Abstract:OBJETIVO Analisar o impacto da pandemia de covid-19 sobre a mortalidade por câncer e por doenças cardiovasculares (DCV) como causa básica e comorbidade no Brasil e em suas regiões em 2020. MÉTODOS Foram utilizadas as bases de dados de 2019 e 2020 do Sistema de Informações de Mortalidade (SIM), analisando os óbitos ocorridos entre março e dezembro de cada ano que tiveram o câncer e as DCV como causa básica ou como comorbidade. Também foram analisados os óbitos por covid-19 em 2020. Para o cálculo da Razão de Mo… Show more
“…Of these, 56 were excluded after the full-text screening. We extracted relevant data from the final set of 22 articles (Brant et al 2020 ; Fernandes et al 2021 ; Décarie and Michaud 2021 ; Liu et al 2021 ; Li et al 2021 ; Shoaib et al 2021 ; Wu et al 2021a , b ; Al Wahaibi et al 2021 ; Wang et al 2021 ; Wu et al 2021a , b ; Kontopantelis et al 2021 ; Sharma et al 2021 ; Faust et al 2021 ; Grande et al 2022 ; Palacio-Mejía et al 2022 ; Gobiņa et al 2022 ; Perotti et al 2022 ; Jardim et al 2022 ; Glei 2022 ; Orellana and de Souza 2022 ; Odd et al 2022 ; Chen et al 2022 ). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Among the 22 articles selected for data extraction, one was published in 2020 (Brant et al 2020 ), 12 were published in 2021 (Fernandes et al 2021 ; Décarie and Michaud 2021 ; Liu et al 2021 ; Li et al 2021 ; Shoaib et al 2021 ; Wu et al 2021a , b ; Al Wahaibi et al 2021 ; Wang et al 2021 ; Wu et al 2021a , b ; Kontopantelis et al 2021 ; Sharma et al 2021 ; Faust et al 2021 ) and nine were published in 2022 (Grande et al 2022 ; Palacio-Mejía et al 2022 ; Gobiņa et al 2022 ; Perotti et al 2022 ; Jardim et al 2022 ; Glei 2022 ; Orellana and de Souza 2022 ; Odd et al 2022 ; Chen et al 2022 ). All the included studies used data from official governmental sources to perform their analysis.…”
Section: Resultsmentioning
confidence: 99%
“…The studies included in our review have study settings with heterogeneous granularity. For instance, studies investigate mortality at a city level (Brant et al 2020 ; Fernandes et al 2021 ; Liu et al 2021 ; Li et al 2021 ; Perotti et al 2022 ), subnational region/state level (Décarie and Michaud 2021 ; Liu et al 2021 ; Shoaib et al 2021 ; Kontopantelis et al 2021 ; Sharma et al 2021 ; Chen et al 2022 ) and country levels (Shoaib et al 2021 ; Wu et al 2021a , b ; Al Wahaibi et al 2021 ; Wang et al 2021 ; Wu et al 2021a , b ; Kontopantelis et al 2021 ; Faust et al 2021 ; Grande et al 2022 ; Palacio-Mejía et al 2022 ; Gobiņa et al 2022 ; Jardim et al 2022 ; Glei 2022 ; Orellana and de Souza 2022 ; Odd et al 2022 ).…”
Section: Resultsmentioning
confidence: 99%
“…All studies reported the methods used to estimate the expected mortality for the respective study period. The Poisson regression model ( n = 7) (Brant et al 2020 ; Décarie and Michaud 2021 ; Li et al 2021 ; Shoaib et al 2021 ; Sharma et al 2021 ; Jardim et al 2022 ; Palacio-Mejía et al 2022 ) was the most widely employed method, followed by the mean number of deaths of the previous years ( n = 4) (Shoaib et al 2021 ; Wang et al 2021 ; Grande et al 2022 ; Perotti et al 2022 ), the Farrington surveillance algorithm ( n = 3) (Liu et al 2021 ; Al Wahaibi et al 2021 : Wu et al 2021a , b ), and the negative binomial regression model ( n = 3) (Kontopantelis et al 2021 ; Glei 2022 ; Odd et al 2022 ). Other methods included the autoregressive integrated moving average (ARIMA) model ( n = 2) (Faust et al 2021 ; Chen et al 2022 ) and generalized additive modeling (GAM) ( n = 2; Gobina et al 2022; Orellana and de Souza 2022 ).…”
Background
Understanding the effects of the COVID-19 pandemic on cause-specific mortality should be a priority, as this metric allows for a detailed analysis of the true burden of the pandemic. The aim of this systematic literature review is to estimate the impact of the pandemic on different causes of death, providing a quantitative and qualitative analysis of the phenomenon.
Methods
We searched MEDLINE, Scopus, and ProQuest for studies that reported cause-specific mortality during the COVID-19 pandemic, extracting relevant data.
Results
A total of 2413 articles were retrieved, and after screening 22 were selected for data extraction. Cause-specific mortality results were reported using different units of measurement. The most frequently analyzed cause of death was cardiovascular diseases (n = 16), followed by cancer (n = 14) and diabetes (n = 11). We reported heterogeneous patterns of cause-specific mortality, except for suicide and road accident.
Conclusions
Evidence on non-COVID-19 cause-specific deaths is not exhaustive. Reliable scientific evidence is needed by policymakers to make the best decisions in an unprecedented and extremely uncertain historical period. We advocate for the urgent need to find an international consensus to define reliable methodological approaches to establish the true burden of the COVID-19 pandemic on non-COVID-19 mortality.
“…Of these, 56 were excluded after the full-text screening. We extracted relevant data from the final set of 22 articles (Brant et al 2020 ; Fernandes et al 2021 ; Décarie and Michaud 2021 ; Liu et al 2021 ; Li et al 2021 ; Shoaib et al 2021 ; Wu et al 2021a , b ; Al Wahaibi et al 2021 ; Wang et al 2021 ; Wu et al 2021a , b ; Kontopantelis et al 2021 ; Sharma et al 2021 ; Faust et al 2021 ; Grande et al 2022 ; Palacio-Mejía et al 2022 ; Gobiņa et al 2022 ; Perotti et al 2022 ; Jardim et al 2022 ; Glei 2022 ; Orellana and de Souza 2022 ; Odd et al 2022 ; Chen et al 2022 ). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Among the 22 articles selected for data extraction, one was published in 2020 (Brant et al 2020 ), 12 were published in 2021 (Fernandes et al 2021 ; Décarie and Michaud 2021 ; Liu et al 2021 ; Li et al 2021 ; Shoaib et al 2021 ; Wu et al 2021a , b ; Al Wahaibi et al 2021 ; Wang et al 2021 ; Wu et al 2021a , b ; Kontopantelis et al 2021 ; Sharma et al 2021 ; Faust et al 2021 ) and nine were published in 2022 (Grande et al 2022 ; Palacio-Mejía et al 2022 ; Gobiņa et al 2022 ; Perotti et al 2022 ; Jardim et al 2022 ; Glei 2022 ; Orellana and de Souza 2022 ; Odd et al 2022 ; Chen et al 2022 ). All the included studies used data from official governmental sources to perform their analysis.…”
Section: Resultsmentioning
confidence: 99%
“…The studies included in our review have study settings with heterogeneous granularity. For instance, studies investigate mortality at a city level (Brant et al 2020 ; Fernandes et al 2021 ; Liu et al 2021 ; Li et al 2021 ; Perotti et al 2022 ), subnational region/state level (Décarie and Michaud 2021 ; Liu et al 2021 ; Shoaib et al 2021 ; Kontopantelis et al 2021 ; Sharma et al 2021 ; Chen et al 2022 ) and country levels (Shoaib et al 2021 ; Wu et al 2021a , b ; Al Wahaibi et al 2021 ; Wang et al 2021 ; Wu et al 2021a , b ; Kontopantelis et al 2021 ; Faust et al 2021 ; Grande et al 2022 ; Palacio-Mejía et al 2022 ; Gobiņa et al 2022 ; Jardim et al 2022 ; Glei 2022 ; Orellana and de Souza 2022 ; Odd et al 2022 ).…”
Section: Resultsmentioning
confidence: 99%
“…All studies reported the methods used to estimate the expected mortality for the respective study period. The Poisson regression model ( n = 7) (Brant et al 2020 ; Décarie and Michaud 2021 ; Li et al 2021 ; Shoaib et al 2021 ; Sharma et al 2021 ; Jardim et al 2022 ; Palacio-Mejía et al 2022 ) was the most widely employed method, followed by the mean number of deaths of the previous years ( n = 4) (Shoaib et al 2021 ; Wang et al 2021 ; Grande et al 2022 ; Perotti et al 2022 ), the Farrington surveillance algorithm ( n = 3) (Liu et al 2021 ; Al Wahaibi et al 2021 : Wu et al 2021a , b ), and the negative binomial regression model ( n = 3) (Kontopantelis et al 2021 ; Glei 2022 ; Odd et al 2022 ). Other methods included the autoregressive integrated moving average (ARIMA) model ( n = 2) (Faust et al 2021 ; Chen et al 2022 ) and generalized additive modeling (GAM) ( n = 2; Gobina et al 2022; Orellana and de Souza 2022 ).…”
Background
Understanding the effects of the COVID-19 pandemic on cause-specific mortality should be a priority, as this metric allows for a detailed analysis of the true burden of the pandemic. The aim of this systematic literature review is to estimate the impact of the pandemic on different causes of death, providing a quantitative and qualitative analysis of the phenomenon.
Methods
We searched MEDLINE, Scopus, and ProQuest for studies that reported cause-specific mortality during the COVID-19 pandemic, extracting relevant data.
Results
A total of 2413 articles were retrieved, and after screening 22 were selected for data extraction. Cause-specific mortality results were reported using different units of measurement. The most frequently analyzed cause of death was cardiovascular diseases (n = 16), followed by cancer (n = 14) and diabetes (n = 11). We reported heterogeneous patterns of cause-specific mortality, except for suicide and road accident.
Conclusions
Evidence on non-COVID-19 cause-specific deaths is not exhaustive. Reliable scientific evidence is needed by policymakers to make the best decisions in an unprecedented and extremely uncertain historical period. We advocate for the urgent need to find an international consensus to define reliable methodological approaches to establish the true burden of the COVID-19 pandemic on non-COVID-19 mortality.
“…Cause-specific expected mortality indicated deficits for Respiratory, Other Infectious Diseases, Neoplasms and Cardiovascular Diseases. With the social distancing recommendations, we expect lower exposure to risk factors associated with the first two, while deficits for Neoplasms and Cardiovascular diseases might be explained by the evidence that people suffering from these illnesses were at high risk for serious COVID-19 conditions and died from COVID-19 in the first wave [14, 35–37]. While these arguments are plausible, we should keep in mind that misdiagnosis is always an issue mainly to deaths that happened outside hospitals and clinics and in regions with poor resources for proper diagnosis [13].…”
We estimated the impact of the COVID-19 pandemic on mortality in Brazil for 2020 and 2021 years. We used mortality data (2015-2021) from the Health Ministry, Brazil government, to fit linear mixed models for forecasting baseline deaths under non-pandemic conditions. An advantage of the linear mixed model is the flexibility to capture year-trend while dealing with the correlations among death counts over time. Following a specified model building strategy, estimation of all-cause excess deaths at the country level and stratified by sex, age, ethnicity and region of residence, from March 2020 to August 2021. We also considered the estimation of excess deaths by specific causes. Estimated all-cause excess deaths was 199,108 (95% PI: 171,007; 227,209, P-Score=17.3%) for weeks 10-53, 2020, and 417,167 (95% PI: 372,075; 462,259, P-Score=50.1%) for weeks 1-32, 2021. P-scores ranged from 5.4% (RS, South) to 36.2% (AM, North) in 2020 and from 29.3% (AL, Northeast) to 94.9%$ (RO, North) in 2021. Differences among men (18.9%) and women (14.2%) appeared in 2020 only, and the P-scores were about 51% for both sexes in 2021. Except for youngsters (<20 years old), all adult age groups were badly hit, especially those from 40 to 79 years old. In 2020, the Indigenous+East Asian population had the highest P-score (27%), and the Black population suffered the greatest impact (61.9%) in 2021. The pandemic impact had enormous regional heterogeneity and substantial differences according to socio-demographic factors, mainly during the first wave, showing some population strata benefits from the social distancing measures when able to adhere to them. In the second wave, the burden was very high for all but extremely high for some, highlighting our society needs to tackle the health inequalities experienced by groups of different socio-demographic and economic status.
COVID-19 has caused many deaths since the first outbreak in 2019. The burden on healthcare systems around the world has been reduced by the success of vaccines.However, population adherence and the occurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are still challenging tasks to be affronted. In addition, the newly approved drug presents some limitations in terms of side effects and drug interference, highlighting the importance of searching for new antiviral agents against SARS-CoV-2. The SARS-CoV-2 main protease (M pro ) represents a versatile target to search for new drug candidates due to its essential role in proteolytic activities responsible for the virus replication. In this work, a series of 190 compounds, composed of 27 natural ones and 163 synthetic compounds, were screened in vitro for their inhibitory effects against SARS-CoV-2 M pro . Twentyfive compounds inhibited M pro with inhibitory constant values (K i ) between 23.2 and 241 µM. Among them, a thiosemicarbazone derivative was the most active compound. Molecular docking studies using Protein Data Bank ID 5RG1, 5RG2, and 5RG3 crystal structures of M pro revealed important interactions identified as hydrophobic, hydrogen bonding and steric interactions with amino acid residues in the active site cavity. Overall, our findings indicate the described thiosemicarbazones as good candidates to be further explored to develop antiviral leads against SARS-CoV-2. Moreover, the studies showed the importance of careful evaluation of test results to detect and exclude false-positive findings.
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