Quantifying Plasmodium falciparum infections clustering within households to inform household-based intervention strategies for malaria control programs: An observational study and meta-analysis from 41 malaria-endemic countries.
Quantifying Plasmodium falciparum infections clustering within households to inform household-based intervention strategies for malaria control programs: An observational study and meta-analysis from 41 malaria-endemic countries.
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量化家庭内聚集的恶性疟原虫感染，为疟疾控制项目的家庭干预战略提供信息：来自41个疟疾流行国家的观察性研究和荟萃分析。
DOI：
10.1371/journal.pmed.1003370
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发表时间：
2020-10
期刊：
影响因子：
15.8
通讯作者：
Cook J
中科院分区：
文献类型：
--
作者：
Stresman G;Whittaker C;Slater HC;Bousema T;Cook J
应对疟疾战略的假设是，疟疾感染者聚集在家庭或社区内。尽管反应战略得到广泛的有计划的实施，但很少有经验证据表明这种战略是否适当，如果适当，如何最有效地实施。我们整理了2个不同的数据集，以评估家庭内疟疾感染的聚集性：（i）人口健康调查（DHS）数据，整合了家庭信息和疟疾感染，近期发热和儿童近期治疗状况;（ii）来自横断面和反应性检测研究的数据，包含所有年龄段个体的家庭和疟疾感染状况（专利和亚专利）的信息。这两个数据集都用于评估指数家庭内感染聚集的几率，其中指数家庭是根据是否包含通过以下3种方案之一可检测到的感染来定义的：（a）通过确认的临床病例分类的反应性病例检测（RACD），（B）通过发热、症状性感染分类的大规模筛查和治疗（MSAT），和（c）通过使用常规诊断可检测的感染分类的大规模测试和治疗（MTAT）。数据包括2006年11月至2018年12月期间从23个非洲国家进行的57项DHS中通过显微镜/快速诊断测试（RDT）检测的208，140名7岁以下儿童（中位年龄= 2岁，最小= 2岁，最大= 7岁）的59，050例感染。2006年4月至2019年5月在20个非洲、美洲、亚洲和中东国家发表的43项研究中，通过分子工具在132，590名个体中检测到所有年龄段（中位年龄= 22岁，最小值= 0.5，最大值= 100）的11，349例感染的数据来自已发表的文献。广泛的聚类被排除了--总体而言，（95%可信区间[CrI] 0.35-20.45; P < 0.001）通畅感染的几率（根据国土安全部的数据）和5.13更大的几率（95% CI 3.85-6.84; P < 0.001）的分子检测感染（来自已发表的文献）检测到的家庭中，在编程检测感染居住。聚合酶链反应（PCR）/环介导等温扩增（LAMP）鉴定的最强聚类程度使用MTAT策略观察（比值比[OR] = 6.79，95% CI 4.42-10.43），但与MSAT相比无显著差异（OR = 5.2，95% CI 3.22-8.37; P-差异= 0.883）和RACD（OR = 4.08，95% CI 2.55-6.53; P-差异= 0.29）。在这两个数据集中，当传播率较低时，聚类变得更加突出。然而，我们分析的局限性包括没有考虑任何疟疾控制干预措施、疟疾季节性或研究地点内传播的可能异质性。因此，集群可能被低估了。在疟疾传播为家庭周围传播的地区，有一些方案可供选择，以查明可能发现残余感染的家庭，将这些检测战略与在一段持续时间内假定治疗指标家庭的居民相结合，可有助于消除疟疾的努力。Gillian Stresman及其同事报告了家庭内疟疾感染的聚集性，以进一步采取疾病预防和治疗方法。疟疾是一种媒介传播的寄生虫感染，导致有症状和无症状的感染，在非洲、南美和东南亚国家是一个特别重要的问题。当疟疾传播率降低时，疟疾感染往往在人群中聚集。在这种情况下，疟疾规划可以改进其战略，并开始将疟疾干预措施具体针对在卫生设施中通过社区筛查发热个体或通过大规模检测疟疾个体而发现的疟疾感染者的家庭成员。为了就方案是否以及何时应考虑以家庭为目标的疟疾干预措施做出明智的决定，需要有证据表明疟疾感染是否一贯在家庭中聚集，以及哪种战略最能针对无症状感染。我们分析了2006年11月至2018年12月期间从DHS收集的208，140名非洲儿童的数据，并对2006年4月至2019年5月期间发表的来自世界各地所有疟疾流行环境的132，590名各年龄段的个体进行了荟萃分析。这两个数据集都显示，疟疾感染确实在所有传播强度下在家庭中聚集，但随着传播强度的下降，聚集变得更加明显（即，人口中受感染的人集中在较少的家庭中的比例较大）。如果指标病例的所有家庭成员都成为干预目标，一旦传播强度非常低，他们就有可能治疗社区中大约75%的感染。在当地发生疟疾传播的地方，疟疾感染集中在指数病例的家庭中，无论指数病例是如何确定的。针对家庭的战略，例如，向指标家庭的所有居民提供治疗剂量的有效抗疟药物，为疟疾控制方案提供了一个选择，可以很容易地将感染作为目标，否则可能无法检测到。今后，了解反应性战略如何有助于实现消灭疟疾的目标，对于确定最佳战略和确定该战略应持续多久至关重要。
Reactive malaria strategies are predicated on the assumption that individuals infected with malaria are clustered within households or neighbourhoods. Despite the widespread programmatic implementation of reactive strategies, little empirical evidence exists as to whether such strategies are appropriate and, if so, how they should be most effectively implemented. We collated 2 different datasets to assess clustering of malaria infections within households: (i) demographic health survey (DHS) data, integrating household information and patent malaria infection, recent fever, and recent treatment status in children; and (ii) data from cross-sectional and reactive detection studies containing information on the household and malaria infection status (patent and subpatent) of all-aged individuals. Both datasets were used to assess the odds of infections clustering within index households, where index households were defined based on whether they contained infections detectable through one of 3 programmatic strategies: (a) Reactive Case Detection (RACD) classifed by confirmed clinical cases, (b) Mass Screen and Treat (MSAT) classifed by febrile, symptomatic infections, and (c) Mass Test and Treat (MTAT) classifed by infections detectable using routine diagnostics. Data included 59,050 infections in 208,140 children under 7 years old (median age = 2 years, minimum = 2, maximum = 7) by microscopy/rapid diagnostic test (RDT) from 57 DHSs conducted between November 2006 and December 2018 from 23 African countries. Data representing 11,349 infections across all ages (median age = 22 years, minimum = 0.5, maximum = 100) detected by molecular tools in 132,590 individuals in 43 studies published between April 2006 and May 2019 in 20 African, American, Asian, and Middle Eastern countries were obtained from the published literature. Extensive clustering was observed—overall, there was a 20.40 greater (95% credible interval [CrI] 0.35–20.45; P < 0.001) odds of patent infections (according to the DHS data) and 5.13 greater odds (95% CI 3.85–6.84; P < 0.001) of molecularly detected infections (from the published literature) detected within households in which a programmatically detectable infection resides. The strongest degree of clustering identified by polymerase chain reaction (PCR)/ loop mediated isothermal amplification (LAMP) was observed using the MTAT strategy (odds ratio [OR] = 6.79, 95% CI 4.42–10.43) but was not significantly different when compared to MSAT (OR = 5.2, 95% CI 3.22–8.37; P-difference = 0.883) and RACD (OR = 4.08, 95% CI 2.55–6.53; P-difference = 0.29). Across both datasets, clustering became more prominent when transmission was low. However, limitations to our analysis include not accounting for any malaria control interventions in place, malaria seasonality, or the likely heterogeneity of transmission within study sites. Clustering may thus have been underestimated. In areas where malaria transmission is peri-domestic, there are programmatic options for identifying households where residual infections are likely to be found. Combining these detection strategies with presumptively treating residents of index households over a sustained time period could contribute to malaria elimination efforts. Gillian Stresman and co-workers report on clustering of malaria infections within households to further approaches to disease prevention and treatment. Malaria is a vector-borne parasitic infection that results in both symptomatic and asymptomatic infections and is a particularly important problem in African, South American, and Southeast Asian countries. When malaria transmission becomes low, malaria infections tend to become clustered within populations. In such situations, malaria programs can refine their strategies and begin to target malaria interventions specifically to include household members of malaria-infected individuals detected at the health facility, through community screening of febrile individuals or through mass testing individuals for malaria. To make informed decisions on whether, and when, programs should consider household-targeted malaria interventions, evidence is needed on whether malaria infections consistently cluster in households and which strategy is best able to target asymptomatic infections. We analysed data from 208,140 African children collected from the DHSs between November 2006 and December 2018 and conducted a meta-analysis of 132,590 individuals of all ages from all malaria-endemic settings around the world published between April 2006 and May 2019. Both datasets show that malaria infections do cluster in households at all transmission intensities, but clustering becomes more pronounced as transmission intensity declines (i.e., a larger proportion of infected individuals within a population are clustered in fewer households). If all household members of index cases were targeted with interventions, they could potentially treat approximately 75% of all infections in a community once transmission intensity is very low. In locations where local malaria transmission occurs, malaria infections cluster within households of index cases, regardless of how that index case was identified. Household targeted strategies, e.g., giving all residents of index households a curative dose of an effective antimalarial drug, provide malaria control programs with an option to easily target infections that otherwise may not be detected. In the future, understanding how reactive strategies contribute to achieving malaria elimination will be important in identifying the best strategy and for determining how long it should be sustained.
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影响因子：
3.7
作者：
Smith, Jennifer L.;Auala, Joyce;Sturrock, Hugh J. W.
通讯作者：
Sturrock, Hugh J. W.
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
168.9
作者：
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通讯作者：
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影响因子：
9.3
作者：
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通讯作者：
D'Alessandro, Umberto