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Understanding the effects of social determinants of health on health outcomesrequires data on characteristics of the neighborhoods in which subjects live.However, estimates of these characteristics are often aggregated over space andtime in a fashion that diminishes their utility. Take, for example, estimatesfrom the American Community Survey (ACS), a multi-year nationwide surveyadministered by the U.S. Census Bureau: estimates for small municipal areas areaggregated over 5-year periods, whereas 1-year estimates are only available formunicipal areas with populations $>$65,000. Researchers may wish to use ACSestimates in studies of population health to characterize neighborhood-levelexposures. However, 5-year estimates may not properly characterize temporalchanges or align temporally with other data in the study, while the coarsespatial resolution of the 1-year estimates diminishes their utility incharacterizing neighborhood exposure. To circumvent this issue, in this paperwe propose a modeling framework to disaggregate estimates of proportionsderived from sampling surveys which explicitly accounts for the survey designeffect. We illustrate the utility of our model by applying it to the ACS data,generating estimates of poverty for the state of Michigan at finespatio-temporal resolution.

Accounting for survey design in Bayesian disaggregation of survey-based  areal estimates of proportions: an application to the American Community  Survey