Oduma Colins O, Ombok Maurice, Zhao Xingyuan, Huwe Tiffany, Ondigo Bartholomew N, Kazura James W, Grieco John, Achee Nicole, Liu Fang, Ochomo Eric, Koepfli Cristian
Department of Biochemistry and Molecular Biology, Egerton University, Nakuru, Kenya.
Kenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya.
PLOS Glob Public Health. 2023 Apr 17;3(4):e0001505. doi: 10.1371/journal.pgph.0001505. eCollection 2023.
Progress in malaria control has stalled over the recent years. Knowledge on main drivers of transmission explaining small-scale variation in prevalence can inform targeted control measures. We collected finger-prick blood samples from 3061 individuals irrespective of clinical symptoms in 20 clusters in Busia in western Kenya and screened for Plasmodium falciparum parasites using qPCR and microscopy. Clusters spanned an altitude range of 207 meters (1077-1284 m). We mapped potential mosquito larval habitats and determined their number within 250 m of a household and distances to households using ArcMap. Across all clusters, P. falciparum parasites were detected in 49.8% (1524/3061) of individuals by qPCR and 19.5% (596/3061) by microscopy. Across the clusters, prevalence ranged from 26% to 70% by qPCR. Three to 34 larval habitats per cluster and 0-17 habitats within a 250m radius around households were observed. Using a generalized linear mixed effect model (GLMM), a 5% decrease in the odds of getting infected per each 10m increase in altitude was observed, while the number of larval habitats and their proximity to households were not statistically significant predictors for prevalence. Kitchen located indoors, open eaves, a lower level of education of the household head, older age, and being male were significantly associated with higher prevalence. Pronounced variation in prevalence at small scales was observed and needs to be taken into account for malaria surveillance and control. Potential larval habitat frequency had no direct impact on prevalence.
近年来，疟疾控制工作陷入停滞。了解传播的主要驱动因素，解释患病率的小规模差异，可为有针对性的控制措施提供依据。我们在肯尼亚西部布西亚的20个集群中，采集了3061名个体的指尖血样，无论其有无临床症状，并使用定量聚合酶链反应（qPCR）和显微镜检查法筛查恶性疟原虫寄生虫。这些集群的海拔范围为207米（1077 - 1284米）。我们绘制了潜在蚊虫幼虫栖息地的地图，并使用ArcMap确定了每个家庭250米范围内的栖息地数量以及与家庭的距离。在所有集群中，通过qPCR检测到49.8%（1524/3061）的个体感染了恶性疟原虫，通过显微镜检查法检测到19.5%（596/3061）的个体感染。在各个集群中，qPCR检测到的患病率在26%至70%之间。每个集群观察到3至34个幼虫栖息地，在家庭周围250米半径范围内观察到0至17个栖息地。使用广义线性混合效应模型（GLMM），观察到海拔每升高10米，感染几率降低5%，而幼虫栖息地的数量及其与家庭的距离并不是患病率的统计学显著预测因素。室内厨房、开放式屋檐、户主教育程度较低、年龄较大以及男性与较高的患病率显著相关。观察到小规模患病率存在明显差异，在疟疾监测和控制中需要考虑这一点。潜在幼虫栖息地频率对患病率没有直接影响。
