Kilifi KEMRI-Wellcome Trust Collaborative Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya.
PLoS Med. 2010 Jul 6;7(7):e1000304. doi: 10.1371/journal.pmed.1000304.
Infectious diseases often demonstrate heterogeneity of transmission among host populations. This heterogeneity reduces the efficacy of control strategies, but also implies that focusing control strategies on "hotspots" of transmission could be highly effective.
In order to identify hotspots of malaria transmission, we analysed longitudinal data on febrile malaria episodes, asymptomatic parasitaemia, and antibody titres over 12 y from 256 homesteads in three study areas in Kilifi District on the Kenyan coast. We examined heterogeneity by homestead, and identified groups of homesteads that formed hotspots using a spatial scan statistic. Two types of statistically significant hotspots were detected; stable hotspots of asymptomatic parasitaemia and unstable hotspots of febrile malaria. The stable hotspots were associated with higher average AMA-1 antibody titres than the unstable clusters (optical density [OD] = 1.24, 95% confidence interval [CI] 1.02-1.47 versus OD = 1.1, 95% CI 0.88-1.33) and lower mean ages of febrile malaria episodes (5.8 y, 95% CI 5.6-6.0 versus 5.91 y, 95% CI 5.7-6.1). A falling gradient of febrile malaria incidence was identified in the penumbrae of both hotspots. Hotspots were associated with AMA-1 titres, but not seroconversion rates. In order to target control measures, homesteads at risk of febrile malaria could be predicted by identifying the 20% of homesteads that experienced an episode of febrile malaria during one month in the dry season. That 20% subsequently experienced 65% of all febrile malaria episodes during the following year. A definition based on remote sensing data was 81% sensitive and 63% specific for the stable hotspots of asymptomatic malaria.
Hotspots of asymptomatic parasitaemia are stable over time, but hotspots of febrile malaria are unstable. This finding may be because immunity offsets the high rate of febrile malaria that might otherwise result in stable hotspots, whereas unstable hotspots necessarily affect a population with less prior exposure to malaria.
传染病在宿主人群中的传播往往存在异质性。这种异质性降低了控制策略的效果,但也意味着将控制策略集中在传播的“热点”上可能非常有效。
为了确定疟疾传播的热点,我们分析了来自肯尼亚沿海基利菲区三个研究地区的 256 个住家 12 年的发热性疟疾发作、无症状寄生虫血症和抗体滴度的纵向数据。我们通过住家分析了异质性,并使用空间扫描统计数据确定了形成热点的住家群。检测到两种具有统计学意义的热点;无症状寄生虫血症的稳定热点和发热性疟疾的不稳定热点。稳定的热点与较高的平均 AMA-1 抗体滴度相关,而不稳定的聚类则较低(光密度 [OD] = 1.24,95%置信区间 [CI] 1.02-1.47 与 OD = 1.1,95% CI 0.88-1.33),发热性疟疾发作的平均年龄也较低(5.8 岁,95% CI 5.6-6.0 与 5.91 岁,95% CI 5.7-6.1)。在两个热点的半影区都发现了发热性疟疾发病率的下降梯度。热点与 AMA-1 滴度相关,但与血清转化率无关。为了针对控制措施,可通过识别在旱季一个月内经历发热性疟疾发作的 20%住家来预测有发热性疟疾风险的住家。随后,这 20%的住家在次年经历了 65%的发热性疟疾发作。基于遥感数据的定义对无症状疟疾的稳定热点具有 81%的敏感性和 63%的特异性。
无症状寄生虫血症的热点是稳定的,但发热性疟疾的热点是不稳定的。这一发现可能是因为免疫力抵消了可能导致稳定热点的高热率的发热性疟疾,而不稳定的热点必然会影响一个以前接触过疟疾的人群。