205 General Discussion choices were driven by data availability. In Chapter 5, for example, the only data available on SES was at the local group level, i.e. average monthly earnings of individuals employed and the proportion of unemployed. There are two main limitations with focusing on a particular dimensions of SES, such as income. First, the fact that it does not capture the intersectionality behind inequalities, according to which several characteristics may exhibit synergy and interact to define the most vulnerable groups (for example, low-income unemployed individuals). Second, the lack of information to interpret the mechanisms behind the observed inequalities. A good example is available from Chapter 3, where the effects of cost-sharing were concentrated among young females from the two lower-income quartiles of the Dutch population. It is reasonable to assume that part of this group decided not to have treatment due to limited liquidity and budget constraints to pay for higher deductibles. However, this is not likely the sole reason, given the reasonable Dutch welfare provisions and targeted mechanisms to support health care costs for low-income individuals. Effects were detected in the second lower-income quartile with 21,963 euros median equivalised household income, largely above the country’s low-income limits. We, therefore, hypothesise that the decision of not pursuing care was driven by other characteristics associated with lower income which influence how individuals perceive mental health needs, how they value treatment and their willingness to pay for care. These mechanisms would have been easier to disentangle with more information on SES, such as the parental and the youngster’s education. Or by looking at SES interaction with aspects such as cultural norms and stigma, which often have particular patterns in some ethnic and religious groups or across different geographies (e.g., rural/urban). In sum, and while single-dimension measures of SES provide more objective and tangible interpretation of findings than complex and compound measures, complementary information and analysis looking at the different dimensions remains crucial to meaningful inequalities research. Last but not least the challenge of measuring outcomes resulting from inequalities in care. Population-level research has not been able to properly establish the link between inequalities in access, use and quality of care and inequalities in health. To our knowledge, no research causally links both, and mixed conclusions persist from studies using international comparison to describe their association. In favour of the link between (inequalities in) health care and inequalities in health is the association between higher health care expenditure and the narrowing of absolute inequalities by education in amenable mortality among 17 European countries [25]. On the other hand, the lack of variation between inequalities in mortality amenable and non-amenable to health care in most countries raised doubts about the link between health care and mortality disparities. These doubts are reinforced by the fact that mortality amenable to health care was equally associated with inequalities in health care use and common risk factors (e.g. smoking), rather than strongly linked with the former [26]. From a policy perspective, this knowledge gap should caution against the direct interpretation of inequalities in health care as 7
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