Previous research suggested that during the COVID-19 pandemic, mental distress did not affect all people equally. This longitudinal study aims to examine joint trajectories of depressive, anxiety, and stress symptoms in a sample of Italian adults during the pandemic, and to identify psychosocial predictors of distress states. We analyzed four-wave panel data from 3,931 adults who had received assessments of depressive, anxiety and stress symptoms between April 2020 and May 2021. Trajectories of individual psychological distress were identified by Latent Class Growth Analysis (LCGA) with parallel processes, and multinomial regression models were conducted to identify baseline predictors. Parallel process LCGA identified three joint trajectory classes for depression, anxiety and stress symptoms. Most individuals (54%) showed a resilient trajectory. However, two subgroups showed vulnerable joint trajectories for depression, anxiety and stress. Expressive suppression, intolerance to uncertainty, and fear of COVID-19 were risk characteristics associated with vulnerable trajectories for mental health distress. Moreover, vulnerability to mental health distress was higher in females, younger age groups and those unemployed during the first lockdown. Findings support the fact that group heterogeneity could be detected in the trajectories of mental health distress during the pandemic and it may help to identify subgroups at risk of worsening states.
Keywords: Anxiety; COVID-19; Depression; Emotion regulation; Intolerance of uncertainty; Parallel-process latent class growth analysis.
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