Factors Associated With Death in Confirmed Cases of COVID-19 in the State of Rio De Janeiro

Oliveira, Marcella Cini; Eleutério, Tatiana de Araújo; Corrêa, Allan Bruno de Andrade; Silva, Lucas Dalsenter Romano da; Rodrigues, Renata Coelho; Oliveira, Bruna Andrade de; Martins, Marlos Melo; Raymundo, Carlos Eduardo; Medronho, Roberto de Andrade

doi:10.21203/rs.3.rs-435549/v1

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Dyspnea, polypnea, chest pain, myalgia, arthralgia, fever, and chills were also the most frequent symptoms. In agreement with those results, dyspnea and fever were also reported by Aktar and Cini Oliveira [25,26]. However, our results indicate that headache, odynophagia, and cough failed to contribute to lethality.…”

Section: Symptoms As Risk Markerssupporting

confidence: 93%

“…Several diagnostic and prognostic models have been proposed for COVID-19, for instance the studies carried out by Aktar et al [ 25 ] and Cini Oliveira et al [ 26 ]. Both approaches evaluated several machine learning models to identify the best predictors of COVID-19 mortality based on demographic data, symptoms and signs, and comorbidities.…”

Section: Discussionmentioning

confidence: 99%

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Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach

et al. 2023

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Background Mexico ranks fifth worldwide in the number of deaths due to COVID-19. Identifying risk markers through easily accessible clinical data could help in the initial triage of COVID-19 patients and anticipate a fatal outcome, especially in the most socioeconomically disadvantaged regions. This study aims to identify markers that increase lethality risk in patients diagnosed with COVID-19, based on machine learning (ML) methods. Markers were differentiated by sex and age-group. Methods A total of 11,564 cases of COVID-19 in Mexico were extracted from the Epidemiological Surveillance System for Viral Respiratory Disease. Four ML classification methods were trained to predict lethality, and an interpretability approach was used to identify those markers. Results Models based on Extreme Gradient Boosting (XGBoost) yielded the best performance in a test set. This model achieved a sensitivity of 0.91, a specificity of 0.69, a positive predictive value of 0.344, and a negative predictive value of 0.965. For female patients, the leading markers are diabetes and arthralgia. For males, the main markers are chronic kidney disease (CKD) and chest pain. Dyspnea, hypertension, and polypnea increased the risk of death in both sexes. Conclusions ML-based models using an interpretability approach successfully identified risk markers for lethality by sex and age. Our results indicate that age is the strongest demographic factor for a fatal outcome, while all other markers were consistent with previous clinical trials conducted in a Mexican population. The markers identified here could be used as an initial triage, especially in geographic areas with limited resources.

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Section: Symptoms As Risk Markerssupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach

et al. 2023

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“…32 Some authors 3 reported that the spatial clusters of social vulnerability were signi cantly associated with increased mortality rates due to COVID-19 and that a higher proportion of African Americans in Chicago had high levels of social vulnerability and several risk factors. At the individual level, Black 33 and non-White people have a higher risk of mortality. 34 Regarding the spatio-temporal progression of the three outcomes, the north region consistently showed a higher excess risk for COVID-19 incidence and mortality during the entire study period.…”

Section: Discussionmentioning

confidence: 99%

Spatial-temporal distribution of incidence, mortality, and case-fatality ratios of coronavirus disease 2019 and its social determinants in Brazilian municipalities

Raymundo

Oliveira

Eleuterio

et al. 2022

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The COVID-19 pandemic caused impact on public health worldwide. Brazil gained prominence during the pandemic due to the magnitude of disease. This study aimed to evaluate the spatial-temporal dynamics of incidence, mortality, and case fatality of COVID-19 and its associations with social determinants in Brazilian municipalities and epidemiological week. We modelled incidence, mortality, and case fatality rates using spatial-temporal Bayesian model. The rates were standardised using the indirect method. “Bolsa Família Programme” (BOLSAFAM) and “proportional mortality ratio” (PMR) were inversely associated with the standardised incidence ratio (SIR), while “health insurance coverage” (HEALTHINSUR) and “Gini index” were directly associated with the SIR. BOLSAFAM and PMR were inversely associated with the standardised mortality ratio (SMR) and standardised case fatality ratio (SCFR), while HEALTHINSUR and “proportion of black and pardo population” were directly associated with the SMR and SCFR. The highest proportion of excess risk for SIR and the SMR started in the North, expanding to the Midwest, Southeast, and South regions. However, this excess risk was detected predominantly in the North region in all periods. The highest proportion of excess risk for the SCFR outcome was observed in some municipalities in the North region and in the other Brazilian regions. The COVID-19 incidence and mortality in municipalities that most benefited from the cash transfer programme and with better social development decreased. The municipalities with a higher proportion of non-white people were at greater risk of having their population becoming ill and dying from the disease.

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Factors Associated With Death in Confirmed Cases of COVID-19 in the State of Rio De Janeiro

Cited by 2 publications

References 17 publications

Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach

Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach

Spatial-temporal distribution of incidence, mortality, and case-fatality ratios of coronavirus disease 2019 and its social determinants in Brazilian municipalities

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