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Abstract
Effective policy and intervention strategies to combat human trafficking for child sexual exploitation material (CSEM) production require accurate prevalence estimates. Traditional Network Scale Up Method (NSUM) models often necessitate standalone surveys for each geographic region, escalating costs and complexity. This study introduces a partially pooled NSUM model, using a hierarchical Bayesian framework that efficiently aggregates and utilizes data across multiple regions without increasing sample sizes. We developed this model for a novel national survey dataset from the Philippines and we demonstrate its ability to produce detailed municipal-level prevalence estimates of trafficking for CSEM production. Our results not only underscore the model’s precision in estimating hidden populations but also highlight its potential for broader application in other areas of social science and public health research, offering significant implications for resource allocation and intervention planning.
Original language | English |
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Article number | qlaf033 |
Journal | Journal of the Royal Statistical Society Series C (Applied Statistics) |
Early online date | 27 May 2025 |
DOIs | |
Publication status | E-pub ahead of print - 27 May 2025 |
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Computational Statistics to Tackle Modern Slavery
Seymour, R. (Principal Investigator)
1/06/24 → 31/05/28
Project: Research