Prenatal Depression, Descriptive

Data

library(haven)

ddi <- read_ipums_ddi("/Volumes/Jyoti/Stat 2 /PROJECT/nhis_00013.xml")
data <- read_ipums_micro(ddi)

## Use of data from IPUMS NHIS is subject to conditions including that users
## should cite the data appropriately. Use command `ipums_conditions()` for more
## details.

data<- haven::zap_labels(data)

names(data) <- tolower(gsub(pattern = "_",replacement =  "",x =  names(data)))

Recode the variables

data<- filter(data, data$pregnantnow ==2)


#depression level
data$depfeelevl<-as.factor(data$depfeelevl)
data$depfeelevl<- car::Recode(data$depfeelevl,
                       recodes="1='ALot'; 2='A Little'; 
                       3='Between a Little and a Lot'; 7:9=NA; else=NA",
                      as.factor=T)


# medication for depression
data$deprx <- as.factor(data$deprx)

data$deprx<- car::Recode(data$deprx,
                       recodes="1='No'; 2='Yes';else=NA",
                       as.factor=T)



#currently Pregnant 
data$pregnantnow<-as.factor(data$pregnantnow)
data$curpreg<-car::Recode(data$pregnantnow,
                          recodes="2='Yes';else=NA",
                          as.factor=T)
        
data$educ<-Recode(data$educ,
                        recodes="100:116 ='Less than HS'; 200:202='HS Diploma/GED'; 300:303='Some college';400= 'Undergraduate Degree'; 500:503:= 'Graduate Degree';else=NA", as.factor = T)
data$educ<-as.factor(data$educ)



#employment status

data$empstat<- car::Recode(data$empstat,
                       recodes="100='Employed'; 200='Unemployed';else=NA",
                       as.factor=T)


# income grouping

data$famtotinc_cat<-Recode(data$famtotinc, recodes = "0:49999='Less than 50k'; 50000:99999='50-100k';100000:149999='100-150k';150000:199999='150-200k';200000:250000='200-250k';else=NA", as.factor = T)
data$famtotinc_cat<-as.ordered(data$famtotinc)


##race
data$race<- car::Recode(data$racea,
                       recodes="100 ='White'; 200 ='African American'; 
                       400:590= 'Asian/Others'; else=NA", 
                       as.factor=T)


#race/ethnicity
data$black<- car::Recode(data$hisprace,
                       recodes="03=1; 99=NA; else=0")

data$white<- car::Recode(data$hisprace,
                       recodes="02=1; 99=NA; else=0")

data$other<- car::Recode(data$hisprace,
                      recodes="4:7=1; 99=NA; else=0")

data$hispanic<- car::Recode(data$hisprace,
                       recodes="01=1; 99=NA; else=0")

data$hisprace<- as.factor(data$hisprace)

data$race_eth<-car::Recode(data$hisprace,
recodes="01='Hispanic'; 02='NH_White'; 03='NH_Black';04:07='NH_Other'; else=NA",
as.factor = T)
data$race_eth<-relevel(data$race_eth,
                          ref = "NH_White")

## marital status
data$mars<- car::Recode(data$marstat, 
                        recodes ="10:13='Married'; 30:40='Divorced/Separated'; 
                        ; 50='Never Married'; else=NA", 
                        as.factor=T)

Filter data

data<-data%>%
  filter(is.na(educ)==F)
data<-data%>%
  filter(is.na(curpreg)==F)
data<-data%>%
  filter(is.na(deprx)==F)
data<-data%>%
  filter(is.na(empstat)==F)
data<-data%>%
  filter(is.na(marstat)==F)
data<-data%>%
  filter(is.na(race_eth)==F)

Survey design

#First we tell R our survey design
options(survey.lonely.psu = "adjust")

library(dplyr)
sub<-data%>%
  select(curpreg, educ, deprx, empstat, race_eth, mars, sampweight,strata) %>%
  filter( complete.cases(.))


#First we tell R our survey design

options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~1,
               strata=~strata,
               weights=~sampweight,
               data =sub)

## count education frequency
countedu <- sub %>% 
  group_by(educ)%>%
dplyr::summarise(numedu=n())
countedu

## count employment frequency
countemp <- sub %>% 
  group_by(empstat)%>%
dplyr::summarise(numemp=n())
countemp

## count marital status frequency
countmar <- sub %>% 
  group_by(mars)%>%
dplyr::summarise(nummar=n())
countmar

label(data$depfeelevl) <- "Depression Level"
label(data$deprx) <- "Medication for Depression"
label(data$pregnantnow) <-"Currently Pregnant"
label(data$educ) <- "Education Level"
label(data$empstat)<- "Employment Status"
label(data$race_eth)<- "Race/Ethnicity"
label(data$mars)<- "Marital Status"

Results

## Table 1: Demographic Characteristics of currently pregnant women who are either taking or not taking prescription medication for depression
table<-table1(~ educ + empstat + race_eth + mars | deprx, data=sub)
table

	No (N=202)	Yes (N=20)	Overall (N=222)
educ
Graduate Degree	32 (15.8%)	4 (20.0%)	36 (16.2%)
HS Diploma/GED	33 (16.3%)	2 (10.0%)	35 (15.8%)
Less than HS	14 (6.9%)	1 (5.0%)	15 (6.8%)
Some college	54 (26.7%)	10 (50.0%)	64 (28.8%)
Undergraduate Degree	69 (34.2%)	3 (15.0%)	72 (32.4%)
empstat
Employed	153 (75.7%)	15 (75.0%)	168 (75.7%)
Unemployed	49 (24.3%)	5 (25.0%)	54 (24.3%)
race_eth
NH_White	129 (63.9%)	17 (85.0%)	146 (65.8%)
Hispanic	35 (17.3%)	3 (15.0%)	38 (17.1%)
NH_Black	18 (8.9%)	0 (0%)	18 (8.1%)
NH_Other	20 (9.9%)	0 (0%)	20 (9.0%)
mars
Divorced/Separated	10 (5.0%)	1 (5.0%)	11 (5.0%)
Married	140 (69.3%)	13 (65.0%)	153 (68.9%)
Never Married	52 (25.7%)	6 (30.0%)	58 (26.1%)

## Figure 1: Depression Among currently Pregnant Women by Education
Fig1 <- ggplot(data = sub, aes(x=educ, fill=deprx)) + 
  geom_bar(position='fill')+
    geom_text(data=countedu, 
            aes(x=educ, y=0.05, label=numedu), 
            size=5, colour="white", inherit.aes=FALSE)+
labs(title="Depression medication among currently pregnant women 
     by Education", 
         x="Education", y = "Population Proportion", fill ="Legend")+
  theme(legend.position="right")
Fig1

## Figure 2: Depression Among Currently Pregnant Women by Employment Status
Fig2 <- ggplot(data = sub, aes(x=empstat, fill=deprx)) + 
  geom_bar(position='fill')+
  geom_text(data=countemp, 
            aes(x=empstat, y=0.05, label=numemp), 
            size=5, colour="white", inherit.aes=FALSE)+
  labs(title="Depression medication among currently pregnant women by Employment Status", 
         x="Employment", y = "Population Proportion", fill ="Legend")+
  theme(legend.position="right")
Fig2

# Figure 3: Depression Among Currently Pregnant Women by Marital Status
Fig3 <- ggplot(data = sub, aes(x=mars, fill=deprx)) + 
  geom_bar(position='fill')+
  geom_text(data=countmar, 
            aes(x=mars, y=0.05, label=nummar), 
            size=5, colour="white", inherit.aes=FALSE)+
  labs(title="Depression medication among currently pregnant women by Marital Status", 
         x="Marital Status", y = "Population Proportion", fill ="Legend")+
  theme(legend.position="right")
Fig3