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Supporting pandemic disease preparedness: Development of a composite index of area vulnerability.
Saghapour, Tayebeh; Giles-Corti, Billie; Jafari, Afshin; Qaisrani, Muhammad Arif; Turrell, Gavin.
  • Saghapour T; Centre for Urban Research, RMIT University, 124 La Trobe St, Melbourne, Victoria, 3000, Australia. Electronic address: tayebeh.saghapour@rmit.edu.au.
  • Giles-Corti B; Centre for Urban Research, RMIT University, 124 La Trobe St, Melbourne, Victoria, 3000, Australia.
  • Jafari A; Centre for Urban Research, RMIT University, 124 La Trobe St, Melbourne, Victoria, 3000, Australia.
  • Qaisrani MA; The Centre for Transportation, Equity, Decisions and Dollars, University of Texas at Arlington, 701 Nedderman Drive, Arlington, TX, 76019, USA.
  • Turrell G; Centre for Urban Research, RMIT University, 124 La Trobe St, Melbourne, Victoria, 3000, Australia.
Health Place ; 70: 102629, 2021 07.
Article in English | MEDLINE | ID: covidwho-1322110
ABSTRACT
Although pandemics are rare, planning and preparation for responding to them plays a crucial role in preventing their spread. The management and control of pandemics such as COVID-19 relies heavily on a country's health capacity. Measuring vulnerability to pandemics in geographical areas could potentially delay a pandemic's exponential growth and reduce the number of cases, which would alleviate the disease impact on communities and the health care sector. The aim of this study is to generate an area-level COVID-19 Pandemic Vulnerability Index (CPVI) and to assess its correlation with COVID-19 cases. Data were collected for Local Government Areas (LGAs) across Australia from different sources including Australia Bureau of Statistics, Australian Institute of Health and Welfare, and General Transit Feed Specification. Based on recent official reports about the COVID-19 outbreak, 18 factors were identified as influencing vulnerability to the disease within LGAs. Using factor analysis, four latent factors were identified and named as sociodemographic, medical conditions, transportation, and land use. Predicted factor scores were summed to generate a CPVI for each LGA. The CPVI was evaluated by correlating with confirmed cases of COVID-19 standardised by adult population in New South Wales and Victoria, the two Australian states with the highest numbers of confirmed cases. There was a statistically significant correlation between the CPVI and COVID-19 in New South Wales (r = 0.49) and Victoria (r = 0.48). LGAs scoring higher on the CPVI also had a higher absolute number of cases. The CPVI could be used by policymakers to identify at-risk areas and to develop preparedness and response plans to help mitigate the spread of COVID-19 and future pandemics.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Outbreaks / Civil Defense / COVID-19 / Local Government Type of study: Experimental Studies / Observational study / Prognostic study Limits: Adult / Humans Country/Region as subject: Oceania Language: English Journal: Health Place Journal subject: Epidemiology / Public Health Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Outbreaks / Civil Defense / COVID-19 / Local Government Type of study: Experimental Studies / Observational study / Prognostic study Limits: Adult / Humans Country/Region as subject: Oceania Language: English Journal: Health Place Journal subject: Epidemiology / Public Health Year: 2021 Document Type: Article