Dataset Information

Estimates of mortality attributable to COVID-19: a statistical model for monitoring COVID-19 and seasonal influenza, Denmark, spring 2020.

ABSTRACT: BackgroundTimely monitoring of COVID-19 impact on mortality is critical for rapid risk assessment and public health action.AimBuilding upon well-established models to estimate influenza-related mortality, we propose a new statistical Attributable Mortality Model (AttMOMO), which estimates mortality attributable to one or more pathogens simultaneously (e.g. SARS-CoV-2 and seasonal influenza viruses), while adjusting for seasonality and excess temperatures.MethodsData from Nationwide Danish registers from 2014-week(W)W27 to 2020-W22 were used to exemplify utilities of the model, and to estimate COVID-19 and influenza attributable mortality from 2019-W40 to 2020-W20.ResultsSARS-CoV-2 was registered in Denmark from 2020-W09. Mortality attributable to COVID-19 in Denmark increased steeply, and peaked in 2020-W14. As preventive measures and national lockdown were implemented from 2020-W12, the attributable mortality started declining within a few weeks. Mortality attributable to COVID-19 from 2020-W09 to 2020-W20 was estimated to 16.2 (95%?confidence interval?(CI):?12.0?to?20.4) per 100,000 person-years. The 2019/20 influenza season was mild with few deaths attributable to influenza, 3.2 (95%?CI:?1.1?to?5.4) per 100,000 person-years.ConclusionAttMOMO estimates mortality attributable to several pathogens simultaneously, providing a fuller picture of mortality by COVID-19 during the pandemic in the context of other seasonal diseases and mortality patterns. Using Danish data, we show that the model accurately estimates mortality attributable to COVID-19 and influenza, respectively. We propose using standardised indicators for pathogen circulation in the population, to make estimates comparable between countries and applicable for timely monitoring.

SUBMITTER: Nielsen J

PROVIDER: S-EPMC7908066 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimates of mortality attributable to COVID-19: a statistical model for monitoring COVID-19 and seasonal influenza, Denmark, spring 2020.

Nielsen Jens J Rod Naja Hulvej NH Vestergaard Lasse S LS Lange Theis T

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 20210201 8

BackgroundTimely monitoring of COVID-19 impact on mortality is critical for rapid risk assessment and public health action.AimBuilding upon well-established models to estimate influenza-related mortality, we propose a new statistical Attributable Mortality Model (AttMOMO), which estimates mortality attributable to one or more pathogens simultaneously (e.g. SARS-CoV-2 and seasonal influenza viruses), while adjusting for seasonality and excess temperatures.MethodsData from Nationwide Danish regist ...[more]

PMID: 33632375

Similar Datasets

Project description:ImportanceThe COVID-19 pandemic has posed a number of unprecedented challenges to the health care system in France, where hip fractures in the elderly population are a major public health concern.ObjectiveTo explore the association of the first nationwide COVID-19 lockdown in France with the absolute number of hip fractures among patients 50 years or older.Design, setting, and participantsThis retrospective cohort study used data from the French national hospitals database to identify patients 50 years or older who were hospitalized for hip fracture in France from January to July 2019 and January to July 2020.ExposuresThe first nationwide COVID-19 lockdown in France from March 16 to May 10, 2020.Main outcomes and measuresThe main outcome was the number of hospitalizations for hip fracture from January to July 2020 (study period) compared with the number of hospitalizations for hip fracture during the same period in 2019 (control period). Hospitalization rate ratios (HRRs) comparing the study period with the control period were calculated for 3 intervals (before lockdown [January 1 to March 15], during lockdown [March 16 to May 10], and after lockdown [May 11 to July 31]) and were stratified by gender, age and hospital type.ResultsThe study included 46 393 patients hospitalized for hip fracture during January to July 2019 (34 589 [74.4%] women; mean [SD] age, 82.8 [10.5] years) and 44 767 patients hospitalized for hip fracture from January to July 2020 (33 160 [74.1%] women; mean [SD] age, 82.9 [10.5] years). During the lockdown in 2020, 10 429 patients (23.30%) were hospitalized for hip fracture compared with 11 782 patients (25.40%) during the same period in 2019 (HRR, 0.89; 95% CI, 0.86-0.91; P < .001). The lockdown period was associated with a decrease in the number of hip fractures of 11% among women (from 8756 in 2019 to 7788 in 2020) and 13% among men (from 3026 in 2019 to 2641 in 2020). When the absolute number of hip fractures was stratified by age group, the lockdown period was associated with a decrease in the number of hip fractures in all age groups except in patients older than 89 years (HRR, 0.97; 95% CI, 0.92-1.01; P = .17). In the group of patients aged 80 to 89 years, the number of hip fractures decreased from 4925 to 4370 (HRR, 0.89; 95% CI, 0.85-0.92; P < .001). During the lockdown, hospitalizations decreased by 33% (HRR, 0.67; 95% CI, 0.63-0.71; P < .001) in public university hospitals and by 24% (HRR, 0.76; 95% CI, 0.73-0.79; P < .001) in public general hospitals but increased by 46% (HRR, 1.46; 95% CI,1.38-1.54; P < .001) in private for-profit hospitals.Conclusions and relevanceIn this cohort study, hospitalizations for hip fractures in France decreased by 11% during the first nationwide COVID-19 lockdown. Further studies are needed to investigate the long-lasting consequences of the COVID-19 pandemic on the incidence of osteoporotic fractures.

Dataset Information

Estimates of mortality attributable to COVID-19: a statistical model for monitoring COVID-19 and seasonal influenza, Denmark, spring 2020.

Publications

Estimates of mortality attributable to COVID-19: a statistical model for monitoring COVID-19 and seasonal influenza, Denmark, spring 2020.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets