Sex Differences in Falls Among the Elderly Community-Dwelling Swiss Population: A Population-Based Cross-Sectional Survey
Andri O. Gerber1
Department of Health Professions, Bern University of Applied Sciences, Bern, SwitzerlandApril 22, 2022
1 R setup and required packages
library(pacman)
pacman::p_load(
rio,
ggplot2,
dplyr,
survey,
hexbin,
psych,
DT,
plotly,
DataExplorer,
tableone,
jtools,
ggstance,
MatchIt,
Publish,
tidyverse,
Hmisc,
mice,
VIM,
corrplot,
sjPlot,
codebook,
officer,
flextable,
pander
)2 Loading and formatting the dataset
The dataset of the Swiss Health Interview survey (SHIS) were anonymised prior to the analysis (for further information) and permission to analyse and publish the restults was obtained through a contract with the Swiss Federal Statistical Office (SFSO).
2.1 Load and subset cross sectional data (\(\geqq\) 60 years of age)
dat <- rio::import("shs_2007_2012_2017_combined.dta") # SHIS Data
dat <- subset(dat, alter >= 60)2.2 Formatting
- Factorize: levels(), labels()
- Dichotomize
- Categorize
2.2.1 Outcome variables
dat$fall <- factor(
dat$taltp01,
levels = c(1, 2),
labels = c("fall last 12 months", "no fall last 12 months")
)
# integrate no fall (0) into variable taltp02 (fall_count)
dat$fall_count_4 <- dat$taltp02
dat$fall_count_4[dat$taltp01==2] <- 0
dat$fall_count <- factor(
dat$fall_count_4,
levels = c(0, 1, 2, 3),
labels = c(
"no fall last 12 months",
"1 fall last 12 months",
"2 falls last 12 months",
"more then 2 falls last 12 months"
)
)
dat$fall_count_collapsed <- fct_collapse(dat$fall_count, two_falls_or_more = c("2 falls last 12 months", "more then 2 falls last 12 months"), no_or_one_fall = c("no fall last 12 months", "1 fall last 12 months"))
outcome_fall <- c("fall")
outcome_fall_count <- c("fall_count")
outcome_fall_count_collapsed <- c("fall_count_collapsed")
c(outcome_fall, outcome_fall_count, outcome_fall_count_collapsed)## [1] "fall" "fall_count" "fall_count_collapsed"2.2.2 Exposure variables
dat$gender <- factor(dat$sex,
levels = c(1, 2),
labels = c("man", "woman"))
exposure_variable <- c("gender")
exposure_variable## [1] "gender"2.2.3 Socio-economic variable
dat$education <- factor(
dat$ausbild3,
levels = c(1, 2, 3),
labels = c(
"compulsory education",
"upper secondary education",
"tertiary education"
)
)
socio_economic_covariate <- c("education")
socio_economic_covariate## [1] "education"2.2.4 Socio-demographic variables
dat$urban_rural <- factor(dat$stala,
levels = c(1, 2),
labels = c("urban area", "rural area"))
dat$nationality <- factor(dat$nation2,
levels = c(1, 2),
labels = c("swiss", "foreigner"))
dat$language <- factor(
dat$sprache,
levels = c(1, 2, 3),
labels = c("german", "french", "italian")
)
socio_demographic_covariate <- c("urban_rural", "nationality", "language")
socio_demographic_covariate## [1] "urban_rural" "nationality" "language"2.2.5 Biological variables
# categorize age in 3 groups
dat$age_cut[dat$alter < 70] <- "60-69"
dat$age_cut[dat$alter >= 70 & dat$alter < 80] <- "70-79"
dat$age_cut[dat$alter >= 80] <- "80+"
dat$age_cut <- as.factor(dat$age_cut)
# Set reference category
dat$age_cut <- relevel(dat$age_cut, ref = "60-69")
dat$age <- dat$alter
# Functional limitations (vision, hearing, speech, walking)
dat$FL <- factor(
dat$limfonc,
levels = c(1, 2, 3, 4),
labels = c(
"No difficulty",
"Slight difficulty",
"Great difficulty",
"Inability"
)
)
# Categorize bmi into 4 groups
dat$bmi_cut[dat$bmi < 18.5] <- "underweight"
dat$bmi_cut[dat$bmi >= 18.5 & dat$bmi < 25] <- "normal weight"
dat$bmi_cut[dat$bmi >= 25 & dat$bmi < 30] <- "overweight"
dat$bmi_cut[dat$bmi >= 30] <- "obesity"
dat$bmi_cut <- as.factor(dat$bmi_cut)
dat$bmi_cut <- relevel(dat$bmi_cut, ref = "normal weight") # set reference for normal weight class
# Self-perceived health status
dat$SHS <- factor(
dat$tsubg_allyears,
levels = c(1, 2, 3, 4, 5),
labels = c("very good", "good", "average", "poor", "very poor")
)
# Self-perceived health status for models (collapse very good and good)
dat$SHS_collapsed <- factor(
dat$tsubg_allyears,
levels = c(1, 2, 3, 4, 5),
labels = c("very good", "good", "average", "poor", "very poor")
)
dat$SHS_collapsed <- fct_collapse(dat$SHS_collapsed, good = c("very good", "good"))
dat$diabetes <- factor(dat$diabete,
levels = c(1, 2),
labels = c("yes", "no"))
# Set reference category
dat$diabetes <- relevel(dat$diabetes, ref = "no")
dat$osteoarthritis <- factor(dat$tkran10c,
levels = c(1, 2),
labels = c("yes", "no"))
# Set reference category
dat$osteoarthritis <- relevel(dat$osteoarthritis, ref = "no")
dat$heart_attack <- factor(dat$tkran10f,
levels = c(1, 2),
labels = c("yes", "no"))
# Set reference category
dat$heart_attack <- relevel(dat$heart_attack, ref = "no")
dat$stroke <- factor(dat$tkran10g,
levels = c(1, 2),
labels = c("yes", "no"))
# Set reference category
dat$stroke <- relevel(dat$stroke, ref = "no")
dat$urinary_incontinence <- factor(dat$tkran10j,
levels = c(1, 2),
labels = c("yes", "no"))
# Set reference category
dat$urinary_incontinence <- relevel(dat$urinary_incontinence, ref = "no")
dat$osteoporosis <- factor(dat$tkran10d,
levels = c(1, 2),
labels = c("yes", "no"))
# Set reference category
dat$osteoporosis <- relevel(dat$osteoporosis, ref = "no")
biological_covariates <- c("age_cut", "age", "FL", "bmi_cut", "bmi", "SHS", "diabetes", "osteoarthritis", "heart_attack", "stroke", "urinary_incontinence", "osteoporosis")
biological_covariates## [1] "age_cut" "age" "FL"
## [4] "bmi_cut" "bmi" "SHS"
## [7] "diabetes" "osteoarthritis" "heart_attack"
## [10] "stroke" "urinary_incontinence" "osteoporosis"2.2.6 Behavioral variables
# Physical activity (weekly)
dat$activity <- factor(
dat$actphy3,
levels = c(1, 2, 3),
labels = c("inactive", "partially active", "active")
)
dat$alcohol <- factor(
dat$alcchron3,
levels = c(1, 2, 3),
labels = c("abstinent", "low risk", "moderate or increased risk")
)
dat$smoke <- factor(
dat$tabac3,
levels = c(1, 2, 3),
labels = c("no smoker", "former smoker", "smoker")
)
behavioral_covariate <- c("activity", "alcohol", "smoke")
behavioral_covariate## [1] "activity" "alcohol" "smoke"2.2.7 Stratae variable
dat$strata <- factor(
dat$kanton,
levels = c(1:27),
labels = c(
"Zurich",
"Bern",
"Lucerne",
"Uri",
"Schwyz",
"Obwalden",
"Nidwalden",
"Glarus",
"Zug",
"Fribourg",
"Solothurn",
"Basel-City",
"Basel-Country",
"Schaffhausen",
"Appenzell Ausserrhoden",
"Appenzell Innerrhoden",
"St. Gallen",
"Graubünden",
"Aargau",
"Thurgau",
"Tessin",
"Vaud",
"Wallis",
"Neuchâtel",
"Geneva",
"Jura",
"Liechstenstein"
)
)
strata <- c("strata")
strata## [1] "strata"2.2.8 Weight variable
dat$weight <- dat$wght
weight <- c("weight")
weight## [1] "weight"- Get variable names
variable_names <-
c(
"fall",
"fall_count",
"gender",
"education",
"language",
"urban_rural",
"nationality",
"smoke",
"FL",
"bmi_cut",
"bmi",
"age_cut",
"age",
"SHS",
"diabetes",
"osteoarthritis",
"heart_attack",
"stroke",
"urinary_incontinence",
"osteoporosis",
"activity",
"alcohol",
"strata",
"weight"
)
variable_names_fall <- c(
"fall",
"gender",
"education",
"language",
"urban_rural",
"nationality",
"smoke",
"FL",
"bmi_cut",
"bmi",
"age_cut",
"age",
"SHS",
"diabetes",
"osteoarthritis",
"heart_attack",
"stroke",
"urinary_incontinence",
"osteoporosis",
"activity",
"alcohol",
"strata",
"weight"
)
variable_names_fall_count <- c(
"fall_count",
"gender",
"education",
"language",
"urban_rural",
"nationality",
"smoke",
"FL",
"bmi_cut",
"bmi",
"age_cut",
"age",
"SHS",
"diabetes",
"osteoarthritis",
"heart_attack",
"stroke",
"urinary_incontinence",
"osteoporosis",
"activity",
"alcohol",
"strata",
"weight"
)
variable_names_nogender <-
c(
"fall",
"fall_count",
"education",
"language",
"urban_rural",
"nationality",
"smoke",
"FL",
"bmi_cut",
"bmi",
"age_cut",
"age",
"SHS",
"diabetes",
"osteoarthritis",
"heart_attack",
"stroke",
"urinary_incontinence",
"osteoporosis",
"activity",
"alcohol",
"strata",
"weight"
)2.3 Subset the dataset (year, sex)
dat_07 <- subset(dat, intjahr == 2007)
dat_12 <- subset(dat, intjahr == 2012)
dat_17 <- subset(dat, intjahr == 2017) # for further analysis
dat_17_m <- subset(dat_17, gender == "man")
dat_17_f <- subset(dat_17, gender == "woman")2.4 Variable selection:
dat_17_select <- select(dat_17, variable_names)
dat_17_select_m <- select(dat_17_m, variable_names)
dat_17_select_f <- select(dat_17_f, variable_names)
DataExplorer::plot_str(dat_17_select, print_network = TRUE)2.5 Codebook (Metadata)
codebook_dat_17_select<-codebook::codebook_table(dat_17_select)3 Summary (unweighted) sample
3.1 Check missing values
The variable diabetes has the most missing values because of a proxy question (the question was not asked because of a previous question). For the further calculations, the complete-case-analysis was applied, as this procedure was also used in previous studies. Therefor missing values were excluded because their types cannot be identified according to the classes of Rubin et al. [1].
DataExplorer::plot_missing(dat_17_select, title = "Missing values SHIS 2017")
DataExplorer::plot_missing(dat_17_select_m, title = "Missing values SHIS (male) 2017")
DataExplorer::plot_missing(dat_17_select_f, title = "Missing values SHIS (female) 2017")
- Missing-pattern over all variables
mice::md.pattern(dat_17_select)
- Missing-pattern in fall, bmi, osteoarthritis, alcohol, activity, diabetes:
mice::md.pattern(select(dat_17_select, "fall", "bmi", "osteoarthritis", "alcohol", "activity", "diabetes")) # select variables with over 0.5% missing values
- Missing values in age - bmi (histogram)
VIM::histMiss(dat_17_select[, c("age", "bmi")])
3.2 Descriptive statistics
3.2.1 Summary table of the data
dat_17_select_describe <- Hmisc::describe(dat_17_select)
Hmisc::html(dat_17_select_describe, size = 80, title = "SHIS dataset 2017")dat_17_select
24 Variables 6882 Observations
24 Variables 6882 Observations
fall
| n | missing | distinct |
|---|---|---|
| 6870 | 12 | 2 |
Value fall last 12 months no fall last 12 months Frequency 1638 5232 Proportion 0.238 0.762
fall_count
| n | missing | distinct |
|---|---|---|
| 6857 | 25 | 4 |
Value no fall last 12 months 1 fall last 12 months
Frequency 5232 1059
Proportion 0.763 0.154
Value 2 falls last 12 months more then 2 falls last 12 months
Frequency 313 253
Proportion 0.046 0.037
gender
| n | missing | distinct |
|---|---|---|
| 6882 | 0 | 2 |
Value man woman Frequency 3214 3668 Proportion 0.467 0.533
education
| n | missing | distinct |
|---|---|---|
| 6853 | 29 | 3 |
Value compulsory education upper secondary education
Frequency 1510 3583
Proportion 0.220 0.523
Value tertiary education
Frequency 1760
Proportion 0.257
language
| n | missing | distinct |
|---|---|---|
| 6882 | 0 | 3 |
Value german french italian Frequency 4704 1642 536 Proportion 0.684 0.239 0.078
urban_rural
| n | missing | distinct |
|---|---|---|
| 6882 | 0 | 2 |
Value urban area rural area Frequency 4688 2194 Proportion 0.681 0.319
nationality
| n | missing | distinct |
|---|---|---|
| 6882 | 0 | 2 |
Value swiss foreigner Frequency 6096 786 Proportion 0.886 0.114
smoke
| n | missing | distinct |
|---|---|---|
| 6881 | 1 | 3 |
Value no smoker former smoker smoker Frequency 3374 2358 1149 Proportion 0.490 0.343 0.167
FL
| n | missing | distinct |
|---|---|---|
| 6864 | 18 | 4 |
Value No difficulty Slight difficulty Great difficulty Inability Frequency 5307 1196 222 139 Proportion 0.773 0.174 0.032 0.020
bmi_cut
| n | missing | distinct |
|---|---|---|
| 6806 | 76 | 4 |
Value normal weight obesity overweight underweight Frequency 3047 1029 2571 159 Proportion 0.448 0.151 0.378 0.023
bmi
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6806 | 76 | 1753 | 1 | 25.82 | 4.861 | 19.49 | 20.58 | 22.86 | 25.30 | 28.29 | 31.25 | 33.70 |
age_cut
| n | missing | distinct |
|---|---|---|
| 6882 | 0 | 3 |
Value 60-69 70-79 80+ Frequency 3298 2489 1095 Proportion 0.479 0.362 0.159
age
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6882 | 0 | 40 | 0.998 | 70.98 | 8.871 | 60 | 61 | 64 | 70 | 76 | 82 | 86 |
SHS
| n | missing | distinct |
|---|---|---|
| 6877 | 5 | 5 |
| lowest : | very good | good | average | poor | very poor |
| highest: | very good | good | average | poor | very poor |
Value very good good average poor very poor Frequency 1846 3234 1419 310 68 Proportion 0.268 0.470 0.206 0.045 0.010
diabetes
| n | missing | distinct |
|---|---|---|
| 6169 | 713 | 2 |
Value no yes Frequency 5553 616 Proportion 0.9 0.1
osteoarthritis
| n | missing | distinct |
|---|---|---|
| 6824 | 58 | 2 |
Value no yes Frequency 4541 2283 Proportion 0.665 0.335
heart_attack
| n | missing | distinct |
|---|---|---|
| 6878 | 4 | 2 |
Value no yes Frequency 6796 82 Proportion 0.988 0.012
stroke
| n | missing | distinct |
|---|---|---|
| 6874 | 8 | 2 |
Value no yes Frequency 6814 60 Proportion 0.991 0.009
urinary_incontinence
| n | missing | distinct |
|---|---|---|
| 6871 | 11 | 2 |
Value no yes Frequency 6161 710 Proportion 0.897 0.103
osteoporosis
| n | missing | distinct |
|---|---|---|
| 6805 | 77 | 2 |
Value no yes Frequency 6217 588 Proportion 0.914 0.086
activity
| n | missing | distinct |
|---|---|---|
| 6388 | 494 | 3 |
Value inactive partially active active Frequency 786 941 4661 Proportion 0.123 0.147 0.730
alcohol
| n | missing | distinct |
|---|---|---|
| 6562 | 320 | 3 |
Value abstinent low risk
Frequency 1075 5075
Proportion 0.164 0.773
Value moderate or increased risk
Frequency 412
Proportion 0.063
strata
| n | missing | distinct |
|---|---|---|
| 6882 | 0 | 26 |
| lowest : | Zurich | Bern | Lucerne | Uri | Schwyz |
| highest: | Vaud | Wallis | Neuchâtel | Geneva | Jura |
weight
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6882 | 0 | 867 | 1 | 288 | 187 | 54.22 | 102.66 | 165.07 | 246.19 | 370.91 | 542.97 | 626.74 |
| lowest : | 45.90269 | 48.23451 | 48.93422 | 49.12796 | 49.93771 |
| highest: | 1265.79823 | 1332.50151 | 1340.80848 | 1408.21435 | 1571.58855 |
3.2.2 Missing value plot
dat_17_select_describe_plot <- plot(dat_17_select_describe)
dat_17_select_describe_plot$Categorical
dat_17_select_describe_plot$Continuous
3.2.3 Variables stratified by fall
tableone::CreateTableOne(data = dat_17_select, strata = "fall", includeNA = TRUE)## Stratified by fall
## fall last 12 months
## n 1638
## fall (%)
## fall last 12 months 1638 (100.0)
## no fall last 12 months 0 ( 0.0)
## NA 0 ( 0.0)
## fall_count (%)
## no fall last 12 months 0 ( 0.0)
## 1 fall last 12 months 1059 ( 64.7)
## 2 falls last 12 months 313 ( 19.1)
## more then 2 falls last 12 months 253 ( 15.4)
## NA 13 ( 0.8)
## gender = woman (%) 899 ( 54.9)
## education (%)
## compulsory education 343 ( 20.9)
## upper secondary education 842 ( 51.4)
## tertiary education 445 ( 27.2)
## NA 8 ( 0.5)
## language (%)
## german 1147 ( 70.0)
## french 395 ( 24.1)
## italian 96 ( 5.9)
## urban_rural = rural area (%) 533 ( 32.5)
## nationality = foreigner (%) 148 ( 9.0)
## smoke (%)
## no smoker 803 ( 49.0)
## former smoker 577 ( 35.2)
## smoker 258 ( 15.8)
## NA 0 ( 0.0)
## FL (%)
## No difficulty 1178 ( 71.9)
## Slight difficulty 321 ( 19.6)
## Great difficulty 81 ( 4.9)
## Inability 52 ( 3.2)
## NA 6 ( 0.4)
## bmi_cut (%)
## normal weight 743 ( 45.4)
## obesity 255 ( 15.6)
## overweight 582 ( 35.5)
## underweight 42 ( 2.6)
## NA 16 ( 1.0)
## bmi (mean (SD)) 25.74 (4.72)
## age_cut (%)
## 60-69 681 ( 41.6)
## 70-79 615 ( 37.5)
## 80+ 342 ( 20.9)
## age (mean (SD)) 72.22 (8.33)
## SHS (%)
## very good 370 ( 22.6)
## good 735 ( 44.9)
## average 408 ( 24.9)
## poor 102 ( 6.2)
## very poor 23 ( 1.4)
## NA 0 ( 0.0)
## diabetes (%)
## no 1275 ( 77.8)
## yes 160 ( 9.8)
## NA 203 ( 12.4)
## osteoarthritis (%)
## no 968 ( 59.1)
## yes 658 ( 40.2)
## NA 12 ( 0.7)
## heart_attack (%)
## no 1615 ( 98.6)
## yes 22 ( 1.3)
## NA 1 ( 0.1)
## stroke (%)
## no 1611 ( 98.4)
## yes 25 ( 1.5)
## NA 2 ( 0.1)
## urinary_incontinence (%)
## no 1392 ( 85.0)
## yes 245 ( 15.0)
## NA 1 ( 0.1)
## osteoporosis (%)
## no 1445 ( 88.2)
## yes 164 ( 10.0)
## NA 29 ( 1.8)
## activity (%)
## inactive 213 ( 13.0)
## partially active 224 ( 13.7)
## active 1074 ( 65.6)
## NA 127 ( 7.8)
## alcohol (%)
## abstinent 244 ( 14.9)
## low risk 1212 ( 74.0)
## moderate or increased risk 95 ( 5.8)
## NA 87 ( 5.3)
## strata (%)
## Zurich 164 ( 10.0)
## Bern 160 ( 9.8)
## Lucerne 84 ( 5.1)
## Uri 30 ( 1.8)
## Schwyz 72 ( 4.4)
## Obwalden 30 ( 1.8)
## Nidwalden 8 ( 0.5)
## Glarus 9 ( 0.5)
## Zug 7 ( 0.4)
## Fribourg 67 ( 4.1)
## Solothurn 29 ( 1.8)
## Basel-City 29 ( 1.8)
## Basel-Country 77 ( 4.7)
## Schaffhausen 10 ( 0.6)
## Appenzell Ausserrhoden 71 ( 4.3)
## Appenzell Innerrhoden 2 ( 0.1)
## St. Gallen 73 ( 4.5)
## Graubünden 88 ( 5.4)
## Aargau 99 ( 6.0)
## Thurgau 77 ( 4.7)
## Tessin 91 ( 5.6)
## Vaud 127 ( 7.8)
## Wallis 82 ( 5.0)
## Neuchâtel 31 ( 1.9)
## Geneva 86 ( 5.3)
## Jura 35 ( 2.1)
## Liechstenstein 0 ( 0.0)
## weight (mean (SD)) 298.06 (180.81)
## Stratified by fall
## no fall last 12 months p test
## n 5232
## fall (%) NaN
## fall last 12 months 0 ( 0.0)
## no fall last 12 months 5232 (100.0)
## NA 0 ( 0.0)
## fall_count (%) <0.001
## no fall last 12 months 5232 (100.0)
## 1 fall last 12 months 0 ( 0.0)
## 2 falls last 12 months 0 ( 0.0)
## more then 2 falls last 12 months 0 ( 0.0)
## NA 0 ( 0.0)
## gender = woman (%) 2760 ( 52.8) 0.139
## education (%) 0.322
## compulsory education 1163 ( 22.2)
## upper secondary education 2736 ( 52.3)
## tertiary education 1312 ( 25.1)
## NA 21 ( 0.4)
## language (%) 0.003
## german 3550 ( 67.9)
## french 1242 ( 23.7)
## italian 440 ( 8.4)
## urban_rural = rural area (%) 1657 ( 31.7) 0.530
## nationality = foreigner (%) 635 ( 12.1) 0.001
## smoke (%) 0.549
## no smoker 2566 ( 49.0)
## former smoker 1775 ( 33.9)
## smoker 890 ( 17.0)
## NA 1 ( 0.0)
## FL (%) <0.001
## No difficulty 4123 ( 78.8)
## Slight difficulty 870 ( 16.6)
## Great difficulty 141 ( 2.7)
## Inability 86 ( 1.6)
## NA 12 ( 0.2)
## bmi_cut (%) 0.440
## normal weight 2299 ( 43.9)
## obesity 774 ( 14.8)
## overweight 1984 ( 37.9)
## underweight 117 ( 2.2)
## NA 58 ( 1.1)
## bmi (mean (SD)) 25.84 (4.47) 0.445
## age_cut (%) <0.001
## 60-69 2611 ( 49.9)
## 70-79 1870 ( 35.7)
## 80+ 751 ( 14.4)
## age (mean (SD)) 70.58 (7.66) <0.001
## SHS (%) <0.001
## very good 1474 ( 28.2)
## good 2493 ( 47.6)
## average 1009 ( 19.3)
## poor 206 ( 3.9)
## very poor 45 ( 0.9)
## NA 5 ( 0.1)
## diabetes (%) 0.002
## no 4272 ( 81.7)
## yes 455 ( 8.7)
## NA 505 ( 9.7)
## osteoarthritis (%) <0.001
## no 3566 ( 68.2)
## yes 1622 ( 31.0)
## NA 44 ( 0.8)
## heart_attack (%) 0.814
## no 5169 ( 98.8)
## yes 60 ( 1.1)
## NA 3 ( 0.1)
## stroke (%) 0.005
## no 5191 ( 99.2)
## yes 35 ( 0.7)
## NA 6 ( 0.1)
## urinary_incontinence (%) <0.001
## no 4761 ( 91.0)
## yes 461 ( 8.8)
## NA 10 ( 0.2)
## osteoporosis (%) <0.001
## no 4763 ( 91.0)
## yes 424 ( 8.1)
## NA 45 ( 0.9)
## activity (%) 0.062
## inactive 572 ( 10.9)
## partially active 717 ( 13.7)
## active 3580 ( 68.4)
## NA 363 ( 6.9)
## alcohol (%) 0.375
## abstinent 828 ( 15.8)
## low risk 3857 ( 73.7)
## moderate or increased risk 317 ( 6.1)
## NA 230 ( 4.4)
## strata (%) NaN
## Zurich 595 ( 11.4)
## Bern 455 ( 8.7)
## Lucerne 229 ( 4.4)
## Uri 133 ( 2.5)
## Schwyz 226 ( 4.3)
## Obwalden 106 ( 2.0)
## Nidwalden 14 ( 0.3)
## Glarus 9 ( 0.2)
## Zug 35 ( 0.7)
## Fribourg 218 ( 4.2)
## Solothurn 86 ( 1.6)
## Basel-City 64 ( 1.2)
## Basel-Country 245 ( 4.7)
## Schaffhausen 20 ( 0.4)
## Appenzell Ausserrhoden 181 ( 3.5)
## Appenzell Innerrhoden 3 ( 0.1)
## St. Gallen 273 ( 5.2)
## Graubünden 259 ( 5.0)
## Aargau 305 ( 5.8)
## Thurgau 251 ( 4.8)
## Tessin 414 ( 7.9)
## Vaud 333 ( 6.4)
## Wallis 246 ( 4.7)
## Neuchâtel 151 ( 2.9)
## Geneva 253 ( 4.8)
## Jura 128 ( 2.4)
## Liechstenstein 0 ( 0.0)
## weight (mean (SD)) 284.95 (174.02) 0.0083.2.4 Univariate distribution
3.2.4.1 Bar chart (by frequency)
DataExplorer::plot_bar(dat_17_select)
3.2.4.2 Bar chart by fall
DataExplorer::plot_bar(dat_17_select)
3.2.4.3 Histogram
- age and bmi
plot_histogram(dat_17_select[, c("age", "bmi")])
3.2.4.4 QQ-Plot
- age and bmi
qq_data <- dat_17_select[, c("age", "bmi")]
DataExplorer::plot_qq(qq_data)
# Quantile-Quantile plot to visualize the deviation from probability distribution.- bmi seems skewed on both tails, apply a simple log transformation
log_qq_data <- DataExplorer::update_columns(qq_data, 2, function(x) log(x + 1))
plot_qq(log_qq_data["bmi"])
3.2.5 Correlation Analysis
plot_correlation(na.omit(dat_17_select[, c("age", "bmi")]), type = "c") #only continuous
plot_correlation(na.omit(dat_17_select[variable_names_fall]), type = "d", maxcat = 5L, geom_text_args = (size = 0.05)) #only descrete## 1 features with more than 5 categories ignored!
## strata: 26 categories
# Bivariate Summaries Computed Separately by a Series of Predictors
var.summ <- spearman2(fall~ ., data = na.omit(dat_17_select[variable_names_fall]))
plot(var.summ, cex = 0.8)
3.2.5.1 Identify collinear predictors
# Variable Clustering:
# hclust hierarchical clustering function
# default is squared Spearman correlation coefficients
# to detect monotonic but nonlinear relationships
var.cluster <- Hmisc::varclus(~., data = dat_17_select[c(
"fall",
"gender",
"education",
"language",
"urban_rural",
"nationality",
"smoke",
"FL",
"bmi_cut",
"bmi",
"age_cut",
"age",
"SHS",
"diabetes",
"osteoarthritis",
"heart_attack",
"stroke",
"urinary_incontinence",
"osteoporosis",
"activity",
"alcohol")])
# var.cluster
plot(var.cluster, abbrev=FALSE, cex = 0.8)
3.2.6 Boxplots
# Visualize the distribution of all continuous features based on fall with a boxplot
DataExplorer::plot_boxplot(dat_17_select, by = "fall")
3.2.7 Control of the dataframe size, i.e. extent of missing values
dat_17_select_fall <- select(dat_17, variable_names_fall) # create dataset without fall_count variable
dat_17_select_fall_count <- select(dat_17, variable_names_fall_count) # create dataset without fall variable
dat_17_select_no_missing_fall <- as.data.frame(na.omit(dat_17_select_fall))
dat_17_select_no_missing_fall_count <- as.data.frame(na.omit(dat_17_select_fall_count))
dim(dat_17_select) # all variables with missings## [1] 6882 24dim(dat_17_select_fall) # all variables fall dataset## [1] 6882 23dim(dat_17_select_fall_count) # all variables count_fall dataset## [1] 6882 23dim(dat_17_select_no_missing_fall) # without missings fall dataset## [1] 5833 23dim(dat_17_select_no_missing_fall_count) # without missings fall_count dataset; Lot of missings are due to no questioning if no fall has happened.## [1] 5822 234 Surveydesign
4.1 Creating/subset (weighted) survey.design-object
- (year, sex)
wdat<-svydesign(id = ~idno, strata = ~kanton, weights = ~wght, nest = T, data = dat)
wdat_07<-svydesign(id = ~idno, strata = ~kanton, weights = ~wght, nest = T, data = dat_07)
wdat_12<-svydesign(id = ~idno, strata = ~kanton, weights = ~wght, nest = T, data = dat_12)
wdat_17<-svydesign(id = ~idno, strata = ~kanton, weights = ~wght, nest = T, data = dat_17)
wdat_17_m <- subset(wdat_17, gender =="man")
wdat_17_f <- subset(wdat_17, gender == "woman")4.2 Variable selection
- Same as previous in the (unweighted) dataset
5 Summary weighted statistics
5.1 Falls by sex SHIS 2007, 2012, 2017
fall_year_gender_svy <- svyby(formula = ~fall,
by = ~intjahr + gender,
design = wdat,
FUN = svymean,
na.rm = TRUE,
keep.names = FALSE)
# datatable(fall_year_gender_svy, filter = 'top')
dodge <- position_dodge(width=0.9)
fall_year_gender_svy_1<-ggplot(data = fall_year_gender_svy, mapping = aes(x = factor(intjahr), y = `fallfall last 12 months`, ymin = `fallfall last 12 months`-(2*`se.fallfall last 12 months`), ymax = `fallfall last 12 months`+(2*`se.fallfall last 12 months`), fill = gender)) +
geom_bar(position="dodge", stat="identity") +
labs(y = "Prop_fall", x = "Year") +
geom_errorbar(width = 0.2, size = 3, position = dodge) + labs(fill = "Gender")
fall_year_gender_svy_2<-fall_year_gender_svy_1 + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(fall_year_gender_svy_2)5.2 Table 1
The following steps are used to create Table 1 of the publication.
For each variable of interest, the number of respondents in each group, the (weighted) percentages of the population in each group and the (weighted) percentages of the population with the outcome of interest per group were calculated separatly for men and women. For all weighted confidence intervals for proportions, the Rao-Scott method was used [2]. Since it uses a scaled chi-squared distribution for the log-likelihood from a binomial distribution, it can be more accurate near 0 and 1. For this purpose, the function svyciprop() from the package survey by Thomas Lumley was used [3].
5.2.1 Summary row total
options(width = 500)
Row_1<-svyCreateTableOne(vars = "fall", data = wdat_17, includeNA = TRUE)
print(Row_1, showAllLevels = TRUE, includeNA = TRUE, contDigit = 1)##
## level Overall
## n 1982204.6
## fall (%) fall last 12 months 488220.1 (24.6)
## no fall last 12 months 1490847.5 (75.2)
## <NA> 3137.1 ( 0.2)5.2.2 Summary row total men & women
# total fall
Row_1_CI_fall<- svyciprop(formula = ~I(fall == "fall last 12 months"),
design = wdat_17,
na.rm = TRUE,
method = "li")
Row_1_CI_fall## 2.5% 97.5%
## I(fall == "fall last 12 months") 0.247 0.235 0.26# total woman
Row_1_CI_woman<- svyciprop(formula = ~I(gender == "woman"),
design = wdat_17,
na.rm = TRUE,
method = "li")
Row_1_CI_woman## 2.5% 97.5%
## I(gender == "woman") 0.533 0.520 0.55# total man
Row_1_CI_man<- svyciprop(formula = ~I(gender == "man"),
design = wdat_17,
na.rm = TRUE,
method = "li")
Row_1_CI_man## 2.5% 97.5%
## I(gender == "man") 0.467 0.453 0.48# Proportion fall % (CI 95) by gender
tab_amount_fall_tot_gender <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ gender,
design = wdat_17,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_tot_gender## gender I(fall == "fall last 12 months") ci_l ci_u
## 1 man 0.2367221 0.2196090 0.2544236
## 2 woman 0.2554283 0.2389467 0.27238905.2.3 Summary (unweighted) sample (n)
options(width = 500)
sample_size_gender<- tableone::CreateTableOne(data = dat_17_select, strata = "gender", includeNA = TRUE)
sample_size_gendera<-print(sample_size_gender, showAllLevels = TRUE, includeNA = TRUE, contDigit = 0)## Stratified by gender
## level man woman p test
## n 3214 3668
## fall (%) fall last 12 months 739 ( 23.0) 899 ( 24.5) 0.103
## no fall last 12 months 2472 ( 76.9) 2760 ( 75.2)
## <NA> 3 ( 0.1) 9 ( 0.2)
## fall_count (%) no fall last 12 months 2472 ( 76.9) 2760 ( 75.2) <0.001
## 1 fall last 12 months 450 ( 14.0) 609 ( 16.6)
## 2 falls last 12 months 132 ( 4.1) 181 ( 4.9)
## more then 2 falls last 12 months 147 ( 4.6) 106 ( 2.9)
## <NA> 13 ( 0.4) 12 ( 0.3)
## gender (%) man 3214 (100.0) 0 ( 0.0) <0.001
## woman 0 ( 0.0) 3668 (100.0)
## education (%) compulsory education 462 ( 14.4) 1048 ( 28.6) <0.001
## upper secondary education 1548 ( 48.2) 2035 ( 55.5)
## tertiary education 1194 ( 37.1) 566 ( 15.4)
## <NA> 10 ( 0.3) 19 ( 0.5)
## language (%) german 2262 ( 70.4) 2442 ( 66.6) 0.003
## french 723 ( 22.5) 919 ( 25.1)
## italian 229 ( 7.1) 307 ( 8.4)
## urban_rural (%) urban area 2180 ( 67.8) 2508 ( 68.4) 0.646
## rural area 1034 ( 32.2) 1160 ( 31.6)
## nationality (%) swiss 2770 ( 86.2) 3326 ( 90.7) <0.001
## foreigner 444 ( 13.8) 342 ( 9.3)
## smoke (%) no smoker 1190 ( 37.0) 2184 ( 59.5) <0.001
## former smoker 1397 ( 43.5) 961 ( 26.2)
## smoker 626 ( 19.5) 523 ( 14.3)
## <NA> 1 ( 0.0) 0 ( 0.0)
## FL (%) No difficulty 2491 ( 77.5) 2816 ( 76.8) 0.023
## Slight difficulty 574 ( 17.9) 622 ( 17.0)
## Great difficulty 81 ( 2.5) 141 ( 3.8)
## Inability 59 ( 1.8) 80 ( 2.2)
## <NA> 9 ( 0.3) 9 ( 0.2)
## bmi_cut (%) normal weight 1195 ( 37.2) 1852 ( 50.5) <0.001
## obesity 541 ( 16.8) 488 ( 13.3)
## overweight 1438 ( 44.7) 1133 ( 30.9)
## underweight 16 ( 0.5) 143 ( 3.9)
## <NA> 24 ( 0.7) 52 ( 1.4)
## bmi (mean (SD)) 27 (4) 25 (5) <0.001
## age_cut (%) 60-69 1515 ( 47.1) 1783 ( 48.6) 0.015
## 70-79 1216 ( 37.8) 1273 ( 34.7)
## 80+ 483 ( 15.0) 612 ( 16.7)
## age (mean (SD)) 71 (8) 71 (8) 0.301
## SHS (%) very good 854 ( 26.6) 992 ( 27.0) 0.002
## good 1533 ( 47.7) 1701 ( 46.4)
## average 617 ( 19.2) 802 ( 21.9)
## poor 165 ( 5.1) 145 ( 4.0)
## very poor 41 ( 1.3) 27 ( 0.7)
## <NA> 4 ( 0.1) 1 ( 0.0)
## diabetes (%) no 2517 ( 78.3) 3036 ( 82.8) <0.001
## yes 377 ( 11.7) 239 ( 6.5)
## <NA> 320 ( 10.0) 393 ( 10.7)
## osteoarthritis (%) no 2401 ( 74.7) 2140 ( 58.3) <0.001
## yes 788 ( 24.5) 1495 ( 40.8)
## <NA> 25 ( 0.8) 33 ( 0.9)
## heart_attack (%) no 3156 ( 98.2) 3640 ( 99.2) <0.001
## yes 56 ( 1.7) 26 ( 0.7)
## <NA> 2 ( 0.1) 2 ( 0.1)
## stroke (%) no 3178 ( 98.9) 3636 ( 99.1) 0.575
## yes 32 ( 1.0) 28 ( 0.8)
## <NA> 4 ( 0.1) 4 ( 0.1)
## urinary_incontinence (%) no 2925 ( 91.0) 3236 ( 88.2) 0.001
## yes 284 ( 8.8) 426 ( 11.6)
## <NA> 5 ( 0.2) 6 ( 0.2)
## osteoporosis (%) no 3123 ( 97.2) 3094 ( 84.4) <0.001
## yes 70 ( 2.2) 518 ( 14.1)
## <NA> 21 ( 0.7) 56 ( 1.5)
## activity (%) inactive 306 ( 9.5) 480 ( 13.1) <0.001
## partially active 398 ( 12.4) 543 ( 14.8)
## active 2284 ( 71.1) 2377 ( 64.8)
## <NA> 226 ( 7.0) 268 ( 7.3)
## alcohol (%) abstinent 303 ( 9.4) 772 ( 21.0) <0.001
## low risk 2540 ( 79.0) 2535 ( 69.1)
## moderate or increased risk 214 ( 6.7) 198 ( 5.4)
## <NA> 157 ( 4.9) 163 ( 4.4)
## strata (%) Zurich 355 ( 11.0) 405 ( 11.0) NaN
## Bern 295 ( 9.2) 323 ( 8.8)
## Lucerne 160 ( 5.0) 153 ( 4.2)
## Uri 73 ( 2.3) 91 ( 2.5)
## Schwyz 140 ( 4.4) 158 ( 4.3)
## Obwalden 71 ( 2.2) 65 ( 1.8)
## Nidwalden 9 ( 0.3) 13 ( 0.4)
## Glarus 4 ( 0.1) 14 ( 0.4)
## Zug 19 ( 0.6) 23 ( 0.6)
## Fribourg 140 ( 4.4) 146 ( 4.0)
## Solothurn 53 ( 1.6) 62 ( 1.7)
## Basel-City 47 ( 1.5) 46 ( 1.3)
## Basel-Country 155 ( 4.8) 167 ( 4.6)
## Schaffhausen 14 ( 0.4) 16 ( 0.4)
## Appenzell Ausserrhoden 121 ( 3.8) 131 ( 3.6)
## Appenzell Innerrhoden 1 ( 0.0) 4 ( 0.1)
## St. Gallen 167 ( 5.2) 180 ( 4.9)
## Graubünden 167 ( 5.2) 181 ( 4.9)
## Aargau 198 ( 6.2) 206 ( 5.6)
## Thurgau 161 ( 5.0) 167 ( 4.6)
## Tessin 213 ( 6.6) 292 ( 8.0)
## Vaud 198 ( 6.2) 264 ( 7.2)
## Wallis 153 ( 4.8) 175 ( 4.8)
## Neuchâtel 84 ( 2.6) 99 ( 2.7)
## Geneva 139 ( 4.3) 201 ( 5.5)
## Jura 77 ( 2.4) 86 ( 2.3)
## Liechstenstein 0 ( 0.0) 0 ( 0.0)
## weight (mean (SD)) 288 (174) 288 (177) 0.908# To export to Excel-format
# write.csv2(sample_size_gendera, "sample_size.csv")5.2.4 Summary (weighted) population (N) for men & women
- to get the missing values (NA):
Row_2_NA<-svyCreateTableOne(vars = c(variable_names), strata = "gender", data = wdat_17, includeNA = TRUE, test = TRUE) # with NA
Row_2_NAa<- print(Row_2_NA, showAllLevels = TRUE, includeNA = TRUE, contDigit = 0)## Stratified by gender
## level man woman p test
## n 924880.1 1057324.6
## fall (%) fall last 12 months 218797.0 ( 23.7) 269423.1 ( 25.5) NA
## no fall last 12 months 705481.0 ( 76.3) 785366.5 ( 74.3)
## <NA> 602.1 ( 0.1) 2535.0 ( 0.2)
## fall_count (%) no fall last 12 months 705481.0 ( 76.3) 785366.5 ( 74.3) NA
## 1 fall last 12 months 132989.4 ( 14.4) 181678.0 ( 17.2)
## 2 falls last 12 months 40660.7 ( 4.4) 56386.0 ( 5.3)
## more then 2 falls last 12 months 42134.1 ( 4.6) 30567.1 ( 2.9)
## <NA> 3614.9 ( 0.4) 3326.9 ( 0.3)
## gender (%) man 924880.1 (100.0) 0.0 ( 0.0) <0.001
## woman 0.0 ( 0.0) 1057324.6 (100.0)
## education (%) compulsory education 119633.0 ( 12.9) 286475.7 ( 27.1) NA
## upper secondary education 441111.5 ( 47.7) 600107.3 ( 56.8)
## tertiary education 360739.5 ( 39.0) 164653.2 ( 15.6)
## <NA> 3396.1 ( 0.4) 6088.3 ( 0.6)
## language (%) german 704489.9 ( 76.2) 765618.4 ( 72.4) 0.001
## french 179829.5 ( 19.4) 237021.1 ( 22.4)
## italian 40560.7 ( 4.4) 54685.0 ( 5.2)
## urban_rural (%) urban area 683856.2 ( 73.9) 779943.5 ( 73.8) 0.884
## rural area 241023.8 ( 26.1) 277381.1 ( 26.2)
## nationality (%) swiss 794615.9 ( 85.9) 946493.5 ( 89.5) <0.001
## foreigner 130264.2 ( 14.1) 110831.0 ( 10.5)
## smoke (%) no smoker 335647.1 ( 36.3) 637343.1 ( 60.3) NA
## former smoker 400072.5 ( 43.3) 273965.2 ( 25.9)
## smoker 188631.9 ( 20.4) 146016.2 ( 13.8)
## <NA> 528.6 ( 0.1) 0.0 ( 0.0)
## FL (%) No difficulty 727412.3 ( 78.6) 802495.0 ( 75.9) NA
## Slight difficulty 155641.7 ( 16.8) 185432.4 ( 17.5)
## Great difficulty 22074.8 ( 2.4) 40791.2 ( 3.9)
## Inability 17144.7 ( 1.9) 25970.9 ( 2.5)
## <NA> 2606.6 ( 0.3) 2635.1 ( 0.2)
## bmi_cut (%) normal weight 351786.3 ( 38.0) 531072.9 ( 50.2) NA
## obesity 147513.6 ( 15.9) 144292.0 ( 13.6)
## overweight 413869.7 ( 44.7) 328003.5 ( 31.0)
## underweight 4190.7 ( 0.5) 39596.1 ( 3.7)
## <NA> 7519.8 ( 0.8) 14359.9 ( 1.4)
## bmi (mean (SD)) 27 (4) 25 (5) <0.001
## age_cut (%) 60-69 451660.1 ( 48.8) 480787.8 ( 45.5) 0.001
## 70-79 329497.6 ( 35.6) 371310.7 ( 35.1)
## 80+ 143722.4 ( 15.5) 205226.0 ( 19.4)
## age (mean (SD)) 71 (8) 72 (8) <0.001
## SHS (%) very good 247212.1 ( 26.7) 281898.3 ( 26.7) NA
## good 441164.0 ( 47.7) 488525.9 ( 46.2)
## average 174930.5 ( 18.9) 235278.6 ( 22.3)
## poor 46282.0 ( 5.0) 44699.5 ( 4.2)
## very poor 13663.7 ( 1.5) 6819.4 ( 0.6)
## <NA> 1627.8 ( 0.2) 102.9 ( 0.0)
## diabetes (%) no 726010.8 ( 78.5) 858516.6 ( 81.2) NA
## yes 107190.7 ( 11.6) 74067.4 ( 7.0)
## <NA> 91678.6 ( 9.9) 124740.6 ( 11.8)
## osteoarthritis (%) no 698865.0 ( 75.6) 616134.9 ( 58.3) NA
## yes 217081.2 ( 23.5) 431274.7 ( 40.8)
## <NA> 8933.9 ( 1.0) 9915.0 ( 0.9)
## heart_attack (%) no 908635.9 ( 98.2) 1048664.2 ( 99.2) NA
## yes 15331.6 ( 1.7) 8237.2 ( 0.8)
## <NA> 912.6 ( 0.1) 423.2 ( 0.0)
## stroke (%) no 913585.6 ( 98.8) 1046222.6 ( 98.9) NA
## yes 9359.6 ( 1.0) 9809.0 ( 0.9)
## <NA> 1934.9 ( 0.2) 1293.1 ( 0.1)
## urinary_incontinence (%) no 842202.1 ( 91.1) 924960.9 ( 87.5) NA
## yes 80472.2 ( 8.7) 130696.2 ( 12.4)
## <NA> 2205.8 ( 0.2) 1667.5 ( 0.2)
## osteoporosis (%) no 900173.6 ( 97.3) 889870.5 ( 84.2) NA
## yes 18375.3 ( 2.0) 150618.8 ( 14.2)
## <NA> 6331.2 ( 0.7) 16835.3 ( 1.6)
## activity (%) inactive 88197.6 ( 9.5) 147599.3 ( 14.0) NA
## partially active 113122.0 ( 12.2) 150020.1 ( 14.2)
## active 662528.5 ( 71.6) 671214.2 ( 63.5)
## <NA> 61032.1 ( 6.6) 88490.9 ( 8.4)
## alcohol (%) abstinent 92020.4 ( 9.9) 216784.0 ( 20.5) NA
## low risk 731840.6 ( 79.1) 731121.1 ( 69.1)
## moderate or increased risk 58119.0 ( 6.3) 53580.6 ( 5.1)
## <NA> 42900.0 ( 4.6) 55838.9 ( 5.3)
## strata (%) Zurich 172107.8 ( 18.6) 188351.4 ( 17.8) NaN
## Bern 123872.8 ( 13.4) 136660.2 ( 12.9)
## Lucerne 46051.1 ( 5.0) 42902.9 ( 4.1)
## Uri 4039.1 ( 0.4) 4856.4 ( 0.5)
## Schwyz 16132.8 ( 1.7) 18273.4 ( 1.7)
## Obwalden 4135.9 ( 0.4) 3900.0 ( 0.4)
## Nidwalden 5419.5 ( 0.6) 7831.4 ( 0.7)
## Glarus 1938.1 ( 0.2) 7771.6 ( 0.7)
## Zug 11024.6 ( 1.2) 13410.8 ( 1.3)
## Fribourg 30090.9 ( 3.3) 30296.2 ( 2.9)
## Solothurn 31508.0 ( 3.4) 39188.1 ( 3.7)
## Basel-City 26443.4 ( 2.9) 25383.1 ( 2.4)
## Basel-Country 38618.3 ( 4.2) 41310.1 ( 3.9)
## Schaffhausen 8547.9 ( 0.9) 9750.0 ( 0.9)
## Appenzell Ausserrhoden 7319.1 ( 0.8) 7715.8 ( 0.7)
## Appenzell Innerrhoden 513.7 ( 0.1) 2289.8 ( 0.2)
## St. Gallen 58586.3 ( 6.3) 64713.9 ( 6.1)
## Graubünden 25590.9 ( 2.8) 27379.6 ( 2.6)
## Aargau 80607.4 ( 8.7) 85534.6 ( 8.1)
## Thurgau 30519.8 ( 3.3) 32154.6 ( 3.0)
## Tessin 37902.7 ( 4.1) 52241.7 ( 4.9)
## Vaud 59681.6 ( 6.5) 85426.9 ( 8.1)
## Wallis 40443.8 ( 4.4) 44553.2 ( 4.2)
## Neuchâtel 15314.2 ( 1.7) 19187.4 ( 1.8)
## Geneva 39840.4 ( 4.3) 56552.9 ( 5.3)
## Jura 8629.7 ( 0.9) 9688.5 ( 0.9)
## Liechstenstein 0.0 ( 0.0) 0.0 ( 0.0)
## weight (mean (SD)) 393 (206) 397 (218) 0.696# To export to Excel-format
# write.csv2(Row_2_NAa, file = "Row_2NA.csv")- to get the t-test mean of weighted characteristics men vs. women:
Row_2<-svyCreateTableOne(vars = c(variable_names), strata = "gender", data = wdat_17, includeNA = FALSE, test = TRUE) # without NA and t-test gender
Row_2a<-print(Row_2, showAllLevels = TRUE, includeNA = FALSE, contDigit = 0)## Stratified by gender
## level man woman p test
## n 924880.1 1057324.6
## fall (%) fall last 12 months 218797.0 ( 23.7) 269423.1 ( 25.5) 0.130
## no fall last 12 months 705481.0 ( 76.3) 785366.5 ( 74.5)
## fall_count (%) no fall last 12 months 705481.0 ( 76.6) 785366.5 ( 74.5) <0.001
## 1 fall last 12 months 132989.4 ( 14.4) 181678.0 ( 17.2)
## 2 falls last 12 months 40660.7 ( 4.4) 56386.0 ( 5.3)
## more then 2 falls last 12 months 42134.1 ( 4.6) 30567.1 ( 2.9)
## gender (%) man 924880.1 (100.0) 0.0 ( 0.0) <0.001
## woman 0.0 ( 0.0) 1057324.6 (100.0)
## education (%) compulsory education 119633.0 ( 13.0) 286475.7 ( 27.3) <0.001
## upper secondary education 441111.5 ( 47.9) 600107.3 ( 57.1)
## tertiary education 360739.5 ( 39.1) 164653.2 ( 15.7)
## language (%) german 704489.9 ( 76.2) 765618.4 ( 72.4) 0.001
## french 179829.5 ( 19.4) 237021.1 ( 22.4)
## italian 40560.7 ( 4.4) 54685.0 ( 5.2)
## urban_rural (%) urban area 683856.2 ( 73.9) 779943.5 ( 73.8) 0.884
## rural area 241023.8 ( 26.1) 277381.1 ( 26.2)
## nationality (%) swiss 794615.9 ( 85.9) 946493.5 ( 89.5) <0.001
## foreigner 130264.2 ( 14.1) 110831.0 ( 10.5)
## smoke (%) no smoker 335647.1 ( 36.3) 637343.1 ( 60.3) <0.001
## former smoker 400072.5 ( 43.3) 273965.2 ( 25.9)
## smoker 188631.9 ( 20.4) 146016.2 ( 13.8)
## FL (%) No difficulty 727412.3 ( 78.9) 802495.0 ( 76.1) 0.007
## Slight difficulty 155641.7 ( 16.9) 185432.4 ( 17.6)
## Great difficulty 22074.8 ( 2.4) 40791.2 ( 3.9)
## Inability 17144.7 ( 1.9) 25970.9 ( 2.5)
## bmi_cut (%) normal weight 351786.3 ( 38.3) 531072.9 ( 50.9) <0.001
## obesity 147513.6 ( 16.1) 144292.0 ( 13.8)
## overweight 413869.7 ( 45.1) 328003.5 ( 31.4)
## underweight 4190.7 ( 0.5) 39596.1 ( 3.8)
## bmi (mean (SD)) 27 (4) 25 (5) <0.001
## age_cut (%) 60-69 451660.1 ( 48.8) 480787.8 ( 45.5) 0.001
## 70-79 329497.6 ( 35.6) 371310.7 ( 35.1)
## 80+ 143722.4 ( 15.5) 205226.0 ( 19.4)
## age (mean (SD)) 71 (8) 72 (8) <0.001
## SHS (%) very good 247212.1 ( 26.8) 281898.3 ( 26.7) 0.001
## good 441164.0 ( 47.8) 488525.9 ( 46.2)
## average 174930.5 ( 18.9) 235278.6 ( 22.3)
## poor 46282.0 ( 5.0) 44699.5 ( 4.2)
## very poor 13663.7 ( 1.5) 6819.4 ( 0.6)
## diabetes (%) no 726010.8 ( 87.1) 858516.6 ( 92.1) <0.001
## yes 107190.7 ( 12.9) 74067.4 ( 7.9)
## osteoarthritis (%) no 698865.0 ( 76.3) 616134.9 ( 58.8) <0.001
## yes 217081.2 ( 23.7) 431274.7 ( 41.2)
## heart_attack (%) no 908635.9 ( 98.3) 1048664.2 ( 99.2) 0.007
## yes 15331.6 ( 1.7) 8237.2 ( 0.8)
## stroke (%) no 913585.6 ( 99.0) 1046222.6 ( 99.1) 0.764
## yes 9359.6 ( 1.0) 9809.0 ( 0.9)
## urinary_incontinence (%) no 842202.1 ( 91.3) 924960.9 ( 87.6) <0.001
## yes 80472.2 ( 8.7) 130696.2 ( 12.4)
## osteoporosis (%) no 900173.6 ( 98.0) 889870.5 ( 85.5) <0.001
## yes 18375.3 ( 2.0) 150618.8 ( 14.5)
## activity (%) inactive 88197.6 ( 10.2) 147599.3 ( 15.2) <0.001
## partially active 113122.0 ( 13.1) 150020.1 ( 15.5)
## active 662528.5 ( 76.7) 671214.2 ( 69.3)
## alcohol (%) abstinent 92020.4 ( 10.4) 216784.0 ( 21.6) <0.001
## low risk 731840.6 ( 83.0) 731121.1 ( 73.0)
## moderate or increased risk 58119.0 ( 6.6) 53580.6 ( 5.4)
## strata (%) Zurich 172107.8 ( 18.6) 188351.4 ( 17.8) NaN
## Bern 123872.8 ( 13.4) 136660.2 ( 12.9)
## Lucerne 46051.1 ( 5.0) 42902.9 ( 4.1)
## Uri 4039.1 ( 0.4) 4856.4 ( 0.5)
## Schwyz 16132.8 ( 1.7) 18273.4 ( 1.7)
## Obwalden 4135.9 ( 0.4) 3900.0 ( 0.4)
## Nidwalden 5419.5 ( 0.6) 7831.4 ( 0.7)
## Glarus 1938.1 ( 0.2) 7771.6 ( 0.7)
## Zug 11024.6 ( 1.2) 13410.8 ( 1.3)
## Fribourg 30090.9 ( 3.3) 30296.2 ( 2.9)
## Solothurn 31508.0 ( 3.4) 39188.1 ( 3.7)
## Basel-City 26443.4 ( 2.9) 25383.1 ( 2.4)
## Basel-Country 38618.3 ( 4.2) 41310.1 ( 3.9)
## Schaffhausen 8547.9 ( 0.9) 9750.0 ( 0.9)
## Appenzell Ausserrhoden 7319.1 ( 0.8) 7715.8 ( 0.7)
## Appenzell Innerrhoden 513.7 ( 0.1) 2289.8 ( 0.2)
## St. Gallen 58586.3 ( 6.3) 64713.9 ( 6.1)
## Graubünden 25590.9 ( 2.8) 27379.6 ( 2.6)
## Aargau 80607.4 ( 8.7) 85534.6 ( 8.1)
## Thurgau 30519.8 ( 3.3) 32154.6 ( 3.0)
## Tessin 37902.7 ( 4.1) 52241.7 ( 4.9)
## Vaud 59681.6 ( 6.5) 85426.9 ( 8.1)
## Wallis 40443.8 ( 4.4) 44553.2 ( 4.2)
## Neuchâtel 15314.2 ( 1.7) 19187.4 ( 1.8)
## Geneva 39840.4 ( 4.3) 56552.9 ( 5.3)
## Jura 8629.7 ( 0.9) 9688.5 ( 0.9)
## Liechstenstein 0.0 ( 0.0) 0.0 ( 0.0)
## weight (mean (SD)) 393 (206) 397 (218) 0.696# To export to Excel-format
# write.csv2(Row_2a, file = "Row_2.csv")5.2.5 Summary proportion (%) and confidence interval (CI 95%) of population (weighted number of people) by characteristic
5.2.5.1 Men
# men
# education
Row_4_compulsory_education_m<- svyciprop(formula = ~I(education == "compulsory education"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_compulsory_education_m*100)[1]## I(education == "compulsory education")
## 12.98265Row_4_compulsory_education_m_CI = attr(Row_4_compulsory_education_m,"ci")
Row_4_compulsory_education_m_CI*100## 2.5% 97.5%
## 11.70425 14.33665Row_4_upper_secondary_education_m <- svyciprop(formula = ~I(education == "upper secondary education"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_upper_secondary_education_m*100)[1]## I(education == "upper secondary education")
## 47.86968Row_4_upper_secondary_education_m_CI = attr(Row_4_upper_secondary_education_m,"ci")
Row_4_upper_secondary_education_m_CI*100## 2.5% 97.5%
## 45.85509 49.88890Row_4_tertiary_education_m <- svyciprop(formula = ~I(education == "tertiary education"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_tertiary_education_m*100)[1]## I(education == "tertiary education")
## 39.14767Row_4_tertiary_education_m_CI = attr(Row_4_tertiary_education_m,"ci")
Row_4_tertiary_education_m_CI*100## 2.5% 97.5%
## 37.17234 41.14697# language
Row_4_german_m<- svyciprop(formula = ~I(language == "german"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_german_m*100)[1]## I(language == "german")
## 76.17094Row_4_german_m_CI = attr(Row_4_german_m,"ci")
Row_4_german_m_CI*100## 2.5% 97.5%
## 74.92261 77.39002Row_4_french_m <- svyciprop(formula = ~I(language == "french"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_french_m*100)[1]## I(language == "french")
## 19.44355Row_4_french_m_CI = attr(Row_4_french_m,"ci")
Row_4_french_m_CI*100## 2.5% 97.5%
## 18.30687 20.61485Row_4_italian_m <- svyciprop(formula = ~I(language == "italian"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_italian_m*100)[1]## I(language == "italian")
## 4.385509Row_4_italian_m_CI = attr(Row_4_italian_m,"ci")
Row_4_italian_m_CI*100## 2.5% 97.5%
## 3.908192 4.898342# urban_rural
Row_4_urban_area_m<- svyciprop(formula = ~I(urban_rural == "urban area"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_urban_area_m*100)[1]## I(urban_rural == "urban area")
## 73.93999Row_4_urban_area_m_CI = attr(Row_4_urban_area_m,"ci")
Row_4_urban_area_m_CI*100## 2.5% 97.5%
## 72.28104 75.55456Row_4_rural_arean_m <- svyciprop(formula = ~I(urban_rural == "rural area"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_rural_arean_m*100)[1]## I(urban_rural == "rural area")
## 26.06001Row_4_rural_arean_m_CI = attr(Row_4_rural_arean_m,"ci")
Row_4_rural_arean_m_CI*100## 2.5% 97.5%
## 24.44544 27.71896# nationality
Row_4_swiss_m<- svyciprop(formula = ~I(nationality == "swiss"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_swiss_m*100)[1]## I(nationality == "swiss")
## 85.91556Row_4_swiss_m_CI = attr(Row_4_swiss_m,"ci")
Row_4_swiss_m_CI*100## 2.5% 97.5%
## 84.47532 87.27814Row_4_foreigner_m <- svyciprop(formula = ~I(nationality == "foreigner"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_foreigner_m*100)[1]## I(nationality == "foreigner")
## 14.08444Row_4_foreigner_m_CI = attr(Row_4_foreigner_m,"ci")
Row_4_foreigner_m_CI*100## 2.5% 97.5%
## 12.72186 15.52468# smoke
Row_4_no_smoker_m<- svyciprop(formula = ~I(smoke == "no smoker"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_no_smoker_m*100)[1]## I(smoke == "no smoker")
## 36.31163Row_4_no_smoker_m_CI = attr(Row_4_no_smoker_m,"ci")
Row_4_no_smoker_m_CI*100## 2.5% 97.5%
## 34.39143 38.26136Row_4_former_smoker_m <- svyciprop(formula = ~I(smoke == "former smoker"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_former_smoker_m*100)[1]## I(smoke == "former smoker")
## 43.28143Row_4_former_smoker_m_CI = attr(Row_4_former_smoker_m,"ci")
Row_4_former_smoker_m_CI*100## 2.5% 97.5%
## 41.28173 45.29581Row_4_smoker_m <- svyciprop(formula = ~I(smoke == "smoker"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_smoker_m*100)[1]## I(smoke == "smoker")
## 20.40694Row_4_smoker_m_CI = attr(Row_4_smoker_m,"ci")
Row_4_smoker_m_CI*100## 2.5% 97.5%
## 18.79218 22.08762# FL
Row_4_FL_no_difficulty_m<- svyciprop(formula = ~I(FL == "No difficulty"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_FL_no_difficulty_m*100)[1]## I(FL == "No difficulty")
## 78.87165Row_4_FL_no_difficulty_m_CI = attr(Row_4_FL_no_difficulty_m,"ci")
Row_4_FL_no_difficulty_m_CI*100## 2.5% 97.5%
## 77.24392 80.44041Row_4_FL_slight_difficulty_m <- svyciprop(formula = ~I(FL == "Slight difficulty"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_FL_slight_difficulty_m*100)[1]## I(FL == "Slight difficulty")
## 16.87587Row_4_FL_slight_difficulty_m_CI = attr(Row_4_FL_slight_difficulty_m,"ci")
Row_4_FL_slight_difficulty_m_CI*100## 2.5% 97.5%
## 15.45430 18.36403Row_4_FL_great_difficulty_m<- svyciprop(formula = ~I(FL == "Great difficulty"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_FL_great_difficulty_m*100)[1]## I(FL == "Great difficulty")
## 2.393518Row_4_FL_great_difficulty_m_CI = attr(Row_4_FL_great_difficulty_m,"ci")
Row_4_FL_great_difficulty_m_CI*100## 2.5% 97.5%
## 1.832830 3.055305Row_4_FL_inability_m<- svyciprop(formula = ~I(FL == "Inability"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_FL_inability_m*100)[1]## I(FL == "Inability")
## 1.858958Row_4_FL_inability_m_CI = attr(Row_4_FL_inability_m,"ci")
Row_4_FL_inability_m_CI*100## 2.5% 97.5%
## 1.374362 2.443602# bmi_cut
Row_4_normal_weight_m<- svyciprop(formula = ~I(bmi_cut == "normal weight"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_normal_weight_m*100)[1]## I(bmi_cut == "normal weight")
## 38.34768Row_4_normal_weight_m_CI = attr(Row_4_normal_weight_m,"ci")
Row_4_normal_weight_m_CI*100## 2.5% 97.5%
## 36.37449 40.34678Row_4_obesity_m <- svyciprop(formula = ~I(bmi_cut == "obesity"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_obesity_m*100)[1]## I(bmi_cut == "obesity")
## 16.08022Row_4_obesity_m_CI = attr(Row_4_obesity_m,"ci")
Row_4_obesity_m_CI*100## 2.5% 97.5%
## 14.63571 17.59820Row_4_overweight_m<- svyciprop(formula = ~I(bmi_cut == "overweight"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_overweight_m*100)[1]## I(bmi_cut == "overweight")
## 45.11529Row_4_overweight_m_CI = attr(Row_4_overweight_m,"ci")
Row_4_overweight_m_CI*100## 2.5% 97.5%
## 43.10301 47.13826Row_4_underweight_m <- svyciprop(formula = ~I(bmi_cut == "underweight"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_underweight_m*100)[1]## I(bmi_cut == "underweight")
## 0.4568165Row_4_underweight_m_CI = attr(Row_4_underweight_m,"ci")
Row_4_underweight_m_CI*100## 2.5% 97.5%
## 0.2555068 0.7427229# age_cut
Row_4_age_cut_60to64_m<- svyciprop(formula = ~I(age_cut == "60-69"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_age_cut_60to64_m*100)[1]## I(age_cut == "60-69")
## 48.83445Row_4_age_cut_60to64_m_CI = attr(Row_4_age_cut_60to64_m,"ci")
Row_4_age_cut_60to64_m_CI*100## 2.5% 97.5%
## 46.81092 50.86053Row_4_age_cut_65to74_m <- svyciprop(formula = ~I(age_cut == "70-79"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_age_cut_65to74_m*100)[1]## I(age_cut == "70-79")
## 35.62598Row_4_age_cut_65to74_m_CI = attr(Row_4_age_cut_65to74_m,"ci")
Row_4_age_cut_65to74_m_CI*100## 2.5% 97.5%
## 33.73542 37.54687Row_4_age_cut_75_m<- svyciprop(formula = ~I(age_cut == "80+"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_age_cut_75_m*100)[1]## I(age_cut == "80+")
## 15.53957Row_4_age_cut_75_m_CI = attr(Row_4_age_cut_75_m,"ci")
Row_4_age_cut_75_m_CI*100## 2.5% 97.5%
## 14.10949 17.04501# SHS
Row_4_SHS_very_good_m<- svyciprop(formula = ~I(SHS == "very good"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_SHS_very_good_m*100)[1]## I(SHS == "very good")
## 26.77622Row_4_SHS_very_good_m_CI = attr(Row_4_SHS_very_good_m,"ci")
Row_4_SHS_very_good_m_CI*100## 2.5% 97.5%
## 24.99203 28.61213Row_4_SHS_good_m<- svyciprop(formula = ~I(SHS == "good"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_SHS_good_m*100)[1]## I(SHS == "good")
## 47.78369Row_4_SHS_good_m_CI = attr(Row_4_SHS_good_m,"ci")
Row_4_SHS_good_m_CI*100## 2.5% 97.5%
## 45.76562 49.80660Row_4_SHS_average_m <- svyciprop(formula = ~I(SHS == "average"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_SHS_average_m*100)[1]## I(SHS == "average")
## 18.94721Row_4_SHS_average_m_CI = attr(Row_4_SHS_average_m,"ci")
Row_4_SHS_average_m_CI*100## 2.5% 97.5%
## 17.40730 20.55379Row_4_SHS_poor_m<- svyciprop(formula = ~I(SHS == "poor"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_SHS_poor_m*100)[1]## I(SHS == "poor")
## 5.012931Row_4_SHS_poor_m_CI = attr(Row_4_SHS_poor_m,"ci")
Row_4_SHS_poor_m_CI*100## 2.5% 97.5%
## 4.176222 5.948335Row_4_SHS_very_poor_m<- svyciprop(formula = ~I(SHS == "very poor"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_SHS_very_poor_m*100)[1]## I(SHS == "very poor")
## 1.47995Row_4_SHS_very_poor_m_CI = attr(Row_4_SHS_very_poor_m,"ci")
Row_4_SHS_very_poor_m_CI*100## 2.5% 97.5%
## 1.031493 2.040422# diabetes
Row_4_diabetes_yes_m<- svyciprop(formula = ~I(diabetes == "yes"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_diabetes_yes_m*100)[1]## I(diabetes == "yes")
## 12.86492Row_4_diabetes_yes_m_CI = attr(Row_4_diabetes_yes_m,"ci")
Row_4_diabetes_yes_m_CI*100## 2.5% 97.5%
## 11.48906 14.32980Row_4_diabetes_no_m <- svyciprop(formula = ~I(diabetes == "no"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_diabetes_no_m*100)[1]## I(diabetes == "no")
## 87.13508Row_4_diabetes_no_m_CI = attr(Row_4_diabetes_no_m,"ci")
Row_4_diabetes_no_m_CI*100## 2.5% 97.5%
## 85.67020 88.51094# osteoarthritis
Row_4_osteoarthritis_yes_m<- svyciprop(formula = ~I(osteoarthritis == "yes"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_osteoarthritis_yes_m*100)[1]## I(osteoarthritis == "yes")
## 23.70022Row_4_osteoarthritis_yes_m_CI = attr(Row_4_osteoarthritis_yes_m,"ci")
Row_4_osteoarthritis_yes_m_CI*100## 2.5% 97.5%
## 22.03221 25.42395Row_4_osteoarthritis_no_m <- svyciprop(formula = ~I(osteoarthritis == "no"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_osteoarthritis_no_m*100)[1]## I(osteoarthritis == "no")
## 76.29978Row_4_osteoarthritis_no_m_CI = attr(Row_4_osteoarthritis_no_m,"ci")
Row_4_osteoarthritis_no_m_CI*100## 2.5% 97.5%
## 74.57605 77.96779# heart_attack
Row_4_heart_attack_yes_m<- svyciprop(formula = ~I(heart_attack == "yes"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_heart_attack_yes_m*100)[1]## I(heart_attack == "yes")
## 1.659322Row_4_heart_attack_yes_m_CI = attr(Row_4_heart_attack_yes_m,"ci")
Row_4_heart_attack_yes_m_CI*100## 2.5% 97.5%
## 1.199294 2.222010Row_4_heart_attack_no_m <- svyciprop(formula = ~I(heart_attack == "no"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_heart_attack_no_m*100)[1]## I(heart_attack == "no")
## 98.34068Row_4_heart_attack_no_m_CI = attr(Row_4_heart_attack_no_m,"ci")
Row_4_heart_attack_no_m_CI*100## 2.5% 97.5%
## 97.77799 98.80071# stroke
Row_4_stroke_yes_m<- svyciprop(formula = ~I(stroke == "yes"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_stroke_yes_m*100)[1]## I(stroke == "yes")
## 1.014104Row_4_stroke_yes_m_CI = attr(Row_4_stroke_yes_m,"ci")
Row_4_stroke_yes_m_CI*100## 2.5% 97.5%
## 0.6663048 1.4644590Row_4_stroke_no_m <- svyciprop(formula = ~I(stroke == "no"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_stroke_no_m*100)[1]## I(stroke == "no")
## 98.9859Row_4_stroke_no_m_CI = attr(Row_4_stroke_no_m,"ci")
Row_4_stroke_no_m_CI*100## 2.5% 97.5%
## 98.53554 99.33370# urinary_incontinence
Row_4_urinary_incontinence_yes_m<- svyciprop(formula = ~I(urinary_incontinence == "yes"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_urinary_incontinence_yes_m*100)[1]## I(urinary_incontinence == "yes")
## 8.721623Row_4_urinary_incontinence_yes_m_CI = attr(Row_4_urinary_incontinence_yes_m,"ci")
Row_4_urinary_incontinence_yes_m_CI*100## 2.5% 97.5%
## 7.653567 9.874844Row_4_urinary_incontinence_no_m <- svyciprop(formula = ~I(urinary_incontinence == "no"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_urinary_incontinence_no_m*100)[1]## I(urinary_incontinence == "no")
## 91.27838Row_4_urinary_incontinence_no_m_CI = attr(Row_4_urinary_incontinence_no_m,"ci")
Row_4_urinary_incontinence_no_m_CI*100## 2.5% 97.5%
## 90.12516 92.34643# osteoporosis
Row_4_osteoporosis_yes_m<- svyciprop(formula = ~I(osteoporosis == "yes"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_osteoporosis_yes_m*100)[1]## I(osteoporosis == "yes")
## 2.00047Row_4_osteoporosis_yes_m_CI = attr(Row_4_osteoporosis_yes_m,"ci")
Row_4_osteoporosis_yes_m_CI*100## 2.5% 97.5%
## 1.521314 2.568718Row_4_osteoporosis_no_m <- svyciprop(formula = ~I(osteoporosis == "no"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_osteoporosis_no_m*100)[1]## I(osteoporosis == "no")
## 97.99953Row_4_osteoporosis_no_m_CI = attr(Row_4_osteoporosis_no_m,"ci")
Row_4_osteoporosis_no_m_CI*100## 2.5% 97.5%
## 97.43128 98.47869# activity
Row_4_inactive_m<- svyciprop(formula = ~I(activity == "inactive"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_inactive_m*100)[1]## I(activity == "inactive")
## 10.20985Row_4_inactive_m_CI = attr(Row_4_inactive_m,"ci")
Row_4_inactive_m_CI*100## 2.5% 97.5%
## 8.993919 11.517827Row_4_partially_active_m <- svyciprop(formula = ~I(activity == "partially active"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_partially_active_m*100)[1]## I(activity == "partially active")
## 13.09512Row_4_partially_active_m_CI = attr(Row_4_partially_active_m,"ci")
Row_4_partially_active_m_CI*100## 2.5% 97.5%
## 11.74245 14.53181Row_4_active_m<- svyciprop(formula = ~I(activity == "active"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_active_m*100)[1]## I(activity == "active")
## 76.69503Row_4_active_m_CI = attr(Row_4_active_m,"ci")
Row_4_active_m_CI*100## 2.5% 97.5%
## 74.90727 78.42136# alcohol
Row_4_alcohol_abstinent_m<- svyciprop(formula = ~I(alcohol == "abstinent"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_alcohol_abstinent_m*100)[1]## I(alcohol == "abstinent")
## 10.43339Row_4_alcohol_abstinent_m_CI = attr(Row_4_alcohol_abstinent_m,"ci")
Row_4_alcohol_abstinent_m_CI*100## 2.5% 97.5%
## 9.177882 11.784710Row_4_alcohol_low_risk_m<- svyciprop(formula = ~I(alcohol == "low risk"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_alcohol_low_risk_m*100)[1]## I(alcohol == "low risk")
## 82.977Row_4_alcohol_low_risk_m_CI = attr(Row_4_alcohol_low_risk_m,"ci")
Row_4_alcohol_low_risk_m_CI*100## 2.5% 97.5%
## 81.35704 84.51919Row_4_alcohol_moderate_or_increased_risk_m<- svyciprop(formula = ~I(alcohol == "moderate or increased risk"),
design = wdat_17_m,
na.rm = TRUE,
method = "li")
(Row_4_alcohol_moderate_or_increased_risk_m*100)[1]## I(alcohol == "moderate or increased risk")
## 6.589606Row_4_alcohol_moderate_or_increased_risk_m_CI = attr(Row_4_alcohol_moderate_or_increased_risk_m,"ci")
Row_4_alcohol_moderate_or_increased_risk_m_CI*100## 2.5% 97.5%
## 5.611971 7.6662925.2.5.2 Women
# women
## education
Row_4_compulsory_education_f<- svyciprop(formula = ~I(education == "compulsory education"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_compulsory_education_f*100)[1]## I(education == "compulsory education")
## 27.25132Row_4_compulsory_education_f_CI = attr(Row_4_compulsory_education_f,"ci")
Row_4_compulsory_education_f_CI*100## 2.5% 97.5%
## 25.60385 28.94136Row_4_upper_secondary_education_f <- svyciprop(formula = ~I(education == "upper secondary education"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_upper_secondary_education_f*100)[1]## I(education == "upper secondary education")
## 57.08586Row_4_upper_secondary_education_f_CI = attr(Row_4_upper_secondary_education_f,"ci")
Row_4_upper_secondary_education_f_CI*100## 2.5% 97.5%
## 55.20178 58.95636Row_4_tertiary_education_f <- svyciprop(formula = ~I(education == "tertiary education"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_tertiary_education_f*100)[1]## I(education == "tertiary education")
## 15.66282Row_4_tertiary_education_f_CI = attr(Row_4_tertiary_education_f,"ci")
Row_4_tertiary_education_f_CI*100## 2.5% 97.5%
## 14.31736 17.07407# language
Row_4_german_f<- svyciprop(formula = ~I(language == "german"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_german_f*100)[1]## I(language == "german")
## 72.41092Row_4_german_f_CI = attr(Row_4_german_f,"ci")
Row_4_german_f_CI*100## 2.5% 97.5%
## 71.25977 73.54258Row_4_french_f <- svyciprop(formula = ~I(language == "french"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_french_f*100)[1]## I(language == "french")
## 22.41707Row_4_french_f_CI = attr(Row_4_french_f,"ci")
Row_4_french_f_CI*100## 2.5% 97.5%
## 21.35928 23.49905Row_4_italian_f <- svyciprop(formula = ~I(language == "italian"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_italian_f*100)[1]## I(language == "italian")
## 5.172018Row_4_italian_f_CI = attr(Row_4_italian_f,"ci")
Row_4_italian_f_CI*100## 2.5% 97.5%
## 4.747738 5.619434# urban_rural
Row_4_urban_area_f<- svyciprop(formula = ~I(urban_rural == "urban area"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_urban_area_f*100)[1]## I(urban_rural == "urban area")
## 73.76576Row_4_urban_area_f_CI = attr(Row_4_urban_area_f,"ci")
Row_4_urban_area_f_CI*100## 2.5% 97.5%
## 72.21985 75.27350Row_4_rural_arean_f <- svyciprop(formula = ~I(urban_rural == "rural area"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_rural_arean_f*100)[1]## I(urban_rural == "rural area")
## 26.23424Row_4_rural_arean_f_CI = attr(Row_4_rural_arean_f,"ci")
Row_4_rural_arean_f_CI*100## 2.5% 97.5%
## 24.72650 27.78015# nationality
Row_4_swiss_f<- svyciprop(formula = ~I(nationality == "swiss"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_swiss_f*100)[1]## I(nationality == "swiss")
## 89.51778Row_4_swiss_f_CI = attr(Row_4_swiss_f,"ci")
Row_4_swiss_f_CI*100## 2.5% 97.5%
## 88.29170 90.66488Row_4_foreigner_f <- svyciprop(formula = ~I(nationality == "foreigner"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_foreigner_f*100)[1]## I(nationality == "foreigner")
## 10.48222Row_4_foreigner_f_CI = attr(Row_4_foreigner_f,"ci")
Row_4_foreigner_f_CI*100## 2.5% 97.5%
## 9.335124 11.708297# smoke
Row_4_no_smoker_f<- svyciprop(formula = ~I(smoke == "no smoker"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_no_smoker_f*100)[1]## I(smoke == "no smoker")
## 60.27885Row_4_no_smoker_f_CI = attr(Row_4_no_smoker_f,"ci")
Row_4_no_smoker_f_CI*100## 2.5% 97.5%
## 58.41183 62.12614Row_4_former_smoker_f <- svyciprop(formula = ~I(smoke == "former smoker"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_former_smoker_f*100)[1]## I(smoke == "former smoker")
## 25.91118Row_4_former_smoker_f_CI = attr(Row_4_former_smoker_f,"ci")
Row_4_former_smoker_f_CI*100## 2.5% 97.5%
## 24.26261 27.60649Row_4_smoker_f <- svyciprop(formula = ~I(smoke == "smoker"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_smoker_f*100)[1]## I(smoke == "smoker")
## 13.80997Row_4_smoker_f_CI = attr(Row_4_smoker_f,"ci")
Row_4_smoker_f_CI*100## 2.5% 97.5%
## 12.56373 15.12251# FL
Row_4_FL_no_difficulty_f<- svyciprop(formula = ~I(FL == "No difficulty"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_FL_no_difficulty_f*100)[1]## I(FL == "No difficulty")
## 76.08828Row_4_FL_no_difficulty_f_CI = attr(Row_4_FL_no_difficulty_f,"ci")
Row_4_FL_no_difficulty_f_CI*100## 2.5% 97.5%
## 74.40957 77.71478Row_4_FL_slight_difficulty_f <- svyciprop(formula = ~I(FL == "Slight difficulty"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_FL_slight_difficulty_f*100)[1]## I(FL == "Slight difficulty")
## 17.5817Row_4_FL_slight_difficulty_f_CI = attr(Row_4_FL_slight_difficulty_f,"ci")
Row_4_FL_slight_difficulty_f_CI*100## 2.5% 97.5%
## 16.14397 19.08387Row_4_FL_great_difficulty_f<- svyciprop(formula = ~I(FL == "Great difficulty"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_FL_great_difficulty_f*100)[1]## I(FL == "Great difficulty")
## 3.867598Row_4_FL_great_difficulty_f_CI = attr(Row_4_FL_great_difficulty_f,"ci")
Row_4_FL_great_difficulty_f_CI*100## 2.5% 97.5%
## 3.183639 4.638955Row_4_FL_inability_f<- svyciprop(formula = ~I(FL == "Inability"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_FL_inability_f*100)[1]## I(FL == "Inability")
## 2.462422Row_4_FL_inability_f_CI = attr(Row_4_FL_inability_f,"ci")
Row_4_FL_inability_f_CI*100## 2.5% 97.5%
## 1.856840 3.183637# bmi_cut
Row_4_normal_weight_f<- svyciprop(formula = ~I(bmi_cut == "normal weight"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_normal_weight_f*100)[1]## I(bmi_cut == "normal weight")
## 50.91955Row_4_normal_weight_f_CI = attr(Row_4_normal_weight_f,"ci")
Row_4_normal_weight_f_CI*100## 2.5% 97.5%
## 49.00546 52.83185Row_4_obesity_f <- svyciprop(formula = ~I(bmi_cut == "obesity"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_obesity_f*100)[1]## I(bmi_cut == "obesity")
## 13.8348Row_4_obesity_f_CI = attr(Row_4_obesity_f,"ci")
Row_4_obesity_f_CI*100## 2.5% 97.5%
## 12.51322 15.23102Row_4_overweight_f<- svyciprop(formula = ~I(bmi_cut == "overweight"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_overweight_f*100)[1]## I(bmi_cut == "overweight")
## 31.44915Row_4_overweight_f_CI = attr(Row_4_overweight_f,"ci")
Row_4_overweight_f_CI*100## 2.5% 97.5%
## 29.70096 33.23310Row_4_underweight_f <- svyciprop(formula = ~I(bmi_cut == "underweight"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_underweight_f*100)[1]## I(bmi_cut == "underweight")
## 3.796499Row_4_underweight_f_CI = attr(Row_4_underweight_f,"ci")
Row_4_underweight_f_CI*100## 2.5% 97.5%
## 3.129617 4.548016# age_cut
Row_4_age_cut_60to64_f<- svyciprop(formula = ~I(age_cut == "60-69"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_age_cut_60to64_f*100)[1]## I(age_cut == "60-69")
## 45.47211Row_4_age_cut_60to64_f_CI = attr(Row_4_age_cut_60to64_f,"ci")
Row_4_age_cut_60to64_f_CI*100## 2.5% 97.5%
## 43.59342 47.35944Row_4_age_cut_65to74_f <- svyciprop(formula = ~I(age_cut == "70-79"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_age_cut_65to74_f*100)[1]## I(age_cut == "70-79")
## 35.11795Row_4_age_cut_65to74_f_CI = attr(Row_4_age_cut_65to74_f,"ci")
Row_4_age_cut_65to74_f_CI*100## 2.5% 97.5%
## 33.31109 36.95368Row_4_age_cut_75_f<- svyciprop(formula = ~I(age_cut == "80+"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_age_cut_75_f*100)[1]## I(age_cut == "80+")
## 19.40993Row_4_age_cut_75_f_CI = attr(Row_4_age_cut_75_f,"ci")
Row_4_age_cut_75_f_CI*100## 2.5% 97.5%
## 17.87821 21.00539# SHS
Row_4_SHS_very_good_f<- svyciprop(formula = ~I(SHS == "very good"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_SHS_very_good_f*100)[1]## I(SHS == "very good")
## 26.66407Row_4_SHS_very_good_f_CI = attr(Row_4_SHS_very_good_f,"ci")
Row_4_SHS_very_good_f_CI*100## 2.5% 97.5%
## 25.01644 28.35604Row_4_SHS_good_f<- svyciprop(formula = ~I(SHS == "good"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_SHS_good_f*100)[1]## I(SHS == "good")
## 46.20847Row_4_SHS_good_f_CI = attr(Row_4_SHS_good_f,"ci")
Row_4_SHS_good_f_CI*100## 2.5% 97.5%
## 44.31668 48.10755Row_4_SHS_average_f <- svyciprop(formula = ~I(SHS == "average"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_SHS_average_f*100)[1]## I(SHS == "average")
## 22.25442Row_4_SHS_average_f_CI = attr(Row_4_SHS_average_f,"ci")
Row_4_SHS_average_f_CI*100## 2.5% 97.5%
## 20.69361 23.86910Row_4_SHS_poor_f<- svyciprop(formula = ~I(SHS == "poor"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_SHS_poor_f*100)[1]## I(SHS == "poor")
## 4.228014Row_4_SHS_poor_f_CI = attr(Row_4_SHS_poor_f,"ci")
Row_4_SHS_poor_f_CI*100## 2.5% 97.5%
## 3.478123 5.073572Row_4_SHS_very_poor_f<- svyciprop(formula = ~I(SHS == "very poor"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_SHS_very_poor_f*100)[1]## I(SHS == "very poor")
## 0.6450267Row_4_SHS_very_poor_f_CI = attr(Row_4_SHS_very_poor_f,"ci")
Row_4_SHS_very_poor_f_CI*100## 2.5% 97.5%
## 0.4156183 0.9457708# diabetes
Row_4_diabetes_yes_f<- svyciprop(formula = ~I(diabetes == "yes"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_diabetes_yes_f*100)[1]## I(diabetes == "yes")
## 7.942166Row_4_diabetes_yes_f_CI = attr(Row_4_diabetes_yes_f,"ci")
Row_4_diabetes_yes_f_CI*100## 2.5% 97.5%
## 6.857244 9.125631Row_4_diabetes_no_f <- svyciprop(formula = ~I(diabetes == "no"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_diabetes_no_f*100)[1]## I(diabetes == "no")
## 92.05783Row_4_diabetes_no_f_CI = attr(Row_4_diabetes_no_f,"ci")
Row_4_diabetes_no_f_CI*100## 2.5% 97.5%
## 90.87437 93.14276# osteoarthritis
Row_4_osteoarthritis_yes_f<- svyciprop(formula = ~I(osteoarthritis == "yes"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_osteoarthritis_yes_f*100)[1]## I(osteoarthritis == "yes")
## 41.17536Row_4_osteoarthritis_yes_f_CI = attr(Row_4_osteoarthritis_yes_f,"ci")
Row_4_osteoarthritis_yes_f_CI*100## 2.5% 97.5%
## 39.30930 43.05849Row_4_osteoarthritis_no_f <- svyciprop(formula = ~I(osteoarthritis == "no"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_osteoarthritis_no_f*100)[1]## I(osteoarthritis == "no")
## 58.82464Row_4_osteoarthritis_no_f_CI = attr(Row_4_osteoarthritis_no_f,"ci")
Row_4_osteoarthritis_no_f_CI*100## 2.5% 97.5%
## 56.94151 60.69070# heart_attack
Row_4_heart_attack_yes_f<- svyciprop(formula = ~I(heart_attack == "yes"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_heart_attack_yes_f*100)[1]## I(heart_attack == "yes")
## 0.7793696Row_4_heart_attack_yes_f_CI = attr(Row_4_heart_attack_yes_f,"ci")
Row_4_heart_attack_yes_f_CI*100## 2.5% 97.5%
## 0.4672237 1.2054567Row_4_heart_attack_no_f <- svyciprop(formula = ~I(heart_attack == "no"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_heart_attack_no_f*100)[1]## I(heart_attack == "no")
## 99.22063Row_4_heart_attack_no_f_CI = attr(Row_4_heart_attack_no_f,"ci")
Row_4_heart_attack_no_f_CI*100## 2.5% 97.5%
## 98.79454 99.53278# stroke
Row_4_stroke_yes_f<- svyciprop(formula = ~I(stroke == "yes"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_stroke_yes_f*100)[1]## I(stroke == "yes")
## 0.9288504Row_4_stroke_yes_f_CI = attr(Row_4_stroke_yes_f,"ci")
Row_4_stroke_yes_f_CI*100## 2.5% 97.5%
## 0.5908282 1.3748218Row_4_stroke_no_f <- svyciprop(formula = ~I(stroke == "no"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_stroke_no_f*100)[1]## I(stroke == "no")
## 99.07115Row_4_stroke_no_f_CI = attr(Row_4_stroke_no_f,"ci")
Row_4_stroke_no_f_CI*100## 2.5% 97.5%
## 98.62518 99.40917# urinary_incontinence
Row_4_urinary_incontinence_yes_f<- svyciprop(formula = ~I(urinary_incontinence == "yes"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_urinary_incontinence_yes_f*100)[1]## I(urinary_incontinence == "yes")
## 12.38055Row_4_urinary_incontinence_yes_f_CI = attr(Row_4_urinary_incontinence_yes_f,"ci")
Row_4_urinary_incontinence_yes_f_CI*100## 2.5% 97.5%
## 11.11915 13.72010Row_4_urinary_incontinence_no_f <- svyciprop(formula = ~I(urinary_incontinence == "no"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_urinary_incontinence_no_f*100)[1]## I(urinary_incontinence == "no")
## 87.61945Row_4_urinary_incontinence_no_f_CI = attr(Row_4_urinary_incontinence_no_f,"ci")
Row_4_urinary_incontinence_no_f_CI*100## 2.5% 97.5%
## 86.27990 88.88085# osteoporosis
Row_4_osteoporosis_yes_f<- svyciprop(formula = ~I(osteoporosis == "yes"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_osteoporosis_yes_f*100)[1]## I(osteoporosis == "yes")
## 14.47577Row_4_osteoporosis_yes_f_CI = attr(Row_4_osteoporosis_yes_f,"ci")
Row_4_osteoporosis_yes_f_CI*100## 2.5% 97.5%
## 13.17181 15.84818Row_4_osteoporosis_no_f <- svyciprop(formula = ~I(osteoporosis == "no"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_osteoporosis_no_f*100)[1]## I(osteoporosis == "no")
## 85.52423Row_4_osteoporosis_no_f_CI = attr(Row_4_osteoporosis_no_f,"ci")
Row_4_osteoporosis_no_f_CI*100## 2.5% 97.5%
## 84.15182 86.82819# activity
Row_4_inactive_f<- svyciprop(formula = ~I(activity == "inactive"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_inactive_f*100)[1]## I(activity == "inactive")
## 15.23474Row_4_inactive_f_CI = attr(Row_4_inactive_f,"ci")
Row_4_inactive_f_CI*100## 2.5% 97.5%
## 13.82513 16.71949Row_4_partially_active_f <- svyciprop(formula = ~I(activity == "partially active"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_partially_active_f*100)[1]## I(activity == "partially active")
## 15.48461Row_4_partially_active_f_CI = attr(Row_4_partially_active_f,"ci")
Row_4_partially_active_f_CI*100## 2.5% 97.5%
## 14.11078 16.92818Row_4_active_f<- svyciprop(formula = ~I(activity == "active"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_active_f*100)[1]## I(activity == "active")
## 69.28064Row_4_active_f_CI = attr(Row_4_active_f,"ci")
Row_4_active_f_CI*100## 2.5% 97.5%
## 67.44248 71.07889# alcohol
Row_4_alcohol_abstinent_f<- svyciprop(formula = ~I(alcohol == "abstinent"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_alcohol_abstinent_f*100)[1]## I(alcohol == "abstinent")
## 21.64624Row_4_alcohol_abstinent_f_CI = attr(Row_4_alcohol_abstinent_f,"ci")
Row_4_alcohol_abstinent_f_CI*100## 2.5% 97.5%
## 20.07156 23.27820Row_4_alcohol_low_risk_f<- svyciprop(formula = ~I(alcohol == "low risk"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_alcohol_low_risk_f*100)[1]## I(alcohol == "low risk")
## 73.00365Row_4_alcohol_low_risk_f_CI = attr(Row_4_alcohol_low_risk_f,"ci")
Row_4_alcohol_low_risk_f_CI*100## 2.5% 97.5%
## 71.26769 74.69396Row_4_alcohol_moderate_or_increased_risk_f<- svyciprop(formula = ~I(alcohol == "moderate or increased risk"),
design = wdat_17_f,
na.rm = TRUE,
method = "li")
(Row_4_alcohol_moderate_or_increased_risk_f*100)[1]## I(alcohol == "moderate or increased risk")
## 5.350114Row_4_alcohol_moderate_or_increased_risk_f_CI = attr(Row_4_alcohol_moderate_or_increased_risk_f,"ci")
Row_4_alcohol_moderate_or_increased_risk_f_CI*100## 2.5% 97.5%
## 4.567451 6.2121545.2.6 Summary proportion (%) and CI 95% of population with falls by characteristic
5.2.6.1 Men
# men
# education
tab_amount_fall_education_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ education,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_education_m*100## education I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 19.85768 15.66504 24.54853
## 2 NA 22.77108 20.40425 25.25952
## 3 NA 26.21775 23.31754 29.26283# language
tab_amount_fall_language_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ language,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_language_m*100## language I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 24.22165 22.16252 26.36338
## 2 NA 22.65386 19.46623 26.06824
## 3 NA 18.64615 13.57350 24.54846# urban_rural
tab_amount_fall_urban_rural_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ urban_rural,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_urban_rural_m*100## urban_rural I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 23.38946 21.35948 25.50444
## 2 NA 24.47442 21.39538 27.73868# nationality
tab_amount_fall_nationality_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ nationality,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_nationality_m*100## nationality I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 24.86303 22.98294 26.80875
## 2 NA 16.41364 12.70048 20.64099# smoke
tab_amount_fall_smoke_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ smoke,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_smoke_m*100## smoke I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 23.07225 20.35888 25.94328
## 2 NA 24.71608 22.06310 27.50309
## 3 NA 22.59429 18.90181 26.59605# FL
tab_amount_fall_FL_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ FL,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_FL_m*100## FL I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 22.81874 20.88739 24.83004
## 2 NA 24.11051 20.32016 28.19325
## 3 NA 40.53428 27.98901 53.97527
## 4 NA 34.39144 21.18945 49.46500# bmi_cut
tab_amount_fall_bmi_cut_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ bmi_cut,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_bmi_cut_m*100## bmi_cut I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 23.56776 20.79323 26.50166
## 2 NA 24.87046 20.59600 29.50056
## 3 NA 23.41424 20.91458 26.04388
## 4 NA 38.10953 11.14708 71.77210# age_cut
tab_amount_fall_age_cut_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ age_cut,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_age_cut_m*100## age_cut I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 21.72215 19.32382 24.25527
## 2 NA 24.02362 21.24775 26.95396
## 3 NA 28.99578 24.43703 33.85649# SHS
tab_amount_fall_SHS_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ SHS,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_SHS_m*100## SHS I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 22.01380 18.72610 25.55495
## 2 NA 23.46568 21.04619 26.00627
## 3 NA 24.63606 20.78396 28.77936
## 4 NA 29.47983 21.68690 38.17040
## 5 NA 31.19853 16.01732 49.77763# diabetes
tab_amount_fall_diabetes_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ diabetes,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_diabetes_m*100## diabetes I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 23.08905 21.17181 25.08355
## 2 NA 27.55444 22.38761 33.15648# osteoarthritis
tab_amount_fall_osteoarthritis_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ osteoarthritis,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_osteoarthritis_m*100## osteoarthritis I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 21.98076 20.05555 23.99026
## 2 NA 29.19377 25.54539 33.03017# heart_attack
tab_amount_fall_heart_attack_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ heart_attack,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_heart_attack_m*100## heart_attack I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 23.58210 21.85908 25.36516
## 2 NA 30.41774 16.81601 46.84486# stroke
tab_amount_fall_stroke_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ stroke,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_stroke_m*100## stroke I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 23.55358 21.83642 25.33049
## 2 NA 33.30601 15.16562 55.69365# urinary_incontinence
tab_amount_fall_urinary_incontinence_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ urinary_incontinence,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_urinary_incontinence_m*100## urinary_incontinence I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 22.62365 20.86522 24.44900
## 2 NA 35.38263 28.95637 42.18273# osteoporosis
tab_amount_fall_osteoporosis_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ osteoporosis,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_osteoporosis_m*100## osteoporosis I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 23.37635 21.64873 25.16521
## 2 NA 35.91822 23.39578 49.89099# activity
tab_amount_fall_activity_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ activity,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_activity_m*100## activity I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 25.91317 20.24444 32.17836
## 2 NA 25.46715 20.65547 30.71525
## 3 NA 23.36547 21.34844 25.46656# alcohol
tab_amount_fall_alcohol_m <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ alcohol,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_alcohol_m*100## alcohol I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 20.88088 15.76847 26.69494
## 2 NA 24.26244 22.31688 26.28137
## 3 NA 23.73551 17.53584 30.785905.2.6.2 Women
# women
# education
tab_amount_fall_education_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ education,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_education_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## education I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 25.58549 22.51454 28.82678
## 2 NA 25.39804 23.19951 27.68358
## 3 NA 25.50011 21.46293 29.83901# language
tab_amount_fall_language_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ language,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_language_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## language I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 25.78792 23.75072 27.89763
## 2 NA 26.25770 23.24247 29.42942
## 3 NA 19.03335 14.56549 24.11140# urban_rural
tab_amount_fall_urban_rural_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ urban_rural,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_urban_rural_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## urban_rural I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 25.47602 23.52489 27.49498
## 2 NA 25.73064 22.73116 28.89076# nationality
tab_amount_fall_nationality_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ nationality,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_nationality_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## nationality I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 25.83928 24.09431 27.63698
## 2 NA 22.99168 18.16858 28.34136# smoke
tab_amount_fall_smoke_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ smoke,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_smoke_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## smoke I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 25.15711 23.04109 27.35494
## 2 NA 27.88961 24.55621 31.39442
## 3 NA 22.83275 18.91841 27.09036# FL
tab_amount_fall_FL_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ FL,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_FL_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## FL I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 23.10017 21.30097 24.96718
## 2 NA 31.27858 27.04272 35.73394
## 3 NA 37.88652 28.68745 47.71052
## 4 NA 38.04728 25.54567 51.72266# bmi_cut
tab_amount_fall_bmi_cut_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ bmi_cut,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_bmi_cut_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## bmi_cut I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 26.60158 24.22806 29.06869
## 2 NA 25.17228 20.88206 29.81314
## 3 NA 24.03888 21.20875 27.02954
## 4 NA 25.41585 17.67966 34.35905# age_cut
tab_amount_fall_age_cut_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ age_cut,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_age_cut_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## age_cut I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 20.31848 18.18715 22.56698
## 2 NA 26.53584 23.72813 29.47611
## 3 NA 35.95031 31.63216 40.42583# SHS
tab_amount_fall_SHS_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ SHS,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_SHS_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## SHS I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 20.45410 17.55242 23.57529
## 2 NA 23.52557 21.18924 25.97418
## 3 NA 33.36065 29.63385 37.23149
## 4 NA 36.42046 27.12977 46.44554
## 5 NA 40.16408 20.22265 62.65513# diabetes
tab_amount_fall_diabetes_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ diabetes,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_diabetes_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## diabetes I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 24.22775 22.45181 26.06476
## 2 NA 29.65368 22.95672 36.99295# osteoarthritis
tab_amount_fall_osteoarthritis_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ osteoarthritis,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_osteoarthritis_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## osteoarthritis I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 22.38463 20.33455 24.52785
## 2 NA 30.11422 27.40807 32.91592# heart_attack
tab_amount_fall_heart_attack_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ heart_attack,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_heart_attack_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## heart_attack I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 25.52858 23.872841 27.23274
## 2 NA 25.64959 9.652918 48.07335# stroke
tab_amount_fall_stroke_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ stroke,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_stroke_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## stroke I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 25.31564 23.66355 27.01663
## 2 NA 51.67638 29.11522 73.77259# urinary_incontinence
tab_amount_fall_urinary_incontinence_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ urinary_incontinence,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_urinary_incontinence_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## urinary_incontinence I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 24.12366 22.40599 25.89880
## 2 NA 35.79056 30.51328 41.30843# osteoporosis
tab_amount_fall_osteoporosis_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ osteoporosis,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_osteoporosis_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## osteoporosis I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 24.82317 23.04898 26.65583
## 2 NA 27.33264 22.98785 31.98564# activity
tab_amount_fall_activity_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ activity,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_activity_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## activity I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 30.43798 25.77617 35.38436
## 2 NA 24.75886 20.56238 29.30033
## 3 NA 23.78882 21.81281 25.84308# alcohol
tab_amount_fall_alcohol_f <- svyby(
formula = ~I(fall == "fall last 12 months"),
by = ~ alcohol,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
tab_amount_fall_alcohol_f*100## Warning in Ops.factor(left, right): '*' not meaningful for factors## alcohol I(fall == "fall last 12 months") ci_l ci_u
## 1 NA 26.42494 22.72913 30.35595
## 2 NA 24.90682 22.96970 26.91346
## 3 NA 22.66173 16.22787 30.097305.2.7 Summary intergroupdifference - test
Intergroupdifference (Prop fall % by subset of groups)
- \(svychi^2-test\) \(Pearson's\_x^2\) with Rao & Scott adjustment
“
svychisq()computes first and second-order Rao-Scott corrections to the Pearson chisquared test, and two Wald-type tests. The default (statistic=”F”) is the Rao-Scott second-order correction. The p-values are computed with a Satterthwaite approximation to the distribution. The alternative statistic=“Chisq” adjusts the Pearson chisquared statistic by a design effect estimate and then compares it to the chisquared distribution it would have under simple random sampling” [2].
Here a description of Prof Thomas Lumley
- Fall - Sex
# men vs women
survey::svychisq(~fall + gender, wdat_17)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + gender, wdat_17)
F = 2.2946, ndf = 1, ddf = 6856, p-value = 0.1299
5.2.7.1 Men
# men
# education
survey::svychisq(~fall + education, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + education, wdat_17_m)
F = 3.0815, ndf = 1.9979, ddf = 6369.4265, p-value = 0.04601
# language
survey::svychisq(~fall + language, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + language, wdat_17_m)
F = 1.473, ndf = 1.9237, ddf = 6132.8699, p-value = 0.2298
# urban_rural
survey::svychisq(~fall + urban_rural, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + urban_rural, wdat_17_m)
F = 0.31923, ndf = 1, ddf = 3188, p-value = 0.5721
# nationality
survey::svychisq(~fall + nationality, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + nationality, wdat_17_m)
F = 11.349, ndf = 1, ddf = 3188, p-value = 0.0007641
# smoke
survey::svychisq(~fall + smoke, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + smoke, wdat_17_m)
F = 0.53085, ndf = 1.9989, ddf = 6372.3946, p-value = 0.588
# FL
survey::svychisq(~fall + FL, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + FL, wdat_17_m)
F = 4.3154, ndf = 2.9936, ddf = 9543.7307, p-value = 0.004803
# bmi_cut
survey::svychisq(~fall + bmi_cut, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + bmi_cut, wdat_17_m)
F = 0.53968, ndf = 2.9671, ddf = 9459.0986, p-value = 0.6531
# bmi
# survey::svychisq(~fall + bmi, wdat_17_m)
# age_cut
survey::svychisq(~fall + age_cut, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + age_cut, wdat_17_m)
F = 4.0318, ndf = 1.9994, ddf = 6373.9621, p-value = 0.0178
# age
# survey::svychisq(~fall + age, wdat_17_m)
# SHS
survey::svychisq(~fall + SHS, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + SHS, wdat_17_m)
F = 1.0713, ndf = 3.9886, ddf = 12715.5751, p-value = 0.3687
# diabetes
survey::svychisq(~fall + diabetes, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + diabetes, wdat_17_m)
F = 2.5506, ndf = 1, ddf = 3188, p-value = 0.1104
# osteoarthritis
survey::svychisq(~fall + osteoarthritis, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + osteoarthritis, wdat_17_m)
F = 12.231, ndf = 1, ddf = 3188, p-value = 0.0004765
# heart_attack
survey::svychisq(~fall + heart_attack, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + heart_attack, wdat_17_m)
F = 0.95028, ndf = 1, ddf = 3188, p-value = 0.3297
# stroke
survey::svychisq(~fall + stroke, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + stroke, wdat_17_m)
F = 1.2191, ndf = 1, ddf = 3188, p-value = 0.2696
# urinary_incontinence
survey::svychisq(~fall + urinary_incontinence, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + urinary_incontinence, wdat_17_m)
F = 16.584, ndf = 1, ddf = 3188, p-value = 4.768e-05
# osteoporosis
survey::svychisq(~fall + osteoporosis, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + osteoporosis, wdat_17_m)
F = 4.3965, ndf = 1, ddf = 3188, p-value = 0.03609
# activity
survey::svychisq(~fall + activity, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + activity, wdat_17_m)
F = 0.55522, ndf = 1.9975, ddf = 6368.1113, p-value = 0.5738
# alcohol
survey::svychisq(~fall + alcohol, wdat_17_m)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + alcohol, wdat_17_m)
F = 0.64744, ndf = 1.9887, ddf = 6339.9501, p-value = 0.5226
5.2.7.2 Women
# women
# education
survey::svychisq(~fall + education, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + education, wdat_17_f)
F = 0.0046757, ndf = 1.9996, ddf = 7282.6569, p-value = 0.9953
# language
survey::svychisq(~fall + language, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + language, wdat_17_f)
F = 2.3529, ndf = 1.9077, ddf = 6947.7606, p-value = 0.09784
# urban_rural
survey::svychisq(~fall + urban_rural, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + urban_rural, wdat_17_f)
F = 0.018618, ndf = 1, ddf = 3642, p-value = 0.8915
# nationality
survey::svychisq(~fall + nationality, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + nationality, wdat_17_f)
F = 1.0103, ndf = 1, ddf = 3642, p-value = 0.3149
# smoke
survey::svychisq(~fall + smoke, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + smoke, wdat_17_f)
F = 1.8053, ndf = 1.9927, ddf = 7257.4910, p-value = 0.1646
# FL
survey::svychisq(~fall + FL, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + FL, wdat_17_f)
F = 8.5626, ndf = 2.982, ddf = 10860.306, p-value = 1.183e-05
# bmi_cut
survey::svychisq(~fall + bmi_cut, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + bmi_cut, wdat_17_f)
F = 0.6055, ndf = 2.9971, ddf = 10915.3607, p-value = 0.6112
# bmi
# survey::svychisq(~fall + bmi, wdat_17_f)
# age_cut
survey::svychisq(~fall + age_cut, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + age_cut, wdat_17_f)
F = 22.791, ndf = 1.994, ddf = 7262.329, p-value = 1.437e-10
# age
# survey::svychisq(~fall + age, wdat_17_f)
# SHS
survey::svychisq(~fall + SHS, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + SHS, wdat_17_f)
F = 10.194, ndf = 3.9156, ddf = 14260.6820, p-value = 4.138e-08
# diabetes
survey::svychisq(~fall + diabetes, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + diabetes, wdat_17_f)
F = 2.4123, ndf = 1, ddf = 3642, p-value = 0.1205
# osteoarthritis
survey::svychisq(~fall + osteoarthritis, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + osteoarthritis, wdat_17_f)
F = 19.622, ndf = 1, ddf = 3642, p-value = 9.713e-06
# heart_attack
survey::svychisq(~fall + heart_attack, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + heart_attack, wdat_17_f)
F = 0.00017813, ndf = 1, ddf = 3642, p-value = 0.9894
# stroke
survey::svychisq(~fall + stroke, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + stroke, wdat_17_f)
F = 7.7581, ndf = 1, ddf = 3642, p-value = 0.005375
# urinary_incontinence
survey::svychisq(~fall + urinary_incontinence, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + urinary_incontinence, wdat_17_f)
F = 19.218, ndf = 1, ddf = 3642, p-value = 1.199e-05
# osteoporosis
survey::svychisq(~fall + osteoporosis, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + osteoporosis, wdat_17_f)
F = 1.0785, ndf = 1, ddf = 3642, p-value = 0.2991
# activity
survey::svychisq(~fall + activity, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + activity, wdat_17_f)
F = 3.5277, ndf = 1.9994, ddf = 7281.7001, p-value = 0.02944
# alcohol
survey::svychisq(~fall + alcohol, wdat_17_f)
Pearson's X^2: Rao & Scott adjustment
data: survey::svychisq(~fall + alcohol, wdat_17_f)
F = 0.48448, ndf = 1.9963, ddf = 7270.6664, p-value = 0.6157
5.3 Univariate models
Univariate logistic models separately for men and women.
5.3.1 Men
# men
# education
uni_education_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ education,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_education_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3201
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.004
## Pseudo-R² (McFadden) = 0.003
## AIC = NA
##
## -------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ---------------------------- ----------- ------- ------- -------- -------
## (Intercept) 0.248 0.188 0.327 -9.813 0.000
## educationupper secondary 1.190 0.872 1.624 1.097 0.273
## education
## educationtertiary 1.434 1.043 1.971 2.221 0.026
## education
## -------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_education_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_education_m")
# language
uni_language_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ language,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_language_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3211
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.001
## Pseudo-R² (McFadden) = 0.001
## AIC = NA
##
## -------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## --------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.320 0.285 0.358 -19.542 0.000
## languagefrench 0.916 0.735 1.142 -0.778 0.437
## languageitalian 0.717 0.491 1.046 -1.724 0.085
## -------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_language_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_language_m")
# urban_rural
uni_urban_rural_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ urban_rural,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_urban_rural_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3211
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = -0.000
## Pseudo-R² (McFadden) = -0.000
## AIC = NA
##
## -------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## --------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.305 0.272 0.343 -20.114 0.000
## urban_ruralrural area 1.061 0.863 1.305 0.565 0.572
## -------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_urban_rural_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_urban_rural_m")
# nationality
uni_nationality_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ nationality,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_nationality_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3211
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.007
## Pseudo-R² (McFadden) = 0.004
## AIC = NA
##
## ------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## -------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.331 0.299 0.367 -21.177 0.000
## nationalityforeigner 0.593 0.437 0.806 -3.339 0.001
## ------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_nationality_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_nationality_m")
# smoke
uni_smoke_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ smoke,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_smoke_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3210
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.001
## Pseudo-R² (McFadden) = 0.000
## AIC = NA
##
## ----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------ ----------- ------- ------- --------- -------
## (Intercept) 0.300 0.256 0.351 -15.015 0.000
## smokeformer smoker 1.095 0.883 1.357 0.826 0.409
## smokesmoker 0.973 0.743 1.275 -0.197 0.844
## ----------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_smoke_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_smoke_m")
# FL
uni_FL_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ FL,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_FL_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3202
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.007
## Pseudo-R² (McFadden) = 0.004
## AIC = NA
##
## -----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.296 0.264 0.331 -21.343 0.000
## FLSlight difficulty 1.075 0.844 1.369 0.582 0.560
## FLGreat difficulty 2.306 1.336 3.980 2.999 0.003
## FLInability 1.773 0.951 3.307 1.801 0.072
## -----------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_FL_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_FL_m")
# bmi_cut
uni_bmi_cut_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ bmi_cut,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_bmi_cut_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3188
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.002
## Pseudo-R² (McFadden) = 0.001
## AIC = NA
##
## ----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------ ----------- ------- ------- --------- -------
## (Intercept) 0.308 0.263 0.361 -14.560 0.000
## bmi_cutobesity 1.074 0.806 1.429 0.486 0.627
## bmi_cutoverweight 0.991 0.801 1.227 -0.079 0.937
## bmi_cutunderweight 1.997 0.646 6.169 1.202 0.230
## ----------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_bmi_cut_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_bmi_cut_m")
# bmi
uni_bmi_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ bmi,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_bmi_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3188
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.001
## Pseudo-R² (McFadden) = 0.001
## AIC = NA
##
## --------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------- ----------- ------- ------- -------- -------
## (Intercept) 0.288 0.152 0.547 -3.809 0.000
## bmi 1.003 0.979 1.027 0.244 0.807
## --------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_bmi_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_bmi_m")
# age_cut
uni_age_cut_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ age_cut,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_age_cut_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3211
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.005
## Pseudo-R² (McFadden) = 0.003
## AIC = NA
##
## ----------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------ ----------- ------- ------- --------- -------
## (Intercept) 0.278 0.240 0.321 -17.334 0.000
## age_cut70-79 1.139 0.921 1.410 1.201 0.230
## age_cut80+ 1.472 1.123 1.928 2.803 0.005
## ----------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_age_cut_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_age_cut_m")
# age
uni_age_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ age,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_age_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3211
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.005
## Pseudo-R² (McFadden) = 0.003
## AIC = NA
##
## --------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------- ----------- ------- ------- -------- -------
## (Intercept) 0.090 0.036 0.221 -5.245 0.000
## age 1.018 1.005 1.030 2.736 0.006
## --------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_age_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_age_m")
# SHS
uni_SHS_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ SHS,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_SHS_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3207
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.003
## Pseudo-R² (McFadden) = 0.002
## AIC = NA
##
## ----------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------ ----------- ------- ------- --------- -------
## (Intercept) 0.282 0.231 0.344 -12.477 0.000
## SHSgood 1.086 0.853 1.384 0.669 0.503
## SHSaverage 1.158 0.864 1.552 0.982 0.326
## SHSpoor 1.481 0.952 2.303 1.743 0.081
## SHSvery poor 1.606 0.732 3.524 1.183 0.237
## ----------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_SHS_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_SHS_m")
# SHS_collapsed
uni_SHS_collapsed_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ SHS_collapsed,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_SHS_collapsed_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3207
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.003
## Pseudo-R² (McFadden) = 0.002
## AIC = NA
##
## --------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ---------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.298 0.266 0.334 -20.936 0.000
## SHS_collapsedaverage 1.098 0.861 1.400 0.752 0.452
## SHS_collapsedpoor 1.404 0.932 2.116 1.621 0.105
## SHS_collapsedvery poor 1.523 0.706 3.284 1.073 0.283
## --------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_SHS_collapsed_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_SHS_m")
# diabetes
uni_diabetes_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ diabetes,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_diabetes_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 2892
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.001
## Pseudo-R² (McFadden) = 0.000
## AIC = NA
##
## ---------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------- ----------- ------- ------- --------- -------
## (Intercept) 0.300 0.269 0.335 -21.420 0.000
## diabetesyes 1.267 0.947 1.695 1.594 0.111
## ---------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_diabetes_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_diabetes_m")
# osteoarthritis
uni_osteoarthritis_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ osteoarthritis,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_osteoarthritis_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3187
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.010
## Pseudo-R² (McFadden) = 0.006
## AIC = NA
##
## ---------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.282 0.251 0.316 -21.651 0.000
## osteoarthritisyes 1.463 1.181 1.813 3.485 0.000
## ---------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.999# jtools::plot_summs(uni_osteoarthritis_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_osteoarthritis_m")
# heart_attack
uni_heart_attack_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ heart_attack,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_heart_attack_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3209
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.001
## Pseudo-R² (McFadden) = 0.000
## AIC = NA
##
## -------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## --------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.309 0.280 0.340 -23.697 0.000
## heart_attackyes 1.417 0.701 2.863 0.970 0.332
## -------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_heart_attack_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_heart_attack_m")
# stroke
uni_stroke_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ stroke,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_stroke_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3207
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.002
## Pseudo-R² (McFadden) = 0.001
## AIC = NA
##
## ---------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------- ----------- ------- ------- --------- -------
## (Intercept) 0.308 0.280 0.339 -23.790 0.000
## strokeyes 1.621 0.682 3.851 1.094 0.274
## ---------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_stroke_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_stroke_m")
# urinary_incontinence
uni_urinary_incontinence_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ urinary_incontinence,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_urinary_incontinence_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3206
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.011
## Pseudo-R² (McFadden) = 0.007
## AIC = NA
##
## ---------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.292 0.264 0.324 -23.550 0.000
## urinary_incontinenceyes 1.873 1.379 2.543 4.021 0.000
## ---------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_urinary_incontinence_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_urinary_incontinence_m")
# osteoporosis
uni_osteoporosis_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ osteoporosis,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_osteoporosis_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3192
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.003
## Pseudo-R² (McFadden) = 0.002
## AIC = NA
##
## -------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## --------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.305 0.277 0.337 -23.709 0.000
## osteoporosisyes 1.837 1.032 3.271 2.067 0.039
## -------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_osteoporosis_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_osteoporosis_m")
# activity
uni_activity_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ activity,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_activity_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 2987
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = -0.007
## Pseudo-R² (McFadden) = -0.004
## AIC = NA
##
## ---------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------------ ----------- ------- ------- -------- -------
## (Intercept) 0.350 0.256 0.477 -6.637 0.000
## activitypartially active 0.977 0.650 1.469 -0.112 0.911
## activityactive 0.872 0.626 1.214 -0.813 0.416
## ---------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.005# jtools::plot_summs(uni_activity_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_activity_m")
# alcohol
uni_alcohol_m <-
survey::svyglm(
I(fall=="fall last 12 months") ~ alcohol,
design = wdat_17_m,
family = quasibinomial(logit)
)
jtools::summ(uni_alcohol_m,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3055
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = -0.004
## Pseudo-R² (McFadden) = -0.002
## AIC = NA
##
## -----------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## -------------------------------- ----------- ------- ------- -------- -------
## (Intercept) 0.264 0.190 0.367 -7.922 0.000
## alcohollow risk 1.214 0.858 1.717 1.095 0.274
## alcoholmoderate or increased 1.179 0.721 1.928 0.657 0.511
## risk
## -----------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.003# jtools::plot_summs(uni_alcohol_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_alcohol_m")5.3.2 Women
# women
# education
uni_education_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ education,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_education_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3640
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.001
## Pseudo-R² (McFadden) = 0.001
## AIC = NA
##
## --------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ---------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.344 0.291 0.406 -12.630 0.000
## educationupper secondary 0.990 0.808 1.214 -0.095 0.924
## education
## educationtertiary 0.996 0.756 1.311 -0.032 0.975
## education
## --------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_education_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_education_f")
# language
uni_language_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ language,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_language_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3659
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.002
## Pseudo-R² (McFadden) = 0.001
## AIC = NA
##
## -------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## --------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.347 0.312 0.387 -19.127 0.000
## languagefrench 1.025 0.845 1.243 0.248 0.804
## languageitalian 0.676 0.488 0.938 -2.341 0.019
## -------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_language_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_language_f")
# urban_rural
uni_urban_rural_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ urban_rural,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_urban_rural_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3659
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = -0.000
## Pseudo-R² (McFadden) = -0.000
## AIC = NA
##
## -------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## --------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.342 0.308 0.380 -20.126 0.000
## urban_ruralrural area 1.013 0.836 1.228 0.136 0.891
## -------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_urban_rural_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_urban_rural_f")
# nationality
uni_nationality_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ nationality,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_nationality_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3659
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.001
## Pseudo-R² (McFadden) = 0.000
## AIC = NA
##
## ------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## -------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.348 0.318 0.382 -22.362 0.000
## nationalityforeigner 0.857 0.634 1.158 -1.004 0.315
## ------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_nationality_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_nationality_f")
# smoke
uni_smoke_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ smoke,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_smoke_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3659
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.002
## Pseudo-R² (McFadden) = 0.001
## AIC = NA
##
## ----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------ ----------- ------- ------- --------- -------
## (Intercept) 0.336 0.300 0.377 -18.661 0.000
## smokeformer smoker 1.151 0.937 1.412 1.341 0.180
## smokesmoker 0.880 0.680 1.140 -0.968 0.333
## ----------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_smoke_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_smoke_f")
# FL
uni_FL_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ FL,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_FL_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3650
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.015
## Pseudo-R² (McFadden) = 0.009
## AIC = NA
##
## -----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.300 0.271 0.333 -22.845 0.000
## FLSlight difficulty 1.515 1.208 1.901 3.589 0.000
## FLGreat difficulty 2.031 1.341 3.074 3.347 0.001
## FLInability 2.044 1.168 3.579 2.503 0.012
## -----------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_FL_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_FL_f")
# bmi_cut
uni_bmi_cut_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ bmi_cut,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_bmi_cut_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3608
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.000
## Pseudo-R² (McFadden) = 0.000
## AIC = NA
##
## ----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------ ----------- ------- ------- --------- -------
## (Intercept) 0.362 0.320 0.410 -16.050 0.000
## bmi_cutobesity 0.928 0.711 1.212 -0.547 0.585
## bmi_cutoverweight 0.873 0.713 1.069 -1.316 0.188
## bmi_cutunderweight 0.940 0.597 1.482 -0.266 0.791
## ----------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_bmi_cut_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_bmi_cut_f")
# bmi
uni_bmi_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ bmi,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_bmi_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3608
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = -0.000
## Pseudo-R² (McFadden) = -0.000
## AIC = NA
##
## --------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------- ----------- ------- ------- -------- -------
## (Intercept) 0.417 0.260 0.669 -3.628 0.000
## bmi 0.992 0.974 1.011 -0.822 0.411
## --------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_bmi_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_bmi_f")
# age_cut
uni_age_cut_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ age_cut,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_age_cut_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3659
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.025
## Pseudo-R² (McFadden) = 0.015
## AIC = NA
##
## ----------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------ ----------- ------- ------- --------- -------
## (Intercept) 0.255 0.223 0.292 -19.803 0.000
## age_cut70-79 1.417 1.159 1.731 3.406 0.001
## age_cut80+ 2.201 1.742 2.781 6.616 0.000
## ----------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_age_cut_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_age_cut_f")
# age
uni_age_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ age,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_age_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3659
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.023
## Pseudo-R² (McFadden) = 0.014
## AIC = NA
##
## --------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------- ----------- ------- ------- -------- -------
## (Intercept) 0.030 0.014 0.065 -8.828 0.000
## age 1.034 1.023 1.045 6.176 0.000
## --------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_age_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_age_f")
# SHS
uni_SHS_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ SHS,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_SHS_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3658
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.021
## Pseudo-R² (McFadden) = 0.013
## AIC = NA
##
## ----------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------ ----------- ------- ------- --------- -------
## (Intercept) 0.257 0.214 0.309 -14.391 0.000
## SHSgood 1.196 0.952 1.503 1.541 0.123
## SHSaverage 1.947 1.514 2.504 5.187 0.000
## SHSpoor 2.228 1.413 3.512 3.449 0.001
## SHSvery poor 2.610 1.107 6.153 2.193 0.028
## ----------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_SHS_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_SHS_f")
# SHS_collapsed
uni_SHS_collapsed_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ SHS_collapsed,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_SHS_collapsed_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3658
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.020
## Pseudo-R² (McFadden) = 0.012
## AIC = NA
##
## --------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ---------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.289 0.259 0.322 -22.598 0.000
## SHS_collapsedaverage 1.734 1.417 2.122 5.344 0.000
## SHS_collapsedpoor 1.984 1.291 3.050 3.126 0.002
## SHS_collapsedvery poor 2.325 1.000 5.409 1.959 0.050
## --------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.001# jtools::plot_summs(uni_SHS_collapsed_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_SHS_m")
# diabetes
uni_diabetes_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ diabetes,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_diabetes_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3270
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.022
## Pseudo-R² (McFadden) = 0.013
## AIC = NA
##
## ---------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------- ----------- ------- ------- --------- -------
## (Intercept) 0.320 0.290 0.353 -22.716 0.000
## diabetesyes 1.318 0.929 1.870 1.549 0.121
## ---------------------------------------------------------------
##
## Estimated dispersion parameter = 0.988# jtools::plot_summs(uni_diabetes_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_diabetes_f")
# osteoarthritis
uni_osteoarthritis_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ osteoarthritis,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_osteoarthritis_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3627
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.012
## Pseudo-R² (McFadden) = 0.007
## AIC = NA
##
## ---------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.288 0.256 0.325 -20.201 0.000
## osteoarthritisyes 1.494 1.250 1.786 4.417 0.000
## ---------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_osteoarthritis_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_osteoarthritis_f")
# heart_attack
uni_heart_attack_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ heart_attack,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_heart_attack_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3657
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = -0.000
## Pseudo-R² (McFadden) = -0.000
## AIC = NA
##
## -------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## --------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.343 0.314 0.374 -23.751 0.000
## heart_attackyes 1.006 0.396 2.559 0.013 0.989
## -------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_heart_attack_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_heart_attack_f")
# stroke
uni_stroke_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ stroke,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_stroke_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3655
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.004
## Pseudo-R² (McFadden) = 0.003
## AIC = NA
##
## ---------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------- ----------- ------- ------- --------- -------
## (Intercept) 0.339 0.310 0.370 -23.917 0.000
## strokeyes 3.155 1.345 7.398 2.642 0.008
## ---------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_stroke_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_stroke_f")
# urinary_incontinence
uni_urinary_incontinence_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ urinary_incontinence,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_urinary_incontinence_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3653
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.011
## Pseudo-R² (McFadden) = 0.006
## AIC = NA
##
## ---------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ----------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.318 0.289 0.350 -23.546 0.000
## urinary_incontinenceyes 1.753 1.361 2.259 4.344 0.000
## ---------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_urinary_incontinence_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_urinary_incontinence_f")
# osteoporosis
uni_osteoporosis_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ osteoporosis,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_osteoporosis_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3604
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.002
## Pseudo-R² (McFadden) = 0.001
## AIC = NA
##
## -------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## --------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.330 0.300 0.364 -22.477 0.000
## osteoporosisyes 1.139 0.891 1.457 1.038 0.299
## -------------------------------------------------------------------
##
## Estimated dispersion parameter = 1# jtools::plot_summs(uni_osteoporosis_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_osteoporosis_f")
# activity
uni_activity_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ activity,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_activity_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3393
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.023
## Pseudo-R² (McFadden) = 0.014
## AIC = NA
##
## ---------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------------ ----------- ------- ------- -------- -------
## (Intercept) 0.438 0.349 0.549 -7.154 0.000
## activitypartially active 0.752 0.543 1.042 -1.714 0.087
## activityactive 0.713 0.554 0.918 -2.626 0.009
## ---------------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.989# jtools::plot_summs(uni_activity_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_activity_f")
# alcohol
uni_alcohol_f <-
survey::svyglm(
I(fall=="fall last 12 months") ~ alcohol,
design = wdat_17_f,
family = quasibinomial(logit)
)
jtools::summ(uni_alcohol_f,exp = TRUE, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 3498
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.015
## Pseudo-R² (McFadden) = 0.009
## AIC = NA
##
## ------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## -------------------------------- ----------- ------- ------- --------- -------
## (Intercept) 0.359 0.295 0.437 -10.245 0.000
## alcohollow risk 0.923 0.739 1.154 -0.701 0.484
## alcoholmoderate or increased 0.816 0.525 1.267 -0.906 0.365
## risk
## ------------------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.992# jtools::plot_summs(uni_alcohol_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_alcohol_f")jtools::summ(uni_FL_f, exp = TRUE, confint = TRUE, digits = 4)## MODEL INFO:
## Observations: 3650
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.0147
## Pseudo-R² (McFadden) = 0.0088
## AIC = NA
##
## ---------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p
## ------------------------- ----------- -------- -------- ---------- --------
## (Intercept) 0.3004 0.2709 0.3330 -22.8449 0.0000
## FLSlight difficulty 1.5152 1.2075 1.9012 3.5887 0.0003
## FLGreat difficulty 2.0305 1.3411 3.0745 3.3465 0.0008
## FLInability 2.0444 1.1678 3.5791 2.5029 0.0124
## ---------------------------------------------------------------------------
##
## Estimated dispersion parameter = 1.00035.4 Trend test for prevalence of falls
5.4.1 Men
After checking summary table, following variables get a trend test of prevalence of falls:
- education
## CochranArmitageTest
## creating matrix for education
camatrix_education_m <- matrix(c(0.1985768*119633, 0.2277108*441112, 0.2621775*360740, 0.8014232*119633, 0.7722892*441112, 0.7378225*360740), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, education=1:3))
dimnames(camatrix_education_m) <- list(
fall = c("yes", "no"),
education = c("primary", "secondary", "tertiary")
)
camatrix_education_m## education
## fall primary secondary tertiary
## yes 23756.34 100446 94577.91
## no 95876.66 340666 266162.09## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_education_m)
Cochran-Armitage test for trend
data: camatrix_education_m
Z = 49.397, dim = 3, p-value < 2.2e-16
alternative hypothesis: two.sided
DescTools::CochranArmitageTest(camatrix_education_m, "increasing")
Cochran-Armitage test for trend
data: camatrix_education_m
Z = 49.397, dim = 3, p-value = 1
alternative hypothesis: increasing
DescTools::CochranArmitageTest(camatrix_education_m, "decreasing")
Cochran-Armitage test for trend
data: camatrix_education_m
Z = 49.397, dim = 3, p-value < 2.2e-16
alternative hypothesis: decreasing
## test with prop_trend_test function
rstatix::prop_trend_test(camatrix_education_m)## # A tibble: 1 x 6
## n statistic p p.signif df method
## * <dbl> <dbl> <dbl> <chr> <dbl> <chr>
## 1 921485 2440. 0 **** 1 Chi-square trend test## test with prop.trend.test function
fallers_education_m <- c(0.1985768*119633, 0.2277108*441112, 0.2621775*360740)
nofallers_education_m <- c( 0.8014232*119633, 0.7722892*441112, 0.7378225*360740)
prop.trend.test(fallers_education_m, nofallers_education_m)
Chi-squared Test for Trend in Proportions
data: fallers_education_m out of nofallers_education_m ,
using scores: 1 2 3
X-squared = 4609.3, df = 1, p-value < 2.2e-16
- age_cut
## CochranArmitageTest
## creating matrix for age_cut
camatrix_age_cut_m <- matrix(c(451660.1*0.2172215,329497.6*0.2402362, 143722.4*0.2899578, 0.7827785*451660.1, 0.7597638*329497.6, 0.7100422*143722.4), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, age_cut=1:3))
dimnames(camatrix_age_cut_m) <- list(
fall = c("yes", "no"),
age_cut = c("60-69", "70-79", "80+")
)
camatrix_age_cut_m## age_cut
## fall 60-69 70-79 80+
## yes 98110.28 79157.25 41673.43
## no 353549.82 250340.35 102048.97## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_age_cut_m)
Cochran-Armitage test for trend
data: camatrix_age_cut_m
Z = 55.156, dim = 3, p-value < 2.2e-16
alternative hypothesis: two.sided
DescTools::CochranArmitageTest(camatrix_age_cut_m, "increasing")
Cochran-Armitage test for trend
data: camatrix_age_cut_m
Z = 55.156, dim = 3, p-value = 1
alternative hypothesis: increasing
DescTools::CochranArmitageTest(camatrix_age_cut_m, "decreasing")
Cochran-Armitage test for trend
data: camatrix_age_cut_m
Z = 55.156, dim = 3, p-value < 2.2e-16
alternative hypothesis: decreasing
- SHS
## CochranArmitageTest
## creating matrix for SHS
camatrix_SHS_m <- matrix(c(247212*0.220138, 441164*0.2346568, 174931*0.2463606, 46282*0.2947983, 13664*0.3119853, 0.779862*247212, 0.7653432*441164, 0.7536394*174931, 0.7052017*46282, 0.6880147*13664), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, SHS=1:5))
dimnames(camatrix_SHS_m) <- list(
fall = c("yes", "no"),
SHS = c("very good", "good", "average", "poor", "very poor")
)
camatrix_SHS_m## SHS
## fall very good good average poor very poor
## yes 54420.76 103522.1 43096.11 13643.85 4262.967
## no 192791.24 337641.9 131834.89 32638.15 9401.033## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_SHS_m)
Cochran-Armitage test for trend
data: camatrix_SHS_m
Z = 39.235, dim = 5, p-value < 2.2e-16
alternative hypothesis: two.sided
DescTools::CochranArmitageTest(camatrix_SHS_m, "increasing")
Cochran-Armitage test for trend
data: camatrix_SHS_m
Z = 39.235, dim = 5, p-value = 1
alternative hypothesis: increasing
DescTools::CochranArmitageTest(camatrix_SHS_m, "decreasing")
Cochran-Armitage test for trend
data: camatrix_SHS_m
Z = 39.235, dim = 5, p-value < 2.2e-16
alternative hypothesis: decreasing
5.4.2 Women
After checking summary table, following variables get a trend-test of prevalence of falls:
- FL
## CochranArmitageTest
## creating matrix for FL
camatrix_FL_f <- matrix(c(802495*0.2310017, 185432*0.3127858, 40791*0.3788652, 25971*0.3804728, 802495*0.7689983, 185432*0.6872142, 40791*0.6211348, 25971*0.6195272), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, FL=1:4))
dimnames(camatrix_FL_f) <- list(
fall = c("yes", "no"),
FL = c("no difficulties", "slight difficulties", "great difficulties", "inability")
)
camatrix_FL_f## FL
## fall no difficulties slight difficulties great difficulties inability
## yes 185377.7 58000.5 15454.29 9881.259
## no 617117.3 127431.5 25336.71 16089.741## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_FL_f)
Cochran-Armitage test for trend
data: camatrix_FL_f
Z = 102.73, dim = 4, p-value < 2.2e-16
alternative hypothesis: two.sided
DescTools::CochranArmitageTest(camatrix_FL_f, "increasing")
Cochran-Armitage test for trend
data: camatrix_FL_f
Z = 102.73, dim = 4, p-value = 1
alternative hypothesis: increasing
DescTools::CochranArmitageTest(camatrix_FL_f, "decreasing")
Cochran-Armitage test for trend
data: camatrix_FL_f
Z = 102.73, dim = 4, p-value < 2.2e-16
alternative hypothesis: decreasing
- age_cut
## CochranArmitageTest
## creating matrix for age_cut
camatrix_age_cut_f <- matrix(c(480788*0.2031848, 371311*0.2653584, 205226*0.3595031, 480788*0.7968152, 371311*0.7346416, 205226*0.6404969), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, age_cut=1:3))
dimnames(camatrix_age_cut_f) <- list(
fall = c("yes", "no"),
age_cut = c("60-69", "70-79", "80+")
)
camatrix_age_cut_f## age_cut
## fall 60-69 70-79 80+
## yes 97688.81 98530.49 73779.38
## no 383099.19 272780.51 131446.62## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_age_cut_f)
Cochran-Armitage test for trend
data: camatrix_age_cut_f
Z = 135.94, dim = 3, p-value < 2.2e-16
alternative hypothesis: two.sided
DescTools::CochranArmitageTest(camatrix_age_cut_f, "increasing")
Cochran-Armitage test for trend
data: camatrix_age_cut_f
Z = 135.94, dim = 3, p-value = 1
alternative hypothesis: increasing
DescTools::CochranArmitageTest(camatrix_age_cut_f, "decreasing")
Cochran-Armitage test for trend
data: camatrix_age_cut_f
Z = 135.94, dim = 3, p-value < 2.2e-16
alternative hypothesis: decreasing
- SHS
## CochranArmitageTest
## creating matrix for SHS
camatrix_SHS_f <- matrix(c(281898*0.204541, 488526*0.2352557, 235279*0.3336065, 44700*0.3642046, 6819*0.4016408, 281898*0.795459, 488526*0.7647443, 235279*0.6663935, 44700*0.6357954, 6819*0.5983592), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, SHS=1:5))
dimnames(camatrix_SHS_f) <- list(
fall = c("yes", "no"),
SHS = c("very good", "good", "average", "poor", "very poor")
)
camatrix_SHS_f## SHS
## fall very good good average poor very poor
## yes 57659.7 114928.5 78490.6 16279.95 2738.789
## no 224238.3 373597.5 156788.4 28420.05 4080.211## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_SHS_f)
Cochran-Armitage test for trend
data: camatrix_SHS_f
Z = 120.05, dim = 5, p-value < 2.2e-16
alternative hypothesis: two.sided
DescTools::CochranArmitageTest(camatrix_SHS_f, "increasing")
Cochran-Armitage test for trend
data: camatrix_SHS_f
Z = 120.05, dim = 5, p-value = 1
alternative hypothesis: increasing
DescTools::CochranArmitageTest(camatrix_SHS_f, "decreasing")
Cochran-Armitage test for trend
data: camatrix_SHS_f
Z = 120.05, dim = 5, p-value < 2.2e-16
alternative hypothesis: decreasing
5.5 Multivariate models, model integration by covariate groups
Multivariate logistic regression models were calculated for both men and women, characterised by the agglomerative inclusion of the socio-demographic, biological and behavioural covariate groups. In this way, three regression models result per gender.
5.5.1 Men
socio_economic_covariate&socio_demographic_covariate:
Model_socio_economic_socio_demographic_1_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language,
design = wdat_17_m,
family = quasibinomial(logit))
Model_socio_economic_socio_demographic_1_ma<- jtools::summ(Model_socio_economic_socio_demographic_1_m,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_1_ma## MODEL INFO:
## Observations: 3201
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.011211
## Pseudo-R² (McFadden) = 0.006836
## AIC = NA
##
## ------------------------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p VIF
## ---------------------------- ----------- ---------- ---------- ----------- ---------- ----------
## (Intercept) 0.294871 0.212749 0.408694 -7.332554 0.000000
## educationupper secondary 1.042592 0.751690 1.446073 0.249893 0.802686 1.162608
## education
## educationtertiary 1.260577 0.900159 1.765304 1.347773 0.177828 1.162608
## education
## urban_ruralrural area 1.068080 0.867743 1.314669 0.621444 0.534352 1.017359
## nationalityforeigner 0.631289 0.456083 0.873800 -2.773302 0.005582 1.152385
## languagefrench 0.952702 0.764280 1.187577 -0.430944 0.666539 1.013671
## languageitalian 0.767277 0.523550 1.124466 -1.358420 0.174427 1.013671
## ------------------------------------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.999636socio_economic_covariate&socio_demographic_covariate&biological_covariates:- SHS-Variable factor: “very good and good” get combined
Model_socio_economic_socio_demographic_biological_1_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis,
design = wdat_17_m,
family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_1_ma<- jtools::summ(Model_socio_economic_socio_demographic_biological_1_m,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_biological_1_ma## MODEL INFO:
## Observations: 2828
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.042516
## Pseudo-R² (McFadden) = 0.026209
## AIC = NA
##
## -------------------------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p VIF
## ----------------------------- ----------- ---------- ---------- ----------- ---------- ----------
## (Intercept) 0.199805 0.128419 0.310873 -7.140391 0.000000
## educationupper secondary 1.136667 0.780902 1.654513 0.668801 0.503678 1.233670
## education
## educationtertiary 1.362527 0.923694 2.009843 1.559746 0.118934 1.233670
## education
## urban_ruralrural area 1.123140 0.897690 1.405211 1.015840 0.309794 1.029986
## nationalityforeigner 0.645481 0.448115 0.929772 -2.351028 0.018791 1.192418
## languagefrench 0.852573 0.668861 1.086746 -1.288123 0.197810 1.059940
## languageitalian 0.710992 0.467573 1.081137 -1.595134 0.110796 1.059940
## age_cut70-79 1.060741 0.842915 1.334857 0.502808 0.615139 1.113286
## age_cut80+ 1.393211 1.028964 1.886398 2.144622 0.032070 1.113286
## FLSlight difficulty 1.064687 0.815822 1.389468 0.461434 0.644523 1.194495
## FLGreat difficulty 2.308789 1.181437 4.511883 2.447713 0.014438 1.194495
## FLInability 1.271891 0.523131 3.092351 0.530578 0.595753 1.194495
## bmi_cutobesity 1.179584 0.860098 1.617746 1.024821 0.305537 1.138953
## bmi_cutoverweight 1.081343 0.858068 1.362716 0.662746 0.507548 1.138953
## bmi_cutunderweight 1.522016 0.431365 5.370237 0.652944 0.513846 1.138953
## SHS_collapsedaverage 0.942092 0.711960 1.246609 -0.417443 0.676387 1.377553
## SHS_collapsedpoor 0.876788 0.522881 1.470233 -0.498569 0.618123 1.377553
## SHS_collapsedvery poor 1.067305 0.464738 2.451143 0.153551 0.877975 1.377553
## diabetesyes 1.197038 0.880426 1.627507 1.147462 0.251289 1.098968
## osteoarthritisyes 1.299990 1.023252 1.651572 2.148173 0.031786 1.084873
## heart_attackyes 1.536092 0.673878 3.501495 1.021057 0.307316 1.036031
## strokeyes 1.548223 0.514002 4.663397 0.776971 0.437242 1.069099
## urinary_incontinenceyes 1.689601 1.184689 2.409707 2.895634 0.003813 1.102968
## osteoporosisyes 1.739026 0.971133 3.114103 1.861426 0.062790 1.058181
## -------------------------------------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.997454socio_economic_covariate&socio_demographic_covariate&biological_covariates&behavioral_covariate:
Model_socio_economic_socio_demographic_biological_behavioral_1_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke,
design = wdat_17_m,
family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_behavioral_1_ma<- jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_1_m,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_biological_behavioral_1_ma## MODEL INFO:
## Observations: 2768
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.042970
## Pseudo-R² (McFadden) = 0.026494
## AIC = NA
##
## ----------------------------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p VIF
## -------------------------------- ----------- ---------- ---------- ----------- ---------- ----------
## (Intercept) 0.158913 0.080449 0.313906 -5.295964 0.000000
## educationupper secondary 1.174047 0.798276 1.726704 0.815250 0.415001 1.269988
## education
## educationtertiary 1.386633 0.930822 2.065649 1.607437 0.108075 1.269988
## education
## urban_ruralrural area 1.114032 0.887463 1.398446 0.930836 0.352021 1.039235
## nationalityforeigner 0.681316 0.471351 0.984810 -2.041393 0.041308 1.249889
## languagefrench 0.840052 0.656178 1.075451 -1.382841 0.166827 1.083117
## languageitalian 0.698974 0.458844 1.064772 -1.667714 0.095488 1.083117
## age_cut70-79 1.036558 0.818032 1.313461 0.297240 0.766306 1.242023
## age_cut80+ 1.351552 0.984185 1.856047 1.861461 0.062787 1.242023
## FLSlight difficulty 1.073057 0.818427 1.406907 0.510184 0.609964 1.252043
## FLGreat difficulty 2.338672 1.183314 4.622094 2.444209 0.014580 1.252043
## FLInability 1.239215 0.505666 3.036894 0.468975 0.639125 1.252043
## bmi_cutobesity 1.137540 0.824992 1.568496 0.786233 0.431800 1.188105
## bmi_cutoverweight 1.060622 0.837726 1.342826 0.488960 0.624909 1.188105
## bmi_cutunderweight 1.501284 0.403750 5.582306 0.606400 0.544300 1.188105
## SHS_collapsedaverage 0.924504 0.694210 1.231197 -0.537036 0.591287 1.514232
## SHS_collapsedpoor 0.903522 0.528076 1.545897 -0.370254 0.711222 1.514232
## SHS_collapsedvery poor 1.079794 0.463211 2.517116 0.177786 0.858904 1.514232
## diabetesyes 1.162885 0.849664 1.591572 0.942478 0.346032 1.114410
## osteoarthritisyes 1.289375 1.010250 1.645621 2.041890 0.041259 1.101873
## heart_attackyes 1.595019 0.691984 3.676510 1.095801 0.273263 1.037301
## strokeyes 1.691612 0.554417 5.161371 0.923620 0.355766 1.064558
## urinary_incontinenceyes 1.713025 1.194064 2.457536 2.923182 0.003493 1.114426
## osteoporosisyes 1.920661 1.072780 3.438673 2.196383 0.028149 1.078834
## activitypartially active 1.186220 0.760831 1.849449 0.753645 0.451128 1.301776
## activityactive 0.970302 0.660305 1.425835 -0.153517 0.878002 1.301776
## alcohollow risk 1.202038 0.817038 1.768455 0.934164 0.350302 1.224839
## alcoholmoderate or increased 1.355957 0.795630 2.310898 1.119474 0.263037 1.224839
## risk
## smokeformer smoker 1.130223 0.888919 1.437031 0.999018 0.317875 1.182758
## smokesmoker 1.042427 0.772414 1.406829 0.271660 0.785904 1.182758
## ----------------------------------------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.9973245.5.1.1 Model comparison male
export_summs(Model_socio_economic_socio_demographic_1_m, Model_socio_economic_socio_demographic_biological_1_m, Model_socio_economic_socio_demographic_biological_behavioral_1_m, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), exp = TRUE, scale = TRUE, error_format = "({conf.low}-{conf.high})", to.file = "docx", file.name = "Multivariate_1-3_male.docx")| Model 1 | Model 2 | Model 3 | |
|---|---|---|---|
| Secondary education | 1.04 | 1.14 | 1.17 |
| (0.75-1.45) | (0.78-1.65) | (0.80-1.73) | |
| Tertiary education | 1.26 | 1.36 | 1.39 |
| (0.90-1.77) | (0.92-2.01) | (0.93-2.07) | |
| Rural area | 1.07 | 1.12 | 1.11 |
| (0.87-1.31) | (0.90-1.41) | (0.89-1.40) | |
| Foreigner | 0.63 ** | 0.65 * | 0.68 * |
| (0.46-0.87) | (0.45-0.93) | (0.47-0.98) | |
| French | 0.95 | 0.85 | 0.84 |
| (0.76-1.19) | (0.67-1.09) | (0.66-1.08) | |
| Italian | 0.77 | 0.71 | 0.70 |
| (0.52-1.12) | (0.47-1.08) | (0.46-1.06) | |
| 70-79 | 1.06 | 1.04 | |
| (0.84-1.33) | (0.82-1.31) | ||
| 80+ | 1.39 * | 1.35 | |
| (1.03-1.89) | (0.98-1.86) | ||
| Slight difficulty (FL) | 1.06 | 1.07 | |
| (0.82-1.39) | (0.82-1.41) | ||
| Great difficulty (FL) | 2.31 * | 2.34 * | |
| (1.18-4.51) | (1.18-4.62) | ||
| Inability (FL) | 1.27 | 1.24 | |
| (0.52-3.09) | (0.51-3.04) | ||
| Obesity | 1.18 | 1.14 | |
| (0.86-1.62) | (0.82-1.57) | ||
| Overweight | 1.08 | 1.06 | |
| (0.86-1.36) | (0.84-1.34) | ||
| Underweight | 1.52 | 1.50 | |
| (0.43-5.37) | (0.40-5.58) | ||
| Average (SHS) | 0.94 | 0.92 | |
| (0.71-1.25) | (0.69-1.23) | ||
| Poor (SHS) | 0.88 | 0.90 | |
| (0.52-1.47) | (0.53-1.55) | ||
| Very poor (SHS) | 1.07 | 1.08 | |
| (0.46-2.45) | (0.46-2.52) | ||
| Diabetes | 1.20 | 1.16 | |
| (0.88-1.63) | (0.85-1.59) | ||
| Osteoarthritis | 1.30 * | 1.29 * | |
| (1.02-1.65) | (1.01-1.65) | ||
| Heart attack | 1.54 | 1.60 | |
| (0.67-3.50) | (0.69-3.68) | ||
| Stroke | 1.55 | 1.69 | |
| (0.51-4.66) | (0.55-5.16) | ||
| Urinary incontinence | 1.69 ** | 1.71 ** | |
| (1.18-2.41) | (1.19-2.46) | ||
| Osteoporosis | 1.74 | 1.92 * | |
| (0.97-3.11) | (1.07-3.44) | ||
| Partially active | 1.19 | ||
| (0.76-1.85) | |||
| Active | 0.97 | ||
| (0.66-1.43) | |||
| Alcohol low risk | 1.20 | ||
| (0.82-1.77) | |||
| Alcohol moderate/increased risk | 1.36 | ||
| (0.80-2.31) | |||
| Former smoker | 1.13 | ||
| (0.89-1.44) | |||
| Smoker | 1.04 | ||
| (0.77-1.41) | |||
| N | 3201 | 2828 | 2768 |
| R2 | |||
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | |||
publication_layout=theme_bw()+
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.border=element_blank(),
axis.line=element_line(),
text=element_text(family='Times New Roman'),
legend.title=element_blank(),
axis.text=element_text(size=10),
axis.title=element_text(size=12),
legend.text = element_text(size = 12))
plot_summs(Model_socio_economic_socio_demographic_1_m, Model_socio_economic_socio_demographic_biological_1_m, Model_socio_economic_socio_demographic_biological_behavioral_1_m, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), scale = TRUE, plot.distributions = FALSE, exp = TRUE) + coord_cartesian(xlim = c(0, NA)) + scale_x_continuous(breaks = c(0.1, 0.2, 0.5, 1, 2, 5, 10), limits=c(0.1, 10)) + coord_trans(x="log10") + publication_layout + labs(x = "\n aOR \n ", y = NULL)
5.5.2 Women
socio_economic_covariate&socio_demographic_covariate:
Model_socio_economic_socio_demographic_1_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language,
design = wdat_17_f,
family = quasibinomial(logit))
Model_socio_economic_socio_demographic_1_fa<- jtools::summ(Model_socio_economic_socio_demographic_1_f,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_1_fa## MODEL INFO:
## Observations: 3640
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.003353
## Pseudo-R² (McFadden) = 0.002007
## AIC = NA
##
## ------------------------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p VIF
## ---------------------------- ----------- ---------- ---------- ----------- ---------- ----------
## (Intercept) 0.361929 0.293195 0.446775 -9.457973 0.000000
## educationupper secondary 0.963205 0.778560 1.191641 -0.345255 0.729923 1.121088
## education
## educationtertiary 0.973588 0.735943 1.287971 -0.187476 0.851298 1.121088
## education
## urban_ruralrural area 0.994207 0.816429 1.210696 -0.057802 0.953910 1.055647
## nationalityforeigner 0.854778 0.623665 1.171534 -0.975627 0.329315 1.078425
## languagefrench 1.013741 0.833640 1.232753 0.136754 0.891233 1.036373
## languageitalian 0.683901 0.492011 0.950629 -2.261305 0.023800 1.036373
## ------------------------------------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.999914socio_economic_covariate&socio_demographic_covariate&biological_covariates:- SHS-Variable factor: “very good and good” get combined
Model_socio_economic_socio_demographic_biological_1_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis,
design = wdat_17_f,
family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_1_fa<- jtools::summ(Model_socio_economic_socio_demographic_biological_1_f,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_biological_1_fa## MODEL INFO:
## Observations: 3156
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.082383
## Pseudo-R² (McFadden) = 0.051194
## AIC = NA
##
## ---------------------------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p VIF
## ----------------------------- ----------- ---------- ----------- ------------ ---------- ----------
## (Intercept) 0.167475 0.123007 0.228019 -11.349129 0.000000
## educationupper secondary 1.202547 0.942648 1.534103 1.484568 0.137760 1.216235
## education
## educationtertiary 1.362430 0.981175 1.891828 1.846499 0.064915 1.216235
## education
## urban_ruralrural area 1.198368 0.964181 1.489436 1.631167 0.102956 1.054207
## nationalityforeigner 0.942866 0.638679 1.391929 -0.296024 0.767232 1.100032
## languagefrench 0.920950 0.735912 1.152513 -0.719602 0.471824 1.107236
## languageitalian 0.592316 0.415361 0.844657 -2.892334 0.003850 1.107236
## age_cut70-79 1.380904 1.106771 1.722936 2.858472 0.004285 1.152481
## age_cut80+ 2.098496 1.577972 2.790725 5.095987 0.000000 1.152481
## FLSlight difficulty 1.187261 0.912234 1.545207 1.276719 0.201797 1.398322
## FLGreat difficulty 1.477915 0.851459 2.565284 1.388420 0.165109 1.398322
## FLInability 0.934081 0.405688 2.150689 -0.160260 0.872687 1.398322
## bmi_cutobesity 0.801148 0.581248 1.104242 -1.354269 0.175749 1.266738
## bmi_cutoverweight 0.809819 0.641510 1.022285 -1.774548 0.076070 1.266738
## bmi_cutunderweight 0.958735 0.573927 1.601550 -0.160967 0.872130 1.266738
## SHS_collapsedaverage 1.434019 1.111861 1.849520 2.776745 0.005524 1.482412
## SHS_collapsedpoor 1.527280 0.894295 2.608295 1.550840 0.121042 1.482412
## SHS_collapsedvery poor 1.882548 0.646359 5.483005 1.159863 0.246194 1.482412
## diabetesyes 1.087878 0.738837 1.601813 0.426681 0.669641 1.134409
## osteoarthritisyes 1.460976 1.185806 1.800001 3.560591 0.000376 1.143368
## heart_attackyes 0.479147 0.130169 1.763727 -1.106557 0.268571 1.084565
## strokeyes 3.274561 0.861884 12.441064 1.741718 0.081657 1.038618
## urinary_incontinenceyes 1.236390 0.910831 1.678313 1.360942 0.173631 1.129313
## osteoporosisyes 0.803351 0.606177 1.064663 -1.523891 0.127638 1.095982
## ---------------------------------------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.983845socio_economic_covariate&socio_demographic_covariate&biological_covariates&behavioral_covariate:
Model_socio_economic_socio_demographic_biological_behavioral_1_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke,
design = wdat_17_f,
family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_behavioral_1_fa<- jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_1_f,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_biological_behavioral_1_fa## MODEL INFO:
## Observations: 3065
## Dependent Variable: I(fall == "fall last 12 months")
## Type: Analysis of complex survey design
## Family: quasibinomial
## Link function: logit
##
## MODEL FIT:
## Pseudo-R² (Cragg-Uhler) = 0.091564
## Pseudo-R² (McFadden) = 0.057122
## AIC = NA
##
## -----------------------------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% t val. p VIF
## -------------------------------- ----------- ---------- ----------- ----------- ---------- ----------
## (Intercept) 0.163618 0.101916 0.262677 -7.494836 0.000000
## educationupper secondary 1.234831 0.961700 1.585534 1.653778 0.098277 1.258383
## education
## educationtertiary 1.385087 0.986703 1.944318 1.882607 0.059850 1.258383
## education
## urban_ruralrural area 1.192011 0.953765 1.489769 1.543871 0.122725 1.075028
## nationalityforeigner 0.977425 0.664534 1.437637 -0.115992 0.907666 1.103011
## languagefrench 0.934645 0.743847 1.174383 -0.580174 0.561841 1.137404
## languageitalian 0.611793 0.426773 0.877026 -2.674083 0.007534 1.137404
## age_cut70-79 1.383365 1.102491 1.735794 2.802623 0.005101 1.276296
## age_cut80+ 2.142949 1.586329 2.894880 4.966913 0.000001 1.276296
## FLSlight difficulty 1.172889 0.895107 1.536878 1.156404 0.247608 1.483496
## FLGreat difficulty 1.303189 0.734267 2.312920 0.904706 0.365693 1.483496
## FLInability 0.920788 0.392071 2.162493 -0.189446 0.849756 1.483496
## bmi_cutobesity 0.756321 0.543548 1.052386 -1.657026 0.097619 1.319778
## bmi_cutoverweight 0.819288 0.646719 1.037905 -1.651667 0.098707 1.319778
## bmi_cutunderweight 0.856635 0.506354 1.449231 -0.576845 0.564088 1.319778
## SHS_collapsedaverage 1.448427 1.115040 1.881493 2.775836 0.005540 1.530794
## SHS_collapsedpoor 1.583383 0.925745 2.708198 1.678208 0.093410 1.530794
## SHS_collapsedvery poor 1.903134 0.649625 5.575395 1.173398 0.240729 1.530794
## diabetesyes 1.030284 0.694861 1.527624 0.148460 0.881989 1.146133
## osteoarthritisyes 1.461684 1.183817 1.804773 3.528566 0.000424 1.140394
## heart_attackyes 0.488241 0.128345 1.857337 -1.051717 0.293014 1.112289
## strokeyes 3.500293 0.798676 15.340455 1.661787 0.096660 1.065701
## urinary_incontinenceyes 1.250249 0.920242 1.698601 1.428380 0.153286 1.137360
## osteoporosisyes 0.794171 0.596006 1.058224 -1.573555 0.115695 1.106902
## activitypartially active 0.870268 0.601316 1.259514 -0.736704 0.461360 1.345886
## activityactive 0.887059 0.649162 1.212138 -0.752298 0.451931 1.345886
## alcohollow risk 1.034832 0.800311 1.338077 0.261127 0.794012 1.172462
## alcoholmoderate or increased 0.787736 0.471645 1.315667 -0.911678 0.362011 1.172462
## risk
## smokeformer smoker 1.378179 1.089051 1.744067 2.670061 0.007625 1.153834
## smokesmoker 1.043433 0.767888 1.417855 0.271765 0.785821 1.153834
## -----------------------------------------------------------------------------------------------------
##
## Estimated dispersion parameter = 0.9825695.5.2.1 Model comparison women
export_summs(Model_socio_economic_socio_demographic_1_f, Model_socio_economic_socio_demographic_biological_1_f, Model_socio_economic_socio_demographic_biological_behavioral_1_f, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), exp = TRUE, scale = TRUE, error_format = "({conf.low}-{conf.high})", to.file = "docx", file.name = "Multivariate_1-3_female.docx")| Model 1 | Model 2 | Model 3 | |
|---|---|---|---|
| Secondary education | 0.96 | 1.20 | 1.23 |
| (0.78-1.19) | (0.94-1.53) | (0.96-1.59) | |
| Tertiary education | 0.97 | 1.36 | 1.39 |
| (0.74-1.29) | (0.98-1.89) | (0.99-1.94) | |
| Rural area | 0.99 | 1.20 | 1.19 |
| (0.82-1.21) | (0.96-1.49) | (0.95-1.49) | |
| Foreigner | 0.85 | 0.94 | 0.98 |
| (0.62-1.17) | (0.64-1.39) | (0.66-1.44) | |
| French | 1.01 | 0.92 | 0.93 |
| (0.83-1.23) | (0.74-1.15) | (0.74-1.17) | |
| Italian | 0.68 * | 0.59 ** | 0.61 ** |
| (0.49-0.95) | (0.42-0.84) | (0.43-0.88) | |
| 70-79 | 1.38 ** | 1.38 ** | |
| (1.11-1.72) | (1.10-1.74) | ||
| 80+ | 2.10 *** | 2.14 *** | |
| (1.58-2.79) | (1.59-2.89) | ||
| Slight difficulty (FL) | 1.19 | 1.17 | |
| (0.91-1.55) | (0.90-1.54) | ||
| Great difficulty (FL) | 1.48 | 1.30 | |
| (0.85-2.57) | (0.73-2.31) | ||
| Inability (FL) | 0.93 | 0.92 | |
| (0.41-2.15) | (0.39-2.16) | ||
| Obesity | 0.80 | 0.76 | |
| (0.58-1.10) | (0.54-1.05) | ||
| Overweight | 0.81 | 0.82 | |
| (0.64-1.02) | (0.65-1.04) | ||
| Underweight | 0.96 | 0.86 | |
| (0.57-1.60) | (0.51-1.45) | ||
| Average (SHS) | 1.43 ** | 1.45 ** | |
| (1.11-1.85) | (1.12-1.88) | ||
| Poor (SHS) | 1.53 | 1.58 | |
| (0.89-2.61) | (0.93-2.71) | ||
| Very poor (SHS) | 1.88 | 1.90 | |
| (0.65-5.48) | (0.65-5.58) | ||
| Diabetes | 1.09 | 1.03 | |
| (0.74-1.60) | (0.69-1.53) | ||
| Osteoarthritis | 1.46 *** | 1.46 *** | |
| (1.19-1.80) | (1.18-1.80) | ||
| Heart attack | 0.48 | 0.49 | |
| (0.13-1.76) | (0.13-1.86) | ||
| Stroke | 3.27 | 3.50 | |
| (0.86-12.44) | (0.80-15.34) | ||
| Urinary incontinence | 1.24 | 1.25 | |
| (0.91-1.68) | (0.92-1.70) | ||
| Osteoporosis | 0.80 | 0.79 | |
| (0.61-1.06) | (0.60-1.06) | ||
| Partially active | 0.87 | ||
| (0.60-1.26) | |||
| Active | 0.89 | ||
| (0.65-1.21) | |||
| Alcohol low risk | 1.03 | ||
| (0.80-1.34) | |||
| Alcohol moderate/increased risk | 0.79 | ||
| (0.47-1.32) | |||
| Former smoker | 1.38 ** | ||
| (1.09-1.74) | |||
| Smoker | 1.04 | ||
| (0.77-1.42) | |||
| N | 3640 | 3156 | 3065 |
| R2 | |||
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | |||
plot_summs(Model_socio_economic_socio_demographic_1_f, Model_socio_economic_socio_demographic_biological_1_f, Model_socio_economic_socio_demographic_biological_behavioral_1_f, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), scale = TRUE, plot.distributions = FALSE, exp = TRUE) + publication_layout + labs(x = "\n AOR \n ", y = NULL) + coord_cartesian(xlim = c(0, NA)) + scale_x_continuous(limits=c(0.1, 20), breaks = c(0, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20)) + coord_trans(x="log10") + publication_layout + labs(x = "\n aOR \n ", y = NULL)
5.5.3 Model comparison men vs. women
- Model with
socio_economic_covariate&socio_demographic_covariate&biological_covariates&behavioral_covariate:
export_summs(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_1_f, exp = TRUE, scale = TRUE, error_format = "[{conf.low}, {conf.high}]")| Model 1 | Model 2 | |
|---|---|---|
| (Intercept) | 0.18 *** | 0.20 *** |
| [0.09, 0.35] | [0.13, 0.32] | |
| educationupper secondary education | 1.17 | 1.23 |
| [0.80, 1.73] | [0.96, 1.59] | |
| educationtertiary education | 1.39 | 1.39 |
| [0.93, 2.07] | [0.99, 1.94] | |
| urban_rural | 1.11 | 1.19 |
| [0.89, 1.40] | [0.95, 1.49] | |
| nationality | 0.68 * | 0.98 |
| [0.47, 0.98] | [0.66, 1.44] | |
| languagefrench | 0.84 | 0.93 |
| [0.66, 1.08] | [0.74, 1.17] | |
| languageitalian | 0.70 | 0.61 ** |
| [0.46, 1.06] | [0.43, 0.88] | |
| age_cut70-79 | 1.04 | 1.38 ** |
| [0.82, 1.31] | [1.10, 1.74] | |
| age_cut80+ | 1.35 | 2.14 *** |
| [0.98, 1.86] | [1.59, 2.89] | |
| FLSlight difficulty | 1.07 | 1.17 |
| [0.82, 1.41] | [0.90, 1.54] | |
| FLGreat difficulty | 2.34 * | 1.30 |
| [1.18, 4.62] | [0.73, 2.31] | |
| FLInability | 1.24 | 0.92 |
| [0.51, 3.04] | [0.39, 2.16] | |
| bmi_cutobesity | 1.14 | 0.76 |
| [0.82, 1.57] | [0.54, 1.05] | |
| bmi_cutoverweight | 1.06 | 0.82 |
| [0.84, 1.34] | [0.65, 1.04] | |
| bmi_cutunderweight | 1.50 | 0.86 |
| [0.40, 5.58] | [0.51, 1.45] | |
| SHS_collapsedaverage | 0.92 | 1.45 ** |
| [0.69, 1.23] | [1.12, 1.88] | |
| SHS_collapsedpoor | 0.90 | 1.58 |
| [0.53, 1.55] | [0.93, 2.71] | |
| SHS_collapsedvery poor | 1.08 | 1.90 |
| [0.46, 2.52] | [0.65, 5.58] | |
| diabetes | 1.16 | 1.03 |
| [0.85, 1.59] | [0.69, 1.53] | |
| osteoarthritis | 1.29 * | 1.46 *** |
| [1.01, 1.65] | [1.18, 1.80] | |
| heart_attack | 1.60 | 0.49 |
| [0.69, 3.68] | [0.13, 1.86] | |
| stroke | 1.69 | 3.50 |
| [0.55, 5.16] | [0.80, 15.34] | |
| urinary_incontinence | 1.71 ** | 1.25 |
| [1.19, 2.46] | [0.92, 1.70] | |
| osteoporosis | 1.92 * | 0.79 |
| [1.07, 3.44] | [0.60, 1.06] | |
| activitypartially active | 1.19 | 0.87 |
| [0.76, 1.85] | [0.60, 1.26] | |
| activityactive | 0.97 | 0.89 |
| [0.66, 1.43] | [0.65, 1.21] | |
| alcohollow risk | 1.20 | 1.03 |
| [0.82, 1.77] | [0.80, 1.34] | |
| alcoholmoderate or increased risk | 1.36 | 0.79 |
| [0.80, 2.31] | [0.47, 1.32] | |
| smokeformer smoker | 1.13 | 1.38 ** |
| [0.89, 1.44] | [1.09, 1.74] | |
| smokesmoker | 1.04 | 1.04 |
| [0.77, 1.41] | [0.77, 1.42] | |
| N | 2768 | 3065 |
| R2 | ||
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||
plot_summs(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_1_f, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), scale = TRUE, plot.distributions = FALSE, exp = TRUE) + publication_layout + labs(x = "\n AOR \n ", y = NULL) + coord_cartesian(xlim = c(0, NA)) + scale_x_continuous(limits=c(0.1, 20), breaks = c(0, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20)) + coord_trans(x="log10") + publication_layout + labs(x = "\n aOR \n ", y = NULL)
5.6 Interactions in male and female Model 3
Interaction terms with age categories (\(age\_categories * x\))
\(x = education, urban\_rural, nationality, language, FL, bmi\_cut, SHS\_collapsed, diabetes,\\ osteoarthritis, heart\_attack, stroke, urinary\_incontinence, osteoporosis, activity, alcohol, \\smoke\)
5.6.1 Men
# education
Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + education * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_m, exp = TRUE, confint = TRUE, digits = 3)
# model comparison and LR-test p < 0.05
Interaction_LRT_age_education_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_m)
# urban_rural
Model_socio_economic_socio_demographic_biological_behavioral_interactions_2_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urban_rural * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_urban_rural_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_2_m)
# nationality
Model_socio_economic_socio_demographic_biological_behavioral_interactions_3_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + nationality * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_nationality_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_3_m)
# language
Model_socio_economic_socio_demographic_biological_behavioral_interactions_4_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + language * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_language_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_4_m)
# FL
Model_socio_economic_socio_demographic_biological_behavioral_interactions_5_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + FL * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_FL_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_5_m)
# bmi_cut
Model_socio_economic_socio_demographic_biological_behavioral_interactions_6_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + bmi_cut * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_bmi_cut_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_6_m)
# SHS_collapsed
Model_socio_economic_socio_demographic_biological_behavioral_interactions_7_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + SHS_collapsed * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_SHS_collapsed_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_7_m)
# diabetes
Model_socio_economic_socio_demographic_biological_behavioral_interactions_8_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + diabetes * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_diabetes_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_8_m)
# osteoarthritis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_9_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoarthritis * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_osteoarthritis_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_9_m)
# heart_attack
Model_socio_economic_socio_demographic_biological_behavioral_interactions_10_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + heart_attack * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_heart_attack_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_10_m)
# stroke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_11_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + stroke * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_stroke_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_11_m)
# urinary_incontinence
Model_socio_economic_socio_demographic_biological_behavioral_interactions_12_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urinary_incontinence * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_urinary_incontinence_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_12_m)
# osteoporosis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_13_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoporosis * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_osteoporosis_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_13_m)
# activity
Model_socio_economic_socio_demographic_biological_behavioral_interactions_14_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + activity * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_activity_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_14_m)
# alcohol
Model_socio_economic_socio_demographic_biological_behavioral_interactions_15_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + alcohol * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_alcohol_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_15_m)
# smoke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_16_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + smoke * I(age_cut),
design = wdat_17_m,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_smoke_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_16_m)
Interaction_age_m <- round(c(Interaction_LRT_age_education_m$p, Interaction_LRT_age_urban_rural_m$p, Interaction_LRT_age_nationality_m$p, Interaction_LRT_age_language_m$p, Interaction_LRT_age_FL_m$p, Interaction_LRT_age_bmi_cut_m$p, Interaction_LRT_age_SHS_collapsed_m$p, Interaction_LRT_age_diabetes_m$p, Interaction_LRT_age_osteoarthritis_m$p, Interaction_LRT_age_heart_attack_m$p, Interaction_LRT_age_stroke_m$p, Interaction_LRT_age_urinary_incontinence_m$p, Interaction_LRT_age_osteoporosis_m$p, Interaction_LRT_age_activity_m$p, Interaction_LRT_age_alcohol_m$p, Interaction_LRT_age_smoke_m$p), 3)
Interaction_names<- c("Education level", "Residential area", "Nationality", "Language region", "Functional limitations", "BMI group", "Self-perceived health status", "Diabetes", "Osteoarthritis", "Heart attack", "Stroke", "Urinary incontinence", "Osteoporosis", "Physical activity", "Chronic alcohol consumption", "Smoking (smoker)")
Interaction_age_table_m <- data.frame(Interaction_names, Interaction_age_m)
knitr::kable(Interaction_age_table_m, col.names = c('Age categories * Variable', '$P$'), caption = "Likelihood Ratio Test and $P$-value for the
addition of the interactions to the Main Effects Model 3")| Age categories * Variable | \(P\) |
|---|---|
| Education level | 0.566 |
| Residential area | 0.412 |
| Nationality | 0.279 |
| Language region | 0.748 |
| Functional limitations | 0.369 |
| BMI group | 0.480 |
| Self-perceived health status | 0.033 |
| Diabetes | 0.515 |
| Osteoarthritis | 0.352 |
| Heart attack | 0.971 |
| Stroke | 0.270 |
| Urinary incontinence | 0.048 |
| Osteoporosis | 0.337 |
| Physical activity | 0.664 |
| Chronic alcohol consumption | 0.056 |
| Smoking (smoker) | 0.534 |
5.6.2 Women
# education
Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + education * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_f, exp = TRUE, confint = TRUE, digits = 3)
# model comparison and LR-test p < 0.05
Interaction_LRT_age_education_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_f)
# urban_rural
Model_socio_economic_socio_demographic_biological_behavioral_interactions_2_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urban_rural * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_urban_rural_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_2_f)
# nationality
Model_socio_economic_socio_demographic_biological_behavioral_interactions_3_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + nationality * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_nationality_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_3_f)
# language
Model_socio_economic_socio_demographic_biological_behavioral_interactions_4_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + language * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_language_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_4_f)
# FL
Model_socio_economic_socio_demographic_biological_behavioral_interactions_5_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + FL * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_FL_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_5_f)
# bmi_cut
Model_socio_economic_socio_demographic_biological_behavioral_interactions_6_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + bmi_cut * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_bmi_cut_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_6_f)
# SHS_collapsed
Model_socio_economic_socio_demographic_biological_behavioral_interactions_7_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + SHS_collapsed * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_SHS_collapsed_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_7_f)
# diabetes
Model_socio_economic_socio_demographic_biological_behavioral_interactions_8_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + diabetes * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_diabetes_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_8_f)
# osteoarthritis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_9_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoarthritis * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_osteoarthritis_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_9_f)
# heart_attack
Model_socio_economic_socio_demographic_biological_behavioral_interactions_10_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + heart_attack * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_heart_attack_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_10_f)
# stroke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_11_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + stroke * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_stroke_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_11_f)
# urinary_incontinence
Model_socio_economic_socio_demographic_biological_behavioral_interactions_12_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urinary_incontinence * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_urinary_incontinence_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_12_f)
# osteoporosis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_13_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoporosis * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_osteoporosis_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_13_f)
# activity
Model_socio_economic_socio_demographic_biological_behavioral_interactions_14_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + activity * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_activity_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_14_f)
# alcohol
Model_socio_economic_socio_demographic_biological_behavioral_interactions_15_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + alcohol * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_alcohol_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_15_f)
# smoke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_16_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + smoke * I(age_cut),
design = wdat_17_f,
family = quasibinomial(logit))
# model comparison and LR-test p < 0.05
Interaction_LRT_age_smoke_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_16_f)
Interaction_age_f <- round(c(Interaction_LRT_age_education_f$p, Interaction_LRT_age_urban_rural_f$p, Interaction_LRT_age_nationality_f$p, Interaction_LRT_age_language_f$p, Interaction_LRT_age_FL_f$p, Interaction_LRT_age_bmi_cut_f$p, Interaction_LRT_age_SHS_collapsed_f$p, Interaction_LRT_age_diabetes_f$p, Interaction_LRT_age_osteoarthritis_f$p, Interaction_LRT_age_heart_attack_f$p, Interaction_LRT_age_stroke_f$p, Interaction_LRT_age_urinary_incontinence_f$p, Interaction_LRT_age_osteoporosis_f$p, Interaction_LRT_age_activity_f$p, Interaction_LRT_age_alcohol_f$p, Interaction_LRT_age_smoke_f$p), 3)
Interaction_age_table_f <- data.frame(Interaction_names, Interaction_age_f)
knitr::kable(Interaction_age_table_f, col.names = c('Age categories * Variable', '$P$'), caption = "Likelihood Ratio Test and $P$-value for the
addition of the interactions to the Main Effects Model 3")| Age categories * Variable | \(P\) |
|---|---|
| Education level | 0.259 |
| Residential area | 0.642 |
| Nationality | 0.123 |
| Language region | 0.520 |
| Functional limitations | 0.967 |
| BMI group | 0.606 |
| Self-perceived health status | 0.613 |
| Diabetes | 0.828 |
| Osteoarthritis | 0.768 |
| Heart attack | 0.892 |
| Stroke | 0.073 |
| Urinary incontinence | 0.613 |
| Osteoporosis | 0.992 |
| Physical activity | 0.026 |
| Chronic alcohol consumption | 0.126 |
| Smoking (smoker) | 0.550 |
5.7 Interaction genderunspecific Model
Interaction terms with sex (\(age\_categories * x\))
\(x = education, urban\_rural, nationality, language, age\_cut, FL, bmi\_cut, SHS\_collapsed, diabetes,\\ osteoarthritis, heart\_attack, stroke, urinary\_incontinence, osteoporosis, activity, alcohol, \\smoke\)
# education
Model_socio_economic_socio_demographic_biological_behavioral_1 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke,
design = wdat_17,
family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_behavioral_interactions_1 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + education *I(sex),
design = wdat_17,
family = quasibinomial(logit))
# jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_interactions_1,exp = TRUE, confint = TRUE, digits = 3)
Interaction_LRT_sex_education <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_1)
# urban_rural
Model_socio_economic_socio_demographic_biological_behavioral_interactions_2 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urban_rural *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_urban_rural <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_2)
# nationality
Model_socio_economic_socio_demographic_biological_behavioral_interactions_3 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + nationality *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_nationality <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_3)
# language
Model_socio_economic_socio_demographic_biological_behavioral_interactions_4 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + language *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_language <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_4)
# age_cut
Model_socio_economic_socio_demographic_biological_behavioral_interactions_5 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + age_cut *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_age_cut <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_5)
# FL
Model_socio_economic_socio_demographic_biological_behavioral_interactions_6 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + FL *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_FL <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_6)
# bmi_cut
Model_socio_economic_socio_demographic_biological_behavioral_interactions_7 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + bmi_cut *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_bmi_cut <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_7)
# SHS_collapsed
Model_socio_economic_socio_demographic_biological_behavioral_interactions_8 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + SHS_collapsed *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_SHS_collapsed <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_8)
# diabetes
Model_socio_economic_socio_demographic_biological_behavioral_interactions_9 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + diabetes *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_diabetes <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_9)
# osteoarthritis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_10 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoarthritis *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_osteoarthritis <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_10)
# heart_attack
Model_socio_economic_socio_demographic_biological_behavioral_interactions_11 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + heart_attack *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_heart_attack <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_11)
# stroke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_12 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + stroke *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_stroke <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_12)
# urinary_incontinence
Model_socio_economic_socio_demographic_biological_behavioral_interactions_13 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urinary_incontinence *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_urinary_incontinence <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_13)
# osteoporosis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_14 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoporosis *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_osteoporosis <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_14)
# activity
Model_socio_economic_socio_demographic_biological_behavioral_interactions_15 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + activity *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_activity <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_15)
# alcohol
Model_socio_economic_socio_demographic_biological_behavioral_interactions_16 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + alcohol *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_alcohol <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_16)
# smoke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_17 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + smoke *I(sex),
design = wdat_17,
family = quasibinomial(logit))
Interaction_LRT_sex_smoke <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_17)
Interaction_names_1<- c("Education level", "Residential area", "Nationality", "Language region", "Age categories", "Functional limitations", "BMI group", "Self-perceived health status", "Diabetes", "Osteoarthritis", "Heart attack", "Stroke", "Urinary incontinence", "Osteoporosis", "Physical activity", "Chronic alcohol consumption", "Smoking (smoker)")
Interaction_sex <- round(c(Interaction_LRT_sex_education$p, Interaction_LRT_sex_urban_rural$p, Interaction_LRT_sex_nationality$p, Interaction_LRT_sex_language$p, Interaction_LRT_sex_age_cut$p, Interaction_LRT_sex_FL$p, Interaction_LRT_sex_bmi_cut$p, Interaction_LRT_sex_SHS_collapsed$p, Interaction_LRT_sex_diabetes$p, Interaction_LRT_sex_osteoarthritis$p, Interaction_LRT_sex_heart_attack$p, Interaction_LRT_sex_stroke$p, Interaction_LRT_sex_urinary_incontinence$p, Interaction_LRT_sex_osteoporosis$p, Interaction_LRT_sex_activity$p, Interaction_LRT_sex_alcohol$p, Interaction_LRT_sex_smoke$p), 3)
Interaction_sex_table <- data.frame(Interaction_names_1, Interaction_sex)
knitr::kable(Interaction_sex_table, col.names = c('Sex * Variable', '$P$'), caption = "Likelihood Ratio Test and $P$-value for the
addition of the interactions to the Main Effects Model 3")| Sex * Variable | \(P\) |
|---|---|
| Education level | 0.868 |
| Residential area | 0.876 |
| Nationality | 0.176 |
| Language region | 0.770 |
| Age categories | 0.064 |
| Functional limitations | 0.565 |
| BMI group | 0.349 |
| Self-perceived health status | 0.182 |
| Diabetes | 0.805 |
| Osteoarthritis | 0.349 |
| Heart attack | 0.191 |
| Stroke | 0.371 |
| Urinary incontinence | 0.457 |
| Osteoporosis | 0.054 |
| Physical activity | 0.404 |
| Chronic alcohol consumption | 0.365 |
| Smoking (smoker) | 0.432 |
5.8 Analysis of fall count
5.8.1 Prevalence of multiple falls by characteristic
\(y = fall\_count\) (levels: 1,2,3,4; no fall, 1 fall, two falls, more then two falls)
\(y = fall\_count\_collapsed\) is used in this analysis and is generated by dichotomization from \(fall\_count\) (1,2 = no fall or 1; 3,4 = two falls or more)
Where \(y\) represents the dependent variable
Due to the scope of this publication, the analysis was limited to the prevalence of multiple falls estimation.
5.8.1.1 Men
- education
tab_amount_fall_count_collapsed_education_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ education,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_education_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 7.9 5.0 11.6
## 2 8.4 6.9 10.2
## 3 10.1 8.2 12.3- urban_rural
tab_amount_fall_count_collapsed_urban_rural_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ urban_rural,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_urban_rural_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.6 7.3 10.1
## 2 10.0 7.9 12.5- nationality
tab_amount_fall_count_collapsed_nationality_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ nationality,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_nationality_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 9.6 8.4 11.0
## 2 5.0 2.9 7.9- language
tab_amount_fall_count_collapsed_language_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ language,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_language_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.9 7.5 10.4
## 2 9.3 7.1 11.7
## 3 9.8 6.2 14.5- age_cut
tab_amount_fall_count_collapsed_age_cut_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ age_cut,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_age_cut_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.0 6.5 9.8
## 2 8.5 6.7 10.6
## 3 13.1 9.8 16.9- FL
tab_amount_fall_count_collapsed_FL_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ FL,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_FL_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 7.9 6.7 9.3
## 2 11.2 8.6 14.2
## 3 26.0 15.2 39.3
## 4 13.6 5.6 25.8- bmi_cut
tab_amount_fall_count_collapsed_bmi_cut_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ bmi_cut,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_bmi_cut_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.6 6.8 10.6
## 2 9.6 6.7 13.2
## 3 9.2 7.5 11.0
## 4 15.8 1.7 47.5- SHS
tab_amount_fall_count_collapsed_SHS_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ SHS,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_SHS_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 6.4 4.6 8.6
## 2 8.7 7.1 10.6
## 3 11.8 9.0 15.1
## 4 11.8 6.9 18.3
## 5 19.4 7.9 36.0- diabetes
tab_amount_fall_count_collapsed_diabetes_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ diabetes,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_diabetes_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.9 7.6 10.3
## 2 9.0 6.1 12.5- osteoarthritis
tab_amount_fall_count_collapsed_osteoarthritis_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ osteoarthritis,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_osteoarthritis_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.1 6.8 9.5
## 2 11.8 9.3 14.6- heart_attack
tab_amount_fall_count_collapsed_heart_attack_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ heart_attack,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_heart_attack_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.9 7.8 10.2
## 2 13.0 4.6 26.7- stroke
tab_amount_fall_count_collapsed_stroke_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ stroke,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_stroke_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 9.0 7.8 10.2
## 2 9.6 2.4 23.5- urinary_incontinence
tab_amount_fall_count_collapsed_urinary_incontinence_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ urinary_incontinence,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_urinary_incontinence_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.4 7.3 9.6
## 2 15.5 10.7 21.3- osteoporosis
tab_amount_fall_count_collapsed_osteoporosis_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ osteoporosis,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_osteoporosis_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.8 7.6 10.0
## 2 17.1 8.7 28.6- activity
tab_amount_fall_count_collapsed_activity_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ activity,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_activity_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 11.9 7.9 16.9
## 2 10.8 7.5 14.8
## 3 8.3 7.0 9.8- alcohol
tab_amount_fall_count_collapsed_alcohol_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ alcohol,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_alcohol_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 6.9 3.8 11.2
## 2 9.4 8.1 10.8
## 3 8.2 4.7 12.9- smoke
tab_amount_fall_count_collapsed_smoke_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ smoke,
design = wdat_17_m,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_smoke_m[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 9.3 7.5 11.3
## 2 8.8 7.1 10.7
## 3 8.8 6.3 11.85.8.1.2 Women
- education
tab_amount_fall_count_collapsed_education_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ education,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_education_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.6 6.8 10.7
## 2 7.7 6.4 9.2
## 3 9.4 6.8 12.5- urban_rural
tab_amount_fall_count_collapsed_urban_rural_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ urban_rural,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_urban_rural_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.2 7.0 9.5
## 2 8.4 6.5 10.4- nationality
tab_amount_fall_count_collapsed_nationality_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ nationality,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_nationality_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.4 7.3 9.5
## 2 7.4 4.7 10.8- language
tab_amount_fall_count_collapsed_language_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ language,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_language_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.2 7.0 9.6
## 2 8.4 6.6 10.4
## 3 8.1 5.2 11.9- age_cut
tab_amount_fall_count_collapsed_age_cut_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ age_cut,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_age_cut_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 6.8 5.5 8.2
## 2 8.1 6.4 10.1
## 3 11.9 9.2 15.1- FL
tab_amount_fall_count_collapsed_FL_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ FL,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_FL_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 6.4 5.3 7.5
## 2 12.8 9.7 16.3
## 3 18.1 11.7 25.9
## 4 17.3 9.3 27.9- bmi_cut
tab_amount_fall_count_collapsed_bmi_cut_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ bmi_cut,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_bmi_cut_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 7.7 6.3 9.4
## 2 7.4 5.3 10.1
## 3 8.7 6.9 10.8
## 4 11.2 5.9 18.5- SHS
tab_amount_fall_count_collapsed_SHS_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ SHS,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_SHS_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 4.8 3.4 6.6
## 2 6.7 5.3 8.2
## 3 13.1 10.5 16.0
## 4 18.4 11.4 27.2
## 5 30.7 13.0 53.5- diabetes
tab_amount_fall_count_collapsed_diabetes_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ diabetes,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_diabetes_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 7.4 6.3 8.6
## 2 11.9 7.4 17.7- osteoarthritis
tab_amount_fall_count_collapsed_osteoarthritis_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ osteoarthritis,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_osteoarthritis_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 6.1 5.0 7.4
## 2 11.4 9.5 13.4- heart_attack
tab_amount_fall_count_collapsed_heart_attack_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ heart_attack,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_heart_attack_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.3 7.2 9.4
## 2 5.7 0.6 19.3- stroke
tab_amount_fall_count_collapsed_stroke_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ stroke,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_stroke_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.1 7.0 9.2
## 2 29.0 11.9 51.5- urinary_incontinence
tab_amount_fall_count_collapsed_urinary_incontinence_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ urinary_incontinence,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_urinary_incontinence_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 7.1 6.1 8.2
## 2 16.4 12.5 20.9- osteoporosis
tab_amount_fall_count_collapsed_osteoporosis_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ osteoporosis,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_osteoporosis_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 7.8 6.7 9.0
## 2 10.5 7.6 13.8- activity
tab_amount_fall_count_collapsed_activity_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ activity,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_activity_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 10.9 7.9 14.4
## 2 9.6 6.6 13.2
## 3 7.0 5.8 8.2- alcohol
tab_amount_fall_count_collapsed_alcohol_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ alcohol,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_alcohol_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.5 6.2 11.2
## 2 7.9 6.7 9.2
## 3 6.9 3.6 11.6- smoke
tab_amount_fall_count_collapsed_smoke_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
by = ~ smoke,
design = wdat_17_f,
FUN = svyciprop,
na.rm = TRUE,
keep.names = FALSE,
vartype="ci",
method = "likelihood"
)
round(tab_amount_fall_count_collapsed_smoke_f[,2:4],3)*100## I(fall_count_collapsed == "two_falls_or_more") ci_l ci_u
## 1 8.0 6.7 9.3
## 2 9.4 7.2 12.0
## 3 7.4 5.1 10.26 References
1.
RUBIN DB. Inference and missing data. Biometrika. 1976;63: 581–592. doi:10.1093/biomet/63.3.581
2.
Rao JNK, Scott AJ. On chi-squared tests for multiway contingency tables with cell proportions estimated from survey data. The Annals of Statistics. 1984;12. doi:10.1214/aos/1176346391
3.
Lumley T. Complex surveys. 2010. doi:10.1002/9780470580066
7 Appendix
In the appendix, we report a printout of all R packages used in the analysis and their versions to facilitate the reproducibility of the analysis/results.
7.1 Appendix
pander(sessionInfo(), compact = TRUE)R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale: LC_COLLATE=German_Switzerland.1252, LC_CTYPE=German_Switzerland.1252, LC_MONETARY=German_Switzerland.1252, LC_NUMERIC=C and LC_TIME=German_Switzerland.1252
attached base packages: grid, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: pander(v.0.6.4), flextable(v.0.7.0), officer(v.0.4.1), codebook(v.0.9.2), sjPlot(v.2.8.9), corrplot(v.0.90), VIM(v.6.1.1), colorspace(v.1.4-1), mice(v.3.13.0), Hmisc(v.4.5-0), Formula(v.1.2-4), lattice(v.0.20-45), forcats(v.0.5.1), stringr(v.1.4.0), purrr(v.0.3.4), readr(v.2.0.2), tidyr(v.1.1.4), tibble(v.3.1.5), tidyverse(v.1.3.1), Publish(v.2020.12.23), prodlim(v.2019.11.13), MatchIt(v.4.3.0), ggstance(v.0.3.5), jtools(v.2.1.4), tableone(v.0.13.0), DataExplorer(v.0.8.2), plotly(v.4.9.4.1), DT(v.0.19), psych(v.2.1.9), hexbin(v.1.28.2), survey(v.4.1-1), survival(v.3.2-13), Matrix(v.1.3-4), dplyr(v.1.0.7), ggplot2(v.3.3.5), rio(v.0.5.27) and pacman(v.0.5.1)
loaded via a namespace (and not attached): utf8(v.1.2.2), tidyselect(v.1.1.1), lme4(v.1.1-27.1), htmlwidgets(v.1.5.4), ranger(v.0.13.1), munsell(v.0.5.0), codetools(v.0.2-18), effectsize(v.0.5), future(v.1.22.1), withr(v.2.4.2), highr(v.0.9), knitr(v.1.30), uuid(v.0.1-4), rstudioapi(v.0.13), DescTools(v.0.99.43), robustbase(v.0.93-9), vcd(v.1.4-8), listenv(v.0.8.0), labeling(v.0.4.2), emmeans(v.1.7.0), repr(v.1.1.3), mnormt(v.2.0.2), farver(v.2.1.0), datawizard(v.0.2.1), coda(v.0.19-4), parallelly(v.1.28.1), vctrs(v.0.3.8), generics(v.0.1.0), TH.data(v.1.1-0), xfun(v.0.26), R6(v.2.5.1), assertthat(v.0.2.1), networkD3(v.0.4), scales(v.1.1.1), multcomp(v.1.4-17), nnet(v.7.3-16), rootSolve(v.1.8.2.3), gtable(v.0.3.0), globals(v.0.14.0), lmom(v.2.8), sandwich(v.3.0-1), rlang(v.0.4.11), systemfonts(v.1.0.4), splines(v.4.0.2), rstatix(v.0.7.0), lazyeval(v.0.2.2), broom(v.0.7.9), checkmate(v.2.0.0), reshape2(v.1.4.4), yaml(v.2.2.1), abind(v.1.4-5), modelr(v.0.1.8), crosstalk(v.1.1.1), backports(v.1.2.1), tools(v.4.0.2), lava(v.1.6.10), ellipsis(v.0.3.2), jquerylib(v.0.1.4), RColorBrewer(v.1.1-2), proxy(v.0.4-26), plyr(v.1.8.6), Rcpp(v.1.0.7), base64enc(v.0.1-3), rpart(v.4.1-15), viridis(v.0.6.2), zoo(v.1.8-9), haven(v.2.4.3), cluster(v.2.1.2), fs(v.1.5.0), magrittr(v.2.0.1), data.table(v.1.14.2), openxlsx(v.4.2.4), lmtest(v.0.9-38), reprex(v.2.0.1), tmvnsim(v.1.0-2), mvtnorm(v.1.1-2), sjmisc(v.2.8.7), hms(v.1.1.1), evaluate(v.0.14), xtable(v.1.8-4), sjstats(v.0.18.1), jpeg(v.0.1-9), broom.mixed(v.0.2.7), readxl(v.1.3.1), gridExtra(v.2.3), ggeffects(v.1.1.1), compiler(v.4.0.2), crayon(v.1.4.1), minqa(v.1.2.4), htmltools(v.0.5.2), tzdb(v.0.1.2), expm(v.0.999-6), Exact(v.3.0), lubridate(v.1.7.10), DBI(v.1.1.1), sjlabelled(v.1.1.8), dbplyr(v.2.1.1), MASS(v.7.3-54), boot(v.1.3-28), car(v.3.0-11), cli(v.3.0.1), mitools(v.2.4), huxtable(v.5.4.0), parallel(v.4.0.2), insight(v.0.14.4), igraph(v.1.2.6), pkgconfig(v.2.0.3), foreign(v.0.8-81), laeken(v.0.5.2), skimr(v.2.1.3), sp(v.1.4-5), xml2(v.1.3.2), bslib(v.0.3.0), estimability(v.1.3), rvest(v.1.0.1), digest(v.0.6.25), parameters(v.0.14.0), rmarkdown(v.2.11), cellranger(v.1.1.0), htmlTable(v.2.2.1), gld(v.2.6.2), gdtools(v.0.2.4), curl(v.4.3.2), commonmark(v.1.7), nloptr(v.1.2.2.2), lifecycle(v.1.0.1), nlme(v.3.1-153), jsonlite(v.1.7.2), carData(v.3.0-4), viridisLite(v.0.4.0), fansi(v.0.5.0), labelled(v.2.8.0), pillar(v.1.6.3), fastmap(v.1.1.0), httr(v.1.4.2), DEoptimR(v.1.0-9), glue(v.1.4.2), bayestestR(v.0.11.0), zip(v.2.2.0), png(v.0.1-7), class(v.7.3-19), stringi(v.1.7.5), sass(v.0.4.0), performance(v.0.8.0), latticeExtra(v.0.6-29), e1071(v.1.7-9) and future.apply(v.1.8.1)
Email: andri.gerber@gmx.ch | Department of Health Professions, Bern University of Applied Sciences, Bern, Switzerland: BFH | ORCiD: ID↩︎
---
title: > 
  Sex Differences in Falls Among the Elderly Community-Dwelling Swiss Population:
  A Population-Based Cross-Sectional Survey
author:
  name: 'Andri O. Gerber ^[Email: andri.gerber@gmx.ch | Department of Health Professions, Bern University of Applied Sciences, Bern, Switzerland: <a href="https://www.bfh.ch/de/?gclid=Cj0KCQjwl7qSBhD-ARIsACvV1X2PLd-PdfjCQDMBqKPvAfzXiFFAHHAaJmiZJGydIKaoT92X-AiuS4EaAgZ6EALw_wcB">BFH</a> | ORCiD: <a href="https://orcid.org/0000-0002-9793-041X">ID</a>]'
  affiliation: Department of Health Professions, Bern University of Applied Sciences, Bern, Switzerland
  footnote: Corresponding author
date: "`r format(Sys.time(), '%B %d, %Y')`"
output:
  html_document:
    toc: yes
    toc_float: yes
    number_sections: yes
    theme: simplex
    paged_df: yes
    code_folding: show
    code_download: yes
  includes:
    in_header: structure.tex
  pdf_document:
    toc: yes
bibliography: references.bib
csl: plos-one.csl
link-citations: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# R setup and required packages

```{r warning=FALSE}
library(pacman)
pacman::p_load(
  rio,
  ggplot2,
  dplyr,
  survey,
  hexbin,
  psych,
  DT,
  plotly,
  DataExplorer,
  tableone,
  jtools,
  ggstance,
  MatchIt,
  Publish,
  tidyverse,
  Hmisc,
  mice,
  VIM,
  corrplot,
  sjPlot,
  codebook,
  officer,
  flextable,
  pander
)
```

# Loading and formatting the dataset

The dataset of the Swiss Health Interview survey (SHIS) were anonymised prior to the analysis (for [further information](https://dam-api.bfs.admin.ch/hub/api/dam/assets/14839332/master)) and permission to analyse and publish the restults was obtained through a contract with the Swiss Federal Statistical Office (SFSO).

## Load and subset cross sectional data ($\geqq$ 60 years of age)

```{r}
dat <- rio::import("shs_2007_2012_2017_combined.dta") # SHIS Data
dat <- subset(dat, alter >= 60)
```

## Formatting {.tabset}

-   Factorize: levels(), labels()
-   Dichotomize
-   Categorize

![[Risk factor model for falls in older age WHO](https://www.who.int/publications/i/item/9789241563536)](Risk%20factor%20model%20for%20falls%20in%20older%20age%20WHO.PNG)

### Outcome variables

```{r}
dat$fall <- factor(
  dat$taltp01,
  levels = c(1, 2),
  labels = c("fall last 12 months", "no fall last 12 months")
)

# integrate no fall (0) into variable taltp02 (fall_count)
dat$fall_count_4 <- dat$taltp02
dat$fall_count_4[dat$taltp01==2] <- 0

dat$fall_count <- factor(
  dat$fall_count_4,
  levels = c(0, 1, 2, 3),
  labels = c(
    "no fall last 12 months",
    "1 fall last 12 months",
    "2 falls last 12 months",
    "more then 2 falls last 12 months"
  )
)

dat$fall_count_collapsed <- fct_collapse(dat$fall_count, two_falls_or_more = c("2 falls last 12 months", "more then 2 falls last 12 months"), no_or_one_fall = c("no fall last 12 months", "1 fall last 12 months"))


outcome_fall <- c("fall")
outcome_fall_count <- c("fall_count")
outcome_fall_count_collapsed <- c("fall_count_collapsed")
c(outcome_fall, outcome_fall_count, outcome_fall_count_collapsed)
```

### Exposure variables

```{r,echo = TRUE}
dat$gender <- factor(dat$sex,
                     levels = c(1, 2),
                     labels = c("man", "woman"))

exposure_variable <- c("gender")
exposure_variable
```

### Socio-economic variable

```{r}
dat$education <- factor(
  dat$ausbild3,
  levels = c(1, 2, 3),
  labels = c(
    "compulsory education",
    "upper secondary education",
    "tertiary education"
  )
)

socio_economic_covariate <- c("education")
socio_economic_covariate
```

### Socio-demographic variables

```{r}
dat$urban_rural <- factor(dat$stala,
                          levels = c(1, 2),
                          labels = c("urban area", "rural area"))

dat$nationality <- factor(dat$nation2,
                          levels = c(1, 2),
                          labels = c("swiss", "foreigner"))

dat$language <- factor(
  dat$sprache,
  levels = c(1, 2, 3),
  labels = c("german", "french", "italian")
)

socio_demographic_covariate <- c("urban_rural", "nationality", "language")
socio_demographic_covariate
```

### Biological variables

```{r,echo = TRUE}
# categorize age in 3 groups
dat$age_cut[dat$alter < 70] <- "60-69"
dat$age_cut[dat$alter >= 70 & dat$alter < 80] <- "70-79"
dat$age_cut[dat$alter >= 80] <- "80+"
dat$age_cut <- as.factor(dat$age_cut)
# Set reference category
dat$age_cut <- relevel(dat$age_cut, ref = "60-69")

dat$age <- dat$alter

# Functional limitations (vision, hearing, speech, walking)
dat$FL <- factor(
  dat$limfonc,
  levels = c(1, 2, 3, 4),
  labels = c(
    "No difficulty",
    "Slight difficulty",
    "Great difficulty",
    "Inability"
  )
)

# Categorize bmi into 4 groups
dat$bmi_cut[dat$bmi < 18.5] <- "underweight"
dat$bmi_cut[dat$bmi >= 18.5 & dat$bmi < 25] <- "normal weight"
dat$bmi_cut[dat$bmi >= 25 & dat$bmi < 30] <- "overweight"
dat$bmi_cut[dat$bmi >= 30] <- "obesity"
dat$bmi_cut <- as.factor(dat$bmi_cut)
dat$bmi_cut <- relevel(dat$bmi_cut, ref = "normal weight") # set reference for normal weight class

# Self-perceived health status
dat$SHS <- factor(
  dat$tsubg_allyears,
  levels = c(1, 2, 3, 4, 5),
  labels = c("very good", "good", "average", "poor", "very poor")
) 

# Self-perceived health status for models (collapse very good and good)

dat$SHS_collapsed <- factor(
  dat$tsubg_allyears,
  levels = c(1, 2, 3, 4, 5),
  labels = c("very good", "good", "average", "poor", "very poor")
) 

dat$SHS_collapsed <- fct_collapse(dat$SHS_collapsed, good = c("very good", "good"))



dat$diabetes <- factor(dat$diabete,
                       levels = c(1, 2),
                       labels = c("yes", "no"))
# Set reference category
dat$diabetes <- relevel(dat$diabetes, ref = "no")

dat$osteoarthritis <- factor(dat$tkran10c,
                             levels = c(1, 2),
                             labels = c("yes", "no"))
# Set reference category
dat$osteoarthritis <- relevel(dat$osteoarthritis, ref = "no")

dat$heart_attack <- factor(dat$tkran10f,
                           levels = c(1, 2),
                           labels = c("yes", "no"))
# Set reference category
dat$heart_attack <- relevel(dat$heart_attack, ref = "no")

dat$stroke <- factor(dat$tkran10g,
                     levels = c(1, 2),
                     labels = c("yes", "no"))
# Set reference category
dat$stroke <- relevel(dat$stroke, ref = "no")

dat$urinary_incontinence <- factor(dat$tkran10j,
                                   levels = c(1, 2),
                                   labels = c("yes", "no"))
# Set reference category
dat$urinary_incontinence <- relevel(dat$urinary_incontinence, ref = "no")

dat$osteoporosis <- factor(dat$tkran10d,
                           levels = c(1, 2),
                           labels = c("yes", "no"))
# Set reference category
dat$osteoporosis <- relevel(dat$osteoporosis, ref = "no")


biological_covariates <- c("age_cut", "age", "FL", "bmi_cut", "bmi", "SHS", "diabetes", "osteoarthritis", "heart_attack", "stroke", "urinary_incontinence", "osteoporosis")
biological_covariates
```

### Behavioral variables

```{r}
# Physical activity (weekly)
dat$activity <- factor(
  dat$actphy3,
  levels = c(1, 2, 3),
  labels = c("inactive", "partially active", "active")
)

dat$alcohol <- factor(
  dat$alcchron3,
  levels = c(1, 2, 3),
  labels = c("abstinent", "low risk", "moderate or increased risk")
)

dat$smoke <- factor(
  dat$tabac3,
  levels = c(1, 2, 3),
  labels = c("no smoker", "former smoker", "smoker")
)

behavioral_covariate <- c("activity", "alcohol", "smoke")
behavioral_covariate
```

### Stratae variable

```{r}
dat$strata <- factor(
  dat$kanton,
  levels = c(1:27),
  labels = c(
    "Zurich",
    "Bern",
    "Lucerne",
    "Uri",
    "Schwyz",
    "Obwalden",
    "Nidwalden",
    "Glarus",
    "Zug",
    "Fribourg",
    "Solothurn",
    "Basel-City",
    "Basel-Country",
    "Schaffhausen",
    "Appenzell Ausserrhoden",
    "Appenzell Innerrhoden",
    "St. Gallen",
    "Graubünden",
    "Aargau",
    "Thurgau",
    "Tessin",
    "Vaud",
    "Wallis",
    "Neuchâtel",
    "Geneva",
    "Jura",
    "Liechstenstein"
  )
)

strata <- c("strata")
strata
```

### Weight variable

```{r}
dat$weight <- dat$wght

weight <- c("weight")
weight
```

-   Get variable names

```{r}
variable_names <-
  c(
    "fall",
    "fall_count",
    "gender",
    "education",
    "language",
    "urban_rural",
    "nationality",
    "smoke",
    "FL",
    "bmi_cut",
    "bmi",
    "age_cut",
    "age",
    "SHS",
    "diabetes",
    "osteoarthritis",
    "heart_attack",
    "stroke",
    "urinary_incontinence",
    "osteoporosis",
    "activity",
    "alcohol",
    "strata",
    "weight"
  )
variable_names_fall <-   c(
    "fall",
    "gender",
    "education",
    "language",
    "urban_rural",
    "nationality",
    "smoke",
    "FL",
    "bmi_cut",
    "bmi",
    "age_cut",
    "age",
    "SHS",
    "diabetes",
    "osteoarthritis",
    "heart_attack",
    "stroke",
    "urinary_incontinence",
    "osteoporosis",
    "activity",
    "alcohol",
    "strata",
    "weight"
  )
variable_names_fall_count <-  c(
    "fall_count",
    "gender",
    "education",
    "language",
    "urban_rural",
    "nationality",
    "smoke",
    "FL",
    "bmi_cut",
    "bmi",
    "age_cut",
    "age",
    "SHS",
    "diabetes",
    "osteoarthritis",
    "heart_attack",
    "stroke",
    "urinary_incontinence",
    "osteoporosis",
    "activity",
    "alcohol",
    "strata",
    "weight"
  )

variable_names_nogender <-
  c(
    "fall",
    "fall_count",
    "education",
    "language",
    "urban_rural",
    "nationality",
    "smoke",
    "FL",
    "bmi_cut",
    "bmi",
    "age_cut",
    "age",
    "SHS",
    "diabetes",
    "osteoarthritis",
    "heart_attack",
    "stroke",
    "urinary_incontinence",
    "osteoporosis",
    "activity",
    "alcohol",
    "strata",
    "weight"
  )
```

## Subset the dataset (*year, sex*)

```{r}
dat_07 <- subset(dat, intjahr == 2007)
dat_12 <- subset(dat, intjahr == 2012)
dat_17 <- subset(dat, intjahr == 2017) # for further analysis
dat_17_m <- subset(dat_17, gender == "man")
dat_17_f <- subset(dat_17, gender == "woman")
```

## Variable selection:

```{r echo=TRUE, message=FALSE, warning=FALSE}
dat_17_select <- select(dat_17, variable_names)
dat_17_select_m <- select(dat_17_m, variable_names)
dat_17_select_f <- select(dat_17_f, variable_names)
DataExplorer::plot_str(dat_17_select, print_network = TRUE)
```

## Codebook (Metadata)

```{r}
codebook_dat_17_select<-codebook::codebook_table(dat_17_select)
```

# Summary (unweighted) sample

## Check missing values

The variable `diabetes` has the most missing values because of a proxy question (the question was not asked because of a previous question). For the further calculations, the complete-case-analysis was applied, as this procedure was also used in previous studies. Therefor missing values were excluded because their types cannot be identified according to the classes of Rubin et al. [@rubin1976].

```{r warning = FALSE, message=FALSE}
DataExplorer::plot_missing(dat_17_select, title = "Missing values SHIS 2017")
DataExplorer::plot_missing(dat_17_select_m, title = "Missing values SHIS (male) 2017")
DataExplorer::plot_missing(dat_17_select_f, title = "Missing values SHIS (female) 2017")
```

-   Missing-pattern over all variables

```{r results='hide'}
mice::md.pattern(dat_17_select)
```

-   Missing-pattern in fall, bmi, osteoarthritis, alcohol, activity, diabetes:

```{r results='hide'}
mice::md.pattern(select(dat_17_select, "fall", "bmi", "osteoarthritis", "alcohol", "activity", "diabetes")) # select variables with over 0.5% missing values
```

-   Missing values in age - bmi (histogram)

```{r}
VIM::histMiss(dat_17_select[, c("age", "bmi")])
```

## Descriptive statistics {.tabset}

### Summary table of the data

```{r warning = FALSE}
dat_17_select_describe <- Hmisc::describe(dat_17_select)
Hmisc::html(dat_17_select_describe, size = 80, title = "SHIS dataset 2017")
```

### Missing value plot

```{r}
dat_17_select_describe_plot <- plot(dat_17_select_describe)
dat_17_select_describe_plot$Categorical
dat_17_select_describe_plot$Continuous
```

### Variables stratified by fall

```{r}
tableone::CreateTableOne(data = dat_17_select, strata = "fall", includeNA = TRUE)
```

### Univariate distribution

#### Bar chart (by frequency)

```{r}
DataExplorer::plot_bar(dat_17_select)
```

#### Bar chart by fall

```{r}
DataExplorer::plot_bar(dat_17_select)
```

#### Histogram

-   age and bmi

```{r echo=TRUE}
plot_histogram(dat_17_select[, c("age", "bmi")])
```

#### QQ-Plot

-   age and bmi

```{r warning=FALSE}
qq_data <- dat_17_select[, c("age", "bmi")]

DataExplorer::plot_qq(qq_data)
# Quantile-Quantile plot to visualize the deviation from probability distribution.
```

-   bmi seems skewed on both tails, apply a simple log transformation

```{r echo=TRUE, warning=FALSE}
log_qq_data <- DataExplorer::update_columns(qq_data, 2, function(x) log(x + 1))
plot_qq(log_qq_data["bmi"])
```

### Correlation Analysis

```{r}
plot_correlation(na.omit(dat_17_select[, c("age", "bmi")]), type = "c") #only continuous
```

```{r fig1, fig.height = 10, fig.width = 10, warning=FALSE}
plot_correlation(na.omit(dat_17_select[variable_names_fall]), type = "d", maxcat = 5L, geom_text_args = (size = 0.05)) #only descrete
```

```{r}
# Bivariate Summaries Computed Separately by a Series of Predictors
var.summ <- spearman2(fall~ ., data = na.omit(dat_17_select[variable_names_fall]))
plot(var.summ, cex = 0.8)
```

#### Identify collinear predictors

```{r}
# Variable Clustering:  
# hclust hierarchical clustering function
# default is squared Spearman correlation coefficients
# to detect monotonic but nonlinear relationships
var.cluster <- Hmisc::varclus(~., data = dat_17_select[c(
    "fall",
    "gender",
    "education",
    "language",
    "urban_rural",
    "nationality",
    "smoke",
    "FL",
    "bmi_cut",
    "bmi",
    "age_cut",
    "age",
    "SHS",
    "diabetes",
    "osteoarthritis",
    "heart_attack",
    "stroke",
    "urinary_incontinence",
    "osteoporosis",
    "activity",
    "alcohol")])
# var.cluster
plot(var.cluster, abbrev=FALSE, cex = 0.8)
```

### Boxplots

```{r warning=FALSE}
# Visualize the distribution of all continuous features based on fall with a boxplot
DataExplorer::plot_boxplot(dat_17_select, by = "fall")
```

### Control of the dataframe size, i.e. extent of missing values

```{r message=FALSE, warning=FALSE}

dat_17_select_fall <- select(dat_17, variable_names_fall) # create dataset without fall_count variable
dat_17_select_fall_count <- select(dat_17, variable_names_fall_count) # create dataset without fall variable

dat_17_select_no_missing_fall <- as.data.frame(na.omit(dat_17_select_fall))
dat_17_select_no_missing_fall_count <- as.data.frame(na.omit(dat_17_select_fall_count))

dim(dat_17_select) # all variables with missings
dim(dat_17_select_fall) # all variables fall dataset
dim(dat_17_select_fall_count) # all variables count_fall dataset
dim(dat_17_select_no_missing_fall) # without missings fall dataset
dim(dat_17_select_no_missing_fall_count) # without missings fall_count dataset; Lot of missings are due to no questioning if no fall has happened.
```

# Surveydesign

## Creating/subset (weighted) survey.design-object

-   (*year, sex)*

```{r}
wdat<-svydesign(id = ~idno, strata = ~kanton, weights = ~wght, nest = T, data = dat)
wdat_07<-svydesign(id = ~idno, strata = ~kanton, weights = ~wght, nest = T, data = dat_07)
wdat_12<-svydesign(id = ~idno, strata = ~kanton, weights = ~wght, nest = T, data = dat_12)
wdat_17<-svydesign(id = ~idno, strata = ~kanton, weights = ~wght, nest = T, data = dat_17)
wdat_17_m <- subset(wdat_17, gender =="man")
wdat_17_f <- subset(wdat_17, gender == "woman")
```

## Variable selection

-   Same as previous in the (unweighted) dataset

# Summary weighted statistics

## Falls by sex SHIS 2007, 2012, **2017**

```{r}
fall_year_gender_svy <- svyby(formula = ~fall, 
      by = ~intjahr + gender, 
      design = wdat, 
      FUN = svymean, 
      na.rm = TRUE, 
      keep.names = FALSE)
# datatable(fall_year_gender_svy, filter = 'top')
dodge <- position_dodge(width=0.9)

fall_year_gender_svy_1<-ggplot(data = fall_year_gender_svy, mapping = aes(x = factor(intjahr), y = `fallfall last 12 months`, ymin = `fallfall last 12 months`-(2*`se.fallfall last 12 months`), ymax = `fallfall last 12 months`+(2*`se.fallfall last 12 months`), fill = gender)) +
   geom_bar(position="dodge", stat="identity") + 
   labs(y =  "Prop_fall", x = "Year") + 
   geom_errorbar(width = 0.2, size = 3, position = dodge) + labs(fill = "Gender")
fall_year_gender_svy_2<-fall_year_gender_svy_1 + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(fall_year_gender_svy_2)
```

## Table 1 {.tabset}

The following steps are used to create *Table 1* of the publication.

For each variable of interest, the number of respondents in each group, the (weighted) percentages of the population in each group and the (weighted) percentages of the population with the outcome of interest per group were calculated separatly for men and women. For all weighted confidence intervals for proportions, the Rao-Scott method was used [@rao1984]. Since it uses a scaled chi-squared distribution for the log-likelihood from a binomial distribution, it can be more accurate near 0 and 1. For this purpose, the function [`svyciprop()`](https://www.rdocumentation.org/packages/survey/versions/4.1-1/topics/svyciprop) from the package [survey](http://cran.fhcrc.org/web/packages/survey/index.html) by Thomas Lumley was used [@lumley2010].

### Summary row total

```{r}
options(width = 500)
Row_1<-svyCreateTableOne(vars = "fall", data = wdat_17, includeNA = TRUE)
print(Row_1, showAllLevels = TRUE, includeNA = TRUE, contDigit = 1)
```

### Summary row total men & women

```{r}
# total fall
Row_1_CI_fall<- svyciprop(formula = ~I(fall == "fall last 12 months"),
          design = wdat_17,
          na.rm = TRUE,
          method = "li")
Row_1_CI_fall

# total woman
Row_1_CI_woman<- svyciprop(formula = ~I(gender == "woman"),
          design = wdat_17,
          na.rm = TRUE,
          method = "li")
Row_1_CI_woman

# total man
Row_1_CI_man<- svyciprop(formula = ~I(gender == "man"),
          design = wdat_17,
          na.rm = TRUE,
          method = "li")
Row_1_CI_man

# Proportion fall % (CI 95) by gender
tab_amount_fall_tot_gender <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ gender,
  design = wdat_17,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_tot_gender
```

### Summary (unweighted) sample (*n*)

```{r}
options(width = 500)

sample_size_gender<- tableone::CreateTableOne(data = dat_17_select, strata = "gender", includeNA = TRUE)
sample_size_gendera<-print(sample_size_gender, showAllLevels = TRUE, includeNA = TRUE, contDigit = 0)
# To export to Excel-format
# write.csv2(sample_size_gendera, "sample_size.csv")
```

### Summary (weighted) population (N) for men & women

-   to get the missing values (NA):

```{r}
Row_2_NA<-svyCreateTableOne(vars = c(variable_names), strata = "gender", data = wdat_17, includeNA = TRUE, test = TRUE) # with NA
Row_2_NAa<- print(Row_2_NA, showAllLevels = TRUE, includeNA = TRUE, contDigit = 0)
# To export to Excel-format
# write.csv2(Row_2_NAa, file = "Row_2NA.csv")
```

-   to get the t-test mean of weighted characteristics men vs. women:

```{r}
Row_2<-svyCreateTableOne(vars = c(variable_names), strata = "gender", data = wdat_17, includeNA = FALSE, test = TRUE) # without NA and t-test gender
Row_2a<-print(Row_2, showAllLevels = TRUE, includeNA = FALSE, contDigit = 0)
# To export to Excel-format
# write.csv2(Row_2a, file = "Row_2.csv")
```

### Summary proportion (%) and confidence interval (CI 95%) of population (weighted number of people) by characteristic

#### Men

```{r}
# men
# education
Row_4_compulsory_education_m<- svyciprop(formula = ~I(education == "compulsory education"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_compulsory_education_m*100)[1]
Row_4_compulsory_education_m_CI = attr(Row_4_compulsory_education_m,"ci")
Row_4_compulsory_education_m_CI*100

Row_4_upper_secondary_education_m <- svyciprop(formula = ~I(education == "upper secondary education"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_upper_secondary_education_m*100)[1]
Row_4_upper_secondary_education_m_CI = attr(Row_4_upper_secondary_education_m,"ci")
Row_4_upper_secondary_education_m_CI*100

Row_4_tertiary_education_m <- svyciprop(formula = ~I(education == "tertiary education"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_tertiary_education_m*100)[1]
Row_4_tertiary_education_m_CI = attr(Row_4_tertiary_education_m,"ci")
Row_4_tertiary_education_m_CI*100

# language
Row_4_german_m<- svyciprop(formula = ~I(language == "german"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_german_m*100)[1]
Row_4_german_m_CI = attr(Row_4_german_m,"ci")
Row_4_german_m_CI*100

Row_4_french_m <- svyciprop(formula = ~I(language == "french"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_french_m*100)[1]
Row_4_french_m_CI = attr(Row_4_french_m,"ci")
Row_4_french_m_CI*100

Row_4_italian_m <- svyciprop(formula = ~I(language == "italian"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_italian_m*100)[1]
Row_4_italian_m_CI = attr(Row_4_italian_m,"ci")
Row_4_italian_m_CI*100

# urban_rural
Row_4_urban_area_m<- svyciprop(formula = ~I(urban_rural == "urban area"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_urban_area_m*100)[1]
Row_4_urban_area_m_CI = attr(Row_4_urban_area_m,"ci")
Row_4_urban_area_m_CI*100

Row_4_rural_arean_m <- svyciprop(formula = ~I(urban_rural == "rural area"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_rural_arean_m*100)[1]
Row_4_rural_arean_m_CI = attr(Row_4_rural_arean_m,"ci")
Row_4_rural_arean_m_CI*100

# nationality
Row_4_swiss_m<- svyciprop(formula = ~I(nationality == "swiss"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_swiss_m*100)[1]
Row_4_swiss_m_CI = attr(Row_4_swiss_m,"ci")
Row_4_swiss_m_CI*100

Row_4_foreigner_m <- svyciprop(formula = ~I(nationality == "foreigner"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_foreigner_m*100)[1]
Row_4_foreigner_m_CI = attr(Row_4_foreigner_m,"ci")
Row_4_foreigner_m_CI*100

# smoke              
Row_4_no_smoker_m<- svyciprop(formula = ~I(smoke == "no smoker"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_no_smoker_m*100)[1]
Row_4_no_smoker_m_CI = attr(Row_4_no_smoker_m,"ci")
Row_4_no_smoker_m_CI*100

Row_4_former_smoker_m <- svyciprop(formula = ~I(smoke == "former smoker"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_former_smoker_m*100)[1]
Row_4_former_smoker_m_CI = attr(Row_4_former_smoker_m,"ci")
Row_4_former_smoker_m_CI*100

Row_4_smoker_m <- svyciprop(formula = ~I(smoke == "smoker"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_smoker_m*100)[1]
Row_4_smoker_m_CI = attr(Row_4_smoker_m,"ci")
Row_4_smoker_m_CI*100

# FL
Row_4_FL_no_difficulty_m<- svyciprop(formula = ~I(FL == "No difficulty"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_FL_no_difficulty_m*100)[1]
Row_4_FL_no_difficulty_m_CI = attr(Row_4_FL_no_difficulty_m,"ci")
Row_4_FL_no_difficulty_m_CI*100

Row_4_FL_slight_difficulty_m <- svyciprop(formula = ~I(FL == "Slight difficulty"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_FL_slight_difficulty_m*100)[1]
Row_4_FL_slight_difficulty_m_CI = attr(Row_4_FL_slight_difficulty_m,"ci")
Row_4_FL_slight_difficulty_m_CI*100

Row_4_FL_great_difficulty_m<- svyciprop(formula = ~I(FL == "Great difficulty"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_FL_great_difficulty_m*100)[1]
Row_4_FL_great_difficulty_m_CI = attr(Row_4_FL_great_difficulty_m,"ci")
Row_4_FL_great_difficulty_m_CI*100

Row_4_FL_inability_m<- svyciprop(formula = ~I(FL == "Inability"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_FL_inability_m*100)[1]
Row_4_FL_inability_m_CI = attr(Row_4_FL_inability_m,"ci")
Row_4_FL_inability_m_CI*100

# bmi_cut
Row_4_normal_weight_m<- svyciprop(formula = ~I(bmi_cut == "normal weight"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_normal_weight_m*100)[1]
Row_4_normal_weight_m_CI = attr(Row_4_normal_weight_m,"ci")
Row_4_normal_weight_m_CI*100

Row_4_obesity_m <- svyciprop(formula = ~I(bmi_cut == "obesity"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_obesity_m*100)[1]
Row_4_obesity_m_CI = attr(Row_4_obesity_m,"ci")
Row_4_obesity_m_CI*100

Row_4_overweight_m<- svyciprop(formula = ~I(bmi_cut == "overweight"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_overweight_m*100)[1]
Row_4_overweight_m_CI = attr(Row_4_overweight_m,"ci")
Row_4_overweight_m_CI*100

Row_4_underweight_m <- svyciprop(formula = ~I(bmi_cut == "underweight"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_underweight_m*100)[1]
Row_4_underweight_m_CI = attr(Row_4_underweight_m,"ci")
Row_4_underweight_m_CI*100

# age_cut
Row_4_age_cut_60to64_m<- svyciprop(formula = ~I(age_cut == "60-69"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_age_cut_60to64_m*100)[1]
Row_4_age_cut_60to64_m_CI = attr(Row_4_age_cut_60to64_m,"ci")
Row_4_age_cut_60to64_m_CI*100

Row_4_age_cut_65to74_m <- svyciprop(formula = ~I(age_cut == "70-79"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_age_cut_65to74_m*100)[1]
Row_4_age_cut_65to74_m_CI = attr(Row_4_age_cut_65to74_m,"ci")
Row_4_age_cut_65to74_m_CI*100

Row_4_age_cut_75_m<- svyciprop(formula = ~I(age_cut == "80+"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_age_cut_75_m*100)[1]
Row_4_age_cut_75_m_CI = attr(Row_4_age_cut_75_m,"ci")
Row_4_age_cut_75_m_CI*100

# SHS     
Row_4_SHS_very_good_m<- svyciprop(formula = ~I(SHS == "very good"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_very_good_m*100)[1]
Row_4_SHS_very_good_m_CI = attr(Row_4_SHS_very_good_m,"ci")
Row_4_SHS_very_good_m_CI*100

Row_4_SHS_good_m<- svyciprop(formula = ~I(SHS == "good"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_good_m*100)[1]
Row_4_SHS_good_m_CI = attr(Row_4_SHS_good_m,"ci")
Row_4_SHS_good_m_CI*100

Row_4_SHS_average_m <- svyciprop(formula = ~I(SHS == "average"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_average_m*100)[1]
Row_4_SHS_average_m_CI = attr(Row_4_SHS_average_m,"ci")
Row_4_SHS_average_m_CI*100

Row_4_SHS_poor_m<- svyciprop(formula = ~I(SHS == "poor"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_poor_m*100)[1]
Row_4_SHS_poor_m_CI = attr(Row_4_SHS_poor_m,"ci")
Row_4_SHS_poor_m_CI*100

Row_4_SHS_very_poor_m<- svyciprop(formula = ~I(SHS == "very poor"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_very_poor_m*100)[1]
Row_4_SHS_very_poor_m_CI = attr(Row_4_SHS_very_poor_m,"ci")
Row_4_SHS_very_poor_m_CI*100

# diabetes
Row_4_diabetes_yes_m<- svyciprop(formula = ~I(diabetes == "yes"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_diabetes_yes_m*100)[1]
Row_4_diabetes_yes_m_CI = attr(Row_4_diabetes_yes_m,"ci")
Row_4_diabetes_yes_m_CI*100

Row_4_diabetes_no_m <- svyciprop(formula = ~I(diabetes == "no"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_diabetes_no_m*100)[1]
Row_4_diabetes_no_m_CI = attr(Row_4_diabetes_no_m,"ci")
Row_4_diabetes_no_m_CI*100

# osteoarthritis
Row_4_osteoarthritis_yes_m<- svyciprop(formula = ~I(osteoarthritis == "yes"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_osteoarthritis_yes_m*100)[1]
Row_4_osteoarthritis_yes_m_CI = attr(Row_4_osteoarthritis_yes_m,"ci")
Row_4_osteoarthritis_yes_m_CI*100

Row_4_osteoarthritis_no_m <- svyciprop(formula = ~I(osteoarthritis == "no"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_osteoarthritis_no_m*100)[1]
Row_4_osteoarthritis_no_m_CI = attr(Row_4_osteoarthritis_no_m,"ci")
Row_4_osteoarthritis_no_m_CI*100

# heart_attack
Row_4_heart_attack_yes_m<- svyciprop(formula = ~I(heart_attack == "yes"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_heart_attack_yes_m*100)[1]
Row_4_heart_attack_yes_m_CI = attr(Row_4_heart_attack_yes_m,"ci")
Row_4_heart_attack_yes_m_CI*100

Row_4_heart_attack_no_m <- svyciprop(formula = ~I(heart_attack == "no"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_heart_attack_no_m*100)[1]
Row_4_heart_attack_no_m_CI = attr(Row_4_heart_attack_no_m,"ci")
Row_4_heart_attack_no_m_CI*100

# stroke
Row_4_stroke_yes_m<- svyciprop(formula = ~I(stroke == "yes"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_stroke_yes_m*100)[1]
Row_4_stroke_yes_m_CI = attr(Row_4_stroke_yes_m,"ci")
Row_4_stroke_yes_m_CI*100

Row_4_stroke_no_m <- svyciprop(formula = ~I(stroke == "no"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_stroke_no_m*100)[1]
Row_4_stroke_no_m_CI = attr(Row_4_stroke_no_m,"ci")
Row_4_stroke_no_m_CI*100

# urinary_incontinence
Row_4_urinary_incontinence_yes_m<- svyciprop(formula = ~I(urinary_incontinence == "yes"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_urinary_incontinence_yes_m*100)[1]
Row_4_urinary_incontinence_yes_m_CI = attr(Row_4_urinary_incontinence_yes_m,"ci")
Row_4_urinary_incontinence_yes_m_CI*100

Row_4_urinary_incontinence_no_m <- svyciprop(formula = ~I(urinary_incontinence == "no"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_urinary_incontinence_no_m*100)[1]
Row_4_urinary_incontinence_no_m_CI = attr(Row_4_urinary_incontinence_no_m,"ci")
Row_4_urinary_incontinence_no_m_CI*100

# osteoporosis
Row_4_osteoporosis_yes_m<- svyciprop(formula = ~I(osteoporosis == "yes"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_osteoporosis_yes_m*100)[1]
Row_4_osteoporosis_yes_m_CI = attr(Row_4_osteoporosis_yes_m,"ci")
Row_4_osteoporosis_yes_m_CI*100

Row_4_osteoporosis_no_m <- svyciprop(formula = ~I(osteoporosis == "no"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_osteoporosis_no_m*100)[1]
Row_4_osteoporosis_no_m_CI = attr(Row_4_osteoporosis_no_m,"ci")
Row_4_osteoporosis_no_m_CI*100

# activity
Row_4_inactive_m<- svyciprop(formula = ~I(activity == "inactive"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_inactive_m*100)[1]
Row_4_inactive_m_CI = attr(Row_4_inactive_m,"ci")
Row_4_inactive_m_CI*100

Row_4_partially_active_m <- svyciprop(formula = ~I(activity == "partially active"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_partially_active_m*100)[1]
Row_4_partially_active_m_CI = attr(Row_4_partially_active_m,"ci")
Row_4_partially_active_m_CI*100

Row_4_active_m<- svyciprop(formula = ~I(activity == "active"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_active_m*100)[1]
Row_4_active_m_CI = attr(Row_4_active_m,"ci")
Row_4_active_m_CI*100


# alcohol
Row_4_alcohol_abstinent_m<- svyciprop(formula = ~I(alcohol == "abstinent"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_alcohol_abstinent_m*100)[1]
Row_4_alcohol_abstinent_m_CI = attr(Row_4_alcohol_abstinent_m,"ci")
Row_4_alcohol_abstinent_m_CI*100

Row_4_alcohol_low_risk_m<- svyciprop(formula = ~I(alcohol == "low risk"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_alcohol_low_risk_m*100)[1]
Row_4_alcohol_low_risk_m_CI = attr(Row_4_alcohol_low_risk_m,"ci")
Row_4_alcohol_low_risk_m_CI*100

Row_4_alcohol_moderate_or_increased_risk_m<- svyciprop(formula = ~I(alcohol == "moderate or increased risk"),
          design = wdat_17_m,
          na.rm = TRUE,
          method = "li")
(Row_4_alcohol_moderate_or_increased_risk_m*100)[1]
Row_4_alcohol_moderate_or_increased_risk_m_CI = attr(Row_4_alcohol_moderate_or_increased_risk_m,"ci")
Row_4_alcohol_moderate_or_increased_risk_m_CI*100
```

#### Women

```{r}
# women
## education
Row_4_compulsory_education_f<- svyciprop(formula = ~I(education == "compulsory education"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_compulsory_education_f*100)[1]
Row_4_compulsory_education_f_CI = attr(Row_4_compulsory_education_f,"ci")
Row_4_compulsory_education_f_CI*100

Row_4_upper_secondary_education_f <- svyciprop(formula = ~I(education == "upper secondary education"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_upper_secondary_education_f*100)[1]
Row_4_upper_secondary_education_f_CI = attr(Row_4_upper_secondary_education_f,"ci")
Row_4_upper_secondary_education_f_CI*100

Row_4_tertiary_education_f <- svyciprop(formula = ~I(education == "tertiary education"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_tertiary_education_f*100)[1]
Row_4_tertiary_education_f_CI = attr(Row_4_tertiary_education_f,"ci")
Row_4_tertiary_education_f_CI*100

# language
Row_4_german_f<- svyciprop(formula = ~I(language == "german"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_german_f*100)[1]
Row_4_german_f_CI = attr(Row_4_german_f,"ci")
Row_4_german_f_CI*100

Row_4_french_f <- svyciprop(formula = ~I(language == "french"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_french_f*100)[1]
Row_4_french_f_CI = attr(Row_4_french_f,"ci")
Row_4_french_f_CI*100

Row_4_italian_f <- svyciprop(formula = ~I(language == "italian"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_italian_f*100)[1]
Row_4_italian_f_CI = attr(Row_4_italian_f,"ci")
Row_4_italian_f_CI*100

# urban_rural
Row_4_urban_area_f<- svyciprop(formula = ~I(urban_rural == "urban area"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_urban_area_f*100)[1]
Row_4_urban_area_f_CI = attr(Row_4_urban_area_f,"ci")
Row_4_urban_area_f_CI*100

Row_4_rural_arean_f <- svyciprop(formula = ~I(urban_rural == "rural area"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_rural_arean_f*100)[1]
Row_4_rural_arean_f_CI = attr(Row_4_rural_arean_f,"ci")
Row_4_rural_arean_f_CI*100

# nationality
Row_4_swiss_f<- svyciprop(formula = ~I(nationality == "swiss"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_swiss_f*100)[1]
Row_4_swiss_f_CI = attr(Row_4_swiss_f,"ci")
Row_4_swiss_f_CI*100

Row_4_foreigner_f <- svyciprop(formula = ~I(nationality == "foreigner"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_foreigner_f*100)[1]
Row_4_foreigner_f_CI = attr(Row_4_foreigner_f,"ci")
Row_4_foreigner_f_CI*100

# smoke              
Row_4_no_smoker_f<- svyciprop(formula = ~I(smoke == "no smoker"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_no_smoker_f*100)[1]
Row_4_no_smoker_f_CI = attr(Row_4_no_smoker_f,"ci")
Row_4_no_smoker_f_CI*100

Row_4_former_smoker_f <- svyciprop(formula = ~I(smoke == "former smoker"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_former_smoker_f*100)[1]
Row_4_former_smoker_f_CI = attr(Row_4_former_smoker_f,"ci")
Row_4_former_smoker_f_CI*100

Row_4_smoker_f <- svyciprop(formula = ~I(smoke == "smoker"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_smoker_f*100)[1]
Row_4_smoker_f_CI = attr(Row_4_smoker_f,"ci")
Row_4_smoker_f_CI*100

# FL
Row_4_FL_no_difficulty_f<- svyciprop(formula = ~I(FL == "No difficulty"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_FL_no_difficulty_f*100)[1]
Row_4_FL_no_difficulty_f_CI = attr(Row_4_FL_no_difficulty_f,"ci")
Row_4_FL_no_difficulty_f_CI*100

Row_4_FL_slight_difficulty_f <- svyciprop(formula = ~I(FL == "Slight difficulty"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_FL_slight_difficulty_f*100)[1]
Row_4_FL_slight_difficulty_f_CI = attr(Row_4_FL_slight_difficulty_f,"ci")
Row_4_FL_slight_difficulty_f_CI*100

Row_4_FL_great_difficulty_f<- svyciprop(formula = ~I(FL == "Great difficulty"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_FL_great_difficulty_f*100)[1]
Row_4_FL_great_difficulty_f_CI = attr(Row_4_FL_great_difficulty_f,"ci")
Row_4_FL_great_difficulty_f_CI*100

Row_4_FL_inability_f<- svyciprop(formula = ~I(FL == "Inability"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_FL_inability_f*100)[1]
Row_4_FL_inability_f_CI = attr(Row_4_FL_inability_f,"ci")
Row_4_FL_inability_f_CI*100

# bmi_cut
Row_4_normal_weight_f<- svyciprop(formula = ~I(bmi_cut == "normal weight"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_normal_weight_f*100)[1]
Row_4_normal_weight_f_CI = attr(Row_4_normal_weight_f,"ci")
Row_4_normal_weight_f_CI*100

Row_4_obesity_f <- svyciprop(formula = ~I(bmi_cut == "obesity"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_obesity_f*100)[1]
Row_4_obesity_f_CI = attr(Row_4_obesity_f,"ci")
Row_4_obesity_f_CI*100

Row_4_overweight_f<- svyciprop(formula = ~I(bmi_cut == "overweight"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_overweight_f*100)[1]
Row_4_overweight_f_CI = attr(Row_4_overweight_f,"ci")
Row_4_overweight_f_CI*100

Row_4_underweight_f <- svyciprop(formula = ~I(bmi_cut == "underweight"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_underweight_f*100)[1]
Row_4_underweight_f_CI = attr(Row_4_underweight_f,"ci")
Row_4_underweight_f_CI*100

# age_cut
Row_4_age_cut_60to64_f<- svyciprop(formula = ~I(age_cut == "60-69"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_age_cut_60to64_f*100)[1]
Row_4_age_cut_60to64_f_CI = attr(Row_4_age_cut_60to64_f,"ci")
Row_4_age_cut_60to64_f_CI*100

Row_4_age_cut_65to74_f <- svyciprop(formula = ~I(age_cut == "70-79"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_age_cut_65to74_f*100)[1]
Row_4_age_cut_65to74_f_CI = attr(Row_4_age_cut_65to74_f,"ci")
Row_4_age_cut_65to74_f_CI*100

Row_4_age_cut_75_f<- svyciprop(formula = ~I(age_cut == "80+"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_age_cut_75_f*100)[1]
Row_4_age_cut_75_f_CI = attr(Row_4_age_cut_75_f,"ci")
Row_4_age_cut_75_f_CI*100

# SHS     
Row_4_SHS_very_good_f<- svyciprop(formula = ~I(SHS == "very good"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_very_good_f*100)[1]
Row_4_SHS_very_good_f_CI = attr(Row_4_SHS_very_good_f,"ci")
Row_4_SHS_very_good_f_CI*100

Row_4_SHS_good_f<- svyciprop(formula = ~I(SHS == "good"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_good_f*100)[1]
Row_4_SHS_good_f_CI = attr(Row_4_SHS_good_f,"ci")
Row_4_SHS_good_f_CI*100

Row_4_SHS_average_f <- svyciprop(formula = ~I(SHS == "average"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_average_f*100)[1]
Row_4_SHS_average_f_CI = attr(Row_4_SHS_average_f,"ci")
Row_4_SHS_average_f_CI*100

Row_4_SHS_poor_f<- svyciprop(formula = ~I(SHS == "poor"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_poor_f*100)[1]
Row_4_SHS_poor_f_CI = attr(Row_4_SHS_poor_f,"ci")
Row_4_SHS_poor_f_CI*100

Row_4_SHS_very_poor_f<- svyciprop(formula = ~I(SHS == "very poor"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_SHS_very_poor_f*100)[1]
Row_4_SHS_very_poor_f_CI = attr(Row_4_SHS_very_poor_f,"ci")
Row_4_SHS_very_poor_f_CI*100

# diabetes
Row_4_diabetes_yes_f<- svyciprop(formula = ~I(diabetes == "yes"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_diabetes_yes_f*100)[1]
Row_4_diabetes_yes_f_CI = attr(Row_4_diabetes_yes_f,"ci")
Row_4_diabetes_yes_f_CI*100

Row_4_diabetes_no_f <- svyciprop(formula = ~I(diabetes == "no"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_diabetes_no_f*100)[1]
Row_4_diabetes_no_f_CI = attr(Row_4_diabetes_no_f,"ci")
Row_4_diabetes_no_f_CI*100

# osteoarthritis
Row_4_osteoarthritis_yes_f<- svyciprop(formula = ~I(osteoarthritis == "yes"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_osteoarthritis_yes_f*100)[1]
Row_4_osteoarthritis_yes_f_CI = attr(Row_4_osteoarthritis_yes_f,"ci")
Row_4_osteoarthritis_yes_f_CI*100

Row_4_osteoarthritis_no_f <- svyciprop(formula = ~I(osteoarthritis == "no"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_osteoarthritis_no_f*100)[1]
Row_4_osteoarthritis_no_f_CI = attr(Row_4_osteoarthritis_no_f,"ci")
Row_4_osteoarthritis_no_f_CI*100

# heart_attack
Row_4_heart_attack_yes_f<- svyciprop(formula = ~I(heart_attack == "yes"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_heart_attack_yes_f*100)[1]
Row_4_heart_attack_yes_f_CI = attr(Row_4_heart_attack_yes_f,"ci")
Row_4_heart_attack_yes_f_CI*100

Row_4_heart_attack_no_f <- svyciprop(formula = ~I(heart_attack == "no"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_heart_attack_no_f*100)[1]
Row_4_heart_attack_no_f_CI = attr(Row_4_heart_attack_no_f,"ci")
Row_4_heart_attack_no_f_CI*100

# stroke
Row_4_stroke_yes_f<- svyciprop(formula = ~I(stroke == "yes"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_stroke_yes_f*100)[1]
Row_4_stroke_yes_f_CI = attr(Row_4_stroke_yes_f,"ci")
Row_4_stroke_yes_f_CI*100

Row_4_stroke_no_f <- svyciprop(formula = ~I(stroke == "no"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_stroke_no_f*100)[1]
Row_4_stroke_no_f_CI = attr(Row_4_stroke_no_f,"ci")
Row_4_stroke_no_f_CI*100

# urinary_incontinence
Row_4_urinary_incontinence_yes_f<- svyciprop(formula = ~I(urinary_incontinence == "yes"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_urinary_incontinence_yes_f*100)[1]
Row_4_urinary_incontinence_yes_f_CI = attr(Row_4_urinary_incontinence_yes_f,"ci")
Row_4_urinary_incontinence_yes_f_CI*100

Row_4_urinary_incontinence_no_f <- svyciprop(formula = ~I(urinary_incontinence == "no"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_urinary_incontinence_no_f*100)[1]
Row_4_urinary_incontinence_no_f_CI = attr(Row_4_urinary_incontinence_no_f,"ci")
Row_4_urinary_incontinence_no_f_CI*100

# osteoporosis
Row_4_osteoporosis_yes_f<- svyciprop(formula = ~I(osteoporosis == "yes"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_osteoporosis_yes_f*100)[1]
Row_4_osteoporosis_yes_f_CI = attr(Row_4_osteoporosis_yes_f,"ci")
Row_4_osteoporosis_yes_f_CI*100

Row_4_osteoporosis_no_f <- svyciprop(formula = ~I(osteoporosis == "no"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_osteoporosis_no_f*100)[1]
Row_4_osteoporosis_no_f_CI = attr(Row_4_osteoporosis_no_f,"ci")
Row_4_osteoporosis_no_f_CI*100

# activity
Row_4_inactive_f<- svyciprop(formula = ~I(activity == "inactive"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_inactive_f*100)[1]
Row_4_inactive_f_CI = attr(Row_4_inactive_f,"ci")
Row_4_inactive_f_CI*100

Row_4_partially_active_f <- svyciprop(formula = ~I(activity == "partially active"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_partially_active_f*100)[1]
Row_4_partially_active_f_CI = attr(Row_4_partially_active_f,"ci")
Row_4_partially_active_f_CI*100

Row_4_active_f<- svyciprop(formula = ~I(activity == "active"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_active_f*100)[1]
Row_4_active_f_CI = attr(Row_4_active_f,"ci")
Row_4_active_f_CI*100


# alcohol
Row_4_alcohol_abstinent_f<- svyciprop(formula = ~I(alcohol == "abstinent"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_alcohol_abstinent_f*100)[1]
Row_4_alcohol_abstinent_f_CI = attr(Row_4_alcohol_abstinent_f,"ci")
Row_4_alcohol_abstinent_f_CI*100

Row_4_alcohol_low_risk_f<- svyciprop(formula = ~I(alcohol == "low risk"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_alcohol_low_risk_f*100)[1]
Row_4_alcohol_low_risk_f_CI = attr(Row_4_alcohol_low_risk_f,"ci")
Row_4_alcohol_low_risk_f_CI*100

Row_4_alcohol_moderate_or_increased_risk_f<- svyciprop(formula = ~I(alcohol == "moderate or increased risk"),
          design = wdat_17_f,
          na.rm = TRUE,
          method = "li")
(Row_4_alcohol_moderate_or_increased_risk_f*100)[1]
Row_4_alcohol_moderate_or_increased_risk_f_CI = attr(Row_4_alcohol_moderate_or_increased_risk_f,"ci")
Row_4_alcohol_moderate_or_increased_risk_f_CI*100
```

### Summary proportion (%) and CI 95% of population with falls by characteristic

#### Men

```{r warning=FALSE}

# men
# education
tab_amount_fall_education_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ education,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_education_m*100

# language
tab_amount_fall_language_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ language,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_language_m*100

# urban_rural
tab_amount_fall_urban_rural_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ urban_rural,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_urban_rural_m*100

# nationality
tab_amount_fall_nationality_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ nationality,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_nationality_m*100

# smoke              
tab_amount_fall_smoke_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ smoke,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_smoke_m*100

# FL
tab_amount_fall_FL_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ FL,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_FL_m*100

# bmi_cut
tab_amount_fall_bmi_cut_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ bmi_cut,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_bmi_cut_m*100

# age_cut
tab_amount_fall_age_cut_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ age_cut,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_age_cut_m*100

# SHS     
tab_amount_fall_SHS_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ SHS,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_SHS_m*100

# diabetes
tab_amount_fall_diabetes_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ diabetes,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_diabetes_m*100

# osteoarthritis
tab_amount_fall_osteoarthritis_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ osteoarthritis,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_osteoarthritis_m*100

# heart_attack
tab_amount_fall_heart_attack_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ heart_attack,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_heart_attack_m*100

# stroke
tab_amount_fall_stroke_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ stroke,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_stroke_m*100

# urinary_incontinence
tab_amount_fall_urinary_incontinence_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ urinary_incontinence,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_urinary_incontinence_m*100

# osteoporosis
tab_amount_fall_osteoporosis_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ osteoporosis,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_osteoporosis_m*100

# activity
tab_amount_fall_activity_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ activity,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_activity_m*100

# alcohol
tab_amount_fall_alcohol_m <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ alcohol,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_alcohol_m*100
```

#### Women

```{r}
# women
# education
tab_amount_fall_education_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ education,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_education_f*100

# language
tab_amount_fall_language_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ language,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_language_f*100

# urban_rural
tab_amount_fall_urban_rural_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ urban_rural,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_urban_rural_f*100

# nationality
tab_amount_fall_nationality_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ nationality,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_nationality_f*100

# smoke              
tab_amount_fall_smoke_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ smoke,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_smoke_f*100

# FL
tab_amount_fall_FL_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ FL,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_FL_f*100

# bmi_cut
tab_amount_fall_bmi_cut_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ bmi_cut,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_bmi_cut_f*100

# age_cut
tab_amount_fall_age_cut_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ age_cut,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_age_cut_f*100

# SHS     
tab_amount_fall_SHS_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ SHS,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_SHS_f*100

# diabetes
tab_amount_fall_diabetes_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ diabetes,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_diabetes_f*100

# osteoarthritis
tab_amount_fall_osteoarthritis_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ osteoarthritis,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_osteoarthritis_f*100

# heart_attack
tab_amount_fall_heart_attack_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ heart_attack,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_heart_attack_f*100

# stroke
tab_amount_fall_stroke_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ stroke,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_stroke_f*100

# urinary_incontinence
tab_amount_fall_urinary_incontinence_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ urinary_incontinence,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_urinary_incontinence_f*100

# osteoporosis
tab_amount_fall_osteoporosis_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ osteoporosis,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_osteoporosis_f*100

# activity
tab_amount_fall_activity_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ activity,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_activity_f*100

# alcohol
tab_amount_fall_alcohol_f <- svyby(
  formula = ~I(fall == "fall last 12 months"),
  by = ~ alcohol,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
tab_amount_fall_alcohol_f*100
```

### Summary intergroupdifference - test

-   Intergroupdifference (Prop fall % by subset of groups)

    -   $svychi^2-test$ $Pearson's\_x^2$ with Rao & Scott adjustment

-   "`svychisq()` computes first and second-order Rao-Scott corrections to the Pearson chisquared test, and two Wald-type tests. The default (statistic="F") is the Rao-Scott second-order correction. The p-values are computed with a Satterthwaite approximation to the distribution. The alternative statistic="Chisq" adjusts the Pearson chisquared statistic by a design effect estimate and then compares it to the chisquared distribution it would have under simple random sampling" [@rao1984].

![Here a description of Prof Thomas Lumley](svychi.PNG)

-   Fall - Sex

```{r}
# men vs women
survey::svychisq(~fall + gender, wdat_17)
```

#### Men

```{r}
# men

# education
survey::svychisq(~fall + education, wdat_17_m)
# language
survey::svychisq(~fall + language, wdat_17_m)
# urban_rural
survey::svychisq(~fall + urban_rural, wdat_17_m)
# nationality
survey::svychisq(~fall + nationality, wdat_17_m)
# smoke
survey::svychisq(~fall + smoke, wdat_17_m)
# FL
survey::svychisq(~fall + FL, wdat_17_m)
# bmi_cut
survey::svychisq(~fall + bmi_cut, wdat_17_m)
# bmi
# survey::svychisq(~fall + bmi, wdat_17_m)
# age_cut
survey::svychisq(~fall + age_cut, wdat_17_m)
# age
# survey::svychisq(~fall + age, wdat_17_m)
# SHS
survey::svychisq(~fall + SHS, wdat_17_m)
# diabetes
survey::svychisq(~fall + diabetes, wdat_17_m)
# osteoarthritis
survey::svychisq(~fall + osteoarthritis, wdat_17_m)
# heart_attack
survey::svychisq(~fall + heart_attack, wdat_17_m)
# stroke
survey::svychisq(~fall + stroke, wdat_17_m)
# urinary_incontinence
survey::svychisq(~fall + urinary_incontinence, wdat_17_m)
# osteoporosis
survey::svychisq(~fall + osteoporosis, wdat_17_m)
# activity
survey::svychisq(~fall + activity, wdat_17_m)
# alcohol
survey::svychisq(~fall + alcohol, wdat_17_m)
```

#### Women

```{r}
# women

# education
survey::svychisq(~fall + education, wdat_17_f)
# language
survey::svychisq(~fall + language, wdat_17_f)
# urban_rural
survey::svychisq(~fall + urban_rural, wdat_17_f)
# nationality
survey::svychisq(~fall + nationality, wdat_17_f)
# smoke
survey::svychisq(~fall + smoke, wdat_17_f)
# FL
survey::svychisq(~fall + FL, wdat_17_f)
# bmi_cut
survey::svychisq(~fall + bmi_cut, wdat_17_f)
# bmi
# survey::svychisq(~fall + bmi, wdat_17_f)
# age_cut
survey::svychisq(~fall + age_cut, wdat_17_f)
# age
# survey::svychisq(~fall + age, wdat_17_f)
# SHS
survey::svychisq(~fall + SHS, wdat_17_f)
# diabetes
survey::svychisq(~fall + diabetes, wdat_17_f)
# osteoarthritis
survey::svychisq(~fall + osteoarthritis, wdat_17_f)
# heart_attack
survey::svychisq(~fall + heart_attack, wdat_17_f)
# stroke
survey::svychisq(~fall + stroke, wdat_17_f)
# urinary_incontinence
survey::svychisq(~fall + urinary_incontinence, wdat_17_f)
# osteoporosis
survey::svychisq(~fall + osteoporosis, wdat_17_f)
# activity
survey::svychisq(~fall + activity, wdat_17_f)
# alcohol
survey::svychisq(~fall + alcohol, wdat_17_f)

```

## Univariate models {.tabset}

Univariate logistic models separately for men and women.

### Men

```{r}
# men

# education
uni_education_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ education,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_education_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_education_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_education_m")

# language
uni_language_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ language,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_language_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_language_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_language_m")

# urban_rural
uni_urban_rural_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ urban_rural,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_urban_rural_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_urban_rural_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_urban_rural_m")

# nationality
uni_nationality_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ nationality,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_nationality_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_nationality_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_nationality_m")

# smoke
uni_smoke_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ smoke,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_smoke_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_smoke_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_smoke_m")

# FL
uni_FL_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ FL,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_FL_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_FL_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_FL_m")

# bmi_cut
uni_bmi_cut_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ bmi_cut,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_bmi_cut_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_bmi_cut_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_bmi_cut_m")

# bmi
uni_bmi_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ bmi,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_bmi_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_bmi_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_bmi_m")

# age_cut
uni_age_cut_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ age_cut,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_age_cut_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_age_cut_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_age_cut_m")

# age
uni_age_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ age,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_age_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_age_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_age_m")

# SHS     
uni_SHS_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ SHS,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_SHS_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_SHS_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_SHS_m")

# SHS_collapsed     
uni_SHS_collapsed_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ SHS_collapsed,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_SHS_collapsed_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_SHS_collapsed_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_SHS_m")

# diabetes
uni_diabetes_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ diabetes,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_diabetes_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_diabetes_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_diabetes_m")

# osteoarthritis
uni_osteoarthritis_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ osteoarthritis,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_osteoarthritis_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_osteoarthritis_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_osteoarthritis_m")

# heart_attack
uni_heart_attack_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ heart_attack,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_heart_attack_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_heart_attack_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_heart_attack_m")

# stroke
uni_stroke_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ stroke,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_stroke_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_stroke_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_stroke_m")

# urinary_incontinence
uni_urinary_incontinence_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ urinary_incontinence,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_urinary_incontinence_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_urinary_incontinence_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_urinary_incontinence_m")

# osteoporosis
uni_osteoporosis_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ osteoporosis,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_osteoporosis_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_osteoporosis_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_osteoporosis_m")

# activity
uni_activity_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ activity,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_activity_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_activity_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_activity_m")

# alcohol
uni_alcohol_m <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ alcohol,
    design = wdat_17_m,
    family = quasibinomial(logit)
  )
jtools::summ(uni_alcohol_m,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_alcohol_m, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_alcohol_m")
```

### Women

```{r}
# women

# education
uni_education_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ education,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_education_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_education_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_education_f")

# language
uni_language_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ language,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_language_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_language_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_language_f")

# urban_rural
uni_urban_rural_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ urban_rural,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_urban_rural_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_urban_rural_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_urban_rural_f")

# nationality
uni_nationality_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ nationality,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_nationality_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_nationality_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_nationality_f")

# smoke
uni_smoke_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ smoke,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_smoke_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_smoke_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_smoke_f")

# FL
uni_FL_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ FL,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_FL_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_FL_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_FL_f")

# bmi_cut
uni_bmi_cut_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ bmi_cut,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_bmi_cut_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_bmi_cut_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_bmi_cut_f")

# bmi
uni_bmi_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ bmi,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_bmi_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_bmi_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_bmi_f")

# age_cut
uni_age_cut_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ age_cut,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_age_cut_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_age_cut_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_age_cut_f")

# age
uni_age_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ age,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_age_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_age_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_age_f")

# SHS     
uni_SHS_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ SHS,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_SHS_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_SHS_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_SHS_f")

# SHS_collapsed     
uni_SHS_collapsed_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ SHS_collapsed,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_SHS_collapsed_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_SHS_collapsed_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_model_SHS_m")

# diabetes
uni_diabetes_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ diabetes,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_diabetes_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_diabetes_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_diabetes_f")

# osteoarthritis
uni_osteoarthritis_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ osteoarthritis,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_osteoarthritis_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_osteoarthritis_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_osteoarthritis_f")

# heart_attack
uni_heart_attack_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ heart_attack,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_heart_attack_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_heart_attack_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_heart_attack_f")

# stroke
uni_stroke_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ stroke,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_stroke_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_stroke_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_stroke_f")

# urinary_incontinence
uni_urinary_incontinence_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ urinary_incontinence,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_urinary_incontinence_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_urinary_incontinence_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_urinary_incontinence_f")

# osteoporosis
uni_osteoporosis_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ osteoporosis,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_osteoporosis_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_osteoporosis_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_osteoporosis_f")

# activity
uni_activity_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ activity,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_activity_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_activity_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_activity_f")

# alcohol
uni_alcohol_f <-
  survey::svyglm(
    I(fall=="fall last 12 months") ~ alcohol,
    design = wdat_17_f,
    family = quasibinomial(logit)
  )
jtools::summ(uni_alcohol_f,exp = TRUE, confint = TRUE, digits = 3)
# jtools::plot_summs(uni_alcohol_f, exp = TRUE, colors = "Rainbow", legend.title = "unadjusted_fodel_alcohol_f")
```

```{r}
jtools::summ(uni_FL_f, exp = TRUE, confint = TRUE, digits = 4)
```

## Trend test for prevalence of falls {.tabset}

-   [CochranArmitageTest()](https://rdrr.io/cran/DescTools/man/CochranArmitageTest.html)
-   [prop_trend_test()](https://rdrr.io/cran/rstatix/man/prop_trend_test.html)
-   [prop.trend.test()](https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/prop.trend.test)

### Men

After checking summary table, following variables get a trend test of prevalence of falls:

-   education

```{r}
## CochranArmitageTest
## creating matrix for education 
camatrix_education_m <- matrix(c(0.1985768*119633, 0.2277108*441112, 0.2621775*360740, 0.8014232*119633, 0.7722892*441112, 0.7378225*360740), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, education=1:3))
dimnames(camatrix_education_m) <- list(
  fall = c("yes", "no"),
  education = c("primary", "secondary", "tertiary")
)
camatrix_education_m
## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_education_m)
DescTools::CochranArmitageTest(camatrix_education_m, "increasing")
DescTools::CochranArmitageTest(camatrix_education_m, "decreasing")

## test with prop_trend_test function
rstatix::prop_trend_test(camatrix_education_m)

## test with prop.trend.test function
fallers_education_m <- c(0.1985768*119633, 0.2277108*441112, 0.2621775*360740)
nofallers_education_m <- c( 0.8014232*119633, 0.7722892*441112, 0.7378225*360740)
prop.trend.test(fallers_education_m, nofallers_education_m)
```

-   age_cut

```{r}
## CochranArmitageTest
## creating matrix for age_cut 
camatrix_age_cut_m <- matrix(c(451660.1*0.2172215,329497.6*0.2402362, 143722.4*0.2899578, 0.7827785*451660.1, 0.7597638*329497.6, 0.7100422*143722.4), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, age_cut=1:3))
dimnames(camatrix_age_cut_m) <- list(
  fall = c("yes", "no"),
  age_cut = c("60-69", "70-79", "80+")
)
camatrix_age_cut_m
## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_age_cut_m)
DescTools::CochranArmitageTest(camatrix_age_cut_m, "increasing")
DescTools::CochranArmitageTest(camatrix_age_cut_m, "decreasing")
```

-   SHS

```{r}
## CochranArmitageTest
## creating matrix for SHS 
camatrix_SHS_m <- matrix(c(247212*0.220138, 441164*0.2346568, 174931*0.2463606, 46282*0.2947983, 13664*0.3119853, 0.779862*247212, 0.7653432*441164, 0.7536394*174931, 0.7052017*46282, 0.6880147*13664), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, SHS=1:5))
dimnames(camatrix_SHS_m) <- list(
  fall = c("yes", "no"),
  SHS = c("very good", "good", "average", "poor", "very poor")
)
camatrix_SHS_m
## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_SHS_m)
DescTools::CochranArmitageTest(camatrix_SHS_m, "increasing")
DescTools::CochranArmitageTest(camatrix_SHS_m, "decreasing")
```

### Women

After checking summary table, following variables get a trend-test of prevalence of falls:

-   FL

```{r}
## CochranArmitageTest
## creating matrix for FL 
camatrix_FL_f <- matrix(c(802495*0.2310017, 185432*0.3127858, 40791*0.3788652, 25971*0.3804728, 802495*0.7689983, 185432*0.6872142, 40791*0.6211348, 25971*0.6195272), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, FL=1:4))
dimnames(camatrix_FL_f) <- list(
  fall = c("yes", "no"),
  FL = c("no difficulties", "slight difficulties", "great difficulties", "inability")
)
camatrix_FL_f
## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_FL_f)
DescTools::CochranArmitageTest(camatrix_FL_f, "increasing")
DescTools::CochranArmitageTest(camatrix_FL_f, "decreasing")
```

-   age_cut

```{r}
## CochranArmitageTest
## creating matrix for age_cut 
camatrix_age_cut_f <- matrix(c(480788*0.2031848, 371311*0.2653584, 205226*0.3595031, 480788*0.7968152, 371311*0.7346416, 205226*0.6404969), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, age_cut=1:3))
dimnames(camatrix_age_cut_f) <- list(
  fall = c("yes", "no"),
  age_cut = c("60-69", "70-79", "80+")
)
camatrix_age_cut_f
## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_age_cut_f)
DescTools::CochranArmitageTest(camatrix_age_cut_f, "increasing")
DescTools::CochranArmitageTest(camatrix_age_cut_f, "decreasing")
```

-   SHS

```{r}
## CochranArmitageTest
## creating matrix for SHS 
camatrix_SHS_f <- matrix(c(281898*0.204541, 488526*0.2352557, 235279*0.3336065, 44700*0.3642046, 6819*0.4016408, 281898*0.795459, 488526*0.7647443, 235279*0.6663935, 44700*0.6357954, 6819*0.5983592), byrow=TRUE, nrow=2, dimnames=list(fall=0:1, SHS=1:5))
dimnames(camatrix_SHS_f) <- list(
  fall = c("yes", "no"),
  SHS = c("very good", "good", "average", "poor", "very poor")
)
camatrix_SHS_f
## test with CochranArmitageTest function
DescTools::CochranArmitageTest(camatrix_SHS_f)
DescTools::CochranArmitageTest(camatrix_SHS_f, "increasing")
DescTools::CochranArmitageTest(camatrix_SHS_f, "decreasing")
```

## Multivariate models, model integration by covariate groups {.tabset}

Multivariate logistic regression models were calculated for both men and women, characterised by the agglomerative inclusion of the `socio-demographic`, `biological` and `behavioural covariate groups`. In this way, three regression models result per gender.

### Men

-   `socio_economic_covariate` & `socio_demographic_covariate`:

```{r}
Model_socio_economic_socio_demographic_1_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language, 
               design = wdat_17_m,
               family = quasibinomial(logit))
Model_socio_economic_socio_demographic_1_ma<- jtools::summ(Model_socio_economic_socio_demographic_1_m,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_1_ma
```

-   `socio_economic_covariate` & `socio_demographic_covariate` & `biological_covariates`:
-   SHS-Variable factor: "very good and good" get combined

```{r}
Model_socio_economic_socio_demographic_biological_1_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis, 
               design = wdat_17_m,
               family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_1_ma<- jtools::summ(Model_socio_economic_socio_demographic_biological_1_m,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_biological_1_ma
```

-   `socio_economic_covariate` & `socio_demographic_covariate` & `biological_covariates` & `behavioral_covariate`:

```{r}
Model_socio_economic_socio_demographic_biological_behavioral_1_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke, 
               design = wdat_17_m,
               family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_behavioral_1_ma<- jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_1_m,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_biological_behavioral_1_ma
```

#### Model comparison male

```{r message=FALSE, warning=FALSE}
export_summs(Model_socio_economic_socio_demographic_1_m, Model_socio_economic_socio_demographic_biological_1_m, Model_socio_economic_socio_demographic_biological_behavioral_1_m, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), exp = TRUE, scale = TRUE, error_format = "({conf.low}-{conf.high})", to.file = "docx", file.name = "Multivariate_1-3_male.docx")

publication_layout=theme_bw()+
theme(panel.grid.major=element_blank(),
    panel.grid.minor=element_blank(),
    panel.border=element_blank(),
    axis.line=element_line(),
    text=element_text(family='Times New Roman'),
    legend.title=element_blank(), 
    axis.text=element_text(size=10),
    axis.title=element_text(size=12),
    legend.text = element_text(size = 12))


plot_summs(Model_socio_economic_socio_demographic_1_m, Model_socio_economic_socio_demographic_biological_1_m, Model_socio_economic_socio_demographic_biological_behavioral_1_m, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), scale = TRUE, plot.distributions = FALSE, exp = TRUE) + coord_cartesian(xlim = c(0, NA)) + scale_x_continuous(breaks = c(0.1, 0.2, 0.5, 1, 2, 5, 10), limits=c(0.1, 10)) + coord_trans(x="log10") + publication_layout + labs(x = "\n aOR \n ", y = NULL)
```

### Women

-   `socio_economic_covariate` & `socio_demographic_covariate`:

```{r}
Model_socio_economic_socio_demographic_1_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language, 
               design = wdat_17_f,
               family = quasibinomial(logit))
Model_socio_economic_socio_demographic_1_fa<- jtools::summ(Model_socio_economic_socio_demographic_1_f,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_1_fa
```

-   `socio_economic_covariate` & `socio_demographic_covariate` & `biological_covariates`:
-   SHS-Variable factor: "very good and good" get combined

```{r}
Model_socio_economic_socio_demographic_biological_1_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis, 
               design = wdat_17_f,
               family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_1_fa<- jtools::summ(Model_socio_economic_socio_demographic_biological_1_f,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_biological_1_fa
```

-   `socio_economic_covariate` & `socio_demographic_covariate` & `biological_covariates` & `behavioral_covariate`:

```{r}
Model_socio_economic_socio_demographic_biological_behavioral_1_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke, 
               design = wdat_17_f,
               family = quasibinomial(logit))
Model_socio_economic_socio_demographic_biological_behavioral_1_fa<- jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_1_f,exp = TRUE, vifs = TRUE, confint = TRUE, digits = 6)
Model_socio_economic_socio_demographic_biological_behavioral_1_fa
```

#### Model comparison women

```{r message=FALSE, warning=FALSE}
export_summs(Model_socio_economic_socio_demographic_1_f, Model_socio_economic_socio_demographic_biological_1_f, Model_socio_economic_socio_demographic_biological_behavioral_1_f, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), exp = TRUE, scale = TRUE, error_format = "({conf.low}-{conf.high})", to.file = "docx", file.name = "Multivariate_1-3_female.docx")

plot_summs(Model_socio_economic_socio_demographic_1_f, Model_socio_economic_socio_demographic_biological_1_f, Model_socio_economic_socio_demographic_biological_behavioral_1_f, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), scale = TRUE, plot.distributions = FALSE, exp = TRUE) + publication_layout + labs(x = "\n AOR \n ", y = NULL) + coord_cartesian(xlim = c(0, NA)) + scale_x_continuous(limits=c(0.1, 20), breaks = c(0, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20)) + coord_trans(x="log10") + publication_layout + labs(x = "\n aOR \n ", y = NULL)
```

### Model comparison men vs. women

-   Model with `socio_economic_covariate` & `socio_demographic_covariate` & `biological_covariates` & `behavioral_covariate`:

```{r message=FALSE, warning=FALSE}
export_summs(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_1_f, exp = TRUE, scale = TRUE, error_format = "[{conf.low}, {conf.high}]")

plot_summs(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_1_f, coefs = c("Secondary education" = "educationupper secondary education", "Tertiary education" = "educationtertiary education", "Rural area" = "urban_rural", "Foreigner" = "nationality", "French" = "languagefrench", "Italian" = "languageitalian", "70-79" = "age_cut70-79", "80+" = "age_cut80+", "Slight difficulty (FL)" = "FLSlight difficulty", "Great difficulty (FL)" = "FLGreat difficulty", "Inability (FL)" = "FLInability", "Obesity" = "bmi_cutobesity", "Overweight" = "bmi_cutoverweight", "Underweight" = "bmi_cutunderweight", "Average (SHS)" = "SHS_collapsedaverage", "Poor (SHS)" = "SHS_collapsedpoor", "Very poor (SHS)" = "SHS_collapsedvery poor", "Diabetes" = "diabetes", "Osteoarthritis" = "osteoarthritis", "Heart attack" = "heart_attack", "Stroke" = "stroke", "Urinary incontinence" = "urinary_incontinence", "Osteoporosis" = "osteoporosis", "Partially active" = "activitypartially active", "Active" = "activityactive", "Alcohol low risk" = "alcohollow risk", "Alcohol moderate/increased risk" = "alcoholmoderate or increased risk", "Former smoker" = "smokeformer smoker", "Smoker" = "smokesmoker"), scale = TRUE, plot.distributions = FALSE, exp = TRUE) + publication_layout + labs(x = "\n AOR \n ", y = NULL) + coord_cartesian(xlim = c(0, NA)) + scale_x_continuous(limits=c(0.1, 20), breaks = c(0, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20)) + coord_trans(x="log10") + publication_layout + labs(x = "\n aOR \n ", y = NULL)
```

## Interactions in male and female Model 3 {.tabset}

-   Interaction terms with age categories ($age\_categories * x$)

    $x = education, urban\_rural, nationality, language, FL, bmi\_cut, SHS\_collapsed, diabetes,\\ osteoarthritis, heart\_attack, stroke, urinary\_incontinence, osteoporosis, activity, alcohol, \\smoke$

### Men

```{r}
# education 
Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + education * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_m, exp = TRUE, confint = TRUE, digits = 3)

# model comparison and LR-test p < 0.05
Interaction_LRT_age_education_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_m)

# urban_rural
Model_socio_economic_socio_demographic_biological_behavioral_interactions_2_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urban_rural * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_urban_rural_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_2_m)

# nationality
Model_socio_economic_socio_demographic_biological_behavioral_interactions_3_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + nationality * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_nationality_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_3_m)

# language
Model_socio_economic_socio_demographic_biological_behavioral_interactions_4_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + language * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_language_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_4_m)

# FL 
Model_socio_economic_socio_demographic_biological_behavioral_interactions_5_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + FL * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_FL_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_5_m)

# bmi_cut
Model_socio_economic_socio_demographic_biological_behavioral_interactions_6_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + bmi_cut * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_bmi_cut_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_6_m)

# SHS_collapsed 
Model_socio_economic_socio_demographic_biological_behavioral_interactions_7_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + SHS_collapsed * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_SHS_collapsed_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_7_m)

# diabetes
Model_socio_economic_socio_demographic_biological_behavioral_interactions_8_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + diabetes * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_diabetes_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_8_m)

# osteoarthritis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_9_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoarthritis * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_osteoarthritis_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_9_m)

# heart_attack
Model_socio_economic_socio_demographic_biological_behavioral_interactions_10_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + heart_attack * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_heart_attack_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_10_m)

# stroke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_11_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + stroke * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_stroke_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_11_m)

# urinary_incontinence
Model_socio_economic_socio_demographic_biological_behavioral_interactions_12_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urinary_incontinence * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_urinary_incontinence_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_12_m)

# osteoporosis 
Model_socio_economic_socio_demographic_biological_behavioral_interactions_13_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoporosis * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_osteoporosis_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_13_m)

# activity
Model_socio_economic_socio_demographic_biological_behavioral_interactions_14_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + activity * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_activity_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_14_m)

# alcohol
Model_socio_economic_socio_demographic_biological_behavioral_interactions_15_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + alcohol * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_alcohol_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_15_m)

# smoke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_16_m <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + smoke * I(age_cut), 
               design = wdat_17_m,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_smoke_m<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_m, Model_socio_economic_socio_demographic_biological_behavioral_interactions_16_m)


Interaction_age_m <- round(c(Interaction_LRT_age_education_m$p, Interaction_LRT_age_urban_rural_m$p, Interaction_LRT_age_nationality_m$p, Interaction_LRT_age_language_m$p, Interaction_LRT_age_FL_m$p, Interaction_LRT_age_bmi_cut_m$p, Interaction_LRT_age_SHS_collapsed_m$p, Interaction_LRT_age_diabetes_m$p, Interaction_LRT_age_osteoarthritis_m$p, Interaction_LRT_age_heart_attack_m$p, Interaction_LRT_age_stroke_m$p, Interaction_LRT_age_urinary_incontinence_m$p, Interaction_LRT_age_osteoporosis_m$p, Interaction_LRT_age_activity_m$p, Interaction_LRT_age_alcohol_m$p, Interaction_LRT_age_smoke_m$p), 3)

Interaction_names<- c("Education level", "Residential area", "Nationality", "Language region", "Functional limitations", "BMI group", "Self-perceived health status", "Diabetes", "Osteoarthritis", "Heart attack", "Stroke", "Urinary incontinence", "Osteoporosis", "Physical activity", "Chronic alcohol consumption", "Smoking (smoker)")

Interaction_age_table_m <- data.frame(Interaction_names, Interaction_age_m)
knitr::kable(Interaction_age_table_m, col.names = c('Age categories * Variable', '$P$'), caption = "Likelihood Ratio Test and $P$-value for the
addition of the interactions to the Main Effects Model 3")
```

### Women

```{r}
# education 
Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + education * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_f, exp = TRUE, confint = TRUE, digits = 3)

# model comparison and LR-test p < 0.05
Interaction_LRT_age_education_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_1_f)

# urban_rural
Model_socio_economic_socio_demographic_biological_behavioral_interactions_2_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urban_rural * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_urban_rural_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_2_f)

# nationality
Model_socio_economic_socio_demographic_biological_behavioral_interactions_3_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + nationality * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_nationality_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_3_f)

# language
Model_socio_economic_socio_demographic_biological_behavioral_interactions_4_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + language * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_language_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_4_f)

# FL 
Model_socio_economic_socio_demographic_biological_behavioral_interactions_5_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + FL * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_FL_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_5_f)

# bmi_cut
Model_socio_economic_socio_demographic_biological_behavioral_interactions_6_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + bmi_cut * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_bmi_cut_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_6_f)

# SHS_collapsed 
Model_socio_economic_socio_demographic_biological_behavioral_interactions_7_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + SHS_collapsed * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_SHS_collapsed_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_7_f)

# diabetes
Model_socio_economic_socio_demographic_biological_behavioral_interactions_8_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + diabetes * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_diabetes_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_8_f)

# osteoarthritis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_9_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoarthritis * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_osteoarthritis_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_9_f)

# heart_attack
Model_socio_economic_socio_demographic_biological_behavioral_interactions_10_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + heart_attack * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_heart_attack_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_10_f)

# stroke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_11_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + stroke * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_stroke_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_11_f)

# urinary_incontinence
Model_socio_economic_socio_demographic_biological_behavioral_interactions_12_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urinary_incontinence * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_urinary_incontinence_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_12_f)

# osteoporosis 
Model_socio_economic_socio_demographic_biological_behavioral_interactions_13_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoporosis * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_osteoporosis_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_13_f)

# activity
Model_socio_economic_socio_demographic_biological_behavioral_interactions_14_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + activity * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_activity_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_14_f)

# alcohol
Model_socio_economic_socio_demographic_biological_behavioral_interactions_15_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + alcohol * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_alcohol_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_15_f)

# smoke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_16_f <- svyglm(I(fall=="fall last 12 months") ~ education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + smoke * I(age_cut), 
               design = wdat_17_f,
               family = quasibinomial(logit))

# model comparison and LR-test p < 0.05
Interaction_LRT_age_smoke_f<-anova(Model_socio_economic_socio_demographic_biological_behavioral_1_f, Model_socio_economic_socio_demographic_biological_behavioral_interactions_16_f)


Interaction_age_f <- round(c(Interaction_LRT_age_education_f$p, Interaction_LRT_age_urban_rural_f$p, Interaction_LRT_age_nationality_f$p, Interaction_LRT_age_language_f$p, Interaction_LRT_age_FL_f$p, Interaction_LRT_age_bmi_cut_f$p, Interaction_LRT_age_SHS_collapsed_f$p, Interaction_LRT_age_diabetes_f$p, Interaction_LRT_age_osteoarthritis_f$p, Interaction_LRT_age_heart_attack_f$p, Interaction_LRT_age_stroke_f$p, Interaction_LRT_age_urinary_incontinence_f$p, Interaction_LRT_age_osteoporosis_f$p, Interaction_LRT_age_activity_f$p, Interaction_LRT_age_alcohol_f$p, Interaction_LRT_age_smoke_f$p), 3)

Interaction_age_table_f <- data.frame(Interaction_names, Interaction_age_f)
knitr::kable(Interaction_age_table_f, col.names = c('Age categories * Variable', '$P$'), caption = "Likelihood Ratio Test and $P$-value for the
addition of the interactions to the Main Effects Model 3")
```

## Interaction genderunspecific Model

-   Interaction terms with sex ($age\_categories * x$)

    $x = education, urban\_rural, nationality, language, age\_cut, FL, bmi\_cut, SHS\_collapsed, diabetes,\\ osteoarthritis, heart\_attack, stroke, urinary\_incontinence, osteoporosis, activity, alcohol, \\smoke$

```{r}
# education
Model_socio_economic_socio_demographic_biological_behavioral_1 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke, 
               design = wdat_17,
               family = quasibinomial(logit))

Model_socio_economic_socio_demographic_biological_behavioral_interactions_1 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + education *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))
# jtools::summ(Model_socio_economic_socio_demographic_biological_behavioral_interactions_1,exp = TRUE, confint = TRUE, digits = 3)

Interaction_LRT_sex_education <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_1)

# urban_rural
Model_socio_economic_socio_demographic_biological_behavioral_interactions_2 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urban_rural *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_urban_rural <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_2)

# nationality
Model_socio_economic_socio_demographic_biological_behavioral_interactions_3 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + nationality *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_nationality <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_3)

# language
Model_socio_economic_socio_demographic_biological_behavioral_interactions_4 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + language *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_language <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_4)

# age_cut
Model_socio_economic_socio_demographic_biological_behavioral_interactions_5 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + age_cut *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_age_cut <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_5)

# FL
Model_socio_economic_socio_demographic_biological_behavioral_interactions_6 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + FL *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_FL <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_6)

# bmi_cut
Model_socio_economic_socio_demographic_biological_behavioral_interactions_7 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + bmi_cut *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_bmi_cut <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_7)

# SHS_collapsed
Model_socio_economic_socio_demographic_biological_behavioral_interactions_8 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + SHS_collapsed *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_SHS_collapsed <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_8)

# diabetes
Model_socio_economic_socio_demographic_biological_behavioral_interactions_9 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + diabetes *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_diabetes <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_9)

# osteoarthritis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_10 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoarthritis *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_osteoarthritis <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_10)

# heart_attack
Model_socio_economic_socio_demographic_biological_behavioral_interactions_11 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + heart_attack *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_heart_attack <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_11)

# stroke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_12 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + stroke *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_stroke <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_12)

# urinary_incontinence
Model_socio_economic_socio_demographic_biological_behavioral_interactions_13 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + urinary_incontinence *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_urinary_incontinence <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_13)

# osteoporosis
Model_socio_economic_socio_demographic_biological_behavioral_interactions_14 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + osteoporosis *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_osteoporosis <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_14)

# activity
Model_socio_economic_socio_demographic_biological_behavioral_interactions_15 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + activity *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_activity <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_15)

# alcohol
Model_socio_economic_socio_demographic_biological_behavioral_interactions_16 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + alcohol *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_alcohol <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_16)

# smoke
Model_socio_economic_socio_demographic_biological_behavioral_interactions_17 <- svyglm(I(fall=="fall last 12 months") ~ sex + education + urban_rural + nationality + language + age_cut + FL + bmi_cut + SHS_collapsed + diabetes + osteoarthritis + heart_attack + stroke + urinary_incontinence + osteoporosis + activity + alcohol + smoke + smoke *I(sex), 
               design = wdat_17,
               family = quasibinomial(logit))

Interaction_LRT_sex_smoke <- anova(Model_socio_economic_socio_demographic_biological_behavioral_1, Model_socio_economic_socio_demographic_biological_behavioral_interactions_17)

Interaction_names_1<- c("Education level", "Residential area", "Nationality", "Language region", "Age categories", "Functional limitations", "BMI group", "Self-perceived health status", "Diabetes", "Osteoarthritis", "Heart attack", "Stroke", "Urinary incontinence", "Osteoporosis", "Physical activity", "Chronic alcohol consumption", "Smoking (smoker)")

Interaction_sex <- round(c(Interaction_LRT_sex_education$p, Interaction_LRT_sex_urban_rural$p, Interaction_LRT_sex_nationality$p, Interaction_LRT_sex_language$p, Interaction_LRT_sex_age_cut$p, Interaction_LRT_sex_FL$p, Interaction_LRT_sex_bmi_cut$p, Interaction_LRT_sex_SHS_collapsed$p, Interaction_LRT_sex_diabetes$p, Interaction_LRT_sex_osteoarthritis$p, Interaction_LRT_sex_heart_attack$p, Interaction_LRT_sex_stroke$p, Interaction_LRT_sex_urinary_incontinence$p, Interaction_LRT_sex_osteoporosis$p, Interaction_LRT_sex_activity$p, Interaction_LRT_sex_alcohol$p, Interaction_LRT_sex_smoke$p), 3)

Interaction_sex_table <- data.frame(Interaction_names_1, Interaction_sex)
knitr::kable(Interaction_sex_table, col.names = c('Sex * Variable', '$P$'), caption = "Likelihood Ratio Test and $P$-value for the
addition of the interactions to the Main Effects Model 3")

```

## Analysis of fall count

### Prevalence of multiple falls by characteristic {.tabset}

-   $y = fall\_count$ (levels: 1,2,3,4; no fall, 1 fall, two falls, more then two falls)

-   $y = fall\_count\_collapsed$ is used in this analysis and is generated by dichotomization from $fall\_count$ (1,2 = no fall or 1; 3,4 = two falls or more)

-   Where $y$ represents the dependent variable

-   Due to the scope of this publication, the analysis was limited to the prevalence of multiple falls estimation.

#### Men

-   education

```{r}
tab_amount_fall_count_collapsed_education_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ education,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_education_m[,2:4],3)*100
```

-   urban_rural

```{r}
tab_amount_fall_count_collapsed_urban_rural_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ urban_rural,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_urban_rural_m[,2:4],3)*100
```

-   nationality

```{r}
tab_amount_fall_count_collapsed_nationality_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ nationality,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_nationality_m[,2:4],3)*100
```

-   language

```{r}
tab_amount_fall_count_collapsed_language_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ language,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_language_m[,2:4],3)*100
```

-   age_cut

```{r}
tab_amount_fall_count_collapsed_age_cut_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ age_cut,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_age_cut_m[,2:4],3)*100
```

-   FL

```{r}
tab_amount_fall_count_collapsed_FL_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ FL,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_FL_m[,2:4],3)*100
```

-   bmi_cut

```{r}
tab_amount_fall_count_collapsed_bmi_cut_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ bmi_cut,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_bmi_cut_m[,2:4],3)*100
```

-   SHS

```{r}
tab_amount_fall_count_collapsed_SHS_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ SHS,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_SHS_m[,2:4],3)*100
```

-   diabetes

```{r}
tab_amount_fall_count_collapsed_diabetes_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ diabetes,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_diabetes_m[,2:4],3)*100
```

-   osteoarthritis

```{r}
tab_amount_fall_count_collapsed_osteoarthritis_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ osteoarthritis,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_osteoarthritis_m[,2:4],3)*100
```

-   heart_attack

```{r}
tab_amount_fall_count_collapsed_heart_attack_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ heart_attack,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_heart_attack_m[,2:4],3)*100
```

-   stroke

```{r}
tab_amount_fall_count_collapsed_stroke_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ stroke,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_stroke_m[,2:4],3)*100
```

-   urinary_incontinence

```{r}
tab_amount_fall_count_collapsed_urinary_incontinence_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ urinary_incontinence,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_urinary_incontinence_m[,2:4],3)*100
```

-   osteoporosis

```{r}
tab_amount_fall_count_collapsed_osteoporosis_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ osteoporosis,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_osteoporosis_m[,2:4],3)*100
```

-   activity

```{r}
tab_amount_fall_count_collapsed_activity_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ activity,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_activity_m[,2:4],3)*100
```

-   alcohol

```{r}
tab_amount_fall_count_collapsed_alcohol_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ alcohol,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_alcohol_m[,2:4],3)*100
```

-   smoke

```{r}
tab_amount_fall_count_collapsed_smoke_m <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ smoke,
  design = wdat_17_m,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_smoke_m[,2:4],3)*100
```

#### Women

-   education

```{r}
tab_amount_fall_count_collapsed_education_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ education,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_education_f[,2:4],3)*100
```

-   urban_rural

```{r}
tab_amount_fall_count_collapsed_urban_rural_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ urban_rural,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_urban_rural_f[,2:4],3)*100
```

-   nationality

```{r}
tab_amount_fall_count_collapsed_nationality_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ nationality,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_nationality_f[,2:4],3)*100
```

-   language

```{r}
tab_amount_fall_count_collapsed_language_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ language,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_language_f[,2:4],3)*100
```

-   age_cut

```{r}
tab_amount_fall_count_collapsed_age_cut_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ age_cut,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_age_cut_f[,2:4],3)*100
```

-   FL

```{r}
tab_amount_fall_count_collapsed_FL_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ FL,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_FL_f[,2:4],3)*100
```

-   bmi_cut

```{r}
tab_amount_fall_count_collapsed_bmi_cut_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ bmi_cut,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_bmi_cut_f[,2:4],3)*100
```

-   SHS

```{r}
tab_amount_fall_count_collapsed_SHS_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ SHS,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_SHS_f[,2:4],3)*100
```

-   diabetes

```{r}
tab_amount_fall_count_collapsed_diabetes_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ diabetes,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_diabetes_f[,2:4],3)*100
```

-   osteoarthritis

```{r}
tab_amount_fall_count_collapsed_osteoarthritis_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ osteoarthritis,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_osteoarthritis_f[,2:4],3)*100
```

-   heart_attack

```{r}
tab_amount_fall_count_collapsed_heart_attack_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ heart_attack,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_heart_attack_f[,2:4],3)*100
```

-   stroke

```{r}
tab_amount_fall_count_collapsed_stroke_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ stroke,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_stroke_f[,2:4],3)*100
```

-   urinary_incontinence

```{r}
tab_amount_fall_count_collapsed_urinary_incontinence_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ urinary_incontinence,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_urinary_incontinence_f[,2:4],3)*100
```

-   osteoporosis

```{r}
tab_amount_fall_count_collapsed_osteoporosis_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ osteoporosis,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_osteoporosis_f[,2:4],3)*100
```

-   activity

```{r}
tab_amount_fall_count_collapsed_activity_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ activity,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_activity_f[,2:4],3)*100
```

-   alcohol

```{r}
tab_amount_fall_count_collapsed_alcohol_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ alcohol,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_alcohol_f[,2:4],3)*100
```

-   smoke

```{r}
tab_amount_fall_count_collapsed_smoke_f <- svyby(formula = ~I(fall_count_collapsed=="two_falls_or_more"),
  by = ~ smoke,
  design = wdat_17_f,
  FUN = svyciprop,
  na.rm = TRUE,
  keep.names = FALSE,
  vartype="ci",
  method = "likelihood"
)
round(tab_amount_fall_count_collapsed_smoke_f[,2:4],3)*100
```

------------------------------------------------------------------------

# References

::: {#refs}
:::

------------------------------------------------------------------------

# Appendix

In the appendix, we report a printout of all R packages used in the analysis and their versions to facilitate the reproducibility of the analysis/results.

## Appendix

```{r}
pander(sessionInfo(), compact = TRUE)
```
