Quality of Care and Outcomes in Type 2 Diabetic Patients: A comparison between general practice and diabetes clinics

All Italian citizens are covered by a government health insurance and are registered with a GP. Primary care for diabetes is provided by GPs and in DOCs. Patients can choose one of these two ways of accessing the health care system according to their preferences or they can be referred to DOCs by their GPs. The DOCs are usually staffed by diabetologists, internists, and/or endocrinologists, who are primarily responsible for diabetes care; other specialists (e.g., ophthalmologists, cardiologists) may also practice part time in the clinic. Given the existence in Italy of a specialty in “diabetes and metabolic disorders,” the term “diabetologist” refers only to a physician with such specific training. In some DOCs, patients are always cared for by the same specialist, whereas in other clinics patients can be seen by different physicians on different occasions.

The study involved 212 physicians practicing in 125 DOCs and 103 general practice offices. Details on the study design and sampling of physicians have already been reported elsewhere (8–10).

Briefly, all patients with type 2 diabetes (adult onset of disease, fasting venous plasma glucose concentration ≥7.8 mmol/l on at least two separate occasions, diabetes not requiring insulin treatment at diagnosis) were considered eligible for this project, irrespective of age, duration of diabetes, and treatment. In diabetes clinics, patients were sampled using random lists, stratified by patient age (<65 or ≥65 years). Each center was asked to recruit at least 30 patients, whereas GPs enrolled all consecutive patients for whom they were primarily responsible for diabetes care, up to a maximum of 10 patients. Patients were enrolled between March 1998 and December 1999.

Clinical information was abstracted from clinical records by the participating physicians and reported in ad hoc forms. Data were collected at baseline and 6-month intervals. Patients are being followed for 5 years; the present analysis refers to data collected during the first 2 years.

All recruited patients were requested to fill in a questionnaire regarding the presence and severity of diabetes complications and comorbidities and the SF-36 Health Survey. The presence and severity of diabetes complications and comorbidities were summarized using the Total Illness Burden Index (TIBI), a widely used comorbidity measure specifically developed for diabetic outpatient populations (11).

Based on the recommendations of the American Diabetes Association (12), we identified process measures that could be considered as representative of quality of diabetes care. Process measures included frequency of HbA_1c, lipid profile (total and HDL cholesterol, triglycerides), microalbuminuria, and serum creatinine measurements and frequency of foot and dilated eye examination. Although blood pressure measurement is an important process indicator, we did not consider it as all patients had at least two measurements per year.

We defined the assessment of HbA_1c as adequate if at least two measurements per year were performed, whereas all other process measures were considered appropriate if at least one measurement/examination per year was performed.

We also identified intermediate outcome measures, including mean HbA_1c, blood pressure, and total and LDL cholesterol levels over 2 years. For each outcome, we considered the proportion of patients with satisfactory values as well as those with unacceptably high values. Outcomes were considered satisfactory if HbA_1c levels were ≤7.0%, blood pressure values were ≤130/85 mmHg, total cholesterol levels were ≤5.18 mmol/l, and LDL cholesterol levels were <2.86 mmol/l. Unsatisfactory outcomes included HbA_1c levels >8%, blood pressure values ≥140/90 mmHg, total cholesterol levels >5.70 mmol/l, and LDL cholesterol levels >3.37 mmol/l. LDL cholesterol was estimated by the Friedwald equation.

Patient characteristics and the percent of patients conforming with process and outcomes measures were compared using χ² statistics for categorical variables and Mann-Whitney U test for continuous variables.

To compare the two settings of care, for each process and outcome measure we used three regression models to calculate estimated odds ratios (ORs) and 95% CIs. In the first model, we estimated unadjusted ORs. In the second model, ORs were adjusted for patient case−mix variables, including age, sex, school education, BMI, duration of diabetes, treatment of diabetes, TIBI, and physical functioning, as measured by the specific 10-item SF-36 subscale. In the third model, to account for the multilevel nature of the data (patients clustered within physician or practice) and to control simultaneously for the possible confounding effects of the different variables, we used multivariate multilevel logistic regression models (13,14).

Because the clustering effect within practices could be more relevant for those patients always followed by the same physician within a clinic, we ran additional analyses on this subgroup. In particular, we tested whether being followed by a GP or always by the same physician practicing in a DOC had an impact in terms of process and outcome measures. Moreover, we also tested whether the specialty of the physician played a role (GP versus endocrinologists, diabetologists, internists, or other specialists).

For process measures, an OR >1.0 indicated a higher probability to perform the exam or the measurement for patients cared for in DOCs as opposed to those followed by GPs. For intermediate outcome measures, an OR >1 indicated a higher likelihood to present adequate/inadequate values for patients cared for by DOCs as opposed to those followed by GPs.

All the analyses were performed using SAS Statistical Package version 8.2 (SAS Institute, Cary, NC). Multilevel logistic regression was performed using the SAS language macro routine GLIMMIX.

Overall, 3,437 patients were enrolled, of whom 2,658 were recruited by DOCs and 779 by GPs. Of the 3,437 subjects, 2,130 were always followed by the same physician. In DOCs, 31% of the physicians were diabetologists, 15% were internists, 38% were endocrinologists, and 16% were either from other specialties (10%) or had no further training (6%).

Patients’ characteristics according to the setting of care are shown in Table 1. Patients seen by GPs tended to be older, be female, have a shorter diabetes duration, and have hypertension or dyslipidemia, whereas those followed in DOCs were more likely to be treated with insulin and to suffer from retinopathy and neuropathy. Overall, patients seen in the two settings did not significantly differ in terms of physical functioning and severity of clinical conditions, as expressed by the TIBI score.

The percent of patients conforming with process measures are reported in Table 2. Statistically significant differences in favor of patients treated by DOCs were found for HbA_1c, HDL cholesterol, and microalbuminuria testing, as well as for foot and eye examinations. The differences in the percent of patients conforming with process measures were even more marked when the comparison was restricted to patients always seen by the same physician (Table 2).

As for outcome measures, more DOC patients showed satisfactory blood pressure and total cholesterol values compared with those seen by GPs, whereas high total and LDL cholesterol levels were found more often among patients cared for by GPs. Similar figures emerged when analyzing patients always seen by the same physician (Table 2).

Results for unadjusted, case-mix adjusted, and both case-mix and physician level−clustering adjusted ORs are shown in Table 3. In the whole sample, adjustment for patient case mix did not substantially modify the results, confirming that patients followed by DOCs and GPs had similar health conditions. Accounting for physician-level clustering increased all the ORs relative to process measures, except for microalbuminuria testing and foot examination. The statistical significance was achieved for HbA_1c, HDL cholesterol, triglycerides, serum creatinine, and microalbuminuria measurements and for eye examination.

As for the outcome measures considered, after accounting for case mix and physician-level clustering, patients treated in DOCs still showed a higher probability of having adequate total cholesterol levels (≤5.18 mmol/l). On the other hand, the difference in the proportion of patients with inadequate total and LDL cholesterol levels and with adequate blood pressure levels were no longer significant.

To better evaluate the role of physician-level clustering, the analyses were repeated after excluding patients followed by different physicians within the same structure. Even in this analysis, the adjustment for physician-level clustering substantially increased ORs for most of the pro-cess measures considered, confirming their statistical significance, with the only exception being foot examination (Table 3).

As far as outcomes measures are concerned, the only statistically significant difference found referred to patients with adequate total cholesterol levels.

The additional analyses on health care delivery modalities within DOCs and according to physician specialty offered important additional information. In particular, being followed by different physicians within DOCs was associated with performance rates of the different process measures similar to those of GPs, with the only exception being higher rates of patients conforming with HbA_1c measurement (clustering- and case mix−adjusted OR 2.55, CI 1.51–4.30) and eye examination (OR 2.09, CI 1.32–2.30). No difference was found for any of the outcome measures considered.

Quality of diabetes care for patients treated by GPs or always followed by the same physician in DOCs was further differentiated when the specialty of physicians practicing in DOCs was taken into account. Case mix− and clustering-adjusted ORs for process measures are reported in Fig. 1, with patients followed by diabetology specialists as the reference category. Diabetologists performed significantly better than GPs on all process measures considered. Differences between GPs and the other specialists practicing in DOCs were less consistent; in fact, no statistically significant difference emerged for lipid monitoring and foot examination between those groups, whereas internists tended to perform better than GPs in terms of HbA_1c measurement (OR 8.11, CI 4.01–16.4) and serum creatinine and microalbuminuria testing (OR 2.43, CI 1.06–5.58 and OR 2.79, CI 1.07–7.31, respectively). Endocrinologists performed better than GPs in terms of HbA_1c measurement (OR 2.49, CI 1.31–4.70), serum creatinine testing (OR 2.83, CI 1.29–6.22), and eye examination (OR 2.91, CI 1.58–5.37). Within DOCs, the proportion of patients conforming with HbA_1c measurement was significantly lower among those treated by endocrinologists compared with diabetologists. Similarly, the proportion of patients who received at least one eye examination per year was significantly lower for patients cared for by specialists in internal medicine or with other specialties as compared with those treated by diabetologists.

As for the outcomes considered, no statistically significant difference related to physician specialty emerged (Fig. 2).

Our study involved a large number of GPs and physicians practicing in DOCs and offered an important opportunity to compare the quality of diabetes care provided by generalists and specialists using appropriate statistical techniques (5,15).

Overall, the performance of Italian physicians participating in the project was very similar in terms of process measures to that reported in other studies conducted in the U.S. comparing generalists with specialists (4–6,16). As for the outcomes considered, good metabolic control was attained in the vast majority of patients, with only 26% showing HbA_1c values >8%. Control of cardiovascular risk factors was less satisfactory, with 65% of the patients showing total cholesterol levels >5.18 mmol/l and blood pressure levels ≥140/90 mmHg.

We found several differences between generalists and specialists for both process and outcome measures, despite the fact that patients followed in the two settings did not differ in terms of overall disease severity and functional status. This substantial similarity in the overall clinical conditions is the result of a mix of favorable and unfavorable characteristics for the patients cared for by GPs as compared with those followed in DOCs.

The lack of major clinical differences was confirmed by the analyses adjusted for patient case mix, which did not modify specialty differences found for process and outcomes measures.

Physician level−clustering adjustment showed that all the process measures were performed significantly more often by physicians practicing in DOCs than in general practice offices, particularly when the analysis was restricted to those patients always cared for by the same physician. The latter finding suggests that modalities of health care delivery within DOCs can influence quality of care. In fact, only minor differences in process measures emerged when comparing patients followed by GPs with those followed by different physicians within a same DOC.

Our results were consistent with those reported in the existing literature indicating that specialists are more resource intensive than generalists and adhere to process of care guidelines more closely (4–6). Nevertheless, it is not possible to exclude the possibility that reasons other than physician attitude can be at least partially responsible for our findings. One possibility is that patients followed by GPs could have less access to the testing facilities that are easily available in DOCs, which are usually located within hospitals. This could be particularly true for older individuals living in rural areas, for whom the access to testing facilities and DOCs might represent a major problem. It is also possible that patients cared for by specialists prefer a more aggressive style of care and thus could be motivated in having their tests completed. Therefore, the differences in process measures documented in our study could be at least partially attributed to differences in the study populations not captured by case-mix and clustering adjustment.

The evaluation of physician specialty added another important element in comparing diabetes care provided by generalists with that by specialists. In fact, diabetologists performed consistently better than GPs for all the process measures considered, whereas the differences between GPs and the other specialists practicing in DOCs tended to be less marked and not systematic. As for the outcomes measures considered, satisfactory total cholesterol levels were obtained more often in DOCs than in general practice offices, irrespective of the specialty of physicians practicing in DOCs.

Process measures have been criticized as often lacking strong links to outcomes (17). From this point of view, it is of particular interest to note that more frequent monitoring of blood lipids in patients attending DOCs was also associated with a higher proportion of patients with satisfactory total cholesterol levels. This finding is further supported by the higher proportion of dyslipidemic patients treated with cholesterol-lowering drugs in DOCs as compared with treatment received in general practice offices (51 vs. 42%; P = 0.03). On the other hand, a significantly higher frequency of HbA_1c monitoring in DOCs was not associated with better outcomes in terms of metabolic control. We have previously shown that personal attitudes and beliefs of the individual physician, rather than physician specialty or setting of care, influence metabolic control (8). Furthermore, it is also possible that patients who are more difficult to manage in terms of metabolic control are referred to specialists by GPs.

Our study also offered interesting methodological hints. In a previous study, it has been shown that the failure to account for physician-level clustering could lead to overestimation of the statistical significance of the groups being compared (5). We have documented that the use of inappropriate statistical techniques can also lead to underestimation of the differences when comparing generalists with specialists. In fact, some nonstatistically significant unadjusted differences between the two settings (i.e., triglycerides and serum creatinine measurements) (Table 3) became statistically significant once the physician-level variation was taken into account.

Some of the potential limitations of our study need to be discussed. First, physicians were selected according to their willingness to participate in the project. They could thus represent those clinicians, particularly GPs, who are more interested in diabetes care and therefore not be reflective of diabetes care delivered by Italian physicians in general. From this point of view, the differences documented in our study could be underestimated and the true variability in process and outcomes measures could be even greater.

Second, some of the differences in process measures could be related to disparities in the accuracy of reporting. Nevertheless, the consistency of our findings across the whole spectrum of measures considered strongly suggests a true difference in physicians’ performance.

Finally, because of the relatively short period of observation, we could select only intermediate outcome measures. It is therefore not clear to what extent the noted differences could determine different outcomes in terms of major clinical events in the long term. The analysis after the completion of the 5-year follow-up will allow a deeper understanding of the relation between process and major outcomes.

In conclusion, our findings suggest that being followed always by the same physician practicing in a DOC, particularly if the physician has a specialty in diabetes, ensures better quality of care in terms of process measures. In the short term, care provided by specialists was also associated with better intermediate outcome measures, such as total cholesterol levels. Because of the short period of observation, we could not document whether long-term outcomes were also affected.

The increasing number of patients with diabetes, together with the progressive rise in demand for diabetes care stemming from increased life expectancy, call for an efficient and coordinated health care delivery. In this respect, the involvement of general practice represents a crucial aspect. Therefore, more attention should be paid to minimizing quality of care differences, decreasing unnecessary and inappropriate care, improving the referral process for patients with complicated conditions, and promoting a comanagement and teamwork approach to diabetes care. To this end, an important first step is to ensure that the same level of care is provided by physicians with a diabetes specialty. Unfortunately, no single approach has been shown to be effective for all physicians in changing their practice. Multiple interventions, including educational programs, practice guidelines, financial incentives, regulatory measures, and total quality management techniques are likely to enhance physician performance (18). The ongoing transformation of general practice in Italy, moving from solo practice to group practice and the reorganization of team function and practice systems (e.g., appointments and follow-up) to meet the needs of chronically ill patients will also represent an important step forward in improving the standard of diabetes care. Finally, the recent implementation of shared information systems that allow a bidirectional information flux between generalists and specialists will greatly facilitate the interaction between the different health care providers while ensuring the continuity of diabetes care.

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Sabbi D. (Arquata Scrivia, AL); Mazzarino A. (Aversa, CE); Lippa L. (Avezzano, AQ); Casassa Vigna M. (Balangero, TO); D’Alessandro A . (Bari); Caniglia N. (Barrea, AQ); Brancati F. (Brugherio, MI); Omati G. (Bussero, MI); Danti G. (Buttapietra, VR); Pascali L. (Camerano, AN); Ragazzi G. (Camisano Vicentino, VI); Di Paolo L. (Campo Di Giove, AQ); Di Febo E. (Carsoli, AQ); Ferrari P., Ballarini L. (Castel D’azzano, VR); Tonello P. (Castelgomberto, VI); Capilupi V. (Catanzaro); De Giorgi D. (Cavallino, LE); Spiezio C. (Ciriè, TO); Della Cagnoletta F. (Colorina, SO); Beretta E. (Concorezzo, MI); Nepote Fus M.T., Rapacciuolo T. (Corio, TO); Cannelli B. (Corridonia, MC); Metrucci A. (Cutrofiano, LE); Veldorale A. (Druento, TO); Ioverno E., Visentin G. (Dueville, VI); Bellino L. (Firenze); Brizio E. (Fossano, CN); Zanellato E. (Front, TO); Frapporti G. (Fumane, VR); Della Vedova R. (Gradisca d’Isonzo, GO); Gesualdi F. (Latronico, PZ); Mola E., Bosco T., Fiume D. (Lecce); Falcoz M. (Loira, TV); Martinelli G. (Lovere, BG); Tombesi M., Caraceni L. (Macerata); Di Giovanbattista E. (Magnano in Riviera, UD); Ermacora T. (Maiano, UD); Gualtiero A. (Malo, VI); Morelli F., Capozza G .(Matera); Musso M. (Mathi, TO); Pagliani S., Longoni P. (Milano); Caimi V., Parma E., Riva M.G., Bosisio M. (Monza, MI); Bertini L. (Monzuno, BO); Barra R., D’Alessandro F.M., Alano R. (Napoli); Mezzasalma G. (Nole Canavese, TO); Barberio L. (Paganica, AQ); Petrona Baviera F. (Palermo); De Matteis C. (Paola, CS); Anglano B. (Verona); Scarpolini P. (Pescantina, VR); Milano M., Bernabè S. (Pianezza, TO); Ferrara F. (Pisticci, MT); Filippi S. (Pontremoli, MS); Tosetti C. (Porretta Terme, BO); Dorato P. (Pozzuoli, NA); Moro A. (Preganziol, TV); La Terra Bella B. (Ragusa); Marziani M. (Reggio Emilia); Burzacca S. (Rivalta Di Torino, TO); Zamboni A. (Ro, FE); Saliceti F., Bartoletti P.L., Spalletta L. (Roma); Bonicatto L. (San Francesco al Campo, TO); Catalano A. (San Leucio del Sannio, BN); Crapesi L. (San Lorenzo Isontino, GO); Greco M. (San Pietro in Lama, LE); Mattana G. (San Sperate, CA); Agnolio M.L. (Sandrigo, VI); Piazza G. (Santorso, VI); Lattuada G. (Saronno, VA); Gambarelli L. (Scandiano, RE); Bussotti A. (Sesto Fiorentino, FI); Pinsuti A. (Sinalunga, SI); Signorati L. (Sommacampagna, VR); Baggi V. (Sordio, LO); Riundi R. (Sumirago, VA); Uberti M., Mondazzi A.R., Massaro R., Botto Micca M. (Torino); Massignani D. (Valdagno, VI); Gazzetta F., Bianchetti F., Molla D. (Varese); Marino R., Gribaldo E. (Venaria, TO); Aramini E. (Vercelli); Galopin T., Pettenella G., Bonollo E. (Verona); Luvisi P.F. (Viareggio, LU); Frigo A., Cabri G., Simionato C. (Vicenza); Bevilacqua S., Longhi L. (Viterbo); Dezio G. (Vittoria, RG).

Giorgia De Berardis, MSc (Pharm Chem); Fabio Pellegrini, MS; Monica Franciosi, MSc (Biol); Maurizio Belfiglio, MD; Barbara Di Nardo, HSDip; Sheldon Greenfield, MD; Sherrie H. Kaplan, PhD, MPH; Maria C.E. Rossi, MSc (Pharm Chem); Michele Sacco, MD; Gianni Tognoni, MD; Miriam Valentini, MD; Antonio Nicolucci, MD.

Vittorio Caimi, MD; Fabio Capani, MD; Andrea Corsi, MD; Roberto Della Vedova, MD; Massimo Massi Benedetti, MD; Antonio Nicolucci, MD; Claudio Toboga, MD; Massimo Tombesi, MD; Giacomo Vespasiani, MD.

This study was supported by Pfizer Italiana SpA., and partially supported by Italian Ministero dell’Istruzione, dell’Università e della Ricerca DM 623/96–2002.