Chronic HCV is associated with a higher risk of mortality.SVR from DAAs was associated with a significant reduction in the risk of all-cause, liver-and drug-related mortality.Older age and cirrhosis were associated with higher risk of liverrelated mortality.Younger age, injection drug use, and problematic alcohol use were associated with higher risk of drug-related mortality.

IMPORTANCE Osteoporosis medication treatment is recommended after hip fracture, yet contemporary estimates of rates of initiation and clinical benefit in the patient population receiving routine care are not well documented. OBJECTIVES To report osteoporosis treatment initiation rates between January 1, 2004, and September 30, 2015, and to estimate the risk reduction in subsequent nonvertebral fractures associated with treatment initiation in patients with hip fracture. DESIGN, SETTING, AND PARTICIPANTS In this cohort study, data from a commercial insurance claims database from the United States were analyzed. Patients 50 years and older who had a hip fracture and were not receiving treatment with osteoporosis medications before their fracture were included. EXPOSURE Prescription dispensing of an osteoporosis medication within 180 days of a hip fracture hospitalization. MAIN OUTCOMES AND MEASURES Each initiation episode was matched with 10 nonuse episodes on person-time after the index hip fracture event to preclude immortal time bias and followed up for the outcome of nonvertebral fracture until change in exposure or a censoring event. An instrumental variable analysis using 2-stage residual inclusion method was conducted using calendar year, specialist access, geographical variation in prescribing patterns, and hospital preference. RESULTS Among 97 169 patients with a hip fracture identified, the mean (SD) age was 80.2 (10.8) years, and 64 164 (66.0%) were women. A continuous decline over the study years was observed in osteoporosis medication initiation rates from 9.8% (95% CI, 9.0%-10.6%) in 2004 to 3.3% (95% CI, 2.9%-3.8%) in 2015. In the effectiveness analyses, the hospital preference instrumental variable had a stronger association with treatment (pseudo R 2 = 0.20) than the other 3 instrumental variables (specialist access: pseudo R 2 = 0.04; calendar year: pseudo R 2 = 0.05; and geographic variation: pseudo R 2 = 0.07). Instrumental variable analysis with hospital preference suggested a rate difference of 4.2 events (95% CI, 1.1-7.3) per 100 person-years in subsequent fractures associated with osteoporosis treatment initiation compared with nonuse in an additive hazard model. CONCLUSIONS AND RELEVANCE Low rates of osteoporosis treatment initiation after a hip fracture in recent years were observed. Clinically meaningful reduction in subsequent nonvertebral fracture (continued) Key Points Question Among patients with a hip fracture, what is the frequency and effectiveness of initiating osteoporosis medications for prevention of subsequent fractures? Findings In this cohort study of 97 169 patients with hip fracture, a continuous decline was observed in osteoporosis medication initiation rates, from 9.8% in 2004 to 3.3% in 2015. After adjusting for measured and unmeasured confounding with an instrumental variable approach, a difference of 4.2 events per 100 person-years was observed in the rate of subsequent fractures associated with treatment initiation. Meaning The findings of low initiation rates of ost...

Propensity score based statistical methods, such as matching, regression, stratification, inverse probability weighting (IPW), and doubly robust (DR) estimating equations, have become popular in estimating average treatment effect (ATE) and average treatment effect among treated (ATT) in observational studies. Propensity score is the conditional probability receiving a treatment assignment with given covariates, and propensity score is usually estimated by logistic regression. However, a misspecification of the propensity score model may result in biased estimates for ATT and ATE. As an alternative, the generalized boosting method (GBM) has been proposed to estimate the propensity score. GBM uses regression trees as weak predictors and captures nonlinear and interactive effects of the covariate. For GBM-based propensity score, only IPW methods have been investigated in the literature. In this article, we provide a comparative study of the commonly used propensity score based methods for estimating ATT and ATE, and examine their performances when propensity score is estimated by logistic regression and GBM, respectively. Extensive simulation results indicate that the estimators for ATE and ATT may vary greatly due to different methods. We concluded that (i) regression may not be suitable for estimating ATE and ATT regardless of the estimation method of propensity score; (ii) IPW and stratification usually provide reliable estimates of ATT when propensity score model is correctly specified; (iii) the estimators of ATE based on stratification, IPW, and DR are close to the underlying true value of ATE when propensity score is correctly specified by logistic regression or estimated using GBM.

Objective To assess the association between long term prescription opioid treatment medically dispensed for non-cancer pain and the initiation of injection drug use (IDU) among individuals without a history of substance use. Design Retrospective cohort study. Setting Large administrative data source (containing information for about 1.7 million individuals tested for hepatitis C virus or HIV in British Columbia, Canada) with linkage to administrative health databases, including dispensations from community pharmacies. Participants Individuals age 11-65 years and without a history of substance use (except alcohol) at baseline. Main outcome measures Episodes of prescription opioid use for non-cancer pain were identified based on drugs dispensed between 2000 and 2015. Episodes were classified by the increasing length and intensity of opioid use (acute (lasting <90 episode days), episodic (lasting ≥90 episode days; with <90 days’ drug supply and/or <50% episode intensity), and chronic (lasting ≥90 episode days; with ≥90 days’ drug supply and ≥50% episode intensity)). People with a chronic episode were matched 1:1:1:1 on socioeconomic variables to those with episodic or acute episodes and to those who were opioid naive. IDU initiation was identified by a validated administrative algorithm with high specificity. Cox models weighted by inverse probability of treatment weights assessed the association between opioid use category (chronic, episodic, acute, opioid naive) and IDU initiation. Results 59 804 participants (14 951 people from each opioid use category) were included in the matched cohort, and followed for a median of 5.8 years. 1149 participants initiated IDU. Cumulative probability of IDU initiation at five years was highest for participants with chronic opioid use (4.0%), followed by those with episodic use (1.3%) and acute use (0.7%), and those who were opioid naive (0.4%). In the inverse probability of treatment weighted Cox model, risk of IDU initiation was 8.4 times higher for those with chronic opioid use versus those who were opioid naive (95% confidence interval 6.4 to 10.9). In a sensitivity analysis limited to individuals with a history of chronic pain, cumulative risk for those with chronic use (3.4% within five years) was lower than the primary results, but the relative risk was not (hazard ratio 9.7 (95% confidence interval 6.5 to 14.5)). IDU initiation was more frequent at higher opioid doses and younger ages. Conclusions The rate of IDU initiation among individuals who received chronic prescription opioid treatment for non-cancer pain was infrequent overall (3-4% within five years) but about eight times higher than among opioid naive individuals. These findings could have implications for strategies to prevent IDU initiation, but should not be used as a reason to support involuntary tapering or discontinuation of long term prescription opioid treatment.

PurposePrescription opioids (POs) are widely prescribed for chronic non-cancer pain but are associated with several risks and limited long-term benefit. Large, linked data sources are needed to monitor their harmful effects. We developed and characterised a retrospective cohort of people dispensed POs.ParticipantsWe used a large linked administrative database to create the Opioid Prescribing Evaluation and Research Activities cohort of individuals dispensed POs for non-cancer pain in British Columbia (BC), Canada (1996–2015). We created definitions to categorise episodes of PO use based on a review of the literature (acute, episodic, chronic), developed an algorithm for inferring clinical indication and assessed patterns of PO use across a range of characteristics.Findings to dateThe current cohort includes 1.1 million individuals and 3.4 million PO episodes (estimated to capture 40%–50% of PO use in BC). The majority of episodes were acute (81%), with most prescribed for dental or surgical pain. Chronic use made up 3% of episodes but 88% of morphine equivalents (MEQ). Across the acute to episodic to chronic episode gradient, there was an increasing prevalence of higher potency POs (hydromorphone, oxycodone, fentanyl, morphine), long-acting formulations and chronic pain related indications (eg, back, neck, joint pain). Average daily dose (MEQ) was similar for acute/episodic but higher for chronic episodes. Approximately 7% of the cohort had a chronic episode and chronic pain was the characteristic most strongly associated with chronic PO use. Individuals initiating a chronic episode were also more likely to have higher social/material deprivation and previous experience with a mental health condition or a problem related to alcohol or opioid use. Overall, these findings suggest our episode definitions have face validity and also provide insight into characteristics of people initiating chronic PO therapy.Future plansThe cohort will be refreshed every 2 years. Future analyses will explore the association between POs and adverse outcomes.

Purpose: Bootstrapping can account for uncertainty in propensity score (PS) estimation and matching processes in 1:1 PS-matched cohort studies. While theory suggests that the classical bootstrap can fail to produce proper coverage, practical impact of this theoretical limitation in settings typical to pharmacoepidemiology is not well studied. Methods:In a plasmode-based simulation study, we compared performance of the standard parametric approach, which ignores uncertainty in PS estimation and matching, with two bootstrapping methods. The first method only accounted for uncertainty introduced during the matching process (the observation resampling approach). The second method accounted for uncertainty introduced during both PS estimation and matching processes (the PS reestimation approach). Variance was estimated based on percentile and empirical standard errors, and treatment effect estimation was based on median and mean of the estimated treatment effects across 1000 bootstrap resamples. Two treatment prevalence scenarios (5% and 29%) across two treatment effect scenarios (hazard ratio of 1.0 and 2.0) were evaluated in 500 simulated cohorts of 10 000 patients each. Results:We observed that 95% confidence intervals from the bootstrapping approaches but not the standard approach, resulted in inaccurate coverage rates (98%-100% for the observation resampling approach, 99%-100% for the PS reestimation approach, and 95%-96% for standard approach). Treatment effect estimation based on bootstrapping approaches resulted in lower bias than the standard approach (less than 1.4% vs 4.1%) at 5% treatment prevalence; however, the performance was equivalent at 29% treatment prevalence. Conclusion:Use of bootstrapping led to variance overestimation and inconsistent coverage, while coverage remained more consistent with parametric estimation.

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Background In late 2021, the Omicron SARS-CoV-2 variant emerged and rapidly replaced Delta as the dominant variant globally. The increased transmissibility of the variant led to surges in case rates as well as increases in hospitalizations, however, the true severity of the variant remained unclear. We aimed to provide robust estimates of Omicron severity relative to Delta. Methods This study was conducted using a retrospective cohort design with data from the British Columbia COVID-19 Cohort – a large provincial surveillance platform with linkage to administrative datasets. To capture the time of co-circulation with Omicron and Delta, December 2021 was chosen as the study period. We included individuals diagnosed with Omicron or Delta infection, as determined by whole genome sequencing (WGS). To assess the severity (hospitalization, ICU admission, length of stay), we conducted adjusted Cox proportional hazard models, weighted by inverse probability of treatment weights (IPTW), accounting for age, sex, underlying comorbidities, vaccination, sociodemographic status, and geographical variation. Results The cohort was composed of 13,128 individuals (7,729 Omicron and 5,399 Delta). There were 419 COVID-19 hospitalizations, with 118 (22%) among people diagnosed with Omicron (crude rate = 1.5% Omicron, 5.6% Delta). In multivariable IPTW analysis, Omicron was associated with a 50% lower risk of hospitalization compared to Delta (aHR = 0.50; 95%CI = 0·43-0.59), a 73% lower risk of ICU admission (aHR = 0.27; 95%CI = 0.19-0.38), and a 5 days shorter hospital stay on average (aß=-5.03; 95% CI=-8.01, -2.05). Conclusions Our analysis supports findings from other studies demonstrating lower risk of severe outcomes in Omicron-infected individuals relative to Delta.