Understanding the effects of neighborhood change on health requires data on characteristics of the neighborhoods in which subjects live. However, estimates of these characteristics are often aggregated over space and time in a fashion that diminishes their utility. Take, for example, estimates derived from the American Community Survey (ACS), in which estimates for small municipal areas are aggregated over 5-year periods, while 1-year estimates are only available for municipal areas with populations >65,000. Researchers may wish to use ACS estimates in studies of population health to characterize neighborhood-level exposures. However, 5-year estimates may not properly characterize temporal changes or align temporally with other data in the study, while the coarse spatial resolution of the 1-year estimates diminishes their utility in characterizing neighborhood exposure. To circumvent this issue, we propose a modeling framework to disaggregate estimates of proportions derived from sampling surveys, and account for the survey design effect. Application to ACS estimates of poverty and race demonstrate its utility in disaggregating these estimates to a fine spatio-temporal resolution.