Evidence of Sample Use Among New Users of Statins: Implications for Pharmacoepidemiology

Epidemiologic studies of prescription medications increasingly rely on large administrative healthcare databases. These data do not capture patients’ use of medication samples. This could potentially bias studies of short-term effects where date of initiation may be inaccurate

Comparative Health-Care Cost Advantage of Ipratropium over Tiotropium in COPD Patients

Objective: To compare the total direct health-care costs of patients treated with tiotropium and ipratropium. Methods: We conducted a cohort study of health-care costs in British Columbia, Canada, by comparing new patients on tiotropium with new patients on ipratropium. Direct health-care costs for study patients were measured in the first 2 years after initiating inhaled anticholinergic treatment. Differences in direct health-care costs between tiotropium and ipratropium patients were estimated by using quantile regression. We analyzed cost differences in the 10th percentile, median, and 90th percentile of patients by cost. High-dimensional propensity score analysis was used as a method of adjustment for potential confounding factors. Results: The study population had 3,140 tiotropium patients and 26,182 ipratropium patients. Higher health system costs in patients who started on tiotropium instead of ipratropium were observed in patients in the median and 10th percentile. The magnitude of these increases was comparable to the price difference between the two drugs. Health system costs in the 90th percentile were not significantly different between tiotropium and ipratropium patients. Conclusions: The results of this study did not support the preferential use of tiotropium over ipratropium as a basis for savings in direct health-care costs

Propensity Score Estimation to Address Calendar Time-Specific Channeling in Comparative Effectiveness Research of Second Generation Antipsychotics

BackgroundChanneling occurs when a medication and its potential comparators are selectively prescribed based on differences in underlying patient characteristics. Drug safety advisories can provide new information regarding the relative safety or effectiveness of a drug product which might increase selective prescribing. In particular, when reported adverse effects vary among drugs within a therapeutic class, clinicians may channel patients toward or away from a drug based on the patient's underlying risk for an adverse outcome. If channeling is not identified and appropriately managed it might lead to confounding in observational comparative effectiveness studies.ObjectiveTo demonstrate channeling among new users of second generation antipsychotics following a Food and Drug Administration safety advisory and to evaluate the impact of channeling on cardiovascular risk estimates over time.Data SourceFlorida Medicaid data from 2001–2006.Study DesignRetrospective cohort of adults initiating second generation antipsychotics. We used propensity scores to match olanzapine initiators with other second generation antipsychotic initiators. To evaluate channeling away from olanzapine following an FDA safety advisory, we estimated calendar time-specific propensity scores. We compare the performance of these calendar time-specific propensity scores with conventionally-estimated propensity scores on estimates of cardiovascular risk.Principal FindingsIncreased channeling away from olanzapine was evident for some, but not all, cardiovascular risk factors and corresponded with the timing of the FDA advisory. Covariate balance was optimized within period and across all periods when using the calendar time-specific propensity score. Hazard ratio estimates for cardiovascular outcomes did not differ across models (Conventional PS: 0.97, 95%CI: 0.81–3.18 versus calendar time-specific PS: 0.93, 95%CI: 0.77–3.04).ConclusionsChanges in channeling over time was evident for several covariates but had limited impact on cardiovascular risk estimates, possibly due to unmeasured confounding. Although calendar time-specific propensity scores appear to improve covariate balance, the impact on comparative effectiveness results is limited in this setting

Effect of glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors on colorectal cancer incidence and its precursors

Incretin-based antihyperglycemic therapies increase intestinal mucosal expansion and polyp growth in mouse models. We aimed to evaluate the effect of dipeptidyl peptidase-4 inhibitors (DPP-4i) or glucagon-like peptide-1 receptor agonists (GLP-1ra) initiation on colorectal cancer incidence

Gender Disparities in Surgical Treatment of Axis Fractures in Older Adults

Study Design: Retrospective cohort study. Objectives: Gender appears to play in important role in surgical outcomes following acute cervical spine trauma, with current literature suggesting males have a significantly higher mortality following spine surgery. However, no well-adjusted population-based studies of gender disparities in incidence and outcomes of spine surgery following acute traumatic axis injuries exist to our knowledge. We hypothesized that females would receive surgery less often than males, but males would have a higher 1-year mortality following isolated traumatic axis fractures. Methods: We performed a retrospective cohort study using Medicare claims data that identified US citizens aged 65 and older with ICD-9 (International Classification of Diseases, Ninth Revision) code diagnosis corresponding to isolated acute traumatic axis fracture between 2007 and 2014. Our primary outcome was defined as cumulative incidence of surgical treatment, and our secondary outcome was 1-year mortality. Propensity weighted analysis was performed to balance covariates between genders. Our institutional review board approved the study (IRB #16-0533). Results: There was no difference in incidence of surgery between males and females following acute isolated traumatic axis fractures (7.4 and 7.5 per 100 fractures, respectively). Males had significantly higher 1-year weighted mortality overall (41.7 and 28.9 per 100 fractures, respectively, P < .001). Conclusion: Our well-adjusted data suggest there was no significant gender disparity in incidence of surgical treatment over the study period. The data also support previous observations that males have worse outcomes in comparison to females in the setting of axis fractures and spinal trauma regardless of surgical intervention

Considerations for observational study design: Comparing the evidence of opioid use between electronic health records and insurance claims

Purpose: Pharmacoepidemiology studies often use insurance claims and/or electronic health records (EHR) to capture information about medication exposure. The choice between these data sources has important implications. Methods: We linked EHR from a large academic health system (2015-2017) to Medicare insurance claims for patients undergoing surgery. Drug utilization was characterized based on medication order dates in the EHR, and prescription fill dates in Medicare claims. We compared opioid use documented in EHR orders to prescription claims in four time periods: 1) Baseline (182 days before surgery); 2) Perioperative period; 3) Discharge date; 4) Follow-up (90 days after surgery). Results: We identified 11 128 patients undergoing surgery. During baseline, 34.4% (EHR) versus 44.1% (claims) had evidence of opioid use, and 56.9% of all baseline use was reflected only in one data source. During the perioperative period, 78.8% (EHR) versus 47.6% (claims) had evidence of use. On the day of discharge, 59.6% (EHR) versus 45.5% (claims) had evidence of use, and 51.8% of all discharge use was reflected only in one data source. During follow-up, 4.3% (EHR) versus 10.4% (claims) were identified with prolonged opioid use following surgery with 81.4% of all prolonged use reflected only in one data source. Conclusions: When characterizing opioid exposure, we found substantial discrepancies between EHR medication orders and prescription claims data. In all time periods assessed, most patients' use was reflected only in the EHR, or only in the claims, not both. The potential for misclassification of drug utilization must be evaluated carefully, and choice of data source may have large impacts on key study design elements

Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study: PS VARIABLE SELECTION FOR MULTIPLE OUTCOMES

It is often preferable to simplify the estimation of treatment effects on multiple outcomes by using a single propensity score (PS) model. Variable selection in PS models impacts the efficiency and validity of treatment effects. However, the impact of different variable selection strategies on the estimated treatment effects in settings involving multiple outcomes is not well understood. The authors use simulations to evaluate the impact of different variable selection strategies on the bias and precision of effect estimates to provide insight into the performance of various PS models in settings with multiple outcomes

Propensity Score Methods for Confounding Control in Nonexperimental Research

Nonexperimental studies are increasingly used to investigate the safety and effectiveness of medical products as they are used in routine care. One of the primary challenges of such studies is confounding, systematic differences in prognosis between patients exposed to an intervention of interest and the selected comparator group. In the presence of uncontrolled confounding, any observed difference in outcome risk between the groups cannot be attributed solely to a causal effect of the exposure on the outcome. Confounding in studies of medical products can arise from a variety of different sociomedical processes.1 The most common form of confounding arises from good medical practice, physicians prescribing medications and performing procedures on patients who are most likely to benefit from them. This leads to a bias known as confounding by indication, which can cause medical interventions to appear to cause events that they prevent.2,3 Conversely, patients who are perceived by a physician to be near the end of life may be less likely to receive preventive medications, leading to confounding by frailty or comorbidity.4–6 Additional sources of confounding bias can result from patients’ health-related behaviors. For example, patients who initiate a preventive medication may be more likely than other patients to engage in other healthy, prevention-oriented behaviors leading to bias known as the healthy user/adherer effect.7–9 Many statistical approaches can be used to remove the confounding effects of such factors if they are captured in the data. The most common statistical approaches for confounding control are based on multivariable regression models of the outcome. To yield unbiased estimates of treatment effects, these approaches require that the researcher correctly models the effect of the treatment and covariates on the outcome. However, correct specification of an outcome model can be challenging, particularly in studies