World Analysis of the Determinants of the Inequality in Health. Is the Measurement of Inequality Important?

AbstractWe develop a normative approach to the measurement of inequality of opportunity. That is, we measure inequality of opportunity by the welfare gain obtained in moving from the actual income distribution to the optimal income distribution of the total available income. Our study brings together the main approaches in the literature: we axiomatically characterize social welfare functions, we obtain prominent allocation rules as their optima, and we derive familiar classes of inequality of opportunity measures. Our analysis captures moreover the key philosophical distinctions in the literature: ex post versus ex ante compensation, and liberal versus utilitarian reward.

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Measurement of Inequality and Compression of Mortality in India for 4 Decades: 1970–2015

Innovation in Aging ◽

10.1093/geroni/igaa057.222 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 68-68

Author(s):

Mukesh Parmar

Keyword(s):

Standard Deviation ◽

Gini Coefficient ◽

Age At Death ◽

Registration System ◽

Death Rates ◽

Male And Female ◽

Inequality Measures ◽

Sample Registration System ◽

Dispersion Measures ◽

Measurement Of Inequality

Abstract The studies relating to measurement of compression of Mortality in India is scarce. Most of the studies relating to mortality in India are focused on either life expectancy, or adult, and child mortality. We have used methods suggested by Kannisto (2000) and Canudos (2008) to measure the compression of mortality phenomenon for India for four decades viz. 1970-2015. Dispersion measures like simple mean, median, modal age at death; and some complicated measures like life disparity, standard deviation above mode, standard deviation in highest quartile, Interquartile range, Gini coefficient, AID and C-family were calculated for India from 1970-2015. We used the age specific death rates from abridged Life tables given by Sample Registration System published by Govt. of India. Our results show that inequality in mortality is decreasing in general but the gap between male and female is increasing. There was an average of three years difference in mean and modal age at death between male females in 2011-15. Overall, mean, median and modal age at death has increased in four decades but other inequality measures like Gini coefficient, AID, Standard deviation (SD) and coefficient of variation has decreased in four decades in India. C50 indicator, which indicates that 50 percent of deaths are happening in that age interval, declined from 26 years to 20 years for males and 27 years to 17 years for females, thus indicating the rate of compression of mortality is higher for females than males in India during 1970-75 till 2011-15.

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Socio-economic differences in smoking among adolescents in a school-based smoking intervention: The X:IT II study

Scandinavian Journal of Public Health ◽

10.1177/14034948211007683 ◽

2021 ◽

pp. 140349482110076

Author(s):

Lotus S. Bast ◽

Lisbeth Lund ◽

Stine G. LauemØller ◽

Simone G. Kjeld ◽

Pernille Due ◽

...

Keyword(s):

Preventive Intervention ◽

First Year ◽

Difference In Difference ◽

Current Smoking ◽

Smoking Intervention ◽

School Time ◽

School Based ◽

Inequality In Health ◽

Baseline Response

Aims: Socio-economic inequalities in health behaviour may be influenced by health interventions. We examined whether the X:IT II intervention, aiming at preventing smoking in adolescence, was equally effective among students from different occupational social classes (OSC). Methods: We used data from the multi-component school-based smoking preventive intervention X:IT II, targeting 13- to 15-year-olds in Denmark. The intervention was tested in 46 schools with 2307 eligible students at baseline (response rate=86.6%) and had three main intervention components: smoke-free school time, smoke-free curriculum and parental involvement. We used a difference-in-difference design and estimated the change in current smoking after the first year of implementation in high versus low OSC. Analyses were based on available cases ( N=1190) and imputation of missing data at follow-up ( N=1967). Results: We found that 1% of the students from high OSC and 4.9% from low OSC were smokers at baseline (imputed data), and 8.2% of the students from high OSC and 12.2% from low OSC were smokers at follow-up. Difference-in-difference estimates were close to zero, indicating no differential trajectory. Conclusions: As intended, the X:IT II intervention, designed to apply equally to students from all socio-economic groups, did not seem to create different trajectories in current smoking among adolescents in high and low socio-economic groups. To diminish social inequality in health, future studies should carefully consider the ability to affect all socio-economic groups equally, or even to appeal mainly to participants from lower socio-economic groups, as they are often the ones most in need of intervention.

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Visualizing Inequality in Health and Socioeconomic Wellbeing in the EU: Findings from the SHARE Survey

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17217747 ◽

2020 ◽

Vol 17 (21) ◽

pp. 7747

Author(s):

Aurea Grané ◽

Irene Albarrán ◽

Roger Lumley

Keyword(s):

Physical Health ◽

Mixed Type ◽

Clustering Algorithm ◽

Demographic Variables ◽

Health And Nutrition ◽

Composite Indices ◽

Inequality In Health ◽

Perception Of Health ◽

Older Europeans ◽

The Eu

The main objective of this paper is to visualize profiles of older Europeans to better understand differing levels of dependency across Europe. Data comes from wave 6 of the Survey of Health, Ageing and Retirement in Europe (SHARE), carried out in 18 countries and representing over 124 million aged individuals in Europe. Using the information of around 30 mixed-type variables, we design four composite indices of wellbeing for each respondent: self-perception of health, physical health and nutrition, mental agility, and level of dependency. Next, by implementing the k-prototypes clustering algorithm, profiles are created by combining those indices with a collection of socio-economic and demographic variables about the respondents. Five profiles are established that segment the dataset into the least to the most individuals at risk of health and socio-economic wellbeing. The methodology we propose is wide enough to be extended to other surveys or disciplines.

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