School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa.
Biostatistics Unit, South African Medical Research Council, Pretoria 0001, South Africa.
Int J Environ Res Public Health. 2021 Oct 26;18(21):11215. doi: 10.3390/ijerph182111215.
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.
尽管南非在应对艾滋病疫情方面取得了重大进展，但该国仍有 770 万人感染艾滋病，是世界上艾滋病疫情最严重的国家。政府与发展伙伴和机构合作，加强了对艾滋病疫情的应对措施，以更好地提供艾滋病护理、治疗策略和预防服务。基于人群的家庭艾滋病毒调查随着时间的推移，为监测和了解艾滋病毒疫情的规模和异质性做出了贡献。地方一级监测艾滋病毒和艾滋病防治工作进展，对于决策越来越重要。先前的研究已经提供了证据，表明艾滋病毒疫情在次国家一级存在很大差异。本研究利用 2016 年南非人口与健康调查的艾滋病毒流行率数据，比较了三种空间平滑模型，即固有条件自回归正态模型、拉普拉斯模型和偏斜 t 模型（ICAR-正态模型、ICAR-拉普拉斯模型和 ICAR-偏斜 t 模型），以估计南非 52 个地区的艾滋病毒流行率。使用贝叶斯方法估计了所得模型的参数。ICAR-偏斜 t 模型的偏度参数在统计上不显著，表明艾滋病毒流行率数据不存在偏度。基于偏差信息准则（DIC）模型选择，ICAR-正态模型和 ICAR-拉普拉斯模型的 DIC 值分别为 291.3 和 315，低于 ICAR-偏斜 t 模型的 348.1。然而，根据使用条件预测有序数（CPO）的模型充分性标准，ICAR-偏斜 t 分布的 CPO 值最低。因此，在我们的研究中，ICAR-偏斜 t 分布是估计艾滋病毒流行率的最佳空间平滑模型。
