Load Packages

library(tidyverse)
library(codebookr)
library(summarytools)
library(broom) 
library(performance)
library(gt)
library(gtsummary)
library(janitor)
library(forcats)
library(here)
library(margins)
library(ggplot2)

Import Data

load(file = "~/Desktop/R-Code/SDOH_Vax/vax_clean1.rda")

Data cleaning

shingrix1 <- vax_clean1[ which(vax_clean1$age_yrs > 49.999), ]

shingrix1 %>% 
mutate(full_shingrix = case_when(total_shingrix>= 2 ~ '1',TRUE ~ "0")) -> shingrix_clean1

shingrix_clean1$full_shingrix = as.numeric(shingrix_clean1$full_shingrix)

Baseline Characteristics

shingrix_clean1 %>% 
  dplyr::select(ibd_3, age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, act_tob, max_ch, IC, pop_dens,r_pct, total_shingrix, full_shingrix, RPL_THEMES, RPL_4, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4) -> shingrix_baseline
shingrix_baseline %>% tbl_summary(label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Primary 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", pop_dens ~ "Population Density", RPL_4 ~ "SVI Quartiles", r_pct ~ "Percent Republican", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", ibd_3 ~ "IBD Type", IC ~ "Immunocompromised", total_shingrix ~ "Total Shingrix Vaccines", full_shingrix ~ "Fully Vaccinated"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")
Characteristic N = 7,4651
IBD Type
    CD 3,419 (46%)
    UC 3,965 (53%)
    Unspecified 81 (1.1%)
Age 66 (10)
Gender
    Male 3,330 (45%)
    Female 4,135 (55%)
Race
    White 6,707 (90%)
    Black 368 (4.9%)
    Asian 112 (1.5%)
    Native 27 (0.4%)
    Other 251 (3.4%)
Ethnicity
    NonHispanic 7,061 (99%)
    UNKNOWN 0 (0%)
    Hispanic 95 (1.3%)
    CHOOSE NOT TO DISCLOSE 0 (0%)
    (Missing) 309
Primary Language
    English 7,389 (99%)
    Other 76 (1.0%)
Any Religious Affiliation
    Yes 4,759 (68%)
    No 2,283 (32%)
    UNKNOWN 0 (0%)
    PATIENT REFUSED 0 (0%)
    (Missing) 423
Marital Status
    Married 4,257 (57%)
    Unknown 1,322 (18%)
    Unmarried 1,219 (16%)
    DivorcedSeparated 372 (5.0%)
    Widow 295 (4.0%)
Active Tobacco Use
    No 6,221 (88%)
    Yes 875 (12%)
    NOT ASKED 0 (0%)
    (Missing) 369
Charlson Comorbidity Index 5.3 (5.7)
    (Missing) 240
Immunocompromised 1,544 (49%)
    (Missing) 4,303
Population Density 1,921 (2,136)
    (Missing) 258
Percent Republican 47 (17)
    (Missing) 1,142
Total Shingrix Vaccines
    0 5,970 (80%)
    1 310 (4.2%)
    2 1,086 (15%)
    3 87 (1.2%)
    4 10 (0.1%)
    5 2 (<0.1%)
Fully Vaccinated 1,185 (16%)
Total SVI 0.37 (0.26)
    (Missing) 79
SVI Quartiles
    First 2,936 (40%)
    Second 2,209 (30%)
    Third 1,542 (21%)
    Fourth 699 (9.5%)
    (Missing) 79
Soceioeconomic Status 0.35 (0.25)
    (Missing) 104
Household Composition 0.41 (0.26)
    (Missing) 79
Minority Status and Language 0.46 (0.28)
    (Missing) 77
Housing and Transportation 0.43 (0.28)
    (Missing) 92
1 n (%); Mean (SD)

Baseline characteristics by SVI Quartile

shingrix_baseline %>% 
tbl_summary(by = RPL_4,
         label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Primary 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", pop_dens ~ "Population Density", RPL_4 ~ "SVI Quartiles", r_pct ~ "Percent Republican", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", ibd_3 ~ "IBD Type", IC ~ "Immunocompromised", total_shingrix ~ "Total Shingrix Vaccines", full_shingrix ~ "Fully Vaccinated"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)") %>% add_p()
79 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 'race_5', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, : FEXACT error 5.
The hash table key cannot be computed because the largest key
is larger than the largest representable int.
The algorithm cannot proceed.
Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.
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, 2L, 1L, 1L, 2L, 1L, : FEXACT error 6.  LDKEY=578 is too small for this problem,
  (ii := key2[itp=103] = 8900078, ldstp=17340)
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(2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, : FEXACT error 7(location). LDSTP=17340 is too small for this problem,
  (pastp=896.636, ipn_0:=ipoin[itp=253]=20, stp[ipn_0]=864.174).
Increase workspace or consider using 'simulate.p.value=TRUE'
There was an error in 'add_p()/add_difference()' for variable 'total_shingrix', p-value omitted:
Error in stats::fisher.test(c(0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 2, 2, 3, 0, : FEXACT error 5.
The hash table key cannot be computed because the largest key
is larger than the largest representable int.
The algorithm cannot proceed.
Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.
Characteristic First, N = 2,9361 Second, N = 2,2091 Third, N = 1,5421 Fourth, N = 6991 p-value2
IBD Type <0.001
    CD 1,248 (43%) 986 (45%) 768 (50%) 386 (55%)
    UC 1,662 (57%) 1,199 (54%) 757 (49%) 301 (43%)
    Unspecified 26 (0.9%) 24 (1.1%) 17 (1.1%) 12 (1.7%)
Age 66 (10) 66 (10) 66 (11) 65 (10) 0.2
Gender 0.004
    Male 1,379 (47%) 961 (44%) 643 (42%) 307 (44%)
    Female 1,557 (53%) 1,248 (56%) 899 (58%) 392 (56%)
Race
    White 2,719 (93%) 2,012 (91%) 1,375 (89%) 532 (76%)
    Black 62 (2.1%) 84 (3.8%) 85 (5.5%) 129 (18%)
    Asian 51 (1.7%) 34 (1.5%) 25 (1.6%) 2 (0.3%)
    Native 8 (0.3%) 10 (0.5%) 6 (0.4%) 2 (0.3%)
    Other 96 (3.3%) 69 (3.1%) 51 (3.3%) 34 (4.9%)
Ethnicity 0.057
    NonHispanic 2,771 (99%) 2,099 (99%) 1,458 (99%) 658 (97%)
    UNKNOWN 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Hispanic 33 (1.2%) 24 (1.1%) 21 (1.4%) 17 (2.5%)
    CHOOSE NOT TO DISCLOSE 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    (Missing) 132 86 63 24
Primary Language <0.001
    English 2,921 (99%) 2,188 (99%) 1,521 (99%) 681 (97%)
    Other 15 (0.5%) 21 (1.0%) 21 (1.4%) 18 (2.6%)
Any Religious Affiliation
    Yes 1,898 (68%) 1,401 (67%) 992 (68%) 430 (67%)
    No 887 (32%) 676 (33%) 467 (32%) 214 (33%)
    UNKNOWN 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    PATIENT REFUSED 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    (Missing) 151 132 83 55
Marital Status <0.001
    Married 1,924 (66%) 1,255 (57%) 759 (49%) 277 (40%)
    Unknown 461 (16%) 385 (17%) 307 (20%) 158 (23%)
    Unmarried 342 (12%) 343 (16%) 314 (20%) 199 (28%)
    DivorcedSeparated 101 (3.4%) 127 (5.7%) 92 (6.0%) 49 (7.0%)
    Widow 108 (3.7%) 99 (4.5%) 70 (4.5%) 16 (2.3%)
Active Tobacco Use
    No 2,587 (91%) 1,837 (88%) 1,226 (84%) 510 (79%)
    Yes 242 (8.6%) 259 (12%) 230 (16%) 135 (21%)
    NOT ASKED 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    (Missing) 107 113 86 54
Charlson Comorbidity Index 5.2 (5.7) 5.6 (5.9) 5.2 (5.6) 5.1 (5.6) 0.2
    (Missing) 78 66 61 33
Immunocompromised 612 (45%) 485 (50%) 307 (52%) 140 (55%) 0.003
    (Missing) 1,588 1,236 954 446
Population Density 1,760 (1,933) 1,793 (2,117) 2,053 (2,235) 2,743 (2,567) <0.001
    (Missing) 35 48 63 35
Percent Republican 47 (16) 48 (17) 48 (17) 39 (21) <0.001
    (Missing) 338 408 282 110
Total Shingrix Vaccines
    0 2,182 (74%) 1,779 (81%) 1,311 (85%) 633 (91%)
    1 146 (5.0%) 75 (3.4%) 67 (4.3%) 19 (2.7%)
    2 557 (19%) 327 (15%) 153 (9.9%) 39 (5.6%)
    3 47 (1.6%) 24 (1.1%) 9 (0.6%) 6 (0.9%)
    4 4 (0.1%) 4 (0.2%) 1 (<0.1%) 1 (0.1%)
    5 0 (0%) 0 (0%) 1 (<0.1%) 1 (0.1%)
Fully Vaccinated 608 (21%) 355 (16%) 164 (11%) 47 (6.7%) <0.001
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) 0.77 (0.11) <0.001
    (Missing) 25 0 0 0
Household Composition 0.22 (0.14) 0.40 (0.21) 0.60 (0.23) 0.77 (0.19) <0.001
Minority Status and Language 0.40 (0.27) 0.45 (0.28) 0.50 (0.28) 0.63 (0.25) <0.001
Housing and Transportation 0.19 (0.15) 0.48 (0.20) 0.64 (0.21) 0.83 (0.16) <0.001
    (Missing) 13 0 0 0
1 n (%); Mean (SD)
2 Pearson's Chi-squared test; Kruskal-Wallis rank sum test; Fisher's exact test

Shingrix Count (negative binomial models)

SVI Continuous

shingrix_nb <- glm.nb(total_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_THEMES,
               data = shingrix_clean1) 
summary(shingrix_nb)

Call:
glm.nb(formula = total_shingrix ~ ibd_3 + age_yrs + gender + 
    race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + 
    max_ch + IC + pop_dens + r_pct + RPL_THEMES, data = shingrix_clean1, 
    init.theta = 0.76526448, link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3407  -0.9215  -0.7885   0.4675   2.1666  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -7.098e-01  3.123e-01  -2.273 0.023046 *  
ibd_3UC                   1.119e-01  7.557e-02   1.481 0.138637    
ibd_3Unspecified         -1.547e-01  5.774e-01  -0.268 0.788818    
age_yrs                   1.341e-02  3.822e-03   3.510 0.000448 ***
genderFemale              1.413e-01  7.243e-02   1.951 0.051106 .  
race_5Black              -4.150e-01  1.779e-01  -2.333 0.019641 *  
race_5Asian               1.995e-01  2.313e-01   0.863 0.388373    
race_5Native             -6.365e-02  5.178e-01  -0.123 0.902181    
race_5Other              -2.757e-02  2.355e-01  -0.117 0.906816    
ethnic_3Hispanic          2.661e-01  2.665e-01   0.999 0.317974    
lang_3Other              -5.905e-01  3.739e-01  -1.579 0.114254    
relig_affilNo            -6.319e-03  7.822e-02  -0.081 0.935610    
mstat_5Unknown           -7.357e-02  9.848e-02  -0.747 0.455021    
mstat_5Unmarried         -1.654e-01  1.057e-01  -1.564 0.117798    
mstat_5DivorcedSeparated -2.080e-01  1.828e-01  -1.138 0.255274    
mstat_5Widow             -5.358e-01  1.993e-01  -2.689 0.007177 ** 
act_tobYes               -4.134e-01  1.342e-01  -3.082 0.002059 ** 
max_ch                    6.984e-03  6.069e-03   1.151 0.249801    
IC                        2.262e-01  7.508e-02   3.013 0.002584 ** 
pop_dens                 -1.183e-05  1.950e-05  -0.606 0.544209    
r_pct                    -1.531e-02  2.532e-03  -6.047 1.47e-09 ***
RPL_THEMES               -7.466e-01  1.545e-01  -4.832 1.35e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(0.7653) family taken to be 1)

    Null deviance: 2258.8  on 2609  degrees of freedom
Residual deviance: 2127.3  on 2588  degrees of freedom
  (4855 observations deleted due to missingness)
AIC: 5213.7

Number of Fisher Scoring iterations: 1

              Theta:  0.7653 
          Std. Err.:  0.0788 

 2 x log-likelihood:  -5167.6870 
broom::glance(shingrix_nb)
broom::tidy(shingrix_nb, exponentiate = TRUE)
model_performance(shingrix_nb)
# Indices of model performance

AIC      |      BIC | Nagelkerke's R2 |  RMSE | Sigma | Score_log | Score_spherical
-----------------------------------------------------------------------------------
5213.687 | 5348.630 |           0.085 | 0.874 | 0.907 |    -1.003 |           0.017
tbl_regression(shingrix_nb, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_THEMES ~ "Total SVI", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)
Characteristic IRR1 95% CI1 p-value
IBD Type
    CD — —
    UC 1.12 0.96, 1.30 0.14
    Unspecified 0.86 0.26, 2.51 0.8
Age 1.01 1.01, 1.02 <0.001
Gender
    Male — —
    Female 1.15 1.00, 1.33 0.051
Race
    White — —
    Black 0.66 0.46, 0.93 0.020
    Asian 1.22 0.77, 1.93 0.4
    Native 0.94 0.33, 2.58 >0.9
    Other 0.97 0.61, 1.55 >0.9
Ethnicity
    NonHispanic — —
    Hispanic 1.30 0.77, 2.21 0.3
Preferred Language
    English — —
    Other 0.55 0.25, 1.14 0.11
Any Religious Affiliation
    Yes — —
    No 0.99 0.85, 1.16 >0.9
Marital Status
    Married — —
    Unknown 0.93 0.76, 1.13 0.5
    Unmarried 0.85 0.69, 1.04 0.12
    DivorcedSeparated 0.81 0.57, 1.15 0.3
    Widow 0.59 0.39, 0.86 0.007
Active Tobacco Use
    No — —
    Yes 0.66 0.51, 0.86 0.002
Charlson Comorbidity Index 1.01 1.0, 1.02 0.2
Immunocompromised 1.25 1.08, 1.45 0.003
Population Density 1.00 1.00, 1.00 0.5
Percent Republican 0.98 0.98, 0.99 <0.001
Total SVI 0.47 0.35, 0.64 <0.001
1 IRR = Incidence Rate Ratio, CI = Confidence Interval 

# NB Residual Plot
shingrix_nb_res <- resid(shingrix_nb)
plot(fitted(shingrix_nb), shingrix_nb_res, col='steelblue', pch=16,
     xlab='Predicted Vaccines', ylab='Standardized Residuals', main='Negative Binomial')
abline(0,0)

# NB regression more appropriate because residuals of the model are smaller 

# Likelihood ratio test 
pchisq(2 * (logLik(shingrix_nb) - logLik(shingrix_nb)), df = 1, lower.tail = FALSE)
'log Lik.' 1 (df=23)
# p-value of loglik is < 0.05 so NB regression is the more appropriate model 

performance::check_model(shingrix_nb, panel = TRUE)

SVI Quartiles

shingrix_nb2 <- glm.nb(total_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_4,
               data = shingrix_clean1) 
summary(shingrix_nb2)

Call:
glm.nb(formula = total_shingrix ~ ibd_3 + age_yrs + gender + 
    race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + 
    max_ch + IC + pop_dens + r_pct + RPL_4, data = shingrix_clean1, 
    init.theta = 0.7567736182, link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3208  -0.9197  -0.7949   0.4628   2.1532  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -7.861e-01  3.120e-01  -2.520 0.011740 *  
ibd_3UC                   1.175e-01  7.570e-02   1.552 0.120778    
ibd_3Unspecified         -1.906e-01  5.785e-01  -0.329 0.741837    
age_yrs                   1.336e-02  3.828e-03   3.491 0.000481 ***
genderFemale              1.475e-01  7.260e-02   2.032 0.042192 *  
race_5Black              -4.487e-01  1.788e-01  -2.510 0.012076 *  
race_5Asian               2.093e-01  2.321e-01   0.902 0.367198    
race_5Native             -9.039e-02  5.198e-01  -0.174 0.861956    
race_5Other              -4.401e-02  2.362e-01  -0.186 0.852227    
ethnic_3Hispanic          2.650e-01  2.676e-01   0.990 0.321965    
lang_3Other              -6.077e-01  3.748e-01  -1.622 0.104880    
relig_affilNo            -7.549e-03  7.836e-02  -0.096 0.923248    
mstat_5Unknown           -8.281e-02  9.872e-02  -0.839 0.401554    
mstat_5Unmarried         -1.716e-01  1.058e-01  -1.622 0.104754    
mstat_5DivorcedSeparated -2.126e-01  1.828e-01  -1.163 0.244983    
mstat_5Widow             -5.388e-01  1.996e-01  -2.699 0.006951 ** 
act_tobYes               -4.195e-01  1.342e-01  -3.127 0.001766 ** 
max_ch                    6.852e-03  6.082e-03   1.127 0.259902    
IC                        2.233e-01  7.523e-02   2.968 0.002998 ** 
pop_dens                 -1.485e-05  1.956e-05  -0.759 0.447700    
r_pct                    -1.538e-02  2.545e-03  -6.041 1.54e-09 ***
RPL_4Second              -2.023e-01  8.337e-02  -2.426 0.015265 *  
RPL_4Third               -3.230e-01  1.029e-01  -3.138 0.001700 ** 
RPL_4Fourth              -5.008e-01  1.570e-01  -3.190 0.001424 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(0.7568) family taken to be 1)

    Null deviance: 2250.6  on 2609  degrees of freedom
Residual deviance: 2125.3  on 2586  degrees of freedom
  (4855 observations deleted due to missingness)
AIC: 5223.3

Number of Fisher Scoring iterations: 1

              Theta:  0.7568 
          Std. Err.:  0.0775 

 2 x log-likelihood:  -5173.2960 
broom::glance(shingrix_nb2)
broom::tidy(shingrix_nb2, exponentiate = TRUE)
model_performance(shingrix_nb2)
# Indices of model performance

AIC      |      BIC | Nagelkerke's R2 |  RMSE | Sigma | Score_log | Score_spherical
-----------------------------------------------------------------------------------
5223.296 | 5369.973 |           0.081 | 0.875 | 0.907 |    -1.005 |           0.017
tbl_regression(shingrix_nb2, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_4 ~ "SVI Quartile", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)
Characteristic IRR1 95% CI1 p-value
IBD Type
    CD — —
    UC 1.12 0.97, 1.31 0.12
    Unspecified 0.83 0.25, 2.43 0.7
Age 1.01 1.01, 1.02 <0.001
Gender
    Male — —
    Female 1.16 1.01, 1.34 0.042
Race
    White — —
    Black 0.64 0.45, 0.90 0.012
    Asian 1.23 0.78, 1.96 0.4
    Native 0.91 0.32, 2.52 0.9
    Other 0.96 0.60, 1.52 0.9
Ethnicity
    NonHispanic — —
    Hispanic 1.30 0.77, 2.21 0.3
Preferred Language
    English — —
    Other 0.54 0.25, 1.13 0.10
Any Religious Affiliation
    Yes — —
    No 0.99 0.85, 1.16 >0.9
Marital Status
    Married — —
    Unknown 0.92 0.76, 1.12 0.4
    Unmarried 0.84 0.68, 1.04 0.10
    DivorcedSeparated 0.81 0.56, 1.15 0.2
    Widow 0.58 0.39, 0.86 0.007
Active Tobacco Use
    No — —
    Yes 0.66 0.50, 0.85 0.002
Charlson Comorbidity Index 1.01 1.0, 1.02 0.3
Immunocompromised 1.25 1.08, 1.45 0.003
Population Density 1.00 1.00, 1.00 0.4
Percent Republican 0.98 0.98, 0.99 <0.001
SVI Quartile
    First — —
    Second 0.82 0.69, 0.96 0.015
    Third 0.72 0.59, 0.89 0.002
    Fourth 0.61 0.44, 0.82 0.001
1 IRR = Incidence Rate Ratio, CI = Confidence Interval 

performance::check_model(shingrix_nb2, panel = TRUE)

All themes

shingrix_nb3 <- glm.nb(total_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_THEME1
                    + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
               data = shingrix_clean1) 
summary(shingrix_nb3)

Call:
glm.nb(formula = total_shingrix ~ ibd_3 + age_yrs + gender + 
    race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + 
    max_ch + IC + pop_dens + r_pct + RPL_THEME1 + RPL_THEME2 + 
    RPL_THEME3 + RPL_THEME4, data = shingrix_clean1, init.theta = 0.7787687096, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3197  -0.9231  -0.7822   0.4586   2.2552  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -7.694e-01  3.411e-01  -2.256 0.024080 *  
ibd_3UC                   1.087e-01  7.541e-02   1.441 0.149495    
ibd_3Unspecified         -1.540e-01  5.744e-01  -0.268 0.788618    
age_yrs                   1.297e-02  3.819e-03   3.397 0.000681 ***
genderFemale              1.406e-01  7.226e-02   1.946 0.051624 .  
race_5Black              -3.933e-01  1.781e-01  -2.208 0.027222 *  
race_5Asian               1.504e-01  2.317e-01   0.649 0.516253    
race_5Native             -2.970e-02  5.112e-01  -0.058 0.953679    
race_5Other              -2.716e-02  2.346e-01  -0.116 0.907833    
ethnic_3Hispanic          2.453e-01  2.662e-01   0.921 0.356808    
lang_3Other              -6.164e-01  3.735e-01  -1.650 0.098855 .  
relig_affilNo            -8.496e-03  7.817e-02  -0.109 0.913447    
mstat_5Unknown           -6.201e-02  9.828e-02  -0.631 0.528073    
mstat_5Unmarried         -1.607e-01  1.056e-01  -1.522 0.128111    
mstat_5DivorcedSeparated -1.963e-01  1.827e-01  -1.075 0.282468    
mstat_5Widow             -5.307e-01  1.990e-01  -2.667 0.007644 ** 
act_tobYes               -4.098e-01  1.340e-01  -3.058 0.002228 ** 
max_ch                    7.069e-03  6.055e-03   1.168 0.242990    
IC                        2.203e-01  7.497e-02   2.938 0.003298 ** 
pop_dens                 -1.005e-05  2.034e-05  -0.494 0.621114    
r_pct                    -1.209e-02  2.991e-03  -4.042  5.3e-05 ***
RPL_THEME1               -3.936e-01  2.196e-01  -1.793 0.073050 .  
RPL_THEME2               -4.660e-01  1.911e-01  -2.438 0.014768 *  
RPL_THEME3                5.068e-02  1.491e-01   0.340 0.734003    
RPL_THEME4               -9.555e-02  1.620e-01  -0.590 0.555320    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(0.7788) family taken to be 1)

    Null deviance: 2271.7  on 2609  degrees of freedom
Residual deviance: 2132.1  on 2585  degrees of freedom
  (4855 observations deleted due to missingness)
AIC: 5212.6

Number of Fisher Scoring iterations: 1

              Theta:  0.7788 
          Std. Err.:  0.0811 

 2 x log-likelihood:  -5160.5730 
broom::glance(shingrix_nb3)
broom::tidy(shingrix_nb3, exponentiate = TRUE)
model_performance(shingrix_nb3)
# Indices of model performance

AIC      |      BIC | Nagelkerke's R2 |  RMSE | Sigma | Score_log | Score_spherical
-----------------------------------------------------------------------------------
5212.573 | 5365.118 |           0.090 | 0.872 | 0.908 |    -1.002 |           0.017
tbl_regression(shingrix_nb3, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)
Characteristic IRR1 95% CI1 p-value
IBD Type
    CD — —
    UC 1.11 0.96, 1.29 0.15
    Unspecified 0.86 0.26, 2.50 0.8
Age 1.01 1.01, 1.02 <0.001
Gender
    Male — —
    Female 1.15 1.00, 1.33 0.052
Race
    White — —
    Black 0.67 0.47, 0.95 0.027
    Asian 1.16 0.74, 1.84 0.5
    Native 0.97 0.34, 2.66 >0.9
    Other 0.97 0.61, 1.54 >0.9
Ethnicity
    NonHispanic — —
    Hispanic 1.28 0.75, 2.16 0.4
Preferred Language
    English — —
    Other 0.54 0.25, 1.11 0.10
Any Religious Affiliation
    Yes — —
    No 0.99 0.85, 1.16 >0.9
Marital Status
    Married — —
    Unknown 0.94 0.77, 1.14 0.5
    Unmarried 0.85 0.69, 1.05 0.13
    DivorcedSeparated 0.82 0.57, 1.17 0.3
    Widow 0.59 0.40, 0.87 0.008
Active Tobacco Use
    No — —
    Yes 0.66 0.51, 0.86 0.002
Charlson Comorbidity Index 1.01 1.00, 1.02 0.2
Immunocompromised 1.25 1.08, 1.45 0.003
Population Density 1.00 1.00, 1.00 0.6
Percent Republican 0.99 0.98, 0.99 <0.001
Soceioeconomic Status 0.67 0.44, 1.04 0.073
Household Composition 0.63 0.43, 0.91 0.015
Minority Status and Language 1.05 0.78, 1.41 0.7
Housing and Transportation 0.91 0.66, 1.25 0.6
1 IRR = Incidence Rate Ratio, CI = Confidence Interval 

performance::check_model(shingrix_nb3, panel = TRUE)

Fully Vaccinated (logistic)

SVI Continuous

shingrix_full1 <- glm(full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_THEMES,
                   family = binomial, 
               data = shingrix_clean1) 
summary(shingrix_full1)

Call:
glm(formula = full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + 
    ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + 
    IC + pop_dens + r_pct + RPL_THEMES, family = binomial, data = shingrix_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3480  -0.7710  -0.6258  -0.3827   2.3826  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -9.746e-01  4.175e-01  -2.334  0.01958 *  
ibd_3UC                   1.231e-01  1.015e-01   1.212  0.22547    
ibd_3Unspecified         -1.721e-01  8.008e-01  -0.215  0.82987    
age_yrs                   1.589e-02  5.144e-03   3.088  0.00201 ** 
genderFemale              1.738e-01  9.749e-02   1.783  0.07461 .  
race_5Black              -5.162e-01  2.402e-01  -2.149  0.03165 *  
race_5Asian               3.698e-01  3.044e-01   1.215  0.22439    
race_5Native             -9.060e-02  7.028e-01  -0.129  0.89743    
race_5Other              -1.457e-01  3.235e-01  -0.450  0.65255    
ethnic_3Hispanic          4.587e-01  3.567e-01   1.286  0.19843    
lang_3Other              -7.416e-01  5.148e-01  -1.440  0.14975    
relig_affilNo             6.335e-02  1.045e-01   0.606  0.54434    
mstat_5Unknown           -1.362e-01  1.337e-01  -1.019  0.30808    
mstat_5Unmarried         -2.537e-01  1.435e-01  -1.767  0.07721 .  
mstat_5DivorcedSeparated -2.089e-01  2.439e-01  -0.856  0.39180    
mstat_5Widow             -5.383e-01  2.624e-01  -2.051  0.04026 *  
act_tobYes               -5.023e-01  1.841e-01  -2.728  0.00638 ** 
max_ch                    9.487e-03  8.161e-03   1.162  0.24506    
IC                        3.168e-01  1.012e-01   3.131  0.00174 ** 
pop_dens                 -3.101e-05  2.638e-05  -1.175  0.23984    
r_pct                    -2.317e-02  3.414e-03  -6.787 1.15e-11 ***
RPL_THEMES               -1.157e+00  2.113e-01  -5.472 4.44e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2875.7  on 2609  degrees of freedom
Residual deviance: 2732.6  on 2588  degrees of freedom
  (4855 observations deleted due to missingness)
AIC: 2776.6

Number of Fisher Scoring iterations: 4
broom::glance(shingrix_full1)
broom::tidy(shingrix_full1, exponentiate = TRUE)
model_performance(shingrix_full1)
# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
2776.622 | 2905.698 |     0.055 | 0.415 | 1.028 |    0.523 |  -176.245 |       3.897e-04 | 0.655
tbl_regression(shingrix_full1, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_THEMES ~ "Total SVI", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
IBD Type
    CD — —
    UC 1.13 0.93, 1.38 0.2
    Unspecified 0.84 0.12, 3.39 0.8
Age 1.02 1.01, 1.03 0.002
Gender
    Male — —
    Female 1.19 0.98, 1.44 0.075
Race
    White — —
    Black 0.60 0.37, 0.94 0.032
    Asian 1.45 0.79, 2.61 0.2
    Native 0.91 0.19, 3.31 0.9
    Other 0.86 0.44, 1.59 0.7
Ethnicity
    NonHispanic — —
    Hispanic 1.58 0.77, 3.14 0.2
Preferred Language
    English — —
    Other 0.48 0.15, 1.21 0.15
Any Religious Affiliation
    Yes — —
    No 1.07 0.87, 1.31 0.5
Marital Status
    Married — —
    Unknown 0.87 0.67, 1.13 0.3
    Unmarried 0.78 0.58, 1.02 0.077
    DivorcedSeparated 0.81 0.49, 1.29 0.4
    Widow 0.58 0.34, 0.96 0.040
Active Tobacco Use
    No — —
    Yes 0.61 0.42, 0.86 0.006
Charlson Comorbidity Index 1.01 0.99, 1.03 0.2
Immunocompromised 1.37 1.13, 1.67 0.002
Population Density 1.00 1.00, 1.00 0.2
Percent Republican 0.98 0.97, 0.98 <0.001
Total SVI 0.31 0.21, 0.47 <0.001
1 OR = Odds Ratio, CI = Confidence Interval 

performance::check_model(shingrix_full1, panel = TRUE)

SVI Quartiles

shingrix_full2 <- glm(full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_4,
                   family = binomial,
               data = shingrix_clean1) 
summary(shingrix_full2)

Call:
glm(formula = full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + 
    ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + 
    IC + pop_dens + r_pct + RPL_4, family = binomial, data = shingrix_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3071  -0.7703  -0.6308  -0.3852   2.3398  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -1.124e+00  4.159e-01  -2.701 0.006905 ** 
ibd_3UC                   1.300e-01  1.014e-01   1.282 0.199781    
ibd_3Unspecified         -2.157e-01  7.978e-01  -0.270 0.786862    
age_yrs                   1.590e-02  5.135e-03   3.095 0.001966 ** 
genderFemale              1.803e-01  9.741e-02   1.851 0.064172 .  
race_5Black              -5.500e-01  2.407e-01  -2.285 0.022340 *  
race_5Asian               3.862e-01  3.044e-01   1.269 0.204483    
race_5Native             -1.214e-01  7.022e-01  -0.173 0.862788    
race_5Other              -1.628e-01  3.234e-01  -0.503 0.614744    
ethnic_3Hispanic          4.662e-01  3.571e-01   1.306 0.191655    
lang_3Other              -7.571e-01  5.148e-01  -1.471 0.141377    
relig_affilNo             6.346e-02  1.043e-01   0.608 0.543049    
mstat_5Unknown           -1.431e-01  1.335e-01  -1.072 0.283733    
mstat_5Unmarried         -2.672e-01  1.433e-01  -1.865 0.062200 .  
mstat_5DivorcedSeparated -2.225e-01  2.435e-01  -0.914 0.360889    
mstat_5Widow             -5.423e-01  2.621e-01  -2.069 0.038519 *  
act_tobYes               -5.153e-01  1.839e-01  -2.802 0.005073 ** 
max_ch                    9.333e-03  8.148e-03   1.145 0.252050    
IC                        3.143e-01  1.011e-01   3.109 0.001875 ** 
pop_dens                 -3.461e-05  2.636e-05  -1.313 0.189303    
r_pct                    -2.318e-02  3.419e-03  -6.781  1.2e-11 ***
RPL_4Second              -2.448e-01  1.107e-01  -2.210 0.027080 *  
RPL_4Third               -4.969e-01  1.403e-01  -3.542 0.000398 ***
RPL_4Fourth              -8.249e-01  2.218e-01  -3.720 0.000200 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2875.7  on 2609  degrees of freedom
Residual deviance: 2740.1  on 2586  degrees of freedom
  (4855 observations deleted due to missingness)
AIC: 2788.1

Number of Fisher Scoring iterations: 4
broom::glance(shingrix_full2)
broom::tidy(shingrix_full2, exponentiate = TRUE)
model_performance(shingrix_full2)
# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
2788.101 | 2928.912 |     0.052 | 0.416 | 1.029 |    0.525 |  -175.945 |       3.833e-04 | 0.654
tbl_regression(shingrix_full2, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_4 ~ "SVI Quartile", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
IBD Type
    CD — —
    UC 1.14 0.93, 1.39 0.2
    Unspecified 0.81 0.12, 3.22 0.8
Age 1.02 1.01, 1.03 0.002
Gender
    Male — —
    Female 1.20 0.99, 1.45 0.064
Race
    White — —
    Black 0.58 0.35, 0.91 0.022
    Asian 1.47 0.80, 2.65 0.2
    Native 0.89 0.19, 3.20 0.9
    Other 0.85 0.44, 1.56 0.6
Ethnicity
    NonHispanic — —
    Hispanic 1.59 0.77, 3.17 0.2
Preferred Language
    English — —
    Other 0.47 0.15, 1.19 0.14
Any Religious Affiliation
    Yes — —
    No 1.07 0.87, 1.31 0.5
Marital Status
    Married — —
    Unknown 0.87 0.66, 1.12 0.3
    Unmarried 0.77 0.58, 1.01 0.062
    DivorcedSeparated 0.80 0.49, 1.27 0.4
    Widow 0.58 0.34, 0.96 0.039
Active Tobacco Use
    No — —
    Yes 0.60 0.41, 0.85 0.005
Charlson Comorbidity Index 1.01 0.99, 1.03 0.3
Immunocompromised 1.37 1.12, 1.67 0.002
Population Density 1.00 1.00, 1.00 0.2
Percent Republican 0.98 0.97, 0.98 <0.001
SVI Quartile
    First — —
    Second 0.78 0.63, 0.97 0.027
    Third 0.61 0.46, 0.80 <0.001
    Fourth 0.44 0.28, 0.67 <0.001
1 OR = Odds Ratio, CI = Confidence Interval 

performance::check_model(shingrix_full2, panel = TRUE)

All Themes

shingrix_full3 <- glm(full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_THEME1
                    + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
                   family = binomial,
               data = shingrix_clean1) 
summary(shingrix_full3)

Call:
glm(formula = full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + 
    ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + 
    IC + pop_dens + r_pct + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + 
    RPL_THEME4, family = binomial, data = shingrix_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3010  -0.7695  -0.6161  -0.3752   2.3850  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -1.096e+00  4.591e-01  -2.388  0.01694 *  
ibd_3UC                   1.214e-01  1.018e-01   1.192  0.23324    
ibd_3Unspecified         -1.582e-01  7.973e-01  -0.198  0.84276    
age_yrs                   1.561e-02  5.166e-03   3.021  0.00252 ** 
genderFemale              1.735e-01  9.772e-02   1.775  0.07589 .  
race_5Black              -4.768e-01  2.410e-01  -1.978  0.04788 *  
race_5Asian               2.815e-01  3.066e-01   0.918  0.35859    
race_5Native             -9.599e-02  7.034e-01  -0.136  0.89146    
race_5Other              -1.664e-01  3.250e-01  -0.512  0.60874    
ethnic_3Hispanic          4.400e-01  3.586e-01   1.227  0.21984    
lang_3Other              -7.794e-01  5.149e-01  -1.514  0.13006    
relig_affilNo             6.467e-02  1.049e-01   0.616  0.53774    
mstat_5Unknown           -1.275e-01  1.342e-01  -0.950  0.34219    
mstat_5Unmarried         -2.527e-01  1.441e-01  -1.753  0.07952 .  
mstat_5DivorcedSeparated -1.860e-01  2.445e-01  -0.761  0.44679    
mstat_5Widow             -5.317e-01  2.627e-01  -2.024  0.04296 *  
act_tobYes               -4.953e-01  1.845e-01  -2.685  0.00726 ** 
max_ch                    9.513e-03  8.186e-03   1.162  0.24517    
IC                        3.100e-01  1.016e-01   3.052  0.00227 ** 
pop_dens                 -2.899e-05  2.757e-05  -1.052  0.29298    
r_pct                    -1.801e-02  4.046e-03  -4.452 8.51e-06 ***
RPL_THEME1               -5.728e-01  2.974e-01  -1.926  0.05411 .  
RPL_THEME2               -8.025e-01  2.600e-01  -3.086  0.00203 ** 
RPL_THEME3                8.795e-02  2.011e-01   0.437  0.66193    
RPL_THEME4               -1.249e-01  2.202e-01  -0.567  0.57055    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2875.7  on 2609  degrees of freedom
Residual deviance: 2721.6  on 2585  degrees of freedom
  (4855 observations deleted due to missingness)
AIC: 2771.6

Number of Fisher Scoring iterations: 4
broom::glance(shingrix_full3)
broom::tidy(shingrix_full3, exponentiate = TRUE)
model_performance(shingrix_full3)
# Indices of model performance

AIC      |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
------------------------------------------------------------------------------------------------
2771.594 | 2918.271 |     0.059 | 0.414 | 1.026 |    0.521 |  -176.657 |       3.938e-04 | 0.657
tbl_regression(shingrix_full3, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
IBD Type
    CD — —
    UC 1.13 0.93, 1.38 0.2
    Unspecified 0.85 0.13, 3.40 0.8
Age 1.02 1.01, 1.03 0.003
Gender
    Male — —
    Female 1.19 0.98, 1.44 0.076
Race
    White — —
    Black 0.62 0.38, 0.98 0.048
    Asian 1.33 0.72, 2.40 0.4
    Native 0.91 0.19, 3.30 0.9
    Other 0.85 0.43, 1.56 0.6
Ethnicity
    NonHispanic — —
    Hispanic 1.55 0.75, 3.09 0.2
Preferred Language
    English — —
    Other 0.46 0.15, 1.16 0.13
Any Religious Affiliation
    Yes — —
    No 1.07 0.87, 1.31 0.5
Marital Status
    Married — —
    Unknown 0.88 0.67, 1.14 0.3
    Unmarried 0.78 0.58, 1.03 0.080
    DivorcedSeparated 0.83 0.50, 1.32 0.4
    Widow 0.59 0.34, 0.97 0.043
Active Tobacco Use
    No — —
    Yes 0.61 0.42, 0.87 0.007
Charlson Comorbidity Index 1.01 0.99, 1.03 0.2
Immunocompromised 1.36 1.12, 1.66 0.002
Population Density 1.00 1.00, 1.00 0.3
Percent Republican 0.98 0.97, 0.99 <0.001
Soceioeconomic Status 0.56 0.31, 1.01 0.054
Household Composition 0.45 0.27, 0.74 0.002
Minority Status and Language 1.09 0.74, 1.62 0.7
Housing and Transportation 0.88 0.57, 1.36 0.6
1 OR = Odds Ratio, CI = Confidence Interval 

performance::check_model(shingrix_full3, panel = TRUE)

Mediation Analysis?

---
title: "Shingrix Models"
output:
  html_notebook:
    themes: paper
    toc: yes
    toc_float: yes
editor_options:
  chunk_output_type: inline
date: "2022-11-27"
---
# Load Packages 
```{r}
library(tidyverse)
library(codebookr)
library(summarytools)
library(broom) 
library(performance)
library(gt)
library(gtsummary)
library(janitor)
library(forcats)
library(here)
library(margins)
library(ggplot2)
```

# Import Data 
```{r}
load(file = "~/Desktop/R-Code/SDOH_Vax/vax_clean1.rda")


```

# Data cleaning
```{r}
shingrix1 <- vax_clean1[ which(vax_clean1$age_yrs > 49.999), ]

shingrix1 %>% 
mutate(full_shingrix = case_when(total_shingrix>= 2 ~ '1',TRUE ~ "0")) -> shingrix_clean1

shingrix_clean1$full_shingrix = as.numeric(shingrix_clean1$full_shingrix)
```

# Baseline Characteristics 
```{r}
shingrix_clean1 %>% 
  dplyr::select(ibd_3, age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, act_tob, max_ch, IC, pop_dens,r_pct, total_shingrix, full_shingrix, RPL_THEMES, RPL_4, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4) -> shingrix_baseline
shingrix_baseline %>% tbl_summary(label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Primary 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", pop_dens ~ "Population Density", RPL_4 ~ "SVI Quartiles", r_pct ~ "Percent Republican", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", ibd_3 ~ "IBD Type", IC ~ "Immunocompromised", total_shingrix ~ "Total Shingrix Vaccines", full_shingrix ~ "Fully Vaccinated"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")
```
# Baseline characteristics by SVI Quartile 
```{r}
shingrix_baseline %>% 
tbl_summary(by = RPL_4,
         label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "Primary 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", pop_dens ~ "Population Density", RPL_4 ~ "SVI Quartiles", r_pct ~ "Percent Republican", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", ibd_3 ~ "IBD Type", IC ~ "Immunocompromised", total_shingrix ~ "Total Shingrix Vaccines", full_shingrix ~ "Fully Vaccinated"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)") %>% add_p()
```

# Shingrix Count (negative binomial models) {.tabset}

## SVI Continuous 
```{r}
shingrix_nb <- glm.nb(total_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_THEMES,
               data = shingrix_clean1) 
summary(shingrix_nb)
broom::glance(shingrix_nb)
broom::tidy(shingrix_nb, exponentiate = TRUE)
model_performance(shingrix_nb)
tbl_regression(shingrix_nb, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_THEMES ~ "Total SVI", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)

# NB Residual Plot
shingrix_nb_res <- resid(shingrix_nb)
plot(fitted(shingrix_nb), shingrix_nb_res, col='steelblue', pch=16,
     xlab='Predicted Vaccines', ylab='Standardized Residuals', main='Negative Binomial')
abline(0,0)
# NB regression more appropriate because residuals of the model are smaller 

# Likelihood ratio test 
pchisq(2 * (logLik(shingrix_nb) - logLik(shingrix_nb)), df = 1, lower.tail = FALSE)
# p-value of loglik is < 0.05 so NB regression is the more appropriate model 

performance::check_model(shingrix_nb, panel = TRUE)
```
## SVI Quartiles 
```{r}
shingrix_nb2 <- glm.nb(total_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_4,
               data = shingrix_clean1) 
summary(shingrix_nb2)
broom::glance(shingrix_nb2)
broom::tidy(shingrix_nb2, exponentiate = TRUE)
model_performance(shingrix_nb2)
tbl_regression(shingrix_nb2, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_4 ~ "SVI Quartile", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)

performance::check_model(shingrix_nb2, panel = TRUE)
```
## All themes 
```{r}
shingrix_nb3 <- glm.nb(total_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_THEME1
                    + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
               data = shingrix_clean1) 
summary(shingrix_nb3)
broom::glance(shingrix_nb3)
broom::tidy(shingrix_nb3, exponentiate = TRUE)
model_performance(shingrix_nb3)
tbl_regression(shingrix_nb3, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)

performance::check_model(shingrix_nb3, panel = TRUE)
```


# Fully Vaccinated (logistic) {.tabset}
## SVI Continuous 
```{r}
shingrix_full1 <- glm(full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_THEMES,
                   family = binomial, 
               data = shingrix_clean1) 
summary(shingrix_full1)
broom::glance(shingrix_full1)
broom::tidy(shingrix_full1, exponentiate = TRUE)
model_performance(shingrix_full1)
tbl_regression(shingrix_full1, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_THEMES ~ "Total SVI", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)

performance::check_model(shingrix_full1, panel = TRUE)

```

## SVI Quartiles 
```{r}
shingrix_full2 <- glm(full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_4,
                   family = binomial,
               data = shingrix_clean1) 
summary(shingrix_full2)
broom::glance(shingrix_full2)
broom::tidy(shingrix_full2, exponentiate = TRUE)
model_performance(shingrix_full2)
tbl_regression(shingrix_full2, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_4 ~ "SVI Quartile", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)

performance::check_model(shingrix_full2, panel = TRUE)
```

## All Themes 
```{r}
shingrix_full3 <- glm(full_shingrix ~ ibd_3 + age_yrs + gender + race_5 + ethnic_3 + lang_3 + relig_affil + mstat_5 + act_tob + max_ch + IC + pop_dens + r_pct + RPL_THEME1
                    + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
                   family = binomial,
               data = shingrix_clean1) 
summary(shingrix_full3)
broom::glance(shingrix_full3)
broom::tidy(shingrix_full3, exponentiate = TRUE)
model_performance(shingrix_full3)
tbl_regression(shingrix_full3, label = list(age_yrs ~ "Age", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "Preferred Language", race_5 ~ "Race", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", r_pct ~ "Percent Republican", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density", act_tob ~"Active Tobacco Use", max_ch ~ "Charlson Comorbidity Index", IC ~ "Immunocompromised", ibd_3 ~ "IBD Type"), exponentiate = TRUE)

performance::check_model(shingrix_full3, panel = TRUE)
```
# Mediation Analysis?
