A beta regression analysis of COVID-19 mortality in Brazil

Cribari‐Neto, Francisco

doi:10.1016/j.idm.2023.02.005

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…This is because, while the Northeast region had the worst scenario, with excess suicides in females and males, in all age groups and all 4-month periods evaluated, the South region did not display excess suicide in a single 4-month period. Our results also reveal how suicide increased in the Southeast region during the second pandemic year (reaching 28% among females aged 60 years and over), particularly in July to October 2021, when excess suicide reached 50% (81/54), which may be related to the high rate of urbanization (Cribari-Neto, 2023).…”

Section: Discussionmentioning

confidence: 54%

Excess suicides in Brazil during the first 2 years of the COVID-19 pandemic: Gender, regional and age group inequalities

Orellana,

de Souza,

Horta

2023

Int J Soc Psychiatry

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Background: While the COVID-19 pandemic’s total impact on global mortality is uncertain, an estimated 15 million excess deaths occurred during the first two pandemic years, suggesting that a broad impact, since several causes of death showed a substantial rise. Aims: To estimate excess suicides in Brazil and evaluate differences within and between subgroups during the first 2 years of the COVID-19 pandemic. Method: Based on suicide data from the mortality information system of the Brazilian Ministry of Health, the expected number of suicides was estimated by age group, gender, 4-month periods and regions through quasi-Poisson generalized additive models. Analyses were performed in R software and RStudio. Results: Between March 2020 and February 2022, 29,295 suicides were reported in Brazil, close to what would be expected (30,116; 95% Confidence Interval (95% CI): [28,009, 32,224]), albeit in males and females aged 30 to 59 years and 60 years and over, there were excess suicides in at least one of the six 4-month periods evaluated, especially in the second pandemic year. In the Southeast region, a 28% increase was observed in women 60 years and older during the second year. In the North region, suicide increased 23% and 32% among women aged 30 to 59 years during the first and second pandemic years, respectively. The Northeast region had a 16% excess in suicides among men aged 30 to 59 years and 61% among women 60 years old and older during the second pandemic year, reaching 83% in July to October 2021. Conclusions: During the first 2 pandemic years, the pattern of suicides was not homogeneous in Brazil. There were substantial excess suicides in women aged 30 to 59 years from the North and Northeast, while among the elderly and men there was a consistent pattern in several four-month periods throughout Brazil.

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Section: Discussionmentioning

confidence: 54%

Excess suicides in Brazil during the first 2 years of the COVID-19 pandemic: Gender, regional and age group inequalities

Orellana,

de Souza,

Horta

2023

Int J Soc Psychiatry

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“…We also did not detect a significant relation between state performance index, which is not a COVID-19-specific indicator and excess deaths. Cribari-Neto [44] used the differences between the predicted and observed mortality rates as a proxy for local government's administrative efficiencies, though these were related to COVID-19-specific interventions. In Sweden, the hospitalisations and deaths due to COVID-19 were positively associated with population density and the proportion of immigrants while the proportion of the population aged >65 years showed a negative association [20].…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a covariate-based model for district-level estimation of excess deaths due to COVID-19 in India

Krishnan,

Dubey,

Kumar

et al. 2024

J Glob Health

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Background Different statistical approaches for estimating excess deaths due to coronavirus disease 2019 (COVID-19) pandemic have led to varying estimates. In this study, we developed and validated a covariate-based model (CBM) with imputation for prediction of district-level excess deaths in India. MethodsWe used data extracted from deaths registered under the Civil Registration System for 2015-19 for 684 of 713 districts in India to estimate expected deaths for 2020 through a negative binomial regression model (NBRM) and to calculate excess observed deaths. Specifically, we used 15 covariates across four domains (state, health system, population, COVID-19) in a zero inflated NBRM to identify covariates significantly (P < 0.05) associated with excess deaths estimate in 460 districts. We then validated this CBM in 140 districts by comparing predicted and estimated excess. For 84 districts with missing covariates, we validated the imputation with CBM by comparing estimated with predicted excess deaths. We imputed covariate data to predict excess deaths for 29 districts which did not have data on deaths. ResultsThe share of elderly and urban population, the under-five mortality rate, prevalence of diabetes, and bed availability were significantly associated with estimated excess deaths and were used for CBM. The mean of the CBM-predicted excess deaths per district (x̄ = 989, standard deviation (SD) = 1588) was not significantly different from the estimated one (x̄ = 1448, SD = 3062) (P = 0.25). The estimated excess deaths (n = 67 540; 95% confidence interval (CI) = 35 431, 99 648) were similar to the predicted excess death (n = 64 570; 95% CI = 54 140, 75 000) by CBM with imputation. The total national estimate of excess deaths for all 713 districts was 794 989 (95% CI = 664 895, 925 082).Conclusions A CBM with imputation can be used to predict excess deaths in an appropriate context.

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“…Different studies throughout the pandemic demonstrated the relationship between socioeconomic and demographic indicators and COVID-19 outcomes [Docherty et al 2020, Zeiser et al 2022, Barough et al 2023, Cribari-Neto 2023. These studies explored different indicators and aspects of the pandemic, providing valuable insights into the disparities and inequalities observed among affected populations.…”

Section: Related Workmentioning

confidence: 99%

Integration of Epidemiologic, Socioeconomic, and Sociodemographic Indicators to Predict Early COVID-19 In-Hospital Outcomes

Matos,

Schmitt,

Zeiser

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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The COVID-19 pandemic is an unprecedented challenge for healthcare systems around the world. In Brazil, the COVID-19 pandemic affected the population differently. Sociodemographic and socioeconomic characteristics were important indicators of early access and quality of the health system. In this way, we combine epidemiological, socioeconomic, and sociodemographic data to predict in-hospital outcomes of COVID-19. The proposed approach utilizes models such as Random Forest, XGBoost, TabNet, and CatBoost, and employs Bayesian optimization for automatic hyperparameter selection. The results demonstrate that all models exhibit a relatively higher ability to correctly identify hospital discharge outcomes than mortality cases. However, XGBoost showed the best result, with a Precision of 0.72, Recall of 0.74, F1-score of 0.64, Accuracy of 0.74, and AUC of 0.83. The quantitative and qualitative results demonstrate that our method can effectively suggest high-quality in-hospital outcomes and demonstrate the possibility of using our methodology as a tool to assist healthcare professionals.

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A beta regression analysis of COVID-19 mortality in Brazil

Cited by 4 publications

References 27 publications

Excess suicides in Brazil during the first 2 years of the COVID-19 pandemic: Gender, regional and age group inequalities

Excess suicides in Brazil during the first 2 years of the COVID-19 pandemic: Gender, regional and age group inequalities

Construction and validation of a covariate-based model for district-level estimation of excess deaths due to COVID-19 in India

Integration of Epidemiologic, Socioeconomic, and Sociodemographic Indicators to Predict Early COVID-19 In-Hospital Outcomes

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