Life expectancy drop in 2020. Estimates based on Human Mortality Database.
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ABSTRACT:
SUBMITTER: Mazzuco S
PROVIDER: S-EPMC8803147 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Accurately counting the human cost of the COVID-19 at both the national and regional level is a policy priority. The Russian Federation currently reports one of the higher COVID-19 mortality rates in the world; but estimates of mortality differ significantly. Using a statistical method accounting for changes in the population age structure, we present the first national and regional estimates of excess mortality for 2021; calculations of excess mortality by age, gender, and urban/rural status for 2020; and mean remaining years of life expectancy lost at the regional level. We estimate that there were 351,158 excess deaths in 2020 and 678,022 in 2021 in the Russian Federation; and, in 2020, around 2.0 years of life expectancy lost. While the Russian Federation exhibits very high levels of excess mortality compared to other countries, there is a wide degree of regional variation: in 2021, excess deaths expressed as a percentage of expected deaths at the regional level range from 27% to 52%. Life expectancy loss is generally greater for males; while excess mortality is greater in urban areas. For Russia as whole, an average person who died due to the pandemic in 2020 would have otherwise lived for a further 14 more years (and as high as 18 years in some regions), disproving the widely held view that excess mortality during the pandemic period was concentrated among those with few years of life remaining-especially for females. At a regional level, less densely populated, more remote regions, rural regions appear to have fared better regarding excess mortality and life expectancy loss-however, a part of this differential could be owing to measurement issues. The calculations demonstrate more clearly the true degree of the human cost of the pandemic in the Russian Federation.
| S-EPMC9629588 | biostudies-literature
Project description:Mortality information of populations is aggregated in life tables that serve as a basis for calculation of life expectancy and various life disparity measures. Conventional life-table methods address right-censoring inadequately by assuming a constant hazard in the last open-ended age group. As a result, life expectancy can be substantially distorted, especially in the case when the last age group in a life table contains a large proportion of the population. Previous research suggests addressing censoring in a gamma-Gompertz-Makeham model setting as this framework incorporates all major features of adult mortality. In this article, we quantify the difference between gamma-Gompertz-Makeham life expectancy values and those published in the largest publicly available high-quality life-table databases for human populations, drawing attention to populations for which life expectancy values should be reconsidered. We also advocate the use of gamma-Gompertz-Makeham life expectancy for three reasons. First, model-based life-expectancy calculation successfully handles the problem of data quality or availability, resulting in severe censoring due to the unification of a substantial number of deaths in the last open-end age group. Second, model-based life expectancies are preferable in the case of data scarcity, i.e. when data contain numerous age groups with zero death counts: here, we provide an example of hunter-gatherer populations. Third, gamma-Gompertz-Makeham-based life expectancy values are almost identical to the ones provided by the major high-quality human mortality databases that use more complicated procedures. Applying a gamma-Gompertz-Makeham model to adult mortality data can be used to revise life-expectancy trends for historical populations that usually serve as input for mortality forecasts.
| S-EPMC5986123 | biostudies-literature
Project description:Background:The Lee-Carter method and its later variants are widely accepted extrapolative methods for forecasting mortality and life expectancy in industrial countries due to their simplicity and availability of high quality long time series data. Objective:We compared and contrasted mortality forecasting models for higher mortality regimes that lack long time series data of good quality, which is common in several Central and Eastern European (CEE) countries. Data and methods:We utilized seven different variants of the Lee-Carter method and coherent mortality forecasts of various CEE countries, and the Bayesian Hierarchical Model used by the United Nations to produce probabilistic forecasts. The data of nine CEE countries with comparatively higher mortality have been considered. Results:The performance of the forecasting models for the nine CEE countries was found to be lower than that observed for low-mortality countries. No model gives uniquely best performance for all the nine CEE countries. Most of the LC variants produced lower forecasts of life expectancies than current life expectancy values for Belarus, Russia, and Ukraine. A coherent mortality forecast could not overcome the limitations of single population forecasting techniques due to increasing mortality differences between these countries over the fitting period (mortality divergence). In the same context, the use of the probabilistic forecasting technique from the Bayesian framework resulted in a better forecast than some of the extrapolative methods but also produced a wider prediction interval for several countries. The more detailed analysis for Hungary indicates that a better fit of certain forecasting methods may occur in the later part of the life span rather than the whole life span. Conclusion:These findings imply the necessity of inventing a new forecasting technique for high-mortality countries.
| S-EPMC6223892 | biostudies-other
Project description:ObjectivesSince the 1980s, life expectancy at birth (e0) in the United States has fallen steadily behind that of other high-income countries, widening the U.S. e0 disadvantage. We estimate how that disadvantage was affected by high mortality rates in 2020, the first full year of the coronavirus disease 2019 pandemic.MethodsWe contrast male and female e0 in the United States and 18 peer countries for years 1980, 1995, 2010, 2019, and 2020. Using Arriaga decomposition, we show how differences in age-specific death rates have contributed to U.S. e0 disadvantages.ResultsIn 2020, U.S. male and female e0 changed by -2.33 (-2.50, -2.15) and -1.69 (-1.85, -1.53) years, respectively, whereas corresponding changes in peer countries averaged -0.67 (-0.82, -0.51) and -0.50 (-0.65, -0.35) years, respectively. This accelerated a longstanding and widening U.S. e0 disadvantage relative to its peers, which increased from 3.49 to 5.15 years in males and from 2.78 to 3.97 years in females between 2019 and 2020. Whereas deaths before age 65 accounted for 55% and 40% of declines in U.S. male and female life expectancy, respectively, they accounted for only 24% and 11% of the respective declines in peer countries.DiscussionU.S. life expectancy declines in 2020 were larger than in peer countries and involved deaths across a broader age range, particularly among young and middle-aged adults. Both the longstanding U.S. e0 disadvantage and acute losses of life in 2020 signal the need for systemic policy changes in the United States.
| S-EPMC9154231 | biostudies-literature
Project description:ObjectiveTo estimate the changes in life expectancy and years of life lost in 2020 associated with the covid-19 pandemic.DesignTime series analysis.Setting37 upper-middle and high income countries or regions with reliable and complete mortality data.ParticipantsAnnual all cause mortality data from the Human Mortality Database for 2005-20, harmonised and disaggregated by age and sex.Main outcome measuresReduction in life expectancy was estimated as the difference between observed and expected life expectancy in 2020 using the Lee-Carter model. Excess years of life lost were estimated as the difference between the observed and expected years of life lost in 2020 using the World Health Organization standard life table.ResultsReduction in life expectancy in men and women was observed in all the countries studied except New Zealand, Taiwan, and Norway, where there was a gain in life expectancy in 2020. No evidence was found of a change in life expectancy in Denmark, Iceland, and South Korea. The highest reduction in life expectancy was observed in Russia (men: -2.33, 95% confidence interval -2.50 to -2.17; women: -2.14, -2.25 to -2.03), the United States (men: -2.27, -2.39 to -2.15; women: -1.61, -1.70 to -1.51), Bulgaria (men: -1.96, -2.11 to -1.81; women: -1.37, -1.74 to -1.01), Lithuania (men: -1.83, -2.07 to -1.59; women: -1.21, -1.36 to -1.05), Chile (men: -1.64, -1.97 to -1.32; women: -0.88, -1.28 to -0.50), and Spain (men: -1.35, -1.53 to -1.18; women: -1.13, -1.37 to -0.90). Years of life lost in 2020 were higher than expected in all countries except Taiwan, New Zealand, Norway, Iceland, Denmark, and South Korea. In the remaining 31 countries, more than 222 million years of life were lost in 2020, which is 28.1 million (95% confidence interval 26.8m to 29.5m) years of life lost more than expected (17.3 million (16.8m to 17.8m) in men and 10.8 million (10.4m to 11.3m) in women). The highest excess years of life lost per 100 000 population were observed in Bulgaria (men: 7260, 95% confidence interval 6820 to 7710; women: 3730, 2740 to 4730), Russia (men: 7020, 6550 to 7480; women: 4760, 4530 to 4990), Lithuania (men: 5430, 4750 to 6070; women: 2640, 2310 to 2980), the US (men: 4350, 4170 to 4530; women: 2430, 2320 to 2550), Poland (men: 3830, 3540 to 4120; women: 1830, 1630 to 2040), and Hungary (men: 2770, 2490 to 3040; women: 1920, 1590 to 2240). The excess years of life lost were relatively low in people younger than 65 years, except in Russia, Bulgaria, Lithuania, and the US where the excess years of life lost was >2000 per 100 000.ConclusionMore than 28 million excess years of life were lost in 2020 in 31 countries, with a higher rate in men than women. Excess years of life lost associated with the covid-19 pandemic in 2020 were more than five times higher than those associated with the seasonal influenza epidemic in 2015.
| S-EPMC8564739 | biostudies-literature
Project description:AimsSince dietary sodium intake has been identified as a risk factor for cardiovascular disease and premature death, a high sodium intake can be expected to curtail life span. We tested this hypothesis by analysing the relationship between sodium intake and life expectancy as well as survival in 181 countries worldwide.Methods and resultsWe correlated age-standardized estimates of country-specific average sodium consumption with healthy life expectancy at birth and at age of 60 years, death due to non-communicable diseases and all-cause mortality for the year of 2010, after adjusting for potential confounders such as gross domestic product per capita and body mass index. We considered global health estimates as provided by World Health Organization. Among the 181 countries included in this analysis, we found a positive correlation between sodium intake and healthy life expectancy at birth (β = 2.6 years/g of daily sodium intake, R2 = 0.66, P < 0.001), as well as healthy life expectancy at age 60 (β = 0.3 years/g of daily sodium intake, R2 = 0.60, P = 0.048) but not for death due to non-communicable diseases (β = 17 events/g of daily sodium intake, R2 = 0.43, P = 0.100). Conversely, all-cause mortality correlated inversely with sodium intake (β = -131 events/g of daily sodium intake, R2 = 0.60, P < 0.001). In a sensitivity analysis restricted to 46 countries in the highest income class, sodium intake continued to correlate positively with healthy life expectancy at birth (β = 3.4 years/g of daily sodium intake, R2 = 0.53, P < 0.001) and inversely with all-cause mortality (β = -168 events/g of daily sodium intake, R2 = 0.50, P < 0.001).ConclusionOur observation of sodium intake correlating positively with life expectancy and inversely with all-cause mortality worldwide and in high-income countries argues against dietary sodium intake being a culprit of curtailing life span or a risk factor for premature death. These data are observational and should not be used as a base for nutritional interventions.
| S-EPMC8169157 | biostudies-literature
Project description:IntroductionIn the 2016 U.S. Presidential election, voters in communities with recent stagnation or decline in life expectancy were more likely to vote for the Republican candidate than in prior Presidential elections. We aimed to assess the association between change in life expectancy and voting patterns in the 2020 Presidential election.MethodsWith data on county-level life expectancy from the Institute for Health Metrics and Evaluation and voting data from a GitHub repository of results scraped from news outlets, we used weighted multivariable linear regression to estimate the association between the change in life expectancy from 1980 to 2014 and the proportion of votes for the Republican candidate and change in the proportion of votes cast for the Republican candidate in the 2020 Presidential election.ResultsAmong 3110 U.S counties and Washington, D.C., change in life expectancy at the county level was negatively associated with Republican share of the vote in the 2020 Presidential election (parameter estimate -7.2, 95% confidence interval, -7.8 to -6.6). With the inclusion of state, sociodemographic, and economic variables in the model, the association was attenuated (parameter estimate -0.8; 95% CI, -1.5 to -0.2). County-level change in life expectancy was positively associated with change in Republican vote share 0.29 percentage points (95% CI, 0.23 to 0.36). The association was attenuated when state, sociodemographic, and economic variables were added (parameter estimate 0.24; 95% CI, 0.15 to 0.33).ConclusionCounties with a less positive trajectory in life expectancy were more likely to vote for the Republican candidate in the 2020 U.S. Presidential election, but the Republican candidate's share improved in some counties that experienced marked gains in life expectancy. Associations were moderated by demographic, social and economic factors.
| S-EPMC8209240 | biostudies-literature
Project description:BackgroundConducting a distinct comparison between the life expectancy (LE) and healthy life expectancy (HALE) of people with disabilities (PWDs) and the general population is necessary due to the various environmental and health conditions they encounter. Therefore, this study aimed to develop the life table for PWDs and calculate those of LE and HALE based on sex, severity, and disability types among the registered Korean PWDs.MethodsWe used aggregated data of registered PWDs from the Korea National Rehabilitation Center database between 2014 and 2018. Overall, 345,595 deaths were included among 12,627,428 registered PWDs. First, we calculated the LE for total PWDs and non-disabled people using a standard life table, extending the old age mortality among nine models. Subsequently, we calculated the LE for each type of disability using the relationship between the mortality of total PWDs and those of each type of disability. Finally, HALE was calculated using the Sullivan method for three types as follows: disability-free and perceived health (PH) using the National Survey, and hospitalized for ≥ 7 days using the Korea National Health Insurance System (NHIS) database.ResultsThe calculated LE/HALE-NHIS (years) at registration in males and females were 81.32/73.32 and 87.38/75.58, 68.54/58.98 and 71.43/59.24, 73.87/65.43 and 78.25/67.51, and 61.53/50.48 and 62.41/49.72 years among non-disabled, total PWDs, mild disabled, and severe disabled, respectively. LE/HALE-NHIS was lowest and highest in respiratory dysfunction and hearing disabilities, respectively.ConclusionsMales with disabilities had shorter LE and HALE at registration than females, except for those with severe disabilities, and there were variabilities in the LE based on the disability types.
| S-EPMC10485940 | biostudies-literature
Project description:The concept of a so-called urban advantage in health ignores the possibility of heterogeneity in health outcomes across cities. Using a harmonized dataset from the SALURBAL project, we describe variability and predictors of life expectancy and proportionate mortality in 363 cities across nine Latin American countries. Life expectancy differed substantially across cities within the same country. Cause-specific mortality also varied across cities, with some causes of death (unintentional and violent injuries and deaths) showing large variation within countries, whereas other causes of death (communicable, maternal, neonatal and nutritional, cancer, cardiovascular disease and other noncommunicable diseases) varied substantially between countries. In multivariable mixed models, higher levels of education, water access and sanitation and less overcrowding were associated with longer life expectancy, a relatively lower proportion of communicable, maternal, neonatal and nutritional deaths and a higher proportion of deaths from cancer, cardiovascular disease and other noncommunicable diseases. These results highlight considerable heterogeneity in life expectancy and causes of death across cities of Latin America, revealing modifiable factors that could be amenable to urban policies aimed toward improving urban health in Latin America and more generally in other urban environments.
| S-EPMC7960508 | biostudies-literature
Project description:PurposeAccurate life expectancy estimates are required to inform prostate cancer treatment decisions. However, few models are specific to the population served or easily implemented in a clinical setting. We sought to create life expectancy estimates specific to Veterans diagnosed with prostate cancer.Materials and methodsUsing national Veterans Health Administration electronic health records, we identified Veterans diagnosed with prostate cancer between 2000 and 2015. We abstracted demographics, comorbidities, oncologic staging, and treatment information. We fit Cox Proportional Hazards models to determine the impact of age, comorbidity, cancer risk, and race on survival. We stratified life expectancy estimates by age, comorbidity and cancer stage.ResultsOur analytic cohort included 145,678 patients. Survival modeling demonstrated the importance of age and comorbidity across all cancer risk categories. Life expectancy estimates generated from age and comorbidity data were predictive of overall survival (C-index 0.676, 95% CI 0.674-0.679) and visualized using Kaplan-Meier plots and heatmaps stratified by age and comorbidity. Separate life expectancy estimates were generated for patients with localized or advanced disease. These life expectancy estimates calibrate well across prostate cancer risk categories.ConclusionsLife expectancy estimates are essential to providing patient-centered prostate cancer care. We developed accessible life expectancy estimation tools for Veterans diagnosed with prostate cancer that can be used in routine clinical practice to inform medical-decision making.
| S-EPMC7438257 | biostudies-literature