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Successive sampling-population size estimation (SS-PSE) accurately estimates hard-to-reach populations using respondent-driven sampling data. This method provides reliable population size estimates without needing extra studies, improving public health research.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Population Health

Background:

  • Respondent-driven sampling (RDS) is crucial for estimating HIV/AIDS prevalence in hard-to-reach populations.
  • Accurate estimation of population size is vital for public health initiatives but often lacks reliable methods.
  • Existing methods for population size estimation may rely on external data and be prone to bias.

Purpose of the Study:

  • To introduce and evaluate the Successive Sampling-Population Size Estimation (SS-PSE) method.
  • To assess SS-PSE's effectiveness in estimating the size of hard-to-reach populations using RDS data.
  • To determine if SS-PSE can provide reliable estimates without supplementary studies.

Main Methods:

  • Employed Successive Sampling-Population Size Estimation (SS-PSE).
  • Integrated network size imputation to refine estimates.
  • Compared SS-PSE results with expert opinions and alternative estimation methods.

Main Results:

  • Calculated ten population size estimates for key populations (people who inject drugs, female sex workers, men who have sex with men, migrants) in Morocco.
  • SS-PSE estimates closely aligned with expert-provided values and results from other established methods.
  • Network size imputation smoothed network sizes, enhancing the accuracy of SS-PSE.

Conclusions:

  • SS-PSE is an effective method for estimating hard-to-reach population sizes, leveraging RDS data.
  • Network size imputation improves SS-PSE accuracy by smoothing network size data.
  • Caution is advised with SS-PSE when populations are clustered or sample sizes are small relative to population size.