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--- title: "Social Insurance Inequality in China" author: - Li Sun, University of Leeds - Juntao Lyu (presenter), University of Leeds - Ying Zhao, Renmin University of China - Tao Liu, University of Duisburg-Essen date: "10 September 2019" output: ioslides_presentation: logo: background.gif smaller: yes template: quarterly-report.html widescreen: yes --- ```{r setup, include=FALSE, fig.align='center'} library(knitr) knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE, optipng=hook_optipng) ``` # Introduction ## Introduction {.smaller} * Data source: - Chinese Livelihood Survey 2014, 8 Provinces, 9283 samples. * Methods: - Multilevel linear regression & Multilevel logistic regression modelling in R Studio. <div class="red2"> * Main aims: - examining health insurance inequalities in relation to entitlements, beneficial level and its impact on health seeking patterns; - contextualizing health insurance with other socio-demographic factors; - exploring and explaining the social determinants of choosing self-medication rather than visiting doctors as the first health seeking option. </div> ## Introduction {.smaller .flexbox .vcenter} {width=800px} ## Introduction {.smaller .flexbox .vcenter} * Health Insurance Enrollment ```{r} setwd("D:/R/leedsdata") library(mapdata) library(maptools) load("mapdata.Rda") load("china_map_data.Rda") mapdata1<-mapdata mapdata1$Coverage<-NULL mapdata1$Coverage[mapdata1$NAME== "上海市"] <- "0.902" mapdata1$Coverage[mapdata1$NAME== "安徽省"] <- "0.991" mapdata1$Coverage[mapdata1$NAME== "广东省"] <- "0.915" mapdata1$Coverage[mapdata1$NAME== "河北省"] <- "0.975" mapdata1$Coverage[mapdata1$NAME== "黑龙江省"] <- "0.953" mapdata1$Coverage[mapdata1$NAME== "陕西省"] <- "0.988" mapdata1$Coverage[mapdata1$NAME== "四川省"] <- "0.986" mapdata1$Coverage[mapdata1$NAME== "浙江省"] <- "0.958" library(ggplot2) library(tidyverse) library(plyr) china_data <- join(china_map_data,mapdata1,type='full') ggplot(china_data, aes(x = long, y = lat, group = group,fill = Coverage)) + geom_polygon(colour="white") + coord_map("polyconic") + theme( panel.grid = element_blank(), panel.background = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(), legend.position = c(0.9,0.3) ) ``` # Variables ## Dependent variables {.smaller .flexbox .vcenter} * Health insurance benefit level: reimbursement ratio $ \frac{\text{paid by medical insurance}}{\text{paid by medical insurance} + \text{paid out-of-pocket}} \times {100}{\%} $ ## Dependent variables {.smaller .flexbox .vcenter} Reimbursement ratio: box-plot ```{r} load("h02.Rda") load("h04.Rda") load("h03.Rda") boxplot(h03$medcover ~ h03$province,las=2) ``` ## Dependent variables {.smaller .flexbox .vcenter} {width=800px} ## Independent variables {.smaller .flexbox .vcenter} {width=800px} ## Independent variables {.smaller .flexbox .vcenter} {width=500px} # Data Analyses ## Multilevel linear regression modelling for beneficial level {.smaller .flexbox .vcenter} ```{r} if (!require("pacman")) install.packages("pacman") pacman::p_load(sjPlot, sjlabelled, sjmisc, ggplot2,afex,dotwhisker,broom.mixed) library("lme4") ## basic (G)LMMs library("nlme") ## more LMMs library("afex") ## helper functions library("emmeans") load("h03.Rda") contrasts(h03$age) <- contr.treatment(4) contrasts(h03$edu) <- contr.treatment(4) contrasts(h03$inc) <- contr.treatment(5) library(lme4) library(lmerTest) m1<-lmer(medcover ~ age+female+hukou+edu+inc+occ+nrcmi+private+uebmi+urbmi+no_ins + (1 | province), data=h03) plot_model(m1, vline.color = "red",show.values = TRUE, value.offset = .5) summary(m1) ``` ## Multilevel logistic regression modelling for self-medication {.smaller .flexbox .vcenter} ```{r} contrasts(h04$age) <- contr.treatment(4) contrasts(h04$edu) <- contr.treatment(4) contrasts(h04$inc) <- contr.treatment(5) library(lme4) m2<-glmer(self_med ~ (1 | province), data = h04, family = binomial) m3<-glmer(self_med ~ age + female + edu + inc + occ + med_type + hukou + (1 | province), data = h04, family = binomial,nAGQ = 0) library(Hmisc) dotplot(ranef(m2,condVar=TRUE)) summary(m3) ``` ## Multilevel logistic regression modelling for self-medication {.smaller .flexbox .vcenter} ```{r} plot_model(m3, vline.color = "red",show.values = TRUE, value.offset = .5) ``` # Summary ## Summary { .smaller} - The health insurance enrollment rates are very high (average 96% are insured) <div class="red2"> - Beneficial level - Significant negative factors: - not enrolled in health insurances; - rural hukou (both rural residents and rural-to-urban migrants) - working in informal sectors </div> - Significant positive factors: - secondary educational level (high school finishers) - middle income level (80,000<=family annual income < 150,000 RMB) - private health insurances ## Summary { .smaller} Impact on health seeking patterns <div class="red2"> - self-medication is significantly encouraged by: - migrants (both rural-to-urban migrants and urban-to-urban migrants) - working in informal sectors </div> - self-medication is significantly reduced by: - income level (family annual income >= 80,000, closely related) - education level (high school finishers) - health insurances (except URBMI) ## Discussion { .flexbox .vcenter} - Flaws - Only 8 provinces - The number of migrants is too small - No provincial level data - Future research - Regional comparative research is needed - Considering the cultural factors of self-medication: Guangdong province (slide 14) ## Summary <div class="centered"> <div class="blue"> Thank you for listening. This is an unpublished paper, you are welcome to give any suggestions or ask any questions</div> </div> <div align="left"> - All data analyses and slides are produced in RStudio, all r codes are available on my website: <div class="blue"> <a href="https://jtlyu.com/">https://jtlyu.com/</a> </div> - Presented by: <div class="blue"> Juntao Lyu</div> - Doctoral Candidate - School of Sociology and Social Policy - Room 9.02 Social Sciences Building - University of Leeds | LS2 9JT - e-mail: ssjly@leeds.ac.uk </div>