Differences In Healthy Life Expectancy Research Articles

Decomposing the gaps in healthy and unhealthy life expectancies between Indigenous and non-Indigenous Australians: a burden of disease and injury study

BackgroundThe gaps in healthy life expectancy (HLE) between Indigenous and non-Indigenous Australians are significant. Detailed and accurate information is required to develop strategies that will close these health disparities. This paper aims to quantify and compare the causes and their relative contributions to the life expectancy (LE) gaps between the Indigenous and non-Indigenous population in the Northern Territory (NT), Australia.MethodsThe age-cause decomposition was used to analyse the differences in HLE and unhealthy life expectancy (ULE), where LE = HLE + ULE. The data was sourced from the burden of disease and injury study in the NT between 2014 and 2018.ResultsIn 2014–2018, the HLE at birth in the NT Indigenous population was estimated at 43.3 years in males and 41.4 years in females, 26.5 and 33.5 years shorter than the non-Indigenous population. This gap approximately doubled the LE gap (14.0 years in males, 16.6 years in females) at birth. In contrast to LE and HLE, ULE at birth was longer in the Indigenous than non-Indigenous population. The leading causes of the ULE gap at birth were endocrine conditions (explaining 2.9–4.4 years, 23–26%), followed by mental conditions in males and musculoskeletal conditions in females (1.92 and 1.94 years, 15% and 12% respectively), markedly different from the causes of the LE gap (cardiovascular disease, cancers and unintentional injury).ConclusionsThe ULE estimates offer valuable insights into the patterns of morbidity particularly useful in terms of primary and secondary prevention.

Regional differences and trends in healthy life expectancy in Germany

Against the background of increasing life expectancy, the question arises in which state of health the additional years of life are spent. The aim of this study is to assess for the first time regional differences in healthy life expectancy for Germany. The concept of healthy life expectancy allows for the combination of regional differences in health status and mortality in asingle measure. This article uses the concept of partial healthy life expectancy. We use official data on deaths and population numbers to calculate abridged life tables. Data from the Socio-Economic Panel (SOEP) are used to determine the age- and sex-specific prevalences of health status. Regional differences are analyzed from 2002 to 2019 by dividing Germany into four regions (North, South, East, West). The regional differences in healthy life expectancy in Germany are greater than differences in life expectancy, and trends in healthy life expectancy partly differ from the corresponding trends in mortality. These differences over time also vary according to age: while healthy life expectancy has tended to stagnate and, in some cases, decline among the population aged between 20and 64, the number and proportion of years in good health has increased among older adults up to the age of79. There are striking regional differences and trends in the distribution of expected years in good health in Germany. The timely identification of regionally divergent developments could facilitate the implementation of targeted health-promoting measures.

Decomposing the differences in healthy life expectancy between migrants and natives: the ‘healthy migrant effect’ and its age variations in Australia

Decomposing the differences in healthy life expectancy between migrants and natives: the ‘healthy migrant effect’ and its age variations in Australia

Impact of access to childhood health services on healthy life expectancy of the older population.

Life course theory provides new perspectives on the impact of early experiences on health in old age, where unfortunate childhood experiences can alter an individual's health trajectory. This study aims to calculate the healthy life expectancy of the older population in China under different childhood experiences, and to explore the influence of childhood medical and health services on the health level of older adults. Differences in healthy life expectancy of the older population under different childhood experiences were analyzed using the multi-state life table method to calculate the healthy life expectancy by sex and place of birth, based on the cohort data of Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2008 to 2018. The probability of the transition risk from health to non-health gradually increased with age, while the probability of the transition risk from non-health to health decreased with age; In both urban and rural areas, on the probability of the transition risk from health to non-health, the older adults who were able to receive timely medical and health services in childhood were lower than those who failed to receive medical services in time (Z = -5.833, P < 0.05), but the probability of the transition risk from non-health to health was the opposite (Z = -5.334, P < 0.05); The probability of the transition risk from health to death is also higher in older adults who were unable to receive timely medical care in childhood (Z = -5.88, P < 0.05); The healthy life expectancy and its proportion in the remaining life expectancy of older people who received medical and health services in time during childhood were significantly higher than those of their peers (Z = -5.88, P < 0.05). The lack of medical services in childhood has a negative effect on the health of older adults. The healthy life expectancy and its proportion of remaining life expectancy were higher for rural older adults than for urban older adults under the same health care conditions in childhood; the health benefits of good access to health care environment or conditions in childhood were greater for rural older females.

Ожидаемая продолжительность здоровой жизни в контексте Десятилетия здорового старения ООН

The article examines the dynamics of the healthy life expectancy (HALE) in the world, in the WHO European Region and in Russia. It provides cross-country comparisons, revealing the main trends in the dynamics of healthy life expectancy at birth and at older ages. The author notes that the most intense growth in healthy life expectancy can be observed in low-income countries; as wealth increases, growth in healthy life expectancy slows down. The author analyzes the differences in healthy life expectancy between the regions of the Russian Federation. It is noted that the inequality between Russian regions is higher compared to the inequality between groups of low- and high-income countries, between WHO regions, and between countries of the WHO European Region. Russia is one of the countries for which the territorial aspect of inequality in all its manifestations is of great importance. The article discusses various methods for assessing healthy life expectancy, adopted in international and Russian statistics. The study concludes that the HALE indicator used by WHO is optimal for assessing the performance of health systems, for monitoring the health situation of the population. The indicators calculated by Eurostat and Russian statistics are based on the population's self-perceived state of health. Self-perceived health status is essential for assessing the quality of life and determining the need for health care, social services and care, although it is subjective. This is especially important in terms of promoting healthy ageing. The process of population ageing in the world is accelerating more and more. Medical, social, economic, psychological problems associated with the ageing of the population are most acute in economically developed countries. Increasing healthy life expectancy, active longevity, and the development of the ‘silver economy’ are responses to the global demographic challenge of population ageing. Active longevity sets new directions for the development of various spheres of society: medicine, social protection, education and the labour market. At the same time, active longevity creates new types of inequality in these areas, associated with the availability of the necessary resources both at the country level and at the level of population groups and individuals.

Healthy life expectancy for registered residents in 2017 in Shanghai Jing'an Districts

Objective: To estimate the healthy life expectancy (HALE) of registered residents in Jing'an District, Shanghai City. Methods: From June to August in 2017, 14 districts (towns) were selected as the research sites, and 4 159 registered residents were selected as the subjects. The health status data of subjects were collected by using the self-rated health scale, and the health rate of subjects was obtained by using the CHOPIT model. The health rate of residents under 18 years old was replaced by the parameters of 18-year-old group, and Sullivan method was used to calculate the HALE of registered residents in Jing'an District, Shanghai City. Results: The age of 4 159 subjects was (56.46±15.19) years old, ranging from 18 to 98 years old. There were 1 768 males (42.5%). The overall health rate of subjects was 74.96%, of which the health rates of male and female were 76.87% and 72.45% respectively. With the increase of age, the health rate decreased (Z=265.51, P<0.001), and the health rate of male was higher than that of female (χ²=2 154.54, P<0.001). The HALE of the 0-year-old group was 64.29 years old, in which the male and female were 66.25 and 63.57 years old respectively. Among the 18-year-old group, the HALE was 48.18 years old, with 49.07 years old for male and 47.46 years old for female. The HALE of male was higher than that of female in all age groups. With the increase of age, the HALE decreased gradually. Conclusion: There are significant sex and age differences in HALE in Jing'an District, Shanghai City. The health issue of female and older people should be given more attention.

Toward the development of a vibrant, super-aged society: The future of medicine and society in Japan.

Toward the development of a vibrant, super-aged society: The future of medicine and society in Japan.

Associations Between Healthcare Resources and Healthy Life Expectancy: A Descriptive Study across Secondary Medical Areas in Japan.

Japan has the highest life expectancy in the world. However, this does not guarantee an improved quality of life. There is a gap between life expectancy and healthy life expectancy. This study aimed to reveal the features of healthy life expectancy across all secondary medical areas (n = 344) in Japan and examine the relationship among healthcare resources, life expectancy, and healthy life expectancy at birth. Data were collected from Japan’s population registry and long-term insurance records. Differences in healthy life expectancy by gender were calculated using the Sullivan method. Maps of healthy life expectancy were drawn up. Descriptive statistics and correlation analysis were used for analysis. The findings revealed significant regional disparities. The number of doctors and therapists, support clinics for home healthcare facilities and home-visit treatments, and dentistry expenditure per capita were positively correlated with life expectancy and healthy life expectancy (correlation coefficients > 0.2). They also revealed gender differences. Despite controlling for population density, inequalities in healthy life expectancy were observed, highlighting the need to promote social policies to reduce regional disparities. Japanese policymakers should consider optimal levels of health resources to improve life expectancy and healthy life expectancy. The geographical distribution of healthcare resources should also be reconstituted.

Increasing Education-Based Disparities in Healthy Life Expectancy Among U.S. Non-Hispanic Whites, 2000-2010.

To examine changes in Healthy Life Expectancy (HLE) against the backdrop of rising mortality among less-educated white Americans during the first decade of the twenty-first century. This study documented changes in HLE by education among U.S. non-Hispanic whites, using data from the U.S. Multiple Cause of Death public-use files, the Integrated Public Use Microdata Sample (IPUMS) of the 2000 Census and the 2010 American Community Survey, and the Health and Retirement Study (HRS). Changes in HLE were decomposed into contributions from: (i) change in age-specific mortality rates; and (ii) change in disability prevalence, measured via Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). Between 2000 and 2010, HLE significantly decreased for white men and women with less than 12 years of schooling. In contrast, HLE increased among college-educated white men and women. Declines or stagnation in HLE among less-educated whites reflected increases in disability prevalence over the study period, whereas improvements among the college educated reflected decreases in both age-specific mortality rates and disability prevalence at older ages. Differences in HLE between education groups increased among non-Hispanic whites from 2000 to 2010. In fact, education-based differences in HLE were larger than differences in total life expectancy. Thus, the lives of less-educated whites were not only shorter, on average, compared with their college-educated counterparts, but they were also more burdened with disability.

Sex differences in healthy life expectancy among nonagenarians: A multistate survival model using data from the Vitality 90+ study.

Little is known about sex differences in healthy life expectancy among the oldest old, the fastest growing segment of the older population. This study examines sex differences in total, healthy and unhealthy life expectancy among nonagenarians. Longitudinal data of 884 older adults aged 90 and over participating in the Vitality 90+ study (Tampere, Finland) were used, including 2501 observations (health or death states) from 5 measurement waves between 2001 and 2014. Using the MSM and ELECT packages in R, multistate survival models were performed to estimate the transition probabilities of older adults through the different health states and to calculate life expectancies. The analyses were done separately for two health indicators (disability and multimorbidity) to see whether patterns were consistent. Women had higher total life expectancies than men (about 8 months), but also higher unhealthy life expectancies. Men had a higher disability-free life expectancy between the age of 90 and 95 compared to women. For multimorbidity, no sex differences in healthy life expectancy were found. This study showed that the male-female health-survival paradox remains at very old age. Women aged 90+ live longer than men, and spend more time in poor health.

Gender difference in trends in healthy life expectancy in 2005-2012 for adults aged 50 years and older in South Africa

Data characterizing older people’s life expectancy by good or poor health is important for policy and fiscal planning. This study aims to examine trends and investigate gender differences in healthy life expectancy (HLE) for older people in South Africa for the period 2005–2012. Using data from three repeated cross-sectional surveys conducted in 2005, 2008, and 2012, we applied a self-rated health measure to estimating HLE. The Sullivan method was used in the calculations. We found that unhealthy life expectancy decreased over the period, while HLE and the proportion of life spent in good health increased more than total life expectancy in the same period. Gender disparities were evident: Women had higher life expectancy than men, yet they spent a greater proportion of their lifetime in poor health. We concluded that HLE of older people in South Africa has improved over the period under investigation.

Differences in healthy life expectancy between older migrants and non-migrants in three European countries over time

ObjectivesWe analysed differences in healthy life expectancy at age 50 (HLE50) between migrants and non-migrants in Belgium , the Netherlands, and England and Wales, and their trends over time between 2001 and 2011 in the latter two countries.MethodsPopulation, mortality and health data were derived from registers, census or surveys. HLE50 and the share of remaining healthy life years were calculated for non-migrants, western and non-western migrants by sex. We applied decomposition techniques to answer whether differences in HLE50 between origin groups and changes in HLE50 over time were attributable to either differences in mortality or health.ResultsIn all three countries, older (non-western) migrants could expect to live less years in good health than older non-migrants. Differences in HLE50 between migrants and non-migrants diminished over time in the Netherlands, but they increased in England and Wales. General health, rather than mortality, mainly explained (trends in) inequalities in healthy life expectancy between migrants and non-migrants.ConclusionsInterventions aimed at reducing the health and mortality inequalities between older migrants and non-migrants should focus on prevention, and target especially non-western migrants.

Inequalities in healthy life expectancy by Brazilian geographic regions: findings from the National Health Survey, 2013.

BackgroundThe demographic shift and epidemiologic transition in Brazil have drawn attention to ways of measuring population health that complement studies of mortality. In this paper, we investigate regional differences in healthy life expectancy based on information from the National Health Survey (PNS), 2013.MethodsIn the survey, a three-stage cluster sampling (census tracts, households and individuals) with stratification of the primary sampling units and random selection in all stages was used to select 60,202 Brazilian adults (18 years and over). Healthy life expectancies (HLE) were estimated by Sullivan’s method according to sex, age and geographic region, using poor self-rated health for defining unhealthy status. Logistic regression models were used to investigate socioeconomic and regional inequalities in poor self-rated health, after controlling by sex and age.ResultsWide disparities by geographic region were found with the worst indicators in the North and Northeast regions, whether considering educational attainment, material deprivation, or health care utilization. Life expectancy at birth for women and men living in the richest regions was 5 years longer than for those living in the less wealthy regions. Modeling the variation across regions for poor self-rated health, statistically significant effects (p < 0.001) were found for the North and Northeast when compared to the Southeast, even after controlling for age, sex, diagnosis of at least one non-communicable chronic disease, and schooling or socioeconomic class. Marked regional inequalities in HLE were found, with the loss of healthy life much higher among residents of the poorest regions, especially among the elderly.ConclusionsBy combining data on self-rated health status and mortality in a single indicator, Healthy Life Expectancy, this study demonstrated the excess burden of poor health experienced by populations in the less wealthy regions of Brazil. To mitigate the effects of social exclusion, the development of strategies at the regional level is essential to provide health care to all persons in need, reduce risk exposures, support prevention policies for adoption of healthy behaviors. Such strategies should prioritize population groups that will experience the greatest impact from such interventions.

Socioeconomic differences in healthy life expectancy: Evidence from four prospective cohort studies

Socioeconomic differences in healthy life expectancy: Evidence from four prospective cohort studies

Monitoring socio-economic differences in healthy life expectancy in the Netherlands

Background In the Netherlands, like in other countries, health differs between those with a low and a high socio-economic position. This is shown by several figures on socio-economic differences in health. Insight in the meaning of these figures and their trends is a basis for public health policy (e.g. the Dutch National Prevention Program aims to increase healthy life …

Live longer and better? Estimates of healthy life expectancy in the Brazilian population

This study analyzed differences in healthy life expectancy in the elderly based on three health dimensions in Brazil from 1998 to 2008: disability-free life expectancy, healthy life expectancy based on self-rated health, and chronic disease-free life expectancy. The Sullivan method was used, combining life tables from the Brazilian Institute of Geography and Statistics (IBGE) and interval estimates of the prevalence of functional disability, self-rated health, and chronic diseases according to the Brazilian National Household Sample Survey (PNAD, 1998 and 2008). Besides the increase in life expectancy, the study showed significant and similar increases in disability-free life expectancy and healthy life expectancy based on self-rated health at almost all ages. Women had higher life expectancies than men, but expected to live longer with poor health, regardless of the indicator used to measure health. Although the studies measured health differently (making comparisons difficult), women showed a consistent disadvantage in healthy life expectancy.

Gender differences in health expectancies across the disablement process among older Thais.

ObjectivesTo estimate health expectancies based on measures that more fully cover the stages in the disablement process for the older Thais and examine gender differences in these health expectancies.MethodsHealth expectancies by genders using Sullivan’s method were computed from the fourth Thai National Health Examination Survey conducted in 2009. A total of 9,210 participants aged 60 years and older were included in the analysis. Health measures included chronic diseases; cognitive impairment; depression; disability in instrumental activities of daily living (IADL); and disability in activities of daily living (ADL).ResultsThe average number of years lived with and without morbidity and disability as measured by multiple dimensions of health varied and gender differences were not consistent across measures. At age 60, males could expect to live the most years on average free of depression (18.6 years) and ADL disability (18.6 years) and the least years free of chronic diseases (9.1 years). Females, on the contrary, could expect to live the most years free of ADL disability (21.7 years) and the least years free of IADL disability (8.1 years), and they consistently spent more years with all forms of morbidity and disability. Finally, and for both genders, years lived with cognitive impairment, depression and ADL disability were almost constant with increasing age.ConclusionThis study adds knowledge of gender differences in healthy life expectancy in the older Thai population using a wider spectrum of health which provides useful information to diverse policy audiences.

Differences in healthy life expectancy for the US population by sex, race/ethnicity and geographic region: 2008.

Healthy life expectancy (HLE) varies among demographic segments of the US population and by geography. To quantify that variation, we estimated the national and regional HLE for the US population by sex, race/ethnicity and geographic region in 2008. National HLEs were calculated using the published 2008 life table and the self-reported health status data from the National Health Interview Survey (NHIS). Regional HLEs were calculated using the combined 2007-09 mortality, population and NHIS health status data. In 2008, HLE in the USA varied significantly by sex, race/ethnicity and geographical regions. At 25 years of age, HLE for females was 47.3 years and ∼2.9 years greater than that for males at 44.4 years. HLE for non-Hispanic white adults was 2.6 years greater than that for Hispanic adults and 7.8 years greater than that for non-Hispanic black adults. By region, the Northeast had the longest HLE and the South had the shortest. The HLE estimates in this report can be used to monitor trends in the health of populations, compare estimates across populations and identify health inequalities that require attention.

Gender differences in healthy life expectancy among Brazilian elderly.

BackgroundThis study examined gender differences in healthy life expectancy (HLE) and unhealthy life expectancy (UHLE) among people aged 60 years or older living in a large Brazilian city.MethodsBased on Chiang method, abridged life tables were constructed for men and for women. To calculate HLE, the Sullivan method was applied. Estimates of the prevalence of self-rated health and self-reported functional disability (global, mild/moderate, and severe) were obtained from a population-based household survey carried out in 2008, which involved non-institutionalized individuals.ResultsFindings showed that women live longer and these extra years would be spent in good self-rated health. For example, women aged 60 would live, on average, 4 more years in good health in comparison to men. In terms of global limitations and mild/moderate limitations, no gender differences were detected for HLE. However, UHLE was statistically higher among women than among men at all ages in the global limitations and mild/moderate limitations (except for the age 80). Women at age 60, for instance, could expect to live 3.1 years longer with mild/moderate limitations compared to men. Gender differences were identified for severe limitations for either HLE or UHLE. In comparison to men, women at age 60, for example, would expect to live 2.5 and 2.0 more years without and with severe limitations.ConclusionsBy showing that the advantage of longer life expectancy among women is not necessarily accompanied by worse health conditions, these findings add some evidence to the debate about male-female health-survival paradox. Policy efforts are necessary to reduce gender differences in the quantity and quality of years to be lived, providing equal opportunities to women and men live longer with quality of life, autonomy, and independence.

Educational differences in disability-free life expectancy: a comparative study of long-standing activity limitation in eight European countries

Healthy life expectancy is a composite measure of length and quality of life and an important indicator of health in aging populations. There are few cross-country comparisons of socioeconomic differences in healthy life expectancy. Most of the existing comparisons focus on Western Europe and the United States, often relying on older data. To address these deficiencies, we estimated educational differences in disability-free life expectancy for eight countries from all parts of Europe in the early 2000s. Long-standing severe disability was measured as a Global Activity Limitation Indicator (GALI) derived from the European Union Statistics on Income and Living Conditions (EU-SILC) survey. Census-linked mortality data were collected by a recent project comparing health inequalities between European countries (the EURO-GBD-SE project). We calculated sex-specific educational differences in disability-free life expectancy between the ages of 30 and 79 years using the Sullivan method. The lowest disability-free life expectancy was found among Lithuanian men and women (33.1 and 39.1 years, respectively) and the highest among Italian men and women (42.8 and 44.4 years, respectively). Life expectancy and disability-free life expectancy were directly related to the level of education, but the educational differences were much greater in the latter in all countries. The difference in the disability-free life expectancy between those with a primary or lower secondary education and those with a tertiary education was over 10 years for males in Lithuania and approximately 7 years for males in Austria, Finland and France, as well as for females in Lithuania. The difference was smallest in Italy (4 and 2 years among men and women, respectively). Highly educated Europeans can expect to live longer and spend more years in better health than those with lower education. The size of the educational difference in disability-free life expectancy varies significantly between countries. The smallest and largest differences appear to be in Southern Europe and in Eastern and Northern Europe, respectively.