A linear mixed model to estimate COVID-19-induced excess mortality.
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ABSTRACT:
SUBMITTER: Verbeeck J
PROVIDER: S-EPMC8652760 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy. The first case in Italy was found in Lombardy in early 2020, and the first wave was mainly centered in Lombardy. The other three waves, in Autumn 2020, March 2021 and Summer 2021 are also characterized by a high number of cases in absolute terms. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15-64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes.
| S-EPMC8743436 | biostudies-literature
Project description:ObjectivesThe first wave of the SARS-CoV-2 pandemic in Germany lasted from week 10 to 23 in 2020. The aim is to provide estimates of excess mortality in Germany during this time.MethodsWe analyzed age-specific numbers of deaths per week from 2016 to week 26 in 2020. We used weekly mean numbers of deaths of 2016-2019 to estimate expected weekly numbers for 2020. We estimated standardized mortality ratios (SMR) and 95% confidence intervals.ResultsDuring the first wave observed numbers of deaths were higher than expected for age groups 60-69, 80-89, and 90+. The age group 70-79 years did not show excess mortality. The net excess number of deaths for weeks 10-23 was +8,071. The overall SMR was 1•03 (95%CI 1•03-1•04). The largest increase occurred among people aged 80-89 and 90+ (SMR=1•08 and SMR=1•09). A sensitivity analysis that accounts for demographic changes revealed an overall SMR of 0•98 (95%CI 0•98-0•99) and a deficit of 4,926 deaths for week 10-23, 2020.ConclusionsThe excess mortality existed for two months. The favorable course of the first wave may be explained by a younger age at infection at the beginning of the pandemic, lower contact rates, and a more efficient pandemic management.
| S-EPMC7501062 | biostudies-literature
Project description:Although childhood cancer survival rates have dramatically increased, survivors face elevated risk for life-threatening late effects, including secondary cancer.To estimate the cumulative effect of disease- and treatment-related mortality risks on survivor life expectancy.State-transition model to simulate the lifetime clinical course of childhood cancer survivors.Childhood Cancer Survivor Study.Five-year survivors of childhood cancer.Probabilities of risk for death from the original cancer diagnosis, excess mortality from subsequent cancer and cardiac, pulmonary, external, and other complications, and background mortality (age-specific mortality rates for the general population) were estimated over the lifetime of survivors of childhood cancer.For a cohort of 5-year survivors aged 15 years who received a diagnosis of cancer at age 10 years, the average lifetime probability was 0.10 for late-recurrence mortality; 0.15 for treatment-related subsequent cancer and death from cardiac, pulmonary, and external causes; and 0.05 for death from other excess risks. Life expectancy for the cohort of persons aged 15 years was 50.6 years, a loss of 10.4 years (17.1%) compared with the general population. Reduction in life expectancy varied by diagnosis, ranging from 4.0 years (6.0%) for kidney tumor survivors to more than 17.8 years (> or =28.0%) for brain and bone tumor survivors, and was sensitive to late-recurrence mortality risk and duration of excess mortality risk.Estimates are based on data for survivors who received treatment 20 to 40 years ago; patients who received treatment more recently may have more favorable outcomes.Childhood cancer survivors face considerable mortality during adulthood, with excess risks reducing life expectancy by as much as 28%. Monitoring the health of current survivors and carefully evaluating therapies with known late toxicities in patients with newly diagnosed cancer are needed.
| S-EPMC3086591 | biostudies-literature
Project description:AimDecreased cancer incidence and reported changes to clinical management indicate that the COVID-19 pandemic has delayed cancer diagnosis and treatment. This study aimed to develop and apply a flexible model to estimate the impact of delayed diagnosis and treatment on survival outcomes and healthcare costs based on a shift in the disease stage at treatment initiation.MethodsA model was developed and made publicly available to estimate population-level health economic outcomes by extrapolating and weighing stage-specific outcomes by the distribution of stages at treatment initiation. It was applied to estimate the impact of 3- and 6-month delays based on Australian data for stage I breast cancer, colorectal cancer, and lung cancer patients, and for T1 melanoma. Two approaches were explored to estimate stage shifts following a delay: (a) based on the relation between time to treatment initiation and overall survival (breast, colorectal, and lung cancer), and (b) based on the tumor growth rate (melanoma).ResultsUsing a conservative once-off 3-month delay and considering only shifts from stage I/T1 to stage II/T2, 88 excess deaths and $12 million excess healthcare costs were predicted in Australia over 5 years for all patients diagnosed in 2020. For a 6-month delay, excess mortality and healthcare costs were 349 deaths and $46 million over 5 years.ConclusionsThe health and economic impacts of delays in treatment initiation cause an imminent policy concern. More accurate individual patient data on shifts in stage of disease during and after the COVID-19 pandemic are critical for further analyses.
| S-EPMC8014813 | biostudies-literature
Project description:BackgroundCauses of death other than COVID-19 seem to contribute significantly to the excess mortality observed during the 2020-2022 pandemic. In this study, we explore changes in non-COVID-19 causes of death in Norway during the COVID-19 pandemic from March 2020 to December 2022.MethodsWe performed a population-based cross-sectional study on data from the Norwegian Cause of Death Registry. All recorded deaths from 1st January 2010 to 31st December 2022 were included. The main outcome measures were the number of deaths and age-standardised death rate (ASMR) per 100000 population from the major cause of death groups in 2020, 2021 and 2022. The predicted number of deaths and ASMRs were forecasted with a 95% prediction interval constructed from a general linear regression model based on the corresponding number of deaths and rates from the preceding ten prepandemic years (2010-2019). We also examined whether there were deviations from expected seasonality in the pandemic period based on prepandemic monthly data from 2010-2019. The cumulative number of deaths and ASMR were estimated based on monthly mortality data.ResultsThere was significant excess mortality (number of deaths) in 2021 and 2022 for all causes (3.7% and 14.5%), for cardiovascular diseases (14.3% and 22.0%), and for malignant tumours in 2022 (3.5%). In terms of ASMR, there was excess mortality in 2021 and 2022 for all causes (2.9% and 13.7%), and for cardiovascular diseases (16.0% and 25,8%). ASMR was higher than predicted in 2022 for malignant tumours (2.3%). There were fewer deaths than predicted from respiratory diseases (except COVID-19) in 2020 and 2021, and from dementia in 2021 and 2022. From March 2020 to December 2022, there were cumulatively 3754 (ASMR 83.8) more non-COVID-19 deaths than predicted, of which 3453 (ASMR: 79.6) were excess deaths from cardiovascular disease, 509 (ASMR 4.0) from malignant tumours. Mortality was lower than predicted for respiratory diseases (-1889 (ASMR: -44.3)), and dementia (-530 (ASMR -18.5)).ConclusionsThere was considerable excess non-COVID-19 mortality in Norway from March 2020 until December 2022, mainly due to excess cardiovascular deaths. For respiratory diseases and dementia, mortality was lower than predicted.
| S-EPMC10801945 | biostudies-literature
Project description:The coronavirus disease 2019 (COVID-19) pandemic has produced vastly disproportionate deaths for communities of color in the United States. Minnesota seemingly stands out as an exception to this national pattern, with white Minnesotans accounting for 80 percent of the population and 82 percent of COVID-19 deaths. The authors examine confirmed COVID-19 mortality alongside deaths indirectly attributable to the pandemic-"excess mortality"-in Minnesota. This analysis reveals profound racial disparities: age-adjusted excess mortality rates for whites are exceeded by a factor of 2.8 to 5.3 for all other racial groups, with the highest rates among Black, Latino, and Native Minnesotans. The seemingly small disparities in COVID-19 deaths in Minnesota reflect the interaction of three factors: the natural history of the disease, whose early toll was heavily concentrated in nursing homes; an exceptionally divergent age distribution in the state; and a greatly different proportion of excess mortality captured in confirmed COVID-19 rates for white Minnesotans compared with most other groups.
| S-EPMC7780067 | biostudies-literature
Project description:Mortality due to massive events like the COVID-19 pandemic is underestimated because of several reasons, among which the impossibility to track all positive cases and the inadequacy of coding systems are presumably the most relevant. Therefore, the most affordable method to estimate COVID-19-related mortality is excess mortality (EM). Very often, though, EM is calculated on large spatial units that may entail different EM patterns and without stratifying deaths by age or sex, while, especially in the case of epidemics, it is important to identify the areas that suffered a higher death toll or that were spared. We developed the Stata COVID19_EM.ado procedure that estimates EM within municipalities in six subgroups defined by sex and age class using official data provided by ISTAT (Italian National Statistics Bureau) on deaths occurred from 2015 to 2020. Using simple linear regression models, we estimated the mortality trend in each age-and-sex subgroup and obtained the expected deaths of 2020 by extrapolating the linear trend. The results are then displayed using choropleth maps. Subsequently, local autocorrelation maps, which allow to appreciate the presence of local clusters of high or low EM, may be obtained using an R procedure that we developed.•We focused on estimating excess mortality in small-scale spatial units (municipalities) and in population strata defined by age and sex.•This method gives a deeper insight on excess mortality than summary figures at regional or national level, enabling to identify the local areas that suffered the most and the high-risk population subgroups within them.•This type of analysis could be replicated on different time frames, which might correspond to successive epidemic waves, as well as to periods in which containment measures were enforced and for different age classes; moreover, it could be applied in every instance of mortality crisis within a region or a country.
| S-EPMC8105045 | biostudies-literature
Project description:Introduction Understanding educational patterns in excess mortality during the COVID-19 pandemic may help identify strategies to reduce disparities. It is unclear whether educational inequalities in COVID-19 mortality have persisted throughout the pandemic, spanned the full range of educational attainment, or varied by other demographic indicators of COVID-19 risk, such as age or occupation. Methods This study analyzed individual-level California Department of Public Health data on deaths occurring between January 2016 and February 2021 among individuals aged ≥25 years (1,502,202 deaths). Authors applied ARIMA (autoregressive integrated moving average) models to subgroups defined by highest level of education and other demographics (age, sex, race/ethnicity, U.S. nativity, occupational sector, and urbanicity). Authors estimated excess deaths (the number of observed deaths minus the number of deaths expected to occur under the counterfactual of no pandemic) and excess deaths per 100,000 individuals. Results Educational inequalities in excess mortality emerged early in the pandemic and persisted throughout the first year. The greatest per capita excess occurred among people without high school diplomas (533 excess deaths/100,000), followed by those with a high school diploma but no college (466/100,000), some college (156/100,000), Bachelor's degrees (120/100,000) and smallest among people with graduate/professional degrees (101/100,000). Educational inequalities occurred within every subgroup examined. For example, per-capita excess mortality among Latinos with no college experience was 3.7 times higher than among Latinos with at least some college experience. Conclusions Pervasive educational inequalities in excess mortality during the pandemic suggest multiple potential intervention points to reduce disparities.
| S-EPMC9325680 | biostudies-literature
Project description:Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.
| S-EPMC7852240 | biostudies-literature
Project description:Measuring mortality has been a challenge during the COVID-19 pandemic. Here, we compared the results from the Spanish daily mortality surveillance system (MoMo) of excess mortality estimates, using a time series analysis, with those obtained for the confirmed COVID-19 deaths reported to the National Epidemiological Surveillance Network (RENAVE). The excess mortality estimated at the beginning of March 2020 was much greater than what has been observed in previous years, and clustered in a very short time. The cumulated excess mortality increased with age. In the first epidemic wave, the excess mortality estimated by MoMo was 1.5 times higher than the confirmed COVID-19 deaths reported to RENAVE, but both estimates were similar in the following pandemic waves. Estimated excess mortality and confirmed COVID-19 mortality rates were geographically distributed in a very heterogeneous way. The greatest increase in mortality that has taken place in Spain in recent years was detected early by MoMo, coinciding with the spread of the COVID-19 pandemic. MoMo is able to identify risk situations for public health in a timely manner, relying on mortality in general as an indirect indicator of various important public health problems.
| S-EPMC8703729 | biostudies-literature