Policy stringency and mental health during the COVID-19 pandemic: a longitudinal analysis of data from 15 countries
Notice bibliographique
Résumé
BACKGROUND: To date, public health policies implemented during the COVID-19 pandemic have been evaluated on the basis of their ability to reduce transmission and minimise economic harm. We aimed to assess the association between COVID-19 policy restrictions and mental health during the COVID-19 pandemic. METHODS: In this longitudinal analysis, we combined daily policy stringency data from the Oxford COVID-19 Government Response Tracker with psychological distress scores and life evaluations captured in the Imperial College London-YouGov COVID-19 Behaviour Tracker Global Survey in fortnightly cross-sections from samples of 15 countries between April 27, 2020, and June 28, 2021. The mental health questions provided a sample size of 432 642 valid responses, with an average of 14 918 responses every 2 weeks. To investigate how policy stringency was associated with mental health, we considered two potential mediators: observed physical distancing and perceptions of the government's handling of the pandemic. Countries were grouped on the basis of their response to the COVID-19 pandemic as those pursuing an elimination strategy (countries that aimed to eliminate community transmission of SARS-CoV-2 within their borders) or those pursuing a mitigation strategy (countries that aimed to control SARS-CoV-2 transmission). Using a combined dataset of country-level and individual-level data, we estimated linear regression models with country-fixed effects (ie, dummy variables representing the countries in our sample) and with individual and contextual covariates. Additionally, we analysed data from a sample of Nordic countries, to compare Sweden (that pursued a mitigation strategy) to other Nordic countries (that adopted a near-elimination strategy). FINDINGS: Controlling for individual and contextual variables, higher policy stringency was associated with higher mean psychological distress scores and lower life evaluations (standardised coefficients β=0·014 [95% CI 0·005 to 0·023] for psychological distress; β=-0·010 [-0·015 to -0·004] for life evaluation). Pandemic intensity (number of deaths per 100 000 inhabitants) was also associated with higher mean psychological distress scores and lower life evaluations (standardised coefficients β=0·016 [0·008 to 0·025] for psychological distress; β=-0·010 [-0·017 to -0·004] for life evaluation). The negative association between policy stringency and mental health was mediated by observed physical distancing and perceptions of the government's handling of the pandemic. We observed that countries pursuing an elimination strategy used different policy timings and intensities compared with countries pursuing a mitigation strategy. The containment policies of countries pursuing elimination strategies were on average less stringent, and fewer deaths were observed. INTERPRETATION: Changes in mental health measures during the first 15 months of the COVID-19 pandemic were small. More stringent COVID-19 policies were associated with poorer mental health. Elimination strategies minimised transmission and deaths, while restricting mental health effects. FUNDING: None.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».