Are Guideline-concordant Processes of Care Consistent Across the Rural-Urban Continuum? A Retrospective Cohort Study of Adults Newly Treated for Type 2 Diabetes.

OBJECTIVES: Our aim in this study was to identify the association between place of residence (metropolitan, urban, rural) and guideline-concordant processes of care in the first year of type 2 diabetes management. METHODS: We conducted a retrospective cohort study of new metformin users between April 2015 and March 2020 in Alberta, Canada. Outcomes were identified as guideline-concordant processes of care through the review of clinical practice guidelines and published literature. Using multivariable logistic regression, the following outcomes were examined by place of residence: dispensation of a statin, angiotensin-converting enzyme inhibitor (ACEi) or angiotensin II receptor blocker (ARB), eye examination, glycated hemoglobin (A1C), cholesterol, and kidney function testing. RESULTS: Of 60,222 new metformin users, 67% resided in a metropolitan area, 10% in an urban area, and 23% in a rural area. After confounder adjustment, rural residents were less likely to have a statin dispensed (adjusted odds ratio [aOR] 0.83, 95% confidence interval [CI] 0.79 to 0.87) or undergo cholesterol testing (aOR 0.86, 95% CI 0.83 to 0.90) when compared with metropolitan residents. In contrast, rural residents were more likely to receive A1C and kidney function testing (aOR 1.14, 95% CI 1.08 to 1.21 and aOR 1.17, 95% CI 1.11 to 1.24, respectively). ACEi/ARB use and eye examinations were similar across place of residence. CONCLUSIONS: Processes of care varied by place of residence. Limited cholesterol management in rural areas is concerning because this may lead to increased cardiovascular outcomes.

Rurality is associated with lower likelihood of dipeptidyl peptidase 4 inhibitor use for treatment intensification.

Background: Antihyperglycemic drug utilization studies are conducted frequently and describe the uptake of new drug therapies across may jurisdictions. An increasingly important, yet often absent, aspect of these studies is the impact of rurality on drug utilization. Objectives: The objective of this study was to explore the association between place of residence (rural, urban, metropolitan) and the use of dipeptidyl peptidase 4 inhibitors (DPP-4i) for first treatment intensification of type 2 diabetes. Methods: A retrospective cohort study was conducted from April 1, 2008 to March 31, 2019 of new metformin users. A multivariable logistic regression analysis was performed to determine the association between place of residence (using postal codes) and likelihood of DPP-4i dispensing. Results: After adjusting for confounders, analysis revealed that rural-dwellers are less likely to have a DPP-4i dispensed, compared with metropolitan-dwellers (aOR:0.64; 95%CI:0.61-0.67) and over-time, the uptake in rural areas was slower. Conclusions: This study demonstrates that rurality can have an impact on drug therapy decisions at first treatment intensification, with respect to the utilization of new therapies.

Predicting 30-day risk from benzodiazepine/Z-drug dispensations in older adults using administrative data: A prognostic machine learning approach.

OBJECTIVE: To develop a machine-learning (ML) model using administrative data to estimate risk of adverse outcomes within 30-days of a benzodiazepine (BZRA) dispensation in older adults for use by health departments/regulators. DESIGN, SETTING AND PARTICIPANTS: This study was conducted in Alberta, Canada during 2018-2019 in Albertans 65 years of age and older. Those with any history of malignancy or palliative care were excluded. EXPOSURE: Each BZRA dispensation from a community pharmacy served as the unit of analysis. MAIN OUTCOMES AND MEASURES: ML algorithms were developed on 2018 administrative data to predict risk of any-cause hospitalization, emergency department visit or death within 30-days of a BZRA dispensation. Validation on 2019 administrative data was done using XGBoost to evaluate discrimination, calibration and other relevant metrics on ranked predictions. Daily and quarterly predictions were simulated on 2019 data. RESULTS: 65,063 study participants were included which represented 633,333 BZRA dispensation during 2018-2019. The validation set had 314,615 dispensations linked to 55,928 all-cause outcomes representing a pre-test probability of 17.8%. C-statistic for the XGBoost model was 0.75. Measuring risk at the end of 2019, the top 0.1 percentile of predicted risk had a LR + of 40.31 translating to a post-test probability of 90%. Daily and quarterly classification simulations resulted in uninformative predictions with positive likelihood ratios less than 10 in all risk prediction categories. Previous history of admissions was ranked highest in variable importance. CONCLUSION: Developing ML models using only administrative health data may not provide health regulators with sufficient informative predictions to use as decision aids for potential interventions, especially if considering daily or quarterly classifications of BZRA risks in older adults. ML models may be informative for this context if yearly classifications are preferred. Health regulators should have access to other types of data to improve ML prediction.

Predicting falls-related admissions in older adults in Alberta, Canada: a machine-learning falls prevention tool developed using population administrative health data.

OBJECTIVE: To construct a machine-learning (ML) model for health systems with organised falls prevention programmes to identify older adults at risk for fall-related admissions. DESIGN: This prognostic study used population-level administrative health data to develop an ML prediction model. SETTING: This study took place in Alberta, Canada during 2018-2019. PARTICIPANTS: Albertans aged 65 and older with at least one prior admission. Those with palliative conditions or emigrated out of Alberta were excluded. EXPOSURE: Unit of analysis was the individual person. MAIN OUTCOMES/MEASURES: We identified fall-related admissions. A CatBoost model was developed on 2018 data to predict risk of fall-related emergency department visits or hospitalisations. Temporal validation was done using 2019 data to evaluate model performance. We reported discrimination, calibration and other relevant metrics measured at the end of 2019 on both ranked predictions and predicted probability thresholds. A cost-savings simulation was performed using 2019 data. RESULTS: Final number of study participants was 224 445. The validation set had 203 584 participants with 19 389 fall-related events (9.5% pretest probability) and an ML model c-statistic of 0.70. The highest ranked predictions had post-test probabilities ranging from 40% to 50%. Net benefit analysis presented mixed results with some net benefit using the ML model in the 6%-30% range. The top 50 percentile of predicted risks represented nearly $C60 million in health system costs related to falls. Intervening on the top 25 or 50 percentiles of predicted risk could realise substantial (up to $C16 million) savings. CONCLUSION: ML prediction models based on population-level administrative data can assist health systems with fall prevention programmes identify older adults at risk of fall-related admissions and reduce costs. ML predictions based on ranked predictions or probability thresholds could guide subsequent interventions to mitigate fall risks. Increased access to diverse forms of data could improve ML performance and further reduce costs.

Rural Residence Is Associated With a Delayed Trend Away From Sulfonylurea Use for Treatment Intensification of Type 2 Diabetes.

OBJECTIVE: To examine the intersection between location of residence along the rural-urban continuum (metropolitan, urban, and rural) and sulfonylurea dispensation records for the management of type 2 diabetes. RESEARCH DESIGN AND METHODS: This retrospective cohort study used administrative health records of adult new metformin users between April 2008 and March 2019 in Alberta, Canada. Multivariable logistic regression was performed to examine the association between sulfonylurea-based treatment intensification and location of residence. RESULTS: Treatment was intensified in 66,084 (38%) of 171,759 new metformin users after a mean of 1.5 years. At treatment intensification, mean age was 55 years, 62% of users were male, and 27% were rural residents. The most common antihyperglycemic drug, given to 30,297 people (46%) for treatment intensification, was a sulfonylurea. At the beginning of our observation period, the proportion of people dispensed a sulfonylurea at first treatment intensification was highest in rural (57%), compared with urban (54%) and metropolitan (52%) areas (P = 0.009). Although proportions decreased over time across the province, rural residents continued to constitute the highest proportion of sulfonylurea users (45%), compared with urban (35%) and metropolitan (37%) residents (P < 0.001), and the trend away from sulfonylurea use was delayed by ∼4 years for rural residents. Adjusting for potential sources of confounding, rural residence was associated with a significantly higher likelihood of using a sulfonylurea compared with metropolitan residence (adjusted odds ratio 1.34; 95% CI 1.29-1.39). CONCLUSIONS: Variation in sulfonylurea dispensation across the rural-urban continuum provides a basis for continued research in the differences in process of care by location.

A survey of Alberta pharmacists' actions and opinions in regard to administering vaccines and medications by injection.

BACKGROUND: Pharmacists in Alberta have been authorized to administer vaccines and other medications by injection for more than 10 years; however, little is known about the provision of this service and their opinions regarding this service. Understanding pharmacists' experiences regarding injection services would inform development of strategies to improve provision of injection services. OBJECTIVES: To describe the actions related to administering an injection, including identification of commonly administered medications, and to identify perceived barriers and facilitators pharmacists face when providing injection services. METHODS: An online survey was developed and loaded into REDCap, and e-mail invitations were sent to 5714 pharmacists registered with the Alberta College of Pharmacy in October 2020. Responses were analyzed using descriptive statistics. Pharmacists who administered at least one injection in the previous year were considered active providers, and their opinions regarding injection services were compared with nonactive providers. RESULTS: A total of 397 pharmacists responded to our survey, mean age was 42 years, 66% were female, 82% were community pharmacists, and 90% were active providers. The most common injection, administered by 98% of active providers, was influenza vaccine, followed by vitamin B12 (95%), herpes zoster vaccine (88%), hepatitis vaccines (86%), and pneumococcal vaccines (82%). Nonactive providers were more likely than active providers to report that comfort with administering injections (P < 0.001) and managing adverse reactions (P = 0.013) were moderate or major barriers to providing injections. More than 60% of pharmacists indicated that access and automated reporting to the provincial immunization registry would be essential to increasing the frequency of providing injection services. CONCLUSION: We identified that Alberta pharmacists administer a wide variety of vaccines and other medications by injection. Respondents identified several barriers and facilitators to providing these services. Addressing these barriers may help improve provision of injection services by pharmacists.

Development and Validation of a Machine Learning Model to Estimate Risk of Adverse Outcomes Within 30 Days of Opioid Dispensation.

Importance: Machine learning approaches can assist opioid stewardship by identifying high-risk opioid prescribing for potential interventions. Objective: To develop a machine learning model for deployment that can estimate the risk of adverse outcomes within 30 days of an opioid dispensation as a potential component of prescription drug monitoring programs using access to real-world data. Design, Setting, and Participants: This prognostic study used population-level administrative health data to construct a machine learning model. This study took place in Alberta, Canada (from January 1, 2018, to December 31, 2019), and included all patients 18 years and older who received at least 1 opioid dispensation from a community pharmacy within the province. Exposures: Each opioid dispensation served as the unit of analysis. Main Outcomes and Measures: Opioid-related adverse outcomes were identified from administrative data sets. An XGBoost model was developed on 2018 data to estimate the risk of hospitalization, an emergency department visit, or mortality within 30 days of an opioid dispensation; validation on 2019 data was done to evaluate model performance. Model discrimination, calibration, and other relevant metrics are reported using daily and weekly predictions on both ranked predictions and predicted probability thresholds using all data from 2019. Results: A total of 853 324 participants represented 6 181 025 opioid dispensations, with 145 016 outcome events reported (2.3%); 46.4% of the participants were men and 53.6% were women, with a mean (SD) age of 49.1 (15.6) years for men and 51.0 (18.0) years for women. Of the outcome events, 77 326 (2.6% pretest probability) occurred within 30 days of a dispensation in the validation set (XGBoost C statistic, 0.82 [95% CI, 0.81-0.82]). The top 0.1 percentile of estimated risk had a positive likelihood ratio (LR) of 28.7, which translated to a posttest probability of 43.1%. In our simulations, the weekly measured predictions had higher positive LRs in both the highest-risk dispensations and percentiles of estimated risk compared with predictions measured daily. Net benefit analysis showed that using machine learning prediction may not add additional benefit over the entire range of probability thresholds. Conclusions and Relevance: These findings suggest that prescription drug monitoring programs can use machine learning classifiers to identify patients at risk of opioid-related adverse outcomes and intervene on high-risk ranked predictions. Better access to available administrative and clinical data could improve the prediction performance of machine learning classifiers and thus expand opioid stewardship efforts.

Association Between Antidepressant Use and Adherence to Anti-hyperglycemic Medications in Adults With Type 2 Diabetes and Depression: A Retrospective Cohort Study.

OBJECTIVES: Depression is a known risk factor for poor medication adherence, but it is unclear whether depression treatment affects adherence rates. In this study, we examined the association between pharmacologic treatment of a new depressive episode and subsequent adherence to oral anti-hyperglycemic medications. METHODS: In this retrospective cohort study we used administrative health data to follow adult new metformin users in Alberta, Canada, between 2008 and 2018. Depressive episodes starting ≥1 year after metformin initiation were identified and individuals starting antidepressant treatment within the first 90 days were compared with those who did not. The proportion of days covered (PDC) with oral anti-hyperglycemic medications in the subsequent year (days 91 to 455) was used to estimate adherence. The association between antidepressant treatment and poor adherence (PDC<0.8) was examined using multivariate logistic regression models. RESULTS: A new depressive episode occurred in 6,201 people, with a mean age of 56.0 (standard deviation [SD], 15.4) years. Of this cohort, 3,303 (53.2%) were women. Mean PDC was 0.55 (SD, 0.41); 924 (57.0%) of 1,621 people who started antidepressant treatment and 2,709 (59.2%) of 4,580 controls had poor adherence (p=0.13). After adjusting for baseline comorbidities and other characteristics, antidepressant treatment was associated with a lower likelihood of poor adherence (adjusted odds ratio, 0.85; 95% confidence interval, 0.75 to 0.96; p=0.007). CONCLUSIONS: Although overall adherence to anti-hyperglycemic medications was low after onset of a depressive episode, antidepressant treatment was associated with a lower likelihood of poor adherence.

Foregone Opportunities? Time to Treatment Intensification Among Adults With Type 2 Diabetes and Elevated Glycated Hemoglobin on Metformin Monotherapy, 2009-2018.

OBJECTIVES: In this study, we aimed to characterize time to treatment intensification (TTI) in people on metformin with uncontrolled hyperglycemia, and estimated the frequency of physician visits until intensification. METHODS: This work was a cohort study of Albertan adults with glycated hemoglobin (A1C) of >7.5% after at least 3 months of metformin monotherapy, using administrative databases from 2009 to 2018, with each subject followed for up to 4 years. Therapeutic intensification was defined as dispensation of an additional class of antihyperglycemic medication. Median TTI and the median number of physician visits were estimated from Kaplan-Meier functions within age/A1C strata. A Cox proportional hazards model was fitted to examine predictors of therapeutic intensification. RESULTS: We included 38,846 people (average age, 57 years; 37% female; mean A1C, 8.8%). Overall, therapeutic intensification was observed in 23,077 (59%; 40% at 1 year). Median TTI was 1.4 years, varying from 0.7 years (A1C >8.5%, age <65 years) to 3.3 years (age ≥75 years, any A1C). The median number of physician visits until intensification was 9, varying between 5 (A1C >8.5%, age <65 years) and ≥30 (age ≥75 years); 93% of people awaiting intensification had at least 2 visits by 1 year. Higher A1C and younger age were the strongest predictors of intensification. Results were similar in people with ischemic heart disease. CONCLUSIONS: Despite ample contacts with community physicians, TTI exceeds the 6-month target recommended by guidelines, particularly in older adults. Further study is needed to better understand these foregone opportunities as guidelines call for wider promulgation of agents with cardiorenal benefits.

Predicting 30-Day Readmissions in Patients With Heart Failure Using Administrative Data: A Machine Learning Approach.

BACKGROUND: We sought to develop machine learning (ML) models trained on administrative data which predict risk of readmission in patients with heart failure and to evaluate and compare the ML model with the currently used LaCE score using clinically informative metrics. METHODS AND RESULTS: This prognostic study was conducted in Alberta, Canada, on 9845 patients with confirmed heart failure admitted to hospital between 2012 and 2019. The outcome was unplanned all-cause hospital readmission within 30 days of discharge. We used 80% of the data for the ML model development and 20% for independent validation. We reported, using the validation set, c-statistics (area under the receiver operating characteristic curves)and performance metrics (likelihood ratio, positive predictive values) for the XGBoost model and a modified LaCE score within their respective predictive thresholds. Boosted tree-based classifiers had higher area under the receiver operating characteristic curves (0.65 for XGBoost) compared with others (0.58 for neural networks) and 0.57 for the modified LaCE. Within the predicted threshold range of the XGBoost classifier, the positive likelihood ratio was 1.00 at the low end of predicted risk and 6.12 at the high end, resulting in a positive predictive value (post-test probability) range of 21%-62%; the pretest probability of readmission was 20.9% using prevalence. The corresponding positive likelihood ratios and positive predictive values across LaCE score thresholds were 1.00-1.20 and 21%-24%, respectively. CONCLUSIONS: Despite predicting readmissions better than the LaCE, even the best ML model trained on administrative health data (XGBoost) did not provide substantially informative prediction performance as it only generated a moderate shift from pre to post-test probability. Health systems wishing to deploy such a tool should consider training ML models with additional data. Adding other techniques like natural language processing, along with ML, to use other clinical information (like chart notes) might improve prediction performance.

Depression and antecedent medication adherence in a cohort of new metformin users.

AIMS: The association between depression and poor medication adherence is based on cross-sectional studies and cohort studies that measure adherence rates after depression status is determined. However, depressive symptoms occur well before diagnosis. This study examined adherence patterns in the year before a depressive episode. METHODS: This retrospective cohort study followed new metformin users identified in Alberta Health's administrative data between 2008 and 2018. Depressive episodes starting ≥1 year after metformin initiation were identified using a validated case definition. Controls were randomly assigned a pseudo depression date. Adherence to oral antihyperglycemic medications was estimated using proportion of days covered (PDC) and group-based trajectory models to explore the association between depression and poor adherence (PDC<0.8). RESULTS: A depressive episode occurred in 17,418 (10.6%) of 165,056 new metformin users. Individuals with depression were more likely to have poor adherence compared to controls (adjusted odds ratio 1.21; 95% CI 1.17, 1.26). Five trajectories were identified: nearly perfect adherence (PDC >0.95 [34.8% of cohort]), discontinued (PDC=0 [18.3% of cohort], poor initial adherence (PDC 0.75) that declined either rapidly (9.2% of cohort) or gradually (30.1% of cohort), and poor initial adherence (PDC 0.26) that increased gradually (7.6% of cohort). Individuals with depression were more likely to be in one of the four trajectories of poor adherence compared to controls (adjusted odds ratio 1.24; 95% CI 1.19-1.29). CONCLUSIONS: Poor medication adherence occurs in the year before a depressive episode; therefore, poor medication use patterns could be used as an early warning sign for depression.

Medication adherence in patients with mental illness and recent homelessness: contributing factors and perceptions on mobile technology use.

OBJECTIVES: The primary objective was to determine medication-taking behaviours and factors influencing adherence in patients with mental illness and recent homelessness. Secondary objectives were to explore patients' perceptions on mobile technology use to support adherence. METHODS: A constructivist approach and qualitative description method was used. The sample population consisted of patients with recent homelessness and mental illness affiliated with a community-based outreach programme in Canada. Participants were purposefully selected; semi-structured interviews were conducted to elicit information on medication-taking strategies and mobile technology to support adherence. A standardized questionnaire collected demographic and medical information; the Medication Adherence Rating Scale (MARS) was used to evaluate self-reported adherence. Questionnaire data were analysed using summary descriptive statistics. Interview data were subject to qualitative content analysis. KEY FINDINGS: Fifteen participants with a mean age of 44 years were included. The mean MARS score ± standard deviation was 7.3 ± 1.5. Themes arising from the data included patient factors (i.e. insight, attitudes towards medications, coping strategies) and external factors (i.e. therapeutic alliance, family support that impacted adherence) and technology use and health. Eight participants (53%) had access to a mobile phone. There was a moderate interest in the use of mobile technology to support adherence, with cost and technology literacy identified as barriers. CONCLUSION: External supports and individual medication management strategies were important in supporting medication adherence in this patient group. Perceived need for mobile technology, in addition to existing supports for adherence, was not high. Challenges accessing and maintaining consistent mobile technology and individual preferences should be considered when developing mobile technology-based interventions.

Concurrent use of opioids and benzodiazepines/Z-drugs in Alberta, Canada and the risk of hospitalisation and death: a case cross-over study.

OBJECTIVES: Coprescribing of benzodiazepines/Z-drugs (BZDs) and opioids is a drug-use pattern of considerable concern due to risk of adverse events. The objective of this study is to estimate the effect of concurrent use of BZDs on the risk of hospitalisations/emergency department (ED) visits and deaths among opioid users. DESIGN, SETTING AND PARTICIPANTS: We conducted a population-based case cross-over study during 2016-2018 involving Albertans 18 years of age and over who received opioids. From this group, we identified 1 056 773 people who were hospitalised or visited the ED, and 31 998 who died. INTERVENTION: Concurrent use of opioids and BZDs. OUTCOMES: We estimated the risk of incident all-cause hospitalisation/ED visits and all-cause mortality associated with concurrent BZD use by applying a matched-pair analyses comparing concurrent use to opioid only use. RESULTS: Concurrent BZD use occurred in 17% of opioid users (179 805/1 056 773). Overall, concurrent use was associated with higher risk of hospitalisation/ED visit (OR 1.13, p<0.001) and all cause death (OR 1.90; p<0.001). The estimated risk of hospitalisation/ED visit was highest in those >65 (OR 1.5; p<0.001), using multiple health providers (OR 1.67; p<0.001) and >365 days of opioid use (OR 1.76; p<0.001). Events due to opioid toxicity were also associated with concurrent use (OR 1.8; p<0.001). Opioid dose-response effects among concurrent patients who died were also noted (OR 3.13; p<0.001). INTERPRETATION: Concurrent use of opioids and BZDs further contributes to the risk of hospitalisation/ED visits and mortality in Alberta, Canada over opioid use alone, with higher opioid doses, older age and increased number of unique health providers carrying higher risks. Regulatory bodies and health providers should reinforce safe drug-use practices and be vigilant about coprescribing.

Time to Treatment Intensification in Newly Treated Adult Type 2 Diabetes Patients: Comparison of Canada and the United States.

OBJECTIVES: Our aim in this study was to determine whether differences exist in time to treatment intensification in newly treated type 2 diabetes patients in Canada and the United States (US). METHODS: Two separate retrospective cohorts of diabetes patients were used from Canada and the US. Time to treatment intensification (i.e. addition of a second antihyperglycemic agent after initial metformin use) was determined using multivariate Cox proportional hazard models. RESULTS: Among new metformin users in 2004‒2007 (2,116 Canadians and 2,631 Americans) >65 years of age, the median time to treatment intensification was 362 days for Canadians and 170 days for Americans (adjusted hazard ratio, 1.99; 95% confidence interval, 1.69 to 2.36). In a second cohort of all adult ages with clinical data between 2008 and 2010 (23,022 Canadians and 19,318 Americans), the median time to treatment intensification was 197 days for Canadians and 119 days for Americans (adjusted hazard ratio, 5.62; 95% confidence interval, 5.246 to 6.029). At treatment intensification, the mean glycated hemoglobin was 9.0% (standard deviation, 2.0) in Canada and 8.6% (standard deviation, 2.2) in the US (p<0.01). CONCLUSIONS: Although clinical practice guidelines are similar between Canada and the US, Canadian clinicians have historically demonstrated more clinical inertia compared with their US counterparts with respect to intensifying antihyperglycemic therapy. It is relatively unknown whether these differences currently exist or whether Canadian clinicians have closed the gap.

Characterisation of concurrent use of prescription opioids and benzodiazepine/Z-drugs in Alberta, Canada: a population-based study.

OBJECTIVE: The objective of this study is to characterise concurrent use of benzodiazepine receptor modulators and opioids among prescription opioid users in Alberta in 2017. DESIGN: A population based retrospective study. SETTING: Alberta, Canada, in the year 2017. PARTICIPANTS: All individuals in Alberta, Canada, with at least one dispensation record from a community pharmacy for an opioid in the year 2017. EXPOSURE: Concurrent use of a benzodiazepine receptor modulator and opioid, defined as overlap of supply for both drugs for at least 1 day. MAIN OUTCOME MEASURES: Prevalence of concurrency was estimated among subgroups of patient characteristics that were considered clinically relevant or associated with inappropriate medication use. RESULTS: Among the 547 709 Albertans who were dispensed opioid prescriptions in 2017, 132 156 (24%) also received prescriptions for benzodiazepine receptor modulators. There were 96 581 (17.6%) prescription opioid users who concurrently used benzodiazepine receptor modulators with an average of 98 days (SD=114, 95% CI 97 to 99) of total cumulative concurrency and a median of 37 days (IQR 10 to 171). The average longest duration of consecutive days of concurrency was 45 (SD=60, 95% CI 44.6 to 45.4) with a median of 24 days (IQR 8 to 59). Concurrency was more prevalent in females, patients using an average daily oral morphine equivalent >90 mg, opioid dependence therapy patients, chronic opioid users, patients utilising a high number of unique providers, lower median household incomes and those older than 65 (p value<0.001 for all comparisons). CONCLUSIONS: Concurrent prescribing of opioids and benzodiazepine receptor modulators is common in Alberta despite the ongoing guidance of many clinical resources. Older patients, those taking higher doses of opioids, and for longer durations may be at particular risk of adverse outcomes and may be worthy of closer follow-up for assessment for dose tapering or discontinuations. As well, those with higher healthcare utilisation (seeking multiple providers) should also be closely monitored. Continued surveillance of concurrent use of these medications is warranted to ensure that safe drug use recommendations are being followed by health providers.

Evaluation of Healthy User Effects With Metformin and Other Oral Antihyperglycemia Medication Users in Adult Patients With Type 2 Diabetes.

OBJECTIVES: Healthy user bias, whereby health-seeking patients are more likely to initiate preventive therapies and engage in healthy lifestyle behaviours, is well known in observational studies, particularly with statins. However, its influence in studies of oral antihyperglycemic therapies is unknown. We sought to explore the healthy user effects in metformin users vs. nonusers on various health outcomes that should not be associated with metformin use. METHODS: We conducted a retrospective cohort study using data from Alberta, between 2008 and 2015, to examine the association between metformin use and various health outcomes. RESULTS: We identified 135,301 new users of oral antihyperglycemic agents. The mean age was 55 years, 75,949 (56%) were men and 130,725 (97%) had had at least 1 metformin prescription during a mean follow-up period of 3.4 years. Metformin users were less likely to be involved in accident-related events (adjusted hazard ratio [aHR] 0.90; 95% CI 0.85 to 0.96), were more likely to have preventive screening services (aHR 1.16; 95% CI 1.11 to 1.21), were less likely to experience other clinical events, such as asthma and gout attacks (aHR 0.90; 95% CI 0.84 to 0.97), and had lower risks for all-cause mortality (aHR 0.57; 95% CI 0.51 to 0.63) compared to nonusers. CONCLUSIONS: Our results suggest that metformin users are more likely to initiate preventive therapies and engage in other healthy behaviours. Failure to account for these behaviours may introduce healthy user bias into studies evaluating the effects of oral antihyperglycemic therapies.

A Randomized Trial of Video-based Education in Patients With Heart Failure: The Congestive Heart Failure Outreach Program of Education (COPE).

BACKGROUND: Heart failure (HF) exacerbations often relate to poor self-care. Education programs improve outcomes, but are resource-intensive. We developed a video-based educational intervention and evaluated it in patients with HF. METHODS: Congestive Heart Failure Outreach Program of Education was a pragmatic multicenter randomized trial. We included subjects with HF if they were hospitalized, seen in the emergency department (ED), or high-risk outpatients, and randomized them to intervention or control. Intervention included a 20-minute video, supplementary booklet, and 3 bimonthly newsletters focusing on salt and fluid restriction, daily weights, and medications. Subjects watched the video and were encouraged to review it at home, along with the booklet/newsletters. Control subjects received the booklet only. The primary outcome was the difference in cardiovascular hospitalizations or ED visits between groups at 6 months. Secondary outcomes included clinical events and in-hospital days. RESULTS: We recruited 539 subjects from 22 centers in Canada and the United States. Baseline characteristics were similar in both groups: 64% were male and had a mean age of 66 (± 13) years, mean ejection fraction 31% (± 13.5), and 65% New York Heart Association Functional Classification III/IV. The primary outcome occurred in 57 subjects (21%) in the intervention group compared with 61 subjects (23%) in the control group (P = 0.66). There were no significant differences in prespecified secondary outcomes; however, death occurred in 18 subjects (7%) in the intervention group and 33 subjects (12%) in the control group (P = 0.03). CONCLUSION: Video education on self-care did not reduce hospitalizations or ED visits in patients with HF. Of note, mortality was lower in the intervention group.

INTRODUCTION: L'exacerbation de l'insuffisance cardiaque (IC) est souvent liée à une mauvaise prise en charge autonome des soins. Les programmes d'enseignement améliorent les résultats cliniques, mais exigent beaucoup de ressources. Nous avons conçu une intervention éducative par vidéo et l'avons évaluée auprès de patients atteints d'IC. MÉTHODES: Le Congestive Heart Failure Outreach Program of Education était une étude pragmatique multicentrique à répartition aléatoire. Nous avons sélectionné les sujets atteints d'IC s'ils étaient hospitalisés, vus au service des urgences (SU) ou patients en consultation externe exposés à un risque élevé, et les avons répartis de manière aléatoire au groupe d'intervention ou au groupe témoin. L'intervention a consisté en une vidéo de 20 minutes, un livret supplémentaire et 3 bulletins bimensuels portant sur la restriction du sel et des liquides, les mesures quotidiennes du poids et les médicaments. Après que les sujets eurent regardé la vidéo, nous les avons encouragés à la revoir à la maison, en plus de lire le livret et les bulletins. Les sujets témoins ont reçu seulement le livret. Le critère de jugement principal était la différence dans les hospitalisations en raison d'une maladie cardiovasculaire ou les visites au SU entre les groupes après 6 mois. Les critères de jugement secondaires étaient les événements cliniques et les jours d'hospitalisation. RÉSULTATS: Nous avons recruté 539 sujets de 22 centres au Canada et aux États-Unis. Les caractéristiques initiales étaient similaires dans les 2 groupes : 64 % étaient des hommes et avaient un âge moyen de 66 ans (± 13), une fraction d'éjection moyenne de 31 % (± 13,5), et 65 % avaient une classification fonctionnelle III/IV de la New York Heart Association. Le critère de jugement principal est survenu chez 57 sujets (21 %) dans le groupe d'intervention et chez 61 sujets (23 %) dans le groupe témoin (P = 0,66). Il n'y a eu aucune différence significative dans les critères secondaires prédéfinis. Toutefois, 18 sujets (7 %) du groupe d'intervention et 33 sujets (12 %) du groupe témoin sont morts (P = 0,03). CONCLUSION: L'enseignement sur les autosoins par vidéo n'a pas réduit les hospitalisations ou les visites au SU des patients atteints d'IC. Notamment, la mortalité a été plus faible dans le groupe d'intervention.

Impact of Drug Exposure Definitions on Observed Associations in Pharmacoepidemiology Research.

BACKGROUND - A variety of methods are used to define exposure in pharmacoepidemiologic studies. Although each method has known biases, the relative effect of these biases on an observed association has not been fully examined. OBJECTIVE - To explore the influence of different exposure definitions on estimates, using the association between metformin and all-cause mortality as a proto-typical model. METHODS - New users of oral anti-hyperglycemic drugs were identified using administrative health databases from Alberta, Canada between 1998 and 2010. Drug exposure was described using definitions that are commonly used in observational studies. All analyses included the same covariates of age, gender, and a comorbidity score, and subjects not exposed to metformin served as the reference group. The measure of association was assessed using a Cox Proportional Hazards model for cohort studies and conditional logistic regression for case-control studies. RESULTS - We identified 64,293 new oral anti-hyperglycemic drugs users; mean age 68.9 years, 33,131 (52%) males, and 24,745 (39%) deaths during a mean follow-up of 6 years.  In adjusted models, the association between metformin and mortality ranged from 0.23 (95% CI 0.22-0.25) to 0.92 (95% CI 0.88-0.95) reduction. Most metformin exposure definitions, however, provided estimates in the 0.6-0.8 reduction range, aligning with the results of previous observational studies. CONCLUSIONS - The variety of exposure definitions tested in this analysis produced a wide range of associations between metformin and mortality risk. Therefore, pharmacoepidemiological studies should implement sensitivity analyses including at least two exposure definitions to provide more robust and potentially valid study estimates.

Financial remuneration is positively correlated with the number of clinical activities: an example from diabetes management in Alberta community pharmacies.

OBJECTIVE: To determine whether use of a compensation plan to remunerate pharmacists for clinical pharmacy services was associated with the number of diabetes management activities provided. METHODS: Alberta pharmacists were asked about compensation plan use and frequency they provide a list of 80 diabetes management activities. KEY FINDINGS: A total of 168 community pharmacists responded to the survey. When compensation plan use, diabetes-specific training, practice characteristics and additional authorizations were incorporated into a factorial ANOVA, pharmacists who used the compensation plan reported a mean of 42.9 (95% CI 39.4 to 46.4) diabetes management activities, while those who did not reported a mean of 29.9 (95% CI 21.4 to 38.4) activities (P = 0.016). CONCLUSIONS: After considering other important influencing factors, use of the compensation plan is positively correlated with the number of diabetes management activities pharmacists provided.