A0580
Title: A Bayesian hierarchical model to account for temporal misalignment in American community survey explanatory variables
Authors: Staci Hepler - Wake Forest University (United States)
Jihyeon Kwon - Drexel University (United States) [presenting]
David Kline - Wake Forest University School of Medicine (United States)
Abstract: The American Community Survey (ACS) is one of the most vital public sources for demographic and socioeconomic characteristics of communities in the United States and is administered by the U.S. Census Bureau every year. The ACS publishes 5-year estimates of community characteristics for all geographical areas and 1-year estimates for areas with a population of at least 65,000. Many epidemiological and public health studies use 5-year ACS estimates as explanatory variables in models. However, doing so ignores the uncertainty and averages over variability during the time period, which may lead to biased estimates of covariate effects of interest. A Bayesian hierarchical model is proposed that accounts for the uncertainty and disentangles the temporal misalignment in the ACS multi-year time-period estimates. It is shown via simulation that the proposed model more accurately recovers covariate effects compared to models that ignore the temporal misalignment. Lastly, the proposed model is implemented to quantify the relationship between yearly, county-level characteristics and the prevalence of frequent mental distress for counties in North Carolina from 2014 to 2018.
