Chimoyi Lucy A, Musenge Eustasius
Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
BMC Public Health. 2014 Jun 5;14:555. doi: 10.1186/1471-2458-14-555.
The HIV epidemic in East Africa is of public health importance with an increasing number of young people getting infected. This study sought to identify spatial clusters and examine the geographical variation of HIV infection at a regional level while accounting for risk factors associated with HIV/AIDS among young people in Uganda.
A secondary data analysis was conducted on a survey cross-sectional design whose data were obtained from the 2011 Uganda Demographic and Health Survey (DHS) and AIDS Indicator Survey (AIS) for 7 518 young people aged 15-24 years. The analysis was performed in three stages while incorporating population survey sampling weights. Maximum likelihood-based logistic regression models were used to explore the non-spatially adjusted factors associated with HIV infection. Spatial scan statistic was used to identify geographical clusters of elevated HIV infections which justified modelling using a spatial random effects model by Bayesian-based logistic regression models.
In this study, 309/533 HIV sero-positive female participants were selected with majority residing in the rural areas [386(72%)]. Compared to singles, those currently [Adjusted Odds Ratio (AOR) =3.64; (95% CI; 1.25-10.27)] and previously married [AOR = 5.62; (95% CI: 1.52-20.75)] participants had significantly higher likelihood of HIV infections. Sexually Transmitted Infections [AOR = 2.21; (95% CI: 1.35-3.60)] were more than twice likely associated with HIV infection. One significant (p < 0.05) primary cluster of HIV prevalence around central Uganda emerged from the SaTScan cluster analysis. Spatial analysis disclosed behavioural factors associated with greater odds of HIV infection such as; alcohol use before sexual intercourse [Posterior Odds Ratio (POR) =1.32; 95% (BCI: 1.11-1.63)]. Condom use [POR = 0.54; (95% BCI: 0.41-0.69)] and circumcision [POR = 0.66; (95% BCI: 0.45-0.99)] provided a protective effect against HIV.
The study revealed associations between high-risk sexual behaviour and HIV infection. Behavioural change interventions should therefore be pertinent to the prevention of HIV. Spatial analysis further revealed a significant HIV cluster towards the Central and Eastern areas of Uganda. We propose that interventions targeting young people should initially focus on these regions and subsequently spread out across Uganda.
东非的艾滋病疫情对公共卫生至关重要，感染艾滋病的年轻人数量不断增加。本研究旨在识别空间聚集区，并在考虑乌干达年轻人中与艾滋病毒/艾滋病相关的风险因素的同时，在区域层面研究艾滋病毒感染的地理差异。
对一项调查横断面设计进行二次数据分析，其数据来自2011年乌干达人口与健康调查（DHS）和艾滋病指标调查（AIS），涉及7518名15 - 24岁的年轻人。分析分三个阶段进行，同时纳入人口调查抽样权重。基于最大似然的逻辑回归模型用于探索与艾滋病毒感染相关的非空间调整因素。空间扫描统计用于识别艾滋病毒感染率升高的地理聚集区，这为使用基于贝叶斯的逻辑回归模型的空间随机效应模型进行建模提供了依据。
在本研究中，选取了309/533名艾滋病毒血清阳性女性参与者，其中大多数居住在农村地区[386(72%)]。与单身者相比，目前[调整后的优势比（AOR）=3.64；(95%置信区间；1.25 - 10.27)]和以前结婚的参与者[AOR = 5.62；(95%置信区间：1.52 - 20.75)]感染艾滋病毒的可能性显著更高。性传播感染[AOR = 2.21；(95%置信区间：1.35 - 3.60)]与艾滋病毒感染的关联可能性是其两倍多。通过空间扫描统计聚类分析发现，乌干达中部周围出现了一个艾滋病毒流行的显著（p < 0.05）主要聚集区。空间分析揭示了与艾滋病毒感染几率增加相关的行为因素，如：性交前饮酒[后验优势比（POR）=1.32；95%（贝叶斯可信区间：1.11 - 1.63）]。使用避孕套[POR = 0.54；(95%贝叶斯可信区间：0.41 - 0.69)]和包皮环切术[POR = 0.66；(95%贝叶斯可信区间：0.45 - 0.99)]对艾滋病毒有保护作用。
该研究揭示了高危性行为与艾滋病毒感染之间的关联。因此，行为改变干预措施应与预防艾滋病毒相关。空间分析进一步揭示了乌干达中部和东部地区存在一个显著的艾滋病毒聚集区。我们建议，针对年轻人的干预措施应首先关注这些地区，随后在乌干达各地推广。
