A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya
A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya
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DOI：
10.1136/bmjgh-2020-003014
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发表时间：
2020-08-01
影响因子：
8.1
通讯作者：
Okiro, Emelda A.
中科院分区：
文献类型：
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作者：
Macharia, Peter M.;Joseph, Noel K.;Okiro, Emelda A.
背景 应对 2019 年冠状病毒病 (COVID-19) 大流行需要精准的公共卫生，这反映出我们对谁是最脆弱人群及其地理位置的了解有所加深。我们创建了三个脆弱性指数，以确定需要更多支持的地区和人群，同时阐明健康不平等现象，为肯尼亚的应急响应提供信息。方法 组合地理空间指标以创建三个脆弱性指数；肯尼亚295个县的社会脆弱性指数（SVI）、流行病学脆弱性指数（EVI）以及两者的综合，即社会流行病学脆弱性指数（SEVI）。 SVI包括19个影响疾病传播的指标；社会经济剥夺、获得服务的机会和人口动态，而 EVI 包括描述与 COVID-19 严重疾病进展相关的合并症的 5 个指标。将指标缩放至通用测量尺度，通过算术平均值进行空间重叠并等权。该指数分为七类，1-2 表示低脆弱性，6-7 表示高脆弱性。对漏洞类别内的人口进行了量化。结果肯尼亚各地每个指数的空间变化存在异质性。 49 个西北和部分东部县（690 万人）非常容易受到 SVI 影响，而肯尼亚西部和中部的 58 个县（970 万人）最容易受到 SVI 影响。就 EVI 而言，中部及邻近地区的 48 个县（720 万人）和肯尼亚北部的 81 个县（1,320 万人）分别是最脆弱和最不脆弱的。总体而言 (SEVI)，中部和东南部周围的 46 个县（700 万人）较为脆弱，而 81 个县（1,440 万人）最不脆弱。结论 创建的脆弱性指数是与县、国家政府和利益相关者相关的工具，用于确定优先顺序和改进规划。脆弱性指数的异质性决定了需要根据各县的需求采取有针对性的优先行动。
Background Response to the coronavirus disease 2019 (COVID-19) pandemic calls for precision public health reflecting our improved understanding of who is the most vulnerable and their geographical location. We created three vulnerability indices to identify areas and people who require greater support while elucidating health inequities to inform emergency response in Kenya. Methods Geospatial indicators were assembled to create three vulnerability indices; Social VulnerabilityIndex (SVI), Epidemiological Vulnerability Index (EVI) and a composite of the two, that is, Social Epidemiological Vulnerability Index (SEVI) resolved at 295 subcounties in Kenya. SVI included 19 indicators that affect the spread of disease; socioeconomic deprivation, access to services and population dynamics, whereas EVI comprised 5 indicators describing comorbidities associated with COVID-19 severe disease progression. The indicators were scaled to a common measurement scale, spatially overlaid via arithmetic mean and equally weighted. The indices were classified into seven classes, 1-2 denoted low vulnerability and 6-7, high vulnerability. The population within vulnerabilities classes was quantified. Results The spatial variation of each index was heterogeneous across Kenya. Forty-nine northwestern and partly eastern subcounties (6.9 million people) were highly vulnerable, whereas 58 subcounties (9.7 million people) in western and central Kenya were the least vulnerable for SVI. For EVI, 48 subcounties (7.2 million people) in central and the adjacent areas and 81 subcounties (13.2 million people) in northern Kenya were the most and least vulnerable, respectively. Overall (SEVI), 46 subcounties (7.0 million people) around central and southeastern were more vulnerable, whereas 81 subcounties (14.4 million people) were least vulnerable. Conclusion The vulnerability indices created are tools relevant to the county, national government and stakeholders for prioritisation and improved planning. The heterogeneous nature of the vulnerability indices underpins the need for targeted and prioritised actions based on the needs across the subcounties.