Citation

Gause EL, Schumacher AE, Ellyson AM, Withers SD, Mayer JD, Rowhani-Rahbar A. Am. J. Epidemiol. 2024; ePub(ePub): ePub.

Copyright

(Copyright © 2024, Oxford University Press)

DOI

10.1093/aje/kwae005

PMID

38375682

Abstract

This article introduces Bayesian spatial smoothing models for disease mapping, a specific application of small area estimation where the full universe of data is known, to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York and Mollié (BYM) Poisson spatial and space-time smoothing models were fit to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0-24.81 deaths per 10,000 people. However, the highest mortality rate was highly unstable based on only 2 deaths in a population of approximately 800, and 82.4% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06-4.05 deaths per 10,000 people and could be reported for all counties. The space-time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gained precision from adjacent neighbors and their own rates in adjacent years. Bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R have made fitting these models more accessible to researchers.

Language: en

Keywords

Bayesian spatial smoothing; disease mapping; firearm suicide; small area estimation; spatial statistics