BRFSS Data

Reviewing the 2017 BRFSS data.

Examining the relationship between social determinants of health and sleep in older adults (age 50+). We want to look at this relationship between AA and Caucasian Also, we want to examine the mediating role of HRQL on this relationship.

Variables of interest: Demographics, Sleep Disorders, Social Determinants of Health.

#Get 2017 BRFSS data
df<-read.csv("llcp2017.csv")

#Save data to LLCP_2017
LLCP_2017<-df

Identify which state(s) participated in the optional modules

#Identify variables from optional modules
#Find states that have answers to desired optional modules
StateCounts<-LLCP_2017 %>% 
            filter(SLEPTIM1 >= 1 & HOWSAFE1 >= 1) %>% 
            group_by(X_STATE) %>% 
            summarise()

#Subset States with optional modules
#27 = Minnesota. The only state that participated
LLCP_2017<- LLCP_2017 %>% 
  filter(X_STATE %in% StateCounts)

Variable Selection and Descriptions

Study Variables

#Study variables necessary for analysis and weighting
StudyVars<-c("X_PSU","X_STSTR","X_LLCPWT","IMONTH")

X_PSU
- Primary Sampling Unit (Equal to Annual Sequence Number)
X_STSTR
- Sample Design Stratification Variable (Prior to 2011: _STSTR is a five digit number that combines the values for _STATE (first two characters), _GEOSTR (third and fourth character), and _DENSTR2 (final character)
X_LLCPWT
- Final weight assigned to each respondent: Land-line and cell-phone data (Raking derived weight)
IMONTH
- Month of interview

Demographic Variables

#Demographic Variables
DemoVars<-c("SEX","MARITAL","EDUCA","EMPLOY1","INCOME2","X_BMI5CAT",
            "X_RACE","X_AGEG5YR","CHILDREN","MSCODE","X_STATE")

SEX
- 1- Male
- 2- Female
- 9- Refused
MARITAL
- 1- Married
- 2- Divorced
- 3- Widowed
- 4- Separated
- 5- Never married
- 6- A member of an unmarried couple
- 9- Refused
- BLANK -Not asked or Missing
EDUCA
- 1- Never attended school or only kindergarten
- 2- Grades 1 through 8 (Elementary)
- 3- Grades 9 through 11 (Some high school)
- 4- Grade 12 or GED (High school graduate)
- 5- College 1 year to 3 years (Some college or technical school)
- 6- College 4 years or more (College graduate)
- 9- Refused
- BLANK- Not asked or Missing
EMPLOY1
- 1- Employed for wages
- 2- Self-employed
- 3- Out of work for 1 year or more
- 4- Out of work for less than 1 year
- 5- A homemaker
- 6- A student
- 7- Retired
- 8- Unable to work
- 9- Refused
- BLANK- Not asked or missing
INCOME2
- 1- Less than $10,000
- 2- Less than $15,000 ($10,000 to less than $15,000)
- 3- Less than $20,000 ($15,000 to less than $20,000)
- 4- Less than $25,000 ($20,000 to less than $25,000)
- 5- Less than $35,000 ($25,000 to less than $35,000)
- 6- Less than $50,000 ($35,000 to less than $50,000)
- 7- Less than $75,000 ($50,000 to less than $75,000)
- 8- $75,000 or more
- 77- Don’t know/Not Sure
- 99- Refused
- BLANK- Not asked or missing
X_BMI5CAT
- 1- Underweight BMI< 18.5
- 2- Normal Weight 18.5 <= BMI < 25
- 3- Overweight 25 <= BMI < 30
- 4- Obese 30<= BMI < 99
- Blank- Don’t know/Refused/Missing
X_RACE
- 1- White only, non-Hispanic
- 2- Black only, non-Hispanic
- 3- American Indian or Alaskan Native only, Non-Hispanic
- 4- Asian only, non-Hispanic
- 5- Native Hawaiian or other Pacific Islander only, Non-Hispanic
- 6- Other race only, non-Hispanic
- 7- Multiracial, non-Hispanic
- 8- Hispanic
- 9- Don’t know/Not sure/Refused
X_AGEG5YR
- 1- Age 18 to 24
- 2- Age 25 to 29
- 3- Age 30 to 34
- 4- Age 35 to 39
- 5- Age 40 to 44
- 6- Age 45 to 49
- 7- Age 50 to 54
- 8- Age 55 to 59
- 9- Age 60 to 64
- 10- Age 65 to 69
- 11- Age 70 to 74
- 12- Age 75 to 79
- 13- Age 80 or older
- 14- Don’t know/Refused/Missing
CHILDREN
- 1-87 Number of children
- 88- None
- 77- Don’t know/Not sure
- 99- Refused
MSCODE- Metropolitan Status Code
- 1- In the center city of an MSA
- 2- Outside the center city of an MSA but inside the county containing the center city
- 3- Inside a suburban county of the MSA
- 4- Not in an MSA

Sleep Variables

#Sleep optional modules
SleepVars<- c("SLEPTIM1","ADSLEEP")

SLEPTIM1 - ON average, how many hours of sleep do you get in a 24-hour period?
- 1-24 hours
- 77- Don’t know/Not sure
- 99- Refused
- BLANK- Missing
ADSLEEP- Over the last 2 weeks, how many days have you had trouble falling asleep or staying asleep or sleeping too much?
- 1-14 days
- 88- None
- 77- Don’t know/Not sure
- 99- Refused

Social Determinants of Health

#Social Determinants optional module
SDHVars<- c("SDHBILLS","HOWSAFE1","SDHFOOD","SDHMEALS","SDHMONEY","SDHSTRES")

SDHBILLS- During the last 12 months, was there a time when you were not able to pay your mortgage, rent or utility bills?
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
HOWSAFE1- How safe from crime do you consider your neighborhood to be?
- 1- Extremely Safe
- 2- Safe
- 3- Unsafe
- 4- Extremely Unsafe
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
SDHFOOD- “The food that I bought just didn’t last, and I didn’t have money to get more.” Was that often, sometimes, or never true for you in the last 12 months?
- 1- Often True
- 2- Sometimes True
- 3- Never True
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
SDHMEALS- I couldn’t afford to eat balanced meals.” Was that often, sometimes, or never true for you in the last 12 months?
- 1- Often True
- 2- Sometimes True
- 3- Never True
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
SDHMONEY- In general, how do your finances usually work out at the end of the month? Do you find that you usually:
- 1- End up with some money left over
- 2- Have just enough money to make ends meet
- 3- Not have enough money to make ends meet
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
SDHSTRES- Question Prologue: Stress means a situation in which a person feels tense, restless, nervous, or anxious, or is unable to sleep at night because his/her mind is troubled all the time. Question: Within the last 30 days, how often have you felt this kind of stress?
- 1- None of the time
- 2- A little of the time
- 3- Some of the time
- 4- Most of the time
- 5- All of the time
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing

Health Related Quality of Life

#HRQL modules
HRQLVars<-c("GENHLTH", "PHYSHLTH","MENTHLTH","POORHLTH" )

GENHLTH- Would you say that in general your health is:
- 1- Excellent
- 2- Very Good
- 3- Good
- 4- Fair
- 5- Poor
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
PHYSHLTH- Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?
- 1-30 Number of days
- 88- None
- 77- Don’t know/Not sure
- 99- Refused
- BLANK- Not asked or Missing
MENTHLTH- Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?
- 1-30 Number of days
- 88- None
- 77- Don’t know/Not sure
- 99- Refused
- BLANK- Not asked or Missing
POORHLTH- During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?
- 1-30 Number of days
- 88- None
- 77- Don’t know/Not sure
- 99- Refused
- BLANK- Not asked or Missing

Illness Variables

#Illness Modules
ILLNESSVars<-c("SMOKDAY2","ECIGNOW","CVDSTRK3","CVDCRHD4","DIABETE3","ADDEPEV2","HAVARTH3","CHCCOPD1")

SMOKDAY2- Do you now smoke cigarettes every day, some days, or not at all?
- 1- Every day
- 2- Some days
- 3- Not at all
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
ECIGNOW-
- 1- Every day
- 2- Some days
- 3- Not at all
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
CVDSTRK3- (Ever told) you had a stroke.
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
CVDCRHD4- (Ever told) you had angina or coronary heart disease?
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
DIABETE3- (Ever told) you have diabetes (If ´Yes´ and respondent is female, ask ´Was this only when you were pregnant?´. If Respondent says pre-diabetes or borderline diabetes, use response code 4.)
- 1- Yes
- 2- Yes, but female told only during pregnancy
- 3- No
- 4- No, pre-diabetes or borderline diabetes
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
ADDEPEV2- (Ever told) you that you have a depressive disorder, including depression, major depression, dysthymia, or minor depression?
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
HAVARTH3- (Ever told) you have some form of arthritis, rheumatoid arthritis, gout, lupus, or fibromyalgia? (Arthritis diagnoses include: rheumatism, polymyalgia rheumatica; osteoarthritis (not osteporosis); tendonitis, bursitis, bunion, tennis elbow; carpal tunnel syndrome, tarsal tunnel syndrome; joint infection, etc.)
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
CHCCOPD1- (Ever told) you have Chronic Obstructive Pulmonary Disease or COPD, emphysema or chronic bronchitis?
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing

Disability Variables

#Illness Modules
DisVars<-c("DEAF","BLIND", "DECIDE","DIFFWALK","DIFFDRES","DIFFALON")

DEAF-
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
BLIND-
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
DECIDE-
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
DIFFWALK-
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
DIFFDRES-
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing
DIFFALON-
- 1- Yes
- 2- No
- 7- Don’t Know/Not sure
- 9- Refused
- BLANK- Not asked or Missing

Create Variable to Hold All Study Variables

#Decide which variables to keep
#Combine all variables into one variable
KeepVars<- c(DemoVars,SleepVars,SDHVars, HRQLVars,ILLNESSVars, DisVars, StudyVars)

Cleaning Variables

Functions for NA/Missing values

#Identify variables with 9- "Refused" . Convert to NA
Remove_9<- c("SEX", "MARITAL", "EDUCA", "EMPLOY1", "X_RACE")
#Function to recode "9" as NA
Remove_9_fun<-function(x){ifelse(x==9,NA,x)}


#Identify variables with 77- "Don't Know/Not Sure", 88- "None", and 99- "Refused".
Remove_77_88_99<- c("INCOME2", "CHILDREN","SLEPTIM1", "ADSLEEP", 
                 "PHYSHLTH","MENTHLTH","POORHLTH")
# Function to recode "77" and "99" as NA, "88" as 0 
Remove_77_88_99_fun<-function(x){ifelse(x %in% c(77,99),NA,ifelse(x==88,0,x))}



#Identify variables with 14- "Don't Know/Refused/Missing" . Convert to NA
Remove_14<- c("X_AGEG5YR")
#Function to recode "14" as NA
Remove_14_fun<-function(x){ifelse(x==14,NA,x)}


#Identify variables with 7,8,9- "Don't Know/Not Sure" or "Refused".
Remove_7_8_9<- c("SDHBILLS", "HOWSAFE1","SDHFOOD", "SDHMEALS","SDHMONEY", 
              "SDHSTRES","GENHLTH", "SMOKDAY2","ECIGNOW","CVDSTRK3","CVDCRHD4",   
              "DIABETE3","ADDEPEV2", "HAVARTH3", "CHCCOPD1", "DEAF","BLIND","DECIDE", 
              "DIFFWALK", "DIFFDRES", "DIFFALON")
# Function to recode "7","8", and  "9" as NA
Remove_7_8_9_fun<-function(x){ifelse(x %in% c(7:9),NA,x)}

Functions for Binary/Ordinal Values

# Identify variables with a 14 day scale
Day_Vars<-c("ADSLEEP")
Group_Days<-function(x){
    cut(x,breaks = c(-Inf,0,3,6,7,10,13,14),labels=c(0:6))
}

New Values. Will convert to Binary as well for 7 days and greater. - 0 = 0 days - 1 = 1-3 days - 2 = 4-6 days - 3 = 7 days - 4 = 8-10 days - 5 = 11-13 days - 6 = 14 days

Month_Vars<-c("PHYSHLTH","MENTHLTH","POORHLTH")
Group_Month_Fun<-function(x){
  cut(x,breaks = c(-Inf,0,6,13,20,29,Inf),labels=c(0:5))
}

New Values - 0 = 0 days - 1 = 1-6 days - 2 = 7-13 days - 3 = 14-20 days - 4 = 21-28 days - 5 = 29-31 days

Yes_No_Group<- c("SDHBILLS","CVDSTRK3","CVDCRHD4","DIABETE3","ADDEPEV2","HAVARTH3","CHCCOPD1",
               "DEAF","BLIND","DECIDE","DIFFWALK","DIFFDRES","DIFFALON")
Yes_1_Fun<-function(x){            
         as.numeric(recode(x, "1" = "1", .default = "0"))}

New Values - 1 = 1 - All else = 0

#Recoding for binary/logistic regressions
ref0<-c("CHILDREN")
fun0<- function(x){as.factor(recode(x,"88"="ref",.default="com",.missing="com"))}

ref1<-c("SEX","MARITAL","EMPLOY1","X_RACE","MSCODE","SDHBILLS","CVDSTRK3",
            "CVDCRHD4","DIABETE3","ADDEPEV2","HAVARTH3","CHCCOPD1",
            "DEAF","BLIND","DECIDE","DIFFWALK","DIFFDRES","DIFFALON")
fun1<- function(x){as.factor(recode(x,"1"="ref",.default="com",.missing="com"))}

ref2<-c("X_BMI5CAT")
fun2<- function(x){as.factor(recode(x,"2"="ref",.default="com",.missing="com"))}

ref4<-c("EDUCA")
fun4<- function(x){as.factor(recode(x,"4"="ref",.default="com",.missing="com"))}
  
ref6<-c("INCOME2")
fun6<- function(x){as.factor(recode(x,"6"="ref",.default="com",.missing="com"))}

ref7<-c("X_AGEG5YR")
fun7<- function(x){as.factor(recode(x,"7"="ref",.default="com",.missing="com"))}

ref12<-c("SDHFOOD","SDHMEALS","SMOKDAY2","ECIGNOW")
fun12<- function(x){as.factor(recode(x,"1"="ref","2"="ref",
                                          .default="com",.missing="com"))}
ref23<-c("SDHMONEY")
fun23<- function(x){as.factor(recode(x,"2"="ref","3"="ref",
                                          .default="com",.missing="com"))}
ref34<-c("HOWSAFE1")
fun34<- function(x){as.factor(recode(x,"3"="ref","4"="ref",
                                          .default="com",.missing="com"))}
ref45<-c("SDHSTRES","GENHLTH")
fun45<- function(x){as.factor(recode(x,"4"="ref","5"="ref",
                                          .default="com",.missing="com"))}
ref30<-c("PHYSHLTH","POORHLTH","MENTHLTH")
fun30<- function(x){as.factor(ifelse(x==88,"ref",ifelse(x<=13,"ref","com")))}

ref14<-c("ADSLEEP")
fun14<- function(x){as.factor(ifelse(x==88,"ref",ifelse(x<=6,"ref","com")))}

ref79<-c("SLEPTIM1")
fun79<- function(x){as.factor(ifelse(x %in% c(7:9),"ref","com"))}

Create Survey Objects

Descriptive Stats Survey

#Clean all variables
brfss_2017<- LLCP_2017 %>% 
  dplyr::select(KeepVars, -X_STATE) %>% 
  mutate_at(Remove_9,Remove_9_fun) %>% 
  mutate_at(Remove_77_88_99,Remove_77_88_99_fun) %>% 
  mutate_at(Remove_14,Remove_14_fun) %>% 
  mutate_at(Remove_7_8_9,Remove_7_8_9_fun) %>% 
  mutate_at(Day_Vars,Group_Days) %>% 
  mutate_at(Yes_No_Group,Yes_1_Fun) %>% 
  mutate_at(Month_Vars,Group_Month_Fun) %>% 
  mutate(SEX= as.factor(ifelse(SEX==1,"Male","Female")),
         MARITAL= factor(MARITAL,
          levels = c(1:6),
          labels = c("Married","Divorced","Widowed","Separated",
            "Never Married","Unmarried Couple")),
         EDUCA= factor(EDUCA,
          levels= c(1:6),
          labels = c("Never attended school or only kindergarten" , 
            "Grades 1 through 8 (Elementary)" ,
            "Grades 9 through 11 (Some high school)",
            "Grade 12 or GED (High school graduate)", 
            "College 1 year to 3 years (Some college or technical school)",
            "College 4 years or more (College graduate)"), ordered=TRUE),
         EMPLOY1= factor(EMPLOY1,
          levels=c(1:8),
          labels=c("Employed for wages","Self-Employed","Out of work for less than 1 year",
            "Out of work for 1 year or more", "A homemaker", "A student",
            "Retired","Unable to Work")),
         INCOME2=factor(INCOME2,
          levels= c(1:8),
          labels= c("Less than 10K","10 to 15K", "15 to 20K",
            "20 to 25K","25 to 35K","35 to 50K", "50 to 75K",
            "greater than 75K"), ordered=TRUE),
         X_RACE=factor(X_RACE,
          levels=c(1:8),
          labels=c("White only, non-Hispanic", "Black only, non-Hispanic",
            "American Indian or Alaskan Native Only, non-Hispanic", "Asian only, non-Hispanic",
            "Native Hawaiian or other Pacific Islander only, non-Hispanic",
            "Other race only, non-Hispanic","Multiracial, non-Hispanic","Hispanic")),
         MSCODE= factor(MSCODE,
          levels=c(1:4), 
          labels= c("In the center of an MSA",
            "Outside MSA center city but inside the center city county",
            "Inside a suburban county of the MSA", "Not in an MSA")),
         X_BMI5CAT= factor(X_BMI5CAT, 
          levels= c(1:4),
          labels=c("Underweight","Normal Weight","Overweight","Obese")),
         X_AGEG5YR= factor(X_AGEG5YR,
          levels= c(1:13),
          labels=c( "18 to 24",  "25 to 29",  "30 to 34", "35 to 39", "40 to 44",
            "45 to 49", "50 to 54", "55 to 59", "60 to 64","65 to 69","70 to 74", 
            "75 to 79", "80+"), ordered=TRUE),         
         CHILDREN=as.numeric(CHILDREN),
         SLEPTIM_B=ifelse(SLEPTIM1 %in% c(7:9),1,0),
         SLEPTIM1=ifelse(SLEPTIM1 %in% c(0:6),0,
                         ifelse(SLEPTIM1 %in% c(7:9),1,
                         ifelse(SLEPTIM1>=10,2,0))),
         SLEPTIM1=factor(SLEPTIM1,
            levels=c(0:2),
            labels=c("Less than 7 hours","7-9 hours","Greater than 9 hours")),
         ADSLEEP=factor(ADSLEEP,
                        levels=c(0:6),
                        labels=c("0 days","1-3 days","4-6 days","7 days",
                                 "8-10 days","11-13 days","14 days"),ordered = T),
         ADSLEEP_B= ifelse(ADSLEEP<"7 days",1,0), 
         HOWSAFE1= factor(HOWSAFE1,
            levels=c(1:4),
            labels=c("Extremely safe", "Safe", "Unsafe", "Extremely Unsafe") ,
            ordered= TRUE),
         SDHFOOD= factor(SDHFOOD,
            levels=c(1:3),
            labels=c("Often True","Sometimes True","Never True"), ordered= TRUE),
         SDHMEALS= factor(SDHMEALS,
            levels=c(1:3),
            labels=c("Often True","Sometimes True","Never True"), ordered= TRUE),
         SDHMONEY= factor(SDHMONEY,
            levels=c(1:3),
            labels=c("Often True","Sometimes True","Never True"), ordered= TRUE),
         SDHSTRES= factor(SDHSTRES,
            levels=c(1:5),
            labels=c("None of the time","A little of the time","Some of the time",
                     "Most of the time","All of the time"), ordered= TRUE),
         GENHLTH= factor(GENHLTH,
            levels=c(1:5),
            labels=c("Excellent","Very Good","Good","Fair","Poor"), ordered= TRUE),
         SMOKDAY2= factor(SMOKDAY2,
            levels=c(1:3),
            labels=c("Every day","Some days","Not at all"), ordered= TRUE),
         ECIGNOW= factor(ECIGNOW,
            levels=c(1:3),
            labels=c("Every day","Some days","Not at all"), ordered= TRUE))

#Create weighted survey
options(survey.lonely.psu = "adjust")
des<-svydesign(id=~X_PSU,strata=~X_STSTR,weights =~X_LLCPWT,data=brfss_2017,nest=T)

Regression Survey

# Build survey based on selected variables and binary functions
brfss2017B<- LLCP_2017 %>% 
  dplyr::select(KeepVars,-X_STATE) %>% 
  mutate(RACE=factor(X_RACE,
          levels=c(1:8),
          labels=c("White only, non-Hispanic", "Black only, non-Hispanic",
            "American Indian or Alaskan Native Only, non-Hispanic", "Asian only, non-Hispanic",
            "Native Hawaiian or other Pacific Islander only, non-Hispanic",
            "Other race only, non-Hispanic","Multiracial, non-Hispanic","Hispanic"))) %>% 
  mutate_at(ref0,fun0) %>% 
  mutate_at(ref1,fun1) %>% 
  mutate_at(ref2,fun2) %>% 
  mutate_at(ref4,fun4) %>% 
  mutate_at(ref6,fun6) %>% 
  mutate_at(ref7,fun7) %>% 
  mutate_at(ref12,fun12) %>% 
  mutate_at(ref23,fun23) %>% 
  mutate_at(ref34,fun34) %>% 
  mutate_at(ref45,fun45) %>% 
  mutate_at(ref30,fun30) %>% 
  mutate_at(ref14,fun14) %>% 
  mutate_at(ref79,fun79) 


#Create weighted survey
options(survey.lonely.psu = "adjust")
svydes<-svydesign(id=~X_PSU,strata=~X_STSTR,weights =~X_LLCPWT,data=brfss2017B,nest=T)

Data

Raw Data

DT::datatable(brfss_2017, options = list(scrollX=TRUE))

Unweighted

Unweighted_table<- tableby(X_RACE~., data= brfss_2017[,-c(37:42)], control = my_controls)
summary(Unweighted_table,labelTranslations = my_labels, 
        title="Unweighted Statistics")

Unweighted Statistics
	White only, non-Hispanic (N=14917)	Black only, non-Hispanic (N=466)	American Indian or Alaskan Native Only, non-Hispanic (N=222)	Asian only, non-Hispanic (N=308)	Native Hawaiian or other Pacific Islander only, non-Hispanic (N=14)	Other race only, non-Hispanic (N=55)	Multiracial, non-Hispanic (N=176)	Hispanic (N=645)	Total (N=16803)	p value
Sex										0.012
Female	7896 (52.9%)	231 (49.6%)	129 (58.1%)	141 (45.8%)	7 (50.0%)	22 (40.0%)	80 (45.5%)	336 (52.1%)	8842 (52.6%)
Male	7021 (47.1%)	235 (50.4%)	93 (41.9%)	167 (54.2%)	7 (50.0%)	33 (60.0%)	96 (54.5%)	309 (47.9%)	7961 (47.4%)
Missing	0	0	0	0	0	0	0	0	0
Marital Status										< 0.001
Married	8511 (57.4%)	171 (37.0%)	69 (31.2%)	151 (49.2%)	5 (35.7%)	28 (53.8%)	61 (35.1%)	313 (48.8%)	9309 (55.7%)
Divorced	1911 (12.9%)	64 (13.9%)	30 (13.6%)	12 (3.9%)	2 (14.3%)	6 (11.5%)	29 (16.7%)	51 (8.0%)	2105 (12.6%)
Widowed	1471 (9.9%)	17 (3.7%)	14 (6.3%)	6 (2.0%)	1 (7.1%)	4 (7.7%)	10 (5.7%)	19 (3.0%)	1542 (9.2%)
Separated	129 (0.9%)	16 (3.5%)	9 (4.1%)	1 (0.3%)	0 (0.0%)	2 (3.8%)	3 (1.7%)	30 (4.7%)	190 (1.1%)
Never Married	2432 (16.4%)	188 (40.7%)	84 (38.0%)	129 (42.0%)	6 (42.9%)	11 (21.2%)	61 (35.1%)	162 (25.3%)	3073 (18.4%)
Unmarried Couple	380 (2.6%)	6 (1.3%)	15 (6.8%)	8 (2.6%)	0 (0.0%)	1 (1.9%)	10 (5.7%)	66 (10.3%)	486 (2.9%)
Missing	83	4	1	1	0	3	2	4	98
Education Level										< 0.001
Missing	34	0	3	3	0	4	0	5	49
Never attended school or only kindergarten	3 (0.0%)	3 (0.6%)	1 (0.5%)	1 (0.3%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	18 (2.8%)	26 (0.2%)
Grades 1 through 8 (Elementary)	84 (0.6%)	10 (2.1%)	3 (1.4%)	5 (1.6%)	0 (0.0%)	1 (2.0%)	0 (0.0%)	112 (17.5%)	215 (1.3%)
Grades 9 through 11 (Some high school)	321 (2.2%)	31 (6.7%)	27 (12.3%)	5 (1.6%)	0 (0.0%)	1 (2.0%)	7 (4.0%)	86 (13.4%)	478 (2.9%)
Grade 12 or GED (High school graduate)	3597 (24.2%)	140 (30.0%)	70 (32.0%)	56 (18.4%)	3 (21.4%)	19 (37.3%)	40 (22.7%)	165 (25.8%)	4090 (24.4%)
College 1 year to 3 years (Some college or technical school)	4647 (31.2%)	145 (31.1%)	63 (28.8%)	67 (22.0%)	4 (28.6%)	16 (31.4%)	54 (30.7%)	138 (21.6%)	5134 (30.6%)
College 4 years or more (College graduate)	6231 (41.9%)	137 (29.4%)	55 (25.1%)	171 (56.1%)	7 (50.0%)	14 (27.5%)	75 (42.6%)	121 (18.9%)	6811 (40.7%)
Employment										< 0.001
Employed for wages	7003 (47.3%)	253 (55.2%)	98 (46.2%)	187 (62.8%)	6 (42.9%)	24 (44.4%)	101 (57.7%)	349 (55.0%)	8021 (48.2%)
Self-Employed	1451 (9.8%)	34 (7.4%)	11 (5.2%)	19 (6.4%)	3 (21.4%)	9 (16.7%)	8 (4.6%)	60 (9.5%)	1595 (9.6%)
Out of work for less than 1 year	363 (2.5%)	17 (3.7%)	16 (7.5%)	8 (2.7%)	2 (14.3%)	2 (3.7%)	3 (1.7%)	15 (2.4%)	426 (2.6%)
Out of work for 1 year or more	233 (1.6%)	27 (5.9%)	14 (6.6%)	7 (2.3%)	0 (0.0%)	1 (1.9%)	8 (4.6%)	26 (4.1%)	316 (1.9%)
A homemaker	475 (3.2%)	11 (2.4%)	13 (6.1%)	8 (2.7%)	1 (7.1%)	1 (1.9%)	1 (0.6%)	73 (11.5%)	583 (3.5%)
A student	318 (2.1%)	32 (7.0%)	5 (2.4%)	47 (15.8%)	2 (14.3%)	0 (0.0%)	14 (8.0%)	38 (6.0%)	456 (2.7%)
Retired	4441 (30.0%)	55 (12.0%)	28 (13.2%)	11 (3.7%)	0 (0.0%)	14 (25.9%)	25 (14.3%)	40 (6.3%)	4614 (27.7%)
Unable to Work	516 (3.5%)	29 (6.3%)	27 (12.7%)	11 (3.7%)	0 (0.0%)	3 (5.6%)	15 (8.6%)	33 (5.2%)	634 (3.8%)
Missing	117	8	10	10	0	1	1	11	158
Income										< 0.001
Missing	2301	62	30	57	2	11	20	91	2574
Less than 10K	301 (2.4%)	36 (8.9%)	24 (12.5%)	17 (6.8%)	0 (0.0%)	2 (4.5%)	5 (3.2%)	45 (8.1%)	430 (3.0%)
10 to 15K	387 (3.1%)	20 (5.0%)	19 (9.9%)	8 (3.2%)	0 (0.0%)	3 (6.8%)	4 (2.6%)	42 (7.6%)	483 (3.4%)
15 to 20K	634 (5.0%)	50 (12.4%)	18 (9.4%)	10 (4.0%)	2 (16.7%)	1 (2.3%)	11 (7.1%)	81 (14.6%)	807 (5.7%)
20 to 25K	964 (7.6%)	67 (16.6%)	15 (7.8%)	28 (11.2%)	1 (8.3%)	5 (11.4%)	17 (10.9%)	86 (15.5%)	1183 (8.3%)
25 to 35K	1214 (9.6%)	50 (12.4%)	25 (13.0%)	27 (10.8%)	0 (0.0%)	8 (18.2%)	20 (12.8%)	88 (15.9%)	1432 (10.1%)
35 to 50K	1853 (14.7%)	63 (15.6%)	27 (14.1%)	24 (9.6%)	3 (25.0%)	13 (29.5%)	26 (16.7%)	78 (14.1%)	2087 (14.7%)
50 to 75K	2275 (18.0%)	47 (11.6%)	33 (17.2%)	40 (15.9%)	3 (25.0%)	6 (13.6%)	21 (13.5%)	47 (8.5%)	2472 (17.4%)
greater than 75K	4988 (39.5%)	71 (17.6%)	31 (16.1%)	97 (38.6%)	3 (25.0%)	6 (13.6%)	52 (33.3%)	87 (15.7%)	5335 (37.5%)
BMI Category										< 0.001
Underweight	188 (1.4%)	6 (1.5%)	6 (3.1%)	16 (6.0%)	1 (7.1%)	2 (4.1%)	2 (1.2%)	7 (1.6%)	228 (1.5%)
Normal Weight	4296 (31.4%)	133 (32.6%)	35 (17.9%)	132 (49.6%)	8 (57.1%)	16 (32.7%)	34 (21.0%)	110 (24.7%)	4764 (31.3%)
Overweight	5128 (37.4%)	141 (34.6%)	74 (37.8%)	80 (30.1%)	3 (21.4%)	18 (36.7%)	65 (40.1%)	173 (38.9%)	5682 (37.3%)
Obese	4082 (29.8%)	128 (31.4%)	81 (41.3%)	38 (14.3%)	2 (14.3%)	13 (26.5%)	61 (37.7%)	155 (34.8%)	4560 (29.9%)
Missing	1223	58	26	42	0	6	14	200	1569
Age Group										< 0.001
Missing	138	4	2	4	0	1	1	6	156
18 to 24	854 (5.8%)	64 (13.9%)	25 (11.4%)	66 (21.7%)	1 (7.1%)	1 (1.9%)	25 (14.3%)	81 (12.7%)	1117 (6.7%)
25 to 29	687 (4.6%)	49 (10.6%)	16 (7.3%)	50 (16.4%)	4 (28.6%)	5 (9.3%)	31 (17.7%)	70 (11.0%)	912 (5.5%)
30 to 34	849 (5.7%)	57 (12.3%)	21 (9.5%)	46 (15.1%)	3 (21.4%)	6 (11.1%)	6 (3.4%)	72 (11.3%)	1060 (6.4%)
35 to 39	878 (5.9%)	51 (11.0%)	14 (6.4%)	41 (13.5%)	0 (0.0%)	6 (11.1%)	22 (12.6%)	90 (14.1%)	1102 (6.6%)
40 to 44	828 (5.6%)	40 (8.7%)	13 (5.9%)	21 (6.9%)	3 (21.4%)	5 (9.3%)	16 (9.1%)	76 (11.9%)	1002 (6.0%)
45 to 49	946 (6.4%)	46 (10.0%)	21 (9.5%)	25 (8.2%)	2 (14.3%)	2 (3.7%)	13 (7.4%)	73 (11.4%)	1128 (6.8%)
50 to 54	1327 (9.0%)	39 (8.4%)	31 (14.1%)	20 (6.6%)	0 (0.0%)	7 (13.0%)	12 (6.9%)	49 (7.7%)	1485 (8.9%)
55 to 59	1616 (10.9%)	36 (7.8%)	24 (10.9%)	14 (4.6%)	0 (0.0%)	2 (3.7%)	14 (8.0%)	45 (7.0%)	1751 (10.5%)
60 to 64	1805 (12.2%)	33 (7.1%)	24 (10.9%)	8 (2.6%)	0 (0.0%)	3 (5.6%)	11 (6.3%)	32 (5.0%)	1916 (11.5%)
65 to 69	1673 (11.3%)	21 (4.5%)	18 (8.2%)	2 (0.7%)	0 (0.0%)	6 (11.1%)	10 (5.7%)	19 (3.0%)	1749 (10.5%)
70 to 74	1295 (8.8%)	13 (2.8%)	8 (3.6%)	3 (1.0%)	0 (0.0%)	3 (5.6%)	5 (2.9%)	13 (2.0%)	1340 (8.0%)
75 to 79	894 (6.0%)	4 (0.9%)	1 (0.5%)	3 (1.0%)	0 (0.0%)	0 (0.0%)	7 (4.0%)	10 (1.6%)	919 (5.5%)
80+	1127 (7.6%)	9 (1.9%)	4 (1.8%)	5 (1.6%)	1 (7.1%)	8 (14.8%)	3 (1.7%)	9 (1.4%)	1166 (7.0%)
Number of Children										< 0.001
Mean (SD)	0 (1)	1 (2)	1 (1)	1 (1)	1 (1)	1 (1)	1 (1)	1 (1)	1 (1)
Median (Q1, Q3)	0 (0, 0)	0 (0, 2)	0 (0, 2)	0 (0, 2)	0 (0, 1)	0 (0, 1)	0 (0, 1)	1 (0, 2)	0 (0, 1)
Min - Max	0 - 11	0 - 10	0 - 8	0 - 6	0 - 3	0 - 7	0 - 6	0 - 12	0 - 12
Missing	69	4	0	3	0	2	0	4	82
Metropolitan Status
In the center of an MSA	2270 (59.7%)	75 (87.2%)	16 (64.0%)	40 (81.6%)	2 (100.0%)	8 (66.7%)	13 (59.1%)	46 (69.7%)	2470 (60.7%)
Outside MSA center city but inside the center city county	391 (10.3%)	0 (0.0%)	2 (8.0%)	0 (0.0%)	0 (0.0%)	1 (8.3%)	1 (4.5%)	2 (3.0%)	397 (9.8%)
Inside a suburban county of the MSA	1144 (30.1%)	11 (12.8%)	7 (28.0%)	9 (18.4%)	0 (0.0%)	3 (25.0%)	8 (36.4%)	18 (27.3%)	1200 (29.5%)
Not in an MSA	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
Missing	11112	380	197	259	12	43	154	579	12736
Hours of Sleep per Night										< 0.001
Less than 7 hours	3571 (27.3%)	165 (44.7%)	63 (36.8%)	68 (28.8%)	2 (22.2%)	18 (41.9%)	60 (40.3%)	156 (28.5%)	4103 (28.1%)
7-9 hours	9145 (70.0%)	189 (51.2%)	102 (59.6%)	162 (68.6%)	7 (77.8%)	24 (55.8%)	84 (56.4%)	358 (65.3%)	10071 (69.0%)
Greater than 9 hours	347 (2.7%)	15 (4.1%)	6 (3.5%)	6 (2.5%)	0 (0.0%)	1 (2.3%)	5 (3.4%)	34 (6.2%)	414 (2.8%)
Missing	1854	97	51	72	5	12	27	97	2215
Days with difficulty sleeping										< 0.001
Missing	1993	99	49	76	5	11	31	102	2366
0 days	6971 (53.9%)	214 (58.3%)	73 (42.2%)	140 (60.3%)	5 (55.6%)	16 (36.4%)	62 (42.8%)	315 (58.0%)	7796 (54.0%)
1-3 days	2610 (20.2%)	63 (17.2%)	34 (19.7%)	55 (23.7%)	2 (22.2%)	13 (29.5%)	23 (15.9%)	83 (15.3%)	2883 (20.0%)
4-6 days	1057 (8.2%)	24 (6.5%)	14 (8.1%)	13 (5.6%)	0 (0.0%)	5 (11.4%)	21 (14.5%)	44 (8.1%)	1178 (8.2%)
7 days	385 (3.0%)	8 (2.2%)	8 (4.6%)	4 (1.7%)	0 (0.0%)	1 (2.3%)	5 (3.4%)	26 (4.8%)	437 (3.0%)
8-10 days	450 (3.5%)	5 (1.4%)	8 (4.6%)	8 (3.4%)	1 (11.1%)	3 (6.8%)	8 (5.5%)	15 (2.8%)	498 (3.4%)
11-13 days	129 (1.0%)	6 (1.6%)	2 (1.2%)	1 (0.4%)	0 (0.0%)	0 (0.0%)	2 (1.4%)	2 (0.4%)	142 (1.0%)
14 days	1322 (10.2%)	47 (12.8%)	34 (19.7%)	11 (4.7%)	1 (11.1%)	6 (13.6%)	24 (16.6%)	58 (10.7%)	1503 (10.4%)
Bill Security										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	1888	102	49	72	6	11	29	101	2258
How safe from crime do you consider your neighborhood to be?										< 0.001
Missing	1922	103	53	71	6	11	31	108	2305
Extremely safe	6621 (51.0%)	121 (33.3%)	48 (28.4%)	69 (29.1%)	1 (12.5%)	10 (22.7%)	66 (45.5%)	181 (33.7%)	7117 (49.1%)
Safe	6097 (46.9%)	213 (58.7%)	96 (56.8%)	157 (66.2%)	6 (75.0%)	32 (72.7%)	63 (43.4%)	320 (59.6%)	6984 (48.2%)
Unsafe	245 (1.9%)	21 (5.8%)	16 (9.5%)	11 (4.6%)	1 (12.5%)	2 (4.5%)	12 (8.3%)	32 (6.0%)	340 (2.3%)
Extremely Unsafe	32 (0.2%)	8 (2.2%)	9 (5.3%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	4 (2.8%)	4 (0.7%)	57 (0.4%)
Food Security										< 0.001
Missing	1958	105	53	71	6	12	31	104	2340
Often True	264 (2.0%)	25 (6.9%)	19 (11.2%)	12 (5.1%)	0 (0.0%)	2 (4.7%)	8 (5.5%)	25 (4.6%)	355 (2.5%)
Sometimes True	756 (5.8%)	84 (23.3%)	27 (16.0%)	28 (11.8%)	1 (12.5%)	8 (18.6%)	23 (15.9%)	99 (18.3%)	1026 (7.1%)
Never True	11939 (92.1%)	252 (69.8%)	123 (72.8%)	197 (83.1%)	7 (87.5%)	33 (76.7%)	114 (78.6%)	417 (77.1%)	13082 (90.5%)
Balanced Meals										< 0.001
Missing	1957	101	53	72	6	12	32	110	2343
Often True	443 (3.4%)	34 (9.3%)	16 (9.5%)	11 (4.7%)	0 (0.0%)	2 (4.7%)	13 (9.0%)	28 (5.2%)	547 (3.8%)
Sometimes True	935 (7.2%)	80 (21.9%)	34 (20.1%)	37 (15.7%)	1 (12.5%)	11 (25.6%)	29 (20.1%)	100 (18.7%)	1227 (8.5%)
Never True	11582 (89.4%)	251 (68.8%)	119 (70.4%)	188 (79.7%)	7 (87.5%)	30 (69.8%)	102 (70.8%)	407 (76.1%)	12686 (87.7%)
Financial Security										< 0.001
Missing	2195	112	52	80	6	12	32	116	2605
Often True	7933 (62.4%)	134 (37.9%)	60 (35.3%)	122 (53.5%)	2 (25.0%)	19 (44.2%)	66 (45.8%)	201 (38.0%)	8537 (60.1%)
Sometimes True	4180 (32.9%)	166 (46.9%)	79 (46.5%)	91 (39.9%)	6 (75.0%)	17 (39.5%)	65 (45.1%)	257 (48.6%)	4861 (34.2%)
Never True	609 (4.8%)	54 (15.3%)	31 (18.2%)	15 (6.6%)	0 (0.0%)	7 (16.3%)	13 (9.0%)	71 (13.4%)	800 (5.6%)
Within the last 30 days, how often have you felt stress?										< 0.001
Missing	2014	104	54	70	6	11	31	103	2393
None of the time	6310 (48.9%)	162 (44.8%)	59 (35.1%)	106 (44.5%)	4 (50.0%)	13 (29.5%)	54 (37.2%)	224 (41.3%)	6932 (48.1%)
A little of the time	3755 (29.1%)	86 (23.8%)	61 (36.3%)	67 (28.2%)	1 (12.5%)	18 (40.9%)	37 (25.5%)	138 (25.5%)	4163 (28.9%)
Some of the time	1854 (14.4%)	60 (16.6%)	17 (10.1%)	45 (18.9%)	2 (25.0%)	7 (15.9%)	29 (20.0%)	124 (22.9%)	2138 (14.8%)
Most of the time	686 (5.3%)	34 (9.4%)	17 (10.1%)	13 (5.5%)	1 (12.5%)	3 (6.8%)	13 (9.0%)	35 (6.5%)	802 (5.6%)
All of the time	298 (2.3%)	20 (5.5%)	14 (8.3%)	7 (2.9%)	0 (0.0%)	3 (6.8%)	12 (8.3%)	21 (3.9%)	375 (2.6%)
General Health										< 0.001
Missing	27	2	1	0	0	1	0	2	33
Excellent	2723 (18.3%)	109 (23.5%)	24 (10.9%)	70 (22.7%)	4 (28.6%)	12 (22.2%)	12 (6.8%)	116 (18.0%)	3070 (18.3%)
Very Good	5631 (37.8%)	100 (21.6%)	50 (22.6%)	92 (29.9%)	3 (21.4%)	9 (16.7%)	51 (29.0%)	132 (20.5%)	6068 (36.2%)
Good	4608 (30.9%)	171 (36.9%)	86 (38.9%)	114 (37.0%)	6 (42.9%)	23 (42.6%)	76 (43.2%)	240 (37.3%)	5324 (31.7%)
Fair	1438 (9.7%)	60 (12.9%)	43 (19.5%)	27 (8.8%)	1 (7.1%)	6 (11.1%)	24 (13.6%)	126 (19.6%)	1725 (10.3%)
Poor	490 (3.3%)	24 (5.2%)	18 (8.1%)	5 (1.6%)	0 (0.0%)	4 (7.4%)	13 (7.4%)	29 (4.5%)	583 (3.5%)
How many days during the past 30 days was your physical health not good?										< 0.001
0	9845 (67.1%)	331 (71.8%)	123 (56.4%)	216 (72.0%)	12 (85.7%)	38 (69.1%)	100 (57.5%)	465 (74.3%)	11130 (67.4%)
1	2786 (19.0%)	59 (12.8%)	42 (19.3%)	55 (18.3%)	1 (7.1%)	11 (20.0%)	46 (26.4%)	83 (13.3%)	3083 (18.7%)
2	524 (3.6%)	22 (4.8%)	12 (5.5%)	9 (3.0%)	0 (0.0%)	1 (1.8%)	6 (3.4%)	16 (2.6%)	590 (3.6%)
3	498 (3.4%)	19 (4.1%)	11 (5.0%)	10 (3.3%)	1 (7.1%)	1 (1.8%)	6 (3.4%)	26 (4.2%)	572 (3.5%)
4	152 (1.0%)	5 (1.1%)	6 (2.8%)	1 (0.3%)	0 (0.0%)	0 (0.0%)	1 (0.6%)	3 (0.5%)	168 (1.0%)
5	862 (5.9%)	25 (5.4%)	24 (11.0%)	9 (3.0%)	0 (0.0%)	4 (7.3%)	15 (8.6%)	33 (5.3%)	972 (5.9%)
Missing	250	5	4	8	0	0	2	19	288
How many days during the past 30 days was your mental health not good?										< 0.001
0	10440 (70.9%)	321 (69.3%)	124 (56.4%)	215 (71.0%)	8 (57.1%)	36 (65.5%)	90 (51.7%)	437 (68.9%)	11671 (70.3%)
1	2538 (17.2%)	59 (12.7%)	37 (16.8%)	53 (17.5%)	3 (21.4%)	11 (20.0%)	35 (20.1%)	82 (12.9%)	2818 (17.0%)
2	560 (3.8%)	26 (5.6%)	15 (6.8%)	14 (4.6%)	1 (7.1%)	2 (3.6%)	14 (8.0%)	40 (6.3%)	672 (4.1%)
3	584 (4.0%)	24 (5.2%)	22 (10.0%)	14 (4.6%)	2 (14.3%)	3 (5.5%)	16 (9.2%)	35 (5.5%)	700 (4.2%)
4	109 (0.7%)	7 (1.5%)	4 (1.8%)	3 (1.0%)	0 (0.0%)	0 (0.0%)	3 (1.7%)	6 (0.9%)	132 (0.8%)
5	497 (3.4%)	26 (5.6%)	18 (8.2%)	4 (1.3%)	0 (0.0%)	3 (5.5%)	16 (9.2%)	34 (5.4%)	598 (3.6%)
Missing	189	3	2	5	0	0	2	11	212
Poor health days										0.002
0	4183 (59.5%)	91 (45.7%)	63 (46.3%)	72 (56.2%)	3 (50.0%)	15 (51.7%)	63 (58.3%)	154 (55.6%)	4644 (58.7%)
1	1551 (22.1%)	48 (24.1%)	29 (21.3%)	29 (22.7%)	3 (50.0%)	6 (20.7%)	25 (23.1%)	64 (23.1%)	1755 (22.2%)
2	348 (5.0%)	18 (9.0%)	16 (11.8%)	13 (10.2%)	0 (0.0%)	2 (6.9%)	5 (4.6%)	17 (6.1%)	419 (5.3%)
3	428 (6.1%)	18 (9.0%)	12 (8.8%)	10 (7.8%)	0 (0.0%)	3 (10.3%)	7 (6.5%)	23 (8.3%)	501 (6.3%)
4	91 (1.3%)	6 (3.0%)	4 (2.9%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	2 (1.9%)	2 (0.7%)	105 (1.3%)
5	425 (6.0%)	18 (9.0%)	12 (8.8%)	4 (3.1%)	0 (0.0%)	3 (10.3%)	6 (5.6%)	17 (6.1%)	485 (6.1%)
Missing	7891	267	86	180	8	26	68	368	8894
Do you smoke cigarettes every day, some days, or not at all?										< 0.001
Missing	8689	335	83	262	9	29	90	435	9932
Every day	1319 (21.2%)	51 (38.9%)	52 (37.4%)	16 (34.8%)	1 (20.0%)	10 (38.5%)	25 (29.1%)	52 (24.8%)	1526 (22.2%)
Some days	532 (8.5%)	34 (26.0%)	29 (20.9%)	5 (10.9%)	0 (0.0%)	0 (0.0%)	11 (12.8%)	44 (21.0%)	655 (9.5%)
Not at all	4377 (70.3%)	46 (35.1%)	58 (41.7%)	25 (54.3%)	4 (80.0%)	16 (61.5%)	50 (58.1%)	114 (54.3%)	4690 (68.3%)
Do you use vaping products every day, some days, or not at all?										0.617
Missing	12856	388	166	266	10	44	121	540	14391
Every day	152 (7.4%)	3 (3.8%)	2 (3.6%)	1 (2.4%)	0 (0.0%)	0 (0.0%)	3 (5.5%)	5 (4.8%)	166 (6.9%)
Some days	251 (12.2%)	15 (19.2%)	10 (17.9%)	6 (14.3%)	0 (0.0%)	0 (0.0%)	5 (9.1%)	13 (12.4%)	300 (12.4%)
Not at all	1658 (80.4%)	60 (76.9%)	44 (78.6%)	35 (83.3%)	4 (100.0%)	11 (100.0%)	47 (85.5%)	87 (82.9%)	1946 (80.7%)
(Ever told) you had a stroke.										0.100
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	32	0	1	1	0	1	0	7	42
(Ever told) you had angina or coronary heart disease?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	101	1	2	6	0	1	1	6	118
(Ever told) you have diabetes										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	13	1	1	0	0	2	0	1	18
(Ever told) you that you have a depressive disorder?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 1)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 1)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	50	2	2	3	0	3	1	6	67
(Ever told) you have some form of arthritis?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 1)	0 (0, 0)	0 (0, 1)	0 (0, 0)	0 (0, 0)	0 (0, 1)	0 (0, 1)	0 (0, 0)	0 (0, 1)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	65	1	3	4	0	3	1	4	81
(Ever told) you have COPD, emphysema or chronic bronchitis?										0.019
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	51	0	3	4	0	4	1	3	66
Are you deaf or do you have serious difficulty hearing?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	302	17	14	16	2	2	7	17	377
Are you blind or do you have serious difficulty seeing, even when wearing glasses?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	329	18	13	20	2	2	7	17	408
Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	365	24	15	17	2	2	10	20	455
Do you have serious difficulty walking or climbing stairs?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 1)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	361	27	13	18	2	3	7	23	454
Do you have difficulty dressing or bathing?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	365	26	14	19	2	3	7	24	460
Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone?										< 0.001
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	407	29	14	20	2	3	9	25	509

Weighted

Weighted_Table<- tableby(X_RACE~., data=brfss_2017[,-c(37,38,40:42)], control = my_controls, 
                         weights = X_LLCPWT)
summary(Weighted_Table,labelTranslations = my_labels, 
        title="Weighted Statistics")

Weighted Statistics
	White only, non-Hispanic (N=3501803)	Black only, non-Hispanic (N=216193)	American Indian or Alaskan Native Only, non-Hispanic (N=41207)	Asian only, non-Hispanic (N=198660)	Native Hawaiian or other Pacific Islander only, non-Hispanic (N=4531)	Other race only, non-Hispanic (N=11248)	Multiracial, non-Hispanic (N=44054)	Hispanic (N=183148)	Total (N=4200845)
Sex
Female	1791248 (51.2%)	112199 (51.9%)	24276 (58.9%)	100861 (50.8%)	2470 (54.5%)	4795 (42.6%)	20200 (45.9%)	85074 (46.5%)	2141123 (51.0%)
Male	1710556 (48.8%)	103993 (48.1%)	16931 (41.1%)	97799 (49.2%)	2062 (45.5%)	6453 (57.4%)	23853 (54.1%)	98074 (53.5%)	2059722 (49.0%)
Missing	0	0	0	0	0	0	0	0	0
Marital Status
Married	1992074 (57.2%)	77640 (36.5%)	13709 (33.7%)	86012 (43.3%)	1663 (36.7%)	6510 (59.0%)	13252 (30.8%)	87479 (47.9%)	2278338 (54.6%)
Divorced	377418 (10.8%)	31135 (14.6%)	4689 (11.5%)	6895 (3.5%)	364 (8.0%)	965 (8.8%)	5227 (12.2%)	10594 (5.8%)	437287 (10.5%)
Widowed	228935 (6.6%)	6572 (3.1%)	2889 (7.1%)	4365 (2.2%)	278 (6.1%)	415 (3.8%)	2058 (4.8%)	3450 (1.9%)	248964 (6.0%)
Separated	26336 (0.8%)	7521 (3.5%)	1342 (3.3%)	745 (0.4%)	0 (0.0%)	269 (2.4%)	459 (1.1%)	8527 (4.7%)	45198 (1.1%)
Never Married	735487 (21.1%)	86588 (40.7%)	15325 (37.7%)	93789 (47.3%)	2226 (49.1%)	2663 (24.1%)	19073 (44.4%)	45797 (25.1%)	1000949 (24.0%)
Unmarried Couple	121376 (3.5%)	3374 (1.6%)	2742 (6.7%)	6681 (3.4%)	0 (0.0%)	210 (1.9%)	2888 (6.7%)	26797 (14.7%)	164067 (3.9%)
Missing	20177	3363	512	172	0	215	1097	505	26041
Education Level
Missing	6833	0	401	1103	0	367	0	1232	9936
Never attended school or only kindergarten	1708 (0.0%)	3091 (1.4%)	380 (0.9%)	940 (0.5%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	6187 (3.4%)	12306 (0.3%)
Grades 1 through 8 (Elementary)	33100 (0.9%)	9324 (4.3%)	59 (0.1%)	7334 (3.7%)	0 (0.0%)	284 (2.6%)	0 (0.0%)	46866 (25.8%)	96967 (2.3%)
Grades 9 through 11 (Some high school)	146516 (4.2%)	25100 (11.6%)	9601 (23.5%)	7039 (3.6%)	0 (0.0%)	585 (5.4%)	3892 (8.8%)	41333 (22.7%)	234066 (5.6%)
Grade 12 or GED (High school graduate)	911647 (26.1%)	70480 (32.6%)	13768 (33.7%)	50896 (25.8%)	888 (19.6%)	3867 (35.5%)	12644 (28.7%)	38673 (21.3%)	1102861 (26.3%)
College 1 year to 3 years (Some college or technical school)	1263036 (36.1%)	66929 (31.0%)	10349 (25.4%)	50512 (25.6%)	2311 (51.0%)	4439 (40.8%)	12927 (29.3%)	31708 (17.4%)	1442210 (34.4%)
College 4 years or more (College graduate)	1138964 (32.6%)	41269 (19.1%)	6650 (16.3%)	80836 (40.9%)	1333 (29.4%)	1706 (15.7%)	14591 (33.1%)	17149 (9.4%)	1302498 (31.1%)
Employment
Employed for wages	1885983 (54.4%)	118052 (55.5%)	18140 (46.3%)	113427 (59.1%)	1702 (37.6%)	4355 (41.3%)	24207 (55.0%)	97391 (54.1%)	2263257 (54.6%)
Self-Employed	338369 (9.8%)	18865 (8.9%)	3222 (8.2%)	14928 (7.8%)	529 (11.7%)	2115 (20.1%)	2600 (5.9%)	20843 (11.6%)	401472 (9.7%)
Out of work for less than 1 year	80345 (2.3%)	6653 (3.1%)	2172 (5.5%)	6403 (3.3%)	489 (10.8%)	617 (5.9%)	542 (1.2%)	3194 (1.8%)	100415 (2.4%)
Out of work for 1 year or more	57251 (1.7%)	13685 (6.4%)	2203 (5.6%)	3369 (1.8%)	0 (0.0%)	342 (3.2%)	2638 (6.0%)	6525 (3.6%)	86013 (2.1%)
A homemaker	120193 (3.5%)	5519 (2.6%)	3689 (9.4%)	6996 (3.6%)	770 (17.0%)	422 (4.0%)	3 (0.0%)	23139 (12.9%)	160731 (3.9%)
A student	138155 (4.0%)	17522 (8.2%)	783 (2.0%)	34826 (18.2%)	1040 (23.0%)	0 (0.0%)	4861 (11.0%)	12272 (6.8%)	209460 (5.0%)
Retired	742711 (21.4%)	20410 (9.6%)	3824 (9.8%)	4093 (2.1%)	0 (0.0%)	2463 (23.4%)	3810 (8.7%)	8051 (4.5%)	785362 (18.9%)
Unable to Work	102052 (2.9%)	12053 (5.7%)	5146 (13.1%)	7778 (4.1%)	0 (0.0%)	220 (2.1%)	5375 (12.2%)	8565 (4.8%)	141189 (3.4%)
Missing	36745	3432	2028	6839	0	714	17	3169	52943
Income
Missing	535283	30837	8505	34419	287	2522	7310	27852	647015
Less than 10K	68250 (2.3%)	18639 (10.1%)	4589 (14.0%)	12020 (7.3%)	0 (0.0%)	269 (3.1%)	768 (2.1%)	13250 (8.5%)	117786 (3.3%)
10 to 15K	71544 (2.4%)	11045 (6.0%)	3329 (10.2%)	6070 (3.7%)	0 (0.0%)	745 (8.5%)	887 (2.4%)	10928 (7.0%)	104549 (2.9%)
15 to 20K	126466 (4.3%)	24376 (13.2%)	2164 (6.6%)	8851 (5.4%)	905 (21.3%)	284 (3.2%)	2894 (7.9%)	25461 (16.4%)	191402 (5.4%)
20 to 25K	212524 (7.2%)	31198 (16.8%)	3271 (10.0%)	24506 (14.9%)	278 (6.6%)	697 (8.0%)	3921 (10.7%)	26461 (17.0%)	302856 (8.5%)
25 to 35K	261969 (8.8%)	18297 (9.9%)	3582 (11.0%)	15936 (9.7%)	0 (0.0%)	1135 (13.0%)	3951 (10.8%)	28512 (18.4%)	333382 (9.4%)
35 to 50K	409972 (13.8%)	29424 (15.9%)	5619 (17.2%)	17280 (10.5%)	998 (23.5%)	3466 (39.7%)	6285 (17.1%)	22791 (14.7%)	495835 (14.0%)
50 to 75K	534070 (18.0%)	20708 (11.2%)	4845 (14.8%)	22563 (13.7%)	1221 (28.8%)	796 (9.1%)	5361 (14.6%)	9975 (6.4%)	599540 (16.9%)
greater than 75K	1281724 (43.2%)	31668 (17.1%)	5303 (16.2%)	57015 (34.7%)	842 (19.8%)	1336 (15.3%)	12675 (34.5%)	17917 (11.5%)	1408481 (39.6%)
BMI Category
Underweight	46541 (1.4%)	2336 (1.3%)	1511 (4.3%)	10990 (6.3%)	500 (11.0%)	168 (1.6%)	468 (1.1%)	2381 (2.0%)	64895 (1.7%)
Normal Weight	1063252 (33.0%)	62399 (33.8%)	6826 (19.4%)	83460 (48.0%)	1988 (43.9%)	3606 (35.2%)	7643 (18.4%)	30497 (25.8%)	1259670 (33.3%)
Overweight	1194439 (37.1%)	59527 (32.2%)	13026 (37.0%)	50244 (28.9%)	1223 (27.0%)	4458 (43.5%)	17589 (42.3%)	43066 (36.4%)	1383572 (36.5%)
Obese	913025 (28.4%)	60413 (32.7%)	13845 (39.3%)	29070 (16.7%)	821 (18.1%)	2020 (19.7%)	15914 (38.2%)	42397 (35.8%)	1077505 (28.5%)
Missing	284547	31519	6000	24895	0	996	2440	64806	415202
Age Group
Missing	32265	3104	246	2600	0	15	340	1732	40302
18 to 24	373807 (10.8%)	37212 (17.5%)	6814 (16.6%)	56270 (28.7%)	389 (8.6%)	7 (0.1%)	11094 (25.4%)	25055 (13.8%)	510649 (12.3%)
25 to 29	226209 (6.5%)	22010 (10.3%)	3285 (8.0%)	31599 (16.1%)	1496 (33.0%)	1161 (10.3%)	5935 (13.6%)	20135 (11.1%)	311830 (7.5%)
30 to 34	310213 (8.9%)	31118 (14.6%)	4253 (10.4%)	29081 (14.8%)	1341 (29.6%)	2078 (18.5%)	1408 (3.2%)	21926 (12.1%)	401419 (9.6%)
35 to 39	276330 (8.0%)	22080 (10.4%)	1882 (4.6%)	26600 (13.6%)	0 (0.0%)	1125 (10.0%)	7446 (17.0%)	28735 (15.8%)	364199 (8.8%)
40 to 44	251319 (7.2%)	19768 (9.3%)	1926 (4.7%)	10042 (5.1%)	731 (16.1%)	1559 (13.9%)	3507 (8.0%)	24905 (13.7%)	313757 (7.5%)
45 to 49	235493 (6.8%)	19914 (9.3%)	3648 (8.9%)	11624 (5.9%)	497 (11.0%)	277 (2.5%)	1401 (3.2%)	17668 (9.7%)	290523 (7.0%)
50 to 54	328689 (9.5%)	16472 (7.7%)	5049 (12.3%)	12309 (6.3%)	0 (0.0%)	1350 (12.0%)	2216 (5.1%)	12086 (6.7%)	378170 (9.1%)
55 to 59	314909 (9.1%)	15132 (7.1%)	3980 (9.7%)	8615 (4.4%)	0 (0.0%)	716 (6.4%)	4506 (10.3%)	11705 (6.5%)	359563 (8.6%)
60 to 64	342717 (9.9%)	13911 (6.5%)	5338 (13.0%)	5518 (2.8%)	0 (0.0%)	533 (4.7%)	2389 (5.5%)	8245 (4.5%)	378649 (9.1%)
65 to 69	261477 (7.5%)	6686 (3.1%)	2490 (6.1%)	715 (0.4%)	0 (0.0%)	744 (6.6%)	1565 (3.6%)	4169 (2.3%)	277845 (6.7%)
70 to 74	203406 (5.9%)	4363 (2.0%)	1572 (3.8%)	1310 (0.7%)	0 (0.0%)	594 (5.3%)	843 (1.9%)	2902 (1.6%)	214991 (5.2%)
75 to 79	151771 (4.4%)	562 (0.3%)	132 (0.3%)	1058 (0.5%)	0 (0.0%)	0 (0.0%)	986 (2.3%)	2234 (1.2%)	156743 (3.8%)
80+	193200 (5.6%)	3860 (1.8%)	594 (1.4%)	1319 (0.7%)	77 (1.7%)	1089 (9.7%)	418 (1.0%)	1650 (0.9%)	202206 (4.9%)
Number of Children
Mean (SD)	1 (1)	1 (1)	1 (1)	1 (1)	1 (1)	1 (1)	1 (1)	1 (1)	1 (1)
Median (Q1, Q3)	0 (0, 1)	1 (0, 2)	0 (0, 2)	1 (0, 2)	1 (0, 3)	0 (0, 1)	0 (0, 1)	1 (0, 2)	0 (0, 1)
Min - Max	0 - 11	0 - 10	0 - 8	0 - 6	0 - 3	0 - 7	0 - 6	0 - 12	0 - 12
Missing	19040	3577	0	2165	0	728	0	1039	26549
Metropolitan Status
In the center of an MSA	449781 (55.7%)	21117 (84.9%)	2870 (38.4%)	19867 (77.0%)	287 (100.0%)	1877 (72.7%)	2723 (65.9%)	9853 (65.0%)	508375 (57.2%)
Outside MSA center city but inside the center city county	95126 (11.8%)	0 (0.0%)	710 (9.5%)	0 (0.0%)	0 (0.0%)	146 (5.7%)	205 (5.0%)	542 (3.6%)	96729 (10.9%)
Inside a suburban county of the MSA	263022 (32.6%)	3760 (15.1%)	3903 (52.2%)	5920 (23.0%)	0 (0.0%)	559 (21.6%)	1205 (29.2%)	4766 (31.4%)	283135 (31.9%)
Not in an MSA	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
Missing	2693874	191316	33725	172874	4244	8666	39920	167987	3312605
Hours of Sleep per Night
Less than 7 hours	870879 (28.5%)	73155 (43.6%)	9591 (31.0%)	44672 (29.4%)	753 (23.9%)	3911 (47.4%)	15116 (41.1%)	41728 (26.6%)	1059803 (29.4%)
7-9 hours	2114591 (69.2%)	89135 (53.2%)	18603 (60.2%)	101733 (66.9%)	2402 (76.1%)	4296 (52.1%)	19967 (54.2%)	104331 (66.5%)	2455060 (68.0%)
Greater than 9 hours	69664 (2.3%)	5310 (3.2%)	2723 (8.8%)	5648 (3.7%)	0 (0.0%)	40 (0.5%)	1729 (4.7%)	10782 (6.9%)	95896 (2.7%)
Missing	446669	48592	10291	46607	1377	3001	7243	26307	590086
Days with difficulty sleeping
Missing	480498	51438	9933	47495	1377	2804	8521	29224	631290
0 days	1605295 (53.1%)	98303 (59.7%)	13662 (43.7%)	92409 (61.1%)	1132 (35.9%)	3009 (35.6%)	14035 (39.5%)	96948 (63.0%)	1924792 (53.9%)
1-3 days	601393 (19.9%)	26430 (16.0%)	6098 (19.5%)	36571 (24.2%)	601 (19.1%)	1528 (18.1%)	5251 (14.8%)	20276 (13.2%)	698150 (19.6%)
4-6 days	265813 (8.8%)	10344 (6.3%)	4131 (13.2%)	6745 (4.5%)	0 (0.0%)	1344 (15.9%)	7043 (19.8%)	12211 (7.9%)	307630 (8.6%)
7 days	94054 (3.1%)	2819 (1.7%)	1001 (3.2%)	1273 (0.8%)	0 (0.0%)	765 (9.1%)	1297 (3.6%)	6919 (4.5%)	108126 (3.0%)
8-10 days	114739 (3.8%)	1680 (1.0%)	1809 (5.8%)	4846 (3.2%)	651 (20.6%)	120 (1.4%)	1148 (3.2%)	3495 (2.3%)	128489 (3.6%)
11-13 days	30131 (1.0%)	3301 (2.0%)	54 (0.2%)	884 (0.6%)	0 (0.0%)	0 (0.0%)	289 (0.8%)	405 (0.3%)	35065 (1.0%)
14 days	309880 (10.3%)	21879 (13.3%)	4519 (14.4%)	8437 (5.6%)	770 (24.4%)	1677 (19.9%)	6470 (18.2%)	13669 (8.9%)	367302 (10.3%)
Bill Security
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (1)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 1)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	464166	51109	9479	46205	1766	2804	7580	28651	611759
How safe from crime do you consider your neighborhood to be?
Missing	473537	51169	10134	45022	1766	2804	8186	29064	621684
Extremely safe	1498980 (49.5%)	53616 (32.5%)	9553 (30.7%)	45948 (29.9%)	500 (18.1%)	1832 (21.7%)	14887 (41.5%)	49330 (32.0%)	1674646 (46.8%)
Safe	1459253 (48.2%)	97343 (59.0%)	17796 (57.3%)	100773 (65.6%)	1614 (58.4%)	5570 (66.0%)	17353 (48.4%)	93027 (60.4%)	1792728 (50.1%)
Unsafe	60213 (2.0%)	10252 (6.2%)	2555 (8.2%)	6917 (4.5%)	651 (23.5%)	1041 (12.3%)	3041 (8.5%)	10762 (7.0%)	95433 (2.7%)
Extremely Unsafe	9820 (0.3%)	3814 (2.3%)	1169 (3.8%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	587 (1.6%)	964 (0.6%)	16354 (0.5%)
Food Security
Missing	478911	53169	9765	44873	1766	2865	8118	28481	627948
Often True	69410 (2.3%)	12380 (7.6%)	3782 (12.0%)	8433 (5.5%)	0 (0.0%)	589 (7.0%)	1883 (5.2%)	6732 (4.4%)	103209 (2.9%)
Sometimes True	202443 (6.7%)	42573 (26.1%)	5692 (18.1%)	19722 (12.8%)	770 (27.9%)	1897 (22.6%)	6792 (18.9%)	28138 (18.2%)	308028 (8.6%)
Never True	2751039 (91.0%)	108071 (66.3%)	21968 (69.9%)	125632 (81.7%)	1995 (72.1%)	5897 (70.3%)	27261 (75.9%)	119797 (77.5%)	3161660 (88.5%)
Balanced Meals
Missing	478990	50242	9765	44835	1766	3029	8593	30475	627694
Often True	113176 (3.7%)	15365 (9.3%)	1702 (5.4%)	5904 (3.8%)	0 (0.0%)	592 (7.2%)	3891 (11.0%)	6144 (4.0%)	146775 (4.1%)
Sometimes True	264022 (8.7%)	38254 (23.1%)	6835 (21.7%)	27453 (17.8%)	500 (18.1%)	2216 (27.0%)	7655 (21.6%)	27339 (17.9%)	374273 (10.5%)
Never True	2645616 (87.5%)	112333 (67.7%)	22905 (72.8%)	120468 (78.3%)	2266 (81.9%)	5411 (65.8%)	23915 (67.4%)	119190 (78.1%)	3052102 (85.4%)
Financial Security
Missing	533114	55515	10021	52988	1766	2865	8459	31477	696204
Often True	1810315 (61.0%)	56604 (35.2%)	12237 (39.2%)	75430 (51.8%)	269 (9.7%)	3027 (36.1%)	17547 (49.3%)	53128 (35.0%)	2028557 (57.9%)
Sometimes True	1010346 (34.0%)	76979 (47.9%)	13909 (44.6%)	61889 (42.5%)	2496 (90.3%)	4096 (48.9%)	14891 (41.8%)	80114 (52.8%)	1264720 (36.1%)
Never True	148029 (5.0%)	27095 (16.9%)	5040 (16.2%)	8354 (5.7%)	0 (0.0%)	1259 (15.0%)	3157 (8.9%)	18429 (12.2%)	211363 (6.0%)
Within the last 30 days, how often have you felt stress?
Missing	497774	51341	10385	43751	1766	2236	7748	28442	643441
None of the time	1399472 (46.6%)	73182 (44.4%)	11356 (36.8%)	65660 (42.4%)	557 (20.1%)	2978 (33.0%)	15132 (41.7%)	66044 (42.7%)	1634380 (45.9%)
A little of the time	894627 (29.8%)	36479 (22.1%)	11250 (36.5%)	46058 (29.7%)	287 (10.4%)	3475 (38.6%)	7780 (21.4%)	39995 (25.9%)	1039951 (29.2%)
Some of the time	465804 (15.5%)	27345 (16.6%)	3656 (11.9%)	27003 (17.4%)	1270 (45.9%)	797 (8.8%)	6051 (16.7%)	36738 (23.7%)	568665 (16.0%)
Most of the time	173489 (5.8%)	15650 (9.5%)	2301 (7.5%)	9656 (6.2%)	651 (23.5%)	515 (5.7%)	3566 (9.8%)	7912 (5.1%)	213740 (6.0%)
All of the time	70638 (2.4%)	12196 (7.4%)	2260 (7.3%)	6531 (4.2%)	0 (0.0%)	1247 (13.8%)	3777 (10.4%)	4018 (2.6%)	100667 (2.8%)
General Health
Missing	4944	834	59	0	0	7	0	360	6205
Excellent	667712 (19.1%)	51150 (23.8%)	7218 (17.5%)	42893 (21.6%)	1310 (28.9%)	2136 (19.0%)	3584 (8.1%)	34129 (18.7%)	810132 (19.3%)
Very Good	1350959 (38.6%)	46697 (21.7%)	8684 (21.1%)	57317 (28.9%)	857 (18.9%)	1053 (9.4%)	11390 (25.9%)	31829 (17.4%)	1508787 (36.0%)
Good	1077042 (30.8%)	80654 (37.5%)	14824 (36.0%)	77715 (39.1%)	2229 (49.2%)	6074 (54.0%)	21186 (48.1%)	68173 (37.3%)	1347898 (32.1%)
Fair	302942 (8.7%)	28064 (13.0%)	7366 (17.9%)	19066 (9.6%)	135 (3.0%)	1440 (12.8%)	5031 (11.4%)	40650 (22.2%)	404694 (9.6%)
Poor	98205 (2.8%)	8792 (4.1%)	3055 (7.4%)	1670 (0.8%)	0 (0.0%)	538 (4.8%)	2863 (6.5%)	8006 (4.4%)	123130 (2.9%)
How many days during the past 30 days was your physical health not good?
0	2316184 (67.1%)	157275 (73.4%)	24611 (61.0%)	135250 (69.4%)	3864 (85.3%)	7870 (70.0%)	26134 (61.2%)	137350 (77.0%)	2808539 (67.8%)
1	705480 (20.4%)	26773 (12.5%)	6268 (15.5%)	40332 (20.7%)	278 (6.1%)	2482 (22.1%)	10892 (25.5%)	20228 (11.3%)	812733 (19.6%)
2	112996 (3.3%)	10435 (4.9%)	2046 (5.1%)	7005 (3.6%)	0 (0.0%)	74 (0.7%)	874 (2.0%)	3391 (1.9%)	136822 (3.3%)
3	110759 (3.2%)	7825 (3.7%)	3826 (9.5%)	5151 (2.6%)	389 (8.6%)	31 (0.3%)	1382 (3.2%)	6505 (3.6%)	135869 (3.3%)
4	29828 (0.9%)	2098 (1.0%)	752 (1.9%)	2381 (1.2%)	0 (0.0%)	0 (0.0%)	340 (0.8%)	1353 (0.8%)	36751 (0.9%)
5	177793 (5.1%)	9934 (4.6%)	2848 (7.1%)	4876 (2.5%)	0 (0.0%)	790 (7.0%)	3092 (7.2%)	9517 (5.3%)	208850 (5.0%)
Missing	48764	1852	856	3666	0	0	1340	4803	61282
How many days during the past 30 days was your mental health not good?
0	2337776 (67.7%)	145976 (68.2%)	25447 (61.8%)	134536 (69.4%)	2747 (60.6%)	7433 (66.1%)	23665 (54.4%)	128874 (71.6%)	2806454 (67.7%)
1	666528 (19.3%)	26256 (12.3%)	6182 (15.0%)	36830 (19.0%)	1040 (22.9%)	2244 (20.0%)	7837 (18.0%)	21344 (11.9%)	768261 (18.5%)
2	154482 (4.5%)	12900 (6.0%)	1265 (3.1%)	9749 (5.0%)	77 (1.7%)	773 (6.9%)	2594 (6.0%)	10305 (5.7%)	192146 (4.6%)
3	152568 (4.4%)	11891 (5.6%)	5401 (13.1%)	6670 (3.4%)	667 (14.7%)	113 (1.0%)	3872 (8.9%)	8016 (4.5%)	189198 (4.6%)
4	26714 (0.8%)	3243 (1.5%)	945 (2.3%)	4187 (2.2%)	0 (0.0%)	0 (0.0%)	660 (1.5%)	1404 (0.8%)	37153 (0.9%)
5	116866 (3.4%)	13695 (6.4%)	1926 (4.7%)	1837 (0.9%)	0 (0.0%)	685 (6.1%)	4845 (11.1%)	9953 (5.5%)	149807 (3.6%)
Missing	46869	2232	41	4852	0	0	581	3250	57825
Poor health days
0	1034161 (60.2%)	43728 (48.3%)	13131 (53.6%)	43701 (50.3%)	568 (31.8%)	3944 (64.4%)	14072 (57.7%)	39641 (55.4%)	1192945 (58.9%)
1	398138 (23.2%)	21603 (23.9%)	5214 (21.3%)	23768 (27.4%)	1217 (68.2%)	1062 (17.3%)	5176 (21.2%)	16226 (22.7%)	472404 (23.3%)
2	83986 (4.9%)	7650 (8.4%)	2377 (9.7%)	7705 (8.9%)	0 (0.0%)	38 (0.6%)	1437 (5.9%)	5226 (7.3%)	108419 (5.4%)
3	96104 (5.6%)	7954 (8.8%)	1690 (6.9%)	10574 (12.2%)	0 (0.0%)	681 (11.1%)	1518 (6.2%)	5222 (7.3%)	123743 (6.1%)
4	18279 (1.1%)	976 (1.1%)	975 (4.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	712 (2.9%)	454 (0.6%)	21396 (1.1%)
5	87180 (5.1%)	8658 (9.6%)	1119 (4.6%)	1122 (1.3%)	0 (0.0%)	400 (6.5%)	1468 (6.0%)	4822 (6.7%)	104767 (5.2%)
Missing	1783956	125624	16702	111790	2747	5123	19671	111558	2177169
Do you smoke cigarettes every day, some days, or not at all?
Missing	2050881	162422	16938	168026	3097	6068	26072	125699	2559203
Every day	329568 (22.7%)	23436 (43.6%)	8987 (37.0%)	9275 (30.3%)	135 (9.4%)	2482 (47.9%)	6335 (35.2%)	13835 (24.1%)	394053 (24.0%)
Some days	143534 (9.9%)	14988 (27.9%)	5593 (23.0%)	6320 (20.6%)	0 (0.0%)	0 (0.0%)	1677 (9.3%)	13391 (23.3%)	185502 (11.3%)
Not at all	977820 (67.4%)	15347 (28.5%)	9689 (39.9%)	15040 (49.1%)	1300 (90.6%)	2698 (52.1%)	9970 (55.4%)	30223 (52.6%)	1062087 (64.7%)
Do you use vaping products every day, some days, or not at all?
Missing	2891422	180511	30107	165823	3574	8366	30303	153899	3464005
Every day	44960 (7.4%)	1231 (3.4%)	643 (5.8%)	2381 (7.2%)	0 (0.0%)	0 (0.0%)	381 (2.8%)	1101 (3.8%)	50696 (6.9%)
Some days	76843 (12.6%)	6301 (17.7%)	2620 (23.6%)	4891 (14.9%)	0 (0.0%)	0 (0.0%)	955 (6.9%)	4312 (14.7%)	95922 (13.0%)
Not at all	488579 (80.0%)	28151 (78.9%)	7838 (70.6%)	25566 (77.9%)	958 (100.0%)	2882 (100.0%)	12414 (90.3%)	23835 (81.5%)	590222 (80.1%)
(Ever told) you had a stroke.
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	6189	0	562	644	0	140	0	1267	8801
(Ever told) you had angina or coronary heart disease?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	20810	212	191	2930	0	140	118	2161	26562
(Ever told) you have diabetes
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	2915	212	478	0	0	349	0	210	4165
(Ever told) you that you have a depressive disorder?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 1)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 1)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	12463	834	100	2215	0	401	453	1486	17952
(Ever told) you have some form of arthritis?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 1)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	12485	436	509	1635	0	494	118	1603	17280
(Ever told) you have COPD, emphysema or chronic bronchitis?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	10938	0	342	2971	0	719	245	860	16075
Are you deaf or do you have serious difficulty hearing?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	69348	10161	2912	10096	821	423	1753	5368	100882
Are you blind or do you have serious difficulty seeing, even when wearing glasses?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	74961	10488	3156	11550	821	423	1753	5581	108732
Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1	0 - 1
Missing	83761	12953	3309	10462	821	423	2266	6903	120897
Do you have serious difficulty walking or climbing stairs?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	81916	16366	2807	12104	821	686	1753	6999	123450
Do you have difficulty dressing or bathing?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	83816	13803	2883	12788	821	686	1753	7836	124385
Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone?
Mean (SD)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Median (Q1, Q3)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)	0 (0, 0)
Min - Max	0 - 1	0 - 1	0 - 1	0 - 1	0 - 0	0 - 1	0 - 1	0 - 1	0 - 1
Missing	93344	15097	2940	14028	821	686	2207	7439	136562
X_LLCPWT
Mean (SD)	383 (276)	660 (369)	375 (332)	905 (520)	465 (223)	365 (216)	421 (283)	429 (253)	425 (321)
Median (Q1, Q3)	313 (194, 492)	556 (385, 889)	268 (163, 475)	753 (533, 1134)	500 (287, 651)	342 (182, 526)	343 (220, 531)	377 (231, 552)	340 (208, 538)
Min - Max	2 - 2296	4 - 1857	4 - 1434	4 - 2589	70 - 770	3 - 765	3 - 1315	4 - 1256	2 - 2589
Missing	0	0	0	0	0	0	0	0	0

Weighted tables

SLEPTIM1-

On average, how many hours of sleep do you get per night?
- Less than 7 hours
- 7-9 hours
- Greater than 9 hours

stargazer(data.frame(prop.table(svytable(~SLEPTIM1+X_RACE,design= des),
                      margin=2)), summary = FALSE,
          title="Descriptive Statistics",
          type = "html", digits= 3)

**Descriptive Statistics**

	SLEPTIM1	X_RACE	Freq

1	Less than 7 hours	White only, non-Hispanic	0.285
2	7-9 hours	White only, non-Hispanic	0.692
3	Greater than 9 hours	White only, non-Hispanic	0.023
4	Less than 7 hours	Black only, non-Hispanic	0.436
5	7-9 hours	Black only, non-Hispanic	0.532
6	Greater than 9 hours	Black only, non-Hispanic	0.032
7	Less than 7 hours	American Indian or Alaskan Native Only, non-Hispanic	0.310
8	7-9 hours	American Indian or Alaskan Native Only, non-Hispanic	0.602
9	Greater than 9 hours	American Indian or Alaskan Native Only, non-Hispanic	0.088
10	Less than 7 hours	Asian only, non-Hispanic	0.294
11	7-9 hours	Asian only, non-Hispanic	0.669
12	Greater than 9 hours	Asian only, non-Hispanic	0.037
13	Less than 7 hours	Native Hawaiian or other Pacific Islander only, non-Hispanic	0.239
14	7-9 hours	Native Hawaiian or other Pacific Islander only, non-Hispanic	0.761
15	Greater than 9 hours	Native Hawaiian or other Pacific Islander only, non-Hispanic	0
16	Less than 7 hours	Other race only, non-Hispanic	0.474
17	7-9 hours	Other race only, non-Hispanic	0.521
18	Greater than 9 hours	Other race only, non-Hispanic	0.005
19	Less than 7 hours	Multiracial, non-Hispanic	0.411
20	7-9 hours	Multiracial, non-Hispanic	0.542
21	Greater than 9 hours	Multiracial, non-Hispanic	0.047
22	Less than 7 hours	Hispanic	0.266
23	7-9 hours	Hispanic	0.665
24	Greater than 9 hours	Hispanic	0.069

stargazer(svyby(formula= ~SLEPTIM1, by = ~X_RACE, 
                design=des,FUN =svymean,na.rm=T),
          title="Descriptive Statistics",
          summary = F, type="html",digits = 2)

**Descriptive Statistics**

	X_RACE	SLEPTIM1Less than 7 hours	SLEPTIM17-9 hours	SLEPTIM1Greater than 9 hours	se.SLEPTIM1Less than 7 hours	se.SLEPTIM17-9 hours	se.SLEPTIM1Greater than 9 hours

White only, non-Hispanic	White only, non-Hispanic	0.29	0.69	0.02	0.01	0.01	0.002
Black only, non-Hispanic	Black only, non-Hispanic	0.44	0.53	0.03	0.03	0.03	0.01
American Indian or Alaskan Native Only, non-Hispanic	American Indian or Alaskan Native Only, non-Hispanic	0.31	0.60	0.09	0.05	0.06	0.05
Asian only, non-Hispanic	Asian only, non-Hispanic	0.29	0.67	0.04	0.04	0.04	0.02
Native Hawaiian or other Pacific Islander only, non-Hispanic	Native Hawaiian or other Pacific Islander only, non-Hispanic	0.24	0.76	0	0.18	0.18	0
Other race only, non-Hispanic	Other race only, non-Hispanic	0.47	0.52	0.005	0.11	0.10	0.005
Multiracial, non-Hispanic	Multiracial, non-Hispanic	0.41	0.54	0.05	0.05	0.05	0.02
Hispanic	Hispanic	0.27	0.67	0.07	0.02	0.02	0.01

ADSLEEP

Over the last 2 weeks, how many days have you had trouble falling asleep or sleeping too much??
- 0- 6 days
- 7 or greater

stargazer(data.frame(prop.table(svytable(~ADSLEEP_B+X_RACE,design= des),
                      margin=2)),title="Descriptive Statistics",
                      summary = FALSE,type = "html", digits= 3)

**Descriptive Statistics**

	ADSLEEP_B	X_RACE	Freq

1	0	White only, non-Hispanic	0.182
2	1	White only, non-Hispanic	0.818
3	0	Black only, non-Hispanic	0.180
4	1	Black only, non-Hispanic	0.820
5	0	American Indian or Alaskan Native Only, non-Hispanic	0.236
6	1	American Indian or Alaskan Native Only, non-Hispanic	0.764
7	0	Asian only, non-Hispanic	0.102
8	1	Asian only, non-Hispanic	0.898
9	0	Native Hawaiian or other Pacific Islander only, non-Hispanic	0.451
10	1	Native Hawaiian or other Pacific Islander only, non-Hispanic	0.549
11	0	Other race only, non-Hispanic	0.303
12	1	Other race only, non-Hispanic	0.697
13	0	Multiracial, non-Hispanic	0.259
14	1	Multiracial, non-Hispanic	0.741
15	0	Hispanic	0.159
16	1	Hispanic	0.841

stargazer(svyby(formula= ~ADSLEEP_B, by = ~X_RACE, 
                design=des,FUN =svymean,na.rm=T),
         title="Descriptive Statistics", 
         summary = F, type="html",digits = 2)

**Descriptive Statistics**

	X_RACE	ADSLEEP_B	se

White only, non-Hispanic	White only, non-Hispanic	0.82	0.004
Black only, non-Hispanic	Black only, non-Hispanic	0.82	0.02
American Indian or Alaskan Native Only, non-Hispanic	American Indian or Alaskan Native Only, non-Hispanic	0.76	0.04
Asian only, non-Hispanic	Asian only, non-Hispanic	0.90	0.02
Native Hawaiian or other Pacific Islander only, non-Hispanic	Native Hawaiian or other Pacific Islander only, non-Hispanic	0.55	0.21
Other race only, non-Hispanic	Other race only, non-Hispanic	0.70	0.11
Multiracial, non-Hispanic	Multiracial, non-Hispanic	0.74	0.05
Hispanic	Hispanic	0.84	0.02

Regression

All

ADSLEEP- Difficulty sleeping past 14 days

#Function to save odds ratio from each variable
or_fun <- function(y) {
or_table <- exp(cbind(OR = coef(or), confint(or)))[2, ]
or_table$hv <- c(y)
unlist(or_table)
as.data.frame(or_table)

}

#Binomial logistic regression for each variable
or<-svyglm(ADSLEEP~SEX, family=quasibinomial,design=svydes)
#OR saved for each variable
a <- or_fun("SEX")

#Repeat
or <- svyglm(ADSLEEP~ MARITAL, family = quasibinomial, design = svydes)
b <- or_fun("MARITAL")

or <- svyglm(ADSLEEP~ EDUCA, family = quasibinomial, design = svydes)
c <- or_fun("EDUCA")

or <- svyglm(ADSLEEP~ EMPLOY1, family = quasibinomial, design = svydes)
d <- or_fun("EMPLOY1")

or <- svyglm(ADSLEEP~ INCOME2, family = quasibinomial, design = svydes)
e <- or_fun("INCOME2")

or <- svyglm(ADSLEEP~ X_BMI5CAT, family = quasibinomial, design = svydes)
f <- or_fun("X_BMI5CAT")

or <- svyglm(ADSLEEP~ X_RACE, family = quasibinomial, design = svydes)
g <- or_fun("X_RACE")

or <- svyglm(ADSLEEP~ X_AGEG5YR, family = quasibinomial, design = svydes)
h <- or_fun("X_AGEG5YR")

or <- svyglm(ADSLEEP~ CHILDREN, family = quasibinomial, design = svydes)
i <- or_fun("CHILDREN")

or <- svyglm(ADSLEEP~ SDHBILLS, family = quasibinomial, design = svydes)
j <- or_fun("SDHBILLS")

or <- svyglm(ADSLEEP~ HOWSAFE1, family = quasibinomial, design = svydes)
k <- or_fun("HOWSAFE1")

or <- svyglm(ADSLEEP~ SDHFOOD, family = quasibinomial, design = svydes)
l <- or_fun("SDHFOOD")

or <- svyglm(ADSLEEP~ SDHMEALS, family = quasibinomial, design = svydes)
m <- or_fun("SDHMEALS")

or <- svyglm(ADSLEEP~ SDHMONEY, family = quasibinomial, design = svydes)
n <- or_fun("SDHMONEY")

or <- svyglm(ADSLEEP~ SDHSTRES, family = quasibinomial, design = svydes)
o <- or_fun("SDHSTRES")

or <- svyglm(ADSLEEP~ GENHLTH, family = quasibinomial, design = svydes)
p <- or_fun("GENHLTH")

or <- svyglm(ADSLEEP~ PHYSHLTH, family = quasibinomial, design = svydes)
q <- or_fun("PHYSHLTH")

or <- svyglm(ADSLEEP~ MENTHLTH, family = quasibinomial, design = svydes)
r <- or_fun("MENTHLTH")

or <- svyglm(ADSLEEP~ POORHLTH, family = quasibinomial, design = svydes)
s <- or_fun("POORHLTH")

or <- svyglm(ADSLEEP~ SMOKDAY2, family = quasibinomial, design = svydes)
t <- or_fun("SMOKDAY2")

or <- svyglm(ADSLEEP~ ECIGNOW, family = quasibinomial, design = svydes)
u <- or_fun("ECIGNOW")

or <- svyglm(ADSLEEP~ CVDSTRK3, family = quasibinomial, design = svydes)
v <- or_fun("CVDSTRK3")

or <- svyglm(ADSLEEP~ CVDCRHD4, family = quasibinomial, design = svydes)
w <- or_fun("CVDCRHD4")

or <- svyglm(ADSLEEP~ DIABETE3, family = quasibinomial, design = svydes)
x <- or_fun("DIABETE3")

or <- svyglm(ADSLEEP~ ADDEPEV2, family = quasibinomial, design = svydes)
y <- or_fun("ADDEPEV2")

or <- svyglm(ADSLEEP~ HAVARTH3, family = quasibinomial, design = svydes)
z <- or_fun("HAVARTH3")

or <- svyglm(ADSLEEP~ CHCCOPD1, family = quasibinomial, design = svydes)
aa <- or_fun("CHCCOPD1")

or <- svyglm(ADSLEEP~ DEAF, family = quasibinomial, design = svydes)
bb <- or_fun("DEAF")

or <- svyglm(ADSLEEP~ BLIND, family = quasibinomial, design = svydes)
cc <- or_fun("BLIND")

or <- svyglm(ADSLEEP~ DECIDE, family = quasibinomial, design = svydes)
dd <- or_fun("DECIDE")

or <- svyglm(ADSLEEP~ DIFFWALK, family = quasibinomial, design = svydes)
ee <- or_fun("DIFFWALK")

or <- svyglm(ADSLEEP~ DIFFDRES, family = quasibinomial, design = svydes)
ff <- or_fun("DIFFDRES")

or <- svyglm(ADSLEEP~ DIFFALON, family = quasibinomial, design = svydes)
gg <- or_fun("DIFFALON")


# Combine OR into 1 table
all_or <- bind_rows(a, b, c, d, e, f, g, h, i, j, k, l, m,n,o,p,
                    q,r,s,t,u,v,w,x,y,z,aa,bb,cc,dd,ee,ff,gg)
all_or <- as.data.frame(all_or)


# Run all variables in logistic regression for adjusted OR
alr <- svyglm(ADSLEEP ~ SEX + MARITAL + EDUCA + EMPLOY1 + INCOME2 + X_BMI5CAT
              + X_RACE + X_AGEG5YR + CHILDREN  + SDHBILLS + HOWSAFE1 + SDHFOOD + 
                SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + MENTHLTH + 
                POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 +  CVDCRHD4 + DIABETE3 + 
                ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + DECIDE + 
                DIFFWALK + DIFFDRES + DIFFALON, family = quasibinomial, 
              design = svydes)

summary(alr)

## 
## Call:
## svyglm(formula = ADSLEEP ~ SEX + MARITAL + EDUCA + EMPLOY1 + 
##     INCOME2 + X_BMI5CAT + X_RACE + X_AGEG5YR + CHILDREN + SDHBILLS + 
##     HOWSAFE1 + SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + 
##     PHYSHLTH + MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 + 
##     CVDCRHD4 + DIABETE3 + ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + 
##     BLIND + DECIDE + DIFFWALK + DIFFDRES + DIFFALON, design = svydes, 
##     family = quasibinomial)
## 
## Survey design:
## svydesign(id = ~X_PSU, strata = ~X_STSTR, weights = ~X_LLCPWT, 
##     data = brfss2017B, nest = T)
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.22876    0.20263   6.064 1.40e-09 ***
## SEXref        0.23258    0.08048   2.890 0.003866 ** 
## MARITALref   -0.01702    0.08055  -0.211 0.832637    
## EDUCAref     -0.11168    0.08792  -1.270 0.204042    
## EMPLOY1ref   -0.09371    0.08488  -1.104 0.269623    
## INCOME2ref    0.05052    0.11069   0.456 0.648123    
## X_BMI5CATref  0.21380    0.08921   2.397 0.016576 *  
## X_RACEref    -0.26406    0.12051  -2.191 0.028470 *  
## X_AGEG5YRref -0.05044    0.12524  -0.403 0.687161    
## CHILDRENref  -0.12839    0.09052  -1.418 0.156107    
## SDHBILLSref  -0.33833    0.14633  -2.312 0.020804 *  
## HOWSAFE1ref   0.07706    0.19780   0.390 0.696860    
## SDHFOODref   -0.04931    0.14162  -0.348 0.727680    
## SDHMEALSref  -0.22102    0.12372  -1.787 0.074061 .  
## SDHMONEYref   0.03728    0.08751   0.426 0.670134    
## SDHSTRESref  -1.19604    0.11196 -10.682  < 2e-16 ***
## GENHLTHref   -0.37895    0.10304  -3.678 0.000237 ***
## PHYSHLTHref   0.41555    0.10389   4.000 6.40e-05 ***
## MENTHLTHref   0.32346    0.10505   3.079 0.002085 ** 
## POORHLTHref   0.16705    0.12399   1.347 0.177940    
## SMOKDAY2ref  -0.11691    0.11106  -1.053 0.292546    
## ECIGNOWref   -0.04889    0.19157  -0.255 0.798591    
## CVDSTRK3ref   0.21287    0.20329   1.047 0.295079    
## CVDCRHD4ref   0.20579    0.16232   1.268 0.204908    
## DIABETE3ref  -0.02922    0.11977  -0.244 0.807242    
## ADDEPEV2ref  -0.40113    0.08886  -4.514 6.47e-06 ***
## HAVARTH3ref  -0.21351    0.08566  -2.492 0.012709 *  
## CHCCOPD1ref  -0.01844    0.14730  -0.125 0.900364    
## DEAFref      -0.11244    0.13053  -0.861 0.389074    
## BLINDref     -0.13190    0.17407  -0.758 0.448621    
## DECIDEref    -0.42124    0.11336  -3.716 0.000204 ***
## DIFFWALKref  -0.07368    0.11632  -0.633 0.526469    
## DIFFDRESref   0.02556    0.18117   0.141 0.887824    
## DIFFALONref  -0.18098    0.14857  -1.218 0.223232    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 1.008297)
## 
## Number of Fisher Scoring iterations: 4

confint(alr)

##                    2.5 %      97.5 %
## (Intercept)   0.83162547  1.62590232
## SEXref        0.07483836  0.39032102
## MARITALref   -0.17489396  0.14084959
## EDUCAref     -0.28399424  0.06063963
## EMPLOY1ref   -0.26008293  0.07265610
## INCOME2ref   -0.16642450  0.26745653
## X_BMI5CATref  0.03894897  0.38864900
## X_RACEref    -0.50025534 -0.02786605
## X_AGEG5YRref -0.29590750  0.19503090
## CHILDRENref  -0.30579702  0.04901603
## SDHBILLSref  -0.62514070 -0.05152191
## HOWSAFE1ref  -0.31062481  0.46474321
## SDHFOODref   -0.32687704  0.22824799
## SDHMEALSref  -0.46350179  0.02146010
## SDHMONEYref  -0.13424074  0.20879734
## SDHSTRESref  -1.41548215 -0.97659071
## GENHLTHref   -0.58089593 -0.17699937
## PHYSHLTHref   0.21193280  0.61915824
## MENTHLTHref   0.11755960  0.52935755
## POORHLTHref  -0.07597198  0.41007866
## SMOKDAY2ref  -0.33458955  0.10077158
## ECIGNOWref   -0.42436541  0.32659313
## CVDSTRK3ref  -0.18557174  0.61131249
## CVDCRHD4ref  -0.11234688  0.52391735
## DIABETE3ref  -0.26397792  0.20552911
## ADDEPEV2ref  -0.57529862 -0.22696248
## HAVARTH3ref  -0.38139947 -0.04561521
## CHCCOPD1ref  -0.30714755  0.27026162
## DEAFref      -0.36828013  0.14340719
## BLINDref     -0.47307946  0.20927028
## DECIDEref    -0.64342757 -0.19905690
## DIFFWALKref  -0.30167731  0.15430820
## DIFFDRESref  -0.32953171  0.38064459
## DIFFALONref  -0.47217812  0.11022394

exp(coef(alr))

##  (Intercept)       SEXref   MARITALref     EDUCAref   EMPLOY1ref   INCOME2ref 
##    3.4170032    1.2618510    0.9831219    0.8943328    0.9105437    1.0518137 
## X_BMI5CATref    X_RACEref X_AGEG5YRref  CHILDRENref  SDHBILLSref  HOWSAFE1ref 
##    1.2383737    0.7679269    0.9508126    0.8795099    0.7129590    1.0801060 
##   SDHFOODref  SDHMEALSref  SDHMONEYref  SDHSTRESref   GENHLTHref  PHYSHLTHref 
##    0.9518817    0.8017000    1.0379819    0.3023904    0.6845815    1.5151971 
##  MENTHLTHref  POORHLTHref  SMOKDAY2ref   ECIGNOWref  CVDSTRK3ref  CVDCRHD4ref 
##    1.3818989    1.1818173    0.8896662    0.9522896    1.2372243    1.2284893 
##  DIABETE3ref  ADDEPEV2ref  HAVARTH3ref  CHCCOPD1ref      DEAFref     BLINDref 
##    0.9711985    0.6695626    0.8077462    0.9817261    0.8936541    0.8764246 
##    DECIDEref  DIFFWALKref  DIFFDRESref  DIFFALONref 
##    0.6562311    0.9289647    1.0258858    0.8344545

exp(cbind(AOR = coef(alr), confint(alr)))

##                    AOR     2.5 %    97.5 %
## (Intercept)  3.4170032 2.2970495 5.0830035
## SEXref       1.2618510 1.0777099 1.4774550
## MARITALref   0.9831219 0.8395460 1.1512515
## EDUCAref     0.8943328 0.7527710 1.0625159
## EMPLOY1ref   0.9105437 0.7709876 1.0753607
## INCOME2ref   1.0518137 0.8466867 1.3066368
## X_BMI5CATref 1.2383737 1.0397174 1.4749867
## X_RACEref    0.7679269 0.6063758 0.9725186
## X_AGEG5YRref 0.9508126 0.7438562 1.2153485
## CHILDRENref  0.8795099 0.7365361 1.0502372
## SDHBILLSref  0.7129590 0.5351861 0.9497828
## HOWSAFE1ref  1.0801060 0.7329888 1.5916054
## SDHFOODref   0.9518817 0.7211724 1.2563969
## SDHMEALSref  0.8017000 0.6290769 1.0216920
## SDHMONEYref  1.0379819 0.8743795 1.2321953
## SDHSTRESref  0.3023904 0.2428085 0.3765928
## GENHLTHref   0.6845815 0.5593970 0.8377803
## PHYSHLTHref  1.5151971 1.2360648 1.8573639
## MENTHLTHref  1.3818989 1.1247487 1.6978412
## POORHLTHref  1.1818173 0.9268422 1.5069363
## SMOKDAY2ref  0.8896662 0.7156318 1.1060240
## ECIGNOWref   0.9522896 0.6541848 1.3862373
## CVDSTRK3ref  1.2372243 0.8306292 1.8428485
## CVDCRHD4ref  1.2284893 0.8937342 1.6886297
## DIABETE3ref  0.9711985 0.7679905 1.2281747
## ADDEPEV2ref  0.6695626 0.5625369 0.7969507
## HAVARTH3ref  0.8077462 0.6829050 0.9554095
## CHCCOPD1ref  0.9817261 0.7355421 1.3103072
## DEAFref      0.8936541 0.6919233 1.1541997
## BLINDref     0.8764246 0.6230806 1.2327781
## DECIDEref    0.6562311 0.5254882 0.8195033
## DIFFWALKref  0.9289647 0.7395767 1.1668505
## DIFFDRESref  1.0258858 0.7192605 1.4632275
## DIFFALONref  0.8344545 0.6236424 1.1165281

exp(cbind(AOR = coef(alr), confint(alr)))[-1, ]

##                    AOR     2.5 %    97.5 %
## SEXref       1.2618510 1.0777099 1.4774550
## MARITALref   0.9831219 0.8395460 1.1512515
## EDUCAref     0.8943328 0.7527710 1.0625159
## EMPLOY1ref   0.9105437 0.7709876 1.0753607
## INCOME2ref   1.0518137 0.8466867 1.3066368
## X_BMI5CATref 1.2383737 1.0397174 1.4749867
## X_RACEref    0.7679269 0.6063758 0.9725186
## X_AGEG5YRref 0.9508126 0.7438562 1.2153485
## CHILDRENref  0.8795099 0.7365361 1.0502372
## SDHBILLSref  0.7129590 0.5351861 0.9497828
## HOWSAFE1ref  1.0801060 0.7329888 1.5916054
## SDHFOODref   0.9518817 0.7211724 1.2563969
## SDHMEALSref  0.8017000 0.6290769 1.0216920
## SDHMONEYref  1.0379819 0.8743795 1.2321953
## SDHSTRESref  0.3023904 0.2428085 0.3765928
## GENHLTHref   0.6845815 0.5593970 0.8377803
## PHYSHLTHref  1.5151971 1.2360648 1.8573639
## MENTHLTHref  1.3818989 1.1247487 1.6978412
## POORHLTHref  1.1818173 0.9268422 1.5069363
## SMOKDAY2ref  0.8896662 0.7156318 1.1060240
## ECIGNOWref   0.9522896 0.6541848 1.3862373
## CVDSTRK3ref  1.2372243 0.8306292 1.8428485
## CVDCRHD4ref  1.2284893 0.8937342 1.6886297
## DIABETE3ref  0.9711985 0.7679905 1.2281747
## ADDEPEV2ref  0.6695626 0.5625369 0.7969507
## HAVARTH3ref  0.8077462 0.6829050 0.9554095
## CHCCOPD1ref  0.9817261 0.7355421 1.3103072
## DEAFref      0.8936541 0.6919233 1.1541997
## BLINDref     0.8764246 0.6230806 1.2327781
## DECIDEref    0.6562311 0.5254882 0.8195033
## DIFFWALKref  0.9289647 0.7395767 1.1668505
## DIFFDRESref  1.0258858 0.7192605 1.4632275
## DIFFALONref  0.8344545 0.6236424 1.1165281

adj_or <- exp(cbind(AOR = coef(alr), confint(alr)))[-1,]
adj_or <- as.data.frame(adj_or)

# Combine All OR with Adjusted OR
stargazer(bind_cols(all_or, adj_or),summary=F,type = "html", digits=2)


	OR	X2.5..	X97.5..	hv	AOR	2.5 %	97.5 %

SEXref	1.51	1.36	1.69	SEX	1.26	1.08	1.48
MARITALref	1.53	1.37	1.70	MARITAL	0.98	0.84	1.15
EDUCAref	0.89	0.79	1.00	EDUCA	0.89	0.75	1.06
EMPLOY1ref	1.26	1.13	1.40	EMPLOY1	0.91	0.77	1.08
INCOME2ref	0.97	0.83	1.14	INCOME2	1.05	0.85	1.31
X_BMI5CATref	1.27	1.12	1.43	X_BMI5CAT	1.24	1.04	1.47
X_RACEref	1.00	0.85	1.17	X_RACE	0.77	0.61	0.97
X_AGEG5YRref	0.93	0.79	1.10	X_AGEG5YR	0.95	0.74	1.22
CHILDRENref	0.85	0.75	0.96	CHILDREN	0.88	0.74	1.05
SDHBILLSref	0.29	0.24	0.34	SDHBILLS	0.71	0.54	0.95
HOWSAFE1ref	0.40	0.31	0.52	HOWSAFE1	1.08	0.73	1.59
SDHFOODref	0.36	0.31	0.42	SDHFOOD	0.95	0.72	1.26
SDHMEALSref	0.39	0.34	0.45	SDHMEALS	0.80	0.63	1.02
SDHMONEYref	0.55	0.49	0.61	SDHMONEY	1.04	0.87	1.23
SDHSTRESref	0.13	0.11	0.16	SDHSTRES	0.30	0.24	0.38
GENHLTHref	0.29	0.25	0.33	GENHLTH	0.68	0.56	0.84
PHYSHLTHref	3.89	3.40	4.45	PHYSHLTH	1.52	1.24	1.86
MENTHLTHref	4.82	4.15	5.60	MENTHLTH	1.38	1.12	1.70
POORHLTHref	3.48	2.92	4.14	POORHLTH	1.18	0.93	1.51
SMOKDAY2ref	0.51	0.44	0.58	SMOKDAY2	0.89	0.72	1.11
ECIGNOWref	0.57	0.43	0.76	ECIGNOW	0.95	0.65	1.39
CVDSTRK3ref	0.57	0.43	0.75	CVDSTRK3	1.24	0.83	1.84
CVDCRHD4ref	0.68	0.55	0.84	CVDCRHD4	1.23	0.89	1.69
DIABETE3ref	0.73	0.62	0.85	DIABETE3	0.97	0.77	1.23
ADDEPEV2ref	0.29	0.25	0.32	ADDEPEV2	0.67	0.56	0.80
HAVARTH3ref	0.50	0.44	0.55	HAVARTH3	0.81	0.68	0.96
CHCCOPD1ref	0.38	0.31	0.46	CHCCOPD1	0.98	0.74	1.31
DEAFref	0.58	0.49	0.70	DEAF	0.89	0.69	1.15
BLINDref	0.48	0.38	0.60	BLIND	0.88	0.62	1.23
DECIDEref	0.19	0.16	0.22	DECIDE	0.66	0.53	0.82
DIFFWALKref	0.33	0.29	0.38	DIFFWALK	0.93	0.74	1.17
DIFFDRESref	0.22	0.17	0.27	DIFFDRES	1.03	0.72	1.46
DIFFALONref	0.19	0.16	0.23	DIFFALON	0.83	0.62	1.12

table1 <- bind_cols(all_or, adj_or)

table1 <- table1 %>% 
  dplyr::select("hv", "OR", "X2.5..", "X97.5..", "AOR", "2.5 %", "97.5 %")


table1$orll <- table1$X2.5..
table1$orul <- table1$X97.5..
table1$aorll <- table1$"2.5 %"
table1$aorul <- table1$"97.5 %"
table1 <- table1 %>% dplyr::select(hv, OR, orll, orul, AOR, aorll, aorul)


#Plot OR
ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) +
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=orll, ymax=orul), 
                width = .2, position = position_dodge(.9))

#Plot AOR
ggplot(table1, aes(x=hv, y = AOR)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=aorll, ymax=aorul), 
                width = .2, position = position_dodge(.9))

ORtable <- table1 %>% dplyr::select(hv, OR, orll, orul)
ORtable$type <- c("OR")
ORtable$stat <- ORtable$OR
ORtable$ll <- ORtable$orll
ORtable$ul <- ORtable$orul
ORtable <- ORtable %>% dplyr::select(hv, stat, type, ll, ul)

AORtable <- table1 %>% dplyr::select(hv, AOR, aorll, aorul)
AORtable$type <- c("AOR")
AORtable$stat <- AORtable$AOR
AORtable$ll <- AORtable$aorll
AORtable$ul <- AORtable$aorul
AORtable <- AORtable %>% dplyr::select(hv, stat, type, ll, ul)

table2 <- rbind(ORtable, AORtable)

figureoraor <- ggplot(table2, aes(x=hv, y = stat, fill = type)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=ll, ymax=ul), 
                width = .2, position = position_dodge(.9))

#Plot OR compared to Adjusted OR
print(figureoraor + ggtitle("OR & AOR"))

SLEPTIM1- Hours per sleep at night

#Binomial logistic regression for each variable
or<-svyglm(SLEPTIM1~SEX, family=quasibinomial,design=svydes)
#OR saved for each variable
a <- or_fun("SEX")

#Repeat
or <- svyglm(SLEPTIM1~ MARITAL, family = quasibinomial, design = svydes)
b <- or_fun("MARITAL")

or <- svyglm(SLEPTIM1~ EDUCA, family = quasibinomial, design = svydes)
c <- or_fun("EDUCA")

or <- svyglm(SLEPTIM1~ EMPLOY1, family = quasibinomial, design = svydes)
d <- or_fun("EMPLOY1")

or <- svyglm(SLEPTIM1~ INCOME2, family = quasibinomial, design = svydes)
e <- or_fun("INCOME2")

or <- svyglm(SLEPTIM1~ X_BMI5CAT, family = quasibinomial, design = svydes)
f <- or_fun("X_BMI5CAT")

or <- svyglm(SLEPTIM1~ X_RACE, family = quasibinomial, design = svydes)
g <- or_fun("X_RACE")

or <- svyglm(SLEPTIM1~ X_AGEG5YR, family = quasibinomial, design = svydes)
h <- or_fun("X_AGEG5YR")

or <- svyglm(SLEPTIM1~ CHILDREN, family = quasibinomial, design = svydes)
i <- or_fun("CHILDREN")

or <- svyglm(SLEPTIM1~ SDHBILLS, family = quasibinomial, design = svydes)
j <- or_fun("SDHBILLS")

or <- svyglm(SLEPTIM1~ HOWSAFE1, family = quasibinomial, design = svydes)
k <- or_fun("HOWSAFE1")

or <- svyglm(SLEPTIM1~ SDHFOOD, family = quasibinomial, design = svydes)
l <- or_fun("SDHFOOD")

or <- svyglm(SLEPTIM1~ SDHMEALS, family = quasibinomial, design = svydes)
m <- or_fun("SDHMEALS")

or <- svyglm(SLEPTIM1~ SDHMONEY, family = quasibinomial, design = svydes)
n <- or_fun("SDHMONEY")

or <- svyglm(SLEPTIM1~ SDHSTRES, family = quasibinomial, design = svydes)
o <- or_fun("SDHSTRES")

or <- svyglm(SLEPTIM1~ GENHLTH, family = quasibinomial, design = svydes)
p <- or_fun("GENHLTH")

or <- svyglm(SLEPTIM1~ PHYSHLTH, family = quasibinomial, design = svydes)
q <- or_fun("PHYSHLTH")

or <- svyglm(SLEPTIM1~ MENTHLTH, family = quasibinomial, design = svydes)
r <- or_fun("MENTHLTH")

or <- svyglm(SLEPTIM1~ POORHLTH, family = quasibinomial, design = svydes)
s <- or_fun("POORHLTH")

or <- svyglm(SLEPTIM1~ SMOKDAY2, family = quasibinomial, design = svydes)
t <- or_fun("SMOKDAY2")

or <- svyglm(SLEPTIM1~ ECIGNOW, family = quasibinomial, design = svydes)
u <- or_fun("ECIGNOW")

or <- svyglm(SLEPTIM1~ CVDSTRK3, family = quasibinomial, design = svydes)
v <- or_fun("CVDSTRK3")

or <- svyglm(SLEPTIM1~ CVDCRHD4, family = quasibinomial, design = svydes)
w <- or_fun("CVDCRHD4")

or <- svyglm(SLEPTIM1~ DIABETE3, family = quasibinomial, design = svydes)
x <- or_fun("DIABETE3")

or <- svyglm(SLEPTIM1~ ADDEPEV2, family = quasibinomial, design = svydes)
y <- or_fun("ADDEPEV2")

or <- svyglm(SLEPTIM1~ HAVARTH3, family = quasibinomial, design = svydes)
z <- or_fun("HAVARTH3")

or <- svyglm(SLEPTIM1~ CHCCOPD1, family = quasibinomial, design = svydes)
aa <- or_fun("CHCCOPD1")

or <- svyglm(SLEPTIM1~ DEAF, family = quasibinomial, design = svydes)
bb <- or_fun("DEAF")

or <- svyglm(SLEPTIM1~ BLIND, family = quasibinomial, design = svydes)
cc <- or_fun("BLIND")

or <- svyglm(SLEPTIM1~ DECIDE, family = quasibinomial, design = svydes)
dd <- or_fun("DECIDE")

or <- svyglm(SLEPTIM1~ DIFFWALK, family = quasibinomial, design = svydes)
ee <- or_fun("DIFFWALK")

or <- svyglm(SLEPTIM1~ DIFFDRES, family = quasibinomial, design = svydes)
ff <- or_fun("DIFFDRES")

or <- svyglm(SLEPTIM1~ DIFFALON, family = quasibinomial, design = svydes)
gg <- or_fun("DIFFALON")


# Combine OR into 1 table
all_or <- bind_rows(a, b, c, d, e, f, g, h, i, j, k, l, m,n,o,p,
                    q,r,s,t,u,v,w,x,y,z,aa,bb,cc,dd,ee,ff,gg)
all_or <- as.data.frame(all_or)


# Run all variables in logistic regression for adjusted OR
alr <- svyglm(SLEPTIM1 ~ SEX + MARITAL + EDUCA + EMPLOY1 + INCOME2 +
                X_BMI5CAT + X_RACE + X_AGEG5YR + CHILDREN  + SDHBILLS + 
                HOWSAFE1 + SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + 
                PHYSHLTH + MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW +
                CVDSTRK3 +  CVDCRHD4 + DIABETE3 + ADDEPEV2 + HAVARTH3 + 
                CHCCOPD1 + DEAF + BLIND + DECIDE + DIFFWALK + DIFFDRES +
                DIFFALON, family = quasibinomial, design = svydes)

summary(alr)

## 
## Call:
## svyglm(formula = SLEPTIM1 ~ SEX + MARITAL + EDUCA + EMPLOY1 + 
##     INCOME2 + X_BMI5CAT + X_RACE + X_AGEG5YR + CHILDREN + SDHBILLS + 
##     HOWSAFE1 + SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + 
##     PHYSHLTH + MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 + 
##     CVDCRHD4 + DIABETE3 + ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + 
##     BLIND + DECIDE + DIFFWALK + DIFFDRES + DIFFALON, design = svydes, 
##     family = quasibinomial)
## 
## Survey design:
## svydesign(id = ~X_PSU, strata = ~X_STSTR, weights = ~X_LLCPWT, 
##     data = brfss2017B, nest = T)
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.630022   0.172344  -3.656 0.000258 ***
## SEXref       -0.077728   0.063435  -1.225 0.220488    
## MARITALref    0.257943   0.063768   4.045 5.28e-05 ***
## EDUCAref     -0.168772   0.072701  -2.321 0.020287 *  
## EMPLOY1ref   -0.163115   0.067362  -2.421 0.015480 *  
## INCOME2ref   -0.114752   0.093423  -1.228 0.219369    
## X_BMI5CATref  0.079891   0.071506   1.117 0.263919    
## X_RACEref     0.404703   0.095466   4.239 2.27e-05 ***
## X_AGEG5YRref -0.317459   0.099133  -3.202 0.001369 ** 
## CHILDRENref   0.418076   0.072892   5.736 1.01e-08 ***
## SDHBILLSref  -0.131381   0.130492  -1.007 0.314056    
## HOWSAFE1ref   0.112011   0.168495   0.665 0.506216    
## SDHFOODref    0.380721   0.135520   2.809 0.004977 ** 
## SDHMEALSref  -0.084301   0.118490  -0.711 0.476821    
## SDHMONEYref   0.426318   0.072351   5.892 3.96e-09 ***
## SDHSTRESref  -0.451402   0.109257  -4.132 3.64e-05 ***
## GENHLTHref   -0.425845   0.088635  -4.804 1.58e-06 ***
## PHYSHLTHref  -0.061632   0.088960  -0.693 0.488449    
## MENTHLTHref   0.257765   0.087544   2.944 0.003245 ** 
## POORHLTHref   0.245183   0.107670   2.277 0.022802 *  
## SMOKDAY2ref  -0.373156   0.089999  -4.146 3.41e-05 ***
## ECIGNOWref    0.175950   0.160832   1.094 0.273989    
## CVDSTRK3ref   0.118320   0.164820   0.718 0.472854    
## CVDCRHD4ref  -0.054346   0.135776  -0.400 0.688971    
## DIABETE3ref  -0.108432   0.097819  -1.108 0.267680    
## ADDEPEV2ref   0.047199   0.074719   0.632 0.527612    
## HAVARTH3ref  -0.181609   0.072468  -2.506 0.012229 *  
## CHCCOPD1ref  -0.262680   0.117140  -2.242 0.024960 *  
## DEAFref      -0.005874   0.109725  -0.054 0.957311    
## BLINDref     -0.256423   0.159788  -1.605 0.108585    
## DECIDEref    -0.237306   0.103165  -2.300 0.021459 *  
## DIFFWALKref   0.206776   0.101710   2.033 0.042086 *  
## DIFFDRESref  -0.249518   0.156452  -1.595 0.110785    
## DIFFALONref  -0.224213   0.133028  -1.685 0.091939 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 1.023553)
## 
## Number of Fisher Scoring iterations: 4

confint(alr)

##                     2.5 %      97.5 %
## (Intercept)  -0.967809080 -0.29223470
## SEXref       -0.202058279  0.04660155
## MARITALref    0.132959785  0.38292703
## EDUCAref     -0.311262106 -0.02628115
## EMPLOY1ref   -0.295142236 -0.03108843
## INCOME2ref   -0.297857895  0.06835400
## X_BMI5CATref -0.060258917  0.22004039
## X_RACEref     0.217592988  0.59181276
## X_AGEG5YRref -0.511757490 -0.12316139
## CHILDRENref   0.275210777  0.56094178
## SDHBILLSref  -0.387141761  0.12437900
## HOWSAFE1ref  -0.218233556  0.44225474
## SDHFOODref    0.115105721  0.64633546
## SDHMEALSref  -0.316537885  0.14793596
## SDHMONEYref   0.284511848  0.56812355
## SDHSTRESref  -0.665541753 -0.23726185
## GENHLTHref   -0.599565688 -0.25212347
## PHYSHLTHref  -0.235989669  0.11272553
## MENTHLTHref   0.086181978  0.42934832
## POORHLTHref   0.034154619  0.45621235
## SMOKDAY2ref  -0.549549451 -0.19676175
## ECIGNOWref   -0.139274978  0.49117438
## CVDSTRK3ref  -0.204720310  0.44136111
## CVDCRHD4ref  -0.320462453  0.21176946
## DIABETE3ref  -0.300154164  0.08328952
## ADDEPEV2ref  -0.099248063  0.19364570
## HAVARTH3ref  -0.323643232 -0.03957411
## CHCCOPD1ref  -0.492270436 -0.03308933
## DEAFref      -0.220930370  0.20918320
## BLINDref     -0.569602037  0.05675655
## DECIDEref    -0.439505292 -0.03510761
## DIFFWALKref   0.007427802  0.40612398
## DIFFDRESref  -0.556157868  0.05712206
## DIFFALONref  -0.484943054  0.03651623

exp(coef(alr))

##  (Intercept)       SEXref   MARITALref     EDUCAref   EMPLOY1ref   INCOME2ref 
##    0.5325801    0.9252157    1.2942656    0.8447018    0.8494932    0.8915873 
## X_BMI5CATref    X_RACEref X_AGEG5YRref  CHILDRENref  SDHBILLSref  HOWSAFE1ref 
##    1.0831687    1.4988571    0.7279962    1.5190365    0.8768833    1.1185247 
##   SDHFOODref  SDHMEALSref  SDHMONEYref  SDHSTRESref   GENHLTHref  PHYSHLTHref 
##    1.4633387    0.9191546    1.5316073    0.6367350    0.6532179    0.9402288 
##  MENTHLTHref  POORHLTHref  SMOKDAY2ref   ECIGNOWref  CVDSTRK3ref  CVDCRHD4ref 
##    1.2940349    1.2778558    0.6885581    1.1923781    1.1256047    0.9471039 
##  DIABETE3ref  ADDEPEV2ref  HAVARTH3ref  CHCCOPD1ref      DEAFref     BLINDref 
##    0.8972396    1.0483304    0.8339276    0.7689880    0.9941436    0.7738148 
##    DECIDEref  DIFFWALKref  DIFFDRESref  DIFFALONref 
##    0.7887495    1.2297070    0.7791763    0.7991446

exp(cbind(AOR = coef(alr), confint(alr)))

##                    AOR     2.5 %    97.5 %
## (Intercept)  0.5325801 0.3799145 0.7465933
## SEXref       0.9252157 0.8170473 1.0477045
## MARITALref   1.2942656 1.1422041 1.4665710
## EDUCAref     0.8447018 0.7325219 0.9740612
## EMPLOY1ref   0.8494932 0.7444257 0.9693898
## INCOME2ref   0.8915873 0.7424068 1.0707443
## X_BMI5CATref 1.0831687 0.9415207 1.2461271
## X_RACEref    1.4988571 1.2430810 1.8072616
## X_AGEG5YRref 0.7279962 0.5994411 0.8841210
## CHILDRENref  1.5190365 1.3168082 1.7523220
## SDHBILLSref  0.8768833 0.6789948 1.1324450
## HOWSAFE1ref  1.1185247 0.8039377 1.5562121
## SDHFOODref   1.4633387 1.1219920 1.9085341
## SDHMEALSref  0.9191546 0.7286674 1.1594386
## SDHMONEYref  1.5316073 1.3291131 1.7649521
## SDHSTRESref  0.6367350 0.5139950 0.7887847
## GENHLTHref   0.6532179 0.5490500 0.7771488
## PHYSHLTHref  0.9402288 0.7897888 1.1193247
## MENTHLTHref  1.2940349 1.0900047 1.5362561
## POORHLTHref  1.2778558 1.0347446 1.5780854
## SMOKDAY2ref  0.6885581 0.5772098 0.8213863
## ECIGNOWref   1.1923781 0.8699888 1.6342343
## CVDSTRK3ref  1.1256047 0.8148752 1.5548221
## CVDCRHD4ref  0.9471039 0.7258133 1.2358629
## DIABETE3ref  0.8972396 0.7407040 1.0868564
## ADDEPEV2ref  1.0483304 0.9055181 1.2136662
## HAVARTH3ref  0.8339276 0.7235083 0.9611987
## CHCCOPD1ref  0.7689880 0.6112370 0.9674521
## DEAFref      0.9941436 0.8017725 1.2326708
## BLINDref     0.7738148 0.5657505 1.0583981
## DECIDEref    0.7887495 0.6443551 0.9655015
## DIFFWALKref  1.2297070 1.0074555 1.5009886
## DIFFDRESref  0.7791763 0.5734079 1.0587850
## DIFFALONref  0.7991446 0.6157323 1.0371911

exp(cbind(AOR = coef(alr), confint(alr)))[-1, ]

##                    AOR     2.5 %    97.5 %
## SEXref       0.9252157 0.8170473 1.0477045
## MARITALref   1.2942656 1.1422041 1.4665710
## EDUCAref     0.8447018 0.7325219 0.9740612
## EMPLOY1ref   0.8494932 0.7444257 0.9693898
## INCOME2ref   0.8915873 0.7424068 1.0707443
## X_BMI5CATref 1.0831687 0.9415207 1.2461271
## X_RACEref    1.4988571 1.2430810 1.8072616
## X_AGEG5YRref 0.7279962 0.5994411 0.8841210
## CHILDRENref  1.5190365 1.3168082 1.7523220
## SDHBILLSref  0.8768833 0.6789948 1.1324450
## HOWSAFE1ref  1.1185247 0.8039377 1.5562121
## SDHFOODref   1.4633387 1.1219920 1.9085341
## SDHMEALSref  0.9191546 0.7286674 1.1594386
## SDHMONEYref  1.5316073 1.3291131 1.7649521
## SDHSTRESref  0.6367350 0.5139950 0.7887847
## GENHLTHref   0.6532179 0.5490500 0.7771488
## PHYSHLTHref  0.9402288 0.7897888 1.1193247
## MENTHLTHref  1.2940349 1.0900047 1.5362561
## POORHLTHref  1.2778558 1.0347446 1.5780854
## SMOKDAY2ref  0.6885581 0.5772098 0.8213863
## ECIGNOWref   1.1923781 0.8699888 1.6342343
## CVDSTRK3ref  1.1256047 0.8148752 1.5548221
## CVDCRHD4ref  0.9471039 0.7258133 1.2358629
## DIABETE3ref  0.8972396 0.7407040 1.0868564
## ADDEPEV2ref  1.0483304 0.9055181 1.2136662
## HAVARTH3ref  0.8339276 0.7235083 0.9611987
## CHCCOPD1ref  0.7689880 0.6112370 0.9674521
## DEAFref      0.9941436 0.8017725 1.2326708
## BLINDref     0.7738148 0.5657505 1.0583981
## DECIDEref    0.7887495 0.6443551 0.9655015
## DIFFWALKref  1.2297070 1.0074555 1.5009886
## DIFFDRESref  0.7791763 0.5734079 1.0587850
## DIFFALONref  0.7991446 0.6157323 1.0371911

adj_or <- exp(cbind(AOR = coef(alr), confint(alr)))[-1,]
adj_or <- as.data.frame(adj_or)

# Combine All OR with Adjusted OR
stargazer(bind_cols(all_or, adj_or),summary=F,type = "html", digits=2)


	OR	X2.5..	X97.5..	hv	AOR	2.5 %	97.5 %

SEXref	0.86	0.80	0.93	SEX	0.93	0.82	1.05
MARITALref	1.41	1.30	1.53	MARITAL	1.29	1.14	1.47
EDUCAref	0.81	0.74	0.89	EDUCA	0.84	0.73	0.97
EMPLOY1ref	0.88	0.82	0.96	EMPLOY1	0.85	0.74	0.97
INCOME2ref	0.97	0.86	1.10	INCOME2	0.89	0.74	1.07
X_BMI5CATref	1.28	1.16	1.40	X_BMI5CAT	1.08	0.94	1.25
X_RACEref	1.62	1.44	1.82	X_RACE	1.50	1.24	1.81
X_AGEG5YRref	0.83	0.72	0.95	X_AGEG5YR	0.73	0.60	0.88
CHILDRENref	1.32	1.21	1.45	CHILDREN	1.52	1.32	1.75
SDHBILLSref	0.67	0.56	0.80	SDHBILLS	0.88	0.68	1.13
HOWSAFE1ref	0.74	0.57	0.96	HOWSAFE1	1.12	0.80	1.56
SDHFOODref	0.85	0.73	0.98	SDHFOOD	1.46	1.12	1.91
SDHMEALSref	0.85	0.74	0.97	SDHMEALS	0.92	0.73	1.16
SDHMONEYref	1.16	1.06	1.27	SDHMONEY	1.53	1.33	1.76
SDHSTRESref	0.45	0.38	0.53	SDHSTRES	0.64	0.51	0.79
GENHLTHref	0.53	0.47	0.60	GENHLTH	0.65	0.55	0.78
PHYSHLTHref	1.59	1.41	1.80	PHYSHLTH	0.94	0.79	1.12
MENTHLTHref	2.02	1.76	2.31	MENTHLTH	1.29	1.09	1.54
POORHLTHref	1.97	1.68	2.32	POORHLTH	1.28	1.03	1.58
SMOKDAY2ref	0.63	0.56	0.71	SMOKDAY2	0.69	0.58	0.82
ECIGNOWref	0.78	0.62	1.00	ECIGNOW	1.19	0.87	1.63
CVDSTRK3ref	0.85	0.67	1.07	CVDSTRK3	1.13	0.81	1.55
CVDCRHD4ref	0.90	0.75	1.09	CVDCRHD4	0.95	0.73	1.24
DIABETE3ref	0.90	0.79	1.03	DIABETE3	0.90	0.74	1.09
ADDEPEV2ref	0.75	0.68	0.83	ADDEPEV2	1.05	0.91	1.21
HAVARTH3ref	0.89	0.81	0.97	HAVARTH3	0.83	0.72	0.96
CHCCOPD1ref	0.61	0.51	0.72	CHCCOPD1	0.77	0.61	0.97
DEAFref	0.90	0.77	1.05	DEAF	0.99	0.80	1.23
BLINDref	0.63	0.50	0.79	BLIND	0.77	0.57	1.06
DECIDEref	0.51	0.44	0.59	DECIDE	0.79	0.64	0.97
DIFFWALKref	0.77	0.69	0.87	DIFFWALK	1.23	1.01	1.50
DIFFDRESref	0.45	0.36	0.56	DIFFDRES	0.78	0.57	1.06
DIFFALONref	0.47	0.39	0.56	DIFFALON	0.80	0.62	1.04

table1 <- bind_cols(all_or, adj_or)

table1 <- table1 %>% 
  dplyr::select("hv", "OR", "X2.5..", "X97.5..", "AOR", "2.5 %", "97.5 %")


table1$orll <- table1$X2.5..
table1$orul <- table1$X97.5..
table1$aorll <- table1$"2.5 %"
table1$aorul <- table1$"97.5 %"
table1 <- table1 %>% dplyr::select(hv, OR, orll, orul, AOR, aorll, aorul)


#Plot OR

ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) +
  geom_errorbar(aes(ymin=orll, ymax=orul), 
                width = .2, position = position_dodge(.9))

#Plot AOR
ggplot(table1, aes(x=hv, y = AOR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_errorbar(aes(ymin=aorll, ymax=aorul), 
                width = .2, position = position_dodge(.9))

ORtable <- table1 %>% dplyr::select(hv, OR, orll, orul)
ORtable$type <- c("OR")
ORtable$stat <- ORtable$OR
ORtable$ll <- ORtable$orll
ORtable$ul <- ORtable$orul
ORtable <- ORtable %>% dplyr::select(hv, stat, type, ll, ul)

AORtable <- table1 %>% dplyr::select(hv, AOR, aorll, aorul)
AORtable$type <- c("AOR")
AORtable$stat <- AORtable$AOR
AORtable$ll <- AORtable$aorll
AORtable$ul <- AORtable$aorul
AORtable <- AORtable %>% dplyr::select(hv, stat, type, ll, ul)

table2 <- rbind(ORtable, AORtable)

figureoraor <- ggplot(table2, aes(x=hv, y = stat, fill = type)) +
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=ll, ymax=ul), 
                width = .2, position = position_dodge(.9))

#OR compared to Adjusted OR
print(figureoraor + ggtitle("OR & AOR"))

Non-White

ADSLEEP - Difficulty sleeping past 14 days

svydesNW<-subset(svydes,RACE != "White only, non-Hispanic")

#Binomial logistic regression for each variable
or<-svyglm(ADSLEEP~SEX, family=quasibinomial,design=svydesNW)
#OR saved for each variable
a <- or_fun("SEX")

#Repeat
or <- svyglm(ADSLEEP~ MARITAL, family = quasibinomial, design = svydesNW)
b <- or_fun("MARITAL")

or <- svyglm(ADSLEEP~ EDUCA, family = quasibinomial, design = svydesNW)
c <- or_fun("EDUCA")

or <- svyglm(ADSLEEP~ EMPLOY1, family = quasibinomial, design = svydesNW)
d <- or_fun("EMPLOY1")

or <- svyglm(ADSLEEP~ INCOME2, family = quasibinomial, design = svydesNW)
e <- or_fun("INCOME2")

or <- svyglm(ADSLEEP~ X_BMI5CAT, family = quasibinomial, design = svydesNW)
f <- or_fun("X_BMI5CAT")

or <- svyglm(ADSLEEP~ X_AGEG5YR, family = quasibinomial, design = svydesNW)
h <- or_fun("X_AGEG5YR")

or <- svyglm(ADSLEEP~ CHILDREN, family = quasibinomial, design = svydesNW)
i <- or_fun("CHILDREN")

or <- svyglm(ADSLEEP~ SDHBILLS, family = quasibinomial, design = svydesNW)
j <- or_fun("SDHBILLS")

or <- svyglm(ADSLEEP~ HOWSAFE1, family = quasibinomial, design = svydesNW)
k <- or_fun("HOWSAFE1")

or <- svyglm(ADSLEEP~ SDHFOOD, family = quasibinomial, design = svydesNW)
l <- or_fun("SDHFOOD")

or <- svyglm(ADSLEEP~ SDHMEALS, family = quasibinomial, design = svydesNW)
m <- or_fun("SDHMEALS")

or <- svyglm(ADSLEEP~ SDHMONEY, family = quasibinomial, design = svydesNW)
n <- or_fun("SDHMONEY")

or <- svyglm(ADSLEEP~ SDHSTRES, family = quasibinomial, design = svydesNW)
o <- or_fun("SDHSTRES")

or <- svyglm(ADSLEEP~ GENHLTH, family = quasibinomial, design = svydesNW)
p <- or_fun("GENHLTH")

or <- svyglm(ADSLEEP~ PHYSHLTH, family = quasibinomial, design = svydesNW)
q <- or_fun("PHYSHLTH")

or <- svyglm(ADSLEEP~ MENTHLTH, family = quasibinomial, design = svydesNW)
r <- or_fun("MENTHLTH")

or <- svyglm(ADSLEEP~ POORHLTH, family = quasibinomial, design = svydesNW)
s <- or_fun("POORHLTH")

or <- svyglm(ADSLEEP~ SMOKDAY2, family = quasibinomial, design = svydesNW)
t <- or_fun("SMOKDAY2")

or <- svyglm(ADSLEEP~ ECIGNOW, family = quasibinomial, design = svydesNW)
u <- or_fun("ECIGNOW")

or <- svyglm(ADSLEEP~ CVDSTRK3, family = quasibinomial, design = svydesNW)
v <- or_fun("CVDSTRK3")

or <- svyglm(ADSLEEP~ CVDCRHD4, family = quasibinomial, design = svydesNW)
w <- or_fun("CVDCRHD4")

or <- svyglm(ADSLEEP~ DIABETE3, family = quasibinomial, design = svydesNW)
x <- or_fun("DIABETE3")

or <- svyglm(ADSLEEP~ ADDEPEV2, family = quasibinomial, design = svydesNW)
y <- or_fun("ADDEPEV2")

or <- svyglm(ADSLEEP~ HAVARTH3, family = quasibinomial, design = svydesNW)
z <- or_fun("HAVARTH3")

or <- svyglm(ADSLEEP~ CHCCOPD1, family = quasibinomial, design = svydesNW)
aa <- or_fun("CHCCOPD1")

or <- svyglm(ADSLEEP~ DEAF, family = quasibinomial, design = svydesNW)
bb <- or_fun("DEAF")

or <- svyglm(ADSLEEP~ BLIND, family = quasibinomial, design = svydesNW)
cc <- or_fun("BLIND")

or <- svyglm(ADSLEEP~ DECIDE, family = quasibinomial, design = svydesNW)
dd <- or_fun("DECIDE")

or <- svyglm(ADSLEEP~ DIFFWALK, family = quasibinomial, design = svydesNW)
ee <- or_fun("DIFFWALK")

or <- svyglm(ADSLEEP~ DIFFDRES, family = quasibinomial, design = svydesNW)
ff <- or_fun("DIFFDRES")

or <- svyglm(ADSLEEP~ DIFFALON, family = quasibinomial, design = svydesNW)
gg <- or_fun("DIFFALON")


# Combine OR into 1 table
all_or <- bind_rows(a, b, c, d, e, f, h, i, j, k, l, m,n,o,p,
                    q,r,s,t,u,v,w,x,y,z,aa,bb,cc,dd,ee,ff,gg)
all_or <- as.data.frame(all_or)


# Run all variables in logistic regression for adjusted OR

alr <- svyglm(ADSLEEP ~ SEX + MARITAL + EDUCA + EMPLOY1 + INCOME2 + X_BMI5CAT
               + X_AGEG5YR + CHILDREN  + SDHBILLS + HOWSAFE1 + SDHFOOD + 
                SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + MENTHLTH + 
                POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 +  CVDCRHD4 + DIABETE3 + 
                ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + DECIDE + 
                DIFFWALK + DIFFDRES + DIFFALON, family = quasibinomial, 
              design = svydesNW)

summary(alr)

## 
## Call:
## svyglm(formula = ADSLEEP ~ SEX + MARITAL + EDUCA + EMPLOY1 + 
##     INCOME2 + X_BMI5CAT + X_AGEG5YR + CHILDREN + SDHBILLS + HOWSAFE1 + 
##     SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + 
##     MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 + CVDCRHD4 + 
##     DIABETE3 + ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + 
##     DECIDE + DIFFWALK + DIFFDRES + DIFFALON, design = svydesNW, 
##     family = quasibinomial)
## 
## Survey design:
## subset(svydes, RACE != "White only, non-Hispanic")
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.93935    0.49684   1.891  0.05909 .  
## SEXref        0.70491    0.24019   2.935  0.00345 ** 
## MARITALref   -0.18148    0.27367  -0.663  0.50746    
## EDUCAref      0.42831    0.26966   1.588  0.11266    
## EMPLOY1ref    0.08239    0.26443   0.312  0.75547    
## INCOME2ref    0.03632    0.34778   0.104  0.91684    
## X_BMI5CATref  0.25613    0.28124   0.911  0.36276    
## X_AGEG5YRref  0.13407    0.47878   0.280  0.77954    
## CHILDRENref  -0.49377    0.24819  -1.989  0.04704 *  
## SDHBILLSref  -0.29833    0.33199  -0.899  0.36918    
## HOWSAFE1ref   0.04694    0.34750   0.135  0.89259    
## SDHFOODref    0.16259    0.33896   0.480  0.63161    
## SDHMEALSref  -0.28154    0.32201  -0.874  0.38225    
## SDHMONEYref  -0.42647    0.26906  -1.585  0.11342    
## SDHSTRESref  -1.38205    0.28505  -4.848 1.54e-06 ***
## GENHLTHref   -0.31268    0.28793  -1.086  0.27788    
## PHYSHLTHref   0.39879    0.33059   1.206  0.22812    
## MENTHLTHref   0.05579    0.31683   0.176  0.86029    
## POORHLTHref   0.80582    0.35118   2.295  0.02205 *  
## SMOKDAY2ref  -0.44586    0.30106  -1.481  0.13907    
## ECIGNOWref    1.07632    0.46705   2.304  0.02149 *  
## CVDSTRK3ref   0.06605    0.68031   0.097  0.92268    
## CVDCRHD4ref   0.69074    0.71454   0.967  0.33404    
## DIABETE3ref  -0.03410    0.35035  -0.097  0.92250    
## ADDEPEV2ref  -0.49793    0.27839  -1.789  0.07411 .  
## HAVARTH3ref  -0.22729    0.29748  -0.764  0.44510    
## CHCCOPD1ref   0.01137    0.45798   0.025  0.98020    
## DEAFref       0.11685    0.36499   0.320  0.74895    
## BLINDref     -1.05938    0.40282  -2.630  0.00873 ** 
## DECIDEref    -0.05746    0.29643  -0.194  0.84635    
## DIFFWALKref   0.08239    0.37092   0.222  0.82428    
## DIFFDRESref   1.15865    0.51305   2.258  0.02423 *  
## DIFFALONref  -0.37283    0.43841  -0.850  0.39539    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 0.9092306)
## 
## Number of Fisher Scoring iterations: 5

confint(alr)

##                    2.5 %       97.5 %
## (Intercept)  -0.03445004  1.913140096
## SEXref        0.23413894  1.175684310
## MARITALref   -0.71786684  0.354900063
## EDUCAref     -0.10020683  0.956832181
## EMPLOY1ref   -0.43589540  0.600668617
## INCOME2ref   -0.64531553  0.717964992
## X_BMI5CATref -0.29508577  0.807339086
## X_AGEG5YRref -0.80432334  1.072465335
## CHILDRENref  -0.98020730 -0.007329393
## SDHBILLSref  -0.94902191  0.352367690
## HOWSAFE1ref  -0.63415198  0.728027871
## SDHFOODref   -0.50175454  0.826935338
## SDHMEALSref  -0.91266977  0.349588240
## SDHMONEYref  -0.95381855  0.100881330
## SDHSTRESref  -1.94074225 -0.823351148
## GENHLTHref   -0.87700195  0.251650215
## PHYSHLTHref  -0.24916776  1.046740733
## MENTHLTHref  -0.56518645  0.676756989
## POORHLTHref   0.11751615  1.494122149
## SMOKDAY2ref  -1.03593394  0.144214402
## ECIGNOWref    0.16091432  1.991726489
## CVDSTRK3ref  -1.26733133  1.399435835
## CVDCRHD4ref  -0.70974218  2.091217917
## DIABETE3ref  -0.72077877  0.652587559
## ADDEPEV2ref  -1.04356604  0.047699936
## HAVARTH3ref  -0.81034830  0.355769384
## CHCCOPD1ref  -0.88626319  0.908999736
## DEAFref      -0.59851962  0.832219873
## BLINDref     -1.84889799 -0.269855220
## DECIDEref    -0.63846005  0.523531803
## DIFFWALKref  -0.64458770  0.809372969
## DIFFDRESref   0.15308752  2.164212023
## DIFFALONref  -1.23209027  0.486431796

exp(coef(alr))

##  (Intercept)       SEXref   MARITALref     EDUCAref   EMPLOY1ref   INCOME2ref 
##    2.5583053    2.0236678    0.8340321    1.5346659    1.0858755    1.0369925 
## X_BMI5CATref X_AGEG5YRref  CHILDRENref  SDHBILLSref  HOWSAFE1ref   SDHFOODref 
##    1.2919163    1.1434740    0.6103221    0.7420586    1.0480570    1.1765547 
##  SDHMEALSref  SDHMONEYref  SDHSTRESref   GENHLTHref  PHYSHLTHref  MENTHLTHref 
##    0.7546202    0.6528104    0.2510642    0.7314870    1.4900154    1.0573706 
##  POORHLTHref  SMOKDAY2ref   ECIGNOWref  CVDSTRK3ref  CVDCRHD4ref  DIABETE3ref 
##    2.2385294    0.6402736    2.9338642    1.0682825    1.9951872    0.9664791 
##  ADDEPEV2ref  HAVARTH3ref  CHCCOPD1ref      DEAFref     BLINDref    DECIDEref 
##    0.6077856    0.7966901    1.0114331    1.1239510    0.3466719    0.9441558 
##  DIFFWALKref  DIFFDRESref  DIFFALONref 
##    1.0858821    3.1856290    0.6887828

exp(cbind(AOR = coef(alr), confint(alr)))

##                    AOR     2.5 %    97.5 %
## (Intercept)  2.5583053 0.9661366 6.7743275
## SEXref       2.0236678 1.2638201 3.2403596
## MARITALref   0.8340321 0.4877917 1.4260381
## EDUCAref     1.5346659 0.9046503 2.6034362
## EMPLOY1ref   1.0858755 0.6466854 1.8233375
## INCOME2ref   1.0369925 0.5244970 2.0502567
## X_BMI5CATref 1.2919163 0.7444677 2.2419344
## X_AGEG5YRref 1.1434740 0.4473906 2.9225758
## CHILDRENref  0.6103221 0.3752333 0.9926974
## SDHBILLSref  0.7420586 0.3871195 1.4224314
## HOWSAFE1ref  1.0480570 0.5303851 2.0709923
## SDHFOODref   1.1765547 0.6054674 2.2863013
## SDHMEALSref  0.7546202 0.4014510 1.4184834
## SDHMONEYref  0.6528104 0.3852670 1.1061454
## SDHSTRESref  0.2510642 0.1435973 0.4389582
## GENHLTHref   0.7314870 0.4160283 1.2861461
## PHYSHLTHref  1.4900154 0.7794492 2.8483524
## MENTHLTHref  1.0573706 0.5682542 1.9674868
## POORHLTHref  2.2385294 1.1246998 4.4554236
## SMOKDAY2ref  0.6402736 0.3548948 1.1551317
## ECIGNOWref   2.9338642 1.1745843 7.3281749
## CVDSTRK3ref  1.0682825 0.2815821 4.0529128
## CVDCRHD4ref  1.9951872 0.4917710 8.0947679
## DIABETE3ref  0.9664791 0.4863733 1.9205038
## ADDEPEV2ref  0.6077856 0.3521965 1.0488559
## HAVARTH3ref  0.7966901 0.4447032 1.4272784
## CHCCOPD1ref  1.0114331 0.4121932 2.4818388
## DEAFref      1.1239510 0.5496247 2.2984153
## BLINDref     0.3466719 0.1574105 0.7634900
## DECIDEref    0.9441558 0.5281051 1.6879787
## DIFFWALKref  1.0858821 0.5248789 2.2464989
## DIFFDRESref  3.1856290 1.1654270 8.7077377
## DIFFALONref  0.6887828 0.2916822 1.6265022

exp(cbind(AOR = coef(alr), confint(alr)))[-1, ]

##                    AOR     2.5 %    97.5 %
## SEXref       2.0236678 1.2638201 3.2403596
## MARITALref   0.8340321 0.4877917 1.4260381
## EDUCAref     1.5346659 0.9046503 2.6034362
## EMPLOY1ref   1.0858755 0.6466854 1.8233375
## INCOME2ref   1.0369925 0.5244970 2.0502567
## X_BMI5CATref 1.2919163 0.7444677 2.2419344
## X_AGEG5YRref 1.1434740 0.4473906 2.9225758
## CHILDRENref  0.6103221 0.3752333 0.9926974
## SDHBILLSref  0.7420586 0.3871195 1.4224314
## HOWSAFE1ref  1.0480570 0.5303851 2.0709923
## SDHFOODref   1.1765547 0.6054674 2.2863013
## SDHMEALSref  0.7546202 0.4014510 1.4184834
## SDHMONEYref  0.6528104 0.3852670 1.1061454
## SDHSTRESref  0.2510642 0.1435973 0.4389582
## GENHLTHref   0.7314870 0.4160283 1.2861461
## PHYSHLTHref  1.4900154 0.7794492 2.8483524
## MENTHLTHref  1.0573706 0.5682542 1.9674868
## POORHLTHref  2.2385294 1.1246998 4.4554236
## SMOKDAY2ref  0.6402736 0.3548948 1.1551317
## ECIGNOWref   2.9338642 1.1745843 7.3281749
## CVDSTRK3ref  1.0682825 0.2815821 4.0529128
## CVDCRHD4ref  1.9951872 0.4917710 8.0947679
## DIABETE3ref  0.9664791 0.4863733 1.9205038
## ADDEPEV2ref  0.6077856 0.3521965 1.0488559
## HAVARTH3ref  0.7966901 0.4447032 1.4272784
## CHCCOPD1ref  1.0114331 0.4121932 2.4818388
## DEAFref      1.1239510 0.5496247 2.2984153
## BLINDref     0.3466719 0.1574105 0.7634900
## DECIDEref    0.9441558 0.5281051 1.6879787
## DIFFWALKref  1.0858821 0.5248789 2.2464989
## DIFFDRESref  3.1856290 1.1654270 8.7077377
## DIFFALONref  0.6887828 0.2916822 1.6265022

adj_or <- exp(cbind(AOR = coef(alr), confint(alr)))[-1,]
adj_or <- as.data.frame(adj_or)

# Combine All OR with Adjusted OR
stargazer(bind_cols(all_or, adj_or),summary=F,type = "html", digits=2)


	OR	X2.5..	X97.5..	hv	AOR	2.5 %	97.5 %

SEXref	1.96	1.41	2.72	SEX	2.02	1.26	3.24
MARITALref	1.90	1.37	2.65	MARITAL	0.83	0.49	1.43
EDUCAref	1.09	0.75	1.59	EDUCA	1.53	0.90	2.60
EMPLOY1ref	1.95	1.40	2.72	EMPLOY1	1.09	0.65	1.82
INCOME2ref	1.09	0.68	1.75	INCOME2	1.04	0.52	2.05
X_BMI5CATref	1.54	1.04	2.27	X_BMI5CAT	1.29	0.74	2.24
X_AGEG5YRref	0.72	0.41	1.25	X_AGEG5YR	1.14	0.45	2.92
CHILDRENref	0.87	0.63	1.21	CHILDREN	0.61	0.38	0.99
SDHBILLSref	0.34	0.23	0.50	SDHBILLS	0.74	0.39	1.42
HOWSAFE1ref	0.50	0.31	0.82	HOWSAFE1	1.05	0.53	2.07
SDHFOODref	0.44	0.31	0.63	SDHFOOD	1.18	0.61	2.29
SDHMEALSref	0.43	0.30	0.61	SDHMEALS	0.75	0.40	1.42
SDHMONEYref	0.52	0.37	0.73	SDHMONEY	0.65	0.39	1.11
SDHSTRESref	0.11	0.07	0.18	SDHSTRES	0.25	0.14	0.44
GENHLTHref	0.31	0.21	0.44	GENHLTH	0.73	0.42	1.29
PHYSHLTHref	4.34	2.88	6.55	PHYSHLTH	1.49	0.78	2.85
MENTHLTHref	4.31	2.89	6.44	MENTHLTH	1.06	0.57	1.97
POORHLTHref	4.21	2.53	6.98	POORHLTH	2.24	1.12	4.46
SMOKDAY2ref	0.35	0.24	0.52	SMOKDAY2	0.64	0.35	1.16
ECIGNOWref	0.63	0.23	1.75	ECIGNOW	2.93	1.17	7.33
CVDSTRK3ref	0.31	0.13	0.79	CVDSTRK3	1.07	0.28	4.05
CVDCRHD4ref	0.44	0.22	0.91	CVDCRHD4	2.00	0.49	8.09
DIABETE3ref	0.85	0.53	1.34	DIABETE3	0.97	0.49	1.92
ADDEPEV2ref	0.26	0.18	0.38	ADDEPEV2	0.61	0.35	1.05
HAVARTH3ref	0.43	0.29	0.63	HAVARTH3	0.80	0.44	1.43
CHCCOPD1ref	0.29	0.15	0.55	CHCCOPD1	1.01	0.41	2.48
DEAFref	0.45	0.23	0.88	DEAF	1.12	0.55	2.30
BLINDref	0.46	0.27	0.78	BLIND	0.35	0.16	0.76
DECIDEref	0.21	0.14	0.31	DECIDE	0.94	0.53	1.69
DIFFWALKref	0.32	0.22	0.49	DIFFWALK	1.09	0.52	2.25
DIFFDRESref	0.34	0.19	0.62	DIFFDRES	3.19	1.17	8.71
DIFFALONref	0.16	0.10	0.28	DIFFALON	0.69	0.29	1.63

table1 <- bind_cols(all_or, adj_or)

table1 <- table1 %>% 
  dplyr::select("hv", "OR", "X2.5..", "X97.5..", "AOR", "2.5 %", "97.5 %")


table1$orll <- table1$X2.5..
table1$orul <- table1$X97.5..
table1$aorll <- table1$"2.5 %"
table1$aorul <- table1$"97.5 %"
table1 <- table1 %>% dplyr::select(hv, OR, orll, orul, AOR, aorll, aorul)


#Plot OR
ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) +
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=orll, ymax=orul), 
                width = .2, position = position_dodge(.9))

#Plot AOR
ggplot(table1, aes(x=hv, y = AOR)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=aorll, ymax=aorul), 
                width = .2, position = position_dodge(.9))

ORtable <- table1 %>% dplyr::select(hv, OR, orll, orul)
ORtable$type <- c("OR")
ORtable$stat <- ORtable$OR
ORtable$ll <- ORtable$orll
ORtable$ul <- ORtable$orul
ORtable <- ORtable %>% dplyr::select(hv, stat, type, ll, ul)

AORtable <- table1 %>% dplyr::select(hv, AOR, aorll, aorul)
AORtable$type <- c("AOR")
AORtable$stat <- AORtable$AOR
AORtable$ll <- AORtable$aorll
AORtable$ul <- AORtable$aorul
AORtable <- AORtable %>% dplyr::select(hv, stat, type, ll, ul)

table2 <- rbind(ORtable, AORtable)

figureoraor <- ggplot(table2, aes(x=hv, y = stat, fill = type)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=ll, ymax=ul), 
                width = .2, position = position_dodge(.9))

#OR compared to Adjusted OR
print(figureoraor + ggtitle("OR & AOR"))

SLEPTIM1 - Hours per sleep at night

#Binomial logistic regression for each variable
or<-svyglm(SLEPTIM1~SEX, family=quasibinomial,design=svydesNW)
#OR saved for each variable
a <- or_fun("SEX")

#Repeat
or <- svyglm(SLEPTIM1~ MARITAL, family = quasibinomial, design = svydesNW)
b <- or_fun("MARITAL")

or <- svyglm(SLEPTIM1~ EDUCA, family = quasibinomial, design = svydesNW)
c <- or_fun("EDUCA")

or <- svyglm(SLEPTIM1~ EMPLOY1, family = quasibinomial, design = svydesNW)
d <- or_fun("EMPLOY1")

or <- svyglm(SLEPTIM1~ INCOME2, family = quasibinomial, design = svydesNW)
e <- or_fun("INCOME2")

or <- svyglm(SLEPTIM1~ X_BMI5CAT, family = quasibinomial, design = svydesNW)
f <- or_fun("X_BMI5CAT")

or <- svyglm(SLEPTIM1~ X_AGEG5YR, family = quasibinomial, design = svydesNW)
h <- or_fun("X_AGEG5YR")

or <- svyglm(SLEPTIM1~ CHILDREN, family = quasibinomial, design = svydesNW)
i <- or_fun("CHILDREN")

or <- svyglm(SLEPTIM1~ SDHBILLS, family = quasibinomial, design = svydesNW)
j <- or_fun("SDHBILLS")

or <- svyglm(SLEPTIM1~ HOWSAFE1, family = quasibinomial, design = svydesNW)
k <- or_fun("HOWSAFE1")

or <- svyglm(SLEPTIM1~ SDHFOOD, family = quasibinomial, design = svydesNW)
l <- or_fun("SDHFOOD")

or <- svyglm(SLEPTIM1~ SDHMEALS, family = quasibinomial, design = svydesNW)
m <- or_fun("SDHMEALS")

or <- svyglm(SLEPTIM1~ SDHMONEY, family = quasibinomial, design = svydesNW)
n <- or_fun("SDHMONEY")

or <- svyglm(SLEPTIM1~ SDHSTRES, family = quasibinomial, design = svydesNW)
o <- or_fun("SDHSTRES")

or <- svyglm(SLEPTIM1~ GENHLTH, family = quasibinomial, design = svydesNW)
p <- or_fun("GENHLTH")

or <- svyglm(SLEPTIM1~ PHYSHLTH, family = quasibinomial, design = svydesNW)
q <- or_fun("PHYSHLTH")

or <- svyglm(SLEPTIM1~ MENTHLTH, family = quasibinomial, design = svydesNW)
r <- or_fun("MENTHLTH")

or <- svyglm(SLEPTIM1~ POORHLTH, family = quasibinomial, design = svydesNW)
s <- or_fun("POORHLTH")

or <- svyglm(SLEPTIM1~ SMOKDAY2, family = quasibinomial, design = svydesNW)
t <- or_fun("SMOKDAY2")

or <- svyglm(SLEPTIM1~ ECIGNOW, family = quasibinomial, design = svydesNW)
u <- or_fun("ECIGNOW")

or <- svyglm(SLEPTIM1~ CVDSTRK3, family = quasibinomial, design = svydesNW)
v <- or_fun("CVDSTRK3")

or <- svyglm(SLEPTIM1~ CVDCRHD4, family = quasibinomial, design = svydesNW)
w <- or_fun("CVDCRHD4")

or <- svyglm(SLEPTIM1~ DIABETE3, family = quasibinomial, design = svydesNW)
x <- or_fun("DIABETE3")

or <- svyglm(SLEPTIM1~ ADDEPEV2, family = quasibinomial, design = svydesNW)
y <- or_fun("ADDEPEV2")

or <- svyglm(SLEPTIM1~ HAVARTH3, family = quasibinomial, design = svydesNW)
z <- or_fun("HAVARTH3")

or <- svyglm(SLEPTIM1~ CHCCOPD1, family = quasibinomial, design = svydesNW)
aa <- or_fun("CHCCOPD1")

or <- svyglm(SLEPTIM1~ DEAF, family = quasibinomial, design = svydesNW)
bb <- or_fun("DEAF")

or <- svyglm(SLEPTIM1~ BLIND, family = quasibinomial, design = svydesNW)
cc <- or_fun("BLIND")

or <- svyglm(SLEPTIM1~ DECIDE, family = quasibinomial, design = svydesNW)
dd <- or_fun("DECIDE")

or <- svyglm(SLEPTIM1~ DIFFWALK, family = quasibinomial, design = svydesNW)
ee <- or_fun("DIFFWALK")

or <- svyglm(SLEPTIM1~ DIFFDRES, family = quasibinomial, design = svydesNW)
ff <- or_fun("DIFFDRES")

or <- svyglm(SLEPTIM1~ DIFFALON, family = quasibinomial, design = svydesNW)
gg <- or_fun("DIFFALON")



# Combine OR into 1 table
all_or <- bind_rows(a, b, c, d, e, f, h, i, j, k, l, m,n,o,p,
                    q,r,s,t,u,v,w,x,y,z,aa,bb,cc,dd,ee,ff,gg)
all_or <- as.data.frame(all_or)


# Run all variables in logistic regression for adjusted OR
alr <- svyglm(SLEPTIM1 ~ SEX + MARITAL + EDUCA + EMPLOY1 + INCOME2 +
                X_BMI5CAT  + X_AGEG5YR + CHILDREN  + SDHBILLS + 
                HOWSAFE1 + SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + 
                PHYSHLTH + MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW +
                CVDSTRK3 +  CVDCRHD4 + DIABETE3 + ADDEPEV2 + HAVARTH3 + 
                CHCCOPD1 + DEAF + BLIND + DECIDE + DIFFWALK + DIFFDRES +
                DIFFALON, family = quasibinomial, design = svydesNW)

summary(alr)

## 
## Call:
## svyglm(formula = SLEPTIM1 ~ SEX + MARITAL + EDUCA + EMPLOY1 + 
##     INCOME2 + X_BMI5CAT + X_AGEG5YR + CHILDREN + SDHBILLS + HOWSAFE1 + 
##     SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + 
##     MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 + CVDCRHD4 + 
##     DIABETE3 + ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + 
##     DECIDE + DIFFWALK + DIFFDRES + DIFFALON, design = svydesNW, 
##     family = quasibinomial)
## 
## Survey design:
## subset(svydes, RACE != "White only, non-Hispanic")
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.486463   0.412058  -1.181  0.23811   
## SEXref        0.003219   0.197687   0.016  0.98701   
## MARITALref    0.151877   0.206031   0.737  0.46123   
## EDUCAref      0.092460   0.221439   0.418  0.67639   
## EMPLOY1ref   -0.072104   0.199899  -0.361  0.71841   
## INCOME2ref    0.089505   0.312375   0.287  0.77454   
## X_BMI5CATref -0.012613   0.216676  -0.058  0.95359   
## X_AGEG5YRref -0.988308   0.357519  -2.764  0.00583 **
## CHILDRENref   0.078938   0.197141   0.400  0.68895   
## SDHBILLSref   0.344526   0.294958   1.168  0.24312   
## HOWSAFE1ref  -0.059916   0.330047  -0.182  0.85599   
## SDHFOODref    0.191431   0.327404   0.585  0.55891   
## SDHMEALSref  -0.252173   0.323163  -0.780  0.43542   
## SDHMONEYref   0.696371   0.215423   3.233  0.00127 **
## SDHSTRESref  -0.318749   0.292474  -1.090  0.27610   
## GENHLTHref   -0.559765   0.232160  -2.411  0.01612 * 
## PHYSHLTHref  -0.531784   0.263073  -2.021  0.04355 * 
## MENTHLTHref   0.216274   0.242082   0.893  0.37190   
## POORHLTHref   0.464774   0.317912   1.462  0.14413   
## SMOKDAY2ref  -0.052441   0.239235  -0.219  0.82654   
## ECIGNOWref    0.005501   0.484845   0.011  0.99095   
## CVDSTRK3ref   0.138494   0.514483   0.269  0.78785   
## CVDCRHD4ref   0.398824   0.500209   0.797  0.42549   
## DIABETE3ref  -0.350238   0.316336  -1.107  0.26854   
## ADDEPEV2ref   0.088590   0.250363   0.354  0.72354   
## HAVARTH3ref  -0.445409   0.261940  -1.700  0.08942 . 
## CHCCOPD1ref  -0.245914   0.390360  -0.630  0.52889   
## DEAFref      -0.136697   0.373910  -0.366  0.71476   
## BLINDref     -0.349231   0.370686  -0.942  0.34640   
## DECIDEref    -0.108435   0.270561  -0.401  0.68869   
## DIFFWALKref   0.352457   0.308539   1.142  0.25364   
## DIFFDRESref  -0.188618   0.409472  -0.461  0.64518   
## DIFFALONref  -0.088949   0.403318  -0.221  0.82550   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 0.9381753)
## 
## Number of Fisher Scoring iterations: 4

confint(alr)

##                   2.5 %      97.5 %
## (Intercept)  -1.2940809  0.32115487
## SEXref       -0.3842414  0.39067880
## MARITALref   -0.2519360  0.55568984
## EDUCAref     -0.3415527  0.52647301
## EMPLOY1ref   -0.4638977  0.31969036
## INCOME2ref   -0.5227375  0.70174820
## X_BMI5CATref -0.4372905  0.41206367
## X_AGEG5YRref -1.6890332 -0.28758345
## CHILDRENref  -0.3074518  0.46532802
## SDHBILLSref  -0.2335809  0.92263244
## HOWSAFE1ref  -0.7067960  0.58696401
## SDHFOODref   -0.4502697  0.83313154
## SDHMEALSref  -0.8855616  0.38121522
## SDHMONEYref   0.2741502  1.11859239
## SDHSTRESref  -0.8919863  0.25448928
## GENHLTHref   -1.0147894 -0.10474074
## PHYSHLTHref  -1.0473977 -0.01616926
## MENTHLTHref  -0.2581977  0.69074632
## POORHLTHref  -0.1583230  1.08787007
## SMOKDAY2ref  -0.5213328  0.41645034
## ECIGNOWref   -0.9447788  0.95577994
## CVDSTRK3ref  -0.8698749  1.14686201
## CVDCRHD4ref  -0.5815672  1.37921515
## DIABETE3ref  -0.9702458  0.26976957
## ADDEPEV2ref  -0.4021119  0.57929123
## HAVARTH3ref  -0.9588008  0.06798382
## CHCCOPD1ref  -1.0110050  0.51917635
## DEAFref      -0.8695462  0.59615319
## BLINDref     -1.0757615  0.37729915
## DECIDEref    -0.6387246  0.42185562
## DIFFWALKref  -0.2522685  0.95718333
## DIFFDRESref  -0.9911683  0.61393303
## DIFFALONref  -0.8794376  0.70153864

exp(coef(alr))

##  (Intercept)       SEXref   MARITALref     EDUCAref   EMPLOY1ref   INCOME2ref 
##    0.6147971    1.0032239    1.1640170    1.0968694    0.9304344    1.0936332 
## X_BMI5CATref X_AGEG5YRref  CHILDRENref  SDHBILLSref  HOWSAFE1ref   SDHFOODref 
##    0.9874658    0.3722058    1.0821373    1.4113204    0.9418437    1.2109811 
##  SDHMEALSref  SDHMONEYref  SDHSTRESref   GENHLTHref  PHYSHLTHref  MENTHLTHref 
##    0.7771101    2.0064586    0.7270584    0.5713433    0.5875561    1.2414429 
##  POORHLTHref  SMOKDAY2ref   ECIGNOWref  CVDSTRK3ref  CVDCRHD4ref  DIABETE3ref 
##    1.5916537    0.9489101    1.0055157    1.1485423    1.4900713    0.7045203 
##  ADDEPEV2ref  HAVARTH3ref  CHCCOPD1ref      DEAFref     BLINDref    DECIDEref 
##    1.0926322    0.6405625    0.7819892    0.8722349    0.7052301    0.8972376 
##  DIFFWALKref  DIFFDRESref  DIFFALONref 
##    1.4225591    0.8281031    0.9148918

exp(cbind(AOR = coef(alr), confint(alr)))

##                    AOR     2.5 %    97.5 %
## (Intercept)  0.6147971 0.2741497 1.3787191
## SEXref       1.0032239 0.6809670 1.4779837
## MARITALref   1.1640170 0.7772945 1.7431431
## EDUCAref     1.0968694 0.7106660 1.6929507
## EMPLOY1ref   0.9304344 0.6288279 1.3767014
## INCOME2ref   1.0936332 0.5928953 2.0172762
## X_BMI5CATref 0.9874658 0.6457838 1.5099306
## X_AGEG5YRref 0.3722058 0.1846980 0.7500740
## CHILDRENref  1.0821373 0.7353183 1.5925365
## SDHBILLSref  1.4113204 0.7916935 2.5159047
## HOWSAFE1ref  0.9418437 0.4932220 1.7985198
## SDHFOODref   1.2109811 0.6374562 2.3005116
## SDHMEALSref  0.7771101 0.4124825 1.4640627
## SDHMONEYref  2.0064586 1.3154123 3.0605431
## SDHSTRESref  0.7270584 0.4098409 1.2898027
## GENHLTHref   0.5713433 0.3624788 0.9005580
## PHYSHLTHref  0.5875561 0.3508496 0.9839608
## MENTHLTHref  1.2414429 0.7724425 1.9952040
## POORHLTHref  1.5916537 0.8535740 2.9679458
## SMOKDAY2ref  0.9489101 0.5937287 1.5165687
## ECIGNOWref   1.0055157 0.3887655 2.6006982
## CVDSTRK3ref  1.1485423 0.4190040 3.1482981
## CVDCRHD4ref  1.4900713 0.5590216 3.9717832
## DIABETE3ref  0.7045203 0.3789899 1.3096626
## ADDEPEV2ref  1.0926322 0.6689059 1.7847730
## HAVARTH3ref  0.6405625 0.3833523 1.0703480
## CHCCOPD1ref  0.7819892 0.3638531 1.6806428
## DEAFref      0.8722349 0.4191417 1.8151229
## BLINDref     0.7052301 0.3410380 1.4583405
## DECIDEref    0.8972376 0.5279653 1.5247884
## DIFFWALKref  1.4225591 0.7770360 2.6043505
## DIFFDRESref  0.8281031 0.3711428 1.8476841
## DIFFALONref  0.9148918 0.4150162 2.0168535

exp(cbind(AOR = coef(alr), confint(alr)))[-1, ]

##                    AOR     2.5 %    97.5 %
## SEXref       1.0032239 0.6809670 1.4779837
## MARITALref   1.1640170 0.7772945 1.7431431
## EDUCAref     1.0968694 0.7106660 1.6929507
## EMPLOY1ref   0.9304344 0.6288279 1.3767014
## INCOME2ref   1.0936332 0.5928953 2.0172762
## X_BMI5CATref 0.9874658 0.6457838 1.5099306
## X_AGEG5YRref 0.3722058 0.1846980 0.7500740
## CHILDRENref  1.0821373 0.7353183 1.5925365
## SDHBILLSref  1.4113204 0.7916935 2.5159047
## HOWSAFE1ref  0.9418437 0.4932220 1.7985198
## SDHFOODref   1.2109811 0.6374562 2.3005116
## SDHMEALSref  0.7771101 0.4124825 1.4640627
## SDHMONEYref  2.0064586 1.3154123 3.0605431
## SDHSTRESref  0.7270584 0.4098409 1.2898027
## GENHLTHref   0.5713433 0.3624788 0.9005580
## PHYSHLTHref  0.5875561 0.3508496 0.9839608
## MENTHLTHref  1.2414429 0.7724425 1.9952040
## POORHLTHref  1.5916537 0.8535740 2.9679458
## SMOKDAY2ref  0.9489101 0.5937287 1.5165687
## ECIGNOWref   1.0055157 0.3887655 2.6006982
## CVDSTRK3ref  1.1485423 0.4190040 3.1482981
## CVDCRHD4ref  1.4900713 0.5590216 3.9717832
## DIABETE3ref  0.7045203 0.3789899 1.3096626
## ADDEPEV2ref  1.0926322 0.6689059 1.7847730
## HAVARTH3ref  0.6405625 0.3833523 1.0703480
## CHCCOPD1ref  0.7819892 0.3638531 1.6806428
## DEAFref      0.8722349 0.4191417 1.8151229
## BLINDref     0.7052301 0.3410380 1.4583405
## DECIDEref    0.8972376 0.5279653 1.5247884
## DIFFWALKref  1.4225591 0.7770360 2.6043505
## DIFFDRESref  0.8281031 0.3711428 1.8476841
## DIFFALONref  0.9148918 0.4150162 2.0168535

adj_or <- exp(cbind(AOR = coef(alr), confint(alr)))[-1,]
adj_or <- as.data.frame(adj_or)

# Combine All OR with Adjusted OR
stargazer(bind_cols(all_or, adj_or),summary=F,type = "html", digits=2)


	OR	X2.5..	X97.5..	hv	AOR	2.5 %	97.5 %

SEXref	0.93	0.74	1.18	SEX	1.00	0.68	1.48
MARITALref	1.48	1.17	1.87	MARITAL	1.16	0.78	1.74
EDUCAref	0.80	0.61	1.05	EDUCA	1.10	0.71	1.69
EMPLOY1ref	1.00	0.79	1.27	EMPLOY1	0.93	0.63	1.38
INCOME2ref	0.91	0.65	1.30	INCOME2	1.09	0.59	2.02
X_BMI5CATref	1.26	0.95	1.66	X_BMI5CAT	0.99	0.65	1.51
X_AGEG5YRref	0.46	0.30	0.72	X_AGEG5YR	0.37	0.18	0.75
CHILDRENref	0.88	0.69	1.12	CHILDREN	1.08	0.74	1.59
SDHBILLSref	1.19	0.83	1.71	SDHBILLS	1.41	0.79	2.52
HOWSAFE1ref	1.02	0.64	1.63	HOWSAFE1	0.94	0.49	1.80
SDHFOODref	1.19	0.88	1.61	SDHFOOD	1.21	0.64	2.30
SDHMEALSref	1.08	0.80	1.44	SDHMEALS	0.78	0.41	1.46
SDHMONEYref	1.65	1.29	2.10	SDHMONEY	2.01	1.32	3.06
SDHSTRESref	0.56	0.36	0.87	SDHSTRES	0.73	0.41	1.29
GENHLTHref	0.66	0.48	0.89	GENHLTH	0.57	0.36	0.90
PHYSHLTHref	1.26	0.87	1.82	PHYSHLTH	0.59	0.35	0.98
MENTHLTHref	1.82	1.27	2.60	MENTHLTH	1.24	0.77	2.00
POORHLTHref	1.59	0.99	2.55	POORHLTH	1.59	0.85	2.97
SMOKDAY2ref	0.73	0.53	1.01	SMOKDAY2	0.95	0.59	1.52
ECIGNOWref	0.73	0.36	1.47	ECIGNOW	1.01	0.39	2.60
CVDSTRK3ref	0.59	0.25	1.40	CVDSTRK3	1.15	0.42	3.15
CVDCRHD4ref	0.61	0.28	1.30	CVDCRHD4	1.49	0.56	3.97
DIABETE3ref	0.64	0.42	0.99	DIABETE3	0.70	0.38	1.31
ADDEPEV2ref	0.71	0.51	1.00	ADDEPEV2	1.09	0.67	1.78
HAVARTH3ref	0.75	0.53	1.06	HAVARTH3	0.64	0.38	1.07
CHCCOPD1ref	0.62	0.34	1.14	CHCCOPD1	0.78	0.36	1.68
DEAFref	0.63	0.34	1.20	DEAF	0.87	0.42	1.82
BLINDref	0.86	0.51	1.46	BLIND	0.71	0.34	1.46
DECIDEref	0.70	0.49	1.01	DECIDE	0.90	0.53	1.52
DIFFWALKref	0.90	0.62	1.31	DIFFWALK	1.42	0.78	2.60
DIFFDRESref	0.63	0.35	1.14	DIFFDRES	0.83	0.37	1.85
DIFFALONref	0.57	0.34	0.95	DIFFALON	0.91	0.42	2.02

table1 <- bind_cols(all_or, adj_or)

table1 <- table1 %>% 
  dplyr::select("hv", "OR", "X2.5..", "X97.5..", "AOR", "2.5 %", "97.5 %")


table1$orll <- table1$X2.5..
table1$orul <- table1$X97.5..
table1$aorll <- table1$"2.5 %"
table1$aorul <- table1$"97.5 %"
table1 <- table1 %>% dplyr::select(hv, OR, orll, orul, AOR, aorll, aorul)


#Plot OR
ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) +
  geom_errorbar(aes(ymin=orll, ymax=orul), 
                width = .2, position = position_dodge(.9))

#Plot AOR
ggplot(table1, aes(x=hv, y = AOR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_errorbar(aes(ymin=aorll, ymax=aorul), 
                width = .2, position = position_dodge(.9))

ORtable <- table1 %>% dplyr::select(hv, OR, orll, orul)
ORtable$type <- c("OR")
ORtable$stat <- ORtable$OR
ORtable$ll <- ORtable$orll
ORtable$ul <- ORtable$orul
ORtable <- ORtable %>% dplyr::select(hv, stat, type, ll, ul)

AORtable <- table1 %>% dplyr::select(hv, AOR, aorll, aorul)
AORtable$type <- c("AOR")
AORtable$stat <- AORtable$AOR
AORtable$ll <- AORtable$aorll
AORtable$ul <- AORtable$aorul
AORtable <- AORtable %>% dplyr::select(hv, stat, type, ll, ul)

table2 <- rbind(ORtable, AORtable)

figureoraor <- ggplot(table2, aes(x=hv, y = stat, fill = type)) +
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=ll, ymax=ul), 
                width = .2, position = position_dodge(.9))

#OR compared to Adjusted OR
print(figureoraor + ggtitle("OR & AOR"))

AA

ADSLEEP - Difficulty sleeping past 14 days

svydesAA<-subset(svydes,RACE == "Black only, non-Hispanic")

#Binomial logistic regression for each variable
or<-svyglm(ADSLEEP~SEX, family=quasibinomial,design=svydesAA)
#OR saved for each variable
a <- or_fun("SEX")

#Repeat

or <- svyglm(ADSLEEP~ MARITAL, family = quasibinomial, design = svydesAA)
b <- or_fun("MARITAL")

or <- svyglm(ADSLEEP~ EDUCA, family = quasibinomial, design = svydesAA)
c <- or_fun("EDUCA")

or <- svyglm(ADSLEEP~ EMPLOY1, family = quasibinomial, design = svydesAA)
d <- or_fun("EMPLOY1")

or <- svyglm(ADSLEEP~ INCOME2, family = quasibinomial, design = svydesAA)
e <- or_fun("INCOME2")

or <- svyglm(ADSLEEP~ X_BMI5CAT, family = quasibinomial, design = svydesAA)
f <- or_fun("X_BMI5CAT")

or <- svyglm(ADSLEEP~ X_AGEG5YR, family = quasibinomial, design = svydesAA)
h <- or_fun("X_AGEG5YR")

or <- svyglm(ADSLEEP~ CHILDREN, family = quasibinomial, design = svydesAA)
i <- or_fun("CHILDREN")

or <- svyglm(ADSLEEP~ SDHBILLS, family = quasibinomial, design = svydesAA)
j <- or_fun("SDHBILLS")

or <- svyglm(ADSLEEP~ HOWSAFE1, family = quasibinomial, design = svydesAA)
k <- or_fun("HOWSAFE1")

or <- svyglm(ADSLEEP~ SDHFOOD, family = quasibinomial, design = svydesAA)
l <- or_fun("SDHFOOD")

or <- svyglm(ADSLEEP~ SDHMEALS, family = quasibinomial, design = svydesAA)
m <- or_fun("SDHMEALS")

or <- svyglm(ADSLEEP~ SDHMONEY, family = quasibinomial, design = svydesAA)
n <- or_fun("SDHMONEY")

or <- svyglm(ADSLEEP~ SDHSTRES, family = quasibinomial, design = svydesAA)
o <- or_fun("SDHSTRES")

or <- svyglm(ADSLEEP~ GENHLTH, family = quasibinomial, design = svydesAA)
p <- or_fun("GENHLTH")

or <- svyglm(ADSLEEP~ PHYSHLTH, family = quasibinomial, design = svydesAA)
q <- or_fun("PHYSHLTH")

or <- svyglm(ADSLEEP~ MENTHLTH, family = quasibinomial, design = svydesAA)
r <- or_fun("MENTHLTH")

or <- svyglm(ADSLEEP~ POORHLTH, family = quasibinomial, design = svydesAA)
s <- or_fun("POORHLTH")

or <- svyglm(ADSLEEP~ SMOKDAY2, family = quasibinomial, design = svydesAA)
t <- or_fun("SMOKDAY2")

or <- svyglm(ADSLEEP~ ECIGNOW, family = quasibinomial, design = svydesAA)
u <- or_fun("ECIGNOW")

or <- svyglm(ADSLEEP~ CVDSTRK3, family = quasibinomial, design = svydesAA)
v <- or_fun("CVDSTRK3")

or <- svyglm(ADSLEEP~ CVDCRHD4, family = quasibinomial, design = svydesAA)
w <- or_fun("CVDCRHD4")

or <- svyglm(ADSLEEP~ DIABETE3, family = quasibinomial, design = svydesAA)
x <- or_fun("DIABETE3")

or <- svyglm(ADSLEEP~ ADDEPEV2, family = quasibinomial, design = svydesAA)
y <- or_fun("ADDEPEV2")

or <- svyglm(ADSLEEP~ HAVARTH3, family = quasibinomial, design = svydesAA)
z <- or_fun("HAVARTH3")

or <- svyglm(ADSLEEP~ CHCCOPD1, family = quasibinomial, design = svydesAA)
aa <- or_fun("CHCCOPD1")

or <- svyglm(ADSLEEP~ DEAF, family = quasibinomial, design = svydesAA)
bb <- or_fun("DEAF")

or <- svyglm(ADSLEEP~ BLIND, family = quasibinomial, design = svydesAA)
cc <- or_fun("BLIND")

or <- svyglm(ADSLEEP~ DECIDE, family = quasibinomial, design = svydesAA)
dd <- or_fun("DECIDE")

or <- svyglm(ADSLEEP~ DIFFWALK, family = quasibinomial, design = svydesAA)
ee <- or_fun("DIFFWALK")

or <- svyglm(ADSLEEP~ DIFFDRES, family = quasibinomial, design = svydesAA)
ff <- or_fun("DIFFDRES")

or <- svyglm(ADSLEEP~ DIFFALON, family = quasibinomial, design = svydesAA)
gg <- or_fun("DIFFALON")



# Combine OR into 1 table
all_or <- bind_rows(a, b, c, d, e, f, h, i, j, k, l, m,n,o,p,
                    q,r,s,t,u,v,w,x,y,z,aa,bb,cc,dd,ee,ff,gg)
all_or <- as.data.frame(all_or)


# Run all variables in logistic regression for adjusted OR
alr <- svyglm(ADSLEEP ~ SEX + MARITAL + EDUCA + EMPLOY1 + INCOME2 + X_BMI5CAT
               + X_AGEG5YR + CHILDREN  + SDHBILLS + HOWSAFE1 + SDHFOOD + 
                SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + MENTHLTH + 
                POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 +  CVDCRHD4 + DIABETE3 + 
                ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + DECIDE + 
                DIFFWALK + DIFFDRES + DIFFALON, family = quasibinomial, 
              design = svydesAA)

summary(alr)

## 
## Call:
## svyglm(formula = ADSLEEP ~ SEX + MARITAL + EDUCA + EMPLOY1 + 
##     INCOME2 + X_BMI5CAT + X_AGEG5YR + CHILDREN + SDHBILLS + HOWSAFE1 + 
##     SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + 
##     MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 + CVDCRHD4 + 
##     DIABETE3 + ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + 
##     DECIDE + DIFFWALK + DIFFDRES + DIFFALON, design = svydesAA, 
##     family = quasibinomial)
## 
## Survey design:
## subset(svydes, RACE == "Black only, non-Hispanic")
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -1.31234    1.26157  -1.040  0.30035   
## SEXref        0.43239    0.74355   0.582  0.56200   
## MARITALref   -0.39624    0.65724  -0.603  0.54774   
## EDUCAref      0.13641    0.55015   0.248  0.80461   
## EMPLOY1ref   -0.27664    0.61929  -0.447  0.65591   
## INCOME2ref    0.32929    0.75264   0.438  0.66255   
## X_BMI5CATref  0.47065    0.63570   0.740  0.46055   
## X_AGEG5YRref  0.85310    1.35886   0.628  0.53135   
## CHILDRENref  -1.19006    0.59083  -2.014  0.04626 * 
## SDHBILLSref   1.11476    0.83952   1.328  0.18679   
## HOWSAFE1ref   1.62134    0.80180   2.022  0.04543 * 
## SDHFOODref    1.74079    0.71772   2.425  0.01680 * 
## SDHMEALSref  -1.86351    0.94254  -1.977  0.05036 . 
## SDHMONEYref  -1.00565    0.66875  -1.504  0.13531   
## SDHSTRESref  -2.24050    0.85175  -2.630  0.00966 **
## GENHLTHref   -0.08057    0.69527  -0.116  0.90794   
## PHYSHLTHref   0.52187    0.80097   0.652  0.51597   
## MENTHLTHref   0.77870    0.78212   0.996  0.32147   
## POORHLTHref   2.91294    1.01200   2.878  0.00475 **
## SMOKDAY2ref   0.74699    0.68089   1.097  0.27484   
## ECIGNOWref   -0.58684    0.90278  -0.650  0.51693   
## CVDSTRK3ref   0.82761    1.22117   0.678  0.49928   
## CVDCRHD4ref   1.69176    1.69764   0.997  0.32103   
## DIABETE3ref   0.06063    0.90822   0.067  0.94689   
## ADDEPEV2ref  -0.76009    0.64882  -1.171  0.24376   
## HAVARTH3ref  -0.53299    0.70415  -0.757  0.45060   
## CHCCOPD1ref   0.59693    1.09170   0.547  0.58556   
## DEAFref      -0.94386    0.96071  -0.982  0.32788   
## BLINDref     -2.10381    0.78541  -2.679  0.00845 **
## DECIDEref     1.11495    0.67857   1.643  0.10303   
## DIFFWALKref   1.42801    0.96992   1.472  0.14360   
## DIFFDRESref   1.60295    0.91496   1.752  0.08238 . 
## DIFFALONref  -0.73187    0.95972  -0.763  0.44723   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 1.145577)
## 
## Number of Fisher Scoring iterations: 6

confint(alr)

##                    2.5 %      97.5 %
## (Intercept)  -3.78497723  1.16029429
## SEXref       -1.02493369  1.88971420
## MARITALref   -1.68440340  0.89191896
## EDUCAref     -0.94186690  1.21467812
## EMPLOY1ref   -1.49042940  0.93715906
## INCOME2ref   -1.14586949  1.80443951
## X_BMI5CATref -0.77529914  1.71659381
## X_AGEG5YRref -1.81021458  3.51640577
## CHILDRENref  -2.34805778 -0.03206520
## SDHBILLSref  -0.53066042  2.76018516
## HOWSAFE1ref   0.04983491  3.19283909
## SDHFOODref    0.33409400  3.14748835
## SDHMEALSref  -3.71085296 -0.01617393
## SDHMONEYref  -2.31637915  0.30507431
## SDHSTRESref  -3.90989333 -0.57110923
## GENHLTHref   -1.44326928  1.28212398
## PHYSHLTHref  -1.04801257  2.09174414
## MENTHLTHref  -0.75422479  2.31162190
## POORHLTHref   0.92946733  4.89641535
## SMOKDAY2ref  -0.58753290  2.08150938
## ECIGNOWref   -2.35625627  1.18257870
## CVDSTRK3ref  -1.56584785  3.22106535
## CVDCRHD4ref  -1.63555527  5.01907477
## DIABETE3ref  -1.71945543  1.84071311
## ADDEPEV2ref  -2.03174860  0.51157474
## HAVARTH3ref  -1.91309747  0.84710983
## CHCCOPD1ref  -1.54276704  2.73661910
## DEAFref      -2.82681730  0.93908767
## BLINDref     -3.64318698 -0.56443159
## DECIDEref    -0.21502311  2.44492100
## DIFFWALKref  -0.47300293  3.32901431
## DIFFDRESref  -0.19032439  3.39623375
## DIFFALONref  -2.61289272  1.14914621

exp(coef(alr))

##  (Intercept)       SEXref   MARITALref     EDUCAref   EMPLOY1ref   INCOME2ref 
##    0.2691890    1.5409364    0.6728437    1.1461467    0.7583311    1.3899740 
## X_BMI5CATref X_AGEG5YRref  CHILDRENref  SDHBILLSref  HOWSAFE1ref   SDHFOODref 
##    1.6010303    2.3469007    0.3042026    3.0488436    5.0598508    5.7018528 
##  SDHMEALSref  SDHMONEYref  SDHSTRESref   GENHLTHref  PHYSHLTHref  MENTHLTHref 
##    0.1551266    0.3658059    0.1064052    0.9225879    1.6851689    2.1786350 
##  POORHLTHref  SMOKDAY2ref   ECIGNOWref  CVDSTRK3ref  CVDCRHD4ref  DIABETE3ref 
##   18.4108717    2.1106337    0.5560824    2.2878414    5.4290260    1.0625045 
##  ADDEPEV2ref  HAVARTH3ref  CHCCOPD1ref      DEAFref     BLINDref    DECIDEref 
##    0.4676258    0.5868454    1.8165263    0.3891210    0.1219908    3.0494125 
##  DIFFWALKref  DIFFDRESref  DIFFALONref 
##    4.1703739    4.9676887    0.4810071

exp(cbind(AOR = coef(alr), confint(alr)))

##                     AOR      2.5 %      97.5 %
## (Intercept)   0.2691890 0.02270938   3.1908722
## SEXref        1.5409364 0.35882026   6.6174771
## MARITALref    0.6728437 0.18555510   2.4398070
## EDUCAref      1.1461467 0.38989925   3.3692094
## EMPLOY1ref    0.7583311 0.22527590   2.5527190
## INCOME2ref    1.3899740 0.31794734   6.0765647
## X_BMI5CATref  1.6010303 0.46056599   5.5655389
## X_AGEG5YRref  2.3469007 0.16361902  33.6632174
## CHILDRENref   0.3042026 0.09555457   0.9684434
## SDHBILLSref   3.0488436 0.58821637  15.8027688
## HOWSAFE1ref   5.0598508 1.05109756  24.3574824
## SDHFOODref    5.7018528 1.39667442  23.2775260
## SDHMEALSref   0.1551266 0.02445665   0.9839562
## SDHMONEYref   0.3658059 0.09863006   1.3567258
## SDHSTRESref   0.1064052 0.02004264   0.5648985
## GENHLTHref    0.9225879 0.23615444   3.6042870
## PHYSHLTHref   1.6851689 0.35063392   8.0990287
## MENTHLTHref   2.1786350 0.47037511  10.0907776
## POORHLTHref  18.4108717 2.53315948 133.8092599
## SMOKDAY2ref   2.1106337 0.55569655   8.0165598
## ECIGNOWref    0.5560824 0.09477437   3.2627771
## CVDSTRK3ref   2.2878414 0.20891081  25.0547982
## CVDCRHD4ref   5.4290260 0.19484415 151.2712781
## DIABETE3ref   1.0625045 0.17916369   6.3010300
## ADDEPEV2ref   0.4676258 0.13110607   1.6679157
## HAVARTH3ref   0.5868454 0.14762242   2.3328946
## CHCCOPD1ref   1.8165263 0.21378872  15.4347136
## DEAFref       0.3891210 0.05920097   2.5576469
## BLINDref      0.1219908 0.02616881   0.5686833
## DECIDEref     3.0494125 0.80652280  11.5296387
## DIFFWALKref   4.1703739 0.62312824  27.9108166
## DIFFDRESref   4.9676887 0.82669092  29.8514599
## DIFFALONref   0.4810071 0.07332214   3.1554976

exp(cbind(AOR = coef(alr), confint(alr)))[-1, ]

##                     AOR      2.5 %      97.5 %
## SEXref        1.5409364 0.35882026   6.6174771
## MARITALref    0.6728437 0.18555510   2.4398070
## EDUCAref      1.1461467 0.38989925   3.3692094
## EMPLOY1ref    0.7583311 0.22527590   2.5527190
## INCOME2ref    1.3899740 0.31794734   6.0765647
## X_BMI5CATref  1.6010303 0.46056599   5.5655389
## X_AGEG5YRref  2.3469007 0.16361902  33.6632174
## CHILDRENref   0.3042026 0.09555457   0.9684434
## SDHBILLSref   3.0488436 0.58821637  15.8027688
## HOWSAFE1ref   5.0598508 1.05109756  24.3574824
## SDHFOODref    5.7018528 1.39667442  23.2775260
## SDHMEALSref   0.1551266 0.02445665   0.9839562
## SDHMONEYref   0.3658059 0.09863006   1.3567258
## SDHSTRESref   0.1064052 0.02004264   0.5648985
## GENHLTHref    0.9225879 0.23615444   3.6042870
## PHYSHLTHref   1.6851689 0.35063392   8.0990287
## MENTHLTHref   2.1786350 0.47037511  10.0907776
## POORHLTHref  18.4108717 2.53315948 133.8092599
## SMOKDAY2ref   2.1106337 0.55569655   8.0165598
## ECIGNOWref    0.5560824 0.09477437   3.2627771
## CVDSTRK3ref   2.2878414 0.20891081  25.0547982
## CVDCRHD4ref   5.4290260 0.19484415 151.2712781
## DIABETE3ref   1.0625045 0.17916369   6.3010300
## ADDEPEV2ref   0.4676258 0.13110607   1.6679157
## HAVARTH3ref   0.5868454 0.14762242   2.3328946
## CHCCOPD1ref   1.8165263 0.21378872  15.4347136
## DEAFref       0.3891210 0.05920097   2.5576469
## BLINDref      0.1219908 0.02616881   0.5686833
## DECIDEref     3.0494125 0.80652280  11.5296387
## DIFFWALKref   4.1703739 0.62312824  27.9108166
## DIFFDRESref   4.9676887 0.82669092  29.8514599
## DIFFALONref   0.4810071 0.07332214   3.1554976

adj_or <- exp(cbind(AOR = coef(alr), confint(alr)))[-1,]
adj_or <- as.data.frame(adj_or)

# Combine All OR with Adjusted OR
stargazer(bind_cols(all_or, adj_or),summary=F,type = "html", digits=2)


	OR	X2.5..	X97.5..	hv	AOR	2.5 %	97.5 %

SEXref	2.42	1.28	4.56	SEX	1.54	0.36	6.62
MARITALref	2.54	1.27	5.07	MARITAL	0.67	0.19	2.44
EDUCAref	1.17	0.59	2.32	EDUCA	1.15	0.39	3.37
EMPLOY1ref	2.17	1.15	4.07	EMPLOY1	0.76	0.23	2.55
INCOME2ref	1.12	0.47	2.72	INCOME2	1.39	0.32	6.08
X_BMI5CATref	0.98	0.48	1.98	X_BMI5CAT	1.60	0.46	5.57
X_AGEG5YRref	0.36	0.14	0.92	X_AGEG5YR	2.35	0.16	33.66
CHILDRENref	0.82	0.44	1.53	CHILDREN	0.30	0.10	0.97
SDHBILLSref	0.39	0.19	0.78	SDHBILLS	3.05	0.59	15.80
HOWSAFE1ref	1.55	0.57	4.25	HOWSAFE1	5.06	1.05	24.36
SDHFOODref	0.49	0.26	0.94	SDHFOOD	5.70	1.40	23.28
SDHMEALSref	0.36	0.19	0.68	SDHMEALS	0.16	0.02	0.98
SDHMONEYref	0.49	0.25	0.98	SDHMONEY	0.37	0.10	1.36
SDHSTRESref	0.09	0.04	0.19	SDHSTRES	0.11	0.02	0.56
GENHLTHref	0.16	0.08	0.33	GENHLTH	0.92	0.24	3.60
PHYSHLTHref	9.45	4.18	21.35	PHYSHLTH	1.69	0.35	8.10
MENTHLTHref	6.96	3.26	14.86	MENTHLTH	2.18	0.47	10.09
POORHLTHref	8.93	3.46	23.09	POORHLTH	18.41	2.53	133.81
SMOKDAY2ref	0.52	0.26	1.04	SMOKDAY2	2.11	0.56	8.02
ECIGNOWref	1.42	0.38	5.34	ECIGNOW	0.56	0.09	3.26
CVDSTRK3ref	0.37	0.09	1.49	CVDSTRK3	2.29	0.21	25.05
CVDCRHD4ref	1.10	0.24	4.96	CVDCRHD4	5.43	0.19	151.27
DIABETE3ref	1.17	0.46	2.94	DIABETE3	1.06	0.18	6.30
ADDEPEV2ref	0.15	0.07	0.31	ADDEPEV2	0.47	0.13	1.67
HAVARTH3ref	0.38	0.18	0.79	HAVARTH3	0.59	0.15	2.33
CHCCOPD1ref	0.28	0.09	0.85	CHCCOPD1	1.82	0.21	15.43
DEAFref	0.18	0.05	0.64	DEAF	0.39	0.06	2.56
BLINDref	0.47	0.19	1.15	BLIND	0.12	0.03	0.57
DECIDEref	0.19	0.09	0.39	DECIDE	3.05	0.81	11.53
DIFFWALKref	0.37	0.18	0.77	DIFFWALK	4.17	0.62	27.91
DIFFDRESref	0.34	0.12	0.99	DIFFDRES	4.97	0.83	29.85
DIFFALONref	0.11	0.04	0.28	DIFFALON	0.48	0.07	3.16

table1 <- bind_cols(all_or, adj_or)

table1 <- table1 %>% 
  dplyr::select("hv", "OR", "X2.5..", "X97.5..", "AOR", "2.5 %", "97.5 %")


table1$orll <- table1$X2.5..
table1$orul <- table1$X97.5..
table1$aorll <- table1$"2.5 %"
table1$aorul <- table1$"97.5 %"
table1 <- table1 %>% dplyr::select(hv, OR, orll, orul, AOR, aorll, aorul)


#Plot OR
ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) +
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=orll, ymax=orul), 
                width = .2, position = position_dodge(.9))

#Plot AOR
ggplot(table1, aes(x=hv, y = AOR)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=aorll, ymax=aorul), 
                width = .2, position = position_dodge(.9))

ORtable <- table1 %>% dplyr::select(hv, OR, orll, orul)
ORtable$type <- c("OR")
ORtable$stat <- ORtable$OR
ORtable$ll <- ORtable$orll
ORtable$ul <- ORtable$orul
ORtable <- ORtable %>% dplyr::select(hv, stat, type, ll, ul)

AORtable <- table1 %>% dplyr::select(hv, AOR, aorll, aorul)
AORtable$type <- c("AOR")
AORtable$stat <- AORtable$AOR
AORtable$ll <- AORtable$aorll
AORtable$ul <- AORtable$aorul
AORtable <- AORtable %>% dplyr::select(hv, stat, type, ll, ul)

table2 <- rbind(ORtable, AORtable)

figureoraor <- ggplot(table2, aes(x=hv, y = stat, fill = type)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=ll, ymax=ul), 
                width = .2, position = position_dodge(.9))

#OR compared to Adjusted OR
print(figureoraor + ggtitle("OR & AOR"))

SLEPTIM1 - Hours per sleep at night

#Binomial logistic regression for each variable
or<-svyglm(SLEPTIM1~SEX, family=quasibinomial,design=svydesAA)
#OR saved for each variable
a <- or_fun("SEX")

#Repeat

or <- svyglm(SLEPTIM1~ MARITAL, family = quasibinomial, design = svydesAA)
b <- or_fun("MARITAL")

or <- svyglm(SLEPTIM1~ EDUCA, family = quasibinomial, design = svydesAA)
c <- or_fun("EDUCA")

or <- svyglm(SLEPTIM1~ EMPLOY1, family = quasibinomial, design = svydesAA)
d <- or_fun("EMPLOY1")

or <- svyglm(SLEPTIM1~ INCOME2, family = quasibinomial, design = svydesAA)
e <- or_fun("INCOME2")

or <- svyglm(SLEPTIM1~ X_BMI5CAT, family = quasibinomial, design = svydesAA)
f <- or_fun("X_BMI5CAT")

or <- svyglm(SLEPTIM1~ X_AGEG5YR, family = quasibinomial, design = svydesAA)
h <- or_fun("X_AGEG5YR")

or <- svyglm(SLEPTIM1~ CHILDREN, family = quasibinomial, design = svydesAA)
i <- or_fun("CHILDREN")

or <- svyglm(SLEPTIM1~ SDHBILLS, family = quasibinomial, design = svydesAA)
j <- or_fun("SDHBILLS")

or <- svyglm(SLEPTIM1~ HOWSAFE1, family = quasibinomial, design = svydesAA)
k <- or_fun("HOWSAFE1")

or <- svyglm(SLEPTIM1~ SDHFOOD, family = quasibinomial, design = svydesAA)
l <- or_fun("SDHFOOD")

or <- svyglm(SLEPTIM1~ SDHMEALS, family = quasibinomial, design = svydesAA)
m <- or_fun("SDHMEALS")

or <- svyglm(SLEPTIM1~ SDHMONEY, family = quasibinomial, design = svydesAA)
n <- or_fun("SDHMONEY")

or <- svyglm(SLEPTIM1~ SDHSTRES, family = quasibinomial, design = svydesAA)
o <- or_fun("SDHSTRES")

or <- svyglm(SLEPTIM1~ GENHLTH, family = quasibinomial, design = svydesAA)
p <- or_fun("GENHLTH")

or <- svyglm(SLEPTIM1~ PHYSHLTH, family = quasibinomial, design = svydesAA)
q <- or_fun("PHYSHLTH")

or <- svyglm(SLEPTIM1~ MENTHLTH, family = quasibinomial, design = svydesAA)
r <- or_fun("MENTHLTH")

or <- svyglm(SLEPTIM1~ POORHLTH, family = quasibinomial, design = svydesAA)
s <- or_fun("POORHLTH")

or <- svyglm(SLEPTIM1~ SMOKDAY2, family = quasibinomial, design = svydesAA)
t <- or_fun("SMOKDAY2")

or <- svyglm(SLEPTIM1~ ECIGNOW, family = quasibinomial, design = svydesAA)
u <- or_fun("ECIGNOW")

or <- svyglm(SLEPTIM1~ CVDSTRK3, family = quasibinomial, design = svydesAA)
v <- or_fun("CVDSTRK3")

or <- svyglm(SLEPTIM1~ CVDCRHD4, family = quasibinomial, design = svydesAA)
w <- or_fun("CVDCRHD4")

or <- svyglm(SLEPTIM1~ DIABETE3, family = quasibinomial, design = svydesAA)
x <- or_fun("DIABETE3")

or <- svyglm(SLEPTIM1~ ADDEPEV2, family = quasibinomial, design = svydesAA)
y <- or_fun("ADDEPEV2")

or <- svyglm(SLEPTIM1~ HAVARTH3, family = quasibinomial, design = svydesAA)
z <- or_fun("HAVARTH3")

or <- svyglm(SLEPTIM1~ CHCCOPD1, family = quasibinomial, design = svydesAA)
aa <- or_fun("CHCCOPD1")

or <- svyglm(SLEPTIM1~ DEAF, family = quasibinomial, design = svydesAA)
bb <- or_fun("DEAF")

or <- svyglm(SLEPTIM1~ BLIND, family = quasibinomial, design = svydesAA)
cc <- or_fun("BLIND")

or <- svyglm(SLEPTIM1~ DECIDE, family = quasibinomial, design = svydesAA)
dd <- or_fun("DECIDE")

or <- svyglm(SLEPTIM1~ DIFFWALK, family = quasibinomial, design = svydesAA)
ee <- or_fun("DIFFWALK")

or <- svyglm(SLEPTIM1~ DIFFDRES, family = quasibinomial, design = svydesAA)
ff <- or_fun("DIFFDRES")

or <- svyglm(SLEPTIM1~ DIFFALON, family = quasibinomial, design = svydesAA)
gg <- or_fun("DIFFALON")



# Combine OR into 1 table
all_or <- bind_rows(a, b, c, d, e, f, h, i, j, k, l, m,n,o,p,
                    q,r,s,t,u,v,w,x,y,z,aa,bb,cc,dd,ee,ff,gg)
all_or <- as.data.frame(all_or)


# Run all variables in logistic regression for adjusted OR
alr <- svyglm(SLEPTIM1 ~ SEX + MARITAL + EDUCA + EMPLOY1 + INCOME2 +
                X_BMI5CAT  + X_AGEG5YR + CHILDREN  + SDHBILLS + 
                HOWSAFE1 + SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + 
                PHYSHLTH + MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW +
                CVDSTRK3 +  CVDCRHD4 + DIABETE3 + ADDEPEV2 + HAVARTH3 + 
                CHCCOPD1 + DEAF + BLIND + DECIDE + DIFFWALK + DIFFDRES +
                DIFFALON, family = quasibinomial, design = svydesAA)

summary(alr)

## 
## Call:
## svyglm(formula = SLEPTIM1 ~ SEX + MARITAL + EDUCA + EMPLOY1 + 
##     INCOME2 + X_BMI5CAT + X_AGEG5YR + CHILDREN + SDHBILLS + HOWSAFE1 + 
##     SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + 
##     MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 + CVDCRHD4 + 
##     DIABETE3 + ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + 
##     DECIDE + DIFFWALK + DIFFDRES + DIFFALON, design = svydesAA, 
##     family = quasibinomial)
## 
## Survey design:
## subset(svydes, RACE == "Black only, non-Hispanic")
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.62742    0.90683  -0.692   0.4900  
## SEXref       -0.20130    0.49023  -0.411   0.6819  
## MARITALref   -0.23568    0.48871  -0.482   0.6303  
## EDUCAref      0.45842    0.46931   0.977   0.3302  
## EMPLOY1ref    0.13554    0.43661   0.310   0.7566  
## INCOME2ref   -0.42123    0.76287  -0.552   0.5816  
## X_BMI5CATref  0.20938    0.43897   0.477   0.6341  
## X_AGEG5YRref -2.03222    0.83837  -2.424   0.0165 *
## CHILDRENref   0.59703    0.46920   1.272   0.2051  
## SDHBILLSref  -0.07563    0.60316  -0.125   0.9004  
## HOWSAFE1ref  -0.18871    0.71208  -0.265   0.7913  
## SDHFOODref    0.13073    0.62349   0.210   0.8342  
## SDHMEALSref   0.46633    0.68921   0.677   0.4997  
## SDHMONEYref   0.88944    0.51011   1.744   0.0832 .
## SDHSTRESref  -0.87431    0.65981  -1.325   0.1871  
## GENHLTHref   -0.54989    0.52878  -1.040   0.3000  
## PHYSHLTHref  -1.00914    0.63925  -1.579   0.1164  
## MENTHLTHref   0.25294    0.60193   0.420   0.6749  
## POORHLTHref   0.02806    0.63465   0.044   0.9648  
## SMOKDAY2ref   0.24493    0.48012   0.510   0.6107  
## ECIGNOWref    0.28705    1.03603   0.277   0.7821  
## CVDSTRK3ref   0.18916    0.88087   0.215   0.8302  
## CVDCRHD4ref  -0.13472    1.10345  -0.122   0.9030  
## DIABETE3ref  -0.91694    0.70020  -1.310   0.1923  
## ADDEPEV2ref  -0.85519    0.61957  -1.380   0.1695  
## HAVARTH3ref  -0.13938    0.58886  -0.237   0.8132  
## CHCCOPD1ref   1.31833    0.78257   1.685   0.0941 .
## DEAFref      -1.41685    0.99942  -1.418   0.1583  
## BLINDref     -0.29090    0.73501  -0.396   0.6928  
## DECIDEref     0.55292    0.59251   0.933   0.3522  
## DIFFWALKref  -0.02382    0.59344  -0.040   0.9680  
## DIFFDRESref  -0.16828    0.79104  -0.213   0.8318  
## DIFFALONref   0.42188    0.86288   0.489   0.6256  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 0.9916475)
## 
## Number of Fisher Scoring iterations: 5

confint(alr)

##                   2.5 %     97.5 %
## (Intercept)  -2.4047672  1.1499266
## SEXref       -1.1621402  0.7595395
## MARITALref   -1.1935435  0.7221793
## EDUCAref     -0.4614196  1.3782519
## EMPLOY1ref   -0.7202046  0.9912854
## INCOME2ref   -1.9164255  1.0739585
## X_BMI5CATref -0.6509900  1.0697412
## X_AGEG5YRref -3.6753920 -0.3890556
## CHILDRENref  -0.3225854  1.5166404
## SDHBILLSref  -1.2577929  1.1065343
## HOWSAFE1ref  -1.5843556  1.2069308
## SDHFOODref   -1.0912911  1.3527610
## SDHMEALSref  -0.8845012  1.8171547
## SDHMONEYref  -0.1103658  1.8892369
## SDHSTRESref  -2.1675148  0.4188932
## GENHLTHref   -1.5862819  0.4864941
## PHYSHLTHref  -2.2620353  0.2437600
## MENTHLTHref  -0.9268279  1.4326992
## POORHLTHref  -1.2158312  1.2719467
## SMOKDAY2ref  -0.6960900  1.1859417
## ECIGNOWref   -1.7435352  2.3176414
## CVDSTRK3ref  -1.5373195  1.9156442
## CVDCRHD4ref  -2.2974362  2.0279976
## DIABETE3ref  -2.2893055  0.4554217
## ADDEPEV2ref  -2.0695211  0.3591419
## HAVARTH3ref  -1.2935248  1.0147741
## CHCCOPD1ref  -0.2154827  2.8521344
## DEAFref      -3.3756745  0.5419682
## BLINDref     -1.7314932  1.1496864
## DECIDEref    -0.6083741  1.7142057
## DIFFWALKref  -1.1869441  1.1393136
## DIFFDRESref  -1.7186805  1.3821237
## DIFFALONref  -1.2693386  2.1131084

exp(coef(alr))

##  (Intercept)       SEXref   MARITALref     EDUCAref   EMPLOY1ref   INCOME2ref 
##    0.5339675    0.8176668    0.7900318    1.5815670    1.1451555    0.6562369 
## X_BMI5CATref X_AGEG5YRref  CHILDRENref  SDHBILLSref  HOWSAFE1ref   SDHFOODref 
##    1.2329080    0.1310438    1.8167106    0.9271598    0.8280246    1.1396657 
##  SDHMEALSref  SDHMONEYref  SDHSTRESref   GENHLTHref  PHYSHLTHref  MENTHLTHref 
##    1.5941278    2.4337555    0.4171494    0.5770110    0.3645332    1.2878004 
##  POORHLTHref  SMOKDAY2ref   ECIGNOWref  CVDSTRK3ref  CVDCRHD4ref  DIABETE3ref 
##    1.0284551    1.2775266    1.3324949    1.2082371    0.8739612    0.3997396 
##  ADDEPEV2ref  HAVARTH3ref  CHCCOPD1ref      DEAFref     BLINDref    DECIDEref 
##    0.4252026    0.8699014    3.7371597    0.2424758    0.7475879    1.7383142 
##  DIFFWALKref  DIFFDRESref  DIFFALONref 
##    0.9764661    0.8451186    1.5248330

exp(cbind(AOR = coef(alr), confint(alr)))

##                    AOR      2.5 %     97.5 %
## (Intercept)  0.5339675 0.09028651  3.1579610
## SEXref       0.8176668 0.31281597  2.1372917
## MARITALref   0.7900318 0.30314518  2.0589153
## EDUCAref     1.5815670 0.63038808  3.9679592
## EMPLOY1ref   1.1451555 0.48665266  2.6946961
## INCOME2ref   0.6562369 0.14713194  2.9269430
## X_BMI5CATref 1.2329080 0.52152923  2.9146251
## X_AGEG5YRref 0.1310438 0.02533947  0.6776966
## CHILDRENref  1.8167106 0.72427408  4.5568902
## SDHBILLSref  0.9271598 0.28428078  3.0238603
## HOWSAFE1ref  0.8280246 0.20507990  3.3432079
## SDHFOODref   1.1396657 0.33578270  3.8680907
## SDHMEALSref  1.5941278 0.41292010  6.1543225
## SDHMONEYref  2.4337555 0.89550650  6.6143192
## SDHSTRESref  0.4171494 0.11446172  1.5202780
## GENHLTHref   0.5770110 0.20468524  1.6266034
## PHYSHLTHref  0.3645332 0.10413832  1.2760380
## MENTHLTHref  1.2878004 0.39580726  4.1899935
## POORHLTHref  1.0284551 0.29646349  3.5677913
## SMOKDAY2ref  1.2775266 0.49853075  3.2737684
## ECIGNOWref   1.3324949 0.17490099 10.1517020
## CVDSTRK3ref  1.2082371 0.21495651  6.7913123
## CVDCRHD4ref  0.8739612 0.10051622  7.5988548
## DIABETE3ref  0.3997396 0.10133682  1.5768381
## ADDEPEV2ref  0.4252026 0.12624623  1.4321001
## HAVARTH3ref  0.8699014 0.27430220  2.7587400
## CHCCOPD1ref  3.7371597 0.80615223 17.3247208
## DEAFref      0.2424758 0.03419504  1.7193876
## BLINDref     0.7475879 0.17701989  3.1572027
## DECIDEref    1.7383142 0.54423500  5.5522635
## DIFFWALKref  0.9764661 0.30515236  3.1246230
## DIFFDRESref  0.8451186 0.17930259  3.9833522
## DIFFALONref  1.5248330 0.28101742  8.2739196

exp(cbind(AOR = coef(alr), confint(alr)))[-1, ]

##                    AOR      2.5 %     97.5 %
## SEXref       0.8176668 0.31281597  2.1372917
## MARITALref   0.7900318 0.30314518  2.0589153
## EDUCAref     1.5815670 0.63038808  3.9679592
## EMPLOY1ref   1.1451555 0.48665266  2.6946961
## INCOME2ref   0.6562369 0.14713194  2.9269430
## X_BMI5CATref 1.2329080 0.52152923  2.9146251
## X_AGEG5YRref 0.1310438 0.02533947  0.6776966
## CHILDRENref  1.8167106 0.72427408  4.5568902
## SDHBILLSref  0.9271598 0.28428078  3.0238603
## HOWSAFE1ref  0.8280246 0.20507990  3.3432079
## SDHFOODref   1.1396657 0.33578270  3.8680907
## SDHMEALSref  1.5941278 0.41292010  6.1543225
## SDHMONEYref  2.4337555 0.89550650  6.6143192
## SDHSTRESref  0.4171494 0.11446172  1.5202780
## GENHLTHref   0.5770110 0.20468524  1.6266034
## PHYSHLTHref  0.3645332 0.10413832  1.2760380
## MENTHLTHref  1.2878004 0.39580726  4.1899935
## POORHLTHref  1.0284551 0.29646349  3.5677913
## SMOKDAY2ref  1.2775266 0.49853075  3.2737684
## ECIGNOWref   1.3324949 0.17490099 10.1517020
## CVDSTRK3ref  1.2082371 0.21495651  6.7913123
## CVDCRHD4ref  0.8739612 0.10051622  7.5988548
## DIABETE3ref  0.3997396 0.10133682  1.5768381
## ADDEPEV2ref  0.4252026 0.12624623  1.4321001
## HAVARTH3ref  0.8699014 0.27430220  2.7587400
## CHCCOPD1ref  3.7371597 0.80615223 17.3247208
## DEAFref      0.2424758 0.03419504  1.7193876
## BLINDref     0.7475879 0.17701989  3.1572027
## DECIDEref    1.7383142 0.54423500  5.5522635
## DIFFWALKref  0.9764661 0.30515236  3.1246230
## DIFFDRESref  0.8451186 0.17930259  3.9833522
## DIFFALONref  1.5248330 0.28101742  8.2739196

adj_or <- exp(cbind(AOR = coef(alr), confint(alr)))[-1,]
adj_or <- as.data.frame(adj_or)

# Combine All OR with Adjusted OR
stargazer(bind_cols(all_or, adj_or),summary=F,type = "html", digits=2)


	OR	X2.5..	X97.5..	hv	AOR	2.5 %	97.5 %

SEXref	0.95	0.61	1.48	SEX	0.82	0.31	2.14
MARITALref	1.97	1.25	3.09	MARITAL	0.79	0.30	2.06
EDUCAref	1.00	0.62	1.60	EDUCA	1.58	0.63	3.97
EMPLOY1ref	1.25	0.80	1.94	EMPLOY1	1.15	0.49	2.69
INCOME2ref	1.13	0.60	2.11	INCOME2	0.66	0.15	2.93
X_BMI5CATref	1.11	0.68	1.82	X_BMI5CAT	1.23	0.52	2.91
X_AGEG5YRref	0.23	0.10	0.53	X_AGEG5YR	0.13	0.03	0.68
CHILDRENref	0.94	0.61	1.46	CHILDREN	1.82	0.72	4.56
SDHBILLSref	1.35	0.73	2.47	SDHBILLS	0.93	0.28	3.02
HOWSAFE1ref	1.19	0.49	2.87	HOWSAFE1	0.83	0.21	3.34
SDHFOODref	1.42	0.85	2.37	SDHFOOD	1.14	0.34	3.87
SDHMEALSref	1.33	0.80	2.22	SDHMEALS	1.59	0.41	6.15
SDHMONEYref	1.68	1.08	2.63	SDHMONEY	2.43	0.90	6.61
SDHSTRESref	0.50	0.23	1.08	SDHSTRES	0.42	0.11	1.52
GENHLTHref	0.67	0.37	1.24	GENHLTH	0.58	0.20	1.63
PHYSHLTHref	0.94	0.47	1.90	PHYSHLTH	0.36	0.10	1.28
MENTHLTHref	1.95	0.96	3.98	MENTHLTH	1.29	0.40	4.19
POORHLTHref	0.96	0.42	2.17	POORHLTH	1.03	0.30	3.57
SMOKDAY2ref	0.89	0.50	1.56	SMOKDAY2	1.28	0.50	3.27
ECIGNOWref	1.27	0.43	3.74	ECIGNOW	1.33	0.17	10.15
CVDSTRK3ref	0.53	0.14	1.90	CVDSTRK3	1.21	0.21	6.79
CVDCRHD4ref	0.73	0.18	3.00	CVDCRHD4	0.87	0.10	7.60
DIABETE3ref	0.73	0.32	1.68	DIABETE3	0.40	0.10	1.58
ADDEPEV2ref	0.45	0.23	0.89	ADDEPEV2	0.43	0.13	1.43
HAVARTH3ref	0.85	0.45	1.62	HAVARTH3	0.87	0.27	2.76
CHCCOPD1ref	1.46	0.54	3.95	CHCCOPD1	3.74	0.81	17.32
DEAFref	0.30	0.08	1.04	DEAF	0.24	0.03	1.72
BLINDref	1.14	0.48	2.71	BLIND	0.75	0.18	3.16
DECIDEref	0.73	0.37	1.42	DECIDE	1.74	0.54	5.55
DIFFWALKref	0.95	0.50	1.80	DIFFWALK	0.98	0.31	3.12
DIFFDRESref	0.77	0.28	2.14	DIFFDRES	0.85	0.18	3.98
DIFFALONref	0.62	0.25	1.57	DIFFALON	1.52	0.28	8.27

table1 <- bind_cols(all_or, adj_or)

table1 <- table1 %>% 
  dplyr::select("hv", "OR", "X2.5..", "X97.5..", "AOR", "2.5 %", "97.5 %")


table1$orll <- table1$X2.5..
table1$orul <- table1$X97.5..
table1$aorll <- table1$"2.5 %"
table1$aorul <- table1$"97.5 %"
table1 <- table1 %>% dplyr::select(hv, OR, orll, orul, AOR, aorll, aorul)


#Plot OR
ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) +
  geom_errorbar(aes(ymin=orll, ymax=orul), 
                width = .2, position = position_dodge(.9))

# Plot AOR
ggplot(table1, aes(x=hv, y = AOR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_errorbar(aes(ymin=aorll, ymax=aorul), 
                width = .2, position = position_dodge(.9))

ORtable <- table1 %>% dplyr::select(hv, OR, orll, orul)
ORtable$type <- c("OR")
ORtable$stat <- ORtable$OR
ORtable$ll <- ORtable$orll
ORtable$ul <- ORtable$orul
ORtable <- ORtable %>% dplyr::select(hv, stat, type, ll, ul)

AORtable <- table1 %>% dplyr::select(hv, AOR, aorll, aorul)
AORtable$type <- c("AOR")
AORtable$stat <- AORtable$AOR
AORtable$ll <- AORtable$aorll
AORtable$ul <- AORtable$aorul
AORtable <- AORtable %>% dplyr::select(hv, stat, type, ll, ul)

table2 <- rbind(ORtable, AORtable)

figureoraor <- ggplot(table2, aes(x=hv, y = stat, fill = type)) +
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=ll, ymax=ul), 
                width = .2, position = position_dodge(.9))

#OR compared to Adjusted OR
print(figureoraor + ggtitle("OR & AOR"))

White

ADSLEEP - Difficulty sleeping past 14 days

svydesW<-subset(svydes,RACE == "White only, non-Hispanic")

#Binomial logistic regression for each variable
or<-svyglm(ADSLEEP~SEX, family=quasibinomial,design=svydesW)
#OR saved for each variable
a <- or_fun("SEX")

#Repeat
or <- svyglm(ADSLEEP~ MARITAL, family = quasibinomial, design = svydesW)
b <- or_fun("MARITAL")

or <- svyglm(ADSLEEP~ EDUCA, family = quasibinomial, design = svydesW)
c <- or_fun("EDUCA")

or <- svyglm(ADSLEEP~ EMPLOY1, family = quasibinomial, design = svydesW)
d <- or_fun("EMPLOY1")

or <- svyglm(ADSLEEP~ INCOME2, family = quasibinomial, design = svydesW)
e <- or_fun("INCOME2")

or <- svyglm(ADSLEEP~ X_BMI5CAT, family = quasibinomial, design = svydesW)
f <- or_fun("X_BMI5CAT")

or <- svyglm(ADSLEEP~ X_AGEG5YR, family = quasibinomial, design = svydesW)
h <- or_fun("X_AGEG5YR")

or <- svyglm(ADSLEEP~ CHILDREN, family = quasibinomial, design = svydesW)
i <- or_fun("CHILDREN")

or <- svyglm(ADSLEEP~ SDHBILLS, family = quasibinomial, design = svydesW)
j <- or_fun("SDHBILLS")

or <- svyglm(ADSLEEP~ HOWSAFE1, family = quasibinomial, design = svydesW)
k <- or_fun("HOWSAFE1")

or <- svyglm(ADSLEEP~ SDHFOOD, family = quasibinomial, design = svydesW)
l <- or_fun("SDHFOOD")

or <- svyglm(ADSLEEP~ SDHMEALS, family = quasibinomial, design = svydesW)
m <- or_fun("SDHMEALS")

or <- svyglm(ADSLEEP~ SDHMONEY, family = quasibinomial, design = svydesW)
n <- or_fun("SDHMONEY")

or <- svyglm(ADSLEEP~ SDHSTRES, family = quasibinomial, design = svydesW)
o <- or_fun("SDHSTRES")

or <- svyglm(ADSLEEP~ GENHLTH, family = quasibinomial, design = svydesW)
p <- or_fun("GENHLTH")

or <- svyglm(ADSLEEP~ PHYSHLTH, family = quasibinomial, design = svydesW)
q <- or_fun("PHYSHLTH")

or <- svyglm(ADSLEEP~ MENTHLTH, family = quasibinomial, design = svydesW)
r <- or_fun("MENTHLTH")

or <- svyglm(ADSLEEP~ POORHLTH, family = quasibinomial, design = svydesW)
s <- or_fun("POORHLTH")

or <- svyglm(ADSLEEP~ SMOKDAY2, family = quasibinomial, design = svydesW)
t <- or_fun("SMOKDAY2")

or <- svyglm(ADSLEEP~ ECIGNOW, family = quasibinomial, design = svydesW)
u <- or_fun("ECIGNOW")

or <- svyglm(ADSLEEP~ CVDSTRK3, family = quasibinomial, design = svydesW)
v <- or_fun("CVDSTRK3")

or <- svyglm(ADSLEEP~ CVDCRHD4, family = quasibinomial, design = svydesW)
w <- or_fun("CVDCRHD4")

or <- svyglm(ADSLEEP~ DIABETE3, family = quasibinomial, design = svydesW)
x <- or_fun("DIABETE3")

or <- svyglm(ADSLEEP~ ADDEPEV2, family = quasibinomial, design = svydesW)
y <- or_fun("ADDEPEV2")

or <- svyglm(ADSLEEP~ HAVARTH3, family = quasibinomial, design = svydesW)
z <- or_fun("HAVARTH3")

or <- svyglm(ADSLEEP~ CHCCOPD1, family = quasibinomial, design = svydesW)
aa <- or_fun("CHCCOPD1")

or <- svyglm(ADSLEEP~ DEAF, family = quasibinomial, design = svydesW)
bb <- or_fun("DEAF")

or <- svyglm(ADSLEEP~ BLIND, family = quasibinomial, design = svydesW)
cc <- or_fun("BLIND")

or <- svyglm(ADSLEEP~ DECIDE, family = quasibinomial, design = svydesW)
dd <- or_fun("DECIDE")

or <- svyglm(ADSLEEP~ DIFFWALK, family = quasibinomial, design = svydesW)
ee <- or_fun("DIFFWALK")

or <- svyglm(ADSLEEP~ DIFFDRES, family = quasibinomial, design = svydesW)
ff <- or_fun("DIFFDRES")

or <- svyglm(ADSLEEP~ DIFFALON, family = quasibinomial, design = svydesW)
gg <- or_fun("DIFFALON")



# Combine OR into 1 table
all_or <- bind_rows(a, b, c, d, e, f,h, i, j, k, l, m,n,o,p,
                    q,r,s,t,u,v,w,x,y,z,aa,bb,cc,dd,ee,ff,gg)
all_or <- as.data.frame(all_or)


# Run all variables in logistic regression for adjusted OR
alr <- svyglm(ADSLEEP ~ SEX + MARITAL + EDUCA + EMPLOY1 + INCOME2 + X_BMI5CAT
               + X_AGEG5YR + CHILDREN  + SDHBILLS + HOWSAFE1 + SDHFOOD + 
                SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + MENTHLTH + 
                POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 +  CVDCRHD4 + DIABETE3 + 
                ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + DECIDE + 
                DIFFWALK + DIFFDRES + DIFFALON, family = quasibinomial, 
              design = svydesW)

summary(alr)

## 
## Call:
## svyglm(formula = ADSLEEP ~ SEX + MARITAL + EDUCA + EMPLOY1 + 
##     INCOME2 + X_BMI5CAT + X_AGEG5YR + CHILDREN + SDHBILLS + HOWSAFE1 + 
##     SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + 
##     MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 + CVDCRHD4 + 
##     DIABETE3 + ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + 
##     DECIDE + DIFFWALK + DIFFDRES + DIFFALON, design = svydesW, 
##     family = quasibinomial)
## 
## Survey design:
## subset(svydes, RACE == "White only, non-Hispanic")
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.029806   0.202191   5.093 3.63e-07 ***
## SEXref        0.155904   0.086069   1.811 0.070131 .  
## MARITALref   -0.015277   0.085356  -0.179 0.857963    
## EDUCAref     -0.192942   0.093910  -2.055 0.039967 *  
## EMPLOY1ref   -0.091723   0.090886  -1.009 0.312910    
## INCOME2ref    0.007006   0.117050   0.060 0.952274    
## X_BMI5CATref  0.197275   0.094766   2.082 0.037411 *  
## X_AGEG5YRref -0.080365   0.127179  -0.632 0.527471    
## CHILDRENref  -0.067428   0.097282  -0.693 0.488266    
## SDHBILLSref  -0.258732   0.168803  -1.533 0.125389    
## HOWSAFE1ref   0.059096   0.255823   0.231 0.817319    
## SDHFOODref   -0.107492   0.156874  -0.685 0.493236    
## SDHMEALSref  -0.199265   0.135536  -1.470 0.141559    
## SDHMONEYref   0.096672   0.092586   1.044 0.296465    
## SDHSTRESref  -1.142877   0.122262  -9.348  < 2e-16 ***
## GENHLTHref   -0.370999   0.111057  -3.341 0.000841 ***
## PHYSHLTHref   0.401980   0.110949   3.623 0.000293 ***
## MENTHLTHref   0.350342   0.113853   3.077 0.002099 ** 
## POORHLTHref   0.087465   0.133658   0.654 0.512884    
## SMOKDAY2ref  -0.063023   0.122781  -0.513 0.607764    
## ECIGNOWref   -0.197395   0.204339  -0.966 0.334075    
## CVDSTRK3ref   0.273279   0.214554   1.274 0.202815    
## CVDCRHD4ref   0.249225   0.166348   1.498 0.134129    
## DIABETE3ref  -0.064444   0.125948  -0.512 0.608901    
## ADDEPEV2ref  -0.430179   0.094681  -4.543 5.64e-06 ***
## HAVARTH3ref  -0.181831   0.090980  -1.999 0.045698 *  
## CHCCOPD1ref  -0.042012   0.154690  -0.272 0.785949    
## DEAFref      -0.199872   0.138532  -1.443 0.149131    
## BLINDref      0.045269   0.191497   0.236 0.813135    
## DECIDEref    -0.509417   0.124774  -4.083 4.51e-05 ***
## DIFFWALKref  -0.088769   0.122289  -0.726 0.467929    
## DIFFDRESref  -0.254140   0.190235  -1.336 0.181623    
## DIFFALONref  -0.108623   0.159349  -0.682 0.495474    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 1.028312)
## 
## Number of Fisher Scoring iterations: 4

confint(alr)

##                    2.5 %       97.5 %
## (Intercept)   0.63351982  1.426092049
## SEXref       -0.01278870  0.324596656
## MARITALref   -0.18257200  0.152018617
## EDUCAref     -0.37700257 -0.008880921
## EMPLOY1ref   -0.26985610  0.086409173
## INCOME2ref   -0.22240763  0.236419455
## X_BMI5CATref  0.01153696  0.383013356
## X_AGEG5YRref -0.32963134  0.168900813
## CHILDRENref  -0.25809776  0.123242571
## SDHBILLSref  -0.58957883  0.072114850
## HOWSAFE1ref  -0.44230819  0.560501137
## SDHFOODref   -0.41496040  0.199975788
## SDHMEALSref  -0.46491110  0.066380674
## SDHMONEYref  -0.08479312  0.278137306
## SDHSTRESref  -1.38250681 -0.903247870
## GENHLTHref   -0.58866699 -0.153331945
## PHYSHLTHref   0.18452354  0.619436369
## MENTHLTHref   0.12719481  0.573488570
## POORHLTHref  -0.17449996  0.349429371
## SMOKDAY2ref  -0.30366919  0.177623687
## ECIGNOWref   -0.59789231  0.203102852
## CVDSTRK3ref  -0.14723895  0.693797569
## CVDCRHD4ref  -0.07681153  0.575262515
## DIABETE3ref  -0.31129839  0.182410472
## ADDEPEV2ref  -0.61575126 -0.244607410
## HAVARTH3ref  -0.36014879 -0.003513027
## CHCCOPD1ref  -0.34519955  0.261175735
## DEAFref      -0.47138911  0.071645073
## BLINDref     -0.33005927  0.420596625
## DECIDEref    -0.75397027 -0.264863270
## DIFFWALKref  -0.32845048  0.150912296
## DIFFDRESref  -0.62699314  0.118714048
## DIFFALONref  -0.42094207  0.203695372

exp(coef(alr))

##  (Intercept)       SEXref   MARITALref     EDUCAref   EMPLOY1ref   INCOME2ref 
##    2.8005223    1.1687140    0.9848394    0.8245300    0.9123574    1.0070305 
## X_BMI5CATref X_AGEG5YRref  CHILDRENref  SDHBILLSref  HOWSAFE1ref   SDHFOODref 
##    1.2180792    0.9227792    0.9347954    0.7720299    1.0608776    0.8980834 
##  SDHMEALSref  SDHMONEYref  SDHSTRESref   GENHLTHref  PHYSHLTHref  MENTHLTHref 
##    0.8193326    1.1014991    0.3189001    0.6900443    1.4947814    1.4195525 
##  POORHLTHref  SMOKDAY2ref   ECIGNOWref  CVDSTRK3ref  CVDCRHD4ref  DIABETE3ref 
##    1.0914037    0.9389221    0.8208666    1.3142673    1.2830313    0.9375887 
##  ADDEPEV2ref  HAVARTH3ref  CHCCOPD1ref      DEAFref     BLINDref    DECIDEref 
##    0.6503924    0.8337423    0.9588584    0.8188355    1.0463089    0.6008459 
##  DIFFWALKref  DIFFDRESref  DIFFALONref 
##    0.9150568    0.7755836    0.8970682

exp(cbind(AOR = coef(alr), confint(alr)))

##                    AOR     2.5 %    97.5 %
## (Intercept)  2.8005223 1.8842311 4.1624009
## SEXref       1.1687140 0.9872927 1.3834725
## MARITALref   0.9848394 0.8331247 1.1641819
## EDUCAref     0.8245300 0.6859143 0.9911584
## EMPLOY1ref   0.9123574 0.7634894 1.0902523
## INCOME2ref   1.0070305 0.8005890 1.2667055
## X_BMI5CATref 1.2180792 1.0116038 1.4666976
## X_AGEG5YRref 0.9227792 0.7191888 1.1840027
## CHILDRENref  0.9347954 0.7725197 1.1311588
## SDHBILLSref  0.7720299 0.5545608 1.0747788
## HOWSAFE1ref  1.0608776 0.6425516 1.7515500
## SDHFOODref   0.8980834 0.6603664 1.2213732
## SDHMEALSref  0.8193326 0.6281909 1.0686334
## SDHMONEYref  1.1014991 0.9187023 1.3206675
## SDHSTRESref  0.3189001 0.2509487 0.4052513
## GENHLTHref   0.6900443 0.5550667 0.8578449
## PHYSHLTHref  1.4947814 1.2026453 1.8578806
## MENTHLTHref  1.4195525 1.1356382 1.7744465
## POORHLTHref  1.0914037 0.8398769 1.4182580
## SMOKDAY2ref  0.9389221 0.7381050 1.1943758
## ECIGNOWref   0.8208666 0.5499696 1.2251985
## CVDSTRK3ref  1.3142673 0.8630877 2.0013012
## CVDCRHD4ref  1.2830313 0.9260644 1.7775971
## DIABETE3ref  0.9375887 0.7324953 1.2001067
## ADDEPEV2ref  0.6503924 0.5402349 0.7830119
## HAVARTH3ref  0.8337423 0.6975725 0.9964931
## CHCCOPD1ref  0.9588584 0.7080790 1.2984558
## DEAFref      0.8188355 0.6241347 1.0742740
## BLINDref     1.0463089 0.7188811 1.5228699
## DECIDEref    0.6008459 0.4704948 0.7673109
## DIFFWALKref  0.9150568 0.7200386 1.1628947
## DIFFDRESref  0.7755836 0.5341956 1.1260479
## DIFFALONref  0.8970682 0.6564281 1.2259246

exp(cbind(AOR = coef(alr), confint(alr)))[-1, ]

##                    AOR     2.5 %    97.5 %
## SEXref       1.1687140 0.9872927 1.3834725
## MARITALref   0.9848394 0.8331247 1.1641819
## EDUCAref     0.8245300 0.6859143 0.9911584
## EMPLOY1ref   0.9123574 0.7634894 1.0902523
## INCOME2ref   1.0070305 0.8005890 1.2667055
## X_BMI5CATref 1.2180792 1.0116038 1.4666976
## X_AGEG5YRref 0.9227792 0.7191888 1.1840027
## CHILDRENref  0.9347954 0.7725197 1.1311588
## SDHBILLSref  0.7720299 0.5545608 1.0747788
## HOWSAFE1ref  1.0608776 0.6425516 1.7515500
## SDHFOODref   0.8980834 0.6603664 1.2213732
## SDHMEALSref  0.8193326 0.6281909 1.0686334
## SDHMONEYref  1.1014991 0.9187023 1.3206675
## SDHSTRESref  0.3189001 0.2509487 0.4052513
## GENHLTHref   0.6900443 0.5550667 0.8578449
## PHYSHLTHref  1.4947814 1.2026453 1.8578806
## MENTHLTHref  1.4195525 1.1356382 1.7744465
## POORHLTHref  1.0914037 0.8398769 1.4182580
## SMOKDAY2ref  0.9389221 0.7381050 1.1943758
## ECIGNOWref   0.8208666 0.5499696 1.2251985
## CVDSTRK3ref  1.3142673 0.8630877 2.0013012
## CVDCRHD4ref  1.2830313 0.9260644 1.7775971
## DIABETE3ref  0.9375887 0.7324953 1.2001067
## ADDEPEV2ref  0.6503924 0.5402349 0.7830119
## HAVARTH3ref  0.8337423 0.6975725 0.9964931
## CHCCOPD1ref  0.9588584 0.7080790 1.2984558
## DEAFref      0.8188355 0.6241347 1.0742740
## BLINDref     1.0463089 0.7188811 1.5228699
## DECIDEref    0.6008459 0.4704948 0.7673109
## DIFFWALKref  0.9150568 0.7200386 1.1628947
## DIFFDRESref  0.7755836 0.5341956 1.1260479
## DIFFALONref  0.8970682 0.6564281 1.2259246

adj_or <- exp(cbind(AOR = coef(alr), confint(alr)))[-1,]
adj_or <- as.data.frame(adj_or)

# Combine All OR with Adjusted OR
stargazer(bind_cols(all_or, adj_or),summary=F,type = "html", digits=2)


	OR	X2.5..	X97.5..	hv	AOR	2.5 %	97.5 %

SEXref	1.42	1.27	1.59	SEX	1.17	0.99	1.38
MARITALref	1.47	1.32	1.65	MARITAL	0.98	0.83	1.16
EDUCAref	0.84	0.74	0.96	EDUCA	0.82	0.69	0.99
EMPLOY1ref	1.18	1.05	1.31	EMPLOY1	0.91	0.76	1.09
INCOME2ref	0.94	0.79	1.11	INCOME2	1.01	0.80	1.27
X_BMI5CATref	1.23	1.08	1.40	X_BMI5CAT	1.22	1.01	1.47
X_AGEG5YRref	0.96	0.81	1.15	X_AGEG5YR	0.92	0.72	1.18
CHILDRENref	0.85	0.74	0.96	CHILDREN	0.93	0.77	1.13
SDHBILLSref	0.27	0.22	0.33	SDHBILLS	0.77	0.55	1.07
HOWSAFE1ref	0.36	0.26	0.49	HOWSAFE1	1.06	0.64	1.75
SDHFOODref	0.32	0.27	0.38	SDHFOOD	0.90	0.66	1.22
SDHMEALSref	0.36	0.31	0.42	SDHMEALS	0.82	0.63	1.07
SDHMONEYref	0.54	0.48	0.60	SDHMONEY	1.10	0.92	1.32
SDHSTRESref	0.13	0.11	0.16	SDHSTRES	0.32	0.25	0.41
GENHLTHref	0.28	0.24	0.32	GENHLTH	0.69	0.56	0.86
PHYSHLTHref	3.78	3.27	4.36	PHYSHLTH	1.49	1.20	1.86
MENTHLTHref	5.01	4.26	5.89	MENTHLTH	1.42	1.14	1.77
POORHLTHref	3.43	2.85	4.12	POORHLTH	1.09	0.84	1.42
SMOKDAY2ref	0.55	0.47	0.64	SMOKDAY2	0.94	0.74	1.19
ECIGNOWref	0.56	0.42	0.74	ECIGNOW	0.82	0.55	1.23
CVDSTRK3ref	0.61	0.46	0.81	CVDSTRK3	1.31	0.86	2.00
CVDCRHD4ref	0.70	0.56	0.88	CVDCRHD4	1.28	0.93	1.78
DIABETE3ref	0.70	0.60	0.83	DIABETE3	0.94	0.73	1.20
ADDEPEV2ref	0.29	0.25	0.33	ADDEPEV2	0.65	0.54	0.78
HAVARTH3ref	0.51	0.45	0.57	HAVARTH3	0.83	0.70	1.00
CHCCOPD1ref	0.40	0.33	0.49	CHCCOPD1	0.96	0.71	1.30
DEAFref	0.59	0.49	0.71	DEAF	0.82	0.62	1.07
BLINDref	0.46	0.35	0.59	BLIND	1.05	0.72	1.52
DECIDEref	0.17	0.15	0.21	DECIDE	0.60	0.47	0.77
DIFFWALKref	0.33	0.29	0.38	DIFFWALK	0.92	0.72	1.16
DIFFDRESref	0.18	0.14	0.23	DIFFDRES	0.78	0.53	1.13
DIFFALONref	0.20	0.16	0.24	DIFFALON	0.90	0.66	1.23

table1 <- bind_cols(all_or, adj_or)

table1 <- table1 %>% 
  dplyr::select("hv", "OR", "X2.5..", "X97.5..", "AOR", "2.5 %", "97.5 %")


table1$orll <- table1$X2.5..
table1$orul <- table1$X97.5..
table1$aorll <- table1$"2.5 %"
table1$aorul <- table1$"97.5 %"
table1 <- table1 %>% dplyr::select(hv, OR, orll, orul, AOR, aorll, aorul)


#Plot
ggplot(table1, aes(x=hv, y = OR)) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_bar(stat = "identity", position = "dodge")

ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  theme(axis.text.x = element_text(angle = 90)) +  
  geom_abline(slope = 0, intercept = 1)

ggplot(table1, aes(x=hv, y = OR)) +
  geom_bar(stat = "identity", position = "dodge") +
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_abline(slope = 0, intercept = 1, color = "red")

ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) +
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=orll, ymax=orul), 
                width = .2, position = position_dodge(.9))

ggplot(table1, aes(x=hv, y = AOR)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=aorll, ymax=aorul), 
                width = .2, position = position_dodge(.9))

ORtable <- table1 %>% dplyr::select(hv, OR, orll, orul)
ORtable$type <- c("OR")
ORtable$stat <- ORtable$OR
ORtable$ll <- ORtable$orll
ORtable$ul <- ORtable$orul
ORtable <- ORtable %>% dplyr::select(hv, stat, type, ll, ul)

AORtable <- table1 %>% dplyr::select(hv, AOR, aorll, aorul)
AORtable$type <- c("AOR")
AORtable$stat <- AORtable$AOR
AORtable$ll <- AORtable$aorll
AORtable$ul <- AORtable$aorul
AORtable <- AORtable %>% dplyr::select(hv, stat, type, ll, ul)

table2 <- rbind(ORtable, AORtable)

figureoraor <- ggplot(table2, aes(x=hv, y = stat, fill = type)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=ll, ymax=ul), 
                width = .2, position = position_dodge(.9))

#OR compared to Adjusted OR
print(figureoraor + ggtitle("OR & AOR"))

SLEPTIM1 - Hours per sleep at night

#Binomial logistic regression for each variable
or<-svyglm(SLEPTIM1~SEX, family=quasibinomial,design=svydesW)
#OR saved for each variable
a <- or_fun("SEX")

#Repeat
or <- svyglm(SLEPTIM1~ MARITAL, family = quasibinomial, design = svydesW)
b <- or_fun("MARITAL")

or <- svyglm(SLEPTIM1~ EDUCA, family = quasibinomial, design = svydesW)
c <- or_fun("EDUCA")

or <- svyglm(SLEPTIM1~ EMPLOY1, family = quasibinomial, design = svydesW)
d <- or_fun("EMPLOY1")

or <- svyglm(SLEPTIM1~ INCOME2, family = quasibinomial, design = svydesW)
e <- or_fun("INCOME2")

or <- svyglm(SLEPTIM1~ X_BMI5CAT, family = quasibinomial, design = svydesW)
f <- or_fun("X_BMI5CAT")

or <- svyglm(SLEPTIM1~ X_AGEG5YR, family = quasibinomial, design = svydesW)
h <- or_fun("X_AGEG5YR")

or <- svyglm(SLEPTIM1~ CHILDREN, family = quasibinomial, design = svydesW)
i <- or_fun("CHILDREN")

or <- svyglm(SLEPTIM1~ SDHBILLS, family = quasibinomial, design = svydesW)
j <- or_fun("SDHBILLS")

or <- svyglm(SLEPTIM1~ HOWSAFE1, family = quasibinomial, design = svydesW)
k <- or_fun("HOWSAFE1")

or <- svyglm(SLEPTIM1~ SDHFOOD, family = quasibinomial, design = svydesW)
l <- or_fun("SDHFOOD")

or <- svyglm(SLEPTIM1~ SDHMEALS, family = quasibinomial, design = svydesW)
m <- or_fun("SDHMEALS")

or <- svyglm(SLEPTIM1~ SDHMONEY, family = quasibinomial, design = svydesW)
n <- or_fun("SDHMONEY")

or <- svyglm(SLEPTIM1~ SDHSTRES, family = quasibinomial, design = svydesW)
o <- or_fun("SDHSTRES")

or <- svyglm(SLEPTIM1~ GENHLTH, family = quasibinomial, design = svydesW)
p <- or_fun("GENHLTH")

or <- svyglm(SLEPTIM1~ PHYSHLTH, family = quasibinomial, design = svydesW)
q <- or_fun("PHYSHLTH")

or <- svyglm(SLEPTIM1~ MENTHLTH, family = quasibinomial, design = svydesW)
r <- or_fun("MENTHLTH")

or <- svyglm(SLEPTIM1~ POORHLTH, family = quasibinomial, design = svydesW)
s <- or_fun("POORHLTH")

or <- svyglm(SLEPTIM1~ SMOKDAY2, family = quasibinomial, design = svydesW)
t <- or_fun("SMOKDAY2")

or <- svyglm(SLEPTIM1~ ECIGNOW, family = quasibinomial, design = svydesW)
u <- or_fun("ECIGNOW")

or <- svyglm(SLEPTIM1~ CVDSTRK3, family = quasibinomial, design = svydesW)
v <- or_fun("CVDSTRK3")

or <- svyglm(SLEPTIM1~ CVDCRHD4, family = quasibinomial, design = svydesW)
w <- or_fun("CVDCRHD4")

or <- svyglm(SLEPTIM1~ DIABETE3, family = quasibinomial, design = svydesW)
x <- or_fun("DIABETE3")

or <- svyglm(SLEPTIM1~ ADDEPEV2, family = quasibinomial, design = svydesW)
y <- or_fun("ADDEPEV2")

or <- svyglm(SLEPTIM1~ HAVARTH3, family = quasibinomial, design = svydesW)
z <- or_fun("HAVARTH3")

or <- svyglm(SLEPTIM1~ CHCCOPD1, family = quasibinomial, design = svydesW)
aa <- or_fun("CHCCOPD1")

or <- svyglm(SLEPTIM1~ DEAF, family = quasibinomial, design = svydesW)
bb <- or_fun("DEAF")

or <- svyglm(SLEPTIM1~ BLIND, family = quasibinomial, design = svydesW)
cc <- or_fun("BLIND")

or <- svyglm(SLEPTIM1~ DECIDE, family = quasibinomial, design = svydesW)
dd <- or_fun("DECIDE")

or <- svyglm(SLEPTIM1~ DIFFWALK, family = quasibinomial, design = svydesW)
ee <- or_fun("DIFFWALK")

or <- svyglm(SLEPTIM1~ DIFFDRES, family = quasibinomial, design = svydesW)
ff <- or_fun("DIFFDRES")

or <- svyglm(SLEPTIM1~ DIFFALON, family = quasibinomial, design = svydesW)
gg <- or_fun("DIFFALON")


# Combine OR into 1 table
all_or <- bind_rows(a, b, c, d, e, f, h, i, j, k, l, m,n,o,p,
                    q,r,s,t,u,v,w,x,y,z,aa,bb,cc,dd,ee,ff,gg)
all_or <- as.data.frame(all_or)


# Run all variables in logistic regression for adjusted OR
alr <- svyglm(SLEPTIM1 ~ SEX + MARITAL + EDUCA + EMPLOY1 + INCOME2 +
                X_BMI5CAT  + X_AGEG5YR + CHILDREN  + SDHBILLS + 
                HOWSAFE1 + SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + 
                PHYSHLTH + MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW +
                CVDSTRK3 +  CVDCRHD4 + DIABETE3 + ADDEPEV2 + HAVARTH3 + 
                CHCCOPD1 + DEAF + BLIND + DECIDE + DIFFWALK + DIFFDRES +
                DIFFALON, family = quasibinomial, design = svydesW)

summary(alr)

## 
## Call:
## svyglm(formula = SLEPTIM1 ~ SEX + MARITAL + EDUCA + EMPLOY1 + 
##     INCOME2 + X_BMI5CAT + X_AGEG5YR + CHILDREN + SDHBILLS + HOWSAFE1 + 
##     SDHFOOD + SDHMEALS + SDHMONEY + SDHSTRES + GENHLTH + PHYSHLTH + 
##     MENTHLTH + POORHLTH + SMOKDAY2 + ECIGNOW + CVDSTRK3 + CVDCRHD4 + 
##     DIABETE3 + ADDEPEV2 + HAVARTH3 + CHCCOPD1 + DEAF + BLIND + 
##     DECIDE + DIFFWALK + DIFFDRES + DIFFALON, design = svydesW, 
##     family = quasibinomial)
## 
## Survey design:
## subset(svydes, RACE == "White only, non-Hispanic")
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.281555   0.169994  -1.656 0.097714 .  
## SEXref       -0.088035   0.066159  -1.331 0.183348    
## MARITALref    0.257863   0.067377   3.827 0.000131 ***
## EDUCAref     -0.191481   0.076332  -2.509 0.012147 *  
## EMPLOY1ref   -0.176538   0.072062  -2.450 0.014318 *  
## INCOME2ref   -0.136601   0.096812  -1.411 0.158290    
## X_BMI5CATref  0.079353   0.074461   1.066 0.286602    
## X_AGEG5YRref -0.281058   0.103384  -2.719 0.006573 ** 
## CHILDRENref   0.484119   0.077826   6.221 5.24e-10 ***
## SDHBILLSref  -0.284643   0.145187  -1.961 0.049974 *  
## HOWSAFE1ref   0.092733   0.188987   0.491 0.623667    
## SDHFOODref    0.426491   0.144986   2.942 0.003276 ** 
## SDHMEALSref  -0.038634   0.125376  -0.308 0.757978    
## SDHMONEYref   0.388551   0.075886   5.120 3.13e-07 ***
## SDHSTRESref  -0.463598   0.115635  -4.009 6.16e-05 ***
## GENHLTHref   -0.361649   0.093233  -3.879 0.000106 ***
## PHYSHLTHref   0.042737   0.093390   0.458 0.647242    
## MENTHLTHref   0.257628   0.093495   2.756 0.005875 ** 
## POORHLTHref   0.202654   0.113224   1.790 0.073522 .  
## SMOKDAY2ref  -0.409506   0.096944  -4.224 2.43e-05 ***
## ECIGNOWref    0.173646   0.173027   1.004 0.315619    
## CVDSTRK3ref   0.135723   0.174706   0.777 0.437265    
## CVDCRHD4ref  -0.147531   0.138695  -1.064 0.287499    
## DIABETE3ref  -0.037566   0.104113  -0.361 0.718244    
## ADDEPEV2ref   0.061030   0.076254   0.800 0.423538    
## HAVARTH3ref  -0.143975   0.075834  -1.899 0.057666 .  
## CHCCOPD1ref  -0.250401   0.122433  -2.045 0.040872 *  
## DEAFref       0.007028   0.111291   0.063 0.949651    
## BLINDref     -0.254311   0.166054  -1.531 0.125692    
## DECIDEref    -0.316578   0.108778  -2.910 0.003622 ** 
## DIFFWALKref   0.139587   0.103975   1.343 0.179475    
## DIFFDRESref  -0.313138   0.158807  -1.972 0.048670 *  
## DIFFALONref  -0.233796   0.135851  -1.721 0.085300 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 1.039705)
## 
## Number of Fisher Scoring iterations: 4

confint(alr)

##                    2.5 %        97.5 %
## (Intercept)  -0.61473802  5.162788e-02
## SEXref       -0.21770400  4.163476e-02
## MARITALref    0.12580602  3.899204e-01
## EDUCAref     -0.34108972 -4.187245e-02
## EMPLOY1ref   -0.31777752 -3.529871e-02
## INCOME2ref   -0.32634832  5.314618e-02
## X_BMI5CATref -0.06658904  2.252942e-01
## X_AGEG5YRref -0.48368715 -7.842849e-02
## CHILDRENref   0.33158308  6.366541e-01
## SDHBILLSref  -0.56920485 -8.152809e-05
## HOWSAFE1ref  -0.27767547  4.631405e-01
## SDHFOODref    0.14232324  7.106595e-01
## SDHMEALSref  -0.28436677  2.070979e-01
## SDHMONEYref   0.23981817  5.372845e-01
## SDHSTRESref  -0.69023785 -2.369587e-01
## GENHLTHref   -0.54438339 -1.789155e-01
## PHYSHLTHref  -0.14030421  2.257779e-01
## MENTHLTHref   0.07438150  4.408748e-01
## POORHLTHref  -0.01926094  4.245692e-01
## SMOKDAY2ref  -0.59951264 -2.194989e-01
## ECIGNOWref   -0.16548175  5.127730e-01
## CVDSTRK3ref  -0.20669410  4.781400e-01
## CVDCRHD4ref  -0.41936846  1.243066e-01
## DIABETE3ref  -0.24162474  1.664921e-01
## ADDEPEV2ref  -0.08842560  2.104847e-01
## HAVARTH3ref  -0.29260755  4.657855e-03
## CHCCOPD1ref  -0.49036415 -1.043690e-02
## DEAFref      -0.21109826  2.251537e-01
## BLINDref     -0.57976984  7.114857e-02
## DECIDEref    -0.52977930 -1.033763e-01
## DIFFWALKref  -0.06419964  3.433735e-01
## DIFFDRESref  -0.62439277 -1.882309e-03
## DIFFALONref  -0.50005792  3.246692e-02

exp(coef(alr))

##  (Intercept)       SEXref   MARITALref     EDUCAref   EMPLOY1ref   INCOME2ref 
##    0.7546094    0.9157292    1.2941618    0.8257352    0.8381668    0.8723182 
## X_BMI5CATref X_AGEG5YRref  CHILDRENref  SDHBILLSref  HOWSAFE1ref   SDHFOODref 
##    1.0825860    0.7549847    1.6227441    0.7522826    1.0971682    1.5318733 
##  SDHMEALSref  SDHMONEYref  SDHSTRESref   GENHLTHref  PHYSHLTHref  MENTHLTHref 
##    0.9621023    1.4748427    0.6290162    0.6965265    1.0436632    1.2938576 
##  POORHLTHref  SMOKDAY2ref   ECIGNOWref  CVDSTRK3ref  CVDCRHD4ref  DIABETE3ref 
##    1.2246488    0.6639783    1.1896339    1.1453645    0.8628358    0.9631306 
##  ADDEPEV2ref  HAVARTH3ref  CHCCOPD1ref      DEAFref     BLINDref    DECIDEref 
##    1.0629303    0.8659095    0.7784889    1.0070525    0.7754509    0.7286383 
##  DIFFWALKref  DIFFDRESref  DIFFALONref 
##    1.1497987    0.7311493    0.7915237

exp(cbind(AOR = coef(alr), confint(alr)))

##                    AOR     2.5 %    97.5 %
## (Intercept)  0.7546094 0.5407825 1.0529838
## SEXref       0.9157292 0.8043635 1.0425136
## MARITALref   1.2941618 1.1340622 1.4768632
## EDUCAref     0.8257352 0.7109951 0.9589921
## EMPLOY1ref   0.8381668 0.7277647 0.9653170
## INCOME2ref   0.8723182 0.7215538 1.0545838
## X_BMI5CATref 1.0825860 0.9355796 1.2526912
## X_AGEG5YRref 0.7549847 0.6165060 0.9245682
## CHILDRENref  1.6227441 1.3931719 1.8901460
## SDHBILLSref  0.7522826 0.5659753 0.9999185
## HOWSAFE1ref  1.0971682 0.7575426 1.5890566
## SDHFOODref   1.5318733 1.1529493 2.0353331
## SDHMEALSref  0.9621023 0.7524906 1.2301030
## SDHMONEYref  1.4748427 1.2710180 1.7113534
## SDHSTRESref  0.6290162 0.5014568 0.7890239
## GENHLTHref   0.6965265 0.5801994 0.8361765
## PHYSHLTHref  1.0436632 0.8690938 1.2532973
## MENTHLTHref  1.2938576 1.0772177 1.5540662
## POORHLTHref  1.2246488 0.9809234 1.5289316
## SMOKDAY2ref  0.6639783 0.5490792 0.8029210
## ECIGNOWref   1.1896339 0.8474853 1.6699155
## CVDSTRK3ref  1.1453645 0.8132684 1.6130713
## CVDCRHD4ref  0.8628358 0.6574619 1.1323630
## DIABETE3ref  0.9631306 0.7853508 1.1811542
## ADDEPEV2ref  1.0629303 0.9153712 1.2342762
## HAVARTH3ref  0.8659095 0.7463150 1.0046687
## CHCCOPD1ref  0.7784889 0.6124033 0.9896174
## DEAFref      1.0070525 0.8096945 1.2525152
## BLINDref     0.7754509 0.5600272 1.0737407
## DECIDEref    0.7286383 0.5887349 0.9017875
## DIFFWALKref  1.1497987 0.9378178 1.4096951
## DIFFDRESref  0.7311493 0.5355866 0.9981195
## DIFFALONref  0.7915237 0.6064955 1.0329997

exp(cbind(AOR = coef(alr), confint(alr)))[-1, ]

##                    AOR     2.5 %    97.5 %
## SEXref       0.9157292 0.8043635 1.0425136
## MARITALref   1.2941618 1.1340622 1.4768632
## EDUCAref     0.8257352 0.7109951 0.9589921
## EMPLOY1ref   0.8381668 0.7277647 0.9653170
## INCOME2ref   0.8723182 0.7215538 1.0545838
## X_BMI5CATref 1.0825860 0.9355796 1.2526912
## X_AGEG5YRref 0.7549847 0.6165060 0.9245682
## CHILDRENref  1.6227441 1.3931719 1.8901460
## SDHBILLSref  0.7522826 0.5659753 0.9999185
## HOWSAFE1ref  1.0971682 0.7575426 1.5890566
## SDHFOODref   1.5318733 1.1529493 2.0353331
## SDHMEALSref  0.9621023 0.7524906 1.2301030
## SDHMONEYref  1.4748427 1.2710180 1.7113534
## SDHSTRESref  0.6290162 0.5014568 0.7890239
## GENHLTHref   0.6965265 0.5801994 0.8361765
## PHYSHLTHref  1.0436632 0.8690938 1.2532973
## MENTHLTHref  1.2938576 1.0772177 1.5540662
## POORHLTHref  1.2246488 0.9809234 1.5289316
## SMOKDAY2ref  0.6639783 0.5490792 0.8029210
## ECIGNOWref   1.1896339 0.8474853 1.6699155
## CVDSTRK3ref  1.1453645 0.8132684 1.6130713
## CVDCRHD4ref  0.8628358 0.6574619 1.1323630
## DIABETE3ref  0.9631306 0.7853508 1.1811542
## ADDEPEV2ref  1.0629303 0.9153712 1.2342762
## HAVARTH3ref  0.8659095 0.7463150 1.0046687
## CHCCOPD1ref  0.7784889 0.6124033 0.9896174
## DEAFref      1.0070525 0.8096945 1.2525152
## BLINDref     0.7754509 0.5600272 1.0737407
## DECIDEref    0.7286383 0.5887349 0.9017875
## DIFFWALKref  1.1497987 0.9378178 1.4096951
## DIFFDRESref  0.7311493 0.5355866 0.9981195
## DIFFALONref  0.7915237 0.6064955 1.0329997

adj_or <- exp(cbind(AOR = coef(alr), confint(alr)))[-1,]
adj_or <- as.data.frame(adj_or)

# Combine All OR with Adjusted OR
stargazer(bind_cols(all_or, adj_or),summary=F,type = "html", digits=2)


	OR	X2.5..	X97.5..	hv	AOR	2.5 %	97.5 %

SEXref	0.85	0.78	0.92	SEX	0.92	0.80	1.04
MARITALref	1.33	1.22	1.45	MARITAL	1.29	1.13	1.48
EDUCAref	0.82	0.74	0.90	EDUCA	0.83	0.71	0.96
EMPLOY1ref	0.85	0.78	0.93	EMPLOY1	0.84	0.73	0.97
INCOME2ref	0.98	0.86	1.11	INCOME2	0.87	0.72	1.05
X_BMI5CATref	1.26	1.14	1.39	X_BMI5CAT	1.08	0.94	1.25
X_AGEG5YRref	0.87	0.75	1.00	X_AGEG5YR	0.75	0.62	0.92
CHILDRENref	1.37	1.25	1.50	CHILDREN	1.62	1.39	1.89
SDHBILLSref	0.57	0.46	0.69	SDHBILLS	0.75	0.57	1.00
HOWSAFE1ref	0.70	0.51	0.95	HOWSAFE1	1.10	0.76	1.59
SDHFOODref	0.82	0.69	0.97	SDHFOOD	1.53	1.15	2.04
SDHMEALSref	0.85	0.73	0.98	SDHMEALS	0.96	0.75	1.23
SDHMONEYref	1.11	1.00	1.22	SDHMONEY	1.47	1.27	1.71
SDHSTRESref	0.44	0.37	0.53	SDHSTRES	0.63	0.50	0.79
GENHLTHref	0.54	0.47	0.61	GENHLTH	0.70	0.58	0.84
PHYSHLTHref	1.68	1.48	1.91	PHYSHLTH	1.04	0.87	1.25
MENTHLTHref	2.00	1.72	2.32	MENTHLTH	1.29	1.08	1.55
POORHLTHref	1.99	1.68	2.35	POORHLTH	1.22	0.98	1.53
SMOKDAY2ref	0.62	0.54	0.70	SMOKDAY2	0.66	0.55	0.80
ECIGNOWref	0.80	0.62	1.03	ECIGNOW	1.19	0.85	1.67
CVDSTRK3ref	0.94	0.75	1.20	CVDSTRK3	1.15	0.81	1.61
CVDCRHD4ref	0.89	0.73	1.07	CVDCRHD4	0.86	0.66	1.13
DIABETE3ref	0.98	0.85	1.12	DIABETE3	0.96	0.79	1.18
ADDEPEV2ref	0.74	0.66	0.82	ADDEPEV2	1.06	0.92	1.23
HAVARTH3ref	0.86	0.79	0.95	HAVARTH3	0.87	0.75	1.00
CHCCOPD1ref	0.60	0.50	0.72	CHCCOPD1	0.78	0.61	0.99
DEAFref	0.93	0.80	1.09	DEAF	1.01	0.81	1.25
BLINDref	0.64	0.50	0.81	BLIND	0.78	0.56	1.07
DECIDEref	0.48	0.42	0.57	DECIDE	0.73	0.59	0.90
DIFFWALKref	0.75	0.66	0.85	DIFFWALK	1.15	0.94	1.41
DIFFDRESref	0.42	0.33	0.53	DIFFDRES	0.73	0.54	1.00
DIFFALONref	0.46	0.38	0.56	DIFFALON	0.79	0.61	1.03

table1 <- bind_cols(all_or, adj_or)

table1 <- table1 %>% 
  dplyr::select("hv", "OR", "X2.5..", "X97.5..", "AOR", "2.5 %", "97.5 %")


table1$orll <- table1$X2.5..
table1$orul <- table1$X97.5..
table1$aorll <- table1$"2.5 %"
table1$aorul <- table1$"97.5 %"
table1 <- table1 %>% dplyr::select(hv, OR, orll, orul, AOR, aorll, aorul)


#Plot OR
ggplot(table1, aes(x=hv, y = OR)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) +
  geom_errorbar(aes(ymin=orll, ymax=orul), 
                width = .2, position = position_dodge(.9))

#Plot AOR
ggplot(table1, aes(x=hv, y = AOR)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_errorbar(aes(ymin=aorll, ymax=aorul), 
                width = .2, position = position_dodge(.9))

ORtable <- table1 %>% dplyr::select(hv, OR, orll, orul)
ORtable$type <- c("OR")
ORtable$stat <- ORtable$OR
ORtable$ll <- ORtable$orll
ORtable$ul <- ORtable$orul
ORtable <- ORtable %>% dplyr::select(hv, stat, type, ll, ul)

AORtable <- table1 %>% dplyr::select(hv, AOR, aorll, aorul)
AORtable$type <- c("AOR")
AORtable$stat <- AORtable$AOR
AORtable$ll <- AORtable$aorll
AORtable$ul <- AORtable$aorul
AORtable <- AORtable %>% dplyr::select(hv, stat, type, ll, ul)

table2 <- rbind(ORtable, AORtable)

figureoraor <- ggplot(table2, aes(x=hv, y = stat, fill = type)) +
  geom_bar(stat = "identity", position = "dodge") + 
  geom_abline(slope = 0, intercept = 1, color = "red", size = 2) + 
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_errorbar(aes(ymin=ll, ymax=ul), 
                width = .2, position = position_dodge(.9))

#OR compared to Adjusted OR
print(figureoraor + ggtitle("OR & AOR"))

BRFSS Sleep Data

Josh Gunter

2020-12-03

BRFSS Data

Variable Selection and Descriptions

Study Variables

Demographic Variables

Sleep Variables

Illness Variables

Disability Variables

Create Variable to Hold All Study Variables

Cleaning Variables

Functions for NA/Missing values

Functions for Binary/Ordinal Values

Create Survey Objects

Descriptive Stats Survey

Regression Survey

Data

Raw Data

Unweighted

Weighted

Weighted tables

SLEPTIM1-

ADSLEEP

Regression

All

ADSLEEP- Difficulty sleeping past 14 days

SLEPTIM1- Hours per sleep at night

Non-White

ADSLEEP - Difficulty sleeping past 14 days

SLEPTIM1 - Hours per sleep at night

AA

ADSLEEP - Difficulty sleeping past 14 days

SLEPTIM1 - Hours per sleep at night

White

ADSLEEP - Difficulty sleeping past 14 days

SLEPTIM1 - Hours per sleep at night