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This paper empirically examines the inequalities related to social class and income using individual self‐reported health status data. Health inequalities are estimated by different indexes using individual standardised and unstandardised health status data. The population was divided into income and social class, respectively. From this two main results are obtatined: inequalities are sensitive to the health status variable and the social position variable employed. It was found that significant health related social class inequalities were insignificant when income was employed as a reference variable.

This paper investigates the effect of income inequality on health status. A model of health status was specified in which the main variables were income level, income inequality, the level of savings and the level of education. The model was estimated using a panel data set for 44 countries covering six time periods. The results indicate that income inequality (measured by the Gini coefficient) has a significant effect on health status when we control for the levels of income, savings and education. The relationship is consistent regardless of the specification of health status and income. Thus, the study results provide some empirical support for the income inequality hypothesis.

Scholars and activists working both within and outside the massive health-related machinery of government and the private sector and within and outside communities of color address the same fundamental questions: Why do health disparities exist? Why have they persisted over such a long time? What can be done to significantly reduce or eliminate them?

The chapter suggests two methodologies to measure inequality of opportunity in health in Israel, an ex-ante and an ex-post approach. In both cases, following the strategy recently suggested by Trannoy, Tubeuf, Jusot, and Devaux (2010), the chapter starts by introducing the production function of health, taking into account circumstances (the father’s years of education, his country of birth, the religion of the individual, his or her country of birth, age and gender) as well as effort variables (the level of education of the individual, his or her occupation and a variable describing his or her smoking habits).

The chapter also suggests then a decomposition of the overall health inequality into a legitimate and an illegitimate component, using the mean logarithmic deviation as inequality index, such a breakdown being applied to both the ex-ante and the ex-post approaches to equality of opportunity.

There is an on-going debate as to whether health is negatively affected by economic inequality. Still, we have limited knowledge of the mechanisms relating inequality to individual health and very little evidence comes from less-developed economies. We use individual and multi-level data from Zambia on child nutritional health to test three hypotheses consistent with a negative correlation between income inequality and population health: the absolute income hypothesis (AIH), the relative income hypothesis (RIH) and the income inequality hypothesis (IIH). The results confirm that absolute income positively affects health. For the RIH we find sensitivity to the reference group used. Most interestingly, we find higher income inequality to robustly associate with better child health. The same pattern appears in a cross country regression. To explain the conflicting results in the literature we suggest examining potential mediators such as generosity, food sharing, trust and purchasing power.

Many countries have a renewed focus on health inequalities since COVID-19. In England, integrated care systems (ICSs), formed in 2022 to promote integration, are required to reduce health inequalities. Integration is supported by population health management (PHM) which links data across health and care organisations to inform service delivery. It is not well-understood how PHM can help ICSs reduce health inequalities. This paper describes development of a programme theory to advance this understanding.

This study was conducted as a mixed-methods process evaluation in a local ICS using PHM. The study used Framework to analyse interviews with health and care professionals about a PHM tool, the COVID-19 vaccination uptake Dashboard. Quantitative data on staff Dashboard usage were analysed descriptively. To develop a wider programme theory, local findings were discussed with national PHM stakeholders.

ICS staff used PHM in heterogeneous ways to influence programme delivery and reduce inequalities in vaccine uptake. PHM data was most influential where it highlighted action was needed for “targetable” populations. PHM is more likely to influence decisions on reducing inequalities where data are trusted and valued, data platforms are underpinned by positive inter-organisational relationships and where the health inequality is a shared priority.

The COVID-19 pandemic accelerated a shift toward use of digital health platforms and integrated working across ICSs. This paper used an evaluation of integrated data to reduce inequalities in COVID-19 vaccine delivery to propose a novel programme theory for how integrated data can support ICS staff to tackle health inequalities.

What change in the distribution of a population’s health preserves the level of inequality? The answer to this analogous question in the context of income inequality lies somewhere between a uniform and a proportional change. These polar positions represent the absolute and relative inequality equivalence criterion (IEC), respectively. A bounded health variable may be presented in terms of both health attainments and shortfalls. As a distributional change cannot simultaneously be proportional to attainments and to shortfalls, relative inequality measures may rank populations differently from the two perspectives. In contrast to the literature that stresses the importance of measuring inequality in attainments and shortfalls consistently using an absolute IEC, this chapter formalizes a new compromise concept for a bounded variable by explicitly considering the two relative IECs, defined with respect to attainments and shortfalls, to represent the polar cases of defensible positions.

We use a surplus-sharing approach to provide new insights on commonly used inequality indices by evaluating the underpinning IECs in terms of how infinitesimal surpluses of health must be successively distributed to preserve the level of inequality. We derive a one-parameter IEC that, unlike those implicit in commonly used indices, assigns constant weights to the polar cases independent of the health distribution.

Much of the theoretical literature on inequality assumes that the equalisand is a cardinal variable like income or wealth. However, health status is generally measured as a categorical variable expressing a qualitative order. Traditional solutions involve reclassifying the variable by means of qualitative models and relying on inequality measures that are mean independent. We argue that the way status is conceptualised has important theoretical implications for measurement as well as for policy analysis. We also bring to the data a recently proposed approach to measuring self-reported health inequality that meets both rigorous and practical considerations. We draw upon the World Health Survey data to examine alternative pragmatic methods for making health-inequality comparisons. Findings suggest significant differences in health-inequality measurement and that regional and country patterns of inequality orderings do not coincide with any reasonable categorisation of countries by health system organisation.

Health outcomes are often described according to two dimensions: quality of life and quantity of life. We analyze the measurement of inequality of health distributions referring to these two dimensions. Our analysis relies on a novel treatment of the quality-of-life dimension, which might not have a standard mathematical structure. We single out two families of (absolute and relative) multidimensional health inequality indices, inspired by the classical normative approach to income inequality measurement. We also discuss how to extend the analysis to deal with the related problem of health deprivation measurement in this setting.

Most politicians and ethical observers are not interested in pure health inequalities, as they want to distinguish between different causes of health differences. Measures of “unfair” inequality – direct unfairness and the fairness gap, but also the popular standardized concentration index (CI) – therefore neutralize the effects of what are considered to be “legitimate” causes of inequality. This neutralization is performed by putting a subset of the explanatory variables at reference values, for example, their means. We analyze how the inequality ranking of different policies depends on the specific choice of reference values. We show with mortality data from the Netherlands that the problem is empirically relevant and we suggest a statistical method for fixing the reference values.