Inequalities In Healthy Life Expectancy Research Articles

Geographical socioeconomic inequalities in healthy life expectancy in Japan, 2010-2014: An ecological study

ABSTRACTSBackgroundArea differences in life expectancy (LE) and healthy life expectancy (HLE) in large geographical units have been monitored around the world. Area characteristics may be based on culture, history, socioeconomic status and discrimination in smaller geographical units, so it is important to consider these when looking at health inequality. We aimed to evaluate LE, HLE, and non-healthy life expectancy (NHLE) in 1707 municipalities using Areal Deprivation Index (ADI) in Japan for the first time.MethodsWe calculated the observed LE, HLE, and NHLE using death, population, and Long-term care insurance data for 2010-2014 and applied the variance weighted least squares model to estimate LE, HLE, and NHLE by 100 percentiles using the standardized ADI.FindingsThe estimated LE, HLE, and NHLE became lower as the deprivation index worsened: the differences between the most and least deprived areas for HLE were 2·49 years for LE and 2·32 years for HLE in males; 1·22 years for LE and 0·93 years for HLE in females. The observed LE and HLE in the most deprived areas were much lower than other areas.InterpretationUsing ADI has enabled us to see the disparity within municipalities precisely. LE and HLE outlier for the 100th percentile might be linked to historical areal deprivation and marginalization. Precise monitoring of socioeconomic status-based health inequalities could help manage these inequalities by identifying the groups most in need of intervention.FundingThe Ministry of Education, Science and Culture of Japan (a Grant-in-Aid for Scientific Research [A] No. 20H00040 and 18H04071).

On the Postponement of Increases in State Pension Age through Health Improvement and Active Ageing

The UK population is predicted to grow from 65 m in 2015 to 71 m in 2030 and 75 m in 2040, with the number aged 65-plus rising to 18 m. Successive governments have been reluctant to increase taxes but this looks increasingly unsustainable, if the increasing demand for health and social care is to be met. Increasing state pension age is the customary response for keeping pension contributions and benefits in balance in Pay As You Go (PAYG systems). However, this policy raises concerns about the capability of people to work to ever higher pension ages. Using newly available labour market data on health and disability, the paper finds limits to how far pension age can be increased without necessary health improvements. If improvements were forthcoming, planned pension age rises could be postponed. However, inequalities in healthy life expectancy across the UK are a major barrier to its achievement.

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.

Inequalities in Life Expectancy With Frailty Among Brazilian Older Adults: A Multistate Approach

Background and ObjectivesFrailty is considered one of the major conditions faced by aging societies. Little has been reported about the effect of becoming frail on life expectancy among older adults in developing countries. The aim was to estimate total life expectancy and life expectancy with or without frailty by age, sex, and education among older adults in Brazil.Data and Methods: The study was developed based on information provided by the Health, Well-being, and Aging (SABE) Study. The sample included 1,399 older adults (≥60 years old) followed up from 2006 to 2010. Frailty status was classified according to the Fried criteria. Additional variables include age (in years), gender, and years of education. Estimates of total life expectancy, frailty-free life expectancy, and frailty life expectancy were obtained using the multistate life table method.ResultsAt the baseline, the proportion of individuals with frailty was 13.7% and participants had, on average, 4.0 years of education. Men had more years of education than women (4.6 vs. 3.7, p < .001). Older adults with higher education live fewer years with frailty. Compared with older adults with no education, those with 6 years of education have higher frailty-free life expectancy. At age 70, men with no education expect to live 9.1 years (95% confidence interval [CI] = 7.8, 10.4) without frailty compared with 10.6 years (95% CI = 9.4, 11.8) among those with 6 years of education. Among women age 70, frailty-free life expectancy reaches 11.7 years (95% CI = 10.6, 12.8) among those with no education, but 13.9 years (95% CI = 12.5, 15.3) among those with 6 years.ImplicationsGiven the recent changes in educational achievement in Brazil, we believe that educational policies are powerful ways in addressing inequalities in healthy life expectancy. Public health policies aimed at avoiding the development of frailty among elderly at risk should be encouraged.

Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

SummaryBackgroundHow long one lives, how many years of life are spent in good and poor health, and how the population’s state of health and leading causes of disability change over time all have implications for policy, planning, and provision of services. We comparatively assessed the patterns and trends of healthy life expectancy (HALE), which quantifies the number of years of life expected to be lived in good health, and the complementary measure of disability-adjusted life-years (DALYs), a composite measure of disease burden capturing both premature mortality and prevalence and severity of ill health, for 359 diseases and injuries for 195 countries and territories over the past 28 years.MethodsWe used data for age-specific mortality rates, years of life lost (YLLs) due to premature mortality, and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to calculate HALE and DALYs from 1990 to 2017. We calculated HALE using age-specific mortality rates and YLDs per capita for each location, age, sex, and year. We calculated DALYs for 359 causes as the sum of YLLs and YLDs. We assessed how observed HALE and DALYs differed by country and sex from expected trends based on Socio-demographic Index (SDI). We also analysed HALE by decomposing years of life gained into years spent in good health and in poor health, between 1990 and 2017, and extra years lived by females compared with males.FindingsGlobally, from 1990 to 2017, life expectancy at birth increased by 7·4 years (95% uncertainty interval 7·1–7·8), from 65·6 years (65·3–65·8) in 1990 to 73·0 years (72·7–73·3) in 2017. The increase in years of life varied from 5·1 years (5·0–5·3) in high SDI countries to 12·0 years (11·3–12·8) in low SDI countries. Of the additional years of life expected at birth, 26·3% (20·1–33·1) were expected to be spent in poor health in high SDI countries compared with 11·7% (8·8–15·1) in low-middle SDI countries. HALE at birth increased by 6·3 years (5·9–6·7), from 57·0 years (54·6–59·1) in 1990 to 63·3 years (60·5–65·7) in 2017. The increase varied from 3·8 years (3·4–4·1) in high SDI countries to 10·5 years (9·8–11·2) in low SDI countries. Even larger variations in HALE than these were observed between countries, ranging from 1·0 year (0·4–1·7) in Saint Vincent and the Grenadines (62·4 years [59·9–64·7] in 1990 to 63·5 years [60·9–65·8] in 2017) to 23·7 years (21·9–25·6) in Eritrea (30·7 years [28·9–32·2] in 1990 to 54·4 years [51·5–57·1] in 2017). In most countries, the increase in HALE was smaller than the increase in overall life expectancy, indicating more years lived in poor health. In 180 of 195 countries and territories, females were expected to live longer than males in 2017, with extra years lived varying from 1·4 years (0·6–2·3) in Algeria to 11·9 years (10·9–12·9) in Ukraine. Of the extra years gained, the proportion spent in poor health varied largely across countries, with less than 20% of additional years spent in poor health in Bosnia and Herzegovina, Burundi, and Slovakia, whereas in Bahrain all the extra years were spent in poor health. In 2017, the highest estimate of HALE at birth was in Singapore for both females (75·8 years [72·4–78·7]) and males (72·6 years [69·8–75·0]) and the lowest estimates were in Central African Republic (47·0 years [43·7–50·2] for females and 42·8 years [40·1–45·6] for males). Globally, in 2017, the five leading causes of DALYs were neonatal disorders, ischaemic heart disease, stroke, lower respiratory infections, and chronic obstructive pulmonary disease. Between 1990 and 2017, age-standardised DALY rates decreased by 41·3% (38·8–43·5) for communicable diseases and by 49·8% (47·9–51·6) for neonatal disorders. For non-communicable diseases, global DALYs increased by 40·1% (36·8–43·0), although age-standardised DALY rates decreased by 18·1% (16·0–20·2).InterpretationWith increasing life expectancy in most countries, the question of whether the additional years of life gained are spent in good health or poor health has been increasingly relevant because of the potential policy implications, such as health-care provisions and extending retirement ages. In some locations, a large proportion of those additional years are spent in poor health. Large inequalities in HALE and disease burden exist across countries in different SDI quintiles and between sexes. The burden of disabling conditions has serious implications for health system planning and health-related expenditures. Despite the progress made in reducing the burden of communicable diseases and neonatal disorders in low SDI countries, the speed of this progress could be increased by scaling up proven interventions. The global trends among non-communicable diseases indicate that more effort is needed to maximise HALE, such as risk prevention and attention to upstream determinants of health.FundingBill & Melinda Gates Foundation.

Socioeconomic differences in healthy and disease-free life expectancy between ages 50 and 75: a multi-cohort study.

BackgroundThere are striking socioeconomic differences in life expectancy, but less is known about inequalities in healthy life expectancy and disease-free life expectancy. We estimated socioeconomic differences in health expectancies in four studies in England, Finland, France and Sweden.MethodsWe estimated socioeconomic differences in health expectancies using data drawn from repeated waves of the four cohorts for two indicators: (i) self-rated health and (ii) chronic diseases (cardiovascular, cancer, respiratory and diabetes). Socioeconomic position was measured by occupational position. Multistate life table models were used to estimate healthy and chronic disease-free life expectancy from ages 50 to 75.ResultsIn all cohorts, we found inequalities in healthy life expectancy according to socioeconomic position. In England, both women and men in the higher positions could expect 82–83% of their life between ages 50 and 75 to be in good health compared to 68% for those in lower positions. The figures were 75% compared to 47–50% for Finland; 85–87% compared to 77–79% for France and 80–83% compared to 72–75% for Sweden. Those in higher occupational positions could expect more years in good health (2.1–6.8 years) and without chronic diseases (0.5–2.3 years) from ages 50 to 75.ConclusionThere are inequalities in healthy life expectancy between ages 50 and 75 according to occupational position. These results suggest that reducing socioeconomic inequalities would make an important contribution to extending healthy life expectancy and disease-free life expectancy.

Decennial trends and inequalities in healthy life expectancy: The HUNT Study, Norway.

Norway is experiencing a rising life expectancy combined with an increasing dependency ratio - the ratio of those outside over those within the working force. To provide data relevant for future health policy we wanted to study trends in total and healthy life expectancy in a Norwegian population over three decades (1980s, 1990s and 2000s), both overall and across gender and educational groups. Data were obtained from the HUNT Study, and the Norwegian Educational Database. We calculated total life expectancy and used the Sullivan method to calculate healthy life expectancies based on self-rated health and self-reported longstanding limiting illness. The change in health expectancies was decomposed into mortality and disability effects. During three consecutive decades we found an increase in life expectancy for 30-year-olds (~7 years) and expected lifetime in self-rated good health (~6 years), but time without longstanding limiting illness increased less (1.5 years). Women could expect to live longer than men, but the extra life years for females were spent in poor self-rated health and with longstanding limiting illness. Differences in total life expectancy between educational groups decreased, whereas differences in expected lifetime in self-rated good health and lifetime without longstanding limiting illness increased. The increase in total life expectancy was accompanied by an increasing number of years spent in good self-rated health but more years with longstanding limiting illness. This suggests increasing health care needs for people with chronic diseases, given an increasing number of elderly. Socioeconomic health inequalities remain a challenge for increasing pensioning age.

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 Federated States.

ABSTRACTOBJECTIVES To estimate the healthy life expectancy at 60 years by sex and Federated States and to investigate geographical inequalities by socioeconomic status.METHODS Healthy life expectancy was estimated by the Sullivan method, based on the information of the National Survey on Health, 2013. Three criteria were adopted for the definition of “unhealthy state”: self-assessment of bad health, functionality for performing the activities of daily living, and the presence of noncommunicable disease with intense degree of limitation. The indicator of socioeconomic status was built based on the number of goods at household and educational level of the head of household. To analyze the geographical inequalities and socioeconomic level, inequality measures were calculated, such as the ratio, the difference, and the angular coefficient.RESULTS Healthy life expectancy among men ranged from 13.8 (Alagoas) to 20.9 (Espírito Santo) for the self-assessment criterion of bad health. Among women, the corresponding estimates were always higher and ranged from 14.9 (Maranhão) to 22.2 (São Paulo). As to the ratio of inequality by Federated State, the medians were always higher for healthy life expectancy than for life expectancy, regardless of the definition adopted for healthy state. Regarding the differences per Federated State, the healthy life expectancy was seven years higher in one state than in another. By socioeconomic status, differences of three and four years were found, approximately, between the last and first fifth, for men and women, respectively.CONCLUSIONS Despite the association of the mortality indicators with living conditions, the inequalities are even more pronounced when the welfare and the limitations in usual activities are considered, showing the necessity to promote actions and programs to reduce the socio-spatial gradient.

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.

Labor force participation and secondary education of gender inequality index (GII) associated with healthy life expectancy (HLE) at birth.

BackgroundWhat is the factor that affects healthy life expectancy? Healthy life expectancy (HLE) at birth may be influenced by components of the gender inequality index (GII). Notably, this claim is not tested on the between components of the GII, such as population at least secondary education (PLSE) with ages 25 and older, labor force participation rate (LFPR) with ages 15 and older, and the HLE in the world’s countries. Thus, this study estimates the associations between the PLSE, LFPR of components of the GII and the HLE.MethodsThe data for the analysis of HLE in 148 countries were obtained from the World Health Organization. Information regarding the GII indicators for this study was obtained from the United Nations database. Associations between these factors and HLE were assessed using Pearson correlation coefficients and regression models.ResultsAlthough significant negative correlations were found between HLE and the LFPR, positive correlations were found between HLE and PLSE. Finally, the HLE predictors were used to form a model of the components of the GII, with higher PLSE as secondary education and lower LFPR as labor force (R2 = 0.552, P <0.001).ConclusionsGender inequality of the attainment secondary education and labor force participation seems to have an important latent effect on healthy life expectancy at birth. Therefore, in populations with high HLE, the gender inequalities in HLE are smaller because of a combination of a larger secondary education advantage and a smaller labor force disadvantage in male-females.

Inequalities in healthy life expectancy between ethnic groups in England and Wales in 2001

Objectives. We aim to develop robust estimates of disability-free life expectancy (DFLE) and healthy life expectancy (HLE) for ethnic groups in England and Wales in 2001 and to examine observed variations across ethnic groups.Design. DFLE and HLE by age and gender for five-year age groups were computed for 16 ethnic groups by combining the 2001 Census data on ethnicity, self-reported limiting long-term illness and self-rated health using mortality by ethnic group estimated by two methods: the Standardised Illness Ratio (SIR) method and the Geographically Weighted Method (GWM).Results. The SIR and GWM methods differed somewhat in their estimates of life expectancy (LE) at birth but produced very similar estimates of DFLE and HLE by ethnic group. For the more conservative method (GWM), the range in DFLE at birth was 10.5 years for men and 11.9 years for women, double that in LE. DFLE at birth was highest for Chinese men (64.7 years, 95% CI 64.0–65.3) and women (67.0 years, 95% CI 66.4–67.6). Over half of the ethnic minority groups (men: 10; women: 9) had significantly lower DFLE at birth than White British men (61.7 years, 95% CI 61.7–61.7) or women (64.1 years, 95% CI 64.1–64.2), mostly the Black, Asian and mixed ethnic groups. The lowest DFLE observed was for Bangladeshi men (54.3 years, 95% CI 53.7–54.8) and Pakistani women (55.1 years, 95% CI 54.8–55.4). Notable were Indian women whose LE was similar to White British women but who had 4.3 years less disability-free (95% CI 4.0–4.6).Conclusions. Inequalities in DFLE between ethnic groups are large and exceed those in LE. Moreover, certain ethnic groups have a larger burden of disability that does not seem to be associated with shorter LE. With the increasing population of the non-White British community, it is essential to be able to identify the ethnic groups at higher risk of disability, in order to target appropriate interventions.

Inequalities in Healthy Life Expectancy in Eastern Europe

Compared to the large body of research on mortality differentials between East Central Europe and the former Soviet Union, little attention has been paid to how overall population health status differs between these two country groups. This article investigates disparities in population health, measured by healthy life expectancy (HLE) between ages 20 and 74, for 23 Eastern European countries in 2008. There are substantial disparities in partial HLE between East Central Europe and the former Soviet Union, amounting to differences of 10 years on average for men and women. In addition, factors reflecting the malfunction of existing social structure are inversely associated with partial HLE. Accordingly, populations in countries where corruption, restriction of freedom, and violence are prevalent spend fewer years in good health.

Gini Impact Analysis: Measuring Pure Health Inequity before and after Interventions

The aims of the paper are (i) to introduce a framework for reasoning about equity in health distribution before and after interventions, and (ii) to assess various Gini measures applied to healthy life expectancy against explicit normative concerns. Part 1 discusses different ways of measuring pure health inequality and suggests that a modified Gini measure could be used to measure inequity in health before and after treatment. Part 2 introduces a framework for reasoning about distributions of health. Part 3 discusses three normative concerns that any acceptable measure of inequity should satisfy. Part 4 describes the standard Gini measure for measuring pure inequalities in healthy life expectancy. It also examines Wagstaff's Extended Gini measure of health inequity and discusses how it can be used to make priority weights to the worst off explicit. The final section expresses some programmatic worries and possible applications. It is argued that the impact of interventions should be evaluated both in terms of reduced inequity (as measured by the Extended Gini or the Extended Proportional Gini) and improved efficiency (measured by gains in average healthy life expectancy). Measurement of pure health inequity could supplement cost-effectiveness analysis as a basis for fair priority setting.

Inequalities in healthy life expectancy by social class and area type: England, 2001–03

Healthy life expectancy (HLE) is an important indicator which combines longevity with health status. This article examines inequalities in HLE by socio-economic position in England, and between Local Authorities (LAs) in the deprived 'Spearhead group' and other LAs. Census and vital event data available from the ONS Longitudinal Study were used to calculate estimates of HLE based on general health status for each Registrar General's Social Class (RGSC) in 2001-03, in England as a whole and within the 'Spearhead group' and non-Spearhead LAs. A predominantly linear relationship was present with HLE increasing with rising social class. The differences observed in HLE at birth and at age 65 between people assigned to the professional and unskilled manual social classes were statistically significant and substantial, demonstrating a clear social inequality in the amount of life, the quality of those years lived, the absolute number of healthy life years, and thus the relative proportion of life spent in 'Good' or 'Fairly Good' health.HLEs at birth were statistically significantly higher for women than for men in the non-Spearhead LAs for all social classes; however, in the 'Spearhead group' of areas higher HLEs in women were only present among the skilled non-manual, skilled manual and partly skilled social classes. This suggests that an interaction effect between sex and area in the level of HLE may be operating.The magnitude of inequality in HLE between professionals and the unskilled manual class in the proportion of life spent in 'Good' or 'Fairly Good' health also varied between the 'Spearhead group' and non-Spearhead LAs: among men, a difference of 6.6 per cent was present in non-Spearhead LAs, compared with 10.6 per cent in the 'Spearhead group'; among women, the equivalent differences were 5.3 per cent and 7.6 per cent. The within-class HLEs for men and women were lower in the 'Spearhead group' compared with the non-Spearhead LAs, and statistically significantly lower among the intervening social classes for men; among women they are significant for all social classes other than the professional class. The scale of inequalities shown is important for policy responses and understanding of differences in service needs.