OBJECTIVE

The 2013 Diabetes Canada guidelines launched targeted dissemination tools and a simple assessment for vascular protection. We aimed to 1) examine changes associated with the launch of the 2013 guidelines and additional dissemination efforts in the rates of vascular protective medications prescribed in primary care for older patients with diabetes and 2) examine differences in the rates of prescriptions of vascular protective medications by patient and provider characteristics.

RESEARCH DESIGN AND METHODS

The study population included patients (≥40 years of age) from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) with type 2 diabetes and at least one clinic visit from April 2010 to December 2015. An interrupted time series analysis was used to assess the proportion of eligible patients prescribed a statin, ACE inhibitor (ACEI)/angiotensin receptor blocker (ARB), or antiplatelet prescription in each quarter. Proton pump inhibitor (PPI) prescriptions were the reference control.

RESULTS

A dynamic cohort was used where participants were enrolled each quarter using a prespecified set of conditions (range 25,985–70,693 per quarter). There were no significant changes in statin (P = 0.43), ACEI/ARB (P = 0.42), antiplatelet (P = 0.39), or PPI (P = 0.16) prescriptions at baseline (guideline intervention). After guideline publication, there was a significant change in slope for statin (−0.52% per quarter, SE 0.15, P < 0.05), ACEI/ARB (−0.38% per quarter, SE 0.13, P < 0.05), and reference PPI (−0.18% per quarter, SE 0.05, P < 0.05) prescriptions.

CONCLUSIONS

There was a decrease in prescribing trends over time that was not specific to vascular protective medications. More effective knowledge translation strategies are needed to improve vascular protection in diabetes in order for patients to receive the most effective interventions.

The World Health Organization describes the “rule of halves” whereby half of the people with long-term conditions are not known, half of those conditions known are not treated, and half of those conditions treated are not controlled (1). As a result of these failures, patients with diabetes are at increased risk of developing significant morbidity and mortality related to atherosclerotic cardiovascular complications including coronary heart disease, stroke, and peripheral vascular disease (2). The lifetime 10-year cardiovascular disease (CVD) risk is >20% for patients aged >40 years and living with diabetes (3). Effective risk factor modification with vascular protective medications, including statins, antihypertension agents, and antiplatelet agents, is an essential component of diabetes management to improve both quantity and quality of life-years (3,4). Clinical trial data show a 22–37% risk reduction in CVD for patients age >40 years on statin therapy and a 25% risk reduction for patients age >55 years taking ACE inhibitors (ACEIs) (46). Despite evidence-based support for these therapies, a national physician survey in 2012 estimated that 43% of patients with diabetes do not meet guideline-recommended LDL targets (≤2 mmol/L) and 64% fail to meet blood pressure (BP) targets (<130/80 mmHg), suggesting suboptimal vascular protection management (7).

For bridging of this evidence-to-practice gap for vascular protection in patients with diabetes, dissemination tools were launched with the 2013 Diabetes Canada (previously Canadian Diabetes Association) evidence-based guidelines. Compared with previous (2008) guidelines, the 2013 Diabetes Canada guidelines no longer require providers to stratify patients into different risk categories prior to recommending vascular protective therapy, thereby simplifying the assessment for vascular protection (Supplementary Table 1). Statin use is recommended for all patients ≥40 years old and living with diabetes; ACEIs or angiotensin receptor blockers (ARBs) are recommended for patients ≥55 years old with diabetes. Antiplatelet medications are no longer recommended for routine use in the primary prevention of CVD for patients with diabetes (3). Diabetes Canada also expanded on its patient- and provider-directed dissemination and implementation strategy, which was based on the Knowledge-to-Action Cycle and included a variety of new dissemination tools (8). The nationwide dissemination strategy launched in April 2013 and targeted multiple national and provincial systems-level groups (e.g., government agencies, nongovernmental agencies, disease advocacy groups, and professional associations), as well as health care providers and people living with diabetes across Canada via large-scale communications campaigns (e.g., television, radio, digital and print media) (9) (Supplementary Table 2). Interventions including in-person lecture series, conferences, webinars, web-based professional and patient resources such as flow sheets, electronic point of care decision support, a mobile application, and electronic medical record (EMR) templates were rolled out over 24 months. An evaluation of the effectiveness of this dissemination strategy has been published elsewhere (9).

Multiple studies examining vascular protective agents in Canada have shown an increase in statins and ACEIs/ARBs prescribed and used over the past two decades (1012). Estimates from one Canadian province (Ontario) suggest that statin prescriptions in patients with diabetes aged >65 years have increased 53% from 1996 to 2010 and ACEI/ARB prescriptions have increased by 22% over a 6-year period (1995–2001) (11,12). These studies used data from provincial drug registries, which failed to capture nonprescription medications including antiplatelets and typically involved smaller geographic areas (i.e., city- or province-level data), making it difficult to determine whether dissemination and implementation of the Diabetes Canada guidelines achieved a population-level impact across Canada. Drug registry data also exclude the population of patients with diabetes age <65 years who may be eligible for vascular protective therapy. In addition, there are no studies that examined the prescription of these medications before and after the 2013 Diabetes Canada guidelines were released and disseminated, leaving the impact of the guidelines unknown.

Successful guideline evaluation is critical in assessing whether suggested recommendations have been adopted into practice at a population level. The current study used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a pan-Canadian EMR surveillance system, to capture longitudinal trends in vascular protective agent prescriptions. The specific aims were to 1) examine changes associated with the launch of the 2013 Diabetes Canada Guidelines and additional dissemination efforts in the rates of vascular protective medications prescribed in primary care for older patients with diabetes and 2) examine differences in the rates of vascular protective medication prescriptions according to provider and patient characteristics.

Study Design and Population

We used a retrospective cohort design and followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines (13). We included patients living with diabetes whose data were in the CPCSSN database from April 2010 to December 2015. Diabetes was defined according to the CPCSSN-validated case definition (14) (Supplementary Table 3). The study cohort was dynamic, and patients were enrolled quarterly when the following three temporal conditions were met: 1) onset of diabetes recorded in the EMR prior to or during each quarter of interest, 2) first patient visit recorded within a given quarter or any quarter preceding it, and 3) patient met eligibility criteria for the medication being analyzed defined as per the 2013 Diabetes Canada guideline criteria: patients age ≥40 years for statins and age ≥55 years for ACEI/ARBs. For antiplatelet agents, patients age ≥40 years with no cardiovascular event were included, as guideline changes suggest that these medications should no longer be used for primary prevention in this group. Priorities set were based on well-accepted guideline recommendations and framed such that in our defined cohorts, those prescribed the agent are “controlled” and those not prescribed the agent are “uncontrolled.” Achievement of LDL targets (≤2 mmol/L) and BP targets (<130/80 mmHg) was assessed in patients in the following age categories: age ≥40 years for LDL and age ≥55 years for BP. We employed a censoring point whereby patients were removed from the cohort using the last encounter date if they had not seen their family doctor as indicated by an encounter in the EMR in the last 2 years. The study was approved by the University of Toronto’s Research Ethics Board (REB#33127).

CPCSSN Database

CPCSSN, established in 2008, is an EMR-based information system designed for chronic disease surveillance. Every 3 months, EMR data from primary care practices in 10 practice-based research networks (PBRNs) across Canada are extracted, cleaned, and merged into a single database housed at the Centre for Advanced Computing at Queen’s University in Kingston, Ontario, Canada (15). Contributing PBRNs are located in Alberta (two), British Columbia (one), Manitoba (one), Newfoundland and Labrador (one), Nova Scotia (one), Ontario (three), and Quebec (one), Canada. The network is composed of >1,100 family physicians contributing data for >1,500,000 patients. Information contained in the database includes network and provider identifiers, de-identified patient demographic information, date and type of each patient’s encounters, patient health conditions, risk factors, referrals, laboratory investigations, procedures, and medications (15). CPCSSN captures all medications recorded in the EMR, including medications prescribed by primary care providers or specialists and those purchased over the counter.

Data Collection and Processing

Data from 1 January 2010 to 31 December 2015 were used for this project. The project data included patient demographic variables (year and month of birth, sex, and neighborhood-level socioeconomic status [SES] indicators derived from residential postal codes), physical measurements (height, weight, BMI, systolic BP, and diastolic BP), diabetes-related laboratory records (i.e., total cholesterol, LDL, HDL, triglycerides, and urine albumin-to-creatinine ratio), comorbidities/risk factors (i.e., hypertension, CVD, dyslipidemia, microalbuminuria, and smoking history), all medications prescribed to patients (i.e., lipid-lowering agents, ACEIs, ARBs, antiplatelets, and proton pump inhibitors [PPIs] as a reference control), provider/site characteristics (i.e., province of site and rurality of site) for each quarter of interest, and CPCSSN patient and provider data table (containing information on hierarchical relationship between patient, care provider, site, and network). Year of medical school (MD) graduation for providers was collected from the University of Toronto Practice-Based Research Network (UTOPIAN) network only owing to limited availability at other PBRNs.

All medications in CPCSSN database were coded using Anatomical Therapeutic Chemical codes; these codes were used to identify the relevant medications for this project (16). Long-term conditions were identified using CPCSSN-validated algorithms where available (i.e., diabetes and hypertension) (Supplementary Table 3). For the rest of the conditions, we used coded and free text diagnosis data from the CPCSSN health conditions table; this table populates data from the Cumulative Patient Profile (14). CVD was defined according to guideline and antiplatelet indications to include ICD-9 codes for coronary heart disease (410–414), cerebrovascular disease (430–438), and peripheral vascular disease (440–445) (17). Dyslipidemia was defined using appropriate medication history and coded and free text diagnosis data and included patients on lipid-lowering therapy or with LDL >2.0 mmol/L. Albuminuria was defined according to Diabetes Canada guidelines, whereby a urine albumin creatinine ratio <2 mg/mmol was categorized as normal, 2–20 mg/mmol as microalbuminuria, and >20 mg/mmol as macroalbuminuria (18). SES was measured in accordance with the Canadian Institute for Health Information method of deriving neighborhood-level deprivation indices on a quintile scale for each patient (19). Rurality was defined according to postal code whereby a second letter of “0” was considered rural and a second letter “1–9” considered urban (20). In accordance with recommendations of the World Health Organization and Health Canada, BMI was classified into categories of underweight (<18.5 kg/m2), normal weight (18.5–24.99 kg/m2), overweight (25–29.99 kg/m2), obese class I (30–34.99 kg/m2), obese class II (35–39.99 kg/m2), and class III (≥40 kg/m2) (21).

Analysis

This study used an interrupted time series (ITS) design. For the primary analysis, the proportion of eligible patients who had prescriptions for statins, ACEI/ARBs, or antiplatelets in each quarter was computed using a longitudinal data analysis (22). Quarterly intervals were chosen to introduce less fluctuation in the time series curve compared with a monthly approach (23). If patients had multiple visits within a quarter, the most recent record was used for all variables studied. A total of eight quarters before, one quarter during, and nine quarters after the intervention were analyzed. Patients were deemed to be prescribed a statin, ACEI/ARB, or antiplatelet agent if they had any prescription record in the four preceding quarters and one quarter after each quarter of interest (lag4, lead1) (Fig. 1). This approach was used to account for variation in refill protocols and prescription procedures among family physicians (i.e., some provide a prescription every 3 months and others provide multiple repeats) (24). Having four quarters before and one after each quarter of interest would provide a sufficient time frame to capture prescriptions given on a yearly basis. We also examined other approaches, including (lag0, lead0), (lag1, lead1), (lag2, lead2), (lag3, lead3), (lag4, lead4), and (lag4, lead1). We found that prescription rates for the latter three approaches were very similar, further justifying our selection of the (lag4, lead1) approach.

Figure 1

(Lag4, lead1) approach for sample quarter (2013 Q2). 2013 Q2 represents the quarter of interest. As illustrated, data from four lag quarters (2012 Q2–2013 Q1) and one lead quarter (2013 Q3) are included in the analysis for 2013 Q2.

Figure 1

(Lag4, lead1) approach for sample quarter (2013 Q2). 2013 Q2 represents the quarter of interest. As illustrated, data from four lag quarters (2012 Q2–2013 Q1) and one lead quarter (2013 Q3) are included in the analysis for 2013 Q2.

The launch of the Diabetes Canada guidelines and dissemination strategy in April 2013 was considered the intervention point (2013 quarter 2 [Q2]). A segmented regression model was used to assess baseline trend/slope, the level change immediately after intervention, and the trend/gradual change (25). We also measured the prescription rates of statin, ACEI/ARB, and antiplatelets using a cross-sectional study design, which deemed a patient to be using the medication if they had at least one prescription during the entire study period (2010 Q1–2015 Q4). This approach serves as a sensitivity analysis for the (lag4, lead1) approach and allowed us to test for subgroup differences in the proportion prescribed statin, ACEI/ARB, and antiplatelets.

Secondary analyses included the proportion of eligible patients attaining LDL (≤2.0 mmol/L) or BP (<130/80 mmHg) target. A carryover approach was implemented: each patient’s most recent measurement was used in the following quarters until a new measurement was recorded in the EMR. A segmented regression model was used to assess the baseline trend/slope, level change, and trend/gradual change for secondary outcomes (i.e., eligible patients attaining LDL or BP target).

Results were stratified by patient characteristics (age, sex, BMI, and SES), presence of risk factors/comorbidities (smoking, CVD, hypertension, dyslipidemia, and albuminuria), and provider characteristics (province of care and rurality). We assessed the statistical significance across patient, provider, and geographical characteristics for prescription rates for statins, ACEI/ARBs, and antiplatelet agents using χ2 test (adjusted for multiple hypothesis testing). We used the method of false discovery rate to control for inflated type I error rates in multiple hypothesis testing procedures (26). PPI prescriptions were used as a reference to control for confounding factors. Using modified hypothesis testing, we look at the temporal difference in prescription rates of PPIs compared with those of all three vascular protective medications (e.g., statin 2011 Q2–2015 Q3 vs. PPI 2011 Q2–2015 Q3) and considered statistical significance at P < 0.05. The analyses were conducted using SAS, version 9.4 (27).

Primary Outcomes

The total number of patients enrolled in each dynamic cohort is outlined in Table 1. Quarterly cohort size ranged from 23,016 to 70,693 patients (Supplementary Table 4). Variation in population size was attributed to increasing recruitment of practices into the CPCSSN database over time. Results of the ITS analysis are presented in Table 2 and graphically in Fig. 2. There were no significant changes in the rate of statin, ACEI/ARB, antiplatelet, or PPI prescriptions prior to the release of the 2013 guidelines. After guideline publication, there was a significant change in slope for statins (−0.52% per quarter, SE 0.15, P < 0.05) and ACEI/ARBs (−0.38% per quarter, SE 0.13, P < 0.05) prescribed. A significant change in slope was also seen for PPI prescriptions (−0.18% per quarter, SE 0.05, P < 0.05). The change in slope was not significant for antiplatelets. The absolute difference in the rates of statin, ACEI/ARB, or antiplatelet prescriptions from the start (2011 Q2) to the end (2015 Q4) of the study period was significantly less than that of the PPI reference control (P < 0.0001).

Table 1

Patient, provider, and geographical characteristics for statin, ACEI/ARB, and antiplatelet prescriptions among patients with type 2 diabetes

Statin medication
ACEI/ARB medication
Antiplatelet medication
N%PN%PN%P
Age (years)          
 40–49 4,084 56.1 0.0006 — — — 814 13.4 0.001 
 50–59 9,766 58.6  3,023 39.8 <0.0001 2,125 15.3  
 60–69 13,448 58.7  11,142 48.7  2,982 15.6  
 70–79 10,769 58.3  8,910 48.2  2,377 15.4  
 ≥80 8,589 59.2  7,076 48.7  1,781 14.7  
BMI (kg/cm2         
 <18.5 227 59.1 0.33 163 51.1 0.004 48 15.1 0.34 
 18.5–24.9 4,162 60.5  2,877 50.1  902 15.8  
 25–29.9 9,455 60.4  6,431 49.3  1,947 14.8  
 30–34.9 8,734 59.9  5,575 47.8  1,839 15.1  
 35–39.9 4,857 59.3  2,915 47.5  987 14.4  
 ≥40 3,992 59.0  2,196 47.1  821 14.6  
 Missing 15,229 55.5  9,994 45.6  3,535 15.5  
Sex          
 Female 22,147 58.4 0.99 14,365 47.6 0.71 4,723 14.9 0.15 
 Male 24,505 58.4  15,786 47.4  5,355 15.3  
 Missing 44.4     14.3  
SES quintiles          
 1 (highest) 3,958 59.4 <0.0001 2,574 47.3 0.001 847 15.2 0.001 
 2 5,071 61.6  3,233 49.5  989 14.6  
 3 4,577 61.1  2,919 48.4  948 15.1  
 4 3,957 58.2  2,603 48.1  833 14.7  
 5 (lowest) 4,134 56.0  2,595 45.7  778 12.7  
 Missing 24,959 57.6  16,227 47.2  5,684 15.7  
Smoking status          
 Current smoker 4,280 59.5 <0.0001 2,460 48.3 0.015 926 15.5 0.008 
 Nonsmoker 8,065 58.0  5,356 49.0  1,614 14.0  
 Past smoker 9,798 63.4  6,500 50.5  1,956 15.1  
 Missing 24,513 56.6  15,835 45.8  5,583 15.4  
Hypertension          
 No 16,551 50.0 <0.0001 8,160 31.0 <0.0001 3,481 11.7 <0.0001 
 Yes 30,105 64.4  21,991 59.2  6,598 17.8  
Dyslipidemia          
 No 8,461 41.3 <0.0001 6,485 39.3 <0.0001 2,240 13.2 <0.0001 
 Yes 38,195 64.3  23,666 50.3  7,839 15.8  
Albuminuria          
 Microalbuminuria 11,190 69.5 <0.0001 7,623 60.0 <0.0001 2,645 20.5 <0.0001 
 Normal 13,861 63.2  8,438 48.7  2,898 15.4  
 Macroalbuminuria 3,372 73.1  2,493 68.2  838 24.7  
 Missing 18,233 49.0  11,597 38.9  3,698 11.7  
CVD          
 No 36,395 54.6 <0.0001 23,456 44.2 <0.0001 10,079 15.1 N/A 
 Yes 10,261 77.8  6,695 64.1     
Rurality          
 Rural 8,895 53.9 <0.0001 5,984 44.5 <0.0001 1,976 14.5 0.001 
 Urban 33,049 61.1  21,137 49.2  7,094 15.7  
 Missing 4,712 50.9  3,030 42.9  1,009 12.8  
Province          
 AB 5,804 45.1 <0.0001 4,022 39.5 <0.0001 1,076 9.9 <0.0001 
 BC 385 35.2  366 41.5  85 9.5  
 MB 9,044 59.2  5,758 48.6  1,977 15.8  
 MP 1,636 64.8  1,048 50.5  540 26.3  
 NL 1,941 54.0  1,421 48.6  494 16.8  
 NW 722 56.9  439 55.4  200 19.2  
 ON 26,194 62.4  16,483 48.9  5,519 15.6  
 QC 930 74.2  614 55.9  188 17.9  
MD graduation year          
 1965–1979 1,871 70.6 0.15 1,241 57.0 0.18 472 20.9 <0.0001 
 1980–1994 5,769 67.8  3,764 54.9  1,094 15.3  
 ≥1995 5,872 69.2  3,688 56.1  1,323 18.3  
 Non-UTOPIAN 33,144 55.0  21,458 44.8  7,190 14.4  
Total number of patients prescribed medication 46,656 58.4  30,151 47.5  10,079 15.1  
Total number of patients enrolled in dynamic cohort over study period 79,880 100  63,494 100  66,683 100  
Statin medication
ACEI/ARB medication
Antiplatelet medication
N%PN%PN%P
Age (years)          
 40–49 4,084 56.1 0.0006 — — — 814 13.4 0.001 
 50–59 9,766 58.6  3,023 39.8 <0.0001 2,125 15.3  
 60–69 13,448 58.7  11,142 48.7  2,982 15.6  
 70–79 10,769 58.3  8,910 48.2  2,377 15.4  
 ≥80 8,589 59.2  7,076 48.7  1,781 14.7  
BMI (kg/cm2         
 <18.5 227 59.1 0.33 163 51.1 0.004 48 15.1 0.34 
 18.5–24.9 4,162 60.5  2,877 50.1  902 15.8  
 25–29.9 9,455 60.4  6,431 49.3  1,947 14.8  
 30–34.9 8,734 59.9  5,575 47.8  1,839 15.1  
 35–39.9 4,857 59.3  2,915 47.5  987 14.4  
 ≥40 3,992 59.0  2,196 47.1  821 14.6  
 Missing 15,229 55.5  9,994 45.6  3,535 15.5  
Sex          
 Female 22,147 58.4 0.99 14,365 47.6 0.71 4,723 14.9 0.15 
 Male 24,505 58.4  15,786 47.4  5,355 15.3  
 Missing 44.4     14.3  
SES quintiles          
 1 (highest) 3,958 59.4 <0.0001 2,574 47.3 0.001 847 15.2 0.001 
 2 5,071 61.6  3,233 49.5  989 14.6  
 3 4,577 61.1  2,919 48.4  948 15.1  
 4 3,957 58.2  2,603 48.1  833 14.7  
 5 (lowest) 4,134 56.0  2,595 45.7  778 12.7  
 Missing 24,959 57.6  16,227 47.2  5,684 15.7  
Smoking status          
 Current smoker 4,280 59.5 <0.0001 2,460 48.3 0.015 926 15.5 0.008 
 Nonsmoker 8,065 58.0  5,356 49.0  1,614 14.0  
 Past smoker 9,798 63.4  6,500 50.5  1,956 15.1  
 Missing 24,513 56.6  15,835 45.8  5,583 15.4  
Hypertension          
 No 16,551 50.0 <0.0001 8,160 31.0 <0.0001 3,481 11.7 <0.0001 
 Yes 30,105 64.4  21,991 59.2  6,598 17.8  
Dyslipidemia          
 No 8,461 41.3 <0.0001 6,485 39.3 <0.0001 2,240 13.2 <0.0001 
 Yes 38,195 64.3  23,666 50.3  7,839 15.8  
Albuminuria          
 Microalbuminuria 11,190 69.5 <0.0001 7,623 60.0 <0.0001 2,645 20.5 <0.0001 
 Normal 13,861 63.2  8,438 48.7  2,898 15.4  
 Macroalbuminuria 3,372 73.1  2,493 68.2  838 24.7  
 Missing 18,233 49.0  11,597 38.9  3,698 11.7  
CVD          
 No 36,395 54.6 <0.0001 23,456 44.2 <0.0001 10,079 15.1 N/A 
 Yes 10,261 77.8  6,695 64.1     
Rurality          
 Rural 8,895 53.9 <0.0001 5,984 44.5 <0.0001 1,976 14.5 0.001 
 Urban 33,049 61.1  21,137 49.2  7,094 15.7  
 Missing 4,712 50.9  3,030 42.9  1,009 12.8  
Province          
 AB 5,804 45.1 <0.0001 4,022 39.5 <0.0001 1,076 9.9 <0.0001 
 BC 385 35.2  366 41.5  85 9.5  
 MB 9,044 59.2  5,758 48.6  1,977 15.8  
 MP 1,636 64.8  1,048 50.5  540 26.3  
 NL 1,941 54.0  1,421 48.6  494 16.8  
 NW 722 56.9  439 55.4  200 19.2  
 ON 26,194 62.4  16,483 48.9  5,519 15.6  
 QC 930 74.2  614 55.9  188 17.9  
MD graduation year          
 1965–1979 1,871 70.6 0.15 1,241 57.0 0.18 472 20.9 <0.0001 
 1980–1994 5,769 67.8  3,764 54.9  1,094 15.3  
 ≥1995 5,872 69.2  3,688 56.1  1,323 18.3  
 Non-UTOPIAN 33,144 55.0  21,458 44.8  7,190 14.4  
Total number of patients prescribed medication 46,656 58.4  30,151 47.5  10,079 15.1  
Total number of patients enrolled in dynamic cohort over study period 79,880 100  63,494 100  66,683 100  

AB, Alberta; BC, British Columbia, MB, Manitoba; MP, Maritime provinces; NL, Newfoundland and Labrador; NW, Northwest territories; N/A, not applicable; ON, Ontario; QC, Quebec.

Table 2

ITS regression analysis of vascular protective medication prescribing rates

EstimateSEP
Statin    
 Intercept B0 50.97 0.95 <0.0001 
 Preintervention trend B1 0.33 0.10 0.0072 
 Level change 0.35 0.43 0.4280 
 Trend change −0.52 0.15 0.0045 
ACEI/ARB    
 Intercept B0 41.66 0.85 <0.0001 
 Preintervention trend B1 0.14 0.09 0.1691 
 Level change 0.37 0.45 0.4218 
 Trend change −0.38 0.13 0.0127 
Antiplatelet    
 Intercept B0 9.75 0.44 <0.0001 
 Preintervention trend B1 0.13 0.05 0.3918 
 Level change −0.26 0.30 0.3918 
 Trend change −0.06 0.06 0.3129 
PPI    
 Intercept B0 18.92 0.36 <0.0001 
 Preintervention trend B1 0.39 0.04 <0.0001 
 Level change 0.31 0.21 0.1586 
 Trend change −0.18 0.05 0.0044 
EstimateSEP
Statin    
 Intercept B0 50.97 0.95 <0.0001 
 Preintervention trend B1 0.33 0.10 0.0072 
 Level change 0.35 0.43 0.4280 
 Trend change −0.52 0.15 0.0045 
ACEI/ARB    
 Intercept B0 41.66 0.85 <0.0001 
 Preintervention trend B1 0.14 0.09 0.1691 
 Level change 0.37 0.45 0.4218 
 Trend change −0.38 0.13 0.0127 
Antiplatelet    
 Intercept B0 9.75 0.44 <0.0001 
 Preintervention trend B1 0.13 0.05 0.3918 
 Level change −0.26 0.30 0.3918 
 Trend change −0.06 0.06 0.3129 
PPI    
 Intercept B0 18.92 0.36 <0.0001 
 Preintervention trend B1 0.39 0.04 <0.0001 
 Level change 0.31 0.21 0.1586 
 Trend change −0.18 0.05 0.0044 

Preintervention trend, before guideline intervention; level change, at guideline intervention; trend change, difference in slope before/after guideline intervention.

Figure 2

A: Proportion of eligible patients prescribed vascular protective medications in Canada, quarterly, from 2011 to 2015. Solid vertical line indicates the intervention point of guideline publication and dissemination (2013 Q2). B: Proportion of eligible patients achieving recommended LDL and BP targets in Canada, quarterly, from 2011 to 2015. Solid vertical line indicates the intervention point of guideline publication and dissemination (2013 Q2).

Figure 2

A: Proportion of eligible patients prescribed vascular protective medications in Canada, quarterly, from 2011 to 2015. Solid vertical line indicates the intervention point of guideline publication and dissemination (2013 Q2). B: Proportion of eligible patients achieving recommended LDL and BP targets in Canada, quarterly, from 2011 to 2015. Solid vertical line indicates the intervention point of guideline publication and dissemination (2013 Q2).

Geographic Characteristics

Prescription rates in eligible patients were significantly higher in urban than rural practices for statins, ACEI/ARBs, and antiplatelets across the study period (Table 1). χ2 test revealed significant differences in prescriptions rates between provinces for statins (P < 0.001), ACEI/ARBs (P < 0.001), and antiplatelets (P < 0.001). Statin and ACEI/ARB prescription rates were highest in the province of Quebec (74.2% for statins and 55.9% for ACEI/ARBs) and in the Maritimes provinces (64.8% for stains and 50.5% for ACEI/ARBs) and lowest in Alberta (45.1% for statins and 39.5% ACEI/ARBs) and British Columbia (35.2% for statins and 41.5% ACEI/ARBs).

Provider Characteristics

There were no significant differences between MD graduation groups for statin (P = 0.152) or ACEI/ARB (P = 0.18) prescriptions in the UTOPIAN population (Table 1). However, there were significant differences between MD graduation groups for antiplatelet prescriptions (P < 0.001). Antiplatelet prescription rates were 20.9% for MD graduation years of 1965–1979, 15.3% for 1980–1994, and 18.3% for ≥1995.

Patient Demographics and Risk Factors

Prescription rates according to patient characteristics are seen in Table 1. There were no significant sex-based differences in prescription rates for statins (P = 0.99), ACEI/ARBs (P = 0.71), or antiplatelet agents (P = 0.15). Patients with hypertension had significantly higher statin (P < 0.001), ACEI/ARB (P < 0.001), and antiplatelet (P < 0.001) prescription rates compared with patients without hypertension. Patients with CVD also had higher statin (P < 0.001) and ACEI/ARB (P < 0.001) prescription rates compared with patients without CVD. Patients without dyslipidemia had significantly lower rates of statin prescription (P < 0.001), ACEI/ARB prescriptions (P < 0.001), and antiplatelet prescriptions (P < 0.001) compared with those with dyslipidemia. Significant differences were detected for age groups, SES quintiles, smoking status, hypertension, dyslipidemia, and albuminuria for all three vascular protective medications (P < 0.05). Significant differences between BMI groups were seen with respect to ACEI/ARB prescriptions (P < 0.001).

Secondary Outcomes

LDL and BP Targets

Across the study period, LDL and BP targets were achieved by 47–57% and 61–66% of patients, respectively (Fig. 2B and C). The release of the 2013 guidelines did not significantly impact the proportion of patients achieving BP targets, as there were no significant changes in this proportion before, during, or after guideline release (Table 2). The release of the guidelines did not impact the proportion of patients achieving LDL targets, as there were no significant changes in this proportion during or after guideline release.

The results of this large study suggest that dissemination of the 2013 Diabetes Canada guidelines was not associated with any further improvements in physician prescribing behavior as measured by EMR prescription estimates for primary vascular protection in patients age ≥40 years with diabetes. While there was no change in prescriptions at the intervention point, a small but statistically significant decrease in slope after guideline publication was observed with statins and ACEI/ARBs, as well as the reference control. A negative change in slope (improved guideline adherence) was also observed with antiplatelets; however, this change was not significant. There were no significant changes with respect to LDL and BP targets at or after guideline intervention. Overall prescription rates were influenced by factors including rurality, province, and patient age and SES. While statistically significant changes were seen across patient/provider characteristics, these do not necessarily represent clinical significance given the large sample size.

Our time series analysis revealed a negative change in slope for statins, ACEI/ARBs, and PPIs. Owing to a lack of sufficient variation in the time series curves shown in Fig. 2A, we caution that this statistical inference can be affected by minuscule changes in prescription rates during any quarter pre– and post–guideline implementation (28). This decrease was also seen with the PPI comparator, suggesting that external factors cannot be excluded. It is possible that there was no actual improvement in vascular protective medication prescribing after release of the 2013 guidelines. The CPCSSN population also involves a dynamic cohort, so a change in composition of patients, physicians, or sites included in the database in each quarter could also influence prescription rates. Entry to the dynamic cohort was random, and there would be no reason to suspect that patients with diabetes from practices joining CPCSSN later would be more likely to have lower prescribing rates. We also did not observe any remarkable shifts with respect to composition of site locations but did observe an increase in patients age 40–49 years from 5.89% to 9.15% across the study period. Patients in this age category had lower statin and ACEI/ARB prescription rates, and thus an increase in the proportion of these patients may have contributed to the measurable decrease seen in prescribing rates over time. It is also possible that an emerging focus on resource stewardship in medical education and practice influenced physicians to prescribe more cautiously, decreasing prescription rates over time (29).

Our prescription estimates are lower than those from 2003 to 2008 Diabetes Canada guideline evaluations (7,30). These studies used physician self-reported survey data subject to selection bias, as physicians willing to participate may be those who prescribe in accordance with guideline recommendations. A validation study is currently ongoing to compare our results with data from the Institute for Clinical and Evaluative Sciences (ICES), which encompasses publicly funded administrative health data for the Ontario population eligible for universal health coverage (31). Preliminary results from this study confirm no improvement in statin, ACEI, and ARB prescriptions at or after guideline intervention (P > 0.05). Statin prescription estimates were similar (62–65%) to those in the current study (52–56%), and increased rates may be attributed to the older population cohort included in ICES (age ≥65 years).

We observed better guideline adherence in urban areas and provinces, including Quebec, Ontario, and the Maritimes. It is possible that dissemination outreach strategies were less effective for removed locations including rural areas and provinces such as Northwest Territories, Alberta, and British Colombia. Increased distance from health care access leading to reduced frequency of physician visits may inhibit physicians in rural areas from adhering to guidelines (32). Physicians in rural areas report increased patient resistance to medical or preventative care as a barrier to guideline adherence (32). We also observed lower prescription rates among patients from lower SES brackets, which is consistent with low SES cohorts being less likely to receive guideline-recommended diabetes care (33).

National survey data from primary care providers across Canada highlight significant gaps in knowledge and behavior change constructs for vascular protection (9). While awareness of the 2013 Diabetes Canada vascular protective guidelines was 71% among survey respondents, only 22% had correct knowledge of guideline content for vascular protection upon questioning and only 21% reported adhering to guideline recommendations (9). It remains possible that despite ongoing dissemination efforts, prescription rates may have reached a ceiling in terms of any further improvements in uptake.

Limitations of this study should be acknowledged. First, the CPCSSN cohort involves a convenient sample of patients, providers, and practices, which limits the generalizability of findings. Second, results must be interpreted carefully, as prescription estimates may not correspond with actual medication usage. Third, the CPCSSN-validated case definition for diabetes differed from the 2013 Diabetes Canada guidelines definition. The CPCSSN definition includes both type 1 and type 2 diabetes, with an HbA1c ≥7% or fasting glucose ≥7 mmol/L, while Diabetes Canada includes an HbA1c ≥6.5% or fasting glucose ≥7 mmol/L. We suspect that CPCSSN was unable to capture some patients who would otherwise be considered to have diabetes by the Diabetes Canada guidelines, potentially overestimating the rates of statin and ACEI/ARB prescriptions, as usage of these medications is associated with higher HbA1c levels (34,35). However, we suspect that this number would have been small, as patients can be captured by other diabetes criteria according to the CPCSSN case definition (14). Fourth, a lack of a validated case definition for CVD, microalbuminuria, and dyslipidemia in CPCSSN may reduce the specificity and sensitivity of multiple estimates. Finally, ITS analyses cannot infer cause-effect relationships, as external factors are not accounted for. An analysis of PPI prescriptions showed trends similar to those of the vascular protective prescriptions under investigation, suggesting that the potential for confounding effects cannot be excluded.

There are limitations unique to using practice-based EMRs, whereby accuracy and completeness can vary across different systems, providers, and sites, in turn affecting the validity of the study (36). It is possible that a full record of patient medications including those requiring manual input (nonprescription, specialist prescriptions) are not recorded in the EMR by all providers. Accordingly, data from nonprescription medications as a group are typically less accurate than prescription estimates (37). Antiplatelet agents carry significant bleeding risk, and we suspect that they would be more likely to be recorded in the EMR than other nonprescription medications with lower-risk profiles. There is also no reason to expect differences in the completeness of data pre– and post–guideline implementation.

Despite these limitations, this study captured trends in vascular protective medication prescriptions in a large sample of Canadian primary care patients with diabetes. Previous studies have primarily used provincial data and are limited in their generalizability at a national level. Use of longitudinal data enabled assessment of trends over time and allowed for effective guideline evaluation at a population level. EMR data allowed for a more valid assessment of prescription rates, as self-reported data often overestimate medication adherence behavior (38). EMR data also allowed us to capture the rates of nonprescription medications including antiplatelet agents and data across greater age ranges (i.e., age <65 years), which are often unavailable with drug registry data.

We carried out statistical inference using an ITS design, which is considered a stronger research design than the traditional before-and-after longitudinal design and remains the recommended quasi-experiment design when a randomized control trial is not feasible at the community level (39,40). The total number of patients prescribed medications per quarter were aggregated into a single population-level proportion, and therefore results correspond with an intervention effect at the population level rather than the individual level. ITS has also been mainly used in conditions applicable to our study: a macro-level intervention (nationwide guideline dissemination strategy), a unidimensional outcome (prescription rates for vascular protective agents), and a large population (of primary care patients with diabetes) (25).

This study highlights a persistent gap in the management of vascular protection among patients with diabetes. Qualitative assessments are needed to assess the collective experience of physicians and elicit factors not captured in our quantitative design (i.e., registries which capture information about why a physician did not prescribe a guideline-recommended treatment). Further dissemination efforts should be targeted at groups with lower adherence rates including physicians from rural areas and those treating patients with lower SES. They may also involve EMR-based prompts to serve as reminders for providers. It is possible that a ceiling level of vascular protective medication uptake has been achieved; however, it is necessary to explore other reasons for guideline nonadherence and revise dissemination strategies accordingly before arriving at this conclusion.

Acknowledgments. The authors would like to thank Carolyn Gall Casey (Diabetes Canada, Toronto, Ontario, Canada) for her continued support and contributions in securing study funding.

Funding. A.V.R. was supported by the Comprehensive Research Experience for Medical Students (CREMS) Stipend. The authors are also grateful to Diabetes Canada for providing funds for a component of data collection and analysis. The Canadian Diabetes Association is the registered owner of the name Diabetes Canada.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. A.V.R. wrote the manuscript. A.V.R., J.N., and C.Y. initially conceived the concept of this study. S.K., B.A., and R.M. performed the statistical analyses. B.A. obtained and managed the data. A.V.R., S.K., J.N., B.A., M.G., R.M., F.S., and C.Y. made substantial contributions to the interpretation of data and revised the manuscript for important intellectual content. C.Y. is the guarantor of this work and as such, had full access to all the data in the study and takes responsibility for the integrity of data and the accuracy of the data analysis.

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Supplementary data