Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic
Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic
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DOI：
10.1093/ije/dyp148
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发表时间：
2009-08-01
影响因子：
7.7
通讯作者：
Newell, Marie-Louise
中科院分区：
文献类型：
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作者：
Tanser, Frank;Barnighausen, Till;Newell, Marie-Louise
方法所有12 221名参与者谁同意在一个连续的人口监测的人群中进行HIV检测，他们的家园和地理定位在地理信息系统（精度为2米）。然后，我们使用半径为3公里的二维高斯核来产生对HIV流行率的稳健估计，这些估计在连续的地理空间中变化。我们还采用了Kulldorff空间扫描统计（伯努利模型），正式确定集群的感染（P 0.05）。结果显示相当大的地理差异，在当地的艾滋病毒感染率（范围636）在这个相对同质的人群，并提供明确的经验证据，本地化的聚集性艾滋病毒感染。沿沿着，通过Kulldorff统计量确定了三个高风险、重叠的空间集群[相对风险（RR）1.341.62（P 0.01），而三个低风险集群（RR 0.20.38）在研究区域的其他地方发现（P 0.017）结论研究结果表明，存在几种不同强度的局部HIV流行病，这些流行病部分包含在地理定义的范围内。社区.尽管南非许多农村地区艾滋病毒的总体流行率很高，但研究结果表明，有必要采取针对风险最大的社会地理空间（社区）的干预措施，以补充针对普通民众的措施。
Methods All 12 221 participants who consented to an HIV test in a population under continuous demographical surveillance were linked to their homesteads and geo-located in a geographical information system (accuracy of 2 m). We then used a two-dimensional Gaussian kernel of radius 3 km to produce robust estimates of HIV prevalence that vary across continuous geographical space. We also applied a Kulldorff spatial scan statistic (Bernoulli model) to formally identify clusters of infections (P 0.05).Results The results reveal considerable geographical variation in local HIV prevalence (range 636) within this relatively homogenous population and provide clear empirical evidence for the localized clustering of HIV infections. Three high-risk, overlapping spatial clusters [Relative Risk (RR) 1.341.62] were identified by the Kulldorff statistic along the National Road (P 0.01), whereas three low risk clusters (RR 0.20.38) were identified elsewhere in the study area (P 0.017).Conclusions The findings show the existence of several localized HIV epidemics of varying intensity that are partly contained within geographically defined communities. Despite the overall high prevalence of HIV in many rural South African settings, the results support the need for interventions that target socio-geographic spaces (communities) at greatest risk to supplement measures aimed at the general population.