Bousema Teun, Stresman Gillian, Baidjoe Amrish Y, Bradley John, Knight Philip, Stone William, Osoti Victor, Makori Euniah, Owaga Chrispin, Odongo Wycliffe, China Pauline, Shagari Shehu, Doumbo Ogobara K, Sauerwein Robert W, Kariuki Simon, Drakeley Chris, Stevenson Jennifer, Cox Jonathan
Radboud Institute for Health Sciences, Department of Medical Microbiology, Radboud University Medical Center, Nijmegen, the Netherlands.
Department of Infectious & Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
PLoS Med. 2016 Apr 12;13(4):e1001993. doi: 10.1371/journal.pmed.1001993. eCollection 2016 Apr.
Malaria transmission is highly heterogeneous, generating malaria hotspots that can fuel malaria transmission across a wider area. Targeting hotspots may represent an efficacious strategy for reducing malaria transmission. We determined the impact of interventions targeted to serologically defined malaria hotspots on malaria transmission both inside hotspots and in surrounding communities.
Twenty-seven serologically defined malaria hotspots were detected in a survey conducted from 24 June to 31 July 2011 that included 17,503 individuals from 3,213 compounds in a 100-km2 area in Rachuonyo South District, Kenya. In a cluster-randomized trial from 22 March to 15 April 2012, we randomly allocated five clusters to hotspot-targeted interventions with larviciding, distribution of long-lasting insecticide-treated nets, indoor residual spraying, and focal mass drug administration (2,082 individuals in 432 compounds); five control clusters received malaria control following Kenyan national policy (2,468 individuals in 512 compounds). Our primary outcome measure was parasite prevalence in evaluation zones up to 500 m outside hotspots, determined by nested PCR (nPCR) at baseline and 8 wk (16 June-6 July 2012) and 16 wk (21 August-10 September 2012) post-intervention by technicians blinded to the intervention arm. Secondary outcome measures were parasite prevalence inside hotpots, parasite prevalence in the evaluation zone as a function of distance from the hotspot boundary, Anopheles mosquito density, mosquito breeding site productivity, malaria incidence by passive case detection, and the safety and acceptability of the interventions. Intervention coverage exceeded 87% for all interventions. Hotspot-targeted interventions did not result in a change in nPCR parasite prevalence outside hotspot boundaries (p ≥ 0.187). We observed an average reduction in nPCR parasite prevalence of 10.2% (95% CI -1.3 to 21.7%) inside hotspots 8 wk post-intervention that was statistically significant after adjustment for covariates (p = 0.024), but not 16 wk post-intervention (p = 0.265). We observed no statistically significant trend in the effect of the intervention on nPCR parasite prevalence in the evaluation zone in relation to distance from the hotspot boundary 8 wk (p = 0.27) or 16 wk post-intervention (p = 0.75). Thirty-six patients with clinical malaria confirmed by rapid diagnostic test could be located to intervention or control clusters, with no apparent difference between the study arms. In intervention clusters we caught an average of 1.14 female anophelines inside hotspots and 0.47 in evaluation zones; in control clusters we caught an average of 0.90 female anophelines inside hotspots and 0.50 in evaluation zones, with no apparent difference between study arms. Our trial was not powered to detect subtle effects of hotspot-targeted interventions nor designed to detect effects of interventions over multiple transmission seasons.
Despite high coverage, the impact of interventions targeting malaria vectors and human infections on nPCR parasite prevalence was modest, transient, and restricted to the targeted hotspot areas. Our findings suggest that transmission may not primarily occur from hotspots to the surrounding areas and that areas with highly heterogeneous but widespread malaria transmission may currently benefit most from an untargeted community-wide approach. Hotspot-targeted approaches may have more validity in settings where human settlement is more nuclear.
ClinicalTrials.gov NCT01575613.
疟疾传播具有高度异质性,形成了疟疾热点地区,这些热点地区可推动疟疾在更广泛区域的传播。针对热点地区采取措施可能是减少疟疾传播的有效策略。我们确定了针对血清学定义的疟疾热点地区的干预措施对热点地区内部及周边社区疟疾传播的影响。
在2011年6月24日至7月31日进行的一项调查中,在肯尼亚拉乔尼奥南区100平方公里区域内,对来自3213个居住点的17503人进行检测,发现了27个血清学定义的疟疾热点地区。在2012年3月22日至4月15日的一项整群随机试验中,我们将五个整群随机分配至针对热点地区的干预措施组,包括杀幼虫、分发长效驱虫蚊帐、室内滞留喷洒以及集中群体药物治疗(432个居住点的2082人);五个对照整群按照肯尼亚国家政策进行疟疾防控(512个居住点的2468人)。我们的主要结局指标是在热点地区外500米范围内评估区域的寄生虫感染率,在基线时以及干预后8周(2012年6月16日至7月6日)和16周(2012年8月21日至9月10日)通过巢式聚合酶链反应(nPCR)进行测定,由对干预组不知情的技术人员操作。次要结局指标包括热点地区内的寄生虫感染率、评估区域内寄生虫感染率与距热点地区边界距离的函数关系、按蚊密度、蚊虫孳生地生产力、通过被动病例检测得出的疟疾发病率以及干预措施的安全性和可接受性。所有干预措施的覆盖率均超过87%。针对热点地区的干预措施并未导致热点地区边界外nPCR寄生虫感染率发生变化(p≥0.187)。我们观察到干预后8周热点地区内nPCR寄生虫感染率平均降低了10.2%(95%置信区间为-1.3%至21.7%),在对协变量进行调整后具有统计学意义(p = 0.024),但在干预后16周时无统计学意义(p = 0.265)。我们未观察到干预措施对评估区域内nPCR寄生虫感染率的影响与距热点地区边界距离在干预后8周(p = 0.27)或16周(p = 0.75)时存在统计学显著趋势。通过快速诊断检测确诊的36例临床疟疾患者可定位至干预组或对照组整群,两组之间无明显差异。在干预组整群中,我们在热点地区内平均捕获1.14只雌性按蚊,在评估区域内捕获0.47只;在对照组整群中,我们在热点地区内平均捕获0.90只雌性按蚊,在评估区域内捕获0.50只,两组之间无明显差异。我们的试验没有足够的效力来检测针对热点地区的干预措施的细微效果,也未设计用于检测多个传播季节的干预效果。
尽管覆盖率高,但针对疟疾病媒和人类感染的干预措施对nPCR寄生虫感染率的影响较小、短暂且仅限于目标热点地区。我们的研究结果表明,传播可能并非主要从热点地区传播至周边地区,而且疟疾传播高度异质但广泛存在的地区目前可能从非针对性的全社区方法中获益最多。针对热点地区的方法在人类定居更为集中的环境中可能更有效。
ClinicalTrials.gov NCT01575613