Evidence map›Paper›PMID 33662028›Full record

ArticlePloS one2021

The exposure risk to COVID-19 in most affected countries: A vulnerability assessment model.

Adriana Nascimento Santos Cartaxo, Francisco Iran Cartaxo Barbosa, Paulo Henrique de Souza Bermejo, Marina Figueiredo Moreira, David Nadler Prata

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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

11 citing papers in PubMed.

  1. Article
  2. Social media engagement in the maritime industry during the pandemic.Technological forecasting and social change · 2023
    Article
  3. Review
  4. Article
  5. Article
  6. 5G, Big Data, and AI for Smart City and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
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.

Adriana Nascimento Santos CartaxoGeneral-Coordination of the Health Industrial Complex, Secretariat of Science Technology Innovation and Strategic Supplies in Health, Ministry of Health, Brasília, Federal District, Brazil.ORCID 0000-0001-6518-6959
Francisco Iran Cartaxo BarbosaMedical Equipment Office, Brazil Health Surveillance Agency - ANVISA, Brasília, Federal District, Brazil.
Paulo Henrique de Souza BermejoDepartment of Administration, University of Brasilia, Brasília, Federal District, Brazil.
Marina Figueiredo MoreiraBusiness School, University of Brasilia, Brasília, Federal District, Brazil.
David Nadler PrataDepartment of Computation Modelling, Institute of Regional Development, Federal University of Tocantins, Palmas, Tocantins, Brazil.ORCID 0000-0002-1414-4000

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The world is facing the coronavirus pandemic (COVID-19), which began in China. By August 18, 2020, the United States, Brazil, and India were the most affected countries. Health infrastructure and socioeconomic vulnerabilities may be affecting the response capacities of these countries. We compared official indicators to identify which vulnerabilities better determined the exposure risk to COVID-19 in both the most and least affected countries. To achieve this purpose, we collected indicators from the Infectious Disease Vulnerability Index (IDVI), the World Health Organization (WHO), the World Bank, and the Brazilian Geography and Statistics Institute (IBGE). All indicators were normalized to facilitate comparisons. Speed, incidence, and population were used to identify the groups of countries with the highest and lowest risks of infection. Countries' response capacities were determined based on socioeconomic, political, and health infrastructure conditions. Vulnerabilities were identified based on the indicator sensitivity. The highest-risk group included the U.S., Brazil, and India, whereas the lowest-risk group (with the largest population by continent) consisted of China, New Zealand, and Germany. The high-sensitivity cluster had 18 indicators (50% extra IDVI), such as merchandise trade, immunization, public services, maternal mortality, life expectancy at birth, hospital beds, GINI index, adolescent fertility, governance, political stability, transparency/corruption, industry, and water supply. The greatest vulnerability of the highest-risk group was related first to economic factors (merchandise trade), followed by public health (immunization), highlighting global dependence on Chinese trade, such as protective materials, equipment, and diagnostic tests. However, domestic political factors had more indicators, beginning with high sensitivity and followed by healthcare and economic conditions, which signified a lesser capacity to guide, coordinate, and supply the population with protective measures, such as social distancing.

Indexed as

BrazilChinaCOVID-19Delivery of Health CareGermanyHumansIndiaNew ZealandPandemicsPolitical SystemsRisk AssessmentSocioeconomic FactorsUnited StatesWorld Health Organization

Identifiers

PMID33662028
PMCPMC7932136

What OpenQuestion holds

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LicenceCC BY
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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.