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.
PurposeReal‐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.MethodsAn 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.ResultsThe 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).ConclusionsThe 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.
BackgroundFree-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.MethodsWe 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).ResultsWe 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0255-x) contains supplementary material, which is available to authorized users.
ObjectivesWe 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).SettingElectronic health records and population-based administrative data from six jurisdictions in four countries (UK, Taiwan, USA and Canada).ParticipantsA historical cohort of older adults (≥65 years) who had a new episode of antidepressant use between 2009 and 2014.Outcome measuresThe 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).ResultsThe 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.ConclusionAntidepressant 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.
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.
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.
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.
Background: The U.S. opioid epidemic has led to similar concerns about prescribed opioids in the U.K. In new users, escalation to more potent and high-dose opioids may contribute to long-term use as well as opioid-related morbidity/mortality. The scale of such escalation is unclear for non-cancer pain. Additionally, physician prescribing behaviour has been described as a key driver of rising opioid prescriptions and long-term opioid use. No studies have investigated the extent to which regions, practices, prescribers, vary in opioid prescribing, whilst accounting for case-mix. Methods: Using a retrospective cohort study we used U.K. primary-care electronic health records from Clinical Practice Research Datalink to: (i)describe prescribing trends between 2006-17 (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 (iv)quantify the variation of long-term use attributed to region, practice and prescriber, accounting for case-mix and chance variation. Adult patients with a new prescription of an opioid without cancer were included. Findings: 1,968,742 new-users of opioids were identified. Rates of codeine use were highest, increasing five-fold from 2006-2017, reaching up to 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 (100-200 Morphine Milligram Equivalents [MME]/day) or very high dose opioids (>200 MME/day), 4.9% and 10.3% remained in the same or higher MME/day category throughout 2-years, respectively. Following opioid initiation, 15% became long-term opioid users. In the fully adjusted model, MME at initiation, older-age, social deprivation, fibromyalgia, rheumatological conditions, substance abuse, suicide/self-harm and gabapentinoid use were associated with the highest odds of long-term use. After adjustment for case-mix, the North-West, Yorkshire, South-West; 103 practices (25.6%) and 540 prescribers (3.5%) were associated with a significantly higher risk of long-term use. Interpretation: Patients commenced on high MMEs were more likely to stay in the same state for a subsequent 2-years and were at increased risk of long-term use. In the first UK study evaluating long-term opioid prescribing with adjustment for patient-level characteristics, variation in regions and especially practices and prescribers were observed. Our findings support greater calls for action for reduction in practice and prescriber variation by promoting safe practice in opioid prescribing.
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