Estimating county-level dental care utilization among adults in California using multilevel modeling with raking approach

Regular dental visits are essential for oral health, yet disparities between regions exist due to socioeconomic and geographic factors. While national surveys provide valuable data on dental care utilization, they generally lack sufficient sample sizes at the local level to generate reliable county-level estimates. Small area estimation techniques, such as multilevel regression and post-stratification (MRP), can help address this gap by producing robust estimates for smaller geographic areas. However, the MRP approach relies on detailed population data in the form of joint distributions and cannot be applied when only marginal distributions are available.

This paper introduces a hybrid approach combining multilevel modeling with the raking procedure. We used individual-level data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) and census data from American Community Survey to estimate county-level dental care utilization among adults in California.

The proposed approach accounts for individual- and area-level factors while overcoming data constraints that limit the application of MRP. The findings demonstrate the feasibility of this approach in generating county-level estimates, supporting public health planning and targeted interventions to reduce disparities in dental care utilization.