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A BAYESIAN HIERARCHICAL MODEL FOR COMBINING MULTIPLE DATA SOURCES IN POPULATION SIZE ESTIMATION.

Jacob Parsons1, Xiaoyue Niu2, Le Bao2

  • 1GlaxoSmithKline.

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|May 3, 2023
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Summary
This summary is machine-generated.

Estimating the size of key populations, such as people who inject drugs, is crucial for combating HIV/AIDS. This study introduces a Bayesian hierarchical model to reconcile conflicting data and improve population size estimates.

Keywords:
HIV/AIDS epidemicMultiplier methodkey affected populationnetwork scale-uppeople who inject drugs

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Accurate population size estimates are vital for effective HIV/AIDS interventions targeting key populations.
  • Key populations, including sex workers, people who inject drugs, and men who have sex with men, are difficult to enumerate directly.
  • Existing indirect estimation methods often yield conflicting results, necessitating robust reconciliation approaches.

Purpose of the Study:

  • To develop and present a Bayesian hierarchical model for combining and reconciling multiple indirect estimates of key population sizes.
  • To address systematic errors inherent in different data sources used for population estimation.
  • To apply the model for estimating the size of people who inject drugs in Ukraine and evaluate its performance.

Main Methods:

  • Development of a Bayesian hierarchical model incorporating data from multiple sources and years.
  • Explicit modeling of systematic error within each data source.
  • Application of the model to estimate the population size of people who inject drugs in Ukraine.

Main Results:

  • The Bayesian hierarchical model successfully integrated diverse data sources to produce a reconciled estimate for the target population.
  • The model's ability to account for systematic error improved the reliability of the final estimates.
  • Evaluation provided insights into the contribution of individual data sources to the overall estimate.

Conclusions:

  • The proposed Bayesian hierarchical model offers a principled framework for combining and reconciling size estimates of key populations.
  • This approach enhances the accuracy and reliability of estimates crucial for public health planning and HIV/AIDS control.
  • The model's application in Ukraine demonstrates its utility for informing targeted interventions.