Covariates

We adjusted our model for factors known to be associated with the BE and weight change. Beyond inclusion of baseline age, sex, and race/ethnicity as main effects in the models, we adjusted for having Medicaid (yes, no), baseline weight (nonlinearly via spline terms with 5 degrees of freedom (DF), where the association of baseline weight was allowed to differ by sex), and residential property values (inflation-adjusted, year-specific deciles). Due to the adjustment for baseline weights, we estimate the association between baseline BE exposure and weight change, beyond any observed relationship between baseline BE exposures, or exposures prior to the study period, and baseline weight. The main effect term of baseline age was included in the model nonlinearly via natural cubic spline terms with 10 DF and knots at quantiles. Residential property value was our primary proxy measure for socioeconomic status (SES) since the EHR does not capture metrics typically used to evaluate SES (e.g., income and education). Prior health and social science research have demonstrated that residential property values are highly correlated with individual and area-level SES and are predictive of health [3,34]. Residential property values were measured at the tax-parcel level, and reflect the combined relative, local value of a given home and the land it on which it rests [35]. Medicaid adjustment served as an additional proxy indicator for SES.