Using generalized estimating equations (GEE) with robust standard errors and accounting for the clustering effect at county level, we performed multivariable analyses of smoking prevalence and age-adjusted lung cancer rates with-respect-to the strengths of smoke-free ordinances at the county level over time. The county level characteristics over time served as control variables in each GEE model. This model allowed flexibility to modify the indicator for strength of municipal SFOs to reflect policy changes that occurred over time. These included changing from no ordinances to a municipal ordinance or strengthening of initial ordinance after implementation. Estimates of differences between smoking prevalence and lung cancer incidence between counties and corresponding 95% confidence intervals were calculated for each model effect, and significance levels for the estimates were determined using Wald tests. We conducted our analysis with Stata/SE 14.2 [StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP] software.

Our study was granted exemption status by the Indiana University Institutional Review Board. All data samples were fully anonymized before we accessed them.

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