National all-cause mortality during the COVID-19 pandemic: a Danish registry-based study.
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ABSTRACT:
SUBMITTER: Mills EHA
PROVIDER: S-EPMC7505217 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundExcess all-cause mortality is helpful to assess the full extent of the health impact, including direct and indirect deaths of coronavirus disease 2019 (COVID-19). The study aimed to estimate overall and regional excess all-cause mortality during the pandemic in Korea.MethodsWe obtained all-cause death data and population statistics from January 2010 to December 2020. The expected mortality in 2020 was estimated using a quasi-Poisson regression model. The model included death year, seasonal variation, cold wave (January), average death counts in the previous month, and population. Excess mortality was defined as the difference between the observed mortality and the expected mortality. Regions were classified into three areas according to the numbers of COVID-19 cases.ResultsThere was no annual excess all-cause mortality in 2020 at the national and regional level compared to the average death for the previous ten years. The observed mortality in 2020 was 582.9 per 100,000 people, and the expected mortality was 582.3 per 100,000 people (95% confidence interval, 568.3-596.7). However, we found monthly and regional variations depending on the waves of the COVID-19 pandemic in Korea. While the mortality in August, October, and November exceeded the expected range, the mortality in September was lower than the expected range. The months in which excess deaths were identified differed by region.ConclusionOur results show that the mortality in 2020 was similar to the historical trend. However, in the era of the COVID-19 pandemic, it would be necessary to regularly investigate COVID-19-related mortality and determine its direct and indirect causes.
| S-EPMC8506417 | biostudies-literature
Project description:Objective/backgroundNo research has yet assessed the impact of the coronavirus disease 2019 (COVID-19) pandemic on restless legs syndrome (RLS). We hypothesized that RLS symptom severity would be increased during the COVID-19 pandemic in a sample of patients with diagnosed RLS.Patients/methodsThe National RLS Opioid Registry is a longitudinal observational study of patients using opioid medications for treatment of RLS. Questionnaires assessing RLS symptom severity, medication dosages, sleep disturbance, depression, and anxiety are administered at baseline and at recurring 6-month surveys. Survey responses from the outset of the pandemic in April/May 2020 were compared to responses completed by other participants in January/February 2020 (between-subjects analysis), as well as responses by the same participants at baseline, approximately six months later in September 2020 through February 2021, and approximately one year later in March through June 2021 (within-subjects analyses).ResultsThese analyses provide evidence for higher RLS symptom severity scores at the outset of the COVID-19 pandemic in the US. Symptom severity scores were still elevated on subsequent questionnaires completed over six months into the pandemic but had returned towards baseline by the spring of 2021. Participants with increases in RLS severity were significantly more likely than others to see increases in sleep disturbance, depression, and anxiety.ConclusionsThis is the first study demonstrating increased RLS symptom severity during the earliest stage of the COVID-19 pandemic. These findings warrant similar investigations in other patient populations and suggest that clinicians should attend to RLS symptoms during times of socioeconomic and/or political uncertainty.
| S-EPMC8783980 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other
Project description:BackgroundIndia has been severely affected by the ongoing COVID-19 pandemic. However, due to shortcomings in disease surveillance, the burden of mortality associated with COVID-19 remains poorly understood. We aimed to assess changes in mortality during the pandemic in Chennai, Tamil Nadu, using data on all-cause mortality within the district.MethodsFor this observational study, we analysed comprehensive death registrations in Chennai, from Jan 1, 2016, to June 30, 2021. We estimated expected mortality without the effects of the COVID-19 pandemic by fitting models to observed mortality time series during the pre-pandemic period, with stratification by age and sex. Additionally, we considered three periods of interest: the first 4 weeks of India's first lockdown (March 24 to April 20, 2020), the 4-month period including the first wave of the pandemic in Chennai (May 1 to Aug 31, 2020), and the 4-month period including the second wave of the pandemic in Chennai (March 1 to June 30, 2021). We computed the difference between observed and expected mortality from March 1, 2020, to June 30, 2021, and compared pandemic-associated mortality across socioeconomically distinct communities (measured with use of 2011 census of India data) with regression analyses.FindingsBetween March 1, 2020, and June 30, 2021, 87 870 deaths were registered in areas of Chennai district represented by the 2011 census, exceeding expected deaths by 25 990 (95% uncertainty interval 25 640-26 360) or 5·18 (5·11-5·25) excess deaths per 1000 people. Stratified by age, excess deaths numbered 21·02 (20·54-21·49) excess deaths per 1000 people for individuals aged 60-69 years, 39·74 (38·73-40·69) for those aged 70-79 years, and 96·90 (93·35-100·16) for those aged 80 years or older. Neighbourhoods with lower socioeconomic status had 0·7% to 2·8% increases in pandemic-associated mortality per 1 SD increase in each measure of community disadvantage, due largely to a disproportionate increase in mortality within these neighbourhoods during the second wave. Conversely, differences in excess mortality across communities were not clearly associated with socioeconomic status measures during the first wave. For each increase by 1 SD in measures of community disadvantage, neighbourhoods had 3·6% to 8·6% lower pandemic-associated mortality during the first 4 weeks of India's country-wide lockdown, before widespread SARS-CoV-2 circulation was underway in Chennai. The greatest reductions in mortality during this early lockdown period were observed among men aged 20-29 years, with 58% (54-62) fewer deaths than expected from pre-pandemic trends.InterpretationMortality in Chennai increased substantially but heterogeneously during the COVID-19 pandemic, with the greatest burden concentrated in disadvantaged communities. Reported COVID-19 deaths greatly underestimated pandemic-associated mortality.FundingNational Institute of General Medical Sciences, Bill & Melinda Gates Foundation, National Science Foundation.TranslationFor the Hindi translation of the abstract see Supplementary Materials section.
| S-EPMC8694707 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic resulted in unprecedented increases in mortality in the U.S. and worldwide. To better understand the impact of the COVID-19 pandemic on mortality in the state of Minnesota, U.S.A., we characterize the changes in the causes of death during 2020 (COVID-19 period), compared to 2018-2019 (baseline period), assessing for differences across ages, races, ethnicities, sexes, and geographic characteristics.MethodsLongitudinal population-based study using Minnesota death certificate data, 2018-2020. Using Poisson regression models adjusted for age and sex, we calculated all-cause and cause-specific (by underlying causes of death) mortality rates per 100,000 Minnesotans, the demographics of the deceased, and years of life lost (YLL) using the Chiang's life table method in 2020 relative to 2018-2019.ResultsWe identified 89,910 deaths in 2018-2019 and 52,030 deaths in 2020. The mean daily mortality rate increased from 123.1 (SD 11.7) in 2018-2019 to 144.2 (SD 22.1) in 2020. COVID-19 comprised 9.9% of deaths in 2020. Other categories of causes of death with significant increases in 2020 compared to 2018-2019 included assault by firearms (RR 1.68, 95% CI 1.34-2.11), accidental poisonings (RR 1.49, 95% CI 1.37-1.61), malnutrition (RR 1.48, 95% CI 1.17-1.87), alcoholic liver disease (RR, 95% CI 1.14-1.40), and cirrhosis and other chronic liver diseases (RR 1.28, 95% CI 1.09-1.50). Mortality rates due to COVID-19 and non-COVID-19 causes were higher among racial and ethnic minority groups, older adults, and non-rural residents.ConclusionsThe COVID-19 pandemic was associated with a 17% increase in the death rate in Minnesota relative to 2018-2019, driven by both COVID-19 and non-COVID-19 causes. As the COVID-19 pandemic enters its third year, it is imperative to examine and address the factors contributing to excess mortality in the short-term and monitor for additional morbidity and mortality in the years to come.
| S-EPMC9727873 | biostudies-literature
Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.
| 2346925 | ecrin-mdr-crc
Project description:During the COVID-19 pandemic, excess mortality has been reported worldwide, but its magnitude has varied depending on methodological differences that hinder between-study comparability. Our aim was to estimate variability attributable to different methods, focusing on specific causes of death with different pre-pandemic trends. Monthly mortality figures observed in 2020 in the Veneto Region (Italy) were compared with those forecasted using: (1) 2018-2019 monthly average number of deaths; (2) 2015-2019 monthly average age-standardized mortality rates; (3) Seasonal Autoregressive Integrated Moving Average (SARIMA) models; (4) Generalized Estimating Equations (GEE) models. We analyzed deaths due to all-causes, circulatory diseases, cancer, and neurologic/mental disorders. Excess all-cause mortality estimates in 2020 across the four approaches were: +17.2% (2018-2019 average number of deaths), +9.5% (five-year average age-standardized rates), +15.2% (SARIMA), and +15.7% (GEE). For circulatory diseases (strong pre-pandemic decreasing trend), estimates were +7.1%, -4.4%, +8.4%, and +7.2%, respectively. Cancer mortality showed no relevant variations (ranging from -1.6% to -0.1%), except for the simple comparison of age-standardized mortality rates (-5.5%). The neurologic/mental disorders (with a pre-pandemic growing trend) estimated excess corresponded to +4.0%/+5.1% based on the first two approaches, while no major change could be detected based on the SARIMA and GEE models (-1.3%/+0.3%). The magnitude of excess mortality varied largely based on the methods applied to forecast mortality figures. The comparison with average age-standardized mortality rates in the previous five years diverged from the other approaches due to the lack of control over pre-existing trends. Differences across other methods were more limited, with GEE models probably representing the most versatile option.
| S-EPMC10252246 | biostudies-literature
Project description:In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four months of 2016-2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, the city of Bergamo (Lombardia) showed the largest percent excess, 88.9% (81.9% to 95.2%), at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.
| S-EPMC7546500 | biostudies-literature
Project description:BackgroundMortality statistics are fundamental to understand the magnitude of the COVID-19 pandemic. Due to limitation of real-time data availability, researchers had used mathematical models to estimate excess mortality globally during COVID-19 pandemic. As they demonstrated variations in scope, assumptions, estimations, and magnitude of the pandemic, and hence raised a controversy all over the world. This paper aims to review the mathematical models and their estimates of mortality due to COVID-19 in the Indian context.MethodsThe PRISMA and SWiM guidelines were followed to the best possible extent. A two-step search strategy was used to identify studies that estimated excess deaths from January 2020 to December 2021 on Medline, Google Scholar, MedRxiv and BioRxiv available until 0100 h, 16 May 2022 (IST). We selected 13 studies based on a predefined criteria and extracted data on a standardised, pre-piloted form by two investigators, independently. Any discordance was resolved through consensus with a senior investigator. Estimated excess mortality was analysed using statistical software and depicted using appropriate graphs.ResultsSignificant variations in scope, population, data sources, time period, and modelling strategies existed across studies along with a high risk of bias. Most of the models were based on Poisson regression. Predicted excess mortality by various models ranged from 1.1 to 9.5 million.ConclusionThe review presents a summary of all the estimates of excess deaths and is important to understand the different strategies used for estimation, and it highlights the importance of data availability, assumptions, and estimates.
| S-EPMC10105293 | biostudies-literature
Project description:AIM:The Danish National Multiple Myeloma Registry (DMMR) is a population-based clinical quality database established in January 2005. The primary aim of the database is to ensure that diagnosis and treatment of plasma cell dyscrasia are of uniform quality throughout the country. Another aim is to support research. Patients are registered with their unique Danish personal identification number, and the combined use of DMMR, other Danish National registries, and the Danish National Cancer Biobank offers a unique platform for population-based translational research. STUDY POPULATION:All newly diagnosed patients with multiple myeloma (MM), smoldering MM, solitary plasmacytomas, and plasma cell leukemia in Denmark are registered annually; ~350 patients. Amyloid light-chain amyloidosis, POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes syndrome), monoclonal gammopathy of undetermined significance and monoclonal gammopathy of undetermined significance with polyneuropathy have been registered since 2014. MAIN VARIABLES:The main registered variables at diagnosis are patient demographics, baseline disease characteristics, myeloma-defining events, clinical complications, prognostics, first- and second-line treatments, treatment responses, progression free, and overall survival. DESCRIPTIVE DATA:Up to June 2015, 2,907 newly diagnosed patients with MM, 485 patients with smoldering MM, 64 patients with plasma cell leukemia, and 191 patients with solitary plasmacytomas were registered. Registration completeness of new patients is ~100%. A data validation study performed in 2013-2014 by the Danish Myeloma Study Group showed >95% data correctness. CONCLUSION:The DMMR is a population-based data validated database eligible for clinical, epidemiological, and translational research.
| S-EPMC5094522 | biostudies-literature