Inequalities In General Health Research Articles

Does gender moderate the association between socioeconomic status and health? Results from an observational study in persons with spinal cord injury living in Morocco.

Socioeconomic status (SES) and gender are well-known social determinants of health. However, their impact on health in populations with physical disabilities in low-resource countries is still lacking. Therefore, the objective of this study was to investigate associations of individual SES with health and the moderating effect of gender on this association in a Moroccan population with a physical disability, namely spinal cord injury. Cross-sectional survey data from 385 participants with spinal cord injury living in Morocco were analyzed. SES was operationalized by education level, household income, financial hardship, and subjective social status. Health indicators included secondary conditions, pain, vitality, quality of life, and general health. Associations between SES and health indicators were investigated using linear and logistic regressions. To test the potential moderation of gender, interaction terms between SES and gender were introduced in regression models. Financial hardship and lower subjective social status were associated with poorer health outcomes in four out of five indicators in the total sample. In contrast, education and income were inconsistently associated with health. Overall, gender did not moderate the association between SES and health, except that educational inequalities in general health were more pronounced in women, and the observation of a trend for a stronger negative effect of subjective social status on men's than woman's health (p > 0.05). This study revealed that subjective indicators of SES negatively impact on health, whereas evidence for the moderating role of gender in this association was weak. These findings underline the importance to reduce social marginalization and poverty in populations with disabilities in low-resource countries to reduce their double burden of living with a disability and encountering social disadvantages through low SES.

Social, Clinical and Psychometric Factors Affecting Self-Rated Oral Health, Self-Rated Health and Wellbeing in Adults: A Cross-Sectional Survey

Background: Many studies examined and reported oral and general health inequalities in clinical health, SROH and SRH. Objectives: The study aims to explore the social influences, gradients and predictors of self-rated oral health (SROH) and self-rated health (SRH) and wellbeing in Greek adults. Methods: Cross-sectional study, of men and women, aged 65 years and over (N = 743) in Greece. Descriptive and statistical analyses were performed for dentate and edentulous participants. For the association between socioeconomic exposures and binary outcomes, logistic regression was performed to estimate Odds Ratios and 95% Confidence Intervals (OR, 95% CI); levels of association and Cramer’s V were applied to calculate associations and p-values. Results: The objective socioeconomic measures, such as household income, education level and last main occupation were significant predictors and determinants of both SROH and self-rated health (SRH). For Satisfaction with life (SWL), there was a diversity in the results analogous to the dental status of the participants. Household income and SSS were predictors of SWL in dentate participants. In the total sample Household income, occupation and SSS, were predictors of SWL, while in edentulous participants only occupation and SSS were statistically significant (p p p < 0.05). Conclusion: There are socioeconomic gradient inequalities in SROH and SRH in Greek adults living in Attica area. Subjective social status is a predictor of SROH, SRH and SWL. The need to prioritize interventions to eliminate disparities and inequalities in oral and general health and wellbeing of elders is evident.

Educational inequalities in general health: Does the curricular tracking system matter?

Educational tracking amplifies social inequalities in a wide range of outcomes. From an institutional perspective, the current study examines whether this holds for educational disparities in general health. To investigate this question, we use information from the European Social Survey (Rounds 1–8: 2002–2016) for individuals between the ages of 18 and 45 years ( N = 99,771) in 22 European countries. The estimated three-level hierarchical models indicate that tracking is indeed associated with larger educational inequalities in overall health. Individuals who have attained vocational education fare worse in terms of general health than do individuals who have pursued academic qualifications. However, the strength of association is much higher in countries with highly tracked systems (e.g. Germany and Czech Republic) than it is in countries with more comprehensive systems (e.g. the United Kingdom and Scandinavian countries). This result suggests that health inequalities between educational groups can be reduced by reorganizing secondary educational systems.

Embodying pervasive discrimination: a decomposition of sexual orientation inequalities in health in a population-based cross-sectional study in Northern Sweden

BackgroundStudies from Sweden and abroad have established health inequalities between heterosexual and non-heterosexual people. Few studies have examined the underpinnings of such sexual orientation inequalities in health. To expand this literature, the present study aimed to employ decomposition analysis to explain health inequalities between people with heterosexual and non-heterosexual orientation in Sweden, a country with an international reputation for heeding the human rights of non-heterosexual people.MethodsParticipants (N = 23,446) came from a population-based cross-sectional survey in the four northernmost counties in Sweden in 2014. Participants completed self-administered questionnaires, covering sexual orientation, mental and general physical health, social conditions and unmet health care needs, and sociodemographic data was retrieved from total population registers. Sexual orientation inequalities in health were decomposed by Blinder-Oaxaca decomposition analysis.ResultsResults showed noticeable mental and general health inequalities between heterosexual and non-heterosexual orientation groups. Health inequalities were partly explained (total explained fraction 64-74%) by inequalities in degrading treatment (24-26% of the explained fraction), but to a considerable degree also by material conditions (38-45%) and unmet care needs (25-43%).ConclusionsPsychosocial experiences may be insufficient to explain and understand health inequalities by sexual orientation in a reputedly ‘gay-friendly’ setting. Less overt forms of structural discrimination may need to be considered to capture the pervasive material discrimination that seems to underpin the embodiment of sexual minority inequalities. This ought to be taken into consideration in research, policy-making and monitoring aiming to work towards equity in health across sexual orientations.

Associations between measures of child poverty, health-care use, and health outcomes: observational study of nationally representative English data

BackgroundThe UK Government has recently proposed the abolition of income-based measures of child poverty in favour of environmental, educational, and employment measures of deprivation. We aimed to study how strongly these proposed measures are associated with health outcomes among children and young people compared with a relative income measure. MethodsWith data from Hospital Episode Statistics for 2013–14 (n=16·4 million), we compared inequalities in inpatient admissions of children and young people aged 0–24 years per thousand (total and chronic conditions) using five deprivation measures (deciles of area-based measures: index of multiple deprivation [IMD], income, index of income deprivation affecting children [IDACI], education, living environment). With data from Health Survey for England for 2014 (n=3085), we compared inequalities in general health and long-standing illness reported by an individual, parent, or carer using equivalised household income quintiles, IMD quintiles, and whether the household reference person was employed (n=2417, 78·3%); not working (397, 12·9%); or retired, long-term sick, or other (274, 8·9%). The ratios of hospital admission rates and weighted prevalence of poor self-reported health were compared between the most and least deprived groups within each measure. FindingsTotal admission rates were higher among the most versus least deprived IMD deciles (ratio 1·60, 95% CI 1·59–1·61). The ratio was greater when income deciles were compared (1·69, 1·68–1·71) and smaller when analysis was by education (1·59, 1·58–1·60), IDACI (1·52, 1·51–1·53), and living environment (1·01, 1·00–1·02). The ratio was lower for admissions for chronic conditions (IMD 1·18, 1·16–1·20). Inequalities were largest when analysis was by income decile (1·25, 1·22–1·27). In the household-level analyses, inequalities in fair or poor, self or parent-reported health were seen when comparing lowest versus highest income quintiles (12·2% vs 3·9; ratio 3·12, 1·99–5·87), not working versus being employed (14·1 vs 6·1; 2·30, 1·68–3·03), and most versus least deprived IMD quintiles (9·8 vs 6·5; 1·49, 1·05–2·21). For long-standing illness, the equivalent data were: income (22·0 vs 11·0; 1·99, 1·50–2·77), employment (23·6 vs 15·8; 1·50, 1·21–1·81), and IMD (18·9 vs 17·2; 1·10, 0·88–1·39). InterpretationAlthough some important aspects of health such as use of primary care and community services are not included, this study shows that child poverty measures differ significantly in their association with key indicators of population health and health-care use. Of the deprivation measures studied here, hospital admissions were most strongly associated with income inequality. Self-reported health outcomes were also more strongly linked to household income than area-level IMD. FundingNone.

The mouth as a site of structural inequalities; the experience of Aboriginal Australians.

To address the mouth as a site of structural inequalities looking through the lens of Aboriginal Australian experience. This is a critical review of published literature relevant to our objective. Criteria for selection included articles on: the social context of oral and general health inequalities for Aboriginal Australians; Aboriginal perceptions and meanings of the mouth and experiences of oral health care and the role of the current political-economic climate in promoting or compromising oral health for Aboriginal Australians. Evidence suggests oral health is important for Aboriginal Australians yet constrained by challenges beyond their control as individuals, including accessing dental services. Competing demands on limited budgets often led to oral health dropping off the radar unless there was an emergency. Structural (social, political and economic) factors often inhibited Aboriginal people making optimum health choices to prevent oral disease and access services for treatment. Factors included cost of services, limited education about oral health, intense advertising of sugary drinks and discrimination from service providers. Yet the literature indicates individuals, rather than structural factors, are held responsible and blamed for the poor state of their oral health. The current neoliberal climate focuses on individual responsibility for health and wellbeing often ignoring the social context. To avoid the mouth becoming an ongoing site for structural inequality, critically reviewing oral health policies and practices for whether they promote or compromise Aboriginal Australians' oral health is a step towards accountability-related oral health outcomes.

Associations between hair cortisol concentration, income, income dynamics and status incongruity in healthy middle-aged women

A body of research demonstrates that financial disadvantage is associated with general health inequalities and higher mortality rates. Most studies make use of cross-sectional analyses, although income can also be viewed as a dynamic concept. The use of endocrine-markers as proxies for health can provide information about the pathways involved in these associations. Hair cortisol analysis has been developed as a method for assessing sustained cortisol output as it provides an estimate of cumulative cortisol secretion over a prolonged time. The present study assessed income and income trajectory over a 4-year period in 164 working women (aged 26–65) in relation to hair cortisol in a longitudinal design. A negative association between hair cortisol and concurrent income was found (p=0.025) and hair cortisol and changes in income over 4 years (p<0.001), after adjustment for age, BMI, smoking status, hair treatment and country. Status incongruity, a mismatch between educational status and income group, was related to higher cortisol levels compared with status congruity (p=0.009). These findings suggest that psychoneuroendocrinological pathways might partially explain the relationship between lower socio-economic status and adverse health outcomes. Future longitudinal research using hair cortisol analysis is warranted to clarify the time course of social mobility in relation to long-term cortisol, to investigate other underlying psychosocial factors implicated in these associations, and to determine the exact health implications of the neuroendocrine perturbations in individuals with limited economic resources.

Decomposition of Health Inequality Determinants in Shiraz, South-west Iran.

Despite the enhancement in health outcomes worldwide, health inequity and inequality is one of the most relevant topics both for health policy and public health. This research was designed to decompose the health inequality of people living in Shiraz, south-west Iran. Data were obtained from a multistage-sample survey conducted in Shiraz from April to May 2012, to find determinants of health related quality of life (HRQoL). General health (GH) and mental health (MH) were used as health status. As a measure of socioeconomic inequality, a concentration index of GH and MH was used and decomposed into its determinants. The overall concentration indices of MH and GH in Shiraz were 0.023 (95% CI: 0.015, 0.031) and 0.016 (95% CI: 0.009, 0.022), respectively. Decomposition of the concentration indices indicated that income made the largest contribution (39.92% for GH and 39.82% for MH) to income-related health inequality. Education (about 25% for GH and 34% for MH), insurance (about 14% for GH and 11% for MH), and occupation (about 12% for GH and 11% for MH) also proved important contributors to the health inequality in Shiraz. There exist MH and GH inequalities in Shiraz. Apart from insurance, most of the health inequalities in Shiraz can be explained through factors beyond the health sector. Hence, implementing redistributive policies and education expansion programs as well as providing an insurance scheme and secure career conditions could decrease these unethical health inequalities.

Socio-Economic Inequalities in Oral Health: The Case of Greece

Background: The literature on the socioeconomic determinants of oral health inequalities is rather limited. Since oral health is an integral part of general health, the investigation of oral and general health inequalities becomes an important research topic. The recent economic crisis has aggravated the level of general and oral health in Greece and evidence on inequalities is interesting for policy guidelines. Objective: The purpose of the present study is to measure the magnitude of self reported oral health inequalities in relation to certain demographic and socio-economic indicators such as gender, age, education and income status. Methods: We launched a cross-sectional survey across urban and rural population in Greece and we selected a random sample of 1500 individuals consisting of three age groups: 15 - 18 years, 35 - 45 years, and 65 to 74 years old. Self-reported general and oral health were both recorded and analyzed by means of an ordinal scale ranging from 0 = worse health to 100 = perfect health. Likert-type of scale questions were further used to capture self assessments of oral health. Dental Inequalities were assessed with reference to the following socio-economic variables: age, education, and income levels. Results: Statistically significant oral health inequalities were identified among the socio-economic groups in Greece. Lower levels of oral health were associated with those confronting financial difficulties, the aged and the less educated. The Gini Coefficient for Oral Health Goral = 0.344 (s.d. = 0.0033) was higher in comparison to general health Ggeneral = 0.289 (s.d. = 0.0016) indicating the need for public health intervention in the area of oral health. Conclusions: The paper demonstrates the significance of socioeconomic inequalities in oral health in Greece.

Comparing Inequalities in Oral and General Health: Findings of the Canadian Health Measures Survey

The aim of this study was to measure and compare the magnitude of income-related inequalities for oral and general health outcomes in Canada. Data for this study were from the 2007/09 Canadian Health Measures Survey (CHMS). The sample size consisted of 3,413 Canadians aged 18-79 (1,601 men and 1,812 women). Oral health indicators were the total number of decayed and missing teeth. General health was measured as obesity and high blood pressure. Socio-economic status was measured as equivalized household income. We used the concentration index (CI) to quantify income-related inequalities in these outcomes. Values for the concentration index range from -1 to +1 with negative (or positive) concentration indices showing that the outcome is more concentrated among the less well off (or among the better off). All statistical analyses were weight-adjusted for the complex survey design and standardized for age. The concentration indices for oral health outcomes (decayed teeth = -0.25, missing teeth = -0.15) were greater than for general health outcomes (obesity = -0.05, high blood pressure = -0.04). The concentration indices for oral health outcomes, in contrast to general health outcomes, were statistically significant. There were income-related inequalities for oral health outcomes with the disease concentrated more among the poor. Inequalities in oral health were greater than inequalities in general health. The variation in the funding of oral health care and general health care is likely to explain the differences in the magnitude of income-related inequalities for oral and general health.

Educational inequalities in general and mental health: differential contribution of physical activity, smoking, alcohol consumption and diet

Behavioural, material and psychosocial risk factors may explain educational inequalities in general health. To what extent these risk factors have similar or different contributions to educational inequalities in mental health is unknown. Data were derived from the Norwegian Survey of Level of Living from 2005, comprising 5791 respondents aged ≥ 25 years. The study objectives were addressed by means of a series of logistic regression analyses in which we examined: (i) educational inequalities in self-reported general and mental health; (ii) the associations between behavioural, material and psychosocial risk factors and general and mental health, controlled for sex, age and education; and (iii) the contribution of risk factors to the observed health gradients. The lower educated were more likely to be in poor health [odds ratio (OR): 3.46 (95% confidence interval, CI: 2.84-4.21)] and to be in poor mental health [OR: 1.41 (95% CI: 1.12-1.78)] than the highest educated. The joint contribution of behavioural, material and psychosocial risk factors explained all the variations of mental health inequalities, whereas these were able to explain ~40% of the inequalities in general health. Both behavioural and material risk factors contributed substantially to the explanation of general and mental health inequalities, whereas the psychosocial risk factor (i.e. having close persons to communicate with) only seemed to make a larger difference for the explanation of mental health inequalities. Policies and interventions to reduce health inequalities should have a broad focus. Combined strategies should be applied to improve physical activity, decrease smoking and improve material and psychosocial conditions among lower educated groups, to achieve the true potential of reducing inequalities in both general and mental health.

Worklessness and regional differences in the social gradient in general health: Evidence from the 2001 English census

BackgroundThere has been much focus on separating contextual and compositional influences on social inequalities in health. However, there has been less focus on the important role of place in shaping the distribution of risk factors. Spatial variations in worklessness are one such factor. In this paper, then we examine the extent to which between and within regional differences in the social gradient in self-rated general health are associated with differences in rates of worklessness. MethodsData were obtained for men and women of working age (25–59) who had ever worked from the Sample of Anonymised Records (Individual SAR)—a 3% representative sample of the 2001 English Census (349,699 women and 349,181 men). Generalised linear models were used to calculate region and age adjusted prevalence difference for not good health by education (as an indicator of socio-economic status) and employment status. The slope index of an inequality was also calculated for each region. ResultsFor both men and women, educational inequalities in worklessness and not good health are largest in those regions with the highest overall levels of worklessness. Adjusting for worklessness considerably attenuated the educational health gradient within all English regions (by over 60%) and virtually eliminated between region differences. DiscussionMacroeconomic policies, which influence the demand for labour, may have an important role in creating inequalities in general health of the working age population both within and between regions. Employment policy may therefore be one important approach to tackling spatial and socio-economic health inequalities.

Intergenerational continuities of ethnic inequalities in general health in England

Background:Previous research strongly suggests that ethnic minority groups are more likely to suffer a poorer health profile compared with the overall population, although it is not clear whether these inequalities...

Socioeconomic inequalities in mortality and importance of perceived control: cohort study

Perceived control has convincingly been suggested to be a key concept in explaining socioeconomic differences in health.1 Some empirical evidence exists of a higher prevalence of low control beliefs (such...

Social inequalities in dental health

The analysis of social inequality is one of the predominant concerns of sociology. Inequalities in general health or utilization of health care services are nearly universal. The purpose of the paper is to describe inequalities in dental health in Denmark. Recent empirical findings are reviewed. Longitudinal data on the number of adults with few or no teeth left show remarkable differences between low and high social classes in 1976 and 1986. Among the younger individuals some reduction in the social differences has been observed over time. This was ascribed to the establishment of a public child dental health service. The level of dental caries and unmet treatment need is higher among workers than officials. Studies of industrial workers also indicate that dental illness or diseases are induced by environmental working factors. The association between health-related behavior and dental health status has been demonstrated as well as the effects of social network relations and lifestyle. Finally, theoretical explanations of the inequalities in dental health are outlined: artefact explanations, theories of natural or social selection, materialist or structuralist explanations, and cultural/behavioral explanations. Arguments for the relevance of the materialist/structuralist and the cultural/behavioral approaches are given and a combined model on dental health is presented. The practical implications of the various models are discussed.