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Novel Approaches for Estimating Female Sex Worker Population Size in Conflict-Affected South Sudan
BACKGROUND: Limited data exist describing the population size of female sex workers (FSW) in South Sudan. A population size estimation exercise among FSW was undertaken in Juba and Nimule during the Eagle Survey. OBJECTIVE: The study aimed to estimate the number of FSW in Juba and Nimule to inform r...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441857/ https://www.ncbi.nlm.nih.gov/pubmed/30882356 http://dx.doi.org/10.2196/11576 |
Sumario: | BACKGROUND: Limited data exist describing the population size of female sex workers (FSW) in South Sudan. A population size estimation exercise among FSW was undertaken in Juba and Nimule during the Eagle Survey. OBJECTIVE: The study aimed to estimate the number of FSW in Juba and Nimule to inform resource allocation and service provision for FSW. METHODS: We utilized service and unique object multipliers, and 3-source capture-recapture methods in conjunction with a respondent-driven sampling (RDS) survey to estimate the number of FSW in Juba and Nimule. For service multiplier, the number of FSW testing for HIV in 2015 (Juba) and 2016 (Nimule) was obtained from the LINKAGES program targeting FSW. Survey participants were asked whether they had been tested for HIV by LINKAGES during the relevant period. A total of 2 separate unique object distributions were conducted in Juba and Nimule. In Nimule, these were combined to produce a 3-source capture-recapture estimate. The exercise involved distribution of key chains and bangles to FSW, documentation of the number of those who received unique objects, and questions during RDS survey to assess whether participants received unique objects. RESULTS: In Juba, the service multiplier method yielded an estimate of 5800 (95% CI 4927-6673) FSW. The unique object estimate (key chain and RDS participation) yielded 5306 (95% CI 4673-5939). Another estimate using RDS participation and receipt of a bangle yielded a much lower estimate of 1863 (95% CI 1776-1951), as did a 2-source estimate of key chain and bangle (2120, 95% CI 2028-2211). A 3-source capture-recapture estimate could not be produced because aggregate rather than individual level data were collected during the third capture. The multiplier estimate using key chain and RDS participation was taken as the final population estimate for FSW in Juba, which constitutes more than 6% of the female population aged 15 to 64 years. In Nimule, the service multiplier method yielded an estimate of 9384 (95% CI 8511-10,257). The 2-source estimates for key chain and RDS yielded 6973 (95% CI 4759-9186); bangles and RDS yielded a higher estimate of 13,104 (95% CI 7101-19,106); key chains and bangles yielded a lower estimate of 1322 (95% CI 1223-1420). The 3-source capture-recapture method using Bayesian nonparametric latent-class model-based estimate yielded a population of 2694 (95% CI 1689-6945), and this was selected as the final estimate for Nimule, which constitutes nearly 40% of female population aged 15 to 64 years. CONCLUSIONS: The service and unique object multiplier, and 3-source capture-recapture methods were successfully used to estimate the number of FSW in Nimule, whereas service and unique object multiplier methods were successfully used in Juba. These methods yielded higher than previously estimated FSW population sizes. These estimates will inform resource allocation and advocacy efforts to support services for FSW. |
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