Estimates created using least-squares means from fitted multilevel models. The models used fixed effects to adjust for age, gender, race/ethnicity, education, insurance, number of clinic visits, language, clinic connectedness, comorbidity and census tract level median household income, poverty rates, ‘food desert’ status, unemployment, numbers living in group quarters, vehicle access and segregation. To account for clustering within practices, we included a practice-level random effects term. To account for area-level clustering, we used a ZIP-level random effects term. These were fit as crossed effects models (ie, we did not nest practices within ZIP codes) to allow for the fact that patients are often seen in practices outside of their ZIP code of residence.

BMI, body mass index.