Assessing the Impact of Pre-Existing Mental Health and Neurocognitive Disorders on the Mortality and Severity of COVID-19 in Those Aged Over 18 Years: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
Aims Since the coronavirus disease 2019 (COVID-19) pandemic began, evidence suggests that people with underlying mental health disorders have worse outcomes from COVID-19 infection. Our aim was to assess the impact of COVID-19 infection on people with pre-existing mental health or neurocognitive disorder including COVID-19 related mortality and severity. Methods We conducted systematic searches of PubMed, EMBASE, and Cochrane library for articles published between 1 December 2019 and 15 March 2021. The language was restricted to English. We included all case control, cohort and cross sectional studies that reported raw data on COVID-19 associated mortality and severity in participants aged 18 years or older with a pre-existing mental health or neurocognitive disorder compared to those without. Three independent reviewers extracted data according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Methodological quality and risk of bias were assessed using the 9-star Newcastle-Ottawa Scale. We calculated the odds ratio as the summary measure along with the corresponding 95% confidence intervals. The random effects model was used to calculate the overall pooled risk estimates. COVID-19 related mortality was the primary outcome measure. The secondary outcome measure was COVID-19 related severity, defined as intensive care unit admission or use of mechanical ventilation. Results Fifteen studies were included in the meta-analysis comprising of 8,021,164 participants. There was a statistically significant increased risk of mortality for participants with a pre-existing mental health or neurocognitive disorder compared to those without (OR = 2.18, 95% CI = 1.63–2.90, P < .00001). Increased mortality risk was found on subgroup analysis for participants with pre-existing schizophrenia (OR = 2.55, 95% CI = 1.38–4.71, P = .003) and dementia (OR = 3.83, 95% CI = 2.42–6.06, P < .00001). There was no statistically significant difference in the severity of illness when comparing the two groups. There was a statistically significant increase in the number of participants with comorbid diabetes and chronic lung disease in those with a pre-existing mental health or neurocognitive disorder compared to those without. Conclusion The results show that people over 18 years with a pre-existing mental health or neurocognitive disorder have an increased risk of mortality from COVID-19 and are more likely to have comorbid diabetes and chronic lung disease. These results highlight the need for better physical health monitoring and management for this group of people and better integration of mental and physical health services, as well as adding to the evidence that they should be prioritised in the ongoing COVID-19 vaccination schedules worldwide.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 tête enseignante, 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 ».