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Keenan Research Centre in the Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Ontario, CanadaDepartment of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, CanadaDepartment of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
Keenan Research Centre in the Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Ontario, CanadaDepartment of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, CanadaDepartment of Family and Community Medicine, University of Toronto, Toronto, Ontario, CanadaInstitute for Clinical Evaluative Sciences, Toronto, Ontario, CanadaInstitute of Health Policy Management and Evaluation, Toronto, Ontario, Canada
This study examined the association between Ontario's differing primary care models and receipt of recommended testing for people with diabetes. We analyzed available administrative data for 757 928 people with diabetes aged 40 years and older. We assigned them to a primary care physician and assessed whether they had received 3 key monitoring tests between 2006 and 2008. We used multivariable generalized estimating equation models to test the associations among various primary care models and receipt of recommended testing.
Ontarians with diabetes who were enrolled in a non-team blended capitation model (OR 1.18, 95% CI 1.09 to 1.27) and those enrolled in a team-based blended capitation model (OR 1.20, 95% CI 1.13 to 1.28) were more likely than those enrolled in a blended fee-for-service model to receive the optimal number of 3 recommended monitoring tests. Patients who were not enrolled in any model and who were assigned to a traditional fee-for-service physician were least likely to receive optimal monitoring compared to those enrolled in a blended fee-for-service model (OR 0.60, 95% CI 0.57 to 0.62).
The biggest gap in diabetes care was for patients not enrolled in any primary care model. Research and policy work is needed to understand and reduce this care gap, especially which provider and patient-level factors are involved. Options may include intensive outreach to patients, knowledge translation to physicians, encouraging enrollment and efforts to remove barriers to care.
Résumé
Cette étude a examiné le lien entre les différents modèles de soins primaires et l’obtention des tests recommandés aux personnes souffrant du diabète de l’Ontario. Nous avons analysé les données administratives disponibles de 757 928 personnes de 40 ans et plus souffrant du diabète. Nous leur avons attribué un médecin de premier recours et évalué s’ils avaient reçu les 3 principaux tests pour la surveillance de la maladie de 2006 à 2008. Nous avons utilisé le modèle multivariable des équations d’estimation généralisée pour vérifier les liens entre les différents modèles de soins primaires et l’obtention des tests recommandés.
Les Ontariens souffrant du diabète qui étaient inscrits dans un modèle de rémunération par capitation combiné non accessible aux groupes (RIA 1,18, IC à 95 % 1,09 à 1,27) et ceux inscrits dans un modèle de rémunération par capitation combiné accessible aux groupes (RIA 1,20, IC à 95 % 1,13 à 1,28) étaient plus susceptibles que ceux inscrits dans un modèle de rémunération à l’acte combiné d’obtenir les 3 tests recommandés pour la surveillance de la maladie. Les patients qui n’étaient inscrits à aucun modèle et pour lesquels un médecin traditionnellement rémunéré à l’acte leur avait été attribué étaient parmi les moins susceptibles de bénéficier d’une surveillance optimale comparativement à ceux inscrits à un modèle de rémunération à l’acte combiné (RIA 0,60, IC à 95 % 0,57, 0,62).
Les lacunes les plus importantes en matière de soins aux diabétiques se trouvaient chez les patients qui n’étaient inscrits à aucun modèle de soins primaires. La recherche et le travail d’élaboration des politiques sont nécessaires pour comprendre et réduire les lacunes en matière de soins, particulièrement celles où les facteurs liés aux prestataires et aux patients interviennent. Les options comprennent la sensibilisation intensive des patients, la transmission des connaissances aux médecins, l’incitation à la participation et les efforts pour éliminer les obstacles à la prestation des soins.
). Diabetes complications, including cardiovascular disease, kidney failure, amputations and vision loss, compose a large healthcare burden that is it at least partially avoidable through diabetes prevention and treatment. Control of blood sugar, blood pressure and lipids and routine retinal screening are part of current guidelines for diabetes care.
A robust primary care sector is now widely recognized to be associated with better health outcomes, greater satisfaction and lower costs (
). Canadian provinces and territories and countries around the world have been engaged for at least a decade in transforming primary care so that it can help to accomplish these goals. Recent reviews suggest that reforms across Canadian jurisdictions have been quite different and that all continue to face ongoing challenges (
Hutchison B, Glazier R. Ontario's primary care reforms have transformed the local care landscape, but a plan is needed for ongoing improvement. Health Affairs 32:695–703.
). Many jurisdictions have implemented after-hours coverage requirements, interprofessional teams, payment reforms and electronic health records. Ontario has arguably gone the furthest in making structural changes, introducing several new physician reimbursement and organizational models over the past decade.
Currently, almost three-quarters of Ontario's population are formally enrolled with a physician practising in a new primary care model, with close to one-fifth being served by an interprofessional team (
Hutchison B, Glazier R. Ontario's primary care reforms have transformed the local care landscape, but a plan is needed for ongoing improvement. Health Affairs 32:695–703.
). Of Ontario's comprehensive primary care physicians, 40% are now being paid through blended capitation.
Although there is evidence that these types of reforms can be associated with improved care, little is known about their impact in Ontario; only a handful of studies have examined differences in access or quality of care between Ontario's primary care models and those in or not in a model (
). In particular, Ontario's Auditor General has asked for evidence of value in the substantial expenditures on primary care transformation in recent years (
Hutchison B, Glazier R. Ontario's primary care reforms have transformed the local care landscape, but a plan is needed for ongoing improvement. Health Affairs 32:695–703.
). In brief, the 2 major payment models include blended fee-for-service and blended capitation. Both types of models require evening and weekend clinics and both have incentive payments for immunizations, cancer screening, smoking cessation and chronic disease management, including diabetes care. Patients are formally enrolled in both models; both the patients and the physicians sign a Ministry of Health and Long-Term Care document. Physician membership in these models is voluntary, and enrollment is voluntary for patients. The largest blended fee-for-service model is the family health group; the blended capitation models are the family health organization and the family health network, which are similar and which are included together in this article as blended capitation models. These models compose the main comparisons in this study, along with the family health team, an interprofessional model composed of blended capitation practices (family health organizations and family health networks).
We accessed administrative healthcare data through a comprehensive research agreement between Ontario's Ministry of Health and Long-Term Care and the Institute for Clinical Evaluative Sciences. All patient identifiers were stripped from the data prior to analysis, and linkage among databases was accomplished using an encrypted identifier. This study was approved by the Research Ethics Board of Sunnybrook Health Sciences Centre in Toronto.
Many of the methods used in this study have been described elsewhere (
). We identified people 40 years of age and older who had diabetes mellitus through a validated algorithm with high sensitivity (86%) and specificity (97%). The algorithm requires a single hospitalization or 2 physician claims within 2 years with a diagnosis of diabetes. It excludes gestational diabetes and does not distinguish between type 1 and type 2 diabetes, although the large majority of people identified would be expected to have type 2 diabetes. The resulting database is cumulative, such that people remain in the database once identified. We limited our study population to those in the database on or prior to August 31, 2006, and we excluded people who resided in long-term care facilities and who first became eligible for healthcare after March 31, 2006, or who died before March 31, 2008. Primary care physicians in active practice in August 2008 were included.
The outcome measures used in this study were based on the availability of data in administrative databases and in the Canadian Diabetes Association 2003 clinical practice guidelines. The main outcome measures were: testing of hemoglobin A1C 4 times within 2 years (at least once every 6 months); testing of lipids twice within 2 years (at least once annually) and a retinal examination by an optometrist or ophthalmologist once within 2 years (at least every 2 years). We considered optimal monitoring to include completion of all 3 types of testing at the intervals specified. The time period examined was between April 1, 2006, and March 31, 2008.
We attributed patients to enrollment models using client agency program enrollment tables and physicians to models using the corporate provider database. Physician specialties and characteristics were also derived from the corporate provider database. We included comparisons for people with diabetes who were not formally enrolled in any primary care model. We matched these patients to a primary care physician using a virtual rostering method whereby a patient is attributed to the primary care physician who performed the majority of their primary care services (
). Non-enrolled patients were treated separately in the analysis and were matched to a primary care physician who practised in an enrollment model or to a physician who did not. Physicians practising outside of an enrollment are reimbursed through traditional fee-for-service; approximately half of these physicians are in specialized practice such as emergency or sports medicine (
Hutchison B, Glazier R. Ontario's primary care reforms have transformed the local care landscape, but a plan is needed for ongoing improvement. Health Affairs 32:695–703.
We determined healthcare eligibility, age, sex, residential postal code and timing of first eligibility for healthcare from the Registered Persons' Database. Statistics Canada's Postal Code Conversion File (PCCF+) was used to assign postal codes of residence to 2006 census subdivisions, which were used to determine the urban-rural status of patients using the Rurality Index of Ontario (21). Neighbourhood household income was derived using the PCCF+ by linking with 2006 census dissemination areas and after taking into account household size and community of residence. The most recent date for which complete data were available was August 31, 2008. We used recent registration with the Ontario Health Insurance Plan (OHIP) as a proxy for immigration and included those born prior to 1998 who first received OHIP coverage on or after September 1, 1998. According to the 2006 Canadian census, 77% of this group was expected to be immigrants, while the remainder would consist of interprovincial migrants (many of whom would also be expected to be immigrants). The prevalence of mental health problems was assessed using a validated algorithm based on ambulatory physician visits (22). Patient comorbidity was determined using Aggregated Diagnosis Groups (ADGs) from the Johns Hopkins Adjusted Clinical Groups Case-Mix System (23) (scores included diabetes as a comorbidity). Physician billing claims to OHIP linked with the Discharge Abstract Database from the Canadian Institute for Health Information were used to identify diagnoses.
We used means, standard deviations, medians, interquartile ranges and proportions to describe the sociodemographic, health, and healthcare-related characteristics of patients in the study and the sociodemographic characteristics of physicians involved in the study. We assessed the proportion of patients receiving recommended monitoring over the 2-year study period. We used multivariable generalized estimating equation models with binomial distribution and logit link (i.e. similar to logistic regression) to examine the association between patients' and physicians' characteristics and whether patients received optimal monitoring using logistic regression. Generalized estimating equation models are similar to typical regression models in their interpretation; however, they adjust for the correlation in outcomes that is observed among patients who see the same physician and among physicians in the same enrollment group (
). Independent variables for these models at the patient level included age, sex, neighbourhood income quintile, recent registrant status, rural residence, mental illness, comorbidity, duration of diabetes, having seen an endocrinologist or general internist, number of primary care physician visits and enrollment model. At the physician level, the models included primary care physician age, sex, years since graduation and Canadian graduate status. We excluded patients from regression models if we could not assign them to a primary care physician.
Results
We analyzed data for 757 928 people with diabetes, who represented approximately 12% of Ontario's population 40 years of age and older. Compared with those without diabetes, people with diabetes were more likely to be male, 65 years or older, to live in lower-income neighbourhoods, to live in urban areas and to have a psychotic mental illness (Table 1). Approximately one-third were diagnosed with diabetes within 3 years, one-third 4 to 9 years prior, and one-third 10 or more years prior. Ontarians with diabetes visited a primary care physician a median of 5 times per year, and 42% had been seen by either an endocrinologist or a general internist within the previous 2 years. Of those with diabetes, 71% were formally enrolled in a primary care model, and 47% were enrolled in a blended fee-for-service model, 11% in a non-team blended capitation model and 13% in a team-based blended capitation model.
Table 1Characteristics of study subjects on August 1, 2006
Total population
No diabetes
All diabetes
Females with diabetes Age 40-64
Females with diabetes Age 65+
Males with diabetes Age 40-64
Males with diabetes Age 65+
All: n (%)
6228398 (100)
5470470 (87.8)
757928 (12.2)
170449 (22.5)
192252 (25.4)
205004 (27.0)
190223 (25.1)
Age: n (%)
40 to 64
4494893 (72.2)
4119440 (75.3)
375453 (49.5)
N/A
N/A
N/A
N/A
65 to 79
1261601 (20.3)
984458 (18.0)
277143 (36.6)
N/A
N/A
N/A
N/A
80 +
471904 (7.6)
366572 (6.7)
105332 (13.9)
N/A
N/A
N/A
N/A
Female: n (%)
3251805 (52.2)
2889104 (52.8)
362701 (47.9)
N/A
N/A
N/A
N/A
Income quintile: n (%)
1 (lowest)
1149438 (18.5)
977906 (17.9)
171532 (22.6)
41493 (24.3)
46836 (24.4)
44470 (21.7)
38733 (20.4)
2
1223321 (19.6)
1058969 (19.4)
164352 (21.7)
36698 (21.5)
43355 (22.6)
43421 (21.2)
40878 (21.5)
3
1233878 (19.8)
1082071 (19.8)
151807 (20.0)
34020 (20.0)
37995 (19.8)
41636 (20.3)
38156 (20.1)
4
1284263 (20.6)
1141145 (20.9)
143118 (18.9)
31446 (18.4)
34263 (17.8)
40122 (19.6)
37287 (19.6)
5 (highest)
1322188 (21.2)
1197339 (21.9)
124849 (16.5)
26210 (15.4)
29304 (15.2)
34703 (16.9)
34632 (18.2)
Missing data
15310 (0.2)
13040 (0.2)
2270 (0.3)
582 (0.3)
499 (0.3)
652 (0.3)
537 (0.3)
New immigrant: n (%)
506591 (8.1)
469837 (8.6)
36754 (4.8)
10407 (6.1)
6657 (3.5)
13983 (6.8)
5707 (3.0)
Rurality Index of Ontario: n (%)
0-9 (major urban)
4005137 (64.3)
3498041 (63.9)
507096 (66.9)
117329 (68.8)
129123 (67.2)
138664 (67.6)
121980 (64.1)
10-44 (non-major urban)
1394661 (22.4)
1240714 (22.7)
153947 (20.3)
32642 (19.2)
39679 (20.6)
40557 (19.8)
41069 (21.6)
45+ (rural)
760133 (12.2)
673956 (12.3)
86177 (11.4)
17531 (10.3)
21267 (11.1)
22577 (11.0)
24802 (13.0)
Missing data
68467 (1.1)
57759 (1.1)
10708 (1.4)
2947 (1.7)
2183 (1.1)
3206 (1.6)
2372 (1.2)
Mental health status: n (%)
Psychotic
116911 (1.9)
97057 (1.8)
19854 (2.6)
6459 (3.8)
4566 (2.4)
5760 (2.8)
3069 (1.6)
Nonpsychotic
1498957 (24.1)
1313284 (24.0)
185673 (24.5)
52897 (31.0)
47513 (24.7)
46165 (22.5)
39098 (20.6)
None
4612530 (74.1)
4060129 (74.2)
552401 (72.9)
111093 (65.2)
140173 (72.9)
153079 (74.7)
148056 (77.8)
No. of ADGs mean (SD)
5.23 (3.40)
5.04 (3.34)
6.55 (3.54)
6.58 (3.40)
7.24 (3.64)
5.42 (3.22)
7.03 (3.58)
Duration of diabetes: n (%)
0 to 3 years
268222 (4.3)
N/A
268222 (35.4)
69386 (40.7)
57420 (29.8)
85766 (41.8)
55650 (29.2)
4 to 9 years
264560 (4.2)
N/A
264560 (34.9)
61043 (35.8)
64883 (33.7)
73335 (35.8)
65299 (34.3)
10 to 14 years
179623 (2.9)
N/A
179623 (23.7)
32651 (19.2)
53755 (28.0)
38501 (18.8)
54716 (28.8)
15 + years
45523 (0.7)
N/A
45523 (6.0)
7369 (4.3)
16194 (8.4)
7402 (3.6)
14558 (7.7)
No. of FP/GP visits in last year: Median (IQR)
3 (1-6)
3 (1-6)
5 (3-9)
5 (3-9)
6 (3-9)
4 (2-7)
6 (3-9)
Seen by an endocrinologist or general internist: n (%)
Frequency and percentages are expressed as percent of subpopulation defined by row and column that have diabetes (e.g. percentage of females aged 40 to 64 years, enrolled in a blended fee-for-service model who have diabetes).
Blended fee-for-service
2592754 (41.6)
2237548 (40.9)
355206 (46.9)
83011 (48.7)
88700 (46.1)
95848 (46.8)
87647 (46.1)
Non-team blended capitation
667652 (10.7)
588024 (10.7)
79628 (10.5)
16382 (9.6)
21358 (11.1)
19760 (9.6)
22128 (11.6)
Team-based blended capitation
868615 (13.9)
769534 (14.1)
99081 (13.1)
20906 (12.3)
26603 (13.8)
24042 (11.7)
27530 (14.5)
Non-enrolled: Virtually rostered to enrolment model physician
579632 (9.3)
520631 (9.5)
59001 (7.8)
14040 (8.2)
13990 (7.3)
18350 (9.0)
12621 (6.6)
Non-enrolled: Virtually rostered to traditional fee-for-service physician
1068246 (17.2)
926188 (16.9)
142058 (18.7)
31962 (18.8)
35340 (18.4)
39607 (19.3)
35149 (18.5)
Unmatched
451499 (7.2)
428545 (7.8)
22954 (3.0)
44148 (2.4)
6261 (3.3)
7397 (3.6)
5148 (2.7)
ADG, aggregated diagnosis group; ACG, derived from Johns Hopkins Adjusted Clinical Groups case mix system (higher numbers indicate higher comorbidity); FP/GP, family physician or general practitioner; IQR, interquartile range.
∗ Frequency and percentages are expressed as percent of subpopulation defined by row and column that have diabetes (e.g. percentage of females aged 40 to 64 years, enrolled in a blended fee-for-service model who have diabetes).
We were able to assign 734 739 (97%) of Ontarians with diabetes to a primary care physician. Compared to other Ontario residents, people with diabetes were more likely to be cared for by an international medical graduate, a male physician or a physician 50 years old or older (Table 2).
Table 2Characteristics of primary care physicians who were assigned to study subjects
Between 2006 and 2008, 68% of Ontarians with diabetes received 1 or more retinal eye exams (Table 3); 78% received at least 1 A1C test and 37% received the optimal number (4) of A1C tests. For cholesterol, 80% received at least 1 cholesterol test, and 59% received the optimal number (2) of cholesterol tests. Overall, only 27% of Ontarians with diabetes received the optimal number of all 3 recommended screening tests.
Table 3Crude percentage of patients with diabetes receiving recommended testing over the 2-year period between 2006 and 2008 by patient characteristic
Non-enrolled: Virtually rostered to traditional fee-for-service physician
66.1
25.0
42.5
32.5
23.4
20.6
56.0
22.5
Non-enrolled: Virtually rostered to enrolment model physician
59.2
27.4
44.3
28.3
26.1
23.6
50.3
18.3
ADG, aggregated diagnosis group derived from Johns Hopkins Adjusted Clinical Group case mix system (higher numbers indicate higher comorbidity); FP/GP, family physician or general practitioner.
Before adjusting for other factors, we found that people with diabetes who were formally enrolled with a physician practising in any of the primary care models were more likely to receive the optimal number of recommended tests compared to those not enrolled with a physician (Table 3). The percentage of patients with diabetes receiving optimal monitoring was similar in the various primary care enrollment models. Other factors associated with higher rates of optimal monitoring were older age, being a long-term resident, not living in a rural area, not having a psychotic mental illness, making more primary care visits and seeing an endocrinologist or general internist.
After adjusting for patient and physician characteristics, Ontarians with diabetes who were enrolled to a non-team blended capitation model (OR 1.18, 95% CI 1.09 to 1.27) and those enrolled in a team-based blended capitation model (OR 1.20, 95% CI 1.13 to 1.28) were more likely than those enrolled with a blended fee-for-service model to receive the optimal number of the 3 recommended tests (Figure 1). Patients who were not enrolled in any model were less likely to receive optimal monitoring compared to those enrolled to a blended fee-for-service model. This finding was true for patients who were not enrolled but were virtually rostered to a physician practising in an enrollment model (OR 0.78, 95% CI 0.74 to 0.83) as well as for those virtually rostered to traditional fee-for-service physicians (OR 0.60, 95% CI 0.57 to 0.62).
Figure 1Multivariable regression results for the association between primary care model and receipt of recommended testing for diabetes after adjustment for patient and provider characteristics.
We found differences in the quality of diabetes care between primary care models in Ontario. Overall, just over one-quarter of Ontarians with diabetes received the optimal number of 3 key monitoring tests recommended to prevent morbidity and mortality due to diabetes. Patients enrolled in a blended capitation model were more likely to receive recommended testing than patients enrolled in a blended fee-for-service model. Patients who were not formally enrolled in a model and who were seeing a traditional fee-for-service physician were least likely to receive recommended testing.
Over the past decade, Ontario has transitioned 40% of its family physicians to blended capitation models with the goal of improving quality of care and reducing costs (
Hutchison B, Glazier R. Ontario's primary care reforms have transformed the local care landscape, but a plan is needed for ongoing improvement. Health Affairs 32:695–703.
). Capitation theoretically incentivizes better chronic disease care by providing family physicians the flexibility to spend more time with patients who have complex illnesses, collaborate with other professionals, and integrate e-mail and phone calls into their practices. Whether these potential advantages translate into better care and better outcomes is not yet clear in the Ontario context. Liddy et al reviewed 82 practices in eastern Ontario and, like us, found that patients with diabetes attending a blended capitation practice were more likely to receive appropriate A1C testing than those receiving care from a physician reimbursed via traditional or blended fee-for-service (
). However, Russell et al found minimal differences in overall quality of diabetes care between fee-for-service and capitation practices, but their study predated the introduction of 1 of Ontario's most prevalent capitation models, the family health organization (
Ours is the first study to include team-based blended capitation practices when comparing the quality of diabetes care between Ontario's primary care models. Team-based practices receive funding for health professionals such as nurse practitioners and dietitians who can augment the diabetes care provided by a solo physician. Studies have shown that allied health professionals generally (
) are associated with higher quality care in cases of chronic disease. In our study, patients enrolled in team-based capitation practices were not more likely to receive recommended diabetes testing than those in non-team capitation practices. However, our study was conducted 1 to 3 years after the first team-based capitation practices were introduced in Ontario, and that may not have been enough time to realize the advantages of an interprofessional team.
Despite large investments in primary care reform, about a quarter of Ontarians with diabetes are not formally enrolled in a primary care model, and these residents face the largest gaps in quality of care. Approximately two-thirds of nonenrolled patients were matched to traditional fee-for-service physicians, and these patients were least likely to receive optimal diabetes monitoring. This finding might be explained by the shortcomings of traditional fee-for-service reimbursement, which has been associated with lower quality diabetes care (
). However, some of these patients may effectively have been unattached because many fee-for-service physicians in Ontario do not practice comprehensive care and instead are walk-in clinic physicians or are in specialized practice, such as emergency or sports medicine. Approximately one-third of non-enrolled patients were seen by physicians who practised in an enrollment model, yet they were less likely to receive recommended testing compared to patients who were formally enrolled with an enrollment-model physician. It is unclear whether these patients opted not to enrol of their own accord or whether physicians opted not to enrol them. Like the other group of non-enrolled patients, it is possible that these patients were effectively unattached, coming into contact with an enrollment-model physician only during the physician's walk-in clinic shift. Nonetheless, this discrepancy in optimal care between enrolled and nonenrolled patients for physicians practising in an enrolment model warrants further investigation, particularly because there is already some evidence that capitation models have preferentially enrolled healthier and wealthier patients (
Our study has 3 limitations of note. First, our study was cross-sectional and could not address causation. A longitudinal analysis is needed to understand whether existing differences in quality of diabetes care were present before physicians' entry into a specific primary care model. Previous work has shown that differences in quality of care have largely predated primary care reforms (
). Second, we relied on administrative data. We likely underestimated testing rates because we were unable to include in our analysis A1C or cholesterol tests done in hospital or eye examinations paid for privately or out of pocket. However, these omissions likely had minimal impact on the comparison between models. Third, we were unable to assess the quality of diabetes care provided by Ontario's community health centres, an interprofessional primary care model in which physicians are paid by salary. Other studies have shown that the quality of diabetes care is higher in community health centres compared to both capitation and fee-for-service practices (
We found that patients enrolled in blended capitation models received higher quality diabetes care than those enrolled in blended fee-for-service models but that the biggest gap in diabetes care occurred for patients not enrolled in any model. Research and policy work is needed to understand and reduce this care gap, especially those in which provider-level and patient-level factors are involved. Options may include intensive outreach to patients, knowledge translation to physicians, encouraging enrollment and efforts to remove barriers to care. We found no differences in quality of diabetes care between capitation practices with and without an interprofessional team, although teams were introduced only shortly before our study began. Further research will be needed to understand whether quality-of-care differences arise once interprofessional teams have had sufficient time to coalesce.
Author Disclosures
This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. Dr. Kiran was supported as a Clinician Investigator and Dr. Glazier as a Clinician Scientist by the Department of Family and Community Medicine at the University of Toronto and at St. Michael's Hospital. We thank Morgan Slater for her help with preparing the figure for publication.
Author Contributions
TK and RG conceived of the study; TK, JCV, AK, BS and RG designed the study; TK, JCV and AK conducted the analysis; TK, JCV, AK, BS and RG interpreted the results; TK and RG drafted the manuscript; and TK, JCV, AK, BS and RG critically revised it. All authors approved the final version.
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Hutchison B, Glazier R. Ontario's primary care reforms have transformed the local care landscape, but a plan is needed for ongoing improvement. Health Affairs 32:695–703.
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