Opioid Exposures Reported to U.S. Poison Centers

Polypharmacy and Healthcare Service Use Among Prescription Opioid Poisoning Cases Age 50

Objectives: To examine relationships between polypharmacy and level of healthcare service use among prescription opioid poisoning cases age 50 and older. Methods: Data came from the American Association of Poison Control Center's National Poison Data System, 2015-2020. We used multinomial logistic regression to examine the study questions. Results: Of the 77 946 cases with prescription opioid exposures, 64.5% were managed at a healthcare facility (HCF). Of HCF-managed cases, 41.2% were treated/evaluated and released and 21.3% and 37.5% were admitted for noncritical care and critical care, respectively. Medications for cardiovascular disease, benzodiazepines, other types of sedatives/hypnotics, antipsychotics, muscle relaxants, acetaminophen, and gabapentin were associated with increased risk of admission to both noncritical and critical care compared to treatment/evaluation and release. Acetaminophen use had the highest relative risk ratios (RRRs) for noncritical care (1.70, 95% CI = 1.51-1.91) and critical care (1.56, 95% CI = 1.39-1.76). Each additional medication/substance used was associated with 1.14 (95% CI = 1.11-1.17) and 1.19 (95% CI = 1.16-1.22) greater risk of noncritical and critical care admissions, respectively. Conclusions: Among older-adult poison control center cases for prescription opioid exposures, co-use of several commonly prescribed/used medicines was associated with increased risk of admissions to both noncritical and critical care units. Careful monitoring of medication use among older adults who use prescription opioids may reduce the risk of unintentional and intentional opioid poisoning.

Variation in adverse drug events of opioids in the United States

Background: The United States (US) ranks high, nationally, in opioid consumption. The ongoing increase in the misuse and mortality amid the opioid epidemic has been contributing to its rising cost. The worsening health and economic impact of opioid use disorder in the US warrants further attention. We, therefore, assessed commonly prescribed opioids to determine the opioids that were over-represented versus under-represented for adverse drug events (ADEs) to better understand their distribution patterns using the Food and Drug Administration's Adverse Event Reporting System (FAERS) while correcting for distribution using the Drug Enforcement Administration's Automation of Reports and Consolidated Orders System (ARCOS). Comparing the ratio of the percentage of adverse drug events as reported by the FAERS relative to the percentage of distribution as reported by the ARCOS database is a novel approach to evaluate post-marketing safety surveillance and may inform healthcare policies and providers to better regulate the use of these opioids. Methods: We analyzed the adverse events for 11 prescription opioids, when correcting for distribution, and their ratios for three periods, 2006-2010, 2011-2016, and 2017-2021, in the US. The opioids include buprenorphine, codeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, and tapentadol. Oral morphine milligram equivalents (MMEs) were calculated by conversions relative to morphine. The relative ADEs of the selected opioids, opioid distributions, and ADEs relative to distribution ratios were analyzed for the 11 opioids. Results: Oxycodone, fentanyl, and morphine accounted for over half of the total number of ADEs (n = 667,969), while meperidine accounted for less than 1%. Opioid distributions were relatively constant over time, with methadone repeatedly accounting for the largest proportions. Many ADE-to-opioid distribution ratios increased over time, with meperidine (60.6), oxymorphone (11.1), tapentadol (10.3), and hydromorphone (7.9) being the most over-represented for ADEs in the most recent period. Methadone was under-represented (<0.20) in all the three periods. Conclusion: The use of the FAERS with the ARCOS provides insights into dynamic changes in ADEs of the selected opioids in the US. There is further need to monitor and address the ADEs of these drugs.

Analysis of Urine Drug Test Results From Substance Use Disorder Treatment Practices and Overdose Mortality Rates, 2013-2020

IMPORTANCE

Drug overdose deaths in the US are currently the highest ever recorded; data collected from public health surveillance sources can help to identify emerging drug use patterns associated with overdose mortality rates, but the time lag in results often limits utility. Urine drug testing (UDT) is one potentially underused source that could augment surveillance efforts through timely data collection.

OBJECTIVE

To evaluate the correlation between real-time UDT results from a proprietary national database and overdose mortality data from the National Vital Statistics System.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cross-sectional study included 500 000 urine specimens submitted for UDT by substance use disorder (SUD) treatment health care practices and collected between January 1, 2013, and December 31, 2020. Real-time UDT data were obtained from the Millennium Health proprietary national database, and overdose mortality data were obtained from the National Vital Statistics System of the Centers for Disease Control and Prevention (CDC WONDER). Specimens were analyzed for specific drugs in 5 categories (cocaine, heroin, methamphetamine, synthetic opioids, and other opioids) using liquid chromatography-tandem mass spectrometry. Participants were adults aged 18 years and older who provided urine specimens at SUD treatment practices.

EXPOSURES

Urine drug testing.

MAIN OUTCOMES AND MEASURES

The primary outcome was the correlation between UDT positivity rates and overdose mortality rates at national, state, and county levels. Univariate and multivariate regression models were also used to evaluate the association between state- and county-level overdose mortality and standardized UDT positivity rates.

RESULTS

Among 500 000 unique patient specimens collected from SUD treatment practices between 2013 and 2020, 288 534 specimens (57.7%) were from men, and the median age of the study population was 34 years (IQR, 17-51 years). On a national level, synthetic opioids and methamphetamine were highly correlated with overdose mortality (Spearman ρ = 0.96 for both). When synthetic opioids were coinvolved, methamphetamine (ρ = 0.98), heroin (ρ = 0.78), cocaine (ρ = 0.94), and other opioids (ρ = 0.83) were also highly correlated with overdose mortality. In the absence of synthetic opioids, all drug categories were highly correlated (ρ = 0.75 for other opioids, 0.81 for heroin, and 0.88 for methamphetamine), with the exception of cocaine (ρ = -0.37). Synthetic opioids (ρ = 0.77) and methamphetamine (ρ = 0.80) had the strongest state-level correlations over time, whereas other opioids had the lowest correlation for both total positivity (ρ = 0.31) and positivity in the absence of synthetic opioids (ρ = 0.23). In Ohio, county-level correlation was strongest for synthetic opioids (ρ = 0.71), followed by heroin (ρ = 0.69) and methamphetamine (ρ = 0.67). At the state level, the multivariate incidence rate ratio (IRR) for synthetic opioids was 1.16 (95% CI, 1.14-1.19; P < .001), and at the county level, the IRR was 1.13 (95% CI, 1.09-1.17; P < .001), suggesting that for every 1-SD increase in the UDT positivity rate, there were 16.2% and 12.8% increases, respectively, in monthly overdose deaths. Both methamphetamine (11.7% increase per 1-SD increase in UDT positivity rate; IRR, 1.12; 95% CI, 1.09-1.14; P < .001) and cocaine (5.1% increase per 1-SD increase in UDT positivity rate; IRR, 1.05; 95% CI, 1.03-1.07; P < .001) also had significant positive associations with mortality rates, but the effect sizes were smaller than that of synthetic opioids (IRR, 1.16).

CONCLUSIONS AND RELEVANCE

In this study, UDT results were highly correlated with mortality rates at national, state, and county levels. These findings suggest that real-time UDT surveillance can help to quickly identify changes in drug use patterns that might inform targeted harm reduction strategies designed to prevent overdose deaths.