The COVID-19 pandemic has impacted various aspects of daily life, leading to increased psychological symptoms and changes in alcohol use, yet little is known about their specific interactions, particularly early stages during the pandemic. We examined the relationship between psychological symptoms and alcohol-related behaviors associated with COVID-19, and determined whether associations shifted already early during the pandemic and whether changes in psychological symptoms from the pre- to during COVID-19 impacted changes in alcohol consumption.

Participants were young adults from a longitudinal cohort (N=435, age: 22–25) from two time points. We applied paired samples t-tests, correlation analyses, SHapley Additive exPlanations, and classification models to examine the multiple associations between psychological symptoms and alcohol use directly pre- and early during COVID-19.

We found significant associations between psychological symptoms and alcohol use pre- compared to during COVID-19. Anxiety was the strongest factor influencing alcohol use pre-pandemic, depression had the greatest impact during COVID-19. Changes in anxiety from pre- to during COVID-19 were the main factor associated with an increase in alcohol use, while changes in depression appeared to be most predictive for a decrease/persistence in alcohol use.

These findings suggest a shift in the association between psychological symptoms and alcohol use following COVID-19, as well as a differential impact of psychological symptoms, depending on their changes related to the pandemic. Changes in anxiety may contribute to riskier alcohol use behaviors following the pandemic, while depression appears to be one of the most critical factors influencing alcohol use during such crisis situations.

Nowadays, both researchers and clinicians alike have to deal with increasingly larger datasets, specifically also in the context of mental health data. Sophisticated tools for dataset visualization of information from various item-based instruments, such as questionnaire data or data from digital applications or clinical documentations, are still lacking, specifically for an integration at multiple levels and for use in both data organization and appropriate construction for its valid use in subsequent analyses.

Here, we introduce ItemComplex, a Python-based framework for ex-post visualization of large datasets. The method exploits the comprehensive recognition of instrument alignments and the identification of new content networks and graphs based on item similarities and shared versus differential conceptual bases within and across data and studies.

The ItemComplex framework was evaluated using four existing large datasets from four different cohort studies and demonstrated successful data visualization across multi-item instruments within and across studies. ItemComplex enables researchers and clinicians to navigate through big datasets reliably, informatively, and quickly. Moreover, it facilitates the extraction of new insights into construct representations and concept identifications within the data.

The ItemComplex app is an efficient tool in the field of big data management and analysis addressing the growing complexity of modern datasets to harness the potential hidden within these extensive collections of information. It is also easily adjustable for individual datasets and user preferences, both in the research and clinical field.

Recent advances in natural language processing (NLP), particularly in language processing methods, have opened new avenues in semantic data analysis. A promising application of NLP is data harmonization in questionnaire-based cohort studies, where it can be used as an additional method, specifically when only different instruments are available for one construct as well as for the evaluation of potentially new construct-constellations. The present article therefore explores embedding models’ potential to detect opportunities for semantic harmonization.

Using models like SBERT and OpenAI’s ADA, we developed a prototype application (“Semantic Search Helper”) to facilitate the harmonization process of detecting semantically similar items within extensive health-related datasets. The approach’s feasibility and applicability were evaluated through a use case analysis involving data from four large cohort studies with heterogeneous data obtained with a different set of instruments for common constructs.

With the prototype, we effectively identified potential harmonization pairs, which significantly reduced manual evaluation efforts. Expert ratings of semantic similarity candidates showed high agreement with model-generated pairs, confirming the validity of our approach.

This study demonstrates the potential of embeddings in matching semantic similarity as a promising add-on tool to assist harmonization processes of multiplex data sets and instruments but with similar content, within and across studies.