A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

Souza, Arthur Pimentel Gomes de; Mota, Caroline Maria de Miranda; Rosa, Amanda Gadelha Ferreira; Figueiredo, Ciro José Jardim de; Candeias, Ana Lúcia Bezerra

doi:10.1371/journal.pone.0268538

Cited by 9 publications

(11 citation statements)

References 75 publications

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“…At the intraurban scale, Souza et al [ 22 ] show that cases of COVID-19 are negatively correlated with income and positively correlated with the population over 60 years of age. Souza et al [ 23 ] show that COVID-19 cases are positively correlated with the proportion of the elderly population and negatively correlated with income and education.…”

Section: Introductionmentioning

confidence: 99%

Social vulnerability and COVID-19 in Maringá, Brazil

et al. 2022

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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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Section: Introductionmentioning

confidence: 99%

Social vulnerability and COVID-19 in Maringá, Brazil

et al. 2022

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“…To reduce the burden of in-hospital deaths due to potentially new epidemics and COVID-19 in particular, the South African government and private stakeholders need to build strong and resilient health care infrastructure which can be accessed equally by both the poor and rich independent of class or race especially in vulnerable districts. Modelling the effects of underlying factors and in-hospital disease mortality at small spatial scale is essential to plan effective control strategies for disease outcome risks (20). The relationship between COVID-19 in-hospital mortality rates and sociodemographic, clinical and districts spatial dependents can consequently guide the development of specific intervention actions for these places.…”

Section: Discussionmentioning

confidence: 99%

Section: Jassat Et Al Described the Demographic And Clinical Characte...mentioning

confidence: 99%

“…In recent years, there has been a growing interest in the application of spatial analysis and modelling techniques as a tool for in-depth understanding of public health problems including identifying hotspots, spatial distribution, patterns and effects (1,16–20). SARS-CoV-2 transmission and infections, and COVID-19 deaths, have been shown to be spatially distributed in Europe(16), England(17), and France(1).…”

Section: Introductionmentioning

confidence: 99%

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Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

Maposa

Welch²,

Ozougwu³

et al. 2022

Preprint

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BackgroundThe first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors.MethodsCOVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian.ResultsThe risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR=4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR=1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR=3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR= 3.16; 95% Credible Interval: 3.10-3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts.ConclusionThe results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

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“…In recent years, there has been a growing interest in the application of spatial analysis and modelling techniques as a tool for indepth understanding of public health problems including identifying hotspots, spatial distribution, patterns and effects (1,(16)(17)(18)(19)(20). SARS-CoV-2 transmission and infections, and COVID-19 deaths, have been shown to be spatially distributed in Europe(16), England (17), and France (1).…”

Section: Introductionmentioning

confidence: 99%

Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

Maposa

Welch

Ozougwu

et al. 2022

Preprint

View full text Add to dashboard Cite

Background The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors. Methods COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian. Results The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05–4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46–1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61–3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10–3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts. Conclusion The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

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A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

Cited by 9 publications

References 75 publications

Social vulnerability and COVID-19 in Maringá, Brazil

Social vulnerability and COVID-19 in Maringá, Brazil

Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

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