The wider determinants of inequalities in health: a decomposition analysis

There is no shortage of empirical evidence illustrating the existence of health inequalities and association between socio-economic position and health inequalities is well established [1‐3]. Reducing health inequalities, especially socioeconomic health inequalities, has therefore been on the agenda of policy-makers in a number of countries [4‐6] and international organisations [7, 8].

Nevertheless, the underlying mechanisms that determine health inequalities are not fully understood [9, 10], which makes it hard for policy-makers to create well-targeted public policy strategies. On the conceptual level, various factors have been proposed to generate socioeconomic health inequalities including material factors, cultural-behavioural factors, and psycho-social factors [11, 12]. Other important factors are ethnic- [13, 14] and gender-based differences [15]. In health economics, the relative importance of these factors is commonly assessed by decomposition methods based on the concentration-index [16]. This process separates the contributions of individual factors to income-related health inequality, in which each contribution is the product of the sensitivity of health with respect to that factor and the degree of income-related inequality associated with that particular factor [16]. Various authors have contributed to this literature refining decomposition methods and their interpretation [17, 18]. As an alternative to income-related health inequalities, education- [19], occupation- [1] and wealth-related health inequalities [20] have been assessed. Studies emerging from public health and epidemiology have used multiple regression analyses differentiated by education level or occupation [21‐23] to assess the importance of different sets of factors [9]. A common starting point of both strands in the literature is that factors affecting health are rooted in or at least channelled through income, education, occupation, wealth or a similar indicator of socioeconomic structure. In the deepest sense, these approaches build on two schools of thought: the Marxian and the Weberian. In a very simplified framework the Marxian approach concentrates on the antagonistic class-relationship based on the distribution of means of production. In this framework, socioeconomic health inequalities emerge because of the different exposures to material factors, the most important being differences in work-related strains between the bourgeoisie and the working class. In the Weberian traditions classes are numerous and reflect the hierarchical structure in a number of dimensions such as prestige or status related to occupation, education, income, and other sources of power [24].

We use Fields' decomposition [25] techniques to decompose health inequalities, rather than socioeconomic inequalities in health, into their sources. This allows us to assess the relative importance of different sets of factors over and above their effect on the variation in health channelled through one of the measures of socioeconomic structure previously outlined. Our approach does not deny the especially worrisome nature of socioeconomic health inequalities, and nor do we suggest that income or other measures of socioeconomic structure are not import. Rather, we investigate if and to what extent different sets of factors can assert systematic influence on the distribution of health of a population independent of the effects channelled through income, education, or wealth. To our knowledge, no decomposition study has so far attempted to scrutinise the effect of different sets of explanatory factors over and above the influence channelled through income, education, and wealth.

Previous work on health inequalities in Germany were conducted from an epidemiological perspective within the framework of large, comparative European research projects [26‐29]. In addition, income-related health inequalities were analysed in a comparative manner across countries applying approaches derived from health economists [30, 31]. The particularities of health inequalities within Germany were discussed in various studies [32, 33]. Overall, these studies suggest a moderate socio-economic gradient in health inequalities [34].

As outlined, different sets of explanatory factors have been proposed to account for health inequalities. We concentrate on three widely discussed sets of factors, namely, material, cultural-behavioural, and psycho-social factors, while also taking into account the life-course perspective. We also assess the capability approach as formulated by Hall and Taylor [35]. Due to limited space, we cannot fully develop the underlying theories and models, but various excellent overviews of the models and theories and their respective strengths and weaknesses exist and can be consulted [11, 35].

The materialist approach explains health inequalities through differences in an individual's socio-economic position. The basic idea is that different social hierarchical positions in socio-economic stratification are linked to differential exposures to the material world, which can be either conducive or unconducive to health (e.g., noise, pollution, material working conditions). Various authors stressed that factors referring to the public infrastructure may determine the private resources available for health production and should also be considered as (neo-) material factors [36, 37].

The psycho-social approach argues that individuals from lower socio-economic backgrounds experience more negative life events [38], less social support [39], less autonomy at work [40, 41], less job security and therefore suffer from poorer health [42]. Various underlying mechanisms are assumed, but the core argument is that stress negatively affects health by reducing resilience and increasing vulnerability to illness [43]. Siegrist [44‐46] puts forward a different variant of the psycho-social explanation, arguing that harmful stress is triggered by a perceived lack of reciprocity in the workplace, i.e., when rewards from employment or other central social roles are threatened or lost, persons become more vulnerable to addiction and other types of high risk behaviour due to biological processes in the brain [11].

The cultural-behavioural approach stresses that culture determines or frames behavioural choices, including decisions affecting health, i.e., engaging in higher risk lifestyles that may include drinking, smoking, or an unhealthy diet. Cultural-behavioural factors are often implicitly motivated by Bourdieu's concept of habitus [47]. Habitus is expressed in daily lifestyle decisions, partialities, body awareness, and consumption patterns. Differences in access to cultural, economic, and social capital are central to the class specific development of habitus patterns. In line with Bordieu's notion of habitus is the well-documented relationship between high educational attainment and health promoting behaviours.

The life-course perspective adds a temporal dimension and explains health inequalities as the result of differences in increasing and decreasing bundles of factors, which influence health at different times in an individual's life [48]. Thus, health is no longer solely the result of current conditions and individual lifestyle choices but is also determined by past living conditions and events [49].

Recently, Hall and Taylor [35] put forward the capability approach. Hall and colleagues follow the general analytical foundations formulated by Amartya Sen and others, arguing that the study of wellbeing, i.e., in our case 'health', should consider factors beyond material or narrow socioeconomic factors by focusing more broadly on the capabilities of people to realize functions (such as 'good health') they value [50, 51]. This approach broadens the perspective on social relations as these can affect health independent of their relationship with people's income, employment, or wealth [35]. Hall et al. define an explicit micro-level explanatory mechanism and argue that an individual's health status is a function of individual capabilities and life challenges over time. Capabilities and challenges are, in turn, determined by socio-economic position, social connectedness, emotional disposition, collective imaginaries (understood as cultural and societal norms), self-determination, and stress. Hall and Taylor hypothesize that the observed socio-economic gradient in health inequalities is generated by differences in individual balances of capabilities and challenges.

The data for the analysis is taken from the 2006 German Socioeconomic Panel (GSOEP). The GSOEP is an annual panel with approximately 20,000 individuals aged 16 from over 11,000 households throughout Germany. Each person in the sample is interviewed individually. The GSOEP collects data on a broad range of issues, such as population and demography, education, training and qualification, earnings and income, health, basic orientation and satisfaction-specific aspects of life.

Our dependent variable physical health is measured through the reliable and validated Short Form-12 Health Survey (SF-12v2), which uses 12 questions to measure functional health and well-being from the individual's point of view and allows scores for physical and mental health to be generated.¹ Four subscales are used to generate a physical health score, and four are used to generate a mental health score respectively. The physical score mainly refers to evaluations of one's ability to perform physical activity. We use the physical health scores derived from the 2006 GSOEP as the dependent variable. As the discourses on mental health are very different [52], extending our analysis to mental health would require us to scrutinize an even greater variety of approaches. Therefore, we limit our analysis to physical health.

Research in the decomposition of factors is rooted in and driven by research applied to income inequality. In 1982, Shorrocks [54] developed a method that decomposes inequality in income by sources of factor components. Later, Murdoch and Sicular [55] and Fields [25] extended Shorrocks' [54] approach to a regression-based decomposition of inequality. They expressed household income as a linear function of explanatory variables and used the regression coefficients to calculate the decomposed variance for all variables in the model. The regression-based decomposition had the advantages that (1) it yielded an exact allocation of contributions to the identified factors, (2) it provided measures of uncertainty around the decomposed values that are part of standard regression analysis, and (3) it allowed for the analysis of multiple factors.

Given that our aim is to scrutinise the direct impact of various sets of explanatory variables on health, we choose to depart from the standard concentration index approach [56, 2] and follow Fields' [25] decomposition method. Following Fields' method, health is first regressed on a range of explanatory variables using a standard least squares regression model of the form:

where Y _i is the health of individual i, X _k is a vector of variables X ₁ , X ₂ ,...X _K thought to determine health (there are k = 1, 2, ... K variables included in X _k ), and β _k is a vector of coefficients β ₁ , β ₂ ,... β _K pertaining to each variable k. ε is an error term with a mean value of zero and a variance of unity, β ₀ is the intercept term. The estimated coefficients are denoted by and the residual term is given by

To deduct the decomposition according to Fields [25], we then first take the variance of the left and right hand sides of equation (1), which is written as:

Dividing (2) by the variance of Y then yields:

The equation partitions the full variance of Y into the share that is explained by the covariance between each of the X factors and the Y values. Fields [25] calls the proportions denoted by s(X _k ) "relative factor inequality weights" or s-weights. It can be described as the share of the variance in health explained by the determinant k, holding all other determinants constant. Dividing the individuals' s-weights for each k by the model R ² (the proportion of variance explained by all determinants X _k taken together) gives the share of each factor in the explained variation of the linear regression, the so called p-weights.⁴ Each p-weight, assigned to a variable k, gives the share of overall R-squared explained by this variable k. The sum of the p-weights is R-squared. The p-weights can therefore be interpreted as "little R-squared" for each of the variables k.

Formally, this is given by:

Furthermore, Fields [25] shows that under six decomposition conditions (Additional File 1), the s-weights and p-weights are the same for any measure of dispersion that is continuous, symmetric, and takes value zero when all Y are identical (namely the Gini coefficient, the Theil index, and the Atkinson index).

Three points are important when interpreting the decomposition results. First, Fields' [25] approach decomposes the predicted value of Y rather than the actual value of Y. Thus, using this approach, we quantify the relative importance of determinants of explained inequality in Y. Second, Fields [25] also pointed out that the weights can take negative values.⁵ Third, this method relies on the linearity of the model.

Scrutinising the results of the analysis allows us to deduce informed hypotheses about mechanisms underlying health inequalities. The analysis suggests that three main factors persistently contribute to variance in health over and above their effect on the variation in health channelled through one of the socioeconomic measures: the capability score, cultural-behavioural variables, and to a lower extent the materialist approach.

Of the three, the capability score illustrates the importance 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. On one hand, the high explanatory power of the variable suggests that independent challenges for health (e.g., low household income or education) can be tackled if the individual has access to a high level of resources (e.g., high social capital). On the other hand, it illustrates the difficulties of maintaining good health if the individual faces many challenges while resources are low. Moreover, our analysis shows that for men and women, the difference between resources and challenges is particularly important during the last stage of work life (46 to 65 years). This may suggest that individuals become increasingly vulnerable when they face a high disparity of challenges and resources over a long time or in older age. Individuals may for example feel more strained as the challenges posed by job and family life persist, but the efficiency in the production of their own health decreases, and therefore, the ability to maintain good health decreases.

The results for the materialist variables complement the findings for the capability score. Our analysis for women and men proposes that materialist variables are important for health inequalities during the last stage of work life. However, considering the relative importance of the capability score compared to materialist variables, this perspective suggests that for old age, inequalities are even better captured if one considers vulnerability in terms of the interacted/compound effects of the discrepancy between resources and challenges.

With regard to the cultural-behavioural variables, our analysis suggests that these increasingly contribute to R-squared for individuals from older age groups (36 to 65 years). Therefore, being overweight is a key contributor to health inequalities for both sexes during later stages of work life. Moreover, our analysis shows that cultural-behavioural variables are important in the youngest generation, especially for women. Again we see that weight problems contribute to health inequalities in women while the consumption of hard liqueur explains a high share of variation in health in men.

The control variables add explanatory power across all models and especially in the first and last age group for women and in the middle age groups for men. Therefore, they should be considered in fully specified analyses.

There are a number of limitations to our analysis. First, we do not claim that the selection of variables exhaustively reflects the notions of the presented theories. Building on previous studies, we try to operationalise the approaches as closely as possible, but some choices in operationalisation will remain ultimately normative. For example, we did not include the interaction between smoking and low levels of household income in the decomposition analysis because we considered the cultural-behavioural effect to be better captured by the interaction between smoking and low levels of education. Although this view was informed by our literature review, the decision remains subjective.

Second, the approaches are operationalised with different numbers of variables. This might marginally affect the contribution to variance in the analysis. However, we are confident that this shortcoming is moderate. For example the capability approach, which is operationalised with one variable, is a major contributor in the analysis. Overall, our aim is to give an impression of the relative importance of the variables. We do not claim that the relative percentages should be taken at face value because they might be slightly distorted by collinearity. Last, in an econometric sense we do not apply methods that allow conclusions about causal mechanisms. Therefore we have to be cautious not to overstate our results.

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. This was particularly evident when we modelled the distance between resources and challenges in life, which in our analysis is a very important factor contributing to inequality in health.

¹ The GSOEP version of the SF-12v2 deviates from the original SF-12v2 to a limited degree with regard to formulation, order of questions, and general layout. See Anderson et al. for more specific information (2007: 172).

² We chose an age of 16 as the lower cut-off point for inclusion in the sample because 16 is the minimum age for legal work in Germany. About 20% of youths between the age of 16 and 18 are in the labour force.

³ We believe that the working population over age 65 represents a selected sample of likely very healthy individuals. To avoid any distortions in the analysis, we decided to use the official retirement age as the upper cut-off for the last age group.

⁴ Note that is the model R ² .

⁵ He explained this result as follows: the covariance between and Y can be expressed as and when we regress of Y on X _k , we obtain the simple regression coefficient . It follows that the p-weights can be written as . This implies that a negative value arises whenever the two beta coefficients have opposite signs (i.e., whenever controlling for multiple factors within a regression framework would reverse the sign from the simple regression).

⁶ The significance level of the single coefficient of dummy variables (household income, education, insurance etc.) in the regression models is low. However their combined effect (e.g., five dummy variables for education) is most of the time significant (using a F-test).