Experiencing violence and other predictors of within-person same-day use of multiple substances in youth: a longitudinal study in emergency settings.

Background: Although experiencing violence is a risk factor for substance use among youth, its association with same-day use of multiple substances (a form of polysubstance use) and mitigating factors is less well understood.Objectives: To identify whether prosocial factors modified the effect of experiencing violence on the frequency of same-day use, and examine gender-specific risk/protective factors for same-day use.Methods: We analyzed longitudinal data from a cohort of youth who use drugs aged 14-24 (n = 599; 58% male) presenting to an urban emergency department between 2009-2011 and assessed biannually for two years. Using Poisson-generalized linear models with person-level fixed effects, we estimated within-person associations between self-reported experiencing violence and same-day use and analyzed gender and peer/parent support as effect modifiers. We adjusted for negative peer influence, parental drug and alcohol use, family conflict, anxiety and depression, and age.Results: Overall, positive parental support corresponded to lower rates of same-day use (rate ratio [RR]:0.93, 95% CI:0.87-0.99) and experiencing violence was associated with higher rates of same-day use (RR:1.25, 95% CI:1.10-1.41). Violence exposure was a risk factor among males (RR:1.42, 95% CI:1.21-1.66), while negative peer influences and parental substance use were risk factors among females (RR:1.63, 95% CI:1.36-1.97 and RR:1.58, 95% CI:1.35-1.83, respectively). Positive peer support reduced the association between violence exposure and same-day use among males (RR:0.69, 95% CI:0.57-0.84, p < .05).Conclusions: Tailored interventions may address gender differences in coping with experiencing violence - including interventions that promote parental support among males and reduce influence from parental substance use among females.

Where Do Adults in the United States Obtain Cannabis? A Nationally Representative Study Examining the Relationships Between Sociodemographic Factors, Cannabis Use Characteristics and Sources of Cannabis.

OBJECTIVE: Characterization of population subgroups based on where they acquire cannabis is unexplored. We examine relationships between sociodemographic characteristics, cannabis use modality, risky cannabis use, and source of cannabis. METHOD: Analyzing a representative sample (unweighted n = 8,089) of U.S. adults living in medical cannabis-permitting states with past-year cannabis use from the 2021 National Survey on Drug Use and Health, we determined source of last cannabis used. Outcome groups were purchased from a dispensary, purchased from another source, or nonpurchased source. Incorporating the complex survey design, descriptive statistics and adjusted multinomial logistic regressions evaluated associations between sociodemographic, individual cannabis use characteristics, and source of cannabis. Secondary analyses described cannabis purchasing characteristics among the subsample who last purchased cannabis. RESULTS: Purchasing from a dispensary was the most common source of cannabis (42.5%). Significant relationships between sociodemographic characteristics, cannabis use modality, risky cannabis use, and source of cannabis were found. Recent cannabis initiates and those with cannabis vaporizer use had an increased likelihood of purchasing cannabis from a dispensary. Purchasing from a nondispensary source was most likely among those with daily cannabis use, past-month blunt use, past-year driving under the influence, cannabis use disorder, and cannabis and alcohol co-use. Among those purchasing cannabis, joints and other forms of cannabis were more likely to be purchased from a dispensary than purchased from other sources. CONCLUSIONS: We identified key sociodemographic and cannabis use characteristics that may influence where individuals obtain cannabis, which are important for cannabis behavior surveillance and cannabis use prevention and intervention strategies to consider.

Assessing the diagnostic utility of the Cannabis Use Disorder Identification Test - Revised (CUDIT-R) among veterans with medical and non-medical cannabis use.

BACKGROUND: Few studies examine the utility of the Cannabis Use Disorder Identification Test - Revised (CUDIT-R) in relation to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, (DSM-5) criteria for cannabis use disorder (CUD). This study assesses the performance of the CUDIT-R among a sample of Veterans with and without medical cannabis use. METHODS: We approached and consented primary care patients presenting to one of three Department of Veterans Affairs (VA) Medical Centers. Veterans with at least monthly cannabis use and complete CUD data at baseline were included in this analysis (n=234). CUDIT-R scores were compared against Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (DSM-5) CUD as the standard to calculate measures of validity (sensitivity, specificity), identify optimal CUDIT-R cutoff values, and assess the diagnostic proficiency of the CUDIT-R using receiver operating characteristic (ROC) curves. We further stratified analyses by active medical cannabis card holder status and DSM-5 CUD severity (any, moderate, and severe). RESULTS: Among the entire sample, 38.9% qualified for any DSM-5 CUD, with 10.7% and 3.0% meeting criteria for moderate and severe CUD, respectively. We identified optimal CUDIT-R scores at 10 for any DSM-5 CUD (sensitivity=0.58; specificity=0.80), at 12 for moderate CUD (sensitivity=0.72; specificity=0.82), and at 14 for severe CUD (sensitivity=0.71; specificity=0.87). ROC curves showed higher CUDIT-R validity among non-card holders compared with medical cannabis card holders. CONCLUSION: The present study identified optimal CUDIT-R cutoff scores for Veterans who use cannabis. Varying DSM-5 validity measures inform the need for population-specific CUDIT-R cutoff values.

Firearms Availability Among High-School Age Youth With Recent Depression or Suicidality.

BACKGROUND AND OBJECTIVES: Limiting firearm access is essential to decreasing teen suicide. Previous efforts have focused on household firearms; however, less is known about firearm access and possession among teens at increased suicide risk. Our objective was to estimate prevalence of firearm possession and access among high school-aged teens with recent depression and/or lifetime history of suicidality (DLHS). METHODS: We conducted a probability-based, cross-sectional Web survey of 1914 parent-teen dyads between June 24, 2020, and July 22, 2020, with data weighted to generate a nationally representative sample of US teenagers (aged 14-18). Logistic regression analyses examined the difference between teens with and without DLHS for: (1) personal firearm possession, (2) perceived firearm access, and (3) method of firearm attainment. RESULTS: Among high school-aged teens, 22.6% (95% confidence interval [CI], 19.4-25.8) reported DLHS, 11.5% (95% CI, 8.7-14.3) reported personal firearm possession, and 44.2% (95% CI, 40.2-48.2) endorsed firearm access. Teens experiencing DLHS had increased perceived access (adjusted odds ratio, 1.56; 95% CI, 1.07-2.28) compared with non-DLHS peers. There was no association between DLHS and personal firearm possession (adjusted odds ratio, 0.97; 95% CI, 0.47-2.00). Among teens reporting firearm possession, those with DLHS were more likely to have acquired it by buying/trading for it (odds ratio, 5.66; 95% CI, 1.17-27.37) and less likely receiving it as a gift (odds ratio, 0.06; 95% CI, 0.01-0.36). CONCLUSIONS: High school-aged teens experiencing DLHS have higher perceived firearm access compared with lower-risk peers. Providers should speak directly to high school-aged teens at increased suicide risk about firearm access, in addition to counseling parents.

Driving under the influence of cannabis, alcohol, and illicit drugs among adults in the United States from 2016 to 2020.

OBJECTIVE: Driving under the influence (DUI) of substances increases motor vehicle crash risk. Understanding current national trends of driving under the influence of alcohol (DUIA), cannabis (DUIC), and drugs other than cannabis (DUID) can inform public health efforts. Herein, we provide updated trends among United States (US) adults regarding DUIA, DUIC, DUID, and DUI of any substance. METHOD: We used nationally-representative National Survey on Drug Use and Health (2016-2020) data to derive prevalence estimates of past-year DUIC, DUIA, DUID, and DUI of any substance among non-institutionalized US adults and among those reporting respective past-year  substance use. Prevalence estimates and adjusted logistic regressions characterized temporal trends of these behaviors among US adults, among those with respective past-year substance use, and among stratified demographic subpopulations. RESULTS: Over 1 in 10 US adults reported DUI of any substance annually from 2016 to 2020.DUIA was most prevalent among all US adults (8.7% in 2017); however, this behavior is decreasing (AOR:0.96; 95%CI:0.94,0.98). No change in DUIC among the US adult population was found, but a decrease was found among those with past-year cannabis use (AOR:0.95; 95%CI:0.93,0.98), which coincided with a 29.1% increase in past-year cannabis use. There were no significant changes in overall DUID; however, females, those ages 26-34 and 65 or older with past-year use displayed increasing trends. DUI of any substance decreased among the US adult population. CONCLUSIONS: DUI remains a salient public health concern in the US and results indicate population subgroups who may benefit from impaired driving prevention interventions.

Patterns in Nonopioid Pain Medication Prescribing After the Release of the 2016 Guideline for Prescribing Opioids for Chronic Pain.

Importance: In 2016, the Centers for Disease Control and Prevention (CDC) released the evidence-based Guideline for Prescribing Opioids for Chronic Pain. How the release of this guideline coincided with changes in nonopioid pain medication prescribing rates remains unknown. Objective: To evaluate changes in nonopioid pain medication prescribing after the 2016 CDC guideline release and to assess the heterogeneity in these changes as a function of patient demographic and clinical characteristics. Design, Setting, and Participants: This cohort study constructed 7 (4 preguideline and 3 postguideline) annual cohorts using claims data from the national Optum Clinformatics Data Mart Database for the period January 1, 2011, through December 31, 2018. The cohorts included adults with commercial insurance, no cancer or palliative care claims, and 2 years of continuous insurance enrollment. Individuals could qualify for inclusion in multiple cohorts. Each cohort covered a 2-year period, with year 1 as the baseline period used to calculate opioid exposure and other clinical characteristics and year 2 as the follow-up period used to calculate prescribing outcomes. Data were analyzed in March 2022. Exposures: The CDC guideline, which was released in March 2016. Main Outcomes and Measures: The primary outcome was receipt of any nonopioid pain medication prescriptions (analgesics or antipyretics, anticonvulsants, antidepressants, and nonsteroidal anti-inflammatory drugs) during the follow-up period. This postguideline prescribing pattern was compared with estimates based on the preguideline prescribing pattern, and then the differences were stratified by patient clinical characteristics (chronic pain, recent opioid exposure, substance use disorder, anxiety disorder, and mood disorder). Results: A total of 15 879 241 individuals (2015 mean [SD] age, 50.2 [18.6] years; 8 298 271 female patients [52.3%]) qualified for inclusion in 1 or more cohorts. Logistic regression models showed that nonopioid pain medication prescribing odds were higher by 3.0% (95% CI, 2.6%-3.3%) in postguideline year 1, by 8.7% (95% CI, 8.3%-9.2%) in postguideline year 2, and by 9.7% (95% CI, 9.2%-10.3%) in postguideline year 3 than the preguideline pattern-based estimates. The magnitude of the postguideline departures from the preguideline pattern varied by several clinical characteristics (chronic pain, recent opioid exposure, anxiety disorder, and mood disorder). The largest departure was found among those with chronic pain, with postguideline prescribing being higher than estimated in postguideline year 2 (13.6%; 95% CI, 12.7%-14.6%) and postguideline year 3 (14.9%; 95% CI, 13.8%-16.0%). Conclusions and Relevance: Results of this study showed increases in nonopioid pain medication prescribing after the release of the 2016 CDC guideline, suggesting that the guideline may be associated with an increase in guideline-concordant care, but additional studies are needed to understand the role of other secular changes in the opioid policy landscape and other sources of nonopioid medication use.

Within- and between-person associations with drug use disorder among adolescents and emerging adults presenting to an urban emergency department.

BACKGROUND: The distinction between within- and between-person associations with drug use disorder (DUD) has implications for intervention targets and content. We used longitudinal data from youth entering an urban emergency department (ED) to identify factors related to changes in DUD diagnosis, with particular emphasis on alcohol use. METHODS: Research staff recruited youth age 14-24 (n = 599) reporting any past six-month drug use from a Level-1 ED; participants were assessed at baseline and four biannual follow-ups. Participants self-reported validated measurements of peer/parental behaviors, violence/crime exposure, drug use self-efficacy, and alcohol use. Research staff performed diagnostic interviews for DUD with nine substances, post-traumatic stress disorder (PTSD), and major depressive disorder (MDD). We used repeated measures logistic regression models with person-level covariate means, and person-mean-centered covariates, as separate variables, to separate within- and between-person covariate effects. RESULTS: Among 2,630 assessments, 1,128 (42.9 %) were DUD diagnoses; 21.7 % were co-diagnoses with multiple drugs. Positive (aOR = 0.81, 95 %CI:[0.70, 0.94]) and negative (aOR = 1.73, 95 %CI:[1.45, 2.07]) peer behaviors related to DUD, primarily through between-person effects. Parental support (aOR = 0.92, 95 %CI:[0.83, 0.99]), community violence/crime (aOR = 1.28, 95 %CI:[1.14, 1.44]), PTSD/MDD diagnosis (aOR = 1.36, 95 %CI:[1.04, 1.79]), and alcohol use quantity (aOR = 1.06, 95 %CI:[1.02, 1.11]) were associated with DUD, showing primarily within-person effects. Other factors, such as interpersonal violence involvement (aOR = 1.47, 95 %CI:[1.21, 1.78]), showed both within- and between-person effects. CONCLUSIONS: DUD is prevalent in this population, and within-person changes in DUD are predictable. Within-person effects suggest the importance of addressing escalating alcohol use, enhancing parental support, crime/violence exposure, and other mental health diagnoses as part of DUD intervention.