Marijuana and Other Substance Use Among Motor Vehicle Operators: A Latent Class Analysis

High-dose benzodiazepine use and QTc interval prolongation, a latent class analysis study

Benzodiazepine (BDZ) addiction is a widespread and multifaceted phenomenon. For many patients, especially females, the concomitant use of other drugs also increases their risk of QTc prolongation, possibly leading to complications such as seizures and even sudden death. However, the relationship between BDZ use and QTc prolongation is currently unclear. The present study aims to examine patterns of polysubstance use among a sample of Italian adults with BDZ dependence in relation with their QTc prolongation risk. We used Latent Class Analysis (LCA) on data collected from 251 inpatients of the Addiction Medicine Unit in Verona to group patients into three classes according to their substance use and their QTc prolongation risk. Results showed no significant relationship between QTc prolongation and BDZ use in any of the classes considered. We conclude that BDZs, even if used long-term and at high dosages, can be considered safe in terms of cardiovascular complications for patients.

Polysubstance Use Patterns among Outpatients Undergoing Substance Use Disorder Treatment: A Latent Class Analysis

Substance Use Disorders (SUDs) pose significant challenges to both individuals and society at large. The primary focus of existing research with clinical SUD populations has been on individual substances, but research is required to better understand the profiles of individuals who use different substances simultaneously. The purpose of the current study was, therefore, to identify patterns of use among subjects (n = 1025) who reported using multiple substances by adopting a Latent Class Analysis (LCA) methodology. The Addiction Severity Index (ASI-lite) was included as a measure of substance misuse, we performed LCA to identify patterns of substance use through the administration of the ASI-Lite. Responses were collected from the following substances: alcohol, cannabis/cannabinoids, opioids and heroin, and cocaine. Results identified two latent classes: (1) alcohol use dominant, and (2) poly-abuser use dominants. Class 1 represented 60.0% of the sample and refers to individuals with the dominant use of alcohol, of those a higher proportion (47%) reported low-frequency use (1 to 7 days per month) and 26% reported a frequency of use of 24 to 30 days per month. Furthermore, 18% used alcohol in combination with cocaine. Class 2 represents 40.0% of the sample. This class is characterized by low-frequency and high-frequency users of several substances. The results obtained highlight the importance of deepening the study of the concomitant use of substances in individuals with SUDs to better understand the health risk of the combined use of two or more substances.

Typologies of Canadian young adults who drive after cannabis use: A two-step cluster analysis

Young adults that drive after cannabis use (DACU) may not share all the same characteristics. This study aimed to identify typologies of Canadians who engage in DACU. About 910 cannabis users with a driver's license (17-35 years old) who have engaged in DACU completed an online questionnaire. Two-step cluster analysis identified four subgroups, based on driving-related behaviors, cannabis use and related problems, and psychological distress. Complementary comparative analysis among the identified subgroups was performed as external validation. The identified subgroups were: (1) frequent cannabis users who regularly DACU; (2) individuals with generalized deviance with diverse risky road behaviors and high levels of psychological distress; (3) alcohol and drug-impaired drivers who were also heavy frequent drinkers; and (4) well-adjusted youths with mild depressive-anxious symptoms. Individuals who engaged in DACU were not a homogenous group. When required, prevention and treatment need to be tailored according to the different profiles.

Polysubstance Use Patterns Among High Dose Benzodiazepine Users: A Latent Class Analysis and Differences Between Male and Female Use

Benzodiazepines (BZDs) represent one of the most widely used groups of pharmaceuticals, but if used for long periods of time they are associated with dependence and an increased risk of harmful effects. High-dose (HD) BZD dependence is a specific substance use disorder associated with a poor quality of life. It is especially important to pinpoint differences in HD BZD addict subgroups in order to tailor treatment to the individual's specific needs, also considering possible comorbidities with other substance use disorders. We conducted a study to evaluate HD BZD dependence (converted doses to diazepam equivalents, mg) in an Italian sample of 1,354 participants. We also investigated if and to which extent participants co-used other substances (alcohol, tobacco, cannabis/cannabinoids, cocaine, and heroin). We then performed latent class analysis (LCA) to identify the use patterns of these substances, finding three classes: participants in Class 1 (4.3% of the sample) had the highest probability of also using cocaine and alcohol (Polysubstance BZD users); Class 2 comprised subjects with the highest probability of being former heroin, cocaine, THC, and alcohol users (Former polysubstance BZD users); Class 3 represented mono-dependence BZD users (78.5% of the sample) and was the most prevalent among women, while young men were most prevalent in Class 1. The present study underlines different characteristics in HD BZD users both concerning other addictions and sex, and also highlights the need for a stricter control of BZD use, ranging from prescriptions to sales.

Latent Classes of Polydrug Users as a Predictor of Crash Involvement and Alcohol Consumption

OBJECTIVE

Polydrug users have been shown to be at higher risk for alcohol consumption and crash involvement. However, research has shown that polydrug groups differ in some important ways. It is currently unknown how polydrug-using groups differ in terms of crash involvement and alcohol consumption.

METHOD

The current study used latent class analysis to examine subgroups of polydrug users (n = 384) among a sample of drivers in Virginia Beach, Virginia (N = 10,512). A series of logistic regression analyses were conducted to determine the relationship between polydrug use categories and crash involvement and alcohol consumption.

RESULTS

Four distinct subclasses of users were identified among polydrug-using drivers: Class 1 is the "marijuana-amphetamines class" and accounts for 21.6% of polydrug users. Class 2 is the "benzo-antidepressant class" and accounts for 39.0% of polydrug users. Class 3 is the "opioid-benzo class" and accounts for 32.7% of polydrug users. Finally, Class 4 is the "marijuana-cocaine class" and accounts for 6.7% of the study sample. Drivers in the opioid-benzo class were significantly more likely than those in any other class as well as non-drug users and single-drug users to be involved in a crash and were more likely than those in most other conditions to consume alcohol. No significant difference was found between marijuana-amphetamine users or benzo-antidepressant users and non-drug users on crash risk.

CONCLUSIONS

Some polydrug users are indeed at greater risk for crash involvement and alcohol consumption; however, not all polydrug users are significantly worse than single-drug users and/or non-drug users, and the practice of lumping polydrug users together when predicting crash risk runs the risk of inaccurately attributing crash involvement to certain drivers.

A latent class analysis of the past-30-day substance use patterns among lifetime cocaine users: Findings from a community sample in North Central Florida

Objectives

Cocaine use is increasing and many cocaine users engage in polysubstance use. Within polysubstance use, relationships among use of individual substances are necessarily complex. To address this complexity, we used latent class analysis (LCA) to identify patterns of polysubstance use among lifetime cocaine users and examine associations among these patterns, demographics, and risk profiles.

Methods

Members of HealthStreet, an ongoing community engagement program, were asked about lifetime and past 30-day use of cocaine, alcohol, tobacco, marijuana, and prescription medications, mental health conditions, recent Emergency Department (ED) visits and demographics. LCA was used to identify classes of past 30-day polysubstance use among individuals who endorsed lifetime cocaine use. Multinomial logistic regression identified factors associated with these classes.

Results

Among 1797 lifetime cocaine users, a five-class LCA model was identified: 1) past 30-day tobacco use only (45%), 2) past 30-day alcohol, marijuana and tobacco use (31%), 3) past 30-day tobacco, prescription opioid and sedative use (13%), 4) past 30-day cocaine, alcohol, marijuana and tobacco use (9%), 5) past 30-day cocaine and multiple polysubstance use (2%). Demographics, ED visits and mental health conditions were associated with class membership.

Conclusions

Approximately 11% of lifetime cocaine users used cocaine in the past 30 days with two different concurrent substance use patterns. Prescription medication (opioids and sedatives) and complex polysubstance use patterns were stronger indicators of negative outcomes than current cocaine use. Cocaine was not used frequently with other stimulants. In addition to polysubstance use, prescription medication use should be targeted for intervention among lifetime cocaine users.

Intercorrelation of Alcohol and Other Drug Use Disorders among a National Sample of Drivers

This study examined the relationship between alcohol, marijuana, cocaine, and painkiller use disorders in a sample of drivers. We studied nighttime drivers aged 16 to 87 (n = 4,277) from the 2007 National Roadside Survey who reported substance use behaviors and provided breath tests for alcohol. Logistic regression analyses assessed the relationships between (1) substance (i.e., alcohol/marijuana/cocaine/pain killer) use disorders; (2) demographic characteristics; and (3) BAC levels. Overall, 13.2% of participants met criteria for marijuana use disorder, 7% met criteria for cocaine use disorder, and 15.4% met criteria for extra-medicinal painkiller use disorder. When self-report data were analyzed, three reciprocal associations emerged: (1) marijuana use disorders and alcohol use disorders were correlated; (2) marijuana use disorders and cocaine use disorders were correlated; and (3) cocaine use disorders and painkiller use disorders were correlated. BAC data revealed that marijuana and cocaine use disorders were both associated with positive BAC levels, but only cocaine use disorders were associated with BAC levels over the legal limit. Results suggest significant poly-substance use disorders in a sample of nighttime drivers, with variations by demographic characteristics. The individual and public health consequences of multiple substance use disorders among drivers are significant.

Determinants of driving under the influence of alcohol or drugs among latino immigrants in miami-dade county, florida

Little is known about the pre- and postimmigration drinking and drunk- and drugged-driving behavior of Latino immigrants. Despite showing risky drinking behaviors, many recent immigrants of low socioeconomic status (SES) do not drive while impaired by alcohol (DWI) due largely to limited access to a vehicle. This effort examines the DWI and driving while impaired by drugs (DWID) behaviors of Latino immigrants who have access to a vehicle. Data came from an ongoing longitudinal sample of Latino immigrants to Miami-Dade County, Florida. Bivariate analyses and logistic regression were applied. Preimmigration DWI and DWID was the best predictor of postimmigration DWI and DWID. Future interventions should make efforts to (a) identify immigrants who had DWI and DWID in their country of origin and (b) design and deliver specific and culturally relevant messages to persuade them not to engage in DWI and DWID after immigration.