Load Packages

tidyverse

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

codebookr

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

summarytools

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

broom

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

forcats

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

Import Data

mh_deidv3 <- read.csv("~/Desktop/R-Code/mh_deidv3.csv")

Data Cleaning

RPL_THEMES erroneous values

mh_deidv3 %>%
  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",
                              unmarried = "SIGNIFICANT OTHER"),
         mstat_5 = fct_relevel(mstat_5, ref = 'married')) %>% 
  mutate(mstat_5 = na_if(mstat_5, "UNKNOWN")) %>% 
mutate(mstat_5 = na_if(mstat_5, "OTHER")) -> exampledf1NADi2MA

Religion

exampledf1NADi2MA %>% 
  mutate(relig_affil = as_factor(PATIENT_RELIGION_DESC),
          relig_affil = fct_recode(relig_affil, yes = "CATHOLIC",
                      no = "NONE", 
                      yes = "CHRISTIAN", yes = "LUTHERAN",
                      yes = "RUSSIAN ORTHODOX",
                      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')) %>% 
mutate(relig_affil = na_if(relig_affil, "UNKNOWN")) %>% 
mutate(relig_affil = na_if(relig_affil, "PATIENT REFUSED")) -> 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",
                  ASIAN = "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",
Other = "TAGALOG", Other = "FRENCH",
Other = "GERMAN", Other = "CHINESE, CANTONESE",
Other = "BOSNIAN", Other = "URDU",
Other = "UNKNOWN"),
lang_3 = fct_relevel(lang_3, ref = 'English')) -> exampledf1NADi2MARelRaGL

Ethnicity

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

tobacco

exampledf1NADi2MARelRaGLEth %>% 
  mutate(tobac_4 = as_factor(TOBACCO_DESC),
 tobac_4 = fct_recode(tobac_4, Yes = "PASSIVE",
Yes = "YES"),
tobac_4 = fct_relevel(tobac_4, ref = 'NEVER')) %>% 
mutate(tobac_4 = na_if(tobac_4, "NOT ASKED")) -> exampledf1NADi2MARelRaGLEthT

Codebook

exampledf1NADi2MARelRaGLEthT %>% 
select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, PATIENT_STATE_CD, EDU_YEARS, tobac_4, 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: 15245 x 25
Duplicates: 0

Variable

Stats / Values

Freqs (% of Valid)

Graph

Valid

Missing

age_yrs [numeric]

Mean (sd) : 49.3 (19.5)

min ≤ med ≤ max:

1 ≤ 49.3 ≤ 108.3

IQR (CV) : 31.5 (0.4)

11557 distinct values

15245 (100.0%)

0 (0.0%)

gender [factor]

1. male

2. female

6973	(	45.7%	)
8272	(	54.3%	)

15245 (100.0%)

0 (0.0%)

race_5 [factor]

1. WHITE OR CAUCASIAN

2. BLACK OR AFRICAN AMERICAN

3. Other

4. ASIAN

5. AMERICAN INDIAN AND ALASK

13230	(	86.8%	)
946	(	6.2%	)
630	(	4.1%	)
383	(	2.5%	)
56	(	0.4%	)

15245 (100.0%)

0 (0.0%)

ethnic_3 [factor]

1. NON-HISPANIC

2. UNKNOWN

3. CHOOSE NOT TO DISCLOSE

4. HISPANIC

14401	(	97.9%	)
0	(	0.0%	)
0	(	0.0%	)
302	(	2.1%	)

14703 (96.4%)

542 (3.6%)

lang_3 [factor]

1. English

2. Other

15081	(	98.9%	)
164	(	1.1%	)

15245 (100.0%)

0 (0.0%)

relig_affil [factor]

1. yes

2. no

3. PATIENT REFUSED

4. UNKNOWN

8211	(	57.4%	)
6085	(	42.6%	)
0	(	0.0%	)
0	(	0.0%	)

14296 (93.8%)

949 (6.2%)

mstat_5 [factor]

1. married

2. UNKNOWN

3. unmarried

4. div_sep

5. widow

6. OTHER

6198	(	51.6%	)
0	(	0.0%	)
4997	(	41.6%	)
507	(	4.2%	)
307	(	2.6%	)
0	(	0.0%	)

12009 (78.8%)

3236 (21.2%)

PATIENT_STATE_CD [character]

1. MI

2. OH

3. FL

4. IN

5. IL

6. CA

7. NY

8. NC

9. TX

10. PA

[ 36 others ]

14299	(	93.8%	)
339	(	2.2%	)
82	(	0.5%	)
60	(	0.4%	)
52	(	0.3%	)
50	(	0.3%	)
50	(	0.3%	)
24	(	0.2%	)
22	(	0.1%	)
21	(	0.1%	)
245	(	1.6%	)

15244 (100.0%)

1 (0.0%)

EDU_YEARS [integer]

Mean (sd) : 15.3 (3.8)

min ≤ med ≤ max:

0 ≤ 16 ≤ 50

IQR (CV) : 5 (0.2)

29 distinct values

960 (6.3%)

14285 (93.7%)

tobac_4 [factor]

1. NEVER

2. QUIT

3. Yes

4. NOT ASKED

8173	(	56.4%	)
4439	(	30.6%	)
1890	(	13.0%	)
0	(	0.0%	)

14502 (95.1%)

743 (4.9%)

depression_2 [numeric]

Min : 0

Mean : 0.2

Max : 1

0	:	12639	(	82.9%	)
1	:	2606	(	17.1%	)

15245 (100.0%)

0 (0.0%)

anxiety_2 [numeric]

Min : 0

Mean : 0.2

Max : 1

0	:	12434	(	81.6%	)
1	:	2811	(	18.4%	)

15245 (100.0%)

0 (0.0%)

ptsd_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	15085	(	99.0%	)
1	:	160	(	1.0%	)

15245 (100.0%)

0 (0.0%)

bipolar_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	15026	(	98.6%	)
1	:	219	(	1.4%	)

15245 (100.0%)

0 (0.0%)

body_image_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	15242	(	100.0%	)
1	:	3	(	0.0%	)

15245 (100.0%)

0 (0.0%)

ocd_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	15166	(	99.5%	)
1	:	79	(	0.5%	)

15245 (100.0%)

0 (0.0%)

seasonalAD_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	15219	(	99.8%	)
1	:	26	(	0.2%	)

15245 (100.0%)

0 (0.0%)

panic_2 [numeric]

Min : 0

Mean : 0

Max : 1

0	:	15062	(	98.8%	)
1	:	183	(	1.2%	)

15245 (100.0%)

0 (0.0%)

any_psych_dx_2 [numeric]

Min : 0

Mean : 0.3

Max : 1

0	:	10929	(	71.7%	)
1	:	4316	(	28.3%	)

15245 (100.0%)

0 (0.0%)

E_TOTPOP [integer]

Mean (sd) : 4362.8 (1701.5)

min ≤ med ≤ max:

182 ≤ 4128 ≤ 20695

IQR (CV) : 2148 (0.4)

2130 distinct values

14966 (98.2%)

279 (1.8%)

RPL_THEMES [numeric]

Mean (sd) : 0.4 (0.3)

min ≤ med ≤ max:

0 ≤ 0.3 ≤ 1

IQR (CV) : 0.4 (0.7)

2539 distinct values

14957 (98.1%)

288 (1.9%)

RPL_THEME1 [numeric]

Mean (sd) : 0.4 (0.3)

min ≤ med ≤ max:

0 ≤ 0.3 ≤ 1

IQR (CV) : 0.4 (0.7)

2477 distinct values

14907 (97.8%)

338 (2.2%)

RPL_THEME2 [numeric]

Mean (sd) : 0.4 (0.3)

min ≤ med ≤ max:

0 ≤ 0.3 ≤ 1

IQR (CV) : 0.4 (0.7)

2451 distinct values

14958 (98.1%)

287 (1.9%)

RPL_THEME3 [numeric]

Mean (sd) : 0.5 (0.3)

min ≤ med ≤ max:

0 ≤ 0.5 ≤ 1

IQR (CV) : 0.5 (0.6)

1832 distinct values

14966 (98.2%)

279 (1.8%)

RPL_THEME4 [numeric]

Mean (sd) : 0.4 (0.3)

min ≤ med ≤ max:

0 ≤ 0.4 ≤ 1

IQR (CV) : 0.5 (0.7)

2455 distinct values

14935 (98.0%)

310 (2.0%)

Generated by summarytools 1.0.1 (R version 4.1.2)
2022-10-18

Patient Characteristics

Baseline Characteristics

mh_clean1 %>% 
  select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, tobac_4, 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", tobac_4 ~ "Tobacco Use"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")

Characteristic	N = 15,245¹
Age	49 (19)
Gender
male	6,973 (46%)
female	8,272 (54%)
Race
WHITE OR CAUCASIAN	13,230 (87%)
BLACK OR AFRICAN AMERICAN	946 (6.2%)
Other	630 (4.1%)
ASIAN	383 (2.5%)
AMERICAN INDIAN AND ALASKA NATIVE	56 (0.4%)
Ethnicity
NON-HISPANIC	14,401 (98%)
UNKNOWN	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)
HISPANIC	302 (2.1%)
(Missing)	542
English Speaking
English	15,081 (99%)
Other	164 (1.1%)
Any Religious Affiliation
yes	8,211 (57%)
no	6,085 (43%)
PATIENT REFUSED	0 (0%)
UNKNOWN	0 (0%)
(Missing)	949
Marital Status
married	6,198 (52%)
UNKNOWN	0 (0%)
unmarried	4,997 (42%)
div_sep	507 (4.2%)
widow	307 (2.6%)
OTHER	0 (0%)
(Missing)	3,236
Tobacco Use
NEVER	8,173 (56%)
QUIT	4,439 (31%)
Yes	1,890 (13%)
NOT ASKED	0 (0%)
(Missing)	743
Depression	2,606 (17%)
Anxiety	2,811 (18%)
PTSD	160 (1.0%)
Any Psychiatric Diagnosis	4,316 (28%)
Total SVI	0.37 (0.26)
(Missing)	288
Soceioeconomic Status	0.35 (0.26)
(Missing)	338
Household Composition	0.40 (0.27)
(Missing)	287
Minority Status and Language	0.48 (0.29)
(Missing)	279
Housing and Transportation	0.44 (0.29)
(Missing)	310
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", tobac_4 ~ "Tobacco Use"),
        missing_text = "(Missing)"
        ) %>% add_p()

There was an error in 'add_p()/add_difference()' for variable 'tobac_4', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, : FEXACT error 6.  LDKEY=531 is too small for this problem,
  (ii := key2[itp=345] = 27473220, ldstp=15930)
Try increasing the size of the workspace and possibly 'mult'

Characteristic	0, N = 12,639¹	1, N = 2,606¹	p-value²
Age	49 (20)	51 (18)	<0.001
Gender			<0.001
male	6,090 (48%)	883 (34%)
female	6,549 (52%)	1,723 (66%)
Race			<0.001
WHITE OR CAUCASIAN	10,927 (86%)	2,303 (88%)
BLACK OR AFRICAN AMERICAN	778 (6.2%)	168 (6.4%)
Other	544 (4.3%)	86 (3.3%)
ASIAN	347 (2.7%)	36 (1.4%)
AMERICAN INDIAN AND ALASKA NATIVE	43 (0.3%)	13 (0.5%)
Ethnicity			0.5
NON-HISPANIC	11,905 (98%)	2,496 (98%)
UNKNOWN	0 (0%)	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)	0 (0%)
HISPANIC	255 (2.1%)	47 (1.8%)
(Missing)	479	63
English Speaking			0.002
English	12,488 (99%)	2,593 (100%)
Other	151 (1.2%)	13 (0.5%)
Any Religious Affiliation			0.2
yes	6,740 (57%)	1,471 (59%)
no	5,043 (43%)	1,042 (41%)
PATIENT REFUSED	0 (0%)	0 (0%)
UNKNOWN	0 (0%)	0 (0%)
(Missing)	856	93
Marital Status			<0.001
married	5,228 (41%)	970 (37%)
unknown	2,727 (22%)	509 (20%)
unmarried	4,095 (32%)	902 (35%)
div_sep	362 (2.9%)	145 (5.6%)
widow	227 (1.8%)	80 (3.1%)
Tobacco Use
NEVER	6,990 (59%)	1,183 (46%)
QUIT	3,499 (29%)	940 (37%)
Yes	1,459 (12%)	431 (17%)
NOT ASKED	0 (0%)	0 (0%)
(Missing)	691	52
Anxiety	1,320 (10%)	1,491 (57%)	<0.001
PTSD	54 (0.4%)	106 (4.1%)	<0.001
Any Psychiatric Diagnosis	1,710 (14%)	2,606 (100%)	<0.001
Total SVI	0.37 (0.26)	0.39 (0.26)	<0.001
(Missing)	261	27
Soceioeconomic Status	0.35 (0.25)	0.37 (0.26)	<0.001
(Missing)	301	37
Household Composition	0.39 (0.27)	0.40 (0.27)	>0.9
(Missing)	260	27
Minority Status and Language	0.48 (0.28)	0.49 (0.29)	0.026
(Missing)	252	27
Housing and Transportation	0.44 (0.29)	0.45 (0.29)	0.004
(Missing)	278	32
¹ Mean (SD); n (%)
² Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test

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", tobac_4 ~ "Tobacco Use"),
        missing_text = "(Missing)"
        ) %>% add_p()

There was an error in 'add_p()/add_difference()' for variable 'tobac_4', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, : FEXACT error 6.  LDKEY=531 is too small for this problem,
  (ii := key2[itp=1030] = 30145890, ldstp=15930)
Try increasing the size of the workspace and possibly 'mult'

Characteristic	0, N = 12,434¹	1, N = 2,811¹	p-value²
Age	50 (20)	47 (18)	<0.001
Gender			<0.001
male	6,058 (49%)	915 (33%)
female	6,376 (51%)	1,896 (67%)
Race			<0.001
WHITE OR CAUCASIAN	10,731 (86%)	2,499 (89%)
BLACK OR AFRICAN AMERICAN	792 (6.4%)	154 (5.5%)
Other	527 (4.2%)	103 (3.7%)
ASIAN	342 (2.8%)	41 (1.5%)
AMERICAN INDIAN AND ALASKA NATIVE	42 (0.3%)	14 (0.5%)
Ethnicity			0.8
NON-HISPANIC	11,713 (98%)	2,688 (98%)
UNKNOWN	0 (0%)	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)	0 (0%)
HISPANIC	248 (2.1%)	54 (2.0%)
(Missing)	473	69
English Speaking			<0.001
English	12,280 (99%)	2,801 (100%)
Other	154 (1.2%)	10 (0.4%)
Any Religious Affiliation			0.9
yes	6,652 (57%)	1,559 (58%)
no	4,935 (43%)	1,150 (42%)
PATIENT REFUSED	0 (0%)	0 (0%)
UNKNOWN	0 (0%)	0 (0%)
(Missing)	847	102
Marital Status			<0.001
married	5,162 (42%)	1,036 (37%)
unknown	2,664 (21%)	572 (20%)
unmarried	3,990 (32%)	1,007 (36%)
div_sep	372 (3.0%)	135 (4.8%)
widow	246 (2.0%)	61 (2.2%)
Tobacco Use
NEVER	6,752 (58%)	1,421 (51%)
QUIT	3,540 (30%)	899 (33%)
Yes	1,447 (12%)	443 (16%)
NOT ASKED	0 (0%)	0 (0%)
(Missing)	695	48
Depression	1,115 (9.0%)	1,491 (53%)	<0.001
PTSD	60 (0.5%)	100 (3.6%)	<0.001
Any Psychiatric Diagnosis	1,505 (12%)	2,811 (100%)	<0.001
Total SVI	0.37 (0.26)	0.36 (0.25)	0.088
(Missing)	256	32
Soceioeconomic Status	0.35 (0.26)	0.34 (0.25)	0.009
(Missing)	296	42
Household Composition	0.40 (0.27)	0.38 (0.26)	<0.001
(Missing)	255	32
Minority Status and Language	0.48 (0.28)	0.49 (0.29)	0.004
(Missing)	247	32
Housing and Transportation	0.44 (0.29)	0.44 (0.28)	0.7
(Missing)	273	37
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", tobac_4 ~ "Tobacco Use", RPL_THEME4 ~ "Housing and Transportation"),
        missing_text = "(Missing)"
        ) %>% add_p()

There was an error in 'add_p()/add_difference()' for variable 'tobac_4', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, : FEXACT error 6.  LDKEY=531 is too small for this problem,
  (ii := key2[itp=1047] = 39726216, ldstp=15930)
Try increasing the size of the workspace and possibly 'mult'

Characteristic	0, N = 10,929¹	1, N = 4,316¹	p-value²
Age	49 (20)	49 (18)	0.7
Gender			<0.001
male	5,444 (50%)	1,529 (35%)
female	5,485 (50%)	2,787 (65%)
Race			<0.001
WHITE OR CAUCASIAN	9,433 (86%)	3,797 (88%)
BLACK OR AFRICAN AMERICAN	678 (6.2%)	268 (6.2%)
Other	475 (4.3%)	155 (3.6%)
ASIAN	307 (2.8%)	76 (1.8%)
AMERICAN INDIAN AND ALASKA NATIVE	36 (0.3%)	20 (0.5%)
Ethnicity			0.7
NON-HISPANIC	10,280 (98%)	4,121 (98%)
UNKNOWN	0 (0%)	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)	0 (0%)
HISPANIC	219 (2.1%)	83 (2.0%)
(Missing)	430	112
English Speaking			<0.001
English	10,787 (99%)	4,294 (99%)
Other	142 (1.3%)	22 (0.5%)
Any Religious Affiliation			0.3
yes	5,804 (57%)	2,407 (58%)
no	4,350 (43%)	1,735 (42%)
PATIENT REFUSED	0 (0%)	0 (0%)
UNKNOWN	0 (0%)	0 (0%)
(Missing)	775	174
Marital Status			<0.001
married	4,553 (42%)	1,645 (38%)
unknown	2,376 (22%)	860 (20%)
unmarried	3,498 (32%)	1,499 (35%)
div_sep	304 (2.8%)	203 (4.7%)
widow	198 (1.8%)	109 (2.5%)
Tobacco Use
NEVER	6,064 (59%)	2,109 (50%)
QUIT	2,988 (29%)	1,451 (34%)
Yes	1,222 (12%)	668 (16%)
NOT ASKED	0 (0%)	0 (0%)
(Missing)	655	88
Depression	0 (0%)	2,606 (60%)	<0.001
Anxiety	0 (0%)	2,811 (65%)	<0.001
PTSD	0 (0%)	160 (3.7%)	<0.001
Total SVI	0.37 (0.26)	0.38 (0.26)	0.3
(Missing)	236	52
Soceioeconomic Status	0.35 (0.25)	0.35 (0.26)	0.5
(Missing)	273	65
Household Composition	0.40 (0.27)	0.39 (0.27)	0.040
(Missing)	236	51
Minority Status and Language	0.47 (0.28)	0.49 (0.29)	0.002
(Missing)	228	51
Housing and Transportation	0.44 (0.29)	0.44 (0.28)	0.2
(Missing)	251	59
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 ~ + race_5 + lang_3 + relig_affil + age_yrs
               + gender + ethnic_3 + tobac_4 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model1a)


Call:
glm(formula = depression_2 ~ +race_5 + lang_3 + relig_affil + 
    age_yrs + gender + ethnic_3 + tobac_4 + RPL_THEMES, family = "binomial", 
    data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9599  -0.6474  -0.6083  -0.4750   2.4939  

Coefficients:
                                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)                             -2.189351   0.086970 -25.174   <2e-16 ***
race_5BLACK OR AFRICAN AMERICAN         -0.012107   0.095732  -0.126   0.8994    
race_5Other                             -0.141798   0.152837  -0.928   0.3535    
race_5ASIAN                             -0.368055   0.186422  -1.974   0.0483 *  
race_5AMERICAN INDIAN AND ALASKA NATIVE  0.283480   0.337460   0.840   0.4009    
lang_3Other                             -0.533237   0.312910  -1.704   0.0884 .  
relig_affilno                           -0.013350   0.047884  -0.279   0.7804    
age_yrs                                  0.001306   0.001316   0.993   0.3208    
genderfemale                             0.599821   0.047966  12.505   <2e-16 ***
ethnic_3HISPANIC                         0.046810   0.175318   0.267   0.7895    
tobac_4QUIT                              0.448447   0.054477   8.232   <2e-16 ***
tobac_4Yes                               0.571420   0.067302   8.490   <2e-16 ***
RPL_THEMES                               0.160805   0.092026   1.747   0.0806 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 12438  on 13063  degrees of freedom
Residual deviance: 12131  on 13051  degrees of freedom
  (2181 observations deleted due to missingness)
AIC: 12157

Number of Fisher Scoring iterations: 4

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
--------------------------------------------------------------------------------------------------
12157.277 | 12254.486 |     0.023 | 0.382 | 0.964 |    0.464 |      -Inf |       7.657e-05 | 0.708

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", RPL_THEMES ~ "Total SVI", tobac_4 ~ "Tobacco Use"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.99	0.82, 1.19	0.9
Other	0.87	0.64, 1.16	0.4
ASIAN	0.69	0.47, 0.98	0.048
AMERICAN INDIAN AND ALASKA NATIVE	1.33	0.66, 2.50	0.4
English Speaking
English	—	—
Other	0.59	0.30, 1.04	0.088
Any Religious Affiliation
yes	—	—
no	0.99	0.90, 1.08	0.8
Age	1.00	1.00, 1.00	0.3
Gender
male	—	—
female	1.82	1.66, 2.00	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	1.05	0.74, 1.46	0.8
Tobacco Use
NEVER	—	—
QUIT	1.57	1.41, 1.74	<0.001
Yes	1.77	1.55, 2.02	<0.001
Total SVI	1.17	0.98, 1.41	0.081
¹ OR = Odds Ratio, CI = Confidence Interval

NA

Depression + RPL_THEMESx4


model1b <- glm(depression_2 ~  lang_3 + relig_affil + age_yrs + race_5
       + tobac_4 + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model1b)


Call:
glm(formula = depression_2 ~ lang_3 + relig_affil + age_yrs + 
    race_5 + tobac_4 + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + 
    RPL_THEME3 + RPL_THEME4, family = "binomial", data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9772  -0.6567  -0.5968  -0.4626   2.5622  

Coefficients:
                                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)                             -2.285510   0.099863 -22.886  < 2e-16 ***
lang_3Other                             -0.630681   0.314163  -2.007  0.04470 *  
relig_affilno                           -0.009748   0.048102  -0.203  0.83940    
age_yrs                                  0.001732   0.001323   1.308  0.19075    
race_5BLACK OR AFRICAN AMERICAN         -0.068330   0.097496  -0.701  0.48340    
race_5Other                             -0.168300   0.153101  -1.099  0.27165    
race_5ASIAN                             -0.451993   0.188107  -2.403  0.01627 *  
race_5AMERICAN INDIAN AND ALASKA NATIVE  0.308150   0.339114   0.909  0.36351    
tobac_4QUIT                              0.456258   0.054880   8.314  < 2e-16 ***
tobac_4Yes                               0.575558   0.067870   8.480  < 2e-16 ***
genderfemale                             0.601136   0.048165  12.481  < 2e-16 ***
ethnic_3HISPANIC                         0.035639   0.175267   0.203  0.83887    
RPL_THEME1                               0.338928   0.132086   2.566  0.01029 *  
RPL_THEME2                              -0.354265   0.114493  -3.094  0.00197 ** 
RPL_THEME3                               0.218637   0.084902   2.575  0.01002 *  
RPL_THEME4                               0.109498   0.096162   1.139  0.25483    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 12369  on 12995  degrees of freedom
Residual deviance: 12042  on 12980  degrees of freedom
  (2249 observations deleted due to missingness)
AIC: 12074

Number of Fisher Scoring iterations: 4

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
--------------------------------------------------------------------------------------------------
12074.247 | 12193.806 |     0.025 | 0.382 | 0.963 |    0.463 |      -Inf |       7.909e-05 | 0.709

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", tobac_4 ~ "Tobacco Use"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
English Speaking
English	—	—
Other	0.53	0.27, 0.95	0.045
Any Religious Affiliation
yes	—	—
no	0.99	0.90, 1.09	0.8
Age	1.00	1.00, 1.00	0.2
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.93	0.77, 1.13	0.5
Other	0.85	0.62, 1.13	0.3
ASIAN	0.64	0.43, 0.91	0.016
AMERICAN INDIAN AND ALASKA NATIVE	1.36	0.67, 2.57	0.4
Tobacco Use
NEVER	—	—
QUIT	1.58	1.42, 1.76	<0.001
Yes	1.78	1.56, 2.03	<0.001
Gender
male	—	—
female	1.82	1.66, 2.01	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	1.04	0.73, 1.45	0.8
Soceioeconomic Status	1.40	1.08, 1.82	0.010
Household Composition	0.70	0.56, 0.88	0.002
Minority Status and Language	1.24	1.05, 1.47	0.010
Housing and Transportation	1.12	0.92, 1.35	0.3
¹ OR = Odds Ratio, CI = Confidence Interval

Anxiety + RPL_THEMES

model2a <- glm(anxiety_2 ~ lang_3 + age_yrs + race_5 + relig_affil
               + tobac_4 + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model2a)


Call:
glm(formula = anxiety_2 ~ lang_3 + age_yrs + race_5 + relig_affil + 
    tobac_4 + gender + ethnic_3 + RPL_THEMES, family = "binomial", 
    data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.1018  -0.7241  -0.5830  -0.4602   2.5735  

Coefficients:
                                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)                             -1.346818   0.080938 -16.640  < 2e-16 ***
lang_3Other                             -0.874349   0.354352  -2.467 0.013608 *  
age_yrs                                 -0.011502   0.001295  -8.879  < 2e-16 ***
race_5BLACK OR AFRICAN AMERICAN         -0.177295   0.097635  -1.816 0.069387 .  
race_5Other                              0.068542   0.139035   0.493 0.622024    
race_5ASIAN                             -0.629592   0.188770  -3.335 0.000852 ***
race_5AMERICAN INDIAN AND ALASKA NATIVE  0.267240   0.330068   0.810 0.418142    
relig_affilno                           -0.053691   0.046441  -1.156 0.247636    
tobac_4QUIT                              0.402107   0.054218   7.417  1.2e-13 ***
tobac_4Yes                               0.511390   0.066184   7.727  1.1e-14 ***
genderfemale                             0.727003   0.047142  15.421  < 2e-16 ***
ethnic_3HISPANIC                        -0.034620   0.166894  -0.207 0.835669    
RPL_THEMES                              -0.233620   0.090939  -2.569 0.010200 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 12977  on 13063  degrees of freedom
Residual deviance: 12579  on 13051  degrees of freedom
  (2181 observations deleted due to missingness)
AIC: 12605

Number of Fisher Scoring iterations: 4

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
--------------------------------------------------------------------------------------------------
12604.858 | 12702.067 |     0.030 | 0.392 | 0.982 |    0.481 |      -Inf |       7.664e-05 | 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", RPL_THEMES ~ "Total SVI", tobac_4 ~ "Tobacco Use"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
English Speaking
English	—	—
Other	0.42	0.19, 0.79	0.014
Age	0.99	0.99, 0.99	<0.001
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.84	0.69, 1.01	0.069
Other	1.07	0.81, 1.40	0.6
ASIAN	0.53	0.36, 0.76	<0.001
AMERICAN INDIAN AND ALASKA NATIVE	1.31	0.66, 2.43	0.4
Any Religious Affiliation
yes	—	—
no	0.95	0.87, 1.04	0.2
Tobacco Use
NEVER	—	—
QUIT	1.49	1.34, 1.66	<0.001
Yes	1.67	1.46, 1.90	<0.001
Gender
male	—	—
female	2.07	1.89, 2.27	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.97	0.69, 1.33	0.8
Total SVI	0.79	0.66, 0.95	0.010
¹ OR = Odds Ratio, CI = Confidence Interval

Anxiety + RPL_THEMESx4

model2b <- glm(anxiety_2 ~  lang_3 + age_yrs + race_5 + relig_affil 
          + tobac_4 + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = mh_clean1)
summary(model2b)


Call:
glm(formula = anxiety_2 ~ lang_3 + age_yrs + race_5 + relig_affil + 
    tobac_4 + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
    RPL_THEME4, family = "binomial", data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.1402  -0.7221  -0.5811  -0.4526   2.6011  

Coefficients:
                                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)                             -1.464848   0.094004 -15.583  < 2e-16 ***
lang_3Other                             -0.955461   0.355321  -2.689 0.007166 ** 
age_yrs                                 -0.011236   0.001302  -8.629  < 2e-16 ***
race_5BLACK OR AFRICAN AMERICAN         -0.235553   0.099262  -2.373 0.017642 *  
race_5Other                              0.039657   0.139322   0.285 0.775920    
race_5ASIAN                             -0.754171   0.190326  -3.963 7.42e-05 ***
race_5AMERICAN INDIAN AND ALASKA NATIVE  0.297927   0.332590   0.896 0.370371    
relig_affilno                           -0.047082   0.046659  -1.009 0.312946    
tobac_4QUIT                              0.422736   0.054662   7.734 1.05e-14 ***
tobac_4Yes                               0.536588   0.066807   8.032 9.59e-16 ***
genderfemale                             0.734696   0.047372  15.509  < 2e-16 ***
ethnic_3HISPANIC                        -0.041189   0.166887  -0.247 0.805058    
RPL_THEME1                              -0.197443   0.130341  -1.515 0.129818    
RPL_THEME2                              -0.255494   0.112778  -2.265 0.023484 *  
RPL_THEME3                               0.313945   0.082664   3.798 0.000146 ***
RPL_THEME4                               0.051412   0.094157   0.546 0.585054    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 12908  on 12995  degrees of freedom
Residual deviance: 12482  on 12980  degrees of freedom
  (2249 observations deleted due to missingness)
AIC: 12514

Number of Fisher Scoring iterations: 4

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
--------------------------------------------------------------------------------------------------
12514.377 | 12633.935 |     0.033 | 0.391 | 0.981 |    0.480 |      -Inf |       8.020e-05 | 0.694

tbl_regression(model2b, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", tobac_4 ~ "Tobacco Use", relig_affil ~ "Any Religious Affiliation"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
English Speaking
English	—	—
Other	0.38	0.18, 0.73	0.007
Age	0.99	0.99, 0.99	<0.001
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.79	0.65, 0.96	0.018
Other	1.04	0.79, 1.36	0.8
ASIAN	0.47	0.32, 0.67	<0.001
AMERICAN INDIAN AND ALASKA NATIVE	1.35	0.68, 2.52	0.4
Any Religious Affiliation
yes	—	—
no	0.95	0.87, 1.05	0.3
Tobacco Use
NEVER	—	—
QUIT	1.53	1.37, 1.70	<0.001
Yes	1.71	1.50, 1.95	<0.001
Gender
male	—	—
female	2.08	1.90, 2.29	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.96	0.69, 1.32	0.8
Soceioeconomic Status	0.82	0.64, 1.06	0.13
Household Composition	0.77	0.62, 0.97	0.023
Minority Status and Language	1.37	1.16, 1.61	<0.001
Housing and Transportation	1.05	0.88, 1.27	0.6
¹ OR = Odds Ratio, CI = Confidence Interval

Any Psych + RPL THEMES

model4a <- glm(any_psych_dx_2 ~ relig_affil + race_5 + lang_3 +
              + tobac_4 + age_yrs + gender + ethnic_3 + RPL_THEMES,
              family = "binomial",
              data = mh_clean1)
summary(model4a)


Call:
glm(formula = any_psych_dx_2 ~ relig_affil + race_5 + lang_3 + 
    +tobac_4 + age_yrs + gender + ethnic_3 + RPL_THEMES, family = "binomial", 
    data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.2110  -0.8732  -0.7174   1.3297   2.1660  

Coefficients:
                                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)                             -1.172352   0.071002 -16.511  < 2e-16 ***
relig_affilno                           -0.038905   0.040379  -0.964 0.335295    
race_5BLACK OR AFRICAN AMERICAN         -0.028279   0.082003  -0.345 0.730203    
race_5Other                              0.029307   0.122218   0.240 0.810491    
race_5ASIAN                             -0.355153   0.145493  -2.441 0.014646 *  
race_5AMERICAN INDIAN AND ALASKA NATIVE  0.206501   0.301479   0.685 0.493370    
lang_3Other                             -0.619971   0.252969  -2.451 0.014255 *  
tobac_4QUIT                              0.426124   0.046582   9.148  < 2e-16 ***
tobac_4Yes                               0.523448   0.058605   8.932  < 2e-16 ***
age_yrs                                 -0.003955   0.001108  -3.569 0.000358 ***
genderfemale                             0.617571   0.039950  15.458  < 2e-16 ***
ethnic_3HISPANIC                         0.059182   0.144512   0.410 0.682149    
RPL_THEMES                              -0.038900   0.078423  -0.496 0.619877    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 15974  on 13063  degrees of freedom
Residual deviance: 15591  on 13051  degrees of freedom
  (2181 observations deleted due to missingness)
AIC: 15617

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
--------------------------------------------------------------------------------------------------
15617.318 | 15714.527 |     0.029 | 0.452 | 1.093 |    0.597 |      -Inf |       7.655e-05 | 0.592

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", RPL_THEMES ~ "Total SVI", tobac_4 ~ "Tobacco Use"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Any Religious Affiliation
yes	—	—
no	0.96	0.89, 1.04	0.3
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.97	0.83, 1.14	0.7
Other	1.03	0.81, 1.31	0.8
ASIAN	0.70	0.52, 0.93	0.015
AMERICAN INDIAN AND ALASKA NATIVE	1.23	0.67, 2.20	0.5
English Speaking
English	—	—
Other	0.54	0.32, 0.86	0.014
Tobacco Use
NEVER	—	—
QUIT	1.53	1.40, 1.68	<0.001
Yes	1.69	1.50, 1.89	<0.001
Age	1.00	0.99, 1.00	<0.001
Gender
male	—	—
female	1.85	1.72, 2.01	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	1.06	0.80, 1.40	0.7
Total SVI	0.96	0.82, 1.12	0.6
¹ OR = Odds Ratio, CI = Confidence Interval

Any Psych + RPL_THEMESx4

model4b <- glm(any_psych_dx_2 ~ relig_affil + race_5 + lang_3 
             + age_yrs + tobac_4 + 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 ~ relig_affil + race_5 + lang_3 + 
    age_yrs + tobac_4 + gender + ethnic_3 + RPL_THEME1 + RPL_THEME2 + 
    RPL_THEME3 + RPL_THEME4, family = "binomial", data = mh_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.2482  -0.8736  -0.7300   1.3302   2.2268  

Coefficients:
                                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)                             -1.271738   0.082156 -15.480  < 2e-16 ***
relig_affilno                           -0.033501   0.040562  -0.826 0.408856    
race_5BLACK OR AFRICAN AMERICAN         -0.081292   0.083559  -0.973 0.330620    
race_5Other                              0.005038   0.122614   0.041 0.967228    
race_5ASIAN                             -0.457314   0.147089  -3.109 0.001877 ** 
race_5AMERICAN INDIAN AND ALASKA NATIVE  0.240394   0.304220   0.790 0.429412    
lang_3Other                             -0.706477   0.254101  -2.780 0.005431 ** 
age_yrs                                 -0.003565   0.001114  -3.200 0.001376 ** 
tobac_4QUIT                              0.439496   0.046946   9.362  < 2e-16 ***
tobac_4Yes                               0.539086   0.059082   9.124  < 2e-16 ***
genderfemale                             0.623325   0.040128  15.533  < 2e-16 ***
ethnic_3HISPANIC                         0.052089   0.144687   0.360 0.718839    
RPL_THEME1                               0.071775   0.112524   0.638 0.523564    
RPL_THEME2                              -0.315640   0.097437  -3.239 0.001198 ** 
RPL_THEME3                               0.252296   0.071866   3.511 0.000447 ***
RPL_THEME4                               0.082906   0.081579   1.016 0.309503    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 15891  on 12995  degrees of freedom
Residual deviance: 15480  on 12980  degrees of freedom
  (2249 observations deleted due to missingness)
AIC: 15512

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
--------------------------------------------------------------------------------------------------
15511.960 | 15631.518 |     0.031 | 0.451 | 1.092 |    0.596 |      -Inf |       7.695e-05 | 0.593

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)

Characteristic	OR¹	95% CI¹	p-value
Any Religious Affiliation
yes	—	—
no	0.97	0.89, 1.05	0.4
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.92	0.78, 1.08	0.3
Other	1.01	0.79, 1.27	>0.9
ASIAN	0.63	0.47, 0.84	0.002
AMERICAN INDIAN AND ALASKA NATIVE	1.27	0.69, 2.29	0.4
English Speaking
English	—	—
Other	0.49	0.29, 0.79	0.005
Age	1.00	0.99, 1.00	0.001
tobac_4
NEVER	—	—
QUIT	1.55	1.42, 1.70	<0.001
Yes	1.71	1.53, 1.92	<0.001
Gender
male	—	—
female	1.87	1.72, 2.02	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	1.05	0.79, 1.39	0.7
Soceioeconomic Status	1.07	0.86, 1.34	0.5
Household Composition	0.73	0.60, 0.88	0.001
Minority Status and Language	1.29	1.12, 1.48	<0.001
Housing and Transportation	1.09	0.93, 1.27	0.3
¹ OR = Odds Ratio, CI = Confidence Interval

---
title: "MH Models, exluding unknowns, excluding marital status"
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_deidv3 <- read.csv("~/Desktop/R-Code/mh_deidv3.csv")
```

# Data Cleaning {.tabset}

## RPL_THEMES erroneous values 
```{r}
mh_deidv3 %>%
  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",
                              unmarried = "SIGNIFICANT OTHER"),
         mstat_5 = fct_relevel(mstat_5, ref = 'married')) %>% 
  mutate(mstat_5 = na_if(mstat_5, "UNKNOWN")) %>% 
mutate(mstat_5 = na_if(mstat_5, "OTHER")) -> exampledf1NADi2MA
```

## Religion 
```{r}
exampledf1NADi2MA %>% 
  mutate(relig_affil = as_factor(PATIENT_RELIGION_DESC),
          relig_affil = fct_recode(relig_affil, yes = "CATHOLIC",
                      no = "NONE", 
                      yes = "CHRISTIAN", yes = "LUTHERAN",
                      yes = "RUSSIAN ORTHODOX",
                      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')) %>% 
mutate(relig_affil = na_if(relig_affil, "UNKNOWN")) %>% 
mutate(relig_affil = na_if(relig_affil, "PATIENT REFUSED")) -> 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",
                  ASIAN = "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",
Other = "TAGALOG", Other = "FRENCH",
Other = "GERMAN", Other = "CHINESE, CANTONESE",
Other = "BOSNIAN", Other = "URDU",
Other = "UNKNOWN"),
lang_3 = fct_relevel(lang_3, ref = 'English')) -> exampledf1NADi2MARelRaGL

```

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

# tobacco 
```{r}
exampledf1NADi2MARelRaGLEth %>% 
  mutate(tobac_4 = as_factor(TOBACCO_DESC),
 tobac_4 = fct_recode(tobac_4, Yes = "PASSIVE",
Yes = "YES"),
tobac_4 = fct_relevel(tobac_4, ref = 'NEVER')) %>% 
mutate(tobac_4 = na_if(tobac_4, "NOT ASKED")) -> exampledf1NADi2MARelRaGLEthT
```


# Codebook 
```{r}
exampledf1NADi2MARelRaGLEthT %>% 
select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, PATIENT_STATE_CD, EDU_YEARS, tobac_4, 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, tobac_4, 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", tobac_4 ~ "Tobacco Use"),
        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", tobac_4 ~ "Tobacco Use"),
        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", tobac_4 ~ "Tobacco Use"),
        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", tobac_4 ~ "Tobacco Use", RPL_THEME4 ~ "Housing and Transportation"),
        missing_text = "(Missing)"
        ) %>% add_p()
```


# Prelim Models {.tabset}

## Depression + RPL_THEMES
```{r} 

model1a <- glm(depression_2 ~ + race_5 + lang_3 + relig_affil + age_yrs
               + gender + ethnic_3 + tobac_4 + 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", RPL_THEMES ~ "Total SVI", tobac_4 ~ "Tobacco Use"), exponentiate = TRUE)

```

## Depression + RPL_THEMESx4 
```{r}

model1b <- glm(depression_2 ~  lang_3 + relig_affil + age_yrs + race_5
       + tobac_4 + 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", tobac_4 ~ "Tobacco Use"), exponentiate = TRUE)
```

## Anxiety + RPL_THEMES
```{r}
model2a <- glm(anxiety_2 ~ lang_3 + age_yrs + race_5 + relig_affil
               + tobac_4 + 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", RPL_THEMES ~ "Total SVI", tobac_4 ~ "Tobacco Use"), exponentiate = TRUE)
```
## Anxiety + RPL_THEMESx4
```{r}
model2b <- glm(anxiety_2 ~  lang_3 + age_yrs + race_5 + relig_affil 
          + tobac_4 + 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", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", tobac_4 ~ "Tobacco Use", relig_affil ~ "Any Religious Affiliation"), exponentiate = TRUE)
```

## Any Psych + RPL THEMES 
```{r}
model4a <- glm(any_psych_dx_2 ~ relig_affil + race_5 + lang_3 +
              + tobac_4 + 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", RPL_THEMES ~ "Total SVI", tobac_4 ~ "Tobacco Use"), exponentiate = TRUE)
```

## Any Psych + RPL_THEMESx4
```{r}
model4b <- glm(any_psych_dx_2 ~ relig_affil + race_5 + lang_3 
             + age_yrs + tobac_4 + 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)
```



MH Models, exluding unknowns, excluding marital status

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

tobacco

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

Any Psych + RPL THEMES

Any Psych + RPL_THEMESx4