Logo image
Back
A Bayesian approach for estimating age‐adjusted rates for low‐prevalence diseases over space and time
Journal article   Peer reviewed

A Bayesian approach for estimating age‐adjusted rates for low‐prevalence diseases over space and time

Melissa Jay, Jacob Oleson, Mary Charlton and Ali Arab
Statistics in medicine, Vol.40(12), pp.2922-2938
05/30/2021
DOI: 10.1002/sim.8948
PMCID: PMC9575652
PMID: 33728679
url
https://www.ncbi.nlm.nih.gov/pmc/articles/9575652View
Open Access

Abstract

Age‐adjusted rates are frequently used by epidemiologists to compare disease incidence and mortality across populations. In small geographic regions, age‐adjusted rates computed directly from the data are subject to considerable variability and are generally unreliable. Therefore, we desire an approach that accounts for the excessive number of zero counts in disease mapping datasets, which are naturally present for low‐prevalence diseases and are further innated when stratifying by age group. Bayesian modeling approaches are naturally suited to employ spatial and temporal smoothing to produce more stable estimates of age‐adjusted rates for small areas. We propose a Bayesian hierarchical spatio‐temporal hurdle model for counts and demonstrate how age‐adjusted rates can be estimated from the hurdle model. We perform a simulation study to evaluate the performance of the proposed model vs a traditional Poisson model on datasets with varying characteristics. The approach is illustrated using two applications to cancer mortality at the county level.
 cancer disease mapping hurdle risk standardization

Details

Metrics

18 Record Views
4 Times Cited - Web of Science
Logo image