Strategically validating error-prone food access measures using map-based software with efficient, equity-focused analyses in mind

Thomas M. Menino Convention & Exhibition Center 

Quantifying neighborhood food environments and understanding their relationships with residents' health is a public health priority. Using simple, error-prone food access metrics (like the shortest straight-line routes) introduces measurement error and leads to bias in downstream statistical models, but measuring the more-accurate, map-based ones (like the shortest map-based driving routes) for entire studies is often implausible. Fortunately, adopting a two-phase design can harness the best of both metrics by combining the error-prone values for the entire study and the more-accurate ones for a chosen subset. Fortunately, this subset to be validated with map-based food access measures can be strategically chosen to not only reduce bias but further improve statistical efficiency in modeling relationships between neighborhood health and the food environment. Using simulations and data for the Piedmont Triad Region of North Carolina, we evaluate various validation sampling designs as we quantify the associations of diabetes and obesity with neighborhood-level access to healthy foods.

Measurement error

Partial validation

Health equity

Food environment

Extreme tail sampling

Driving distance