Determinants of COVID-19 outcomes: A systematic review
Notice bibliographique
Résumé
Abstract Background The current pandemic, COVID-19, caused by a novel coronavirus SARS-CoV-2, has claimed over a million lives worldwide in a year, warranting the need for more research into the wider determinants of COVID-19 outcomes to support evidence-based policies. Objective This study aimed to investigate what factors determined the mortality and length of hospitalisation in individuals with COVID-19. Data Source This is a systematic review with data from four electronic databases: Scopus, Google Scholar, CINAHL and Web of Science. Eligibility Criteria Studies were included in this review if they explored determinants of COVID-19 mortality or length of hospitalisation, were written in the English Language, and had available full-text. Study appraisal and data synthesis The authors assessed the quality of the included studies with the Newcastle□Ottawa Scale and the Agency for Healthcare Research and Quality checklist, depending on their study design. Risk of bias in the included studies was assessed with risk of bias assessment tool for non-randomised studies. A narrative synthesis of the evidence was carried out. The review methods were informed by the Joana Briggs Institute guideline for systematic reviews. Results The review included 22 studies from nine countries, with participants totalling 239,830. The included studies’ quality was moderate to high. The identified determinants were categorised into demographic, biological, socioeconomic and lifestyle risk factors, based on the Dahlgren and Whitehead determinant of health model. Increasing age (ORs 1.04-20.6, 95%CIs 1.01-22.68) was the common demographic determinant of COVID-19 mortality while living with diabetes (ORs 0.50-3.2, 95%CIs −0.2-0.74) was one of the most common biological determinants of COVID-19 length of hospitalisation. Review limitation Meta-analysis was not conducted because of included studies’ heterogeneity. Conclusion COVID-19 outcomes are predicted by multiple determinants, with increasing age and living with diabetes being the most common risk factors. Population-level policies that prioritise interventions for the elderly population and the people living with diabetes may help mitigate the outbreak’s impact. PROSPERO registration number CRD42021237063. Strength and limitations of this review This is the first systematic review synthesising the evidence on determinants of COVID-19 LOS outcome. It is also the first review to provide a comprehensive investigation of contextual determinants of COVID-19 outcomes, based on the determinants of health model; thus, presenting with crucial gaps in the literature on the determinants of COVID-19 outcomes that require urgent attention. The review was restricted in conducting meta-analysis due to included studies’ heterogeneity. The review focused on only papers published in the English Language; hence, other relevant papers written on other languages could have been omitted.
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 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 | 0,018 | 0,088 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,011 |
| Bibliométrie | 0,011 | 0,014 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».