A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa

Despite making significant progress in tackling its HIV epidemic, South Africa, with 7.7 million people living with HIV, still has the biggest HIV epidemic in the world. The Government, in collaboration with developmental partners and agencies, has been strengthening its responses to the HIV epidemi...

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Autores principales:	Kassahun Abere Ayalew, Samuel Manda, Bo Cai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Bayesian disease mapping skew-t distribution ICAR-normal ICAR-Laplace spatial random effects Medicine R
Acceso en línea:	https://doaj.org/article/1dbf7cc5781642fe8b538a92c7912893
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id	oai:doaj.org-article:1dbf7cc5781642fe8b538a92c7912893
record_format	dspace
spelling	oai:doaj.org-article:1dbf7cc5781642fe8b538a92c79128932021-11-11T16:22:05ZA Comparison of Bayesian Spatial Models for HIV Mapping in South Africa10.3390/ijerph1821112151660-46011661-7827https://doaj.org/article/1dbf7cc5781642fe8b538a92c79128932021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11215https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601Despite making significant progress in tackling its HIV epidemic, South Africa, with 7.7 million people living with HIV, still has the biggest HIV epidemic in the world. The Government, in collaboration with developmental partners and agencies, has been strengthening its responses to the HIV epidemic to better target the delivery of HIV care, treatment strategies and prevention services. Population-based household HIV surveys have, over time, contributed to the country’s efforts in monitoring and understanding the magnitude and heterogeneity of the HIV epidemic. Local-level monitoring of progress made against HIV and AIDS is increasingly needed for decision making. Previous studies have provided evidence of substantial subnational variation in the HIV epidemic. Using HIV prevalence data from the 2016 South African Demographic and Health Survey, we compare three spatial smoothing models, namely, the intrinsically conditionally autoregressive normal, Laplace and skew-t (ICAR-normal, ICAR-Laplace and ICAR-skew-t) in the estimation of the HIV prevalence across 52 districts in South Africa. The parameters of the resulting models are estimated using Bayesian approaches. The skewness parameter for the ICAR-skew-t model was not statistically significant, suggesting the absence of skewness in the HIV prevalence data. Based on the deviance information criterion (DIC) model selection, the ICAR-normal and ICAR-Laplace had DIC values of 291.3 and 315, respectively, which were lower than that of the ICAR-skewed t (348.1). However, based on the model adequacy criterion using the conditional predictive ordinates (CPO), the ICAR-skew-t distribution had the lowest CPO value. Thus, the ICAR-skew-t was the best spatial smoothing model for the estimation of HIV prevalence in our study.Kassahun Abere AyalewSamuel MandaBo CaiMDPI AGarticleBayesiandisease mappingskew-t distributionICAR-normalICAR-Laplacespatial random effectsMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11215, p 11215 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian disease mapping skew-t distribution ICAR-normal ICAR-Laplace spatial random effects Medicine R
spellingShingle	Bayesian disease mapping skew-t distribution ICAR-normal ICAR-Laplace spatial random effects Medicine R Kassahun Abere Ayalew Samuel Manda Bo Cai A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa
description	Despite making significant progress in tackling its HIV epidemic, South Africa, with 7.7 million people living with HIV, still has the biggest HIV epidemic in the world. The Government, in collaboration with developmental partners and agencies, has been strengthening its responses to the HIV epidemic to better target the delivery of HIV care, treatment strategies and prevention services. Population-based household HIV surveys have, over time, contributed to the country’s efforts in monitoring and understanding the magnitude and heterogeneity of the HIV epidemic. Local-level monitoring of progress made against HIV and AIDS is increasingly needed for decision making. Previous studies have provided evidence of substantial subnational variation in the HIV epidemic. Using HIV prevalence data from the 2016 South African Demographic and Health Survey, we compare three spatial smoothing models, namely, the intrinsically conditionally autoregressive normal, Laplace and skew-t (ICAR-normal, ICAR-Laplace and ICAR-skew-t) in the estimation of the HIV prevalence across 52 districts in South Africa. The parameters of the resulting models are estimated using Bayesian approaches. The skewness parameter for the ICAR-skew-t model was not statistically significant, suggesting the absence of skewness in the HIV prevalence data. Based on the deviance information criterion (DIC) model selection, the ICAR-normal and ICAR-Laplace had DIC values of 291.3 and 315, respectively, which were lower than that of the ICAR-skewed t (348.1). However, based on the model adequacy criterion using the conditional predictive ordinates (CPO), the ICAR-skew-t distribution had the lowest CPO value. Thus, the ICAR-skew-t was the best spatial smoothing model for the estimation of HIV prevalence in our study.
format	article
author	Kassahun Abere Ayalew Samuel Manda Bo Cai
author_facet	Kassahun Abere Ayalew Samuel Manda Bo Cai
author_sort	Kassahun Abere Ayalew
title	A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa
title_short	A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa
title_full	A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa
title_fullStr	A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa
title_full_unstemmed	A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa
title_sort	comparison of bayesian spatial models for hiv mapping in south africa
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1dbf7cc5781642fe8b538a92c7912893
work_keys_str_mv	AT kassahunabereayalew acomparisonofbayesianspatialmodelsforhivmappinginsouthafrica AT samuelmanda acomparisonofbayesianspatialmodelsforhivmappinginsouthafrica AT bocai acomparisonofbayesianspatialmodelsforhivmappinginsouthafrica AT kassahunabereayalew comparisonofbayesianspatialmodelsforhivmappinginsouthafrica AT samuelmanda comparisonofbayesianspatialmodelsforhivmappinginsouthafrica AT bocai comparisonofbayesianspatialmodelsforhivmappinginsouthafrica
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A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa

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