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

Cannabis Adaptation During and After Alcohol Ignition Interlock Device Installation: A Longitudinal Study

OBJECTIVE

A common intervention to prevent alcohol-impaired driving are alcohol ignition interlock devices (IIDs), which prevent drivers with a blood alcohol concentration greater than .025% from starting the car. These devices force drivers to adapt their drinking to accommodate the device. Prior studies indicated a transfer of risk as some drivers with an IID may increase cannabis use as they decrease alcohol use. This study examines whether this increase in cannabis use persists after IID removal when alcohol use reverts to pre-IID levels.

METHOD

The data are from the Managing Heavy Drinking (MHD) study of drivers in New York State. The MHD is a comprehensive three-wave study of drivers convicted of driving under the influence from 2015 to 2020. Participants (N = 189) completed all waves, and provided oral fluid/blood and hair samples to measure cannabis and alcohol use, respectively. Mixed between-within analysis of variance was conducted to assess cannabis use at IID installation (Time 1), removal (Time 2), and at 6-month follow-up (Time 3).

RESULTS

In aggregate, participants increased their cannabis use over the course of the study. Drivers who decreased their alcohol use while the IID was installed on their car significantly increased their cannabis use while the IID was in place and further increased cannabis use after the device's removal.

CONCLUSIONS

IIDs are efficacious in preventing alcohol-impaired driving. However, in some cases, they may have the unintended effect of increasing other substance use. The current study outlines the need for supplemental treatment interventions while on IID to prevent a transfer of risk to other substances, or polysubstance use after the device is removed.

A latent class analysis of drug and substance use patterns among patients treated in emergency departments for suspected drug overdose

INTRODUCTION

Polysubstance use and misuse can increase risks for nonfatal and fatal drug overdose. To categorize drugs used in combination in nonfatal overdoses, we analyzed data from emergency department (ED) overdose-related visits in 18 states funded by CDC's Enhanced State Opioid Overdose Surveillance (ESOOS) program.

METHODS

From 2017 to 2018, 120,706 ED visits included at least one hospital discharge code indicating acute drug poisoning for opioids, stimulants, hallucinogens, cannabis, anti-depressants, sedatives, alcohol, benzodiazepines, or other psychotropic drugs. Latent class analyses were conducted to determine the groupings of drug combinations in overdose visits.

RESULTS

Latent class analyses indicated a model of 5 classes - mostly heroin overdose (42.5% of visits); mostly non-heroin opioid overdose/use (27.3%); opioid, polysubstance (11.0%); female, younger (<25 years), other non-opioid drugs (10.5%); female, older (>55 years), benzodiazepine (8.0%). Findings indicated that heroin continues to be a large burden to EDs, yet EDs are also seeing overdose survivors with polydrug toxicity.

CONCLUSIONS

Medication-assisted treatment could be initiated in the emergency department following overdose for patients with opioid use disorder, and post-overdose protocols, such as naloxone provision and linkage to treatment and harm reduction services, have the potential to prevent future overdose for those at risk.

Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients

BACKGROUND

Approaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes.

METHODS

This was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes.

FINDINGS

The analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1 was categorized by high hospital utilization with known opioid-related conditions (36.5%); Class 2 included patients with illicit use, low socioeconomic status, and psychoses (12.8%); Class 3 contained patients with alcohol use disorders with complications (39.2%); and class 4 consisted of those with low hospital utilization and incidental opioid misuse (11.5%). The following hospital outcomes were the highest for each subtype when compared against the other subtypes: readmission for class 1 (13.9% vs. 10.5%, p<0.01); discharge against medical advice for class 2 (12.3% vs. 5.3%, p<0.01); and in-hospital death for classes 3 and 4 (3.2% vs. 1.9%, p<0.01).

CONCLUSIONS

A 4-class latent model was the most parsimonious model that defined clinically interpretable and relevant subtypes for opioid misuse. Distinct subtypes were delineated after examining multiple domains of EHR data and applying methods in artificial intelligence. The approach with LCA and readily available class-defining substance use variables from the EHR may be applied as a prognostic enrichment strategy for targeted interventions.

Types of Polydrug Use Among Spanish Students in Health Sciences

Polydrug use among university students may be a predictor for established patterns of multiple substance use and potentially entail long-term health problems. This study examined the types of polydrug use among Spanish students in health sciences. Undergraduate students (n = 968), aged 18-38 years (M = 21.09 years, SD = 4.10), completed the survey. A percentage of 44.3% of the participants were classified as polydrug users. Type A users (alcohol and cigarettes) made up 17.8% of the participants surveyed, whereas 20.1% were Type B (cannabis with cigarettes and/or alcohol), and a further 5.7% were Type C (cannabis with cigarettes and/or alcohol, plus at least another kind of illegal drug). Type A was the most common type among women, whereas Type C was the most common among men. Type B use was higher among women 18-19 years old than among women 25-29 years old, whereas there were no female Type C users younger than 20 years old. Weekend consumption was higher, than weekday consumption, across all polydrug user types and substances. These results suggest that the prevalence of polydrug use among Spanish students in health sciences was similar to students in other disciplines, with Type B as the most prevalent among healthcare and nonhealthcare students. Taking into account the differences based on gender, age, and time of consumption, a specific approach to the different typologies of polydrug users might be a vital step in the successful development of preventive interventions tailored to the changing reality of psychoactive substance use.