Comparative Risk of Injury with Concurrent Use of Opioids and Skeletal Muscle Relaxants.

Concurrent use of skeletal muscle relaxants (SMRs) and opioids has been linked to an increased risk of injury. However, it remains unclear whether the injury risks differ by specific SMR when combined with opioids. We conducted nine retrospective cohort studies within a US Medicaid population. Each cohort consisted exclusively of person-time exposed to both an SMR and one of the three most dispensed opioids-hydrocodone, oxycodone, and tramadol. Opioid users were further divided into three cohorts based on the initiation order of SMRs and opioids-synchronically triggered, opioid-triggered, and SMR-triggered. Within each cohort, we used Cox proportional hazard models to compare the injury rates for different SMRs compared to methocarbamol, adjusting for covariates. We identified 349,543, 139,458, and 218,967 concurrent users of SMRs with hydrocodone, oxycodone, and tramadol, respectively. In the oxycodone-SMR-triggered cohort, the adjusted hazard ratios (HRs) were 1.86 (95% CI, 1.23-2.82) for carisoprodol and 1.73 (1.09-2.73) for tizanidine. In the tramadol-synchronically triggered cohort, the adjusted HRs were 0.69 (0.49-0.97) for metaxalone and 0.62 (0.42-0.90) for tizanidine. In the tramadol-SMR-triggered cohort, the adjusted HRs were 1.51 (1.01-2.26) for baclofen and 1.48 (1.03-2.11) for cyclobenzaprine. All other HRs were statistically nonsignificant. In conclusion, the relative injury rate associated with different SMRs used concurrently with the three most dispensed opioids appears to vary depending on the specific opioid and the order of combination initiation. If confirmed by future studies, clinicians should consider the varying injury rates when prescribing SMRs to individuals using hydrocodone, oxycodone, and tramadol.

Patient-Reported Reasons for Antihypertensive Medication Change: A Quantitative Study Using Social Media.

INTRODUCTION: Hypertension is the leading cause of heart disease in the world, and discontinuation or nonadherence of antihypertensive medication constitutes a significant global health concern. Patients with hypertension have high rates of medication nonadherence. Studies of reasons for nonadherence using traditional surveys are limited, can be expensive, and suffer from response, white-coat, and recall biases. Mining relevant posts by patients on social media is inexpensive and less impacted by the pressures and biases of formal surveys, which may provide direct insights into factors that lead to non-compliance with antihypertensive medication. METHODS: This study examined medication ratings posted to WebMD, an online health forum that allows patients to post medication reviews. We used a previously developed natural language processing classifier to extract indications and reasons for changes in angiotensin receptor II blocker (ARB) and angiotensin-converting enzyme inhibitor (ACEI) treatments. After extraction, ratings were manually annotated and compared with data from the US Food and Drug administration (FDA) Adverse Events Reporting System (FAERS) public database. RESULTS: From a collection of 343,459 WebMD reviews, we automatically extracted 1867 posts mentioning changes in ACEIs or ARBs, and manually reviewed the 300 most recent posts regarding ACEI treatments and the 300 most recent posts regarding ARB treatments. After excluding posts that only mentioned a dose change or were a false-positive mention, 142 posts in the ARBs dataset and 187 posts in the ACEIs dataset remained. The majority of posts (97% ARBs, 91% ACEIs) indicated experiencing an adverse event as the reason for medication change. The most common adverse events reported mapped to the Medical Dictionary for Regulatory Activities were "musculoskeletal and connective tissue disorders" like muscle and joint pain for ARBs, and "respiratory, thoracic, and mediastinal disorders" like cough and shortness of breath for ACEIs. These categories also had the largest differences in percentage points, appearing more frequently on WebMD data than FDA data (p < 0.001). CONCLUSION: Musculoskeletal and respiratory symptoms were the most commonly reported adverse effects in social media postings associated with drug discontinuation. Managing such symptoms is a potential target of interventions seeking to improve medication persistence.

Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia.

PURPOSE: High-dimensional propensity score (hdPS) is a semiautomated method that leverages a vast number of covariates available in healthcare databases to improve confounding adjustment. A novel combined Super Learner (SL)-hdPS approach was proposed to assist with selecting the number of covariates for propensity score inclusion, and was found in plasmode simulation studies to improve bias reduction and precision compared to hdPS alone. However, the approach has not been examined in the applied setting. METHODS: We compared SL-hdPS's performance with that of several hdPS models, each with prespecified covariates and a different number of empirically-identified covariates, using a cohort study comparing real-world bleeding rates between ibrutinib- and bendamustine-rituximab (BR)-treated individuals with chronic lymphocytic leukemia in Optum's de-identified Clinformatics® Data Mart commercial claims database (2013-2020). We used inverse probability of treatment weighting for confounding adjustment and Cox proportional hazards regression to estimate hazard ratios (HRs) for bleeding outcomes. Parameters of interest included prespecified and empirically-identified covariate balance (absolute standardized difference [ASD] thresholds of <0.10 and <0.05) and outcome HR precision (95% confidence intervals). RESULTS: We identified 2423 ibrutinib- and 1102 BR-treated individuals. Including >200 empirically-identified covariates in the hdPS model compromised covariate balance at both ASD thresholds. SL-hdPS balanced more covariates than all individual hdPS models at both ASD thresholds. The bleeding HR 95% confidence intervals were generally narrower with SL-hdPS than with individual hdPS models. CONCLUSION: In a real-world application, hdPS was sensitive to the number of covariates included, while use of SL for covariate selection resulted in improved covariate balance and possibly improved precision.

Comparative safety of antimuscarinics versus mirabegron for overactive bladder in Parkinson disease.

BACKGROUND: Overactive bladder (OAB) is a common non-motor symptom of Parkinson disease (PD), often treated with antimuscarinics or beta-3 agonists. There is lack of evidence to guide OAB management in PD. OBJECTIVES: To assess the comparative safety of antimuscarinics versus beta-3 agonists for OAB treatment in PD. METHODS: We employed a new-user, active-comparator cohort study design. We included Medicare beneficiaries age ≥65 years with PD who were new users of either antimuscarinic or beta-3 agonist. The primary outcome was any acute care encounter (i.e., non-elective hospitalization or emergency department visit) within 90 days of OAB drug initiation. The main secondary outcome was a composite measure of acute care encounters for anticholinergic related adverse events (AEs). Matching on high-dimensional propensity score (hdPS) was used to address potential confounding. We used Cox proportional hazards models to examine the association between OAB drug category and outcomes. We repeated analyses for 30- and 180-day follow-up periods. RESULTS: We identified 27,091 individuals meeting inclusion criteria (mean age: 77.8 years). After hdPS matching, antimuscarinic users had increased risks for any acute care encounter (hazard ratio [HR] 1.23, 95% confidence interval [CI] 1.12-1.37) and encounters for anticholinergic related AEs (HR 1.18, 95% CI 1.04-1.34) compared to beta-3 agonist users. Similar associations were observed for sensitivity analyses. CONCLUSIONS: Among persons with PD, anticholinergic initiation was associated with a higher risk of acute care encounters compared with beta-3 agonist initiation. The long-term safety of anticholinergic vs. beta-3 agonist therapy in the PD population should be evaluated in a prospective study.

Potentially inappropriate medications in older adults with Parkinson disease before and after hospitalization for injury.

BACKGROUND: Parkinson disease (PD) patients are at increased risk of serious injury, such as fall-related fractures. Prescription medications are a modifiable factor for injury risk. OBJECTIVES: To determine the extent to which a serious injury requiring hospitalization affects prescribing of potentially inappropriate medications (PIMs) among older adults with PD. METHODS: We conducted a quasi-experimental difference-in-difference (DID) study using 2013-2017 Medicare data. The cohort consisted of beneficiaries with PD hospitalized for injury versus for other reasons. PIMs were classified into PD and injury-relevant categories (CNS-active PIMs, PD motor symptom PIMs, PD non-motor symptom PIMs, PIMs that reduce bone mineral density). We estimated mean standardized daily doses (SDDs) of medications within each PIM category before and at 3, 6, and 12 months after hospitalization. We used generalized linear regression models to compare changes in mean SDDs for each PIM category between the injury and non-injury group at each timepoint, adjusting for biological, clinical and social determinants of health variables. RESULTS: Both groups discontinued PIMs and/or reduced PIM doses after hospitalization. There were no between-group differences in mean SDD changes, after covariate adjustment, for any PIM category, except for the CNS-active PIMs category at 3 months (DID p-value = 0.00) and for the category of PIMs that reduce bone mineral density at all timepoints (DID p-values = 0.02, 0.04, 0.02 at 3, 6, and 12 months). CONCLUSIONS: Similar patterns of PIM among persons with PD after hospitalization for serious injury versus for other reasons may represent a missed opportunity to deprescribe high-risk medications during care transitions.

Reasons for Discontinuation or Change of Selective Serotonin Reuptake Inhibitors in Online Drug Reviews.

Importance: Selective serotonin reuptake inhibitors (SSRIs) are a commonly prescribed medication class to treat a variety of mental disorders. However, adherence to SSRIs is low, and uncovering the reasons for discontinuation among SSRI users is an important first step to improving medication persistence. Objective: To identify the reasons SSRIs are discontinued or changed, as reported by patients and caregivers in online drug reviews. Design, Setting, and Participants: This qualitative study used natural language processing and machine learning to extract mentions of changes in SSRI intake from 667 drug reviews posted on the online health forum WebMD from September 1, 2007, to August 31, 2021. The type of medication change, including discontinuation, switch to another medication, or dose change and the reason for the change were manually annotated. In each instance in which an adverse event was reported, the event was categorized using Medical Dictionary for Regulatory Activities primary system organ class (SOC) codes, and its relative frequency was compared with that in spontaneous reporting systems maintained by the US Food and Drug Administration and the UK Medicines and Healthcare Products Regulatory Agency. Main Outcomes and Measures: Reasons for SSRI medication change as assessed using SOC codes. Results: In total, 667 reviews posted by 659 patients or caregivers (516 [78%] of patients were female; 410 [62%] 25-54 years of age) were identified that indicated a medication change: 335 posts indicated SSRI discontinuation, 188 posts indicated dose change, and 179 posts indicated switched medications. Most authors 625 (95%) were patients. The most common reason for medication discontinuation or switching was adverse events experienced, and the most common reason for dose change was titration. Both uptitration and downtitration were initiated by either a health care professional or patient. The most common adverse events were classified by SOC codes as psychiatric disorders, including insomnia, loss of libido, and anxiety. Compared with those in regulatory data, psychiatric adverse events, adverse events recorded by investigations (mostly weight gain) and adverse events associated with the reproductive system (mostly erectile dysfunction) were reported disproportionately more often. Conclusions and Relevance: This qualitative study of online drug reviews found that useful information was provided directly by patients or their caregivers regarding their medication behavior, specifically, information regarding SSRI treatment changes that may inform interventions to improve adherence. These findings suggest that these reported adverse events may be associated with SSRI persistence and that people may feel more inclined to report such events on social media than to clinicians or regulatory agencies.

Off-brand: A 6-year study of medication brand and generic name usage in a multifacility academic healthcare system.

BACKGROUND: Usage of medication brand names in electronic health records may introduce conflicts of interest, perpetuate false perceptions of brand superiority, alter prescribing practices, and cause confusion leading to errors. OBJECTIVE: We sought to identify the frequency of brand name medication usage in clinical documentation, as well as factors associated with increased usage. DESIGNS, SETTINGS, AND PARTICIPANTS: We conducted a retrospective analysis of all clinical documentation written at our healthcare system (a multifacility academic urban healthcare system) between 2015 and 2020. MAIN OUTCOMES AND MEASURES: We used string-matching and regular expressions to identify medication mentions. We conducted bivariate analyses to identify associations between brand name usage and author-, note-, and medication-level factors, and a multivariate Poisson regression to clarify independent associations between individual factors and brand usage. RESULTS: A total of 104,456,653 notes from 37,285 unique authors were included in our analysis. A total of 162,906,009 medication mentions were identified, of which 36.0% were brand name mentions with a steady year-over-year decrease. Factors associated with the usage of a brand name include: author role, years since release, length and syllabic complexity of the generic name, service type, and encounter context. Over-the-counter availability did not affect usage. There was sizable individual variation between note writers.

Group sequential testing under instrumented difference-in-differences approach.

Unmeasured confounding is a major obstacle to reliable causal inference based on observational studies. Instrumented difference-in-differences (iDiD), a novel idea connecting instrumental variable and standard DiD, ameliorates the above issue by explicitly leveraging exogenous randomness in an exposure trend. In this article, we utilize the above idea of iDiD, and propose a novel group sequential testing method that provides valid inference even in the presence of unmeasured confounders. At each time point, we estimate the average or conditional average treatment effect under iDiD setting using the data accumulated up to that time point, and test the significance of the treatment effect. We derive the joint distribution of the test statistics under the null using the asymptotic properties of M-estimation, and the group sequential boundaries are obtained using the α $$ \alpha $$ -spending functions. The performance of our proposed approach is evaluated on both synthetic data and Clinformatics Data Mart Database (OptumInsight, Eden Prairie, MN) to examine the association between rofecoxib and acute myocardial infarction, and our method detects significant adverse effect of rofecoxib much earlier than the time when it was finally withdrawn from the market.

Risk of admission to hospital with arterial or venous thromboembolism among patients diagnosed in the ambulatory setting with covid-19 compared with influenza: retrospective cohort study.

Objective: To measure the 90 day risk of arterial thromboembolism and venous thromboembolism among patients diagnosed with covid-19 in the ambulatory (ie, outpatient, emergency department, or institutional) setting during periods before and during covid-19 vaccine availability and compare results to patients with ambulatory diagnosed influenza. Design: Retrospective cohort study. Setting: Four integrated health systems and two national health insurers in the US Food and Drug Administration's Sentinel System. Participants: Patients with ambulatory diagnosed covid-19 when vaccines were unavailable in the US (period 1, 1 April-30 November 2020; n=272 065) and when vaccines were available in the US (period 2, 1 December 2020-31 May 2021; n=342 103), and patients with ambulatory diagnosed influenza (1 October 2018-30 April 2019; n=118 618). Main outcome measures: Arterial thromboembolism (hospital diagnosis of acute myocardial infarction or ischemic stroke) and venous thromboembolism (hospital diagnosis of acute deep venous thrombosis or pulmonary embolism) within 90 days after ambulatory covid-19 or influenza diagnosis. We developed propensity scores to account for differences between the cohorts and used weighted Cox regression to estimate adjusted hazard ratios of outcomes with 95% confidence intervals for covid-19 during periods 1 and 2 versus influenza. Results: 90 day absolute risk of arterial thromboembolism with covid-19 was 1.01% (95% confidence interval 0.97% to 1.05%) during period 1, 1.06% (1.03% to 1.10%) during period 2, and with influenza was 0.45% (0.41% to 0.49%). The risk of arterial thromboembolism was higher for patients with covid-19 during period 1 (adjusted hazard ratio 1.53 (95% confidence interval 1.38 to 1.69)) and period 2 (1.69 (1.53 to 1.86)) than for patients with influenza. 90 day absolute risk of venous thromboembolism with covid-19 was 0.73% (0.70% to 0.77%) during period 1, 0.88% (0.84 to 0.91%) during period 2, and with influenza was 0.18% (0.16% to 0.21%). Risk of venous thromboembolism was higher with covid-19 during period 1 (adjusted hazard ratio 2.86 (2.46 to 3.32)) and period 2 (3.56 (3.08 to 4.12)) than with influenza. Conclusions: Patients diagnosed with covid-19 in the ambulatory setting had a higher 90 day risk of admission to hospital with arterial thromboembolism and venous thromboembolism both before and after covid-19 vaccine availability compared with patients with influenza.

Chemoimmunotherapy vs. Immunotherapy for First Line Treatment of Advanced Non-small Cell Lung Cancer With a PD-L1 Expression ≥50% or ≥90.

BACKGROUND: Evidence about the comparative effectiveness of chemoimmunotherapy vs. immunotherapy alone in patients with advanced non-small cell lung cancer (aNSCLC) and high PD-L1 expression (≥50%) or very high PD-L1 expression (≥90%) is limited because of the lack of head-to-head clinical trials. OBJECTIVE: To compare survival in aNSCLC patients receiving first-line chemoimmunotherapy vs. immunotherapy in both the PD-L1 expression ≥50% or ≥90% subgroups, accounting for potential confounders that may influence physician decision-making. METHODS: This cohort study used a nationwide electronic health record derived database to identify newly diagnosed cases of aNSCLC patients with PD-L1 expression of ≥50% who initiated first-line systemic therapy between October 2016 and October 2021. The exposure of interest was first-line therapy with chemoimmunotherapy or immunotherapy among patients with PD-L1 expression ≥50% or ≥90%. Survival was assessed using Kaplan-Meier curves and Cox regression. Propensity score-based inverse probability of weighting (IPW) was used to control for confounding. Because of nonproportionality of hazards, we estimated hazard ratios over the first 6 months and after 6 months for the overall cohort, and over the first 12 months and after 12 months for a subgroup of persons with a PD-L1 expression ≥90%. RESULTS: We identified 3086 subjects who met inclusion criteria, of whom 32% received chemoimmunotherapy and 68% received immunotherapy alone. Chemoimmunotherapy was associated with no survival advantage vs. immunotherapy alone during the entire follow-up period (IPW-adjusted Hazard Ratio [aHR] 0.98, 95% CI, 0.86-1.12), but was associated with a survival benefit during the first 6 months (aHR 0.74, 95% CI, 0.61-0.90). Similarly, in the subgroup of patients with a PD-L1 expression ≥90%, chemoimmunotherapy was associated with no overall survival advantage during the entire follow-up period (aHR 0.99, 95% CI, 0.87-1.22), but was associated with a survival benefit during the first 12 months (aHR 0.74, 95% CI, 0.57-0.97). CONCLUSION: Chemoimmunotherapy was not associated with an overall benefit over immunotherapy alone, although was associated with an early survival advantage in both the overall cohort and the subgroup of patients with a PD-L1 expression ≥90%. Future studies should focus on identifying the characteristics of higher risk patients that may benefit from the addition of chemotherapy.

Channeling of New Neuropsychiatric Drugs-Impact on Safety and Effectiveness Studies.

This study aimed to examine differential prescribing due to channeling and propensity score non-overlap over time in new versus established treatments for common neurological conditions. We conducted cross-sectional analyses on a national sample of US commercially insured adults using 2005-2019 data. We compared new users of recently approved versus established medications for management of diabetic peripheral neuropathy (pregabalin versus gabapentin), Parkinson disease psychosis (pimavanserin versus quetiapine), and epilepsy (brivaracetam versus levetiracetam). Within these drug pairs, we compared demographic, clinical, and healthcare utilization characteristics of recipients of each drug. In addition, we fit yearly propensity score models for each condition and assessed propensity score non-overlap over time. For all three drug pairs, users of the more recently approved medications more frequently had prior treatment (pregabalin = 73.9%, gabapentin = 38.7%; pimavanserin = 41.1%, quetiapine = 14.0%; brivaracetam = 93.4%, levetiracetam = 32.1%). Propensity score non-overlap and its resulting sample loss after trimming were the greatest in the first year that the more recently approved medication was available (diabetic peripheral neuropathy, 12.4% non-overlap; Parkinson disease psychosis, 6.1%; epilepsy, 43.2%) and subsequently improved. Newer neuropsychiatric therapies appear to be channeled to individuals with refractory disease or intolerance to other treatments, leading to potential confounding and biased comparative effectiveness and safety study findings when compared to established treatments. Propensity score non-overlap should be reported in comparative studies that include newer medications. When studies comparing newer and established treatments are critically needed as soon as new treatments enter the market, investigators should recognize the potential for channeling bias and implement methodological approaches like those demonstrated in this study to understand and improve this issue in such studies.

Robust causal inference of drug-drug interactions.

There is growing interest in developing causal inference methods for multi-valued treatments with a focus on pairwise average treatment effects. Here we focus on a clinically important, yet less-studied estimand: causal drug-drug interactions (DDIs), which quantifies the degree to which the causal effect of drug A is altered by the presence versus the absence of drug B. Confounding adjustment when studying the effects of DDIs can be accomplished via inverse probability of treatment weighting (IPTW), a standard approach originally developed for binary treatments and later generalized to multi-valued treatments. However, this approach generally results in biased results when the propensity score model is misspecified. Motivated by the need for more robust techniques, we propose two empirical likelihood-based weighting approaches that allow for specifying a set of propensity score models, with the second method balancing user-specified covariates directly, by incorporating additional, nonparametric constraints. The resulting estimators from both methods are consistent when the postulated set of propensity score models contains a correct one; this property has been termed multiple robustness. In this paper, we derive two multiply-robust estimators of the causal DDI, and develop inference procedures. We then evaluate their finite sample performance through simulation. The results demonstrate that the proposed estimators outperform the standard IPTW method in terms of both robustness and efficiency. Finally, we apply the proposed methods to evaluate the impact of renin-angiotensin system inhibitors (RAS-I) on the comparative nephrotoxicity of nonsteroidal anti-inflammatory drugs (NSAID) and opioids, using data derived from electronic medical records from a large multi-hospital health system.

Rejoinder to "Instrumented difference-in-differences".

We thank all the discussants for the careful reading and insightful comments. In our rejoinder, we extend the discussion of how the assumptions of instrumented difference-in-differences (iDID) compare to the assumptions of the standard instrumental variable method. We also make additional comments on how iDID is related to the fuzzy DID. We highlight future research directions to enhance the utility of iDID, including extensions to adjust for covariate shift in two-sample iDID design, and generalization of iDID to multiple time points and a multi-valued instrumental variable for DID.

Instrumented difference-in-differences.

Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variables and difference-in-differences, we propose a new method called instrumented difference-in-differences that explicitly leverages exogenous randomness in an exposure trend to estimate the average and conditional average treatment effect in the presence of unmeasured confounding. We develop the identification assumptions using the potential outcomes framework. We propose a Wald estimator and a class of multiply robust and efficient semiparametric estimators, with provable consistency and asymptotic normality. In addition, we extend the instrumented difference-in-differences to a two-sample design to facilitate investigations of delayed treatment effect and provide a measure of weak identification. We demonstrate our results in simulated and real datasets.

Core concepts in pharmacoepidemiology: Key biases arising in pharmacoepidemiologic studies.

Pharmacoepidemiology has an increasingly important role in informing and improving clinical practice, drug regulation, and health policy. Therefore, unrecognized biases in pharmacoepidemiologic studies can have major implications when study findings are translated to the real world. We propose a simple taxonomy for researchers to use as a starting point when thinking through some of the most pervasive biases in pharmacoepidemiology. We organize this discussion according to biases best assessed with respect to the study population (including confounding by indication, channeling bias, healthy user bias, and protopathic bias), the study design (including prevalent user bias and immortal time bias), and the data source (including misclassification bias and missing data/loss to follow up). This tutorial defines, provides a curated list of recommended references, and illustrates through relevant case examples these key biases to consider when planning, conducting, or evaluating pharmacoepidemiologic studies.

Real-World Trends in the Evaluation of Medical Products.

There is a compelling need to evaluate the real-world health effects of medical products outside of tightly controlled preapproval clinical trials. This is done through pharmacoepidemiology, which is the study of the health effects of medical products (including drugs, biologicals, and medical devices and diagnostics) in populations, often using nonrandomized designs. Recent developments in pharmacoepidemiology span changes in the focus of research questions, research designs, data used, and statistical analysis methods. Developments in these areas are thought to improve the value of the evidence produced by such studies, and are prompting greater use of real-world evidence to inform clinical, regulatory, and reimbursement decisions.

Antidepressant drug-drug-drug interactions associated with unintentional traumatic injury: Screening for signals in real-world data.

Antidepressants are associated with traumatic injury and are widely used with other medications. It remains unknown how drug-drug-drug interactions (3DIs) between antidepressants and two other drugs may impact potential injury risks associated with antidepressants. We aimed to generate hypotheses regarding antidepressant 3DI signals associated with elevated injury rates. Using 2000-2020 Optum's de-identified Clinformatics Data Mart, we performed a self-controlled case series study for each drug triad consisting of an antidepressant + codispensed drug (base-pair) with a candidate interacting medication (precipitant). We included persons aged greater than or equal to 16 years who (1) experienced an injury and (2) used a candidate precipitant, during base-pair therapy. We compared injury rates during observation time exposed to the drug triad versus the base-pair only, adjusting for time-varying covariates. We calculated adjusted rate ratios (RRs) using conditional Poisson regression and accounted for multiple comparisons via semi-Bayes shrinkage. Among 147,747 eligible antidepressant users with an injury, we studied 120,714 antidepressant triads, of which 334 (0.3%) were positively associated with elevated injury rates and thus considered potential 3DI signals. Adjusted RRs for signals ranged from 1.31 (1.04-1.65) for sertraline + levothyroxine with tramadol (vs. without tramadol) to 6.60 (3.23-13.46) for escitalopram + simvastatin with aripiprazole (vs. without aripiprazole). Nearly half of the signals (137, 41.0%) had adjusted RRs greater than or equal to 2, suggesting strong associations with injury. The identified signals may represent antidepressant 3DIs of potential clinical concern and warrant future etiologic studies to test these hypotheses.