Data Resource Profile: COVerAGE-DB: a global demographic database of COVID-19 cases and deaths

Riffe, Tim; Acosta, Enrique; Aburto, Diego Manuel; Alburez-Gutierrez, Anna; Altová, Ainhoa; Alustiza, Ugofilippo; Basellini, Simona; Bignami, Didier; Bretón, E Fernández; Choi, Jorge; Cimentada, Gonzalo; Armas, Emanuele De; Fava, Alicia Del; Delgado, Viorela; Diaconu, J; Donzowa, Christian; Dudel, Antonia; Fröhlich, Alain; Gagnon, Mariana; Garcia-Crisóstomo, Victor; Garcia-Guerrero, Armando M; González-Díaz, Irwin; Hecker, Dagnon; Koba, Marina Eric; Kolobova, M. I.; Kühn, Mélanie; Lépori, Chia; Liu, Andrea J.; Lozer, Mădălina; Manea, Lilian; Marey, Muntasir; Masum, Ryohei; Mogi, Céline; Monicolle, Saskia; Morwinsky, Ronald; Musizvingoza, Mikko; Myrskylä, Marília; Nepomuceno, Michelle R; Nickel, Natalie; Nitsche, Anna; Oksuzyan, Samuel; Oladele, Emmanuel; Olamijuwon, Oluwafunke; Omodara, Soumaila; M, Ouedraogo; Paredes, Marius; M, Pascariu; Piriz, Raquel; Pollero, Larbi; Qanni, Federico; Rehermann, Filipe; Ribeiro, Silvia Carreira; Rizzi, F.V.; Rowe, Adil; Sarhan, Isaac R; Sasson, Erez; Shomron, Jiaxin; Shi, Rafael; Silva-Ramirez, Cosmo; Strozza, Catalina; Torres, Sergi; Trias-Llimos, Fumiya; Uchikoshi, Alyson; Raalte, Paola van; Vazquez-Castillo, Estevão; Vilela, Muhammad A; Waqar, Iván Ali; Williams, Virginia; Zarulli, None

doi:10.1093/ije/dyab027

Cited by 76 publications

(59 citation statements)

References 22 publications

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“…However, since vaccination reduces the risk of death from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and most countries have followed an age-based vaccine rollout, the IFR for COVID-19 has varied asynchronously between age groups over time, and this needs to be accounted for in the calculation. We therefore used age-stratified death [3][4][5] and vaccination [6,7] data to infer age-stratified infection time series, accounting for the impact of vaccination on the IFR for each age group over time.…”

Section: Determinants Of Remaining Burdenmentioning

confidence: 99%

“…Source: Vaccination data for European Union countries from the European Centre for Disease Prevention and Control [6] and England from the United Kingdom government COVID-19 dashboard [7]. Sources: Age-stratified death data from the COVerAGE database [3,4] and INED database [5] (data shown in Supplementary Figure S2).…”

Section: Historical Burden and Naturally Acquired Immunitymentioning

confidence: 99%

“…Sources: Age-stratified death data from the COVerAGE database [ 3 , 4 ] and INED database [ 5 ] (data shown in Supplementary Figure S2 ). Seroprevalence data from the SeroTracker database [ 23 ].…”

Section: Historical Burden and Naturally Acquired Immunitymentioning

confidence: 99%

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Unexposed populations and potential COVID-19 hospitalisations and deaths in European countries as per data up to 21 November 2021

et al. 2022

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We estimate the potential remaining COVID-19 hospitalisation and death burdens in 19 European countries by estimating the proportion of each country’s population that has acquired immunity to severe disease through infection or vaccination. Our results suggest many European countries could still face high burdens of hospitalisations and deaths, particularly those with lower vaccination coverage, less historical transmission and/or older populations. Continued non-pharmaceutical interventions and efforts to achieve high vaccination coverage are required in these countries to limit severe COVID-19 outcomes.

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Section: Determinants Of Remaining Burdenmentioning

confidence: 99%

Section: Historical Burden and Naturally Acquired Immunitymentioning

confidence: 99%

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Unexposed populations and potential COVID-19 hospitalisations and deaths in European countries as per data up to 21 November 2021

et al. 2022

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“…Until vital registration data are published in December 2021 by the National Institute of Statistics and Geography (INEGI), publicly available data from the Mexican Health Secretariat on deaths attributed to COVID-19 are the best-suited data for studying mortality. We are aware that no single data source is fully accurate in terms of incidence or fatalities (Riffe et al, 2021).…”

Section: Datamentioning

confidence: 99%

Are homicides and robberies associated with mortality due to COVID-19? Lessons from Urban Mexico

Masferrer¹,

Chávez²

2022

Populationyearbook

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Studies on the symbiosis of crime and COVID-19 have analyzed governmentmandated lockdown effects. However, it is unknown to what extent previous crime rates determined a larger and more mortal spread of the pandemic. We study how homicides and robberies in the pre-pandemic year of 2019 are associated with 2020 mortality rates due to COVID-19 in urban municipalities in Mexico. Considering sex differentials in health, exposure to the virus and experiences of violence, we study whether gender differences in mortality exist in 2020. Using publicly available data on deaths due to COVID-19 provided by the Mexican Secretariat of Health, along with a series of indicators to characterize local pre-pandemic conditions of urban municipalities, we estimate a series of ordinary least squares (OLS) regression models on age-standardized crude death rates (ASCDR) by sex. Findings show that homicides—a proxy for criminal violence that might encourage people to stay home—show significant negative associations with mortality rates. Comparatively, robberies—a proxy of local violence and safety—were positively associated with mortality rates for both sexes. Sex differences in the determinants of ASCDR are discussed.

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“…The investigated period was chosen for the analysis also because the mortality level in the Czech Republic did not fluctuate sharply. For example, it would not be possible to perform the analysis in the current situation where the mortality level is affected by COVID-19 disease and can be problematic in the future as well [39,40].…”

Section: Introductionmentioning

confidence: 99%

Circulatory and Nervous Diseases Mortality Patterns—Comparison of Geomagnetic Storms and Quiet Periods

Podolská

2021

Atmosphere

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The aim of this paper is to statistically examine whether there are different patterns in daily numbers of deaths during the quiet periods of solar activity, in contrast to the periods of the strong solar storms. We considered three periods of solar storms (storm of 14 July 2000 Bastille Day Event, storm of 28 October 2003 Halloween Solar Storms, and storm of 17 March 2015 St. Patrick’s Day event) and three periods of continuous very low solar activity (13 September–24 October 1996, 21 July–20 August 2008, and 31 July–31 August 2009) during the Solar Cycles No. 23 and No. 24. In particular, we focus on diseases of the nervous system (group VI from ICD-10) and diseases of the circulatory system (group IX from ICD-10) separately for both sexes and two age groups (under 39 and 40+). We demonstrate that in the resulting graphical models there was a connection between the daily number of deaths and all indices of solar and geomagnetic activity in periods of low solar activity in contrast to periods of strong solar storms in some monitored groups according to age, sex, and group of diagnosis.

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Data Resource Profile: COVerAGE-DB: a global demographic database of COVID-19 cases and deaths

Cited by 76 publications

References 22 publications

Unexposed populations and potential COVID-19 hospitalisations and deaths in European countries as per data up to 21 November 2021

Unexposed populations and potential COVID-19 hospitalisations and deaths in European countries as per data up to 21 November 2021

Are homicides and robberies associated with mortality due to COVID-19? Lessons from Urban Mexico

Circulatory and Nervous Diseases Mortality Patterns—Comparison of Geomagnetic Storms and Quiet Periods

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