This analysis uses Texas BRFSS data from 2011–2023 to estimate survey-weighted logistic regressions of diagnosed diabetes on routine checkup status, evaluated separately by year. The objective is to assess whether associations between recent healthcare contact and diagnosed diabetes are stable over time or reflect diagnosis and detection dynamics rather than underlying disease risk.

This analysis uses Texas BRFSS data from 2011–2023 to estimate year-by-year survey-weighted logistic regressions of diagnosed diabetes on household income categories. Income is harmonized across years and analyzed one year at a time to evaluate whether its association with diagnosed diabetes is stable or sensitive to survey design and missing-data constraints.

This analysis uses Texas BRFSS data from 2011–2023 to estimate year-by-year survey-weighted logistic regressions of diagnosed diabetes on health insurance coverage. Insurance is coded consistently across years and evaluated one year at a time to assess whether its association with diagnosed diabetes is stable or varies over time.

This analysis uses Texas BRFSS data from 2011–2023 to examine the association between educational attainment and diagnosed diabetes using survey-weighted logistic regression. Education is harmonized across years and analyzed one year at a time to assess whether its relationship with diagnosed diabetes is stable or varies over time.

This script uses Texas BRFSS data from 2011–2023 to construct a binary exercise indicator, define a diagnosed-diabetes outcome, and estimate survey-weighted logistic regressions of diabetes on exercise status for each year. The resulting coefficients are compared across years to evaluate the stability and strength of the exercise–diabetes association over time.

This script loads Texas BRFSS 2011–2023, harmonizes smoking status into consistent categories, constructs a binary diagnosed-diabetes outcome, and runs survey-weighted logistic regressions of diabetes on smoking status for each year. It then extracts the smoking coefficients year by year to assess the stability and magnitude of associations over time.

This script loads Texas BRFSS data from 2011–2023, constructs a cleaned continuous BMI measure from reported height and weight, defines a binary diagnosed-diabetes outcome, and estimates survey-weighted logistic regressions of diabetes on BMI for each year. It then compares the magnitude and statistical strength of the BMI coefficient across years and pooled periods to assess the stability of the BMI–diabetes association over time.

This script loads Texas BRFSS 2011–2023, harmonizes sex coding across years, constructs a binary diagnosed diabetes outcome, and estimates survey weighted logistic regressions of diabetes on sex for each year using the BRFSS sampling design. It then extracts the sex coefficient from each year’s model and compiles the estimates to examine the stability and magnitude of sex differences in diagnosed diabetes over time.

This code restricts BRFSS 2011 to Texas respondents with a valid diagnosed-diabetes outcome and constructs predictor-specific analytic samples with appropriate recoding and missing-data handling. It then fits separate bivariate survey-weighted logistic regressions of diabetes on each predictor and reports sample sizes and tidy coefficient estimates.

This script loads Texas BRFSS 2011–2023, harmonizes age into consistent five-year groups, constructs a binary diagnosed-diabetes outcome, and runs survey-weighted logistic regressions of diabetes on age group for each year plus pooled periods. It then extracts the most statistically significant age-group coefficient per year (and per pooled period) and plots those log-odds estimates with 95% error bars, marking 2014 (ACA) and 2020 (COVID).

This code constructs a Texas-only BRFSS 2011 analytic sample, rigorously cleans and recodes health, behavioral, socioeconomic, and access-to-care variables, applies multiple imputation for missing covariates, and defines survey-weighted designs consistent with the BRFSS sampling structure. It then estimates a sequence of survey-weighted logistic regression models of diagnosed diabetes, progressively adding regional fixed effects, interaction terms, and nonlinear specifications to assess robustness and heterogeneity.

Statistical analysis (logistic regression, decision tree) on publicly accessible kaggle dataset regarding diabetes factors