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Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia.

Brian J Kim1, Lisa G Johnston2, Trdat Grigoryan3

  • 1Joint Program in Survey Methodology, University of Maryland, College Park, Maryland, USA.

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Summary

Estimating hidden populations is crucial for public health. A new method, capture-recapture with successive sampling population size estimation (CR-SS-PSE), offers a more robust approach for accurately sizing these hard-to-reach groups.

Keywords:
Markov chain Monte Carlohuman immunodeficiency viruslink tracing samplingnonprobability samplingsuccessive sampling

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Area of Science:

  • Epidemiology
  • Social Sciences
  • Statistical Modeling

Background:

  • Estimating hidden populations is vital for understanding social needs, healthcare burdens, and risk behaviors.
  • Current methods for sizing hidden populations lack a gold standard and often rely on unrealistic assumptions.
  • There's a need for diagnostic tools to assess method assumptions and compare different estimation techniques.

Purpose of the Study:

  • To introduce and evaluate a novel population size estimation method: capture-recapture with successive sampling population size estimation (CR-SS-PSE).
  • To assess the robustness of CR-SS-PSE to violations of its underlying mathematical assumptions.
  • To compare CR-SS-PSE estimates with those from other common methods for hidden populations.

Main Methods:

  • CR-SS-PSE utilizes data from two sequential respondent-driven sampling surveys.
  • It extends the successive sampling population size estimation (SS-PSE) framework by incorporating overlap data between surveys.
  • The method models the successive sampling process to derive population size estimates.

Main Results:

  • CR-SS-PSE demonstrated greater robustness to violations of successive sampling assumptions compared to SS-PSE.
  • The study applied CR-SS-PSE to data from hidden populations in Armenia over three years.
  • Comparisons revealed significant volatility across different population size estimation methods, highlighting CR-SS-PSE's potential advantages.

Conclusions:

  • CR-SS-PSE provides a more reliable method for estimating the size of hidden populations.
  • The findings underscore the importance of assessing method robustness in real-world survey implementations.
  • This research contributes to the development of better tools for epidemiological and social science research involving hidden populations.