Socioeconomic Differences In Health Research Articles

Cross national patterns in educational inequalities in functional limitations among middle aged and older adults at two time points

IntroductionIn recent decades, the global population has aged rapidly while socioeconomic inequalities in health have widened, with older adults who are most disadvantaged experiencing the poorest health. Functional limitations are key predictors of disability and quality of life and are therefore considered an important measure of how well individuals and populations are aging. We determine if educational inequalities in functioning have widened over time and across countries. MethodsWe used data from five nationally representative surveys of aging, covering 14 high- and middle-income countries, with harmonized measures of functional limitations. We examined change over time in the number of functional limitations among adults aged 50-64 and 65 and older in each country as well as changes in educational inequality in functional limitations over time for both age groups. ResultsIn most high-income countries, the number of functional limitations decreased over time, but they increased in China and Mexico, with the largest increases occurring among adults 65 and older. Educational inequality in functional limitations among those aged 65 and older widened for several countries in our study, but for different reasons. In some countries, it widened due to increased limitations among the least-educated, while in others it widened due to declines in limitations among the most-educated. ConclusionGrowing educational inequality in functional limitations for older adults in several high- and middle-income countries suggests that, despite improvements in health and mortality, socioeconomic differences in functional health among older adults are widening.

Inequities in child health in Luxembourg

Abstract Background In Luxembourg, despite its economic prosperity, 28.6 % of children are estimated to be at the risk of poverty. Research has shown that growing up with socioeconomic disadvantage is associated with worse physical and mental health in childhood. We explore the association of socioeconomic status with health, health behaviours and access to healthcare in Luxembourg for children up to age twelve. Methods We used data from 2014 to 2022 for 11-12 year old children from the Health Behaviour in School-aged Children (HBSC) survey as well as social security data from Luxembourg for a similar time period and children 0 to 12 years old. To assess socioeconomic status, we used information on self-perceived household wealth for the HBSC results and stratified health and health behaviours by well-off, average, or less well-off households. For the social security data, we stratified access to care indicators by equivalized household income quintiles. Results We found that physical activity levels, nutritional quality and daily routines such as eating breakfast and brushing teeth are significantly lower in children from less affluent families. For example, 61% of 11-12-year-old children from well-off families reported eating breakfast every day, but only 41% of children from less well-off families. Only 23% of 11-12 year olds from less affluent families rated their health as excellent, compared to 52% of children from affluent families. There were similarly large differences for multiple health complaints, with 63% of children from less affluent families reporting this indicator for psychosomatic problems, compared to 34% of children from affluent families. We did not find differences in the use of pre- and postnatal care visits. Conclusions Socioeconomic differences in child health, mental health and health behaviours are large, with children from poorer households being in worse physical and mental health and less likely to report positive health behaviours. Key messages • Many children in Luxembourg face socioeconomic challenges that are adversely associated with health and health behaviours. The underlying reasons require further attention to improve child health. • Children from poorer backgrounds are in significantly worse mental health than children from better off families in Luxembourg.

Lifetime healthcare expenditures across socioeconomic groups

BackgroundA socioeconomic gradient affects healthcare expenditures and longevity in opposite directions as less affluent individuals have higher current healthcare expenditures but simultaneously enjoy shorter lives. Yet, it is unclear whether this cross-sectional healthcare expenditure gradient persists from a lifetime perspective. This paper analyzes lifetime healthcare expenditures across socioeconomic groups using detailed individual-level healthcare expenditure data for the entire Danish population.MethodUsing full population healthcare expenditures from Danish registries, we estimate lifetime healthcare expenditures as age-specific mean healthcare expenditures times the probability of being alive at each age. Our data enables the estimation of lifetime healthcare expenditures by sex, socioeconomic status, and by various types of healthcare expenditure.ResultsOnce we account for mortality differences and all types of healthcare expenditures, all socioeconomic groups spend an almost equal amount on healthcare throughout a lifetime. Lower socioeconomic groups incur the lowest lifetime hospital expenditures, whereas higher socioeconomic groups experience the highest lifetime expenditures on long-term care services. Our findings remain robust across various socioeconomic measures and alternative estimation methodologies.ConclusionImproving the health status of lower socioeconomic groups to align with that of higher socioeconomic groups is costly but may ultimately reduce current healthcare expenditures. Enhanced health outcomes likely increase lifespan, leading to extended periods of healthcare consumption. However, since all socioeconomic groups tend to have similar lifetime healthcare expenditures, this prolonged consumption has limited impact on overall lifetime healthcare costs. Additionally, a significant benefit is the deferment of healthcare expenditures into the future. Overall, our results diminish concerns about socially inequitable utilization of healthcare resources while socioeconomic differences in health and longevity persist, even in a universal healthcare system.

Do differences in sport participation contribute to socioeconomic health inequalities? Evidence from the Lifelines cohort study on all-cause mortality, diabetes and obesity

Little is known about the role of sport participation in socioeconomic health inequalities. We studied the association between different aspects of sport participation with all-cause mortality, type 2 diabetes mellitus (T2DM) and obesity, including inequalities between socioeconomic subpopulations. Using the Dutch Lifelines cohort study (n = 84,230), we assessed the associations of sport participation, as well as the amount, intensity, type and number of sports, with all-cause mortality, T2DM and obesity in individuals. We studied the effect of sport participation on health outcomes within and between educational categories. Outcomes were compared with moderate to vigorous physical activity (MVPA). Sport participation was significantly associated with lower mortality (HR = 0.81), T2DM (HR = 0.70), and obesity (HR = 0.77). No significant additional effects of the amount or intensity of sport participation were found, while participating in teams sport was associated with significantly lower mortality (HR = 0.53) compared with other types of sport. These effects were similar among educational categories. Sport participation explained between 11% (T2DM and obesity) and 22% (mortality) of health inequalities between educational categories. This was more than twice the effect size of MVPA. The sensitivity analysis with net income as the socioeconomic indicator showed similar results. Our results suggest that to reduce socioeconomic differences in health, public health policies should focus on increasing sport participation in groups with a low socioeconomic status, rather than increasing the amount or intensity of sport participation, or MVPA in general.

Socioeconomic inequalities in health-related functioning among people with Type 2 Diabetes

Abstract Background Type 2 Diabetes mellitus (T2DM) is a common chronic disease which disproportionally affects disadvantaged groups. People with a low socioeconomic position (SEP) have increased risk of T2DM and people with a low SEP and T2DM have higher HbA1c levels and a higher risk to develop T2DM complications compared to their high SEP counterparts. The aim of this study is to analyze longitudinal socioeconomic differences in health-related functioning in people living with T2DM. Methods Cross-sectional and longitudinal data from 2,004 participants of The Maastricht Study with T2DM were used. SEP was determined by baseline measures of education, occupation and income. Health-related functioning (physical, mental and social) was measured with the SF-36 and the Impact on Participation and Autonomy survey (scores 0-100). Associations of SEP and health-related functioning were studied over a 10-year period (baseline 2010-2018) using linear mixed methods adjusting for demographics, disease characteristics and lifestyle factors. Results Low SEP participants had significantly worse health-related functioning compared to those with a high SEP. For example, participants with low income had lower scores for physical (-4.00[CI 2.62;-5.38]), mental (-2.81[-1.52;-4.10]) and social functioning (-9.10[-6.35;-11.85]) compared to high-income participants. In addition, participants with a low education significantly declined more in mental (interaction estimate time -0.21[-0.05;-0.37]) and social functioning (-0.39[-0.02;-0.76]) compared to participants with high education. Conclusions In individuals with T2DM, socioeconomic health differences are apparent in health-related functioning. Moreover, differences in mental and social functioning widened even more between different levels of education. Beyond disease characteristics and lifestyle, more attention is needed for socioeconomic differences in health-related functioning for people living with T2DM, both in research and practice. Key messages • Among people with T2DM, those with a lower SEP have worse health-related function then individuals with a higher SEP, even with similar demographics, disease characteristics and lifestyle patterns. • Additionally, people with T2DM and low education decline more over time in mental and social functioning than people with T2DM and high education.

In Sickness and in Wealth: Mental Health, Income Levels, and COVID-19

Globally, COVID-19 has brought upon many challenges to mental health. Social distancing and isolation have led people to experience greater anxiety and negative affect, and financial distress has increased due to economic changes. Demographic features may differentiate the severity of distress individuals face. Using data from The Centre for Addiction and Mental Health (CAMH), the present study examined measures of psychological distress across a Canadian sample, identifying differences in age, sex, and income levels. Trends over time were observed. Lower-income Canadians reported higher distress. Women may be at greater risk than men, as well as younger compared to older Canadians. Psychological distress has remained relatively stable throughout the pandemic, although COVID-19 financial worry has lessened as people are not as worried about their finances. The findings of this study are informative of socioeconomic, sex, and age differences in mental health throughout the pandemic in a Canadian sample.

Socioeconomic disparities in health outcomes in the United States in the late 2010s: results from four national population-based studies

BackgroundDespite the importance of monitoring health disparities by multiple socioeconomic categories, there have been no recent updates on the prevalence of general health indicators by socioeconomic categories. The present study aims to update the prevalence estimates of health indicators by education and income categories across three age groups (children, young and middle-aged adults, and older adults) in the late 2010s by using four nationally representative data sources. We also examine socioeconomic differences in health by race/ethnicity subgroups.MethodsData were obtained from four nationally representative data sources from the U.S.: The National Health Interview Survey (2015–2018); the National Health and Nutrition Examination Survey, NHANES (2017–2020); the Behavioral Risk Factor Surveillance System (2016–2020); and the Health & Retirement Study (2016). Respondent-rated health and obesity were selected as the health indicators of interest. Socioeconomic factors included percentages of the federal poverty level and years of educational attainment. We conducted logistic regression analyses to calculate adjusted prevalence rates of respondent-rated (or measured, in the case of obesity in NHANES) poor health and obesity by income and education categories after controlling for sociodemographic characteristics. The complex sampling designs were accounted for in all analyses.ResultsPrevalence rates across racial/ethnic groups and age groups demonstrated clear and consistent socioeconomic gradients in respondent-rated poor health, with the highest rates among those in the lowest income and education categories, and decreased rates as income and education levels increased. On the other hand, there were less evident socioeconomic differences in obesity rates across all data sources, racial/ethnic groups, and age groups.ConclusionsOur results confirmed earlier, persistent evidence indicating socioeconomic disparities in respondent-rated poor health across all age and race/ethnicity groups by using four nationally representative datasets. In comparison to a decade earlier, socioeconomic disparities in poor health appeared to shrink while they emerged or increased for obesity. The results suggest an urgent need for action to alleviate pervasive health disparities by socioeconomic status. Further research is needed to investigate potentially modifiable factors underlying socioeconomic disparities in health, which may help design targeted health promotion programs.

Socioeconomic status and social anxiety: attentional control as a key missing variable?

ABSTRACT Background and Objectives The aim was to examine the role of attentional control as a psychological factor involved in socioeconomic status-related mental health differences, and specifically in social anxiety. Based on the literature on socioeconomic status differences in cognitive abilities and attentional control theory, we hypothesized that attentional control would account for the relation between socioeconomic status and social anxiety. We tested this hypothesis in an integrative model also including trait anxiety and subjective socioeconomic status. Design Cross-sectional. Method Online, 439 French adults were recruited via social media. They completed self-reported measures of attentional control, objective socioeconomic status, subjective socioeconomic status, social anxiety, and trait anxiety. Results Using Structural Equation Modelling, findings showed a positive association between objective (but not subjective) socioeconomic status and attentional control, which in turn was related to social anxiety. Exploratory analyses showed that only income, as objective socioeconomic status indicator, was associated with attentional control. Conclusions The current study is the first to support that low socioeconomic status individuals report less attentional control and more social anxiety symptoms. This suggests that attentional control is a psychological factor involved in social anxiety inequalities.

Pathways to socioeconomic health differences in Armenian adolescents: The role of bullying perpetration.

Bullying perpetration might be an alternative way of hierarchy formation among adolescents. It can potentially compensate for the negative health influences of low socioeconomic status (SES), rewarding this unwanted behavior. This study aimed to investigate the role of bullying perpetration in the relationship between SES and health among Armenian adolescents. A nationally representative sample of 3679 adolescents aged 11–15 years (mean = 13.1, standard deviation = 1.6) participated in the Health Behavior in School-aged Children 2013/14 survey in Armenia. Complex samples multiple logistic regression were used to estimate the associations between two SES measures (family socioeconomic position [SEP] and material well-being) and three health outcomes (perceived health status, psychosocial well-being, and psychosomatic symptoms). Bullying perpetration was not associated with less than good health or low psychosocial well-being (P > 0.05) but increased the odds of reporting high psychosomatic symptoms (P < 0.05). Perpetration did not change the SES-health gradient substantially. However, in stratified analyses, socioeconomic inequalities in health were consistently weaker among perpetrators. The largest observed difference was in the relationship between low family SEP and less than good health (OR = 3.60, 95% CI = 2.77–4.67 vs. OR = 1.80, 95% CI = 1.06–3.04), whereas the smallest difference was in the relationship between low family SEP and high psychosomatic symptoms (OR = 1.27, 95% CI = 1.03–1.56 vs. OR = 1.04, 95% CI = 0.61–1.77). Our findings suggest that bullying perpetration, as an alternative hierarchy, may be looked at as a compensatory but vicious strategy in the face of the negative health influences of low SES in Armenian adolescents. For high-SES adolescents, on the other hand, social, emotional, or psychological problems might contribute to bullying perpetration. Consequently, bullying prevention activities in Armenia should focus on both low and high-SES adolescents, considering SES-specific pathways and mechanisms.

Racial and socioeconomic status differences in stress, posttraumatic growth, and mental health in an older adult cohort during the COVID-19 pandemic.

SummaryBackgroundThe COVID-19 pandemic has disproportionately impacted the most vulnerable and widened the health disparity gap in both physical and mental well-being. Consequentially, it is vital to understand how to best support elderly individuals, particularly Black Americans and people of low socioeconomic status, in navigating stressful situations during the COVID-19 pandemic and beyond. The aim of this study was to investigate perceived levels of stress, posttraumatic growth, coping strategies, socioeconomic status, and mental health between Black and non-Hispanic, White older adults, the majority over the age of 70. Additionally, we investigated which variables, if any, were associated with posttraumatic growth in these populations.MethodsOne hundred seventy-six community dwelling older adults (mean age = 76.30 ±8.94), part of two observational studies (The Harvard Aging Brain Study and Instrumental Activities of Daily Living Study) in Massachusetts, US, were included in this cross-sectional study. The survey, conducted from March 23, 2021 to May 13, 2021, measured perceived stress, behavioral coping strategies, posttraumatic growth, and mental health during the COVID-19 pandemic. We investigated associations with post-traumatic growth in a multiple linear regression model and examined their differences by race with t-tests, Wilcoxon rank-sum tests, and Fisher's exact tests. A second multiple linear regression model was used to examine which coping strategies were associated with posttraumatic growth.FindingsOur results indicated no significant difference between the groups in terms of mental health or stress. However, Black participants showed significantly greater posttraumatic growth compared to non-Hispanic, White participants. Additionally, the coping strategies of religion and positive reframing were found to be significantly associated with posttraumatic growth. Furthermore, even with the effects of stress and coping strategies controlled for, race remained significantly associated with posttraumatic growth.InterpretationThe COVID-19 pandemic has differentially impacted Black and non-Hispanic White older adults. These results may help encourage further analysis on geriatric psychiatry as well as understanding how cultural values and adaptations impact posttraumatic growth and mental health in diverse populations.FundingThe Harvard Aging Brain Study (HABS) has been funded by NIH-NIA P01 AG036694 (PI: Reisa Sperling). The IADL study is funded by the National Institute on Aging (R01 AG053184, PI: Gad A. Marshall).

Education inequalities in cardiovascular and coronary heart disease in Italy and the role of behavioral and biological risk factors

Background and aimsBehavioral and biological risk factors (BBRF) explain part of the variability in socioeconomic differences in health. The present study aimed at evaluating education differences in incidence of cardiovascular disease (CVD) and coronary heart disease (CHD) in Italy and the role of BBRF. Methods and resultsAll subjects aged 30–74 years (n = 132,686) who participated to the National Health Interview Surveys 2000 and 2005 were included and followed-up for ten years. Exposure to smoking, physical activity, overweight/obesity, diabetes and hypertension at baseline was considered. Education level was used as an indicator of socioeconomic status. The outcomes were incident cases of CVD and CHD. Hazard ratios by education level were estimated, adjusting for sociodemographic covariates and stratifying by sex and geographic area. The contribution of BBRF to education inequalities was estimated by counterfactual mediation analysis, in addition to the assessment of the risk attenuation by comparing the models including BBRF or not.22,214 participants had a CVD event and 6173 a CHD event. After controlling for sociodemographic factors, the least educated men showed a 21% higher risk of CVD and a 17% higher risk of CHD compared to the most educated (41% and 61% among women). The mediating effect (natural indirect effect) of BBRF between extreme education levels was 52% for CVD and 84% for CHD among men (16% among women for CVD). ConclusionsMore effective strategies aiming at reducing socioeconomic disparities in CVD and CHD are needed, through programs targeting less educated people in combination with community-wide initiatives.

Socio-economic position and burden of ischemic heart disease in Finland in 2017

Abstract Background Ischemic heart disease (IHD) caused every fourth death in the European region in 2017. Furthermore, it accounted for about 12% of total disease burden estimated with disability adjusted life years (DALYs). DALYs summarize premature mortality (years of life lost, YLL) and morbidity (years lived with disease, YLD) in a specific cause. These disease burden estimates are customarily used to examine time trends or differences between countries or regions. However, they have seldom been calculated by socio-economic position (SEP), especially using individual-level SEP indicators. Methods We calculated DALYs for the Finnish 30+ year-old population in 2017. For YLL, we used Statistics Finland database for total population. For YLD, we used individual level data from the cross-sectional health examination FinHealth 2017 survey (n = 6538), which was linked with administrative register data. Population attributable fractions (PAFs) for selected IHD risk factors (smoking, blood pressure, total cholesterol and body mass index, BMI) were also calculated. Methodology published by the Global Burden of Disease study, such as disability weights for YLD and relative risks for PAFs, was applied where applicable. Results DALYs for IHD per 1000 population were 61.1 for men and 31.0 for women. Premature mortality (YLL) caused over 80% of DALYs. Burden of IHD morbidity (YLD) was higher in low education groups. In the total population, PAFs for IHD risk factors for men and women, respectively, were as follows: 17% and 8% for smoking, 56% and 58% for elevated systolic blood pressure, 25% and 28% for high cholesterol, and 27% and 24% for high BMI. Conclusions IHD morbidity was higher among low education groups. A large part of IHD burden was attributable to major cardiovascular risk factors, most markedly to elevated systolic blood pressure. In the future, the burden of disease could be increasingly assessed also by SEP. Key messages The burden of IHD in Finland was especially high among men, and majority of the burden was due to premature mortality. Over 50% of the IHD burden was attributed to elevated systolic blood pressure. Assessing the burden of diseases by socio-economic position provides a new perspective for examining socio-economic differences in health.

An integrative review of the social determinants of mental health among older caregivers.

AimThe aim of this study was to examine the reported social determinants of mental health among older caregivers in the previous caregiving literature.DesignAn integrative literature review method of Whittemore and Knafl.MethodsCINAHL, MEDLINE (Ovid), PubMed, and Science Direct were searched from January 1999 to December 2019. A systematic approach and data analysis were used. The evidence of a multilevel framework by the World Health Organization was used to examine the social determinants of mental health.ResultsTwelve articles were included. “Levels of physical health” was mainly cited factor linked to mental health inequalities among older caregivers. However, the evidence regarding caregivers’ gender‐related, ethnic and socioeconomic differences in mental health was relatively limited.ConclusionFuture research and screening programs on social determinants of mental health among older caregivers are necessary to tackle mental health inequalities. Implications informed by this research could sustain mental health equity among older caregivers.

Socioeconomic differences in healthcare expenditure and utilization in The Netherlands

BackgroundWorldwide, socioeconomic differences in health and use of healthcare resources have been reported, even in countries providing universal healthcare coverage. However, it is unclear how large these socioeconomic differences are for different types of care and to what extent health status plays a role. Therefore, our aim was to examine to what extent healthcare expenditure and utilization differ according to educational level and income, and whether these differences can be explained by health inequalities.MethodsData from 18,936 participants aged 25–79 years of the Dutch Health Interview Survey were linked at the individual level to nationwide claims data that included healthcare expenditure covered in 2017. For healthcare utilization, participants reported use of different types of healthcare in the past 12 months. The association of education/income with healthcare expenditure/utilization was studied separately for different types of healthcare such as GP and hospital care. Subsequently, analyses were adjusted for general health, physical limitations, and mental health.ResultsFor most types of healthcare, participants with lower educational and income levels had higher healthcare expenditure and used more healthcare compared to participants with the highest educational and income levels. Total healthcare expenditure was approximately between 50 and 150 % higher (depending on age group) among people in the lowest educational and income levels. These differences generally disappeared or decreased after including health covariates in the analyses. After adjustment for health, socioeconomic differences in total healthcare expenditure were reduced by 74–91 %.ConclusionsIn this study among Dutch adults, lower socioeconomic status was associated with increased healthcare expenditure and utilization. These socioeconomic differences largely disappeared after taking into account health status, which implies that, within the universal Dutch healthcare system, resources are being spent where they are most needed. Improving health among lower socioeconomic groups may contribute to decreasing health inequalities and healthcare spending.

Separate and combined effects of individual and neighbourhood socio-economic disadvantage on health-related lifestyle risk factors: a multilevel analysis.

BackgroundSocio-economic disadvantage at both individual and neighbourhood levels has been found to be associated with single lifestyle risk factors. However, it is unknown to what extent their combined effects contribute to a broad lifestyle profile. We aimed to (i) investigate the associations of individual socio-economic disadvantage (ISED) and neighbourhood socio-economic disadvantage (NSED) in relation to an extended score of health-related lifestyle risk factors (lifestyle risk index); and to (ii) investigate whether NSED modified the association between ISED and the lifestyle risk index.MethodsOf 77 244 participants [median age (IQR): 46 (40–53) years] from the Lifelines cohort study in the northern Netherlands, we calculated a lifestyle risk index by scoring the lifestyle risk factors including smoking status, alcohol consumption, diet quality, physical activity, TV-watching time and sleep time. A higher lifestyle risk index was indicative of an unhealthier lifestyle. Composite scores of ISED and NSED based on a variety of socio-economic indicators were calculated separately. Linear mixed-effect models were used to examine the association of ISED and NSED with the lifestyle risk index and to investigate whether NSED modified the association between ISED and the lifestyle risk index by including an interaction term between ISED and NSED.ResultsBoth ISED and NSED were associated with an unhealthier lifestyle, because ISED and NSED were both positively associated with the lifestyle risk index {highest quartile [Q4] ISED beta-coefficient [95% confidence interval (CI)]: 0.64 [0.62–0.66], P < 0.001; highest quintile [Q5] NSED beta-coefficient [95% CI]: 0.17 [0.14–0.21], P < 0.001} after adjustment for age, sex and body mass index. In addition, a positive interaction was found between NSED and ISED on the lifestyle risk index (beta-coefficient 0.016, 95% CI: 0.011–0.021, Pinteraction < 0.001), which indicated that NSED modified the association between ISED and the lifestyle risk index; i.e. the gradient of the associations across all ISED quartiles (Q4 vs Q1) was steeper among participants residing in the most disadvantaged neighbourhoods compared with those who resided in the less disadvantaged neighbourhoods.ConclusionsOur findings suggest that public health initiatives addressing lifestyle-related socio-economic health differences should not only target individuals, but also consider neighbourhood factors.

Foreword

In 2018, Janet Currie visited the Institute for Fiscal Studies and delivered its Annual Lecture.11 Watch Janet's lecture here: https://www.ifs.org.uk/publications/13452. The lecture focused on Janet's latest research work, investigating socio-economic differences in health and mortality, and the extent to which access to health care early in life matters for mortality and other health outcomes in the short and longer term, as children grow up and become adults. In her lecture, Janet compares inequality in mortality in the US, Canada and France, as well as across states in the US, to investigate the value of publicly provided health insurance for children in tackling inequalities in health outcomes and in increasing health outcomes for all. Her work clearly demonstrates the power and value of looking across countries to understand trends in inequality and the role of institutions in shaping them. Following her fascinating and inspiring lecture, we joined forces with Sonya Krutikova and invited Janet to collaborate with us in extending her agenda to a wider group of countries and produce a special issue of Fiscal Studies, showing how inequalities in mortality changed in these countries, which trends and patterns were common and where they differed. We were delighted that Janet was just as excited to embark on this project. Papers were commissioned from research teams working on related issues in North and South, West and East European countries, a combination that we felt would extend the existing analysis of US, Canada and France to a richer and wider variety of contexts in the developed world. We were also incredibly fortunate that the group of lead researchers behind the comparative project, including James Banks, Kjell Salvanes and Hannes Schwandt, joined Janet and Sonya as guest editors for this special issue. This issue contains an introductory piece from the guest editors, a three-country paper by Janet and co-authors that updates her annual lecture, and eight single country papers from different research teams across Europe. The resulting issue makes a terrific contribution to the understanding of how inequalities in health and income have evolved together over the last three decades, demonstrating the diversity of experiences across countries and relating these differences to the historical and institutional features of each country. The Editors of Fiscal Studies,

Can school-based, health behavioural interventions tackle health inequities in childhood?

Abstract Background To improve the health situation of children in primary school, many school-based interventions that are aimed at changing health behaviours, are being set up. Prior to knowing the (long-term) effectiveness of such intervention, we set out to examine whether there is room for such interventions to also tackle the health gap between children from high and low socioeconomic backgrounds. Methods Data from the Dutch quasi-experimental Health Primary School of the Future were used. All children from eight primary schools were included in the period 2015-2019. In the first cross-sectional phase, we studied whether there were socioeconomic differences in various health outcomes (e.g. body mass index) and whether these were smaller in children in the oldest groups (highest classes). In a two-year longitudinal approach, it was studied how socioeconomic differences in health outcomes developed in two years time and whether health behaviours could “explain” any socioeconomic differences in the outcomes. Linear and logistic regression analyses were used, including multilevel variants thereof, as well as mediation analyses. Results Both the cross-sectional and longitudinal analyses indicated substantial and consistent socioeconomic differences in various health outcomes. The socioeconomic differences in health appeared stronger in the oldest children (highest classes). Due to only small socioeconomic differences in health behaviours, such behaviours did not mediate the socioeconomic differences in the children's health outcomes. Conclusions The findings indicate substantial socioeconomic differences in health in primary school. That being longer at school did not attenuate the socioeconomic differences in health and that health behaviours did not mediate the socioeconomic differences in health suggests that there might not be much room for school-based, health behaviour interventions to easily tackle socioeconomic differences in health in childhood. Key messages We found substantial socioeconomic differences in child health in the Netherlands. But we found only little evidence that primary school-based, health behavioural interventions will easily tackle socioeconomic differences in child health.

Health Behavior in School-Aged Children: Summary of the 2013/2014 WHO Survey

The following piece summarizes the key findings of the 2013/2014 Health Behavior in School-Aged Children report 'Growing up unequal: gender and socioeconomic differences in young people's health and well-being'.

Comparison of Health Outcomes Among High- and Low-Income Adults Aged 55 to 64 Years in the US vs England

Socioeconomic differences in life expectancy, health, and disability have been found in European countries as well as in the US. Identifying the extent and pattern of health disparities, both within and across the US and England, may be important for informing public health and public policy aimed at reducing these disparities. To compare the health of US adults aged 55 to 64 years with the health of their peers in England across the high and low ranges of income in each country. Using data from the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA) for 2008-2016, a pooled cross-sectional analysis of comparably measured health outcomes, with adjustment for demographic characteristics and socioeconomic status, was conducted. The analysis sample included community-dwelling adults aged 55 to 64 years from the HRS and ELSA, resulting in 46 887 person-years of observations. Data analysis was conducted from September 17, 2019, to May 12, 2020. Residence in the US or England and yearly income. Sixteen health outcomes were compared, including 5 self-assessed outcomes, 3 directly measured outcomes, and 8 self-reported physician-diagnosed health conditions. This cross-sectional study included 12 879 individuals and 31 928 person-years from HRS (mean [SD] age, 59.2 [2.8] years; 51.9% women) and 5693 individuals and 14 959 person-years from ELSA (mean [SD] age, 59.3 [2.9] years; 51.0% women). After adjusting for individual-level demographic characteristics and socioeconomic status, a substantial health gap between lower-income and higher-income adults was found in both countries, but the health gap between the bottom 20% and the top 20% of the income distribution was significantly greater in the US than England on 13 of 16 measures. The adjusted US-England difference in the prevalence gap between the bottom 20% and the top 20% ranged from 3.6 percentage points (95% CI, 2.0-5.2 percentage points) in stroke to 9.7 percentage points (95% CI, 5.4-13.9 percentage points) for functional limitation. Among individuals in the lowest income group in each country, those in the US group vs the England group had significantly worse outcomes on many health measures (10 of 16 outcomes in the bottom income decile); the significant differences in adjusted prevalence of health problems in the US vs England for the bottom income decile ranged from 7.6% (95% CI, 6.0%-9.3%) vs 3.8% (95% CI, 2.6%-4.9%) for stroke to 75.7% (95% CI, 72.7%-78.8%) vs 59.5% (95% CI, 56.3%-62.7%) for functional limitation. Among individuals in the highest income group, those in the US group vs England group had worse outcomes on fewer health measures (4 of 16 outcomes in the top income decile); the significant differences in adjusted prevalence of health problems in the US vs England for the top income decile ranged from 36.9% (95% CI, 33.4%-40.4%) vs 30.0% (95% CI, 27.2%-32.7%) for hypertension to 35.4% (95% CI, 32.0%-38.7%) vs 22.5% (95% CI, 19.9%-25.1%) for arthritis. For most health outcomes examined in this cross-sectional study, the health gap between adults with low vs high income appeared to be larger in the US than in England, and the health disadvantages in the US compared with England are apparently more pronounced among individuals with low income. Public policy and public health interventions aimed at improving the health of adults with lower income should be a priority in the US.

Impact of Socioeconomic Differences on Distributional Cost-effectiveness Analysis.

Public health decision makers value interventions for their effects on overall health and health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates health inequality concerns into economic evaluation by accounting for how parameters, such as effectiveness, differ across population groups. A good understanding of how and when accounting for socioeconomic differences between groups affects the assessment of intervention impacts on overall health and health inequality could inform decision makers where DCEA would add most value. We interrogated 2 DCEA models of smoking and alcohol policies using first national level and then local authority level information on various socioeconomic differences in health and intervention use. Through a series of scenario analyses, we explored the impact of altering these differences on the DCEA results. When all available evidence on socioeconomic differences was incorporated, provision of a smoking cessation service was estimated to increase overall health and increase health inequality, while the screening and brief intervention for alcohol misuse was estimated to increase overall health and reduce inequality. Ignoring all or some socioeconomic differences resulted in minimal change to the estimated impact on overall health in both models; however, there were larger effects on the estimated impact on health inequality. Across the models, there were no clear patterns in how the extent and direction of socioeconomic differences in the inputs translated into the estimated impact on health inequality. Modifying use or coverage of either intervention so that each population group matched the highest level improved the impacts to a greater degree than modifying intervention effectiveness. When local level socioeconomic differences were considered, the magnitude of the impacts was altered; in some cases, the direction of impact on inequality was also altered.