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A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya
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 supp...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447114/ https://www.ncbi.nlm.nih.gov/pubmed/32839197 http://dx.doi.org/10.1136/bmjgh-2020-003014 |
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author | Macharia, Peter M Joseph, Noel K Okiro, Emelda A |
author_facet | Macharia, Peter M Joseph, Noel K Okiro, Emelda A |
author_sort | Macharia, Peter M |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7447114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-74471142020-08-26 A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya Macharia, Peter M Joseph, Noel K Okiro, Emelda A BMJ Glob Health Original Research 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. BMJ Publishing Group 2020-08 2020-08-23 /pmc/articles/PMC7447114/ /pubmed/32839197 http://dx.doi.org/10.1136/bmjgh-2020-003014 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Original Research Macharia, Peter M Joseph, Noel K Okiro, Emelda A A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya |
title | A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya |
title_full | A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya |
title_fullStr | A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya |
title_full_unstemmed | A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya |
title_short | A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya |
title_sort | vulnerability index for covid-19: spatial analysis at the subnational level in kenya |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447114/ https://www.ncbi.nlm.nih.gov/pubmed/32839197 http://dx.doi.org/10.1136/bmjgh-2020-003014 |
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