Gentrification Yields Racial And Ethnic Disparities In Exposure To Contextual Determinants Of Health

This article examines racial and ethnic disparities in the relationship between gentrification and exposure to contextual determinants of health. In our study, we focused on changes in selected contextual determinants of health (health care access, social deprivation, air pollution, and walkability) and life expectancy during the period 2006-21 among residents of gentrifying census tracts in six large US cities that have experienced different gentrification patterns and have different levels of segregation: Chicago, Illinois; Los Angeles, California; New York, New York; Philadelphia, Pennsylvania; San Francisco, California; and Seattle, Washington. We found that gentrification was associated with overall improvements in the likelihood of living in Medically Underserved Areas across racial and ethnic groups, but it was also associated with increased social deprivation and reduced life expectancy among Black people, Hispanic people, and people of another or undetermined race or ethnicity. In contrast, we found that gentrification was related to better (or unchanged) contextual determinants of health for Asian people and White people. Our findings can inform policies that target communities identified to be particularly at risk for worsening contextual determinants of health as a result of gentrification.

Deaths: Final Data for 2020

Objective-This report presents final 2020 data on U.S. deaths, death rates, life expectancy, infant and maternal mortality, and trends by selected characteristics such as age, sex, Hispanic origin and race, state of residence, and cause of death. Methods-Information reported on death certificates is presented in descriptive tabulations. The original records are filed in state registration offices. Statistical information is compiled in a national database through the Vital Statistics Cooperative Program of the National Center for Health Statistics. Causes of death are processed according to the International Classification of Diseases, 10th Revision. Beginning in 2018, all states and the District of Columbia were using the 2003 revised certificate of death for the entire year, which includes the 1997 Office of Management and Budget revised standards for race. Data based on these revised standards are not completely comparable to previous years. Results-In 2020, a total of 3,383,729 deaths were reported in the United States. The age-adjusted death rate was 835.4 deaths per 100,000 U.S. standard population, an increase of 16.8% from the 2019 rate. Life expectancy at birth was 77.0 years, a decrease of 1.8 years from 2019. Age-specific death rates increased from 2019 to 2020 for age groups 15 years and over and decreased for age group under 1 year. Many of the 15 leading causes of death in 2020 changed from 2019. COVID-19, a new cause of death in 2020, became the third leading cause in 2020. The infant mortality rate decreased 2.9% to a historic low of 5.42 infant deaths per 1,000 live births in 2020. Conclusions-In 2020, the age-adjusted death rate increased and life expectancy at birth decreased for the total, male, and female populations, primarily due to the influence of deaths from COVID-19.

Excess Mortality and Years of Potential Life Lost Among the Black Population in the US, 1999-2020

Importance

Amid efforts in the US to promote health equity, there is a need to assess recent progress in reducing excess deaths and years of potential life lost among the Black population compared with the White population.

Objective

To evaluate trends in excess mortality and years of potential life lost among the Black population compared with the White population.

Design, setting, and participants

Serial cross-sectional study using US national data from the Centers for Disease Control and Prevention from 1999 through 2020. We included data from non-Hispanic White and non-Hispanic Black populations across all age groups.

Exposures

Race as documented in the death certificates.

Main outcomes and measures

Excess age-adjusted all-cause mortality, cause-specific mortality, age-specific mortality, and years of potential life lost rates (per 100 000 individuals) among the Black population compared with the White population.

Results

From 1999 to 2011, the age-adjusted excess mortality rate declined from 404 to 211 excess deaths per 100 000 individuals among Black males (P for trend <.001). However, the rate plateaued from 2011 through 2019 (P for trend = .98) and increased in 2020 to 395-rates not seen since 2000. Among Black females, the rate declined from 224 excess deaths per 100 000 individuals in 1999 to 87 in 2015 (P for trend <.001). There was no significant change between 2016 and 2019 (P for trend = .71) and in 2020 rates increased to 192-levels not seen since 2005. The trends in rates of excess years of potential life lost followed a similar pattern. From 1999 to 2020, the disproportionately higher mortality rates in Black males and females resulted in 997 623 and 628 464 excess deaths, respectively, representing a loss of more than 80 million years of life. Heart disease had the highest excess mortality rates, and the excess years of potential life lost rates were largest among infants and middle-aged adults.

Conclusions and relevance

Over a recent 22-year period, the Black population in the US experienced more than 1.63 million excess deaths and more than 80 million excess years of life lost when compared with the White population. After a period of progress in reducing disparities, improvements stalled, and differences between the Black population and the White population worsened in 2020.

Changes in the Relationship Between Income and Life Expectancy Before and During the COVID-19 Pandemic, California, 2015-2021

IMPORTANCE

The COVID-19 pandemic caused a large decrease in US life expectancy in 2020, but whether a similar decrease occurred in 2021 and whether the relationship between income and life expectancy intensified during the pandemic are unclear.

OBJECTIVE

To measure changes in life expectancy in 2020 and 2021 and the relationship between income and life expectancy by race and ethnicity.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective ecological analysis of deaths in California in 2015 to 2021 to calculate state- and census tract-level life expectancy. Tracts were grouped by median household income (MHI), obtained from the American Community Survey, and the slope of the life expectancy-income gradient was compared by year and by racial and ethnic composition.

EXPOSURES

California in 2015 to 2019 (before the COVID-19 pandemic) and 2020 to 2021 (during the COVID-19 pandemic).

MAIN OUTCOMES AND MEASURES

Life expectancy at birth.

RESULTS

California experienced 1 988 606 deaths during 2015 to 2021, including 654 887 in 2020 to 2021. State life expectancy declined from 81.40 years in 2019 to 79.20 years in 2020 and 78.37 years in 2021. MHI data were available for 7962 of 8057 census tracts (98.8%; n = 1 899 065 deaths). Mean MHI ranged from $21 279 to $232 261 between the lowest and highest percentiles. The slope of the relationship between life expectancy and MHI increased significantly, from 0.075 (95% CI, 0.07-0.08) years per percentile in 2019 to 0.103 (95% CI, 0.098-0.108; P < .001) years per percentile in 2020 and 0.107 (95% CI, 0.102-0.112; P < .001) years per percentile in 2021. The gap in life expectancy between the richest and poorest percentiles increased from 11.52 years in 2019 to 14.67 years in 2020 and 15.51 years in 2021. Among Hispanic and non-Hispanic Asian, Black, and White populations, life expectancy declined 5.74 years among the Hispanic population, 3.04 years among the non-Hispanic Asian population, 3.84 years among the non-Hispanic Black population, and 1.90 years among the non-Hispanic White population between 2019 and 2021. The income-life expectancy gradient in these groups increased significantly between 2019 and 2020 (0.038 [95% CI, 0.030-0.045; P < .001] years per percentile among Hispanic individuals; 0.024 [95% CI: 0.005-0.044; P = .02] years per percentile among Asian individuals; 0.015 [95% CI, 0.010-0.020; P < .001] years per percentile among Black individuals; and 0.011 [95% CI, 0.007-0.015; P < .001] years per percentile among White individuals) and between 2019 and 2021 (0.033 [95% CI, 0.026-0.040; P < .001] years per percentile among Hispanic individuals; 0.024 [95% CI, 0.010-0.038; P = .002] years among Asian individuals; 0.024 [95% CI, 0.011-0.037; P = .003] years per percentile among Black individuals; and 0.013 [95% CI, 0.008-0.018; P < .001] years per percentile among White individuals). The increase in the gradient was significantly greater among Hispanic vs White populations in 2020 and 2021 (P < .001 in both years) and among Black vs White populations in 2021 (P = .04).

CONCLUSIONS AND RELEVANCE

This retrospective analysis of census tract-level income and mortality data in California from 2015 to 2021 demonstrated a decrease in life expectancy in both 2020 and 2021 and an increase in the life expectancy gap by income level relative to the prepandemic period that disproportionately affected some racial and ethnic minority populations. Inferences at the individual level are limited by the ecological nature of the study, and the generalizability of the findings outside of California are unknown.

Research on the prediction of longevity from both individual and family perspectives

Increasing human longevity is of global interest. The present study explored the prediction of longevity from both individual perspective and family perspective based on demographic and psychosocial factors. A total of 186 longevous family members and 237 ordinary elderly family members participated in a cross-sectional study, and a sample of 62 longevous elderly and 57 ordinary elderly were selected for comparative research. The results showed that it was three times more female than male in longevous elderly group. Up to 71.2% of longevous elderly had no experience in education, which was significantly lower than that of ordinary elderly. Due to such extreme age, more widowed (81.4%) elderly than those in married (18.6%). Less than one-seventh of the longevous elderly maintained the habit of smoking, and about one-third of them liked drinking, both were significantly lower than that of ordinary elderly. In terms of psychosocial factors, longevous elderly showed lower neuroticism and social support, while higher extraversion, compared with the ordinary elderly. However, there were no significant differences between the two family groups in demographic and psychosocial variables, except longevous families showing lower scores in neuroticism. Regression analysis found that neuroticism, social support and smoking habit had significant impact on individuals’ life span, then, neuroticism and psychoticism were the key factor to predict families’ longevity. We conclude that good emotional management, benign interpersonal support, and moderation of habits are important factors for individual longevity, and the intergenerational influence of personality is closely related to family longevity.

Why we should monitor disparities in old-age mortality with the modal age at death

Indicators based a fixed “old” age threshold have been widely used for assessing socioeconomic disparities in mortality at older ages. Interpretation of long-term trends and determinants of these indicators is challenging because mortality above a fixed age that in the past would have reflected old age deaths is today mixing premature and old-age mortality. We propose the modal (i.e., most frequent) age at death, M, an indicator increasingly recognized in aging research, but which has been infrequently used for monitoring mortality disparities at older ages. We use mortality and population exposure data by occupational class over the 1971-2017 period from Finnish register data. The modal age and life expectancy indicators are estimated from mortality rates smoothed with penalized B-splines. Over the 1971-2017 period, occupational class disparities in life expectancy at 65 and 75 widened while disparities in M remained relatively stable. The proportion of the group surviving to the modal age was constant across time and occupational class. In contrast, the proportion surviving to age 65 and 75 has roughly doubled since 1971 and showed strong occupational class differences. Increasing socioeconomic disparities in mortality based on fixed old age thresholds may be a feature of changing selection dynamics in a context of overall declining mortality. Unlike life expectancy at a selected fixed old age, M compares individuals with similar survival chances over time and across occupational classes. This property makes trends and differentials in M easier to interpret in countries where old-age survival has improved significantly.

The impact of the SARS-CoV-2 epidemic on mortality in South Africa in 2020

BACKGROUND

The impacts on mortality of both the SARS-CoV-2 epidemic and the interventions to manage it differ between countries. The Rapid Mortality Surveillance System set up by the South African Medical Research Council based on data from the National Population Register (NPR) provides a means of tracking this impact on mortality in South Africa.

OBJECTIVES

To report on the change in key metrics of mortality (numbers of deaths, life expectancy at birth, life expectancy at age 60, and infant, under-5, older child and adolescent, young adult, and adult mortality) over the period 2015 - 2020. The key features of the impact are contrasted with those measured in other countries.

METHODS

The numbers of registered deaths by age and sex recorded on the NPR were increased to account for both registered deaths that are not captured by the NPR and an estimate of deaths not reported. The estimated numbers of deaths together with estimates of the numbers in the population in the middle of each of the years were used to produce life tables and calculate various indicators.

RESULTS

Between 2019 and 2020, the number of deaths increased by nearly 53 000 (65% female), and life expectancy at birth fell by 1 year for females and by only 2.5 months for males. Life expectancy at age 60 decreased by 1.6 years for females and 1.2 years for males. Infant mortality, under-5 mortality and mortality of children aged 5 - 14 decreased by 22%, 20% and 10%, respectively, while that for older children and adolescents decreased by 11% for males and 5% for females. Premature adult mortality, the probability of a 15-year-old dying before age 60, increased by 2% for males and 9% for females.

CONCLUSIONS

COVID-19 and the interventions to manage it had differential impacts on mortality by age and sex. The impact of the epidemic on life expectancy in 2020 differs from that in most other, mainly developed, countries, both in the limited decline and also in the greater impact on females. These empirical estimates of life expectancy and mortality rates are not reflected by estimates from agencies, either because agency estimates have yet to be updated for the impact of the epidemic or because they have not allowed for the impact correctly. Trends in weekly excess deaths suggest that the drop in life expectancy in 2021 will be greater than that in 2020.

Changes in Life Expectancy and Disability-Free Life Expectancy in Successive Birth Cohorts of Older Cancer Survivors: A Longitudinal Modeling Analysis of the US Health and Retirement Study

The population of older cancer survivors in the United States is rapidly growing. However, little is currently known about how the health of older cancer survivors has changed over time and across successive birth cohorts. Using data from the US Health and Retirement Study, we parameterized a demographic microsimulation model to compare partial cohort life expectancy (LE) and disability-free LE for US men and women without cancer and with prevalent and incident cancer diagnoses for 4 successive 10-year birth cohorts, born 1918–1927 to 1948–1957. Disability was defined as being disabled in ≥1 activity of daily living. These cohorts had midpoint ages of 55–64, 65–74, and 75–84 years during the periods 1998–2008 (the “early” period) and 2008–2018 (the “later” period). Across all cohorts and periods, those with incident cancer had the lowest LE, followed by those with prevalent cancer and cancer-free individuals. We observed declines in partial LE and an expansion of life spent disabled among more recent birth cohorts of prevalent-cancer survivors. Our findings suggest that advances in treatments that prolong life for individual cancer patients may have led to population-level declines in conditional LE and disability-free LE across successive cohorts of older cancer survivors.

Predicting life expectancy after geriatric hip fracture: A systematic review

Background

Displaced femoral neck fractures in geriatric patients are typically treated with either hemiarthroplasty or total hip arthroplasty. The choice between hemiarthroplasty and total hip arthroplasty requires a good estimate of the patient’s life expectancy, as the recent HEALTH trial suggests that the benefits of the two operations do not diverge, if at all, until the second year post-operatively. A systematic review was this performed to determine if there sufficient information in the medical literature to estimate a patient’s life expectancy beyond two years and to identify those patient variables affecting survival of that duration.

Methods

Pubmed, Embase, and Cochrane databases were queried for articles reporting survival data for at least two years post-operatively for at least 100 patients, age 65 or greater, treated surgically for an isolated hip fracture. A final set of 43 papers was created. The methods section of all selected papers was then reviewed to determine which variables were collected in the studies and the results section was reviewed to note whether an effect was reported for all collected variables.

Results

There were 43 eligible studies with 25 unique variables identified. Only age, gender, comorbidities, the presence of dementia and fracture type were collected in a majority of studies, and within that, only age and gender were reported in a majority of the results. Most (15/ 25) variables were reported in 5 or fewer of the studies.

Discussion

There are important deficiencies in the literature precluding the evidence-based estimation of 2 year life expectancy. Because the ostensible advantages of total hip arthroplasty are reaped only by those who survive two years or more, there is a need for additional data collection, analysis and reporting regarding survival after geriatric hip fracture.

"Translating" All-Cause Mortality Rate Ratios or Hazard Ratios to Age-, Longevity-, and Probability-Based Measures

Epidemiologists commonly use an adjusted hazard ratio or incidence density ratio, or a standardized mortality ratio, to measure a difference in all-cause mortality rates. They seldom translate it into an age-, time-, or probability-based measure that would be easier to communicate and to relate to. Several articles have shown how to translate from a standardized mortality ratio or hazard ratio to a longevity difference, a difference in actuarial ages, or a probability of being outlived. In this paper, we describe the settings where these translations are and are not appropriate and provide some of the heuristics behind the formulae. The tools that yield differences in "effective age" and in longevity are applicable when both 1) the mortality rate ratio (hazard ratio) is constant over age and 2) the rates themselves are log-linear in age. The "probability/odds of being outlived" metric is applicable whenever the first condition holds, and thus it provides no direct information on the magnitude of the effective age/longevity difference.

Effects of covid-19 pandemic on life expectancy and premature mortality in 2020: time series analysis in 37 countries

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.

Life expectancy and risk of death in 6791 communities in England from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data

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.

How do ethnicity and deprivation impact on life expectancy at birth in people with serious mental illness? Observational study in the UK

BACKGROUND

Across international contexts, people with serious mental illnesses (SMI) experience marked reductions in life expectancy at birth. The intersection of ethnicity and social deprivation on life expectancy in SMI is unclear. The aim of this study was to assess the impact of ethnicity and area-level deprivation on life expectancy at birth in SMI, defined as schizophrenia-spectrum disorders, bipolar disorders and depression, using data from London, UK.

METHODS

Abridged life tables to calculate life expectancy at birth, in a cohort with clinician-ascribed ICD-10 schizophrenia-spectrum disorders, bipolar disorders or depression, managed in secondary mental healthcare. Life expectancy in the study population with SMI was compared with life expectancy in the general population and with those residing in the most deprived areas in England.

RESULTS

Irrespective of ethnicity, people with SMI experienced marked reductions in life expectancy at birth compared with the general population; from 14.5 years loss in men with schizophrenia-spectrum and bipolar disorders, to 13.2 years in women. Similar reductions were noted for people with depression. Across all diagnoses, life expectancy at birth in people with SMI was lower than the general population residing in the most deprived areas in England.

CONCLUSIONS

Irrespective of ethnicity, reductions in life expectancy at birth among people with SMI are worse than the general population residing in the most deprived areas in England. This trend in people with SMI is similar to groups who experience extreme social exclusion and marginalisation. Evidence-based interventions to tackle this mortality gap need to take this into account.

Planning for tomorrow: global cancer incidence and the role of prevention 2020-2070

Cancer is currently the first or second most common contributor to premature mortality in most countries of the world. The global number of patients with cancer is expected to rise over the next 50 years owing to the strong influence of demographic changes, such as population ageing and growth, on the diverging trends in cancer incidence in different regions. Assuming that the latest incidence trends continue for the major cancer types, we predict a doubling of the incidence of all cancers combined by 2070 relative to 2020. The greatest increases are predicted in lower-resource settings, in countries currently assigned a low Human Development Index (HDI), whereas the predicted increases in national burden diminish with increasing levels of national HDI. Herein, we assess studies modelling the future burden of cancer that underscore how comprehensive cancer prevention strategies can markedly reduce the prevalence of major risk factors and, in so doing, the number of future cancer cases. Focusing on an in-depth assessment of prevention strategies that target tobacco smoking, overweight and obesity, and human papillomavirus infection, we discuss how stepwise, population-level approaches with amenable goals can avert millions of future cancer diagnoses worldwide. In the absence of a step-change in cancer prevention delivery, tobacco smoking will remain the leading preventable cause of cancer, and overweight and obesity might well present a comparable opportunity for prevention, given its increasing prevalence globally in the past few decades. Countries must therefore instigate national cancer control programmes aimed at preventing cancer, and with some urgency, if such programmes are to yield the desired public health and economic benefits in this century.

Association of Walnut Consumption with Total and Cause-Specific Mortality and Life Expectancy in U.S. Adults

Walnut consumption is associated with health benefits. We aimed to (1) examine the association between walnut consumption and mortality and (2) estimate life expectancy in relation to walnut consumption in U.S. adults. We included 67,014 women of the Nurses' Health Study (1998-2018) and 26,326 men of the Health Professionals Follow-up Study (1998-2018) who were free of cancer, heart disease, and stroke at baseline. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). During up to 20 years of follow-up, we documented 30,263 deaths. The hazard ratios for total mortality across categories of walnut intake (servings/week), as compared to non-consumers, were 0.95 (95% confidence interval (CI), 0.91, 0.98) for <1 serving/week, 0.94 (95% CI, 0.89, 0.99) for 1 serving/week, 0.87 (95% CI, 0.82, 0.93) for 2-4 servings/week, and 0.86 (95% CI, 0.79, 0.93) for >=5 servings/week (p for trend <0.0001). A greater life expectancy at age 60 (1.30 years in women and 1.26 years in men) was observed among those who consumed walnuts more than 5 servings/week compared to non-consumers. Higher walnut consumption was associated with a lower risk of total and CVD mortality and a greater gained life expectancy among U.S. elder adults.

The triple burden of communicable and non-communicable diseases and injuries on sex differences in life expectancy in Ethiopia

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.

Future life expectancy in Europe taking into account the impact of smoking, obesity, and alcohol

Introduction: In Europe, women can expect to live on average 82 years and men 75 years. Forecasting how life expectancy will develop in the future is essential for society. Most forecasts rely on a mechanical extrapolation of past mortality trends, which leads to unreliable outcomes because of temporal fluctuations in the past trends due to lifestyle 'epidemics'. Methods: We project life expectancy for 18 European countries by taking into account the impact of smoking, obesity, and alcohol on mortality, and the mortality experiences of forerunner populations. Results: We project that life expectancy in these 18 countries will increase from, on average, 83.4 years for women and 78.3 years for men in 2014 to 92.8 years for women and 90.5 years for men in 2065. Compared to others (Lee-Carter, Eurostat, United Nations), we project higher future life expectancy values and more realistic differences between countries and sexes. Conclusions: Our results imply longer individual lifespans, and more elderly in society. Funding: Netherlands Organisation for Scientific Research (NWO) (grant no. 452-13-001).

Lifetime risk of developing diabetes and years of life lost among those with diabetes in Brazil

BACKGROUND

Given the paucity of studies for low- or middle-income countries, we aim to provide the first ever estimations of lifetime risk of diabetes, years of life spent and lost among those with diabetes for Brazilians. Estimates of Brazil´s diabetes burden consist essentially of reports of diabetes prevalence from national surveys and mortality data. However, these additional metrics are at times more meaningful ways to characterize this burden.

METHODS

We joined data on incidence of physician-diagnosed diabetes from the Brazilian risk factor surveillance system, all-cause mortality from national statistics, and diabetes mortality rate ratios from ELSA-Brasil, an ongoing cohort study. To calculate lifetime risk of developing diabetes, we applied an illness-death state model. To calculate years of life lost for those with diabetes and years lived with the disease, we additionally calculated the mortality rates for those with diabetes.

RESULTS

A 35-year-old white adult had a 23.4% (95% CI = 22.5%-25.5%) lifetime risk of developing diabetes by age 80 while a same-aged black/brown adult had a 30.8% risk (95% confidence interval (CI) = 29.6%-33.2%). Men diagnosed with diabetes at age 35 would live 32.9 (95% CI = 32.4-33.2) years with diabetes and lose 5.5 (95% CI = 5.1-6.1) years of life. Similarly-aged women would live 38.8 (95% CI = 38.3-38.9) years with diabetes and lose 2.1 (95% CI = 1.9-2.6) years of life.

CONCLUSIONS

Assuming maintenance of current rates, one-quarter of young Brazilians will develop diabetes over their lifetimes, with this number reaching almost one-third among young, black/brown women. Those developing diabetes will suffer a decrease in life expectancy and will generate a considerable cost in terms of medical care.

A first analysis of excess mortality in Switzerland in 2020

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.

Overall U.S. Life Expectancy Drops One Year, Lowest Level Since 2006

Non-Hispanic Blacks show greatest declines.

Death rates at specific life stages mold the sex gap in life expectancy

Why do women live longer than men? Here, we mine rich lodes of demographic data to reveal that lower female mortality at particular ages is decisive-and that the important ages changed around 1950. Earlier, excess mortality among baby boys was crucial; afterward, the gap largely resulted from elevated mortality among men 60+. Young males bear modest responsibility for the sex gap in life expectancy: Depending on the country and time, their mortality accounts for less than a quarter and often less than a 10th of the gap. Understanding the impact on life expectancy of differences between male and female risks of death by age, over time, and across populations yields insights for research on how the lives of men and women differ.

The life expectancy of older couples and surviving spouses

Individual life expectancies provide information for individuals making retirement decisions and for policy makers. For couples, analogous measures are the expected years both spouses will be alive (joint life expectancy) and the expected years the surviving spouse will be a widow or widower (survivor life expectancy). Using individual life expectancies to calculate summary measures for couples is intuitively appealing but yield misleading results, overstating joint life expectancy and dramatically understating survivor life expectancies. This implies that standard "individual life cycle models" are misleading for couples and that "couple life cycle models" must be substantially more complex. Using the CDC life tables for 2010, we construct joint and survivor life expectancy measures for randomly formed couples. The couples we form are defined by age, race and ethnicity, and education. Due to assortative marriage, inequalities in individual life expectancies are compounded into inequalities in joint and survivor life expectancies. We also calculate life expectancy measures for randomly formed couples for the 1930-2010 decennial years. Trends over time show how the relative rate of decrease in the mortality rates of men and women affect joint and survivor life expectancies. Because our couple life expectancy measures are based on randomly formed couples, they do not capture the effects of differences in spouses' premarital characteristics (apart from sex, age, race and ethnicity, and, in some cases, education) or of correlations in spouses' experiences or behaviors during marriage. However, they provide benchmarks which have been sorely lacking in the public discourse.

Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study

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.

Estimated life expectancy gains with antiretroviral therapy among adults with HIV in Latin America and the Caribbean: a multisite retrospective cohort study

BACKGROUND

There are few data on life expectancy gains among people living with HIV in low-income and middle-income settings where antiretroviral therapy (ART) is increasingly available. We aimed to analyse life expectancy trends from 2003 to 2017 among people with HIV beginning treatment with ART within the Caribbean, central America, and South America.

METHODS

We did a multisite retrospective cohort study and included people with HIV who had started treatment with ART and were aged 16 years or older between Jan 1, 2003, and Dec 31, 2017, from Caribbean, Central and South America network for HIV epidemiology (CCASAnet) sites in Argentina, Brazil, Chile, Haiti, Honduras, Mexico, and Peru, who contributed person-time data from the age of 20 years until date of death, last contact, database closure, or Dec 31, 2017. We used the Chiang method of abridged life tables to estimate life expectancy at age 20 years for three eras (2003-08, 2009-12, and 2013-17) overall and by demographic and clinical characteristics at ART initiation. We used Poisson regression models to weight mortality rates to account for informative censoring.

FINDINGS

30 688 people with HIV were included in the study; 17 491 (57·0%) were from the Haiti site and 13 197 (43·0%) were from all other sites. There were 2637 deaths during the study period: 1470 in Haiti and 1167 in other sites. Crude and weighted mortality rates decreased among all age groups over calendar eras. From 2003-08 to 2013-17, overall life expectancy for people with HIV at age 20 years increased from 13·9 years (95% CI 12·5-15·2) to 61·2 years (59·0-63·4) in Haiti and from 31·0 years (29·3-32·8) to 69·5 years (67·2-71·8) in other sites. Life expectancies at the end of the study period were within 10 years of those of the general population (69·9 years in Haiti and 78·0 years in all other sites in 2018). Disparities in life expectancy among people with HIV by sex or HIV transmission risk factor, CD4 cell count, level of education, and history of tuberculosis at or before ART initiation persisted across calendar eras.

INTERPRETATION

Life expectancy among people with HIV receiving ART has significantly improved in Latin America and the Caribbean. Persistent disparities in life expectancy among people with HIV by demographic and clinical factors at ART initiation highlight vulnerable populations in the region.

FUNDING

National Institutes of Health.

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

US State Disparities in Life Expectancy, Disability-Free Life Expectancy, and Disabled Life Expectancy Among Adults Aged 25 to 89 Years

Objectives. To estimate total life expectancy (TLE), disability-free life expectancy (DFLE), and disabled life expectancy (DLE) by US state for women and men aged 25 to 89 years and examine the cross-state patterns.Methods. We used data from the 2013-2017 American Community Survey and the 2017 US Mortality Database to calculate state-specific TLE, DFLE, and DLE by gender for US adults and hypothetical worst- and best-case scenarios.Results. For men and women, DFLEs and DLEs varied widely by state. Among women, DFLE ranged from 45.8 years in West Virginia to 52.5 years in Hawaii, a 6.7-year gap. Men had a similar range. The gap in DLEs across states was 2.4 years for women and 1.6 years for men. The correlation among DFLE, DLE, and TLE was particularly strong in southern states. The South is doubly disadvantaged: residents have shorter lives and spend a greater proportion of those lives with disability.Conclusions. The stark variation in DFLE and DLE across states highlights the large health inequalities present today across the United States, which have significant implications for individuals' well-being and US states' financial costs and medical care burden.

Life expectancy in adulthood is falling for those without a BA degree, but as educational gaps have widened, racial gaps have narrowed

A 4-y college degree is increasingly the key to good jobs and, ultimately, to good lives in an ever-more meritocratic and unequal society. The bachelor's degree (BA) is increasingly dividing Americans; the one-third with a BA or more live longer and more prosperous lives, while the two-thirds without face rising mortality and declining prospects. We construct a time series, from 1990 to 2018, of a summary of each year's mortality rates and expected years lived from 25 to 75 at the fixed mortality rates of that year. Our measure excludes those over 75 who have done relatively well over the last three decades and focuses on the years when deaths rose rapidly through drug overdoses, suicides, and alcoholic liver disease and when the decline in mortality from cardiovascular disease slowed and reversed. The BA/no-BA gap in our measure widened steadily from 1990 to 2018. Beyond 2010, as those with a BA continued to see increases in our period measure of expected life, those without saw declines. This is true for the population as a whole, for men and for women, and for Black and White people. In contrast to growing education gaps, gaps between Black and White people diminished but did not vanish. By 2018, intraracial college divides were larger than interracial divides conditional on college; by our measure, those with a college diploma are more alike one another irrespective of race than they are like those of the same race who do not have a BA.

Sex differences in mortality: results from a population-based study of 12 longitudinal cohorts

BACKGROUND

Women generally have longer life expectancy than men but have higher levels of disability and morbidity. Few studies have identified factors that explain higher mortality in men. The aim of this study was to identify potential factors contributing to sex differences in mortality at older age and to investigate variation across countries.

METHODS

This study included participants age ≥ 50 yr from 28 countries in 12 cohort studies of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) consortium. Using a 2-step individual participant data meta-analysis framework, we applied Cox proportional hazards modelling to investigate the association between sex and mortality across different countries. We included socioeconomic (education, wealth), lifestyle (smoking, alcohol consumption), social (marital status, living alone) and health factors (cardiovascular disease, diabetes, mental disorders) as covariates or interaction terms with sex to test whether these factors contributed to the mortality gap between men and women.

RESULTS

The study included 179 044 individuals. Men had 60% higher mortality risk than women after adjustment for age (pooled hazard ratio [HR] 1.6; 95% confidence interval 1.5-1.7), yet the effect sizes varied across countries (I 2 = 71.5%, HR range 1.1-2.4). Only smoking and cardiovascular diseases substantially attenuated the effect size (by about 22%).

INTERPRETATION

Lifestyle and health factors may partially account for excess mortality in men compared with women, but residual variation remains unaccounted for. Variation in the effect sizes across countries may indicate contextual factors contributing to gender inequality in specific settings.

Demographic perspectives on the rise of longevity

This article reviews some key strands of demographic research on past trends in human longevity and explores possible future trends in life expectancy at birth. Demographic data on age-specific mortality are used to estimate life expectancy, and validated data on exceptional life spans are used to study the maximum length of life. In the countries doing best each year, life expectancy started to increase around 1840 at a pace of almost 2.5 y per decade. This trend has continued until the present. Contrary to classical evolutionary theories of senescence and contrary to the predictions of many experts, the frontier of survival is advancing to higher ages. Furthermore, individual life spans are becoming more equal, reducing inequalities, with octogenarians and nonagenarians accounting for most deaths in countries with the highest life expectancy. If the current pace of progress in life expectancy continues, most children born this millennium will celebrate their 100th birthday. Considerable uncertainty, however, clouds forecasts: Life expectancy and maximum life span might increase very little if at all, or longevity might rise much faster than in the past. Substantial progress has been made over the past three decades in deepening understanding of how long humans have lived and how long they might live. The social, economic, health, cultural, and political consequences of further increases in longevity are so significant that the development of more powerful methods of forecasting is a priority.

Global patterns and trends in incidence and mortality of thyroid cancer in children and adolescents: a population-based study

BACKGROUND

There has been a considerable increase in thyroid cancer incidence among adults in several countries in the past three decades, attributed primarily to overdiagnosis. We aimed to assess global patterns and trends in incidence and mortality of thyroid cancer in children and adolescents, in view of the increased incidence among adults.

METHODS

We did a population-based study of the observed incidence (in 49 countries and territories) and mortality (in 27 countries) of thyroid cancer in children and adolescents aged 0-19 years using data from the International Incidence of Childhood Cancer Volume 3 study database, the WHO mortality database, and the cancer incidence in five continents database (CI5plus; for adult data [age 20-74 years]). We analysed temporal trends in incidence rates, including absolute changes in rates, and the strength of the correlation between incidence rates in children and adolescents and in adults. We calculated the average annual number of thyroid cancer deaths and the age-standardised mortality rates for children and adolescents.

FINDINGS

Age-standardised incidence rates of thyroid cancer among children and adolescents aged 0-19 years ranged from 0·4 (in Uganda and Kenya) to 13·4 (in Belarus) cancers per 1 million person-years in 2008-12. The variability in the incidence rates was mostly accounted for by the papillary tumour subtype. Incidence rates were almost always higher in girls than in boys and increased with age in both sexes. Rapid increases in incidence between 1998-2002 and 2008-12 were observed in almost all countries. Country-specific incidence rates in children and adolescents were strongly correlated (r>0·8) with rates in adults, as were the temporal changes in the respective incidence rates (r>0·6). Thyroid cancer deaths in those aged younger than 20 years were less than 0·1 per 10 million person-years in each country.

INTERPRETATION

The pattern of thyroid cancer incidence in children and adolescents mirrors the pattern seen in adults, suggesting a major role for overdiagnosis, which, in turn, can lead to overtreatment, lifelong medical care, and side effects that can negatively affect quality of life. We suggest that the existing recommendation against screening for thyroid cancer in the asymptomatic adult population who are free from specific risk factors should be extended to explicitly recommend against screening for thyroid cancer in similar populations of children and adolescents.

FUNDING

International Agency for Research on Cancer and the Union for International Cancer Control; French Institut National du Cancer; Italian Association of Cancer Research; and Italian Ministry of Health.

Estimating (quality-adjusted) life-year losses associated with deaths: With application to COVID-19

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.

The Role of Smoking in Country Differences in Life Expectancy Across Europe, 1985-2014

INTRODUCTION

Smoking contributes substantially to mortality levels and trends. Its role in country differences in mortality has, however, hardly been quantified. The current study formally assesses the-so far unknown-changing contribution of smoking to country differences in life expectancy at birth (e0) across Europe.

METHODS

Using all-cause mortality data and indirectly estimated smoking-attributable mortality rates by age and sex for 30 European countries from 1985 to 2014, the differences in e0 between each individual European country and the weighted average were decomposed into a smoking- and a nonsmoking-related part.

RESULTS

In 2014, e0 ranged from 70.8 years in Russia to 83.1 years in Switzerland. Men exhibited larger country differences than women (variance of 21.9 and 7.0 years, respectively). Country differences in e0 increased up to 2005 and declined thereafter. Among men, the average contribution of smoking to the country differences in e0 was highest around 1990 (47%) and declined to 35% in 2014. Among women, the average relative contribution of smoking declined from 1991 to 2011, and smoking resulted in smaller differences with the average e0 level in the majority of European countries. For both sexes combined, the contribution of smoking to country differences in e0 was higher than 20% throughout the period.

CONCLUSIONS

Smoking contributed substantially to the country differences in e0 in Europe, their increases up to 1991, and their decreases since 2005, especially among men. Policies that discourage smoking can help to reduce inequalities in mortality levels across Europe in the long run.

IMPLICATIONS

Smoking contributes substantially to country differences in life expectancy at birth (e0) in Europe, particularly among men, for whom the contribution was highest around 1990 (47%) and declined to 35% in 2014. In line with the anticipated progression of the smoking epidemic, the differences between European countries in e0 due to smoking are expected to further decline among men, but to increase among women. The role of smoking in mortality convergence since 2005 illustrates that smoking policies can help to reduce inequalities in life expectancy levels across Europe, particularly when they target smoking in countries with low e0.

Deaths: Final Data for 2018

Objectives-This report presents final 2018 data on U.S. deaths, death rates, life expectancy, infant and maternal mortality, and trends by selected characteristics such as age, sex, Hispanic origin and race, state of residence, and cause of death. The race categories are consistent with 1997 Office of Management and Budget (OMB) standards, which are different from previous reports (1977 OMB standards). Methods-Information reported on death certificates is presented in descriptive tabulations. The original records are filed in state registration offices. Statistical information is compiled in a national database through the Vital Statistics Cooperative Program of the National Center for Health Statistics. Causes of death are processed according to the International Classification of Diseases, 10th Revision. As of 2018, all states and the District of Columbia were using the 2003 revised certificate of death, which includes the 1997 OMB revised standards for race. The 2018 data based on the revised standards are not completely comparable to previous years. Selected estimates are presented in this report for both the revised and previous race standards to provide some reference for interpretation of trends. Results-In 2018, a total of 2,839,205 deaths were reported in the United States. The age-adjusted death rate was 723.6 deaths per 100,000 U.S. standard population, a decrease of 1.1% from the 2017 rate. Life expectancy at birth was 78.7 years, an increase of 0.1 year from 2017. Age-specific death rates decreased in 2018 from 2017 for age groups 15-24, 25-34, 45-54, 65-74, 75-84, and 85 and over. The 15 leading causes of death in 2018 remained the same as in 2017. The infant mortality rate decreased 2.2% to a historically low figure of 5.66 infant deaths per 1,000 live births in 2018. Conclusions-The age-adjusted death rate for the total, male, and female populations decreased from 2017 to 2018, and life expectancy at birth increased in 2018 for the total, male, and female populations.

Associations of Leisure-Time Physical Activity and Television Viewing with Life Expectancy Cancer-Free at Age 50: The ARIC Study

BACKGROUND

Physical activity has been associated with longer chronic disease-free life expectancy, but specific cancer types have not been investigated. We examined whether leisure-time moderate-to-vigorous physical activity (LTPA) and television (TV) viewing were associated with life expectancy cancer-free.

METHODS

We included 14,508 participants without a cancer history from the Atherosclerosis Risk in Communities (ARIC) study. We used multistate survival models to separately examine associations of LTPA (no LTPA,

RESULTS

Compared with no LTPA, participants who engaged in LTPA ≥median had a greater life expectancy cancer-free from colorectal [men-2.2 years (95% confidence interval (CI), 1.7-2.7), women-2.3 years (95% CI, 1.7-2.8)], lung [men-2.1 years (95% CI, 1.5-2.6), women-2.1 years (95% CI, 1.6-2.7)], prostate [1.5 years (95% CI, 0.8-2.2)], and postmenopausal breast cancer [2.4 years (95% CI, 1.4-3.3)]. Compared with watching TV often/very often, participants who seldom/never watched TV had a greater colorectal, lung, and postmenopausal breast cancer-free life expectancy of ∼1 year.

CONCLUSIONS

Participating in LTPA was associated with longer life expectancy cancer-free from colorectal, lung, prostate, and postmenopausal breast cancer. Viewing less TV was associated with more years lived cancer-free from colorectal, lung, and postmenopausal breast cancer.

IMPACT

Increasing physical activity and reducing TV viewing may extend the number of years lived cancer-free.

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Key Words Collapse

MESH Headings

• Adult
• Aged
• Exercise/physiology
• Female
• Humans
• Life Expectancy/trends
• Male
• Middle Aged
• Neoplasms/epidemiology
• Risk Factors
• Television/trends
• Time Factors

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Grants

• P30 CA016086 NCI NIH HHS
• HHSN268201700002C NHLBI NIH HHS
• HHSN268201700001I NHLBI NIH HHS
• HHSN268201700004I NHLBI NIH HHS
• HHSN268201700004C NHLBI NIH HHS
• HHSN268201700003I NHLBI NIH HHS
• HHSN268201700005C NHLBI NIH HHS
• HHSN268201700001C NHLBI NIH HHS
• HHSN268201700003C NHLBI NIH HHS
• HHSN268201700002I NHLBI NIH HHS
• HHSN268201700005I NHLBI NIH HHS
• T32 HL007055 NHLBI NIH HHS
• U01 CA164975 NCI NIH HHS

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Affiliation(s)

Carmen C Cuthbertson Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Hazel B Nichols Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
Xianming Tan Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
Anna Kucharska-Newton Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
Gerardo Heiss Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
Corinne E Joshu Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Elizabeth A Platz Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Kelly R Evenson Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

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US State Polarization, Policymaking Power, and Population Health

Policy Points Explanations for the troubling trend in US life expectancy since the 1980s should be grounded in the dynamic changes in policies and political landscapes. Efforts to reverse this trend and put US life expectancy on par with other high-income countries must address those factors. Of prime importance are the shifts in the balance of policymaking power in the United States, the polarization of state policy contexts, and the forces behind those changes.

Trends in disease-free life expectancy at age 65 in Spain: Diverging patterns by sex, region and disease

Life expectancy in Spain is among the highest in the world. Nevertheless, we do not know if improvements in health conditions at older ages have followed postponements of death. Previous studies in Spain show a stable trend in years lived in ill health in the past. In this paper we investigate changes between 2006, 2012 and 2017 in life expectancy with and without disease at age 65 in Spain and, for the first time, in Spanish regions, which have autonomous powers of health planning, public health and healthcare. Results show that, at the country level, disease-free life expectancy reduced between 2006 and 2017 in Spain. This was explained by an expansion of most diseases except for some cardiovascular and respiratory chronic conditions. However, at the regional level the evolution was different, especially regarding each disease and sex. First, regional differences reduced between 2006 and 2012 but largely widened in 2017, suggesting that not all regions had the same ability to recover after the 2008 financial crisis that caused government cuts to health services. Second, regional analysis also highlighted diverging trends by sex. While men experienced expansion of morbidity in most regions, women experienced a compression in about half of them, ending up with women showing higher disease-free life expectancies than men in 9 out of the 17 regions considered. This study, then, calls attention to the importance of focusing the analysis of health surveillance to more disaggregated levels, more in accordance with the level of health management, as regional trends showed heterogeneity in the prevalence of diseases and different progresses in the relationship between sexes.

Why do some countries do better or worse in life expectancy relative to income? An analysis of Brazil, Ethiopia, and the United States of America

BACKGROUND

While in general a country's life expectancy increases with national income, some countries "punch above their weight", while some "punch below their weight" - achieving higher or lower life expectancy than would be predicted by their per capita income. Discovering which conditions or policies contribute to this outcome is critical to improving population health globally.

METHODS

We conducted a mixed-method study which included: analysis of life expectancy relative to income for all countries; an expert opinion study; and scoping reviews of literature and data to examine factors that may impact on life expectancy relative to income in three countries: Ethiopia, Brazil, and the United States. Punching above or below weight status was calculated using life expectancy at birth and gross domestic product per capita for 2014-2018. The scoping reviews covered the political context and history, social determinants of health, civil society, and political participation in each country.

RESULTS

Possible drivers identified for Ethiopia's extra 3 years life expectancy included community-based health strategies, improving access to safe water, female education and gender empowerment, and the rise of civil society organisations. Brazil punched above its weight by 2 years. Possible drivers identified included socio-political and economic improvements, reduced inequality, female education, health care coverage, civil society, and political participation. The United States' neoliberal economics and limited social security, market-based healthcare, limited public health regulation, weak social safety net, significant increases in income inequality and lower levels of political participation may have contributed to the country punching 2.9 years below weight.

CONCLUSIONS

The review highlighted potential structural determinants driving differential performance in population health outcomes cross-nationally. These included greater equity, a more inclusive welfare system, high political participation, strong civil society and access to employment, housing, safe water, a clean environment, and education. We recommend research comparing more countries, and also to examine the processes driving within-country inequities.

Age- and cause-specific contributions to the life expectancy gap between Medical Aid recipients and National Health Insurance beneficiaries in Korea, 2008-2017

Recipients of Medical Aid, a government-funded social assistance program for the poor, have a shorter life expectancy than National Health Insurance beneficiaries in Korea. This study aims to explore the contributions of age and major causes of death to the life expectancy difference between the two groups. We used the National Health Information Database provided by the National Health Insurance Service individually linked to mortality registration data of Statistics Korea between 2008 and 2017. Annual abridged life tables were constructed and Arriaga’s life expectancy decomposition method was employed to estimate age- and cause-specific contributions to the life expectancy gap between National Health Insurance beneficiaries and Medical Aid recipients. The life expectancy difference between National Health Insurance beneficiaries and Medical Aid recipients was 14.5 years during the period of 2008–2017. The age groups between 30 and 64 years accounted for 78.7% and 67.5% of the total life expectancy gap in men and women, respectively. Cancer was the leading cause of death contributing to excess mortality among Medical Aid recipients compared to National Health Insurance beneficiaries. More specifically, alcohol-attributable deaths (such as alcoholic liver disease, liver cancer, liver cirrhosis, and alcohol/substance abuse), suicide, and cardiometabolic risk factor–related deaths (such as cerebrovascular disease, ischemic heart disease, and diabetes) were the leading contributors to the life expectancy gap. To decrease excess deaths in Medical Aid recipients and reduce health inequalities, effective policies for tobacco and alcohol regulation, suicide prevention, and interventions to address cardiometabolic risk factors are needed.

United States Life Tables, 2018

Objectives-This report presents complete period life tables for the United States by Hispanic origin, race, and sex, based on age-specific death rates in 2018. Methods-Data used to prepare the 2018 life tables are 2018 final mortality statistics; July 1, 2018 population estimates based on the 2010 decennial census; and 2018 Medicare data for persons aged 66-99. The methodology used to estimate the life tables for the Hispanic population remains unchanged from that developed for the publication of life tables by Hispanic origin for data year 2006. The methodology used to estimate the 2018 life tables for all other groups was first implemented with data year 2008. In 2018, all 50 states and the District of Columbia reported deaths by race based on the 1997 Office of Management and Budget revised standards for the classification of federal data on race and ethnicity. As a result, race-specific life tables for 2018 presented in this report are based on the new standard and show estimates for single-race groups. These estimates are not completely comparable with those of previous years, which are based on bridged-race groups. To show trends and document the impact of changing to the 1997 standards, life expectancy estimates for 2006-2018 are reported for bridged-race categories that were in use starting with data year 2000. Results-In 2018, the overall expectation of life at birth was 78.7 years, increasing from 78.6 in 2017. Between 2017 and 2018, life expectancy at birth increased by 0.1 year for males (76.1 to 76.2) and females (81.1 to 81.2). In 2018, life expectancy at birth was 81.8 for the Hispanic population, 78.6 for the non-Hispanic single-race white population, and 74.7 for the non-Hispanic single-race black population.

Projected impact of diabetes on the Costa Rican healthcare system

INTRODUCTION

Costa Rica, similar to many other Latin American countries is undergoing population aging at a fast pace. As a result of the epidemiological transition, the prevalence of diabetes has increased. This condition impacts not only individual lives, but also the healthcare system. The goal of this study is to examine the expected impact of diabetes, in terms of economic costs on the healthcare system and lives lost. We will also project how long it will take for the number of elderly individuals who are diabetic to double in Costa Rica.

METHODS

CRELES (Costa Rican Longevity and Healthy Aging Study), a three-wave nationally representative longitudinal study, is the main source of data for this research (n = 2827). The projected impact of diabetes was estimated in three ways: length of time for the number of elderly individuals with diabetes to double; projected economic costs of diabetes-related hospitalizations and outpatient care; and years of life lost to diabetes at age 60. Data analyses and estimations used multiple regression models, longitudinal regression models, and Lee-Carter stochastic population projections.

RESULTS

Doubling time of the diabetic elderly population is projected to occur in 13 calendar years. This will cause increases in hospitalization and outpatient consultation costs. The impact of diabetes on life expectancy at age 60 around the year 2035 is estimated to lead to a loss of about 7 months of life. The rapid pace at which the absolute number of elderly people with diabetes will double is projected to result in a negative economic impact on the healthcare system. Lives will also be lost due to diabetes.

CONCLUSION

Population aging will inevitably lead to an increasing number of elderly individuals, who are at greater risk for diabetes due to their lifelong exposure to risk factors. Actions to increase the quality of life of diabetic elderly are warranted. Decreasing the burden of diabetes on elderly populations and the Costa Rican healthcare system are necessary to impact the quantity and quality of life of incoming cohorts. Health promotion and prevention strategies that reduce diabetes risk factors are needed to improve the health of elderly populations.

GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease.

METHODS

GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden.

FINDINGS

The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51-12·1) deaths (19·2% [16·9-21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12-9·31) deaths (15·4% [14·6-16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253-350) DALYs (11·6% [10·3-13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0-9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10-24 years, alcohol use for those aged 25-49 years, and high systolic blood pressure for those aged 50-74 years and 75 years and older.

INTERPRETATION

Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public.

FUNDING

Bill & Melinda Gates Foundation.

Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease.

METHODS

GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden.

FINDINGS

The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51-12·1) deaths (19·2% [16·9-21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12-9·31) deaths (15·4% [14·6-16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253-350) DALYs (11·6% [10·3-13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0-9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10-24 years, alcohol use for those aged 25-49 years, and high systolic blood pressure for those aged 50-74 years and 75 years and older.

INTERPRETATION

Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public.

FUNDING

Bill & Melinda Gates Foundation.

Years of life lost from ischaemic and haemorrhagic stroke related to ambient nitrogen dioxide exposure: A multicity study in China

Few multicity studies have been conducted in developing countries to distinguish the acute effects of ambient nitrogen dioxide (NO₂) on the years of life lost (YLL) from different subtypes of stroke. We aimed to differentiate the associations between NO₂ exposure and YLL from major pathological types of stroke in China, and estimate the relevant economic loss. A time-series study was conducted to explore the associations between short-term NO₂ exposure and YLL from ischaemic and haemorrhagic stroke from 2013 to 2017 in 48 Chinese cities. Daily NO₂ data and stroke mortality counts for each city were obtained from the National Urban Air Quality Real-time Publishing Platform and Chinese Center for Disease Control and Prevention, respectively. Generalized additive models were applied to estimate the cumulative effects of NO₂ in each city, and meta-analysis was used to combine the city-specific estimates. The relevant economic loss was estimated using the method of the value per statistical life year (VSLY). A 10 μg/m³ increase in ambient NO₂ concentration on the present day and previous day (lag 0-1) would lead to relatively higher increments in percentage change of YLL from ischaemic stroke (0.82%, 95% CI: 0.46%, 1.19%) than haemorrhagic stroke (0.46%, 95% CI: 0.09%, 0.84%). The association was significantly stronger in the low-education population than high-education population for ischaemic stroke. Furthermore, significantly higher association was found in South China than those in North China for both subtypes of stroke. Economic loss due to excess YLL from ischaemic stroke related to NO₂ exposure was higher than that for haemorrhagic stroke. Our study indicated higher association and economic loss of ischaemic than haemorrhagic stroke related to NO₂ exposure in China, which informed priorities for type-specific stroke prevention strategies related to NO₂ pollution and vulnerable population protection.

Deaths and Years of Potential Life Lost From Excessive Alcohol Use - United States, 2011-2015

Excessive alcohol use is a leading cause of preventable death in the United States (1) and costs associated with it, such as those from losses in workplace productivity, health care expenditures, and criminal justice, were $249 billion in 2010 (2). CDC used the Alcohol-Related Disease Impact (ARDI) application* to estimate national and state average annual alcohol-attributable deaths and years of potential life lost (YPLL) during 2011-2015, including deaths from one's own excessive drinking (e.g., liver disease) and from others' drinking (e.g., passengers killed in alcohol-related motor vehicle crashes). This study found an average of 95,158 alcohol-attributable deaths (261 deaths per day) and 2.8 million YPLL (29 years of life lost per death, on average) in the United States each year. Of all alcohol-attributable deaths, 51,078 (53.7%) were caused by chronic conditions, and 52,921 (55.6%) involved adults aged 35-64 years. Age-adjusted alcohol-attributable deaths per 100,000 population ranged from 20.8 in New York to 53.1 in New Mexico. YPLL per 100,000 population ranged from 631.9 in New York to 1,683.5 in New Mexico. Implementation of effective strategies for preventing excessive drinking, including those recommended by the Community Preventive Services Task Force (e.g., increasing alcohol taxes and regulating the number and concentration of alcohol outlets), could reduce alcohol-attributable deaths and YPLL.^†.