Dataset Information

A comparative, correlate analysis and projection of global and regional life expectancy, healthy life expectancy, and their GAP: 1995-2025.

ABSTRACT: Background:Life expectancy (LE) and healthy life expectancy (HALE) are indicators measuring the national health level. GAP is the difference between them. This study systematically analyzed and projected LE, HALE, and GAP across global regions from 1995 to 2025. Methods:We obtained the data of 195 countries/regions on their LE, HALE, and influencing factors from 1995 to 2017. We compared the overall changes of LE, HALE, and GAP. Multiple linear regression analysis examined relationships among LE, HALE, GAP, and the associated factors. Using the Autoregressive Integrated Moving Average (ARIMA) model, we projected trends in LE, HALE, and GAP for 2017-2025. Results:During 1995-2017, LE, HALE, and their GAP in 195 countries/regions in the world showed overall increasing trends. Global average LE increased from 66.20 to 72.98 years, HALE from 57.59 to 63.25 years, and GAP from 8.62 to 9.72 years. LE and HALE in North America, Europe, and Australia were generally higher, while Africa had the lowest rates. Females' LE, HALE, and GAP were all higher than males', but females' growth rates of LE and HALE were lower. Different factors were included to project LE, HALE, and GAP, respectively, and prediction results showed that approximately 18% of the 195 countries/regions might achieve improved LE and HALE and lower GAP. Conclusions:LE, HALE will likely continue to increase in most of countries and regions worldwide in the future and GAP will further expand. While striving to improve LE and HALE, more attention needs be made to reduce GAP and improve quality of life.

SUBMITTER: Cao X

PROVIDER: S-EPMC7568920 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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A comparative, correlate analysis and projection of global and regional life expectancy, healthy life expectancy, and their GAP: 1995-2025.

Cao Xinxi X Hou Yabing Y Zhang Xinyu X Xu Chenjie C Jia Peng P Sun Xiaomin X Sun Li L Gao Ying Y Yang Hongxi H Cui Zhuang Z Wang Youfa Y Wang Yaogang Y

Journal of global health 20201201 2

<h4>Background</h4>Life expectancy (LE) and healthy life expectancy (HALE) are indicators measuring the national health level. GAP is the difference between them. This study systematically analyzed and projected LE, HALE, and GAP across global regions from 1995 to 2025.<h4>Methods</h4>We obtained the data of 195 countries/regions on their LE, HALE, and influencing factors from 1995 to 2017. We compared the overall changes of LE, HALE, and GAP. Multiple linear regression analysis examined relatio ...[more]

PMID: 33110572

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Project description:BACKGROUND:During the past decades, life expectancy has continued to increase in most high-income countries. Previous research suggests that improvements in life expectancy have primarily been driven by advances at the upper end of the health distribution, while parts of the population have lagged behind. Using data from the entire Swedish population, this study aims to examine the life expectancy development among subgroups of individuals with a history of common diseases relative to that of the general population. METHODS:The remaining life expectancy at age 65 was estimated for each year in 1998-2017 among individuals with a history of disease, and for the total Swedish population. We defined population subgroups as individuals with a history of myocardial infarction, ischemic or hemorrhagic stroke, hip fracture, or colon, breast, or lung cancer. We further distinguished between different educational levels and Charlson comorbidity index scores. RESULTS:Life expectancy gains have been larger for men and women with a history of myocardial infarction, ischemic or hemorrhagic stroke, and colon or breast cancer than for the general population. The life expectancy gap between individuals with a history of hip fracture or lung cancer and the general population has, however, been growing. Education and comorbidity have affected mortality levels, but have not altered the rate of increase in life expectancy among individuals with disease history. The female advantage in life expectancy was less pronounced among individuals with disease history than among the general population. CONCLUSIONS:Life expectancy has increased faster in many subpopulations with a history of disease than in the general population, while still remaining at lower levels. Improvements in life expectancy have been observed regardless of comorbidity or educational level. These findings suggest that the rise in overall life expectancy reflects more than just improved survival among the healthy or the delayed onset of disease.

Project description:BackgroundFew studies have investigated the change in life expectancy (LE) and the healthy lifespan among patients with advanced schistosomiasis. This study was to evaluate the LE and healthy life expectancy (HLE) for patients and assess the mechanism responsible for the LE inequality.MethodsWe utilized data from a dynamic advanced schistosomiasis cohort (10,362 patients) for the period from January 2008 to December 2019 in Hunan Province, China, to calculate the LEs of patients, and made a comparison with that of general population (19,642 schistosomiasis-free individuals) in the schistosomiasis endemic areas. LEs were estimated from 15 years of age by constructing period life tables. Arriaga's decomposition method was applied to quantify the influence of the age structure on the difference in LE. HLE for advanced schistosomiasis patients was calculated by using Sullivan method with age-specific disability weight. The LE and HLE were calculated for both males and females to perform further analyses on gender gap.ResultsThe estimated LE for advanced schistosomiasis patients aged 15-19 was 49.51 years (48.86 years for males and 51.07 years for females), which was 20.14 years lower compared with general population (69.65 years), and the LE gap between patients and general population decreased with age. The largest age-specific mortality contribution to the gap (32.06%) occurred at age 80-84 years. Women had a lower LE and HLE than men at age ≥ 60 years (both gender gaps in LE and HLE < 0). For advanced schistosomiasis patients, the gender gap in LE was largely attributed to the difference in mortality among those under the age of 55; the age-specific mortality in women exerted positive influence on the gap at age 25-64 and 75-79 years, with the contribution rate ranging from 0.59% to 57.02%, and made the negative contribution at other age groups.ConclusionsThe LE of advanced schistosomiasis patients was still much lower compared with general population. Strengthened prevention strategies and targeted treatments are needed to reduce morbidity and mortality due to advanced schistosomiasis, especially for younger population and elderly female patients.

Project description:BACKGROUND:Although the black-white gap in life expectancy has narrowed in the U.S., there is considerable variability across states. In Wisconsin, the black-white gap exceeds 6 years, well above the national average. Reducing this disparity is an urgent public health priority, but there is limited understanding of what contributes to Wisconsin's racial gap in longevity. Our investigation identifies causes of death that contribute most to Wisconsin's black-white gap in life expectancy among males and females, and highlights specific ages where each cause of death contributes most to the gap. METHODS:Our study employs 1999-2016 restricted-use mortality data provided by the National Center for Health Statistics. After generating race- and sex-specific life tables for each 3-year period of observation (e.g., 1999-2001), we trace recent trends in the black-white life expectancy gap in Wisconsin. We subsequently conduct a series of analyses to decompose the black-white gap in three time periods into 13 separate causes and 19 different age groups. RESULTS:In 2014-16, Wisconsin's black-white gap in life expectancy was 7.34 years for males (67% larger than the national gap), and 5.61 years for females (115% larger than the national gap). Among males, homicide was the single largest contributor, accounting for 1.56 years of the total gap. Heart disease and cancer followed, contributing 1.43 and 1.42 years, respectively. Among females, heart disease and cancer were the two leading contributors to the gap, accounting for 1.12 and 1.00 years, respectively. Whereas homicide contributed most to the racial gap in male longevity during late adolescence and early adulthood, heart disease and cancer exerted most of their influence between ages 50-70 for both males and females. Other notable contributors were unintentional injuries (males), diabetes and cerebrovascular disease (females), and perinatal conditions (males and females). CONCLUSIONS:Our study identifies targets for future policy interventions that could substantially reduce Wisconsin's racial gap in life expectancy. Concerted efforts to eliminate racial disparities in perinatal mortality and homicide early in the life course, and chronic conditions such as cancer and heart disease in later life, promise to help Wisconsin achieve the public health objective of racial parity in longevity.

Project description:BackgroundHealth financing produce a broad range of healthy life expectancy (HLE) disparities. In West Africa, limited research exists on the association between health financing and HLE at ecological level during a consecutive period of time from the spatial perspectives. This study aimed to determine the existence, quantify the magnitude, and interpret the association between health financing and HLE.MethodsA Dynamic Spatial Durbin model was used to explain the association between HLE and health financing level and structure during 1995-2019 in West Africa. Spatial spillover effects were introduced to interpret the direct and indirect effects caused by health financing level and structure on HLE during the long and short terms.ResultsSpatial dependence and clustering on HLE were observed in West Africa. Although the overall level of total health spending, government health spending, out-of-pocket health spending, and development assistance for health (DAH) increased from 1995 to 2019, government health spending per person experienced a declining trend. Out-of-pocket health spending per total health spending was the highest among other sources of health financing, decreasing from 57% during 1995-1999 to 42% during 2015-2019. Total health spending and out-of-pocket health spending affected HLE positively and negatively in the long term, respectively. Government health spending and prepaid private health spending per person had positive effects on local and adjacent country HLE in the short-term, while DAH had negative effects on the same. The short-term spatial spillover effects of government health spending, DAH, and prepaid private health spending per person were more pronounced than the long-term effects.ConclusionsSpatial variations of HLE existed at country-level in West Africa. Health financing regarding government, non-government, as well as external assistance not only affected HLE disparities at local scale but also among nearby countries. Policymakers should optimise supportive health financing transition policies and narrow the national gap to reduce health disparities and increase HLE. Externalities of policy of those health financing proxies should be took into consideration to promote health equity to improve global health governance.

Project description:BackgroundReliable estimation of future trends in life expectancy and the burden of disability is crucial for ageing societies. Previous forecasts have not considered the potential impact of trends in disease incidence. The present prediction model combines population trends in cardiovascular disease, dementia, disability, and mortality to forecast trends in life expectancy and the burden of disability in England and Wales up to 2025.MethodsWe developed and validated the IMPACT-Better Ageing Model-a probabilistic model that tracks the population aged 35-100 years through ten health states characterised by the presence or absence of cardiovascular disease, dementia, disability (difficulty with one or more activities of daily living) or death up to 2025, by use of evidence-based age-specific, sex-specific, and year-specific transition probabilities. As shown in the English Longitudinal Study of Ageing, we projected continuing declines in dementia incidence (2·7% per annum), cardiovascular incidence, and mortality. The model estimates disability prevalence and disabled and disability-free life expectancy by year.FindingsBetween 2015 and 2025, the number of people aged 65 years and older will increase by 19·4% (95% uncertainty interval [UI] 17·7-20·9), from 10·4 million (10·37-10·41 million) to 12·4 million (12·23-12·57 million). The number living with disability will increase by 25·0% (95% UI 21·3-28·2), from 2·25 million (2·24-2·27 million) to 2·81 million (2·72-2·89 million). The age-standardised prevalence of disability among this population will remain constant, at 21·7% (95% UI 21·5-21·8) in 2015 and 21·6% (21·3-21·8) in 2025. Total life expectancy at age 65 years will increase by 1·7 years (95% UI 0·1-3·6), from 20·1 years (19·9-20·3) to 21·8 years (20·2-23·6). Disability-free life expectancy at age 65 years will increase by 1·0 years (95% UI 0·1-1·9), from 15·4 years (15·3-15·5) to 16·4 years (15·5-17·3). However, life expectancy with disability will increase more in relative terms, with an increase of roughly 15% from 2015 (4·7 years, 95% UI 4·6-4·8) to 2025 (5·4 years, 4·7-6·4).InterpretationThe number of older people with care needs will expand by 25% by 2025, mainly reflecting population ageing rather than an increase in prevalence of disability. Lifespans will increase further in the next decade, but a quarter of life expectancy at age 65 years will involve disability.FundingBritish Heart Foundation.

Dataset Information

A comparative, correlate analysis and projection of global and regional life expectancy, healthy life expectancy, and their GAP: 1995-2025.

Publications

A comparative, correlate analysis and projection of global and regional life expectancy, healthy life expectancy, and their GAP: 1995-2025.

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