Social Vulnerability and Ebola Virus Disease in Rural Liberia.
Social Vulnerability and Ebola Virus Disease in Rural Liberia.
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DOI：
10.1371/journal.pone.0137208
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Stegall CM
中科院分区：
文献类型：
--
作者：
Stanturf JA;Goodrick SL;Warren ML Jr;Charnley S;Stegall CM
埃博拉病毒病（EVD）疫情在利比里亚、塞拉利昂和几内亚三个西非国家造成数千人死亡，凸显了冲突后国家缺乏适应能力。特别是卫生服务的稀缺使这些人口容易受到多重相互作用的压力因素的影响，包括粮食不安全、气候变化和埃博拉病毒病等疾病流行的级联效应。然而，脆弱农村人口的空间分布以及导致其脆弱性的个人压力因素尚不清楚。我们利用人口普查指标制定了社会脆弱性分类，并在利比里亚地区一级绘制了地图。根据这一分类，我们估计社会脆弱性最高的地区位于利比里亚北部和西部的洛法、邦、大角山和博米州。其中三个州以及首都蒙罗维亚和周围的蒙特塞拉多和马吉比州的埃博拉病毒感染率在利比里亚最高。脆弱性有多个层面，从多个变量发展而来的分类比粮食不安全或保健设施稀缺等单一指标更能全面地反映脆弱性。利比里亚农村很少有粮食安全，许多人无法在80分钟内到达诊所。我们的研究结果说明了人口普查和家庭调查数据在县以下一级的空间显示时，可能有助于突出最脆弱家庭和人口的位置。我们的研究结果可以用来确定脆弱性热点的发展战略和资源分配，以解决利比里亚脆弱性的根本原因可能是必要的。我们展示了社会脆弱性指数方法如何应用于疾病暴发的背景下，我们的方法是相关的其他地方。
The Ebola virus disease (EVD) epidemic that has stricken thousands of people in the three West African countries of Liberia, Sierra Leone, and Guinea highlights the lack of adaptive capacity in post-conflict countries. The scarcity of health services in particular renders these populations vulnerable to multiple interacting stressors including food insecurity, climate change, and the cascading effects of disease epidemics such as EVD. However, the spatial distribution of vulnerable rural populations and the individual stressors contributing to their vulnerability are unknown. We developed a Social Vulnerability Classification using census indicators and mapped it at the district scale for Liberia. According to the Classification, we estimate that districts having the highest social vulnerability lie in the north and west of Liberia in Lofa, Bong, Grand Cape Mount, and Bomi Counties. Three of these counties together with the capital Monrovia and surrounding Montserrado and Margibi counties experienced the highest levels of EVD infections in Liberia. Vulnerability has multiple dimensions and a classification developed from multiple variables provides a more holistic view of vulnerability than single indicators such as food insecurity or scarcity of health care facilities. Few rural Liberians are food secure and many cannot reach a medical clinic in <80 minutes. Our results illustrate how census and household survey data, when displayed spatially at a sub-county level, may help highlight the location of the most vulnerable households and populations. Our results can be used to identify vulnerability hotspots where development strategies and allocation of resources to address the underlying causes of vulnerability in Liberia may be warranted. We demonstrate how social vulnerability index approaches can be applied in the context of disease outbreaks, and our methods are relevant elsewhere.