Bayesian Hierarchical Model for Synthesizing Registry and Survey Data on Female Breast Cancer Prevalence

TL;DR

This paper develops a Bayesian hierarchical model to combine registry and survey data for accurate county-level female breast cancer prevalence estimates, aiding public health policy decisions.

Contribution

It introduces a two-stage Bayesian approach that accounts for differences in data collection methods and integrates multiple data sources for improved prevalence estimation.

Findings

Combining registry and survey data yields more reliable prevalence estimates.

Including data source membership improves model accuracy.

The method enhances policy-making by providing detailed prevalence insights.

Abstract

In public health, it is critical for policymakers to assess the relationship between the disease prevalence and associated risk factors or clinical characteristics, facilitating effective resources allocation. However, for diseases like female breast cancer (FBC), reliable prevalence data at specific geographical levels, such as the county-level, are limited because the gold standard data typically come from long-term cancer registries, which do not necessarily collect needed risk factors. In addition, it remains unclear whether fitting each model separately or jointly results in better estimation. In this paper, we identify two data sources to produce reliable county-level prevalence estimates in Missouri, USA: the population-based Missouri Cancer Registry (MCR) and the survey-based Missouri County-Level Study (CLS). We propose a two-stage Bayesian model to synthesize these sources, accounting for their differences in the methodological design, case definitions, and collected information. The first stage involves estimating the county-level FBC prevalence using the raking method for CLS data and the counting method for MCR data, calibrating the differences in the methodological design and case definition. The second stage includes synthesizing two sources with different sets of covariates using a Bayesian generalized linear mixed model with Zeller-Siow prior for the coefficients. Our data analyses demonstrate that using both data sources have better results than at least one data source, and including a data source membership matters when there exist systematic differences in these sources. Finally, we translate results into policy making and discuss methodological differences for data synthesis of registry and survey data.