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BackgroundAlthough little is known about why opioid prescribing practices differ between physicians, clinical competence, specialty training and country of origin may play a role. We hypothesised that physicians with stronger clinical competence and communication skills are less likely to prescribe opioids and prescribe lower doses, as do medical specialists and physicians from Asia.MethodsOpioid prescribing practices were examined among international medical graduates (IMGs) licensed to practise in the USA who evaluated Medicare patients for chronic pain problems in 2014–2015. Clinical competence was assessed by the Educational Commission for Foreign Medical Graduates (ECFMG) Clinical Skills Assessment. Physicians in the ECFMG database were linked to the American Medical Association Masterfile. Patients evaluated for chronic pain were obtained by linkage to Medicare outpatient and prescription files. Opioid prescribing was measured within 90 days of evaluation visits. Prescribed dose was measured using morphine milligram equivalents (MMEs). Generalised estimating equation logistic and linear regression estimated the association of clinical competence, specialty, and country of origin with opioid prescribing and dose.Results7373 IMGs evaluated 65 012 patients for chronic pain; 15.2% received an opioid prescription. Increased clinical competence was associated with reduced opioid prescribing, but only among female physicians. For every 10% increase in the clinical competence score, the odds of prescribing an opioid decreased by 16% for female physicians (OR 0.84, 95% CI 0.75 to 0.94) but not male physicians (OR 0.99, 95% CI 0.92 to 1.07). Country of origin was associated with prescribed opioid dose; US and Canadian citizens prescribed higher doses (adjusted MME difference +3.56). Primary care physicians were more likely to prescribe opioids, but surgical and hospital-based specialists prescribed higher doses.ConclusionsClinical competence at entry into US graduate training, physician gender, specialty and country of origin play a role in opioid prescribing practices.

Objectives Antidepressants increase the risk of falls and fracture in older adults. However, risk estimates vary considerably even in comparable populations, limiting the usefulness of current evidence for clinical decision making. Our aim was to apply a common protocol to cohorts of older antidepressant users in multiple jurisdictions to estimate fracture risk associated with different antidepressant classes, drugs, doses, and potential treatment indications. Design Retrospective (2009-2014) cohort study. Setting Five jurisdictions in the United States, Canada, United Kingdom, and Taiwan. Participants Older antidepressant users-subjects were followed from first antidepressant prescription or dispensation to first fracture or until the end of follow-up. Measurements The risk of fractures with antidepressants was estimated by multivariable Cox proportional hazards models using time-varying measures of antidepressant dose and use vs nonuse, adjusting for patient characteristics. Results Between 42.9% and 55.6% of study cohorts were 75 years and older, and 29.3% to 45.4% were men. Selective serotonin reuptake inhibitors (SSRIs) (48.4%-60.0%) were the predominant class used in North America compared with tricyclic antidepressants (TCAs) in the United Kingdom and Taiwan (49.6%-53.6%). Fracture rates varied from 37.67 to 107.18 per 1,000. The SSRIs citalopram (hazard ratio [HR] = 1.23; 95% confidence interval [CI] = 1.11-1.36 to HR = 1.43; 95% CI = 1.11-1.84) and sertraline (HR = 1.36; 95% CI = 1.10-1.68), the SNRI duloxetine (HR = 1.41; 95% CI = 1.06-1.88), TCAs doxepin (HR = 1.36; 95% CI = 1.00-1.86) and imipramine (HR = 1.16; 95% CI = 1.05-1.28), and atypicals (HR = 1.34; 95% CI = 1.14-1.58) increased fracture risk in some but not all jurisdictions. In the United States and the United Kingdom, fracture risk with all classes was higher when prescribed for depression than chronic pain, a trend that is likely explained by drug choice. Conclusion The fracture risk for patients may be reduced by selecting paroxetine, an SSRI with lower risk than citalopram, the SNRI venlafaxine over duloxetine, and the TCA amitriptyline over imipramine or doxepin. There is uncertainty about the risk associated with the atypical antidepressants. J Am Geriatr Soc 68:1494-1503, 2020.

Objective To examine the career outcomes of 20 years of PhD graduates from Canadian health services and policy research (HSPR) doctoral training programs. Methods The deans of the doctoral training programs were invited to participate in this national cohort study. A standardized career-tracking template was developed. Internet searches of publicly accessible sources were used to track graduates' employment. Descriptive analyses summarized PhD program characteristics and current employment. Results Of the 1,208 trainees who graduated during our study period, 884 (73.2% of 1,208, or 90.3% of the 979 with complete data) could be successfully tracked. HSPR PhD graduates are highly employable, but employment trends have changed over time. Today's graduates are more likely to enter careers in a wider variety of sectors and roles and are less likely to be employed in academia than previous graduates. However, over 50% of graduates are currently employed in professorial positions within the academic sector or in research roles or departments within healthcare delivery organizations. Conclusions This article provides an initial descriptive profile of the career outcomes of HSPR PhD graduates in Canada from 10 university-based doctoral training programs. To ensure that PhD graduates are prepared to contribute fully within diverse sectors and roles, doctoral training must evolve to keep pace with employment trends and encompass, in addition to research skills, the professional skills demanded in the public, private, not-for-profit and healthcare delivery sectors.

Background Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection. Methods We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons. Results Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0–95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician. Conclusion Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.

Background and Purpose— There is no consensus on the comparative efficacy and safety of carotid artery stenting (CAS) versus carotid endarterectomy (CEA) in patients with asymptomatic carotid artery stenosis. To evaluate CAS versus CEA in asymptomatic patients, we conducted a systematic review and meta-analysis of randomized controlled trials. Methods— We systematically searched EMBASE, PubMed, MEDLINE, and the Cochrane Library for randomized controlled trials comparing CAS to CEA in asymptomatic patients using a pre-specified protocol. Two independent reviewers identified randomized controlled trials meeting our inclusion/exclusion criteria, extracted relevant data, and assessed quality using the Cochrane risk of bias tool. Random effects models with inverse-variance weighting were used to estimate pooled risk ratios (RRs) comparing the incidences of periprocedural and long-term outcomes between CAS and CEA. Results— We identified 11 reports of 5 randomized controlled trials for inclusion (n=3019) asymptomatic patients. The pooled incidences of any periprocedural stroke (RR, 1.84; 95% confidence interval [CI], 0.99–3.40), periprocedural nondisabling stroke (RR, 1.95; 95% CI, 0.98–3.89), and any periprocedural stroke or death (RR, 1.72; 95% CI, 0.95–3.11) trended toward an increased risk after CAS. We could not rule out clinically significant differences between treatments for long-term stroke (RR, 1.24; 95% CI, 0.76–2.03) and the composite outcome of periprocedural stroke, death or myocardial infarction, or long-term ipsilateral stroke (RR, 0.92; 95% CI, 0.70–1.21). Conclusions— Although uncertainty surrounds the long-term outcomes of CAS versus CEA, the potential for increased risks of periprocedural stroke and periprocedural stroke or death with CAS suggests that CEA is the preferred option for the management of asymptomatic carotid stenosis.

Depression is common among cardiac patients and associated with adverse cardiovascular outcomes. Bright light therapy has emerged as a promising treatment for depressive symptoms, however it has not yet been investigated in this population. We conducted a double-blind, randomized, placebo-controlled pilot trial to assess the feasibility of a larger-scale trial testing bright light therapy for depressive symptoms in cardiac patients. Patients hospitalized for an acute coronary syndrome or undergoing cardiac surgery were randomized to either bright light (10,000 lux) or dim light placebo (500 lux) lamps for 30 minutes each day over 4 weeks, beginning in-hospital. Depression was quantified using the Patient Health Questionnaire 9 (PHQ-9) and Depression Anxiety and Stress Scales (DASS-21). The Short-Form Health Survey 36 (SF-36) was used to measure quality of life. A total of 175 patients were screened and 15 were randomized (8 treatment, 7 placebo) (8.6%) over 10 months. Despite protocol amendments which broadened the inclusion criteria, the trial was terminated early for infeasibility based on the rate of enrollment (1–2 participants/month), with 39.5% of the target sample (38 participants) enrolled. Future trials should take into account the timing of the onset of depressive symptoms in these patients, and consider a less conservative approach to eligibility as well as ways to increase the acceptability of bright light therapy in hospitalized cardiac patients. Once enrolled, our findings suggest that most participants will adhere to the assigned treatment and complete follow-up.

IntroductionData from electronic medical records is now readily available and records information needed in pharmacoepidemiological studies not usually found in administrative data such as risk factors and biometrics. Yet, EMR data leads to measurement error due to primary non-adherence. Bayesian bias correction could provide corrected estimates from administrative data. Objectives and ApproachWe present a method for correcting risk estimates from EMR data using linked data. In our example, we estimate the risk of cardiovascular events from oral-hypoglycemics in patients with type-2 diabetes in Boston, Quebec, and the UK between 2009 and 2012. Using linked EMR and administrative data in Quebec, we compute a positive and negative predicting value of prescription on dispensation for each class of oral-hypoglycemics. The cardiovascular risk is then analysed using a bayesian Weibull survival model adjusted for potential confounders. A similar model is then computed that accounts for exposure measurement error using the PPV and NPV. ResultsThe Quebec and Boston cohorts have similar sizes with 1197 and 2346 patients, but the UK was bigger at 41370 patients. In Quebec's data, there were important differences in PPV and NPV by class of oral-hypoglycemics with PPVs for Biguanides at 0.81, Sulphonylureas at 0.65, and others at 0.50. The pattern for NPV differed with the same classes having respectively values of 0.56, 0.97, and 0.99. Estimates from the naïve model are typical of similar analysis but compared to their correction, they were generally overprecise and biased towards the null. The adjusted estimated were adequately representing the increased uncertainty with hazard ratios for Sulphonylureas going from 1.72 (1.22, 2.41) to 3.19 (1.36, 5.93), and from 1.09 (0.86, 1.39) to 1.05 (0.45, 2.16) for no drugs Conclusion/ImplicationsBayesian adjustment for measurement error allowed us to use linked data to regenerate uncertainty and to correct the bias in our risk estimates. Our approach was impacted by the observed low predictive value of prescribing, by reduced transportability of our PPV and NPV estimates, and other sources of bias.

Background Smoking cessation and weight management are recommended after acute coronary syndrome ( ACS ); however, little is known about the effects of smoking cessation on weight change after ACS . We aimed to assess the effect of smoking cessation after ACS on weight over a 12‐month follow‐up period. Methods and Results Data were prospectively collected from the EVITA (Evaluation of Varenicline in Smoking Cessation for Patients Post‐Acute Coronary Syndrome) trial. Weight change was compared among 3 groups of patients: those who were completely abstinent (n=70), those who smoked intermittently (n=68), and those who smoked persistently (n=34). Patients' mean baseline weight was 83.9 kg ( SD 17.7) with a mean body mass index of 28.5 ( SD 5.4). Patients smoked a mean of 37.7 years ( SD 17.7) and a mean of 21.0 cigarettes ( SD 9.0) per day prior to their ACS . Weight change varied across groups, with abstainers gaining a mean of 4.8 kg ( SD 8.6), intermittent smokers gaining a mean of 2.0 kg ( SD 8.9) and persistent smokers losing a mean of 0.7 kg ( SD 7.4). At 52 weeks, abstainers were more likely to gain weight than persistent smokers (difference in means 5.5 kg; 95% CI 2.3–8.8). This weight gain was not associated with an increase in the use of antihypertensive or antidiabetic medications. Conclusions Following an ACS , significant weight is gained by patients who quit smoking. Weight‐management interventions among smokers who quit after ACS should be a focus of investigation in future research so that the cardiovascular benefits achieved by smoking cessation are not offset by weight gain in this high‐risk population. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 00794573.

Importance Adverse drug events (ADEs) account for up to 16% of emergency department (ED) visits and 7% of hospital admissions. Medication reconciliation is required for hospital accreditation because it can reduce medication discrepancies, but there is no evidence that reducing discrepancies reduces ADEs or other adverse outcomes. Objective To evaluate whether electronic medication reconciliation reduces ADEs, medication discrepancies, and other adverse outcomes compared with usual care. Design, Setting, and Participants This cluster randomized trial involved 3491 patients who were discharged from 2 medical units and 2 surgical units at the McGill University Health Centre, Montreal, Quebec, Canada, between October 2014 and November 2016. Data analysis took place from July 2017 to July 2019. Intervention The RightRx intervention electronically retrieved community drugs from the provincial insurer and aligned them with in-hospital drugs to facilitate reconciliation and communication at care transitions. Main Outcomes and Measures The primary outcome was ADEs in 30 days after discharge. Secondary outcomes included medication discrepancies, ED visits, hospital readmissions, and a composite outcome of ED visits, readmissions, and death up to 90 days after discharge. Results Of 4656 eligible patients, 3567 (76.6%) consented to participate (2060 [57.8%] men; mean [SD] age, 69.8 [14.9] years). Overall, 76 patients died during the hospital stay, so 3491 patients were included in the analysis. There was no significant difference in the risk of ADEs between intervention and control groups (76 [4.6%] vs 73 [4.0%]; OR, 0.97; 95% CI, 0.33-1.48), ED visits (433 [26.2%] vs 488 [26.6%]; OR, 0.83; 95% CI, 0.36-1.42), hospital readmission (170 [10.3%] vs 261 [14.2%]; OR, 0.22; 95% CI, 0.06-1.14), or the composite outcome (447 [27.0%] vs 506 [27.6%]; OR, 0.75; 95% CI, 0.34-1.27) at 30 days. Medication discrepancies were significantly reduced in the intervention group compared with the control group (437 [26.4%] vs 1029 [56.0%]; OR, 0.24; 95% CI, 0.12-0.57). Changes made to community medications (OR, 1.05; 95% CI, 1.01-1.10) and new medications (OR, 1.09; 95% CI, 1.01-1.18) were significant risk factors for ADEs. Conclusions and Relevance Electronic medication reconciliation reduced medication discrepancies but did not reduce ADEs or other adverse outcomes. Hospital accreditation should focus on interventions that reduce the risk of adverse events for patients with multiple changes to community medications. Trial Registration ClinicalTrials.gov identifier:NCT01179867