library(tidyverse)
library(survey)
library(broom)
library(dplyr)
library(ggplot2)
# Load BRFSS data (pre-processed to RDS)
BRFSS2011 <- readRDS("C:/Users/63650/Downloads/BRFSS2011.RDS")
BRFSS2012 <- readRDS("C:/Users/63650/Downloads/BRFSS2012.RDS")
BRFSS2013 <- readRDS("C:/Users/63650/Downloads/BRFSS2013.RDS")
BRFSS2014 <- readRDS("C:/Users/63650/Downloads/BRFSS2014.RDS")
BRFSS2015 <- readRDS("C:/Users/63650/Downloads/BRFSS2015.RDS")
BRFSS2016 <- readRDS("C:/Users/63650/Downloads/BRFSS2016.RDS")
BRFSS2017 <- readRDS("C:/Users/63650/Downloads/BRFSS2017.RDS")
BRFSS2018 <- readRDS("C:/Users/63650/Downloads/BRFSS2018.RDS")
BRFSS2019 <- readRDS("C:/Users/63650/Downloads/BRFSS2019.RDS")
BRFSS2020 <- readRDS("C:/Users/63650/Downloads/BRFSS2020.RDS")
BRFSS2021 <- readRDS("C:/Users/63650/Downloads/BRFSS2021.RDS")
BRFSS2022 <- readRDS("C:/Users/63650/Downloads/BRFSS2022.RDS")
BRFSS2023 <- readRDS("C:/Users/63650/Downloads/BRFSS2023.RDS")
# -------------------------
# 2011 — BMI
# -------------------------
tx_bmi_2011 <- BRFSS2011 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete3, x.bmi5) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100 # numeric BMI in standard units
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2011 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2011, nest=TRUE)
out_bmi_2011 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2011, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2011, predictor="bmi")
# -------------------------
# 2012 — BMI
# -------------------------
tx_bmi_2012 <- BRFSS2012 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete3, x.bmi5) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2012 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2012, nest=TRUE)
out_bmi_2012 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2012, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2012, predictor="bmi")
# -------------------------
# 2013 — BMI
# -------------------------
tx_bmi_2013 <- BRFSS2013 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete3, x.bmi5) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2013 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2013, nest=TRUE)
out_bmi_2013 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2013, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2013, predictor="bmi")
# -------------------------
# 2014 — BMI
# -------------------------
tx_bmi_2014 <- BRFSS2014 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete3, x.bmi5) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2014 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2014, nest=TRUE)
out_bmi_2014 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2014, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2014, predictor="bmi")
# -------------------------
# 2015 — BMI
# -------------------------
tx_bmi_2015 <- BRFSS2015 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete3, x.bmi5) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2015 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2015, nest=TRUE)
out_bmi_2015 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2015, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2015, predictor="bmi")
# -------------------------
# 2016 — BMI
# -------------------------
tx_bmi_2016 <- BRFSS2016 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete3, x.bmi5) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2016 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2016, nest=TRUE)
out_bmi_2016 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2016, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2016, predictor="bmi")
# -------------------------
# 2017 — BMI
# -------------------------
tx_bmi_2017 <- BRFSS2017 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete3, x.bmi5) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2017 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2017, nest=TRUE)
out_bmi_2017 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2017, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2017, predictor="bmi")
# -------------------------
# 2018 — BMI
# -------------------------
tx_bmi_2018 <- BRFSS2018 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete3, x.bmi5) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2018 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2018, nest=TRUE)
out_bmi_2018 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2018, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2018, predictor="bmi")
# -------------------------
# NOTE:
# Beginning in 2019, BRFSS renames the diabetes variable from DIABETE3 to DIABETE4.
# To preserve consistency across years, DIABETE4 is renamed to DIABETE3
# before any recoding or regression is performed.
# 2019 — BMI
# -------------------------
tx_bmi_2019 <- BRFSS2019 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete4, x.bmi5) %>%
rename(diabete3 = diabete4) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
des_bmi_2019 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2019, nest=TRUE)
out_bmi_2019 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2019, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2019, predictor="bmi")
# -------------------------
# 2020 — BMI
# -------------------------
tx_bmi_2020 <- BRFSS2020 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete4, x.bmi5) %>%
rename(diabete3 = diabete4) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
tx_bmi_2020 <- tx_bmi_2020 %>%
group_by(x.ststr) %>%
filter(n() >= 10) %>%
ungroup()
des_bmi_2020 <- svydesign(
ids = ~x.psu, strata = ~x.ststr, weights = ~x.llcpwt,
data = tx_bmi_2020, nest = TRUE
)
out_bmi_2020 <- broom::tidy(
svyglm(diabetes_dx ~ bmi, design = des_bmi_2020, family = quasibinomial()),
conf.int = FALSE
) %>%
filter(term != "(Intercept)") %>%
mutate(year = 2020, predictor = "bmi")
# -------------------------
# 2021 — BMI
# -------------------------
tx_bmi_2021 <- BRFSS2021 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete4, x.bmi5) %>%
rename(diabete3 = diabete4) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
tx_bmi_2021 <- tx_bmi_2021 %>%
group_by(x.ststr) %>%
filter(n() >= 10) %>%
ungroup()
des_bmi_2021 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2021, nest=TRUE)
out_bmi_2021 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2021, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2021, predictor="bmi")
# -------------------------
# 2022 — BMI
# -------------------------
tx_bmi_2022 <- BRFSS2022 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete4, x.bmi5) %>%
rename(diabete3 = diabete4) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
tx_bmi_2022 <- tx_bmi_2022 %>%
group_by(x.ststr) %>%
filter(n() >= 10) %>%
ungroup()
des_bmi_2022 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2022, nest=TRUE)
out_bmi_2022 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2022, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2022, predictor="bmi")
# -------------------------
# 2023 — BMI
# -------------------------
tx_bmi_2023 <- BRFSS2023 %>%
filter(x.state == 48) %>%
select(x.psu, x.ststr, x.llcpwt, diabete4, x.bmi5) %>%
rename(diabete3 = diabete4) %>%
mutate(
diabete3 = ifelse(diabete3 %in% c(7, 9), NA, diabete3),
diabetes_dx = case_when(diabete3 == 1 ~ 1, diabete3 == 3 ~ 0, TRUE ~ NA_real_),
x.bmi5 = as.numeric(x.bmi5),
x.bmi5 = ifelse(x.bmi5 %in% c(0, 9999), NA, x.bmi5),
x.bmi5 = ifelse(x.bmi5 < 1200 | x.bmi5 > 8000, NA, x.bmi5),
bmi = x.bmi5 / 100
) %>%
filter(!is.na(diabetes_dx), !is.na(bmi))
tx_bmi_2023 <- tx_bmi_2023 %>%
group_by(x.ststr) %>%
filter(n() >= 10) %>%
ungroup()
des_bmi_2023 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=tx_bmi_2023, nest=TRUE)
out_bmi_2023 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2023, family=quasibinomial()), conf.int=FALSE) %>%
filter(term != "(Intercept)") %>% mutate(year=2023, predictor="bmi")
# Pool 2012–2014 (pre-ACA)
pool_bmi_2012_2014 <- bind_rows(
tx_bmi_2012 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi)),
tx_bmi_2013 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi)),
tx_bmi_2014 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi))
)
des_bmi_2012_2014 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=pool_bmi_2012_2014, nest=TRUE)
out_bmi_2012_2014 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2012_2014, family=quasibinomial()), conf.int=FALSE)
strong_bmi_2012_2014 <- out_bmi_2012_2014 %>% filter(term!="(Intercept)") %>% slice_min(p.value, n=1, with_ties=FALSE)
strong_bmi_2012_2014
# Pool 2014–2016 (early ACA)
pool_bmi_2014_2016 <- bind_rows(
tx_bmi_2014 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi)),
tx_bmi_2015 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi)),
tx_bmi_2016 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi))
)
des_bmi_2014_2016 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=pool_bmi_2014_2016, nest=TRUE)
out_bmi_2014_2016 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2014_2016, family=quasibinomial()), conf.int=FALSE)
strong_bmi_2014_2016 <- out_bmi_2014_2016 %>% filter(term!="(Intercept)") %>% slice_min(p.value, n=1, with_ties=FALSE)
strong_bmi_2014_2016
# Pool 2020–2023 (COVID era)
pool_bmi_2020_2023 <- bind_rows(
tx_bmi_2020 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi)),
tx_bmi_2021 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi)),
tx_bmi_2022 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi)),
tx_bmi_2023 %>% select(x.psu,x.ststr,x.llcpwt,diabetes_dx,bmi) %>%
mutate(x.psu=as.numeric(x.psu), x.ststr=as.numeric(x.ststr), x.llcpwt=as.numeric(x.llcpwt),
diabetes_dx=as.numeric(diabetes_dx), bmi=as.numeric(bmi))
)
des_bmi_2020_2023 <- svydesign(ids=~x.psu, strata=~x.ststr, weights=~x.llcpwt, data=pool_bmi_2020_2023, nest=TRUE)
out_bmi_2020_2023 <- broom::tidy(svyglm(diabetes_dx ~ bmi, design=des_bmi_2020_2023, family=quasibinomial()), conf.int=FALSE)
strong_bmi_2020_2023 <- out_bmi_2020_2023 %>% filter(term!="(Intercept)") %>% slice_min(p.value, n=1, with_ties=FALSE)
strong_bmi_2020_2023
# Combine all years
bmi_all <- bind_rows(
out_bmi_2011 %>% mutate(year = 2011),
out_bmi_2012 %>% mutate(year = 2012),
out_bmi_2013 %>% mutate(year = 2013),
out_bmi_2014 %>% mutate(year = 2014),
out_bmi_2015 %>% mutate(year = 2015),
out_bmi_2016 %>% mutate(year = 2016),
out_bmi_2017 %>% mutate(year = 2017),
out_bmi_2018 %>% mutate(year = 2018),
out_bmi_2019 %>% mutate(year = 2019),
out_bmi_2020 %>% mutate(year = 2020),
out_bmi_2021 %>% mutate(year = 2021),
out_bmi_2022 %>% mutate(year = 2022),
out_bmi_2023 %>% mutate(year = 2023)
)
# Keep only the BMI coefficient (drop intercept)
bmi_strongest <- bmi_all %>%
filter(term != "(Intercept)") %>%
group_by(year) %>%
slice_min(order_by = p.value, n = 1, with_ties = FALSE) %>%
ungroup()
# Build bmi_period_table from pooled strongest objects
bmi_period_table <- bind_rows(
strong_bmi_2012_2014 %>% mutate(period = "2012–2014"),
strong_bmi_2014_2016 %>% mutate(period = "2014–2016"),
strong_bmi_2020_2023 %>% mutate(period = "2020–2023")
)
# Pooled-period points (from bmi_period_table)
bmi_pooled_plot <- bmi_period_table %>%
mutate(
year = c(2013, 2015, 2021.5),
label = term
)
# Brackets below lowest CI
y_ci_min <- min(bmi_strongest$estimate - 1.96 * bmi_strongest$std.error, na.rm = TRUE)
y_ci_max <- max(bmi_strongest$estimate + 1.96 * bmi_strongest$std.error, na.rm = TRUE)
y_pad <- 0.08 * (y_ci_max - y_ci_min)
y_br <- y_ci_min - y_pad
# Plot
ggplot(bmi_strongest, aes(x = year, y = estimate)) +
geom_line() +
geom_point(size = 2.5) +
# Error bars for yearly points
geom_errorbar(
aes(
ymin = estimate - 1.96 * std.error,
ymax = estimate + 1.96 * std.error
),
width = 0.2,
linewidth = 0.6
) +
geom_text(
aes(label = term),
vjust = -1.2,
size = 2.8,
check_overlap = TRUE
) +
geom_vline(xintercept = 2014, linetype = "dashed", color = "#1f77b4", linewidth = 0.8) +
geom_vline(xintercept = 2020, linetype = "dashed", color = "#d62728", linewidth = 0.8) +
annotate(
"text",
x = 2014,
y = max(bmi_strongest$estimate, na.rm = TRUE),
label = "ACA",
color = "#1f77b4",
angle = 90,
hjust = -1.1,
vjust = -0.6,
size = 4
) +
annotate(
"text",
x = 2020,
y = max(bmi_strongest$estimate, na.rm = TRUE),
label = "COVID",
color = "#d62728",
angle = 90,
hjust = -0.3,
vjust = -0.6,
size = 4
) +
scale_x_continuous(breaks = 2011:2023) +
labs(
x = "Year",
y = "Log-odds coefficient",
title = "Most Statistically Significant BMI Coefficient by Year (Texas BRFSS)",
subtitle = "Dashed lines mark 2014 (ACA-era onset) and 2020 (COVID-era onset)"
) +
theme_minimal() +
theme(
axis.text.x = element_text(size = 12, face = "bold", angle = 45, hjust = 1),
axis.text.y = element_text(size = 12, face = "bold"),
axis.title.x = element_text(size = 13, face = "bold"),
axis.title.y = element_text(size = 13, face = "bold"),
plot.title = element_text(face = "bold")
) +
# Pooled-era strongest coefficients
geom_point(
data = bmi_pooled_plot,
aes(x = year, y = estimate),
color = "#2E8B57",
size = 2.5
) +
# Error bars for pooled-era points
geom_errorbar(
data = bmi_pooled_plot,
aes(
x = year,
ymin = estimate - 1.96 * std.error,
ymax = estimate + 1.96 * std.error
),
width = 0.4,
linewidth = 0.8,
color = "#2E8B57"
) +
geom_text(
data = bmi_pooled_plot,
aes(x = year, y = estimate, label = label, vjust = ifelse(year == 2021.5, -1.2, 1.4)),
color = "#2E8B57",
size = 3
) +
# Green brackets showing pooled-year ranges
annotate(
"segment",
x = c(2012, 2014, 2020),
xend = c(2014, 2016, 2023),
y = y_br, # CHANGE (was: min(...) - 0.5)
yend = y_br, # CHANGE
linewidth = 1,
color = "#2E8B57"
) +
annotate(
"segment",
x = c(2012, 2014, 2020),
xend = c(2012, 2014, 2020),
y = y_br - 0.005,
yend = y_br + 0.005,
linewidth = 1,
color = "#2E8B57"
) +
annotate(
"segment",
x = c(2014, 2016, 2023),
xend = c(2014, 2016, 2023),
y = y_br - 0.005,
yend = y_br + 0.005,
linewidth = 1,
color = "#2E8B57"
)