JMIR Publications

ABSTRACT

The demand for high-quality population health data at the local level calls for expanded tools for those working to enhance the health of communities across the country to easily calculate small area estimates. Statistical models that generate small area estimates often utilize Bayesian estimation techniques which are computationally complex and not readily accessible to most public health professionals. We developed two tools to facilitate small area estimation. For ESRI users, we developed the RSTbx ArcGIS plugin and for R users we developed the RSTr R package. In this tutorial, we demonstrate how to use these tools to calculate small area estimates and evaluate their reliability. We also demonstrate three key benefits from using either of these tools: 1) decreased number of geographic units with suppressed estimates, 2) flexibility to set the threshold for statistical reliability, and 3) credible intervals that can be used to identify statistically significant differences between geographic units. Additionally, both tools offer built-in age-standardization capabilities. We created census tract-level maps from North Carolina mortality data and Rhode Island hospitalization data to showcase the benefits of generating small area estimates with these tools. RSTbx and RSTr are powerful tools that can be used to meet the demand for high-quality local-level data to inform public health programs and tailor health promotion activities to the needs of communities across the country.