Table 4 presents concentration indices using income as ranking variable. Contributions of socioeconomic, demographic and morbidity determinants to the predicted inequality (the former in percentage of the latter) is presented as well. As income is used as ranking variable, education serves as a control variable only, and thus is applied in the simpler three categories version. Regression coefficients and concentration indices for each of the determinants are given in Table 7 in Appendix.

Table 4 Decomposition of inequality in health care and pharmaceutical usage ranked by income Full size table

Overall, the magnitudes of the figures in the table are modest, reflecting the Danish universal health care system with equal access to treatment [50]. Observed and predicted concentration indices for most of the cost variables are negative meaning that costs concentrate among patients of lower income groups. This is illustrated in Fig. 1, where concentration indices to the left are interpreted as costs accumulating among lower SES groups, while the right-side contributions are interpreted reversely.

Fig. 1 Concentration index (observed and predicted by determinants) of income-related inequalities in cost outcomes Legend: Ciy = Observed concentration index for the outcome variable Ciy predicted = Concentration index predicted by the included determinants for the outcome variable Full size image

In the decomposition analysis, we included patients’ morbidity patterns, degree of complications at time of analysis, and whether the patient was diagnosed or died in the current year (2011). Ideally, patients’ morbidity patterns should explain inequality in the distribution of health care costs, if costs were allocated exactly according to patients’ need. This, of course, is an unrealistic expectation, since morbidity indicators cannot capture patients’ exact need, and since costs of services cannot proxy the exact received number of services needed. From Fig. 1 it is clear that especially in-patient health care services inhibit inequality, favoring patients with lower incomes. This corresponds well to these patients experiencing higher morbidity and mortality [39, 40]. Looking at the decomposition of inequality for in-patient care, (Fig. 2), it is seen that morbidity patterns explain a large part of predicted inequality. Especially, the morbidity indicators severe complications at time of analysis and death in 2011 have marked influences on inequality in that costs accumulate among patients with these morbidity characteristics, who are also those with the lowest educational level. Among immigrants and elder (75+), the pattern, however, is opposite with costs accumulating to a higher extent among the higher income groups.

Fig. 2 Decomposition of income-related inequality in in-patient care costs Full size image

As shown in Table 4, concentration indices for outpatient rehabilitation and specialist treatment in primary care are positive, contrary to the other cost variables. However, contributions from determinants are not significant and thus not illustrated.

Turning to the socioeconomic determinants, especially the higher income patients are receiving outpatient services, whereas the lower income patients are receiving more inpatient services and services in general practice.

According to patients’ ethnicity, negative regression coefficients (Table 7 in Appendix), imply that immigrants accumulate lower costs than do ethnic Danes. Given that immigrants have lower incomes (as shown by the negative concentration indices of Table 7 in Appendix), this observation conflicts with the general observation of costs being concentrated among low income groups. However, a potential explanation may be that costs are relatively more concentrated among the higher socioeconomic groups of immigrants than is the case for ethnic Danes. This somewhat surprising tendency, which is observed for in-patient as well as out-patient care and for pharmaceuticals, even when all other demographics and morbidity patterns are taken into account, may be explained by immigrants experiencing language and cultural barriers hindering them in taking full advantage of the Danish universal health care system [12].

For labor market affiliation, the pattern is much similar across cost variables. Especially, being retired contributes highly to the level of inequality with magnitudes around 20-25% of the predicted inequalities in costs. Only children and patients under education have lower costs than patients in job whereas all the other categories in general incur higher costs, especially early retired. Turning to age and gender, it can be seen that these also contribute markedly to inequality. Given that young people are of better health, it is not surprising that they generate lower costs, and it is also to be expected that they have lower incomes, as many of them are studying or in the beginning of their labor market career. However, for the elder group, a potential interpretation may be that elder with low incomes are disfavored with respect to treatment cost.

From differences across the regions, a pattern is seen, which is also reflected in the level of urbanity, where especially residents in the country side use less resources than patients resident in cities, and where costs are more concentrated among patients from higher income levels.

Turning to marital status, divorced patients generally have better income, as indicated by the positive concentration index (Table 7 in Appendix), and they accumulate more in-patient services but less pharmaceutical costs and general practice costs compared to married. The latter corresponds well with an expectation of divorced being more reluctant or hesitating to see a doctor. The former supports an expectation of divorced patients being in worse conditions when hospitalized and more depending on hospital care, given lack of care from a spouse at home.

Education as ranking variable

Table 5 mirrors Table 4, just with the nine categories educational level used as rank variable instead of income. Likewise, Table 8 Appendix in supplementary materials mirrors Table 7 in Appendix.

Table 5 Decomposition of inequality in health care and pharmaceutical usage ranked by education Full size table

Turning to the regression coefficients (Table 8 in Appendix), some (although minor) differences across regions are found. Thus, the Capital Region and Zealand Region have higher costs for in-patient, out-patient, special care in primary care and pharmaceuticals than the three other regions, whereas the opposite is true for services in general practice. Overall, this pattern is also reflected in the level of urbanity, where especially residents in country side use less resources than patients’ resident in cities, and where costs are more concentrated among patients from higher income levels. This might be explained by the Capital region and cities having more resources to seek up patients and invest in secondary prevention efforts targeting all patients also those belonging to lower SES groups, who might be more difficult to address.

Concentration indices based on educational versus income ranks are shown in Fig. 3. It is seen that costs for outpatient rehabilitation and specialists in primary care concentrates among the higher socioeconomic groups, but with relatively stronger associations when ranking according to education. This may indicate that educational level is more decisive than income for usage of outpatient services, rehabilitation and specialist in primary care among diabetes patients.

Fig. 3 Concentration indices of health care and pharmaceutical usage based on ranking by income and educational level respectively Full size image

Decomposition of inequality of costs for specialists in primary care (Fig. 4) shows that especially for women 45+, residents in the countryside and other regions than the Capital Region, costs concentrate among higher educated patients. For early retired and retired, the opposite pattern is seen with costs concentrating among the lower educated patients, since these patient groups on average have lower educational level than patients in job (as indicated by negative concentration indices in Table 8 in Appendix) and consume more resources since they are more morbid (cf. the positive regression coefficients in Table 8 in Appendix). The same applies for patients with severe complications and for immigrants. For the latter, the explanation is, however, reversed, as immigrants have higher educational level and consume less resources (Table 8 in Appendix). Therefore, their contribution to inequality is in the direction of costs accumulating among the lower educational groups. That immigrants have a higher average educational level than ethnic Danes is counterintuitive. One explanation might be that only the highest educated among immigrants are diagnosed at all, another that the preventive effect of education is not the same among ethnic Danes and immigrants.

Fig. 4 Decomposition of education related inequality in costs for specialists in primary care Full size image

Concentrating on ethnicity, the observed pattern for specialist treatment is likewise observed for all three types of in-patient care and pharmaceutical usage. For general practice, the pattern is opposite indicating that immigrants of lower educational levels have higher usage of these services than do ethnic Danes (Table 8 in Appendix).

Turning to morbidity indicators, severe complications and dead in 2011 generally explain more of income inequality than of educational inequality. While between 62 and 97% of income related inequality in costs for in-patient and out-patient care was explained by having severe complications or dying in 2011, the figures are 25–45% for educational level. For out-patient care, as much as 92% of costs accumulating among lower levels of income were explained from these two morbidity indicators. For education on the contrary, as much as 245% of inequality with costs accumulating among higher educated are explained from these two morbidity indicators.

For labor market affiliation, similar patterns are observed across the two tables, however with larger magnitude of contributions of the determinants for income inequality than educational inequality. For age and gender, patterns overall agree across the two tables.