Socioeconomic inequality of diabetes patients' health care utilization in Denmark

Sortsø, Camilla; Lauridsen, Jørgen; Emneus, Martha; Green, Anders; Jensen, Peter Bjødstrup Article Socioeconomic inequality of diabetes patients' health care utilization in Denmark Health Economics Review Provided in Cooperation with: Springer Nature Suggested Citation: Sortsø, Camilla; Lauridsen, Jørgen; Emneus, Martha; Green, Anders; Jensen, Peter Bjødstrup (2017) : Socioeconomic inequality of diabetes patients' health care utilization in Denmark, Health Economics Review, ISSN 2191-1991, Springer, Heidelberg, Vol. 7, Iss. 21, pp. 1-22, https://doi.org/10.1186/s13561-017-0155-5 This Version is available at: https://hdl.handle.net/10419/175643 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ RESEARCH Open Access Socioeconomic inequality of diabetes patients’health care utilization in Denmark Camilla Sortsø 1,2 , Jørgen Lauridsen 1* , Martha Emneus 2 , Anders Green 2,3 and Peter Bjødstrup Jensen 2 Abstract Understanding socioeconomic inequalities in health care is critical for achieving health equity. The aim of this paper is threefold: 1) to quantify inequality in diabetes health care service utilization; 2) to understand determinants of these inequalities in relation to socio-demographic and clinical morbidity factors; and 3) to compare the empirical outcome of using income level and educational level as proxies for Socio Economic Status (SES). Data on the entire Danish population of diabetes patients in 2011 (N= 318,729) were applied. Patients’unique personal identification number enabled individual patient data from several national registers to be linked. A concentration index approach with decomposition into contributing factors was applied. Differences in diabetes patients’health care utilization patterns suggest that use of services differ among patients of lower and higher SES, despite the Danish universal health care system. Especially, out-patient services, rehabilitation and specialists in primary care show different utilization patterns according to SES. Comparison of the empirical outcome from using educational level and income level as proxy for patients’SES indicate important differences in inequality estimates. While income, alike other measures of labor market attachment, to a certain extent is explained by morbidity and thus endogenous, education is more decisive for patients’ability to take advantage of the more specialized services provided in a universal health care system. JEL Classifications: I10, I12, I14, I18 Background Persistent differences in health by socio-economic status (SES) have long been a serious health policy concern in many European countries [9, 10, 43]. Evidence on the contributing factors to inequality in health in general and disease specific inequality may guide future efforts to reduce unequal distributions of for instance health care [9, 10]. This study presents –to our knowledge - first time evidence on the composition of socioeconomic inequality in diabetes patients’health care utilization patterns. Diabetes Mellitus is one of contemporary time’s most burdensome chronic diseases [52]. It is well known that socio-economic inequality exists in diabetes with higher incidence and mortality among lower socio-economic groups [1, 4, 18, 25, 31, 37]. Despite universal coverage health care systems, social inequalities have been evidenced in most European countries [43]. Several Danish reports have underlined that large differences exist in compliance to treatment, especially preventive efforts and retention of life style changes among chronic patients [11–13]. Access to health care, hence, is not only a question of equal potential access, as in a universal health care system like the Danish. The concept of “realized access”[20] reflects patients’actual use of the available services. In health care systems with universal coverage, realized access may be constrained by financial and organizational barriers to the use of benefits, such as required co-payments or other out-of-pocket payments, restrictions on specialty referrals, or lack of proximity to health care facilities [20]. Differences in use of health care within patient groups of same need provide insight into patients’ability to take advantage of the services provided in a universal health care system. Such knowledge can guide future effort to increase success of * Correspondence: [email protected] 1 Centre of Health Economics Research (COHERE), Department of Business and Economics, University of Southern Denmark, Campusvej 55, DK-5s30 Odense M, Denmark Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Sortsø et al. Health Economics Review (2017) 7:21 DOI 10.1186/s13561-017-0155-5 early detection, secondary prevention and treatment. This is highly important, not only for patients’quality and length of life, but also for societies to control the costs of the increasing diabetes populations [21, 38]. Several studies have assessed the level of socioeconomic inequalities in health using concentration indices and concentration curves [33, 43, 44, 47]. Taking advantage of the detailed Danish registers, we apply data on individual patient level on all Danish diabetes patients [41]. We have previously documented that diabetes patients of lower SES experience higher morbidity and mortality [39, 40]. Access to comprehensive data on patients’morbidity patterns is unique, allowing for investigation of novel associations between SES, morbidity and health care utilization patterns investigating the inquiry whether patients’health care usage reflects their need defined through morbidity. The present study sets out three research inquiries 1) to quantify socioeconomic inequality in diabetes health care and pharmaceutical usage (reflected through cost accounts), 2) to decompose these inequalities by quantifying the contribution attributable to individual demographic determinants and individual morbidity characteristics, and 3) to compare the empirical outcome from using educational level versus income level as proxy for patients’SES. Thus, the outline of the paper is as follows. Next to this introduction, Section 2 briefly presents the econometric methods applied. Following this, Section 3 describes data collection and preparation, while the results are presented in Section 4. Finally, Section 5 provides a discussion of the results, while Section 6 rounds off with concluding remarks. Methods Similar to previous studies initiated by Wagstaff et al. [49] we use the concentration index as our measure of relative socioeconomic inequality in healthcare costs. A concentration curve L(s) plots the cumulative proportion of the population (ranked by socioeconomic status (SES), beginning with lowest SES) against the cumulative proportion of costs. If L(s) coincides with the diagonal everyone is equally off. However, if L(s) lies below the diagonal, then inequality in healthcare costs exists and favors those with high SES. The further L(s) lies from the diagonal, the larger the degree of inequality. The concentration index, C, is defined as twice the area between L(s) and the diagonal and takes a value of 0, when everyone is equally off regardless of SES. The minimum and maximum values of Care −1 and +1, respectively; these occur in the (hypothetical) situation where costs are concentrated in the hand of the least disadvantaged and the most disadvantaged person, respectively. Thus, the larger negative value of C, the more costs concentrate among low SES groups. A computational formula for C was given by Kakwani et al. [27] as C¼2 NμX N i¼1 yiRi−1, where μ¼1 NX N i¼1 yiis the mean of observed costs, Nthe sample size, y i observed costs, and R i the fractional rank defined according to Kakwani et al. as Ri¼i−1 Nþ1 2. Following the same authors, Ccan be conveniently computed as the covariance of y i and R i , i.e. C¼2 μcovwyi;Ri ðÞ¼ 2 NμX N i¼1 yi−μðÞRi− 1 2  . A straightforward way of decomposing the predicted degree of inequality into the contributions of explanatory factors was proposed by Wagstaff et al. [48]. Adapting their approach to the present case, we use a traditional linear regression that links healthcare costs to the determinants, leading to a decomposition of the concentration index of predicted costs as ^ C¼X k βkxk ^ μCk, where ^ μis the mean of predicted costs, xkthe mean of the determinant x k , and C k the concentration index of x k (defined analogously to C). In order to assess sampling variability and to obtain standard errors for the estimated quantities, in particular for the concentration indices and the contributions, i.e. the βkxk ^ μCkparts, we apply a “bootstrap”procedure ([14];) in a four-step manner much similar to van Doorslaer et al. [44]: First, a random sub-sample of the size of the original sample is drawn with replacement. Second, the entire set of calculations, as specified above, are performed onthissample.Third,thiswhole process is repeated 1,000 times, each leading to replicate estimates. Fourth, using the obtained 1,000 replicates, standard deviations and tstatistics can be computed for all calculated quantities. Data The study is part of the Diabetes Impact Study 2013 [21, 22, 38–40]. Data was collected from the Danish National Diabetes Register (NDR) [6], the Danish National Patient Register [34], the Danish National Prescription Registry [29], the Danish National Health Service Register [3], the Danish Civil Registration System [5], and social registers at Statistics Denmark (SD). The study population covers all patients registered in NDR diagnosed before 1 st of January 2012 and alive 1 st of January 2011, described in detail elsewhere (1), leaving N=318,729 patients. The analytical time is a window of one calendar year (2011) in a cross-sectional design. This design does not by definition allow for causational conclusions over time to be drawn, but it enables identification of differences between groups and hence cost pattern exploration [26]. Sortsø et al. Health Economics Review (2017) 7:21 Page 2 of 22 The variables for the study may conveniently be grouped in three overall groups: Patients SES and demographic characteristics; health care usage, and patients’ need for health care. Patients’socioeconomic, demographic and morbidity characteristics are summarized and described in details in Table 1. Patients’annual gross income is applied as ranking variable, since this measure is the most common measure of SES in the literature analysing inequality through concentration indices [43–45]. Several studies, including those mentioned here, use household rather than individual income, given that the former is a more comprehensive expression of the patients’economic abilities. Household information, including household income, was not included in the present study. Furthermore, patients’highest attained educational level, based on the Danish Educational Nomenclatura with 13 educational groups, is applied as ranking variable as well, since this measure is frequently used in public health literature due to its simplicity and universality [19]. When education is used as a ranking variable, the full nine level definition is used, and otherwise a three level definition. Following what has been used in previous studies, included demographic variables are age, gender, ethnicity (Dane,immigrant or descendant), civil status (married/ partnership, unmarried, widowed/longest living partner or divorced/separated), region of residence,degree of urbanity of residence, and labour market status (unemployed,early retired,retired,not in job for other reasons). Finally, relevant and available morbidity characteristics were added, including incidence in 2011, complication group (CG0, CG1 or CG2) and mortality (alive or not in 2011) measures. Alike what has been discussed elsewhere, there may be endogeneity problems connected to in particular labour market status, as this affects not only health but also income rank. However, we expect the endogeneity problems to be less profound when using education as SES measure, given that education is taken relatively early in the life course and thus precedes present labour market status. Turning to health care usage, overall volume of treatment related health care, including pharmaceuticals, received by the individual patient, are approximated by the costs of these services through hospital and health insurance statements. This implies that number or type of services is not considered but merely total costs by sectors. Measurements of health care and pharmaceutical consumption in the categories defined, as well as choices of appropriate cost units, are described in details in Table 2. Specifically, a total of nine health care cost components were available, made up of three components summarizing hospital inpatient care (total inpatient services, inpatient services for stays longer than the average patient in the Diagnosis Related Grouping (DRG) group, and inpatient services for rehabilitation), three components summarizing outpatient care (total outpatient services, outpatient services for stays longer than the average patient in the DRG group, and outpatient services for rehabilitation), two components summarizing primary care (services in general practices and services for privately practising specialists), and one component summarizing prescribed pharmaceutical consumption. Patients’need for health care should ideally be measured by health care professionals’clinical assessment of the individual patient. Unfortunately, such data are unavailable, and instead we apply clinically defined morbidity patterns to proxy patients’need. Patients are classified into three complication groups (complication group 0 –CG0, complication group 1 –CG1, and complication group 2 –CG2), according to the progression of their diabetes. While CG0 indicates patients without registered complications, CG1 covers those with moderate or minor problems, and CG2 those with severe complications; see Table 3 and Green et al. [21] for details. Results 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. 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. 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 Sortsø et al. Health Economics Review (2017) 7:21 Page 3 of 22 experiencing higher morbidity and mortality [39, 40]. Looking at the decomposition of inequality for inpatient 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 Table 1 Definition of socioeconomic, sociodemographic and morbidity characteristics Characteristics Definitions Categories Socioeconomics Highest educational level attained Highest educational level attained at date of data extraction, based on the main groups in the Danish educational Nomenclature with 13 educational groups based on years of education. Variable with 3 or 9 categories: 1) Primary education (<11 years) 2) Middle high education (11 to 15 years 3) Higher education (16+ years) 1) Primary education 2) Upper secondary education 3) Vocational education and training 4) Qualifying educational programmes 5) Short cycle higher education 6) Vocational bachelor’s education 7) Bachelor programmes 8) Master programmes 9) PhD programmes Income level Annual gross income 2011 (DKK) Demographics Gender Gender 1) Male 2) Female Age Age in mid-year Continuous Civil status Marital status 1) Married or in civil partnership 2) Unmarried 3) Widow or longest living partner 4) Divorced or cancelled partnership Ethnicity Based on registrations in the Central Person Register 2011. 1) Ethnic Dane 2) Immigrant 3) Descendant Region of residence Residence 2011 in relation to the five Danish regions 1) ”Capital Region of Denmark” 2) ”Region Zealand” 3) ”Region of Southern Denmark” 4) “Central Denmark Region” 5) “North Denmark Region” Urbanity Residence in type of geographic area in relation to urbanity 1) City 2) Suburbs 3) Outer areas/country side Occupational status Affiliation to the labour market 1) Affiliated to the labour market (employed or self-employed) 2) Unemployed (maternal leave, job seeker allowance) 3) Unemployed (unemployment benefit) 4) Education 5) Early retirement 6) Retired 7) Child Morbidity Incident 2011 Patient diagnosed in calendar year 2011 0) Diagnosed in year ≠2011 1) Diagnosed in 2011 Complication group at present Complication group at 31 st of December 2011 1) CG0 2) CG1 3) CG2 Complication group at diagnosis Complication group at diagnosis 1) CG0 2) CG1 3) CG2 Mortality 2011 Death in 2011 0) Alive 2011 1) Death 2011 Sortsø et al. Health Economics Review (2017) 7:21 Page 4 of 22 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. 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 Table 2 Definition of nine health care usage cost components Cost component Cost unit Inpatient and outpatient services delivered in Danish hospitals registered in the National Patient Register divided into the following components: 1) Inpatient services 2) Inpatient services for stays longer than the average patient in this DRG-group 3) Inpatient services for rehabilitation 4) Outpatient services 5) Outpatient services for stays longer than the average patient in this DAGS-group 6) Outpatient services for rehabilitation Diagnosis Related Grouping (DRG) system and Danish Ambulant Grouping System (DAGS) tariffs - year 2012 [35]. The DRG-tariff system is developed for administrative purpose and based on rough average costs across hospitals for specific diagnostic groups. Excludes interest and depreciation of buildings and equipment while other overhead costs are included Primary care services delivered by general practitioners and privately practicing specialists such as: dentists, physiotherapists, chiropractors, chiropodists who are registered in the National Health Service Register divided into the following components: 1) Services in general practices 2) Services for privately practicing specialists Reimbursement fees between the National Health Insurance scheme and private practicing physicians are used as cost units. General Practitioners are compensated by regions through a combination of per capita fee (app. 30% of total) and fee for service (app. 70%) [32]. To reflect this payment scheme in the unit cost, 43.8% of the fee for service in general practice was added on top. Overhead costs covered by capitation fee were hence not distributed across numbers of visits, as would have been most appropriate, but by resource burden. Prescribed pharmaceuticals dispensed by Danish pharmacies and registered in the Danish national prescription register. (Pharmaceuticals consumed in hospitals are included in DRG-tariffs. Over-the-counter drugs are not included in the statements). Total sales price includes patient out of pocket payments since costs of prescribed pharmaceuticals are shared between the patient and the primary health care sector by a copayment scheme where patients are reimbursed according to their need. These costs were aggregated since total costs are measured regardless of who pays. 20% VAT was subtracted. Table 3 Patient need for health care, as defined by complication state classification Complication group Health state a Complication group 0 (CG0) Diabetes without registered complications Complication group 1 (CG1) Moderate or minor complications, problems with eyes, heart, kidney and nervous system, minor amputations below the ancle, bypass operation and some eye operations Complication group 2 (CG2) – Severe complications: blindness, amputation above the ancle, severe heart failure, kidney transplant or dialysis a ICD codes defined for each complication group is given in Table 6 in Appendix Sortsø et al. Health Economics Review (2017) 7:21 Page 5 of 22 Table 4 Decomposition of inequality in health care and pharmaceutical usage ranked by income Ranked by income SECONDARY CARE PRIMARY CARE PHARMACEUTICALS INPATIENT OUTPATIENT Care long stays Rehabilitation Care Rehabilitation General pracitice Specialist Number of observations 318,684 318,684 318,684 209,530 8,089 318,684 318,684 318,684 Mean 20996.5 *** 718.561 *** 236.955 *** 16836.740 *** 3954.549 *** 3069.671 *** 2108.611 *** 7489.03 *** C (observed) −0.176 *** −0.230 *** −0.202 *** −0.036 *** 0.024 *** −0.063 *** 0.003 ** −0.038 *** C (predicted) −0.180 *** −0.245 *** −0.207 *** −0.043 *** 0.011 * −0.064 *** −0.026 *** −0.040 *** C (unexplained) 0.004 * 0.016 ** 0.004 0.006 *** 0.013 *** 0.001 0.029 *** 0.002 ** Determinants (reference group) C Sig. C Sig. C Sig. C Sig. C Sig. C Sig. C Sig. C Sig. SES Income 0.06 −0.81 0.28 −3.41 −3.72 1.10 * 99.38 −2.81 Educational level (high education) Primary education 3.67 *** 2.73 0.81 −14.15 *** 29.22 14.78 *** 97.09 2.46 Medium education −1.21 *** −1.18 0.39 2.26 −5.93 −4.89 *** 1.99 2.45 *** Age and gender (Males0-14) (Females0-14) M15-29 −0.66 *** −0.86 *** 0.18 ** −3.55 *** 2.49 0.16 *** 1.88 −0.82 *** M30-44 6.73 *** 6.29 *** −1.76 ** 24.22 *** −25.07 −6.20 *** −11.23 9.30 *** M45-59 20.45 *** 20.20 *** −9.69 *** 71.29 *** −58.47 −32.51 *** 28.93 49.04 *** M60-74 1.80 *** 1.97 *** −1.09 *** 3.93 *** −8.11 −3.93 *** 7.23 5.58 *** M75+ −10.65 *** −9.91 *** 9.75 *** −45.51 *** 40.92 26.83 *** −36.67 −33.88 *** F15-29 −0.53 *** −0.55 *** 0.16 ** −1.74 *** 2.06 1.05 *** −4.84 −1.25 *** F30-44 5.83 *** 5.69 *** −2.43 *** 17.72 *** −24.96 −10.59 *** 54.68 17.01 *** F45-59 22.60 *** 19.94 *** −10.61 *** 78.07 *** −98.23 −42.88 *** 205.85 50.83 *** F60-74 2.18 *** 2.04 *** −1.25 *** 4.53 *** −17.74 −4.14 *** 17.25 4.66 *** F75+ −13.21 *** −11.54 *** 10.23 *** −50.92 *** 47.46 24.59 *** −49.24 −27.46 *** Labour market affiliation (in job) Not in job (maternity leave, job seeker allowance) −0.29 *** −0.36 *** −0.30 *** −0.84 *** 4.34 −0.69 *** 2.88 −0.10 Not in job (unemployment benefit) 1.86 *** 0.54 0.84 *** 2.68 *** −4.15 4.40 *** −21.52 2.81 *** Education, training −0.63 ** −1.34 *** 0.40 *** 1.98 ** 5.89 −0.87 *** 4.43 1.60 * Early retired 5.95 *** 5.47 *** 2.98 *** 23.32 *** 0.74 8.56 *** −114.36 40.97 *** Retired 22.60 *** 18.74 *** 14.34 *** 74.14 *** −63.97 33.23 *** −349.57 71.38 *** Child −1.18 *** −2.30 *** 0.91 *** 2.30 7.16 0.73 *** 14.16 −3.47 *** Sortsø et al. Health Economics Review (2017) 7:21 Page 6 of 22 Table 4 Decomposition of inequality in health care and pharmaceutical usage ranked by income (Continued) Marital status (married) Unmarried 0.10 * −0.35 ** −0.07 0.36 ** 1.29 0.25 *** −4.49 −0.75 *** widowed/longest living partner 0.22 0.99 ** 0.88 *** −1.91 *** −0.71 −0.11 20.81 0.71 *** Divorced/cancelled partnership −0.29 *** −0.48 *** −0.40 *** 0.78 ** −1.53 0.00 −5.02 −1.20 *** Ethnicity (Ethic Dane) Immigrant −2.37 *** −2.09 *** −2.07 *** −5.93 *** −10.37 0.79 *** 35.59 −13.87 *** Descendant −0.05 −0.13 *** −0.05 ** −0.17 0.09 0.01 0.93 −0.31 *** Region of residence (Capital Region of Denmark) Region Zealand 0.02 −0.08 0.00 −0.13 4.30 0.04 3.82 −0.03 Region of Southern Denmark −0.64 *** −1.55 *** 2.58 *** 1.84 *** 68.74 1.57 *** 34.67 −0.76 *** Central Denmark Region 0.03 0.11 −0.15 −0.01 −36.14 −0.16 1.31 0.05 North Denmark Region −0.28 *** −0.35 *** 0.64 *** −1.43 *** 0.15 1.10 *** 21.02 0.08 Degree of urbanity of residence (cities) Suburbs −0.01 0.02 0.00 −0.05 1.16 0.06 * 1.03 0.07 * Country side −0.35 ** −0.02 −0.79 *** −3.56 *** −16.87 1.92 *** 25.08 0.46 * Morbidity indicators Incident in 2011 0.18 *** 0.01 0.23 *** −0.40 *** 3.18 2.55 *** −26.42 3.42 *** Complication group CG1 (CG0) * −0.67 *** −0.63 *** −0.45 *** −3.90 *** 17.93 −0.23 *** 2.67 −3.53 *** Complication group CG2 (CG0)* 26.30 *** 20.01 *** 23.68 *** 57.50 *** −69.18 8.06 *** −116.38 50.73 *** Death in 2011 59.76 *** 77.45 *** 37.84 *** 34.38 *** 15.37 −8.04 *** 152.06 −20.36 *** *CG0 = no complications, CG1 = minor complications, CG2 = severe complications Sortsø et al. Health Economics Review (2017) 7:21 Page 7 of 22 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. 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 Fig. 2 Decomposition of income-related inequality in in-patient care costs 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 Sortsø et al. Health Economics Review (2017) 7:21 Page 8 of 22 Table 7 Decomposition of inequality in health care costs ranked by income, regression coefficient (B) and concentration index (CI) for each determinant Ranked by income SECONDARY CARE PRIMARY CARE PHARMACEUTICALS INPATIENT OUTPATIENT Care long stays Rehabilitation Care Rehabilitation General pracitice Specialist Variable (reference group) Mean Mean Mean Mean Mean Mean Mean Mean Income b01** 0.00 0.00 0.00 0.00 0.00 0.00 0.00 * 0.00 ci01*** 0.34 *** 0.34 *** 0.34 *** 0.34 *** 0.31 *** 0.34 *** 0.34 *** 0.34 *** Education (high education) low education b02 1656.98 *** 54.93 4.80 −1036.59 *** −287.87 * 349.09 *** −262.41 *** 86.52 ci02 −0.19 *** −0.19 *** −0.19 *** −0.20 *** −0.19 *** −0.19 *** −0.19 *** −0.19 *** medium education b03 1025.82 *** 44.26 −4.24 −322.94 −137.91 216.14 *** −8.86 −159.67 *** ci03 0.11 *** 0.11 *** 0.11 *** 0.10 *** 0.08 *** 0.11 *** 0.11 *** 0.11 *** Age and gender (Males0-14) (Females0-14) M15-29 b04 −613.27 *** −35.70 *** 2.24 ** −450.86 *** −114.50 *** 7.93 *** −17.02 *** −59.22 *** ci04 −0.16 *** −0.16 *** −0.16 *** −0.17 *** −0.14 −0.16 *** −0.16 *** −0.16 *** M30-44 b05 −423.91 *** −17.71 *** 1.44 ** −244.19 *** −58.19 *** 20.43 *** −7.84 *** −45.28 *** ci05 0.39 *** 0.39 *** 0.39 *** 0.41 *** 0.36 *** 0.39 *** 0.39 *** 0.39 *** M45-59 b06 −283.26 *** −12.50 *** 1.75 *** −181.37 *** −20.66 23.57 *** −1.83 −52.54 *** ci06 0.38 *** 0.38 *** 0.38 *** 0.38 *** 0.35 *** 0.38 *** 0.38 *** 0.38 *** M60-74 b07 −227.19 *** −11.16 *** 1.79 *** −147.29 *** −15.57 26.02 *** 2.06 * −54.51 *** ci07 0.02 *** 0.02 *** 0.02 *** 0.01 *** 0.03 ** 0.02 *** 0.02 *** 0.02 *** M75+ b08 −228.72 *** −9.51 *** 2.72 *** −175.53 *** −26.77 ** 30.16 *** 2.68 *** −56.25 *** ci08 −0.21 *** −0.21 *** −0.21 *** −0.19 *** −0.18 *** −0.21 *** −0.21 *** −0.21 *** F15-29 b09 −492.86 *** −22.93 *** 1.95 ** −220.61 *** −91.68 *** 51.24 *** 10.34 *** −90.40 *** ci09 −0.13 *** −0.13 *** −0.13 *** −0.14 *** −0.07 −0.13 *** −0.13 *** −0.13 *** F30-44 b10 −366.93 *** −16.01 *** 1.99 *** −178.72 *** −57.76 *** 34.93 *** 10.76 *** −82.82 *** ci10 0.32 *** 0.32 *** 0.32 *** 0.34 *** 0.23 *** 0.32 *** 0.32 *** 0.32 *** F45-59 b11 −313.06 *** −12.33 *** 1.91 *** −198.61 *** −33.74 ** 31.09 *** 10.80 *** −54.44 *** ci11 0.48 *** 0.48 *** 0.48 *** 0.39 *** 0.40 *** 0.48 *** 0.48 *** 0.48 *** F60-74 b12 −274.92 *** −11.52 *** 2.05 *** −169.68 *** −32.91 *** 27.37 *** 8.50 *** −45.58 *** ci12 0.02 *** 0.02 *** 0.02 *** 0.01 *** 0.03 ** 0.02 *** 0.02 *** 0.02 *** F75+ b13 −283.56 *** −11.09 *** 2.86 *** −196.36 *** −31.08 *** 27.64 *** 3.61 *** −45.60 *** ci13 −0.16 *** −0.16 *** −0.16 *** −0.15 *** −0.12 *** −0.16 *** −0.16 *** −0.16 *** Sortsø et al. Health Economics Review (2017) 7:21 Page 15 of 22 Table 7 Decomposition of inequality in health care costs ranked by income, regression coefficient (B) and concentration index (CI) for each determinant (Continued) Labour market affiliation (in job) Not in job (maternity leave, job seeker allowance) b14 5656.97 *** 310.30 *** 76.42 *** 2065.49 *** 796.35 ** 705.00 *** 344.42 *** 147.77 ci14 0.09 *** 0.09 *** 0.09 *** 0.11 *** 0.13 *** 0.09 *** 0.09 *** 0.09 *** Not in job (unemployment benefit) b15 7714.01 *** 100.31 45.45 *** 1793.17 *** 355.39 955.25 *** 518.71 *** 900.41 *** ci15 −0.44 *** −0.44 *** −0.44 *** −0.43 *** −0.42 *** −0.44 *** −0.44 *** −0.44 *** Education, training b16 −3160.16 ** −297.82 *** 25.81 *** 1328.96 ** −1347.85 ** −227.81 *** −165.51 *** 621.87 * ci16 −0.90 *** −0.90 *** −0.90 *** −0.91 *** −0.85 *** −0.90 *** −0.90 *** −0.90 *** Early retired b17 12395.85 *** 509.88 *** 80.83 *** 8147.46 *** −67.01 932.87 *** 1113.52 *** 6599.71 *** ci17 −0.15 *** −0.15 *** −0.15 *** −0.14 *** −0.10 *** −0.15 *** −0.15 *** −0.15 *** Retired b18 7303.15 *** 271.48 *** 60.31 *** 4240.48 *** 431.49 * 562.26 *** 511.90 *** 1784.64 *** ci18 −0.21 *** −0.21 *** −0.21 *** −0.20 *** −0.18 *** −0.21 *** −0.21 *** −0.21 *** Child b19 −7903.27 *** −687.25 *** 79.26 *** 1970.06 −2358.20 * 257.09 *** −591.64 *** −1792.82 *** ci19 −0.98 *** −0.98 *** −0.98 *** −0.98 *** −0.99 *** −0.98 *** −0.98 *** −0.98 *** Marital status (marriaged) Unmarried b20 −793.18 * 118.66 ** 7.18 −967.62 *** 91.72 −98.20 *** −144.60 *** 435.56 *** ci20 0.03 *** 0.03 *** 0.03 *** 0.02 *** 0.09 *** 0.03 *** 0.03 *** 0.03 *** widowed/longest living partner b21 546.13 110.96 ** 28.94 *** −1280.55 *** −317.43 * −14.08 −242.16 *** 138.57 *** ci21 −0.09 *** −0.09 *** −0.09 *** −0.06 *** 0.01 −0.09 *** −0.09 *** −0.09 *** Divorced/annuled partnership b22 1960.90 *** 143.55 *** 35.08 *** −729.78 ** −89.68 0.94 −154.77 *** 625.22 *** ci22 0.04 *** 0.04 *** 0.04 *** 0.05 *** 0.08 *** 0.04 *** 0.04 *** 0.04 *** Ethicity (Ethic Dane) Immigrant b23 −4598.66 *** −180.16 *** −52.24 *** −1906.88 *** 556.50 ** 80.42 *** −332.02 *** −2082.04 *** ci23 −0.21 *** −0.21 *** −0.21 *** −0.22 *** −0.18 *** −0.21 *** −0.21 *** −0.21 *** Descendant b24 −2601.88 −272.86 *** −32.93 ** −1257.39 1008.95 19.62 −241.26 *** −1152.62 *** ci24 −0.24 *** −0.24 *** −0.24 *** −0.25 *** −0.01 −0.24 *** −0.24 *** −0.24 *** Region of residence (Capital Region of Denmark) Region Zealand b25 1518.98 *** −218.02 *** −1.39 −1508.86 *** −1203.57 *** 146.81 *** −559.62 *** −121.78 * ci25 0.00 0.00 0.00 0.00 −0.03 0.00 0.00 0.00 Region of Southern Denmark b26 −2690.03 *** −289.19 *** 140.30 *** 1281.61 *** −2932.85 *** 343.49 *** −731.81 *** −246.01 *** ci26 −0.04 *** −0.04 *** −0.04 *** −0.03 *** −0.05 *** −0.04 *** −0.04 *** −0.04 *** Central Denmark Region b27 −2483.33 *** −418.62 *** 154.80 *** −1992.48 *** −2226.42 *** 697.56 *** −766.99 *** −293.93 *** ci27 0.00 0.00 0.00 0.00 0.04 *** 0.00 0.00 0.00 Sortsø et al. Health Economics Review (2017) 7:21 Page 16 of 22 Table 7 Decomposition of inequality in health care costs ranked by income, regression coefficient (B) and concentration index (CI) for each determinant (Continued) North Denmark Region b28 −3087.03 *** −173.55 *** 91.92 *** −3236.22 *** −1330.41 *** 632.16 *** −916.56 *** 69.90 ci28 −0.03 *** −0.03 *** −0.03 *** −0.03 *** 0.00 −0.03 *** −0.03 *** −0.03 *** Urbanity (Cities) Suburbs b29 −750.90 * 52.07 1.25 −439.30 971.95 *** 170.91 *** −162.38 *** 278.42 *** ci29 0.00 * 0.00 * 0.00 * 0.00 0.01 0.00 * 0.00 * 0.00 * Country side b30 −791.07 ** −1.76 −23.64 *** −1447.17 *** 975.13 *** 230.68 *** −206.41 *** 82.10 * ci30 −0.04 *** −0.04 *** −0.04 *** −0.04 *** −0.03 *** −0.04 *** −0.04 *** −0.04 *** Morbidity indicators Incident in 2011 b31 −1287.16 *** −4.70 −21.37 *** 1873.66 *** 293.34 −949.62 *** −1000.48 *** −1880.24 *** ci31 0.05 *** 0.05 *** 0.05 *** 0.02 *** 0.11 *** 0.05 *** 0.05 *** 0.05 *** Complication group CG1 (CG0) * b32 8924.98 *** 370.12 *** 77.74 *** 3043.54 *** 1104.35 *** 158.60 *** 192.82 *** 3609.95 *** ci32 0.01 *** 0.01 *** 0.01 *** 0.04 *** 0.05 *** 0.01 *** 0.01 *** 0.01 *** Complication group CG2 (CG0)* b33 29216.04 *** 993.73 *** 342.16 *** 11567.27 *** 2064.34 *** 469.02 *** 662.58 *** 4359.56 *** ci33 −0.13 *** −0.13 *** −0.13 *** −0.10 *** −0.07 *** −0.13 *** −0.13 *** −0.13 *** Death in 2011 b34 87725.75 *** 5083.04 *** 722.43 *** 8603.19 *** −912.07 *** −617.85 *** −1014.66 *** −2310.90 *** ci34 −0.68 *** −0.68 *** −0.68 *** −0.63 *** −0.53 *** −0.68 *** −0.68 *** −0.68 *** *CG0 = no complications, CG1 = minor complications, CG2 = severe complications ** bN=Regression coefficient of variable N ***ciN=Concentration index of variable N Sortsø et al. Health Economics Review (2017) 7:21 Page 17 of 22 Table 8 Decomposition of inequality in health care costs ranked by education, regression coefficient (B) and concentration index (CI) for each determinant Ranked by education SECONDARY CARE PRIMARY CARE PHARMACEUTICALS INPATIENT OUTPATIENT Care long stays Rehabilitation Care Rehabilitation General pracitice Specialist Variable (reference group) Mean Mean Mean Mean Mean Mean Mean Mean Income b01 0.00 0.00 0.00 0.00 0.00 0.00 * 0.00 * 0.00 ci01 0.11 *** 0.11 *** 0.11 *** 0.11 *** 0.10 *** 0.11 *** 0.11 *** 0.11 *** Education (high education) low education b02 1671.70 *** 57.60 5.09 −1044.36 *** −302.94 * 349.05 *** −260.11 *** 91.10 ci02 −0.58 *** −0.58 *** −0.58 *** −0.58 *** −0.57 *** −0.58 *** −0.58 *** −0.58 *** medium education b03 1055.65 *** 47.62 −3.79 −334.48 −145.09 215.94 *** −6.32 −152.72 ** ci03 0.25 *** 0.25 *** 0.25 *** 0.25 *** 0.28 *** 0.25 *** 0.25 *** 0.25 *** Age and gender (Males0-14) (Females0-14) M15-29 b04 −620.08 *** −35.51 *** 2.27 ** −454.78 *** −112.13 *** 7.99 *** −16.68 *** −58.14 *** ci04 −0.14 *** −0.14 *** −0.14 *** −0.13 *** −0.16 * −0.14 *** −0.14 *** −0.14 *** M30-44 b05 −426.18 *** −17.70 *** 1.47 ** −247.64 *** −56.56 *** 20.41 *** −7.58 *** −45.05 *** ci05 0.08 *** 0.08 *** 0.08 *** 0.10 *** 0.08 ** 0.08 *** 0.08 *** 0.08 *** M45-59 b06 −284.55 *** −12.48 *** 1.77 *** −183.38 *** −19.55 23.57 *** −1.65 −52.41 *** ci06 0.07 *** 0.07 *** 0.07 *** 0.07 *** 0.10 *** 0.07 *** 0.07 *** 0.07 *** M60-74 b07 −229.27 *** −11.19 *** 1.80 *** −149.42 *** −14.46 26.00 *** 2.20 * −54.49 *** ci07 0.05 *** 0.05 *** 0.05 *** 0.06 *** 0.07 *** 0.05 *** 0.05 *** 0.05 *** M75+ b08 −229.99 *** −9.48 *** 2.74 *** −177.51 *** −25.93 ** 30.16 *** 2.81 *** −56.25 *** ci08 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 F15-29 b09 −500.86 *** −23.04 *** 1.98 ** −224.03 *** −88.97 *** 51.19 *** 10.54 *** −90.45 *** ci09 −0.11 *** −0.11 *** −0.11 *** −0.11 *** −0.08 * −0.11 *** −0.11 *** −0.11 *** F30-44 b10 −369.02 *** −15.98 *** 2.02 *** −181.68 *** −56.30 *** 34.92 *** 10.99 *** −82.49 *** ci10 0.07 *** 0.07 *** 0.07 *** 0.08 *** 0.05 ** 0.07 *** 0.07 *** 0.07 *** F45-59 b11 −314.80 *** −12.28 *** 1.93 *** −200.69 *** −32.70 ** 31.10 *** 10.98 *** −54.35 *** ci11 0.09 *** 0.09 *** 0.09 *** 0.08 *** 0.11 *** 0.09 *** 0.09 *** 0.09 *** F60-74 b12 −276.81 *** −11.50 *** 2.07 *** −171.59 *** −31.94 ** 27.36 *** 8.67 *** −45.62 *** ci12 0.07 *** 0.07 *** 0.07 *** 0.06 *** 0.08 *** 0.07 *** 0.07 *** 0.07 *** F75+ b13 −284.82 *** −11.08 *** 2.87 *** −198.21 *** −30.10 *** 27.64 *** 3.74 *** −45.61 *** ci13 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 Sortsø et al. Health Economics Review (2017) 7:21 Page 18 of 22 Table 8 Decomposition of inequality in health care costs ranked by education, regression coefficient (B) and concentration index (CI) for each determinant (Continued) Labour market affiliation (in job) Not injob (maternity leave, job seeker allowance) b14 5647.67 *** 310.49 *** 75.85 *** 2050.64 *** 801.14 * 705.60 *** 343.97 *** 146.35 ci14 0.05 *** 0.05 *** 0.05 *** 0.05 *** 0.04 0.05 *** 0.05 *** 0.05 *** Not in job (unemployment benefit) b15 7754.26 *** 102.21 45.48 *** 1755.88 *** 345.08 954.64 *** 520.03 *** 905.78 *** ci15 −0.05 *** −0.05 *** −0.05 *** −0.06 *** −0.11 ** −0.05 *** −0.05 *** −0.05 *** Education, training b16 −3047.98 ** −296.37 *** 26.77 *** 1239.85 ** −1327.45 ** −226.53 *** −163.62 *** 634.89 ** ci16 −0.36 *** −0.36 *** −0.36 *** −0.38 *** −0.27 *** −0.36 *** −0.36 *** −0.36 *** Early pensioner b17 12424.47 *** 516.77 *** 80.95 *** 8113.88 *** −61.04 932.31 *** 1111.59 *** 6598.69 *** ci17 −0.13 *** −0.13 *** −0.13 *** −0.13 *** −0.12 *** −0.13 *** −0.13 *** −0.13 *** Pensioner b18 7360.21 *** 273.17 *** 60.90 *** 4243.02 *** 430.83 * 563.12 *** 510.89 *** 1791.89 *** ci18 −0.06 *** −0.06 *** −0.06 *** −0.06 *** −0.05 *** −0.06 *** −0.06 *** −0.06 *** Child b19 −8016.24 *** −679.84 *** 79.79 *** 1863.83 −2312.99 * 257.14 *** −578.69 *** −1788.10 *** ci19 0.76 *** 0.76 *** 0.76 *** 0.75 *** 0.66 *** 0.76 *** 0.76 *** 0.76 *** Marital status (marriaged) Unmarried b20 −762.99 * 119.34 ** 7.51 −983.54 *** 87.62 −98.00 *** −144.59 *** 436.33 *** ci20 −0.02 *** −0.02 *** −0.02 *** −0.01 *** 0.00 −0.02 *** −0.02 *** −0.02 *** widowed/longest living partner b21 498.17 111.25 ** 28.21 *** −1264.30 *** −328.09 ** −14.91 −243.26 *** 139.54 *** ci21 −0.11 *** −0.11 *** −0.11 *** −0.11 *** −0.12 *** −0.11 *** −0.11 *** −0.11 *** Divorced/annuled partnership b22 1959.70 *** 144.38 *** 35.48 *** −722.93 ** −93.74 0.99 −155.19 *** 624.10 *** ci22 −0.03 *** −0.03 *** −0.03 *** −0.03 *** 0.00 −0.03 *** −0.03 *** −0.03 *** Ethicity (Ethic Dane) Immigrant b23 −4583.20 *** −184.46 *** −52.36 *** −1895.54 *** 566.97 ** 80.27 *** −331.87 *** −2081.26 *** ci23 0.15 *** 0.15 *** 0.15 *** 0.14 *** 0.11 *** 0.15 *** 0.15 *** 0.15 *** Descendant b24 −2549.32 −270.53 *** −33.09 ** −1229.19 1017.49 19.23 −236.49 *** −1134.00 *** ci24 0.11 *** 0.11 *** 0.11 *** 0.12 *** 0.13 0.11 *** 0.11 *** 0.11 *** Region of residence (Capital Region of Denmark) Region Zealand b25 1529.58 *** −219.20 *** −1.67 −1514.75 *** −1215.46 *** 146.88 *** −560.24 *** −125.67 * ci25 −0.02 *** −0.02 *** −0.02 *** −0.02 *** −0.02 −0.02 *** −0.02 *** −0.02 *** Region of Southern Denmark b26 −2687.59 *** −291.85 *** 140.25 *** 1267.48 *** −2931.74 *** 344.33 *** −731.61 *** −251.59 *** ci26 −0.03 *** −0.03 *** −0.03 *** −0.03 *** −0.04 *** −0.03 *** −0.03 *** −0.03 *** Central Denmark Region b27 −2496.60 *** −422.31 *** 154.16 *** −2012.19 *** −2215.80 *** 696.83 *** −767.81 *** −298.20 *** ci27 −0.01 *** −0.01 *** −0.01 *** −0.02 *** 0.03 *** −0.01 *** −0.01 *** −0.01 *** Sortsø et al. Health Economics Review (2017) 7:21 Page 19 of 22 Table 8 Decomposition of inequality in health care costs ranked by education, regression coefficient (B) and concentration index (CI) for each determinant (Continued) North Denmark Region b28 −3077.69 *** −177.70 *** 91.88 *** −3251.57 *** −1313.66 *** 631.06 *** −916.52 *** 63.75 ci28 −0.06 *** −0.06 *** −0.06 *** −0.06 *** −0.06 *** −0.06 *** −0.06 *** −0.06 *** Urbanity (Cities) Suburbs b29 −769.59 * 52.32 1.32 −421.47 975.00 *** 171.33 *** −162.57 *** 278.79 *** ci29 −0.03 *** −0.03 *** −0.03 *** −0.03 *** −0.01 −0.03 *** −0.03 *** −0.03 *** Country side b30 −794.58 ** −1.55 −23.33 *** −1437.32 *** 979.44 *** 229.74 *** −206.84 *** 79.69 ci30 −0.06 *** −0.06 *** −0.06 *** −0.06 *** −0.05 *** −0.06 *** −0.06 *** −0.06 *** Morbidity indicators Incident in 2011 b31 −1291.89 *** −5.98 −21.65 *** 1862.19 *** 307.23 −950.00 *** −999.99 *** −1878.38 *** ci31 0.02 *** 0.02 *** 0.02 *** 0.02 *** 0.07 *** 0.02 *** 0.02 *** 0.02 *** Complication group CG1 (CG0) * b32 8970.85 *** 371.90 *** 78.17 *** 3050.01 *** 1100.95 *** 159.51 *** 193.67 *** 3614.41 *** ci32 0.00 0.00 0.00 0.01 *** 0.00 0.00 0.00 0.00 Complication group CG2 (CG0)* b33 29218.30 *** 994.70 *** 342.29 *** 11571.10 *** 2057.79 *** 469.32 *** 662.94 *** 4362.94 *** ci33 −0.03 *** −0.03 *** −0.03 *** −0.03 *** −0.01 −0.03 *** −0.03 *** −0.03 *** Death in 2011 b34 87694.00 *** 5094.08 *** 720.90 *** 8658.61 *** −921.76 *** −618.34 *** −1012.35 *** −2311.31 *** ci34 −0.02 *** −0.02 *** −0.02 *** −0.05 *** 0.00 −0.02 *** −0.02 *** −0.02 *** *CG0 = no complications, CG1 = minor complications, CG2 = severe complications ** bN=Regression coefficient of variable N ***ciN=Concentration index of variable N Sortsø et al. Health Economics Review (2017) 7:21 Page 20 of 22 Abbreviations C: Concentration Index; CG0: Complication Group 0; CG1: Complication Group 1; CG2: Complication Group 2; DD2: Diabetes Database 2; DRG: Diagnosis Related Grouping; NDR: Danish National Diabetes Register; SD: Statistics Denmark; SES: Socio-Economic Status; WHO: World Health Organization Acknowledgements This study has been conducted by ApEHR in cooperation with the Danish Diabetes Association and supported by a Ph. D. program from COHERE supported by The Danish Centre for Strategic Research in Type 2 Diabetes, DD2. A consortium of sponsors from the pharmaceutical industry comprising Astra Zeneca/BMS, Novo Nordisk, Merck, Sanofi Aventis and Bayer has provided an unrestricted grant to ApEHR for the conduct of this research. We thank Terkel Christiansen for valuable commenting. Funding Camilla Sortsøs research was financed by an unrestricted grant from The Danish Centre for Strategic Research in Type 2 Diabetes, DD2. Anders Greens, Martha Emneus’and Peter Bjødstrup Jensens research was financed by an unrestricted grant from a consortium of sponsors from the pharmaceutical industry comprising Astra Zeneca/BMS, Novo Nordisk, Merck, Sanofi Aventis and Bayer. Availability of data and materials Access to data were provided through Statistics Denmark. According to the rules of Statistics Denmark, further distribution of the data or the access to them is prohibited. Access to the data can be applied for via Statistics Denmark. Authors’contributions CS: First draft of introduction, literature review, description of data, calculation and presentation of results, first draft of discussion and conclusion. JTL: Description of methodology, preparation of programming, calculation and presentation of results, contribution to introduction, discussion and conclusion. AG: Preparation and description of data, contribution to introduction, discussion and conclusion. ME: Preparation and description of data, contribution to introduction, discussion and conclusion. PBJ: Preparation and description of data, contribution to introduction, discussion and conclusion. All authors have read and approved on the final version of the paper prior to submission. Competing interests Neither the Danish Diabetes Association, the DD2 Centre nor the consortium of sponsors from the pharmaceutical industry has had any influence on the conduct of the study. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Endnotes Not applicable. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details 1 Centre of Health Economics Research (COHERE), Department of Business and Economics, University of Southern Denmark, Campusvej 55, DK-5s30 Odense M, Denmark. 2 Institute of Applied Economics and Health Research (ApEHR), Copenhagen, Denmark. 3 Odense Patient data Explorative Network (OPEN), Odense University Hospital and University of Southern Denmark, Copenhagen, Denmark. Received: 3 November 2016 Accepted: 21 April 2017 References 1. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and metaanalysis. Int J Epidemiol. 2011;40(3):804–18. 2. Allanson P, Paetrie D. On the choice of inequality measure for the longitudinal analysis of income-related health inequalities. Health Econ. 2013;22:353–65. 3. Andersen JS, Olivarius NDF, Krasnik A. 2011. The Danish National Health Service Register. Scand J Public Health. 2011;39(suppl 7). 4. Brown AF, Ettner SL, Piette J, Weinberger M, Gregg E, Shapiro MF, et al. Socioeconomic position and health among persons with diabetes mellitus: a conceptual framework and review of the literature. Epidemiol Rev. 2004;26:63–77. 5. Bruce Wirta S, Hodgkins P, Joseph A. Economic burden associated with chronic constipation in Sweden: a retrospective cohort study. ClinicoEconomics and outcomes research : CEOR. 2014;6:369–79. 6. Carstensen B, Kristensen JK, Marcussen MM, Borch-Johnsen K. 2011. The National Diabetes Register. Scandinavian journal of public health. 2011;39(suppl. 7):58-61. 7. Clarke PM, Gerdtham U-G, Johannesson M, Bingefors K, Smith L. On the measurement of relative and absolute incomwe-related inequality. Soc Sci Med. 2002;55:1923–8. 8. Clarke P, van Ourti T. Correcting the Bias in the Concentration Index When Income is Grouped 2009. 2009. 9. Cutler D, Lleras-Muney A. Education and Health: Evaluating Theories and Evidence. In: Making Americans Healthier. In: House J, Schoeni R, Kaplan G, Pollack H, editors. Social and Economic Policy as Health Policy. New York: Russell Sage Foundation; 2008. 10. DM Cutler DM, Lleras-Muney A, Vogl T, et al. Oxford Handbook of Health Economics. Oxford: Oxford University Press; 2011. p. 124–63. Socioeconomic status and health: dimensions and mechanisms. 11. Danish Health Authority [Sundhedsstyrelsen]. Kronisk sygdom - Patient, sundhedsvæsen og samfund. Danish Health Authority: Forudsætninger for det gode forløb. Copenhagen; 2005. 12. Danish Health Authority [Sundhedsstyrelsen]. Strategi for indsatsvedrørende diabetes blandt etniske minoriteter. Copenhagen: Danish Health Authority; 2007. 13. Danish Health Authority [Sundhedsstyrelsen]. Ulighed i sundhed - Årsager og indsatser. Copenhagen: Danish Health Authority; 2011. 14. Efron B, Tibshirani R. 1993An Introduction to the Bootstrap. London: Chapman & Hall; 1993. 15. Errygers G. Correcting the concentration index. J Health Econ. 2009;28:504–13. 16. Erreygers G, Kessels R. 2013. Regression-based decompositions of rankdependent indicators of socioeconomic inequality of health. Working Papers 2013007, University of Antwerp, Faculty of Applied Economics. 17. Erreygers G, Kessels R. 2015. Socioeconomic status and health: A new approach to the measurement of bivariate inequality. Working Papers 2015017, University of Antwerp, Faculty of Applied Economics. 18. Espelt A, Borrell C, Roskam AJ, Rodriguez-Sanz M, Stirbu I, Dalmau-Bueno A, et al. Socioeconomic inequalities in diabetes mellitus across Europe at the beginning of the 21st century. Diabetologia. 2008;51(11):1971–9. 19. Eze CO, Onwuekwe IO, Agu CE, Kalu UA. 2013. The prevalence of orthostatic hypotension in type 2 diabetes mellitus patients in a diabetic clinic in Enugu South-East Nigeria. Nigerian journal of medicine : journal of the National Association of Resident Doctors of Nigeria. 2013;22(3):175–80. 20. Gold M. 1998. Part I: The Concept of Access and Managed Care.Beyond Coverage and Supply: Measuring Access to Healthcare in Today's Market. Health Services Reserach 1998;33(3). 21. Green A, Sortsø C, Jensen P, Emneus M. 2015a. Incidence, morbidity, mortality and prevalence of diabetes in Denmark, 2000–2011: results from the Diabetes Impact Study 2013. J Clin Epidemiol. 2015;7:421–30. 22. Green A, Sortsø C, Jensen PB, Emneus M. Validation of the Danish National Diabetes Register. Clin Epidemiol. 2015b:7; 5–15. 23. Gundgaard J, Lauridsen J. 2013. Explaining the Source of Income-Related Inequality in Health Care Utilization in Denmark. COHERE: 2013. 24. Heckley G, Gerdtham U-G, Kjellson G. A general method for decomposing the causes of socioeconomic inequality in health. J Health Econ. 2016;48:89–106. Sortsø et al. Health Economics Review (2017) 7:21 Page 21 of 22 25. Imkampe AK, Gulliford MC. Increasing socio-economic inequality in type 2 diabetes prevalence–repeated cross-sectional surveys in England 1994– 2006. Eur J Public Health. 2011;21(4):484–90. 26. Juul S. Epidemiologi og evidens. København: Munksgaard Danmark; 2004. p. 2004. 27. Kakwani N, Wagstaff A, van Doorslaer E. Socioeconomic inequalities in health: Measurement, computation and statistical inference. Journal of Econometrics. 1997;77:87–103. 28. Kessels R, Errygers G. Research Paper 2014–013. Department of Economics: University of Antwerp; 2014. A unified structural equation modeling approach for decomposition of rank-dependent indicators of socioeconomic inequality of health. 29. Kildemoes H, Sørensen H, Hallas J. The Danish National Prescription Registry. Scand J Public Health. 2011;39(7 Supp):38–41. 30. Kjellson G, Gerdtham U-G, Petrie D. Lies, damned lies, and health inequality measurements. Understanding the value judgement. Epidemiology. 2015;26:673–80. 31. Kristensen F, Sigmund H. Health Technology Assessment Handbook. Copenhagen: National Board of Health; 2007. p. 2007. 32. Kristensen T, Olsen KR, Sortsø C, Ejersted C, Thomsen JL, Halling A. Resources allocation and health care needs in diabetescare in Danish GP clinics. Health Policy. 2013;113:206–15. 33. Lauridsen J, Pradhan J. Socio-economic inequality of immunization coverage in India. Health Economics Review. 2011;2011:1(11). 34. Lynge E, Sandegaard JL, Rebolj M. The Danish National Patient Register. Scandinavian journal of public health. 2011;39(suppl7). 35. Ministry of Health. DRG-leksikon 2012. 2012. [Available from: http:// sundhedsdatastyrelsen.dk/da/afregning-og-finansiering/takster-drg. 36. Nocon M, Keil T, Willich SN. Education, income, occupational status and health risk behaviour. J Public Health-Uk. 2007;15(5):401–5. 37. Robbins JM, Vaccarino V, Zhang H, Kasl SV. Socioeconomic status and diagnosed diabetes incidence. Diabetes Res Clin Pract. 2005;68(3):230–6. 38. Sortsø C, Emneus M, Green A, Jensen P, Eriksson T. Societal costs of diabetes mellitus 2015 and 2040 - forecasts based on real world cost evidence and observed epidemiological trends in Denmark. Modern Economy. 2015;6:1150–66. 39. Sortsø C, Green A, Jensen P, Emneus M. Societal costs of diabetes mellitus in Denmark. Diabet Med. 2016;33:877–85. 40. Sortsø C, Lauridsen J, Emneus M, Green A, Jensen PB. COHERE discussion paper 2/2016. Odense: University of Southern Denmark; 2016. Decomposing Inequality in Diabetes Patients’Morbidity Patterns, Survival and Health Care Usage in Denmark. 41. Thygesen LC, Daasnes C, Thaulow I, Hansen HB. Introduction to Danish (nationwide) registers on health and social issues: structure, access, legislation and archiving. Scandinavian journal of public health. 2011;39 (suppl. 7). 42. Van Doorslaer E, Koolman X. Equity II Project Working Paper #1. Rotterdam: Erasmus University; 2000. Income-related inequalities in health: some evidence from European Community Household Panel. 43. van Doorslaer E, Koolman X. Explaining the differences in income-related health inequalities across European countries. Health Econ. 2004;13(7):609–28. 44. van Doorslaer E, Koolman X, Jones AM. Explaining income-related inequalities in doctor utilisation in Europe. Health Econ. 2004;13(7):629–47. 45. van Doorslaer E, Masseria C, Koolman X, 2. Inequalities in access to medical care by income in developed countries. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne. 2006;174:177–83. 46. Wagstaff A. The bounds of the Concentration Index when the variable of interest is binary, with an application to immunization inequality. Health Econ. 2005;14:649–53. 47. Wagstaff A, van Doorslaer E. Overall versus socioeconomic health inequality: a measurement framework and two empirical illustrations. Health Econ. 2004;13(3):297–301. 48. Wagstaff A, van Doorslaer E, Watanabe N. On decomposing the causes of health sector inequalities, with an application to malnutrition inequalities in Vietnam. J Econom. 2003;112:219–27. 49. Wagstaff A, Paci P, van Doorslaer E. On The Measurement Of Inequalities in Health Social Science Medicine 1991;33(5):545–57. 50. Vallgårda S, Krasnik A, et al. In: Vallgårda S, Krasnik A, editors. Sundhedsvæsen og sundhedspolitik. Munksgaard Danmark: København; 2007. 51. WHO. Global status report on non-communicable diseases 2010. Description of the global burden of NCDs,their risk factors and determinants. WHO; 2011. 52. WHO. 2015. Diabetes Fact sheet 2015 [Available from: http://www.who.int/ mediacentre/factsheets/fs312/en/. Submit your manuscript to a journal and benefi t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.com Sortsø et al. Health Economics Review (2017) 7:21 Page 22 of 22