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A Review of Prevalence Estimation Methods for Human Trafficking Populations
Elyssa Schroeder1, Timothy G Edgemon2, Lydia Aletraris1
1308457 African Programming and Research Initiative to End Slavery (APRIES), School of Social Work, University of Georgia, Athens, GA, USA.
Public Health Reports (Washington, D.C. : 1974)
|July 1, 2022
Summary
Estimating human trafficking prevalence is challenging due to hidden populations. This review explores network-based and venue-based methods for more accurate prevalence measurement.
Area of Science:
- Social Sciences
- Public Health
- Criminology
Background:
- Human trafficking has severe, long-lasting impacts on individuals, families, and communities.
- Accurate prevalence data is crucial for effective prevention and intervention but is limited by the hidden nature of trafficking.
- Traditional survey methods are often expensive and ineffective for hidden populations.
Purpose of the Study:
- To review and examine the application of prevalence estimation methods used for hard-to-reach populations in the context of human trafficking.
- To provide a roadmap for estimating human trafficking prevalence.
- To promote equitable treatment and well-being for trafficked individuals.
Main Methods:
- Topical review of prevalence estimation methods for hard-to-reach populations.
- Examination of network-based approaches (e.g., respondent-driven sampling, network scale-up method).
- Consideration of venue-based methods.
Main Results:
- Network-based methods like respondent-driven sampling are suitable when sampling frames are absent or population information is scarce.
- The network scale-up method offers a unique approach by not directly targeting the hidden population.
- Existing prevalence estimation methods need more robust documentation and validation in the United States.
Conclusions:
- Applying methods from hard-to-reach populations can improve human trafficking prevalence estimation.
- Further research is needed to validate these methods internationally and within the U.S.
- Improved prevalence data is essential for addressing the needs of socially disadvantaged populations disproportionately affected by trafficking.
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