On the measurement of healthy lifespan inequality

Background Current measures to monitor population health include indicators of (i) average length-of-life (life expectancy), (ii) average length-of-life spent in good health (health expectancy), and (iii) variability in length-of-life (lifespan inequality). What is lacking is an indicator measuring the extent to which healthy lifespans are unequally distributed across individuals (the so-called ‘healthy lifespan inequality’ indicators). Methods We combine information on age-specific survival with the prevalence of functional limitation or disability in Spain (2014–2017) by sex and level of education to estimate age-at-disability onset distributions. Age-, sex- and education-specific prevalence rates of adult individuals’ daily activities limitations were based on the GALI index derived from Spanish National Health Surveys held in 2014 and 2017. We measured inequality using the Gini index. Results In contemporary Spain, education differences in health expectancy are substantial and greatly exceed differences in life expectancy. The female advantage in life expectancy disappears when considering health expectancy indicators, both overall and across education groups. The highly educated exhibit lower levels of lifespan inequality, and lifespan inequality is systematically higher among men. Our new healthy lifespan inequality indicators suggest that the variability in the ages at which physical daily activity limitations start are substantially larger than the variability in the ages at which individuals die. Healthy lifespan inequality tends to decrease with increasing educational attainment, both for women and for men. The variability in ages at which physical limitations start is slightly higher for women than for men. Conclusions The suggested indicators uncover new layers of health inequality that are not traceable with currently existing approaches. Low-educated individuals tend to not only die earlier and spend a shorter portion of their lives in good health than their highly educated counterparts, but also face greater variation in the eventual time of death and in the age at which they cease enjoying good health—a multiple burden of inequality that should be taken into consideration when evaluating the performance of public health systems and in the elaboration of realistic working-life extension plans and the design of equitable pension reforms.

Health expectancy indicators: what do they measure?

Background Health expectancy indicators aim at capturing the quality dimension of total life expectancy.; however, the underlying approach, definition of health, and information source differ considerably among the indicators available. Objective (1) Review the main concepts and approaches used to estimate health expectancy focusing on two widely used European health indicators: Health-Adjusted Life Expectancy (HALE) and Healthy Life Years (HLY); (2) identify underlying differences between the results yielded by these two indicators. Method Statistical differences between the HALE and HLY indicators by sex at ages 50, 60, and 70 were tested using pairwise and global Student´s t-tests and z-scores based on standard deviation. Data for 29 European countries were collected from the European Health Expectancy Monitoring Unit (EHEMU) information system and the World Health Organization (WHO) Global Burden of Disease Study 2016 (GBD 2016). Results The HALE indicator estimates were smoother across European countries compared with those of the HLY indicator, present a narrower sex gap in morbidity, higher z-scores compared with the average distribution across Europe, and results less sensitive to cross-national variations. Conclusion The HALE estimates indicate that morbidity is more compressed for both sexes, whereas the HLY estimates suggest that morbidity is more compressed for males but more expanded for females. These contrasting results demonstrate that health expectancy indicators should be interpreted with caution.

Socioeconomic inequalities in health in high-income countries

Socioeconomic inequalities in health have been studied extensively in the past decades. In all high-income countries with available data, mortality and morbidity rates are higher among those in less advantaged socioeconomic positions, and as a result differences in health expectancy between socioeconomic groups typically amount to 10 years or more. Good progress has been made in unravelling the determinants of health inequalities, and a number of specific determinants (particularly material, psychosocial, and lifestyle factors) have been identified which contribute to explaining health inequalities in many high-income countries. Although further research is necessary, our understanding of what causes health inequalities has progressed to a stage where rational approaches to reduce health inequalities are becoming feasible. Evidence of a reduction of health inequalities via interventions and policies based on the underlying causes remains scarce, and point to a need to increase efforts.

Trends Over Two Decades in Life Expectancy With Complex Health Problems Among Older Swedes: Implications for The Provision of Integrated Medical and Social Care

Background Due to population aging, it is essential to examine to what extent rises in life expectancy (LE) consist of healthy or unhealthy years. Most health expectancy studies have been based on single health measures and have shown divergent trends. We used a multi-domain indicator, complex health problems (CHP), indicative of the need for integrated medical and social care, to investigate how LE with and without CHP developed in Sweden between 1992 and 2011. We also addressed whether individuals with CHP more commonly lived in the community in 2011 compared to earlier years. Methods CHP were defined as having severe problems in at least two of three health domains related to the need for medical and/or social care: symptoms/diseases, cognition/communication, and mobility. The Swedish Panel Study of Living Conditions of the Oldest Old (SWEOLD), a nationally representative survey of the Swedish population aged ≥ 77 years with waves in 1992, 2002 and 2011 (n ≈ 2000), was used to estimate the prevalence of CHP. Age- and gender-specific death rates were obtained from the Human Mortality Database. The Sullivan method was deployed to calculate the remaining life expectancy with and without CHP. The estimates were decomposed to calculate the contribution of changes from morbidity and mortality to the overall trends in LE without CHP. Results Between 1992 and 2011, both total LE (+ 1.69 years [95% CI 1.56;1.83] and LE without CHP (+ 0.84 years [-0,87;2.55]) at age 77 increased for men, whereas LE at age 77 increased for women (+ 1.33 [1.21;1.47]) but not LE without CHP (-0.06 years [-1.39;1.26]). When decomposing the trend, we found that the increase in LE with CHP was mainly driven by an increase in the prevalence of CHP. Among individuals with CHP the proportion residing in residential care was lower in 2011 (37%) compared to 2002 (58%) and 1992 (53%).

Are Religiosity and Spirituality Related to Self-Reported Health Expectancy? An Analysis of the European Values Survey

Research on religiosity and health has generally focussed on the United States, and outcomes of health or mortality but not both. Using the European Values Survey 2008, we examined cross-sectional associations between four dimensions of religiosity/spirituality: attendance, private prayer, importance of religion, belief in God; and healthy life expectancy (HLE) based on self-reported health across 47 European countries (n = 65,303 individuals). Greater levels of private prayer, importance of religion and belief in God, at a country level, were associated with lower HLE at age 20, after adjustment for confounders, but only in women. The findings may explain HLE inequalities between European countries.

Healthy longevity from incidence-based models: More kinds of health than stars in the sky

Background. Healthy longevity (HL) is an important measure of the prospects for quality of life in ageing societies. Incidence-based (cf. prevalence-based) models describe transitions among age classes and health stages. Despite the probabilistic nature of those transitions, analyses of healthy longevity have focused persistently on means ("health expectancy"), neglecting variances and higher moments. Objectives. Our goal is a comprehensive methodology to analyse HL in terms of any combination of health stages and age classes, or of transitions among health stages, or of values (e.g., quality of life) associated with health stages or transitions. Methods. We construct multistate Markov chains for individuals classified by age and health stage and use Markov chains with rewards to compute all moments of HL. Results. We present a new and straightforward algorithm to create the multistate reward matrices for occupancy, transitions, or values associated with occupancy or transitions. As an example, we analyse a published model for colorectal cancer. The possible definitions of HL in this simple model outnumber the stars in the visible universe. Our method can analyse any of them; we show four examples: longevity without abnormal cells, cancer-free longevity, and longevity with cancer before or after a critical age. Contribution. Our methods make it possible to analyse any incidence-based model, with any number of health stages, any pattern of transitions, and any kind of values assigned to stages. It is easily computable, requires no simulations, provides all the moments of healthy longevity, and solves the inhomogeneity problem.

Unhealthy, Unwealthy and Left Behind – Workforce Participation and Inequality Among Older Australians

BackgroundAustralians born in 2012 can expect to live about 33 years longer than those born 100 years earlier. However, only seven of these additional years are spent in the workforce. Longer life expectancy has driven policies to extend working life and increase retirement age, the current Australian policy, which has increased the eligibility for the pension from 65 to 67 by 2023, assumes that an improvement in longevity corresponds with an improvement in health expectancy. However, there is mixed evidence of health trends in Australia over the past two decades. Although some health outcomes are improving among older age groups, many are either stable or deteriorating. This raises the question of how health trends intersect with policy for older Australians aged from 50-70. This paper considers the interplay between older workers’ health and workforce participation rates over the past 15 years when extended workforce participation has been actively encouraged. MethodsWe compared health and economic outcomes of the older people in following years with the base year (start of the study period), adjusting for some key socio-economic characteristics such as age, sex, ethnicity, education and equivalized household income by applying the Random effects estimator with maximum likelihood estimation technique.Results We find that regardless of increasing longevity, the health of older adults aged between 50-70 has slightly deteriorated. In addition, health gaps between those who were working into their older age and those who were not have widened over the 15-year period. Finally, we find that widening health gaps linked to workforce participation are also accompanied by rising economic inequality in incomes, financial assets and superannuation. With the exception of a small group of healthy and very wealthy retirees, the majority of the older Australians who were not working had low incomes, assets, superannuation, and poor health.ConclusionsThe widening economic and health gap within older population over time indicates a clear and urgent need to add policy actions on income and health, to those that seek to increase workforce participation among older adults.