Income inequality in life expectancy and disability-free life expectancy in Denmark

2020 ◽  
pp. jech-2020-214108
Author(s):  
Henrik Brønnum-Hansen ◽  
Else Foverskov ◽  
Ingelise Andersen
Keyword(s):  
Income Inequality ◽  
Life Expectancy ◽  
Register Data ◽  
Income Quintile ◽  
Mortality Data ◽  
Activity Limitations ◽  
Important Indicator ◽  
Mortality Effect ◽  
Disability Free Life Expectancy ◽  
The Difference

BackgroundIncome has seldom been used to study social differences in disability-free life expectancy (DFLE). This study investigates income inequalities in life expectancy and DFLE at age 50 and 65 and estimates the contributions from the mortality and disability effects on the differences between income groups.MethodsLife tables by income quintile were constructed using Danish register data on equivalised disposable household income and mortality. Data on activity limitations from the Danish part of the Survey of Health, Ageing and Retirement in Europe (SHARE) was linked to register data on income. For each income quintile, life table data and prevalence data of no activity limitations from SHARE were combined to estimate DFLE. Differences between income quintiles in DFLE were decomposed into contributions from mortality and disability effects.ResultsA clear social gradient was seen for life expectancy as well as DFLE. Life expectancy at age 50 differed between the highest and lowest income quintiles by 8.6 years for men and 5.5 years for women. The difference in DFLE was 12.8 and 11.0 years for men and women, respectively. The mortality effect from the decomposition contributed equally for men and slightly more for women to the difference in expected lifetime without than with activity limitations. The disability effect contributed by 8.5 years for men and 8.0 years for women.ConclusionThe income inequality gradient was steeper for DFLE than life expectancy. Since income inequality increases, DFLE by income is an important indicator for monitoring social inequality in the growing share of elderly people.

scholarly journals Income inequality in disability-free life expectancy in Denmark

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
H Brønnum-Hansen ◽  
E Foverskov ◽  
I Andersen
Keyword(s):  
Income Inequality ◽  
Life Expectancy ◽  
Social Inequality ◽  
Socioeconomic Position ◽  
Life Tables ◽  
Register Data ◽  
Disposable Income ◽  
Inequality In Health ◽  
Disability Free Life Expectancy ◽  
The Difference

Abstract Background The state old-age pension in Denmark is adjusted in line with the projected increasing life expectancy without taking social inequality in health and life expectancy into account. The purpose of the study was to estimate income disparities in life expectancy and disability-free life expectancy (DFLE) at age 50. Methods By linking nationwide register data on income and mortality each individual at any age was divided into equivalised disposable income quartiles and life tables were constructed for each quartile. Data from the Danish Survey of Health, Ageing and Retirement in Europe (SHARE) was linked to register data providing access to information on respondents equivalised disposable income. Finally, data from the life tables were combined with prevalence on activity limitations by income quartiles from SHARE to estimate DFLE by Sullivan’s method. Differences in DFLE were investigated and decomposed into contributions from mortality and disability effects. Results A clear social gradient was seen for life expectancy as well as DFLE. Thus, life expectancy at age 50 differed between the highest and lowest income quartile by 8.0 years for men and 5.0 years for women. The difference in DFLE was 11.8 and 10.3 years for men and women, respectively. For men the mortality effect from the decomposition contributed by 4.1 years to the difference of 11.8 years in DFLE and 3.9 years to the difference in expected years with disability of 3.8 years while the disability effect contributed by 7.7 years. Conclusions The study quantifies social inequality in health in Denmark. Although income inequality in life expectancy and DFLE can partly be explained by loss of income due to chronic diseases, one would expect a welfare state to provide better financial security for citizens with health problems. Furthermore, the marked social disparity when approaching retirement age is questioning the fairness of implementing a pension scheme independently of socioeconomic position. Key messages Disability-free life expectancy differs between income quartiles by more than 10 years. Pension age follows the projected increasing life expectancy independently of socioeconomic position. This seems unfair.

scholarly journals A publicly well-accepted measure versus an academically desirable measure of health inequality: cross-sectional comparison of the difference between income quintiles with the slope index of inequality

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e028687 ◽  
Author(s):  
Young-Ho Khang ◽  
Dohee Lim ◽  
Jinwook Bahk ◽  
Ikhan Kim ◽  
Hee-Yeon Kang ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Health Inequality ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Secondary Outcome ◽  
Income Quintile ◽  
Cross Sectional ◽  
Men And Women ◽  
Index Of Inequality ◽  
The Difference

ObjectivesThe difference between income quintiles in health is relatively well accepted by the general public as a measure of health inequality. However, the slope index of inequality (SII) in health reflects the patterns of all social groups, including the middle 60%, and it could therefore be considered more academically desirable. If these two measures are closely correlated, the widespread use of the difference between income quintiles in health would be better supported. This study was conducted to compare differences between income quintiles in life expectancy (LE) and healthy life expectancy (HLE) with the SII.DesignCross-sectional comparison using correlational analysis of district level income differences in LE and HLE with associated SII.SettingAll 252 subnational districts of Korea.ParticipantsA total of 342 439 895 subjects (171 287 729 men, 171 152 166 women) and 1 753 476 deaths (970 928 men, 782 548 women) between 2008 and 2014 were analysed.Primary and secondary outcome measuresDifference in LE and HLE by income quintile and associated SII.ResultsThe Pearson correlation coefficients between differences between income quintiles and the SII were generally high: 0.97 (95% CI 0.96 to 0.98) for LE in men and women combined and 0.96 (95% CI 0.94 to 0.97) for HLE in men and women combined. In most districts, the SII was greater than the difference between income quintiles.ConclusionDifferences between income quintiles were closely correlated with the SII. The widespread use of differences between income quintiles in health as a measure of health inequality may be preferable for communicating results of health inequality measurements to the public.

scholarly journals Disability-Free Life Expectancy among People Over 60 Years Old by Sex, Urban and Rural Areas in Jiangxi Province, China

2021 ◽  
Vol 18 (9) ◽  
pp. 4636
Author(s):  
Shengwei Wang ◽  
Songbo Hu ◽  
Pei Wang ◽  
Yuhang Wu ◽  
Zhitao Liu ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Life Expectancy ◽  
Rural Women ◽  
Rural Areas ◽  
Urban Areas ◽  
Rural Elderly ◽  
Jiangxi Province ◽  
Urban Rural ◽  
Disability Free Life Expectancy ◽  
The Difference

Objective: To estimate and compare age trends and the disability-free life expectancy (DFLE) of the population over 60 years old in 2018 in Jiangxi Province, China, by sex and urban–rural areas. Methods: The model life table was employed to estimate the age-specific mortality rate by sex and urban–rural areas, based on the Summary of Health Statistics of Jiangxi Province in 2018 and the Sixth National Health Service survey of Jiangxi Province. DFLE and its ratio to life expectancy (LE) were obtained by the Sullivan method. Results: In 2018, the DFLE among people over 60 is 17.157 years for men and is 19.055 years for women, accounting for 89.7% and 86.5% of their LE respectively. The DFLE/LE of men is higher than that of women at all ages. LE and DFLE are higher for the population in urban areas than in rural areas. For women, DFLE/LE is higher in urban areas than in rural areas (except at ages 75 and 80). Urban men have a higher DFLE/LE than rural men (except at age 85). The difference in DFLE between men and women over 60 years is 1.898 years, of which 2.260 years are attributable to the mortality rate, and 0.362 years are due to the disability-free prevalence. In addition, the difference in DFLE between urban–rural elderly over 60 years old is mostly attributed to the mortality rate by gender (male: 0.902/1.637; female: 0.893/1.454), but the impact of the disability-free rate cannot be ignored either (male: 0.735/1.637; female: 0.561/1.454). Conclusions: The increase in DFLE is accompanied by the increase in LE, but with increased age, DFLE/LE gradually decreases. With advancing age, the effect of disability on elderly people becomes more severe. The government administration must implement some preventive actions to improve health awareness and the life quality of the elderly. Rural elderly; rural women in particular, need to be paid more attention and acquire more health care.

scholarly journals Effects of covid-19 pandemic on life expectancy and premature mortality in 2020: time series analysis in 37 countries

BMJ ◽  
2021 ◽  
pp. e066768
Author(s):  
Nazrul Islam ◽  
Dmitri A Jdanov ◽  
Vladimir M Shkolnikov ◽  
Kamlesh Khunti ◽  
Ichiro Kawachi ◽  
...  
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Life Expectancy ◽  
Confidence Interval ◽  
Time Series Analysis ◽  
South Korea ◽  
Mortality Data ◽  
Years Of Life Lost ◽  
The Us ◽  
The Difference

Abstract Objective To estimate the changes in life expectancy and years of life lost in 2020 associated with the covid-19 pandemic. Design Time series analysis. Setting 37 upper-middle and high income countries or regions with reliable and complete mortality data. Participants Annual all cause mortality data from the Human Mortality Database for 2005-20, harmonised and disaggregated by age and sex. Main outcome measures Reduction in life expectancy was estimated as the difference between observed and expected life expectancy in 2020 using the Lee-Carter model. Excess years of life lost were estimated as the difference between the observed and expected years of life lost in 2020 using the World Health Organization standard life table. Results Reduction in life expectancy in men and women was observed in all the countries studied except New Zealand, Taiwan, and Norway, where there was a gain in life expectancy in 2020. No evidence was found of a change in life expectancy in Denmark, Iceland, and South Korea. The highest reduction in life expectancy was observed in Russia (men: −2.33, 95% confidence interval −2.50 to −2.17; women: −2.14, −2.25 to −2.03), the United States (men: −2.27, −2.39 to −2.15; women: −1.61, −1.70 to −1.51), Bulgaria (men: −1.96, −2.11 to −1.81; women: −1.37, −1.74 to −1.01), Lithuania (men: −1.83, −2.07 to −1.59; women: −1.21, −1.36 to −1.05), Chile (men: −1.64, −1.97 to −1.32; women: −0.88, −1.28 to −0.50), and Spain (men: −1.35, −1.53 to −1.18; women: −1.13, −1.37 to −0.90). Years of life lost in 2020 were higher than expected in all countries except Taiwan, New Zealand, Norway, Iceland, Denmark, and South Korea. In the remaining 31 countries, more than 222 million years of life were lost in 2020, which is 28.1 million (95% confidence interval 26.8m to 29.5m) years of life lost more than expected (17.3 million (16.8m to 17.8m) in men and 10.8 million (10.4m to 11.3m) in women). The highest excess years of life lost per 100 000 population were observed in Bulgaria (men: 7260, 95% confidence interval 6820 to 7710; women: 3730, 2740 to 4730), Russia (men: 7020, 6550 to 7480; women: 4760, 4530 to 4990), Lithuania (men: 5430, 4750 to 6070; women: 2640, 2310 to 2980), the US (men: 4350, 4170 to 4530; women: 2430, 2320 to 2550), Poland (men: 3830, 3540 to 4120; women: 1830, 1630 to 2040), and Hungary (men: 2770, 2490 to 3040; women: 1920, 1590 to 2240). The excess years of life lost were relatively low in people younger than 65 years, except in Russia, Bulgaria, Lithuania, and the US where the excess years of life lost was >2000 per 100 000. Conclusion More than 28 million excess years of life were lost in 2020 in 31 countries, with a higher rate in men than women. Excess years of life lost associated with the covid-19 pandemic in 2020 were more than five times higher than those associated with the seasonal influenza epidemic in 2015.

scholarly journals Socioeconomic inequalities in life expectancy and disability-free life expectancy among Chilean older adults: evidence from a longitudinal study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ximena Moreno ◽  
Lydia Lera ◽  
Francisco Moreno ◽  
Cecilia Albala
Keyword(s):  
Longitudinal Study ◽  
Life Expectancy ◽  
Older People ◽  
Social Protection ◽  
Socioeconomic Inequalities ◽  
Socioeconomic Differences ◽  
Basic Activity ◽  
Timely Access ◽  
Disability Free Life Expectancy ◽  
The Difference

Abstract Background Chile has one of the longest life expectancies of Latin America. The country is characterised by an important macroeconomic growth and persisting socioeconomic inequalities. This study analyses socioeconomic differences in life expectancy (LE) and disability-free life expectancy (DFLE) among Chilean older people. Methods The sample of the Social Protection Survey, a longitudinal study, was analysed. Five waves, from 2004 to 2016, were considered. The indicator was disability, defined as having difficulties to perform at least one basic activity of daily living. Type of health insurance was used to determine socioeconomic position (SEP). Total LE and DFLE were estimated with multistate life table models. Results At age 60, men in the higher SEP could expect to live 3.7 years longer (22.2; 95% CI 19.6–24.8) compared to men of the same age in the medium SEP (18.4; 95% CI 17.4–19.4), and 4.9 years longer than men of the same age in the lower SEP (17.3; 95% CI 16.4–18.2). They also had a DFLE (19.4; 95% CI 17.1–21.7) 4 (15.4; 95% CI 14.6–16.1) and 5.2 (14.2; 95% CI 13.4–14.9) years longer, compared to the same groups. Women aged 60 years in the higher SEP had a LE (27.2; 95% CI 23.7–30.8) 4.6 (22.7; 95% CI 21.9–23.5) and 5.6 (21.6; 20.6–22.6) years longer, compared to women in the medium and the lower SEP. The difference in DFLE, for the same age and groups was 4.9 and 6.1 years, respectively (high: 21.4; 95% CI 19.5–23.3; medium: 16.5; 95% CI 15.8–17.1; low: 15.3; 95% CI 14.6–16.0). Socioeconomic differences in LE and DFLE were observed among both sexes until advanced age. Discussion Socioeconomic inequalities in LE and DFLE were found among Chilean older men and women. Older people in the highest SEP live longer and healthier lives. Conclusion A reform to the Chilean health system should be considered, in order to guarantee timely access to care and benefits for older people who are not in the wealthiest group.

scholarly journals The impact of smoking and obesity on disability-free life expectancy in older Australians

2020 ◽  
Author(s):  
Andrew Kingston ◽  
Julie Byles ◽  
Kim Kiely ◽  
Kaarin Anstey ◽  
Carol Jagger
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Life Expectancy ◽  
Mortality Data ◽  
Obese Women ◽  
Mobility Disability ◽  
Total Life Expectancy ◽  
Disability Free Life Expectancy ◽  
Adl Disability ◽  
The Impact

Abstract Background Smoking and obesity are two modifiable risk factors for disability. We examine the impact of smoking and obesity on disability-free life expectancy (DFLE) at older ages, using two levels of disability. Methods We used the DYNOPTA dataset, derived by harmonizing and pooling risk factors and disability outcomes from five Australian longitudinal ageing studies. We defined mobility disability as inability to walk one kilometre, and more severe (ADL) disability by the inability to dress or bathe. Mortality data for the analytic sample (N=20,401; 81.2% women) were obtained from Government Records via data linkage. We estimated sex-specific total life expectancy, DFLE, and years spent with disability by Interpolated Markov Chain (IMaCh) software for each combination of smoking (never vs ever), obesity (Body Mass Index ≥30 vs 18.5-<30), and education (left school age 14 or younger vs age 15 or older). Results Compared to those without either risk factor, high educated non-obese smokers at age 65 lived shorter lives (men and women: 2.5 years) and fewer years free of mobility disability (men: 2.1 years; women: 2.0 years), with similar results for ADL disability. Obesity had the largest effect on mobility disability in women; high educated obese non-smoking women lived 1.3 years less than non-smoking, not obese women but had 5.1 years fewer free of mobility disability and 3.2 fewer free of ADL disability. Differences between risk factor groups were similar for the low educated. Conclusions Our findings suggest eliminating obesity would lead to an absolute reduction of disability, particularly in women.

scholarly journals Income Mobility, Income Inequality and Mortality in the U.S.

2018 ◽  
Author(s):  
Sebastian Daza ◽  
Alberto Palloni
Keyword(s):  
Income Inequality ◽  
Life Expectancy ◽  
Causal Effect ◽  
Adult Mortality ◽  
Mortality Data ◽  
Income Mobility ◽  
Data Set ◽  
Intergenerational Income Mobility ◽  
Race Ethnicity ◽  
The U.S

We assess the magnitude of the association between intergenerational income mobility and US adult mortality by gender, age group, race/ethnicity and the causes of death. We use a data set from The Health Inequality Project and CDC mortality data at the county level. We find that under different model specifications the association between income mobility and adult mortality is strong, properly signed, and consistent with our hypotheses. If the association we find reflects a causal effect it would translate into shifts in life expectancy at age 40 of as much as 2.0-4.8 years among males and 0.1-2.0 among females, equivalent to 5.1-12.5 and 0.2-4.7 percent of the U.S. male and female life expectancy at age 40 respectively. On average, these effects are 1.5 to 2.5 times as large as those of income inequality and represent between 40 (males) and 25 (females) percent of the magnitude of an income shift from the lowest to the highest quartile of the U.S. income distribution.

Perbandingan Linieritas Dalam Pendeteksian Faktor-Faktor Yang Mempengaruhi Angka Kematian Bayi Di Provinsi Aceh

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Desfira Ahya ◽  
Inas Salsabila ◽  
Miftahuddin
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Linear Model ◽  
Health Workers ◽  
Additive Model ◽  
Infant Mortality Rate ◽  
Mortality Data ◽  
Important Indicator ◽  
Penalized Spline ◽  
Aceh Province

Angka Kematian Bayi/ Infant Mortality Rate (IMR) merupakan indikator penting dalam mengukur keberhasilan pengembangan kesehatan. Nilai IMR juga dapat digunakan untuk mengetahui tingkat kesehatan ibu, kondisi kesehatan lingkungan dan secara umum, tingkat pengembangan sosio-ekonomi masyarakat. Penelitian ini bertujuan untuk memperoleh model IMR terbaik menggunakan tiga pendekatan: Model Linear, Model Linear Tergeneralisir dan Model Aditif Tergeneralisir dengan basis P-spline. Sebagai tambahan, berdasarkan model tersebut akan terlihat variabel yang mempengaruhi tingkat kematian bayi di provinsi Aceh. Penelitian ini menggunakan data jumlah kematian bayi di tahun 2013-2015. Data dalam penelitian ini diperoleh dari Profil Kesehatan Aceh. Hasil menunjukkan bahwa model terbaik dalam menjelaskan angka kematian bayi di provinsi Aceh tahun 2013-2015 ialah Model Linear Tergeneralisir dengan basis P-spline menggunakan parameter penghalusan 100 dan titik knots 8. Faktor yang sangat mempengaruhi angka kematian ialah jumlah pekerja yang sehat.   Infant mortality rate (IMR) is an important indicator in measuring the success of health development. IMR also can be used to knowing the level of maternal health, environmental health conditions and generally the level of socio-economic development in community. This research aims to get the best model of infant mortality data using three approaches: Linear Model, Generalized Linear Model and Generalized Additive Model with Penalized Spline (P-spline) base. In addition, based on the model can be seen the variables that affect to infant mortality in Aceh Province. This research uses data number of infant mortality in Aceh Province period 2013-2015. The data in this research were obtained from Aceh’s Health Profile. The results show that the best model can be explain infant mortality rate in Aceh Province period 2013-2015 is GAM model with P-spline base using smoothing parameter 100 and knots 8. Factor that high effect to infant mortality is number of health workers.

scholarly journals Multimorbidity, Disadvantage, and Trends in Disability-Free Life Expectancy: The CFAS Studies

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 641-641
Author(s):  
Andrew Kingston ◽  
Holly Bennett ◽  
Louise Robinson ◽  
Lynne Corner ◽  
Carol Brayne ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Life Expectancy ◽  
Longitudinal Data ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Health Conditions ◽  
Disadvantaged Groups ◽  
Disability Free Life Expectancy ◽  
Reported Health

Abstract The combined contribution of multi-morbidity and socio-economic position (SEP) to trends in disability free life expectancy (DFLE) is unknown. We use longitudinal data from the Cognitive Function and Ageing Studies (CFAS I: 1991; CFAS II: 2011), with two year follow up. Disability was defined as difficulty in activities of daily living, and SEP as area-level deprivation. Multi-morbidity was constructed from nine self-reported health conditions and categorised as 0-1, 2-3, 4+ diseases. In 1991 and 2011, shorter total and disability-free years were associated with greater multi-morbidity. Between 1991 and 2011, gains in life expectancy and DFLE were observed at all levels of multi-morbidity, the greatest gain in DFLE being 4 years for men with 0-1 diseases. As multi-morbidity is more prevalent in more disadvantaged groups, further analyses will investigate whether SEP differences remain at all levels of multi-morbidity.

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