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Stevens O, Sabin K, Anderson RL, Garcia SA, Willis K, Rao A, McIntyre AF, Fearon E, Grard E, Stuart-Brown A, Cowan F, Degenhardt L, Stannah J, Zhao J, Hakim AJ, Rucinski K, Sathane I, Boothe M, Atuhaire L, Nyasulu PS, Maheu-Giroux M, Platt L, Rice B, Hladik W, Baral S, Mahy M, Imai-Eaton JW. Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data, 2010-23. Lancet Glob Health 2024; 12:e1400-e1412. [PMID: 39151976 PMCID: PMC11345451 DOI: 10.1016/s2214-109x(24)00236-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/22/2024] [Accepted: 05/31/2024] [Indexed: 08/19/2024]
Abstract
BACKGROUND Key population HIV programmes in sub-Saharan Africa require epidemiological information to ensure equitable and universal access to effective services. We aimed to consolidate and harmonise survey data among female sex workers, men who have sex with men, people who inject drugs, and transgender people to estimate key population size, HIV prevalence, and antiretroviral therapy (ART) coverage for countries in mainland sub-Saharan Africa. METHODS Key population size estimates, HIV prevalence, and ART coverage data from 39 sub-Saharan Africa countries between 2010 and 2023 were collated from existing databases and verified against source documents. We used Bayesian mixed-effects spatial regression to model urban key population size estimates as a proportion of the gender-matched, year-matched, and area-matched population aged 15-49 years. We modelled subnational key population HIV prevalence and ART coverage with age-matched, gender-matched, year-matched, and province-matched total population estimates as predictors. FINDINGS We extracted 2065 key population size data points, 1183 HIV prevalence data points, and 259 ART coverage data points. Across national urban populations, a median of 1·65% (IQR 1·35-1·91) of adult cisgender women were female sex workers, 0·89% (0·77-0·95) were men who have sex with men, 0·32% (0·31-0·34) were men who injected drugs, and 0·10% (0·06-0·12) were women who were transgender. HIV prevalence among key populations was, on average, four to six times higher than matched total population prevalence, and ART coverage was correlated with, but lower than, the total population ART coverage with wide heterogeneity in relative ART coverage across studies. Across sub-Saharan Africa, key populations were estimated as comprising 1·2% (95% credible interval 0·9-1·6) of the total population aged 15-49 years but 6·1% (4·5-8·2) of people living with HIV. INTERPRETATION Key populations in sub-Saharan Africa experience higher HIV prevalence and lower ART coverage, underscoring the need for focused prevention and treatment services. In 2024, limited data availability and heterogeneity constrain precise estimates for programming and monitoring trends. Strengthening key population surveys and routine data within national HIV strategic information systems would support more precise estimates. FUNDING UNAIDS, Bill & Melinda Gates Foundation, and US National Institutes of Health.
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Affiliation(s)
- Oliver Stevens
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
| | - Keith Sabin
- Data for Impact, The Joint United Nations Program on HIV/AIDS (UNAIDS), Geneva, Switzerland
| | - Rebecca L Anderson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Sonia Arias Garcia
- Data for Impact, The Joint United Nations Program on HIV/AIDS (UNAIDS), Geneva, Switzerland
| | - Kalai Willis
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Amrita Rao
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anne F McIntyre
- US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Elizabeth Fearon
- Institute for Global Health, University College London, London, UK
| | - Emilie Grard
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Alice Stuart-Brown
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Frances Cowan
- Liverpool School of Tropical Medicine, Liverpool, UK; Centre for Sexual Health and HIV/AIDS Research, Harare, Zimbabwe
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre, University New South Wales, Sydney, NSW, Australia
| | - James Stannah
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Jinkou Zhao
- The Global Fund to Fight AIDS, Tuberculosis and Malaria, Geneva, Switzerland
| | - Avi J Hakim
- US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | - Makini Boothe
- Data for Impact, The Joint United Nations Program on HIV/AIDS (UNAIDS), Maputo, Mozambique
| | - Lydia Atuhaire
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Peter S Nyasulu
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mathieu Maheu-Giroux
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Lucy Platt
- Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Brian Rice
- Sheffield Centre for Health and Related Research (SCHARR), School of Medicine and Population Health, University of Sheffield, Sheffield, UK
| | - Wolfgang Hladik
- US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Stefan Baral
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mary Mahy
- Data for Impact, The Joint United Nations Program on HIV/AIDS (UNAIDS), Geneva, Switzerland
| | - Jeffrey W Imai-Eaton
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
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Rucinski K, Knight J, Willis K, Wang L, Rao A, Roach MA, Phaswana-Mafuya R, Bao L, Thiam S, Arimi P, Mishra S, Baral S. Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response. Curr HIV/AIDS Rep 2024; 21:208-219. [PMID: 38916675 PMCID: PMC11283392 DOI: 10.1007/s11904-024-00702-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response. RECENT FINDINGS Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.
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Affiliation(s)
- Katherine Rucinski
- Department of International Health, Johns Hopkins School of Public Health, Baltimore, MD, USA.
| | - Jesse Knight
- MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Kalai Willis
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Linwei Wang
- MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada
| | - Amrita Rao
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Mary Anne Roach
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Refilwe Phaswana-Mafuya
- South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research (PACER) Extramural Unit, Johannesburg, South Africa
- Department of Environmental Health, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
| | - Le Bao
- Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Safiatou Thiam
- Conseil National de Lutte Contre Le Sida, Dakar, Senegal
| | - Peter Arimi
- Partners for Health and Development in Africa, Nairobi, Kenya
| | - Sharmistha Mishra
- MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation & Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- ICES, Toronto, ON, Canada
| | - Stefan Baral
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
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Gause EL, Schumacher AE, Ellyson AM, Withers SD, Mayer JD, Rowhani-Rahbar A. An introduction to bayesian spatial smoothing methods for disease mapping: modeling county firearm suicide mortality rates. Am J Epidemiol 2024; 193:1002-1009. [PMID: 38375682 DOI: 10.1093/aje/kwae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 11/20/2023] [Accepted: 02/13/2024] [Indexed: 02/21/2024] Open
Abstract
This article introduces bayesian spatial smoothing models for disease mapping-a specific application of small area estimation where the full universe of data is known-to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York, and Mollié (BYM) Poisson spatial and space-time smoothing models were fitted to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0 to 24.81 deaths per 10 000 people. However, the highest mortality rate was highly unstable, based on only 2 deaths in a population of approximately 800, and 80.5% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06 to 4.05 deaths per 10 000 people and could be reported for all counties. The space-time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gain precision from adjacent neighbors and their own counts in adjacent years. bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R software have made fitting these models more accessible to researchers.
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Loeb T, Willis K, Velishavo F, Lee D, Rao A, Baral S, Rucinski K. Leveraging Routinely Collected Program Data to Inform Extrapolated Size Estimates for Key Populations in Namibia: Small Area Estimation Study. JMIR Public Health Surveill 2024; 10:e48963. [PMID: 38573760 PMCID: PMC11027056 DOI: 10.2196/48963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/07/2023] [Accepted: 12/13/2023] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Estimating the size of key populations, including female sex workers (FSW) and men who have sex with men (MSM), can inform planning and resource allocation for HIV programs at local and national levels. In geographic areas where direct population size estimates (PSEs) for key populations have not been collected, small area estimation (SAE) can help fill in gaps using supplemental data sources known as auxiliary data. However, routinely collected program data have not historically been used as auxiliary data to generate subnational estimates for key populations, including in Namibia. OBJECTIVE To systematically generate regional size estimates for FSW and MSM in Namibia, we used a consensus-informed estimation approach with local stakeholders that included the integration of routinely collected HIV program data provided by key populations' HIV service providers. METHODS We used quarterly program data reported by key population implementing partners, including counts of the number of individuals accessing HIV services over time, to weight existing PSEs collected through bio-behavioral surveys using a Bayesian triangulation approach. SAEs were generated through simple imputation, stratified imputation, and multivariable Poisson regression models. We selected final estimates using an iterative qualitative ranking process with local key population implementing partners. RESULTS Extrapolated national estimates for FSW ranged from 4777 to 13,148 across Namibia, comprising 1.5% to 3.6% of female individuals aged between 15 and 49 years. For MSM, estimates ranged from 4611 to 10,171, comprising 0.7% to 1.5% of male individuals aged between 15 and 49 years. After the inclusion of program data as priors, the estimated proportion of FSW derived from simple imputation increased from 1.9% to 2.8%, and the proportion of MSM decreased from 1.5% to 0.75%. When stratified imputation was implemented using HIV prevalence to inform strata, the inclusion of program data increased the proportion of FSW from 2.6% to 4.0% in regions with high prevalence and decreased the proportion from 1.4% to 1.2% in regions with low prevalence. When population density was used to inform strata, the inclusion of program data also increased the proportion of FSW in high-density regions (from 1.1% to 3.4%) and decreased the proportion of MSM in all regions. CONCLUSIONS Using SAE approaches, we combined epidemiologic and program data to generate subnational size estimates for key populations in Namibia. Overall, estimates were highly sensitive to the inclusion of program data. Program data represent a supplemental source of information that can be used to align PSEs with real-world HIV programs, particularly in regions where population-based data collection methods are challenging to implement. Future work is needed to determine how best to include and validate program data in target settings and in key population size estimation studies, ultimately bridging research with practice to support a more comprehensive HIV response.
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Affiliation(s)
- Talia Loeb
- Data for Implementation (Data.FI), Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Kalai Willis
- Data for Implementation (Data.FI), Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | | | - Daniel Lee
- United States Agency for International Development Dominican Republic, Santo Domingo, Dominican Republic
| | - Amrita Rao
- Data for Implementation (Data.FI), Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Stefan Baral
- Data for Implementation (Data.FI), Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Katherine Rucinski
- Data for Implementation (Data.FI), Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
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Stevens O, Anderson R, Stover J, Teng Y, Stannah J, Silhol R, Jones H, Booton RD, Martin-Hughes R, Johnson L, Maheu-Giroux M, Mishra S, Stone J, Bershteyn A, Kim HY, Sabin K, Mitchell KM, Dimitrov D, Baral S, Donnell D, Korenromp E, Rice B, Hargreaves JR, Vickerman P, Boily MC, Imai-Eaton JW. Comparison of Empirically Derived and Model-Based Estimates of Key Population HIV Incidence and the Distribution of New Infections by Population Group in Sub-Saharan Africa. J Acquir Immune Defic Syndr 2024; 95:e46-e58. [PMID: 38180738 PMCID: PMC10769165 DOI: 10.1097/qai.0000000000003321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
BACKGROUND The distribution of new HIV infections among key populations, including female sex workers (FSWs), gay men and other men who have sex with men (MSM), and people who inject drugs (PWID) are essential information to guide an HIV response, but data are limited in sub-Saharan Africa (SSA). We analyzed empirically derived and mathematical model-based estimates of HIV incidence among key populations and compared with the Joint United Nations Programme on HIV/AIDS (UNAIDS) estimates. METHODS We estimated HIV incidence among FSW and MSM in SSA by combining meta-analyses of empirical key population HIV incidence relative to the total population incidence with key population size estimates (KPSE) and HIV prevalence. Dynamic HIV transmission model estimates of HIV incidence and percentage of new infections among key populations were extracted from 94 country applications of 9 mathematical models. We compared these with UNAIDS-reported distribution of new infections, implied key population HIV incidence and incidence-to-prevalence ratios. RESULTS Across SSA, empirical FSW HIV incidence was 8.6-fold (95% confidence interval: 5.7 to 12.9) higher than total population female 15-39 year incidence, and MSM HIV incidence was 41.8-fold (95% confidence interval: 21.9 to 79.6) male 15-29 year incidence. Combined with KPSE, these implied 12% of new HIV infections in 2021 were among FSW and MSM (5% and 7% respectively). In sensitivity analysis varying KPSE proportions within 95% uncertainty range, the proportion of new infections among FSW and MSM was between 9% and 19%. Insufficient data were available to estimate PWID incidence rate ratios. Across 94 models, median proportion of new infections among FSW, MSM, and PWID was 6.4% (interquartile range 3.2%-11.7%), both much lower than the 25% reported by UNAIDS. CONCLUSION Empirically derived and model-based estimates of HIV incidence confirm dramatically higher HIV risk among key populations in SSA. Estimated proportions of new infections among key populations in 2021 were sensitive to population size assumptions and were substantially lower than estimates reported by UNAIDS.
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Affiliation(s)
- Oliver Stevens
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Rebecca Anderson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - John Stover
- Center for Modeling, Planning and Policy Analysis, Avenir Health, Glastonbury, CT
| | - Yu Teng
- Center for Modeling, Planning and Policy Analysis, Avenir Health, Glastonbury, CT
| | - James Stannah
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, Canada
| | - Romain Silhol
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- HIV Prevention Trials Network Modelling Centre, Imperial College London, London, United Kingdom
| | - Harriet Jones
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ross D. Booton
- United Kingdom Heath Security Agency, London, United Kingdom
| | - Rowan Martin-Hughes
- Macfarlane Burnet Institute for Medical Research and Public Health, Melbourne, Australia
| | - Leigh Johnson
- Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa
| | - Mathieu Maheu-Giroux
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, Canada
| | - Sharmistha Mishra
- Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Canada
| | - Jack Stone
- Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Anna Bershteyn
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Hae-Young Kim
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Keith Sabin
- Data for Impact, The Joint United Nations Program on HIV/AIDS (UNAIDS), Geneva, Switzerland
| | - Kate M. Mitchell
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- Department of Nursing and Community Health, Glasgow Caledonian University London, London, United Kingdom
| | - Dobromir Dimitrov
- HIV Prevention Trials Network Modelling Centre, Imperial College London, London, United Kingdom
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Stefan Baral
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD
| | - Deborah Donnell
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Eline Korenromp
- Data for Impact, The Joint United Nations Program on HIV/AIDS (UNAIDS), Geneva, Switzerland
| | - Brian Rice
- School of Health and Related Research (SchARR), University of Sheffield, Sheffield, United Kingdom; and
| | - James R. Hargreaves
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Peter Vickerman
- Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Marie-Claude Boily
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- HIV Prevention Trials Network Modelling Centre, Imperial College London, London, United Kingdom
| | - Jeffrey W. Imai-Eaton
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA
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Xu C, Jing F, Lu Y, Ni Y, Tucker J, Wu D, Zhou Y, Ong J, Zhang Q, Tang W. Summarizing methods for estimating population size for key populations: a global scoping review for human immunodeficiency virus research. AIDS Res Ther 2022; 19:9. [PMID: 35183203 PMCID: PMC8858560 DOI: 10.1186/s12981-022-00434-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 02/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Estimating the population sizes of key populations(people who inject drugs, men who have sex with men, transgender persons, and commercial sex workers) is critical for understanding the overall Human Immunodeficiency Virus burden. This scoping review aims to synthesize existing methods for population size estimation among key populations, and provide recommendations for future application of the existing methods. METHODS Relevant studies published from 1st January 2000 to 4th August 2020 and related to key population size estimation were retrieved and 120 of 688 studies were assessed. After reading the full texts, 81 studies were further excluded. Therefore, 39 studies were included in this scoping review. Estimation methods included five digital methods, one in-person method, and four hybrid methods. FINDING We summarized and organized the methods for population size estimateion into the following five categories: methods based on independent samples (including capture-recapture method and multiplier method), methods based on population counting (including Delphi method and mapping method), methods based on the official report (including workbook method), methods based on social network (including respondent-driven sampling method and network scale-up method) and methods based on data-driven technologies (Bayesian estimation method, Stochastic simulation method, and Laska, Meisner, and Siegel estimation method). Thirty-six (92%) articles were published after 2010 and 23 (59%) used multiple methods. Among the articles published after 2010, 11 in high-income countries and 28 in low-income countries. A total of 10 estimated the size of commercial sex workers, 14 focused on men who have sex with men, and 10 focused on people who inject drugs. CONCLUSIONS There was no gold standard for population size estimation. Among 120 studies that were related to population size estimation of key populations, the most commonly used population estimation method is the multiplier method (26/120 studies). Every method has its strengths and biases. In recent years, novel methods based on data-driven technologies such as Bayesian estimation have been developed and applied in many surveys.
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Ito H, Yamamoto T, Morita S. The effect of men who have sex with men (MSM) on the spread of sexually transmitted infections. Theor Biol Med Model 2021; 18:18. [PMID: 34635123 PMCID: PMC8504019 DOI: 10.1186/s12976-021-00148-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/30/2021] [Indexed: 11/10/2022] Open
Abstract
Sexually transmitted infections (STIs) have remained a worldwide public health threat. It is difficult to control the spread of STIs, not only because of heterogeneous sexual transmission between men and women but also because of the complicated effects of sexual transmission among men who have sex with men (MSM) and mother-to-child transmission. Many studies point to the existence of a ‘bisexual bridge’, where STIs spread from the MSM network via bisexual connections. However, it is unclear how the MSM network affects heterosexual networks as well as mother-to-child transmission. To analyse the effect of MSM on the spread of STIs, we divided the population into four subpopulations: (i) women, (ii) men who have sex with women only (MSW), (iii) men who have sex with both men and women (MSMW), (iv) men who have sex with men exclusively (MSME). We calculated the type-reproduction numbers of these four subpopulations, and our analysis determined what preventive measures may be effective. Our analysis shows the impact of bisexual bridge on the spread of STIs does not outweigh their population size. Since MSM and mother-to-child transmission rates do not have a strong synergistic effect when combined, complementary prevention measures are needed. The methodologies and findings we have provided here will contribute greatly to the future development of public health.
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Affiliation(s)
- Hiromu Ito
- Department of International Health and Medical Anthropology, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Nagasaki, 852-8523, Japan
| | - Taro Yamamoto
- Department of International Health and Medical Anthropology, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Nagasaki, 852-8523, Japan
| | - Satoru Morita
- Department of Mathematical and Systems Engineering, Shizuoka University, Hamamatsu, Shizuoka, 432-8561, Japan. .,Department of Environment and Energy Systems, Graduate School of Science and Technology, Shizuoka University, Hamamatsu, Shizuoka, 432-8561, Japan.
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Estimating the Population Size of Female Sex Workers in Zimbabwe: Comparison of Estimates Obtained Using Different Methods in Twenty Sites and Development of a National-Level Estimate. J Acquir Immune Defic Syndr 2021; 85:30-38. [PMID: 32379082 PMCID: PMC7417013 DOI: 10.1097/qai.0000000000002393] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
National-level population size estimates (PSEs) for hidden populations are required for HIV programming and modelling. Various estimation methods are available at the site-level, but it remains unclear which are optimal and how best to obtain national-level estimates.
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Niu XM, Rao A, Chen D, Sheng B, Weir S, Umar E, Trapence G, Jumbe V, Kamba D, Rucinski K, Viswasam N, Baral S, Bao L. Using factor analyses to estimate the number of female sex workers across Malawi from multiple regional sources. Ann Epidemiol 2020; 55:34-40. [PMID: 33340655 DOI: 10.1016/j.annepidem.2020.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 11/03/2020] [Accepted: 12/02/2020] [Indexed: 01/27/2023]
Abstract
PURPOSE Human immunodeficiency virus (HIV) risks are heterogeneous in nature even in generalized epidemics. However, data are often missing for those at highest risk of HIV, including female sex workers. Statistical models may be used to address data gaps where direct, empiric estimates do not exist. METHODS We proposed a new size estimation method that combines multiple data sources (the Malawi Biological and Behavioral Surveillance Survey, the Priorities for Local AIDS Control Efforts study, and the Malawi Demographic Household Survey). We used factor analysis to extract information from auxiliary variables and constructed a linear mixed effects model for predicting population size for all districts of Malawi. RESULTS On average, the predicted proportion of female sex workers among women of reproductive age across all districts was about 0.58%. The estimated proportions seemed reasonable in comparing with a recent study Priorities for Local AIDS Control Efforts II (PLACE II). Compared with using a single data source, we observed increased precision and better geographic coverage. CONCLUSIONS We illustrate how size estimates from different data sources may be combined for prediction. Applying this approach to other subpopulations in Malawi and to countries where size estimate data are lacking can ultimately inform national modeling processes and estimate the distribution of risks and priorities for HIV prevention and treatment programs.
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Affiliation(s)
- Xiaoyue Maggie Niu
- Department of Statistics, Eberly College of Science, Pennsylvania State University, University Park
| | - Amrita Rao
- Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - David Chen
- Department of Statistics, Eberly College of Science, Pennsylvania State University, University Park
| | - Ben Sheng
- Department of Statistics, Eberly College of Science, Pennsylvania State University, University Park
| | - Sharon Weir
- Department of Epidemiology, University of North Carolina, Chapel Hill
| | - Eric Umar
- Department of Health Systems and Policy, School of Public Health and Family Medicine, University of Malawi College of Medicine, Blantyre, Malawi
| | | | - Vincent Jumbe
- Department of Health Systems and Policy, School of Public Health and Family Medicine, University of Malawi College of Medicine, Blantyre, Malawi
| | - Dunker Kamba
- Center for Development of People, Blantyre, Malawi
| | - Katherine Rucinski
- Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Nikita Viswasam
- Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Stefan Baral
- Center for Public Health and Human Rights, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Le Bao
- Department of Statistics, Eberly College of Science, Pennsylvania State University, University Park.
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Datta A, Pita A, Rao A, Sithole B, Mnisi Z, Baral S. Size estimation of key populations in the HIV epidemic in eSwatini using incomplete and misaligned capture-recapture data. Ann Appl Stat 2020. [DOI: 10.1214/20-aoas1327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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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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12
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Viswasam N, Lyons CE, MacAllister J, Millett G, Sherwood J, Rao A, Baral SD. The uptake of population size estimation studies for key populations in guiding HIV responses on the African continent. PLoS One 2020; 15:e0228634. [PMID: 32101551 PMCID: PMC7043736 DOI: 10.1371/journal.pone.0228634] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 01/21/2020] [Indexed: 12/02/2022] Open
Abstract
Background There has been a heightened emphasis on prioritizing data to inform evidence-based HIV responses, including data focused on both defining the content and scale of HIV programs in response to evidence-based need. Consequently, population size estimation (PSE) studies for key populations have become increasingly common to define the necessary scale of specific programs for key populations. This study aims to assess the research utilization of these size estimates in informing HIV policy and program documents across the African continent. Methods This study included two phases; Phase 1 was a review of all PSE for key populations, including men who have sex with men (MSM), female sex workers (FSW), people who use drugs (PWUD), and transgender persons in the 54 countries across Africa published from January 2009—December 2017. Phase 2 was a review of 23 different types of documents released between January 2009 –January 2019, with a focus on the US President’s Emergency Plan for AIDS Relief (PEPFAR) and The Global Fund to Fight AIDS, Tuberculosis and Malaria investments, for evidence of stakeholder engagement in PSE studies, as well as key population PSE research utilization to inform HIV programming and international HIV investments. Results Of 118 size estimates identified in 39 studies, less than 15% were utilized in PEPFAR Country Operational Plans or national strategic health plan documents, and less than 2% in Global Fund Concept Notes. Of 39 PSE studies, over 50% engaged stakeholders in study implementation and identified target population stakeholders, a third of studies identified policy or program stakeholders, and 15% involved stakeholders in study design. Conclusion The past decade has seen an increase in PSE studies conducted for key populations in more generalized HIV epidemic settings which involve significant investments of finances and human resources. However, there remains limited evidence of sustained uptake of these data to guide the HIV responses. Increasing uptake necessitates effective stakeholder engagement and data-oriented capacity building to optimize research utilization and facilitate data-driven and human rights-affirming HIV responses.
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Affiliation(s)
- Nikita Viswasam
- Department of Epidemiology, Key Populations Program, Center for Public Health and Human Rights, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- * E-mail:
| | - Carrie E. Lyons
- Department of Epidemiology, Key Populations Program, Center for Public Health and Human Rights, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | | | - Greg Millett
- The Foundation for AIDS Research, amfAR, Washington, DC, United States of America
| | - Jennifer Sherwood
- The Foundation for AIDS Research, amfAR, Washington, DC, United States of America
| | - Amrita Rao
- Department of Epidemiology, Key Populations Program, Center for Public Health and Human Rights, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Stefan D. Baral
- Department of Epidemiology, Key Populations Program, Center for Public Health and Human Rights, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - on behalf of the Global.HIV Research Group
- Department of Epidemiology, Key Populations Program, Center for Public Health and Human Rights, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
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13
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Abstract
Background: National estimates of the sizes of key populations, including female sex workers, men who have sex with men, and transgender women are critical to inform national and international responses to the HIV pandemic. However, epidemiologic studies typically provide size estimates for only limited high priority geographic areas. This article illustrates a two-stage approach to obtain a national key population size estimate in the Dominican Republic using available estimates and publicly available contextual information. Methods: Available estimates of key population size in priority areas were augmented with targeted additional data collection in other areas. To combine information from data collected at each stage, we used statistical methods for handling missing data, including inverse probability weights, multiple imputation, and augmented inverse probability weights. Results: Using the augmented inverse probability weighting approach, which provides some protection against parametric model misspecification, we estimated that 3.7% (95% CI = 2.9, 4.7) of the total population of women in the Dominican Republic between the ages of 15 and 49 years were engaged in sex work, 1.2% (95% CI = 1.1, 1.3) of men aged 15–49 had sex with other men, and 0.19% (95% CI = 0.17, 0.21) of people assigned the male sex at birth were transgender. Conclusions: Viewing the size estimation of key populations as a missing data problem provides a framework for articulating and evaluating the assumptions necessary to obtain a national size estimate. In addition, this paradigm allows use of methods for missing data familiar to epidemiologists.
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