Prevalence and Epidemiology of Diabetes in Canadian Primary Care Practices: A Report from the Canadian Primary Care Sentinel Surveillance Network

Can J Diabetes xxx (2014) 1e7

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Canadian Journal of Diabetes journal homepage: www.canadianjournalofdiabetes.com

Original Article

Prevalence and Epidemiology of Diabetes in Canadian Primary Care Practices: A Report from the Canadian Primary Care Sentinel Surveillance Network Michelle Greiver MD a, b, *, Tyler Williamson PhD c, d, David Barber MD c, Richard Birtwhistle MD c, d, Babak Aliarzadeh MD a, b, Shahriar Khan MSc c, Rachael Morkem MSc c, Gayle Halas MA e, Stewart Harris MD f, Alan Katz MB, ChB e a

Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada North York General Hospital, Toronto, Ontario, Canada Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada d Department of Public Health Sciences, Queen’s University, Kingston, Ontario, Canada e Department of Family Medicine, University of Manitoba, Winnipeg, Manitoba, Canada f Department of Family Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 November 2013 Received in revised form 14 February 2014 Accepted 14 February 2014 Available online xxx

Objective: The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a large, validated national primary care Electronic Medical Records (EMR)-based database. Our objective was to describe the epidemiology of diabetes in this Canadian sample. Methods: We analyzed the records of 272 469 patients10 years of age and older, with at least 1 primary care clinical encounter between January 1, 2011, and December 31, 2012. We calculated the age-gender standardized prevalence of diabetes. We compared health care utilization and comorbidities for 7 selected chronic conditions in patients with and without diabetes. We also examined patterns of medication usage. Results: The estimated population prevalence of diabetes was 7.6%. Specifically, we studied 25 425 people with diabetes who had at least 1 primary care encounter in 2 years. On average, patients with diabetes had 1.42 times as many practice encounters as patients without diabetes (95% CI 1.42 to 1.43, p<0.0001). Patients with diabetes had 1.29 times as many other comorbid conditions as those without diabetes (95% CI 1.27 to 1.31, p<0.0001). We found that 85.2% of patients taking hypoglycemic medications were taking metformin, and 51.8% were taking 2 or more classes of medications. Conclusions: This study is the first national Canadian report describing the epidemiology of diabetes using primary care EMR-based data. We found significantly higher rates of primary care use, and greater numbers of comorbidities in patients with diabetes. Most patients were on first-line hypoglycemic medications. Data routinely recorded in EMRs can be used for surveillance of chronic diseases such as diabetes in Canada. These results can enable comparisons with other national EMR-based datasets. Ó 2014 Canadian Diabetes Association

Keywords: diabetes mellitus drug therapy epidemiology medical record systems computerized

r é s u m é Mots clés : diabète sucré pharmacothérapie épidémiologie systèmes de dossiers médicaux informatisé

Objectif : Le Réseau canadien de surveillance sentinelle en soins primaires (RCSSSP) qui maintient une importante base de données nationales recueille et valide des données de soins primaires à partir de dossiers médicaux électroniques (DMÉ). Notre objectif était de décrire l’épidémiologie du diabète de cet échantillon canadien. Méthodes : Nous avons analysé les dossiers de 272 469 patients de 10 ans et plus ayant eu au moins 1 intervention clinique en soins primaires entre le 1er janvier 2011 et le 31 décembre 2012. Nous avons calculé la prévalence standardisée du diabète selon l’âge et le sexe. Nous avons comparé l’utilisation des soins de santé et les comorbidités de 7 affections chroniques sélectionnées chez les patients souffrant ou non de diabète. Nous avons également examiné les profils d’utilisation des médicaments. Résultats : L’estimation de la prévalence du diabète dans la population était de 7,6 %. Notamment, nous avons étudié 25 425 personnes souffrant de diabète qui avaient eu au moins 1 intervention en soins

* Address for correspondence: Michelle Greiver, MD, North York Family Health Team, 240 Duncan Mill Road, Suite 705, Toronto, Ontario M3B 3S6, Canada. E-mail address: [email protected] 1499-2671/$ e see front matter Ó 2014 Canadian Diabetes Association http://dx.doi.org/10.1016/j.jcjd.2014.02.030

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primaires en 2 ans. En moyenne, les patients souffrant de diabète avaient 1,42 fois plus d’interventions que les patients ne souffrant pas de diabète (IC à 95 % 1,42-1,43, p < 0,0001). Les patients souffrant de diabète avaient 1,29 fois plus d’autres comorbidités que ceux ne souffrant pas diabète (IC à 95 % 1,27-1,31, p < 0,0001). Nous avons constaté que 85,2 % des patients prenant des hypoglycémiants prenaient de la metformine, et que 51,8 % prenaient 2 classes ou plus de médicaments. Conclusions : Cette étude constitue le premier rapport national canadien qui décrit l’épidémiologie du diabète en utilisant les données de soins primaires des DMÉ. Nous avons constaté des taux significativement plus élevés d’utilisation des soins primaires et un plus grand nombre de comorbidités chez les patients souffrant de diabète. La plupart des patients prenaient des hypoglycémiants de première intention. Au Canada, les données systématiquement enregistrées aux DMÉ peuvent être utilisées pour la surveillance des maladies chroniques comme le diabète. Ces résultats peuvent permettre de faire des comparaisons sur le plan national avec d’autres ensembles de données issues des DMÉ. Ó 2014 Canadian Diabetes Association

Introduction Prevalence Primary health care Diabetes is an important and prevalent chronic health condition. It is a significant contributor to cardiovascular morbidity and renal disease. The management of diabetes and its complications is associated with substantial and increasing healthcare costs (1,2). Estimates of the prevalence and reports on the epidemiology of diabetes are needed to help plan and guide the management of this condition. Large-scale reports on the epidemiology of diabetes in Canada have usually relied on administrative datasets such as the National Diabetes Surveillance System (NDSS, a collaborative network supported by the Public Health Agency of Canada) (3) or on survey data such as reports from the Canadian Community Health Survey (4). Similar administrative and survey-based data are available provincially (5e7). These datasets have consistently reported increases in the prevalence of diabetes (5). The most recent NDSS estimate of the prevalence of diagnosed diabetes in Canada was 6.8% in 2009 (3). The accuracy of identification of chronic diseases depends on the data sources and methods used to ascertain the presence of conditions (8,9). There are many potential issues underlying the quality of data in large datasets (10). Health administrative databases use validated algorithms to ascertain disease prevalence and incidence. The assumptions in these algorithms depend on properties of the data (10) such as physician billing behaviour; this can change over time as health systems evolve and billing incentives are modified. Ontario, as an example, has recently switched from a largely fee-for-service billing system to a system in which a majority of family physicians practising comprehensive care are remunerated primarily through capitation for their enrolled patients; physicians receive partial payment (currently 15%) for most fees submitted for services provided (11). The percentage of Canadian family physicians reporting being paid mostly through fee-for-service has decreased from 51% in 2004 to 38% in 2013 (12). The switch to nonefee-for-service payments could be impacting the accuracy of diabetes detection when using administrative data (13). A commonly used Canadian algorithm using administrative data to ascertain the presence of diabetes employs a combination of hospital discharge coding and physician service claims. This had a sensitivity of 86% and a specificity of 97% when validated against primary care chart audits (7); the lower sensitivity leads to an underestimation of true prevalence. A recent systematic review of this algorithm found a pooled sensitivity of 82.3% and noted that periodic revalidations may be needed if trends in the prevalence of diabetes change (14). The sensitivity of survey-based self-reports may be even lower than that of administrative data. A recent study noted that the

prevalence of diabetes in Ontario in 2005 was 7.2% using administrative data and 5.4% using self-reported survey data from the Canadian Community Health Survey; sensitivity was 73% and positive agreement was 82% for self-report as compared to administrative data (8). Administrative datasets can be limited by the lack of clinical data, such as laboratory data, medication prescriptions for patients not covered by provincial plans, routinely collected vital signs or presence of risk factors such as tobacco use. New methods of data capture could provide rich clinical information, augmenting currently available data sources and improving the accuracy and completeness of information available concerning diabetes and other chronic diseases (15,16). Because of the increasing uptake of Electronic Medical Records (EMRs) in Canada (64% of family physicians reported using EMRs in 2013) (17), the collection and analysis of routinely recorded clinical data have recently become feasible (18). In this study, we used data extracted from primary care EMRs across Canada. We provide EMR-based information to complement and extend knowledge about the epidemiology of diabetes in Canadian primary care. Data extracted from EMRs have been used in international settings to study chronic diseases at national levels (19e21). In addition to improving national surveillance data, the availability of national primary care clinical databases can enable comparisons across different countries using a similar approach. Our objective was to describe the epidemiology of diabetes primary care, including utilization, comorbidities and patterns of medication use for this condition. Methods Data sources and study population We extracted data from the EMRs of primary care providers participating in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). CPCSSN is Canada’s first multidisease EMR-based surveillance system (18). It includes 10 primary care practice-based research networks in 8 provinces across Canada. Consenting family physicians and other primary care providers participating in CPCSSN contribute anonymized EMR data to a regional CPCSSN repository; data from all participating networks are aggregated in a single national database (18,22). This study included data from 12 different EMR systems. We used CPCSSN data extracted from the EMRs on December 31, 2012. All patients 10 years of age and older by December 31, 2012, who had at least 1 encounter with the physicians’ practices in the past 24 months and who did not opt out of participation in CPCSSN constituted the study population. Data-extraction procedures have been described previously (18). We chose age 10 for the lower age cutoff because of the increasing incidence of diabetes at younger ages (below age 30) (2). Children under the age of 10 with diabetes

M. Greiver et al. / Can J Diabetes xxx (2014) 1e7

are typically managed by pediatricians; they may not be seen primarily in primary care, and data in a primary care dataset may be limited. For that reason, data concerning children younger than age 10 were not included. The geographic distribution of networks collecting data from primary care sites is available at http://cpcssn.ca/regionalnetworks/. CPCSSN patients were older than those in the 2011 Canadian census (median age in CPCSSN, 48.0 years, census 40.2 years) (23). CPCSSN included a larger proportion of female patients (57.8%) than the population-based proportion (50.4%) (23). Of the patients, 22.6% lived in rural areas, compared to 19% in the census (23,24). The characteristics of the patient population in CPCSSN are shown in Table 1. We classified a patient as having diabetes if he or she met the following criteria: a minimum of 2 occurrences of the International Classification of Diseases, 9th revision (ICD9) code for diabetes (250) in bills within 2 years; or presence of the ICD9 code 250 or free text indicating diabetes in the summary health profile of the chart; or presence of hypoglycemic medication(s); or presence of at least 1 glycated hemoglobin of 7% or greater; or the presence of 2 or more fasting blood glucose results of 7 mmol per liter or more within the same 12-month period. The presence of polycystic ovarian syndrome, gestational diabetes, secondary (chemically induced) diabetes, nonspecific hyperglycemia or neonatal diabetes mellitus made the medication criteria alone insufficient for case definition (25). This approach has been validated in multiple sites across Canada using chart audits (26); sensitivity was 96% and specificity was 97%. Case definition algorithms have been validated for a total of 8 chronic conditions (diabetes, hypertension, chronic obstructive pulmonary disease (COPD), depression, osteoarthritis, dementia, parkinsonism and epilepsy). The 7 additional validated conditions were used to provide estimates of comorbidity. Most patients with chronic conditions have at least 1 primary care visit in 2 years (27), and the majority of care for type 2 diabetes is provided by family physicians (28). In order to estimate the number of patients with diabetes in this primary care population, we included all patients with diabetes and at least 1 encounter in the EMR in a 2-year period (from January 1, 2011, to December 31, 2012). Last, to estimate the population prevalence of diabetes, we

Table 1 Characteristics of the patient population in the Canadian Primary Care Sentinel Surveillance Network for patients age 10 or older as of December 31, 2012, with at least 1 encounter over 2 years (January 1, 2011, to December 31, 2012) Characteristics

n¼272,469

Mean (SD) age Median age % Female % Urban Comorbidity % with hypertension depression COPD osteoarthritis dementia epilepsy parkinsonism Province % from British Columbia Alberta Manitoba Ontario Quebec Nova Scotia Newfoundland

47.1 (20.5) 48.0 57.8 77.4 21.0 15.1 3.7 11.0 2.1 1.0 0.4 3.5 19.2 11.0 41.4 2.0 13.5 9.4

COPD, chronic obstructive pulmonary disease; SD, standard deviation. N, all patients age 10 or older as of December 31, 2012, with at least 1 encounter between January 1, 2011, and December 31, 2012.

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used a corrected yearly contact group approach (29) to estimate a practice population-based denominator. This method has been validated in primary care; it combines data on patients with at least 1 encounter in the past 12 months with the proportion of patients reporting at least 1 encounter in the past year using survey data (29). This proportion is available by gender, age groups and province in Canada (30). The practice population estimate is comparable to that obtained for an enrolled practice population and represents a reasonable estimate of the number of patients who consider themselves part of each practice. We used the following data: dates of each encounter in the past 2 years to estimate utilization in primary care; second element of each patient’s postal code for rurality (a zero indicates a rural address); presence or absence of each of the 8 chronic conditions using the validated algorithms for case definitions; glucose lowering medications prescribed at least once in the 2 years of interest. Statistical analysis We calculated the proportion of patients with diabetes who had had at least 1 primary care encounter in 2 years for each age range and by gender and adjusted the proportion for Canadian population age- and gender-based standards for census year 2011. We used a multivariate Poisson regression analysis controlling for the effects of age, gender, location and comorbid conditions to estimate the relative encounter rate over the 2-year observation window for those with diabetes relative to those without diabetes. To investigate effects at network level (CPCSSN is composed of 10 networks), we carried out a multilevel Poisson regression taking network as a random component to determine whether there exists network-to-network variation in the intercept. The estimate of the variance of the random network intercept was 0.03, and the estimated standard error of this variance component estimate was 0.02. The variation in the intercept due to network effects was not significant. Similarly, we used a log-binomial model to estimate the relative prevalence of the various other conditions for those with and without diabetes, controlling for the effects age and gender along with the corresponding 95% confidence intervals and level of significance. We then determined the age- and gender-adjusted number of comorbid conditions validated by CPCSSN for patients with and without diabetes (excluding diabetes in those without the condition). We compared the mean number of conditions using the t-test and determined the incidence rate ratio (IRR) using the log link function of Poisson distribution, controlling for age and gender. We calculated the mean number of comorbid conditions for patients with and without diabetes by age range in decades and compared this using the t test. Finally, we investigated patterns of usage of hypoglycemic medications by patients with diabetes. We used descriptive data (percentages) for the use of glucose-lowering medications by patients with diabetes by using the drug classifications in a national guideline (2). The analyses were performed using SAS version 9.3. CPCSSN has received ethics approval from the research ethics boards of all host universities for all participating networks and from the Health Canada Research Ethics Board. All participating CPCSSN sentinel primary care providers provided written informed consent for the collection and analysis of their EMR data. Results The population consisted of 272 469 patients and 380 participating primary care providers. Of the patients, 25 425 with diabetes had at least 1 encounter with the practice in 2 years. The age- and

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Table 2 Observed prevalence of diabetes by age group and gender in patients with at least 1 encounter over 2 years (January 1, 2011, to December 31, 2012) Age group

10e19 20e29 30e39 40e49 50e59 60e69 70e79 80þ All ages

Male

Female

%

n

0.6 1.3 2.7 6.6 13.3 21.4 28.5 25.9 11.3

13 13 14 17 20 17 10 6 114

857 983 516 508 562 208 446 832 912

All

%

n

0.5 1.3 3.0 4.4 8.7 15.1 20.0 19.7 7.9

15 21 23 24 26 20 13 10 157

304 715 839 836 812 798 316 937 557

median number of classes of medications used was 2.0 (IQR [Q1 to Q3] 1.0 to 2.0). Discussion

%

n

0.6 1.3 2.8 5.3 10.7 17.9 23.7 22.1 9.3

29 35 38 42 47 38 23 17 272

161 698 355 344 374 006 762 769 469

N, all patients in age group with at least 1 encounter between January 1 2011, and December 31, 2012; %, the patients classified as having diabetes by the Canadian Primary Care Sentinel Surveillance Network algorithm.

gender-adjusted prevalence of diabetes for patients seen in this sample of Canadian primary care practices was 8.2%. The population-based estimate of prevalence, using a corrected yearly contact group practice denominator, was 7.6%. Table 2 provides the age- and gender-specific observed prevalence. Patients with diabetes had a median of 10 encounters (interquartile range [IQR] [Q1 to Q3] 6 to 16) recorded over 2 years; those without diabetes had a median of 5 encounters (IQR 2 to 9). Patients with diabetes had 1.42 times as many primary care encounters over the 2-year period compared to those without diabetes after adjustment for age, gender, location (urban vs. rural) and comorbid conditions (IRR ¼ 1.42, 95% CI 1.42 to 1.43, p<0.0001). Age- and gender-adjusted prevalence ratios for 7 comorbid conditions in patients with and without diabetes are presented in Table 3. The prevalence of most individual comorbid conditions we studied was significantly higher in patients with diabetes. In 68.7% of patients with diabetes, at least 1 other condition existed. The mean number of comorbid conditions for patients with diabetes was 1.06; for those without diabetes, the mean number of conditions, excluding diabetes, was 0.49, a difference of 0.57 (95% CI 0.56 to 0.58, p<0.001). Patients with diabetes had 1.29 times as many other comorbid conditions as those without diabetes (95% CI 1.27 to 1.31, p<0.0001). The Figure 1 presents the mean number of comorbid conditions by age ranges (in decades). Patients with diabetes had a significantly greater number of comorbidities at all age ranges except for patients 80 years of age or older. Table 4 presents the use of glucose-lowering medications by drug classes; 59.9% of patients with diabetes were taking at least 1 medication. The most commonly used medication was metformin; 51% of all patient and 85.2% of patients taking at least 1 medication had received a prescription for this drug. Table 5 presents data concerning the number of classes of medications used by patients on medications. More than half of patients (51.4%) had been prescribed more than 1 class of medication. The

Table 3 Percentage of patients with diabetes on medications that are using each class of glucose-lowering medications Class

% used (n¼15 230*)

Alpha glucosidase inhibitor Dipeptidyl peptidase 4 (dpp-4) inhibitor Glucagon-like peptide-1 (glp1) receptor agonist Insulin Insulin secretagogue, sulphonylurea Insulin secretagogue, meglitinide Metformin Thiazolinedione

0.8 12.4 1.9 27.0 40.0 4.9 85.2 12.9

* 15 230 patients with diabetes of 25 425 patients had at least 1 prescription in the 2 years of interest, based on their last prescription start dates.

We present diabetes prevalence and cross-sectional descriptive data for a new Canadian initiative to study the epidemiology of chronic diseases as they are managed in primary care. This study uses EMRs as a new clinical data source in Canada to provide information on epidemiologic trends and patterns. This is similar to recent approaches using primary care EMR clinical databases for diabetes research and epidemiologic studies in Spain (31), the United Kingdom (32) and the United States (33,34). As in the United States (34), we recognized the importance of having standardized data definitions for chronic diseases across multiple EMR applications and multiple jurisdictional healthcare systems in Canada. Variations in definitions can lead to different estimates of diabetes prevalence for the same patient populations (35). The approach taken by CPCSSN will enable longitudinal comparisons of diabetes incidence, prevalence and management in multiple provinces and territories across Canada. A nationally coordinated approach has recently been recommended by the Institute of Medicine (36). Previous nationally based Canadian studies describing the management of diabetes enrolled smaller numbers of patients because they were limited by the need to collect data using surveys, chart audits or paper-based data collection forms in practices (37,38). Our disease definition algorithm led to an estimated diabetes prevalence of 8.2% in those with an encounter in the past 24 months and an estimated prevalence of 7.6% in the practice population. Both of these estimates are higher than recent Canadian estimates of 6.8% in 2009, derived using administrative algorithms (39) and 6.5% in 2012, derived from survey data (40). Perhaps one of the most obvious reasons for the discrepancy is the higher sensitivity of our algorithm for identifying patients with diabetes in EMR data in contrast to administrative or survey data. Further, one would expect that estimates of prevalence would be higher in a cohort of individuals attending primary care physicians because patients with chronic illnesses are more likely to visit the family physician. This prevalence can be compared to national estimates in other countries where EMR-based data have been used for epidemiologic purposes: 7.6% in Spain in 2009 (31); 6.9% in the United States in 2009 (34); 4.3% in the United Kingdom in 2005 (41) and 4.2% in Denmark in 2007 (42). We found that patients with diabetes have 1.42 times as many primary care encounters as those without diabetes during the same period of time. This is similar to estimates of utilization using administrative data (3). We captured recorded encounter data on non-office-based patient contacts (phone calls, e-mails) as well as office-based encounters. However, the database does not include visits to specialists or other sites of care such as hospitals. Similar to reports by others (43e45), we found that diabetes was associated with a higher prevalence of comorbidities. A framework has recently been developed to help understand and plan care while taking into account comorbid conditions in patients with diabetes (46). These can be classified as concordant or discordant conditions. Concordant conditions share etiologic pathways and require similar management plans; hypertension would be considered a concordant condition. Studies commonly report the prevalence and management of concordant conditions such as cardiovascular conditions (3,47,48). Discordant conditions are unrelated and usually require different medications and management pathways (44,46). Most comorbidities studied here would be considered discordant. Some of these discordant conditions add to the challenges of managing diabetes by making lifestyle measures (for example, ability to exercise) more difficult to undertake or

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Figure 1. Mean number of comorbid conditions by age ranges for patients with and without diabetes.

because of therapeutic drug options that may interfere with diabetes care (44). CPCSSN provides a nationally based longitudinal platform to study the impacts of both concordant and discordant comorbid conditions on diabetes management. Guidelines recommend metformin as first-line treatment for diabetes because this drug is associated with improved cardiovascular outcomes (1,2). In our study, metformin was the most common hypoglycemic medication, with 85.2% of patients on medication having a prescription for this drug. A recent population-based study in Ontario reported that 79% of initial prescriptions for elderly patients were for metformin in 2006 (49). Sulphonylureas represented the next largest class of medications in our study. These patterns of medication usage are similar to those in other reports in Canada (50), the United States (50,51) and the United Kingdom (32). The National Population Health Survey reported that 35.9% of Canadian patients with diabetes were not on any hypoglycemic agent in 2004 (50). We found that 40.1% of primary care patients in our database did not have any hypoglycemic prescription in their record over a 2-year period. Some prescriptions may have been provided in other settings (hospitals, through a specialist) or a prescription may have been provided but not recorded in the chart. Our data definition for diabetes included laboratory results (fasting blood glucose, hemoglobin A1C); we therefore captured diagnosed as well as undiagnosed diabetes. It is unlikely that hypoglycemic medications would be prescribed for undiagnosed diabetes, inflating the percentage of patients managed without medications in our dataset when compared with survey or administrative data. The latter datasets include only diagnosed diabetes. A recent study reported increasingly long intervals between diagnosis of diabetes and initiation of hypoglycemic medications in Ontario (median 1.8 years in 1994 and 4.6 years in 2006) (49), possibly indicating reluctance to initiate medications due to clinical uncertainty about glycemic

Table 4 Percentage and number of patients with diabetes who are on medications by number of medication classes used No. of classes

%

n

1 class 2 classes 3 classes 4þ classes Total

48.6 28.7 14.2 8.5 100.0

7 4 2 1 15

405 378 160 287 230

goals. A substantial number of patients recently diagnosed with diabetes may therefore not yet be taking medications. Further investigations using CPCSSN data could be undertaken to investigate correlations between attainment of glycemic targets and use of medications. Our observational dataset includes certain limitations. CPCSSN practices represent a convenience sample of primary care providers rather than randomly selected practices. The physician sample is reasonably representative when compared to respondents in the 2010 National Physician Survey (24,52). The geographic distribution of practices is similar to that of the National Physician Survey, but CPCSSN physicians are younger and are more likely to be female (24). The data are generalizable to practices similar to those of CPCSSN physicians. As well, the sample includes only physicians using EMRs, who tend to be younger than their colleagues who continue to use paper records (17). Patients are those attending primary care rather than the general population and are therefore slightly older; older patients are more likely to seek healthcare. The data in this sample do not allow us to distinguish between type 1 and type 2 diabetes; most people with diabetes followed in primary care have type 2 diabetes (28). We extracted data from primary care records and therefore we did not include electronic data from systems in hospitals or specialty clinics; however, health information from these settings is usually forwarded to primary care providers, and data may be entered in the EMR. In Canada, the majority of patients have family physicians (53), and diabetes care is provided mainly in primary care (28). This leads to the reasonable assumptions that most of the relevant information on comorbidities and medication use is found within the primary care records, and that most initial prescriptions in hospitals or by specialists will be followed by prescriptions obtained through patients’ primary Table 5 Age- and gender-adjusted prevalence ratios for patients with and without diabetes, for selected comorbidities Comorbidity

Prevalence ratio

Lower 95% CI

Upper 95% CI

p value

Hypertension Depression COPD Osteoarthritis Dementia Epilepsy Parkinsonism

1.30 1.31 1.36 1.08 1.10 0.91 0.93

1.287 1.275 1.302 1.057 1.036 0.790 0.791

1.323 1.347 1.422 1.112 1.169 1.037 1.087

<0.001 <0.001 <0.001 <0.001 0.002 0.151 0.351

CI, confidence Interval; COPD, chronic obstructive pulmonary disease.

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care providers. As with other observational studies, unmeasured confounders are present; data represent only patients seen for care over the period studied. In addition, primary care providers may not consistently enter data using the appropriate structure or in the expected fields of the EMR, leading to inability to capture some data. Our data do not reflect prescriptions filled at pharmacies by patients, only those entered into the record by prescribers; nonetheless, prescription patterns are similar to those in other large datasets. The presence of hypoglycemic medications was used to ascertain the presence of diabetes, which would impact descriptions of medication usage. However, only 7.8% of cases of diabetes were ascertained through medications in the absence of any other criteria; this small proportion makes bias unlikely. As well, we used postal codes as a geographic proxy measure for rurality; areas previously defined as being rural can urbanize over time, with a lag in postal code changes. Notwithstanding these limitations, this study also has some important strengths. These results are obtained from a new Canadian data source that can enrich existing datasets and can be used longitudinally to assess epidemiologic trends and changes in diabetes management. The approach is similar to that taken for other chronic disease surveillance systems in international settings. The data platform and case definitions have been standardized and validated across Canada and across multiple EMR applications, enabling comparisons. CPCSSN data are now available to the research community (applications for use of data are at cpcssn.ca), providing a rich new pan-Canadian resource for the study of diabetes and other chronic conditions. Acknowledgements Grant provided by the Public Health Agency of Canada under a contribution agreement with the College of Family Physicians of Canada, fund # 6271-15-2009-10010002. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada. No potential conflicts of interest relevant to this article were reported. Author Contributions Michelle Greiver drafted the initial version of the article. Michelle Greiver, Alan Katz and Tyler Williamson contributed substantially to conception and design. Tyler Williamson and Shahriar Khan contributed substantially to the analysis of data. All authors contributed to the interpretation of data and drafting of the article. All authors revised the article critically for important intellectual content and gave final approval of the version to be published. References 1. American Diabetes Association. Standards of Medical Care in Diabetes 2010. Diabetes Care 2010;33:S11e61. 2. Canadian Diabetes Association Clinical Practice Guidelines Expert Committee. Canadian Diabetes Association 2013 Clinical Practice Guidelines for the Prevention and Management of Diabetes in Canada. Can J Diabetes 2013;37:S1e212. 3. Report from the National Diabetes Surveillance System: Diabetes in Canada, 2009. Ottawa: Public Health Agency of Canada; 2009. 4. Canadian community health survey. Ottawa: Statistics Canada, 2008. 5. Lipscombe LL, Hux JE. Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995-2005: A population-based study. Lancet 2007;369:750e6. 6. Blanchard JF, Ludwig S, Wajda A, et al. Incidence and prevalence of diabetes in Manitoba, 1986-1991. Diabetes Care 1996;19:807e11. 7. Hux JE, Ivis F, Flintoft V, Bica A. Diabetes in Ontario: Determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 2002;25:512e6. 8. Muggah E, Graves E, Bennett C, Manuel DG. Ascertainment of chronic diseases using population health data: A comparison of health administrative data and patient self-report. BMC Pub Health 2013;13:16.

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