Definitive urine drug test findings in patients prescribed opioids for pain from a large national database.

OBJECTIVE: Clinicians and policymakers have been wrestling with the appropriateness and safety of opioid therapy during the opioid crisis. Policy and clinical decisions have often been made without much current data on trends in drug use in patients with pain. Thus, we evaluated definitive urine drug test (UDT) results in patients being treated for pain to see if those taking their prescribed opioids were less likely to be positive for the primary illicit drugs currently driving overdose deaths: cocaine, heroin, fentanyl, and methamphetamine. DESIGN, SETTING, AND PATIENTS: A cross-sectional study of UDT results from January 1, 2015 to September 30, 2021, from 600,000 patient specimens submitted for testing by pain management specialists. INTERVENTIONS: UDT by liquid chromatography-tandem mass spectrometry as ordered by the treating clinician. MAIN OUTCOME MEASURES: Presence of other substances stratified by whether a patient's prescribed opioid was found. RESULTS: The presence of cocaine, heroin, fentanyl, and methamphetamine for the total population was low (<5 percent). Of the 347,092 patients prescribed opioids, 76 percent (n = 264,961) were positive on UDT for their prescribed opioid ("consistent"). Compared to patients without their prescribed opioid present ("inconsistent"), patients consistent with therapy were 54 percent (incidence rate ratio (IRR) 1.54, 95 percent confidence interval (CI) 1.47-1.59) less likely to be positive for cocaine, 47 percent [IRR 1.47, 95 percent CI 1.34-1.57] less likely to be positive for heroin, and 35 percent [IRR 1.35, 95 percent CI 1.24-1.45] less likely to be positive for methamphetamine, p < 0.001. Differences between the groups for fentanyl were not significant. CONCLUSIONS: Overall positivity rates for cocaine, heroin, fentanyl, and methamphetamine were low. Patients with prescribed opioid present were less likely to be positive for cocaine, heroin, or methamphetamine. Patterns of substance use within this pain management population should be used to inform ongoing policy decisions.

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.

Impact of genetic variation in CYP2C19, CYP2D6, and CYP3A4 on oxycodone and its metabolites in a large database of clinical urine drug tests.

Urine drug testing (UDT) is a tool for monitoring drug use, including oxycodone. While variation in cytochrome P450 (CYP) genes is known to alter oxycodone metabolism, its impact on UDT results of oxycodone and its metabolites has not been well-studied. Here, multivariate analysis was performed on retrospective UDT results of 90,379 specimens collected from 14,684 genotyped patients prescribed oxycodone. Genetic variation in CYP2D6 and CYP2C19 had a significant impact on oxymorphone/oxycodone ratios, with a 6.9-fold difference between CYP2D6 ultrarapid metabolizers (UMs) and poor metabolizers (PMs; p < 10-300) and a 1.6-fold difference between CYP2C19 UMs and PMs (p = 1.50 × 10-4). CYP2D6 variation also significantly impacted noroxycodone/oxycodone ratios (p = 6.95 × 10-38). Oxycodone-positive specimens from CYP2D6 PMs were ~5-fold more likely to be oxymorphone-negative compared to normal metabolizers. These findings indicate that multivariate analysis of UDT data may be used to reveal the real-world impact of genetic and non-genetic factors on drug metabolism.

Polysubstance Use Among Patients Treated With Buprenorphine From a National Urine Drug Test Database.

Importance: Polysubstance use is a concern for patients treated for opioid use disorder (OUD). While buprenorphine can curtail harmful opioid use, co-occurring use of nonprescribed substances, such as cocaine, methamphetamine, and other opioids, may negatively affect treatment outcomes. Objective: To characterize factors associated with urine drug positivity for nonprescribed substances among patients prescribed buprenorphine. Design, Setting, and Participants: This cross-sectional study included patients who had been prescribed buprenorphine and who provided urine specimens for urine drug testing (UDT), as ordered by clinicians in primary care or behavioral health or at substance use disorder treatment centers, from 2013 to 2019. Specimens were analyzed by liquid chromatography-tandem mass spectrometry to assess positivity for several commonly used substances. Exposures: Buprenorphine prescription. Main Outcomes and Measures: Positivity for buprenorphine and several nonprescribed substances. Unadjusted trends in positivity for each nonprescribed substance were compared between specimens that did and did not test positive for buprenorphine. Multivariable logistic regression was used to examine factors associated with positivity; factors included patient age, sex, setting of care, payer, collection year, and census division. Results: The study included first UDT specimens from 150 000 patients, of whom 82 107 (54.74%) were men and 77 300 (51.53%) were aged 18 to 34 years. Across all specimens, 128 240 (85.49%) were positive for buprenorphine, and 71 373 (47.58%) were positive for 1 or more nonprescribed substances. From 2013 to 2019, positivity rates increased for most substances (eg, fentanyl: from 131 of 21 412 [0.61%] to 1464 of 13 597 [10.77%]). Factors associated with positivity varied widely by substance; for example, fentanyl positivity was highest for men (OR, 1.13; 95% CI, 1.06-1.21), patients aged 18 to 24 years (OR for patients ≥55 years, 0.46; 95% CI, 0.39-0.54), patients living in New England (OR, 1.19; 95% CI, 1.07-1.33), and patients with Medicaid (OR, 1.20; 95% CI, 1.11-1.31), whereas oxycodone positivity was greatest for women (OR for men, 0.84; 95% CI, 0.79-0.89), patients older than 55 years (OR, 1.42; 95% CI, 1.22-1.64), patients living in the South Atlantic (OR, 1.45, 95% CI, 1.33-1.58), and patients with private insurance (OR for Medicaid, 0.78; 95% CI, 0.73-0.84). Patients whose specimens were positive for buprenorphine were significantly less likely to be positive for other opioids (eg, fentanyl: OR for buprenorphine-negative samples, 6.71; 95% CI, 6.29-7.16; heroin: OR for buprenorphine-negative samples, 9.93; 95% CI, 9.31-10.59). Conclusions and Relevance: In this cross-sectional study, patterns of nonprescribed substance positivity among patients prescribed buprenorphine varied widely. This study highlights the utility of UDT in public health surveillance efforts related to patients treated with buprenorphine for OUD.

Seasonal and Regional Influences on Alcohol Consumption: An Analysis of Near-Real-Time Urine Drug Test Results in Those Seeking Health Care.

AIMS: To examine the probability of detecting alcohol via urine drug testing (UDT) as influenced by age, gender, seasonality, geography, COVID-19, and time in those seeking health care. METHODS: A cross-sectional study of UDT results from January 1, 2013, to December 31, 2020, was conducted using adult patient specimens submitted for testing by health care professionals as part of routine care. The UDT analysis used LC-MS/MS to detect two alcohol metabolites, ethyl glucuronide and ethyl sulfate. Seasonal adjustment of positivity rates was accomplished using the STL method; trend analysis was performed on seasonally adjusted rates. Logistic regression was used to associate demographic features, and an interaction term for collection year and U.S. census division was included to help understand the changing nature of alcohol use over time and across divisions. RESULTS: Alcohol positivity rate shows strong seasonal changes with an oscillating profile that peaks in the summer and is at a low point in winter. The highest predicted positivity rate for alcohol was in male patients, 45-64 years of age, and from a primary care setting. Alcohol positivity peaked in 2016 and declined the following year. While remaining relatively steady since 2017, a small but significant increase was noted after the COVID-19 emergency declaration on March 13, 2020. The probability of being alcohol-positive varies significantly by geographic region, and not all regions are changing at the same rate. CONCLUSIONS: Alcohol positivity in UDT in patients seeking health care is influenced by multiple factors and has increased during the COVID-19 pandemic.

The Cyclical and Evolving Nature of Methamphetamine Use: An Evaluation of Near Real-time Urine Drug Test Results.

OBJECTIVE: To determine methamphetamine positivity and copositivity with other drugs in urine drug test (UDT) results geographically through time. METHODS: This cross-sectional study of UDT results from January 1, 2014, through December 31, 2019, included patient specimens submitted by health care professionals across the United States. The analysis used LC-MS/MS to detect cocaine, heroin, alcohol, marijuana and nonprescribed methamphetamine, fentanyl, methadone, buprenorphine, benzodiazepines, and other opioids. Logistic regression was used to evaluate association of demographic features and model yearly methamphetamine detection patterns across US census divisions. Odds ratios (OR) from logistic modeling were used to evaluate the impact of methamphetamine positivity on the spatio-temporal detection patterns of additional nonprescribed or illicit drugs. RESULTS: The probability of being positive for methamphetamine increased nationally from 0.010 [0.010-0.011] in 2014 to 0.044 [0.042-0.046] in 2019, a 340% increase after correction for demographic covariates. The highest predicted positivity rate was in male patients, 25- to 34-years-old, from the West North Central division and from substance use disorder treatment centers. Nationally, copositivity ORs for fentanyl, heroin, and other opioids with methamphetamine were highest in 2019. Increases in ORs from 2014 through 2019 were statistically significant for heroin (P = 0.024) and fentanyl (P = 0.0085). Copositivity ORs for methamphetamine and other substances varied by census division. CONCLUSIONS: The probability of being positive for methamphetamine in UDT increased nationwide between 2014 and 2019. Not all census divisions are increasing at the same rate. Copositivity with additional substances is increasing in some census divisions, which further increases the risk of overdose and poor treatment outcomes.

Fentanyl analog positivity among near-real-time urine drug test results in patients seeking health care.

Overdose deaths involving synthetic opioids continue to climb. Fentanyl analogs have been identified as important contributors to these overdoses, but little is known about their prevalence in patients seeking health care. This cross-sectional study of urine drug test (UDT) results from July 15, 2019, through March 12, 2020, included patient specimens analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), submitted by health care professionals as part of routine care to detect fentanyl and fentanyl analogs. A convenience sample approach was used to select patient specimens from diverse health care practices across all 50 states, then stratified by fentanyl prescription status. Positivity rates, geographic distribution, and co-occurrence were quantified. The total positivity rate for ten fentanyl analogs was 40.55% in the non-prescribed fentanyl-positive population. The most common fentanyl analogs in this population were 4-ANPP (4-anilino-N-phenethylpiperidine), 30.74%; acetyl fentanyl, 19.40%; and carfentanil, 3.13%. The total positivity rate for four fentanyl analogs was 8.93% in the prescribed fentanyl-positive population, including 4-ANPP, 8.85%; acetyl fentanyl, 0.19%; acryl fentanyl, 0.05%; and 4-FiBF, 0.03%. Counties in Ohio and Kentucky had the highest positivity rates. Acetyl fentanyl and 4-ANPP copositivity occurred in 11.36% of non-prescribed patient specimens. However, acetyl fentanyl and 4-ANPP positivity may not be consistent with fentanyl analog use since both are process impurities, and 4-ANPP is a metabolite of fentanyl. Near-real-time, definitive UDT results reveal fentanyl analogs in patients seeking health care, helping clinicians and public health officials better understand their contribution to overdoses and help mitigate the risks they pose.

Rate of Fentanyl Positivity Among Urine Drug Test Results Positive for Cocaine or Methamphetamine.

Importance: Drug overdose deaths continue to increase, despite the leveling off of prescription opioid use and policy changes limiting opioid prescribing. Illicit fentanyl is the leading cause of drug overdose death, and it is important to characterize the emerging combination of other illicit drugs with fentanyl, which increases the risk of overdose. Objective: To determine whether rates of the combination of nonprescribed fentanyl with cocaine or methamphetamine have changed in urine drug test (UDT) results through time. Design, Setting, and Participants: This cross-sectional study of UDT results from January 1, 2013, through September 30, 2018, included patient specimens submitted for UDTs by health care professionals as part of routine care. Patients were selected from health care practices across the United States, including substance use disorder treatment centers, pain management practices, primary care practices, behavioral health practices, obstetrics and gynecology practices, and multispecialty groups. The UDT analysis used liquid chromatography-tandem mass spectrometry to detect benzoylecgonine (cocaine metabolite), methamphetamine, fentanyl, and norfentanyl. Specimens from individuals reported to have been prescribed fentanyl were excluded. A convenience sample approach was used to randomly select 1 million unique patient UDT specimens from Millennium Health's UDT database for further analysis. Each specimen had associated cocaine, methamphetamine, and fentanyl UDT results. Exposures: Medically necessary UDT to detect benzoylecgonine (cocaine metabolite), methamphetamine, fentanyl, and norfentanyl, ordered by a health care professional as part of routine patient care. Main Outcomes and Measures: Rates of nonprescribed fentanyl positivity among cocaine- or methamphetamine-positive UDT results, quantified through time. Results: In a sampling of 1 million unique patients' UDT specimens analyzed for cocaine and fentanyl (median [interquartile range] age, 44 [19-69] years; 55.0% women), positivity rates for nonprescribed fentanyl among the cocaine-positive results increased significantly, from 0.9% (n = 84) (95% CI, 0.7%-1.1%) in 2013 to 17.6% (n = 427) (95% CI, 16.1%-19.1%) in 2018, a 1850% increase (τ = 0.78; z = 9.45; P < .001). In the same sampling of 1 million specimens, positivity rates for nonprescribed fentanyl among the methamphetamine-positive results also increased significantly, from 0.9% (n = 29) (95% CI, 0.6%-1.2%) in 2013 to 7.9% (n = 344) (95% CI, 7.1%-8.7%) in 2018, a 798% increase (τ = 0.72; z = 8.75; P < .001). Conclusions and Relevance: An increasing number of UDT results positive for cocaine or methamphetamine were also positive for nonprescribed fentanyl. This provides additional insight into recently reported increases in cocaine- and methamphetamine-related overdoses. Stimulant users who may be opioid naive are at a heightened risk of overdose when exposed to fentanyl. Clinicians need to be aware that patients presenting for treatment of suspected drug overdose or substance use disorder may have been exposed, knowingly or unknowingly, to multiple substances, including the combination of stimulants and opioids.