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

Author:

Stevens Oliver1ORCID,Anderson Rebecca1,Stover John2,Teng Yu2,Stannah James3,Silhol Romain14,Jones Harriet5,Booton Ross D.6,Martin-Hughes Rowan7,Johnson Leigh8,Maheu-Giroux Mathieu3,Mishra Sharmistha910,Stone Jack11,Bershteyn Anna12,Kim Hae-Young12,Sabin Keith13,Mitchell Kate M.114,Dimitrov Dobromir415,Baral Stefan16,Donnell Deborah15,Korenromp Eline13,Rice Brian17,Hargreaves James R.5,Vickerman Peter11,Boily Marie-Claude14,Imai-Eaton Jeffrey W.118

Affiliation:

1. MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom;

2. Center for Modeling, Planning and Policy Analysis, Avenir Health, Glastonbury, CT;

3. Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, Canada;

4. HIV Prevention Trials Network Modelling Centre, Imperial College London, London, United Kingdom;

5. Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom;

6. United Kingdom Heath Security Agency, London, United Kingdom;

7. Macfarlane Burnet Institute for Medical Research and Public Health, Melbourne, Australia;

8. Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa;

9. Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Ontario, Canada;

10. MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Canada;

11. Population Health Sciences, University of Bristol, Bristol, United Kingdom;

12. Department of Population Health, New York University Grossman School of Medicine, New York, NY;

13. Data for Impact, The Joint United Nations Program on HIV/AIDS (UNAIDS), Geneva, Switzerland;

14. Department of Nursing and Community Health, Glasgow Caledonian University London, London, United Kingdom;

15. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA;

16. Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD;

17. School of Health and Related Research (SchARR), University of Sheffield, Sheffield, United Kingdom; and

18. Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA.

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.

Funder

Bill and Melinda Gates Foundation

Foundation for the National Institutes of Health

Medical Research Council

Wellcome Trust

Publisher

Ovid Technologies (Wolters Kluwer Health)

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