brfss_core: BRFSS Demographic, Socioeconomic, Health Care, Status and...
In bencapistrant/brfssR: Behavioral Risk Factor Surveillance Survey (BRFSS) Data

Description Usage Format Source Examples

BRFSS Demographic, Socioeconomic, Health Care, Status and Behaviors Data

1	brfss_core

A long data frame with rows of individual BRFSS respondents in a given state and year and the following column variables:

age_num: numeric, quantiative 18-99
age_cat: factor, categorical 5-year age categories (18-24, 25-29,…95-99)
age_10ycat_fct: factor, categorical 10-year age categories (18-24, 25-34,35-44,45-54,55-64,65+)
millennial_d_num: numeric, dichotomous 1/0
millennial_d_fct: factor, dichotomous Yes/No
agege45_d_num: numeric, dichotomous 1/0
agege45_d_fct: factor, dichotomous Yes/No
agege65_d_num: numeric, dichotomous 1/0
agege65_d_fct: factor, dichotomous Yes/No
mstat_cat_fct: factor, categorical Married, Divorced, Widowed, Separated, Never Married, Coupled
mstat_cat_fct: factor, categorical Married, Divorced, Widowed, Separated, Never Married, Coupled
married_d_fct: factor, dichotomous: Married or Coupled Yes/No (No: divorced, widowed, separated, never married)
chld_num: numeric, quantiative 0-87
chld_cat_fct: factor, categorical None, One, Two or More
kids_d_fct: factor, dichotomous Any Kids Yes/No
race_cat_fct: factor, categorical White only, Black or African American only, American Indian or Alaskan Native only,Asian only, Native Hawaiian or other Pacific Islander only,Other race only,Multiracial

]

ethn_cat_fct: factor, categorical Hispanic, Non-Hispanic
raceth_cat_fct: factor, categorical White Non-Hispanic; Black, Non-Hispanic; Other, Non-Hispanic; Multiple, Non-Hispanic; Hispanic
reth_imp_cat_fct: factor, categorical from imputed race (no missing): White Non-Hispanic; Black, Non-Hispanic; Other, Non-Hispanic; Asian, Non-Hispanic; Native, Non-Hispanic, Hispanic
sex_d_fct: factor, dichotomous female/male
fem_d_num: numeric, dichotomous 0/1 (male/female)
vtrn_d_num: numeric, dichotomous 0/1 (Non-Vet/Veteran)
vtrn_d_fct: factor, dichotomous Non-Veteran, Veteran
msa_cat_fct: factor, categorical City_Center, City_County, Suburb, Outside MSA
msa_d_fct: factor, dichotomous Yes/No
metro_d_fct: factor, dichotomous Yes/No: 2018-2019 only (raw BRFSS variable: _METSTAT)
urban_d_fct: factor, dichotomous Yes/No: 2018-2019 only (raw BRFSS variable: _URBSTAT)
drnkbng_d_fct: factor, dichotomous Yes/No
drnkhvy_d_fct: factor, dichotomous Yes/No
bmi_cat_fct: factor, categorical Underweight, Normal Weight, Overweight, Obese, Unkown
bmi_num: numeric, quantitative value of BMI
bmi_40d_fct: BMI>=40: factor, dichotomous (No/Yes)
obese_d_fct: BMI>=30: factor, dichotomous (No/Yes)
hiv_d_num: numeric, dichotomous 0/1 (No/Yes)
hiv_d_fct: factor, dichotomous Yes/No
fluvac_d_fct: factor, dichotomous Yes/No
fluvac_d_num: numeric, dichotomous 0/1 (No/Yes)
ltpa_d_fct: factor, dichotomous Yes/No
smk_cat_fct: factor, categorical Current smoker, Former smoker, Never smoker
smoker_d_fct: factor, categorical: Current smoker vs. Former/Never Yes/No
drcost_d_fct: factor, dichotomous Yes/No
hcplan_d_fct: factor, dichotomous Yes/No
chckup_cat_fct: factor, categorical <1 year, 1-2 years, 2-5 years, 5+ years, Never
dntst_cat_fct: factor, categorical <1 year, 1-2 years, 2-5 years, 5+ years, Never
chron_num: numeric, quantiative 0-7
chron_d_fct: factor, dichotomous Yes/No
cvd_d_num: Cardiovascular Disease: numeric, dichotomous 0/1 (No/Yes)
strk_d_num: Stroke: numeric, dichotomous 0/1 (No/Yes)
diab_d_num: Diabetes: numeric, dichotomous 0/1 (No/Yes)
asth_d_num: Asthma: numeric, dichotomous 0/1 (No/Yes)
arth_d_num: Arthritis: numeric, dichotomous 0/1 (No/Yes)
copd_d_num: COPD: numeric, dichotomous 0/1 (No/Yes)
cncr_d_num: Cancer: numeric, dichotomous 0/1 (No/Yes)
kidn_d_num: Kidney Disease: numeric, dichotomous 0/1 (No/Yes)
dep_d_fct: factor, dichotomous No/Yes
mentqol_num: numeric, quantitative, 0-30
mentqol14_d_num: numeric, dichotomous 0/1 (No/Yes)
mentqol14_d_fct: factor, dichotomous No/Yes
physqol_num: numeric, quantitative, 0-30
physqol14_d_num: numeric, dichotomous 0/1 (No/Yes)
physqol14_d_fct: factor, dichotomous No/Yes
srh_d_fct: factor, dichotomous: Good+ vs. Fair/Poor
srh_cat_fct: factor, categorical: Excellent, Very Good, Good, Fair, Poor
empl_cat_fct: factor, categorical: Out of work, Employed for wages, Self-employed,A homemaker, A student, Retired, Unable to work
employed_d_fct: factor, dichotomous: Employed for Wages or Self-Employed? Yes / No
inc_cat_fct: factor, categorical: <$10,000, $10,000-$14,999, $15,000-19,999, $20,000-$24,999,$25,000-34,999, $35,000-$49,999,$50,000-74,999, $75,000+,Don't know or refused
income_4cats_fct: factor, categorical: <$20,000; 20,000-34,999; 35,000-74,999; $75,000+
educ_cat_fct: factor, categorical:<High School,High School,Some College,College or More
college_d_fct: factor, categorical: College or More Yes/No
x_state: numeric variable of state
state: state FIPS code, labeled with state alphabetic abbreviation
fips: state FIPS code, numeric
seqno: identificaiton variable
year: Numeric year: 2014-2019
x_psu: Primary Sampling Unit Variable
x_ststr: Sampling Strata Variable
var_wt_raw: Original,raw sampling weight: 2014-2020
version_var: Character, Version of data: Core (X_LLCPWT), V1 (X_LCPWTV1), V2(X_LCPWTV2), V3 (X_LCPWTV3)

BRFSS Annual Survey Data https://www.cdc.gov/brfss/annual_data/annual_data.htm.

# To adjust the sampling weight (var_wt_raw) by dividing
# the sampling weight by the number of instances a state
# is in the data, run:
library(tidyverse)
data(brfss_core)
waves<-brfss_core %>%
  filter(year %in% 2016:2018) %>% #keeping only 2016-2018 for illustration
  group_by(year,state) %>%
  slice(1) %>% #keeping the first observation of each state + year
  ungroup() %>%
  group_by(state) %>% #grouping by state
  count() %>% #counting how many years the state was included
  rename(wave=n) #renaming as wave
brfss_core<-full_join(brfss_core,waves,by="state") %>%
  mutate(var_wt_adj = var_wt_raw/wave) #adjusting the weight by number of waves