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A stochastic simulation model to study respondent-driven recruitment.

Mart L Stein1, Vincent Buskens2, Peter G M van der Heijden3,4

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This summary is machine-generated.

Respondent-driven sampling (RDS) is a chain-recruitment method for enhanced case finding. A simulation model shows that participants sending varied numbers of invitations, rather than a few sending many, yields larger recruitment trees.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Respondent-driven sampling (RDS) is a chain-recruitment technique to sample hidden or hard-to-reach populations, particularly for infectious disease surveillance.
  • Effective recruitment relies on participants inviting contacts, but recruitment often stalls due to low participation or invitation refusal.
  • Understanding factors influencing recruitment success is crucial for optimizing RDS for case finding and research.

Purpose of the Study:

  • To analyze how various factors influence peer recruitment in respondent-driven sampling.
  • To determine thresholds for achieving large recruitment trees and steady-state sample composition.
  • To identify optimal invitation strategies for sustainable recruitment in public health research.

Main Methods:

  • Developed a stochastic simulation model for respondent-driven sampling.
  • Calibrated model parameters using empirical data from an online survey.
  • Analyzed the relationship between invitation distribution (mean/variance) and recruitment tree size.

Main Results:

  • A strategy where participants send a variable number of invitations (1 to max) is more effective for generating large recruitment trees than a strategy where few participants send the maximum number.
  • Identified thresholds for successful recruitment, indicating the minimum number of invitations needed for large trees and specific sample compositions.
  • Determined the number of waves required to reach a steady state in sample characteristics.

Conclusions:

  • The developed mathematical model aids public health professionals in planning respondent-driven sampling studies and contact tracing efforts.
  • Optimizing invitation strategies, specifically encouraging varied invitation rates, is key to maximizing recruitment success.
  • The model provides essential insights into required invitation numbers for achieving desired sample sizes and compositions efficiently.