A global database of COVID-19 vaccinations

[Correction Notice: An Erratum for this article was reported in Vol 5(7) of Nature Human Behaviour (see record 2021-69306-023). In the original article, errors occurred in Figs. 2 and 6. Figure 2 showed a lack of alignment between the years presented on gross domestic product (GDP) per capita values across countries. These figures-presented in GDP per capita adjusted for purchasing power parity, measured in international dollars-have now been aligned. This correction was necessary because the most recent year for which GDP data is available from the World Bank is different across countries. The GDP alignment issue will have affected all countries in the dataset, some to a very small (unnoticeable) degree when GDP has not significantly changed year-to-year, but more visibly for countries with strong growth rates (e.g., China). Figure 6 incorrectly indicated that no vaccine for Ebola exists. In fact, the first vaccine against Ebola was approved in the European Union and the USA in 2019. These errors have been corrected in the PDF and HTML versions of this article.] An effective rollout of vaccinations against COVID-19 offers the most promising prospect of bringing the pandemic to an end. We present the Our World in Data COVID-19 vaccination dataset, a global public dataset that tracks the scale and rate of the vaccine rollout across the world. This dataset is updated regularly and includes data on the total number of vaccinations administered, first and second doses administered, daily vaccination rates and population-adjusted coverage for all countries for which data are available (169 countries as of 7 April 2021). It will be maintained as the global vaccination campaign continues to progress. This resource aids policymakers and researchers in understanding the rate of current and potential vaccine rollout;the interactions with non-vaccination policy responses;the potential impact of vaccinations on pandemic outcomes such as transmission, morbidity and mortality;and global inequalities in vaccine access. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Author correction: A global database of COVID-19 vaccinations

Reports an error in "A global database of COVID-19 vaccinations" by Edouard Mathieu, Hannah Ritchie, Esteban Ortiz-Ospina, Max Roser, Joe Hasell, Cameron Appel, Charlie Giattino and Lucas Rodes-Guirao (Nature Human Behaviour, 2021[Jul], Vol 5[7], 947-953). In the original article, errors occurred in Figs. 2 and 6. Figure 2 showed a lack of alignment between the years presented on gross domestic product (GDP) per capita values across countries. These figures-presented in GDP per capita adjusted for purchasing power parity, measured in international dollars-have now been aligned. This correction was necessary because the most recent year for which GDP data is available from the World Bank is different across countries. The GDP alignment issue will have affected all countries in the dataset, some to a very small (unnoticeable) degree when GDP has not significantly changed year-to-year, but more visibly for countries with strong growth rates (e.g., China). Figure 6 incorrectly indicated that no vaccine for Ebola exists. In fact, the first vaccine against Ebola was approved in the European Union and the USA in 2019. These errors have been corrected in the PDF and HTML versions of this article. (The following of the original article appeared in record 2021-69306-020). An effective rollout of vaccinations against COVID-19 offers the most promising prospect of bringing the pandemic to an end. We present the Our World in Data COVID-19 vaccination dataset, a global public dataset that tracks the scale and rate of the vaccine rollout across the world. This dataset is updated regularly and includes data on the total number of vaccinations administered, first and second doses administered, daily vaccination rates and population-adjusted coverage for all countries for which data are available (169 countries as of 7 April 2021). It will be maintained as the global vaccination campaign continues to progress. This resource aids policymakers and researchers in understanding the rate of current and potential vaccine rollout;the interactions with non-vaccination policy responses;the potential impact of vaccinations on pandemic outcomes such as transmission, morbidity and mortality;and global inequalities in vaccine access. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies.

OBJECTIVES: We conducted a systematic literature review and meta-analysis of observational studies to investigate the association between diabetes, hypertension, body mass index (BMI) or smoking with the risk of death in patients with COVID-19 and to estimate the proportion of deaths attributable to these conditions. METHODS: Relevant observational studies were identified by searches in the PubMed, Cochrane library and Embase databases through 14 November 2020. Random-effects models were used to estimate summary relative risks (SRRs) and 95% CIs. Certainty of evidence was assessed using the Cochrane methods and the Grading of Recommendations, Assessment, Development and Evaluations framework. RESULTS: A total of 186 studies representing 210 447 deaths among 1 304 587 patients with COVID-19 were included in this analysis. The SRR for death in patients with COVID-19 was 1.54 (95% CI 1.44 to 1.64, I2=92%, n=145, low certainty) for diabetes and 1.42 (95% CI 1.30 to 1.54, I2=90%, n=127, low certainty) for hypertension compared with patients without each of these comorbidities. Regarding obesity, the SSR was 1.45 (95% CI 1.31 to 1.61, I2=91%, n=54, high certainty) for patients with BMI ≥30 kg/m2 compared with those with BMI <30 kg/m2 and 1.12 (95% CI 1.07 to 1.17, I2=68%, n=25) per 5 kg/m2 increase in BMI. There was evidence of a J-shaped non-linear dose-response relationship between BMI and mortality from COVID-19, with the nadir of the curve at a BMI of around 22-24, and a 1.5-2-fold increase in COVID-19 mortality with extreme obesity (BMI of 40-45). The SRR was 1.28 (95% CI 1.17 to 1.40, I2=74%, n=28, low certainty) for ever, 1.29 (95% CI 1.03 to 1.62, I2=84%, n=19) for current and 1.25 (95% CI 1.11 to 1.42, I2=75%, n=14) for former smokers compared with never smokers. The absolute risk of COVID-19 death was increased by 14%, 11%, 12% and 7% for diabetes, hypertension, obesity and smoking, respectively. The proportion of deaths attributable to diabetes, hypertension, obesity and smoking was 8%, 7%, 11% and 2%, respectively. CONCLUSION: Our findings suggest that diabetes, hypertension, obesity and smoking were associated with higher COVID-19 mortality, contributing to nearly 30% of COVID-19 deaths. TRIAL REGISTRATION NUMBER: CRD42020218115.

A global database of COVID-19 vaccinations.

An effective rollout of vaccinations against COVID-19 offers the most promising prospect of bringing the pandemic to an end. We present the Our World in Data COVID-19 vaccination dataset, a global public dataset that tracks the scale and rate of the vaccine rollout across the world. This dataset is updated regularly and includes data on the total number of vaccinations administered, first and second doses administered, daily vaccination rates and population-adjusted coverage for all countries for which data are available (169 countries as of 7 April 2021). It will be maintained as the global vaccination campaign continues to progress. This resource aids policymakers and researchers in understanding the rate of current and potential vaccine rollout; the interactions with non-vaccination policy responses; the potential impact of vaccinations on pandemic outcomes such as transmission, morbidity and mortality; and global inequalities in vaccine access.

Diabetes, Hypertension, Body Mass Index, Smoking and COVID-19-Related Mortality: A Systematic Review and Meta-Analysis of Observational Studies (preprint)

Background: Patients who smoke and with preexisting comorbidities have a greater risk of developing severe coronavirus disease 2019 (COVID-19) and have a higher mortality rate. However, the number of deaths attributable to diabetes, hypertension, obesity, or smoking have never been estimated. We conducted a systematic literature review and meta-analysis of observational studies to investigate the association between diabetes, hypertension, body mass index (BMI) or smoking with the risk of death in patients with COVID-19.Methods: Relevant observational studies were identified by searches in the PubMed and Embase databases through October 29, 2020. Random-effects models were used to estimate summary relative risks (SRRs) and 95% confidence intervals (CIs). We further estimated the proportion of deaths attributable to these conditions. Certainty of evidence was assessed using the Cochrane methods and the GRADE framework. This study is registered with PROSPERO, CRD42020218115.Findings: A total of 186 studies representing 210,447 deaths among 1,304,587 patients with COVID-19 were included in this analysis. The SRR for death in COVID-19 patients was 1.54 (95% CI=1.44-1.64, I2=92%, n=145, low certainty) for diabetes and 1.42 (95% CI=1.30-1.54, I2=90%, n=127, low certainty) for hypertension compared to patients without each of these comorbidities. Regarding obesity, the SSR was 1.45 (95% CI=1.31-1.61, I2=91%, n=54, high certainty) for patients with BMI ≥30kg/m2 compared to those with BMI <30kg/m2 and 1.12 (95% CI=1.07-1.17, I2=68%, n=25) per 5 kg/m2 increase in BMI. There was evidence of a J-shaped non-linear dose-response relationship between BMI and mortality from COVID-19, with the nadir of the curve at a BMI of around 22-24, and a 1.5-2 fold increase in COVID-19 mortality with extreme obesity (BMI of 40-50). The SRR was 1.28 (95% CI=1.29-1.50, I2=74.0, n=28, low certainty) for ever, 1.29 (95% CI=1.03-1.62, I2=84%, n=19) for current and 1.26 (95% CI=1.11-1.42, I2=84%, n=14) for former smokers compared to never smokers. The proportion of deaths attributable to diabetes, hypertension, obesity, and smoking was 8%, 7%, 11%, and 2%, respectively.Interpretation: Our findings suggest that diabetes, hypertension, obesity and smoking are major contributors to COVID-19 mortality accounting for nearly 30% of COVID-19 deaths.Funding Statement: There was no funding source for this study.Declaration of Interests: We declare no competing interests.

A cross-country database of COVID-19 testing.

Our understanding of the evolution of the COVID-19 pandemic is built upon data concerning confirmed cases and deaths. This data, however, can only be meaningfully interpreted alongside an accurate understanding of the extent of virus testing in different countries. This new database brings together official data on the extent of PCR testing over time for 94 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series. The database is updated regularly through a combination of automated scraping and manual collection and verification, and is entirely replicable, with sources provided for each observation. In providing accessible cross-country data on testing output, it aims to facilitate the incorporation of this crucial information into epidemiological studies, as well as track a key component of countries' responses to COVID-19.

Effect of hydroxychloroquine with or without azithromycin on the mortality of coronavirus disease 2019 (COVID-19) patients: a systematic review and meta-analysis.

BACKGROUND: Hydroxychloroquine or chloroquine with or without azithromycin have been widely promoted to treat coronavirus disease 2019 (COVID-19) following early in vitro antiviral effects against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: The aim of this systematic review and meta-analysis was to assess whether chloroquine or hydroxychloroquine with or without azithromycin decreased COVID-19 mortality compared with the standard of care. DATA SOURCES: PubMed, Web of Science, Embase Cochrane Library, Google Scholar and MedRxiv were searched up to 25 July 2020. STUDY ELIGIBILITY CRITERIA: We included published and unpublished studies comparing the mortality rate between patients treated with chloroquine or hydroxychloroquine with or without azithromycin and patients managed with standard of care. PARTICIPANTS: Patients ≥18 years old with confirmed COVID-19. INTERVENTIONS: Chloroquine or hydroxychloroquine with or without azithromycin. METHODS: Effect sizes were pooled using a random-effects model. Multiple subgroup analyses were conducted to assess drug safety. RESULTS: The initial search yielded 839 articles, of which 29 met our inclusion criteria. All studies except one were conducted on hospitalized patients and evaluated the effects of hydroxychloroquine with or without azithromycin. Among the 29 articles, three were randomized controlled trials, one was a non-randomized trial and 25 were observational studies, including 11 with a critical risk of bias and 14 with a serious or moderate risk of bias. After excluding studies with critical risk of bias, the meta-analysis included 11 932 participants for the hydroxychloroquine group, 8081 for the hydroxychloroquine with azithromycin group and 12 930 for the control group. Hydroxychloroquine was not significantly associated with mortality: pooled relative risk (RR) 0.83 (95% CI 0.65-1.06, n = 17 studies) for all studies and RR = 1.09 (95% CI 0.97-1.24, n = 3 studies) for randomized controlled trials. Hydroxychloroquine with azithromycin was associated with an increased mortality (RR = 1.27; 95% CI 1.04-1.54, n = 7 studies). We found similar results with a Bayesian meta-analysis. CONCLUSION: Hydroxychloroquine alone was not associated with reduced mortality in hospitalized COVID-19 patients but the combination of hydroxychloroquine and azithromycin significantly increased mortality.