Evidence map›Paper›PMID 34303129›Full record

ArticleHealth & place2021

Supporting pandemic disease preparedness: Development of a composite index of area vulnerability.

Tayebeh Saghapour, Billie Giles-Corti, Afshin Jafari, Muhammad Arif Qaisrani, Gavin Turrell

Abstract read
In one paragraph

Article in Health & place, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. What Makes Urban Communities More Resilient to COVID-19? A Systematic Review of Current Evidence.International journal of environmental research and public health · 2022
    Pooled it
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tayebeh SaghapourCentre for Urban Research, RMIT University, 124 La Trobe St, Melbourne, Victoria, 3000, Australia. Electronic address: tayebeh.saghapour@rmit.edu.au.
Billie Giles-CortiCentre for Urban Research, RMIT University, 124 La Trobe St, Melbourne, Victoria, 3000, Australia.
Afshin JafariCentre for Urban Research, RMIT University, 124 La Trobe St, Melbourne, Victoria, 3000, Australia.
Muhammad Arif QaisraniThe Centre for Transportation, Equity, Decisions and Dollars, University of Texas at Arlington, 701 Nedderman Drive, Arlington, TX, 76019, USA.
Gavin TurrellCentre for Urban Research, RMIT University, 124 La Trobe St, Melbourne, Victoria, 3000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Civil DefenseCOVID-19Local GovernmentAdultAustraliaCapacity BuildingDisease OutbreaksHumans

Identifiers

PMID34303129
PMCPMC8292828

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.