Mapping and characterising areas with high levels of HIV transmission in sub-Saharan Africa: A geospatial analysis of national survey data

English version

Autoři: Caroline A. Bulstra ^aff001; Jan A. C. Hontelez ^aff001; Federica Giardina ^aff001; Richard Steen ^aff001; Nico J. D. Nagelkerke ^aff001; Till Bärnighausen ^aff002; Sake J. de Vlas ^aff001
Působiště autorů: Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ^aff001; Heidelberg Institute of Global Health, Heidelberg University Medical Center, Heidelberg, Germany ^aff002; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America ^aff003; Africa Health Research Institute (AHRI), KwaZulu-Natal, South Africa ^aff004
Vyšlo v časopise: Mapping and characterising areas with high levels of HIV transmission in sub-Saharan Africa: A geospatial analysis of national survey data. PLoS Med 17(3): e32767. doi:10.1371/journal.pmed.1003042
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pmed.1003042

Souhrn

Background

In the generalised epidemics of sub-Saharan Africa (SSA), human immunodeficiency virus (HIV) prevalence shows patterns of clustered micro-epidemics. We mapped and characterised these high-prevalence areas for young adults (15–29 years of age), as a proxy for areas with high levels of transmission, for 7 countries in Eastern and Southern Africa: Kenya, Malawi, Mozambique, Tanzania, Uganda, Zambia, and Zimbabwe.

Methods and findings

We used geolocated survey data from the most recent United States Agency for International Development (USAID) demographic and health surveys (DHSs) and AIDS indicator surveys (AISs) (collected between 2008–2009 and 2015–2016), which included about 113,000 adults—of which there were about 53,000 young adults (27,000 women, 28,000 men)—from over 3,500 sample locations. First, ordinary kriging was applied to predict HIV prevalence at unmeasured locations. Second, we explored to what extent behavioural, socioeconomic, and environmental factors explain HIV prevalence at the individual -⁠ and sample-location level, by developing a series of multilevel multivariable logistic regression models and geospatially visualising unexplained model heterogeneity. National-level HIV prevalence for young adults ranged from 2.2% in Tanzania to 7.7% in Mozambique. However, at the subnational level, we found areas with prevalence among young adults as high as 11% or 15% alternating with areas with prevalence between 0% and 2%, suggesting the existence of areas with high levels of transmission Overall, 15.6% of heterogeneity could be explained by an interplay of known behavioural, socioeconomic, and environmental factors. Maps of the interpolated random effect estimates show that environmental variables, representing indicators of economic activity, were most powerful in explaining high-prevalence areas. Main study limitations were the inability to infer causality due to the cross-sectional nature of the surveys and the likely under-sampling of key populations in the surveys.

Conclusions

We found that, among young adults, micro-epidemics of relatively high HIV prevalence alternate with areas of very low prevalence, clearly illustrating the existence of areas with high levels of transmission. These areas are partially characterised by high economic activity, relatively high socioeconomic status, and risky sexual behaviour. Localised HIV prevention interventions specifically tailored to the populations at risk will be essential to curb transmission. More fine-scale geospatial mapping of key populations,—such as sex workers and migrant populations—could help us further understand the drivers of these areas with high levels of transmission and help us determine how they fuel the generalised epidemics in SSA.

Klíčová slova:

Africa – Behavior – HIV – HIV epidemiology – HIV infections – HIV prevention – Socioeconomic aspects of health – Young adults

Zdroje

1. Nations United. Transforming our World: The 2030 Agenda for Sustainable Development. United Nations Division for Social Policy and Development, Indigenous Peoples; 2017. Available from: https://sustainabledevelopment.un.org/post2015/. [cited 1 June 2019].

2. UNAIDS. Fast-track: ending the AIDS epidemic by 2030. 2014.

3. UNAIDS. UNAIDS report 2017, Joint United Nations Programme on HIV/AIDS (UNAIDS). 2017.

4. Cuadros DF, Li J, Branscum AJ, Akullian A, Jia P, Mziray EN, et al. Mapping the spatial variability of HIV infection in Sub-Saharan Africa: Effective information for localized HIV prevention and control. Nature. 2017;7 : 1–11. doi: 10.1038/s41598-017-09464-y 28831171

5. Dwyer-Lindgren L, Cork MA, Sligar A, Steuben KM, Wilson KF, Provost NR, et al. Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017. Nature. 2019; doi: 10.1038/s41586-019-1200-9 31092927

6. Tanser F, LeSueur D, Solarsh G, Wilkinson D. HIV heterogeneity and proximity of homestead to roads in rural South Africa: An exploration using a geographical information system. Trop Med Int Heal. 2000;5 : 40–46. doi: 10.1046/j.1365-3156.2000.00513.x 10672204

7. Zulu LC, Kalipeni E, Johannes E. Analyzing spatial clustering and the spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: The case of Malawi, 1994–2010. BMC Infect Dis. 2014;14 : 1–21. doi: 10.1186/1471-2334-14-1

8. Barankanira E, Molinari N, Niyongabo T, Laurent C. Spatial analysis of HIV infection and associated individual characteristics in Burundi: Indications for effective prevention. BMC Public Health. 2016;16 : 1–11. doi: 10.1186/s12889-015-2639-8

9. Anderson SJ, Cherutich P, Kilonzo N, Cremin I, Fecht D, Kimanga D, et al. Maximising the effect of combination HIV prevention through prioritisation of the people and places in greatest need: A modelling study. Lancet. 2014;384 : 249–256. doi: 10.1016/S0140-6736(14)61053-9 25042235

10. Mahy M, Garcia-Calleja JM, Marsh KA. Trends in HIV prevalence among young people in generalised epidemics: implications for monitoring the HIV epidemic. Sex Transm Infect. 2012;88: i65–i75. doi: 10.1136/sextrans-2012-050789 23172347

11. Luke N, Kurz K. Cross-generational and transactional sexual relations in sub-Saharan Africa. Washington, DC, International Center for Research on Women. 2002;1, 1–42.

12. Palk L, Blower S. Geographic variation in sexual behavior can explain geospatial heterogeneity in the severity of the HIV epidemic in Malawi. BMC Med. 2018;16 : 22. doi: 10.1186/s12916-018-1006-x 29422096

13. Neve J De, Fink G, Subramanian S V, Moyo S, Bor J. Length of secondary schooling and risk of HIV infection in Botswana: evidence from a natural experiment. Lancet. 2015;3 : 470–477. doi: 10.1016/S2214-109X(15)00087-X 26134875

14. Baird SJ, Garfein RS, McIntosh CT, Özler B. Effect of a cash transfer programme for schooling on prevalence of HIV and herpes simplex type 2 in Malawi: A cluster randomised trial. Lancet. 2012;379 : 1320–1329. doi: 10.1016/S0140-6736(11)61709-1 22341825

15. Auvert B, Taljaard D, Lagarde E, Sobngwi-Tambekou J, Sitta R, Puren A. Randomized, controlled intervention trial of male circumcision for reduction of HIV infection risk: The ANRS 1265 trial. PLoS Med. 2005;2 : 1112–1122. doi: 10.1371/journal.pmed.0020298 16231970

16. JICA. Preparatory Survey for Southern Africa Integrated Regional Transport Program. 2010. Available from: http://open_jicareport.jica.go.jp/pdf/11991007_01.pdf. [cited 1 June 2019].

17. Diggle PJ, Tawn JA, Moyeed RA. Model-Based Geostatistics. J R Stat Soc. 1998;67 : 617–666.

18. Miller HJ. Tobler’s First Law and Spatial Analysis. Ann Assoc Am Geogr. 2004;94 : 284–289. doi: 10.1111/j.1467-8306.2004.09402005.x

19. Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol. 2013;4 : 133–142. doi: 10.1111/j.2041-210x.2012.00261.x

20. Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al. A brief conceptual tutorial of multilevel analysis in social epidemiology: Using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health. 2006;60 : 290–297. doi: 10.1136/jech.2004.029454 16537344

21. Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal—external, and external validation. J Clin Epidemiol. 2016;69 : 245–247. doi: 10.1016/j.jclinepi.2015.04.005 25981519

22. Steyerberg EW. Validation in prediction research: the waste by data splitting. J Clin Epidemiol. 2018;103 : 131–133. doi: 10.1016/j.jclinepi.2018.07.010 30063954

23. Reniers G, Wamukoya M, Urassa M, Nyaguara A, Nakiyingi-Miiro J, Lutalo T, et al. Data resource profile: network for analysing longitudinal population-based HIV/AIDS data on Africa (ALPHA Network). Int J Epidemiol. 2016;45 : 83–93. doi: 10.1093/ije/dyv343 26968480

24. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014;12 : 1495–1499. doi: 10.1016/j.ijsu.2014.07.013 25046131

25. Parkhurst JO. Structural approaches for prevention of sexually transmitted HIV in general populations: definitions and an operational approach. J Int AIDS Soc. 2014;17 : 19052. doi: 10.7448/IAS.17.1.19052 25204872

26. University of Toronto. The Nature of Economic Activity. Available from: https://www.economics.utoronto.ca/jfloyd/modules/neastkf.html. [cited 25 Nov 2019].

27. Cassels S, Camlin CS. Geographical mobility and heterogeneity of the HIV epidemic. Lancet HIV. 2016;3: e339–e341. doi: 10.1016/S2352-3018(16)30048-0 27470023

28. Steen R, Hontelez JAC, Mugurungi O, Mpofu A, Matthijsse SM, de Vlas SJ, et al. Economy, migrant labour and sex work: interplay of HIV epidemic drivers in Zimbabwe over three decades. AIDS. 2019;33 : 123–131. doi: 10.1097/QAD.0000000000002066 30475261

29. Chang LW, Health R, Program S, Program BI, Hopkins J, Grabowski MK, et al. Heterogeneity of the HIV epidemic: an observational epidemiologic study of agrarian, trading, and fishing communities in Rakai, Uganda. Lancet HIV. 2016;3 : 1–20. doi: 10.1016/S2352-3018(16)30034-0.Heterogeneity

30. Tumwesigye NM, Atuyambe L, Wanyenze RK, Kibira SP, Li Q, Wabwire-Mangen F, et al. Alcohol consumption and risky sexual behaviour in the fishing communities: Evidence from two fish landing sites on Lake Victoria in Uganda. BMC Public Health. 2012;12 : 1–11. doi: 10.1186/1471-2458-12-1

31. Faria NR, Rambaut A, Suchard MA, Baele G, Bedford T, Ward MJ, et al. The early spread and epidemic ignition of HIV-1 in human populations. Science. 2014;346 : 56–61. doi: 10.1126/science.1256739 25278604

32. Corno L, Walque D De. Mines, Migration and HIV / AIDS in Southern Africa. J Afr Econ. 2012;21 : 465–498. doi: 10.1093/jae/ejs005

33. Clift S, Anemona A, Watson-Jones D, Kanga Z, Ndeki L, Changalucha J, et al. Variations of HIV and STI prevalences within communities neighbouring new goldmines in Tanzania: importance for intervention design. Sex Transm Infect. 2003;79 : 307 LP –⁠ 312.

34. Bachanas P, Medley A, Pals S, Antelman G, Benech I, Deluca N, et al. Disclosure, Knowledge of Partner Status, and Condom Use Among HIV-Positive Patients Attending Clinical Care in Tanzania, Kenya, and Namibia. AIDS Patient Care STDS. 2013;27 : 425–435. doi: 10.1089/apc.2012.0388 23829332

35. Mavedzenge SMN, Doyle AM, Ross DA. HIV prevention in young people in sub-Saharan Africa: a systematic review. J Adolesc Heal. 2011;49 : 568–586.

36. Pettifor A, Bekker L-G, Hosek S, DiClemente R, Rosenberg M, Bull S, et al. Preventing HIV among young people: research priorities for the future. J Acquir Immune Defic Syndr. 2013;63: S155. doi: 10.1097/QAI.0b013e31829871fb 23764629

37. Baeten JM, Donnell D, Ndase P, Mugo NR, Campbell JD, Wangisi J, et al. Antiretroviral Prophylaxis for HIV Prevention in Heterosexual Men and Women. N Engl J Med. 2012;367 : 399–410. doi: 10.1056/NEJMoa1108524 22784037

38. Creese A, Floyd K, Alban A, Guinness L. Cost-effectiveness of HIV / AIDS interventions in Africa: a systematic review of the evidence. Lancet. 2002;359 : 1635–1642. doi: 10.1016/S0140-6736(02)08595-1 12020523

39. Samuelson J, Dickson K. Progress in scale-up of male circumcision for HIV prevention in Eastern and Southern Africa: Focus on service delivery. World Health Organization and UNAIDS. 2011.

40. Wong VJ, Murray KR, Phelps BR, Vermund SH, McCarraher DR. Adolescents, young people, and the 90-90-90 goals: a call to improve HIV testing and linkage to treatment. AIDS. 2017;31: S191. doi: 10.1097/QAD.0000000000001539 28665876

41. Deane KD, Samwell Ngalya P, Boniface L, Bulugu G, Urassa M. Exploring the relationship between population mobility and HIV risk: Evidence from Tanzania. Glob Public Health. 2016; 1–16. doi: 10.1080/17441692.2016.1178318 27230067

42. McGrath N, Hosegood V, Newell ML, Eaton JW. Migration, sexual behaviour, and HIV risk: A general population cohort in rural South Africa. Lancet HIV. 2015;2: e252–e259. doi: 10.1016/S2352-3018(15)00045-4 26280016

43. Wilson D, Halperin DT. “Know your epidemic, know your response”: a useful approach, if we get it right. Lancet. 2008;372 : 423–426. doi: 10.1016/S0140-6736(08)60883-1 18687462

44. Gasser T, Sroka L, Jennen-Steinmetz C. Residual variance and residual pattern in nonlinear regression. Biometrika. 1986;73 : 625–633.

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