Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Heidelberg Institute of Global Health, Heidelberg University Medical Center, Heidelberg, Germany.
PLoS Med. 2020 Mar 6;17(3):e1003042. doi: 10.1371/journal.pmed.1003042. eCollection 2020 Mar.
In the generalised epidemics of sub-Saharan Africa (SSA), human immunodeficiency virus (HIV) prevalence shows patterns of clustered micro-epidemics. We mapped and characterised these high-prevalence areas for young adults (15-29 years of age), as a proxy for areas with high levels of transmission, for 7 countries in Eastern and Southern Africa: Kenya, Malawi, Mozambique, Tanzania, Uganda, Zambia, and Zimbabwe.
We used geolocated survey data from the most recent United States Agency for International Development (USAID) demographic and health surveys (DHSs) and AIDS indicator surveys (AISs) (collected between 2008-2009 and 2015-2016), which included about 113,000 adults-of which there were about 53,000 young adults (27,000 women, 28,000 men)-from over 3,500 sample locations. First, ordinary kriging was applied to predict HIV prevalence at unmeasured locations. Second, we explored to what extent behavioural, socioeconomic, and environmental factors explain HIV prevalence at the individual- and sample-location level, by developing a series of multilevel multivariable logistic regression models and geospatially visualising unexplained model heterogeneity. National-level HIV prevalence for young adults ranged from 2.2% in Tanzania to 7.7% in Mozambique. However, at the subnational level, we found areas with prevalence among young adults as high as 11% or 15% alternating with areas with prevalence between 0% and 2%, suggesting the existence of areas with high levels of transmission Overall, 15.6% of heterogeneity could be explained by an interplay of known behavioural, socioeconomic, and environmental factors. Maps of the interpolated random effect estimates show that environmental variables, representing indicators of economic activity, were most powerful in explaining high-prevalence areas. Main study limitations were the inability to infer causality due to the cross-sectional nature of the surveys and the likely under-sampling of key populations in the surveys.
We found that, among young adults, micro-epidemics of relatively high HIV prevalence alternate with areas of very low prevalence, clearly illustrating the existence of areas with high levels of transmission. These areas are partially characterised by high economic activity, relatively high socioeconomic status, and risky sexual behaviour. Localised HIV prevention interventions specifically tailored to the populations at risk will be essential to curb transmission. More fine-scale geospatial mapping of key populations,-such as sex workers and migrant populations-could help us further understand the drivers of these areas with high levels of transmission and help us determine how they fuel the generalised epidemics in SSA.
在撒哈拉以南非洲(SSA)的普遍流行中,人类免疫缺陷病毒(HIV)的流行呈现出聚集性微流行的模式。我们对 7 个东非和南非国家(肯尼亚、马拉维、莫桑比克、坦桑尼亚、乌干达、赞比亚和津巴布韦)的青年成年人(15-29 岁)的这些高流行地区进行了制图和特征描述,作为高传播水平地区的代表。
我们使用了最近的美国国际开发署(USAID)人口和健康调查(DHS)和艾滋病指标调查(AIS)的地理位置调查数据(收集于 2008-2009 年和 2015-2016 年),其中包括来自 3500 多个样本地点的约 113000 名成年人,其中约有 53000 名青年成年人(27000 名女性,28000 名男性)。首先,应用普通克里金法预测未测量地点的 HIV 流行率。其次,我们通过开发一系列多水平多变量逻辑回归模型并对未解释的模型异质性进行地理空间可视化,探讨了个体和样本位置水平上的行为、社会经济和环境因素在多大程度上解释了 HIV 流行率。全国范围内,青年成年人的 HIV 流行率从坦桑尼亚的 2.2%到莫桑比克的 7.7%不等。然而,在国家以下一级,我们发现一些地区的青年成年人的流行率高达 11%或 15%,而另一些地区的流行率则在 0%至 2%之间,这表明存在传播水平较高的地区。总的来说,15.6%的异质性可以通过已知的行为、社会经济和环境因素的相互作用来解释。插值随机效应估计值的地图表明,代表经济活动指标的环境变量在解释高流行地区方面最为有力。主要研究限制是由于调查的横断面性质以及调查中可能对关键人群的抽样不足,无法推断因果关系。
我们发现,在青年成年人中,相对较高的 HIV 流行率的微流行与非常低的流行率地区交替出现,这清楚地说明了存在传播水平较高的地区。这些地区部分特征是经济活动水平较高、社会经济地位相对较高和性行为风险较高。针对高危人群的本地化艾滋病毒预防干预措施对于遏制传播至关重要。对性工作者和移民等关键人群进行更精细的地理空间测绘,可以帮助我们进一步了解这些高传播水平地区的驱动因素,并帮助我们确定它们如何助长 SSA 的普遍流行。