TY - JOUR
T1 - Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning
AU - The Subnational Estimates Working Group of the HIV Modelling Consortium
AU - Hallett, Timothy B.
AU - Anderson, Sarah Jane
AU - Asante, Cynthia Adobea
AU - Bartlett, Noah
AU - Bendaud, Victoria
AU - Bhatt, Samir
AU - Burgert, Clara R.
AU - Cuadros, Diego Fernando
AU - Dzangare, Janet
AU - Fecht, Daniela
AU - Gething, Peter William
AU - Ghys, Peter D.
AU - Guwani, James M.
AU - Heard, Nathan Joseph
AU - Kalipeni, Ezekiel
AU - Kandala, Ngianga Bakwin
AU - Kim, Andrea A.
AU - Kwao, Isaiah Doe
AU - Larmarange, Joseph
AU - Manda, Samuel O.M.
AU - Moise, Imelda K.
AU - Montana, Livia S.
AU - Mwai, Daniel N.
AU - Mwalili, Samuel
AU - Shortridge, Ashton
AU - Tanser, Frank
AU - Wanyeki, Ian
AU - Zulu, Leo
N1 - Publisher Copyright:
Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - HIV infections/epidemiology
KW - HIV seroprevalence
KW - HIV/infections prevention and control
KW - Health planning/organization and administration
KW - Health policy
KW - Population surveillance/methods
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U2 - 10.1097/QAD.0000000000001075
DO - 10.1097/QAD.0000000000001075
M3 - Article
C2 - 26919737
AN - SCOPUS:84959186542
SN - 0269-9370
VL - 30
SP - 1467
EP - 1474
JO - AIDS
JF - AIDS
IS - 9
ER -