Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning
Open Access
- 1 June 2016
- journal article
- research article
- Published by Ovid Technologies (Wolters Kluwer Health) in AIDS
- Vol. 30 (9), 1467-1474
- https://doi.org/10.1097/qad.0000000000001075
Abstract
Objective: There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV. Design/methods: Six candidate methods - including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases - were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. Results: Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. Conclusions: Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates. Copyright (C) 2016 Wolters Kluwer Health, Inc. All rights reserved.Keywords
This publication has 19 references indexed in Scilit:
- Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan AfricaInternational Journal of Health Geographics, 2013
- A Long Neglected World Malaria Map: Plasmodium vivax Endemicity in 2010PLoS Neglected Tropical Diseases, 2012
- Decline in HIV Prevalence among Young Women in Zambia: National-Level Estimates of Trends Mask Geographical and Socio-Demographic DifferencesPLOS ONE, 2012
- A new world malaria map: Plasmodium falciparum endemicity in 2010Malaria Journal, 2011
- Bayesian geostatistics in health cartography: the perspective of malariaTrends in Parasitology, 2011
- Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys?AIDS, 2011
- Adaptive kernel estimation of spatial relative riskStatistics in Medicine, 2010
- Investigating spatio-temporal similarities in the epidemiology of childhood leukaemia and diabetesEuropean Journal of Epidemiology, 2009
- Comparison of HIV prevalence estimates from antenatal care surveillance and population-based surveys in sub-Saharan AfricaSexually Transmitted Infections, 2008
- Designing Equitable Antiretroviral Allocation Strategies in Resource-Constrained CountriesPLoS Medicine, 2005