Tracking excess deaths associated with the COVID-19 epidemic as an epidemiological surveillance strategy-preliminary results of the evaluation of six Brazilian capitals

Abstract

Introduction: In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak a pandemic. In Brazil, 110 thousand cases and 5,901 deaths were confirmed by the end of April 2020. The scarcity of laboratory resources, the overload on the service network, and the broad clinical spectrum of the disease make it difficult to document all the deaths due to COVID-19. The aim of this study was to assess the mortality rate in Brazilian capitals with a high incidence of COVID-19. Methods: We assessed the weekly mortality between epidemiological week 1 and 16 in 2020 and the corresponding period in 2019. We estimated the expected mortality at 95% confidence interval by projecting the mortality in 2019 to the population in 2020, using data from the National Association of Civil Registrars (ARPEN-Brasil). Results: In the five capitals with the highest incidence of COVID-19, we identified excess deaths during the pandemic. The age group above 60 years was severely affected, while 31% of the excess deaths occurred in the age group of 20-59 years. There was a strong correlation (r = 0.94) between excess deaths and the number of deaths confirmed by epidemiological monitoring. The epidemiological surveillance captured only 52% of all mortality associated with the COVID-19 pandemic in the cities examined. Conclusions: Considering the simplicity of the method and its low cost, we believe that the assessment of excess mortality associated with the COVID-19 pandemic should be used as a complementary tool for regular epidemiological surveillance.

Characterisation of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data

“…Although different criteria for hospital admissions and other patient characteristics (eg, comorbidities) could explain some of the observed differences in hospital admissions, respiratory support, and mortality between countries, the mismatch between demand and supply leading to a collapse of the health-care system could partly explain the high in-hospital mortality in Brazil. 16 , 17 , 18 …”

“…Although different criteria for hospital admissions and other patient characteristics (eg, comorbidities) could explain some of the observed differences in hospital admissions, respiratory support, and mortality between countries, the mismatch between demand and supply leading to a collapse of the health-care system could partly explain the high in-hospital mortality in Brazil. [16][17][18] Several factors might have contributed to the differences observed in mortality and resource use among the regions of Brazil during the pandemic. These include the existing regional heterogeneity of the health system, followed by the temporal spread of the epidemic, and disparities in adherence to best practices for clinical management of severely ill patients.…”

How many more? Under‐reporting of the COVID‐19 deaths in Brazil in 2020

“…In the first months of the epidemic, excess all-cause mortality was suggested as a means to evaluate the impact of COVID-19 on mortality [28] and applied in some studies [29,30]. In the Italian province of Lombardy, a 50% under-reporting of COVID-19 deaths was found [31], a value similar to that of five Brazilian state capitals with the highest incidence of COVID-19 [30]. On the other hand, the corresponding value for the USA was estimated at 26.3% [32].…”

Performance of vaccination with CoronaVac in a cohort of healthcare workers (HCW) - preliminary report

“…The copyright holder for this preprint this version posted April 15, 2021. ; https://doi.org/10.1101/2021.04.12.21255308 doi: medRxiv preprint our HCWs. It has been discussed that deaths are a more reliable surveillance tool in situations in which testing capacity is limited [17,18] which was especially true in the beginning of the pandemic in São Paulo. Considering this situation we chose not to include data from 2020 epi weeks 9 through 23 when designing our predictive model.…”