Dataset Information

The wider determinants of inequalities in health: a decomposition analysis.

ABSTRACT:

Background

The common starting point of many studies scrutinizing the factors underlying health inequalities is that material, cultural-behavioural, and psycho-social factors affect the distribution of health systematically through income, education, occupation, wealth or similar indicators of socioeconomic structure. However, little is known regarding if and to what extent these factors can assert systematic influence on the distribution of health of a population independent of the effects channelled through income, education, or wealth.

Methods

Using representative data from the German Socioeconomic Panel, we apply Fields' regression based decomposition techniques to decompose variations in health into its sources. Controlling for income, education, occupation, and wealth, we assess the relative importance of the explanatory factors over and above their effect on the variation in health channelled through the commonly applied measures of socioeconomic status.

Results

The analysis suggests that three main factors persistently contribute to variance in health: the capability score, cultural-behavioural variables and to a lower extent, the materialist approach. Of the three, the capability score illustrates the explanatory power of interaction and compound effects as it captures the individual's socioeconomic, social, and psychological resources in relation to his/her exposure to life challenges.

Conclusion

Models that take a reductionist perspective and do not allow for the possibility that health inequalities are generated by factors over and above their effect on the variation in health channelled through one of the socioeconomic measures are underspecified and may fail to capture the determinants of health inequalities.

SUBMITTER: Sundmacher L

PROVIDER: S-EPMC3171309 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Publications

The wider determinants of inequalities in health: a decomposition analysis.

Sundmacher Leonie L Scheller-Kreinsen David D Busse Reinhard R

International journal for equity in health 20110726

<h4>Background</h4>The common starting point of many studies scrutinizing the factors underlying health inequalities is that material, cultural-behavioural, and psycho-social factors affect the distribution of health systematically through income, education, occupation, wealth or similar indicators of socioeconomic structure. However, little is known regarding if and to what extent these factors can assert systematic influence on the distribution of health of a population independent of the effe ...[more]

PMID: 21791075

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Project description:BackgroundThroughout Wales and the world, health inequality remains a problem that is interconnected with a wider and complex social, economic and environmental dynamic. Subsequently, action to tackle inequality in health needs to take place at a structural level, acknowledging the constraints affecting an individual's (or community's) capability and opportunity to enable change. While the 'social determinants of health' is an established concept, fully understanding the composition of the health gap is dependent on capturing the relative contributions of a myriad of social, economic and environmental factors within a quantitative analysis.MethodThe decomposition analysis sought to explain the differences in the prevalence of these outcomes in groups stratified by their ability to save at least £10 a month, whether they were in material deprivation, and the presence of a limiting long-standing illness, disability of infirmity. Responses to over 4,200 questions within the National Survey for Wales (n = 46,189; 2016-17 to 2019-20) were considered for analysis. Variables were included based on (1) their alignment to a World Health Organization (WHO) health equity framework ("Health Equity Status Report initiative") and (2) their ability to allow for stratification of the survey sample into distinct groups where considerable gaps in health outcomes existed. A pooled Blinder-Oaxaca model was used to analyse inequalities in self-reported health (fair/poor health, low mental well-being and low life satisfaction) and were stratified by the variables relating to financial security, material deprivation and disability status.ResultsThe prevalence of fair/poor health was 75% higher in those who were financially insecure and 95% higher in those who are materially deprived. Decomposition of the outcome revealed that just under half of the health gap was "explained" i.e., 45.5% when stratifying by the respondent's ability to save and 46% when stratifying by material deprivation status. Further analysis of the explained component showed that "Social/Human Capital" and "Income Security/Social Protection" determinants accounted the most for disparities observed; it also showed that "Health Services" determinants accounted the least. These findings were consistent across the majority of scenarios modeled.ConclusionThe analysis not only quantified the significant health gaps that existed in the years leading up to the COVID-19 pandemic but it has also shown what determinants of health were most influential. Understanding the factors most closely associated with disparities in health is key in identifying policy levers to reduce health inequalities and improve the health and well-being across populations.

Project description:BackgroundCatastrophic health expenditure (CHE) is frequently used as an indicator of financial protection. CHE exists when health expenditure exceeds a certain threshold of household consumption. Although CHE is reported to have declined in Kenya, it is still unacceptably high and disproportionately affects the poor. This study examines the socioeconomic factors that contribute to inequalities in CHE as well as the change in these inequalities over time in Kenya.MethodsWe used data from the Kenya household health expenditure and utilisation (KHHEUS) surveys in 2007 and 2013. The concertation index was used to measure the socioeconomic inequalities in CHE. Using the Wagstaff (2003) approach, we decomposed the concentration index of CHE to assess the relative contribution of its determinants. We applied Oaxaca-type decomposition to assess the change in CHE inequalities over time and the factors that explain it.ResultsThe findings show that while there was a decline in the incidence of CHE, inequalities in CHE increased from -0.271 to -0.376 and was disproportionately concentrated amongst the less well-off. Higher wealth quintiles and employed household heads positively contributed to the inequalities in CHE, suggesting that they disadvantaged the poor. The rise in CHE inequalities overtime was explained mainly by the changes in the elasticities of the household wealth status.ConclusionInequalities in CHE are persistent in Kenya and are largely driven by the socioeconomic status of the households. This implies that the existing financial risk protection mechanisms have not been sufficient in cushioning the most vulnerable from the financial burden of healthcare payments. Understanding the factors that sustain inequalities in CHE is, therefore, paramount in shaping pro-poor interventions that not only protect the poor from financial hardship but also reduce overall socioeconomic inequalities. This underscores the fundamental need for a multi-sectoral approach to broadly address existing socioeconomic inequalities.

Project description:BACKGROUND:The economic determinants of health have been widely recognised as crucial factors affecting health; however, to date, no comprehensive review has been undertaken to summarise these factors and the ways in which they can influence health. We conceptualise the economy as a complex system made up of underlying approaches, regulation from institutions, markets, finance, labour, the public-private balance as well as production and distributional effects, which collectively impact on health through the effect of moderators. This protocol details the methods for an umbrella review to explore the macro-economic factors, strategies, policies and interventions that affect health outcomes and health inequalities. METHODS:We will identify relevant systematic reviews using search terms derived from the Journal of Economic Literature classification. Reviews will be included if they meet the Database of Abstracts and Reviews of Effects criteria for systematic reviews. Reviews of studies with and without controls will be included; both association and intervention studies will be included. Primary outcomes will include but are not limited to morbidity, mortality, prevalence and incidence of conditions and life expectancy. Secondary outcomes will include health inequalities by gender, ethnicity or socio-economic status. Six databases will be searched using tailored versions of our piloted search strategy to locate relevant reviews. Data will be extracted using a standardized pro forma, and the findings will be synthesized into a conceptual framework to address our review aim. DISCUSSION:Our umbrella review protocol provides a robust method to systematically appraise the evidence in this field, using new conceptual models derived specifically to address the study question. This will yield important information for policymakers, practitioners and researchers at the local, national and international level. It will also help set the future research agenda in this field and guide the development of interventions. SYSTEMATIC REVIEW REGISTRATION:This umbrella review protocol has been registered with PROSPERO CRD42017068357 .

Project description:BACKGROUND:The burden of multimorbidity is a growing clinical and health system problem that is known to be associated with socioeconomic status, yet our understanding of the underlying determinants of inequalities in multimorbidity and longitudinal trends in measured disparities remains limited. METHODS:We included all adult respondents from four cycles of the Canadian Community Health Survey (CCHS) (between 2005 to 2011/12), linked at the individual-level to health administrative data in Ontario, Canada (pooled n = 113,627). Multimorbidity was defined at each survey response as having ≥2 (of 17) high impact chronic conditions, based on claims data. Using a decomposition method of the Erreygers-corrected concentration index (CErreygers), we measured household income inequality and the contribution of the key determinants of multimorbidity (including socio-demographic, socio-economic, lifestyle and health system factors) to these disparities. Differences over time are described. We tested for statistically significant changes to measured inequality using the slope index (SII) and relative index of inequality (RII) with a 2-way interaction on pooled data. RESULTS:Multimorbidity prevalence in 2011/12 was 33.5% and the CErreygers was - 0.085 (CI: -0.108 to - 0.062), indicating a greater prevalence among lower income groups. In decomposition analyses, income itself accounted more than two-thirds (69%) of this inequality. Age (21.7%), marital status (15.2%) and physical inactivity (10.9%) followed, and the contribution of these factors increased from baseline (2005 CCHS survey) with the exception of age. Other lifestyle factors, including heavy smoking and obesity, had minimal contribution to measured inequality (1.8 and 0.4% respectively). Tests for trends (SII/RII) across pooled survey data were not statistically significant (p = 0.443 and 0.405, respectively), indicating no change in inequalities in multimorbidity prevalence over the study period. CONCLUSIONS:A pro-rich income gap in multimorbidity has persisted in Ontario from 2005 to 2011/12. These empirical findings suggest that to advance equality in multimorbidity prevalence, policymakers should target chronic disease prevention and control strategies focused on older adults, non-married persons and those that are physically inactive, in addition to addressing income disparities directly.

Project description:Background The use of maternal health care services tends to rise with women’s empowerment. However, disparities in the use of maternal health care services in Ethiopia that are founded on women’s empowerment are not sufficiently addressed. In light of women’s empowerment equity stratifier, this study seeks to assess inequalities in the uptake of maternal health care services (early antenatal care, four or more antenatal care and postnatal care services). Methods Drawing on data from the four rounds of Ethiopia Demographic and Health Surveys (EDHSs) conducted between 2000 and 2016, we conducted analysis of inequalities in utilization of maternal health care services using women’s empowerment as equity stratifier. We utilized concentration index and concentration curve for assessing the inequalities. We used clorenz and conindex Stata modules to compute the index and curve. Decomposition of the Erreygers normalized concentration index was done to explain the inequalities in terms of other variables’ percent contributions. Complex aspect of the EDHSs data was considered during analysis to produce findings consistent with the data generating process. All analyses were done using Stata v16. Results Utilization of maternal health care services was inequitably distributed between empowered and poorly empowered women, with women in the highly empowered category taking more of the services. For instance, the Erreygers index for quality ANC are 0.240 (95% CI 0.207, 0.273); 0.20 (95% CI 0.169, 0.231) and 0.122 (95% CI 0.087, 0.157), respectively, for the attitude towards violence, social independence and decision-making domains of women’s empowerment. Inequalities in the distribution of other variables like wealth, education, place of residence and women’s empowerment itself underpin the inequalities in the utilization of the services across the women’s empowerment groups. Conclusions Equity in maternal health care services can be improved through redistributive policies that attempt to fairly distribute the socioeconomic determinants of health such as wealth and education between highly and poorly empowered women.

Dataset Information

The wider determinants of inequalities in health: a decomposition analysis.

Background

Methods

Results

Conclusion

Publications

The wider determinants of inequalities in health: a decomposition analysis.

Similar Datasets

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