Clinical outcomes and risk factors for COVID-19 among migrant populations in high-income countries: a systematic review
Notice bibliographique
Résumé
Abstract Background Migrants, including refugees, asylum seekers, labour migrants, and undocumented migrants, now constitute a considerable proportion of most high-income countries’ populations, including their skilled and unskilled workforces. Migrants may be at increased risk of COVID-19 due to their health and social circumstances, yet the extent to which they are being affected and their predisposing risk factors are not clearly understood. We did a systematic review to assess clinical outcomes of COVID-19 in migrant populations (cases, hospitalisations, deaths), indirect health and social impacts, and to determine key risk factors. Methods We did a systematic review following PRISMA guidelines, registered with PROSPERO (CRD42020222135). We searched databases including PubMed, Global Health, Scopus, CINAHL, and pre-print databases (medRxiv) via the WHO Global Research on COVID-19 database to Nov 18, 2020 for peer-reviewed and grey literature pertaining to migrants (defined as foreign born) and COVID-19 in 82 high-income countries. We used our international networks to source national datasets and grey literature. Data were extracted on our primary outcomes (cases, hospitalisations, deaths) and we evaluated secondary outcomes on indirect health and social impacts, and risk factors, using narrative synthesis. Results 3016 data sources were screened with 158 from 15 countries included in the analysis (35 data sources for primary outcomes: cases [21], hospitalisations [4]; deaths [15]; 123 for secondary outcomes). We found that migrants are at increased risk of infection and are disproportionately represented among COVID-19 cases. Available datasets suggest a similarly disproportionate representation of migrants in reported COVID-19 deaths, as well as increased all-cause mortality in migrants in some countries in 2020. Undocumented migrants, migrant health and care workers, and migrants housed in camps and labour compounds may have been especially affected. In general, migrants have higher levels of many risk factors and vulnerabilities relevant to COVID-19, including increased exposure to SARS-CoV-2 due to high-risk occupations and overcrowded accommodation, and barriers to health care including inadequate information, language barriers, and reduced entitlement to healthcare coverage related to their immigration status. Conclusions Migrants in high-income countries are at high risk of exposure to, and infection with, COVID-19. These data are of immediate relevance to national public health responses to the pandemic and should inform policymaking on strategies for reducing transmission of COVID-19 in this population. Robust data on testing uptake and clinical outcomes in migrants, and barriers and facilitators to COVID-19 vaccination, are urgently needed, alongside strengthening engagement with diverse migrant groups.
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 | 0,009 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,007 |
| Bibliométrie | 0,009 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».