Load Packages

tidyverse

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

Loading required package: tidyverse
── Attaching packages ────────────────────────────────────── tidyverse 1.3.1 ──
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✔ 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

Variable

Stats / Values

Freqs (% of Valid)

Graph

Valid

Missing

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%)

gender [factor]

1. male

2. female

5282	(	46.7%	)
6024	(	53.3%	)

11306 (100.0%)

0 (0.0%)

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%)

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%)

lang_3 [factor]

1. English

2. Other

3. unknown

11202	(	99.1%	)
99	(	0.9%	)
5	(	0.0%	)

11306 (100.0%)

0 (0.0%)

relig_affil [factor]

1. yes

2. no

3. unknown

6093	(	53.9%	)
4543	(	40.2%	)
670	(	5.9%	)

11306 (100.0%)

0 (0.0%)

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%)

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%)

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%)

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%)

depression_2 [numeric]

Min : 0

Mean : 0.2

Max : 1

0	:	9227	(	81.6%	)
1	:	2079	(	18.4%	)

11306 (100.0%)

0 (0.0%)

anxiety_2 [numeric]

Min : 0

Mean : 0.2

Max : 1

0	:	9086	(	80.4%	)
1	:	2220	(	19.6%	)

11306 (100.0%)

0 (0.0%)

ptsd_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	11189	(	99.0%	)
1	:	117	(	1.0%	)

11306 (100.0%)

0 (0.0%)

bipolar_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	11144	(	98.6%	)
1	:	162	(	1.4%	)

11306 (100.0%)

0 (0.0%)

body_image_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	11303	(	100.0%	)
1	:	3	(	0.0%	)

11306 (100.0%)

0 (0.0%)

ocd_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	11242	(	99.4%	)
1	:	64	(	0.6%	)

11306 (100.0%)

0 (0.0%)

seasonalAD_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	11284	(	99.8%	)
1	:	22	(	0.2%	)

11306 (100.0%)

0 (0.0%)

panic_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	11160	(	98.7%	)
1	:	146	(	1.3%	)

11306 (100.0%)

0 (0.0%)

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%)

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%)

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%)

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%)

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%)

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%)

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", mstat_5 ~ "Marital Status"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Marital Status
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", mstat_5 ~ "Marital Status"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Marital Status
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)

Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

summary(model3b)


Call:
glm(formula = 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)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9109  -0.1625  -0.1338  -0.1069   3.4453  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)
(Intercept)                              -5.036376   0.482197 -10.445  < 2e-16
mstat_5div_sep                            0.226008   0.478278   0.473 0.636538
mstat_5unmarried                          0.107611   0.240803   0.447 0.654959
mstat_5unknown                           -0.202881   0.281881  -0.720 0.471686
mstat_5widow                            -14.057312 429.995688  -0.033 0.973920
relig_affilno                            -0.257281   0.205616  -1.251 0.210837
relig_affilunknown                       -0.027123   0.404557  -0.067 0.946547
race_5BLACK OR AFRICAN AMERICAN          -0.767403   0.475533  -1.614 0.106577
race_5ASIAN                               0.359802   0.529493   0.680 0.496807
race_5Other                              -0.220810   0.595740  -0.371 0.710899
race_5AMERICAN INDIAN AND ALASKA NATIVE   1.853177   0.622503   2.977 0.002911
lang_3Other                             -14.219274 644.749949  -0.022 0.982405
lang_3unknown                             4.029383   1.437171   2.804 0.005052
age_yrs                                  -0.005243   0.006139  -0.854 0.393075
genderfemale                              0.766358   0.207124   3.700 0.000216
ethnic_3Unknown                          -0.545663   0.731839  -0.746 0.455905
ethnic_3HISPANIC                         -0.168886   0.753752  -0.224 0.822710
RPL_THEME1                                0.821149   0.523639   1.568 0.116844
RPL_THEME2                               -0.590445   0.446115  -1.324 0.185661
RPL_THEME3                                0.317841   0.345681   0.919 0.357853
RPL_THEME4                                0.364396   0.389660   0.935 0.349703
                                           
(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: 1291.9  on 11239  degrees of freedom
Residual deviance: 1243.7  on 11219  degrees of freedom
  (66 observations deleted due to missingness)
AIC: 1285.7

Number of Fisher Scoring iterations: 17

broom::glance(model3b)

broom::tidy(model3b, exponentiate = TRUE)

model_performance(model3b)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
1285.684 | 1439.556 |     0.006 | 0.101 | 0.333 |    0.055 |    -1.204 |           0.008 | 0.980

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", mstat_5 ~ "Marital Status"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Marital Status
married	—	—
div_sep	1.25	0.43, 2.91	0.6
unmarried	1.11	0.69, 1.78	0.7
unknown	0.82	0.46, 1.40	0.5
widow	0.00	0.00, 0.98	>0.9
Any Religious Affiliation
yes	—	—
no	0.77	0.51, 1.15	0.2
unknown	0.97	0.40, 2.01	>0.9
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.46	0.16, 1.07	0.11
ASIAN	1.43	0.43, 3.59	0.5
Other	0.80	0.21, 2.24	0.7
AMERICAN INDIAN AND ALASKA NATIVE	6.38	1.49, 18.6	0.003
English Speaking
English	—	—
Other	0.00	0.00, 1,281	>0.9
unknown	56.2	2.04, 912	0.005
Age	1.0	0.98, 1.01	0.4
Gender
male	—	—
female	2.15	1.45, 3.27	<0.001
Ethnicity
NON-HISPANIC	—	—
Unknown	0.58	0.09, 1.91	0.5
HISPANIC	0.84	0.13, 2.94	0.8
Soceioeconomic Status	2.27	0.81, 6.30	0.12
Household Composition	0.55	0.23, 1.34	0.2
Minority Status and Language	1.37	0.70, 2.71	0.4
Housing and Transportation	1.44	0.67, 3.09	0.3
¹ OR = Odds Ratio, CI = Confidence Interval

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)


Call:
glm(formula = 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)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3109  -0.9078  -0.7324   1.3737   2.1566  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)
(Intercept)                             -1.4122003  0.1055055 -13.385  < 2e-16
mstat_5div_sep                           0.5857490  0.1151871   5.085 3.67e-07
mstat_5unmarried                         0.1958290  0.0550988   3.554 0.000379
mstat_5unknown                           0.1368241  0.0591056   2.315 0.020618
mstat_5widow                             0.2299181  0.1463057   1.571 0.116069
relig_affilno                           -0.0367103  0.0447176  -0.821 0.411682
relig_affilunknown                      -0.4437477  0.1007450  -4.405 1.06e-05
race_5BLACK OR AFRICAN AMERICAN         -0.2022384  0.0898995  -2.250 0.024474
race_5ASIAN                             -0.3630103  0.1525326  -2.380 0.017318
race_5Other                             -0.0188399  0.1205518  -0.156 0.875812
race_5AMERICAN INDIAN AND ALASKA NATIVE  0.3169054  0.3348116   0.947 0.343884
lang_3Other                             -1.1303926  0.3166350  -3.570 0.000357
lang_3unknown                            0.2333023  1.1401234   0.205 0.837862
age_yrs                                  0.0009994  0.0013373   0.747 0.454873
genderfemale                             0.6182865  0.0428196  14.439  < 2e-16
ethnic_3Unknown                         -0.2236926  0.1296669  -1.725 0.084504
ethnic_3HISPANIC                         0.1647553  0.1608399   1.024 0.305673
RPL_THEME1                               0.1438166  0.1200558   1.198 0.230951
RPL_THEME2                              -0.1622048  0.1039931  -1.560 0.118815
RPL_THEME3                               0.2323483  0.0772785   3.007 0.002642
RPL_THEME4                               0.0613382  0.0881746   0.696 0.486651
                                           
(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: 13759  on 11239  degrees of freedom
Residual deviance: 13428  on 11219  degrees of freedom
  (66 observations deleted due to missingness)
AIC: 13470

Number of Fisher Scoring iterations: 4

broom::glance(model4b)

broom::tidy(model4b, exponentiate = TRUE)

model_performance(model4b)

# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
13470.010 | 13623.882 |     0.029 | 0.452 | 1.094 |    0.597 |      -Inf |       8.939e-05 | 0.591

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", mstat_5 ~ "Marital Status"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Marital Status
married	—	—
div_sep	1.80	1.43, 2.25	<0.001
unmarried	1.22	1.09, 1.36	<0.001
unknown	1.15	1.02, 1.29	0.021
widow	1.26	0.94, 1.67	0.12
Any Religious Affiliation
yes	—	—
no	0.96	0.88, 1.05	0.4
unknown	0.64	0.53, 0.78	<0.001
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.82	0.68, 0.97	0.024
ASIAN	0.70	0.51, 0.93	0.017
Other	0.98	0.77, 1.24	0.9
AMERICAN INDIAN AND ALASKA NATIVE	1.37	0.70, 2.63	0.3
English Speaking
English	—	—
Other	0.32	0.17, 0.58	<0.001
unknown	1.26	0.06, 8.98	0.8
Age	1.00	1.00, 1.00	0.5
Gender
male	—	—
female	1.86	1.71, 2.02	<0.001
Ethnicity
NON-HISPANIC	—	—
Unknown	0.80	0.62, 1.03	0.085
HISPANIC	1.18	0.86, 1.61	0.3
Soceioeconomic Status	1.15	0.91, 1.46	0.2
Household Composition	0.85	0.69, 1.04	0.12
Minority Status and Language	1.26	1.08, 1.47	0.003
Housing and Transportation	1.06	0.89, 1.26	0.5
¹ OR = Odds Ratio, CI = Confidence Interval

---
title: "Prelim Mental Health Models"
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", mstat_5 ~ "Marital Status"), 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", mstat_5 ~ "Marital Status"), 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", mstat_5 ~ "Marital Status"), 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", mstat_5 ~ "Marital Status"), exponentiate = TRUE)
```



Prelim Mental Health Models

Load Packages

tidyverse

codebookr

summarytools

broom

performance

gtsummary

janitor

forcats

Import Data

Data Cleaning

RPL_THEMES erroneous values

Mental Health Dich

Marital Status

Religion

Race

Gender

Language

Ethnicity

Codebook

Data Frame Summary

mh_clean1

Patient Characteristics

Baseline Characteristics

Baseline Characteristics by Depression

Baseline Characteristics By Anxiety

Baseline Characteristics by Any Psych Dx

Prelim Models

Depression + RPL_THEMES

Depression + RPL_THEMESx4

Anxiety + RPL_THEMES

Anxiety + RPL_THEMESx4

PTSD + RPL_THEMES

PTSD + RPL_THEMESx4

Any Psych + RPL THEMES

Any Psych + RPL_THEMESx4