A systematic review and meta-analysis of longitudinal cohort studies comparing mental health before versus during the COVID-19 pandemic in 2020

Lockdowns to control the spread of the coronavirus disease 2019 (COVID-19) have had profound effects on everyday life worldwide, but their effect on mental health remains unclear because available meta-analyses and reviews rely mostly on cross-sectional studies. We conducted a rapid review and meta-analysis of longitudinal studies and natural experiments investigating the relationship between COVID-19 lockdowns and mental health. A total of 25 studies involving 72 004 participants and 58 effect sizes were analyzed. Using a random effects model, we found that lockdowns had small effects on mental health symptoms, g = 0.17, s.e. = 0.05, 95% CI (0.06-0.24), p = 0.001, but the effects on positive psychological functioning, g = -0.12, s.e. = 0.11, 95% CI (-0.33 to 0.09), p = 0.27, were not significant. Multivariate analysis of effect sizes revealed significant and relatively small effect sizes for anxiety and depression, while those for social support, loneliness, general distress, negative affect, and suicide risk were not significant. The results indicated substantial heterogeneity among studies, but meta-regression analyses found no significant moderation effects for mean age, gender, continent, COVID-19 death rate, days of lockdown, publication status or study design. The psychological impact of COVID-19 lockdowns is small in magnitude and highly heterogeneous, suggesting that lockdowns do not have uniformly detrimental effects on mental health and that most people are psychologically resilient to their effects.

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The psychological impact of COVID-19 pandemic lockdowns: a review and meta-analysis of longitudinal studies and natural experiments.

The COVID-19 pandemic has had huge effects on the daily lives of most individuals in the first half of 2020. Widespread lockdown and preventative measures have isolated individuals, affected the world economy, and limited access to physical and mental healthcare. While these measures may be necessary to minimize the spread of the virus, the negative physical, psychological, and social effects are evident. In response, technology has been adapted to try and mitigate these effects, offering individuals digital alternatives to many of the day-to-day activities which can no longer be completed normally. However, the elderly population, which has been worst affected by both the virus, and the lockdown measures, has seen the least benefits from these digital solutions. The age based digital divide describes a longstanding inequality in the access to, and skills to make use of, new technology. While this problem is not new, during the COVID-19 pandemic it has created a large portion of the population suffering from the negative effects of the crisis, and unable to make use of many of the digital measures put in place to help. This paper aims to explore the increased negative effects the digital divide is having in the elderly population during the COVID-19 pandemic. It also aims to highlight the need for increased attention and resources to go toward improving digital literacy in the elderly, and the need to put in place measures to offer immediate solutions during the COVID-19 crisis, and solutions to close the digital divide for good in the long-term.

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The Effects of COVID-19 Among the Elderly Population: A Case for Closing the Digital Divide.

Italy was the first European country to implement a national lockdown because of the COVID-19 pandemic. Worldwide, this pandemic had a huge impact on the mental health of people in many countries causing similar reaction in terms of emotions and concerns at the population level. Our study investigated the impact of the COVID-19 pandemic on psychological well-being in a cohort of Italian university students. We conducted a cross-sectional survey in the period immediately after the first lockdown through the administration of a questionnaire on the personal websites of students attending their undergraduate courses at the Universita Cattolica del Sacro Cuore. We used the Patient-Health-Engagement-Scale, Self-Rating-Anxiety-Scale, and Self-Rating-Depression-Scale to assess engagement, anxiety symptoms, and depression symptoms of our sample. The sample size was 501 subjects, of which 35.33% were classified as anxious and 72.93% as depressed. Over 90% of respondents had good understanding of the preventive measures despite over 70% suffered from the impossibility of physically seeing friends and partners. Around 55% of students would have been willing to contribute much more to face the pandemic. An increase in the occurrences of anxiety was associated with being female, being student of the Rome campus, suffering from the impossibility of attending university, being distant from colleagues, and being unable of physically seeing one’s partner. Performing physical activity reduced this likelihood. University students are at risk of psychological distress in the case of traumatic events. The evolution of the pandemic is uncertain and may have long-term effects on mental health. Therefore, it is crucial to study the most effective interventions to identify vulnerable subgroups and to plan for acute and long-term psychological services to control and reduce the burden of psychological problems.

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Impact of the COVID-19 pandemic on psychological well-being of students in an Italian university: a web-based cross-sectional survey.

Students' mental health problems before, during, and after COVID-19 lockdown in Italy.

The lockdown due to coronavirus pandemic may exacerbate depressive symptoms, experts argue. Here we report that students, a high-risk category for mental disorders, report on average worse depressive symptoms than six months before isolation. The prospective data reported herein should alert clinician of a possible aggravation as well as new-onsets of depressive symptoms in students.

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COVID-19 and depressive symptoms in students before and during lockdown

Abstract Aims Prospective studies on the mental health of university students highlighted a major concern. Specifically, young adults in academia are affected by markedly worse mental health status than their peers or adults in other vocations. This situation predisposes to exacerbated disability-adjusted life-years. Methods We enroled 1,388 students at the baseline, 557 of whom completed follow-up after 6 months, incorporating their demographic information and self-report questionnaires on depressive, anxiety and obsessive–compulsive symptoms. We applied multiple regression modelling to determine associations – at baseline – between demographic factors and self-reported mental health measures and supervised machine learning algorithms to predict the risk of poorer mental health at follow-up, by leveraging the demographic and clinical information collected at baseline. Results Approximately one out of five students reported severe depressive symptoms and/or suicidal ideation. An association of economic worry with depression was evidenced both at baseline (when high-frequency worry odds ratio = 3.11 [1.88–5.15]) and during follow-up. The random forest algorithm exhibited high accuracy in predicting the students who maintained well-being (balanced accuracy = 0.85) or absence of suicidal ideation but low accuracy for those whose symptoms worsened (balanced accuracy = 0.49). The most important features used for prediction were the cognitive and somatic symptoms of depression. However, while the negative predictive value of worsened symptoms after 6 months of enrolment was 0.89, the positive predictive value is basically null. Conclusions Students’ severe mental health problems reached worrying levels, and demographic factors were poor predictors of mental health outcomes. Further research including people with lived experience will be crucial to better assess students’ mental health needs and improve the predictive outcome for those most at risk of worsening symptoms.

Paolo Rigobello

Papers

Students' mental health problems before, during, and after COVID-19 lockdown in Italy.

COVID-19 and depressive symptoms in students before and during lockdown

Frequency and machine learning predictors of severe depressive symptoms and suicidal ideation among university students