Contribution of chronic conditions to gender disparities in health expectancies in Belgium, 2001, 2004 and 2008

Decomposition of life expectancy differentials with (and without) conditions by educational attainment for major groups of causes in contemporary Spain: where is the advantage?

Introduction

Healthy life expectancy is higher among individuals with higher socioeconomic standing. However, it is unclear whether such advantage is attributable to longer (i.e., mortality advantage) or to healthier (morbidity advantage) lifespans across different health conditions.

Objective

Estimate the contribution of mortality and morbidity components in differences in condition-free life expectancies (CFLE) and life expectancy with conditions (LEWC) for five major groups of conditions by sex and educational attainment, instead of using a global indicator of morbidity.

Methods

Using the Sullivan Method, we computed remaining life expectancies at age 40 and 65, CFLE, and LEWC and applied a stepwise decomposition technique, using national health surveys along with mortality data, in a cross-sectional analysis.

Results

An educational gradient was present in almost all conditions, with different intensities. For females, morbidity was the main contributor to educational differences in health expectancies, but mainly in the older age groups. For males, the drivers behind higher health expectancies for high-educated males were evenly distributed across mortality and morbidity between ages 40 and 65, but after that, the mortality gradient vanished between high-educated and middle-educated individuals.

Discussion

The changing contribution of the mortality and morbidity gradient for different conditions across age-groups brings evidence to adequately plan health policies to mitigate health gaps and improve quality of life of the populations in a lower social standing.

Socio-spatial inequalities in healthy life expectancy in the elderly, Brazil, 2013 and 2019

The growth in longevity in Brazil has drawn attention to more useful population health measures to complement mortality. In this paper, we investigate socio-spatial differences in life expectancy and healthy life expectancy based on information from the Brazilian National Health Survey (PNS), 2013 and 2019. A three-stage cluster sampling with stratification of the primary sampling units and random selection in all stages was used in both PNS editions. Healthy life expectancy was estimated by Sullivan's method by sex, age, and Federated Units (UF). Severe limitations to at least one noncommunicable chronic disease (NCD) or poor self-rated health were used to define the unhealthy state. Inequality indicators and a Principal Component analysis were used to investigate socio-spatial inequalities. From 2013 to 2019, both life expectancy and healthy life expectancy increased. The analysis by UF show larger disparities in healthy life expectancy than in life expectancy, with healthy life expectancy at age 60 varying from 13.6 to 19.9 years, in 2013, and from 14.9 to 20.1, in 2019. Healthy life expectancy in the wealthiest quintile was 20% longer than for those living in the poorest quintile. Wide socio-spatial disparities were found with the worst indicators in the UF located in the North and Northeast regions, whether considering poverty concentration or health care utilization. The socio-spatial inequalities demonstrated the excess burden of poor health experienced by older adults living in the less developed UF. The development of strategies at subnational levels is essential not only to provide equal access to health care but also to reduce risk exposures and support prevention policies for adoption of health behaviors.

Contextual factors underpinning geographical inequalities in disability-free life expectancy in 100 French départements

The objectives were to estimate disability-free life expectancy (DFLE) and life expectancy with disability (DLE) by gender for the 100 French départements (administrative geographical subdivisions) and to investigate associations with socioeconomic factors, supply of healthcare and services for older persons. DFLE and DLE at age 60 are estimated using the Sullivan method and based on the GALI indicator provided by the French cross-sectional survey Vie Quotidienne et Santé 2014. In 2014, DFLE for men and women aged 60 was 14.3 years and 15.6 years, respectively. Variations across départements were considerable (5.4 years for men, 6.7 years for women). Multivariate random effects meta-regression models indicated a negative association for men between DFLE and some of the socioeconomic contextual indicators (ratio of manual workers to higher-level occupations and unemployment rate); the level of in-home nursing services (HNS) was negatively associated with DFLE and density of nurses positively associated with DLE. Among women, ratio of manual workers to higher-level occupations, unemployment rate, proportion of the population living in large urban areas, density of nurses, and level of HNS were negatively associated with DFLE; density of physiotherapy supply was associated positively with DFLE and negatively with DLE. Our results suggest that geographical inequalities in health expectancies are significantly correlated with socioeconomic status and with healthcare supply, support for older persons, and urban environments, particularly among women. These results underline the importance of monitoring these indicators and disparities at infra-national-level, and of investigating their relations with local context, particularly the supply of healthcare and services.

Trends in health expectancies: a systematic review of international evidence

OBJECTIVES

A clear understanding of whether increases in longevity are spent in good health is necessary to support ageing, health and care-related policy.

DESIGN

We conducted a systematic review to update and summarise evidence on trends in health expectancies, in Organisation for Economic Co-operation and Development (OECD) high-income countries.

DATA SOURCES

Four electronic databases (MEDLINE, 1946-19 September 2019; Embase 1980-2019 week 38; Scopus 1966-22 September 2019, Health Management Information Consortium, 1979-September 2019), and the UK Office for National Statistics website (November 2019).

ELIGIBILITY CRITERIA

English language studies published from 2016 that reported trends in healthy, active and/or disability-free life expectancy in an OECD high-income country.

DATA EXTRACTION AND SYNTHESIS

Records were screened independently by two researchers. Study quality was assessed using published criteria designed to identify sources of bias in studies reporting trends, and evidence summarised by narrative synthesis.

FINDINGS

Twenty-eight publications from 11 countries were included, covering periods from 6 to 40 years, between 1970 and 2017. In most countries, gains in healthy and disability-free life expectancy do not match the growth in total life expectancy. Exceptions were demonstrated for women in Sweden, where there were greater gains in disability-free years than life expectancy. Gains in healthy and disability-free life expectancy were greater for men than women in most countries except the USA (age 85), Japan (birth), Korea (age 65) and Sweden (age 77).

CONCLUSION

An expansion of disability in later life is evident in a number of high-income countries, with implications for the sustainability of health and care systems. The recent COVID-19 pandemic may also impact health expectancies in the longer term.

Women's excess unhealthy life years: disentangling the unhealthy life years gap

Background

Compared to men, women live longer but have more years with disability. We assessed the contribution of gender differences in mortality and disability, total and by cause, to women’s excess unhealthy life years (ULYs).

Methods

We used mortality data for France 2008 from Eurostat, causes of death from the CépiDc-INSERM-database; and disability and chronic conditions data from the French Disability Health Survey 2008–09. ULYs were calculated by the Sullivan method. The contributions of mortality and disability differences to gender differences in ULY were based on decomposition analyses.

Results

Life expectancy of French women aged 50 was 36.3 years of which 19.0 were ULYs; life expectancy of men was 30.4 years of which 14.2 were ULYs. Of the 4.8 excess ULYs in women, 4.0 years were due to lower mortality. Of these 4.0 ULYs, 1.8 ULY originated from women’s lower mortality from cancer, 0.8 ULY from heart disease and 0.3 ULY from accidents. The remaining 0.8 excess ULY in women were from higher disability prevalence, including higher disability from musculoskeletal diseases (+1.8 ULY) and anxiety-depression (+0.6 ULY) partly offset by lower disability from heart diseases (−0.8 ULY) and accidents (−0.3 ULY).

Conclusion

Lower mortality and higher disability prevalence contributed to women’s longer life expectancy with disability. Women’s higher disability prevalence due to non-fatal disabling conditions was partly offset by lower disability from heart disease and accidents. Conditions differentially impact gender differences in ULY, depending on whether they are mainly life-threatening or disabling. The conclusions confirm the health-survival paradox.

Factors Influencing the Quality of Life of Korean Elderly Women by Economic Status

Purpose: This study aimed to determine whether there are differences in the factors affecting the quality of life (QOL) of elderly women in South Korea according to their perceived economic status. Methods: Data were extracted from the Seventh Korea National Health and Nutrition Examination Survey conducted in 2016. The participants were 879 women over the age of 65 years, who were divided into three groups: high, medium, and low based on their perceived economic status. The study variables were classified into three categories: general characteristics, physical factors, and psychological factors. General characteristics included age, education level, employment, activity restriction, frequency of breakfast/week, frequency of lunch/week, and frequency of dinner/week. Physical factors included disease status, weight change, consumption of alcohol, number of days of walking per week, duration of walking at a time, body mass index (BMI), and discomfort due to changes in hearing. Psychological factors included stress, subjective body awareness, subjective health status, depressive symptoms, and QOL. Complex sample crosstabs and chi-square tests were conducted, and regression was performed to examine the association between the variables by economic status. Results: The factors that influenced the QOL of elderly women with low economic status were arthritis, alcohol consumption, subjective health status, and depressive symptoms, with an explanatory power of 54.3% (F = 14.94, p < 0.001). The factors that influenced the QOL of the medium economic status group were activity restriction, frequency of dinner/week, arthritis, number of days of walking per week, BMI, stress, subjective health status, and depressive symptoms, with an explanatory power of 48.6% (F = 9.82, p < 0.001). For the high economic status group, influential factors were age, restricted activity, arthritis, number of days of walking per week, stress, and depressive symptoms with an explanatory power of 49.0% (F = 69.76, p < 0.001). Conclusions: This study identified different factors that contributed to the QOL of elderly women by economic status.