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Stratified-Analysis.Rmd
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Stratified-Analysis.Rmd
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---
title: "Stratified Analysis"
author: "Ottavia Prunas"
date: '2023-09-22'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(ggplot2)
library(duckdb)
library(arrow)
library(lubridate)
library(reshape2)
library(vtable)
library(tidyr)
```
```{r}
a1_stream <- open_dataset("./Data/PCV_COVID_export_table.csv", format = "csv") %>%
to_duckdb() %>%
mutate(COVID_severity_bin1 = if_else(COVID_severity=='non_severe', 0,
if_else(COVID_severity %in%
c("resp_severe",'ICU_crit_care'),1, NA_real_)),
COVID_severity_bin2 = if_else(COVID_severity=='resp_severe', 0,
if_else(COVID_severity %in%
c('ICU_crit_care'),1, NA_real_)),
COVID_severity_bin3 = if_else(COVID_severity=='non_severe', 0,
if_else(COVID_severity %in%
c('ICU_crit_care'),1, NA_real_)),
COVID_resp_bin1 = if_else(COVID_severity=='non_resp_severe', 0,
if_else(COVID_severity %in%
c('resp_severe','ICU_crit_care'),1, NA_real_))
) %>%
select(desy_sort_key,patient_yob,COVID_severity,COVID_severity_bin1,COVID_severity_bin2,COVID_severity_bin3,COVID_resp_bin1, age_grp, PCV_combined,zoster_vacc,flu_vacc,race_code,comorbidities,BMI_30_plus,PPSV23_diff,PCV13_diff,PPSV23_date,PCV13_date,earliest_COVID_date,reference_year,year_month_COVID) %>%
collect()
```
##Filter out Covid(-)
```{r}
a1_stream<-a1_stream[!is.na(a1_stream$earliest_COVID_date),]
```
```{r}
a1_stream <- a1_stream %>% mutate(PPSV23_more5yrs = case_when(
!is.na(PPSV23_date) & PPSV23_diff>5 ~ 1,
TRUE ~ 0 # This is for all other values
),
PPSV23_5yrs = case_when(
!is.na(PPSV23_date) & PPSV23_diff<=5 ~ 1,
TRUE ~ 0 # This is for all other values
),
PCV13 = case_when(
!is.na(PCV13_date) ~ 1,
TRUE ~ 0 # This is for all other values
))
```
#Stratify by COVID-19 variants
## Original strain
```{r}
a1 <- a1_stream %>%
filter(year_month_COVID<"2020-09") %>%
mutate(age_grp=as.factor(age_grp),
PCV_combined= factor( PCV_combined, levels=c("PCV_unvacc","PCV13",'PCV_other',"PPSV23_5yrs","PCV13_PPSV23_5yrs" )),
zoster_vacc = as.factor(zoster_vacc),
flu_vacc = as.factor(flu_vacc),
race_code = as.factor(race_code),
)
```
```{r}
a_symp <- read.csv("./Data/yearly_resp_symptoms.csv")
a_symp_min <- a_symp %>%
group_by(desy_sort_key) %>%
slice(which.min(resp_symp_year)) %>%
ungroup() %>%
mutate(year_COVID = year(earliest_COVID_date)) %>%
filter(resp_symp_year<year_COVID)
a1_symp <- a1 %>%
left_join(a_symp_min,by="desy_sort_key") %>%
mutate(resp_symp=ifelse(!is.na(resp_symp_year),1,0))
a1_symp <- a1_symp %>%
mutate(resp_symp = as.factor(resp_symp))
```
```{r}
#For mod 1, we see effects for PCV, not for zoster or flu
#should definition be hierarchical (1+2+3 vs 2+3 vs 3)
# Mod1: (resp severe + ICU_crit_care) vs non-severe
mod1 <- glm(COVID_severity_bin1 ~ age_grp + PPSV23_more5yrs+PPSV23_5yrs+PCV13+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod1)
m.ci<-confint(mod1)
m.table <- cbind(coef(mod1),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod1)["PCV13"]+coef(mod1)["PPSV23_5yrs"])
SE = sqrt(vcov(mod1)["PCV13","PCV13"]+vcov(mod1)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod1)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod1)["PCV13"]+coef(mod1)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod1)["PCV13","PCV13"]+vcov(mod1)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod1)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#For mod 1, we see effects for PCV, not for zoster or flu
#should definition be hierarchical (1+2+3 vs 2+3 vs 3)
# Mod1: (resp severe + ICU_crit_care) vs non-severe
mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod1)
m.ci<-confint(mod1)
m.table <- cbind(coef(mod1),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
```{r}
#Mod2: (resp severe + ICU_crit_care) vs non-resp-severe
mod2 <- glm(COVID_resp_bin1 ~ age_grp + PPSV23_more5yrs+PPSV23_5yrs+PCV13+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod2)
m.ci<-confint(mod2)
m.table <- cbind(coef(mod2),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod2)["PCV13"]+coef(mod2)["PPSV23_5yrs"])
SE = sqrt(vcov(mod2)["PCV13","PCV13"]+vcov(mod2)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod2)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod2)["PCV13"]+coef(mod2)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod2)["PCV13","PCV13"]+vcov(mod2)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod2)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#Mod2: (resp severe + ICU_crit_care) vs non-resp-severe
mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod2)
m.ci<-confint(mod2)
m.table <- cbind(coef(mod2),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
```{r}
#for mod 3, we see effects fo PCV AND zoster AND fly; suggesting bias (should look at flu prevalence during this period)
#Mod2: ICU crit care vs resp severe
mod3 <- glm(COVID_severity_bin2 ~ age_grp +PPSV23_more5yrs+PPSV23_5yrs+PCV13+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod3)
m.ci<-confint(mod3)
m.table <- cbind(coef(mod3),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod3)["PCV13"]+coef(mod3)["PPSV23_5yrs"])
SE = sqrt(vcov(mod3)["PCV13","PCV13"]+vcov(mod3)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod3)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod3)["PCV13"]+coef(mod3)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod3)["PCV13","PCV13"]+vcov(mod3)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod3)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#for mod 3, we see effects fo PCV AND zoster AND fly; suggesting bias (should look at flu prevalence during this period)
#Mod2: ICU crit care vs resp severe
mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod3)
m.ci<-confint(mod3)
m.table <- cbind(coef(mod3),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
## Model 4: ICU crit care vs non severe
```{r}
#Mod4: ICU crit care vs non-severe
mod4 <- glm(COVID_severity_bin3 ~ age_grp +PPSV23_more5yrs+PPSV23_5yrs+PCV13+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod4)
m.ci<-confint(mod4)
m.table <- cbind(coef(mod4),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod4)["PCV13"]+coef(mod4)["PPSV23_5yrs"])
SE = sqrt(vcov(mod4)["PCV13","PCV13"]+vcov(mod4)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod4)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod4)["PCV13"]+coef(mod4)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod4)["PCV13","PCV13"]+vcov(mod4)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod4)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#Negative control analysis
mod4 <- glm(COVID_severity_bin3 ~ age_grp +zoster_vacc+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod4)
m.ci<-confint(mod4)
m.table <- cbind(coef(mod4),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
#Stratify by COVID-19 variants
## Alpha variant
```{r}
a1 <- a1_stream %>%
filter(year_month_COVID>="2020-09" & year_month_COVID<="2021-03")%>%
mutate(age_grp=as.factor(age_grp),
PCV_combined= factor( PCV_combined, levels=c("PCV_unvacc","PCV13",'PCV_other',"PPSV23_5yrs","PCV13_PPSV23_5yrs" )),
zoster_vacc = as.factor(zoster_vacc),
flu_vacc = as.factor(flu_vacc),
race_code = as.factor(race_code),
)
```
```{r}
a_symp <- read.csv("./Data/yearly_resp_symptoms.csv")
a_symp_min <- a_symp %>%
group_by(desy_sort_key) %>%
slice(which.min(resp_symp_year)) %>%
ungroup() %>%
mutate(year_COVID = year(earliest_COVID_date)) %>%
filter(resp_symp_year<year_COVID)
a1_symp <- a1 %>%
left_join(a_symp_min,by="desy_sort_key") %>%
mutate(resp_symp=ifelse(!is.na(resp_symp_year),1,0))
a1_symp <- a1_symp %>%
mutate(resp_symp = as.factor(resp_symp))
```
```{r}
#For mod 1, we see effects for PCV, not for zoster or flu
#should definition be hierarchical (1+2+3 vs 2+3 vs 3)
# Mod1: (resp severe + ICU_crit_care) vs non-severe
mod1 <- glm(COVID_severity_bin1 ~ age_grp + PPSV23_more5yrs+PPSV23_5yrs+PCV13+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod1)
m.ci<-confint(mod1)
m.table <- cbind(coef(mod1),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod1)["PCV13"]+coef(mod1)["PPSV23_5yrs"])
SE = sqrt(vcov(mod1)["PCV13","PCV13"]+vcov(mod1)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod1)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod1)["PCV13"]+coef(mod1)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod1)["PCV13","PCV13"]+vcov(mod1)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod1)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#For mod 1, we see effects for PCV, not for zoster or flu
#should definition be hierarchical (1+2+3 vs 2+3 vs 3)
# Mod1: (resp severe + ICU_crit_care) vs non-severe
mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod1)
m.ci<-confint(mod1)
m.table <- cbind(coef(mod1),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
```{r}
#Mod2: (resp severe + ICU_crit_care) vs non-resp-severe
mod2 <- glm(COVID_resp_bin1 ~ age_grp + PPSV23_more5yrs+PPSV23_5yrs+PCV13+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod2)
m.ci<-confint(mod2)
m.table <- cbind(coef(mod2),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod2)["PCV13"]+coef(mod2)["PPSV23_5yrs"])
SE = sqrt(vcov(mod2)["PCV13","PCV13"]+vcov(mod2)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod2)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod2)["PCV13"]+coef(mod2)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod2)["PCV13","PCV13"]+vcov(mod2)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod2)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#Mod2: (resp severe + ICU_crit_care) vs non-resp-severe
mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod2)
m.ci<-confint(mod2)
m.table <- cbind(coef(mod2),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
```{r}
#for mod 3, we see effects fo PCV AND zoster AND fly; suggesting bias (should look at flu prevalence during this period)
#Mod2: ICU crit care vs resp severe
mod3 <- glm(COVID_severity_bin2 ~ age_grp +PPSV23_more5yrs+PPSV23_5yrs+PCV13+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod3)
m.ci<-confint(mod3)
m.table <- cbind(coef(mod3),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod3)["PCV13"]+coef(mod3)["PPSV23_5yrs"])
SE = sqrt(vcov(mod3)["PCV13","PCV13"]+vcov(mod3)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod3)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod3)["PCV13"]+coef(mod3)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod3)["PCV13","PCV13"]+vcov(mod3)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod3)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#for mod 3, we see effects fo PCV AND zoster AND fly; suggesting bias (should look at flu prevalence during this period)
#Mod2: ICU crit care vs resp severe
mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod3)
m.ci<-confint(mod3)
m.table <- cbind(coef(mod3),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
## Model 4: ICU crit care vs non severe
```{r}
#Mod4: ICU crit care vs non-severe
mod4 <- glm(COVID_severity_bin3 ~ age_grp +PPSV23_more5yrs+PPSV23_5yrs+PCV13+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod4)
m.ci<-confint(mod4)
m.table <- cbind(coef(mod4),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod4)["PCV13"]+coef(mod4)["PPSV23_5yrs"])
SE = sqrt(vcov(mod4)["PCV13","PCV13"]+vcov(mod4)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod4)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod4)["PCV13"]+coef(mod4)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod4)["PCV13","PCV13"]+vcov(mod4)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod4)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#Negative control analysis
mod4 <- glm(COVID_severity_bin3 ~ age_grp +zoster_vacc+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod4)
m.ci<-confint(mod4)
m.table <- cbind(coef(mod4),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
#Stratify by COVID-19 variants
## Delta variant
```{r}
a1 <- a1_stream %>%
filter(year_month_COVID>"2021-03") %>%
mutate(age_grp=as.factor(age_grp),
PCV_combined= factor( PCV_combined, levels=c("PCV_unvacc","PCV13",'PCV_other',"PPSV23_5yrs","PCV13_PPSV23_5yrs" )),
zoster_vacc = as.factor(zoster_vacc),
flu_vacc = as.factor(flu_vacc),
race_code = as.factor(race_code),
)
```
```{r}
a_symp <- read.csv("./Data/yearly_resp_symptoms.csv")
a_symp_min <- a_symp %>%
group_by(desy_sort_key) %>%
slice(which.min(resp_symp_year)) %>%
ungroup() %>%
mutate(year_COVID = year(earliest_COVID_date)) %>%
filter(resp_symp_year<year_COVID)
a1_symp <- a1 %>%
left_join(a_symp_min,by="desy_sort_key") %>%
mutate(resp_symp=ifelse(!is.na(resp_symp_year),1,0))
a1_symp <- a1_symp %>%
mutate(resp_symp = as.factor(resp_symp))
```
```{r}
#For mod 1, we see effects for PCV, not for zoster or flu
#should definition be hierarchical (1+2+3 vs 2+3 vs 3)
# Mod1: (resp severe + ICU_crit_care) vs non-severe
mod1 <- glm(COVID_severity_bin1 ~ age_grp + PPSV23_more5yrs+PPSV23_5yrs+PCV13+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod1)
m.ci<-confint(mod1)
m.table <- cbind(coef(mod1),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod1)["PCV13"]+coef(mod1)["PPSV23_5yrs"])
SE = sqrt(vcov(mod1)["PCV13","PCV13"]+vcov(mod1)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod1)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod1)["PCV13"]+coef(mod1)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod1)["PCV13","PCV13"]+vcov(mod1)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod1)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#For mod 1, we see effects for PCV, not for zoster or flu
#should definition be hierarchical (1+2+3 vs 2+3 vs 3)
# Mod1: (resp severe + ICU_crit_care) vs non-severe
mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod1 <- glm(COVID_severity_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod1)
m.ci<-confint(mod1)
m.table <- cbind(coef(mod1),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
```{r}
#Mod2: (resp severe + ICU_crit_care) vs non-resp-severe
mod2 <- glm(COVID_resp_bin1 ~ age_grp + PPSV23_more5yrs+PPSV23_5yrs+PCV13+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod2)
m.ci<-confint(mod2)
m.table <- cbind(coef(mod2),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod2)["PCV13"]+coef(mod2)["PPSV23_5yrs"])
SE = sqrt(vcov(mod2)["PCV13","PCV13"]+vcov(mod2)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod2)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod2)["PCV13"]+coef(mod2)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod2)["PCV13","PCV13"]+vcov(mod2)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod2)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#Mod2: (resp severe + ICU_crit_care) vs non-resp-severe
mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+resp_symp+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod2 <- glm(COVID_resp_bin1 ~ age_grp + zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod2)
m.ci<-confint(mod2)
m.table <- cbind(coef(mod2),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
```{r}
#for mod 3, we see effects fo PCV AND zoster AND fly; suggesting bias (should look at flu prevalence during this period)
#Mod2: ICU crit care vs resp severe
mod3 <- glm(COVID_severity_bin2 ~ age_grp +PPSV23_more5yrs+PPSV23_5yrs+PCV13+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod3)
m.ci<-confint(mod3)
m.table <- cbind(coef(mod3),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod3)["PCV13"]+coef(mod3)["PPSV23_5yrs"])
SE = sqrt(vcov(mod3)["PCV13","PCV13"]+vcov(mod3)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod3)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod3)["PCV13"]+coef(mod3)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod3)["PCV13","PCV13"]+vcov(mod3)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod3)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#for mod 3, we see effects fo PCV AND zoster AND fly; suggesting bias (should look at flu prevalence during this period)
#Mod2: ICU crit care vs resp severe
mod3 <- glm(COVID_severity_bin2 ~ age_grp +flu_vacc+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod3)
m.ci<-confint(mod3)
m.table <- cbind(coef(mod3),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```
## Model 4: ICU crit care vs non severe
```{r}
#Mod4: ICU crit care vs non-severe
mod4 <- glm(COVID_severity_bin3 ~ age_grp +PPSV23_more5yrs+PPSV23_5yrs+PCV13+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod4)
m.ci<-confint(mod4)
m.table <- cbind(coef(mod4),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
#Now check PCV13+PPSV23_5yrs
est=exp(coef(mod4)["PCV13"]+coef(mod4)["PPSV23_5yrs"])
SE = sqrt(vcov(mod4)["PCV13","PCV13"]+vcov(mod4)["PPSV23_5yrs","PPSV23_5yrs"]+2*vcov(mod4)["PCV13","PPSV23_5yrs"])
est+1.96*SE
est-1.96*SE
est
#Now check PCV13+PPSV23_more5yrs
est=exp(coef(mod4)["PCV13"]+coef(mod4)["PPSV23_more5yrs"])
SE = sqrt(vcov(mod4)["PCV13","PCV13"]+vcov(mod4)["PPSV23_more5yrs","PPSV23_more5yrs"]+2*vcov(mod4)["PCV13","PPSV23_more5yrs"])
est+1.96*SE
est-1.96*SE
est
```
```{r}
#Negative control analysis
mod4 <- glm(COVID_severity_bin3 ~ age_grp +zoster_vacc+resp_symp+comorbidities+race_code+BMI_30_plus, data=a1_symp, family='binomial')
# mod3 <- glm(COVID_severity_bin2 ~ age_grp +zoster_vacc+comorbidities+race_code+BMI_30_plus, data=a1, family='binomial')
summary(mod4)
m.ci<-confint(mod4)
m.table <- cbind(coef(mod4),m.ci)
colnames(m.table) <- c("estimate","lower","upper")
m.table <- exp(m.table)
```