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BackgroundOverdoses caused by synthetic mu-opioid receptor (MOR) agonists such as fentanyl are causing increasing mortality in the United States. The COVID-19 pandemic continues to have complex effects on public health, including opioid use disorders (OUD). It is unclear whether recent increases in mortality caused by synthetic opioids have reached a plateau (i.e., a stable period), after the onset of the COVID-19 pandemic.MethodThis study examined provisional overdose mortality data from the Centers for Disease Control and Prevention, for synthetic opioids excluding methadone (code T40.4; monthly data available from 39 States, plus New York City and Washington DC), for June 2019–November 2021. Data were first examined as crude mortality rates. The presence of a maximum plateau was analyzed for the last 4 months of available data. For authorities in which a plateau in mortality was detected, sigmoidal Boltzmann equations were used to model parameters of this phenomenon (e.g., level of the plateau).ResultsAt the end of the study period, all but one authority (New Hampshire) reported increases in mortality rates for synthetic opioids, compared to the baseline month of June 2019 (range: 111–745% of baseline). A plateau was observed over the last 4 months of the study period (Aug 2021–Nov 2021) in 29 of the authorities. Ten other authorities had not reached a stable plateau at the end of the study period. For the authorities where a plateau was detected, a sigmoidal Boltzmann model revealed a fitted maximum of 262% rise in mortality over the study period, from the baseline month. The midpoint in the rise in mortality was fitted in September 2020. After separation of data into census regions, the highest plateau was observed in the West region, followed by South, Midwest, and Northeast (fitted plateau values were 409, 262, 204, and 149% of baseline, respectively).DiscussionThere were increases in overdose mortality due to synthetic opioids across most states, ranging considerably in magnitude. A plateau in overdose mortality was detected at the end of the study period in most of these authorities. The reasons for these plateaus should be explored, in order to develop optimized public health interventions.

Background: The impact of increasing non-medical cannabis use on vulnerability to develop opioid use disorders has received considerable attention, with contrasting findings. A dimensional analysis of self-exposure to cannabis and other drugs, in individuals with and without opioid dependence (OD) diagnoses, may clarify this issue.Objective: To examine the age of onset of maximal self-exposure to cannabis, alcohol, cocaine, and heroin, in volunteers diagnosed with OD, using a rapidly administered instrument (the KMSK scales). To then determine whether maximal self-exposure to cannabis, alcohol, and cocaine is a dimensional predictor of odds of OD diagnoses.Methods: This outpatient observational study examined maximal self-exposure to these drugs, in volunteers diagnosed with DSM-IV OD or other drug diagnoses, and normal volunteers. In order to focus more directly on opioid dependence diagnosis as the outcome, volunteers who had cocaine dependence diagnoses were excluded. Male and female adults of diverse ethnicity were consecutively ascertained from the community, and from local drug treatment programs, in 2002–2013 (n = 574, of whom n = 94 had OD diagnoses). The age of onset of maximal self-exposure of these drugs was examined. After propensity score matching for age at ascertainment, gender, and ethnicity, a multiple logistic regression examined how increasing self-exposure to non-medical cannabis, alcohol and cocaine affected odds of OD diagnoses.Results: Volunteers with OD diagnoses had the onset of heaviest use of cannabis in the approximate transition between adolescence and adulthood (mean age = 18.9 years), and onset of heaviest use of alcohol soon thereafter (mean age = 20.1 years). Onset of heaviest use of heroin and cocaine was detected later in the lifespan (mean ages = 24.7 and 25.3 years, respectively). After propensity score matching for demographic variables, we found that the maximal self-exposure to cannabis and cocaine, but not to alcohol, was greater in volunteers with OD diagnoses, than in those without this diagnosis. Also, a multiple logistic regression detected that increasing self-exposure to cannabis and cocaine, but not alcohol, was a positive predictor of OD diagnosis.Conclusions/Importance: Increasing self-exposure to non-medical cannabis, as measured with a rapid dimensional instrument, was a predictor of greater odds of opioid dependence diagnosis, in propensity score-matched samples.

Cocaine use disorders (CUD) cause major morbidity and optimized prevention efforts are critical. It is unclear if trait impulsivity and exposure to cannabis or alcohol are associated with age trajectory of cocaine use (e.g., age of onset of heaviest use, or time of escalation), or with vulnerability to develop a CUD. This is an observational study with volunteers (≥18 years old), from a metropolitan area. The sample (n = 1,010) included: n = 360 normal volunteers, n = 438 with cocaine dependence (CD) diagnoses, and n = 212 with other addictive diseases. Trait impulsivity was examined with BIS-11 scores. Maximal self-exposure to cannabis, alcohol, and cocaine were characterized dimensionally with Kreek-McHugh-Schluger-Kellogg (KMSK) scales. Time of escalation was defined as the interval between age of first use and age of onset of heaviest use. Onset of maximal use of cannabis (median age = 17) and alcohol (median age = 21) preceded that of cocaine (median age = 27), in volunteers with CD. Multivariate Cox regressions in volunteers with CD show that increasing self-exposure to cannabis was a predictor of earlier onset of heaviest use of cocaine. Also, more rapid time of escalation of alcohol was a predictor of more rapid time of escalation of cocaine. A multiple logistic regression shows that increasing self-exposure to cannabis or alcohol was a positive predictor of odds of CD diagnosis. Trait impulsivity and gender were not significant predictors in these multivariate analyses. This study shows that aspects of adolescent exposure to nonmedical cannabis and alcohol are predictors of early onset of CUD, and may be potentially targeted for prevention efforts.