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Tuyishime E, Remera E, Kayitesi C, Malamba S, Sangwayire B, Habimana Kabano I, Ruisenor-Escudero H, Oluoch T, Unna Chukwu A. Estimation of the Population Size of Street- and Venue-Based Female Sex Workers and Sexually Exploited Minors in Rwanda in 2022: 3-Source Capture-Recapture. JMIR Public Health Surveill 2024; 10:e50743. [PMID: 38488847 PMCID: PMC10980986 DOI: 10.2196/50743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/25/2023] [Accepted: 01/10/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND HIV surveillance among key populations is a priority in all epidemic settings. Female sex workers (FSWs) globally as well as in Rwanda are disproportionately affected by the HIV epidemic; hence, the Rwanda HIV and AIDS National Strategic Plan (2018-2024) has adopted regular surveillance of population size estimation (PSE) of FSWs every 2-3 years. OBJECTIVE We aimed at estimating, for the fourth time, the population size of street- and venue-based FSWs and sexually exploited minors aged ≥15 years in Rwanda. METHODS In August 2022, the 3-source capture-recapture method was used to estimate the population size of FSWs and sexually exploited minors in Rwanda. The field work took 3 weeks to complete, with each capture occasion lasting for a week. The sample size for each capture was calculated using shinyrecap with inputs drawn from previously conducted estimation exercises. In each capture round, a stratified multistage sampling process was used, with administrative provinces as strata and FSW hotspots as the primary sampling unit. Different unique objects were distributed to FSWs in each capture round; acceptance of the unique object was marked as successful capture. Sampled FSWs for the subsequent capture occasions were asked if they had received the previously distributed unique object in order to determine recaptures. Statistical analysis was performed in R (version 4.0.5), and Bayesian Model Averaging was performed to produce the final PSE with a 95% credibility set (CS). RESULTS We sampled 1766, 1848, and 1865 FSWs and sexually exploited minors in each capture round. There were 169 recaptures strictly between captures 1 and 2, 210 recaptures exclusively between captures 2 and 3, and 65 recaptures between captures 1 and 3 only. In all 3 captures, 61 FSWs were captured. The median PSE of street- and venue-based FSWs and sexually exploited minors in Rwanda was 37,647 (95% CS 31,873-43,354), corresponding to 1.1% (95% CI 0.9%-1.3%) of the total adult females in the general population. Relative to the adult females in the general population, the western and northern provinces ranked first and second with a higher concentration of FSWs, respectively. The cities of Kigali and eastern province ranked third and fourth, respectively. The southern province was identified as having a low concentration of FSWs. CONCLUSIONS We provide, for the first time, both the national and provincial level population size estimate of street- and venue-based FSWs in Rwanda. Compared with the previous 2 rounds of FSW PSEs at the national level, we observed differences in the street- and venue-based FSW population size in Rwanda. Our study might not have considered FSWs who do not want anyone to know they are FSWs due to several reasons, leading to a possible underestimation of the true PSE.
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Affiliation(s)
- Elysee Tuyishime
- African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
- Division of Global HIV and TB, Global Health Center, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Eric Remera
- Institute of HIV Disease Prevention and Control, Rwanda Biomedical Centre, Rwanda Ministry of Health, Kigali, Rwanda
| | - Catherine Kayitesi
- Institute of HIV Disease Prevention and Control, Rwanda Biomedical Centre, Rwanda Ministry of Health, Kigali, Rwanda
| | - Samuel Malamba
- Division of Global HIV and TB, Global Health Center, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Beata Sangwayire
- Division of Global HIV and TB, Global Health Center, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | | | - Horacio Ruisenor-Escudero
- Key Population Surveillance Team, Epidemiology and Surveillance Branch, Division of Global HIV/TB, Global Health Center, US Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Tom Oluoch
- Division of Global HIV and TB, Global Health Center, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Angela Unna Chukwu
- African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
- Department of Statistics, University of Ibadan, Ibadan, Nigeria
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Tuyishime E, Kayitesi C, Musengimana G, Malamba S, Moges H, Kankindi I, Escudero HR, Habimana Kabano I, Oluoch T, Remera E, Chukwu A. Population Size Estimation of Men Who Have Sex With Men in Rwanda: Three-Source Capture-Recapture Method. JMIR Public Health Surveill 2023; 9:e43114. [PMID: 36972131 PMCID: PMC10131990 DOI: 10.2196/43114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/24/2022] [Accepted: 02/03/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Globally, men who have sex with men (MSM) continue to bear a disproportionately high burden of HIV infection. Rwanda experiences a mixed HIV epidemic, which is generalized in the adult population, with aspects of a concentrated epidemic among certain key populations at higher risk of HIV infection, including MSM. Limited data exist to estimate the population size of MSM at a national scale; hence, an important piece is missing in determining the denominators to use in estimates for policy makers, program managers, and planners to effectively monitor HIV epidemic control. OBJECTIVE The aims of this study were to provide the first national population size estimate (PSE) and geographic distribution of MSM in Rwanda. METHODS Between October and December 2021, a three-source capture-recapture method was used to estimate the MSM population size in Rwanda. Unique objects were distributed to MSM through their networks (first capture), who were then tagged according to MSM-friendly service provision (second capture), and a respondent-driven sampling survey was used as the third capture. Capture histories were aggregated in a 2k-1 contingency table, where k indicates the number of capture occasions and "1" and "0" indicate captured and not captured, respectively. Statistical analysis was performed in R (version 4.0.5) and the Bayesian nonparametric latent-class capture-recapture package was used to produce the final PSE with 95% credibility sets (CS). RESULTS We sampled 2465, 1314, and 2211 MSM in capture one, two, and three, respectively. There were 721 recaptures between captures one and two, 415 recaptures between captures two and three, and 422 recaptures between captures one and three. There were 210 MSM captured in all three captures. The total estimated population size of MSM above 18 years old in Rwanda was 18,100 (95% CS 11,300-29,700), corresponding to 0.70% (95% CI 0.4%-1.1%) of total adult males. Most MSM reside in the city of Kigali (7842, 95% CS 4587-13,153), followed by the Western province (2469, 95% CS 1994-3518), Northern province (2375, 95% CS 842-4239), Eastern province (2287, 95% CS 1927-3014), and Southern province (2109, 95% CS 1681-3418). CONCLUSIONS Our study provides, for the first time, a PSE of MSM aged 18 years or older in Rwanda. MSM are concentrated in the city of Kigali and are almost evenly distributed across the other 4 provinces. The national proportion estimate bounds of MSM out of the total adult males includes the World Health Organization's minimum recommended proportion (at least 1.0%) based on 2012 census population projections for 2021. These results will inform denominators to be used for estimating service coverage and fill existing information gaps to enable policy makers and planners to monitor the HIV epidemic among MSM nationally. There is an opportunity for conducting small-area MSM PSEs for subnational-level HIV treatment and prevention interventions.
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Affiliation(s)
- Elysee Tuyishime
- African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
- Division of Global HIV and Tuberculosis, Center for Global Health, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Catherine Kayitesi
- HIV/AIDS, Sexually Transmitted Infections and Viral Hepatitis Division, Rwanda Biomedical Center, Kigali, Rwanda
| | - Gentille Musengimana
- Division of Global HIV and Tuberculosis, Center for Global Health, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Samuel Malamba
- Division of Global HIV and Tuberculosis, Center for Global Health, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Hailegiorgis Moges
- Division of Global HIV and Tuberculosis, Center for Global Health, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Ida Kankindi
- Division of Global HIV and Tuberculosis, Center for Global Health, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Horacio Ruisenor Escudero
- Key Population Surveillance Team, Epidemiology and Surveillance Branch, Division of Global HIV and Tuberculosis, Center of Global Health, US Centers for Disease Control and Prevention, Atlanta, GA, United States
| | | | - Tom Oluoch
- Division of Global HIV and Tuberculosis, Center for Global Health, US Centers for Disease Control and Prevention, Kigali, Rwanda
| | - Eric Remera
- HIV/AIDS, Sexually Transmitted Infections and Viral Hepatitis Division, Rwanda Biomedical Center, Kigali, Rwanda
| | - Angela Chukwu
- African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
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Abstract
Purpose of review To explore the comparative importance of HIV infections among key populations and their intimate partners as HIV epidemics evolve, and to review implications for guiding responses. Recent findings Even as concentrated epidemics evolve, new infections among current and former key population members and their intimate partners dominate new infections. Prevalent infections in the general population grow primarily because of key population turnover and infections among their intimate partners. In generalized epidemic settings, data and analysis on key populations are often inadequate to assess the impact of key population-focused responses, so they remain limited in coverage and under resourced. Models must incorporate downstream infections in comparing impacts of alternative responses. Summary Recognize that every epidemic is unique, moving beyond the overly simplistic concentrated/generalized epidemic paradigm that can misdirect resources. Guide HIV responses by gathering and using locally relevant data, understanding risk heterogeneity, and applying modeling at both national and sub-national levels to optimize resource allocations among different populations for greatest impact. Translate this improved understanding into clear, unequivocal advice for policymakers on where to focus for impact, breaking them free of the generalized/concentrated paradigm limiting their thinking and affecting their decisions.
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Cheng S, Eck DJ, Crawford FW. Estimating the size of a hidden finite set: Large-sample behavior of estimators. STATISTICS SURVEYS 2020. [DOI: 10.1214/19-ss127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Doshi RH, Apodaca K, Ogwal M, Bain R, Amene E, Kiyingi H, Aluzimbi G, Musinguzi G, Serwadda D, McIntyre AF, Hladik W. Estimating the Size of Key Populations in Kampala, Uganda: 3-Source Capture-Recapture Study. JMIR Public Health Surveill 2019; 5:e12118. [PMID: 31407673 PMCID: PMC6771531 DOI: 10.2196/12118] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 01/09/2019] [Accepted: 05/09/2019] [Indexed: 01/19/2023] Open
Abstract
Background Key populations, including people who inject drugs (PWID), men who have sex with men (MSM), and female sex workers (FSW), are disproportionately affected by the HIV epidemic. Understanding the magnitude of, and informing the public health response to, the HIV epidemic among these populations requires accurate size estimates. However, low social visibility poses challenges to these efforts. Objective The objective of this study was to derive population size estimates of PWID, MSM, and FSW in Kampala using capture-recapture. Methods Between June and October 2017, unique objects were distributed to the PWID, MSM, and FSW populations in Kampala. PWID, MSM, and FSW were each sampled during 3 independent captures; unique objects were offered in captures 1 and 2. PWID, MSM, and FSW sampled during captures 2 and 3 were asked if they had received either or both of the distributed objects. All captures were completed 1 week apart. The numbers of PWID, MSM, and FSW receiving one or both objects were determined. Population size estimates were derived using the Lincoln-Petersen method for 2-source capture-recapture (PWID) and Bayesian nonparametric latent-class model for 3-source capture-recapture (MSM and FSW). Results We sampled 467 PWID in capture 1 and 450 in capture 2; a total of 54 PWID were captured in both. We sampled 542, 574, and 598 MSM in captures 1, 2, and 3, respectively. There were 70 recaptures between captures 1 and 2, 103 recaptures between captures 2 and 3, and 155 recaptures between captures 1 and 3. There were 57 MSM captured in all 3 captures. We sampled 962, 965, and 1417 FSW in captures 1, 2, and 3, respectively. There were 316 recaptures between captures 1 and 2, 214 recaptures between captures 2 and 3, and 235 recaptures between captures 1 and 3. There were 109 FSW captured in all 3 rounds. The estimated number of PWID was 3892 (3090-5126), the estimated number of MSM was 14,019 (95% credible interval (CI) 4995-40,949), and the estimated number of FSW was 8848 (95% CI 6337-17,470). Conclusions Our population size estimates for PWID, MSM, and FSW in Kampala provide critical population denominator data to inform HIV prevention and treatment programs. The 3-source capture-recapture is a feasible method to advance key population size estimation.
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Affiliation(s)
- Reena H Doshi
- Centers for Disease Control and Prevention, Center for Global Health, Division of Global HIV and TB, Atlanta, GA, United States.,Centers for Disease Control and Prevention, Epidemic Intelligence Service, Atlanta, GA, United States
| | - Kevin Apodaca
- Centers for Disease Control and Prevention, Center for Global Health, Division of Global HIV and TB, Atlanta, GA, United States.,Public Health Institute, Oakland, CA, United States
| | - Moses Ogwal
- Makerere University, School of Public Health, Kampala, Uganda
| | - Rommel Bain
- Centers for Disease Control and Prevention, Center for Global Health, Division of Global HIV and TB, Atlanta, GA, United States
| | - Ermias Amene
- Centers for Disease Control and Prevention, Center for Global Health, Division of Global HIV and TB, Atlanta, GA, United States
| | - Herbert Kiyingi
- Centers for Disease Control and Prevention, Division of Global HIV and TB, Entebbe, Uganda
| | - George Aluzimbi
- Centers for Disease Control and Prevention, Division of Global HIV and TB, Entebbe, Uganda
| | | | - David Serwadda
- Makerere University, School of Public Health, Kampala, Uganda
| | - Anne F McIntyre
- Centers for Disease Control and Prevention, Center for Global Health, Division of Global HIV and TB, Atlanta, GA, United States
| | - Wolfgang Hladik
- Centers for Disease Control and Prevention, Center for Global Health, Division of Global HIV and TB, Atlanta, GA, United States
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