Mental health effects of infection containment strategies: quarantine and isolation—a systematic review and meta-analysis
Bibliographic record
Abstract
Due to the ongoing COVID-19 pandemic, an unprecedented number of people worldwide is currently affected by quarantine or isolation. These measures have been suggested to negatively impact on mental health. We conducted the first systematic literature review and meta-analysis assessing the psychological effects in both quarantined and isolated persons compared to non-quarantined and non-isolated persons. PubMed, PsycINFO, and Embase databases were searched for studies until April 22, 2020 (Prospero Registration-No.: CRD42020180043). We followed PRISMA and MOOSE guidelines for data extraction and synthesis and the Newcastle-Ottawa Scale for assessing risk of bias of included studies. A random-effects model was implemented to pool effect sizes of included studies. The primary outcomes were depression, anxiety, and stress-related disorders. All other psychological parameters, such as anger, were reported as secondary outcomes. Out of 6807 screened articles, 25 studies were included in our analyses. Compared to controls, individuals experiencing isolation or quarantine were at increased risk for adverse mental health outcomes, particularly after containment duration of 1 week or longer. Effect sizes were summarized for depressive disorders (odds ratio 2.795; 95% CI 1.467-5.324), anxiety disorders (odds ratio 2.0; 95% CI 0.883-4.527), and stress-related disorders (odds ratio 2.742; 95% CI 1.496-5.027). Among secondary outcomes, elevated levels of anger were reported most consistently. There is compelling evidence for adverse mental health effects of isolation and quarantine, in particular depression, anxiety, stress-related disorders, and anger. Reported determinants can help identify populations at risk and our findings may serve as an evidence-base for prevention and management strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".