Hospitalization and Combined Use of Opioids, Benzodiazepines, and Muscle Relaxants in the United States

New and Persistent Sedative-Hypnotic Use After Adjuvant Chemotherapy for Breast Cancer

BACKGROUND

Sedative-hypnotic medications are used to treat chemotherapy-related nausea, anxiety, and insomnia. However, prolonged sedative-hypnotic use can lead to dependence, misuse, and increased health-care use. We aimed to estimate the rates at which patients who receive adjuvant chemotherapy for breast cancer become new persistent users of sedative-hypnotic medications, specifically benzodiazepines and nonbenzodiazepine sedative-hypnotics (Z-drugs).

METHODS

Using the MarketScan health-care claims database, we identified sedative-hypnotic-naïve patients who received adjuvant chemotherapy for breast cancer. Patients who filled 1 and more prescriptions during chemotherapy and 2 and more prescriptions up to 1 year after chemotherapy were classified as new persistent users. Univariate and multivariable logistic regression analyses were used to estimate odds of new persistent use and associated characteristics.

RESULTS

We identified 22 039 benzodiazepine-naïve patients and 23 816 Z-drug-naïve patients who received adjuvant chemotherapy from 2008 to 2017. Among benzodiazepine-naïve patients, 6159 (27.9%) filled 1 and more benzodiazepine prescriptions during chemotherapy, and 963 of those (15.6%) went on to become new persistent users. Among Z-drug-naïve patients, 1769 (7.4%) filled 1 and more prescriptions during chemotherapy, and 483 (27.3%) became new persistent users. In both groups, shorter durations of chemotherapy and receipt of opioid prescriptions were associated with new persistent use. Medicaid insurance was associated with new persistent benzodiazepine use (odds ratio = 1.88, 95% confidence interval = 1.43 to 2.47) compared with commercial or Medicare insurance.

CONCLUSIONS

Patients who receive sedative-hypnotic medications during adjuvant chemotherapy for breast cancer are at risk of becoming new persistent users of these medications after chemotherapy. Providers should ensure appropriate sedative-hypnotic use through tapering dosages and encouraging nonpharmacologic strategies when appropriate.

Population-based screening to detect benzodiazepine drug-drug-drug interaction signals associated with unintentional traumatic injury

Drug interactions involving benzodiazepines and related drugs (BZDs) are increasingly recognized as a contributor to increased risk of unintentional traumatic injury. Yet, it remains unknown to what extent drug interaction triads (3DIs) may amplify BZDs' inherent injury risk. We identified BZD 3DI signals associated with increased injury rates by conducting high-throughput pharmacoepidemiologic screening of 2000-2019 Optum's health insurance data. Using self-controlled case series design, we included patients aged ≥ 16 years with an injury while using a BZD + co-dispensed medication (i.e., base pair). During base pair-exposed observation time, we identified other co-dispensed medications as candidate interacting precipitants. Within each patient, we compared injury rates during time exposed to the drug triad versus to the base pair only using conditional Poisson regression, adjusting for time-varying covariates. We calculated rate ratios (RRs) with 95% confidence intervals (CIs) and accounted for multiple estimation via semi-Bayes shrinkage. Among the 65,123 BZD triads examined, 79 (0.1%) were associated with increased injury rates and considered 3DI signals. Adjusted RRs for signals ranged from 3.01 (95% CI = 1.53-5.94) for clonazepam + atorvastatin with cefuroxime to 1.42 (95% CI = 1.00-2.02, p = 0.049) for alprazolam + hydrocodone with tizanidine. These signals may help researchers prioritize future etiologic studies to investigate higher-order BZD interactions.

Outpatient Opioid and Naloxone Prescribing Practices at an Academic Medical Center during the COVID-19 Pandemic

While improving opioid safety has been a national priority, the coronavirus disease 2019 (COVID-19) pandemic has been associated with increased rates of opioid overdose. The present study characterized outpatient opioid and naloxone prescribing patterns during the COVID-19 pandemic. A retrospective chart review was conducted of adult patients receiving opioid therapy between August 2020 through October 2020 from outpatient clinics within a Texas health system. The primary outcome was naloxone co-prescription during the study period or within the year prior. During the study period, 1,368 patients received an opioid prescription, most of which were prescribed for chronic pain treatment (63.0%). Most opioid prescriptions (91.5%) were written for < 50 MME/day. For prescriptions written for acute pain, 78% were written for ≤ 10 days supply. While 31.1% of patients received gabapentinoid prescriptions, few (7.9%) received benzodiazepine or Z-hypnotic prescriptions. Twenty-two (1.6%) patients were co-prescribed naloxone. In this study, naloxone was rarely prescribed for outpatients receiving opioid prescriptions during the COVID-19 pandemic. Health systems should continue to prioritize adherence to evidence-based clinical guidelines and increase access to naloxone.

Opioid Drug-Drug-Drug Interactions and Unintentional Traumatic Injury: Screening to Detect Three-Way Drug Interaction Signals

Growing evidence suggests that drug interactions may be responsible for much of the known association between opioid use and unintentional traumatic injury. While prior research has focused on pairwise drug interactions, the role of higher-order (i.e., drug-drug-drug) interactions (3DIs) has not been examined. We aimed to identify signals of opioid 3DIs with commonly co-dispensed medications leading to unintentional traumatic injury, using semi-automated high-throughput screening of US commercial health insurance data. We conducted bi-directional, self-controlled case series studies using 2000-2015 Optum Data Mart database. Rates of unintentional traumatic injury were examined in individuals dispensed opioid-precipitant base pairs during time exposed vs unexposed to a candidate interacting precipitant. Underlying cohorts consisted of 16-90-year-olds with new use of opioid-precipitant base pairs and ≥1 injury during observation periods. We used conditional Poisson regression to estimate rate ratios adjusted for time-varying confounders, and semi-Bayes shrinkage to address multiple estimation. For hydrocodone, tramadol, and oxycodone (the most commonly used opioids), we examined 16,024, 8185, and 9330 drug triplets, respectively. Among these, 75 (0.5%; hydrocodone), 57 (0.7%; tramadol), and 42 (0.5%; oxycodone) were significantly positively associated with unintentional traumatic injury (50 unique base precipitants, 34 unique candidate precipitants) and therefore deemed potential 3DI signals. The signals found in this study provide valuable foundations for future research into opioid 3DIs, generating hypotheses to motivate crucially needed etiologic investigations. Further, this study applies a novel approach for 3DI signal detection using pharmacoepidemiologic screening of health insurance data, which could have broad applicability across drug classes and databases.

Frailty Risks of Prescription Analgesics and Sedatives across Frailty Models: the Health and Retirement Study

INTRODUCTION

Limited evidence for incident frailty risks associated with prescription analgesics and sedatives in older (≥ 65 years) community-living adults prompted a more comprehensive investigation.

METHODS

We used data from older Health and Retirement Study respondents and three frailty models (frailty index, functional domain, frailty phenotype with 8803, 10,470, and 6850 non-frail individuals, respectively) and estimated sub-hazard ratios of regular prescription drug use (co-use, analgesic use, and sedative use), by frailty model. We addressed confounding with covariate adjustment and propensity score matching approaches.

RESULTS

The baseline prevalence of analgesic and sedative co-use, analgesic use, and sedative use among non-frail respondents was 1.8%, 12.8%, and 4.7% for the frailty index model, 4.2%, 16.2%, and 5.3% for the functional domain model, and 4.3%, 15.4%, and 6.1% for the frailty phenotype model, respectively. Cumulative frailty incidence over 10 years was 39.3%, 36.1%, and 14.2% for frailty index, functional domain, and frailty phenotype models, respectively; covariate-adjusted sub-hazard ratio estimates were 2.00 (1.63-2.45), 1.83 (1.57-2.13), and 1.68 (1.21-2.33) for co-use; 1.72 (1.56-1.89), 1.38 (1.27-1.51), and 1.51 (1.27-1.79) for analgesic use; and 1.46 (1.24-1.72), 1.25 (1.07-1.46), and 1.31 (0.97-1.76) for sedative use. Frailty risk ranking (co-use > analgesic use > sedative use) persisted across all model sensitivity analyses.

DISCUSSION

Consistently significant frailty risk estimates of regular prescription analgesic and sedative co-use and of prescription analgesic use support existing clinical, public health, and regulatory guidance on opioid and benzodiazepine co-prescription, on opioid prescription, and on NSAID prescription. Frailty phenotype measurement administration limited power to detect significant frailty risks. Research into specific pharmaceutical exposures and comparison of results across cohorts will be required to contribute to the deprescribing evidence base.

New and persistent controlled substance use among patients undergoing mastectomy and reconstructive surgery

PURPOSE

Prolonged use of controlled substances can place patients at increased risk of dependence and complications. Women who have mastectomy and reconstructive surgery (M + R) may be vulnerable to becoming new persistent users (NPUs) of opioid and sedative-hypnotic medications.

METHODS

Using the MarketScan health-care claims database, we identified opioid- and sedative-hypnotic-naïve women who had M + R from 2008 to 2017. Women who filled ≥ 1 peri-operative prescription and ≥ 2 post-operative prescriptions within one year after surgery were classified as NPUs. Univariate and multivariable logistic regression analyses were used to estimate rates of new persistent use and predictive factors. Risk summary scores were created based on the sum of associated factors.

RESULTS

We evaluated 23,025 opioid-naïve women and 25,046 sedative-hypnotic-naïve women. We found that 17,174 opioid-naïve women filled a peri-operative opioid prescription, and of those, 2962 (17.2%) became opioid NPUs post-operatively. Additionally, 9426 sedative-hypnotic-naïve women filled a peri-operative sedative-hypnotic prescription, and of those, 1612 (17.1%) became sedative-hypnotic NPUs. Development of new persistent sedative-hypnotic use was associated with age ≤ 49 [OR 1.77 (95% CI 1.40-2.24)] and age 50-64 [1.60 (1.27-2.03)] compared to age ≥ 65; Medicaid insurance [2.34 (1.40-3.90)]; southern residence [1.42 (1.22-1.64)]; breast cancer diagnosis [2.24 (1.28-3.91)]; and chemotherapy [2.17 (1.94-2.42)]. Risk of NPU increased with higher risk score. Women with ≥ 3 of these risk factors were three times more likely to become sedative-hypnotic NPUs than patients with 0 or 1 factors [2.94 (2.51-3.43)]. Comparable findings were seen regarding new persistent opioid use.

CONCLUSION

Women who have M + R are at risk of developing both new persistent opioid and new persistent sedative-hypnotic use. A patient's risk of becoming an NPU increases as their number of risk factors increases. Non-pharmacologic strategies are needed to manage pain and anxiety following cancer-related surgery.