Towards the characterization and validation of alcohol use disorder subtypes: integrating consumption and symptom data

Clustering Analysis of Time Series of Affect in Dyadic Interactions

An important goal when analyzing multivariate time series is the identification of heterogeneity, both within and across individuals over time. This heterogeneity can represent different ways in which psychological processes manifest, either between people or within a person across time. In many instances, those differences can have systematic patterns that can be related to future outcomes. In close relationships, for example, the daily exchange of affect between two individuals in a couple can contain a particular structure that is different across people and can result in varying levels of relationship satisfaction. In this paper we use Louvain, a clustering method, as a tool to characterize heterogeneity in multivariate time series data. Using affect measures from dyadic interactions, we first determine that Louvain is adept at detecting homogeneous patterns that are distinct from one another. Additionally, these homogeneous points are linked, at some level, by time. Thus, we find that clustering via Louvain is useful to find time periods of stable, reoccurring patterns. However, using measures founded on information theory reveals that there is some level of information loss that is inevitable when clustering on levels of variable expression. Finally, we evaluate the predictive validity of the clustering method by examining the relation between the identified clusters of affect and measures outside the time series (i.e., relationship satisfaction and breakup taken one and two years later).

Driven by need, shaped by access: Heterogeneity in patient profiles and patterns of service utilization in patients with alcohol use disorders

BACKGROUND

Patients with alcohol-use disorders (AUDs) are highly heterogenous and account for an increasing proportion of general medical hospital visits. However, many patients with AUDs do not present with severe medical or psychiatric needs requiring immediate attention. There may be a mismatch between some patients' needs and the available services, potentially driving re-admissions and re-encounters. The current study aims to identify subgroups of AUD patients and predict differences in patterns of healthcare service use (HSU) over time.

METHODS

Latent class analysis (LCA) was conducted using hospital data incorporating sociodemographic, health behavior, clinical, and service use variables to identify subtypes of AUD patients, then class membership was used to predict patterns of HSU.

RESULTS

Four classes were identified with the following characteristics: (1) Patients with acute medical injuries (30 %); (2) Patients with socioeconomic and psychiatric risk factors, (11 %); (3) Patients with chronic AUD with primarily non-psychiatric medical needs (18 %); and (4) Patients with primary AUDs with low medical-treatment complexity (40 %). Negative binomial models showed that Class 4 patients accounted for the highest frequency of service use, including significantly higher rates of emergency department reencounters at 30 days and 12 months.

CONCLUSIONS

The profile and patterns of HSU exhibited by patients in class 4 suggest that these patients have needs which are not currently being addressed in the emergency department. These have implications for how resources are allocated to meet the needs of patients with AUDs, including those who make frequent visits to the emergency department without high acuity medical needs.

Examining the heterogeneity of polysubstance use patterns in young adulthood by age and college attendance

Substance use in young adulthood and polysubstance users (PSU), in particular, pose unique risks for adverse consequences. Prior research on young adult PSU has identified multiple classes of users, but most work has focused on college students. We examined PSU patterns by age and college attendance during young adulthood in two nationally representative samples. Using National Epidemiological Survey on Alcohol and Related Conditions (NESARC) Wave 1 and NESARC-III data sets, multigroup latent class analysis (MG-LCA) was employed to examine PSU patterns based on age (18-24 vs. 25-34) and determine whether solutions were similar (i.e., statistically invariant) by college attendance/graduation. Classes were estimated by binary past-year use of sedatives, tranquilizers, opioids/painkillers, heroin, amphetamines/stimulants, cocaine, hallucinogens, club drugs, and inhalants, and past-year frequency of alcohol, cigarette, and cannabis use. PSU patterns are largely replicated across waves. Model fit supported 3-class solutions in each MG-LCA: Low frequency-limited-range PSU (alcohol, cigarettes, and cannabis only), medium-to-high frequency limited-range PSU (alcohol, cigarettes, and cannabis only), and extended-range PSU (ER PSU; all substances). Apart from one model, MG-LCA solutions were not invariant by college attendance/graduation, suggesting important differences between these groups. Except for alcohol, cannabis, and cigarette use frequency, results showed that probabilities of illicit and prescription drug use declined in the older age group. Findings also supported examining college and noncollege youth separately when studying PSU. ER PSU may be uniquely vulnerable to coingesting substances, particularly for nongraduates, warranting future research to classify patterns of simultaneous PSU and identify predictors and consequences of high-risk combinations (e.g., alcohol and opioids). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Applying network analysis to investigate substance use symptoms associated with drug overdose

BACKGROUND

Drug overdose deaths have been increasing over the last several decades. While single substance classes, such as opioids, have been implicated in this rise, less is known about the contributions of polysubstance use (PSU) and other combinations of specific substances and symptoms that may be a risk factor for drug overdose.

METHODS

Symptoms of alcohol, cannabis, and other drug use disorders, as well as co-substance use indicators, were assessed and then examined via network analysis in a sample of young adults (N = 1540). Features of the estimated symptom network were investigated, including topology and node centrality, as well as bridge centrality, which further examines node centrality while accounting for the nodes belonging to discrete communities.

RESULTS

Individual symptoms were more strongly associated with other symptoms within the same substance class than across substance classes. Tolerance and withdrawal symptoms were the most central items in the network. However, when accounting for symptoms belonging to discrete substance classes, drug overdose emerged as a strong bridge symptom, among others.

CONCLUSIONS

As a strong bridge symptom, drug overdose had many connections with a variety of substances and symptoms, which might suggest that risk for drug overdose may be a function of overall substance use severity. Altogether, examining alcohol and substance use symptoms using a network analytic framework provided novel insights into the role PSU might play in conferring risk for drug overdose.

Investigating differences in sex, race/ethnicity, and impulsivity across substance user profiles: a person-centered approach

Although substance use is considered normative in college, continued examination of factors associated with problematic use is merited. This study identified latent substance user classes and examined their relations with sex, race/ethnicity, impulsivity-like facets, and substance use outcomes among 702 undergraduate students. Non-Alcohol Abstaining Users (NAA), Minimal Users (MU), and Polysubstance Users (PSU) emerged from latent class analysis. Variable-centered analyses indicated that substance user classes did not differ by sex. Students in the Asian and Other groups were at greater odds of being in the MU class than either NAA or PSU class, compared to White students. Differential patterns of impulsigenic trait levels emerged across latent classes. The present study highlights the utility of integrating person- and variable-centered approaches in studying heterogeneous substance use behaviors among college students. College PSU are particularly at risk for problematic outcomes, thus warranting preventive intervention that may target impulsigenic traits or polysubstance use.

Identification of Distinct Latent Classes Related to Sleep, PTSD, Depression, and Anxiety in Individuals Diagnosed With Severe Alcohol Use Disorder

Objective/Background: Alcohol use disorders (AUDs) are often accompanied by comorbid physiologic and psychosocial conditions, including sleep disturbances. Sleep disturbances in these individuals may be associated with increased risk of relapse to drinking following detoxification and rehabilitation. Participants: The sample of inpatient treatment-seeking individuals with AUDs (N = 164) was 70.1% male and 47.6% African American with a mean age of 45.6 years (±9.5 years). Methods: Latent class analysis (LCA) was used to identify unmeasured class membership based on seven indicators: maximum Clinical Institute Withdrawal Assessment (CIWA) scores; sleep efficiency (actigraphy); sleep disturbances (Pittsburgh Sleep Quality Index-PSQI); anxiety or depression (Comprehensive Psychopathological Rating Scale [CPRS]); and current and lifetime posttraumatic stress disorder (PTSD). Results: The average number of drinking days in the 90 days preceding admission was 72.0 (±22.0 days), with an average of 13.16 drinks per day (±5.70 drinks). Nearly one quarter (24.4%) of respondents reported lifetime PTSD. Three latent classes were identified: Sleep Disturbance (SD); Sleep Disturbance, Anxiety and Depression (SD/AD); and Sleep Disturbance, Anxiety and Depression, and PTSD (SD/AD/PTSD). Members of the SD/AD/PTSD group were more likely to be female and had the highest withdrawal and sleep disturbance scores of all three groups. Conclusion: Findings support the use of LCA to identify subgroups of individuals with AUDs and accompanying sleep disturbances. Class identification may provide clinicians with insight into the integrative tailoring of interventions that meet the varied needs of individuals with AUDs, accompanying comorbidities, and sleep disturbances.

Modeling Trajectories of Sensation Seeking and Impulsivity Dimensions from Early to Late Adolescence: Universal Trends or Distinct Sub-groups?

Developmental imbalance models attribute the rise in risk-taking during adolescence to a universal imbalance between rising reward sensitivity and lagging cognitive control. This study tested predictions of an alternate Lifespan Wisdom Model that distinguishes between exploratory/adaptive (e.g., sensation seeking) and maladaptive (e.g., acting-without-thinking, delay discounting) risk-taking propensities and attributes the latter to a sub-set of youth with weak cognitive control. Latent trajectory modeling of six waves of data from 387 adolescents (52% females; spanning average ages of 11-18 years) revealed distinct sub-groups with heterogeneous trajectory patterns for acting-without-thinking and delay-discounting. Only those trajectory groups with weak cognitive control, characterized as "high-increasing" acting-without thinking and "high-stable" delay discounting were predictive of a maladaptive risk-taking outcome, namely substance use disorder. Sensation seeking demonstrated a universal peak, but high levels of sensation seeking were not associated with weakness in cognitive control and were unrelated to substance use disorder, controlling for impulsivity. The findings suggest that maladaptive risk-taking characterized by weak cognitive control over reward-driven impulses is a phenomenon limited to only a sub-set of youth.

Working memory ability and early drug use progression as predictors of adolescent substance use disorders

AIMS

To test a neurobehavioral model of adolescent substance use disorder (SUD) resulting from an imbalance between a hyperactive reward motivation system and a hypoactive executive control system. Specifically, we tested (1) if early weakness in working memory (WM) and associated imbalance indicators of acting-without-thinking (AWT) and delay discounting (DD) predict SUD in late adolescence and (2) if early drug use progression mediates this relation.

DESIGN

Five waves of longitudinal data collected annually from 2005 to 2010, with a final follow-up in 2012.

SETTING AND PARTICIPANTS

Sample of 387 community adolescents (baseline ages 11-13 years) recruited from the Philadelphia, PA, USA area.

MEASUREMENTS

WM was assessed at baseline using four different computerized tasks. AWT and DD were assessed at baseline using self-reports. Early drug use patterns were modeled using annual self-reports of recent drug use across the first four waves. Final outcome of SUD was assessed at last wave using self-reports matched to the DSM-5 criteria for three commonly used substances: alcohol, marijuana and tobacco.

FINDINGS

Weakness in WM at baseline, associated with neurobehavioral imbalance indicators of AWT, B (SE) = -0.06 (0.02), P < 0.01 and DD, B (SE) = -7.30 (1.93), P < 0.01, was a significant predictor of SUD at final follow-up. WM predicted SUD both independent of early drug use, B (SE) = 0.08 (0.03), P < 0.05, and as mediated by early drug use progression, B (SE) = -0.06 (0.02), P < 0.01.

CONCLUSIONS

Adolescents with weak working memory have less control over impulsive urges, placing them at risk for later substance use disorder with some of the effects mediated by early drug use progression.

Latent classes of alcohol problems in Mauritian men: Results from the Joint Child Health Project

INTRODUCTION AND AIMS

The purpose of this study was to identify latent classes of alcohol problems and their sociodemographic correlates in the east African nation of Mauritius.

DESIGN AND METHODS

Participants were from the Joint Child Health Project, a longitudinal study of a 1969-1970 birth cohort of 1795 individuals. In mid-adulthood (M = 37 years), all available participants (n = 1206; 67% of the original cohort) were assessed for demographic characteristics, and lifetime drinkers were assessed for alcohol-related problems. Given the low endorsement of problems by women, only male lifetime drinkers (n = 520) were included in the latent class analyses.

RESULTS

Analyses indicated the best-fitting model contained four classes of drinkers: Non-problematic (66%), Moderate (16%), Hazardous (11%) and Severe (6%). Lower education and occupation were associated with Moderate and Severe problem classes, whereas higher education and occupation were associated with the Hazardous class. Being Hindu, Tamil and Creole were differentially predictive of class membership, but being Muslim was not.

DISCUSSION AND CONCLUSIONS

Our findings provide evidence of a distinct Hazardous drinking class that has unique demographic correlates and may represent a cluster of problems that is more bound by cultural factors. We also found problem classes on a severity continuum from none to moderate to severe problems. This study highlights the importance of examining societal, subgroup and person-level factors to produce a more nuanced understanding of distinct classes of alcohol-related problems. [Luczak SE, Prescott CA, Venables PH. Latent classes of alcohol problems in Mauritian men: Results from the Joint Child Health Project.

An Empirical Assessment of the Sensitivity of Mixture Models to Changes in Measurement

The current study explored the extent to which variations in self-report measures across studies can produce differences in the results obtained from mixture models. Data (N = 854) come from a laboratory analogue study of methods for creating commensurate scores of alcohol- and substance-use-related constructs when items differ systematically across participants for any given measure. Items were manipulated according to four conditions, corresponding to increasing levels of alteration to item stems, response options, or both. In Study 1, results from latent class analyses (LCA) of alcohol consequences were compared across the four conditions, revealing differences in class enumeration and configuration. In Study 2, results from factor mixture models (FMM) of alcohol expectancies were compared across two of the conditions, revealing differences in patterns and magnitude of the factor loadings and thresholds. The results suggest that even subtle differences in measurement can have substantively meaningful effects on mixture model results.

The identification of symptom-based subtypes of depression: A nationally representative cohort study

BACKGROUND

In recent years, researchers have used various techniques to elucidate the heterogeneity in depressive symptoms. This study seeks to resolve the extent to which variations in depression reflect qualitative differences between symptom categories and/or quantitative differences in severity.

METHODS

Data were used from the Netherlands Mental Health Survey and Incidence Study-2, a nationally representative face-to-face survey of the adult general population. In a subsample of respondents with a lifetime key symptom of depression at baseline and who participated in the first two waves (n=1388), symptom profiles at baseline were based on symptoms reported during their worst lifetime depressive episode. Depressive symptoms and DSM-IV diagnoses were assessed with the Composite International Diagnostic Interview 3.0. Three latent variable techniques (latent class analysis, factor analysis, factor mixture modelling) were used to identify the best subtyping model.

RESULTS

A latent class analysis, adjusted for local dependence between weight change and appetite change, described the data best and resulted in four distinct depressive subtypes: severe depression with anxiety (28.0%), moderate depression with anxiety (29.3%), moderate depression without anxiety (23.6%) and mild depression (19.0%). These classes showed corresponding clinical correlates at baseline and corresponding course and outcome indicators at follow-up (i.e., class severity was linked to lifetime mental disorders at baseline, and service use for mental health problems and current disability at follow-up).

LIMITATIONS

Although the sample was representative of the population on most parameters, the findings are not generalisable to the most severely affected depressed patients.

CONCLUSIONS

Depression could best be described in terms of both qualitative differences between symptom categories and quantitative differences in severity. In particular anxiety was a distinguishing feature within moderate depression. This study stresses the central position anxiety occupies in the concept of depression.