We use a set of biomarkers to measure inequality of opportunity (IOp) in the risk of major chronic conditions in the UK. Applying a direct ex ante IOp approach, we find that inequalities in biomarkers attributed to circumstances account for a non-trivial part of the total variation. For example, observed circumstances account for 20% of the total inequalities in our composite measure of multi-system health risk, allostatic load.We propose an extension to the decomposition of ex ante IOp to complement the meanbased approach, analysing the contribution of circumstances across the quantiles of the biomarker distributions. Shapley decompositions show that, for most of the biomarkers, the percentage contribution of socioeconomic circumstances (education and childhood socioeconomic status), relative to differences attributable to age and gender, increase towards the right tail of the biomarker distribution, where health risks are more pronounced.

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.

We use data from the UK Household Longitudinal Study (UKHLS) to compare measures of socioeconomic inequality in psychological distress, measured by the General Health Questionnaire (GHQ), before (Waves 9 and the Interim 2019 Wave) and during the first wave of the COVID‐19 pandemic (April to July 2020). Based on a caseness measure, the prevalence of psychological distress increased from 18.5% to 27.7% between the 2019 Wave and April 2020 with some reversion to earlier levels in subsequent months. Also, there was a systematic increase in total inequality in the Likert GHQ‐12 score. However, measures of relative socioeconomic inequality have not increased. A Shapley‐Shorrocks decomposition analysis shows that during the peak of the first wave of the pandemic (April 2020) other socioeconomic factors declined in their share of socioeconomic inequality, while age and gender account for a larger share. The most notable increase is evident for younger women. The contribution of working in an industry related to the COVID‐19 response played a small role at Wave 9 and the Interim 2019 Wave, but more than tripled its share in April 2020. As the first wave of COVID‐19 progressed, the contribution of demographics declined from their peak level in April and chronic health conditions, housing conditions, and neighbourhood characteristics increased their contributions to socioeconomic inequality.

We adopt an empirical approach to analyse, measure and decompose inequality of opportunity (IOp) in health, based on a latent class model. This addresses some of the limitations that affect earlier work in this literature concerning the definition of types, such as partial observability, the ad hoc selection of circumstances, the curse of dimensionality and unobserved type-specific heterogeneity that may lead to biased estimates of IOp. We apply our latent class approach to measure IOp in allostatic load, a composite measure of biomarker data. Using data from Understanding Society: The UK Household Longitudinal Study (UKHLS), we find that a latent class model with three latent types best fits the data, with the corresponding types characterised in terms of differences in their observed circumstances. Decomposition analysis shows that about two thirds of the total inequalities in allostatic load can be attributed to the direct and indirect contribution of circumstances and that the direct contribution of effort is small. Further analysis conditional on age-sex groups reveals that the relative (percentage) contribution of circumstances to the total inequalities remains mostly unaffected and the direct contribution of effort remains small. K E Y W O R D S biomarkers, decomposition analysis, equality of opportunity, finite mixture models, health equity, latent class models J E L C L A S S I F I C A T I O N

No abstract

We use self-reported health measures, nurse-administered measurements and blood-based biomarkers to examine the concordance between health states of partners in marital/cohabiting relationships in the UK. A model of cumulative health exposures is used to interpret the empirical pattern of between-partner health correlation in relation to elapsed relationship duration, allowing us to distinguish non-causal correlation due to assortative mating from potentially causal effects of shared lifestyle and environmental factors. We find important differences between the results for different health indicators, with strongest homogamy correlations observed for adiposity, followed by blood pressure, heart rate, inflammatory markers and cholesterol, and also self-assessed general health and functional difficulties. We find no evidence of a "dose-response relationship" for marriage duration, and show that this suggests - perhaps counterintuitively - that shared lifestyle factors and homogamous partner selection make roughly equal contributions to the concordance we observe in most of the health measures we examine.

Systemic inflammation has been proposed as a physiological process linking socio-economic position (SEP) to health. We examined how SEP inequalities in inflammation –assessed using C-reactive protein (CRP) and fibrinogen– varied across the adult age span. Current (household income) and distal (education) markers of SEP were used. Data from 7,943 participants (aged 25+) of Understanding Society (wave 2, 1/2010-3/2012) were employed. We found that SEP inequalities in inflammation followed heterogeneous patterns by age, which differed by the inflammatory marker examined rather than by SEP measures. SEP inequalities in CRP emerged in 30s, increased up to mid-50s or early 60 s when they peaked and then decreased with age. SEP inequalities in fibrinogen decreased with age. Body mass index (BMI), smoking, physical activity and healthy diet explained part, but not all, of the SEP inequalities in inflammation; in general, BMI exerted the largest attenuation. Cumulative advantage theories and those considering age as a leveler for the accumulation of health and economic advantages across the life-span should be dynamically integrated to better understand the observed heterogeneity in SEP differences in health across the lifespan. The attenuating roles of health-related lifestyle indicators suggest that targeting health promotion policies may help reduce SEP inequalities in health.

Socio-economic inequalities in adiposity are of particular interest themselves but also because they may be associated with inequalities in overall health status. Using cross-sectional representative data from Great Britain (1/2010-3/2012) for 13,138 adults (5652 males and 7486 females) over age 20, we aimed to explore the presence of income-related inequalities in alternative adiposity measures by gender and to identify the underlying factors contributing to these inequalities. For this reason, we employed concentration indexes and regression-based decomposition techniques. To control for non-homogeneity in body composition, we employed a variety of adiposity measures including body fat (absolute and percentage) and central adiposity (waist circumference) in addition to the conventional body mass index (BMI). The body fat measures allowed us to distinguish between the fat- and lean-mass components of BMI. We found that the absence of income-related obesity inequalities for males in the existing literature may be attributed to their focus on BMI-based measures. Pro-rich inequalities were evident for the fat-mass and central adiposity measures for males, while this was not the case for BMI. Irrespective of the adiposity measure applied, pro-rich inequalities were evident for females. The decomposition analysis showed that these inequalities were mainly attributable to subjective financial well-being measures (perceptions of financial strain and material deprivation) and education, with the relative contribution of the former being more evident in females. Our findings have important implications for the measurement of socio-economic inequalities in adiposity and indicate that central adiposity and body composition measures should be included health policy agendas. Psycho-social mechanisms, linked to subjective financial well-being, and education -rather than income itself-are more relevant for tackling inequalities.