#Codes - Supplementary File with Directories Redacted 
library(epiR)
library(epicalc)
setwd((redacted))
use("peerj-62792-Suppl_-_Data_for_PeerJ.csv")
 
 
#Putting Provinces into Regions
region <- ifelse(province=="Bangkok" | province=="Samut Prakan" | province=="Nonthaburi" , "1 - Bangkok Metro", "Unknown")
region <- ifelse(province=="Chonburi" | province=="Phra Nakhon Si Ayutthaya" | province=="Nakhon Pathom" , "2 - Central", region)
region <- ifelse(province=="Chiang Mai" | province=="Chiang Rai" | province=="Nakhon Sawan" , "3 - North", region)
region <- ifelse(province=="Khon Kaen" | province=="Nakhon Ratchasima" | province=="Buriram" | province=="Buriram" | province=="Ubon Ratchathani", "4 - Northeast", region)
region <- ifelse(province=="Nakhon Si Thammarat" | province=="Songkhla", "5 - South", region)
label.var(region, "")
table(province, region)
 
#sampling weight addition
pweight <- ifelse(region=="1 - Bangkok Metro", 33747.3, 99999)
pweight <- ifelse(region=="2 - Central", 33064.2, pweight)
pweight <- ifelse(region=="3 - North", 33843.6, pweight)
pweight <- ifelse(region=="4 - Northeast", 33171.1, pweight)
pweight <- ifelse(region=="5 - South", 33272.8, pweight)
tab1(pweight)
label.var(pweight, "Population sampling weights")

 
#TABLE 1
#Sex Female
sex_female <- sex-1
tab1(sex_female)

#Age
summ(age)

#Marital status
tab1(status)
status2 <- ifelse(status==2,1,0)
status3 <- ifelse(status==3,1,0)
status4 <- ifelse(status==4,1,0)
label.var(status2,"")
label.var(status3,"")
label.var(status4,"")


label.var(sex_female,"")
label.var(age,"")
label.var(status,"")

 
status_cat <- ifelse(status==1, "1 - Single",
ifelse(status==2, "2 - Married with child(ren)",
ifelse(status==3, "3 - Married, no children",
ifelse(status==4, "4 - Widowed / divorced / separated", NA))))
table(status, status_cat)
label.var(status_cat,"")


#EDUCATION RECODED
tab1(edu)
edu_recoded <- ifelse(edu<=3, "1 - Junior high or less",
ifelse(edu==4 | edu==5, "2 - High school",
ifelse(edu==6 | edu==7, "3 - Associates degree",
ifelse(edu==8, "4 - Bachelors degree",
ifelse(edu==9, "5 - Graduate education",NA)))))
tab1(edu_recoded)
label.var(edu_recoded,"")

edu2 <- ifelse(edu_recoded=="2 - High school",1,0)
edu3 <- ifelse(edu_recoded=="3 - Associates degree",1,0)
edu4 <- ifelse(edu_recoded=="4 - Bachelors degree",1,0)
edu5 <- ifelse(edu_recoded=="5 - Graduate education",1,0)

label.var(edu2,"")
label.var(edu3,"")
label.var(edu4,"")
label.var(edu5,"")


 
#Personal monthly income
tab1(salary)
income_cat <- ifelse(salary==1, "1 - Less than 5k", 
ifelse(salary==2, "2 - 5k to 10k",
ifelse(salary==3, "3 - 10k to 20k",
ifelse(salary==4, "4 - 20k to 30k",
ifelse(salary==5, "5 - 30k to 40k",
ifelse(salary==6, "6 - 40k to 50k",
ifelse(salary==7, "7 - More than 50k", NA)))))))
tab1(income_cat)
label.var(income_cat,"")

salary2 <- ifelse(salary==2,1,0); salary2 <- ifelse(salary==999,NA,salary2)
salary3 <- ifelse(salary==3,1,0); salary3 <- ifelse(salary==999,NA,salary3)
salary4 <- ifelse(salary==4,1,0); salary4 <- ifelse(salary==999,NA,salary4)
salary5 <- ifelse(salary==5,1,0); salary5 <- ifelse(salary==999,NA,salary5)
salary6 <- ifelse(salary==6,1,0); salary6 <- ifelse(salary==999,NA,salary6)
salary7 <- ifelse(salary==7,1,0); salary7 <- ifelse(salary==999,NA,salary7)

label.var(salary2,"")
label.var(salary3,"")
label.var(salary4,"")
label.var(salary5,"")
label.var(salary6,"")
label.var(salary7,"")



  
#EXPOSURE: EMERGENCY FUND AVAILABILITY

#Emergency expense
#Emergency of 5000 THB - What to do? 
shortcredit <- q11_1
longcredit <- q11_2
cash <- q11_3
loanbank <- q11_4 
borrow <- q11_5
loanshark <- q11_6
sell <- q11_7
pawn <- q11_8
cannot <- q11_9


 
   #Recoding "others"

longcredit <- ifelse(q11_10other=="Talk to lenders",1,longcredit)

      #if anyone said they would work more, we assumed that they would obtain cash (to obtain the most liberal estimate of those with adequate cash reserve)
      #Note: To "sell things" in this section refers to trading goods for profit rather than selling personal belongings to earn cash, thus is considered as raising cash to pay for the 5000 bahts emergency
cash <- ifelse(q11_10other=="Use savings" | q11_10other=="Work - sell things" | q11_10other=="Make more income" | q11_10other=="Make extra income" | q11_10other=="Work more",1,cash)
cash <- ifelse(q11_10other=="Ask for advanced payment",1,cash)

loanbank <- ifelse(q11_10other=="Village funds",1,loanbank)

loanshark <- ifelse(q11_10other=="Take money from rotating savings",1,loanshark)
 
       #Remark: "Talking to parents" = borrowing from parents
borrow <- ifelse(q11_10other=="Talk to parents",1,borrow)
borrow <- ifelse(q11_10other=="Borrow from parents",1,borrow)
 
      #Refinancing a car is considered the same as pawning the car
pawn <- ifelse(q11_10other=="Refinance car",1,pawn)
 
allothers <- shortcredit + longcredit + loanbank + borrow +loanshark + sell + pawn + cannot
tab1(allothers)

cashonly <- ifelse(cash==1 & allothers==0, 1 ,0)
cashonly <- ifelse(q11_99==1,NA,cashonly)
label.var(cashonly, "")


tab1(shortcredit)
tab1(longcredit)
tab1(cash)
tab1(loanbank)
tab1(borrow)
tab1(loanshark)
tab1(sell)
tab1(pawn)
tab1(cannot)

label.var(shortcredit,"")
label.var(longcredit,"")
label.var(cash,"")
label.var(loanbank,"")
label.var(borrow,"")
label.var(loanshark,"")
label.var(sell,"")
label.var(pawn,"")
label.var(cannot,"")

 
#Economic distress - pandemic
distress_a1 <- ifelse(q10_1a==9, NA, q10_1a); distress_a1 <- ifelse(distress_a1==1,1,0)
distress_a2 <- ifelse(q10_2a==9, NA, q10_2a); distress_a2 <- ifelse(distress_a2==1,1,0)
distress_a3 <- ifelse(q10_3a==9, NA, q10_3a); distress_a3 <- ifelse(distress_a3==1,1,0)
distress_a4 <- ifelse(q10_4a==9, NA, q10_4a); distress_a4 <- ifelse(distress_a4==1,1,0)
distress_a5 <- ifelse(q10_5a==9, NA, q10_5a); distress_a5 <- ifelse(distress_a5==1,1,0)
distress_a6 <- ifelse(q10_6a==9, NA, q10_6a); distress_a6 <- ifelse(distress_a6==1,1,0)
distress_a7 <- ifelse(q10_7a==9, NA, q10_7a); distress_a7 <- ifelse(distress_a7==1,1,0)
distress_a8 <- ifelse(q10_8a==9, NA, q10_8a); distress_a8 <- ifelse(distress_a8==1,1,0)
 
tab1(distress_a1)
tab1(distress_a2)
tab1(distress_a3)
tab1(distress_a4)
tab1(distress_a5)
tab1(distress_a6)
tab1(distress_a7)
tab1(distress_a8)

 
   #Distress scores  and experiencing at least 1 distress since pandemic
distress_score <- distress_a1 + distress_a2 + distress_a3 + 
distress_a4 + 
distress_a5 + 
distress_a6 + 
distress_a7 + 
distress_a8
tab1(distress_score)
econ_distress_pandemic <- ifelse(distress_score==0,0,1)
tab1(econ_distress_pandemic)

label.var(distress_a1,"")
label.var(distress_a2,"")
label.var(distress_a3,"")
label.var(distress_a4,"")
label.var(distress_a5,"")
label.var(distress_a6,"")
label.var(distress_a7,"")
label.var(distress_a8,"")
label.var(econ_distress_pandemic,"")

 

#Economic distress in past 30 days
#Economic distress - pandemic
distress_b1 <- ifelse(q10_1b==9, NA, q10_1b); distress_b1 <- ifelse(distress_b1==1,1,0) 
distress_b2 <- ifelse(q10_2b==9, NA, q10_2b); distress_b2 <- ifelse(distress_b2==1,1,0) 
distress_b3 <- ifelse(q10_3b==9, NA, q10_3b); distress_b3 <- ifelse(distress_b3==1,1,0) 
distress_b4 <- ifelse(q10_4b==9, NA, q10_4b); distress_b4 <- ifelse(distress_b4==1,1,0) 
distress_b5 <- ifelse(q10_5b==9, NA, q10_5b); distress_b5 <- ifelse(distress_b5==1,1,0) 
distress_b6 <- ifelse(q10_6b==9, NA, q10_6b); distress_b6 <- ifelse(distress_b6==1,1,0) 
distress_b7 <- ifelse(q10_7b==9, NA, q10_7b); distress_b7 <- ifelse(distress_b7==1,1,0) 
distress_b8 <- ifelse(q10_8b==9, NA, q10_8b); distress_b8 <- ifelse(distress_b8==1,1,0) 
 
tab1(distress_b1)
tab1(distress_b2)
tab1(distress_b3)
tab1(distress_b4)
tab1(distress_b5)
tab1(distress_b6)
tab1(distress_b7)
tab1(distress_b8)

 
   #Distress scores  and experiencing at least 1 distress since pandemic
distress_score <-  distress_b1 + distress_b2 + distress_b3 + 
distress_b4 + 
distress_b5 + 
distress_b6 + 
distress_b7 + 
distress_b8
tab1(distress_score)
econ_distress_30d <- ifelse(distress_score==0,0,1)
tab1(econ_distress_30d)



label.var(distress_b1,"")
label.var(distress_b2,"")
label.var(distress_b3,"")
label.var(distress_b4,"")
label.var(distress_b5,"")
label.var(distress_b6,"")
label.var(distress_b7,"")
label.var(distress_b8,"")
label.var(econ_distress_30d,"")





#Outcome: Behavioral Health
#ANXIETY
anxiety_score <- q13gad_1 +q13gad_2 + q13gad_3 + q13gad_4 + q13gad_5 + q13gad_6 + q13gad_7
tab1(anxiety_score)
   #Cut-off score of 10 points generally recommended for GAD screening
anxiety <- ifelse(anxiety_score>=10, 1, 0)
tab1(anxiety)
label.var(anxiety,"")


 
#DEPRESSION
depression_score <- q13phq_1 + q13phq_2
tab1(depression_score)
   #Cut-off point of 3 or higher is optimal for screening purpose, whereas cut-off point of 2 would enhance sensitivity and 4 would enhance specificity, https://www.chpscc.org › _literature_243927 › The_...
depression <- ifelse(depression_score>=3,1,0)
tab1(depression)
label.var(depression,"")

 

#SLEEP
tab1(q17)
sleep <- ifelse(q17>=3 & q17<=5, 1, 0)
sleep <- ifelse(q17==8, NA, sleep)
sleep <- ifelse(q17==9, NA, sleep)
tab1(sleep)
label.var(sleep,"")
 
 
   #Exercise
tab1(q16)
exercise <- ifelse(q16==0,0,1)
exercise <- ifelse(q16==9,NA,exercise)
tab1(exercise)
label.var(exercise,"")

 
   #gambling
gambling <- ifelse(q18==9, NA, q18)
tab1(gambling)
label.var(gambling,"")


 
   #smoking in past 30 days
smoking <- ifelse(q15==3 | q15==4, 1, 0)
smoking <- ifelse(q15==9, NA, smoking)
tab1(smoking)
label.var(smoking,"")


#DRINKING
ever_drinkers <- ifelse(q1==1,1,0)
drinkers_12m <- ifelse(q2==1,1,0)
table(drinkers_12m, ever_drinkers)
drinking_status1 <- ifelse(ever_drinkers==1 & drinkers_12m==1, "3 - Drank in past year", 
ifelse(ever_drinkers==1 & drinkers_12m==0, "2 - Former drinkers", "1 - Never drinkers"))
tab1(drinking_status1)

drinking_30d <- ifelse(q7==1 | q7==888,0,1)
table(drinking_30d, drinking_status1)

drinking_status2 <- ifelse(drinking_30d==1, "4 - Drank in past 30 days", drinking_status1)

tabpct(drinking_status2, sex)


#Binge in past 12 months
#Q5, exclude Q5=9, Recode Q5=888 as 0, Recode 9 as NA
binge_12m <- ifelse(q5==2,1,0)
binge_12m <- ifelse(q5==9, NA, binge_12m)
tab1(binge_12m)
label.var(binge_12m,"")

 
#Binge in past 30 days
#Q9, exclude Q9=9, Recode Q9=888 as 0, Recode 9 as NA
binge_30d <- ifelse(q9==2,1,0)
binge_30d <- ifelse(q9==9, NA, binge_30d)
tab1(binge_30d)
label.var(binge_30d,"")
  

   #Four groups, for Table 2 (In reverse order now; cash and no distress = 1, cash and distress=2, no cash and no distress = 3; no cash and distress = 4)

groups <- ifelse(econ_distress_30d==0 & cashonly==1, 1,
ifelse(econ_distress_30d==1 & cashonly==1, 2, 
ifelse(econ_distress_30d==0 & cashonly==0, 3, 4)))
label.var(groups, "")

group1 <- ifelse(econ_distress_30d==0 & cashonly==1, 1, 0)
group2 <- ifelse(econ_distress_30d==1 & cashonly==1, 1, 0)
group3 <- ifelse(econ_distress_30d==0 & cashonly==0, 1, 0)
group4 <- ifelse(econ_distress_30d==1 & cashonly==0, 1, 0)
label.var(group1, "")
label.var(group2, "")
label.var(group3, "")
label.var(group4, "")


 #Creating Function for Calculating Pseudo R-squares
R2logit<- function(y,model){
    R2<- 1-(model$deviance/model$null.deviance)
    return(R2)
    }
library(lmtest)

library(DescTools)
#BreslowDayTest(table1)

#Variables for Table 3
   #Stratify outcome for only those with emergency reserves - cashonly
anxiety_cash <- ifelse(cashonly==1, anxiety, NA)
depression_cash <- ifelse(cashonly==1, depression, NA)
exercise_cash <- ifelse(cashonly==1, exercise, NA)
sleep_cash <- ifelse(cashonly==1, sleep, NA)
gambling_cash <- ifelse(cashonly==1, gambling, NA)
smoking_cash <- ifelse(cashonly==1, smoking, NA)
binge_30d_cash <- ifelse(cashonly==1, binge_30d, NA)

label.var(anxiety_cash, "")
label.var(depression_cash, "")
label.var(exercise_cash, "")
label.var(sleep_cash, "")
label.var(gambling_cash, "")
label.var(smoking_cash, "")
label.var(binge_30d_cash, "")



   #Stratify outcome for only those with no emergency reserves - cashonly
anxiety_nocash <- ifelse(cashonly==0, anxiety, NA)
depression_nocash <- ifelse(cashonly==0, depression, NA)
exercise_nocash <- ifelse(cashonly==0, exercise, NA)
sleep_nocash <- ifelse(cashonly==0, sleep, NA)
gambling_nocash <- ifelse(cashonly==0, gambling, NA)
smoking_nocash <- ifelse(cashonly==0, smoking, NA)
binge_30d_nocash <- ifelse(cashonly==0, binge_30d, NA)

label.var(anxiety_nocash, "")
label.var(depression_nocash, "")
label.var(exercise_nocash, "")
label.var(sleep_nocash, "")
label.var(gambling_nocash, "")
label.var(smoking_nocash, "")
label.var(binge_30d_nocash, "")



   #For Table 4 - Alternative definition for having cash reserves
cash2 <- ifelse(shortcredit==1 | cash==1, 1, 0)
allothers2 <- longcredit + loanbank + borrow +loanshark + sell + pawn + cannot
cashonly2 <- ifelse(cash2==1 & allothers2==0, 1 ,0)
cashonly2 <- ifelse(q11_99==1,NA,cashonly2)
label.var(cashonly2, "")

   #Four groups, for Table 3
groups2 <- 
ifelse(econ_distress_30d==1 & cashonly2==0, 4,
ifelse(econ_distress_30d==0 & cashonly2==0, 3, 
ifelse(econ_distress_30d==1 & cashonly2==1, 2, 1)))
label.var(groups2, "")

group12 <- ifelse(econ_distress_30d==0 & cashonly2==1, 1, 0)
group22 <- ifelse(econ_distress_30d==1 & cashonly2==1, 1, 0)
group32 <- ifelse(econ_distress_30d==0 & cashonly2==0, 1, 0)
group42 <- ifelse(econ_distress_30d==1 & cashonly2==0, 1, 0)
label.var(group12, "")
label.var(group22, "")
label.var(group32, "")
label.var(group42, "")
 

   #Stratify outcome for only those with emergency reserves - cashonly
anxiety_cash2 <- ifelse(cashonly2==1, anxiety, NA)
depression_cash2 <- ifelse(cashonly2==1, depression, NA)
exercise_cash2 <- ifelse(cashonly2==1, exercise, NA)
sleep_cash2 <- ifelse(cashonly2==1, sleep, NA)
gambling_cash2 <- ifelse(cashonly2==1, gambling, NA)
smoking_cash2 <- ifelse(cashonly2==1, smoking, NA)
binge_30d_cash2 <- ifelse(cashonly2==1, binge_30d, NA)

label.var(anxiety_cash2, "")
label.var(depression_cash2, "")
label.var(exercise_cash2, "")
label.var(sleep_cash2, "")
label.var(gambling_cash2, "")
label.var(smoking_cash2, "")
label.var(binge_30d_cash2, "")



   #Stratify outcome for only those with no emergency reserves - cashonly
anxiety_nocash2 <- ifelse(cashonly2==0, anxiety, NA)
depression_nocash2 <- ifelse(cashonly2==0, depression, NA)
exercise_nocash2 <- ifelse(cashonly2==0, exercise, NA)
sleep_nocash2 <- ifelse(cashonly2==0, sleep, NA)
gambling_nocash2 <- ifelse(cashonly2==0, gambling, NA)
smoking_nocash2 <- ifelse(cashonly2==0, smoking, NA)
binge_30d_nocash2 <- ifelse(cashonly2==0, binge_30d, NA)

label.var(anxiety_nocash2, "")
label.var(depression_nocash2, "")
label.var(exercise_nocash2, "")
label.var(sleep_nocash2, "")
label.var(gambling_nocash2, "")
label.var(smoking_nocash2, "")
label.var(binge_30d_nocash2, "")


 
#####################
# Weighted Analyses #
#####################
library(survey)

design <- svydesign(id=~id, strata=~region, weights=~pweight, data=.data)



#TABLE 1
svymean(~sex_female, design=design, data=.data, na.rm=TRUE)
  
#Age
svymean(~age, design=design, data=.data, na.rm=TRUE)

#Marital status
svymean(~status_cat, design=design, data=.data, na.rm=TRUE)
 
#Education
svymean(~edu_recoded, design=design, data=.data, na.rm=TRUE)
 
#Personal monthly income
svymean(~income_cat, design=design, data=.data, na.rm=TRUE)
  
#EXPOSURE: EMERGENCY FUND AVAILABILITY
svymean(~shortcredit, design=design, data=.data, na.rm=TRUE)
svymean(~longcredit, design=design, data=.data, na.rm=TRUE)
svymean(~cash, design=design, data=.data, na.rm=TRUE)
svymean(~loanbank, design=design, data=.data, na.rm=TRUE)
svymean(~borrow, design=design, data=.data, na.rm=TRUE)
svymean(~loanshark, design=design, data=.data, na.rm=TRUE)
svymean(~sell, design=design, data=.data, na.rm=TRUE)
svymean(~pawn, design=design, data=.data, na.rm=TRUE)
svymean(~cannot, design=design, data=.data, na.rm=TRUE)

svymean(~cashonly, design=design, data=.data, na.rm=TRUE)

  
#Economic distress - pandemic
svymean(~distress_a1, design=design, data=.data, na.rm=TRUE)
svymean(~distress_a2, design=design, data=.data, na.rm=TRUE)
svymean(~distress_a3, design=design, data=.data, na.rm=TRUE)
svymean(~distress_a4, design=design, data=.data, na.rm=TRUE)
svymean(~distress_a5, design=design, data=.data, na.rm=TRUE)
svymean(~distress_a6, design=design, data=.data, na.rm=TRUE)
svymean(~distress_a7, design=design, data=.data, na.rm=TRUE)
svymean(~distress_a8, design=design, data=.data, na.rm=TRUE)
svymean(~econ_distress_pandemic, design=design, data=.data, na.rm=TRUE)

  
#Economic distress in past 30 days
svymean(~distress_b1, design=design, data=.data, na.rm=TRUE)
svymean(~distress_b2, design=design, data=.data, na.rm=TRUE)
svymean(~distress_b3, design=design, data=.data, na.rm=TRUE)
svymean(~distress_b4, design=design, data=.data, na.rm=TRUE)
svymean(~distress_b5, design=design, data=.data, na.rm=TRUE)
svymean(~distress_b6, design=design, data=.data, na.rm=TRUE)
svymean(~distress_b7, design=design, data=.data, na.rm=TRUE)
svymean(~distress_b8, design=design, data=.data, na.rm=TRUE)
svymean(~econ_distress_30d, design=design, data=.data, na.rm=TRUE)

 
  
#Outcome: Behavioral Health
#ANXIETY
svymean(~anxiety, design=design, data=.data, na.rm=TRUE)

#DEPRESSION
svymean(~depression, design=design, data=.data, na.rm=TRUE)
 
#SLEEP
svymean(~sleep, design=design, data=.data, na.rm=TRUE)
 
   #Exercise
svymean(~exercise, design=design, data=.data, na.rm=TRUE)

   #gambling
svymean(~gambling, design=design, data=.data, na.rm=TRUE)
  
   #smoking in past 30 days
svymean(~smoking, design=design, data=.data, na.rm=TRUE)
 
#DRINKING
#Binge in past 12 months
svymean(~binge_12m, design=design, data=.data, na.rm=TRUE)

#Binge in past 30 days
svymean(~binge_30d, design=design, data=.data, na.rm=TRUE)
 
 





#####################
# Additional Analyses 20220210 #
#####################

#TABLE 2  
#Descriptives
svyby(~anxiety, ~econ_distress_30d, design=design, svymean, na.rm=TRUE) 
svyby(~depression, ~econ_distress_30d, design=design, svymean, na.rm=TRUE) 
svyby(~exercise, ~econ_distress_30d, design=design, svymean, na.rm=TRUE) 
svyby(~sleep, ~econ_distress_30d, design=design, svymean, na.rm=TRUE) 
svyby(~gambling, ~econ_distress_30d, design=design, svymean, na.rm=TRUE) 
svyby(~smoking, ~econ_distress_30d, design=design, svymean, na.rm=TRUE) 
svyby(~binge_30d, ~econ_distress_30d, design=design, svymean, na.rm=TRUE) 


#Odds ratios
   #Anxiety
model1<-(svyglm(anxiety ~ econ_distress_30d, family=binomial, design = design))
model1a<-(svyglm(anxiety ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design))
summary(model1)
summary(model1a)
round(R2logit(anxiety, model1a), digits=3)

model1a <- glm(anxiety ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1a)

 
 
   #Depression
model1<-svyglm(depression ~ econ_distress_30d, family=binomial, design = design)
model1a<-svyglm(depression ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1)
summary(model1a)
round(R2logit(depression, model1a), digits=3)

model1a <- glm(depression ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1a)

 
   #Exercise
model1 <- svyglm(exercise ~ econ_distress_30d, family=binomial, design = design)
model1a <-svyglm(exercise ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1)
summary(model1a)
round(R2logit(exercise, model1a), digits=3)

model1a <- glm(exercise ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1a)



   #Sleep
model1 <- svyglm(sleep  ~ econ_distress_30d, family=binomial, design = design)
model1a <- svyglm(sleep ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1)
summary(model1a)
round(R2logit(sleep, model1a), digits=3)

model1a <- glm(sleep ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1a)
#cannot display, use the following "Log-likelihood" value as proxy
logistic.display(model1a)

 
 
   #Gambling (unadj, adj, p-for-trend, multiplicative interaction
model1 <- svyglm(gambling ~ econ_distress_30d, family=binomial, design = design)
model1a <- svyglm(gambling ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1)
summary(model1a)
round(R2logit(gambling, model1a), digits=3)

model1a <- glm(gambling ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1a)
#cannot display, use the following "Log-likelihood" value as proxy
logistic.display(model1a)


 
   #Smoking
model1<- svyglm(smoking ~ econ_distress_30d, family=binomial, design = design)
model1<- svyglm(smoking ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1)
summary(model1a)
round(R2logit(smoking, model1a), digits=3)

model1a <- glm(smoking ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1a) 
 
   #Drinking
model1<- svyglm(binge_30d ~ econ_distress_30d, family=binomial, design = design)
model1a<- svyglm(binge_30d ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1)
summary(model1a)
round(R2logit(binge_30d, model1a), digits=3)

model1a <- glm(binge_30d ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1a) 




###########################################


#TABLE 3
#Odds ratios
#multivariate logistic regression

   #Anxiety (Adj OR for cash avail, pseudo R2, log-rank test; Adj OR for cash unavail, pseudo R2, log-rank test; p-for-trend; Breslow-Day test)
model1a <- svyglm(anxiety_cash ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(anxiety_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
round(R2logit(anxiety_cash, model1b), digits=3)
model1c <- glm(anxiety_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(anxiety_nocash ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(anxiety_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
round(R2logit(anxiety_nocash, model2b), digits=3)
model2c <- glm(anxiety_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)

#Breslow-Day-Test
table1 <- table(anxiety, econ_distress_30d, cashonly)
BreslowDayTest(table1)

 
 
   #Depression
model1a <- svyglm(depression_cash ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(depression_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)

round(R2logit(depression_cash, model1b), digits=3)
model1c <- glm(depression_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(depression_nocash ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(depression_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)

round(R2logit(depression_nocash, model2b), digits=3)
model2c <- glm(depression_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)

#Breslow-Day-Test
table1 <- table(depression, econ_distress_30d, cashonly)
BreslowDayTest(table1)

 


 
   #Exercise
model1a <- svyglm(exercise_cash ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(exercise_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)

round(R2logit(exercise_cash, model1b), digits=3)
model1c <- glm(exercise_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(exercise_nocash ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(exercise_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)

round(R2logit(exercise_nocash, model2b), digits=3)
model2c <- glm(exercise_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
 
#Breslow-Day-Test
table1 <- table(exercise, econ_distress_30d, cashonly)
BreslowDayTest(table1)



 
   #Sleep
model1a <- svyglm(sleep_cash ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(sleep_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
 
round(R2logit(sleep_cash, model1b), digits=3)
model1c <- glm(sleep_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(sleep_nocash ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(sleep_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
 
round(R2logit(sleep_nocash, model2b), digits=3)
model2c <- glm(sleep_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
 
#Breslow-Day-Test
table1 <- table(sleep, econ_distress_30d, cashonly)
BreslowDayTest(table1)

			
 

 
   #Gambling (unadj, adj, p-for-trend, multiplicative interaction
model1a <- svyglm(gambling_cash ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(gambling_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
 
round(R2logit(gambling_cash, model1b), digits=3)
model1c <- glm(gambling_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(gambling_nocash ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(gambling_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
 
round(R2logit(gambling_nocash, model2b), digits=3)
model2c <- glm(gambling_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
#Breslow-Day-Test
table1 <- table(gambling, econ_distress_30d, cashonly)
BreslowDayTest(table1)

 
   #Smoking
model1a <- svyglm(smoking_cash ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(smoking_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
  
round(R2logit(smoking_cash, model1b), digits=3)
model1c <- glm(smoking_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(smoking_nocash ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(smoking_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)

  
round(R2logit(smoking_nocash, model2b), digits=3)
model2c <- glm(smoking_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
#Breslow-Day-Test
table1 <- table(smoking, econ_distress_30d, cashonly)
BreslowDayTest(table1)

 
 
   #Binge-Drinking in past 30 days
model1a <- svyglm(binge_30d_cash ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(binge_30d_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
   
round(R2logit(binge_30d_cash, model1b), digits=3)
model1c <- glm(binge_30d_cash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(binge_30d_nocash ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(binge_30d_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)

 
round(R2logit(binge_30d_nocash, model2b), digits=3)
model2c <- glm(binge_30d_nocash ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
#Breslow-Day-Test
table1 <- table(binge_30d, econ_distress_30d, cashonly)
BreslowDayTest(table1)

 



#P-for-trends
summary(model1<-(svyglm(anxiety ~ groups + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(depression ~ groups + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(exercise ~ groups + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(sleep ~ groups + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(gambling ~ groups + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(smoking ~ groups + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(binge_30d ~ groups + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))


 
###################################
# Additional Analyses             #
# Additive Interaction Assessment #
###################################
library(epiR)

model1 <- glm(anxiety ~ econ_distress_30d + cashonly + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model2 <- glm(depression ~ econ_distress_30d + cashonly + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model3 <- glm(exercise ~ econ_distress_30d + cashonly + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model4 <- glm(sleep ~ econ_distress_30d + cashonly + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model5 <- glm(gambling ~ econ_distress_30d + cashonly + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model6 <- glm(smoking ~ econ_distress_30d + cashonly + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model7 <- glm(binge_30d ~ econ_distress_30d + cashonly + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)



 
#Model 1
epi.interaction(model = model1, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.4623021, -1.732407, 0.8078031), digits=2)
 

#Model 2
epi.interaction(model = model2, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.1784303, -0.9599094, 0.6030488), digits=2)
  

#Model 3
epi.interaction(model = model3, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.2258106, -0.9669922, 0.515371), digits=2)

#Model 4
epi.interaction(model = model4, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(0.3668917, -0.2048254, 0.9386087), digits=2)

#Model 5
epi.interaction(model = model5, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.09236883, -0.4633308, 0.2785931), digits=2)

#Model 6
epi.interaction(model = model6, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.2262895, -1.051579, 0.5990004), digits=2)

#Model 7
epi.interaction(model = model7, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.03932383, -1.565475, 1.486828), digits=2)



####################################

#Multiplicative interaction terms 
model2 <- svyglm(anxiety ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2)

summary(model1<-(svyglm(depression ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(exercise ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

 summary(model1<-(svyglm(sleep ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(gambling ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(smoking ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(binge_30d ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))




########################################
#                            TABLE 4                                                   #
########################################


#Odds ratios
#multivariate logistic regression

   #Anxiety (Adj OR for cash avail, pseudo R2, log-rank test; Adj OR for cash unavail, pseudo R2, log-rank test; p-for-trend; Breslow-Day test)
model1a <- svyglm(anxiety_cash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(anxiety_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
 
round(R2logit(anxiety_cash2, model1b), digits=3)
model1c <- glm(anxiety_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(anxiety_nocash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(anxiety_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
 
round(R2logit(anxiety_nocash2, model2b), digits=3)
model2c <- glm(anxiety_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)

#Breslow-Day-Test
table1 <- table(anxiety, econ_distress_30d, cashonly2)
BreslowDayTest(table1)

 
 
   #Depression
model1a <- svyglm(depression_cash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(depression_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
 
round(R2logit(depression_cash2, model1b), digits=3)
model1c <- glm(depression_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(depression_nocash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(depression_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)

round(R2logit(depression_nocash2, model2b), digits=3)
model2c <- glm(depression_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)

#Breslow-Day-Test
table1 <- table(depression, econ_distress_30d, cashonly2)
BreslowDayTest(table1)

 


 
   #Exercise
model1a <- svyglm(exercise_cash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(exercise_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
 
round(R2logit(exercise_cash2, model1b), digits=3)
model1c <- glm(exercise_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(exercise_nocash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(exercise_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
 
round(R2logit(exercise_nocash2, model2b), digits=3)
model2c <- glm(exercise_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
 
#Breslow-Day-Test
table1 <- table(exercise, econ_distress_30d, cashonly2)
BreslowDayTest(table1)



 
   #Sleep
model1a <- svyglm(sleep_cash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(sleep_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
 
round(R2logit(sleep_cash2, model1b), digits=3)
model1c <- glm(sleep_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(sleep_nocash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(sleep_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
 
round(R2logit(sleep_nocash2, model2b), digits=3)
model2c <- glm(sleep_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
 
#Breslow-Day-Test
table1 <- table(sleep, econ_distress_30d, cashonly2)
BreslowDayTest(table1)

			
 

 
   #Gambling (unadj, adj, p-for-trend, multiplicative interaction
model1a <- svyglm(gambling_cash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(gambling_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)

round(R2logit(gambling_cash2, model1b), digits=3)
model1c <- glm(gambling_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(gambling_nocash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(gambling_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
 
round(R2logit(gambling_nocash2, model2b), digits=3)
model2c <- glm(gambling_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
#Breslow-Day-Test
table1 <- table(gambling, econ_distress_30d, cashonly2)
BreslowDayTest(table1)

 
   #Smoking
model1a <- svyglm(smoking_cash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(smoking_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
 
round(R2logit(smoking_cash2, model1b), digits=3)
model1c <- glm(smoking_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(smoking_nocash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(smoking_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
 
round(R2logit(smoking_nocash2, model2b), digits=3)
model2c <- glm(smoking_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
#Breslow-Day-Test
table1 <- table(smoking, econ_distress_30d, cashonly2)
BreslowDayTest(table1)

 
 
   #Binge-Drinking in past 30 days
model1a <- svyglm(binge_30d_cash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model1a)
model1b <- svyglm(binge_30d_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model1b)
    
round(R2logit(binge_30d_cash2, model1b), digits=3)
model1c <- glm(binge_30d_cash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model1c)
#Cannot display, try alternative
logistic.display(model1c)


model2a <- svyglm(binge_30d_nocash2 ~ econ_distress_30d, family=binomial, design = design)
summary(model2a)
model2b <- svyglm(binge_30d_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2b)
 
round(R2logit(binge_30d_nocash2, model2b), digits=3)
model2c <- glm(binge_30d_nocash2 ~ econ_distress_30d + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, data=.data)
lrtest(model2c)
#Cannot display, try alternative
logistic.display(model2c)
 
#Breslow-Day-Test
table1 <- table(binge_30d, econ_distress_30d, cashonly2)
BreslowDayTest(table1)

 



#P-for-trends
summary(model1<-(svyglm(anxiety ~ groups2 + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(depression ~ groups2 + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(exercise ~ groups2 + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(sleep ~ groups2 + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(gambling ~ groups2 + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(smoking ~ groups2 + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(binge_30d ~ groups2 + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))


 
###################################
# Additional Analyses             #
# Additive Interaction Assessment #
###################################
library(epiR)

model1 <- glm(anxiety ~ econ_distress_30d + cashonly2 + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model2 <- glm(depression ~ econ_distress_30d + cashonly2 + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model3 <- glm(exercise ~ econ_distress_30d + cashonly2 + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model4 <- glm(sleep ~ econ_distress_30d + cashonly2 + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model5 <- glm(gambling ~ econ_distress_30d + cashonly2 + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model6 <- glm(smoking ~ econ_distress_30d + cashonly2 + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)

model7 <- glm(binge_30d ~ econ_distress_30d + cashonly2 + econ_distress_30d:cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7,  family=binomial, data=.data)



 
#Model 1
epi.interaction(model = model1, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.4497719, -1.684594, 0.7850502), digits=2)

#Model 2
epi.interaction(model = model2, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.279808, -1.1038, 0.5441838), digits=2)
  

#Model 3
epi.interaction(model = model3, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.2716586, -1.01966, 0.4763424), digits=2)

#Model 4
epi.interaction(model = model4, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(0.3405522, -0.2547934, 0.9358978), digits=2)

#Model 5
epi.interaction(model = model5, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.05879851, -0.4280099, 0.3104129), digits=2)

#Model 6
epi.interaction(model = model6, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c(-0.3393988, -1.208191, 0.5293936), digits=2)

#Model 7
epi.interaction(model = model7, param = "product", coef = c(2,3,19),type = "RERI", conf.level = 0.95)
round(c( -0.005366949, -1.47784, 1.467107), digits=2)
 
  
####################################

#Multiplicative interaction terms 
model2 <- svyglm(anxiety ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)
summary(model2)

summary(model1<-(svyglm(depression ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(exercise ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

 summary(model1<-(svyglm(sleep ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(gambling ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(smoking ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

summary(model1<-(svyglm(binge_30d ~ econ_distress_30d + cashonly + econ_distress_30d*cashonly + sex + age + status2 + status3 + status4 + edu2 + edu3 + edu4 + edu5 + salary2 + salary3 + salary4 + salary5 + salary6 + salary7, family=binomial, design = design)))

 

#Calculating Cronbach's Alpha in R 
library(epicalc)
library(ltm)
setwd((redacted))
use("peerj-62792-Suppl_-_Data_for_PeerJ.csv")
 
#Calculation Cronbach's Alpha for the ANXIETY screening questionnaire (GAD-7)
gad_7 <- data.frame(q13gad_1, q13gad_2, q13gad_3, q13gad_4, q13gad_5, q13gad_6, q13gad_7)
cronbach.alpha(gad_7, CI=TRUE)
#0.85; 95% CI = 0.83, 0.87)