Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning
File(s)
Published version
Author(s)
Type
Journal Article
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.
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.
Date Issued
2016-06-01
Date Acceptance
2016-01-25
Citation
AIDS, 2016, 30 (9), pp.1467-1474
ISSN
0269-9370
Publisher
Wolters Kluwer
Start Page
1467
End Page
1474
Journal / Book Title
AIDS
Volume
30
Issue
9
Copyright Statement
© 2016 Wolters Kluwer Health, Inc. All rights reserved. This is an open access article distributed under the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Sponsor
Bill & Melinda Gates Foundation
Grant Number
OPP1084364
Subjects
Science & Technology
Life Sciences & Biomedicine
Immunology
Infectious Diseases
Virology
health planning/organization and administration
health policy
HIV infections/epidemiology
HIV seroprevalence
HIV/infections prevention and control
population surveillance/methods
POPULATION-BASED SURVEYS
SUB-SAHARAN AFRICA
ENDEMICITY
INFECTION
EPIDEMIC
MODEL
RISK
06 Biological Sciences
11 Medical And Health Sciences
17 Psychology And Cognitive Sciences
Publication Status
Published