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      Social vulnerability and COVID-19 in Maringá, Brazil

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          Abstract

          This research explores the relationship between COVID-19 and social vulnerability on an intra-urban scale. For this, two composite indicators of social vulnerability have been constructed. The composite indicator constructed by the Benefit-of-the-Doubt considers spatial heterogeneity. It weakly captures the conceptually most significant individual indicator of social vulnerability ( R =-0.39), as it overestimates the above-average performance sub-indicators. The composite indicator constructed by the Principal Component Analysis considers that the sub-indicators have the same weights in different census tracts, resulting in a highly consistent composite indicator as a multidimensional phenomenon concept ( R =-0.93). These findings allow reaching four conclusions. First, the direction and strength of correlations associated with COVID-19 are sensitive to the method employed to construct the composite indicator and not just the geographic scale and space. Second, Medium and High social vulnerability census tracts concentrate 97% of the population but only 93% of COVID-19 cases and deaths. Third, people living in census tracts of None and Low social vulnerability are 3.87 and 2.13 times more likely to be infected or die from COVID-19. Fourth, policies to combat COVID-19 in the study area should prioritize older populations regardless of their social conditions.

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          The Impact of Social Vulnerability on COVID-19 in the U.S.: An Analysis of Spatially Varying Relationships

          Introduction Because of their inability to access adequate medical care, transportation, and nutrition, socially vulnerable populations are at increased risk of health challenges during disasters. This study estimates the association between case counts of coronavirus disease 2019 (COVID-19) infection and social vulnerability in the U.S., identifying counties at increased vulnerability to the pandemic. Methods Using Social Vulnerability Index and COVID-19 case count data, an ordinary least squares regression model was fitted to assess the “global” relationship between COVID-19 case counts and social vulnerability. Local relationships were assessed using a geographically weighted regression model, which is effective in exploring spatial non-stationarity. Results As of May 12, 2020, a total of 1,320,909 people had been diagnosed with COVID-19 in the U.S. Of the counties included in this study (91.5%, 2,844/3,108), the highest case count was recorded in Trousdale, Tennessee (16,525.22 per 100,000) and the lowest in Tehama, California (1.54 per 100,000). At the “global” level, overall Social Vulnerability Index (e β=1.65, p=0.03) and minority status and language (e β=6.69, p<0.001) were associated with increased COVID-19 case counts. However, based on the “local” geographically weighted model, the association between social vulnerability and COVID-19 varied among counties. Overall, minority status and language, household composition and transportation, and housing and disability predicted COVID-19 infection. Conclusions Large-scale disasters differentially affect the health of marginalized communities. In this study, minority status and language, household composition and transportation, and housing and disability predicted COVID-19 case counts in the U.S. Addressing the social factors that create poor health is essential to reducing inequities in the health impacts of disasters.
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            Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators

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              Is Open Access

              On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness

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                Author and article information

                Contributors
                m4th32s@gmail.com
                osmartinuci@uem.br
                patriciabernardes0208@gmail.com
                nataliacristinachaves@gmail.com
                guilherme.castro@sambahoteis.com
                guerrahl@gmail.com
                eduardoribeiro@maringa.pr.gov.br
                janetefonzar@hotmail.com
                icarodacostafran@hotmail.com
                Journal
                Spat. Inf. Res.
                Spatial Information Research
                Springer Nature Singapore (Singapore )
                2366-3286
                2366-3294
                5 September 2022
                : 1-9
                Affiliations
                [1 ]GRID grid.412520.0, ISNI 0000 0001 2155 6671, Pontifical Catholic University of Minas Gerais, ; Dom José Gaspar street, 500, Coração Eucarístico, 30535-012 Belo Horizonte, Minas Gerais Brazil
                [2 ]GRID grid.271762.7, ISNI 0000 0001 2116 9989, State University of Maringa, ; Colombo avenue, Zone 7, 5790, 87020-900 Maringá, Paraná Brazil
                [3 ]Health secretariat of Maringá, Prudente de Morais avenue, 885, Zone 7, 87010-020 Maringá, Paraná Brazil
                [4 ]GRID grid.441899.9, ISNI 0000 0000 9491 0371, Department of Medicine, , Unicesumar, ; Maringá, Brazil
                Author information
                http://orcid.org/0000-0003-1411-0553
                Article
                479
                10.1007/s41324-022-00479-w
                9442576
                fe5c8db6-2628-49e8-bbeb-862016eb9db1
                © The Author(s), under exclusive licence to Korean Spatial Information Society 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 10 June 2022
                : 19 August 2022
                : 20 August 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002322, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior;
                Award ID: 0001
                Funded by: FundRef http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 151518/2022-0
                Categories
                Article

                social vulnerability,composite indicators,covid-19,principal component analysis,benefit-of-the-doubt

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