Load Packages

library(tidyverse)
library(codebookr)
library(summarytools)
library(broom)
library(performance)
library(gt)
library(gtsummary)
library(janitor)
library(forcats)
library(here)
library(yaml)
library(margins)

Import Data


load("~/Desktop/R-Code/SDOH_ALL/mh_vax_co_sub.rda")
View(mh_vax_co_sub)

Baseline Characteristics

mh_vax_co_sub %>% 
  dplyr::select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, max_ch, anxiety_2, depression_2, act_tob, drug_use, etoh_use, RPL_THEMES, RPL_4, 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 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", RPL_4 ~ "SVI Quartiles", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", act_tob ~ "Active Tobacco Use", drug_use ~ "Drug Abuse", etoh_use ~ "Alcohol Abuse", max_ch ~ "Charlson Comorbidity Index"),
        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	13,230 (87%)
Black	946 (6.2%)
Other	638 (4.2%)
Asian	375 (2.5%)
Native	56 (0.4%)
Ethnicity
NonHispanic	14,401 (98%)
UNKNOWN	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)
Hispanic	302 (2.1%)
(Missing)	542
Preferred Language
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 (41%)
Unknown	3,236 (21%)
Unmarried	4,997 (33%)
DivorcedSeparated	507 (3.3%)
Widow	307 (2.0%)
Charlson Comorbidity Index	3.3 (4.9)
(Missing)	446
Anxiety	2,811 (18%)
Depression	2,606 (17%)
Active Tobacco Use
No	12,612 (87%)
Yes	1,890 (13%)
NOT ASKED	0 (0%)
(Missing)	743
Drug Abuse	581 (3.8%)
(Missing)	41
Alcohol Abuse	233 (1.5%)
(Missing)	41
Total SVI	0.37 (0.26)
(Missing)	288
SVI Quartiles
First	5,843 (39%)
Second	4,506 (30%)
Third	3,063 (20%)
Fourth	1,545 (10%)
(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 SVI

baseline %>% 
tbl_summary(by = RPL_4,
         label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", RPL_4 ~ "SVI Quartiles", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", act_tob ~ "Active Tobacco Use", drug_use ~ "Drug Abuse", etoh_use ~ "Alcohol Abuse", max_ch ~ "Charlson Comorbidity Index"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)") %>% add_p()

288 observations missing `RPL_4` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `RPL_4` column before passing to `tbl_summary()`.
There was an error in 'add_p()/add_difference()' for variable 'relig_affil', p-value omitted:
Error in stats::fisher.test(structure(c(2L, NA, 1L, NA, 1L, 2L, 2L, 1L, : FEXACT error 6.  LDKEY=534 is too small for this problem,
  (ii := key2[itp=940] = 42828805, ldstp=16020)
Try increasing the size of the workspace and possibly 'mult'
There was an error in 'add_p()/add_difference()' for variable 'act_tob', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, : FEXACT error 6.  LDKEY=533 is too small for this problem,
  (ii := key2[itp=674] = 21415546, ldstp=15990)
Try increasing the size of the workspace and possibly 'mult'

Characteristic	First, N = 5,843¹	Second, N = 4,506¹	Third, N = 3,063¹	Fourth, N = 1,545¹	p-value²
Age	49 (20)	50 (19)	50 (19)	48 (19)	0.007
Gender					<0.001
Male	2,761 (47%)	2,053 (46%)	1,305 (43%)	692 (45%)
Female	3,082 (53%)	2,453 (54%)	1,758 (57%)	853 (55%)
Race					<0.001
White	5,301 (91%)	3,969 (88%)	2,627 (86%)	1,093 (71%)
Black	139 (2.4%)	204 (4.5%)	239 (7.8%)	345 (22%)
Other	236 (4.0%)	167 (3.7%)	122 (4.0%)	93 (6.0%)
Asian	153 (2.6%)	142 (3.2%)	65 (2.1%)	7 (0.5%)
Native	14 (0.2%)	24 (0.5%)	10 (0.3%)	7 (0.5%)
Ethnicity					0.007
NonHispanic	5,525 (98%)	4,274 (98%)	2,897 (98%)	1,441 (97%)
UNKNOWN	0 (0%)	0 (0%)	0 (0%)	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Hispanic	100 (1.8%)	87 (2.0%)	60 (2.0%)	49 (3.3%)
(Missing)	218	145	106	55
Preferred Language					<0.001
English	5,809 (99%)	4,454 (99%)	3,019 (99%)	1,513 (98%)
Other	34 (0.6%)	52 (1.2%)	44 (1.4%)	32 (2.1%)
Any Religious Affiliation
Yes	3,238 (59%)	2,403 (57%)	1,657 (58%)	779 (55%)
No	2,286 (41%)	1,816 (43%)	1,205 (42%)	643 (45%)
PATIENT REFUSED	0 (0%)	0 (0%)	0 (0%)	0 (0%)
UNKNOWN	0 (0%)	0 (0%)	0 (0%)	0 (0%)
(Missing)	319	287	201	123
Marital Status					<0.001
Married	2,700 (46%)	1,857 (41%)	1,115 (36%)	444 (29%)
Unknown	1,137 (19%)	946 (21%)	705 (23%)	389 (25%)
Unmarried	1,763 (30%)	1,434 (32%)	1,041 (34%)	620 (40%)
DivorcedSeparated	133 (2.3%)	168 (3.7%)	128 (4.2%)	72 (4.7%)
Widow	110 (1.9%)	101 (2.2%)	74 (2.4%)	20 (1.3%)
Charlson Comorbidity Index	3.2 (4.9)	3.4 (5.1)	3.4 (4.9)	3.3 (4.8)	0.020
(Missing)	137	131	99	62
Anxiety	1,109 (19%)	864 (19%)	538 (18%)	268 (17%)	0.2
Depression	938 (16%)	805 (18%)	539 (18%)	297 (19%)	0.009
Active Tobacco Use
No	5,137 (91%)	3,719 (87%)	2,394 (83%)	1,121 (78%)
Yes	480 (8.5%)	570 (13%)	506 (17%)	309 (22%)
NOT ASKED	0 (0%)	0 (0%)	0 (0%)	0 (0%)
(Missing)	226	217	163	115
Drug Abuse	238 (4.1%)	170 (3.8%)	103 (3.4%)	59 (3.8%)	0.4
(Missing)	11	11	16	1
Alcohol Abuse	79 (1.4%)	73 (1.6%)	50 (1.6%)	29 (1.9%)	0.4
(Missing)	11	11	16	1
Total SVI	0.12 (0.07)	0.37 (0.07)	0.61 (0.07)	0.86 (0.07)	<0.001
Soceioeconomic Status	0.13 (0.11)	0.34 (0.16)	0.56 (0.15)	0.78 (0.11)	<0.001
(Missing)	51	0	0	0
Household Composition	0.21 (0.14)	0.38 (0.22)	0.58 (0.24)	0.76 (0.19)	<0.001
Minority Status and Language	0.41 (0.27)	0.47 (0.29)	0.52 (0.28)	0.66 (0.25)	<0.001
Housing and Transportation	0.19 (0.16)	0.49 (0.20)	0.64 (0.21)	0.81 (0.17)	<0.001
(Missing)	22	0	0	0
¹ Mean (SD); n (%)
² Kruskal-Wallis rank sum test; Pearson's Chi-squared test; Fisher's exact test

Bivariate Analysis

Tobacco Use

tbl_uv_tobac <-
  tbl_uvregression(
    mh_vax_co_sub[c("act_tob", "age_yrs", "gender", "race_5", "ethnic_3", "lang_3", "relig_affil", "mstat_5", "max_ch", "anxiety_2", "depression_2", "RPL_THEMES",  "RPL_THEME1", "RPL_THEME2",  "RPL_THEME3", "RPL_THEME4")],
    method = glm,
    y = act_tob,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"))
    
print(tbl_uv_tobac, method = render)

`...` must be empty.
✖ Problematic argument:
• method = render

Characteristic	N	OR¹	95% CI¹	p-value
Age	14,502	1.00	0.99, 1.00	<0.001
Gender	14,502
Male		—	—
Female		1.01	0.91, 1.11	0.9
Race	14,502
White		—	—
Black		1.41	1.17, 1.69	<0.001
Other		0.92	0.71, 1.17	0.5
Asian		0.19	0.10, 0.34	<0.001
Native		0.99	0.41, 2.06	>0.9
Ethnicity	14,036
NonHispanic		—	—
Hispanic		0.82	0.55, 1.16	0.3
Preferred Language	14,502
English		—	—
Other		0.61	0.33, 1.04	0.092
Any Religious Affiliation	13,668
Yes		—	—
No		1.25	1.13, 1.38	<0.001
Marital Status	14,502
Married		—	—
Unknown		1.34	1.17, 1.53	<0.001
Unmarried		1.70	1.51, 1.91	<0.001
DivorcedSeparated		3.16	2.53, 3.93	<0.001
Widow		1.32	0.91, 1.86	0.12
Charlson Comorbidity Index	14,302	0.99	0.97, 1.00	0.004
Anxiety	14,502	1.36	1.21, 1.52	<0.001
Depression	14,502	1.46	1.30, 1.64	<0.001
Total SVI	14,236	4.37	3.63, 5.25	<0.001
Soceioeconomic Status	14,188	5.46	4.53, 6.57	<0.001
Household Composition	14,237	3.35	2.80, 4.01	<0.001
Minority Status and Language	14,244	0.87	0.73, 1.03	0.11
Housing and Transportation	14,214	2.24	1.89, 2.65	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

NULL

Drug Abuse

tbl_uv_drug <-
  tbl_uvregression(
    mh_vax_co_sub[c("drug_use", "age_yrs", "gender", "race_5", "ethnic_3", "lang_3", "relig_affil", "mstat_5", "max_ch", "anxiety_2", "depression_2", "RPL_THEMES",  "RPL_THEME1", "RPL_THEME2",  "RPL_THEME3", "RPL_THEME4")],
    method = glm,
    y = drug_use,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"))
    
print(tbl_uv_drug, method = render)

`...` must be empty.
✖ Problematic argument:
• method = render

Characteristic	N	OR¹	95% CI¹	p-value
Age	15,204	1.00	1.0, 1.00	0.6
Gender	15,204
Male		—	—
Female		0.87	0.73, 1.02	0.089
Race	15,204
White		—	—
Black		0.81	0.54, 1.15	0.3
Other		0.97	0.62, 1.44	0.9
Asian		0.82	0.43, 1.40	0.5
Native		0.45	0.03, 2.04	0.4
Ethnicity	14,664
NonHispanic		—	—
Hispanic		0.86	0.43, 1.55	0.7
Preferred Language	15,204
English		—	—
Other		0.97	0.38, 2.01	>0.9
Any Religious Affiliation	14,261
Yes		—	—
No		1.06	0.89, 1.26	0.5
Marital Status	15,204
Married		—	—
Unknown		0.81	0.64, 1.02	0.072
Unmarried		0.96	0.80, 1.17	0.7
DivorcedSeparated		0.91	0.55, 1.43	0.7
Widow		0.31	0.10, 0.73	0.021
Charlson Comorbidity Index	14,767	1.00	0.98, 1.02	0.8
Anxiety	15,204	0.85	0.67, 1.06	0.15
Depression	15,204	0.81	0.64, 1.02	0.083
Total SVI	14,918	0.79	0.57, 1.09	0.2
Soceioeconomic Status	14,868	0.87	0.62, 1.21	0.4
Household Composition	14,919	0.88	0.64, 1.21	0.4
Minority Status and Language	14,927	0.88	0.66, 1.18	0.4
Housing and Transportation	14,896	0.80	0.59, 1.07	0.13
¹ OR = Odds Ratio, CI = Confidence Interval

NULL

Alcohol Abuse

tbl_uv_etoh <-
  tbl_uvregression(
    mh_vax_co_sub[c("etoh_use", "age_yrs", "gender", "race_5", "ethnic_3", "lang_3", "relig_affil", "mstat_5", "max_ch", "anxiety_2", "depression_2", "RPL_THEMES",  "RPL_THEME1", "RPL_THEME2",  "RPL_THEME3", "RPL_THEME4")],
    method = glm,
    y = etoh_use,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"))
    
print(tbl_uv_etoh, method = render)

`...` must be empty.
✖ Problematic argument:
• method = render

Characteristic	N	OR¹	95% CI¹	p-value
Age	15,204	1.00	0.99, 1.00	0.2
Gender	15,204
Male		—	—
Female		0.86	0.67, 1.12	0.3
Race	15,204
White		—	—
Black		1.49	0.91, 2.31	0.094
Other		1.89	1.10, 3.04	0.013
Asian		1.12	0.44, 2.33	0.8
Native		1.25	0.07, 5.73	0.8
Ethnicity	14,664
NonHispanic		—	—
Hispanic		2.03	0.96, 3.76	0.041
Preferred Language	15,204
English		—	—
Other		1.22	0.30, 3.23	0.7
Any Religious Affiliation	14,261
Yes		—	—
No		0.89	0.68, 1.17	0.4
Marital Status	15,204
Married		—	—
Unknown		0.99	0.69, 1.41	>0.9
Unmarried		1.20	0.89, 1.62	0.2
DivorcedSeparated		0.82	0.32, 1.73	0.6
Widow		1.37	0.53, 2.90	0.5
Charlson Comorbidity Index	14,767	1.01	0.98, 1.04	0.4
Anxiety	15,204	1.34	0.98, 1.81	0.063
Depression	15,204	1.29	0.93, 1.76	0.11
Total SVI	14,918	1.51	0.92, 2.47	0.10
Soceioeconomic Status	14,868	1.25	0.75, 2.06	0.4
Household Composition	14,919	1.40	0.86, 2.25	0.2
Minority Status and Language	14,927	1.32	0.84, 2.09	0.2
Housing and Transportation	14,896	1.26	0.80, 1.98	0.3
¹ OR = Odds Ratio, CI = Confidence Interval

NULL

Active Tobacco: Multivariable Models

Active tobacco + RPL_THEMES

tobac1 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3 + mstat_5 + relig_affil + 
                max_ch + anxiety_2 + depression_2 + RPL_THEMES,
              family = "binomial",
              data = mh_vax_co_sub)
summary(tobac1)


Call:
glm(formula = act_tob ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + mstat_5 + relig_affil + max_ch + anxiety_2 + depression_2 + 
    RPL_THEMES, family = "binomial", data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.1546  -0.5596  -0.4644  -0.3785   3.1348  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -2.7344071  0.1224732 -22.327  < 2e-16 ***
age_yrs                  -0.0005484  0.0017937  -0.306 0.759820    
genderFemale             -0.1039828  0.0550894  -1.888 0.059089 .  
race_5Black              -0.0406304  0.1028452  -0.395 0.692796    
race_5Other              -0.0036124  0.1668531  -0.022 0.982727    
race_5Asian              -2.0908763  0.4535819  -4.610 4.03e-06 ***
race_5Native             -0.5664562  0.5287427  -1.071 0.284022    
ethnic_3Hispanic         -0.3128105  0.2135729  -1.465 0.143015    
lang_3Other              -0.3013680  0.3468955  -0.869 0.384980    
mstat_5Unknown            0.1496898  0.0781349   1.916 0.055392 .  
mstat_5Unmarried          0.3995811  0.0720074   5.549 2.87e-08 ***
mstat_5DivorcedSeparated  0.9865255  0.1216488   8.110 5.08e-16 ***
mstat_5Widow              0.3490334  0.1922636   1.815 0.069464 .  
relig_affilNo             0.1694701  0.0552203   3.069 0.002148 ** 
max_ch                   -0.0117714  0.0062699  -1.877 0.060457 .  
anxiety_2                 0.2386917  0.0719469   3.318 0.000908 ***
depression_2              0.2701222  0.0730716   3.697 0.000218 ***
RPL_THEMES                1.3922157  0.1041556  13.367  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 9892.5  on 12927  degrees of freedom
Residual deviance: 9462.0  on 12910  degrees of freedom
  (2317 observations deleted due to missingness)
AIC: 9498

Number of Fisher Scoring iterations: 6

broom::glance(tobac1)

broom::tidy(tobac1, exponentiate = TRUE)

tbl_regression(tobac1, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_THEMES ~ "Total SVI", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", mstat_5 ~ "Marital Status", relig_affil ~ "Any Religious Affiliation"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Age	1.00	1.00, 1.00	0.8
Gender
Male	—	—
Female	0.90	0.81, 1.00	0.059
Race
White	—	—
Black	0.96	0.78, 1.17	0.7
Other	1.00	0.71, 1.37	>0.9
Asian	0.12	0.04, 0.27	<0.001
Native	0.57	0.17, 1.42	0.3
Ethnicity
NonHispanic	—	—
Hispanic	0.73	0.47, 1.09	0.14
Preferred Language
English	—	—
Other	0.74	0.35, 1.39	0.4
Marital Status
Married	—	—
Unknown	1.16	1.00, 1.35	0.055
Unmarried	1.49	1.30, 1.72	<0.001
DivorcedSeparated	2.68	2.11, 3.39	<0.001
Widow	1.42	0.96, 2.04	0.069
Any Religious Affiliation
Yes	—	—
No	1.18	1.06, 1.32	0.002
Charlson Comorbidity Index	0.99	0.98, 1.00	0.060
Anxiety	1.27	1.10, 1.46	<0.001
Depression	1.31	1.13, 1.51	<0.001
Total SVI	4.02	3.28, 4.94	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(tobac1)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
9497.991 | 9632.400 |     0.035 | 0.328 | 0.856 |    0.366 |      -Inf |       2.191e-04 | 0.784

performance::check_model(tobac1, panel = TRUE)


## Margins
cplot(tobac1, "RPL_THEMES", what = "prediction", main = "Percent Likelihood of Tobacco Use Given SVI")

Active tobacco + RPL_4

tobac1 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3 + mstat_5 + relig_affil + 
                max_ch + anxiety_2 + depression_2 + RPL_4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(tobac1)


Call:
glm(formula = act_tob ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + mstat_5 + relig_affil + max_ch + anxiety_2 + depression_2 + 
    RPL_4, family = "binomial", data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.0978  -0.5652  -0.4657  -0.3707   3.1279  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -2.6242252  0.1224909 -21.424  < 2e-16 ***
age_yrs                  -0.0004855  0.0017919  -0.271 0.786448    
genderFemale             -0.1044869  0.0550697  -1.897 0.057781 .  
race_5Black               0.0080401  0.1028158   0.078 0.937670    
race_5Other               0.0110039  0.1667656   0.066 0.947390    
race_5Asian              -2.1170466  0.4535899  -4.667 3.05e-06 ***
race_5Native             -0.5642900  0.5290969  -1.067 0.286191    
ethnic_3Hispanic         -0.3036124  0.2131589  -1.424 0.154346    
lang_3Other              -0.2789519  0.3464783  -0.805 0.420758    
mstat_5Unknown            0.1572696  0.0780365   2.015 0.043870 *  
mstat_5Unmarried          0.4061081  0.0719519   5.644 1.66e-08 ***
mstat_5DivorcedSeparated  0.9972110  0.1214716   8.209 2.22e-16 ***
mstat_5Widow              0.3527038  0.1921977   1.835 0.066489 .  
relig_affilNo             0.1675057  0.0551936   3.035 0.002406 ** 
max_ch                   -0.0121584  0.0062673  -1.940 0.052384 .  
anxiety_2                 0.2376703  0.0718769   3.307 0.000944 ***
depression_2              0.2734764  0.0729932   3.747 0.000179 ***
RPL_4Second               0.4772741  0.0697810   6.840 7.94e-12 ***
RPL_4Third                0.7296290  0.0739834   9.862  < 2e-16 ***
RPL_4Fourth               1.0288209  0.0881562  11.670  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 9892.5  on 12927  degrees of freedom
Residual deviance: 9470.9  on 12908  degrees of freedom
  (2317 observations deleted due to missingness)
AIC: 9510.9

Number of Fisher Scoring iterations: 6

broom::glance(tobac1)

broom::tidy(tobac1, exponentiate = TRUE)

tbl_regression(tobac1, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_4 ~ "SVI Quartile", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", mstat_5 ~ "Marital Status", relig_affil ~ "Any Religious Affiliation"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Age	1.00	1.00, 1.00	0.8
Gender
Male	—	—
Female	0.90	0.81, 1.00	0.058
Race
White	—	—
Black	1.01	0.82, 1.23	>0.9
Other	1.01	0.72, 1.39	>0.9
Asian	0.12	0.04, 0.26	<0.001
Native	0.57	0.17, 1.43	0.3
Ethnicity
NonHispanic	—	—
Hispanic	0.74	0.48, 1.10	0.2
Preferred Language
English	—	—
Other	0.76	0.36, 1.42	0.4
Marital Status
Married	—	—
Unknown	1.17	1.00, 1.36	0.044
Unmarried	1.50	1.30, 1.73	<0.001
DivorcedSeparated	2.71	2.13, 3.43	<0.001
Widow	1.42	0.96, 2.05	0.066
Any Religious Affiliation
Yes	—	—
No	1.18	1.06, 1.32	0.002
Charlson Comorbidity Index	0.99	0.98, 1.00	0.052
Anxiety	1.27	1.10, 1.46	<0.001
Depression	1.31	1.14, 1.52	<0.001
SVI Quartile
First	—	—
Second	1.61	1.41, 1.85	<0.001
Third	2.07	1.79, 2.40	<0.001
Fourth	2.80	2.35, 3.32	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(tobac1)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
9510.850 | 9660.194 |     0.034 | 0.329 | 0.857 |    0.366 |      -Inf |       2.644e-04 | 0.784

performance::check_model(tobac1, panel = TRUE)


## Margins
cplot(tobac1, "RPL_4", what = "prediction", main = "Percent Likelihood of Tobacco Use Given SVI Quartile")

Active Tobacco + All Themes

tobac3 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3 + mstat_5 + relig_affil + 
                max_ch + anxiety_2 + depression_2 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
                RPL_THEME4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(tobac3)


Call:
glm(formula = act_tob ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + mstat_5 + relig_affil + max_ch + anxiety_2 + depression_2 + 
    RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4, family = "binomial", 
    data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.1657  -0.5615  -0.4551  -0.3672   3.0537  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -2.6939773  0.1336030 -20.164  < 2e-16 ***
age_yrs                  -0.0007259  0.0018060  -0.402 0.687743    
genderFemale             -0.1042769  0.0553267  -1.885 0.059464 .  
race_5Black              -0.0396828  0.1056774  -0.376 0.707282    
race_5Other               0.0247467  0.1682359   0.147 0.883057    
race_5Asian              -1.9135268  0.4549148  -4.206 2.60e-05 ***
race_5Native             -0.5922414  0.5305655  -1.116 0.264317    
ethnic_3Hispanic         -0.2905460  0.2144564  -1.355 0.175481    
lang_3Other              -0.3229906  0.3500543  -0.923 0.356170    
mstat_5Unknown            0.1358057  0.0784008   1.732 0.083238 .  
mstat_5Unmarried          0.3972267  0.0724874   5.480 4.25e-08 ***
mstat_5DivorcedSeparated  0.9814360  0.1221755   8.033 9.51e-16 ***
mstat_5Widow              0.3626886  0.1929289   1.880 0.060121 .  
relig_affilNo             0.1611219  0.0555181   2.902 0.003706 ** 
max_ch                   -0.0116566  0.0062858  -1.854 0.063676 .  
anxiety_2                 0.2538062  0.0722915   3.511 0.000447 ***
depression_2              0.2605652  0.0733944   3.550 0.000385 ***
RPL_THEME1                1.5729772  0.1516096  10.375  < 2e-16 ***
RPL_THEME2                0.1598526  0.1303013   1.227 0.219901    
RPL_THEME3               -0.2359623  0.0997179  -2.366 0.017967 *  
RPL_THEME4               -0.0584157  0.1115246  -0.524 0.600423    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 9858.5  on 12859  degrees of freedom
Residual deviance: 9364.7  on 12839  degrees of freedom
  (2385 observations deleted due to missingness)
AIC: 9406.7

Number of Fisher Scoring iterations: 6

broom::glance(tobac3)

broom::tidy(tobac3, exponentiate = TRUE)

tbl_regression(tobac3, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", mstat_5 ~ "Marital Status", 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
Age	1.00	1.00, 1.00	0.7
Gender
Male	—	—
Female	0.90	0.81, 1.00	0.059
Race
White	—	—
Black	0.96	0.78, 1.18	0.7
Other	1.03	0.73, 1.41	0.9
Asian	0.15	0.05, 0.32	<0.001
Native	0.55	0.16, 1.39	0.3
Ethnicity
NonHispanic	—	—
Hispanic	0.75	0.48, 1.12	0.2
Preferred Language
English	—	—
Other	0.72	0.34, 1.37	0.4
Marital Status
Married	—	—
Unknown	1.15	0.98, 1.34	0.083
Unmarried	1.49	1.29, 1.72	<0.001
DivorcedSeparated	2.67	2.09, 3.38	<0.001
Widow	1.44	0.97, 2.07	0.060
Any Religious Affiliation
Yes	—	—
No	1.17	1.05, 1.31	0.004
Charlson Comorbidity Index	0.99	0.98, 1.00	0.064
Anxiety	1.29	1.12, 1.48	<0.001
Depression	1.30	1.12, 1.50	<0.001
Soceioeconomic Status	4.82	3.58, 6.49	<0.001
Household Composition	1.17	0.91, 1.52	0.2
Minority Status and Language	0.79	0.65, 0.96	0.018
Housing and Transportation	0.94	0.76, 1.17	0.6
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(tobac3)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
9406.734 | 9563.433 |     0.040 | 0.328 | 0.854 |    0.364 |      -Inf |       2.976e-04 | 0.785

performance::check_model(tobac3, panel = TRUE)


## Margins
cplot(tobac3, "RPL_THEME1", what = "prediction", main = "Percent Likelihood of Tobacco Use Given THEME1")

cplot(tobac3, "RPL_THEME2", what = "prediction", main = "Percent Likelihood of Tobacco Use Given THEME2")

cplot(tobac3, "RPL_THEME3", what = "prediction", main = "Percent Likelihood of Tobacco Use Given THEME3")

cplot(tobac3, "RPL_THEME4", what = "prediction", main = "Percent Likelihood of Tobacco Use Given THEME4")

Drug Use: Multivariable Models

Drug Use + RPL_THEMES

# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

drug1 <- glm(drug_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_THEMES,
              family = "binomial",
              data = mh_vax_co_sub)
summary(drug1)


Call:
glm(formula = drug_use ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + max_ch + anxiety_2 + depression_2 + RPL_THEMES, 
    family = "binomial", data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.3411  -0.2902  -0.2792  -0.2611   2.7816  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -3.004854   0.146927 -20.451   <2e-16 ***
age_yrs          -0.001767   0.002591  -0.682    0.495    
genderFemale     -0.064396   0.090098  -0.715    0.475    
race_5Black      -0.180515   0.206165  -0.876    0.381    
race_5Other       0.021861   0.267822   0.082    0.935    
race_5Asian      -0.276892   0.317107  -0.873    0.383    
race_5Native     -0.655670   1.011507  -0.648    0.517    
ethnic_3Hispanic -0.128914   0.344136  -0.375    0.708    
lang_3Other       0.222463   0.436085   0.510    0.610    
max_ch            0.008666   0.010003   0.866    0.386    
anxiety_2        -0.140645   0.134423  -1.046    0.295    
depression_2     -0.190106   0.140475  -1.353    0.176    
RPL_THEMES       -0.197675   0.179580  -1.101    0.271    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4491.7  on 14013  degrees of freedom
Residual deviance: 4480.8  on 14001  degrees of freedom
  (1231 observations deleted due to missingness)
AIC: 4506.8

Number of Fisher Scoring iterations: 6

broom::glance(drug1)

broom::tidy(drug1, exponentiate = TRUE)

tbl_regression(drug1, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_THEMES ~ "Total SVI", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Age	1.00	0.99, 1.00	0.5
Gender
Male	—	—
Female	0.94	0.79, 1.12	0.5
Race
White	—	—
Black	0.83	0.55, 1.23	0.4
Other	1.02	0.58, 1.68	>0.9
Asian	0.76	0.38, 1.34	0.4
Native	0.52	0.03, 2.38	0.5
Ethnicity
NonHispanic	—	—
Hispanic	0.88	0.42, 1.64	0.7
Preferred Language
English	—	—
Other	1.25	0.48, 2.71	0.6
Charlson Comorbidity Index	1.01	0.99, 1.03	0.4
Anxiety	0.87	0.66, 1.12	0.3
Depression	0.83	0.62, 1.08	0.2
Total SVI	0.82	0.58, 1.16	0.3
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(drug1)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
4506.821 | 4604.943 | 7.427e-04 | 0.190 | 0.566 |    0.160 |   -20.094 |       3.328e-04 | 0.928

performance::check_model(drug1, panel = TRUE)


## Margins
cplot(drug1, "RPL_THEMES", what = "prediction", main = "Percent Likelihood of Drug Use Given SVI")

Drug Use + SVI Quartiles

# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

drug2 <- glm(drug_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(drug2)


Call:
glm(formula = drug_use ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + max_ch + anxiety_2 + depression_2 + RPL_4, family = "binomial", 
    data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.3515  -0.2923  -0.2782  -0.2598   2.7837  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -3.016296   0.143057 -21.085   <2e-16 ***
age_yrs          -0.001717   0.002591  -0.662   0.5077    
genderFemale     -0.061716   0.090134  -0.685   0.4935    
race_5Black      -0.220622   0.207305  -1.064   0.2872    
race_5Other       0.011359   0.267880   0.042   0.9662    
race_5Asian      -0.265612   0.317023  -0.838   0.4021    
race_5Native     -0.660386   1.011580  -0.653   0.5139    
ethnic_3Hispanic -0.134459   0.344073  -0.391   0.6960    
lang_3Other       0.213916   0.436021   0.491   0.6237    
max_ch            0.008653   0.010003   0.865   0.3870    
anxiety_2        -0.140693   0.134437  -1.047   0.2953    
depression_2     -0.192232   0.140493  -1.368   0.1712    
RPL_4Second      -0.069438   0.106448  -0.652   0.5142    
RPL_4Third       -0.208384   0.126407  -1.649   0.0992 .  
RPL_4Fourth      -0.008374   0.158522  -0.053   0.9579    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4491.7  on 14013  degrees of freedom
Residual deviance: 4479.1  on 13999  degrees of freedom
  (1231 observations deleted due to missingness)
AIC: 4509.1

Number of Fisher Scoring iterations: 6

broom::glance(drug2)

broom::tidy(drug2, exponentiate = TRUE)

tbl_regression(drug2, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_4 ~ "SVI Quartile", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Age	1.00	0.99, 1.00	0.5
Gender
Male	—	—
Female	0.94	0.79, 1.12	0.5
Race
White	—	—
Black	0.80	0.52, 1.18	0.3
Other	1.01	0.58, 1.66	>0.9
Asian	0.77	0.39, 1.36	0.4
Native	0.52	0.03, 2.37	0.5
Ethnicity
NonHispanic	—	—
Hispanic	0.87	0.42, 1.63	0.7
Preferred Language
English	—	—
Other	1.24	0.47, 2.69	0.6
Charlson Comorbidity Index	1.01	0.99, 1.03	0.4
Anxiety	0.87	0.66, 1.12	0.3
Depression	0.83	0.62, 1.08	0.2
SVI Quartile
First	—	—
Second	0.93	0.76, 1.15	0.5
Third	0.81	0.63, 1.04	0.10
Fourth	0.99	0.72, 1.34	>0.9
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(drug2)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
4509.075 | 4622.292 | 8.632e-04 | 0.190 | 0.566 |    0.160 |   -20.095 |       3.812e-04 | 0.928

performance::check_model(drug2, panel = TRUE)


## Margins
cplot(drug2, "RPL_4", what = "prediction", main = "Percent Likelihood of Drug Use Given SVI")

Drug Use + All Themes

drug3 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
                RPL_THEME4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(drug3)


Call:
glm(formula = act_tob ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + max_ch + anxiety_2 + depression_2 + RPL_THEME1 + 
    RPL_THEME2 + RPL_THEME3 + RPL_THEME4, family = "binomial", 
    data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.0331  -0.5682  -0.4646  -0.3895   2.9316  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -2.291944   0.103316 -22.184  < 2e-16 ***
age_yrs          -0.003936   0.001538  -2.559 0.010507 *  
genderFemale     -0.066116   0.052934  -1.249 0.211659    
race_5Black      -0.030714   0.102028  -0.301 0.763387    
race_5Other      -0.006250   0.159810  -0.039 0.968806    
race_5Asian      -1.562477   0.362496  -4.310 1.63e-05 ***
race_5Native     -0.703962   0.528518  -1.332 0.182876    
ethnic_3Hispanic -0.251818   0.203988  -1.234 0.217026    
lang_3Other      -0.284355   0.320410  -0.887 0.374826    
max_ch           -0.014111   0.006115  -2.307 0.021031 *  
anxiety_2         0.244696   0.070314   3.480 0.000501 ***
depression_2      0.275414   0.071388   3.858 0.000114 ***
RPL_THEME1        1.610667   0.145762  11.050  < 2e-16 ***
RPL_THEME2        0.193119   0.125365   1.540 0.123451    
RPL_THEME3       -0.220446   0.096098  -2.294 0.021792 *  
RPL_THEME4       -0.025414   0.107348  -0.237 0.812853    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 10497  on 13540  degrees of freedom
Residual deviance: 10074  on 13525  degrees of freedom
  (1704 observations deleted due to missingness)
AIC: 10106

Number of Fisher Scoring iterations: 6

broom::glance(drug3)

broom::tidy(drug3, exponentiate = TRUE)

tbl_regression(drug3, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", 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
Age	1.00	0.99, 1.00	0.011
Gender
Male	—	—
Female	0.94	0.84, 1.04	0.2
Race
White	—	—
Black	0.97	0.79, 1.18	0.8
Other	0.99	0.72, 1.35	>0.9
Asian	0.21	0.09, 0.40	<0.001
Native	0.49	0.15, 1.24	0.2
Ethnicity
NonHispanic	—	—
Hispanic	0.78	0.51, 1.14	0.2
Preferred Language
English	—	—
Other	0.75	0.38, 1.36	0.4
Charlson Comorbidity Index	0.99	0.97, 1.00	0.021
Anxiety	1.28	1.11, 1.46	<0.001
Depression	1.32	1.14, 1.51	<0.001
Soceioeconomic Status	5.01	3.76, 6.66	<0.001
Household Composition	1.21	0.95, 1.55	0.12
Minority Status and Language	0.80	0.66, 0.97	0.022
Housing and Transportation	0.97	0.79, 1.20	0.8
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(drug3)

# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
10106.174 | 10226.389 |     0.032 | 0.332 | 0.863 |    0.372 |      -Inf |       2.283e-04 | 0.780

performance::check_model(drug3, panel = TRUE)


## Margins
cplot(drug3, "RPL_THEME1", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME1")

cplot(drug3, "RPL_THEME2", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME2")

cplot(drug3, "RPL_THEME3", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME3")

cplot(drug3, "RPL_THEME4", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME4")

Sensitivity analysis including mstat and relig_affil

drug4 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + mstat_5 + relig_affil +
                max_ch + anxiety_2 + depression_2 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
                RPL_THEME4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(drug4)


Call:
glm(formula = act_tob ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + mstat_5 + relig_affil + max_ch + anxiety_2 + depression_2 + 
    RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4, family = "binomial", 
    data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.1657  -0.5615  -0.4551  -0.3672   3.0537  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -2.6939773  0.1336030 -20.164  < 2e-16 ***
age_yrs                  -0.0007259  0.0018060  -0.402 0.687743    
genderFemale             -0.1042769  0.0553267  -1.885 0.059464 .  
race_5Black              -0.0396828  0.1056774  -0.376 0.707282    
race_5Other               0.0247467  0.1682359   0.147 0.883057    
race_5Asian              -1.9135268  0.4549148  -4.206 2.60e-05 ***
race_5Native             -0.5922414  0.5305655  -1.116 0.264317    
ethnic_3Hispanic         -0.2905460  0.2144564  -1.355 0.175481    
lang_3Other              -0.3229906  0.3500543  -0.923 0.356170    
mstat_5Unknown            0.1358057  0.0784008   1.732 0.083238 .  
mstat_5Unmarried          0.3972267  0.0724874   5.480 4.25e-08 ***
mstat_5DivorcedSeparated  0.9814360  0.1221755   8.033 9.51e-16 ***
mstat_5Widow              0.3626886  0.1929289   1.880 0.060121 .  
relig_affilNo             0.1611219  0.0555181   2.902 0.003706 ** 
max_ch                   -0.0116566  0.0062858  -1.854 0.063676 .  
anxiety_2                 0.2538062  0.0722915   3.511 0.000447 ***
depression_2              0.2605652  0.0733944   3.550 0.000385 ***
RPL_THEME1                1.5729772  0.1516096  10.375  < 2e-16 ***
RPL_THEME2                0.1598526  0.1303013   1.227 0.219901    
RPL_THEME3               -0.2359623  0.0997179  -2.366 0.017967 *  
RPL_THEME4               -0.0584157  0.1115246  -0.524 0.600423    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 9858.5  on 12859  degrees of freedom
Residual deviance: 9364.7  on 12839  degrees of freedom
  (2385 observations deleted due to missingness)
AIC: 9406.7

Number of Fisher Scoring iterations: 6

broom::glance(drug4)

broom::tidy(drug4, exponentiate = TRUE)

tbl_regression(drug4, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", mstat_5 ~ "Marital Status", relig_affil ~ "Any Religious Affiliation"), exponentiate = TRUE)

Alcohol Use: Multivariable Models

Alcohol Use + RPL_THEMES

# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

etoh1 <- glm(etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_THEMES,
              family = "binomial",
              data = mh_vax_co_sub)
summary(etoh1)


Call:
glm(formula = etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + max_ch + anxiety_2 + depression_2 + RPL_THEMES, 
    family = "binomial", data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.3517  -0.1836  -0.1712  -0.1610   3.0320  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -4.183902   0.229944 -18.195   <2e-16 ***
age_yrs          -0.002896   0.004029  -0.719   0.4723    
genderFemale     -0.172049   0.138884  -1.239   0.2154    
race_5Black       0.286781   0.259450   1.105   0.2690    
race_5Other       0.547041   0.326378   1.676   0.0937 .  
race_5Asian      -0.001881   0.464481  -0.004   0.9968    
race_5Native      0.206969   1.014974   0.204   0.8384    
ethnic_3Hispanic  0.456308   0.385932   1.182   0.2371    
lang_3Other       0.081106   0.614681   0.132   0.8950    
max_ch            0.012970   0.015098   0.859   0.3903    
anxiety_2         0.219257   0.185959   1.179   0.2384    
depression_2      0.122055   0.192606   0.634   0.5263    
RPL_THEMES        0.247149   0.268586   0.920   0.3575    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2247.8  on 14013  degrees of freedom
Residual deviance: 2233.6  on 14001  degrees of freedom
  (1231 observations deleted due to missingness)
AIC: 2259.6

Number of Fisher Scoring iterations: 7

broom::glance(etoh1)

broom::tidy(etoh1, exponentiate = TRUE)

tbl_regression(etoh1, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_THEMES ~ "Total SVI", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Age	1.00	0.99, 1.00	0.5
Gender
Male	—	—
Female	0.84	0.64, 1.11	0.2
Race
White	—	—
Black	1.33	0.78, 2.16	0.3
Other	1.73	0.87, 3.15	0.094
Asian	1.00	0.35, 2.24	>0.9
Native	1.23	0.07, 5.69	0.8
Ethnicity
NonHispanic	—	—
Hispanic	1.58	0.69, 3.19	0.2
Preferred Language
English	—	—
Other	1.08	0.26, 3.10	0.9
Charlson Comorbidity Index	1.01	0.98, 1.04	0.4
Anxiety	1.25	0.86, 1.78	0.2
Depression	1.13	0.77, 1.64	0.5
Total SVI	1.28	0.75, 2.16	0.4
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(etoh1)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
2259.605 | 2357.726 |     0.001 | 0.124 | 0.399 |    0.080 |    -3.400 |           0.007 | 0.969

performance::check_model(etoh1, panel = TRUE)


## Margins
cplot(etoh1, "RPL_THEMES", what = "prediction", main = "Percent Likelihood of Alcohol Use Given SVI")

Alcohol Use + SVI Quartile

# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

etoh2 <- glm(etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(etoh2)


Call:
glm(formula = etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + max_ch + anxiety_2 + depression_2 + RPL_4, family = "binomial", 
    data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.3575  -0.1842  -0.1709  -0.1607   3.0452  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -4.205070   0.226567 -18.560   <2e-16 ***
age_yrs          -0.002904   0.004030  -0.721   0.4712    
genderFemale     -0.173568   0.138932  -1.249   0.2116    
race_5Black       0.308603   0.259976   1.187   0.2352    
race_5Other       0.555829   0.326510   1.702   0.0887 .  
race_5Asian      -0.014895   0.464620  -0.032   0.9744    
race_5Native      0.197112   1.015225   0.194   0.8461    
ethnic_3Hispanic  0.457925   0.386058   1.186   0.2356    
lang_3Other       0.081995   0.614505   0.133   0.8939    
max_ch            0.012755   0.015101   0.845   0.3983    
anxiety_2         0.219555   0.185920   1.181   0.2376    
depression_2      0.121837   0.192571   0.633   0.5269    
RPL_4Second       0.183931   0.167931   1.095   0.2734    
RPL_4Third        0.195687   0.187682   1.043   0.2971    
RPL_4Fourth       0.157627   0.242250   0.651   0.5153    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2247.8  on 14013  degrees of freedom
Residual deviance: 2232.8  on 13999  degrees of freedom
  (1231 observations deleted due to missingness)
AIC: 2262.8

Number of Fisher Scoring iterations: 7

broom::glance(etoh2)

broom::tidy(etoh2, exponentiate = TRUE)

tbl_regression(etoh2, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_4 ~ "SVI Quartile", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"), exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Age	1.00	0.99, 1.00	0.5
Gender
Male	—	—
Female	0.84	0.64, 1.10	0.2
Race
White	—	—
Black	1.36	0.79, 2.21	0.2
Other	1.74	0.88, 3.18	0.089
Asian	0.99	0.34, 2.21	>0.9
Native	1.22	0.07, 5.64	0.8
Ethnicity
NonHispanic	—	—
Hispanic	1.58	0.70, 3.20	0.2
Preferred Language
English	—	—
Other	1.09	0.26, 3.10	0.9
Charlson Comorbidity Index	1.01	0.98, 1.04	0.4
Anxiety	1.25	0.86, 1.78	0.2
Depression	1.13	0.77, 1.63	0.5
SVI Quartile
First	—	—
Second	1.20	0.86, 1.67	0.3
Third	1.22	0.84, 1.75	0.3
Fourth	1.17	0.71, 1.85	0.5
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(etoh2)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
2262.801 | 2376.018 |     0.001 | 0.124 | 0.399 |    0.080 |    -3.400 |           0.007 | 0.969

performance::check_model(etoh2, panel = TRUE)


## Margins
cplot(etoh2, "RPL_4", what = "prediction", main = "Percent Likelihood of Alcohol Use Given SVI Quartile")

Alcohol Use + All Themes

# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

etoh3 <- glm(etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
                RPL_THEME4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(etoh3)


Call:
glm(formula = etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + 
    lang_3 + max_ch + anxiety_2 + depression_2 + RPL_THEME1 + 
    RPL_THEME2 + RPL_THEME3 + RPL_THEME4, family = "binomial", 
    data = mh_vax_co_sub)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.3711  -0.1850  -0.1710  -0.1596   3.0536  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -4.339313   0.272864 -15.903   <2e-16 ***
age_yrs          -0.002741   0.004043  -0.678    0.498    
genderFemale     -0.164875   0.139288  -1.184    0.237    
race_5Black       0.291680   0.264839   1.101    0.271    
race_5Other       0.553851   0.326629   1.696    0.090 .  
race_5Asian      -0.048297   0.471463  -0.102    0.918    
race_5Native      0.251656   1.015239   0.248    0.804    
ethnic_3Hispanic  0.469858   0.385656   1.218    0.223    
lang_3Other       0.109349   0.618042   0.177    0.860    
max_ch            0.011830   0.015189   0.779    0.436    
anxiety_2         0.218766   0.186275   1.174    0.240    
depression_2      0.135770   0.192854   0.704    0.481    
RPL_THEME1       -0.338091   0.393710  -0.859    0.390    
RPL_THEME2        0.480040   0.341512   1.406    0.160    
RPL_THEME3        0.288674   0.254563   1.134    0.257    
RPL_THEME4        0.053214   0.286859   0.186    0.853    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2237.3  on 13944  degrees of freedom
Residual deviance: 2221.1  on 13929  degrees of freedom
  (1300 observations deleted due to missingness)
AIC: 2253.1

Number of Fisher Scoring iterations: 7

broom::glance(etoh3)

broom::tidy(etoh3, exponentiate = TRUE)

tbl_regression(etoh3, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", 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
Age	1.00	0.99, 1.01	0.5
Gender
Male	—	—
Female	0.85	0.65, 1.11	0.2
Race
White	—	—
Black	1.34	0.77, 2.19	0.3
Other	1.74	0.88, 3.17	0.090
Asian	0.95	0.33, 2.17	>0.9
Native	1.29	0.07, 5.96	0.8
Ethnicity
NonHispanic	—	—
Hispanic	1.60	0.70, 3.24	0.2
Preferred Language
English	—	—
Other	1.12	0.26, 3.22	0.9
Charlson Comorbidity Index	1.01	0.98, 1.04	0.4
Anxiety	1.24	0.86, 1.78	0.2
Depression	1.15	0.78, 1.66	0.5
Soceioeconomic Status	0.71	0.33, 1.53	0.4
Household Composition	1.62	0.83, 3.17	0.2
Minority Status and Language	1.33	0.81, 2.20	0.3
Housing and Transportation	1.05	0.60, 1.85	0.9
¹ OR = Odds Ratio, CI = Confidence Interval


## Model performance
model_performance(etoh3)

# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
2253.083 | 2373.769 |     0.002 | 0.124 | 0.399 |    0.080 |    -3.386 |           0.007 | 0.969

performance::check_model(etoh3, panel = TRUE)


## Margins
cplot(etoh3, "RPL_THEME1", what = "prediction", main = "Percent Likelihood of Alcohol Use Given THEME1")

cplot(etoh3, "RPL_THEME2", what = "prediction", main = "Percent Likelihood of Alcohol Use Given THEME2")

cplot(etoh3, "RPL_THEME3", what = "prediction", main = "Percent Likelihood of Alcohol Use Given THEME3")

cplot(etoh3, "RPL_THEME4", what = "prediction", main = "Percent Likelihood of Alcohol Use Given THEME4")

---
title: "2022.11.8 Substance Use IBD"
output:
  html_notebook:
    themes: paper
    toc: yes
    toc_float: yes
editor_options:
  chunk_output_type: inline
date: '2022-11-8'
---

# Load Packages 
```{r}
library(tidyverse)
library(codebookr)
library(summarytools)
library(broom)
library(performance)
library(gt)
library(gtsummary)
library(janitor)
library(forcats)
library(here)
library(yaml)
library(margins)
```

# Import Data 
```{r}

load("~/Desktop/R-Code/SDOH_ALL/mh_vax_co_sub.rda")
View(mh_vax_co_sub)


```


# Baseline Characteristics 
```{r}
mh_vax_co_sub %>% 
  dplyr::select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, max_ch, anxiety_2, depression_2, act_tob, drug_use, etoh_use, RPL_THEMES, RPL_4, 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 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", RPL_4 ~ "SVI Quartiles", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", act_tob ~ "Active Tobacco Use", drug_use ~ "Drug Abuse", etoh_use ~ "Alcohol Abuse", max_ch ~ "Charlson Comorbidity Index"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")
```
# Baseline Characteristics by SVI
```{r}
baseline %>% 
tbl_summary(by = RPL_4,
         label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", RPL_4 ~ "SVI Quartiles", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", act_tob ~ "Active Tobacco Use", drug_use ~ "Drug Abuse", etoh_use ~ "Alcohol Abuse", max_ch ~ "Charlson Comorbidity Index"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)") %>% add_p()
```

# Bivariate Analysis {.tabset}

## Tobacco Use
```{r}
tbl_uv_tobac <-
  tbl_uvregression(
    mh_vax_co_sub[c("act_tob", "age_yrs", "gender", "race_5", "ethnic_3", "lang_3", "relig_affil", "mstat_5", "max_ch", "anxiety_2", "depression_2", "RPL_THEMES",  "RPL_THEME1", "RPL_THEME2",  "RPL_THEME3", "RPL_THEME4")],
    method = glm,
    y = act_tob,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"))
    
print(tbl_uv_tobac, method = render)
```


## Drug Abuse
```{r}
tbl_uv_drug <-
  tbl_uvregression(
    mh_vax_co_sub[c("drug_use", "age_yrs", "gender", "race_5", "ethnic_3", "lang_3", "relig_affil", "mstat_5", "max_ch", "anxiety_2", "depression_2", "RPL_THEMES",  "RPL_THEME1", "RPL_THEME2",  "RPL_THEME3", "RPL_THEME4")],
    method = glm,
    y = drug_use,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"))
    
print(tbl_uv_drug, method = render)
```

## Alcohol Abuse
```{r}
tbl_uv_etoh <-
  tbl_uvregression(
    mh_vax_co_sub[c("etoh_use", "age_yrs", "gender", "race_5", "ethnic_3", "lang_3", "relig_affil", "mstat_5", "max_ch", "anxiety_2", "depression_2", "RPL_THEMES",  "RPL_THEME1", "RPL_THEME2",  "RPL_THEME3", "RPL_THEME4")],
    method = glm,
    y = etoh_use,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"))
    
print(tbl_uv_etoh, method = render)
```

# Active Tobacco: Multivariable Models {.tabset}

## Active tobacco + RPL_THEMES 
```{r}
tobac1 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3 + mstat_5 + relig_affil + 
                max_ch + anxiety_2 + depression_2 + RPL_THEMES,
              family = "binomial",
              data = mh_vax_co_sub)
summary(tobac1)
broom::glance(tobac1)
broom::tidy(tobac1, exponentiate = TRUE)
tbl_regression(tobac1, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_THEMES ~ "Total SVI", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", mstat_5 ~ "Marital Status", relig_affil ~ "Any Religious Affiliation"), exponentiate = TRUE)

## Model performance
model_performance(tobac1)
performance::check_model(tobac1, panel = TRUE)

## Margins
cplot(tobac1, "RPL_THEMES", what = "prediction", main = "Percent Likelihood of Tobacco Use Given SVI")
```

## Active tobacco + RPL_4
```{r}
tobac2 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3 + mstat_5 + relig_affil + 
                max_ch + anxiety_2 + depression_2 + RPL_4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(tobac2)
broom::glance(tobac2)
broom::tidy(tobac2, exponentiate = TRUE)
tbl_regression(tobac2, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_4 ~ "SVI Quartile", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", mstat_5 ~ "Marital Status", relig_affil ~ "Any Religious Affiliation"), exponentiate = TRUE)

## Model performance
model_performance(tobac2)
performance::check_model(tobac2, panel = TRUE)

## Margins
cplot(tobac2, "RPL_4", what = "prediction", main = "Percent Likelihood of Tobacco Use Given SVI Quartile")
```

## Active Tobacco + All Themes 
```{r}
tobac3 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3 + mstat_5 + relig_affil + 
                max_ch + anxiety_2 + depression_2 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
                RPL_THEME4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(tobac3)
broom::glance(tobac3)
broom::tidy(tobac3, exponentiate = TRUE)
tbl_regression(tobac3, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", mstat_5 ~ "Marital Status", 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)

## Model performance
model_performance(tobac3)
performance::check_model(tobac3, panel = TRUE)

## Margins
cplot(tobac3, "RPL_THEME1", what = "prediction", main = "Percent Likelihood of Tobacco Use Given THEME1")
cplot(tobac3, "RPL_THEME2", what = "prediction", main = "Percent Likelihood of Tobacco Use Given THEME2")
cplot(tobac3, "RPL_THEME3", what = "prediction", main = "Percent Likelihood of Tobacco Use Given THEME3")
cplot(tobac3, "RPL_THEME4", what = "prediction", main = "Percent Likelihood of Tobacco Use Given THEME4")
```

# Drug Use: Multivariable Models {.tabset}

## Drug Use + RPL_THEMES
```{r}
# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

drug1 <- glm(drug_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_THEMES,
              family = "binomial",
              data = mh_vax_co_sub)
summary(drug1)
broom::glance(drug1)
broom::tidy(drug1, exponentiate = TRUE)
tbl_regression(drug1, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_THEMES ~ "Total SVI", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"), exponentiate = TRUE)

## Model performance
model_performance(drug1)
performance::check_model(drug1, panel = TRUE)

## Margins
cplot(drug1, "RPL_THEMES", what = "prediction", main = "Percent Likelihood of Drug Use Given SVI")
```

## Drug Use + SVI Quartiles 
```{r}
# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

drug2 <- glm(drug_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(drug2)
broom::glance(drug2)
broom::tidy(drug2, exponentiate = TRUE)
tbl_regression(drug2, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_4 ~ "SVI Quartile", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"), exponentiate = TRUE)

## Model performance
model_performance(drug2)
performance::check_model(drug2, panel = TRUE)

## Margins
cplot(drug2, "RPL_4", what = "prediction", main = "Percent Likelihood of Drug Use Given SVI")
```

## Drug Use + All Themes 
```{r}

# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

drug3 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
                RPL_THEME4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(drug3)
broom::glance(drug3)
broom::tidy(drug3, exponentiate = TRUE)
tbl_regression(drug3, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)

## Model performance
model_performance(drug3)
performance::check_model(drug3, panel = TRUE)

## Margins
cplot(drug3, "RPL_THEME1", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME1")
cplot(drug3, "RPL_THEME2", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME2")
cplot(drug3, "RPL_THEME3", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME3")
cplot(drug3, "RPL_THEME4", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME4")
```

## Sensitivity analysis including mstat and relig_affil 
```{r}
drug4 <- glm(act_tob ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + mstat_5 + relig_affil +
                max_ch + anxiety_2 + depression_2 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
                RPL_THEME4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(drug4)
broom::glance(drug4)
broom::tidy(drug4, exponentiate = TRUE)
tbl_regression(drug4, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", mstat_5 ~ "Marital Status", relig_affil ~ "Any Religious Affiliation"), exponentiate = TRUE)

## Model performance
model_performance(drug4)
performance::check_model(drug4, panel = TRUE)

## Margins
cplot(drug4, "RPL_THEME1", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME1")
cplot(drug4, "RPL_THEME2", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME2")
cplot(drug4, "RPL_THEME3", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME3")
cplot(drug4, "RPL_THEME4", what = "prediction", main = "Percent Likelihood of Drug Use Given THEME4")
```

# Alcohol Use: Multivariable Models {.tabset}

## Alcohol Use + RPL_THEMES 
```{r}
# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

etoh1 <- glm(etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_THEMES,
              family = "binomial",
              data = mh_vax_co_sub)
summary(etoh1)
broom::glance(etoh1)
broom::tidy(etoh1, exponentiate = TRUE)
tbl_regression(etoh1, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_THEMES ~ "Total SVI", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"), exponentiate = TRUE)

## Model performance
model_performance(etoh1)
performance::check_model(etoh1, panel = TRUE)

## Margins
cplot(etoh1, "RPL_THEMES", what = "prediction", main = "Percent Likelihood of Alcohol Use Given SVI")
```

## Alcohol Use + SVI Quartile 
```{r}
# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

etoh2 <- glm(etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(etoh2)
broom::glance(etoh2)
broom::tidy(etoh2, exponentiate = TRUE)
tbl_regression(etoh2, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", RPL_4 ~ "SVI Quartile", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index"), exponentiate = TRUE)

## Model performance
model_performance(etoh2)
performance::check_model(etoh2, panel = TRUE)

## Margins
cplot(etoh2, "RPL_4", what = "prediction", main = "Percent Likelihood of Alcohol Use Given SVI Quartile")
```

## Alcohol Use + All Themes 
```{r}
# removing mstat_5 and relig_affil because of missing data and made no difference in bivariate models 

etoh3 <- glm(etoh_use ~ age_yrs + gender + race_5 + ethnic_3 + lang_3  + 
                max_ch + anxiety_2 + depression_2 + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
                RPL_THEME4,
              family = "binomial",
              data = mh_vax_co_sub)
summary(etoh3)
broom::glance(etoh3)
broom::tidy(etoh3, exponentiate = TRUE)
tbl_regression(etoh3, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", anxiety_2 ~"Anxiety", depression_2 ~ "Depression", max_ch ~ "Charlson Comorbidity Index", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation"), exponentiate = TRUE)

## Model performance
model_performance(etoh3)
performance::check_model(etoh3, panel = TRUE)

## Margins
cplot(etoh3, "RPL_THEME1", what = "prediction", main = "Percent Likelihood of Alcohol Use Given THEME1")
cplot(etoh3, "RPL_THEME2", what = "prediction", main = "Percent Likelihood of Alcohol Use Given THEME2")
cplot(etoh3, "RPL_THEME3", what = "prediction", main = "Percent Likelihood of Alcohol Use Given THEME3")
cplot(etoh3, "RPL_THEME4", what = "prediction", main = "Percent Likelihood of Alcohol Use Given THEME4")
```



2022.11.8 Substance Use IBD

2022-11-8