14 European Journal of Public Health ......................................................................................................... The European Journal of Public Health, Vol. 27, No. 1, 14–19 ß The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. doi:10.1093/eurpub/ckw181 Advance Access published on 10 October 2016 ......................................................................................................... Changes in diabetes care introduced by a Chronic Care Model-based programme in Tuscany: a 4-year cohort study Francesco Profili1, Irene Bellini2, Alfredo Zuppiroli1, Giuseppe Seghieri1, Fabio Barbone3,4, Paolo Francesconi1 Regional Health Agency of Tuscany, Florence, Italy Medical Specialisation School of Hygiene and Preventive Medicine, Florence, Italy Department of Medical Sciences, University of Trieste, Trieste, Italy Department of Medical and Biological Sciences, University of Udine, Udine, Italy Correspondence: Francesco Profili, Regional Health Agency of Tuscany, via Pietro Dazzi 1, 50141 Firenze, Italy, Tel: +393357757645; e-mail: francesco.profili@ars.toscana.it Background: In 2010, Tuscany (Italy) implemented a Chronic Care Model (CCM)-based programme for the management of chronic diseases. The study’s objective was to evaluate its impact on the care of patients with type 2 diabetes. Methods: A population-based cohort study was performed on patients with diabetes, identified by an administrative data algorithm, exposed to a CCM-based programme versus patients not exposed (8486 patients in each group). The groups were matched using a propensity score approach and observed from 2011 to 2014. The outcomes measured were: mortality rate and hazard ratio (HR), hospitalisation incidence rate (IR) (all causes and diabetes-related diseases) and incidence rate ratio (IRR), and Guideline Composite Indicator (GCI) as proxy of adherence to guidelines (IR and IRR). Stratified Cox regression analysis and conditional fixed effect Poisson regression analyses were performed to compute HR and IRR. Results: A significant improvement was observed for GCI (IRR 1.58; 95% CI 1.53–1.62) and for cardiovascular long-term complications (IRR 1.11; 95% CI 1.04–1.18). A protective effect was observed for neurological long-term complications (IRR 0.85; 95% CI 0.76–0.95), acute cardio-cerebrovascular long-term complications—stroke and ST segment elevation myocardial infarction— (IRR 0.81; 95% CI 0.71–0.92) and mortality (HR 0.88; 95% CI 0.81–0.96). Conclusion: The implementation of a CCMbased programme was followed by better management and benefits for the health status of patients. The increase in hospitalisations for cardiovascular long-term complications could engender cost-efficacy issues, but a better integrated care (GPs and specialists) and a more appropriate specialist outpatient services organisation could avoid a part of these, while still maintaining the benefits seen. ......................................................................................................... Introduction uscany is an Italian region with 3.7 million inhabitants, with a Tproportion of old and very old people among the highest in the world, and further increasing. Age-related chronic diseases are therefore very frequent.1 Among chronic diseases, diabetes, a non-communicable disease with a significant impact on the quality of life, health services and costs, is the main cause of morbidity because of its complications.2 In 2013, there were 205 000 diabetics in Tuscany among the population aged >15 years, with a prevalence of 6.4%, with an increasing trend during the last 10 years.3 For the management of chronic diseases, interventions based on Chronic Care Model (CCM) have been widely applied in other countries, such as USA and Northern European countries, and many studies regarding its effectiveness have been published.4–9 Most of them reported an improvement in care quality10–13 and an increased adherence to the standard guidelines11–14 for diabetes, showing a better control of glycated haemoglobin (HbA1c), blood pressure and cholesterol levels.11–21 Other studies evidenced reduced accesses of less urgent cases to emergency departments, a decreased hospitalisation rate, a lower heart disease risk and a higher survival time among patients enrolled.22–25 Tuscany has been applying a CCM-based programme since 2008 (described in more detail below) aimed at better care of chronic patients, among whom diabetic patients are included. It was similar to the experience of patient’s centered medical home (PCMH), where home-dwelling chronic patients are taken in charge by an integrated team with a proactive approach, focused on the improvement of life quality and on the prevention of complications.26–28 The objective of this study was to evaluate the impact on the care of type 2 diabetic patients after 4 years from the start of the CCM-based programme, considering both process (laboratory tests and specialist visits) and outcome indicators (hospitalisation and survival rate). Methods Study design Population-based cohort study, which includes patients with type 2 diabetes (from now on simply diabetes), aged over 15 years, living in Tuscany. Follow-up period went from 1 January 2011 to 12 December 2014. Settings Italy has a tax-based universal health system organised on three levels. The national level has a funding role and dictates the fundamental services, yearly updated, that must be provided to every inhabitant, called LEA (essential levels of assistance). The regional level receives the national funding and organises the health systems autonomously through a network of Local Health Authorities (LHAs). Every inhabitant is entitled to choose a GP, who has a gatekeeper function and a maximum of 1500 patients in charge. Co-payments of some health services might be requested. LHAs are further divided into different health districts, homogeneous for Downloaded from https://academic.oup.com/eurpub/article-abstract/27/1/14/2670163 by guest on 14 June 2020 1 2 3 4 Changes in diabetes care introduced by a Chronic Care Model-based programme in Tuscany some characteristics (e.g. rural vs. urban vs. mountain areas), where primary care is organised. In 2008, the Tuscan Regional Health Ministry launched the ‘Project for proactive health care implementation at community level’,29 based on the CCM30,31 for the management of four chronic diseases: type 2 diabetes, chronic stroke, heart failure and chronic obstructive pulmonary disease. CCM-based programme started on 6 January 2010 with a pilot group (483 GPs out of 2700). Other GPs joined after 2010 reaching 60% in 2014. GP adherence was voluntary throughout all CCM-based programme implementation period. Population  met the diabetes case definition on 1 January 2006 (in order to have a reference to the previous period of disease care);  were still alive on 4 January 2011 (in order to reduce a source of selection bias due to the possibility that patients with a higher short-term death risk were not enrolled in CCM-based programme);  had not been changing GP or LHA of residence since 2006 (in order to reduce exposure misclassification). All patients assisted by GPs who joined the CCM-based programme after 2010 were excluded because the lists of enrolled patients were not available. Three LHAs out of 12 did not send lists of enrolled patients, so analyses were restricted to the remaining 9 LHAs, with 2 573 902 inhabitants older than 15 years, corresponding to 79% of the Tuscan population. Data sources Administrative data from the regional health database were used: hospital discharges, drug prescriptions, diagnostic procedures and referral visits, disease-specific exemptions from co-payment to health care, mortality and registry office database. LHAs regularly updated the list of the GPs adhering to the CCMbased programme and sent the list of patients enrolled during the first 2 months (April–May 2010) to consent to monitoring at regional level. Patients have a unique personal identification code anonymised, according to the privacy law, but allowing record-linkage to be carried out between administrative data and LHAs enrolled lists. Measures Enrolment in CCM-based programme is the exposure variable. The presence of patients in the lists of enrolled patients identifies exposure status. Linking cohort patients with CCM-enrolled patients resulting from LHA reports we defined our exposure groups as follows (patients in enrolled lists are all assisted by GPs that joined the CCM-based programme):  notCCM, diabetic patients assisted by GPs that never joined the CCM-based programme;  enrolled CCM, patients linked with enrolled lists of GPs that joined the CCM-based programme;  not enrolled CCM, patients assisted by GPs who joined the CCM-based programme, but not linked with enrolled lists (observed only in preliminary analysis to evaluate selection bias and then excluded). The CCM-based programme involves a shifting from a reactive to a proactive approach, focusing on the maintenance of health with multi-professional interventions, characterised by: multi-professional teams that manage the patient, active role of patients and health education programmes, personalised evidence-based therapeutic plan for each patient, scheduled follow-up, shared clinical information, implementation of primary prevention. The adherence of GPs to the project was voluntary. GPs chose patients to be enrolled in the project and asked them for their informed consent. Consequently, not every patient assisted by a GP that joined the CCM-based programme was enrolled. The pivotal unit of the implementation was a specific clinical team, including from 5 to up to 15 GPs, a nurse and a health worker. Each team had 10 000 patients. The activity was carried out in the main practice. Health professionals had to adopt a predefined follow-up protocol for each disease. Diabetes patients were periodically monitored for: blood pressure, waist circumference, Hb1Ac, cholesterol, electrolytes, urine, blood glucose and microalbuminuria, BMI, lifestyle, eating habits and adherence to the therapy, foot, cardiovascular, ocular and neurological complications. Desired parameters were personalised in order to assure monitoring of diabetes, and prevent its evolution. The nurse was responsible for data updates, contacting and helping patients for routine services, and carrying out the detection of clinical parameters. Team activities were supported by the appropriate use of electronic health records. As outcomes, the study measured:  The Guideline Composite Indicator (GCI), which is a proxy of fair adherence to follow-up guidelines,33 included an annual assessment of glycated haemoglobin and at least two assessments among eye examinations, total serum cholesterol and microalbuminuria. The GCI indicator was estimated as the number of years in which a patient has done assessment of glycated haemoglobin and at least two assessments among eye examinations, total serum cholesterol and microalbuminuria on total person-years.  General Hospitalisations rate.  Hospitalisations rate for:  uncontrolled diabetes;  short-term diabetes complications (ketoacidosis, hyperosmolarity, coma, hypoglycaemia);34  long-term diabetes complications;35  cardiovascular complications (hypertension, ischemic and other hearth disease, atherosclerosis, aneurysms, embolism, thrombosis, hypotension, ulcers, chest pain);  neurological complications (peripheral autonomic neuropathy, disorders of the peripheral nervous system, cerebrovascular disease, diabetic arthropathy, radiculopathy, diabetic bone diseases);  ophthalmic complications (disorders of the eye, adnexa);  renal diseases (nephritis, nephritic syndrome, nephrosis, diseases of urinary system, proteinuria, albuminuria);  endocrine/metabolic effects (endocrine, metabolic and immunity disorders);  amputations of lower extremities;  acute cardio-cerebrovascular complications (stroke, ST segment elevation myocardial infarction). All ordinary admissions, excluding long-term care and rehabilitation settings, were considered for hospitalisation rates. Specific primary diagnoses and surgery procedure codes selected to define diabetes complications are shown in Table A of Supplementary Data. All long-term complications subgroups are mutually exclusive, except acute cardio-cerebrovascular group that contains specific diagnoses: ST segment elevation myocardial infarction from cardiovascular and stroke from neurological complications.  All causes mortality rate. Other variables measured were: age (at 1 January 2011), gender, LHA of residence, number of different therapeutic/pharmacological Downloaded from https://academic.oup.com/eurpub/article-abstract/27/1/14/2670163 by guest on 14 June 2020 According to an algorithm based on administrative data from the regional health database,32 patients were considered affected by diabetes in case of any of the following criteria: oral antidiabetics or insuline assumption twice during the preceding year, hospital discharge with primary or secondary diagnosis of diabetes, and/or co-payment exemption due to a diabetes diagnosis. All patients selected for the study met the following inclusion criteria: 15 16 European Journal of Public Health Table 1 Baseline matching characteristics of patients, before and after propensity score matching Characteristics Patients before matching Not CCM/not exposed CCM Enrolled/exposed CCM not enrolled Total n:47 277 n:8574 n:5442 n:61 293 % % % % Pa 9.0 11.5 21.2 28.8 29.5 5.2 10.5 24.6 34.3 25.3 <0.001 2.6 9.9 27.4 38.5 21.6 2.6 10.1 27.5 38.3 21.5 0.997 45.7 54.3 49.5 50.5 <0.001 51.4 48.6 51.5 48.5 0.927 28.3 26.9 21.3 23.5 20.0 28.4 26.1 25.5 <0.001 16.2 31.2 27.4 25.3 16.2 31.2 27.5 25.2 0.996 12.3 6.6 9.9 22.7 74.7 4.2 11.8 5.7 9.9 23.5 78.8 2.9 0.007 10.5 <0.001 4.6 0.994 9.1 0.109 23.9 0.001 80.7 <0.001 1.6 10.7 4.8 9.7 24.0 80.6 1.8 0.653 0.636 0.237 0.815 0.892 0.439 a: Pearson’s chi square P value. subgroups drugs level 3 Anatomical Therapeutic Chemical (ATC3) classification taken during last year, all chronic diseases detectable from administrative data (chronic hearth failure, previous stroke, dementia, chronic obstructive pulmonary disease  COPD, chronic ischemic hearth disease, hypertension). Statistical analyses Descriptive analyses and Pearson’s chi square tests were performed to evaluate differences between patients at baseline (enrolment period) for possible confounding factors, then exposed (enrolled CCM patients) and unexposed (not-CCM patients) groups were matched using a propensity score approach (logit model, 1:1 nearest neighbour matching, caliper: 0.02).36,37 Age, gender, LHA of residence, number of different ATC3 taken during the preceding year and other chronic diseases at baseline were included in the propensity score because a priori counfounders, confirmed by preliminary analyses. Crude incidence rates (IR) and incidence rate ratio (IRR), for hospitalisation and GCI outcomes, were estimated for the preceding period (2006–9). Crude mortality rates and incidence rates for hospitalisation and GCI outcomes were, by contrast, estimated in the follow-up period. After matching a conditional (on matching pairs) fixed effect Poisson regression model (robust standard errors) was performed to estimate IRR for hospitalisation and GCI outcomes, adjusting for individual outcome values measured previously (2006–9).37 The Poisson regression model was chosen to model count data and take into account person years of observation. Hazard ratio (HR) of mortality was estimated using stratified Cox regression (robust standard errors).38 Finally, stratified analyses for type of admission (planned or urgent) were also implemented for hospitalisation outcomes whenever possible. Short-term complications, uncontrolled diabetes and acute cardio-cerebrovascular complications were excluded from stratified analyses because they were, by definition, urgent hospitalisations. All analyses were performed using STATA 12. Results Overall, 61 293 prevalent diabetes patients on 1 January 2006, living in one of nine Tuscan LHAs in study, being alive at 4 January 2011, who never changed GP or LHA after 2006, were identified. GPs who joined the CCM-based programme assisted 14 016 diabetes patients, among which 8574 (61.2%) were enrolled (enrolled CCM group or exposed), and 5442 were not (notenrolled CCM group). About 47 277 patients were assisted by a GP who did not join CCM-based programme (not-CCM group or not exposed). Considering firstly the comparability of these three groups, significant differences were found regarding age, gender, comorbidities and drug prescriptions (table 1). There were also geographical differences (LHA of residence) due to different CCM-based programme diffusion level in each LHA (data not shown). Death risk during the follow-up period, adjusted for age, was 5.6% (95% CI 5.5–5.7%) in the not-CCM group, and 5.8% (95% CI 5.4–6.1%) in the not-enrolled CCM group. After propensity score matching was conducted between exposed and not exposed in order to be able to compare groups, data were restricted to 8486 patients in each group, accounting for 99% of the original CCM group and 18% of original not-CCM group (table 1). Matched groups showed some differences in GCI value and hospitalisations for diabetes complications during the 2006–9 period (table 2). Results in follow-up period analysis revealed an important increase of GCI in exposed (46.2%) compared with not exposed (28.8%), resulting in an IRR of 1.58 (95% CI 1.53–1.62, P < 0.001). Exposure to CCM-based programme did not modify all causes hospitalisation (table 3), incidence rates were nearly 273 per 1000 person years in both groups. Type of admission (planned or urgent) did not significantly change either, but a trend toward the increase in planned hospitalisations was observable (table 4). A significant reduction was observed only in urgent hospitalisation for neurological long-term complications, IRR of 0.80 (95% CI 0.69–0.94). Focusing on the long-term causes, hospitalisations for cardiovascular long-term complications increased with an IRR of 1.11 (95% CI 1.04–1.18). Downloaded from https://academic.oup.com/eurpub/article-abstract/27/1/14/2670163 by guest on 14 June 2020 Age <50 5.3 2.6 50–59 10.5 10.0 60–69 24.6 27.3 70–79 34.2 38.3 80+ 25.4 21.9 Gender Male 49.6 51.5 Female 50.4 48.5 Number of different ATC3 drug codes taken during last year <4 19.8 16.2 4–6 28.1 31.0 7–9 26.4 27.5 10+ 25.7 25.3 Prevalence of other chronic diseases COPD 11.9 10.9 Previous stroke 5.8 4.8 Hearth failure 9.9 9.9 Cardiopathy 23.5 24.2 Hypertension 78.9 80.7 Dementia 3.0 1.9 Matched 1:1 groups Not CCM/not exposed CCM Enrolled/exposed n:8486 n:8486 % % Pa 17 Changes in diabetes care introduced by a Chronic Care Model-based programme in Tuscany Table 2 Crude incidence rates and incidence rate ratiosa (CCM vs. not CCM) during 2006–9 Outcomes CCM IR 21.2 25.3 174.4 79.2 1.5 6.2 71.6 5.7 13.3 50.0 0.3 1.4 0.7 7.5 173.8 84.3 1.5 6.1 76.7 4.2 14.0 55.5 0.3 2.1 0.7 8.4 IRR (95% CI) P 1.19 (1.16–1.23) <0.001 0.99 1.06 1.00 0.98 1.07 0.73 1.05 1.11 1.00 1.45 1.00 1.12 (0.96–1.03) (1.01–1.12) (0.68–1.47) (0.81–1.19) (1.01–1.13) (0.59–0.91) (0.92–1.19) (1.04–1.18) (0.43–2.31) (1.01–2.09) (0.57–1.76) (80.94–1.32) 0.847 0.020 1.000 0.845 0.013 0.004 0.470 0.002 1.000 0.045 1.000 0.198 a: Incidence rate ratios, 95% confidence intervals and statistical significance were estimated using Poisson regression analysis. Table 3 Cases, crude incidence rates (per 1000 person-years) and adjusted incidence rate ratiosa,b (CCM vs. not-CCM), during follow-up Hospitalisation Not CCM N (IR) CCM N (IR) IRR (95% CI) P All diagnosis Total diabetes complications Short-term complications Uncontrolled diabetes Total long-term complications Renal Neurological Cardiovascular Endocrinal/metabolic Ophthalmic Amputations Acute cardio-cerebrovascular 8566 (273.2) 3750 (119.6) 78 (2.5) 464 (14.8) 3210 (102.4) 415 (13.2) 668 (21.3) 1989 (63.4) 17 (0.5) 58 (1.8) 62 (2.0) 492 (15.7) 8649 (273.4) 4039 (127.7) 98 (3.1) 489 (15.5) 3459 (109.4) 415 (13.1) 628 (19.9) 2275 (71.9) 8 (0.3) 61 (1.9) 72 (2.3) 435 (13.8) 0.98 1.03 1.32 1.06 1.03 0.96 0.85 1.11 0.43 1.06 1.10 0.81 0.269 0.194 0.093 0.361 0.268 0.631 0.005 0.001 0.063 0.769 0.608 0.002 (0.95–1.01) (0.98–1.08) (0.96–1.81) (0.93–1.21) (0.98–1.08) (0.84–1.11) (0.76–0.95) (1.04–1.18) (0.18–1.05) (0.73–1.54) (0.77–1.56) (0.71–0.92) a: Incidence rate ratios were adjusted for individual outcome values measured during 2006–9. b: Incidence rate ratios, 95% confidence intervals and statistical significance were estimated using conditional fixed effect Poisson regression analysis (robust standard errors). Instead, protective effects for neurological long-term complications risk, IRR of 0.85 (95% CI 0.76–0.95), and for acute cardiocerebrovascular long-term complications (stroke and ST segment elevation myocardial infarction), IRR of 0.81 (95% CI 0.71–0.92), were observed. A reduction in mortality during the follow-up period (starting from 4 January 2011) was registered in the CCM group (mortality rate 4.2%) compared to the not-CCM group (4.6%). The HR between CCM and not-CCM matched groups was 0.88 (95% CI 0.81–0.96, P 0.003). Discussion In Tuscany, implementing the CCM-based programme produced significant impacts on the care of patients with diabetes. Enrolled patients were much more likely to be followed up according to clinical recommendations, all diagnoses hospitalisation rates did not change significantly, though there was an increase in hospitalisation due to chronic cardiovascular complications and a decrease in neurological and acute cardio-cerebrovascular complications. There was an almost significant increase in planned but not in urgent hospitalisation for total long-term complications, a decrease in neurological complications was observed only for urgent admissions while, on the contrary, the increase in chronic cardiovascular complications was more pronounced and significant for planned admissions. Mortality decreased significantly. The increase in GCI value confirmed the efficacy of the more proactive approach of the CCM-based programme. This result is coherent with other studies on CCM-based programmes conducted both in USA and Europe.11–14,20–26 Considering GCI as a proxy of adherence to guidelines, we can assume that a patient enrolled in a CCM-based programme has a probability of 58% higher to be treated according to the guidelines. The interpretation of results pertaining to hospitalisation is more complex. The lack of expected changes in hospitalisation rates for all causes is coherent with some studies on hospitalisation in diabetes patients conducted in PCMH,31 but differs from the findings of other studies, which detected a reduction in hospitalisation or emergency department accesses.27–30 Other experiences of primary care intervention, indeed, showed an increased hospitalisation risk for disease-related diagnoses.39,40 Comparability with other studies can be limited by use of different diagnosis criteria and admission type. The higher risk of admission for cardiovascular long-term complications observed could be due to an initial screening effect of the CCM-based programme. Indeed, an increase in diagnoses of complications in patients with diabetes, undetected in the past, may have been determined through a proactive behaviour by general practitioners who adhered to the CCM-based programme which may have induced additional physical examinations, laboratory analyses, a general greater attention to patient conditions, and consequently a higher hospitalisation rate for cardiovascular long-term Downloaded from https://academic.oup.com/eurpub/article-abstract/27/1/14/2670163 by guest on 14 June 2020 GCI (% person-years) Hospitalisations (per 1000 person-years) All diagnosis Total diabetes complications Short-term complications Uncontrolled diabetes Total long-term complications Renal Neurological Cardiovascular Endocrinal/metabolic Ophthalmic Amputations Acute cardio-cerebrovascular Not CCM IR 18 European Journal of Public Health Table 4 Adjusted incidence rate ratiosa,b (CCM vs. not-CCM) during follow-up, stratified for type of admission Hospitalisation All diagnosis Total long-term complications Renal Neurological Cardiovascular Endocrinal/metabolic Ophthalmic Amputations Planned admission IRR (95% CI) P Urgent admission IRR (95% CI) P 1.06 1.13 1.21 1.05 1.16 0.72 1.15 1.21 0.139 0.053 0.405 0.749 0.053 0.755 0.555 0.550 0.94 0.99 0.90 0.80 1.09 0.37 0.53 0.99 0.085 0.782 0.349 0.005 0.121 0.114 0.296 0.982 (0.98–1.15) (0.99–1.29) (0.77–1.91) (0.79–1.38) (1.00–1.35) (0.09–5.79) (0.73–1.82) (0.65–2.26) (0.89–1.01) (0.90–1.08) (0.72–1.13) (0.69–0.94) (0.98–1.22) (0.11–1.27) (0.16–1.74) (0.50–1.97) complications. What we have found, however, was that this increased admission rate for cardiovascular diseases (ischemic heart disease, heart failure, hypertension) was accompanied by a significant decrease in acute cardio-cerebrovascular long-term diseases. CCM-based programme patients showed, indeed, a reduced risk of stroke and ST segment elevation myocardial infarction. This could be, in our opinion, linked with a greater attention by GPs, who improved their awareness of patients’ clinical conditions which subsequently resulted into a greater hospitalisation rate. This was also suggested by the observation that there was an almost significant increase in planned but not in urgent hospitalisation for total long-term complications, and that the increase in chronic cardiovascular complications was more pronounced and significant for planned than for urgent admissions. In addition, the reduced risk of stroke could be due to the fact that prevention of acute cardio-cerebrovascular disease requires a multifaceted clinical attention to several risk factors (i.e. hypertension, atrial fibrillation and heart failure) which may be cumulatively better addressed by GPs with a more proactive aptitude. This hypothesis finds moreover a plausible explanation in a sort of ‘paradox’ since our data indicate that, even if hospitalisations for cardiovascular long-term complications significantly rise across the follow-up period, results of survival analysis show a significant reduction in mortality rates among the CCM-enrolled patients, consistent with reduction in stroke and myocardial infarction rates. Other studies showed similar results with different follow-up periods,29,30 showing the beneficial effects on health status by a proactive action of GPs. In other words, these results suggest that hospitalisation cannot be regarded as a systematically negative outcome. It was likely that some hospital admissions were life-saving, improving the probability of survival by a reduction of more catastrophic events. In addition, the differences between planned and urgent admission, although not statistically significant, suggest a trend toward the increase in controlled and planned hospitalisations, and thus suggesting that such admissions could have been agreed by GPs with hospital specialists. Limitations and strengths of the study Study limitation may depend on the comparability of enrolled and not-enrolled patients. Firstly, descriptive analyses showed a plausible selection bias due to an opportunistic enrolment of patients by GPs during the first 2 months. Probably GP-enrolled patients visited more frequently, excluding highly compromised patients with more chronic diseases and older than 80 years. Besides, groups showed significant differences in outcomes already before 2010. Secondly, these differences could also depend on unobservable characteristics of GPs and different attitudes in treatment compliance of patients. Propensity score matching and adjustment of each indicator for individual outcome of the previous period was implemented to reduce potential selection bias and place groups at a comparable level, but the exclusive use of administrative data and the lack of clinic information regarding the real health status of patients could represent a limit. Residual uncontrolled bias could be detected through a direct observation of medical records. Even if with the limitation of a retrospective observational design, the main strength of the study was consistent with the availability of a large database from a region with a homogeneous primary care delivery system, allowing researchers to follow up with sufficient affordability robust outcomes such as mortality or reduced risk of acute cardiovascular events from a real world’s scenario. Conclusions The implementation of a regional CCM-based programme for patients with diabetes was followed by a greater adherence to guidelines, a lower risk of acute cardio-cerebrovascular events and by improved survival. This positive scenario for health status of patients could engender cost-efficacy issues in the future, also due to the observed increase in hospitalisations for cardiovascular longterm complications. Certainly, cost-effectiveness studies should be implemented. It is likely that more positive effects on hospitalisations and cost savings will be observed as soon as the described screening effect is reduced. In the same way, better integrated care (GPs and specialists) of patients at community level and more appropriate specialist outpatient services organisation could reduce a part of observed hospitalisation, maintaining the benefits seen for acute cardio-cerebrovascular events and survival. Key points  The evaluation of the CCM for diabetes management in Tuscany revealed:  an improvement in adherence to diabetes guidelines;  a decrease of hospitalisation for acute cardio-cerebrovascular hospitalisation (stroke and ST segment elevation myocardial infarction) and an improved survival. Funding The authors belong to the Regional Health Agency of Tuscany, funded by Regional Health Authority of Tuscany. One author, belonging to the University of Trieste (Italy), has collaborated as advisor. Downloaded from https://academic.oup.com/eurpub/article-abstract/27/1/14/2670163 by guest on 14 June 2020 a: Incidence rate ratios were adjusted for individual outcome values measured during 2006–9. b: Incidence rate ratios, 95% confidence intervals and statistical significance were estimated using conditional fixed effect Poisson regression analysis (robust standard errors).. Changes in diabetes care introduced by a Chronic Care Model-based programme in Tuscany Supplementary data Supplementary data are available at EURPUB online. Conflicts of interest: None declared. References 1 Italian National Institute of Statistics (Istat): demographic indicators 2014 and historical trends: Available at: http://demo.istat.it/altridati/indicatori/index.html (11 January 2015, date last accessed). 19 20 Sunaert P, Bastiaens H, Nobels F, et al. Effectiveness of the introduction of a Chronic Care Model-based program for type 2 diabetes in Belgium. BMC Health Serv Res 2010;10:207. 21 Pilleron S, Pasquier E, Boyoze-Nolasco I, et al. Participative decentralization of diabetes care in Davao City (Philippines) according to the Chronic Care Model: a program evaluation. Diabetes Res Clin Pract 2014;104:189–95. 22 Chiou SJ, Campbell C, Horswell R, et al. Use of the emergency department for lessurgent care among type 2 diabetics under a disease management program. BMC Health Serv Res 2009;9:223. 23 Stock S, Drabik A, Büscher G, et al. German diabetes management programs improve quality of care and curb costs. Health Aff (Millwood) 2010;29:2197–205. 2 Wilson PW. Diabetes mellitus and coronary heart disease. Am J Kidney Dis 1998;32:S89–100. 3 National Institute of Statistics (Istat), Italy: Available at: http://www.dati.stat.it (Accessed 24 October 2015). 4 Coleman K, Austin BT, Brach C, et al. Evidence on the chronic care model in the new millennium. Health Aff (Millwood) 2009;28:75–85. 26 Jackson GL, Powers BJ, Chatterjee R, et al. The patient-centered medical home: a systematic review. Ann Intern Med 2013;158:169–78. 5 Renders CM, et al. Interventions to improve the management of diabetes in primary care, outpatient, and community settings: a systematic review. Diabetes Care 2001;24:1821–33. 27 Carlos RJ, Ferrer RL, Miller WL, et al. Patient outcomes at 26 months in the patientcentered medical home national demonstration project. Ann Fam Med 2010:8(Suppl 1):S57–67. 6 Pimouguet C, Le Goff M, Thiébaut R, et al. Effectiveness of disease-management programs for improving diabetes care: a meta-analysis. CMAJ 2011;183(2):E115–E127. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3033953/ 7 Norris SL, Nichols PJ, Caspersen CJ. The effectiveness of disease and case management for people with diabetes: a systematic review. Am J Prevent Med 22(Suppl 1):15–38. Available at: http://www.sciencedirect.com/science/article/pii/ S0749379702004233. 28 Grumbach K, Grundy P. Outcomes of implementing patient centered medical home interventions: a review of the evidence from prospective evaluation studies in the United States. [Internet]. Washington (DC): Patient-Centered Primary Care Collaborative; [updated 2010 Nov 16]. Available at: http://forwww.pcpcc.net/files/ evidence_outcomes_in_pcmh_2010.pdf. 9 Knight K, Badamgarav E, Henning JM. A systematic review of diabetes disease management programs. Am J Manag Care 2005;11:242–50. Available at: http://www. ajmc.com/journals/issue/2005/2005-04-vol11-n4/Apr05-2012p242-250. Egginton JS, Ridgeway JL, Shah ND. Care management for Type 2 diabetes in the United States: a systematic review and meta-analysis. BMC Health Serv Res 2012;Mar 22:12–72. Available at: http://bmchealthservres.biomedcentral.com/ articles/10.1186/1472-6963-12-72. 10 Steffelson M, Dipnarine K, Stopka C. The chronic care model and diabetes management in US primary care settings: a systematic review. Prev Chronic Dis 2013;10:E26. 11 Stock S, Pitcavage JM, Simic D, et al. Chronic care model strategies in the United States and Germany deliver patient-centered, high-quality diabetes care. Health Aff (Millwood) 2014;33:1540–8. 12 Wagner EH, Grothaus LC, Sandhu N, et al. Chronic care clinics for diabetes in primary care: a system-wide randomized trial. Diabetes Care 2001;24:695–700. 13 Szecsenyi J, Rosemann T, Joos S, et al. German diabetes disease management programs are appropriate for restructuring care according to the Chronic Care Model: an evaluation with the Patient Assessment of Chronic Illness Care instrument. Diabetes Care 2008;31:1150–4. 14 American Diabetes Association. Standards of medical care in diabetes-2015. Diabetes Care 2015;38(Suppl 1):S1–93. 15 Nutting PA, et al. Use of chronic care model elements is associated with higherquality care for diabetes. Ann Fam Med 2007;5:14–20. 16 Frei S, et al. The Chronic CARe for diAbeTes study (CARAT): a cluster randomized controlled trial. Cardiovasc Diabetol 2010; 9:23. 17 Siminerio LM, Piatt G, Zgibor JC. Implementing the chronic care model for improvements in diabetes care and education in a rural primary care practice. Diabetes Educ 2005;31:225–34. 18 Piatt GA, Orchard TJ, Emerson S, et al. Translating the chronic care model into the community: results from a randomized controlled trial of a multifaceted diabetes care intervention. Diabetes Care 2006;29:811–7. 19 Stroebel RJ, Gloor B, Freytag S, et al. Adapting the chronic care model to treat chronic illness at a free medical clinic. J Health Care Poor Underserved 2005;16:286–96. 25 Vargas RB, Mangione CM, Asch S, et al. Can a chronic care model collaborative reduce heart disease risk in patients with diabetes?. J Gen Intern Med 2007;22:215–22. 29 AIRT 2012. Accordo Regionale ai sensi degli artt. 4, 14 e 13bis dell’Accordo collettivo nazionale per la disciplina dei rapporti con i medici di medicina generale 29.07.09. 30 Wagner EH, Austin BT, Davis C, et al. Improving chronic illness care: translating evidence into action. Health Aff (Millwood) 2001;20:64–78. 31 Wagner EH. The role of patient care teams in chronic disease management. BMJ 2000;320:569–72. 32 Gini R, Francesconi P, Mazzaglia G, et al. Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and National survey. BM Public Health 9;13:15. 33 Giorda C, et al. The impact of adherence to screening guidelines and of diabetes clinics referral on morbidity and mortality in diabetes. PLoS One 2012;7(4):e33839. 34 Aubert R. Diabetes in America. National Institutes of Health. National Institute of Diabetes and Digestive and Kidney Diseases. NIH Publications No. 95146, Bethesda, Maryland (USA), 1995. 35 Donnan PT, Leese GP, Morris AD for Diabetes Audit and Research in Tayside, Scotland/Medicine Monitoring Unit Collaboration. Hospitalizations for people with type 1 and type 2 diabetes compared with the nondiabetic population of Tayside, Scotland: a retrospective cohort study of resource use. Diabetes Care 2000;23:1774–9. 36 Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41–55. 37 D’Agostino RB. Tutorial in bio-statistics: propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998;17:2265–81. 38 Kleinbaum D, Klein M. Survival Analysis: A Self-Learning Text, 3rd edn.. Springer, New York, 2012. 39 Bo S, Ciccone G, Grassi G, et al. Patients with type 2 diabetes had higher rates of hospitalization than the general population. J Clin Epidemiol 2004;57:1196–201. 40 Weinberger M, Oddone EZ, Henderson WG. Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. N Engl J Med 1996;334:1441–7. Downloaded from https://academic.oup.com/eurpub/article-abstract/27/1/14/2670163 by guest on 14 June 2020 8 24 Giorda CB. The role of the care model in modifying prognosis in diabetes. Nutr Metab Cardiovasc Dis 2013;23:11–6.