Spatial overlap links seemingly unconnected genotype-matched TB cases in rural Uganda.
Spatial overlap links seemingly unconnected genotype-matched TB cases in rural Uganda.
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DOI：
10.1371/journal.pone.0192666
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Havlir DV
中科院分区：
文献类型：
--
作者：
Chamie G;Kato-Maeda M;Emperador DM;Wandera B;Mugagga O;Crandall J;Janes M;Marquez C;Kamya MR;Charlebois ED;Havlir DV
对艾滋病毒高流行地区结核病传播动态的不完全了解仍然是预防的障碍。了解传播发生的地点可以为发现病例和中断传播提供一个平台。 2012 年至 2015 年，我们试图招募乌干达社区中开始结核病治疗的所有成年人。参与者接受了家庭（HH）接触者调查，并提供了社会接触者姓名、工作地点、医疗保健和社交活动以及两份痰样本。结核分枝杆菌培养阳性标本接受 24 位点 MIRU-VNTR 和 spoligotyping。我们试图通过分析社交网络和绘制病例报告在 1 个月的治疗前花费≥12 小时的位置来确定基因型匹配病例之间的流行病学联系。基因型匹配病例之间空间重叠（≤100m）的位点被认为是潜在的传播位点。我们分析了按基因型聚类状态分层的社交网络，其中病例通过共享位置连接，并按位置类型比较了聚类与非聚类病例之间的网络密度。在 173 名成人结核病患者中，有 131 名 (76%) 被登记，其中 108 名提供了痰液，84/131 (78%) 的结核分枝杆菌培养呈阳性：52% (66/131) 的 HIV 检测呈阳性。在 118 名成年家庭接触者中，105 名 (89%) 接受了筛查，3 名 (2.5%) 诊断为活动性结核病。总体而言，33 例结核病例 (39%) 属于 15 个不同的 MTB 基因型匹配簇。在每个集群内，没有病例共享家庭地址或报告共享非家庭地址。在 6/15 (40%) 的集群中，通过特定位置的空间重叠确定了潜在的流行病学联系：5/6 涉及医疗保健机构。与非集群网络相比，基因型集群结核病社交网络基于共享诊所的网络密度显着更高（p<0.001），并且基于共享市场的网络密度较低（p<0.001）。在这项分子流行病学研究中，MTB 基因型匹配病例之间的联系只能通过共享位置（尤其是医疗保健位置）来识别，而不是通过指定联系人来识别。这表明大多数传播发生在偶然接触之间，并强调需要改善非洲农村医疗机构的感染控制。
Incomplete understanding of TB transmission dynamics in high HIV prevalence settings remains an obstacle for prevention. Understanding where transmission occurs could provide a platform for case finding and interrupting transmission. From 2012–2015, we sought to recruit all adults starting TB treatment in a Ugandan community. Participants underwent household (HH) contact investigation, and provided names of social contacts, sites of work, healthcare and socializing, and two sputum samples. Mycobacterium tuberculosis culture-positive specimens underwent 24-loci MIRU-VNTR and spoligotyping. We sought to identify epidemiologic links between genotype-matched cases by analyzing social networks and mapping locations where cases reported spending ≥12 hours over the one-month pre-treatment. Sites of spatial overlap (≤100m) between genotype-matched cases were considered potential transmission sites. We analyzed social networks stratified by genotype clustering status, with cases linked by shared locations, and compared network density by location type between clustered vs. non-clustered cases. Of 173 adults with TB, 131 (76%) were enrolled, 108 provided sputum, and 84/131 (78%) were MTB culture-positive: 52% (66/131) tested HIV-positive. Of 118 adult HH contacts, 105 (89%) were screened and 3 (2.5%) diagnosed with active TB. Overall, 33 TB cases (39%) belonged to 15 distinct MTB genotype-matched clusters. Within each cluster, no cases shared a HH or reported shared non-HH contacts. In 6/15 (40%) clusters, potential epidemiologic links were identified by spatial overlap at specific locations: 5/6 involved health care settings. Genotype-clustered TB social networks had significantly greater network density based on shared clinics (p<0.001) and decreased density based on shared marketplaces (p<0.001), compared to non-clustered networks. In this molecular epidemiologic study, links between MTB genotype-matched cases were only identifiable via shared locations, healthcare locations in particular, rather than named contacts. This suggests most transmission is occurring between casual contacts, and emphasizes the need for improved infection control in healthcare settings in rural Africa.
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3.7
作者：
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