Population Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
Population Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
BMJ Glob Health. 2020 Aug;5(8). doi: 10.1136/bmjgh-2020-003014.
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.
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措施，这反映了我们对哪些人最脆弱及其地理位置的理解有所提高。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，同时阐明卫生不平等现象，为肯尼亚的应急响应提供信息。
方法：收集地理空间指标以创建三个脆弱性指数；社会脆弱性指数（SVI）、流行病学脆弱性指数（EVI）和两者的综合指数，即社会流行病学脆弱性指数（SEVI），在肯尼亚的 295 个分区中解析。SVI 包括影响疾病传播的 19 个指标；社会经济贫困、服务获取和人口动态，而 EVI 则包括 5 个描述与 COVID-19 严重疾病进展相关的合并症的指标。这些指标被标准化到一个共同的度量尺度上，通过算术平均值和等权重进行空间叠加。这些指数被分为七个类别，1-2 表示低脆弱性，6-7 表示高脆弱性。量化了脆弱性类别内的人口。
结果：每个指数的空间变化在肯尼亚各地都是不均匀的。49 个西北部和部分东部分区（690 万人）高度脆弱，而西部和中部肯尼亚的 58 个分区（970 万人）则是 SVI 最不脆弱的分区。对于 EVI，中部和相邻地区的 48 个分区（720 万人）和北部肯尼亚的 81 个分区（1320 万人）分别是最脆弱和最不脆弱的分区。总体而言（SEVI），中央和东南部周围的 46 个分区（700 万人）更为脆弱，而 81 个分区（1440 万人）则最不脆弱。
结论：创建的脆弱性指数是县、国家政府和利益相关者进行优先排序和改进规划的相关工具。脆弱性指数的异质性支持根据各分区的需求采取有针对性和优先的行动。
BMJ Glob Health. 2020-8
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