Load Packages

tidyverse

if (!require(tidyverse)){
  install.packages("tidyverse", dependencies = TRUE)
  library(tidyverse)
}
Loading required package: tidyverse
── Attaching packages ────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.5      ✔ purrr   0.3.4 
✔ tibble  3.1.6      ✔ dplyr   1.0.10
✔ tidyr   1.2.0      ✔ stringr 1.4.0 
✔ readr   2.1.2      
── Conflicts ───────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

codebookr

if (!require(codebookr)){
  install.packages("codebookr", dependencies = TRUE)
  library(codebookr)
}
Loading required package: codebookr

summarytools

if (!require(summarytools)){
  install.packages("summarytools", dependencies = TRUE)
  library(summarytools)
}
Loading required package: summarytools
Warning: couldn't connect to display ":0"system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)

Attaching package: ‘summarytools’

The following object is masked from ‘package:tibble’:

    view

broom

if (!require(broom)){
  install.packages("broom", dependencies = TRUE)
  library(broom)
}
Loading required package: broom

performance

if (!require(performance)){
  install.packages("performance", dependencies = TRUE)
  library(performance)
}

gtsummary

if (!require(gtsummary)){
  install.packages("gtsummary", dependencies = TRUE)
  library(gtsummary)
}

janitor

if (!require(janitor)){
  install.packages("janitor", dependencies = TRUE)
  library(janitor)
}
Loading required package: janitor

Attaching package: ‘janitor’

The following objects are masked from ‘package:stats’:

    chisq.test, fisher.test

forcats

if (!require(forcats)){
  install.packages("forcats", dependencies = TRUE)
  library(forcats)
}

Import Data

mh_deidv2 <- read.csv("~/Desktop/R-Code/mh_deidv2.csv")

Data Cleaning

RPL_THEMES erroneous values

mh_deidv2 %>%
  select(age_yrs, PATIENT_GENDER_CD, PATIENT_RACE_DESC, PATIENT_ETHNIC_GROUP_DESC, PATIENT_LANGUAGE_DESC, PATIENT_RELIGION_DESC, PATIENT_MARITAL_STATUS_DESC, PATIENT_STATE_CD, EDU_YEARS, TOBACCO_DESC, depression, anxiety, ptsd, bipolar, body_image, ocd, stress, seasonalAD, panic, any_psych_dx,ST_ABBR, E_TOTPOP, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4) -> exampledf1
exampledf1 %>%
mutate(RPL_THEMES = na_if(RPL_THEMES, "-999")) %>%
mutate(RPL_THEMES = na_if(RPL_THEMES, "0")) %>%
mutate(RPL_THEME1 = na_if(RPL_THEME1, "-999")) %>%
mutate(RPL_THEME1 = na_if(RPL_THEME1, "0")) %>%
mutate(RPL_THEME2 = na_if(RPL_THEME2, "-999")) %>%
mutate(RPL_THEME2 = na_if(RPL_THEME2, "0")) %>%
mutate(RPL_THEME3 = na_if(RPL_THEME3, "-999")) %>%
mutate(RPL_THEME3 = na_if(RPL_THEME3, "0")) %>%
mutate(RPL_THEME4 = na_if(RPL_THEME4, "-999")) %>%
mutate(RPL_THEME4 = na_if(RPL_THEME4, "0")) -> exampledf1NA

Mental Health Dich

exampledf1NA %>% mutate(across(depression:any_psych_dx, ~if_else(.x>0.5, 1, 0),
                .names = "{col}_2")) -> exampledf1NADi2

Marital Status

exampledf1NADi2 %>% 
mutate(mstat_5 = as_factor(PATIENT_MARITAL_STATUS_DESC),
         mstat_5 = fct_recode(mstat_5, div_sep = "DIVORCED",
                              div_sep = "LEGALLY SEPARATED", widow = "WIDOWED",
                              married = "MARRIED", unmarried = "SINGLE",
                              unknown = "UNKNOWN", unknown = "OTHER",
                              unmarried = "SIGNIFICANT OTHER"),
         mstat_5 = fct_relevel(mstat_5, ref = 'married')) -> exampledf1NADi2MA

Religion

exampledf1NADi2MA %>% 
  mutate(relig_affil = as_factor(PATIENT_RELIGION_DESC),
          relig_affil = fct_recode(relig_affil, yes = "CATHOLIC",
                      no = "NONE", unknown = "UNKNOWN", unknown = "PATIENT REFUSED", 
                      yes = "CHRISTIAN", yes = "LUTHERAN",
                      yes = "PROTESTANT", yes = "BAPTIST",
                      yes = "METHODIST", yes = "PRESBYTERIAN",
                      yes = "NON-DENOMINATIONAL", yes = "JEWISH",
                      yes = "MUSLIM", yes = "OTHER",
                      yes = "EPISCOPALIAN", yes = "PENTECOSTAL",
                      no = "AGNOSTIC", no = "ATHEIST",
                      yes = "JEHOVAH'S WITNESS", yes = "HINDU",
                      yes = "GREEK ORTHODOX", yes = "CHURCH OF JESUS CHRIST OF LATTER-DAY SAINTS", yes = "BAHAI", no = "SPIRITUAL", yes = "CHURCH OF CHRIST",
         yes = "SEVENTH DAY ADVENTIST", yes = "APOSTOLIC", yes = "BUDDHIST", yes = "NAZARENE", yes = "CONGREGATIONAL", yes = "UNITED CHURCH OF CHR", yes = "REFORMED", yes = "PAGAN", yes = "JAIN", yes = "ASSEMBLY OF GOD", yes = "REORG CHR OF LAT DAY", yes = "QUAKER", yes = "UNITARIAN UNIVERSALIST", yes = "MENNONITE", yes = "FREE METHODIST", yes = "NATIVE AMER SPIRITL", yes = "WICCAN", yes = "ORTHODOX", yes = "SALVATION ARMY", yes = "DISCIPLES OF CHRIST", yes = "AFRICAN METHODIST EP", yes = "SIKH", yes = "CHURCH OF GOD", yes = "TAOIST", yes = "ANGLICAN"),
         relig_affil = fct_relevel(relig_affil, ref = 'yes')) -> exampledf1NADi2MARel

Race

exampledf1NADi2MARel %>% 
mutate(race_5 = as_factor(PATIENT_RACE_DESC),
         race_5 = fct_recode(race_5, Other = "OTHER",
                  Other = "UNKNOWN", Other = "CHOOSE NOT TO DISCLOSE",
                  Other = "NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER", 
                  Other = "MIDDLE EASTERN/NORTH AFRICAN",
                ASIAN = "ASIAN INDIAN", ASIAN = "OTHER ASIAN",
                ASIAN = "JAPANESE", ASIAN = "KOREAN", ASIAN = "FILIPINO",
                ASIAN = "CHINESE"),
         race_5 = fct_relevel(race_5, ref = 'WHITE OR CAUCASIAN')) -> exampledf1NADi2MARelRa

Gender

exampledf1NADi2MARelRa %>% 
mutate(gender = as_factor(PATIENT_GENDER_CD),
         gender = fct_recode(gender, male = "M", female = "F"),
         gender = fct_relevel(gender, ref = "male")) -> exampledf1NADi2MARelRaG

Language

exampledf1NADi2MARelRaG %>% 
  mutate(lang_3 = as_factor(PATIENT_LANGUAGE_DESC),
lang_3 = fct_recode(lang_3, English = "ENGLISH",
Other = "ARABIC", Other = "JAPANESE",
Other = "CHINESE, MANDARIN",
Other = "KOREAN", Other = "SIGN LANGUAGE",
Other = "RUSSIAN", Other = "SPANISH", Other = "ARMENIAN",
Other = "TURKISH", Other = "HINDI", Other = "BENGALI", Other = "FARSI; PERSIAN", Other = "ALBANIAN", Other = "HMONG", Other = "ROMANIAN", 
Other = "PUNJABI", Other = "CROATIAN", Other = "CHALDEAN", 
Other = "BURMESE", Other = "PORTUGUESE", unknown = "UNKNOWN"),
lang_3 = fct_relevel(lang_3, ref = 'English')) -> exampledf1NADi2MARelRaGL

Ethnicity

exampledf1NADi2MARelRaGL %>% 
  mutate(ethnic_3 = as_factor(PATIENT_ETHNIC_GROUP_DESC),
         ethnic_3 = fct_recode(ethnic_3, Unknown = "CHOOSE NOT TO DISCLOSE", Unknown = "UNKNOWN")) -> exampledf1NADi2MARelRaGLEth

Codebook

exampledf1NADi2MARelRaGLEth %>% 
select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, PATIENT_STATE_CD, EDU_YEARS, TOBACCO_DESC, depression_2, anxiety_2, ptsd_2, bipolar_2, body_image_2, ocd_2, seasonalAD_2, panic_2, any_psych_dx_2, E_TOTPOP, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4) -> mh_clean1
print(dfSummary(mh_clean1), method = 'render')

Data Frame Summary

mh_clean1

Dimensions: 11306 x 25
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 age_yrs [numeric]
Mean (sd) : 49.8 (18.4)
min ≤ med ≤ max:
2.8 ≤ 49.3 ≤ 100.4
IQR (CV) : 30.6 (0.4)
 9012 distinct values 11306 (100.0%) 0 (0.0%)
2 gender [factor]
1. male
2. female
5282(46.7%)
6024(53.3%)
 11306 (100.0%) 0 (0.0%)
3 race_5 [factor]
1. WHITE OR CAUCASIAN
2. BLACK OR AFRICAN AMERICAN
3. ASIAN
4. Other
5. AMERICAN INDIAN AND ALASK
9823(86.9%)
726(6.4%)
280(2.5%)
438(3.9%)
39(0.3%)
 11306 (100.0%) 0 (0.0%)
4 ethnic_3 [factor]
1. NON-HISPANIC
2. Unknown
3. HISPANIC
10719(94.8%)
384(3.4%)
203(1.8%)
 11306 (100.0%) 0 (0.0%)
5 lang_3 [factor]
1. English
2. Other
3. unknown
11202(99.1%)
99(0.9%)
5(0.0%)
 11306 (100.0%) 0 (0.0%)
6 relig_affil [factor]
1. yes
2. no
3. unknown
6093(53.9%)
4543(40.2%)
670(5.9%)
 11306 (100.0%) 0 (0.0%)
7 mstat_5 [factor]
1. married
2. div_sep
3. unmarried
4. unknown
5. widow
4824(42.7%)
348(3.1%)
3672(32.5%)
2236(19.8%)
226(2.0%)
 11306 (100.0%) 0 (0.0%)
8 PATIENT_STATE_CD [character]
1. MI
2. OH
3. IN
4. FL
5. CA
6. IL
7. NC
8. NY
9. IA
10. TX
[ 23 others ]
10874(96.2%)
256(2.3%)
47(0.4%)
23(0.2%)
8(0.1%)
7(0.1%)
7(0.1%)
7(0.1%)
6(0.1%)
6(0.1%)
65(0.6%)
 11306 (100.0%) 0 (0.0%)
9 EDU_YEARS [integer]
Mean (sd) : 15.3 (3.7)
min ≤ med ≤ max:
0 ≤ 16 ≤ 30
IQR (CV) : 5 (0.2)
 28 distinct values 822 (7.3%) 10484 (92.7%)
10 TOBACCO_DESC [character]
1. NEVER
2. NOT ASKED
3. PASSIVE
4. QUIT
5. YES
6149(56.2%)
10(0.1%)
325(3.0%)
3443(31.5%)
1005(9.2%)
 10932 (96.7%) 374 (3.3%)
11 depression_2 [numeric]
Min : 0
Mean : 0.2
Max : 1
0:9227(81.6%)
1:2079(18.4%)
 11306 (100.0%) 0 (0.0%)
12 anxiety_2 [numeric]
Min : 0
Mean : 0.2
Max : 1
0:9086(80.4%)
1:2220(19.6%)
 11306 (100.0%) 0 (0.0%)
13 ptsd_2 [numeric]
Min : 0
Mean : 0
Max : 1
0:11189(99.0%)
1:117(1.0%)
 11306 (100.0%) 0 (0.0%)
14 bipolar_2 [numeric]
Min : 0
Mean : 0
Max : 1
0:11144(98.6%)
1:162(1.4%)
 11306 (100.0%) 0 (0.0%)
15 body_image_2 [numeric]
Min : 0
Mean : 0
Max : 1
0:11303(100.0%)
1:3(0.0%)
 11306 (100.0%) 0 (0.0%)
16 ocd_2 [numeric]
Min : 0
Mean : 0
Max : 1
0:11242(99.4%)
1:64(0.6%)
 11306 (100.0%) 0 (0.0%)
17 seasonalAD_2 [numeric]
Min : 0
Mean : 0
Max : 1
0:11284(99.8%)
1:22(0.2%)
 11306 (100.0%) 0 (0.0%)
18 panic_2 [numeric]
Min : 0
Mean : 0
Max : 1
0:11160(98.7%)
1:146(1.3%)
 11306 (100.0%) 0 (0.0%)
19 any_psych_dx_2 [numeric]
Min : 0
Mean : 0.3
Max : 1
0:7900(69.9%)
1:3406(30.1%)
 11306 (100.0%) 0 (0.0%)
20 E_TOTPOP [integer]
Mean (sd) : 4350.1 (1694.9)
min ≤ med ≤ max:
182 ≤ 4113 ≤ 20695
IQR (CV) : 2162 (0.4)
 1925 distinct values 11306 (100.0%) 0 (0.0%)
21 RPL_THEMES [numeric]
Mean (sd) : 0.4 (0.3)
min ≤ med ≤ max:
0 ≤ 0.3 ≤ 1
IQR (CV) : 0.4 (0.7)
 2280 distinct values 11300 (99.9%) 6 (0.1%)
22 RPL_THEME1 [numeric]
Mean (sd) : 0.3 (0.3)
min ≤ med ≤ max:
0 ≤ 0.3 ≤ 1
IQR (CV) : 0.4 (0.7)
 2242 distinct values 11258 (99.6%) 48 (0.4%)
23 RPL_THEME2 [numeric]
Mean (sd) : 0.4 (0.3)
min ≤ med ≤ max:
0 ≤ 0.3 ≤ 1
IQR (CV) : 0.4 (0.7)
 2211 distinct values 11300 (99.9%) 6 (0.1%)
24 RPL_THEME3 [numeric]
Mean (sd) : 0.5 (0.3)
min ≤ med ≤ max:
0 ≤ 0.5 ≤ 1
IQR (CV) : 0.5 (0.6)
 1620 distinct values 11306 (100.0%) 0 (0.0%)
25 RPL_THEME4 [numeric]
Mean (sd) : 0.4 (0.3)
min ≤ med ≤ max:
0 ≤ 0.4 ≤ 1
IQR (CV) : 0.5 (0.7)
 2204 distinct values 11282 (99.8%) 24 (0.2%)

Generated by summarytools 1.0.1 (R version 4.1.2)
2022-10-15

Patient Characteristics

Baseline Characteristics

mh_clean1 %>% 
  select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, depression_2, anxiety_2, ptsd_2, any_psych_dx_2, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4) -> baseline
baseline %>% tbl_summary(label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", depression_2 ~ "Depression", anxiety_2 ~ "Anxiety", ptsd_2 ~ "PTSD", any_psych_dx_2 ~ "Any Psychiatric Diagnosis", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")
Characteristic N = 11,3061
Age 50 (18)
Gender
    male 5,282 (47%)
    female 6,024 (53%)
Race
    WHITE OR CAUCASIAN 9,823 (87%)
    BLACK OR AFRICAN AMERICAN 726 (6.4%)
    ASIAN 280 (2.5%)
    Other 438 (3.9%)
    AMERICAN INDIAN AND ALASKA NATIVE 39 (0.3%)
Ethnicity
    NON-HISPANIC 10,719 (95%)
    Unknown 384 (3.4%)
    HISPANIC 203 (1.8%)
English Speaking
    English 11,202 (99%)
    Other 99 (0.9%)
    unknown 5 (<0.1%)
Any Religious Affiliation
    yes 6,093 (54%)
    no 4,543 (40%)
    unknown 670 (5.9%)
Marital Status
    married 4,824 (43%)
    div_sep 348 (3.1%)
    unmarried 3,672 (32%)
    unknown 2,236 (20%)
    widow 226 (2.0%)
Depression 2,079 (18%)
Anxiety 2,220 (20%)
PTSD 117 (1.0%)
Any Psychiatric Diagnosis 3,406 (30%)
Total SVI 0.37 (0.26)
    (Missing) 6
Soceioeconomic Status 0.35 (0.26)
    (Missing) 48
Household Composition 0.39 (0.27)
    (Missing) 6
Minority Status and Language 0.48 (0.29)
Housing and Transportation 0.44 (0.29)
    (Missing) 24
1 Mean (SD); n (%)

Baseline Characteristics by Depression

baseline %>% tbl_summary(by = depression_2,
         statistic = list(all_continuous() ~ "{mean} ({sd})"),
        label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", anxiety_2 ~ "Anxiety", ptsd_2 ~ "PTSD", any_psych_dx_2 ~ "Any Psychiatric Diagnosis", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"),
        missing_text = "(Missing)"
        ) %>% add_p()
Characteristic 0, N = 9,2271 1, N = 2,0791 p-value2
Age 50 (19) 51 (18) <0.001
Gender <0.001
    male 4,560 (49%) 722 (35%)
    female 4,667 (51%) 1,357 (65%)
Race 0.005
    WHITE OR CAUCASIAN 7,986 (87%) 1,837 (88%)
    BLACK OR AFRICAN AMERICAN 597 (6.5%) 129 (6.2%)
    ASIAN 249 (2.7%) 31 (1.5%)
    Other 367 (4.0%) 71 (3.4%)
    AMERICAN INDIAN AND ALASKA NATIVE 28 (0.3%) 11 (0.5%)
Ethnicity 0.041
    NON-HISPANIC 8,732 (95%) 1,987 (96%)
    Unknown 332 (3.6%) 52 (2.5%)
    HISPANIC 163 (1.8%) 40 (1.9%)
English Speaking 0.004
    English 9,130 (99%) 2,072 (100%)
    Other 92 (1.0%) 7 (0.3%)
    unknown 5 (<0.1%) 0 (0%)
Any Religious Affiliation <0.001
    yes 4,922 (53%) 1,171 (56%)
    no 3,714 (40%) 829 (40%)
    unknown 591 (6.4%) 79 (3.8%)
Marital Status <0.001
    married 4,028 (44%) 796 (38%)
    div_sep 243 (2.6%) 105 (5.1%)
    unmarried 2,966 (32%) 706 (34%)
    unknown 1,827 (20%) 409 (20%)
    widow 163 (1.8%) 63 (3.0%)
Anxiety 1,035 (11%) 1,185 (57%) <0.001
PTSD 41 (0.4%) 76 (3.7%) <0.001
Any Psychiatric Diagnosis 1,327 (14%) 2,079 (100%) <0.001
Total SVI 0.36 (0.26) 0.39 (0.26) <0.001
    (Missing) 6 0
Soceioeconomic Status 0.34 (0.25) 0.37 (0.26) <0.001
    (Missing) 39 9
Household Composition 0.39 (0.27) 0.40 (0.27) 0.3
    (Missing) 6 0
Minority Status and Language 0.48 (0.28) 0.49 (0.29) 0.031
Housing and Transportation 0.43 (0.29) 0.45 (0.29) 0.001
    (Missing) 19 5
1 Mean (SD); n (%)
2 Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test 
NA
NA

Baseline Characteristics By Anxiety

baseline %>% tbl_summary(by = anxiety_2,
       statistic = list(all_continuous() ~ "{mean} ({sd})"),
        label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", depression_2 ~ "Depression", ptsd_2 ~ "PTSD", any_psych_dx_2 ~ "Any Psychiatric Diagnosis", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"),
        missing_text = "(Missing)"
        ) %>% add_p()
Characteristic 0, N = 9,0861 1, N = 2,2201 p-value2
Age 50 (19) 48 (17) <0.001
Gender <0.001
    male 4,550 (50%) 732 (33%)
    female 4,536 (50%) 1,488 (67%)
Race <0.001
    WHITE OR CAUCASIAN 7,851 (86%) 1,972 (89%)
    BLACK OR AFRICAN AMERICAN 601 (6.6%) 125 (5.6%)
    ASIAN 249 (2.7%) 31 (1.4%)
    Other 357 (3.9%) 81 (3.6%)
    AMERICAN INDIAN AND ALASKA NATIVE 28 (0.3%) 11 (0.5%)
Ethnicity 0.012
    NON-HISPANIC 8,599 (95%) 2,120 (95%)
    Unknown 330 (3.6%) 54 (2.4%)
    HISPANIC 157 (1.7%) 46 (2.1%)
English Speaking <0.001
    English 8,987 (99%) 2,215 (100%)
    Other 94 (1.0%) 5 (0.2%)
    unknown 5 (<0.1%) 0 (0%)
Any Religious Affiliation <0.001
    yes 4,860 (53%) 1,233 (56%)
    no 3,638 (40%) 905 (41%)
    unknown 588 (6.5%) 82 (3.7%)
Marital Status <0.001
    married 3,977 (44%) 847 (38%)
    div_sep 250 (2.8%) 98 (4.4%)
    unmarried 2,894 (32%) 778 (35%)
    unknown 1,787 (20%) 449 (20%)
    widow 178 (2.0%) 48 (2.2%)
Depression 894 (9.8%) 1,185 (53%) <0.001
PTSD 43 (0.5%) 74 (3.3%) <0.001
Any Psychiatric Diagnosis 1,186 (13%) 2,220 (100%) <0.001
Total SVI 0.37 (0.26) 0.36 (0.25) 0.6
    (Missing) 6 0
Soceioeconomic Status 0.35 (0.26) 0.34 (0.25) 0.14
    (Missing) 41 7
Household Composition 0.39 (0.27) 0.38 (0.26) 0.025
    (Missing) 6 0
Minority Status and Language 0.48 (0.28) 0.50 (0.29) 0.007
Housing and Transportation 0.44 (0.29) 0.44 (0.28) 0.7
    (Missing) 19 5
1 Mean (SD); n (%)
2 Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test

Baseline Characteristics by Any Psych Dx

baseline %>% tbl_summary(by = any_psych_dx_2,
       statistic = list(all_continuous() ~ "{mean} ({sd})"),
        label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", depression_2 ~ "Depression", ptsd_2 ~ "PTSD", anxiety_2 ~ "Anxiety", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"),
        missing_text = "(Missing)"
        ) %>% add_p()
Characteristic 0, N = 7,9001 1, N = 3,4061 p-value2
Age 50 (19) 50 (18) 0.8
Gender <0.001
    male 4,055 (51%) 1,227 (36%)
    female 3,845 (49%) 2,179 (64%)
Race 0.011
    WHITE OR CAUCASIAN 6,831 (86%) 2,992 (88%)
    BLACK OR AFRICAN AMERICAN 511 (6.5%) 215 (6.3%)
    ASIAN 219 (2.8%) 61 (1.8%)
    Other 316 (4.0%) 122 (3.6%)
    AMERICAN INDIAN AND ALASKA NATIVE 23 (0.3%) 16 (0.5%)
Ethnicity 0.009
    NON-HISPANIC 7,471 (95%) 3,248 (95%)
    Unknown 294 (3.7%) 90 (2.6%)
    HISPANIC 135 (1.7%) 68 (2.0%)
English Speaking <0.001
    English 7,809 (99%) 3,393 (100%)
    Other 87 (1.1%) 12 (0.4%)
    unknown 4 (<0.1%) 1 (<0.1%)
Any Religious Affiliation <0.001
    yes 4,188 (53%) 1,905 (56%)
    no 3,186 (40%) 1,357 (40%)
    unknown 526 (6.7%) 144 (4.2%)
Marital Status <0.001
    married 3,482 (44%) 1,342 (39%)
    div_sep 200 (2.5%) 148 (4.3%)
    unmarried 2,516 (32%) 1,156 (34%)
    unknown 1,560 (20%) 676 (20%)
    widow 142 (1.8%) 84 (2.5%)
Depression 0 (0%) 2,079 (61%) <0.001
Anxiety 0 (0%) 2,220 (65%) <0.001
PTSD 0 (0%) 117 (3.4%) <0.001
Total SVI 0.37 (0.26) 0.37 (0.26) 0.074
    (Missing) 6 0
Soceioeconomic Status 0.34 (0.25) 0.35 (0.26) 0.2
    (Missing) 37 11
Household Composition 0.39 (0.27) 0.39 (0.27) 0.5
    (Missing) 6 0
Minority Status and Language 0.48 (0.28) 0.49 (0.29) 0.006
Housing and Transportation 0.43 (0.29) 0.44 (0.28) 0.071
    (Missing) 17 7
1 Mean (SD); n (%)
2 Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test

Prelim Models

Depression + RPL_THEMES


model1a <- glm(depression_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
               + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model1a)

Call:
glm(formula = depression_2 ~ mstat_5 + relig_affil + race_5 + 
    lang_3 + age_yrs + gender + ethnic_3 + RPL_THEMES, family = "binomial", 
    data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.1083  -0.6910  -0.5682  -0.4884   2.4580  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)
(Intercept)                              -2.391136   0.114571 -20.870  < 2e-16
mstat_5div_sep                            0.697244   0.124876   5.584 2.36e-08
mstat_5unmarried                          0.308344   0.064845   4.755 1.98e-06
mstat_5unknown                            0.191959   0.069916   2.746  0.00604
mstat_5widow                              0.382229   0.157952   2.420  0.01552
relig_affilno                            -0.002777   0.052557  -0.053  0.95786
relig_affilunknown                       -0.505952   0.126398  -4.003 6.26e-05
race_5BLACK OR AFRICAN AMERICAN          -0.222939   0.105035  -2.123  0.03379
race_5ASIAN                              -0.402514   0.195649  -2.057  0.03965
race_5Other                              -0.013541   0.144461  -0.094  0.92532
race_5AMERICAN INDIAN AND ALASKA NATIVE   0.355694   0.361562   0.984  0.32523
lang_3Other                              -1.013604   0.400714  -2.529  0.01142
lang_3unknown                           -10.106624 143.906745  -0.070  0.94401
age_yrs                                   0.006435   0.001561   4.122 3.76e-05
genderfemale                              0.590703   0.051169  11.544  < 2e-16
ethnic_3Unknown                          -0.242978   0.157859  -1.539  0.12375
ethnic_3HISPANIC                          0.098964   0.189854   0.521  0.60218
RPL_THEMES                                0.319466   0.097941   3.262  0.00111
                                           
(Intercept)                             ***
mstat_5div_sep                          ***
mstat_5unmarried                        ***
mstat_5unknown                          ** 
mstat_5widow                            *  
relig_affilno                              
relig_affilunknown                      ***
race_5BLACK OR AFRICAN AMERICAN         *  
race_5ASIAN                             *  
race_5Other                                
race_5AMERICAN INDIAN AND ALASKA NATIVE    
lang_3Other                             *  
lang_3unknown                              
age_yrs                                 ***
genderfemale                            ***
ethnic_3Unknown                            
ethnic_3HISPANIC                           
RPL_THEMES                              ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 10789  on 11299  degrees of freedom
Residual deviance: 10523  on 11282  degrees of freedom
  (6 observations deleted due to missingness)
AIC: 10559

Number of Fisher Scoring iterations: 11
broom::glance(model1a)
broom::tidy(model1a, exponentiate = TRUE)
model_performance(model1a)
# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
10559.176 | 10691.162 |     0.023 | 0.383 | 0.966 |    0.466 |      -Inf |       1.979e-04 | 0.707
tbl_regression(model1a, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
Marital Status
    married — —
    div_sep 2.01 1.57, 2.56 <0.001
    unmarried 1.36 1.20, 1.55 <0.001
    unknown 1.21 1.06, 1.39 0.006
    widow 1.47 1.07, 1.99 0.016
Any Religious Affiliation
    yes — —
    no 1.00 0.90, 1.11 >0.9
    unknown 0.60 0.47, 0.77 <0.001
Race
    WHITE OR CAUCASIAN — —
    BLACK OR AFRICAN AMERICAN 0.80 0.65, 0.98 0.034
    ASIAN 0.67 0.45, 0.97 0.040
    Other 0.99 0.74, 1.30 >0.9
    AMERICAN INDIAN AND ALASKA NATIVE 1.43 0.67, 2.82 0.3
English Speaking
    English — —
    Other 0.36 0.15, 0.74 0.011
    unknown 0.00 >0.9
Age 1.01 1.00, 1.01 <0.001
Gender
    male — —
    female 1.81 1.63, 2.00 <0.001
Ethnicity
    NON-HISPANIC — —
    Unknown 0.78 0.57, 1.06 0.12
    HISPANIC 1.10 0.75, 1.59 0.6
Total SVI 1.38 1.14, 1.67 0.001
1 OR = Odds Ratio, CI = Confidence Interval 
NA

Depression + RPL_THEMESx4


model1b <- glm(depression_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
                + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model1b)

Call:
glm(formula = depression_2 ~ mstat_5 + relig_affil + race_5 + 
    lang_3 + age_yrs + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + 
    RPL_THEME3 + RPL_THEME4, family = "binomial", data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.0991  -0.6863  -0.5686  -0.4819   2.4788  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)
(Intercept)                              -2.509937   0.126050 -19.912  < 2e-16
mstat_5div_sep                            0.700653   0.125131   5.599 2.15e-08
mstat_5unmarried                          0.298353   0.065122   4.581 4.62e-06
mstat_5unknown                            0.189687   0.070154   2.704  0.00685
mstat_5widow                              0.356478   0.159135   2.240  0.02508
relig_affilno                             0.001572   0.052850   0.030  0.97628
relig_affilunknown                       -0.501297   0.126595  -3.960 7.50e-05
race_5BLACK OR AFRICAN AMERICAN          -0.273232   0.106927  -2.555  0.01061
race_5ASIAN                              -0.469145   0.197899  -2.371  0.01776
race_5Other                              -0.042459   0.145395  -0.292  0.77027
race_5AMERICAN INDIAN AND ALASKA NATIVE   0.387898   0.364156   1.065  0.28679
lang_3Other                              -1.086542   0.401741  -2.705  0.00684
lang_3unknown                           -10.134088 143.608197  -0.071  0.94374
age_yrs                                   0.006900   0.001568   4.401 1.08e-05
genderfemale                              0.593142   0.051385  11.543  < 2e-16
ethnic_3Unknown                          -0.238405   0.159147  -1.498  0.13413
ethnic_3HISPANIC                          0.100326   0.190017   0.528  0.59751
RPL_THEME1                                0.410419   0.140795   2.915  0.00356
RPL_THEME2                               -0.194572   0.122036  -1.594  0.11085
RPL_THEME3                                0.225248   0.091203   2.470  0.01352
RPL_THEME4                                0.091691   0.103862   0.883  0.37734
                                           
(Intercept)                             ***
mstat_5div_sep                          ***
mstat_5unmarried                        ***
mstat_5unknown                          ** 
mstat_5widow                            *  
relig_affilno                              
relig_affilunknown                      ***
race_5BLACK OR AFRICAN AMERICAN         *  
race_5ASIAN                             *  
race_5Other                                
race_5AMERICAN INDIAN AND ALASKA NATIVE    
lang_3Other                             ** 
lang_3unknown                              
age_yrs                                 ***
genderfemale                            ***
ethnic_3Unknown                            
ethnic_3HISPANIC                           
RPL_THEME1                              ** 
RPL_THEME2                                 
RPL_THEME3                              *  
RPL_THEME4                                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 10723  on 11239  degrees of freedom
Residual deviance: 10444  on 11219  degrees of freedom
  (66 observations deleted due to missingness)
AIC: 10486

Number of Fisher Scoring iterations: 11
broom::glance(model1b)
broom::tidy(model1b, exponentiate = TRUE)
model_performance(model1b)
# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
10486.462 | 10640.333 |     0.025 | 0.382 | 0.965 |    0.465 |      -Inf |       1.989e-04 | 0.707
tbl_regression(model1b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
mstat_5
    married — —
    div_sep 2.02 1.57, 2.57 <0.001
    unmarried 1.35 1.19, 1.53 <0.001
    unknown 1.21 1.05, 1.39 0.007
    widow 1.43 1.04, 1.94 0.025
Any Religious Affiliation
    yes — —
    no 1.00 0.90, 1.11 >0.9
    unknown 0.61 0.47, 0.77 <0.001
Race
    WHITE OR CAUCASIAN — —
    BLACK OR AFRICAN AMERICAN 0.76 0.61, 0.94 0.011
    ASIAN 0.63 0.42, 0.91 0.018
    Other 0.96 0.72, 1.27 0.8
    AMERICAN INDIAN AND ALASKA NATIVE 1.47 0.69, 2.93 0.3
English Speaking
    English — —
    Other 0.34 0.14, 0.69 0.007
    unknown 0.00 >0.9
Age 1.01 1.00, 1.01 <0.001
Gender
    male — —
    female 1.81 1.64, 2.00 <0.001
Ethnicity
    NON-HISPANIC — —
    Unknown 0.79 0.57, 1.07 0.13
    HISPANIC 1.11 0.75, 1.59 0.6
Soceioeconomic Status 1.51 1.14, 1.99 0.004
Household Composition 0.82 0.65, 1.05 0.11
Minority Status and Language 1.25 1.05, 1.50 0.014
Housing and Transportation 1.10 0.89, 1.34 0.4
1 OR = Odds Ratio, CI = Confidence Interval

Anxiety + RPL_THEMES

model2a <- glm(anxiety_2 ~ mstat_5 + relig_affil + lang_3 + age_yrs + race_5
               + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model2a)

Call:
glm(formula = anxiety_2 ~ mstat_5 + relig_affil + lang_3 + age_yrs + 
    race_5 + gender + ethnic_3 + RPL_THEMES, family = "binomial", 
    data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.0106  -0.7279  -0.5850  -0.4743   2.5193  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)
(Intercept)                              -1.447495   0.110644 -13.082  < 2e-16
mstat_5div_sep                            0.545058   0.127299   4.282 1.85e-05
mstat_5unmarried                          0.140432   0.063508   2.211  0.02702
mstat_5unknown                            0.126650   0.068362   1.853  0.06393
mstat_5widow                              0.196117   0.171701   1.142  0.25337
relig_affilno                            -0.051649   0.051166  -1.009  0.31276
relig_affilunknown                       -0.553278   0.124657  -4.438 9.06e-06
lang_3Other                              -1.347366   0.466192  -2.890  0.00385
lang_3unknown                           -10.299879 143.225055  -0.072  0.94267
age_yrs                                  -0.007212   0.001560  -4.625 3.75e-06
race_5BLACK OR AFRICAN AMERICAN          -0.294426   0.105861  -2.781  0.00542
race_5ASIAN                              -0.596272   0.195727  -3.046  0.00232
race_5Other                               0.032891   0.138157   0.238  0.81183
race_5AMERICAN INDIAN AND ALASKA NATIVE   0.248353   0.362442   0.685  0.49320
genderfemale                              0.707644   0.050370  14.049  < 2e-16
ethnic_3Unknown                          -0.243212   0.155893  -1.560  0.11873
ethnic_3HISPANIC                          0.133510   0.181560   0.735  0.46213
RPL_THEMES                               -0.082192   0.096949  -0.848  0.39656
                                           
(Intercept)                             ***
mstat_5div_sep                          ***
mstat_5unmarried                        *  
mstat_5unknown                          .  
mstat_5widow                               
relig_affilno                              
relig_affilunknown                      ***
lang_3Other                             ** 
lang_3unknown                              
age_yrs                                 ***
race_5BLACK OR AFRICAN AMERICAN         ** 
race_5ASIAN                             ** 
race_5Other                                
race_5AMERICAN INDIAN AND ALASKA NATIVE    
genderfemale                            ***
ethnic_3Unknown                            
ethnic_3HISPANIC                           
RPL_THEMES                                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 11197  on 11299  degrees of freedom
Residual deviance: 10864  on 11282  degrees of freedom
  (6 observations deleted due to missingness)
AIC: 10900

Number of Fisher Scoring iterations: 11
broom::glance(model2a)
broom::tidy(model2a, exponentiate = TRUE)
model_performance(model2a)
# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
10899.868 | 11031.854 |     0.029 | 0.392 | 0.981 |    0.481 |      -Inf |       1.979e-04 | 0.693
tbl_regression(model2a, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
Marital Status
    married — —
    div_sep 1.72 1.34, 2.21 <0.001
    unmarried 1.15 1.02, 1.30 0.027
    unknown 1.14 0.99, 1.30 0.064
    widow 1.22 0.86, 1.69 0.3
Any Religious Affiliation
    yes — —
    no 0.95 0.86, 1.05 0.3
    unknown 0.58 0.45, 0.73 <0.001
English Speaking
    English — —
    Other 0.26 0.09, 0.59 0.004
    unknown 0.00 >0.9
Age 0.99 0.99, 1.00 <0.001
Race
    WHITE OR CAUCASIAN — —
    BLACK OR AFRICAN AMERICAN 0.74 0.60, 0.91 0.005
    ASIAN 0.55 0.37, 0.80 0.002
    Other 1.03 0.78, 1.35 0.8
    AMERICAN INDIAN AND ALASKA NATIVE 1.28 0.60, 2.54 0.5
Gender
    male — —
    female 2.03 1.84, 2.24 <0.001
Ethnicity
    NON-HISPANIC — —
    Unknown 0.78 0.57, 1.06 0.12
    HISPANIC 1.14 0.79, 1.62 0.5
Total SVI 0.92 0.76, 1.11 0.4
1 OR = Odds Ratio, CI = Confidence Interval

Anxiety + RPL_THEMESx4

model2b <- glm(anxiety_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
                + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model2b)

Call:
glm(formula = anxiety_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + 
    age_yrs + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
    RPL_THEME4, family = "binomial", data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.0373  -0.7230  -0.5820  -0.4648   2.5023  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)
(Intercept)                             -1.574e+00  1.220e-01 -12.900  < 2e-16
mstat_5div_sep                           5.569e-01  1.276e-01   4.364 1.28e-05
mstat_5unmarried                         1.373e-01  6.373e-02   2.155 0.031189
mstat_5unknown                           1.292e-01  6.861e-02   1.883 0.059687
mstat_5widow                             1.929e-01  1.722e-01   1.120 0.262565
relig_affilno                           -4.277e-02  5.143e-02  -0.831 0.405706
relig_affilunknown                      -5.364e-01  1.248e-01  -4.297 1.73e-05
race_5BLACK OR AFRICAN AMERICAN         -3.393e-01  1.076e-01  -3.153 0.001615
race_5ASIAN                             -7.059e-01  1.978e-01  -3.569 0.000358
race_5Other                              3.621e-04  1.389e-01   0.003 0.997920
race_5AMERICAN INDIAN AND ALASKA NATIVE  2.755e-01  3.658e-01   0.753 0.451349
lang_3Other                             -1.416e+00  4.668e-01  -3.033 0.002420
lang_3unknown                           -1.036e+01  1.430e+02  -0.072 0.942256
age_yrs                                 -6.739e-03  1.564e-03  -4.309 1.64e-05
genderfemale                             7.113e-01  5.058e-02  14.062  < 2e-16
ethnic_3Unknown                         -2.460e-01  1.572e-01  -1.565 0.117639
ethnic_3HISPANIC                         1.386e-01  1.816e-01   0.763 0.445514
RPL_THEME1                              -1.078e-01  1.392e-01  -0.775 0.438623
RPL_THEME2                              -1.798e-01  1.204e-01  -1.493 0.135353
RPL_THEME3                               2.824e-01  8.902e-02   3.172 0.001512
RPL_THEME4                               9.487e-02  1.019e-01   0.931 0.351965
                                           
(Intercept)                             ***
mstat_5div_sep                          ***
mstat_5unmarried                        *  
mstat_5unknown                          .  
mstat_5widow                               
relig_affilno                              
relig_affilunknown                      ***
race_5BLACK OR AFRICAN AMERICAN         ** 
race_5ASIAN                             ***
race_5Other                                
race_5AMERICAN INDIAN AND ALASKA NATIVE    
lang_3Other                             ** 
lang_3unknown                              
age_yrs                                 ***
genderfemale                            ***
ethnic_3Unknown                            
ethnic_3HISPANIC                           
RPL_THEME1                                 
RPL_THEME2                                 
RPL_THEME3                              ** 
RPL_THEME4                                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 11137  on 11239  degrees of freedom
Residual deviance: 10787  on 11219  degrees of freedom
  (66 observations deleted due to missingness)
AIC: 10829

Number of Fisher Scoring iterations: 11
broom::glance(model2b)
broom::tidy(model2b, exponentiate = TRUE)
model_performance(model2b)
# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
10828.663 | 10982.535 |     0.030 | 0.391 | 0.981 |    0.480 |      -Inf |       1.989e-04 | 0.694
tbl_regression(model2b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
mstat_5
    married — —
    div_sep 1.75 1.35, 2.23 <0.001
    unmarried 1.15 1.01, 1.30 0.031
    unknown 1.14 0.99, 1.30 0.060
    widow 1.21 0.86, 1.69 0.3
Any Religious Affiliation
    yes — —
    no 0.96 0.87, 1.06 0.4
    unknown 0.58 0.46, 0.74 <0.001
Race
    WHITE OR CAUCASIAN — —
    BLACK OR AFRICAN AMERICAN 0.71 0.57, 0.88 0.002
    ASIAN 0.49 0.33, 0.72 <0.001
    Other 1.00 0.76, 1.31 >0.9
    AMERICAN INDIAN AND ALASKA NATIVE 1.32 0.62, 2.63 0.5
English Speaking
    English — —
    Other 0.24 0.08, 0.55 0.002
    unknown 0.00 >0.9
Age 0.99 0.99, 1.00 <0.001
Gender
    male — —
    female 2.04 1.85, 2.25 <0.001
Ethnicity
    NON-HISPANIC — —
    Unknown 0.78 0.57, 1.05 0.12
    HISPANIC 1.15 0.80, 1.63 0.4
Soceioeconomic Status 0.90 0.68, 1.18 0.4
Household Composition 0.84 0.66, 1.06 0.14
Minority Status and Language 1.33 1.11, 1.58 0.002
Housing and Transportation 1.10 0.90, 1.34 0.4
1 OR = Odds Ratio, CI = Confidence Interval

PTSD + RPL_THEMES

model3a <- glm(ptsd_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
                + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(model3a)

Call:
glm(formula = ptsd_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + 
    age_yrs + gender + ethnic_3 + RPL_THEMES, family = "binomial", 
    data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9062  -0.1643  -0.1353  -0.1102   3.4797  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)
(Intercept)                              -4.831906   0.436657 -11.066  < 2e-16
mstat_5div_sep                            0.227699   0.477160   0.477 0.633223
mstat_5unmarried                          0.120949   0.239832   0.504 0.614046
mstat_5unknown                           -0.203905   0.280534  -0.727 0.467319
mstat_5widow                            -14.033161 429.294756  -0.033 0.973923
relig_affilno                            -0.269765   0.204464  -1.319 0.187044
relig_affilunknown                       -0.030234   0.403362  -0.075 0.940251
race_5BLACK OR AFRICAN AMERICAN          -0.691715   0.470877  -1.469 0.141834
race_5ASIAN                               0.522321   0.517598   1.009 0.312915
race_5Other                              -0.168527   0.595370  -0.283 0.777129
race_5AMERICAN INDIAN AND ALASKA NATIVE   1.837626   0.619050   2.968 0.002993
lang_3Other                             -14.121453 642.083639  -0.022 0.982453
lang_3unknown                             4.167621   1.425091   2.924 0.003451
age_yrs                                  -0.005803   0.006112  -0.949 0.342375
genderfemale                              0.735791   0.204894   3.591 0.000329
ethnic_3Unknown                          -0.595795   0.732287  -0.814 0.415870
ethnic_3HISPANIC                         -0.137868   0.752193  -0.183 0.854572
RPL_THEMES                                0.610222   0.366240   1.666 0.095678
                                           
(Intercept)                             ***
mstat_5div_sep                             
mstat_5unmarried                           
mstat_5unknown                             
mstat_5widow                               
relig_affilno                              
relig_affilunknown                         
race_5BLACK OR AFRICAN AMERICAN            
race_5ASIAN                                
race_5Other                                
race_5AMERICAN INDIAN AND ALASKA NATIVE ** 
lang_3Other                                
lang_3unknown                           ** 
age_yrs                                    
genderfemale                            ***
ethnic_3Unknown                            
ethnic_3HISPANIC                           
RPL_THEMES                              .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1302.3  on 11299  degrees of freedom
Residual deviance: 1259.5  on 11282  degrees of freedom
  (6 observations deleted due to missingness)
AIC: 1295.5

Number of Fisher Scoring iterations: 17
broom::glance(model3a)
broom::tidy(model3a, exponentiate = TRUE)
model_performance(model3a)
# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
1295.528 | 1427.514 |     0.006 | 0.101 | 0.334 |    0.056 |    -1.218 |           0.008 | 0.980
tbl_regression(model3a, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
Marital Status
    married — —
    div_sep 1.26 0.43, 2.91 0.6
    unmarried 1.13 0.70, 1.80 0.6
    unknown 0.82 0.46, 1.39 0.5
    widow 0.00 0.00, 1.13 >0.9
Any Religious Affiliation
    yes — —
    no 0.76 0.51, 1.13 0.2
    unknown 0.97 0.40, 2.00 >0.9
Race
    WHITE OR CAUCASIAN — —
    BLACK OR AFRICAN AMERICAN 0.50 0.17, 1.14 0.14
    ASIAN 1.69 0.51, 4.10 0.3
    Other 0.84 0.22, 2.36 0.8
    AMERICAN INDIAN AND ALASKA NATIVE 6.28 1.48, 18.2 0.003
English Speaking
    English — —
    Other 0.00 0.00, 178,273 >0.9
    unknown 64.6 2.37, 1,024 0.003
Age 0.99 0.98, 1.01 0.3
Gender
    male — —
    female 2.09 1.41, 3.16 <0.001
Ethnicity
    NON-HISPANIC — —
    Unknown 0.55 0.09, 1.82 0.4
    HISPANIC 0.87 0.14, 3.02 0.9
Total SVI 1.84 0.89, 3.75 0.10
1 OR = Odds Ratio, CI = Confidence Interval

PTSD + RPL_THEMESx4

model3b <- glm(ptsd_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
                + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model3b)
broom::glance(model3b)
broom::tidy(model3b, exponentiate = TRUE)
model_performance(model3b)
tbl_regression(model3b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)

Any Psych + RPL THEMES

model4a <- glm(any_psych_dx_2 ~ mstat_5 + relig_affil + race_5 + lang_3 +
              age_yrs + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model4a)

Call:
glm(formula = any_psych_dx_2 ~ mstat_5 + relig_affil + race_5 + 
    lang_3 + age_yrs + gender + ethnic_3 + RPL_THEMES, family = "binomial", 
    data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.2918  -0.9162  -0.7340   1.3762   2.1881  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)
(Intercept)                             -1.3070778  0.0957130 -13.656  < 2e-16
mstat_5div_sep                           0.5800994  0.1149104   5.048 4.46e-07
mstat_5unmarried                         0.2052974  0.0548949   3.740 0.000184
mstat_5unknown                           0.1409957  0.0589234   2.393 0.016717
mstat_5widow                             0.2502883  0.1454133   1.721 0.085211
relig_affilno                           -0.0449351  0.0445018  -1.010 0.312622
relig_affilunknown                      -0.4529476  0.1005874  -4.503 6.70e-06
race_5BLACK OR AFRICAN AMERICAN         -0.1597796  0.0882736  -1.810 0.070288
race_5ASIAN                             -0.2856308  0.1504510  -1.898 0.057631
race_5Other                              0.0027715  0.1199491   0.023 0.981566
race_5AMERICAN INDIAN AND ALASKA NATIVE  0.2751616  0.3308584   0.832 0.405601
lang_3Other                             -1.0672151  0.3158365  -3.379 0.000727
lang_3unknown                            0.2752294  1.1385568   0.242 0.808985
age_yrs                                  0.0005733  0.0013322   0.430 0.666931
genderfemale                             0.6149674  0.0426661  14.413  < 2e-16
ethnic_3Unknown                         -0.2291014  0.1290134  -1.776 0.075767
ethnic_3HISPANIC                         0.1604120  0.1605009   0.999 0.317579
RPL_THEMES                               0.1061593  0.0835235   1.271 0.203725
                                           
(Intercept)                             ***
mstat_5div_sep                          ***
mstat_5unmarried                        ***
mstat_5unknown                          *  
mstat_5widow                            .  
relig_affilno                              
relig_affilunknown                      ***
race_5BLACK OR AFRICAN AMERICAN         .  
race_5ASIAN                             .  
race_5Other                                
race_5AMERICAN INDIAN AND ALASKA NATIVE    
lang_3Other                             ***
lang_3unknown                              
age_yrs                                    
genderfemale                            ***
ethnic_3Unknown                         .  
ethnic_3HISPANIC                           
RPL_THEMES                                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 13833  on 11299  degrees of freedom
Residual deviance: 13514  on 11282  degrees of freedom
  (6 observations deleted due to missingness)
AIC: 13550

Number of Fisher Scoring iterations: 4
broom::glance(model4a)
broom::tidy(model4a, exponentiate = TRUE)
model_performance(model4a)
# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
13550.396 | 13682.382 |     0.028 | 0.452 | 1.094 |    0.598 |      -Inf |       1.353e-04 | 0.591
tbl_regression(model4a, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
Marital Status
    married — —
    div_sep 1.79 1.42, 2.24 <0.001
    unmarried 1.23 1.10, 1.37 <0.001
    unknown 1.15 1.03, 1.29 0.017
    widow 1.28 0.96, 1.70 0.085
Any Religious Affiliation
    yes — —
    no 0.96 0.88, 1.04 0.3
    unknown 0.64 0.52, 0.77 <0.001
Race
    WHITE OR CAUCASIAN — —
    BLACK OR AFRICAN AMERICAN 0.85 0.72, 1.01 0.070
    ASIAN 0.75 0.56, 1.00 0.058
    Other 1.00 0.79, 1.27 >0.9
    AMERICAN INDIAN AND ALASKA NATIVE 1.32 0.68, 2.50 0.4
English Speaking
    English — —
    Other 0.34 0.18, 0.61 <0.001
    unknown 1.32 0.07, 9.33 0.8
Age 1.00 1.00, 1.00 0.7
Gender
    male — —
    female 1.85 1.70, 2.01 <0.001
Ethnicity
    NON-HISPANIC — —
    Unknown 0.80 0.61, 1.02 0.076
    HISPANIC 1.17 0.85, 1.60 0.3
Total SVI 1.11 0.94, 1.31 0.2
1 OR = Odds Ratio, CI = Confidence Interval

Any Psych + RPL_THEMESx4

model4b <- glm(any_psych_dx_2 ~ mstat_5 + relig_affil + race_5 + lang_3 +
              age_yrs + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model4b)
broom::glance(model4b)
broom::tidy(model4b, exponentiate = TRUE)
model_performance(model4b)
tbl_regression(model4b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)
---
title: "MH_Codebook_V2"
output: 
  html_notebook:
   themes: paper
   toc: yes
   toc_float: yes
editor_options: 
  chunk_output_type: inline
---

# Load Packages {.tabset}

## tidyverse

```{r}
if (!require(tidyverse)){
  install.packages("tidyverse", dependencies = TRUE)
  library(tidyverse)
}
```

## codebookr
```{r}
if (!require(codebookr)){
  install.packages("codebookr", dependencies = TRUE)
  library(codebookr)
}
```

## summarytools
```{r}
if (!require(summarytools)){
  install.packages("summarytools", dependencies = TRUE)
  library(summarytools)
}
```

## broom 
```{r}
if (!require(broom)){
  install.packages("broom", dependencies = TRUE)
  library(broom)
}
```

## performance
```{r}
if (!require(performance)){
  install.packages("performance", dependencies = TRUE)
  library(performance)
}
```

## gtsummary
```{r}
if (!require(gtsummary)){
  install.packages("gtsummary", dependencies = TRUE)
  library(gtsummary)
}
```

## janitor
```{r}
if (!require(janitor)){
  install.packages("janitor", dependencies = TRUE)
  library(janitor)
}
```

## forcats
```{r}
if (!require(forcats)){
  install.packages("forcats", dependencies = TRUE)
  library(forcats)
}
```

# Import Data
```{r}
mh_deidv2 <- read.csv("~/Desktop/R-Code/mh_deidv2.csv")
```

# Data Cleaning {.tabset}

## RPL_THEMES erroneous values 
```{r}
mh_deidv2 %>%
  select(age_yrs, PATIENT_GENDER_CD, PATIENT_RACE_DESC, PATIENT_ETHNIC_GROUP_DESC, PATIENT_LANGUAGE_DESC, PATIENT_RELIGION_DESC, PATIENT_MARITAL_STATUS_DESC, PATIENT_STATE_CD, EDU_YEARS, TOBACCO_DESC, depression, anxiety, ptsd, bipolar, body_image, ocd, stress, seasonalAD, panic, any_psych_dx,ST_ABBR, E_TOTPOP, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4) -> exampledf1
exampledf1 %>%
mutate(RPL_THEMES = na_if(RPL_THEMES, "-999")) %>%
mutate(RPL_THEMES = na_if(RPL_THEMES, "0")) %>%
mutate(RPL_THEME1 = na_if(RPL_THEME1, "-999")) %>%
mutate(RPL_THEME1 = na_if(RPL_THEME1, "0")) %>%
mutate(RPL_THEME2 = na_if(RPL_THEME2, "-999")) %>%
mutate(RPL_THEME2 = na_if(RPL_THEME2, "0")) %>%
mutate(RPL_THEME3 = na_if(RPL_THEME3, "-999")) %>%
mutate(RPL_THEME3 = na_if(RPL_THEME3, "0")) %>%
mutate(RPL_THEME4 = na_if(RPL_THEME4, "-999")) %>%
mutate(RPL_THEME4 = na_if(RPL_THEME4, "0")) -> exampledf1NA

```

## Mental Health Dich
```{r}
exampledf1NA %>% mutate(across(depression:any_psych_dx, ~if_else(.x>0.5, 1, 0),
                .names = "{col}_2")) -> exampledf1NADi2
```

## Marital Status 
```{r}
exampledf1NADi2 %>% 
mutate(mstat_5 = as_factor(PATIENT_MARITAL_STATUS_DESC),
         mstat_5 = fct_recode(mstat_5, div_sep = "DIVORCED",
                              div_sep = "LEGALLY SEPARATED", widow = "WIDOWED",
                              married = "MARRIED", unmarried = "SINGLE",
                              unknown = "UNKNOWN", unknown = "OTHER",
                              unmarried = "SIGNIFICANT OTHER"),
         mstat_5 = fct_relevel(mstat_5, ref = 'married')) -> exampledf1NADi2MA
```

## Religion 
```{r}
exampledf1NADi2MA %>% 
  mutate(relig_affil = as_factor(PATIENT_RELIGION_DESC),
          relig_affil = fct_recode(relig_affil, yes = "CATHOLIC",
                      no = "NONE", unknown = "UNKNOWN", unknown = "PATIENT REFUSED", 
                      yes = "CHRISTIAN", yes = "LUTHERAN",
                      yes = "PROTESTANT", yes = "BAPTIST",
                      yes = "METHODIST", yes = "PRESBYTERIAN",
                      yes = "NON-DENOMINATIONAL", yes = "JEWISH",
                      yes = "MUSLIM", yes = "OTHER",
                      yes = "EPISCOPALIAN", yes = "PENTECOSTAL",
                      no = "AGNOSTIC", no = "ATHEIST",
                      yes = "JEHOVAH'S WITNESS", yes = "HINDU",
                      yes = "GREEK ORTHODOX", yes = "CHURCH OF JESUS CHRIST OF LATTER-DAY SAINTS", yes = "BAHAI", no = "SPIRITUAL", yes = "CHURCH OF CHRIST",
         yes = "SEVENTH DAY ADVENTIST", yes = "APOSTOLIC", yes = "BUDDHIST", yes = "NAZARENE", yes = "CONGREGATIONAL", yes = "UNITED CHURCH OF CHR", yes = "REFORMED", yes = "PAGAN", yes = "JAIN", yes = "ASSEMBLY OF GOD", yes = "REORG CHR OF LAT DAY", yes = "QUAKER", yes = "UNITARIAN UNIVERSALIST", yes = "MENNONITE", yes = "FREE METHODIST", yes = "NATIVE AMER SPIRITL", yes = "WICCAN", yes = "ORTHODOX", yes = "SALVATION ARMY", yes = "DISCIPLES OF CHRIST", yes = "AFRICAN METHODIST EP", yes = "SIKH", yes = "CHURCH OF GOD", yes = "TAOIST", yes = "ANGLICAN"),
         relig_affil = fct_relevel(relig_affil, ref = 'yes')) -> exampledf1NADi2MARel
```

## Race
```{r}
exampledf1NADi2MARel %>% 
mutate(race_5 = as_factor(PATIENT_RACE_DESC),
         race_5 = fct_recode(race_5, Other = "OTHER",
                  Other = "UNKNOWN", Other = "CHOOSE NOT TO DISCLOSE",
                  Other = "NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER", 
                  Other = "MIDDLE EASTERN/NORTH AFRICAN",
                ASIAN = "ASIAN INDIAN", ASIAN = "OTHER ASIAN",
                ASIAN = "JAPANESE", ASIAN = "KOREAN", ASIAN = "FILIPINO",
                ASIAN = "CHINESE"),
         race_5 = fct_relevel(race_5, ref = 'WHITE OR CAUCASIAN')) -> exampledf1NADi2MARelRa
```

## Gender
```{r}
exampledf1NADi2MARelRa %>% 
mutate(gender = as_factor(PATIENT_GENDER_CD),
         gender = fct_recode(gender, male = "M", female = "F"),
         gender = fct_relevel(gender, ref = "male")) -> exampledf1NADi2MARelRaG
```

## Language 
```{r}
exampledf1NADi2MARelRaG %>% 
  mutate(lang_3 = as_factor(PATIENT_LANGUAGE_DESC),
lang_3 = fct_recode(lang_3, English = "ENGLISH",
Other = "ARABIC", Other = "JAPANESE",
Other = "CHINESE, MANDARIN",
Other = "KOREAN", Other = "SIGN LANGUAGE",
Other = "RUSSIAN", Other = "SPANISH", Other = "ARMENIAN",
Other = "TURKISH", Other = "HINDI", Other = "BENGALI", Other = "FARSI; PERSIAN", Other = "ALBANIAN", Other = "HMONG", Other = "ROMANIAN", 
Other = "PUNJABI", Other = "CROATIAN", Other = "CHALDEAN", 
Other = "BURMESE", Other = "PORTUGUESE", unknown = "UNKNOWN"),
lang_3 = fct_relevel(lang_3, ref = 'English')) -> exampledf1NADi2MARelRaGL

```

## Ethnicity 
```{r}
exampledf1NADi2MARelRaGL %>% 
  mutate(ethnic_3 = as_factor(PATIENT_ETHNIC_GROUP_DESC),
         ethnic_3 = fct_recode(ethnic_3, Unknown = "CHOOSE NOT TO DISCLOSE", Unknown = "UNKNOWN")) -> exampledf1NADi2MARelRaGLEth
```


# Codebook 
```{r}
exampledf1NADi2MARelRaGLEth %>% 
select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, PATIENT_STATE_CD, EDU_YEARS, TOBACCO_DESC, depression_2, anxiety_2, ptsd_2, bipolar_2, body_image_2, ocd_2, seasonalAD_2, panic_2, any_psych_dx_2, E_TOTPOP, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4) -> mh_clean1
print(dfSummary(mh_clean1), method = 'render') 
```

# Patient Characteristics {.tabset}

## Baseline Characteristics 
```{r}
mh_clean1 %>% 
  select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, depression_2, anxiety_2, ptsd_2, any_psych_dx_2, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4) -> baseline
baseline %>% tbl_summary(label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", depression_2 ~ "Depression", anxiety_2 ~ "Anxiety", ptsd_2 ~ "PTSD", any_psych_dx_2 ~ "Any Psychiatric Diagnosis", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")
```


## Baseline Characteristics by Depression 
```{r}
baseline %>% tbl_summary(by = depression_2,
         statistic = list(all_continuous() ~ "{mean} ({sd})"),
        label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", anxiety_2 ~ "Anxiety", ptsd_2 ~ "PTSD", any_psych_dx_2 ~ "Any Psychiatric Diagnosis", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"),
        missing_text = "(Missing)"
        ) %>% add_p()


```

## Baseline Characteristics By Anxiety
```{r}
baseline %>% tbl_summary(by = anxiety_2,
       statistic = list(all_continuous() ~ "{mean} ({sd})"),
        label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", depression_2 ~ "Depression", ptsd_2 ~ "PTSD", any_psych_dx_2 ~ "Any Psychiatric Diagnosis", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"),
        missing_text = "(Missing)"
        ) %>% add_p()
```

## Baseline Characteristics by Any Psych Dx 
```{r}
baseline %>% tbl_summary(by = any_psych_dx_2,
       statistic = list(all_continuous() ~ "{mean} ({sd})"),
        label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", depression_2 ~ "Depression", ptsd_2 ~ "PTSD", anxiety_2 ~ "Anxiety", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"),
        missing_text = "(Missing)"
        ) %>% add_p()
```


# Prelim Models {.tabset}

## Depression + RPL_THEMES
```{r} 

model1a <- glm(depression_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
               + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model1a)
broom::glance(model1a)
broom::tidy(model1a, exponentiate = TRUE)
model_performance(model1a)
tbl_regression(model1a, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI"), exponentiate = TRUE)

```

## Depression + RPL_THEMESx4 
```{r}

model1b <- glm(depression_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
                + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model1b)
broom::glance(model1b)
broom::tidy(model1b, exponentiate = TRUE)
model_performance(model1b)
tbl_regression(model1b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)
```

## Anxiety + RPL_THEMES
```{r}
model2a <- glm(anxiety_2 ~ mstat_5 + relig_affil + lang_3 + age_yrs + race_5
               + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model2a)
broom::glance(model2a)
broom::tidy(model2a, exponentiate = TRUE)
model_performance(model2a)
tbl_regression(model2a, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI"), exponentiate = TRUE)
```
## Anxiety + RPL_THEMESx4
```{r}
model2b <- glm(anxiety_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
                + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model2b)
broom::glance(model2b)
broom::tidy(model2b, exponentiate = TRUE)
model_performance(model2b)
tbl_regression(model2b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)
```
## PTSD + RPL_THEMES
```{r}
model3a <- glm(ptsd_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
                + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model3a)
broom::glance(model3a)
broom::tidy(model3a, exponentiate = TRUE)
model_performance(model3a)
tbl_regression(model3a, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI"), exponentiate = TRUE)
```


## PTSD + RPL_THEMESx4
```{r}
model3b <- glm(ptsd_2 ~ mstat_5 + relig_affil + race_5 + lang_3 + age_yrs
                + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model3b)
broom::glance(model3b)
broom::tidy(model3b, exponentiate = TRUE)
model_performance(model3b)
tbl_regression(model3b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)
```


## Any Psych + RPL THEMES 
```{r}
model4a <- glm(any_psych_dx_2 ~ mstat_5 + relig_affil + race_5 + lang_3 +
              age_yrs + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model4a)
broom::glance(model4a)
broom::tidy(model4a, exponentiate = TRUE)
model_performance(model4a)
tbl_regression(model4a, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI"), exponentiate = TRUE)
```

## Any Psych + RPL_THEMESx4
```{r}
model4b <- glm(any_psych_dx_2 ~ mstat_5 + relig_affil + race_5 + lang_3 +
              age_yrs + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model4b)
broom::glance(model4b)
broom::tidy(model4b, exponentiate = TRUE)
model_performance(model4b)
tbl_regression(model4b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)
```

