Modeling alcohol use disorder as a set of interconnected symptoms - Assessing differences between clinical and population samples and across external factors

Understanding alcohol use and alcohol use disorders from a developmental psychopathology perspective: Research advances, challenges, and future directions

As part of the special issue of Development and Psychopathology honoring the remarkable contributions of Dr Dante Cicchetti, the current paper attempts to describe the recent contributions that a developmental psychopathology perspective has made in understanding the development of alcohol use and alcohol-related problems over the lifespan. The paper also identifies some of the future challenges and research directions. Because the scope of this task far exceeds the confines of a journal length article this paper does not attempt a comprehensive review. Rather, it builds on an earlier review and commentary that was published in Development and Psychopathology in 2013, with a similar goal.)Building on that work and updating its conclusions and suggestions for future directions, the current paper emphasizes findings from the research areas that were identified for further study in 2013 and the findings that have been published since that time.

The Predictive Role of Tolerance and Health Problems in Problem Gambling: A Cross-Sectional and Cross-Lagged Network Analyses

The existing symptomatic networks of problem gambling are all based on cross-sectional data. Thus, there is a need to explore longitudinal symptom networks of problem gambling. Moreover, the replicability of cross-sectional symptom networks can be limited; therefore, further research should assess the convergence between cross-sectional networks of problem gambling symptoms. The present study aimed (i) to examine cross-sectional networks of problem gambling symptoms and evaluate their replicability and (ii) to examine a longitudinal cross-lagged network of problem gambling symptoms. The study included a representative sample of young adult gamblers (born between 1984 and 2000) from the first two waves of the Budapest Longitudinal Study (original sample: N = 2777; final sample: N = 335). The Problem Gambling Severity Index was used to assess symptoms of problem gambling. Cross-sectional symptom networks showed differences in the centrality of nodes. Correlations between the two cross-sectional networks were low in the presence vs. absence of edges, rank order of edge weights, and centrality estimates. However, network invariance tests indicated non-significant differences between them. The cross-lagged network revealed that the symptoms of tolerance and health problems could predict the subsequent presence of multiple problem gambling symptoms. Overall, limited evidence demonstrated the replicability of cross-sectional symptom networks of problem gambling. Future research needs to explore the utility of cross-sectional networks of problem gambling and assess more precisely causal relationships between problem gambling symptoms by distinguishing within- and between-subject effects.

Alcohol, cannabis, and nicotine use have distinct associations with COVID-19 pandemic-related experiences: An exploratory Bayesian network analysis across two timepoints

BACKGROUND

Substance use trends during the COVID-19 pandemic have been extensively documented. However, relatively less is known about the associations between pandemic-related experiences and substance use.

METHOD

In July 2020 and January 2021, a broad U.S. community sample (N = 1123) completed online assessments of past month alcohol, cannabis, and nicotine use and the 92-item Epidemic-Pandemic Impacts Inventory, a multidimensional measure of pandemic-related experiences. We examined links between substance use frequency, and pandemic impact on emotional, physical, economic, and other key domains, using Bayesian Gaussian graphical networks in which edges represent significant associations between variables (referred to as nodes). Bayesian network comparison approaches were used to assess the evidence of stability (or change) in associations between the two timepoints.

RESULTS

After controlling for all other nodes in the network, multiple significant edges connecting substance use nodes and pandemic-experience nodes were observed across both time points, including positive- (r range 0.07-0.23) and negative-associations (r range -0.25 to -0.11). Alcohol was positively associated with social and emotional pandemic impacts and negatively associated with economic impacts. Nicotine was positively associated with economic impact and negatively associated with social impact. Cannabis was positively associated with emotional impact. Network comparison suggested these associations were stable across the two timepoints.

CONCLUSION

Alcohol, nicotine, and cannabis use had unique associations to a few specific domains among a broad range of pandemic-related experiences. Given the cross-sectional nature of these analyses with observational data, further investigation is needed to identify potential causal links.

Alcohol use in firefighters: A network model of behaviors and transdiagnostic risk

BACKGROUND AND AIMS

Firefighters are at heightened risk for developing alcohol use disorder (AUD), possibly due to chronic stress and exposure to potentially traumatic events. Daily trauma experiences and transdiagnostic risk factors (i.e., anxiety sensitivity and distress intolerance) are related to posttraumatic stress and depressive symptoms, as well as alcohol use severity and alcohol as a coping strategy. Although alcohol use has been identified as a key target for addressing mental health in firefighters, prior research has not fully integrated transdiagnostic vulnerabilities, internalizing symptoms, posttraumatic stress symptoms, alcohol coping, and overall alcohol use into a dynamic network model.

METHODS

We assessed the symptom structure of overall alcohol use in firefighters with a likely AUD and transdiagnostic risk factors in all firefighters using network analysis.

RESULTS

Failing to meet expectations (Expected Influence [EI]: 1.32), morning dependence (EI: 1.07), and guilt about drinking (EI: 1.10) were most central to the network model developed for firefighters with a likely AUD. In a transdiagnostic model of use in firefighters overall, anxiety sensitivity cognitive concerns (EI: 1.48) and negative alterations to cognitions and mood related to trauma (EI: 1.87) had the highest influence on the network. Notable correlations were also identified between trauma arousal and overall alcohol use, between depression and alcohol coping motives, and between trauma avoidance and alcohol coping motives.

CONCLUSIONS

Alcohol use behaviors may follow a unique etiologic pathway in firefighters and intervention strategies should target factors found to be more central to symptom networks.

Cross-sectional and longitudinal AUD symptom networks: They tell different stories

Modern theoretical models of Alcohol Use Disorder (AUD) highlight the different functional roles played by various mechanisms associated with different symptoms. Symptom network models (SNMs) offer one approach to modeling AUD symptomatology in a way that could reflect these processes and provide important information on the progression and persistence of disorder. However, much of the research conducted using SNMs relies on cross-sectional data, which has raised questions regarding the extent they reflect dynamic processes. The current study aimed to (a) examine symptom networks of AUD and (b) compare the extent to which cross-sectional network models had similar structures and interpretations as longitudinal network models. 17,360 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were used to model cross-sectional and longitudinal AUD symptom networks. The cross-sectional analyses demonstrate high replicability across waves and central symptoms consistent with other cross-sectional studies on addiction networks. The longitudinal network shared much less similarity than the cross-sectional networks and had a substantially different structure. Given the increasing attention given to the network perspective in psychopathology research, the results of this study raise concerns about interpreting cross-sectional symptom networks as representative of temporal changes occurring within a psychological disorder. We conclude that the psychological symptom network literature should be bolstered with additional research on longitudinal network models.