Ntirampeba D, Neema I, Kazembe L N
Department of Mathematics and Statistics, Namibia University of Science and Technology, Windhoek, 2064 Namibia.
Namibia Statistics Agency (NSA), Windhoek, 2064 Namibia.
Glob Health Res Policy. 2017 Aug 1;2:22. doi: 10.1186/s41256-017-0041-z. eCollection 2017.
In disease mapping field, researchers often encounter data from multiple sources. Such data are fraught with challenges such as lack of a representative sample, often incomplete and most of which may have measurement errors, and may be spatially and temporally misaligned. This paper presents a joint model in the effort to deal with the sampling bias and misalignment.
A joint (bivariate) spatial model was applied to estimate HIV prevalence using two sources: 2014 National HIV Sentinel survey (NHSS) among pregnant women aged 15-49 years attending antenatal care (ANC) and the 2013 Namibia Demographic and Health Surveys (NDHS).
Findings revealed that health districts and constituencies in the northern part of Namibia were found to be highly associated with HIV infection. Also, the study showed that place of residence, gender, gravida, marital status, number of kids dead, wealth index, education, and condom use were significantly associated with HIV infection in Namibia.
This study had shown determinants of HIV infection in Namibia and had revealed areas at high risk through HIV prevalence mapping. Moreover, a joint modelling approach was used in order to deal with spatially misaligned data. Finally, it was shown that prediction of HIV prevalence using the NDHS data source can be enhanced by jointly modelling other HIV data such as NHSS data. These findings would help Namibia to tailor national intervention strategies for specific regions and groups of population.
在疾病地图绘制领域，研究人员经常会遇到来自多个来源的数据。这类数据充满挑战，比如缺乏代表性样本、往往不完整且大多可能存在测量误差，还可能在空间和时间上存在偏差。本文提出一种联合模型，旨在应对抽样偏差和偏差问题。
应用一种联合（双变量）空间模型，利用两个来源的数据估计艾滋病毒感染率：2014年全国艾滋病毒哨点调查（NHSS），针对年龄在15至49岁、接受产前护理（ANC）的孕妇；以及2013年纳米比亚人口与健康调查（NDHS）。
研究结果显示，纳米比亚北部的卫生区和选区与艾滋病毒感染高度相关。此外，研究表明，在纳米比亚，居住地点、性别、妊娠次数、婚姻状况、死亡子女数量、财富指数、教育程度和避孕套使用情况与艾滋病毒感染显著相关。
本研究显示了纳米比亚艾滋病毒感染的决定因素，并通过艾滋病毒感染率地图绘制揭示了高风险地区。此外，采用了联合建模方法来处理空间上不匹配的数据。最后，研究表明，通过对其他艾滋病毒数据（如NHSS数据）进行联合建模，可以提高利用NDHS数据源预测艾滋病毒感染率的准确性。这些研究结果将有助于纳米比亚针对特定地区和人群制定国家干预策略。
