“…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 ).…”
Section: Resultsmentioning
confidence: 99%
“…Cause-specific mortality results were reported using different units of measurement. Specifically, three studies (Brant et al 2020 ; Li et al 2021 ; Grande et al 2022 ) analyzed the percentage change in mortality with respect to the comparison period, seven studies (Décarie and Michaud 2021 ; Kontopantelis et al 2021 ; Sharma et al 2021 ; Palacio-Mejía et al 2022 ; Gobiņa et al 2022 ; Glei 2022 ; Chen et al 2022 ) analyzed the absolute number of excess deaths, two studies (Fernandes et al 2021 ; Al Wahaibi et al 2021 ) reported the standardized mortality ratio, two studies (Wu et al 2021a , b ) reported both the total number of excess deaths and percentage variation, six studies (Liu et al 2021 ; Al Wahaibi et al 2021 ; Faust et al 2021 ; Perotti et al 2022 ; Jardim et al 2022 ; Odd et al 2022 ) reported the observed-to-expected ratio, and one study (Shoaib et al 2021 ) reported the incidence rate ratio.…”
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 ).…”
Section: Resultsmentioning
confidence: 99%
“…Cause-specific mortality results were reported using different units of measurement. Specifically, three studies (Brant et al 2020 ; Li et al 2021 ; Grande et al 2022 ) analyzed the percentage change in mortality with respect to the comparison period, seven studies (Décarie and Michaud 2021 ; Kontopantelis et al 2021 ; Sharma et al 2021 ; Palacio-Mejía et al 2022 ; Gobiņa et al 2022 ; Glei 2022 ; Chen et al 2022 ) analyzed the absolute number of excess deaths, two studies (Fernandes et al 2021 ; Al Wahaibi et al 2021 ) reported the standardized mortality ratio, two studies (Wu et al 2021a , b ) reported both the total number of excess deaths and percentage variation, six studies (Liu et al 2021 ; Al Wahaibi et al 2021 ; Faust et al 2021 ; Perotti et al 2022 ; Jardim et al 2022 ; Odd et al 2022 ) reported the observed-to-expected ratio, and one study (Shoaib et al 2021 ) reported the incidence rate ratio.…”
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.
The study aimed to analyze the influence of the COVID-19 pandemic on mortality rates in patients with systemic autoimmune rheumatic diseases (SARD) in Mexico. We selected SARD-related deaths using National Open Data and Information from the Ministry of Health, Mexico, and ICD-10 codes. We assessed the observed compared to the predicted mortality values for 2020 and 2021, employing trends from 2010 to 2019 with joinpoint and prediction modelling analyses. Among 12,742 deaths due to SARD between 2010 and 2021, the age-standardized mortality rate (ASMR) increased significantly between 2010 and 2019 (pre-pandemic) (annual percentage change [APC] 1.1%; 95% CI 0.2–2.1), followed by a non-significant decrease during the pandemic period (APC 13.9%; 95% CI 13.9–5.3). In addition, the observed ASMR of 1.19 for 2020 for SARD and of 1.14 for 2021 were lower than the predicted values of 1.25 (95% CI 1.22–1.28) for 2020 and 1.25 (95% CI 1.20–1.30) for 2021. Similar findings were identified for specific SARD, mainly systemic lupus erythematosus (SLE), or by sex or age group. Interestingly, the observed mortality rates for SLE in the Southern region of 1.00 in 2020 and 1.01 in 2021 were both significantly greater than the predicted values of 0.71 (95% CI 0.65–0.77) in 2020 and 0.71 (95% CI 0.63–0.79). In Mexico, the observed SARD mortality rates were not higher than the expected values during the pandemic, except for SLE in the Southern region. No differences by sex or age group were identified.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00296-023-05371-w.
The impact of COVID-19 on mortality from specific causes of death remains poorly understood. This study analysed cause-of-death data provided by the World Health Organization from 2011 to 2019 to estimate excess deaths in 2020 in 30 countries. Over-dispersed Poisson regression models were used to estimate the number of deaths that would have been expected if the pandemic had not occurred, separately for men and women. The models included year and age categories to account for temporal trends and changes in size and age structure of the populations. Excess deaths were calculated by subtracting observed deaths from expected ones. Our analysis revealed significant excess deaths from ischemic heart diseases (IHD) (in 10 countries), cerebrovascular diseases (CVD) (in 10 countries), and diabetes (in 19 countries). The majority of countries experienced excess mortality greater than 10%, including Mexico (+ 38·8% for IHD, + 34·9% for diabetes), Guatemala (+ 30·0% for IHD, + 10·2% for CVD, + 39·7% for diabetes), Cuba (+ 18·8% for diabetes), Brazil (+ 12·9% for diabetes), the USA (+ 15·1% for diabetes), Slovenia (+ 33·8% for diabetes), Poland (+ 30·2% for IHD, + 19·5% for CVD, + 26 1% for diabetes), Estonia (+ 26·9% for CVD, + 34·7% for diabetes), Bulgaria (+ 22·8% for IHD, + 11·4% for diabetes), Spain (+ 19·7% for diabetes), Italy (+ 18·0% for diabetes), Lithuania (+ 17·6% for diabetes), Finland (+ 13·2% for diabetes) and Georgia (+ 10·7% for IHD, + 19·0% for diabetes). In 2020, 22 out of 30 countries had a significant increase in total mortality. Some of this excess was attributed to COVID-19, but a substantial increase was also observed in deaths attributed to cardiovascular diseases and diabetes.
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