2022.10.18 Vax IBD

Load Packages

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

Import Data

vax_deid <- read_csv("~/Desktop/R-Code/SDOH_Vax/ibd_vax.csv", show_col_types = FALSE)

ages <- read_csv("~/Desktop/R-Code/SDOH_Vax/sbj_id_age_years.csv", show_col_types = FALSE)

vax_deid_age <- left_join(vax_deid, ages)

Joining, by = "sbj_id"

Data Cleaning

RPL_THEMES Erroneous values

vax_deid_age %>%
  select(age_yrs, PATIENT_GENDER_CD, PATIENT_RACE_DESC, PATIENT_ETHNIC_GROUP_DESC, PATIENT_LANGUAGE_DESC, PATIENT_RELIGION_DESC, PATIENT_MARITAL_STATUS_DESC, STATE, AREA_SQMI, E_TOTPOP, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4, F_THEME1, F_THEME2, F_THEME3, F_THEME4, F_TOTAL, r_pct, flu_2015, flu_2016, flu_2017, flu_2018, flu_2019, flu_2020, flu_2021, flu_2022, total_flu, prevnar, pvax, any_pneum, both_pneum, total_cov_vax, total_shingrix) -> vaxdf
vaxdf %>%
mutate(RPL_THEMES = na_if(RPL_THEMES, "-999")) %>%
mutate(RPL_THEMES = na_if(RPL_THEMES, "0")) %>%
mutate(RPL_THEME1 = na_if(RPL_THEME1, "-999")) %>%
mutate(RPL_THEME1 = na_if(RPL_THEME1, "0")) %>%
mutate(RPL_THEME2 = na_if(RPL_THEME2, "-999")) %>%
mutate(RPL_THEME2 = na_if(RPL_THEME2, "0")) %>%
mutate(RPL_THEME3 = na_if(RPL_THEME3, "-999")) %>%
mutate(RPL_THEME3 = na_if(RPL_THEME3, "0")) %>%
mutate(RPL_THEME4 = na_if(RPL_THEME4, "-999")) %>%
mutate(RPL_THEME4 = na_if(RPL_THEME4, "0")) %>% 
mutate(F_TOTAL = na_if(F_TOTAL, "-999")) %>%
mutate(F_THEME1 = na_if(F_THEME1, "-999")) %>%
mutate(F_THEME2 = na_if(F_THEME2, "-999")) %>%
mutate(F_THEME3 = na_if(F_THEME3, "-999")) %>%
mutate(F_THEME4 = na_if(F_THEME4, "-999")) -> vaxdfTheme

Marital status

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

Religion

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

Race

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

Gender

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

Language

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

Ethnicity

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

Population density

vaxdfThemeMsRelRaGLEth %>% 
  mutate(pop_dens=E_TOTPOP/AREA_SQMI) -> vaxdfThemeMsRelRaGLEthPop

Codebook

vaxdfThemeMsRelRaGLEthPop %>% 
select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, STATE,AREA_SQMI, E_TOTPOP, pop_dens, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4, F_TOTAL, F_THEME1, F_THEME2, F_THEME3, F_THEME4, r_pct, flu_2015, flu_2016, flu_2017, flu_2018, flu_2019, flu_2020, flu_2021, flu_2022, total_flu, prevnar, pvax, any_pneum, any_pneum, total_cov_vax, total_shingrix) -> vax_clean1
print(dfSummary(vax_clean1), method = 'render')

Data Frame Summary

vax_clean1

Dimensions: 15245 x 36
Duplicates: 0

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid	Missing
1	age_yrs [numeric]	Mean (sd) : 49.3 (19.5) min ≤ med ≤ max: 1 ≤ 49.3 ≤ 108.3 IQR (CV) : 31.5 (0.4)	11557 distinct values		15245 (100.0%)	0 (0.0%)
2	gender [factor]	1. male 2. female	6973 ( 45.7% ) 8272 ( 54.3% )		15245 (100.0%)	0 (0.0%)
3	race_5 [factor]	1. WHITE OR CAUCASIAN 2. BLACK OR AFRICAN AMERICAN 3. Other 4. ASIAN 5. AMERICAN INDIAN AND ALASK	13230 ( 86.8% ) 946 ( 6.2% ) 638 ( 4.2% ) 375 ( 2.5% ) 56 ( 0.4% )		15245 (100.0%)	0 (0.0%)
4	ethnic_3 [factor]	1. NON-HISPANIC 2. UNKNOWN 3. CHOOSE NOT TO DISCLOSE 4. HISPANIC	14401 ( 97.9% ) 0 ( 0.0% ) 0 ( 0.0% ) 302 ( 2.1% )		14703 (96.4%)	542 (3.6%)
5	lang_3 [factor]	1. English 2. Other	15081 ( 98.9% ) 164 ( 1.1% )		15245 (100.0%)	0 (0.0%)
6	relig_affil [factor]	1. yes 2. no 3. PATIENT REFUSED 4. UNKNOWN	8211 ( 57.4% ) 6085 ( 42.6% ) 0 ( 0.0% ) 0 ( 0.0% )		14296 (93.8%)	949 (6.2%)
7	mstat_5 [factor]	1. married 2. unknown 3. unmarried 4. div_sep 5. widow	6198 ( 40.7% ) 3236 ( 21.2% ) 4997 ( 32.8% ) 507 ( 3.3% ) 307 ( 2.0% )		15245 (100.0%)	0 (0.0%)
8	STATE [character]	1. FLORIDA 2. ILLINOIS 3. INDIANA 4. MARYLAND 5. MICHIGAN 6. NEW YORK 7. NORTH CAROLINA 8. OHIO 9. PENNSYLVANIA 10. TEXAS	24 ( 0.2% ) 40 ( 0.3% ) 47 ( 0.3% ) 16 ( 0.1% ) 14103 ( 96.5% ) 38 ( 0.3% ) 10 ( 0.1% ) 316 ( 2.2% ) 14 ( 0.1% ) 7 ( 0.0% )		14615 (95.9%)	630 (4.1%)
9	AREA_SQMI [numeric]	Mean (sd) : 15.2 (31.7) min ≤ med ≤ max: 0 ≤ 2.9 ≤ 586.8 IQR (CV) : 13.5 (2.1)	2642 distinct values		14615 (95.9%)	630 (4.1%)
10	E_TOTPOP [numeric]	Mean (sd) : 4368.6 (1703.2) min ≤ med ≤ max: 182 ≤ 4129 ≤ 20695 IQR (CV) : 2151 (0.4)	2110 distinct values		14615 (95.9%)	630 (4.1%)
11	pop_dens [numeric]	Mean (sd) : 2291.1 (4013.3) min ≤ med ≤ max: 2.5 ≤ 1523.4 ≤ 166336.1 IQR (CV) : 2988 (1.8)	2642 distinct values		14615 (95.9%)	630 (4.1%)
12	RPL_THEMES [numeric]	Mean (sd) : 0.4 (0.3) min ≤ med ≤ max: 0 ≤ 0.3 ≤ 1 IQR (CV) : 0.4 (0.7)	2506 distinct values		14607 (95.8%)	638 (4.2%)
13	RPL_THEME1 [numeric]	Mean (sd) : 0.3 (0.3) min ≤ med ≤ max: 0 ≤ 0.3 ≤ 1 IQR (CV) : 0.4 (0.7)	2447 distinct values		14557 (95.5%)	688 (4.5%)
14	RPL_THEME2 [numeric]	Mean (sd) : 0.4 (0.3) min ≤ med ≤ max: 0 ≤ 0.3 ≤ 1 IQR (CV) : 0.4 (0.7)	2421 distinct values		14608 (95.8%)	637 (4.2%)
15	RPL_THEME3 [numeric]	Mean (sd) : 0.5 (0.3) min ≤ med ≤ max: 0 ≤ 0.5 ≤ 1 IQR (CV) : 0.5 (0.6)	1810 distinct values		14615 (95.9%)	630 (4.1%)
16	RPL_THEME4 [numeric]	Mean (sd) : 0.4 (0.3) min ≤ med ≤ max: 0 ≤ 0.4 ≤ 1 IQR (CV) : 0.5 (0.7)	2428 distinct values		14585 (95.7%)	660 (4.3%)
17	F_TOTAL [numeric]	Mean (sd) : 0.7 (1.2) min ≤ med ≤ max: 0 ≤ 0 ≤ 10 IQR (CV) : 1 (1.6)	11 distinct values		14607 (95.8%)	638 (4.2%)
18	F_THEME1 [numeric]	Mean (sd) : 0.1 (0.4) min ≤ med ≤ max: 0 ≤ 0 ≤ 4 IQR (CV) : 0 (5.1)	0 : 13902 ( 95.2% ) 1 : 384 ( 2.6% ) 2 : 151 ( 1.0% ) 3 : 107 ( 0.7% ) 4 : 64 ( 0.4% )		14608 (95.8%)	637 (4.2%)
19	F_THEME2 [numeric]	Mean (sd) : 0.2 (0.5) min ≤ med ≤ max: 0 ≤ 0 ≤ 3 IQR (CV) : 0 (2.3)	0 : 11956 ( 81.8% ) 1 : 2271 ( 15.5% ) 2 : 363 ( 2.5% ) 3 : 18 ( 0.1% )		14608 (95.8%)	637 (4.2%)
20	F_THEME3 [numeric]	Mean (sd) : 0.1 (0.3) min ≤ med ≤ max: 0 ≤ 0 ≤ 2 IQR (CV) : 0 (2.9)	0 : 13064 ( 89.4% ) 1 : 1549 ( 10.6% ) 2 : 2 ( 0.0% )		14615 (95.9%)	630 (4.1%)
21	F_THEME4 [numeric]	Mean (sd) : 0.3 (0.6) min ≤ med ≤ max: 0 ≤ 0 ≤ 4 IQR (CV) : 1 (1.8)	0 : 10502 ( 71.9% ) 1 : 3444 ( 23.6% ) 2 : 559 ( 3.8% ) 3 : 86 ( 0.6% ) 4 : 16 ( 0.1% )		14607 (95.8%)	638 (4.2%)
22	r_pct [numeric]	Mean (sd) : 44.7 (18) min ≤ med ≤ max: 1.2 ≤ 47.9 ≤ 84.8 IQR (CV) : 24.7 (0.4)	2625 distinct values		12974 (85.1%)	2271 (14.9%)
23	flu_2015 [numeric]	Min : 0 Mean : 0.3 Max : 1	0 : 10848 ( 71.2% ) 1 : 4397 ( 28.8% )		15245 (100.0%)	0 (0.0%)
24	flu_2016 [numeric]	Min : 0 Mean : 0.3 Max : 1	0 : 10650 ( 69.9% ) 1 : 4595 ( 30.1% )		15245 (100.0%)	0 (0.0%)
25	flu_2017 [numeric]	Min : 0 Mean : 0.3 Max : 1	0 : 10791 ( 70.8% ) 1 : 4454 ( 29.2% )		15245 (100.0%)	0 (0.0%)
26	flu_2018 [numeric]	Min : 0 Mean : 0.3 Max : 1	0 : 10681 ( 70.1% ) 1 : 4564 ( 29.9% )		15245 (100.0%)	0 (0.0%)
27	flu_2019 [numeric]	Min : 0 Mean : 0.3 Max : 1	0 : 10713 ( 70.3% ) 1 : 4532 ( 29.7% )		15245 (100.0%)	0 (0.0%)
28	flu_2020 [numeric]	Min : 0 Mean : 0.3 Max : 1	0 : 10632 ( 69.7% ) 1 : 4613 ( 30.3% )		15245 (100.0%)	0 (0.0%)
29	flu_2021 [numeric]	Min : 0 Mean : 0.2 Max : 1	0 : 12049 ( 79.0% ) 1 : 3196 ( 21.0% )		15245 (100.0%)	0 (0.0%)
30	flu_2022 [numeric]	Min : 0 Mean : 0 Max : 1	0 : 15081 ( 98.9% ) 1 : 164 ( 1.1% )		15245 (100.0%)	0 (0.0%)
31	total_flu [numeric]	Mean (sd) : 2 (2.3) min ≤ med ≤ max: 0 ≤ 1 ≤ 8 IQR (CV) : 4 (1.2)	0 : 6530 ( 42.8% ) 1 : 2014 ( 13.2% ) 2 : 1376 ( 9.0% ) 3 : 1244 ( 8.2% ) 4 : 1162 ( 7.6% ) 5 : 1058 ( 6.9% ) 6 : 955 ( 6.3% ) 7 : 899 ( 5.9% ) 8 : 7 ( 0.0% )		15245 (100.0%)	0 (0.0%)
32	prevnar [numeric]	Mean (sd) : 0.3 (0.5) min ≤ med ≤ max: 0 ≤ 0 ≤ 8 IQR (CV) : 1 (1.7)	0 : 10734 ( 70.4% ) 1 : 4238 ( 27.8% ) 2 : 210 ( 1.4% ) 3 : 32 ( 0.2% ) 4 : 24 ( 0.2% ) 5 : 4 ( 0.0% ) 6 : 1 ( 0.0% ) 7 : 1 ( 0.0% ) 8 : 1 ( 0.0% )		15245 (100.0%)	0 (0.0%)
33	pvax [numeric]	Mean (sd) : 0.3 (0.5) min ≤ med ≤ max: 0 ≤ 0 ≤ 6 IQR (CV) : 1 (1.8)	0 : 11257 ( 73.8% ) 1 : 3693 ( 24.2% ) 2 : 280 ( 1.8% ) 3 : 12 ( 0.1% ) 5 : 1 ( 0.0% ) 6 : 2 ( 0.0% )		15245 (100.0%)	0 (0.0%)
34	any_pneum [numeric]	Mean (sd) : 0.6 (0.9) min ≤ med ≤ max: 0 ≤ 0 ≤ 9 IQR (CV) : 1 (1.5)	0 : 9506 ( 62.4% ) 1 : 2820 ( 18.5% ) 2 : 2517 ( 16.5% ) 3 : 310 ( 2.0% ) 4 : 65 ( 0.4% ) 5 : 16 ( 0.1% ) 6 : 7 ( 0.0% ) 7 : 2 ( 0.0% ) 8 : 1 ( 0.0% ) 9 : 1 ( 0.0% )		15245 (100.0%)	0 (0.0%)
35	total_cov_vax [numeric]	Mean (sd) : 1.4 (1.6) min ≤ med ≤ max: 0 ≤ 0 ≤ 7 IQR (CV) : 3 (1.1)	0 : 7766 ( 50.9% ) 1 : 434 ( 2.8% ) 2 : 2207 ( 14.5% ) 3 : 3140 ( 20.6% ) 4 : 1489 ( 9.8% ) 5 : 161 ( 1.1% ) 6 : 39 ( 0.3% ) 7 : 9 ( 0.1% )		15245 (100.0%)	0 (0.0%)
36	total_shingrix [numeric]	Mean (sd) : 0.2 (0.6) min ≤ med ≤ max: 0 ≤ 0 ≤ 5 IQR (CV) : 0 (3.1)	0 : 13659 ( 89.6% ) 1 : 367 ( 2.4% ) 2 : 1116 ( 7.3% ) 3 : 91 ( 0.6% ) 4 : 10 ( 0.1% ) 5 : 2 ( 0.0% )		15245 (100.0%)	0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-10-20

Patient Characteristics

Baseline Characteristics

vax_clean1 %>% 
  select(age_yrs, gender, race_5, ethnic_3, lang_3, relig_affil, mstat_5, pop_dens, RPL_THEMES, RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4, flu_2021, total_flu, any_pneum, total_cov_vax, total_shingrix) -> baseline
baseline %>% tbl_summary(label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", 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"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")

Characteristic	N = 15,2451
Age	49 (19)
Gender
male	6,973 (46%)
female	8,272 (54%)
Race
WHITE OR CAUCASIAN	13,230 (87%)
BLACK OR AFRICAN AMERICAN	946 (6.2%)
Other	638 (4.2%)
ASIAN	375 (2.5%)
AMERICAN INDIAN AND ALASKA NATIVE	56 (0.4%)
Ethnicity
NON-HISPANIC	14,401 (98%)
UNKNOWN	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)
HISPANIC	302 (2.1%)
(Missing)	542
English Speaking
English	15,081 (99%)
Other	164 (1.1%)
Any Religious Affiliation
yes	8,211 (57%)
no	6,085 (43%)
PATIENT REFUSED	0 (0%)
UNKNOWN	0 (0%)
(Missing)	949
Marital Status
married	6,198 (41%)
unknown	3,236 (21%)
unmarried	4,997 (33%)
div_sep	507 (3.3%)
widow	307 (2.0%)
Population Density	2,291 (4,013)
(Missing)	630
Total SVI	0.37 (0.26)
(Missing)	638
Soceioeconomic Status	0.35 (0.25)
(Missing)	688
Household Composition	0.39 (0.27)
(Missing)	637
Minority Status and Language	0.48 (0.29)
(Missing)	630
Housing and Transportation	0.44 (0.29)
(Missing)	660
flu_2021	3,196 (21%)
total_flu
0	6,530 (43%)
1	2,014 (13%)
2	1,376 (9.0%)
3	1,244 (8.2%)
4	1,162 (7.6%)
5	1,058 (6.9%)
6	955 (6.3%)
7	899 (5.9%)
8	7 (<0.1%)
any_pneum	0.60 (0.89)
total_cov_vax
0	7,766 (51%)
1	434 (2.8%)
2	2,207 (14%)
3	3,140 (21%)
4	1,489 (9.8%)
5	161 (1.1%)
6	39 (0.3%)
7	9 (<0.1%)
total_shingrix
0	13,659 (90%)
1	367 (2.4%)
2	1,116 (7.3%)
3	91 (0.6%)
4	10 (<0.1%)
5	2 (<0.1%)
1 Mean (SD); n (%)

Baseline characteristics by Flu 2021

baseline %>% tbl_summary(by = flu_2021,
         label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", pop_dens ~ "Population Density"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)") %>% add_p()

There was an error in 'add_p()/add_difference()' for variable 'total_flu', p-value omitted:
Error in stats::fisher.test(c(6, 0, 2, 0, 6, 1, 2, 0, 0, 1, 6, 2, 0, 0, : FEXACT error 6 (f5xact).  LDKEY=526 is too small for this problem: kval=37321247.
Try increasing the size of the workspace.
There was an error in 'add_p()/add_difference()' for variable 'total_cov_vax', p-value omitted:
Error in stats::fisher.test(c(2, 0, 4, 0, 0, 1, 3, 0, 0, 0, 1, 0, 0, 2, : FEXACT error 7(location). LDSTP=15780 is too small for this problem,
  (pastp=120.092, ipn_0:=ipoin[itp=402]=289, stp[ipn_0]=109.428).
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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : FEXACT error 7(location). LDSTP=15780 is too small for this problem,
  (pastp=291.353, ipn_0:=ipoin[itp=483]=3585, stp[ipn_0]=275.121).
Increase workspace or consider using 'simulate.p.value=TRUE'

Characteristic	0, N = 12,0491	1, N = 3,1961	p-value2
Age	49 (19)	51 (19)	<0.001
Gender			<0.001
male	5,604 (47%)	1,369 (43%)
female	6,445 (53%)	1,827 (57%)
Race			<0.001
WHITE OR CAUCASIAN	10,440 (87%)	2,790 (87%)
BLACK OR AFRICAN AMERICAN	786 (6.5%)	160 (5.0%)
Other	533 (4.4%)	105 (3.3%)
ASIAN	246 (2.0%)	129 (4.0%)
AMERICAN INDIAN AND ALASKA NATIVE	44 (0.4%)	12 (0.4%)
Ethnicity			0.8
NON-HISPANIC	11,357 (98%)	3,044 (98%)
UNKNOWN	0 (0%)	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)	0 (0%)
HISPANIC	236 (2.0%)	66 (2.1%)
(Missing)	456	86
English Speaking			0.070
English	11,910 (99%)	3,171 (99%)
Other	139 (1.2%)	25 (0.8%)
Any Religious Affiliation			0.013
yes	6,376 (57%)	1,835 (59%)
no	4,831 (43%)	1,254 (41%)
PATIENT REFUSED	0 (0%)	0 (0%)
UNKNOWN	0 (0%)	0 (0%)
(Missing)	842	107
Marital Status			<0.001
married	4,707 (39%)	1,491 (47%)
unknown	2,605 (22%)	631 (20%)
unmarried	4,103 (34%)	894 (28%)
div_sep	405 (3.4%)	102 (3.2%)
widow	229 (1.9%)	78 (2.4%)
Population Density	2,273 (4,243)	2,356 (3,022)	<0.001
(Missing)	563	67
Total SVI	0.39 (0.26)	0.30 (0.23)	<0.001
(Missing)	570	68
Soceioeconomic Status	0.37 (0.26)	0.28 (0.23)	<0.001
(Missing)	598	90
Household Composition	0.41 (0.27)	0.32 (0.24)	<0.001
(Missing)	569	68
Minority Status and Language	0.47 (0.28)	0.51 (0.29)	<0.001
(Missing)	563	67
Housing and Transportation	0.45 (0.29)	0.39 (0.28)	<0.001
(Missing)	582	78
total_flu
0	6,530 (54%)	0 (0%)
1	1,873 (16%)	141 (4.4%)
2	1,103 (9.2%)	273 (8.5%)
3	914 (7.6%)	330 (10%)
4	734 (6.1%)	428 (13%)
5	527 (4.4%)	531 (17%)
6	346 (2.9%)	609 (19%)
7	22 (0.2%)	877 (27%)
8	0 (0%)	7 (0.2%)
any_pneum	0.43 (0.76)	1.27 (1.02)	<0.001
total_cov_vax
0	7,562 (63%)	204 (6.4%)
1	362 (3.0%)	72 (2.3%)
2	1,758 (15%)	449 (14%)
3	1,776 (15%)	1,364 (43%)
4	559 (4.6%)	930 (29%)
5	29 (0.2%)	132 (4.1%)
6	3 (<0.1%)	36 (1.1%)
7	0 (0%)	9 (0.3%)
total_shingrix
0	11,503 (95%)	2,156 (67%)
1	187 (1.6%)	180 (5.6%)
2	332 (2.8%)	784 (25%)
3	24 (0.2%)	67 (2.1%)
4	3 (<0.1%)	7 (0.2%)
5	0 (0%)	2 (<0.1%)
1 Mean (SD); n (%)
2 Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test

Baseline characteristics by pneumonia

baseline %>% tbl_summary(by = any_pneum,
         label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", pop_dens ~ "Population Density"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)") %>% add_p()

There was an error in 'add_p()/add_difference()' for variable 'gender', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, : FEXACT error 7(location). LDSTP=15780 is too small for this problem,
  (pastp=261.362, ipn_0:=ipoin[itp=270]=4455, stp[ipn_0]=261.333).
Increase workspace or consider using 'simulate.p.value=TRUE'
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, NA, 1L, 2L, 2L, 1L, : FEXACT error 7(location). LDSTP=15960 is too small for this problem,
  (pastp=41.9351, ipn_0:=ipoin[itp=384]=80, stp[ipn_0]=39.532).
Increase workspace or consider using 'simulate.p.value=TRUE'
There was an error in 'add_p()/add_difference()' for variable 'mstat_5', p-value omitted:
Error in stats::fisher.test(structure(c(2L, 1L, 2L, 1L, 2L, 2L, 2L, 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 'flu_2021', p-value omitted:
Error in stats::fisher.test(c(0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, : FEXACT error 7(location). LDSTP=15780 is too small for this problem,
  (pastp=40.9276, ipn_0:=ipoin[itp=439]=1112, stp[ipn_0]=35.7199).
Increase workspace or consider using 'simulate.p.value=TRUE'
There was an error in 'add_p()/add_difference()' for variable 'total_flu', p-value omitted:
Error in stats::fisher.test(c(6, 0, 2, 0, 6, 1, 2, 0, 0, 1, 6, 2, 0, 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.
There was an error in 'add_p()/add_difference()' for variable 'total_cov_vax', p-value omitted:
Error in stats::fisher.test(c(2, 0, 4, 0, 0, 1, 3, 0, 0, 0, 1, 0, 0, 2, : 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 'total_shingrix', p-value omitted:
Error in stats::fisher.test(c(0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : FEXACT error 501.
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	0, N = 9,5061	1, N = 2,8201	2, N = 2,5171	3, N = 3101	4, N = 651	5, N = 161	6, N = 71	7, N = 21	8, N = 11	9, N = 11	p-value2
Age	48 (19)	52 (20)	50 (19)	56 (18)	50 (27)	46 (33)	63 (27)	41 (51)	3 (NA)	69 (NA)	<0.001
Gender
male	4,258 (45%)	1,325 (47%)	1,185 (47%)	161 (52%)	30 (46%)	8 (50%)	4 (57%)	1 (50%)	0 (0%)	1 (100%)
female	5,248 (55%)	1,495 (53%)	1,332 (53%)	149 (48%)	35 (54%)	8 (50%)	3 (43%)	1 (50%)	1 (100%)	0 (0%)
Race
WHITE OR CAUCASIAN	8,205 (86%)	2,441 (87%)	2,232 (89%)	268 (86%)	59 (91%)	14 (88%)	7 (100%)	2 (100%)	1 (100%)	1 (100%)
BLACK OR AFRICAN AMERICAN	604 (6.4%)	187 (6.6%)	133 (5.3%)	19 (6.1%)	2 (3.1%)	1 (6.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Other	437 (4.6%)	108 (3.8%)	78 (3.1%)	12 (3.9%)	2 (3.1%)	1 (6.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
ASIAN	224 (2.4%)	74 (2.6%)	67 (2.7%)	8 (2.6%)	2 (3.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
AMERICAN INDIAN AND ALASKA NATIVE	36 (0.4%)	10 (0.4%)	7 (0.3%)	3 (1.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Ethnicity											0.8
NON-HISPANIC	8,922 (98%)	2,692 (98%)	2,402 (98%)	296 (99%)	62 (97%)	16 (100%)	7 (100%)	2 (100%)	1 (100%)	1 (100%)
UNKNOWN	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
HISPANIC	187 (2.1%)	60 (2.2%)	50 (2.0%)	3 (1.0%)	2 (3.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
(Missing)	397	68	65	11	1	0	0	0	0	0
English Speaking											0.025
English	9,398 (99%)	2,781 (99%)	2,504 (99%)	307 (99%)	65 (100%)	15 (94%)	7 (100%)	2 (100%)	1 (100%)	1 (100%)
Other	108 (1.1%)	39 (1.4%)	13 (0.5%)	3 (1.0%)	0 (0%)	1 (6.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Any Religious Affiliation
yes	4,913 (56%)	1,620 (60%)	1,444 (59%)	182 (60%)	32 (52%)	13 (81%)	3 (43%)	2 (100%)	1 (100%)	1 (100%)
no	3,858 (44%)	1,084 (40%)	985 (41%)	121 (40%)	30 (48%)	3 (19%)	4 (57%)	0 (0%)	0 (0%)	0 (0%)
PATIENT REFUSED	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
UNKNOWN	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
(Missing)	735	116	88	7	3	0	0	0	0	0
Marital Status
married	3,676 (39%)	1,207 (43%)	1,107 (44%)	168 (54%)	30 (46%)	7 (44%)	2 (29%)	1 (50%)	0 (0%)	0 (0%)
unknown	2,179 (23%)	516 (18%)	475 (19%)	48 (15%)	13 (20%)	2 (12%)	1 (14%)	0 (0%)	1 (100%)	1 (100%)
unmarried	3,180 (33%)	904 (32%)	812 (32%)	72 (23%)	20 (31%)	6 (38%)	2 (29%)	1 (50%)	0 (0%)	0 (0%)
div_sep	312 (3.3%)	104 (3.7%)	74 (2.9%)	16 (5.2%)	1 (1.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
widow	159 (1.7%)	89 (3.2%)	49 (1.9%)	6 (1.9%)	1 (1.5%)	1 (6.2%)	2 (29%)	0 (0%)	0 (0%)	0 (0%)
Population Density	2,303 (4,465)	2,266 (3,521)	2,266 (2,519)	2,370 (4,172)	2,309 (2,257)	2,651 (2,273)	1,279 (1,263)	2,373 (178)	4,275 (NA)	1,034 (NA)	0.3
(Missing)	451	109	60	8	2	0	0	0	0	0
Total SVI	0.38 (0.26)	0.35 (0.25)	0.34 (0.25)	0.36 (0.24)	0.41 (0.28)	0.36 (0.23)	0.45 (0.26)	0.09 (0.04)	0.50 (NA)	0.37 (NA)	<0.001
(Missing)	458	110	60	8	2	0	0	0	0	0
Soceioeconomic Status	0.36 (0.26)	0.33 (0.25)	0.32 (0.25)	0.33 (0.24)	0.38 (0.27)	0.36 (0.26)	0.43 (0.26)	0.07 (0.04)	0.28 (NA)	0.45 (NA)	<0.001
(Missing)	483	122	71	10	2	0	0	0	0	0
Household Composition	0.41 (0.27)	0.37 (0.26)	0.35 (0.26)	0.39 (0.26)	0.44 (0.29)	0.41 (0.22)	0.54 (0.21)	0.26 (0.06)	0.38 (NA)	0.77 (NA)	<0.001
(Missing)	457	110	60	8	2	0	0	0	0	0
Minority Status and Language	0.47 (0.28)	0.48 (0.29)	0.49 (0.29)	0.49 (0.29)	0.46 (0.31)	0.51 (0.33)	0.31 (0.24)	0.47 (0.33)	0.70 (NA)	0.08 (NA)	0.12
(Missing)	451	109	60	8	2	0	0	0	0	0
Housing and Transportation	0.45 (0.29)	0.43 (0.29)	0.41 (0.28)	0.43 (0.27)	0.46 (0.27)	0.38 (0.24)	0.52 (0.30)	0.09 (0.03)	0.74 (NA)	0.33 (NA)	<0.001
(Missing)	471	114	65	8	2	0	0	0	0	0
flu_2021	890 (9.4%)	863 (31%)	1,212 (48%)	178 (57%)	34 (52%)	12 (75%)	4 (57%)	2 (100%)	0 (0%)	1 (100%)
total_flu
0	6,025 (63%)	369 (13%)	126 (5.0%)	9 (2.9%)	1 (1.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
1	1,247 (13%)	531 (19%)	217 (8.6%)	15 (4.8%)	3 (4.6%)	1 (6.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
2	656 (6.9%)	399 (14%)	293 (12%)	18 (5.8%)	7 (11%)	0 (0%)	2 (29%)	0 (0%)	1 (100%)	0 (0%)
3	461 (4.8%)	376 (13%)	361 (14%)	32 (10%)	8 (12%)	5 (31%)	0 (0%)	1 (50%)	0 (0%)	0 (0%)
4	346 (3.6%)	353 (13%)	400 (16%)	48 (15%)	12 (18%)	2 (12%)	1 (14%)	0 (0%)	0 (0%)	0 (0%)
5	301 (3.2%)	286 (10%)	396 (16%)	59 (19%)	12 (18%)	4 (25%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
6	267 (2.8%)	258 (9.1%)	346 (14%)	62 (20%)	15 (23%)	2 (12%)	3 (43%)	1 (50%)	0 (0%)	1 (100%)
7	203 (2.1%)	243 (8.6%)	377 (15%)	66 (21%)	7 (11%)	2 (12%)	1 (14%)	0 (0%)	0 (0%)	0 (0%)
8	0 (0%)	5 (0.2%)	1 (<0.1%)	1 (0.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
total_cov_vax
0	6,076 (64%)	1,066 (38%)	560 (22%)	45 (15%)	12 (18%)	4 (25%)	1 (14%)	1 (50%)	1 (100%)	0 (0%)
1	261 (2.7%)	93 (3.3%)	73 (2.9%)	5 (1.6%)	2 (3.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
2	1,252 (13%)	471 (17%)	408 (16%)	55 (18%)	16 (25%)	3 (19%)	2 (29%)	0 (0%)	0 (0%)	0 (0%)
3	1,461 (15%)	746 (26%)	820 (33%)	89 (29%)	19 (29%)	3 (19%)	1 (14%)	0 (0%)	0 (0%)	1 (100%)
4	419 (4.4%)	400 (14%)	555 (22%)	94 (30%)	12 (18%)	6 (38%)	2 (29%)	1 (50%)	0 (0%)	0 (0%)
5	29 (0.3%)	35 (1.2%)	75 (3.0%)	19 (6.1%)	2 (3.1%)	0 (0%)	1 (14%)	0 (0%)	0 (0%)	0 (0%)
6	6 (<0.1%)	7 (0.2%)	22 (0.9%)	2 (0.6%)	2 (3.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
7	2 (<0.1%)	2 (<0.1%)	4 (0.2%)	1 (0.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
total_shingrix
0	9,207 (97%)	2,331 (83%)	1,868 (74%)	192 (62%)	46 (71%)	9 (56%)	3 (43%)	2 (100%)	1 (100%)	0 (0%)
1	82 (0.9%)	131 (4.6%)	136 (5.4%)	15 (4.8%)	3 (4.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
2	204 (2.1%)	330 (12%)	470 (19%)	88 (28%)	13 (20%)	6 (38%)	4 (57%)	0 (0%)	0 (0%)	1 (100%)
3	10 (0.1%)	26 (0.9%)	38 (1.5%)	13 (4.2%)	3 (4.6%)	1 (6.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
4	2 (<0.1%)	2 (<0.1%)	4 (0.2%)	2 (0.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
5	1 (<0.1%)	0 (0%)	1 (<0.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
1 Mean (SD); n (%)
2 Kruskal-Wallis rank sum test; Fisher's exact test

Baseline characteristics by Shingrix

baseline %>% tbl_summary(by = total_shingrix,
         label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", pop_dens ~ "Population Density"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)") %>% add_p()

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, NA, 1L, 2L, 2L, 1L, : FEXACT error 7(location). LDSTP=15960 is too small for this problem,
  (pastp=283.429, ipn_0:=ipoin[itp=172]=4617, stp[ipn_0]=277.031).
Increase workspace or consider using 'simulate.p.value=TRUE'
There was an error in 'add_p()/add_difference()' for variable 'mstat_5', p-value omitted:
Error in stats::fisher.test(structure(c(2L, 1L, 2L, 1L, 2L, 2L, 2L, 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 'flu_2021', p-value omitted:
Error in stats::fisher.test(c(0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, : FEXACT error 7(location). LDSTP=15780 is too small for this problem,
  (pastp=291.353, ipn_0:=ipoin[itp=483]=3585, stp[ipn_0]=275.121).
Increase workspace or consider using 'simulate.p.value=TRUE'
There was an error in 'add_p()/add_difference()' for variable 'total_flu', p-value omitted:
Error in stats::fisher.test(c(6, 0, 2, 0, 6, 1, 2, 0, 0, 1, 6, 2, 0, 0, : FEXACT error 501.
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 'total_cov_vax', p-value omitted:
Error in stats::fisher.test(c(2, 0, 4, 0, 0, 1, 3, 0, 0, 0, 1, 0, 0, 2, : FEXACT error 501.
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	0, N = 13,6591	1, N = 3671	2, N = 1,1161	3, N = 911	4, N = 101	5, N = 21	p-value2
Age	48 (19)	62 (14)	66 (10)	67 (11)	68 (7)	65 (6)	<0.001
Gender							0.2
male	6,285 (46%)	162 (44%)	479 (43%)	41 (45%)	6 (60%)	0 (0%)
female	7,374 (54%)	205 (56%)	637 (57%)	50 (55%)	4 (40%)	2 (100%)
Race
WHITE OR CAUCASIAN	11,809 (86%)	324 (88%)	1,003 (90%)	82 (90%)	10 (100%)	2 (100%)
BLACK OR AFRICAN AMERICAN	883 (6.5%)	22 (6.0%)	37 (3.3%)	4 (4.4%)	0 (0%)	0 (0%)
Other	587 (4.3%)	13 (3.5%)	35 (3.1%)	3 (3.3%)	0 (0%)	0 (0%)
ASIAN	328 (2.4%)	8 (2.2%)	38 (3.4%)	1 (1.1%)	0 (0%)	0 (0%)
AMERICAN INDIAN AND ALASKA NATIVE	52 (0.4%)	0 (0%)	3 (0.3%)	1 (1.1%)	0 (0%)	0 (0%)
Ethnicity							0.7
NON-HISPANIC	12,898 (98%)	352 (98%)	1,052 (99%)	87 (99%)	10 (100%)	2 (100%)
UNKNOWN	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
HISPANIC	279 (2.1%)	6 (1.7%)	16 (1.5%)	1 (1.1%)	0 (0%)	0 (0%)
(Missing)	482	9	48	3	0	0
English Speaking							0.7
English	13,507 (99%)	363 (99%)	1,108 (99%)	91 (100%)	10 (100%)	2 (100%)
Other	152 (1.1%)	4 (1.1%)	8 (0.7%)	0 (0%)	0 (0%)	0 (0%)
Any Religious Affiliation
yes	7,163 (56%)	247 (69%)	729 (67%)	62 (70%)	8 (89%)	2 (100%)
no	5,585 (44%)	113 (31%)	360 (33%)	26 (30%)	1 (11%)	0 (0%)
PATIENT REFUSED	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
UNKNOWN	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
(Missing)	911	7	27	3	1	0
Marital Status
married	5,253 (38%)	206 (56%)	669 (60%)	61 (67%)	7 (70%)	2 (100%)
unknown	2,965 (22%)	71 (19%)	184 (16%)	15 (16%)	1 (10%)	0 (0%)
unmarried	4,748 (35%)	67 (18%)	171 (15%)	10 (11%)	1 (10%)	0 (0%)
div_sep	444 (3.3%)	15 (4.1%)	46 (4.1%)	2 (2.2%)	0 (0%)	0 (0%)
widow	249 (1.8%)	8 (2.2%)	46 (4.1%)	3 (3.3%)	1 (10%)	0 (0%)
Population Density	2,314 (4,165)	1,988 (2,015)	2,059 (2,179)	2,918 (4,801)	2,947 (2,257)	2,127 (641)	0.4
(Missing)	598	4	26	2	0	0
Total SVI	0.38 (0.26)	0.33 (0.25)	0.28 (0.22)	0.29 (0.25)	0.31 (0.25)	0.65 (0.21)	<0.001
(Missing)	606	4	26	2	0	0
Soceioeconomic Status	0.36 (0.25)	0.31 (0.25)	0.25 (0.22)	0.24 (0.25)	0.25 (0.19)	0.61 (0.23)	<0.001
(Missing)	647	4	34	3	0	0
Household Composition	0.40 (0.27)	0.37 (0.26)	0.31 (0.23)	0.34 (0.24)	0.25 (0.21)	0.39 (0.38)	<0.001
(Missing)	605	4	26	2	0	0
Minority Status and Language	0.48 (0.29)	0.47 (0.30)	0.49 (0.29)	0.53 (0.28)	0.46 (0.26)	0.82 (0.15)	0.2
(Missing)	598	4	26	2	0	0
Housing and Transportation	0.44 (0.29)	0.40 (0.28)	0.37 (0.27)	0.38 (0.31)	0.51 (0.31)	0.64 (0.12)	<0.001
(Missing)	621	6	30	3	0	0
flu_2021	2,156 (16%)	180 (49%)	784 (70%)	67 (74%)	7 (70%)	2 (100%)
total_flu
0	6,469 (47%)	33 (9.0%)	27 (2.4%)	1 (1.1%)	0 (0%)	0 (0%)
1	1,929 (14%)	39 (11%)	43 (3.9%)	3 (3.3%)	0 (0%)	0 (0%)
2	1,257 (9.2%)	25 (6.8%)	86 (7.7%)	8 (8.8%)	0 (0%)	0 (0%)
3	1,054 (7.7%)	64 (17%)	114 (10%)	10 (11%)	2 (20%)	0 (0%)
4	919 (6.7%)	61 (17%)	168 (15%)	13 (14%)	1 (10%)	0 (0%)
5	809 (5.9%)	43 (12%)	194 (17%)	12 (13%)	0 (0%)	0 (0%)
6	665 (4.9%)	57 (16%)	209 (19%)	18 (20%)	4 (40%)	2 (100%)
7	553 (4.0%)	45 (12%)	272 (24%)	26 (29%)	3 (30%)	0 (0%)
8	4 (<0.1%)	0 (0%)	3 (0.3%)	0 (0%)	0 (0%)	0 (0%)
any_pneum	0.51 (0.82)	1.25 (0.88)	1.48 (1.01)	1.74 (1.02)	1.60 (1.07)	1.00 (1.41)	<0.001
total_cov_vax
0	7,594 (56%)	87 (24%)	77 (6.9%)	7 (7.7%)	1 (10%)	0 (0%)
1	403 (3.0%)	11 (3.0%)	17 (1.5%)	3 (3.3%)	0 (0%)	0 (0%)
2	2,008 (15%)	65 (18%)	128 (11%)	5 (5.5%)	1 (10%)	0 (0%)
3	2,642 (19%)	116 (32%)	356 (32%)	24 (26%)	1 (10%)	1 (50%)
4	901 (6.6%)	76 (21%)	465 (42%)	42 (46%)	4 (40%)	1 (50%)
5	92 (0.7%)	9 (2.5%)	53 (4.7%)	6 (6.6%)	1 (10%)	0 (0%)
6	15 (0.1%)	3 (0.8%)	16 (1.4%)	3 (3.3%)	2 (20%)	0 (0%)
7	4 (<0.1%)	0 (0%)	4 (0.4%)	1 (1.1%)	0 (0%)	0 (0%)
1 Mean (SD); n (%)
2 Kruskal-Wallis rank sum test; Fisher's exact test

Baseline Characteristics by Total_COVID

baseline %>% tbl_summary(by = total_cov_vax,
         label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", mstat_5 ~ "Marital Status", RPL_THEMES ~ "Total SVI", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", RPL_THEME4 ~ "Housing and Transportation", pop_dens ~ "Population Density"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)") %>% add_p()

There was an error in 'add_p()/add_difference()' for variable 'gender', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, : FEXACT error 7(location). LDSTP=15780 is too small for this problem,
  (pastp=428.53, ipn_0:=ipoin[itp=51]=11, stp[ipn_0]=427.494).
Increase workspace or consider using 'simulate.p.value=TRUE'
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 'ethnic_3', p-value omitted:
Error in stats::fisher.test(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, : FEXACT error 7(location). LDSTP=15900 is too small for this problem,
  (pastp=196.676, ipn_0:=ipoin[itp=361]=811, stp[ipn_0]=197.454).
Increase workspace or consider using 'simulate.p.value=TRUE'
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 7(location). LDSTP=15960 is too small for this problem,
  (pastp=398.221, ipn_0:=ipoin[itp=284]=7733, stp[ipn_0]=398.559).
Increase workspace or consider using 'simulate.p.value=TRUE'
There was an error in 'add_p()/add_difference()' for variable 'mstat_5', p-value omitted:
Error in stats::fisher.test(structure(c(2L, 1L, 2L, 1L, 2L, 2L, 2L, 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 'flu_2021', p-value omitted:
Error in stats::fisher.test(c(0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, : FEXACT error 7(location). LDSTP=15780 is too small for this problem,
  (pastp=120.092, ipn_0:=ipoin[itp=402]=289, stp[ipn_0]=109.428).
Increase workspace or consider using 'simulate.p.value=TRUE'
There was an error in 'add_p()/add_difference()' for variable 'total_flu', p-value omitted:
Error in stats::fisher.test(c(6, 0, 2, 0, 6, 1, 2, 0, 0, 1, 6, 2, 0, 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.
There was an error in 'add_p()/add_difference()' for variable 'total_shingrix', p-value omitted:
Error in stats::fisher.test(c(0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : FEXACT error 501.
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	0, N = 7,7661	1, N = 4341	2, N = 2,2071	3, N = 3,1401	4, N = 1,4891	5, N = 1611	6, N = 391	7, N = 91	p-value2
Age	49 (20)	47 (18)	46 (19)	49 (18)	59 (17)	53 (18)	62 (18)	49 (21)	<0.001
Gender
male	3,587 (46%)	192 (44%)	1,038 (47%)	1,399 (45%)	669 (45%)	70 (43%)	14 (36%)	4 (44%)
female	4,179 (54%)	242 (56%)	1,169 (53%)	1,741 (55%)	820 (55%)	91 (57%)	25 (64%)	5 (56%)
Race
WHITE OR CAUCASIAN	6,756 (87%)	380 (88%)	1,863 (84%)	2,746 (87%)	1,297 (87%)	147 (91%)	34 (87%)	7 (78%)
BLACK OR AFRICAN AMERICAN	504 (6.5%)	20 (4.6%)	169 (7.7%)	166 (5.3%)	80 (5.4%)	4 (2.5%)	2 (5.1%)	1 (11%)
Other	344 (4.4%)	20 (4.6%)	110 (5.0%)	112 (3.6%)	46 (3.1%)	4 (2.5%)	2 (5.1%)	0 (0%)
ASIAN	130 (1.7%)	14 (3.2%)	56 (2.5%)	107 (3.4%)	61 (4.1%)	5 (3.1%)	1 (2.6%)	1 (11%)
AMERICAN INDIAN AND ALASKA NATIVE	32 (0.4%)	0 (0%)	9 (0.4%)	9 (0.3%)	5 (0.3%)	1 (0.6%)	0 (0%)	0 (0%)
Ethnicity
NON-HISPANIC	7,309 (98%)	413 (97%)	2,078 (97%)	2,985 (98%)	1,417 (99%)	154 (99%)	37 (95%)	8 (89%)
UNKNOWN	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
CHOOSE NOT TO DISCLOSE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
HISPANIC	140 (1.9%)	12 (2.8%)	62 (2.9%)	65 (2.1%)	19 (1.3%)	1 (0.6%)	2 (5.1%)	1 (11%)
(Missing)	317	9	67	90	53	6	0	0
English Speaking									0.5
English	7,679 (99%)	428 (99%)	2,177 (99%)	3,110 (99%)	1,479 (99%)	160 (99%)	39 (100%)	9 (100%)
Other	87 (1.1%)	6 (1.4%)	30 (1.4%)	30 (1.0%)	10 (0.7%)	1 (0.6%)	0 (0%)	0 (0%)
Any Religious Affiliation
yes	4,035 (57%)	239 (58%)	1,210 (58%)	1,711 (57%)	894 (62%)	93 (60%)	25 (68%)	4 (44%)
no	3,106 (43%)	174 (42%)	894 (42%)	1,293 (43%)	539 (38%)	62 (40%)	12 (32%)	5 (56%)
PATIENT REFUSED	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
UNKNOWN	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
(Missing)	625	21	103	136	56	6	2	0
Marital Status
married	3,013 (39%)	147 (34%)	782 (35%)	1,362 (43%)	790 (53%)	82 (51%)	16 (41%)	6 (67%)
unknown	1,478 (19%)	120 (28%)	590 (27%)	741 (24%)	262 (18%)	33 (20%)	11 (28%)	1 (11%)
unmarried	2,834 (36%)	143 (33%)	732 (33%)	891 (28%)	348 (23%)	39 (24%)	8 (21%)	2 (22%)
div_sep	280 (3.6%)	17 (3.9%)	65 (2.9%)	95 (3.0%)	42 (2.8%)	5 (3.1%)	3 (7.7%)	0 (0%)
widow	161 (2.1%)	7 (1.6%)	38 (1.7%)	51 (1.6%)	47 (3.2%)	2 (1.2%)	1 (2.6%)	0 (0%)
Population Density	2,281 (4,827)	2,477 (5,536)	2,245 (2,647)	2,282 (2,952)	2,322 (2,424)	2,662 (2,511)	2,869 (3,429)	1,774 (1,641)	<0.001
(Missing)	428	19	67	76	35	3	2	0
Total SVI	0.40 (0.26)	0.36 (0.27)	0.36 (0.26)	0.32 (0.24)	0.30 (0.23)	0.28 (0.23)	0.34 (0.23)	0.31 (0.23)	<0.001
(Missing)	434	19	69	76	35	3	2	0
Soceioeconomic Status	0.39 (0.26)	0.34 (0.26)	0.34 (0.25)	0.30 (0.24)	0.27 (0.23)	0.24 (0.24)	0.26 (0.23)	0.21 (0.17)	<0.001
(Missing)	444	21	73	96	49	3	2	0
Household Composition	0.43 (0.27)	0.39 (0.27)	0.39 (0.26)	0.35 (0.25)	0.31 (0.24)	0.29 (0.24)	0.27 (0.21)	0.32 (0.22)	<0.001
(Missing)	433	19	69	76	35	3	2	0
Minority Status and Language	0.46 (0.28)	0.47 (0.28)	0.48 (0.29)	0.50 (0.29)	0.51 (0.29)	0.55 (0.29)	0.67 (0.25)	0.53 (0.29)	<0.001
(Missing)	428	19	67	76	35	3	2	0
Housing and Transportation	0.46 (0.29)	0.43 (0.28)	0.43 (0.29)	0.40 (0.28)	0.39 (0.28)	0.35 (0.26)	0.44 (0.28)	0.43 (0.27)	<0.001
(Missing)	440	19	71	83	40	5	2	0
flu_2021	204 (2.6%)	72 (17%)	449 (20%)	1,364 (43%)	930 (62%)	132 (82%)	36 (92%)	9 (100%)
total_flu
0	4,886 (63%)	167 (38%)	675 (31%)	601 (19%)	192 (13%)	7 (4.3%)	2 (5.1%)	0 (0%)
1	1,206 (16%)	65 (15%)	336 (15%)	317 (10%)	85 (5.7%)	4 (2.5%)	1 (2.6%)	0 (0%)
2	618 (8.0%)	49 (11%)	259 (12%)	324 (10%)	116 (7.8%)	9 (5.6%)	1 (2.6%)	0 (0%)
3	424 (5.5%)	47 (11%)	247 (11%)	349 (11%)	164 (11%)	10 (6.2%)	2 (5.1%)	1 (11%)
4	304 (3.9%)	35 (8.1%)	240 (11%)	363 (12%)	199 (13%)	13 (8.1%)	5 (13%)	3 (33%)
5	193 (2.5%)	40 (9.2%)	195 (8.8%)	407 (13%)	187 (13%)	28 (17%)	8 (21%)	0 (0%)
6	96 (1.2%)	23 (5.3%)	156 (7.1%)	397 (13%)	247 (17%)	28 (17%)	5 (13%)	3 (33%)
7	39 (0.5%)	8 (1.8%)	99 (4.5%)	380 (12%)	295 (20%)	61 (38%)	15 (38%)	2 (22%)
8	0 (0%)	0 (0%)	0 (0%)	2 (<0.1%)	4 (0.3%)	1 (0.6%)	0 (0%)	0 (0%)
any_pneum	0.31 (0.66)	0.60 (0.84)	0.70 (0.93)	0.88 (0.96)	1.27 (1.01)	1.59 (1.02)	1.67 (0.98)	1.44 (1.01)	<0.001
total_shingrix
0	7,594 (98%)	403 (93%)	2,008 (91%)	2,642 (84%)	901 (61%)	92 (57%)	15 (38%)	4 (44%)
1	87 (1.1%)	11 (2.5%)	65 (2.9%)	116 (3.7%)	76 (5.1%)	9 (5.6%)	3 (7.7%)	0 (0%)
2	77 (1.0%)	17 (3.9%)	128 (5.8%)	356 (11%)	465 (31%)	53 (33%)	16 (41%)	4 (44%)
3	7 (<0.1%)	3 (0.7%)	5 (0.2%)	24 (0.8%)	42 (2.8%)	6 (3.7%)	3 (7.7%)	1 (11%)
4	1 (<0.1%)	0 (0%)	1 (<0.1%)	1 (<0.1%)	4 (0.3%)	1 (0.6%)	2 (5.1%)	0 (0%)
5	0 (0%)	0 (0%)	0 (0%)	1 (<0.1%)	1 (<0.1%)	0 (0%)	0 (0%)	0 (0%)
1 Mean (SD); n (%)
2 Kruskal-Wallis rank sum test; Fisher's exact test

Estimate vax/no vax by demog

vax_clean1 %>% 
  tabyl(mstat_5, total_cov_vax) %>% 
  adorn_totals(c("row", "col")) %>%
  adorn_percentages("row") %>% 
  adorn_pct_formatting(rounding = "half up", digits = 0) %>%
  adorn_ns() %>%
  adorn_title("combined") %>% 
  gt()

mstat_5/total_cov_vax	0	1	2	3	4	5	6	7	Total
married	49% (3013)	2% (147)	13% (782)	22% (1362)	13% (790)	1% (82)	0% (16)	0% (6)	100% (6198)
unknown	46% (1478)	4% (120)	18% (590)	23% (741)	8% (262)	1% (33)	0% (11)	0% (1)	100% (3236)
unmarried	57% (2834)	3% (143)	15% (732)	18% (891)	7% (348)	1% (39)	0% (8)	0% (2)	100% (4997)
div_sep	55% (280)	3% (17)	13% (65)	19% (95)	8% (42)	1% (5)	1% (3)	0% (0)	100% (507)
widow	52% (161)	2% (7)	12% (38)	17% (51)	15% (47)	1% (2)	0% (1)	0% (0)	100% (307)
Total	51% (7766)	3% (434)	14% (2207)	21% (3140)	10% (1489)	1% (161)	0% (39)	0% (9)	100% (15245)

  

vax_clean1 %>% 
  tabyl(ethnic_3, total_cov_vax) %>% 
  adorn_totals(c("row", "col")) %>%
  adorn_percentages("row") %>% 
  adorn_pct_formatting(rounding = "half up", digits = 0) %>%
  adorn_ns() %>%
  adorn_title("combined") %>% 
  gt()

ethnic_3/total_cov_vax	0	1	2	3	4	5	6	7	Total
NON-HISPANIC	51% (7309)	3% (413)	14% (2078)	21% (2985)	10% (1417)	1% (154)	0% (37)	0% (8)	100% (14401)
UNKNOWN	- (0)	- (0)	- (0)	- (0)	- (0)	- (0)	- (0)	- (0)	100% (0)
CHOOSE NOT TO DISCLOSE	- (0)	- (0)	- (0)	- (0)	- (0)	- (0)	- (0)	- (0)	100% (0)
HISPANIC	46% (140)	4% (12)	21% (62)	22% (65)	6% (19)	0% (1)	1% (2)	0% (1)	100% (302)
NA	58% (317)	2% (9)	12% (67)	17% (90)	10% (53)	1% (6)	0% (0)	0% (0)	100% (542)
Total	51% (7766)	3% (434)	14% (2207)	21% (3140)	10% (1489)	1% (161)	0% (39)	0% (9)	100% (15245)

  

vax_clean1 %>% 
  tabyl(race_5, total_cov_vax) %>% 
  adorn_totals(c("row", "col")) %>%
  adorn_percentages("row") %>% 
  adorn_pct_formatting(rounding = "half up", digits = 0) %>%
  adorn_ns() %>%
  adorn_title("combined") %>% 
  gt()

race_5/total_cov_vax	0	1	2	3	4	5	6	7	Total
WHITE OR CAUCASIAN	51% (6756)	3% (380)	14% (1863)	21% (2746)	10% (1297)	1% (147)	0% (34)	0% (7)	100% (13230)
BLACK OR AFRICAN AMERICAN	53% (504)	2% (20)	18% (169)	18% (166)	8% (80)	0% (4)	0% (2)	0% (1)	100% (946)
Other	54% (344)	3% (20)	17% (110)	18% (112)	7% (46)	1% (4)	0% (2)	0% (0)	100% (638)
ASIAN	35% (130)	4% (14)	15% (56)	29% (107)	16% (61)	1% (5)	0% (1)	0% (1)	100% (375)
AMERICAN INDIAN AND ALASKA NATIVE	57% (32)	0% (0)	16% (9)	16% (9)	9% (5)	2% (1)	0% (0)	0% (0)	100% (56)
Total	51% (7766)	3% (434)	14% (2207)	21% (3140)	10% (1489)	1% (161)	0% (39)	0% (9)	100% (15245)

NA

Prelim Vax Models

Model 1: Flu 2021 + RPL_THEMES

Flu2021_SVI <- glm(flu_2021 ~ age_yrs + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES,
              family = "binomial",
              data = vax_clean1)
summary(Flu2021_SVI )

Call:
glm(formula = flu_2021 ~ age_yrs + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, 
    family = "binomial", data = vax_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4060  -0.7430  -0.6046  -0.4186   3.1853  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)    
(Intercept)                              4.324e-01  1.314e-01   3.290 0.001003 ** 
age_yrs                                  2.567e-03  1.400e-03   1.833 0.066821 .  
mstat_5unknown                          -1.400e-01  6.425e-02  -2.178 0.029380 *  
mstat_5unmarried                        -3.650e-01  6.201e-02  -5.886 3.95e-09 ***
mstat_5div_sep                          -2.214e-01  1.338e-01  -1.655 0.097961 .  
mstat_5widow                            -1.405e-01  1.554e-01  -0.904 0.366027    
race_5BLACK OR AFRICAN AMERICAN         -3.273e-01  1.058e-01  -3.095 0.001966 ** 
race_5Other                             -2.775e-01  1.469e-01  -1.888 0.058962 .  
race_5ASIAN                              4.138e-01  1.331e-01   3.108 0.001880 ** 
race_5AMERICAN INDIAN AND ALASKA NATIVE -2.373e-01  4.129e-01  -0.575 0.565395    
lang_3Other                             -5.620e-01  2.636e-01  -2.132 0.032993 *  
relig_affilno                           -8.817e-02  4.788e-02  -1.841 0.065560 .  
genderfemale                             1.722e-01  4.651e-02   3.702 0.000214 ***
ethnic_3HISPANIC                         2.787e-01  1.655e-01   1.684 0.092259 .  
pop_dens                                -4.114e-05  1.008e-05  -4.083 4.45e-05 ***
r_pct                                   -2.624e-02  1.567e-03 -16.746  < 2e-16 ***
RPL_THEMES                              -1.321e+00  9.725e-02 -13.582  < 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: 12367  on 11566  degrees of freedom
Residual deviance: 11745  on 11550  degrees of freedom
  (3678 observations deleted due to missingness)
AIC: 11779

Number of Fisher Scoring iterations: 4

broom::glance(Flu2021_SVI )

broom::tidy(Flu2021_SVI , exponentiate = TRUE)

model_performance(Flu2021_SVI )

# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
11779.314 | 11904.364 |     0.056 | 0.406 | 1.008 |    0.508 |      -Inf |       1.219e-04 | 0.669

tbl_regression(Flu2021_SVI, label = list(age_yrs ~ "Age", gender~ "Gender", race_5 ~ "Race", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEMES ~ "Total SVI", mstat_5 ~ "Marital Status", pop_dens ~ "Population Density"), exponentiate = TRUE)

Characteristic	OR1	95% CI1	p-value
Age	1.00	1.00, 1.01	0.067
Marital Status
married	—	—
unknown	0.87	0.77, 0.99	0.029
unmarried	0.69	0.61, 0.78	<0.001
div_sep	0.80	0.61, 1.04	0.10
widow	0.87	0.64, 1.17	0.4
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.72	0.58, 0.88	0.002
Other	0.76	0.56, 1.00	0.059
ASIAN	1.51	1.16, 1.96	0.002
AMERICAN INDIAN AND ALASKA NATIVE	0.79	0.33, 1.69	0.6
English Speaking
English	—	—
Other	0.57	0.33, 0.94	0.033
Any Religious Affiliation
yes	—	—
no	0.92	0.83, 1.01	0.066
Gender
male	—	—
female	1.19	1.08, 1.30	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	1.32	0.95, 1.82	0.092
Population Density	1.00	1.00, 1.00	<0.001
r_pct	0.97	0.97, 0.98	<0.001
Total SVI	0.27	0.22, 0.32	<0.001
1 OR = Odds Ratio, CI = Confidence Interval

Model 2: Flu_2021 + RPL_THEMESx4

Flu2021_4 <- glm(flu_2021 ~  age_yrs + race_5 + mstat_5 + lang_3 + relig_affil 
       + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4,
              family = "binomial",
              data = vax_clean1)
summary(Flu2021_4)

Call:
glm(formula = flu_2021 ~ age_yrs + race_5 + mstat_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + 
    RPL_THEME2 + RPL_THEME3 + RPL_THEME4, family = "binomial", 
    data = vax_clean1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3362  -0.7469  -0.5995  -0.4171   3.0713  

Coefficients:
                                          Estimate Std. Error z value Pr(>|z|)    
(Intercept)                              4.056e-01  1.603e-01   2.531 0.011381 *  
age_yrs                                  2.738e-03  1.407e-03   1.946 0.051617 .  
race_5BLACK OR AFRICAN AMERICAN         -2.728e-01  1.069e-01  -2.553 0.010693 *  
race_5Other                             -2.947e-01  1.478e-01  -1.994 0.046156 *  
race_5ASIAN                              3.711e-01  1.344e-01   2.760 0.005778 ** 
race_5AMERICAN INDIAN AND ALASKA NATIVE -1.838e-01  4.155e-01  -0.442 0.658183    
mstat_5unknown                          -1.307e-01  6.458e-02  -2.024 0.042973 *  
mstat_5unmarried                        -3.560e-01  6.237e-02  -5.708 1.14e-08 ***
mstat_5div_sep                          -2.220e-01  1.347e-01  -1.648 0.099351 .  
mstat_5widow                            -1.146e-01  1.556e-01  -0.737 0.461409    
lang_3Other                             -5.755e-01  2.639e-01  -2.180 0.029235 *  
relig_affilno                           -8.194e-02  4.823e-02  -1.699 0.089338 .  
genderfemale                             1.689e-01  4.676e-02   3.612 0.000303 ***
ethnic_3HISPANIC                         2.844e-01  1.658e-01   1.715 0.086298 .  
pop_dens                                -3.854e-05  1.034e-05  -3.729 0.000193 ***
r_pct                                   -2.300e-02  1.862e-03 -12.351  < 2e-16 ***
RPL_THEME1                              -7.685e-01  1.395e-01  -5.507 3.64e-08 ***
RPL_THEME2                              -6.473e-01  1.212e-01  -5.340 9.31e-08 ***
RPL_THEME3                              -6.151e-02  9.574e-02  -0.643 0.520529    
RPL_THEME4                              -1.842e-01  1.020e-01  -1.807 0.070837 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 12260  on 11496  degrees of freedom
Residual deviance: 11629  on 11477  degrees of freedom
  (3748 observations deleted due to missingness)
AIC: 11669

Number of Fisher Scoring iterations: 4

broom::glance(Flu2021_4)

broom::tidy(Flu2021_4, exponentiate = TRUE)

model_performance(Flu2021_4)

# Indices of model performance

AIC       |       BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------
11669.325 | 11816.322 |     0.057 | 0.405 | 1.007 |    0.506 |      -Inf |       1.203e-04 | 0.671

tbl_regression(Flu2021_4, label = list(age_yrs ~ "Age", race_5 ~ "Race", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", mstat_5 ~ "Marital Status", RPL_THEME4 ~ "Housing and Transportation", pop_dens ~ "Population Density"), exponentiate = TRUE)

Characteristic	OR1	95% CI1	p-value
Age	1.00	1.00, 1.01	0.052
Race
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	0.76	0.62, 0.94	0.011
Other	0.74	0.55, 0.99	0.046
ASIAN	1.45	1.11, 1.88	0.006
AMERICAN INDIAN AND ALASKA NATIVE	0.83	0.35, 1.80	0.7
Marital Status
married	—	—
unknown	0.88	0.77, 1.00	0.043
unmarried	0.70	0.62, 0.79	<0.001
div_sep	0.80	0.61, 1.04	0.10
widow	0.89	0.65, 1.20	0.5
English Speaking
English	—	—
Other	0.56	0.33, 0.92	0.029
Any Religious Affiliation
yes	—	—
no	0.92	0.84, 1.01	0.089
Gender
male	—	—
female	1.18	1.08, 1.30	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	1.33	0.95, 1.83	0.086
Population Density	1.00	1.00, 1.00	<0.001
r_pct	0.98	0.97, 0.98	<0.001
Soceioeconomic Status	0.46	0.35, 0.61	<0.001
Household Composition	0.52	0.41, 0.66	<0.001
Minority Status and Language	0.94	0.78, 1.13	0.5
Housing and Transportation	0.83	0.68, 1.02	0.071
1 OR = Odds Ratio, CI = Confidence Interval

Model 3: Total Flu + RPL_THEMES

totalflu_SVI <- lm(total_flu ~ age_yrs + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, data = vax_clean1) 
summary(totalflu_SVI)

Call:
lm(formula = total_flu ~ age_yrs + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, 
    data = vax_clean1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7902 -1.8605 -0.8196  1.7601  6.2768 

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              4.226e+00  1.182e-01  35.750  < 2e-16 ***
age_yrs                                 -6.299e-03  1.294e-03  -4.869 1.13e-06 ***
mstat_5unknown                          -5.278e-01  5.989e-02  -8.813  < 2e-16 ***
mstat_5unmarried                        -3.322e-01  5.695e-02  -5.833 5.59e-09 ***
mstat_5div_sep                          -3.101e-01  1.199e-01  -2.585 0.009741 ** 
mstat_5widow                            -2.977e-01  1.505e-01  -1.978 0.047954 *  
race_5BLACK OR AFRICAN AMERICAN         -3.626e-01  9.428e-02  -3.846 0.000121 ***
race_5Other                             -3.144e-01  1.304e-01  -2.410 0.015967 *  
race_5ASIAN                              3.350e-01  1.418e-01   2.363 0.018131 *  
race_5AMERICAN INDIAN AND ALASKA NATIVE  2.004e-01  3.662e-01   0.547 0.584296    
lang_3Other                             -6.062e-01  2.232e-01  -2.715 0.006632 ** 
relig_affilno                           -1.856e-01  4.405e-02  -4.212 2.55e-05 ***
genderfemale                             1.592e-01  4.288e-02   3.713 0.000206 ***
ethnic_3HISPANIC                         3.271e-01  1.584e-01   2.064 0.038994 *  
pop_dens                                -2.767e-05  5.625e-06  -4.919 8.82e-07 ***
r_pct                                   -2.224e-02  1.373e-03 -16.194  < 2e-16 ***
RPL_THEMES                              -1.333e+00  8.491e-02 -15.704  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.279 on 11550 degrees of freedom
  (3678 observations deleted due to missingness)
Multiple R-squared:  0.05746,   Adjusted R-squared:  0.05615 
F-statistic: 44.01 on 16 and 11550 DF,  p-value: < 2.2e-16

broom::glance(totalflu_SVI)

broom::tidy(totalflu_SVI, exponentiate = TRUE)

model_performance(totalflu_SVI)

# Indices of model performance

AIC       |       BIC |    R2 | R2 (adj.) |  RMSE | Sigma
---------------------------------------------------------
51903.558 | 52035.964 | 0.057 |     0.056 | 2.278 | 2.279

tbl_regression(totalflu_SVI, label = list(age_yrs ~ "Age", pop_dens ~ "Population density", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEMES ~ "Total SVI", mstat_5 ~ "Marital Status"))

Characteristic	Beta	95% CI1	p-value
Age	-0.01	-0.01, 0.00	<0.001
Marital Status
married	—	—
unknown	-0.53	-0.65, -0.41	<0.001
unmarried	-0.33	-0.44, -0.22	<0.001
div_sep	-0.31	-0.55, -0.07	0.010
widow	-0.30	-0.59, 0.00	0.048
race_5
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	-0.36	-0.55, -0.18	<0.001
Other	-0.31	-0.57, -0.06	0.016
ASIAN	0.34	0.06, 0.61	0.018
AMERICAN INDIAN AND ALASKA NATIVE	0.20	-0.52, 0.92	0.6
English Speaking
English	—	—
Other	-0.61	-1.0, -0.17	0.007
Any Religious Affiliation
yes	—	—
no	-0.19	-0.27, -0.10	<0.001
Gender
male	—	—
female	0.16	0.08, 0.24	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.33	0.02, 0.64	0.039
Population density	0.00	0.00, 0.00	<0.001
r_pct	-0.02	-0.02, -0.02	<0.001
Total SVI	-1.3	-1.5, -1.2	<0.001
1 CI = Confidence Interval

Model 4: Total Flu + RPL_THEMESx4

totalflu_4 <- lm(total_flu ~ age_yrs + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4, data = vax_clean1) 
summary(totalflu_4)

Call:
lm(formula = total_flu ~ age_yrs + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + 
    RPL_THEME2 + RPL_THEME3 + RPL_THEME4, data = vax_clean1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7702 -1.8459 -0.8139  1.7477  6.2936 

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              4.191e+00  1.434e-01  29.215  < 2e-16 ***
age_yrs                                 -6.070e-03  1.296e-03  -4.685 2.83e-06 ***
mstat_5unknown                          -5.114e-01  5.995e-02  -8.530  < 2e-16 ***
mstat_5unmarried                        -3.219e-01  5.706e-02  -5.641 1.73e-08 ***
mstat_5div_sep                          -2.953e-01  1.199e-01  -2.463 0.013808 *  
mstat_5widow                            -2.688e-01  1.508e-01  -1.782 0.074754 .  
race_5BLACK OR AFRICAN AMERICAN         -3.036e-01  9.524e-02  -3.187 0.001441 ** 
race_5Other                             -3.238e-01  1.307e-01  -2.478 0.013232 *  
race_5ASIAN                              3.028e-01  1.429e-01   2.119 0.034136 *  
race_5AMERICAN INDIAN AND ALASKA NATIVE  2.816e-01  3.703e-01   0.760 0.447125    
lang_3Other                             -6.082e-01  2.242e-01  -2.712 0.006689 ** 
relig_affilno                           -1.790e-01  4.418e-02  -4.053 5.09e-05 ***
genderfemale                             1.558e-01  4.293e-02   3.629 0.000286 ***
ethnic_3HISPANIC                         3.345e-01  1.586e-01   2.109 0.034956 *  
pop_dens                                -2.577e-05  5.696e-06  -4.525 6.10e-06 ***
r_pct                                   -1.907e-02  1.635e-03 -11.661  < 2e-16 ***
RPL_THEME1                              -8.527e-01  1.242e-01  -6.864 7.05e-12 ***
RPL_THEME2                              -5.598e-01  1.089e-01  -5.143 2.76e-07 ***
RPL_THEME3                              -6.353e-02  8.918e-02  -0.712 0.476246    
RPL_THEME4                              -1.816e-01  9.248e-02  -1.964 0.049542 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.275 on 11477 degrees of freedom
  (3748 observations deleted due to missingness)
Multiple R-squared:  0.05859,   Adjusted R-squared:  0.05703 
F-statistic: 37.59 on 19 and 11477 DF,  p-value: < 2.2e-16

broom::glance(totalflu_4)

broom::tidy(totalflu_4, exponentiate = TRUE)

model_performance(totalflu_4)

# Indices of model performance

AIC       |       BIC |    R2 | R2 (adj.) |  RMSE | Sigma
---------------------------------------------------------
51549.376 | 51703.723 | 0.059 |     0.057 | 2.273 | 2.275

tbl_regression(totalflu_4, label = list(age_yrs ~ "Age", pop_dens ~ "Population density", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", mstat_5 ~ "Marital Status", RPL_THEME4 ~ "Housing and Transportation", mstat_5 ~ "Marital Status"))

Characteristic	Beta	95% CI1	p-value
Age	-0.01	-0.01, 0.00	<0.001
Marital Status
married	—	—
unknown	-0.51	-0.63, -0.39	<0.001
unmarried	-0.32	-0.43, -0.21	<0.001
div_sep	-0.30	-0.53, -0.06	0.014
widow	-0.27	-0.56, 0.03	0.075
race_5
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	-0.30	-0.49, -0.12	0.001
Other	-0.32	-0.58, -0.07	0.013
ASIAN	0.30	0.02, 0.58	0.034
AMERICAN INDIAN AND ALASKA NATIVE	0.28	-0.44, 1.0	0.4
English Speaking
English	—	—
Other	-0.61	-1.0, -0.17	0.007
Any Religious Affiliation
yes	—	—
no	-0.18	-0.27, -0.09	<0.001
Gender
male	—	—
female	0.16	0.07, 0.24	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.33	0.02, 0.65	0.035
Population density	0.00	0.00, 0.00	<0.001
r_pct	-0.02	-0.02, -0.02	<0.001
Soceioeconomic Status	-0.85	-1.1, -0.61	<0.001
Household Composition	-0.56	-0.77, -0.35	<0.001
Minority Status and Language	-0.06	-0.24, 0.11	0.5
Housing and Transportation	-0.18	-0.36, 0.00	0.050
1 CI = Confidence Interval

Model 5: Total COVID + RPL_THEMES

totalcov_SVI <- lm(total_cov_vax ~ age_yrs + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, data = vax_clean1) 
summary(totalcov_SVI)

Call:
lm(formula = total_cov_vax ~ age_yrs + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, 
    data = vax_clean1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7849 -1.3433 -0.3826  1.3461  5.9971 

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              2.395e+00  7.867e-02  30.447  < 2e-16 ***
age_yrs                                  7.865e-03  8.608e-04   9.137  < 2e-16 ***
mstat_5unknown                           9.908e-02  3.985e-02   2.486 0.012920 *  
mstat_5unmarried                        -2.015e-01  3.790e-02  -5.318 1.07e-07 ***
mstat_5div_sep                          -2.086e-01  7.981e-02  -2.614 0.008962 ** 
mstat_5widow                            -3.484e-01  1.002e-01  -3.479 0.000506 ***
race_5BLACK OR AFRICAN AMERICAN         -1.823e-01  6.274e-02  -2.906 0.003667 ** 
race_5Other                             -1.078e-01  8.680e-02  -1.242 0.214408    
race_5ASIAN                              3.855e-01  9.433e-02   4.086 4.41e-05 ***
race_5AMERICAN INDIAN AND ALASKA NATIVE -2.358e-01  2.437e-01  -0.968 0.333200    
lang_3Other                             -3.219e-01  1.485e-01  -2.167 0.030256 *  
relig_affilno                           -2.536e-02  2.931e-02  -0.865 0.386999    
genderfemale                             6.411e-02  2.853e-02   2.247 0.024673 *  
ethnic_3HISPANIC                         1.965e-01  1.054e-01   1.864 0.062373 .  
pop_dens                                -2.160e-05  3.743e-06  -5.772 8.05e-09 ***
r_pct                                   -1.922e-02  9.137e-04 -21.036  < 2e-16 ***
RPL_THEMES                              -9.924e-01  5.650e-02 -17.565  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.517 on 11550 degrees of freedom
  (3678 observations deleted due to missingness)
Multiple R-squared:  0.0799,    Adjusted R-squared:  0.07862 
F-statistic: 62.68 on 16 and 11550 DF,  p-value: < 2.2e-16

broom::glance(totalcov_SVI)

broom::tidy(totalcov_SVI, exponentiate = TRUE)

model_performance(totalcov_SVI)

# Indices of model performance

AIC       |       BIC |    R2 | R2 (adj.) |  RMSE | Sigma
---------------------------------------------------------
42480.231 | 42612.637 | 0.080 |     0.079 | 1.516 | 1.517

tbl_regression(totalcov_SVI, label = list(age_yrs ~ "Age", pop_dens ~ "Population density", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEMES ~ "Total SVI", mstat_5 ~ "Marital Status"))

Characteristic	Beta	95% CI1	p-value
Age	0.01	0.01, 0.01	<0.001
Marital Status
married	—	—
unknown	0.10	0.02, 0.18	0.013
unmarried	-0.20	-0.28, -0.13	<0.001
div_sep	-0.21	-0.37, -0.05	0.009
widow	-0.35	-0.54, -0.15	<0.001
race_5
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	-0.18	-0.31, -0.06	0.004
Other	-0.11	-0.28, 0.06	0.2
ASIAN	0.39	0.20, 0.57	<0.001
AMERICAN INDIAN AND ALASKA NATIVE	-0.24	-0.71, 0.24	0.3
English Speaking
English	—	—
Other	-0.32	-0.61, -0.03	0.030
Any Religious Affiliation
yes	—	—
no	-0.03	-0.08, 0.03	0.4
Gender
male	—	—
female	0.06	0.01, 0.12	0.025
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.20	-0.01, 0.40	0.062
Population density	0.00	0.00, 0.00	<0.001
r_pct	-0.02	-0.02, -0.02	<0.001
Total SVI	-0.99	-1.1, -0.88	<0.001
1 CI = Confidence Interval

Model 6: Total COVID + RPL_THEMESx4

totalcov_4 <- lm(total_cov_vax ~ age_yrs + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4, data = vax_clean1) 
summary(totalcov_4)

Call:
lm(formula = total_cov_vax ~ age_yrs + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + 
    RPL_THEME2 + RPL_THEME3 + RPL_THEME4, data = vax_clean1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7200 -1.3354 -0.4144  1.3286  5.9428 

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              2.341e+00  9.545e-02  24.525  < 2e-16 ***
age_yrs                                  7.956e-03  8.620e-04   9.229  < 2e-16 ***
mstat_5unknown                           1.064e-01  3.989e-02   2.667 0.007667 ** 
mstat_5unmarried                        -1.943e-01  3.797e-02  -5.118 3.14e-07 ***
mstat_5div_sep                          -2.010e-01  7.979e-02  -2.519 0.011793 *  
mstat_5widow                            -3.247e-01  1.003e-01  -3.235 0.001218 ** 
race_5BLACK OR AFRICAN AMERICAN         -1.364e-01  6.337e-02  -2.153 0.031370 *  
race_5Other                             -1.143e-01  8.695e-02  -1.315 0.188628    
race_5ASIAN                              3.420e-01  9.510e-02   3.596 0.000324 ***
race_5AMERICAN INDIAN AND ALASKA NATIVE -1.722e-01  2.464e-01  -0.699 0.484776    
lang_3Other                             -3.459e-01  1.492e-01  -2.319 0.020433 *  
relig_affilno                           -2.276e-02  2.939e-02  -0.774 0.438867    
genderfemale                             6.099e-02  2.857e-02   2.135 0.032760 *  
ethnic_3HISPANIC                         1.946e-01  1.055e-01   1.844 0.065215 .  
pop_dens                                -1.952e-05  3.790e-06  -5.151 2.64e-07 ***
r_pct                                   -1.660e-02  1.088e-03 -15.260  < 2e-16 ***
RPL_THEME1                              -7.149e-01  8.266e-02  -8.649  < 2e-16 ***
RPL_THEME2                              -3.517e-01  7.244e-02  -4.855 1.22e-06 ***
RPL_THEME3                               1.960e-03  5.934e-02   0.033 0.973651    
RPL_THEME4                              -1.332e-01  6.154e-02  -2.164 0.030503 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.514 on 11477 degrees of freedom
  (3748 observations deleted due to missingness)
Multiple R-squared:  0.08197,   Adjusted R-squared:  0.08045 
F-statistic: 53.94 on 19 and 11477 DF,  p-value: < 2.2e-16

broom::glance(totalcov_4)

broom::tidy(totalcov_4, exponentiate = TRUE)

model_performance(totalcov_4)

# Indices of model performance

AIC       |       BIC |    R2 | R2 (adj.) |  RMSE | Sigma
---------------------------------------------------------
42182.135 | 42336.482 | 0.082 |     0.080 | 1.512 | 1.514

tbl_regression(totalcov_4, label = list(age_yrs ~ "Age", pop_dens ~ "Population density", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", mstat_5 ~ "Marital Status", RPL_THEME4 ~ "Housing and Transportation", mstat_5 ~ "Marital Status"))

Characteristic	Beta	95% CI1	p-value
Age	0.01	0.01, 0.01	<0.001
Marital Status
married	—	—
unknown	0.11	0.03, 0.18	0.008
unmarried	-0.19	-0.27, -0.12	<0.001
div_sep	-0.20	-0.36, -0.04	0.012
widow	-0.32	-0.52, -0.13	0.001
race_5
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	-0.14	-0.26, -0.01	0.031
Other	-0.11	-0.28, 0.06	0.2
ASIAN	0.34	0.16, 0.53	<0.001
AMERICAN INDIAN AND ALASKA NATIVE	-0.17	-0.66, 0.31	0.5
English Speaking
English	—	—
Other	-0.35	-0.64, -0.05	0.020
Any Religious Affiliation
yes	—	—
no	-0.02	-0.08, 0.03	0.4
Gender
male	—	—
female	0.06	0.01, 0.12	0.033
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.19	-0.01, 0.40	0.065
Population density	0.00	0.00, 0.00	<0.001
r_pct	-0.02	-0.02, -0.01	<0.001
Soceioeconomic Status	-0.71	-0.88, -0.55	<0.001
Household Composition	-0.35	-0.49, -0.21	<0.001
Minority Status and Language	0.00	-0.11, 0.12	>0.9
Housing and Transportation	-0.13	-0.25, -0.01	0.031
1 CI = Confidence Interval

Model 7: Any pneum + RPL_THEMES

any_pneum_SVI <- lm(any_pneum ~ age_yrs + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, data = vax_clean1) 
summary(any_pneum_SVI)

Call:
lm(formula = any_pneum ~ age_yrs + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, 
    data = vax_clean1)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0482 -0.6433 -0.5122  0.4341  8.3621 

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              8.565e-01  4.648e-02  18.426  < 2e-16 ***
age_yrs                                  2.806e-03  5.086e-04   5.517 3.52e-08 ***
mstat_5unknown                          -7.952e-02  2.355e-02  -3.377 0.000734 ***
mstat_5unmarried                        -3.995e-02  2.239e-02  -1.784 0.074418 .  
mstat_5div_sep                          -3.674e-02  4.716e-02  -0.779 0.435979    
mstat_5widow                            -4.199e-02  5.918e-02  -0.710 0.477936    
race_5BLACK OR AFRICAN AMERICAN         -6.742e-02  3.707e-02  -1.819 0.068981 .  
race_5Other                             -6.571e-02  5.129e-02  -1.281 0.200160    
race_5ASIAN                              1.133e-02  5.574e-02   0.203 0.838974    
race_5AMERICAN INDIAN AND ALASKA NATIVE  8.304e-02  1.440e-01   0.577 0.564118    
lang_3Other                             -4.564e-02  8.777e-02  -0.520 0.603102    
relig_affilno                           -2.682e-02  1.732e-02  -1.549 0.121526    
genderfemale                            -6.215e-02  1.686e-02  -3.686 0.000229 ***
ethnic_3HISPANIC                         5.187e-02  6.229e-02   0.833 0.404995    
pop_dens                                -5.140e-06  2.212e-06  -2.324 0.020146 *  
r_pct                                   -4.251e-03  5.399e-04  -7.873 3.76e-15 ***
RPL_THEMES                              -2.088e-01  3.338e-02  -6.256 4.10e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8961 on 11550 degrees of freedom
  (3678 observations deleted due to missingness)
Multiple R-squared:  0.01667,   Adjusted R-squared:  0.01531 
F-statistic: 12.24 on 16 and 11550 DF,  p-value: < 2.2e-16

broom::glance(any_pneum_SVI)

broom::tidy(any_pneum_SVI, exponentiate = TRUE)

model_performance(any_pneum_SVI)

# Indices of model performance

AIC       |       BIC |    R2 | R2 (adj.) |  RMSE | Sigma
---------------------------------------------------------
30307.227 | 30439.633 | 0.017 |     0.015 | 0.895 | 0.896

tbl_regression(any_pneum_SVI, label = list(age_yrs ~ "Age", pop_dens ~ "Population density", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEMES ~ "Total SVI", mstat_5 ~ "Marital Status"))

Characteristic	Beta	95% CI1	p-value
Age	0.00	0.00, 0.00	<0.001
Marital Status
married	—	—
unknown	-0.08	-0.13, -0.03	<0.001
unmarried	-0.04	-0.08, 0.00	0.074
div_sep	-0.04	-0.13, 0.06	0.4
widow	-0.04	-0.16, 0.07	0.5
race_5
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	-0.07	-0.14, 0.01	0.069
Other	-0.07	-0.17, 0.03	0.2
ASIAN	0.01	-0.10, 0.12	0.8
AMERICAN INDIAN AND ALASKA NATIVE	0.08	-0.20, 0.37	0.6
English Speaking
English	—	—
Other	-0.05	-0.22, 0.13	0.6
Any Religious Affiliation
yes	—	—
no	-0.03	-0.06, 0.01	0.12
Gender
male	—	—
female	-0.06	-0.10, -0.03	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.05	-0.07, 0.17	0.4
Population density	0.00	0.00, 0.00	0.020
r_pct	0.00	-0.01, 0.00	<0.001
Total SVI	-0.21	-0.27, -0.14	<0.001
1 CI = Confidence Interval

Model 8: Any pneumo + RPL_THEMESx4

any_pneum_4 <- lm(any_pneum ~ age_yrs + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4, data = vax_clean1) 
summary(any_pneum_4)

Call:
lm(formula = any_pneum ~ age_yrs + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + 
    RPL_THEME2 + RPL_THEME3 + RPL_THEME4, data = vax_clean1)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0106 -0.6439 -0.5091  0.4362  8.4083 

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              8.646e-01  5.651e-02  15.301  < 2e-16 ***
age_yrs                                  2.794e-03  5.104e-04   5.474 4.49e-08 ***
mstat_5unknown                          -7.893e-02  2.362e-02  -3.342 0.000835 ***
mstat_5unmarried                        -4.224e-02  2.248e-02  -1.879 0.060244 .  
mstat_5div_sep                          -3.957e-02  4.724e-02  -0.838 0.402246    
mstat_5widow                            -3.536e-02  5.941e-02  -0.595 0.551701    
race_5BLACK OR AFRICAN AMERICAN         -5.927e-02  3.752e-02  -1.580 0.114207    
race_5Other                             -6.544e-02  5.148e-02  -1.271 0.203701    
race_5ASIAN                             -9.381e-04  5.630e-02  -0.017 0.986707    
race_5AMERICAN INDIAN AND ALASKA NATIVE  1.040e-01  1.459e-01   0.713 0.475788    
lang_3Other                             -6.830e-02  8.833e-02  -0.773 0.439400    
relig_affilno                           -2.542e-02  1.740e-02  -1.461 0.144151    
genderfemale                            -6.326e-02  1.691e-02  -3.741 0.000185 ***
ethnic_3HISPANIC                         4.998e-02  6.248e-02   0.800 0.423751    
pop_dens                                -5.515e-06  2.244e-06  -2.458 0.014000 *  
r_pct                                   -3.736e-03  6.442e-04  -5.799 6.83e-09 ***
RPL_THEME1                              -7.921e-02  4.894e-02  -1.619 0.105569    
RPL_THEME2                              -1.499e-01  4.289e-02  -3.494 0.000477 ***
RPL_THEME3                              -1.176e-02  3.513e-02  -0.335 0.737906    
RPL_THEME4                              -3.125e-02  3.643e-02  -0.858 0.391067    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8962 on 11477 degrees of freedom
  (3748 observations deleted due to missingness)
Multiple R-squared:  0.01741,   Adjusted R-squared:  0.01578 
F-statistic:  10.7 on 19 and 11477 DF,  p-value: < 2.2e-16

broom::glance(any_pneum_4)

broom::tidy(any_pneum_4, exponentiate = TRUE)

model_performance(any_pneum_4)

# Indices of model performance

AIC       |       BIC |    R2 | R2 (adj.) |  RMSE | Sigma
---------------------------------------------------------
30128.890 | 30283.237 | 0.017 |     0.016 | 0.895 | 0.896

tbl_regression(any_pneum_4, label = list(age_yrs ~ "Age", pop_dens ~ "Population density", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", mstat_5 ~ "Marital Status", RPL_THEME4 ~ "Housing and Transportation", mstat_5 ~ "Marital Status"))

Characteristic	Beta	95% CI1	p-value
Age	0.00	0.00, 0.00	<0.001
Marital Status
married	—	—
unknown	-0.08	-0.13, -0.03	<0.001
unmarried	-0.04	-0.09, 0.00	0.060
div_sep	-0.04	-0.13, 0.05	0.4
widow	-0.04	-0.15, 0.08	0.6
race_5
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	-0.06	-0.13, 0.01	0.11
Other	-0.07	-0.17, 0.04	0.2
ASIAN	0.00	-0.11, 0.11	>0.9
AMERICAN INDIAN AND ALASKA NATIVE	0.10	-0.18, 0.39	0.5
English Speaking
English	—	—
Other	-0.07	-0.24, 0.10	0.4
Any Religious Affiliation
yes	—	—
no	-0.03	-0.06, 0.01	0.14
Gender
male	—	—
female	-0.06	-0.10, -0.03	<0.001
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.05	-0.07, 0.17	0.4
Population density	0.00	0.00, 0.00	0.014
r_pct	0.00	0.00, 0.00	<0.001
Soceioeconomic Status	-0.08	-0.18, 0.02	0.11
Household Composition	-0.15	-0.23, -0.07	<0.001
Minority Status and Language	-0.01	-0.08, 0.06	0.7
Housing and Transportation	-0.03	-0.10, 0.04	0.4
1 CI = Confidence Interval

Model 9: Shingrix + RPL_THEMES

vax_clean1 %>% 
mutate(age_2 = case_when(
age_yrs<49.99 ~ 0,
TRUE ~ 1)) -> vax_clean2
Shingrix_SVI <- lm(total_shingrix ~ age_2 + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, data = vax_clean2) 
summary(Shingrix_SVI)

Call:
lm(formula = total_shingrix ~ age_2 + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEMES, 
    data = vax_clean2)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.6635 -0.3450 -0.0885  0.0404  4.5285 

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              3.208e-01  2.352e-02  13.640  < 2e-16 ***
age_2                                    3.681e-01  1.182e-02  31.143  < 2e-16 ***
mstat_5unknown                          -3.288e-02  1.458e-02  -2.255 0.024161 *  
mstat_5unmarried                        -5.017e-02  1.350e-02  -3.715 0.000204 ***
mstat_5div_sep                          -6.495e-02  2.968e-02  -2.188 0.028682 *  
mstat_5widow                            -6.246e-02  3.686e-02  -1.695 0.090197 .  
race_5BLACK OR AFRICAN AMERICAN         -6.082e-02  2.333e-02  -2.607 0.009139 ** 
race_5Other                             -3.630e-02  3.228e-02  -1.125 0.260714    
race_5ASIAN                              6.670e-02  3.504e-02   1.903 0.057016 .  
race_5AMERICAN INDIAN AND ALASKA NATIVE -1.869e-03  9.062e-02  -0.021 0.983543    
lang_3Other                             -9.554e-02  5.524e-02  -1.730 0.083703 .  
relig_affilno                           -6.335e-03  1.091e-02  -0.581 0.561376    
genderfemale                             9.225e-03  1.061e-02   0.869 0.384619    
ethnic_3HISPANIC                         4.975e-02  3.918e-02   1.270 0.204252    
pop_dens                                -2.569e-06  1.391e-06  -1.846 0.064887 .  
r_pct                                   -4.122e-03  3.399e-04 -12.128  < 2e-16 ***
RPL_THEMES                              -2.279e-01  2.099e-02 -10.858  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.564 on 11550 degrees of freedom
  (3678 observations deleted due to missingness)
Multiple R-squared:  0.1205,    Adjusted R-squared:  0.1193 
F-statistic: 98.92 on 16 and 11550 DF,  p-value: < 2.2e-16

broom::glance(Shingrix_SVI)

broom::tidy(Shingrix_SVI, exponentiate = TRUE)

model_performance(Shingrix_SVI)

# Indices of model performance

AIC       |       BIC |    R2 | R2 (adj.) |  RMSE | Sigma
---------------------------------------------------------
19594.799 | 19727.205 | 0.121 |     0.119 | 0.564 | 0.564

tbl_regression(Shingrix_SVI, label = list(age_2 ~ "Age > 50", pop_dens ~ "Population density", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEMES ~ "Total SVI", mstat_5 ~ "Marital Status"))

Characteristic	Beta	95% CI1	p-value
Age > 50	0.37	0.34, 0.39	<0.001
Marital Status
married	—	—
unknown	-0.03	-0.06, 0.00	0.024
unmarried	-0.05	-0.08, -0.02	<0.001
div_sep	-0.06	-0.12, -0.01	0.029
widow	-0.06	-0.13, 0.01	0.090
race_5
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	-0.06	-0.11, -0.02	0.009
Other	-0.04	-0.10, 0.03	0.3
ASIAN	0.07	0.00, 0.14	0.057
AMERICAN INDIAN AND ALASKA NATIVE	0.00	-0.18, 0.18	>0.9
English Speaking
English	—	—
Other	-0.10	-0.20, 0.01	0.084
Any Religious Affiliation
yes	—	—
no	-0.01	-0.03, 0.02	0.6
Gender
male	—	—
female	0.01	-0.01, 0.03	0.4
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.05	-0.03, 0.13	0.2
Population density	0.00	0.00, 0.00	0.065
r_pct	0.00	0.00, 0.00	<0.001
Total SVI	-0.23	-0.27, -0.19	<0.001
1 CI = Confidence Interval

Model 10: Shingrix + RPL_THEMESx4

total_shingrix_4 <- lm(total_shingrix ~ age_2 + mstat_5 + race_5 + lang_3 + relig_affil
               + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4, data = vax_clean2) 
summary(total_shingrix_4)

Call:
lm(formula = total_shingrix ~ age_2 + mstat_5 + race_5 + lang_3 + 
    relig_affil + gender + ethnic_3 + pop_dens + r_pct + RPL_THEME1 + 
    RPL_THEME2 + RPL_THEME3 + RPL_THEME4, data = vax_clean2)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.6572 -0.3381 -0.0930  0.0421  4.5538 

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              3.147e-01  3.073e-02  10.239  < 2e-16 ***
age_2                                    3.658e-01  1.182e-02  30.940  < 2e-16 ***
mstat_5unknown                          -3.281e-02  1.458e-02  -2.251 0.024424 *  
mstat_5unmarried                        -5.023e-02  1.351e-02  -3.718 0.000202 ***
mstat_5div_sep                          -6.614e-02  2.963e-02  -2.232 0.025628 *  
mstat_5widow                            -5.581e-02  3.688e-02  -1.513 0.130257    
race_5BLACK OR AFRICAN AMERICAN         -5.013e-02  2.353e-02  -2.131 0.033142 *  
race_5Other                             -3.617e-02  3.228e-02  -1.120 0.262607    
race_5ASIAN                              5.905e-02  3.528e-02   1.674 0.094204 .  
race_5AMERICAN INDIAN AND ALASKA NATIVE  1.562e-02  9.150e-02   0.171 0.864450    
lang_3Other                             -9.170e-02  5.540e-02  -1.655 0.097883 .  
relig_affilno                           -4.957e-03  1.092e-02  -0.454 0.649923    
genderfemale                             7.349e-03  1.061e-02   0.693 0.488393    
ethnic_3HISPANIC                         4.975e-02  3.917e-02   1.270 0.204014    
pop_dens                                -2.052e-06  1.407e-06  -1.458 0.144740    
r_pct                                   -3.593e-03  4.039e-04  -8.896  < 2e-16 ***
RPL_THEME1                              -1.712e-01  3.069e-02  -5.577 2.51e-08 ***
RPL_THEME2                              -6.480e-02  2.689e-02  -2.410 0.015982 *  
RPL_THEME3                              -2.983e-03  2.203e-02  -0.135 0.892308    
RPL_THEME4                              -3.573e-02  2.285e-02  -1.564 0.117873    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5621 on 11477 degrees of freedom
  (3748 observations deleted due to missingness)
Multiple R-squared:  0.1205,    Adjusted R-squared:  0.119 
F-statistic: 82.73 on 19 and 11477 DF,  p-value: < 2.2e-16

broom::glance(total_shingrix_4)

broom::tidy(total_shingrix_4, exponentiate = TRUE)

model_performance(total_shingrix_4)

# Indices of model performance

AIC       |       BIC |    R2 | R2 (adj.) |  RMSE | Sigma
---------------------------------------------------------
19401.044 | 19555.391 | 0.120 |     0.119 | 0.562 | 0.562

tbl_regression(total_shingrix_4, label = list(age_2 ~ "Age > 50", pop_dens ~ "Population density", gender~ "Gender", ethnic_3 ~ "Ethnicity", lang_3 ~ "English Speaking", relig_affil ~ "Any Religious Affiliation", RPL_THEME1 ~ "Soceioeconomic Status", RPL_THEME2 ~ "Household Composition", RPL_THEME3 ~ "Minority Status and Language", mstat_5 ~ "Marital Status", RPL_THEME4 ~ "Housing and Transportation", mstat_5 ~ "Marital Status"))

Characteristic	Beta	95% CI1	p-value
Age > 50	0.37	0.34, 0.39	<0.001
Marital Status
married	—	—
unknown	-0.03	-0.06, 0.00	0.024
unmarried	-0.05	-0.08, -0.02	<0.001
div_sep	-0.07	-0.12, -0.01	0.026
widow	-0.06	-0.13, 0.02	0.13
race_5
WHITE OR CAUCASIAN	—	—
BLACK OR AFRICAN AMERICAN	-0.05	-0.10, 0.00	0.033
Other	-0.04	-0.10, 0.03	0.3
ASIAN	0.06	-0.01, 0.13	0.094
AMERICAN INDIAN AND ALASKA NATIVE	0.02	-0.16, 0.19	0.9
English Speaking
English	—	—
Other	-0.09	-0.20, 0.02	0.10
Any Religious Affiliation
yes	—	—
no	0.00	-0.03, 0.02	0.6
Gender
male	—	—
female	0.01	-0.01, 0.03	0.5
Ethnicity
NON-HISPANIC	—	—
HISPANIC	0.05	-0.03, 0.13	0.2
Population density	0.00	0.00, 0.00	0.14
r_pct	0.00	0.00, 0.00	<0.001
Soceioeconomic Status	-0.17	-0.23, -0.11	<0.001
Household Composition	-0.06	-0.12, -0.01	0.016
Minority Status and Language	0.00	-0.05, 0.04	0.9
Housing and Transportation	-0.04	-0.08, 0.01	0.12
1 CI = Confidence Interval

```