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. 2023 Jun;65(5):e2200136.
doi: 10.1002/bimj.202200136. Epub 2023 Mar 6.

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

Brian J Kim  1 , Lisa G Johnston  2 , Trdat Grigoryan  3 , Arshak Papoyan  3 , Samvel Grigoryan  3 , Katherine R McLaughlin  4
Affiliations

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

Brian J Kim et al. Biom J. 2023 Jun.

Abstract

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture-recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture-recapture to illustrate volatility across estimation methods.

Keywords: Markov chain Monte Carlo; human immunodeficiency virus; link tracing sampling; nonprobability sampling; successive sampling.

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References

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