We propose a novel, two-step method for rescaling health survey data and creating small area estimates of smoking rates using a Behavioral Risk Factor Surveillance System (BRFSS) survey administered in 2015 to participants living in Allegheny County, in the state of Pennsylvania, USA. The first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize and select from available census tract specific ancillary data on social vulnerability for small area estimation (SAE) of local health risk using an area level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County. The ever-smoking rate was slightly above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (>65%) ever-smoking rates. These small area estimates may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations, and other health-related behaviors and outcomes.
Space–time smoothing of complex survey data: Small area estimation for child mortality
