Opioid prescribing among new users for non-cancer pain in the USA, Canada, UK, and Taiwan: A population-based cohort study.

BACKGROUND: The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. METHODS AND FINDINGS: We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. CONCLUSIONS: In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic.

Time trends and prescribing patterns of opioid drugs in UK primary care patients with non-cancer pain: A retrospective cohort study.

BACKGROUND: The US opioid epidemic has led to similar concerns about prescribed opioids in the UK. In new users, initiation of or escalation to more potent and high dose opioids may contribute to long-term use. Additionally, physician prescribing behaviour has been described as a key driver of rising opioid prescriptions and long-term opioid use. No studies to our knowledge have investigated the extent to which regions, practices, and prescribers vary in opioid prescribing whilst accounting for case mix. This study sought to (i) describe prescribing trends between 2006 and 2017, (ii) evaluate the transition of opioid dose and potency in the first 2 years from initial prescription, (iii) quantify and identify risk factors for long-term opioid use, and (iv) quantify the variation of long-term use attributed to region, practice, and prescriber, accounting for case mix and chance variation. METHODS AND FINDINGS: A retrospective cohort study using UK primary care electronic health records from the Clinical Practice Research Datalink was performed. Adult patients without cancer with a new prescription of an opioid were included; 1,968,742 new users of opioids were identified. Mean age was 51 ± 19 years, and 57% were female. Codeine was the most commonly prescribed opioid, with use increasing 5-fold from 2006 to 2017, reaching 2,456 prescriptions/10,000 people/year. Morphine, buprenorphine, and oxycodone prescribing rates continued to rise steadily throughout the study period. Of those who started on high dose (120-199 morphine milligram equivalents [MME]/day) or very high dose opioids (≥200 MME/day), 10.3% and 18.7% remained in the same MME/day category or higher at 2 years, respectively. Following opioid initiation, 14.6% became long-term opioid users in the first year. In the fully adjusted model, the following were associated with the highest adjusted odds ratios (aORs) for long-term use: older age (≥75 years, aOR 4.59, 95% CI 4.48-4.70, p < 0.001; 65-74 years, aOR 3.77, 95% CI 3.68-3.85, p < 0.001, compared to <35 years), social deprivation (Townsend score quintile 5/most deprived, aOR 1.56, 95% CI 1.52-1.59, p < 0.001, compared to quintile 1/least deprived), fibromyalgia (aOR 1.81, 95% CI 1.49-2.19, p < 0.001), substance abuse (aOR 1.72, 95% CI 1.65-1.79, p < 0.001), suicide/self-harm (aOR 1.56, 95% CI 1.52-1.61, p < 0.001), rheumatological conditions (aOR 1.53, 95% CI 1.48-1.58, p < 0.001), gabapentinoid use (aOR 2.52, 95% CI 2.43-2.61, p < 0.001), and MME/day at initiation (aOR 1.08, 95% CI 1.07-1.08, p < 0.001). After adjustment for case mix, 3 of the 10 UK regions (North West [16%], Yorkshire and the Humber [15%], and South West [15%]), 103 practices (25.6%), and 540 prescribers (3.5%) had a higher proportion of patients with long-term use compared to the population average. This study was limited to patients prescribed opioids in primary care and does not include opioids available over the counter or prescribed in hospitals or drug treatment centres. CONCLUSIONS: Of patients commencing opioids on very high MME/day (≥200), a high proportion stayed in the same category for a subsequent 2 years. Age, deprivation, prescribing factors, comorbidities such as fibromyalgia, rheumatological conditions, recent major surgery, and history of substance abuse, alcohol abuse, and self-harm/suicide were associated with long-term opioid use. Despite adjustment for case mix, variation across regions and especially practices and prescribers in high-risk prescribing was observed. Our findings support greater calls for action for reduction in practice and prescriber variation by promoting safe practice in opioid prescribing.

Assumptions made when preparing drug exposure data for analysis have an impact on results: An unreported step in pharmacoepidemiology studies.

PURPOSE: Real-world data for observational research commonly require formatting and cleaning prior to analysis. Data preparation steps are rarely reported adequately and are likely to vary between research groups. Variation in methodology could potentially affect study outcomes. This study aimed to develop a framework to define and document drug data preparation and to examine the impact of different assumptions on results. METHODS: An algorithm for processing prescription data was developed and tested using data from the Clinical Practice Research Datalink (CPRD). The impact of varying assumptions was examined by estimating the association between 2 exemplar medications (oral hypoglycaemic drugs and glucocorticoids) and cardiovascular events after preparing multiple datasets derived from the same source prescription data. Each dataset was analysed using Cox proportional hazards modelling. RESULTS: The algorithm included 10 decision nodes and 54 possible unique assumptions. Over 11 000 possible pathways through the algorithm were identified. In both exemplar studies, similar hazard ratios and standard errors were found for the majority of pathways; however, certain assumptions had a greater influence on results. For example, in the hypoglycaemic analysis, choosing a different variable to define prescription end date altered the hazard ratios (95% confidence intervals) from 1.77 (1.56-2.00) to 2.83 (1.59-5.04). CONCLUSIONS: The framework offers a transparent and efficient way to perform and report drug data preparation steps. Assumptions made during data preparation can impact the results of analyses. Improving transparency regarding drug data preparation would increase the repeatability, reproducibility, and comparability of published results.

A comparison of methods for estimating the temporal change in a continuous variable: Example of HbA1c in patients with diabetes.

PURPOSE: To compare the more complex technique, functional principal component analysis (FPCA), to simpler methods of estimating values of sparse and irregularly spaced continuous variables at given time points in longitudinal data using a diabetic patient cohort from UK primary care. METHODS: The setting for this study is the Clinical Practice Research Datalink (CPRD), a UK general practice research database. For 16,034 diabetic patients identified in CPRD, with at least 2 measures in a 30-month period, HbA1c was estimated after temporarily omitting (i) the final and (ii) middle known values using linear interpolation, simple linear regression, arithmetic mean, random effects, and FPCA. Performance of each method was assessed using mean prediction error. The influence on predictive accuracy of (1) more homogeneous populations and (2) number and range of known HbA1c values was explored. RESULTS: When estimating the last observation, the predictive accuracy of FPCA was highest with over half of predicted values within 0.4 units, equivalent to laboratory measurement error. Predictive accuracy improved when estimating the middle observation with almost 60% predicted values within 0.4 units for FPCA. These results were marginally better than that achieved by simpler approaches, such as last-occurrence-carried-forward linear interpolation. This pattern persisted with more homogeneous populations as well as when variability in HbA1c measures coupled with frequency of data points were considered. CONCLUSIONS: When estimating change from baseline to prespecified time points in electronic medical records data, a marginal benefit to using the more complex modelling approach of FPCA exists over more traditional methods.

Modelling and extraction of variability in free-text medication prescriptions from an anonymised primary care electronic medical record research database.

BACKGROUND: Free-text medication prescriptions contain detailed instruction information that is key when preparing drug data for analysis. The objective of this study was to develop a novel model and automated text-mining method to extract detailed structured medication information from free-text prescriptions and explore their variability (e.g. optional dosages) in primary care research databases. METHODS: We introduce a prescription model that provides minimum and maximum values for dose number, frequency and interval, allowing modelling variability and flexibility within a drug prescription. We developed a text mining system that relies on rules to extract such structured information from prescription free-text dosage instructions. The system was applied to medication prescriptions from an anonymised primary care electronic record database (Clinical Practice Research Datalink, CPRD). RESULTS: We have evaluated our approach on a test set of 220 CPRD prescription free-text directions. The system achieved an overall accuracy of 91 % at the prescription level, with 97 % accuracy across the attribute levels. We then further analysed over 56,000 most common free text prescriptions from CPRD records and found that 1 in 4 has inherent variability, i.e. a choice in taking medication specified by different minimum and maximum doses, duration or frequency. CONCLUSIONS: Our approach provides an accurate, automated way of coding prescription free text information, including information about flexibility and variability within a prescription. The method allows the researcher to decide how best to prepare the prescription data for drug efficacy and safety analyses in any given setting, and test various scenarios and their impact.

Modeling of cumulative effects of time-varying drug exposures on within-subject changes in a continuous outcome.

An accurate assessment of the safety or effectiveness of drugs in pharmaco-epidemiological studies requires defining an etiologically correct time-varying exposure model, which specifies how previous drug use affects the outcome of interest. To address this issue, we develop, and validate in simulations, a new approach for flexible modeling of the cumulative effects of time-varying exposures on repeated measures of a continuous response variable, such as a quantitative surrogate outcome, or a biomarker. Specifically, we extend the linear mixed effects modeling to estimate how past and recent drug exposure affects the way individual values of the outcome change throughout the follow-up. To account for the dosage, duration and timing of past exposures, we rely on a flexible weighted cumulative exposure methodology to model the cumulative effects of past drug use, as the weighted sum of past doses. Weights, modeled with unpenalized cubic regression B-splines, reflect the relative importance of doses taken at different times in the past. In simulations, we evaluate the performance of the model under different assumptions concerning (i) the shape of the weight function, (ii) the sample size, (iii) the number of the longitudinal observations and (iv) the intra-individual variance. Results demonstrate the accuracy of our estimates of the weight function and of the between- and within-patients variances, and good correlation between the observed and predicted longitudinal changes in the outcome. We then apply the proposed method to re-assess the association between time-varying glucocorticoid exposure and weight gain in people living with rheumatoid arthritis.

Multinational Investigation of Fracture Risk with Antidepressant Use by Class, Drug, and Indication.

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.

Multinational comparison of new antidepressant use in older adults: a cohort study.

OBJECTIVES: We used an international pharmacosurveillance network to estimate the rate and characteristics of antidepressant use in older adults in countries with more conservative (UK) and liberal depression guidelines (Canada, USA). SETTING: Electronic health records and population-based administrative data from six jurisdictions in four countries (UK, Taiwan, USA and Canada). PARTICIPANTS: A historical cohort of older adults (≥65 years) who had a new episode of antidepressant use between 2009 and 2014. OUTCOME MEASURES: The age and sex-standardised cumulative incidence of new episodes of antidepressant use in older adults was measured. Descriptive statistics were used to compare the proportion of new users by the antidepressant prescribed, therapeutic class, potential treatment indication and country, as well as the characteristics of the first treatment episode (standardised daily doses, duration and changes). RESULTS: The incidence of antidepressant use between 2009 and 2014 varied from 4.7% (Montreal and Quebec City) to 18.6% (Taiwan). Tricyclic antidepressants (TCAs) were the most commonly used class in the UK (48.8%) and Taiwan (52.4%) compared with selective serotonin reuptake inhibitors (SSRIs) in North American jurisdictions (42.3%-53.3%). Chronic pain was the most common potential treatment indication (41.2%-68.2%). Among users with chronic pain, TCAs were used most frequently in the UK and Taiwan (55.2%-60.4%), whereas SSRIs were used most frequently in North America (33.5%-46.4%). Treatment was longer (252-525 vs 169-437 days), standardised doses were higher (0.7-1.3 vs 0.5-1.0) and treatment was more likely to be changed (31%-46% vs 21%-34%) among patients with depression (9.1%-43%) than those with chronic pain. CONCLUSION: Antidepressant use in older adults varied 24-fold by country, with the UK, which has the most conservative treatment guidelines, being among the lowest. Chronic pain was the most common potential treatment indication. Evaluation of real-world risks of TCAs is a priority for future research, given high rates of use and the potential for increased toxicity in older adults because of potent anticholinergic effects.

Pharmacosurveillance without borders: electronic health records in different countries can be used to address important methodological issues in estimating the risk of adverse events.

OBJECTIVES: Evaluate methodological advantages and limitations of an international pharmacosurveillance system based on electronic health records (EHRs). STUDY DESIGN AND SETTINGS: Type 2 diabetes was used as an exemplar. Cohorts of newly treated diabetics were followed in each country (Quebec, Canada; Massachusetts, United States; Manchester, UK) from 2009 to 2012 using local EHR systems. Cox proportional hazards models were used to assess the risk of cardiovascular events. RESULTS: A total of 44,913 newly treated diabetics were identified; 82.6% (United States) to 93.1% (Canada) were started on biguanides; 13% of patients failed to fill initial prescriptions. An increased risk of cardiovascular events with sulfonylureas was observed when dispensing [hazard ratio (HR): 2.83] vs. EHR prescribing (HR: 2.47) data were used. The addition of clinical data produced a threefold to 10-fold increase in comorbidity for obesity and renal disease, but had no impact on the risk of different hypoglycemic therapies. The risk of cardiovascular events with sulfonylureas was higher in the United States [HR: 3.4; 95% confidence interval (CI): 2.1, 5.5] compared to England (HR: 1.3; 95% CI: 1.1, 1.6). CONCLUSION: An international surveillance system based on EHRs may provide more timely information about drug safety and new opportunities to estimate potential sources of bias and health system effects on drug-related outcomes.