Damien Foundation, Brussels, Belgium.
University Charité-Universitätsmedizin Berlin, Berlin, Germany.
PLoS Negl Trop Dis. 2022 Sep 12;16(9):e0010764. doi: 10.1371/journal.pntd.0010764. eCollection 2022 Sep.
In India, leprosy clusters at hamlet level but detailed information is lacking. We aim to identify high-incidence hamlets to be targeted for active screening and post-exposure prophylaxis.
We paid home visits to a cohort of leprosy patients registered between April 1st, 2020, and March 31st, 2022. Patients were interviewed and household members were screened for leprosy. We used an open-source app(ODK) to collect data on patients' mobility, screening results of household members, and geographic coordinates of their households. Clustering was analysed with Kulldorff's spatial scan statistic(SaTScan). Outlines of hamlets and population estimates were obtained through an open-source high-resolution population density map(https://data.humdata.org), using kernel density estimation in QGIS, an open-source software.
We enrolled 169 patients and screened 1,044 household contacts in Bisfi and Benipatti blocks of Bihar. Median number of years of residing in the village was 17, interquartile range(IQR)12-30. There were 11 new leprosy cases among 658 household contacts examined(167 per 10,000), of which seven had paucibacillary leprosy, one was a child under 14 years, and none had visible disabilities. We identified 739 hamlets with a total population of 802,788(median 163, IQR 65-774). There were five high incidence clusters including 12% of the population and 46%(78/169) of the leprosy cases. One highly significant cluster with a relative risk (RR) of 4.7(p<0.0001) included 32 hamlets and 27 cases in 33,609 population. A second highly significant cluster included 32 hamlets and 24 cases in 33,809 population with a RR of 4.1(p<0.001). The third highly significant cluster included 16 hamlets and 17 cases in 19,659 population with a RR of 4.8(p<0.001). High-risk clusters still need to be screened door-to-door.
We found a high yield of active household contact screening. Our tools for identifying high-incidence hamlets appear effective. Focusing labour-intensive interventions such as door-to-door screening on such hamlets could increase efficiency.
在印度，麻风病以小村庄为单位聚集，但缺乏详细信息。我们的目标是确定高发小村庄，以便进行主动筛查和接触后预防。
我们对 2020 年 4 月 1 日至 2022 年 3 月 31 日期间登记的麻风病患者队列进行了家访。对患者进行了访谈，并对家庭成员进行了麻风病筛查。我们使用开源应用程序（ODK）收集患者流动性、家庭成员筛查结果和家庭地理位置等数据。聚类使用 Kulldorff 的空间扫描统计（SaTScan）进行分析。村庄轮廓和人口估计值通过开源高分辨率人口密度图（https://data.humdata.org）获得，使用 QGIS 中的核密度估计值，QGIS 是一个开源软件。
我们在比哈尔邦的 Bisfi 和 Benipatti 街区招募了 169 名患者，并对 1044 名家庭接触者进行了筛查。中位数居住在村庄的年限为 17 年，四分位距（IQR）为 12-30 年。在检查的 658 名家庭接触者中发现了 11 例新的麻风病病例（每 10000 人 167 例），其中 7 例为少菌型麻风病，1 例为 14 岁以下儿童，均无可见残疾。我们确定了 739 个有 802788 人（中位数 163，IQR 65-774）的小村庄。有五个高发集群，包括 12%的人口和 46%（78/169）的麻风病病例。一个高度显著的集群，相对风险（RR）为 4.7（p<0.0001），包括 32 个村庄和 33609 人中的 27 例病例。第二个高度显著的集群包括 32 个村庄和 33809 人中的 24 例病例，RR 为 4.1（p<0.001）。第三个高度显著的集群包括 16 个村庄和 19659 人中的 17 例病例，RR 为 4.8（p<0.001）。高风险集群仍需进行逐户筛查。
我们发现主动家庭接触筛查的效果很高。我们用于识别高发村庄的工具似乎很有效。将劳动密集型干预措施（如上门筛查）集中在这些村庄上，可以提高效率。
