Quantifying Poverty as a Driver of Ebola Transmission
Quantifying Poverty as a Driver of Ebola Transmission
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DOI：
10.1371/journal.pntd.0004260
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发表时间：
2015-12-01
影响因子：
3.8
通讯作者：
Galvani, Alison P.
中科院分区：
文献类型：
--
作者：
Fallah, Mosoka P.;Skrip, Laura A.;Galvani, Alison P.
背景贫困被认为是控制目前西非埃博拉疫情的一个挑战。虽然受影响国家之间的差异得到了赞赏，但西非国家内部的差异尚未被调查为埃博拉传播的驱动因素。为了量化贫困在埃博拉传播中的作用，我们分析了利比里亚蒙特塞拉多县300多个社区中埃博拉发病率和传播因素的异质性，这些社区按社会经济地位（SES）分类。方法/主要发现我们评估了2014年2月28日至12月1日期间报告的4，437例埃博拉病例，2014年，蒙特塞拉多州确定SES分层的时间趋势和埃博拉传播的驱动因素。包括症状发作、住院和死亡日期以及指定居住社区的数据集用于将病例分层为高、中、低SES。此外，还为1 585名被追踪者提供了9 129名接触者的信息。为了评估社会经济亚群内和跨社会经济亚群的传播，以及疫情的轨迹，我们用时间依赖性随机模型分析了这些数据。最贫困社区的病例报告的接触次数平均比高社会经济地位社区的病例多3次（p< 0.001）。我们的传播模型显示，来自中等和低社会经济地位社区的感染者分别与高社会经济地位社区的1.5倍（95%CI：1.4-1.6）和3.5倍（95%CI：3.1-3.9）的继发病例相关。此外，大部分的传播埃博拉病毒的整个蒙特塞拉多县起源于较低的SES.Conclusions/Significance个人从贫困地区的传播和传播埃博拉病毒到其他地区的高利率。因此，如果将疾病干预措施针对极端贫困地区，并将资金专门用于满足基本需求的发展项目，就可以最有效地预防或遏制埃博拉。
BackgroundPoverty has been implicated as a challenge in the control of the current Ebola outbreak in West Africa. Although disparities between affected countries have been appreciated, disparities within West African countries have not been investigated as drivers of Ebola transmission. To quantify the role that poverty plays in the transmission of Ebola, we analyzed heterogeneity of Ebola incidence and transmission factors among over 300 communities, categorized by socioeconomic status (SES), within Montserrado County, Liberia.Methodology/Principal FindingsWe evaluated 4,437 Ebola cases reported between February 28, 2014 and December 1, 2014 for Montserrado County to determine SES-stratified temporal trends and drivers of Ebola transmission. A dataset including dates of symptom onset, hospitalization, and death, and specified community of residence was used to stratify cases into high, middle and low SES. Additionally, information about 9,129 contacts was provided for a subset of 1,585 traced individuals. To evaluate transmission within and across socioeconomic subpopulations, as well as over the trajectory of the outbreak, we analyzed these data with a time-dependent stochastic model. Cases in the most impoverished communities reported three more contacts on average than cases in high SES communities (p< 0.001). Our transmission model shows that infected individuals from middle and low SES communities were associated with 1.5 (95% CI: 1.4-1.6) and 3.5 (95% CI: 3.1-3.9) times as many secondary cases as those from high SES communities, respectively. Furthermore, most of the spread of Ebola across Montserrado County originated from areas of lower SES.Conclusions/SignificanceIndividuals from areas of poverty were associated with high rates of transmission and spread of Ebola to other regions. Thus, Ebola could most effectively be prevented or contained if disease interventions were targeted to areas of extreme poverty and funding was dedicated to development projects that meet basic needs.