Predicting New-Onset Psychiatric Disorders Throughout the COVID-19 Pandemic: A Machine Learning Approach.

OBJECTIVE

The investigators estimated new-onset psychiatric disorders (PsyDs) throughout the COVID-19 pandemic in Italian adults without preexisting PsyDs and developed a machine learning (ML) model predictive of at least one new-onset PsyD in subsequent independent samples.

METHODS

Data were from the first (May 18-June 20, 2020) and second (September 15-October 20, 2020) waves of an ongoing longitudinal study, based on a self-reported online survey. Provisional diagnoses of PsyDs (PPsyDs) were assessed via DSM-based screening tools to maximize assessment specificity. Gradient-boosted decision trees as an ML modeling technique and the SHapley Additive exPlanations technique were applied to identify each variable's contribution to the model.

RESULTS

From the original sample of 3,532 participants, the final sample included 500 participants in the first wave and 236 in the second. Some 16.0% of first-wave participants and 18.6% of second-wave participants met criteria for at least one new-onset PPsyD. The final best ML predictive model, trained on the first wave, displayed a sensitivity of 70% and a specificity of 73% when tested on the second wave. The following variables made the largest contributions: low resilience, being an undergraduate student, and being stressed by pandemic-related conditions. Living alone and having ceased physical activity contributed to a lesser extent.

CONCLUSIONS

Substantial rates of new-onset PPsyDs emerged among Italians throughout the pandemic, and the ML model exhibited moderate predictive performance. Results highlight modifiable vulnerability factors that are suitable for targeting by public campaigns or interventions to mitigate the pandemic's detrimental effects on mental health.