ABSTRACT
Given the importance of understanding health outcomes at fine spatial scales, iterative proportional fitting (IPF), a form of small area estimation, was applied to a fixed number of health-related variables (obesity, overweight, diabetes) taken from regionalized 2019 survey responses (n = 5474) from the Idaho Behavioral Risk Factor Surveillance System (BRFSS). Using associated county-level American Community Survey (ACS) census data, a set of constraints, which included age categorization, race, sex, and education level, were used to create county-level weighting matrices for each variable, for each of the seven (7) Idaho public health districts. Using an optimized modeling construction technique, we identified significant constraints and grouping splits for each variable/region, resulting in estimates that were internally and externally validated. Externally validated model results for the most populated counties showed correlations ranging from .79 to .85, with p values all below .05. Estimates indicated higher levels of obesity and overweight individuals for midsouth and southwestern Idaho counties, with a cluster of higher diabetes estimates in the center of the state (Gooding, Lincoln, Minidoka, and Jerome counties). Alternative external sources for health outcomes aligned extremely well with our estimates, with wider confidence intervals in more rural counties with sparse populations.
ABSTRACT
Early public health strategies to prevent the spread of COVID-19 in the United States relied on non-pharmaceutical interventions (NPIs) as vaccines and therapeutic treatments were not yet available. Implementation of NPIs, primarily social distancing and mask wearing, varied widely between communities within the US due to variable government mandates, as well as differences in attitudes and opinions. To understand the interplay of trust, risk perception, behavioral intention, and disease burden, we developed a survey instrument to study attitudes concerning COVID-19 and pandemic behavioral change in three states: Idaho, Texas, and Vermont. We designed our survey (n = 1034) to detect whether these relationships were significantly different in rural populations. The best fitting structural equation models show that trust indirectly affects protective pandemic behaviors via health and economic risk perception. We explore two different variations of this social cognitive model: the first assumes behavioral intention affects future disease burden while the second assumes that observed disease burden affects behavioral intention. In our models we include several exogenous variables to control for demographic and geographic effects. Notably, political ideology is the only exogenous variable which significantly affects all aspects of the social cognitive model (trust, risk perception, and behavioral intention). While there is a direct negative effect associated with rurality on disease burden, likely due to the protective effect of low population density in the early pandemic waves, we found a marginally significant, positive, indirect effect of rurality on disease burden via decreased trust (p = 0.095). This trust deficit creates additional vulnerabilities to COVID-19 in rural communities which also have reduced healthcare capacity. Increasing trust by methods such as in-group messaging could potentially remove some of the disparities inferred by our models and increase NPI effectiveness.
