COVID-19 after two years: trajectories of different components of mental health in the Spanish population

Abstract

Aims Our study aimed to (1) identify trajectories on different mental health components during a two-year follow-up of the COVID-19 pandemic and contextualise them according to pandemic periods; (2) investigate the associations between mental health trajectories and several exposures, and determine whether there were differences among the different mental health outcomes regarding these associations. Methods We included 5535 healthy individuals, aged 40–65 years old, from the Barcelona Brain Health Initiative (BBHI). Growth mixture models (GMM) were fitted to classify individuals into different trajectories for three mental health-related outcomes (psychological distress, personal growth and loneliness). Moreover, we fitted a multinomial regression model for each outcome considering class membership as the independent variable to assess the association with the predictors. Results For the outcomes studied we identified three latent trajectories, differentiating two major trends, a large proportion of participants was classified into ‘resilient’ trajectories, and a smaller proportion into ‘chronic-worsening’ trajectories. For the former, we observed a lower susceptibility to the changes, whereas, for the latter, we noticed greater heterogeneity and susceptibility to different periods of the pandemic. From the multinomial regression models, we found global and cognitive health, and coping strategies as common protective factors among the studied mental health components. Nevertheless, some differences were found regarding the risk factors. Living alone was only significant for those classified into ‘chronic’ trajectories of loneliness, but not for the other outcomes. Similarly, secondary or higher education was only a risk factor for the ‘worsening’ trajectory of personal growth. Finally, smoking and sleeping problems were risk factors which were associated with the ‘chronic’ trajectory of psychological distress. Conclusions Our results support heterogeneity in reactions to the pandemic and the need to study different mental health-related components over a longer follow-up period, as each one evolves differently depending on the pandemic period. In addition, the understanding of modifiable protective and risk factors associated with these trajectories would allow the characterisation of these segments of the population to create targeted interventions.

Functional brain connectivity prior to the COVID‐19 outbreak predicts mental health trajectories during two years of pandemic

“…While acknowledging the hardships caused by COVID‐19, the pandemic also provided a unique opportunity to study mental well‐being and individual vulnerability or resilience 1,2 . Sociodemographic, psychological factors, and lifestyles, have been identified as predictors of mental health during COVID‐19 3 . Our previous study demonstrated the relevance of the interplay between psychological measures and brain networks' functional connectivity (FC) 4 .…”

“…For example, can FC—alone or in combination with other measures – predict longer‐term mental health? Additionally, most studies focus on emotional aspects (psychological distress), although mental health (MH) comprises emotional, psychological (personal growth, [PG]), and social (loneliness) well‐being components, which were differently impacted during the pandemic 3 . This study aims to investigate if there exists specificity between FC measures and long‐term changes across MH components, knowing the links between brain networks and ‘resilience processes’ 5,6 …”

“…We studied 702 healthy, middle‐aged individuals (350 women, age: 50.66 ± 6.98 years) who met the criteria in a 2023 study by Bayes‐Marin and colleagues 3 . All participants gave written informed consent according to the Declaration of Helsinki.…”

“…RSN models included FC for seven RSNs. Full models combined significant RSN measures and significant predictors found in our previous study (age, sex, monthly income, stress coping, personality, general health, and lifestyle habits) (see 3 ). Non‐RSN models were as full models but without RSN data, and through likelihood ratio tests (non‐RSN vs full models) we assessed whether the goodness of fit improved by adding FC measures to sociodemographic, psychological, and lifestyle measures.…”

“…Associations between RSNs SyS and latent trajectories of different components of mental health. Panel (a) depicts the latent trajectories elucidated by Bayes‐Marin et al 3 . subset to the sample of this study.…”