European Journal of Internal Medicine 26 (2015) 203–210

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European Journal of Internal Medicine journal homepage: www.elsevier.com/locate/ejim

Original Article

Global health care use by patients with type-2 diabetes: Does the type of comorbidity matter? A. Calderón-Larrañaga a,⁎, J.M. Abad-Díez a,b, L.A. Gimeno-Feliu a,c, J. Marta-Moreno a,d, F. González-Rubio a,e, M. Clerencia-Sierra a,f, B. Poblador-Plou a, A. Poncel-Falcó a,g, A. Prados-Torres a a

EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), IIS Aragon, Miguel Servet University Hospital, Paseo Isabel La Católica 1–3, 50009 Zaragoza, Spain Dept. of Health, Welfare and Family, DG Planning and Assurance, Government of Aragon, Vía Univérsitas 36, 50009 Zaragoza, Spain San Pablo Health Centre, Aragon Health Service (SALUD), C/Aguadores 7, 50003 Zaragoza, Spain d Miguel Servet University Hospital, Department of Neurology, Aragon Health Service (SALUD), Paseo Isabel La Católica 1–3, 50009 Zaragoza, Spain e Delicias Sur Health Centre, Aragon Health Service (SALUD), C/Manuel Dronda 1, 50009 Zaragoza, Spain f Socio-Sanitary Assessment Unit, Miguel Servet University Hospital, Aragon Health Service (SALUD), Paseo Isabel La Católica 1–3, 50009 Zaragoza, Spain g Zaragoza-Sector III Primary Care Directorate, Aragon Health Service (SALUD), C/Condes de Aragón 30, 50009 Zaragoza, Spain b c

a r t i c l e

i n f o

Available online 10 March 2015 Keywords: Multimorbidity Primary health care Hospitalization Emergency care Electronic health records

a b s t r a c t Aim: To identify patterns of health care use among diabetic patients with multimorbidity across primary, specialised, hospital and emergency care, depending on their type of chronic comorbidity. Methods: Longitudinal study of a population-based retrospective cohort conformed by adult patients with type-2 diabetes assigned to any of the primary care centres in Aragon during 2010 and 2011 (n = 65,716). Negative binomial regressions were run to model the effect of the type of comorbidity on the number of visits to each level of care. Comorbidities were classified as concordant, discordant or mental based on expert consensus and depending on whether they shared the same overall pathophysiologic risk profile and disease management plan designed for type-2 diabetes. Results: Mental comorbidity was independently associated with total and unplanned admissions (incidence rate ratio [IRR]:1.25; 95% confidence interval [CI]:1.12–1.39, IRR:1.21; 95% CI:1.06–1.39), average length of stay (IRR:1.47; 95% CI:1.25–1.73), and total and priority emergency room visits (IRR:1.26; 95% CI:1.17–1.35, IRR:1.30; 95% CI:1.18–1.42). Patients with discordant comorbidities showed the strongest associations with the number of visits to specialists (IRR:1.38; 95% CI:1.33–1.43) and to different specialties (IRR:1.36; 95% CI:1.32–1.39). Differences regarding GP visits were lower but still significant for patients with discordant comorbidity (IRR:1.08; 95% CI:1.06–1.11), but especially for those with mental comorbidity (IRR:1.17; 95% CI:1.14–1.21). Conclusion: In patients with type-2 diabetes, the coexistence of mental comorbidity significantly increases the use of unplanned hospital services, and discordant comorbidities have an important effect on specialised care use. Differences with respect to primary care use are not as prominent. © 2015 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

1. Introduction Higher levels of multimorbidity have consistently been shown to be associated with a greater health care resource use, no matter the kind of population or setting [1,2]. Having a combination of conditions creates more complex care provision scenarios which may result in an increased need of physician services. Multimorbidity not only is it related with recurrent [3] and avoidable [4] hospital admissions, but also with frequent use of primary care [5,6], and higher number of visits to ⁎ Corresponding author at: Hospital Universitario Miguel Servet — Hospital General, 2a Pl (antiguas consultas), Paseo Isabel La Católica 1–3, 50009 Zaragoza, Spain. Tel.: +34 976 76 5500x5371. E-mail address: [email protected] (A. Calderón-Larrañaga).

specialists [7] even for diagnoses not generally considered to require specialist care [8]. Although illness level is acknowledged to be a key determinant of health care use, less is known about which specific types or combinations of illnesses are mostly contributing to higher levels of use. Most studies measuring multimorbidity and health care utilisation are based on simple counts of diseases [2], but the impact of individual comorbidities may be lesser or greater than the simple sum depending on whether diseases share common pathologic mechanism or not [9]. Unrelated disorders seem to be relatively neglected in patients with chronic medical diseases, probably due to time constraints, communication and coordination problems, patient's preferences, and the priorities of physicians themselves [10]. Piette et al. [11] defined the concept of concordant comorbidities as those representing parts of the same

http://dx.doi.org/10.1016/j.ejim.2015.02.011 0953-6205/© 2015 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

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overall pathophysiologic risk profile and, therefore, more likely to be the focus of the same disease management plan designed for a given index disease. While the treatment of a concordant condition generally improves the status of both the concordant condition and the primary disease [12], multimorbid patients may receive lower quality care for discordant conditions due to the lack of specific recommendations [13, 14]. Indeed, diabetes care guidelines focus mainly on diabetes-related micro and macro-vascular complications, paying less attention to diseases that are not directly related [15]. This could in turn put patients at higher risk for intensive resource use. Moreover, a specific group of discordant comorbidities, mental health problems, have shown to interact with physical chronic disorders, such as diabetes, affecting prognosis. Depression affects the ability of patients to cope with their diabetes and to adhere to treatments, increasing the risk of adverse health outcomes [16]. In the same direction, Smith et al. [17] recently found depression to be associated with considerable medical burden and, by extension, additional health care use. The objective of the present study was to study the impact of type-2 diabetes-related concordant, discordant and mental comorbidities on intensity of health care use across primary, specialised, hospital and emergency care. 2. Material & methods This was a longitudinal study of a population-based retrospective cohort. The study population was conformed by all adult patients (i.e. over 14 years) assigned to any of the public primary care centres in Aragon during 2010 and 2011, who had either a diagnosis of type-2 diabetes or were taking antidiabetic medication in 2010. Those patients for whom a unique general practitioner (GP) identifier was not available were excluded from the analyses. The Aragon Health Service is a tax-based system which offers universal coverage and where primary care centres have gatekeeping functions. As a result, 98% of the general population is assigned to primary care centres belonging to the public network [18]. For this study, the demographic information (i.e. age and sex) was extracted from patients' health insurance cards. Diagnostic data was extracted from primary care electronic health records and the Hospital Minimum Basic Dataset (CMBD for its initials in Spanish). In the former, all types of health problems are computer-stored by GPs during patients' visits to primary care, according to the International Classification of Primary Care, Version 1 (ICPC-1) [19]. Diagnosis recording by GPs is not linked to billing systems or financial incentives and there is no limit regarding the number of diagnoses listed per patient, avoiding a selective reporting of heath conditions. In the latter, the clinical information from patients discharged from public and private hospitals in Aragon is coded using the International Classification of Diseases, Ninth Revision (ICD-9) [20]. The CMBD contains information on the reason for admission (principal diagnosis) as well as other comorbidities and complications listed during the admission (secondary diagnoses), and the storage of the data is centrally performed by trained personnel from each hospital in order to guarantee quality and homogeneity standards. To harmonise the diagnostic information, diseases were grouped according to the internationally validated Expanded Diagnostic Clusters (EDC) of the ACG® system [21]. This system groups both ICPC and ICD codes into 260 EDCs based on the clinical, diagnostic and therapeutic similarities of diseases. The selection of chronic EDCs was based on a previous list containing 114 EDCs [22]. Data on the prescribed and dispensed antidiabetic medication was obtained from the pharmacy billing records. The active ingredients were coded according to the Anatomical Therapeutic Chemical Classification System (ATC), considering the first three levels of the classification. Patients with either EDC codes END06 “Type-2 diabetes, w/o complication”/END07 “Type-2 diabetes, w/ complication” and/or ATC codes A10A “Insulins and analogues”/A10B “Oral hipoglucemiants” were

included in this study. We further excluded those patients for whom the only medication received was insulin since these were likely to present type-1 diabetes. Patients with diabetes were classified into four mutually exclusive groups depending on the type of chronic comorbidities they presented in 2010: i) individuals with no chronic comorbidities, ii) individuals with only concordant comorbidities, iii) individuals with at least one discordant physical comorbidity excluding those with mental comorbidities, and iv) individuals with at least one mental comorbidity. The identification of concordant/discordant/mental comorbidities was based on expert consensus, taking the definition of Piette et al. [11] as a basis. Two GPs, one geriatrician, one neurologist and a public health specialist were involved in the consensus process. The results of this process are shown in Table 1. Although the four groups of diabetic patients were designed to be mutually exclusive, those with concordant comorbidity may be distributed across categories ii–iv and those with discordant comorbidity could be part of categories iii and iv. Our rationale behind this classification was that discordant and, even to a greater extent, mental comorbidities have a multiplicative effect on health care use, after considering the total number of chronic conditions. Health care use was measured for year 2011. Utilisation data from primary, specialised, hospital and emergency care were integrated at patient-level. The use of primary care was measured as the number of visits to the GP and to the primary care nurse. Measures of specialised care utilisation included the total number of visits to any specialist and the number of specialties visited. Hospital care use was analysed by looking at total admissions, unplanned admissions, and number of hospital days. All admissions were previously classified as planned or unplanned by each hospital. The use of emergency care was assessed by the total number of visits and the number of priority visits. Priority visits were identified based on the triage level established by the Aragon Health Service; out of the five categories listed, levels 1–3 are normally assigned to priority visits. This study holds approval of the Clinical Research Ethics Committee of Aragon (CEICA). All records were previously made anonymous. 2.1. Statistical analysis Differences in the distribution of age, sex and number of chronic conditions among the four groups of patients were analysed by means of the Kruskal Wallis H non-parametric rank test and the Chi-Squared test. Means and 95% confidence intervals were calculated for the number of visits to each level of care by type of comorbidity. All means were adjusted for age (as a categorical variable: 15/44; 45/64; 65 +), sex and number of chronic comorbidities (as a categorical variable: 0; 1/2; 3/4; 5/6; 7+). Negative binomial regressions were run in order to model the effect of the type of comorbidity on the number of visits to each level of care, after adjusting for the covariates mentioned before. The number of chronic comorbidities was included as a continuous variable in the regression models. Individual GPs may vary in their tendency to admit or refer patients, distorting the studied associations. Thus, cluster-robust standard error terms were included in the models. All analyses were performed using STATA version 12. 3. Results The study population comprised 68,968 persons, which corresponds to all adult patients with type-2 diabetes who met the inclusion criteria. Of these patients, a unique GP identifier was available for 65,716 persons. Patients with no comorbidities were younger (p b 0.001), and there were more women among patients with mental comorbidities (p b 0.001) (Table 2). The mean number of chronic comorbidities was higher in patients with mental comorbidity (5.5) compared to those with discordant physical comorbidity (4.1) or only concordant comorbidity (1.7) (p b 0.001). Within the groups of patients with discordant

A. Calderón-Larrañaga et al. / European Journal of Internal Medicine 26 (2015) 203–210 Table 1 Classification of chronic comorbidities of diabetes by type of comorbidity.

Table 1 (continued) Chronic comorbidities of diabetes

Chronic comorbidities of diabetes

%

Concordant Discordant Mental

Hypertension Disorders of lipid metabolism Degenerative joint disease Obesity Varicose veins of lower extremities Depression Cataract, aphakia Glaucoma Hypothyroidism Dermatitis and eczema Osteoporosis Cardiac arrhythmia Cerebrovascular disease Emphysema, chronic bronchitis, COPD Ischemic heart disease Prostatic hypertrophy Deafness, hearing loss Iron deficiency, other deficiency anaemia Congestive heart failure Low back pain Surgical aftercare Cardiovascular disorders, other Asthma Acute myocardial infarction Neurologic disorders, other Diabetic retinopathy Gout Dementia and delirium Blindness Peripheral neuropathy, neuritis Anxiety, neuroses Chronic ulcer of the skin Other endocrine disorders Hematologic disorders, other Cardiac valve disorders Psoriasis Respiratory disorders, other Thrombophlebitis Renal calculi Diverticular disease of colon Substance use Autoimmune and connective tissue diseases High impact malignant neoplasms Cervical pain syndromes Malignant neoplasms, prostate Schizophrenia and affective psychosis Chronic renal failure Disease of hair and hair follicles Malignant neoplasms of the skin Irritable bowel syndrome Chronic liver disease Peripheral vascular disease Malignant neoplasms, colorectal Parkinson's disease Malignant neoplasms, breast Malignant neoplasms, bladder Retinal disorders Seizure disorder Kyphoscoliosis Utero-vaginal prolapse Generalized atherosclerosis Gastroesophageal reflux Nephritis, nephrosis Sleep apnoea Behaviour problems Disorders of the immune system Tuberculosis infection Personality disorders Inflammatory bowel disease Renal disorders, other Cardiomyopathy Paralytic syndromes, other

59.32 41.40 20.43 18.48 12.35 11.61 11.00 8.40 7.82 7.81 7.42 7.28 7.16 6.83 6.75 6.65 5.73 4.94

X X

4.65 4.61 4.26 4.22 4.07 4.05 3.95 3.44 3.29 3.27 3.07 2.88 2.75 2.72 2.55 2.44 2.30 2.23 2.07 2.06 2.03 2.02 1.76 1.55 1.46 1.42 1.35 1.33 1.30 1.25 1.22 1.19 1.11 1.10 1.10 1.07 0.98 0.89 0.83 0.80 0.79 0.78 0.72 0.71 0.57 0.51 0.47 0.43 0.41 0.40 0.39 0.36 0.36 0.36

205

X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X

Chronic cystic disease of the breast Pulmonary embolism Malignant neoplasms, kidney Malignant neoplasms, lung Congenital anomalies of limbs, hands, and feet Haemolytic anaemia Low impact malignant neoplasms Prostatitis Malignant neoplasms, lymphomas Developmental disorder Acute leukaemia Transplant status Aortic aneurysm Congenital heart disease HIV, AIDS Arthropathy Deep vein thrombosis Chromosomal anomalies Malignant neoplasms, stomach Inherited metabolic disorders Chronic pancreatitis Multiple sclerosis Malignant neoplasms, cervix, uterus Attention deficit disorder Haemophilia, coagulation disorder Malignant neoplasms, pancreas Aplastic anaemia Malignant neoplasms, liver and biliary tract Quadriplegia and paraplegia Spinal cord injury/disorders Tracheostomy Muscular dystrophy Lactose intolerance Vesicoureteral reflux Malignant neoplasms, ovary Malignant neoplasms, oesophagus Endometriosis Hypospadias, other penile anomalies Cerebral palsy Cleft lip and palate Cystic fibrosis Congenital hip dislocation

%

Concordant Discordant Mental

0.35 0.33 0.27 0.26 0.26

X X X X X

0.25 0.23 0.22 0.22 0.19 0.17 0.14 0.14 0.13 0.12 0.12 0.11 0.10 0.10 0.10 0.10 0.09 0.08 0.07 0.07 0.06 0.05 0.04

X X X X X X X X X X X X X X X X X X X X X X

0.03 0.03 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00

X X X X X X X X X X X X X X

X

X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X

and mental comorbidity, 85.3% and 87.1% had at least one concordant comorbidity and 2.8% and 1.3% had over three concordant comorbidities respectively. Individuals with no comorbidities showed a lower mean number of visits regardless of the health care setting (Table 2). On average, a higher use of hospital and emergency care was observed among those with mental comorbidity (Table 3). When comparing this group of patients with those with only concordant comorbidity, the mean number of hospital admissions and the average length of stay (in days) were 25% and 47% higher among the former, respectively (p b 0.001). The incidence rate ratios (IRR) for the number of visits to the emergency room (both total and priority visits) were also high and statistically significant (IRR: 1.26 and 1.30, p b 0.001) for the group of patients with mental comorbidity. The highest IRRs for specialised care use were estimated for diabetic patients with discordant physical comorbidities (Table 3), regarding both the total number of visits (IRR: 1.38, p b 0.001) and the number of visits to different specialties (IRR: 1.36, p b 0.001). This is further confirmed in Fig. 1. Regarding the number of GP visits, differences were lower but still significant both for persons with discordant physical comorbidity (IRR: 1.08, p b 0.001), but especially for those with at least one mental comorbidity (IRR: 1.17, p b 0.001). Hardly any differences were observed concerning the number of visits to primary care nurses.

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Table 2 Use of services by diabetic patients with multimorbidity in 2011, according to type of comorbidity. No comorbidities

Concordanta

Discordantb

Mentalc

6,871 (10.0%)

10,938 (15.9%)

38,111 (55.3%)

13,048 (18.9%)

Demographic and clinical information 15–44 years, % 45–64 years, % 65+, % Women, % Mean # comorbidities

16.5 36.1 47.4 41.2 –

4.9 42.3 52.9 32.8 1.7

3.1 23.4 73.5 47.5 4.1

3.2 23.1 69.8 63.6 5.5

Use of primary care # patients with at least 1 visit to GP (%) Mean # visits to GP (95% CI) # patients with at least 1 visit to nurse (%) Mean # visits to nurse (95% CI)

4825 (70.2) 8.0 (7.3–8.6) 3984 (58.0) 5.1 (4.7–5.5)

10,140 (92.7) 12.6 (11.4–13.7) 9528 (87.1) 13.0 (10.6–15.5)

36,511 (95.8) 13.2 (12.8–13.6) 35,164 (92.3) 11.2 (10.9–11.4)

12,437 (95.3) 14.3 (13.8–14.7) 11,948 (91.6) 11.1 (10.7–11.5)

Use of specialised care # patients with at least 1 visit to specialists (%) Mean # visits (95% CI) Mean # specialties (95% CI)

3883 (56.5) 3.0 (2.9–3.2) 1.4 (1.4–1.5)

6398 (58.5) 4.3 (4–4.6) 2.1 (2–2.2)

29,504 (77.4) 5.3 (5.1–5.4) 2.4 (2.4–2.5)

10,178 (78.0) 5.0 (4.9–5.2) 2.3 (2.2–2.3)

Use of hospital care # patients with at least 1 admission (%) Mean # admissions/10 ind. (95% CI) # patients with at least 1 unplanned admission (%) Mean # unplanned admissions/10 ind. (95% CI) Mean # hospital days

815 (11.9) 1.4 (1.2–1.5) 606 (8.8) 0.9 (0.7–1.0) 1.3 (1.1–1.5)

891 (8.2) 2.2 (1.6–2.7) 576 (5.3) 1.4 (0.9–1.9) 2.2 (1.5–2.9)

5611 (14.7) 2.0 (1.9–2.1) 3470 (9.1) 1.2 (1.1–1.2) 1.7 (1.6–1.8)

2336 (17.9) 2.2 (2.1–2.4) 1641 (12.6) 1.4 (1.3–1.5) 2.1 (1.9–2.3)

Use of emergency care # patients with at least 1 visit to ER (%) Mean # visits/10 ind. (95% CI) # patients with at least 1 priority visit to ER (%) Mean # priority visits/10 ind. (95% CI)

1488 (21.7) 3.2 (2.9–3.4) 1069 (15.6) 2.0 (1.8–2.2)

1976 (18.1) 3.3 (2.7–3.9) 1251 (11.4) 2.2 (1.7–2.8)

10,006 (26.3) 4.3 (4.2–4.5) 6813 (17.9) 2.6 (2.5–2.7)

4272 (32.7) 5.3 (4.8–5.3) 3077 (23.6) 3.4 (3.0–3.4)

N (%)

ER: emergency room. NOTE: All means (except for the mean # comorbidities) are adjusted for age, sex, # of chronic comorbidities (except for the group of “No comorbidities”) and clustering of patients in GPs. a Individuals with only concordant comorbidities. b Individuals with at least one discordant comorbidity, excluding those with mental comorbidities. c Individuals with at least one mental comorbidity.

The lower use of primary, specialised, hospital and emergency care by diabetic patients with concordant comorbidities is clearly depicted in Fig. 1. The increase in health services use – and more specifically in

specialised, hospital and emergency care – with additional discordant physical and mental comorbidities is shown in Fig. 2. 4. Discussion

Table 3 Risk of use of services by diabetic patients, according to type of comorbidity. Incidence rate ratios (IRR): Results of multivariable models of negative binomial regression, adjusted for age, sex, # of chronic comorbidities and clustering of patients in GPs. Concordanta (ref. category)

Discordantb

Mentalc

IRR

95% CI

IRR

95% CI

1 1

1.08⁎⁎ 1.03⁎

1.06 1.00

1.11 1.06

1.17⁎⁎ 1.01

1.14 0.97

1.21 1.04

1 1

1.38⁎⁎ 1.36⁎⁎

1.33 1.32

1.43 1.39

1.30⁎⁎ 1.27⁎⁎

1.25 1.23

1.35 1.31

Use of hospital care Total admissions Unplanned admissions Hospital days

1 1 1

1.17⁎⁎ 1.03 1.13

1.07 0.92 0.99

1.28 1.16 1.29

1.25⁎⁎ 1.21⁎ 1.47⁎⁎

1.12 1.06 1.25

1.39 1.39 1.73

Use of emergency care Total visits Priority visits

1 1

1.12⁎ 1.10⁎

1.05 1.02

1.19 1.19

1.26⁎⁎ 1.30⁎⁎

1.17 1.18

1.35 1.42

Use of primary care Visits to GP Visits to nurse Use of specialised care Total visits Visits to different specialties

IRR = incidence rate ratio; CI = confidence interval; ref. = reference. a Individuals with only concordant comorbidities. b Individuals with at least one discordant comorbidity, excluding those with mental comorbidities. c Individuals with at least one mental comorbidity. ⁎ Significant result at the p b 0.05 level. ⁎⁎ Significant result at the p b 0.001 level.

This study employed the concordant/discordant comorbidity approach defined by Piette et al. [11] to study the use of health care services by diabetic patients. Worth highlighting are the associations observed between higher use of unplanned hospital services and mental comorbidity – especially when more than two mental comorbidities coexist – and between increased use of specialised care and discordant comorbidity. Differences with respect to primary care use were not as prominent. These findings are specially relevant if we consider that the life expectancy of most diabetic patients in developed countries is now higher than ever, leading to the coexistence of multiple chronic diseases associated with ageing [4], and that almost nine in every ten diabetic patients have at least one concomitant chronic illness [23–25]. So far, researchers have avoided the inherent difficulties that the analysis of multimorbidity represents, and have dealt with the problem in relatively simple and fragmented ways. Most indices assessing the level of multimorbidity are based on simple counts of diagnoses, without necessarily attributing any weights to individual diseases [26]. The main disadvantage of a summed measure is that it ignores potentially important interactions between diseases that might differ from their simple sum. Other studies have investigated multimorbidity from the perspective of an index disease (mainly cardiovascular diseases, cancer, musculoskeletal diseases, or diabetes) and most frequently co-existing disease pairs or triads [27]. The central problem behind such a pragmatic approach to diseases selection (i.e. based on their prevalence) is

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ER: emergency room NOTE: All means are adjusted for age, sex and clustering of patients in GPs Fig. 1. Use of services by diabetic patients with multimorbidity in 2011, according to the number of chronic comorbidities and type of comorbidity.

that some important comorbid conditions with a key influence on decision-making by both patients and clinicians could be left out. Other dimensions beyond those described above need to be considered to move forward in multimorbidity research, which improve our understanding of how individual comorbidities impact health outcomes and use of health services. The concordant versus discordant comorbidity approach may respond to such a requirement. In our study, diabetic patients with mental conditions showed higher rates of emergency visits and hospital unplanned admissions than those with concordant or discordant physical comorbidity. We know that there is a bidirectional dose–response effect between mental health problems, such as depressive symptoms, and number of physical health conditions [28], due in part to the broad impact of deprived social environments on different health outcomes [17]. What has been studied to a lesser extent is the differential effect of mental comorbidity on the use, and more specifically, the unplanned use of health services. This

is of major interest not only because of the high and rising costs of emergency visits and unplanned hospital admissions compared to other forms of care, but also because of the disruption it causes to elective health care and to the individuals themselves [29]. In a recent study by Payne et al. [30], they found that patients with mental comorbidity had twice the probability for an unplanned admission in comparison to those with no mental conditions, after adjusting for physical comorbidity burden, deprivation, age and sex. Moreover, according to our results, the co-occurrence of more than two mental health conditions seems to shoot up the number of hospital admissions and visits to the emergency room. This could be related to the previously described additive effect of multiple mental health comorbidities on multimorbidity [31]. Reduced motivation and capacity for self-management of long-term conditions, potential clinical management conflicts and impoverished patient-provider communication could be explaining these results [32].

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ER: emergency room NOTE: All means are adjusted for age, sex, # of chronic comorbidities and clustering of patients in GPs Fig. 2. Use of services by diabetic patients with multimorbidity in 2011, according to the number and type of chornic comorbidity.

One second finding of this study was the strong association between the coexistence of discordant conditions and the higher use of specialised care among diabetic patients, compared to those with only concordant comorbidity. In Spain, where GPs act as gatekeepers of secondary care, the referral of patients to specialists is their exclusive competence. The number of referrals to specialists is expected to rise with increasing morbidity burden, given individuals' higher probability to present health problems that are less common in primary care. However, Starfield et al. [8] demonstrated that numbers are also higher even for comorbidities that are generally in the purview of general practice (i.e. conditions of high prevalence), some of which are likely to have been classified as concordant in the present study. This could not be corroborated in our study but should be investigated in the future, given the acknowledged negative impact of multiple specialists on the management of patients with multimorbidity [33–35]. Last, our study revealed smaller although undeniable differences in the use of primary care by diabetic patients considering their type of comorbidity. A recent primary care study carried out in the Netherlands [6] reported that, although the number of contacts with general practice increased with the number of chronic diseases, this increase was levelled off from a certain number of diseases. According to the authors, the finding of a lower number of contacts per disease in patients with multiple health conditions could be explained by the synergistic management of related diseases both by GPs and patients. However, they

also found a lower than expected number of contacts for disease pairs that were not directly associated. This is also in line with a previous study [36] showing that non diabetes-related comorbidity increases the health care demand as much as diabetes-related comorbidity. In our study, diabetic patients with discordant physical and mental comorbidity had a probability 8% and 17% higher, respectively, for an additional visit to their GP, in comparison with those with only concordant comorbidity. These differences in the findings may derive from the fact that Struijs et al. [36] did not directly compare the effect of concordant versus discordant diabetes comorbidity on health services use, taking the patient group without comorbidity as the reference group instead. Still, in a subsequent analysis, the authors demonstrated that depression, the most prevalent mental health condition in our study, substantially increased GP care, medical specialist care and hospital care [36], which fully corresponds with our findings. 4.1. Implications and future research Although this framework may help to understand the complex relationships between diabetes and comorbid conditions, it still deserves further consideration. To what extent is a higher overall health system use related to quality of care and, most importantly, to patients' health outcomes? The answer is not clear and deserves more research. Patients with more health problems could be receiving better care given their

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higher exposure to health care services and, therefore, their higher chances for intensive disease monitoring and control [37]. In a longitudinal Australian study analysing the impact of comorbid chronic diseases on mortality in older people [38], it was reported that when cardiovascular disease, mental health problem or diabetes was comorbid with arthritis, there is a trend towards increased survival in comparison with the latter conditions alone. The authors hypothesized that this was because patients who had more opportunities to receive care from their GP due to symptomatic diseases such as arthritis were more likely to have comorbid diseases detected and managed [38]. On the other hand, our findings also revealed an increased use of specialist, hospital and emergency care in diabetic patients with discordant (especially mental) comorbidity, which is often undesirable for both the patients and the health system [39]. Moving towards health care services that i) promote patient (instead of disease) centred care, ii) integrate physical and mental healthcare, iii) support crossprovider coordination and communication, and iv) prioritize the social determinants of health, is definitely one of the major barriers that need to be overcome in order to improve the management of patients with discordant physical and mental comorbidities [40]. 4.2. Strengths and limitations The public nature of our health care system and high access of citizens confer an enormous research potential to our clinical and administrative databases, given that selection bias is minimal. By linking data on different health care registrations, we were able to track patients' contacts with the health system across different levels of care. The high number of chronic conditions included in the study enabled considering broader patterns of potential interactions between diabetes and comorbidities. Moreover, morbidity data from primary care electronic medical records was complemented with data registered in the CMBD; an important step towards acquiring a comprehensive picture of patients' morbidity burden. As for the limitations, the following need to be carefully considered. Although the concept of concordance is attractive, its use for comorbidity classification is not free from subjectivity and, moreover, there are no validated lists against which to compare our own taxonomy. Dermatitis, gout and cardiomyopathy are some of the examples for which consensus was not straightforward in our study. Moreover, other key features of comorbid conditions, such as clinical dominance or their symptomatic/asymptomatic nature may also be relevant when studying the impact of comorbidity on diabetes care. We only focused on chronic diseases in order to minimise the risk of under-recording of diagnostic data by GPs. However, the border between chronic and acute is not always clear. As stated by Starfield [39], it is the number of types of conditions – and not chronicity per se – that burdens health systems. We could not adjust by disease severity, neither for diabetes nor coexisting comorbidities, which is likely to affect patients' contact rates with the different care services. It was not possible in this study to consider the chronology of appearance of comorbid disorders, which prevents us from elucidating whether diabetes preceded the comorbidities or vice versa. In the case of unplanned hospital admissions, we did not study the reasons for admission. We can therefore not know whether such admissions where related or not to the effectiveness of primary care. 5. Conclusions In patients with type-2 diabetes, the coexistence of mental comorbidity significantly increases the use of unplanned hospital services, and discordant comorbidities have an important effect on specialised care use. Differences with respect to primary care use are not as notable. Future studies should address whether a higher overall health system use is related to the quality of care and, most importantly, to health outcomes in patients with multimorbidity.

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Conflict of interests None. Acknowledgements This study was funded by grant PI11/01126 from the Carlos III Health Institute. It arises from the Joint Action CHRODIS, which has received funding from the European Union, in the framework of the Health Programme (2008–2013). Sole responsibility lies with the author and the Consumers, Health, Agriculture and Food Executive Agency is not responsible for any use that may be made of the information contained therein. Some of the authors of the present manuscript belong to Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC) and Grupo de Investigación en Servicios Sanitarios (GRISSA). References [1] Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev Mar 23 2011;10(4):430–9. [2] Lehnert T, Heider D, Leicht H, Heinrich S, Corrieri S, Luppa M, et al. Review: health care utilization and costs of elderly persons with multiple chronic conditions. Med Care Res Rev Aug 2011;68(4):387–420. [3] Condelius A, Edberg AK, Jakobsson U, Hallberg IR. Hospital admissions among people 65 + related to multimorbidity, municipal and outpatient care. Arch Gerontol Geriatr Jan 2008;46(1):41–55. [4] Wolff J, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med Nov 11 2002;162(20): 2269–76. [5] Glynn LG, Valderas JM, Healy P, Burke E, Newell J, Gillespie P, et al. The prevalence of multimorbidity in primary care and its effect on health care utilization and cost. Fam Pract Oct 2011;28(5):516–23. [6] van Oostrom SH, Picavet HS, de Bruin SR, Stirbu I, Korevaar JC, Schellevis FG, et al. Multimorbidity of chronic diseases and health care utilization in general practice. BMC Fam Pract Apr 7 2014;15(1):61. [7] Laux G, Kuehlein T, Rosemann T, Szecsenyi J. Co- and multimorbidity patterns in primary care based on episodes of care: results from the German CONTENT project. BMC Health Serv Res 2008;8:14. [8] Starfield B, Lemke KW, Herbert R, Pavlovich WD, Anderson G. Comorbidity and the use of primary care and specialist care in the elderly. Ann Fam Med May 2005;3(3): 215–22. [9] Lash TL, Mor V, Wieland D, Ferrucci L, Satariano W, Silliman RA. Methodology, design, and analytic techniques to address measurement of comorbid disease. J Gerontol A Biol Sci Med Sci Mar 2007;62(3):281–5. [10] Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical diseases. N Engl J Med May 21 1998;338(21):1516–20. [11] Piette JD, Kerr EA. The impact of comorbid chronic conditions on diabetes care. Diabetes Care Mar 2006;29(3):725–31. [12] Laiteerapong N, Huang ES, Chin MH. Prioritization of care in adults with diabetes and comorbidity. Ann N Y Acad Sci Dec 2011;1243:69–87. [13] Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA Aug 10 2005;294(6):716–24. [14] Tinetti ME, Bogardus Jr ST, Agostini JV. Potential pitfalls of disease-specific guidelines for patients with multiple conditions. N Engl J Med Dec 30 2004;351(27):2870–4. [15] Durso SC. Using clinical guidelines designed for older adults with diabetes mellitus and complex health status. JAMA Apr 26 2006;295(16):1935–40. [16] Ciechanowski PS, Katon WJ, Russo JE. Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Arch Intern Med Nov 27 2000; 160(21):3278–85. [17] Smith DJ, Court H, McLean G, Martin D, Langan MJ, Guthrie B, et al. Depression and multimorbidity: a cross-sectional study of 1,751,841 patients in primary care. J Clin Psychiatry Nov 2014;75(11):1202–8. [18] Garcia-Armesto S, Begona Abadia-Taira M, Duran A, Hernandez-Quevedo C, BernalDelgado E. Spain: health system review. Health Syst Transit 2010;12(4):1–295. [19] Lamberts H, Wood M. ICPC: International Classification of Primary Care. Oxford: Oxford University Press; 1987. [20] Información y Estadísticas Sanitarias—Ministerio de Sanidad y Consumo. Clasificación Internacional de Enfermedades, 9ª Revisión, Modificación Clínica; 2008. [21] Johns Hopkins University—Bloomberg School of Public Health. The Johns Hopkins University ACG case–mix system. Available at: http://acg.jhsph.org/. [22] Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract Jan 2011;61(582):e12–21. [23] Teljeur C, Smith SM, Paul G, Kelly A, O'Dowd T. Multimorbidity in a cohort of patients with type 2 diabetes. Eur J Gen Pract Mar 2013;19(1):17–22. [24] Druss BG, Marcus SC, Olfson M, Tanielian T, Elinson L, Pincus HA. Comparing the national economic burden of five chronic conditions. Health Aff (Millwood) Nov 2001; 20(6):233–41.

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