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1.
Annu Rev Public Health ; 42: 381-403, 2021 04 01.
Article in English | MEDLINE | ID: covidwho-1554162

ABSTRACT

In recent years, life expectancy in the United States has stagnated, followed by three consecutive years of decline. The decline is small in absolute terms but is unprecedented and has generated considerable research interest and theorizing about potential causes. Recent trends show that the decline has affected nearly all race/ethnic and gender groups, and the proximate causes of the decline are increases in opioid overdose deaths, suicide, homicide, and Alzheimer's disease. A slowdown in the long-term decline in mortality from cardiovascular diseases has also prevented life expectancy from improving further. Although a popular explanation for the decline is the cumulative decline in living standards across generations, recent trends suggest that distinct mechanisms for specific causes of death are more plausible explanations. Interventions to stem the increase in overdose deaths, reduce access to mechanisms that contribute to violent deaths, and decrease cardiovascular risk over the life course are urgently needed to improve mortality in the United States.


Subject(s)
Life Expectancy/trends , Humans , United States/epidemiology
3.
BMJ ; 375: e066768, 2021 11 03.
Article in English | MEDLINE | ID: covidwho-1501690

ABSTRACT

OBJECTIVE: To estimate the changes in life expectancy and years of life lost in 2020 associated with the covid-19 pandemic. DESIGN: Time series analysis. SETTING: 37 upper-middle and high income countries or regions with reliable and complete mortality data. PARTICIPANTS: Annual all cause mortality data from the Human Mortality Database for 2005-20, harmonised and disaggregated by age and sex. MAIN OUTCOME MEASURES: Reduction 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. RESULTS: Reduction 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. CONCLUSION: More 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.


Subject(s)
COVID-19/mortality , Developed Countries/statistics & numerical data , Global Health/trends , Life Expectancy/trends , Mortality, Premature/trends , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , Databases, Factual , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Young Adult
4.
Lancet Public Health ; 6(11): e805-e816, 2021 11.
Article in English | MEDLINE | ID: covidwho-1467001

ABSTRACT

BACKGROUND: High-resolution data for how mortality and longevity have changed in England, UK are scarce. We aimed to estimate trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6791 middle-layer super output areas (MSOAs) in England. METHODS: We performed a high-resolution spatiotemporal analysis of civil registration data from the UK Small Area Health Statistics Unit research database using de-identified data for all deaths in England from 2002 to 2019, with information on age, sex, and MSOA of residence, and population counts by age, sex, and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs, and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA. FINDINGS: In 2002-06 and 2006-10, all but a few (0-1%) MSOAs had a life expectancy increase for female and male sexes. In 2010-14, female life expectancy decreased in 351 (5·2%) of 6791 MSOAs. By 2014-19, the number of MSOAs with declining life expectancy was 1270 (18·7%) for women and 784 (11·5%) for men. The life expectancy increase from 2002 to 2019 was smaller in MSOAs where life expectancy had been lower in 2002 (mostly northern urban MSOAs), and larger in MSOAs where life expectancy had been higher in 2002 (mostly MSOAs in and around London). As a result of these trends, the gap between the first and 99th percentiles of MSOA life expectancy for women increased from 10·7 years (95% credible interval 10·4-10·9) in 2002 to reach 14·2 years (13·9-14·5) in 2019, and for men increased from 11·5 years (11·3-11·7) in 2002 to 13·6 years (13·4-13·9) in 2019. INTERPRETATION: In the decade before the COVID-19 pandemic, life expectancy declined in increasing numbers of communities in England. To ensure that this trend does not continue or worsen, there is a need for pro-equity economic and social policies, and greater investment in public health and health care throughout the entire country. FUNDING: Wellcome Trust, Imperial College London, Medical Research Council, Health Data Research UK, and National Institutes of Health Research.


Subject(s)
Life Expectancy/trends , Mortality/trends , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , Child , Child, Preschool , England/epidemiology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Registries , Residence Characteristics/statistics & numerical data , Risk Assessment , Spatio-Temporal Analysis , Young Adult
5.
Proc Natl Acad Sci U S A ; 118(40)2021 10 05.
Article in English | MEDLINE | ID: covidwho-1442867

ABSTRACT

Although there is a large gap between Black and White American life expectancies, the gap fell 48.9% between 1990 and 2018, mainly due to mortality declines among Black Americans. We examine age-specific mortality trends and racial gaps in life expectancy in high- and low-income US areas and with reference to six European countries. Inequalities in life expectancy are starker in the United States than in Europe. In 1990, White Americans and Europeans in high-income areas had similar overall life expectancy, while life expectancy for White Americans in low-income areas was lower. However, since then, even high-income White Americans have lost ground relative to Europeans. Meanwhile, the gap in life expectancy between Black Americans and Europeans decreased by 8.3%. Black American life expectancy increased more than White American life expectancy in all US areas, but improvements in lower-income areas had the greatest impact on the racial life expectancy gap. The causes that contributed the most to Black Americans' mortality reductions included cancer, homicide, HIV, and causes originating in the fetal or infant period. Life expectancy for both Black and White Americans plateaued or slightly declined after 2012, but this stalling was most evident among Black Americans even prior to the COVID-19 pandemic. If improvements had continued at the 1990 to 2012 rate, the racial gap in life expectancy would have closed by 2036. European life expectancy also stalled after 2014. Still, the comparison with Europe suggests that mortality rates of both Black and White Americans could fall much further across all ages and in both high-income and low-income areas.


Subject(s)
/statistics & numerical data , Life Expectancy/ethnology , Mortality/ethnology , /statistics & numerical data , Adolescent , Adult , Aged , Child , Child, Preschool , Europe , Humans , Infant , Life Expectancy/trends , Middle Aged , Mortality/trends , United States , Young Adult
6.
Int J Equity Health ; 20(1): 180, 2021 08 03.
Article in English | MEDLINE | ID: covidwho-1365357

ABSTRACT

BACKGROUND: Ethiopia has experienced great improvements in life expectancy (LE) at birth over the last three decades. Despite consistent increases in LE for both males and females in Ethiopia, the country has simultaneously witnessed an increasing discrepancy in LE between males and females. METHODS: This study used Pollard's actuarial method of decomposing LE to compare age- and cause- specific contributions to changes in sex differences in LE between 1995 and 2015 in Ethiopia. RESULTS: Life expectancy at birth in Ethiopia increased for both males and females from 48.28 years and 50.12 years in 1995 to 65.59 years and 69.11 years in 2015, respectively. However, the sex differences in LE at birth also increased from 1.85 years in 1995 to 3.51 years in 2015. Decomposition analysis shows that the higher male mortality was consistently due to injuries and respiratory infections, which contributed to 1.57 out of 1.85 years in 1995 and 1.62 out of 3.51 years in 2015 of the sex differences in LE. Increased male mortality from non-communicable diseases (NCDs) also contributed to the increased difference in LE between males and females over the period, accounting for 0.21 out of 1.85 years and 1.05 out of 3.51 years in 1995 and 2015, respectively. CONCLUSIONS: While injuries and respiratory infections causing male mortality were the most consistent causes of the sex differences in LE in Ethiopia, morality from NCDs is the main cause of the recent increasing differences in LE between males and females. However, unlike the higher exposure of males to death from injuries due to road traffic injuries or interpersonal violence, to what extent sex differences are caused by the higher male mortality compared to female mortality from respiratory infection diseases is unclear. Similarly, despite Ethiopia's weak social security system, an explanation for the increased sex differences after the age of 40 years due to either longer female LE or reduced male LE should be further investigated.


Subject(s)
Communicable Diseases , Health Status Disparities , Life Expectancy , Noncommunicable Diseases , Wounds and Injuries , Adolescent , Adult , Aged , Child , Child, Preschool , Communicable Diseases/epidemiology , Ethiopia/epidemiology , Female , Humans , Infant , Infant, Newborn , Life Expectancy/trends , Male , Middle Aged , Noncommunicable Diseases/epidemiology , Sex Distribution , Wounds and Injuries/epidemiology , Young Adult
8.
PLoS One ; 16(6): e0253505, 2021.
Article in English | MEDLINE | ID: covidwho-1278201

ABSTRACT

OBJECTIVE: To quantify excess all-cause mortality in Switzerland in 2020, a key indicator for assessing direct and indirect consequences of the COVID-19 pandemic. METHODS: Using official data on deaths in Switzerland, all-cause mortality in 2020 was compared with that of previous years using directly standardized mortality rates, age- and sex-specific mortality rates, and life expectancy. RESULTS: The standardized mortality rate was 8.8% higher in 2020 than in 2019, returning to the level observed 5-6 years before, around the year 2015. This increase was greater for men (10.6%) than for women (7.2%) and was statistically significant only for men over 70 years of age, and for women over 75 years of age. The decrease in life expectancy in 2020 compared to 2019 was 0.7%, with a loss of 9.7 months for men and 5.3 months for women. CONCLUSIONS: There was an excess mortality in Switzerland in 2020, linked to the COVID-19 pandemic. However, as this excess only concerned the elderly, the resulting loss of life expectancy was restricted to a few months, bringing the mortality level back to 2015.


Subject(s)
COVID-19/mortality , Cause of Death/trends , Life Expectancy/trends , Mortality/trends , SARS-CoV-2/isolation & purification , Adult , Age Distribution , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/virology , Female , Humans , Male , Middle Aged , Pandemics/prevention & control , SARS-CoV-2/physiology , Switzerland/epidemiology , Time Factors
9.
Public Health ; 193: 48-56, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1129171

ABSTRACT

OBJECTIVES: The COVID-19 pandemic in Wales and the UK has highlighted significant and historic inequalities in health between social groups. To better understand the composition of these inequalities and inform planning after the pandemic, we undertook a decomposition of life expectancy inequalities between the most and least deprived quintiles for men and women by age and cause of death and explored trends between 2002 and 2018. STUDY DESIGN: Statistical decomposition of life expectancy inequalities by age and cause of death using routine population mortality datasets. METHODS: We used routine statistics from the Office for National Statistics for the period 2002-2018 on population and deaths in Wales stratified by age, gender, Welsh Index of Multiple Deprivation (WIMD) 2019 quintile and cause of death, categorised by International Classification of Disease, version 10, code into 15 categories of public health relevance. We aggregated data to 3-year rolling figures to account for low numbers of events in some groups annually. Next, we estimated life expectancy at birth by quintile, gender and period using life table methods. Lastly, we performed a decomposition analysis using the Arriaga method to identify the specific disease categories and ages at which excess deaths occur in more disadvantaged areas to highlight potential areas for action. RESULTS: Life expectancy inequalities between the most and least WIMD quintiles rose for both genders between 2002 and 2018: from 4.69 to 6.02 years for women (an increase of 1.33 years) and from 6.34 to 7.42 years for men (an increase of 1.08 years). Exploratory analysis of these trends suggested that the following were most influential for women: respiratory disease (1.50 years), cancers (1.36 years), circulatory disease (1.35 years) and digestive disease (0.51 years). For men, the gap was driven by circulatory disease (2.01 years), cancers (1.39 years), respiratory disease (1.25 years), digestive disease (0.79 years), drug- and alcohol-related conditions (0.54 years) and external causes (0.54 years). Contributions for women from respiratory disease, cancers, dementia and drug- and alcohol-related conditions appeared to be increasing, while among men, there were rising contributions from respiratory, digestive and circulatory disease. CONCLUSIONS: Life expectancy inequalities in Wales remain wide and have been increasing, particularly among women, with indications of worsening trends since 2010 following the introduction of fiscal austerity. As agencies recover from the pandemic, these findings should be considered alongside any resumption of services in Wales or future health and public policy.


Subject(s)
Health Status Disparities , Life Expectancy/trends , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , COVID-19 , Cause of Death/trends , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Socioeconomic Factors , Wales/epidemiology , Young Adult
10.
Value Health ; 24(5): 648-657, 2021 05.
Article in English | MEDLINE | ID: covidwho-1117765

ABSTRACT

OBJECTIVES: Coronavirus disease 2019 has put unprecedented pressure on healthcare systems worldwide, leading to a reduction of the available healthcare capacity. Our objective was to develop a decision model to estimate the impact of postponing semielective surgical procedures on health, to support prioritization of care from a utilitarian perspective. METHODS: A cohort state-transition model was developed and applied to 43 semielective nonpediatric surgical procedures commonly performed in academic hospitals. Scenarios of delaying surgery from 2 weeks were compared with delaying up to 1 year and no surgery at all. Model parameters were based on registries, scientific literature, and the World Health Organization Global Burden of Disease study. For each surgical procedure, the model estimated the average expected disability-adjusted life-years (DALYs) per month of delay. RESULTS: Given the best available evidence, the 2 surgical procedures associated with most DALYs owing to delay were bypass surgery for Fontaine III/IV peripheral arterial disease (0.23 DALY/month, 95% confidence interval [CI]: 0.13-0.36) and transaortic valve implantation (0.15 DALY/month, 95% CI: 0.09-0.24). The 2 surgical procedures with the least DALYs were placing a shunt for dialysis (0.01, 95% CI: 0.005-0.01) and thyroid carcinoma resection (0.01, 95% CI: 0.01-0.02). CONCLUSION: Expected health loss owing to surgical delay can be objectively calculated with our decision model based on best available evidence, which can guide prioritization of surgical procedures to minimize population health loss in times of scarcity. The model results should be placed in the context of different ethical perspectives and combined with capacity management tools to facilitate large-scale implementation.


Subject(s)
COVID-19/complications , Computer Simulation , Population Health/statistics & numerical data , Surge Capacity/standards , Cohort Studies , Global Burden of Disease , Humans , Life Expectancy/trends , Probability Theory , Quality-Adjusted Life Years , Surge Capacity/statistics & numerical data
12.
Health Econ ; 30(3): 699-707, 2021 03.
Article in English | MEDLINE | ID: covidwho-986047

ABSTRACT

Many epidemiological models of the COVID-19 pandemic have focused on preventing deaths. Questions have been raised as to the frailty of those succumbing to the COVID-19 infection. In this paper we employ standard life table methods to illustrate how the potential quality-adjusted life-year (QALY) losses associated with COVID-19 fatalities could be estimated, while adjusting for comorbidities in terms of impact on both mortality and quality of life. Contrary to some suggestions in the media, we find that even relatively elderly patients with high levels of comorbidity can still lose substantial life years and QALYs. The simplicity of the method facilitates straightforward international comparisons as the pandemic evolves. In particular, we compare five different countries and show that differences in the average QALY losses for each COVID-19 fatality is driven mainly by differing age distributions for those dying of the disease.


Subject(s)
COVID-19/mortality , Life Expectancy/trends , Quality-Adjusted Life Years , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Child , Child, Preschool , Comorbidity , Humans , Infant , Middle Aged , Pandemics , Quality of Life , SARS-CoV-2 , Time Factors , United Kingdom/epidemiology , Young Adult
15.
Proc Natl Acad Sci U S A ; 117(36): 21854-21856, 2020 09 08.
Article in English | MEDLINE | ID: covidwho-729027

ABSTRACT

The COVID-19 pandemic is causing a catastrophic increase in US mortality. How does the scale of this pandemic compare to another US catastrophe: racial inequality? Using demographic models, I estimate how many excess White deaths would raise US White mortality to the best-ever (lowest) US Black level under alternative, plausible assumptions about the age patterning of excess mortality in 2020. I find that 400,000 excess White deaths would be needed to equal the best mortality ever recorded among Blacks. For White mortality in 2020 to reach levels that Blacks experience outside of pandemics, current COVID-19 mortality levels would need to increase by a factor of nearly 6. Moreover, White life expectancy in 2020 will remain higher than Black life expectancy has ever been unless nearly 700,000 excess White deaths occur. Even amid COVID-19, US White mortality is likely to be less than what US Blacks have experienced every year. I argue that, if Black disadvantage operates every year on the scale of Whites' experience of COVID-19, then so too should the tools we deploy to fight it. Our imagination should not be limited by how accustomed the United States is to profound racial inequality.


Subject(s)
Coronavirus Infections/ethnology , Coronavirus Infections/mortality , Pneumonia, Viral/ethnology , Pneumonia, Viral/mortality , African Americans/statistics & numerical data , Betacoronavirus , COVID-19 , Humans , Life Expectancy/ethnology , Life Expectancy/trends , Mortality/ethnology , Mortality/trends , Pandemics , SARS-CoV-2 , Socioeconomic Factors , United States/epidemiology , /statistics & numerical data
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