Journal article
Bayesian Models to Generate Small Area Estimates of Population Health: Tutorial for Using Rate Stabilizing Tools and Their Output
JMIR public health and surveillance, v 12, e83498
30 Jan 2026
PMID: 41616102
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 use Bayesian estimation techniques, which are computationally complex and not readily accessible to most public health professionals. We developed 2 tools to facilitate small area estimation. For ArcGIS Pro users, we developed the Rate Stabilizing Toolbox ArcGIS plugin (RSTbx), and for R users, we developed the Rate Stabilizing Tool R package (RSTr). In this tutorial, we demonstrate how to use these tools to calculate small area estimates and evaluate their reliability. We also demonstrate 3 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. Rate Stabilizing Toolbox and Rate Stabilizing Tool for R 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.
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Details
- Title
- Bayesian Models to Generate Small Area Estimates of Population Health: Tutorial for Using Rate Stabilizing Tools and Their Output
- Creators
- David DeLara (Corresponding Author) - National Center for Chronic Disease Prevention and Health PromotionRyan Zomorrodi - University of Illinois ChicagoHarrison Quick - University of MinnesotaJoshua Tootoo - University of Illinois ChicagoRuiyang Li - University of Illinois ChicagoJustan Baker - Rhode Island Department of HealthJihyeon Kwon - Drexel UniversityMichele Casper - National Center for Chronic Disease Prevention and Health PromotionAdam Vaughan - National Center for Chronic Disease Prevention and Health Promotion
- Publication Details
- JMIR public health and surveillance, v 12, e83498
- Publisher
- Jmir Publications, Inc
- Number of pages
- 17
- Grant note
- The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and PreventionU.S. Department of Health and Human Services
Acknowledgments This research uses data from the North Carolina Center for Health Statistics (SCHS) to illustrate the use of the RSTbx. The authors wish to acknowledge SCHS but are responsible for the analysis and interpretation of these data. The authors attest that there was no use of generative artificial intelligence (AI) technology in the generation of text, figures, or other informational content of this manuscript. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:001682085800001
- Other Identifier
- 991022156300304721