Magnitude Of Health Inequalities Research Articles

Educational Inequalities in Self-Rated Health in Europe and South Korea.

While numerous comparative works on the magnitude of health inequalities in Europe have been conducted, there is a paucity of research that encompasses non-European nations such as Asian countries. This study was conducted to compare Europe and Korea in terms of educational health inequalities, with poor self-rated health (SRH) as the outcome variable. The European Union Statistics on Income and Living Conditions and the Korea National Health and Nutrition Examination Survey in 2017 were used (31 countries). Adult men and women aged 20+ years were included (207,245 men and 238,007 women). The age-standardized, sex-specific prevalence of poor SRH by educational level was computed. The slope index of inequality (SII) and relative index of inequality (RII) were calculated. The prevalence of poor SRH was higher in Korea than in other countries for both low/middle- and highly educated individuals. Among highly educated Koreans, the proportion of less healthy women was higher than that of less healthy men. Korea’s SII was the highest for men (15.7%) and the ninth-highest for women (10.4%). In contrast, Korea’s RII was the third-lowest for men (3.27), and the lowest among women (1.98). This high-SII–low-RII mix seems to have been generated by the high level of baseline poor SRH.

Measuring the Magnitude of Health Inequality Between 2 Population Subgroup Proportions.

In this paper, we evaluate 11 measures of inequality, d(p1, p2), between 2 proportions p1 and p2, some of which are new to the health disparities literature. These measures are selected because they are continuous, nonnegative, equal to 0 if and only if |p1 - p2|=0, and maximal when |p1 - p2|=1. They are also symmetrical [d(p1, p2)=d(p2, p1)] and complement-invariant [d(p1, p2)=d(1 - p2, 1 - p1)]. To study intermeasure agreement, 5 of the 11 measures, including the absolute difference, are retained, because they remain finite and are maximal if and only if |p1 - p2|=1. Even when the 2 proportions are assumed to be drawn at random from a shared distribution-interpreted as the absence of an avoidable difference-the expected value of d(p1, p2) depends on the shape of the distribution (and the choice of d) and can be quite large. To allow for direct comparisons among measures, we propose a standard measurement unit akin to a z score. For skewed underlying beta distributions, 4 of the 5 retained measures, once standardized, offer more conservative assessments of the magnitude of inequality than the absolute difference. We conclude that, even for measures that share the highlighted mathematical properties, magnitude comparisons are most usefully assessed relative to an elicited or estimated underlying distribution for the 2 proportions.

Inequalities in life expectancy in six large Latin American cities from the SALURBAL study: an ecological analysis

SummaryBackgroundLatin America is one of the most unequal regions in the world, but evidence is lacking on the magnitude of health inequalities in urban areas of the region. Our objective was to examine inequalities in life expectancy in six large Latin American cities and its association with a measure of area-level socioeconomic status.MethodsIn this ecological analysis, we used data from the Salud Urbana en America Latina (SALURBAL) study on six large cities in Latin America (Buenos Aires, Argentina; Belo Horizonte, Brazil; Santiago, Chile; San José, Costa Rica; Mexico City, Mexico; and Panama City, Panama), comprising 266 subcity units, for the period 2011–15 (expect for Panama city, which was for 2012–16). We calculated average life expectancy at birth by sex and subcity unit with life tables using age-specific mortality rates estimated from a Bayesian model, and calculated the difference between the ninth and first decile of life expectancy at birth (P90–P10 gap) across subcity units in cities. We also analysed the association between life expectancy at birth and socioeconomic status at the subcity-unit level, using education as a proxy for socioeconomic status, and whether any geographical patterns existed in cities between subcity units.FindingsWe found large spatial differences in average life expectancy at birth in Latin American cities, with the largest P90–P10 gaps observed in Panama City (15·0 years for men and 14·7 years for women), Santiago (8·9 years for men and 17·7 years for women), and Mexico City (10·9 years for men and 9·4 years for women), and the narrowest in Buenos Aires (4·4 years for men and 5·8 years for women), Belo Horizonte (4·0 years for men and 6·5 years for women), and San José (3·9 years for men and 3·0 years for women). Higher area-level socioeconomic status was associated with higher life expectancy, especially in Santiago (change in life expectancy per P90–P10 change unit-level of educational attainment 8·0 years [95% CI 5·8–10·3] for men and 11·8 years [7·1–16·4] for women) and Panama City (7·3 years [2·6–12·1] for men and 9·0 years [2·4–15·5] for women). We saw an increase in life expectancy at birth from east to west in Panama City and from north to south in core Mexico City, and a core-periphery divide in Buenos Aires and Santiago. Whereas for San José the central part of the city had the lowest life expectancy and in Belo Horizonte the central part of the city had the highest life expectancy.InterpretationLarge spatial differences in life expectancy in Latin American cities and their association with social factors highlight the importance of area-based approaches and policies that address social inequalities in improving health in cities of the region.FundingWellcome Trust.

Public awareness of poverty as a determinant of health: survey results from 23 countries

We aim to examine (1) variations in the public awareness of poverty as a determinant of health and (2) associations of individual and macro level factors with awareness. Analyses are based on the International Social Survey Programme. Data stem from 23 countries (N=37,228) that were assigned to six welfare states. Sociodemographic, socio-economic, and health-related factors were considered as individual level characteristics. Gross domestic product, relative poverty rate, Gini coefficient, and magnitude of health inequalities were additionally introduced as macro level factors. About 47% of the respondents in all countries agreed with the statement that people suffer from severe health problems because they are poor (range 30-77%). Multilevel analyses reveal that awareness was least pronounced in Liberal, East European, and East Asian welfare regimes. Moreover, women, older adults, respondents with low education and income, as well as poor health were more likely to show awareness. There is a need to raise public awareness of the adverse health effects of poverty as the public opinion can be an important driver of political will on health and social issues.

Trends in health inequalities in developing countries

This is a review of current professional thinking about health inequalities in developing countries and how to reduce them. It is in four parts. The first provides a brief history of recent trends in concern about health inequalities and related issues. The second is a discussion of the concept of health inequalities, and of the similarities and differences between other distributional measures in current use. The third summarizes what is known about the dimensions and magnitude of health inequalities. The fourth presents a comparable summary of current thought about how best to reduce inequalities. The review closes with a brief conclusion.

Social inequalities and health: monitoring in the era of non-communicable diseases

Abstract: Social inequalities in health have persisted or increased in the era of non-communicable diseases. They are sensitive to changes in underlying structural mechanisms which are ‘socialy produced’. Research in this field is increasingly concerned with changes over time in the magnitude of health inequalities between social groups and related structural mechanisms contributing to this change. Equity-oriented monitoring systems are essential for this task, but not yet well established in Germany.

Health, Homelessness, and Poverty: A Way Forward

Health, Homelessness, and Poverty: A Way Forward

The magnitude of oral health inequalities in Canada: findings of the Canadian health measures survey

This study aimed to measure the magnitude of income-related inequality for four oral health outcomes in Canada. The degree of oral health inequality according to sex was also compared. Data for this study are from the year 2007 to 2009 Canadian Health Measure Survey (CHMS). The sample size consisted of 4951 Canadians aged 6-79 (2409 men and 2542 women). The oral health indicators used were the number of decayed teeth, number of missing teeth, number of filled teeth, and oral pain in the past year. Socioeconomic status was measured as equivalized household income. We used the relative concentration index to quantify health inequalities. Data analyses were performed using STATA 11.1 and ADePT (4.0). The number of decayed teeth, the number of missing teeth and the prevalence of oral pain decreased with increasing income, while the number of filled teeth increased with increasing income. The relative concentration indices for decayed teeth, missing teeth, filled teeth and for oral pain were -0.264, -0.157, 0.085, and -0.120, respectively. There was a statistically significant deviation from equality for the four oral health outcomes and this was generally present for both sexes. The relative concentration indices for decayed teeth were statistically significantly larger than other oral health outcomes. The relative concentration indices for women were greater than those of males indicating a greater magnitude of inequality among women. There was a higher concentration of decayed teeth, missing teeth and oral pain in the worse off, while the more affluent had a greater concentration of filled teeth. The numbers of decayed teeth was the most unequal aspect of oral health comparatively. There was a sex difference in the pattern of oral health inequalities with greater magnitude of inequality present among women in terms of the number of decayed and missing teeth. Health policymakers should consider the magnitude of health inequalities according to outcome and between sexes in their decision to tackle oral health inequalities.

Health inequalities policy in Korea: current status and future challenges.

In recent years, health inequalities have become an important public health concern and the subject of both research and policy attention in Korea. Government reports, as well as many epidemiological studies, have provided evidence that a wide range of health outcomes and health-related behaviors are socioeconomically patterned, and that the magnitude of health inequalities is even increasing. However, except for the revised Health Plan 2010 targets for health equity, few government policies have explicitly addressed health inequalities. Although a number of economic and social policies may have had an impact on health inequalities, such impact has scarcely been evaluated. In this review, we describe the current status of research and policy on health inequalities in Korea. We also suggest future challenges of approaches and policies to reduce health inequalities and highlight the active and intensive engagement of many policy sectors and good evidence for interventions that will make meaningful reduction of health inequalities possible.

Social inequalities in health in Tunisia: a reality whose magnitude is underestimated

This study aims to analyse the influence of determinants of health on the extent of socioeconomic inequalities in health. A material and social deprivation index into two versions has been used to assess the magnitude of social inequalities in health in Tunisian population. The results show that the individual version of the index produces differences in healthy life expectancy and life expectancy without disability of greater magnitude than the geographic version. The relative risk of mortality and the proportional risk of disability are increased with deprivation index, used the geographical or individual version. The differences between the two versions are also stronger for the disability for mortality and more marked in interior regions than in larger cities and the coastal regions. Finally, the advantages and limitations of this index are also put into perspective, as are the possibilities it offers for research and decision-making.

P2-25 Trends and social inequalities on chronic diseases in Brazilian population: PNAD, 2003–2008

IntroductionNational Health Surveys provide essential data to monitoring health conditions and the magnitude of health inequalities. The aims of this study were to evaluate the 2008 prevalence of chronic diseases...

Socioeconomic inequalities in old-age mortality: A comparison of Denmark and the USA

Previous studies have reported important variations in the magnitude of health inequalities between countries that belong to different welfare systems. This suggests that there is scope for reducing health inequalities by means of country-level interventions. The present study adds to this literature by exploring whether the magnitude of socioeconomic inequalities in mortality is associated with social inequality levels. Denmark and the USA belong to fundamentally different welfare systems (social democratic and liberal) and our study thereby contributes to the ongoing debate on whether welfare systems are linked to health inequalities. We analyze Denmark and the USA in terms of socioeconomic differences in mortality above age 58. The data sources were Danish register data from 1980 to 2002 (n = 2,029,324), and survey data from the US Health and Retirement Study (HRS) from 1992 to 2006 (n = 9374). Survival analysis was used to study the impact of socioeconomic status on mortality and the magnitude of mortality differences between the two countries was compared. The results showed surprisingly that mortality differentials were larger in Denmark than in the USA even after controlling for a number of covariates: The poorest 10 percent of the Danish elderly population have a mortality rate ratio of 3.32 (men) and 3.70 (women) compared to the richest 25 percent. In the USA the corresponding rate ratios are 1.67 and 1.56. Low income seems to be a more powerful risk factor for mortality than low education. A number of possible explanations for higher mortality differences in Denmark are discussed: unintended positive correlation between generous health services and health inequality, early life influences, mortality selection, and relative deprivation.

Health inequalities in European welfare states

A controversial and puzzling finding from studies on health inequalities is that countries that have managed to reduce inequalities in social and economic conditions, itself powerful determinants of health, are not necessarily characterized by smaller inequalities in health. Fifteen years after the Black Report, a breakthrough in research comparing the magnitude of health inequalities across western European countries was made, providing the first comprehensive evidence on the unexpected patterning of health inequalities: relative inequalities in ill-health and mortality were not smaller in the Nordic welfare states than elsewhere in western Europe.1 Since then scholars have been puzzled by the issue of why affluent Nordic countries with advanced welfare state arrangements and egalitarian policies share large health inequalities with other western European countries. Subsequent studies looking at changes over time have not altered the picture.2–4 A number of viewpoints can be … Correspondence: Eero Lahelma, Department of Public Health, University of Helsinki, P.O.B. 41, 00014 University of Helsinki, Finland, e-mail: eero.lahelma{at}helsinki.fi

Socioeconomic Inequalities in Health in 22 European Countries

A b s t r ac t Background Comparisons among countries can help to identify opportunities for the reduction of inequalities in health. We compared the magnitude of inequalities in mortality and self-assessed health among 22 countries in all parts of Europe. Methods We obtained data on mortality according to education level and occupational class from census-based mortality studies. Deaths were classified according to cause, including common causes, such as cardiovascular disease and cancer; causes related to smoking; causes related to alcohol use; and causes amenable to medical intervention, such as tuberculosis and hypertension. Data on self-assessed health, smoking, and obesity according to education and income were obtained from health or multipurpose surveys. For each country, the association between socioeconomic status and health outcomes was measured with the use of regression-based inequality indexes. Results In almost all countries, the rates of death and poorer self-assessments of health were substantially higher in groups of lower socioeconomic status, but the magnitude of the inequalities between groups of higher and lower socioeconomic status was much larger in some countries than in others. Inequalities in mortality were small in some southern European countries and very large in most countries in the eastern and Baltic regions. These variations among countries appeared to be attributable in part to causes of death related to smoking or alcohol use or amenable to medical intervention. The magnitude of inequalities in self-assessed health also varied substantially among countries, but in a different pattern. Conclusions We observed variation across Europe in the magnitude of inequalities in health associated with socioeconomic status. These inequalities might be reduced by improving educational opportunities, income distribution, health-related behavior, or access to health care.

Differential Effects of Family Income on Self-rated Health by Age: Analysis of Seoul Citizens Health Indicators Survey 2001, 2005

This study was conducted in order to determine how the association between socioeconomic position(SEP) and health status changes with age among Seoul residents aged 25 and over. We utilized the 2001 and 2005 Seoul Citizens Health Indicators Surveys. We used self-rated 'poor' health status as an outcome variable, and family income as an indicator of SEP. In order to characterize the differential effects of socioeconomic position on health by age, we conducted separate multivariate analyses by 10-year age groups, controlling for sociodemographic covariates. In order to assess the relative health inequality across socioeconomic groups, we estimated the Relative Index of Inequality (RII). The risk of 'poor health' is significantly high in low family income groups, and this increased risk is seen at all ages. However, the magnitude of relative socioeconomic inequality in health, as measured by the odds ratio and RII, is not identical across age groups. The difference in health across income groups is small in early adulthood (ages 25-34), but increases with age until relatively late in life (ages 35-64). It then decreases among the elderly population (ages more than 65). When the RII reported in 2005 is compared to that reported in 2001, RII can be seen to have increased across all ages, with the exception of individuals aged 25-34. The magnitude of health inequality is the greatest during mid- to late adulthood (ages 45-64). In addition, health inequalities have worsened between 2001 and 2005 across all age groups after economic crisis.

Socioeconomic differences in the burden of disease in Sweden.

We sought to analyse how much of the total burden of disease in Sweden, measured in disability-adjusted life years (DALYs), is a result of inequalities in health between socioeconomic groups. We also sought to determine how this unequal burden is distributed across different disease groups and socioeconomic groups. Our analysis used data from the Swedish Burden of Disease Study. We studied all Swedish men and women in three age groups (15-44, 45-64, 65-84) and five major socioeconomic groups. The 18 disease and injury groups that contributed to 65% of the total burden of disease were analysed using attributable fractions and the slope index of inequality and the relative index of inequality. About 30% of the burden of disease among women and 37% of the burden among men is a differential burden resulting from socioeconomic inequalities in health. A large part of this unequally distributed burden falls on unskilled manual workers. The largest contributors to inequalities in health for women are ischaemic heart disease, depression and neurosis, and stroke. For men, the largest contributors are ischaemic heart disease, alcohol addiction and self-inflicted injuries. This is the first study to use socioeconomic differences, measured by socioeconomic position, to assess the burden of disease using DALYs. We found that in Sweden one-third of the burden of the diseases we studied is unequally distributed. Studies of socioeconomic inequalities in the burden of disease that take both mortality and morbidity into account can help policy-makers understand the magnitude of inequalities in health for different disease groups.

Health inequalities by socioeconomic status among men in the Nord-Trøndelag Health Study, Norway

Aims: This study describes inequalities and trends in health according to socioeconomic status in the Nord-Trøndelag Health Study (HUNT I and II) and contribute to the ongoing discussion on the magnitude of inequalities in health in the Nordic welfare states. To produce data comparable to recent European studies, occupational data in the HUNT Study were reclassifi ed according to the international Erikson Goldthorpe Portocarero (EGP) social class scheme. Methods: Two cross-sectional health surveys were undertaken with a 10-year interval, HUNT I (1984-86) and HUNT II (1995-97). This was a primary healthcare, total-county population study, participants for this analysis being men aged 25-69 years. Results: A consistent pattern was found of increasing health problems with decreasing socioeconomic status for four health variables: self-perceived health, temporary disability, any long-standing health problem, and chronic conditions. The prevalence ratio between the highest and lowest status groups for ``perceived health less than good'' was 2.0 in the first survey and 2.1 in the second 10 years later. The magnitude of differences for the other health outcomes was at this level or smaller, with no signifi cant overall time trend from the mid-1980s to the mid-1990s. Conclusions: The magnitude of socioeconomic gradients in health in this study seemed somewhat smaller than results from national studies, and on the average compared to studies from other European countries; there was no detectable time trend in health differentials. International comparative studies have suggested considerably larger inequalities in health according to social class in Norway using national data.

Toward understanding socio-economic inequalities in injury incidence and mortality

The existing knowledge on socio-economic inequality in injury risks is scarce and inconsistent and there is still a great need for sound epidemiological studies in this field. When performing these studies, the injury research community could benefit from the experience gained within other areas of public health. A recent project from the European Union has led, for example, to a set of proposed guidelines on several methodological aspects. Many of these are probably relevant for the injury problem field and will be addressed. Major issues are: – The distinction between individual-level and area-based studies; – The selection, measurement and classification of possible socio-economic indicators; – The expression of the magnitude of health inequalities with the help of possible summary indices; – The definition of the outcome measure (morbidity or mortality, general or specific). This paper illustrates the relevance of the aforementioned methodological issues with empirical data. Results are shown from both individual-level and area-based studies, using different socio-economic indicators and outcome measures. It is concluded that a systematic approach towards understanding socio-economic differences in injury incidence and mortality has not yet been conducted and seems highly needed. Future work in this area could benefit from experience gained within other areas of public health.

Social inequalities in health: are there gender differences?

Some studies suggest that socio-economic status (SES) inequalities in health are smaller in women than men, but the evidence is inconsistent as to whether this applies across various health measures and life stages. The first aim of this paper was to establish whether the magnitude of social inequality in health differs for men and women during early adulthood, specifically in respect to self rated health, limiting long-standing illness, psychological distress, respiratory symptoms, asthma/wheezing, height and obesity; second, to determine whether explanations for socioeconomic inequality in poor self rated health differ for men and women. Analyses are based on longitudinal data from the British 1958 birth cohort study using information from birth to age 33. When gender differences in inequalities were examined using social class, no significant differences emerged across the seven health measures examined at ages 23 and 33. SES inequalities based on education, however, showed greater inequality among men at age 33 for limiting long-standing illness and respiratory symptoms, but greater inequality among women for poor rated health at age 23 and psychological distress at age 33. Hence, gender differences in the magnitude of health inequality were inconsistent across age and health measures. An analysis of the contribution of explanatory factors to social class differences in self-rated health suggested that causes of inequality were similar for men and women. However, some discrepancies emerged, notably in the greater contribution of job insecurity to class differences for men and in the greater contribution of age at first child for women. The magnitude and explanations for gender differences in SES health inequalities are likely to vary according to life stage and health measure.