Development of Prediction Models of COVID-19 Vaccine Uptake among Lebanese and Syrians in a district of Beirut, Lebanon: a population-based study
Notice bibliographique
Résumé
Abstract Introduction Vaccines are essential to prevent infection and reduce morbidity of infectious diseases. Previous evidence has shown that migrants and refugees are particularly vulnerable to exclusion and discrimination, and low COVID-19 vaccine intention and uptake were observed among refugees globally. This study aimed to develop and internally validate prediction models of COVID-19 vaccine uptake by nationality. Methods This is a nested prognostic population-based cross-sectional analysis. Data was collected between June and October 2022 in Sin-El-Fil, a district of Beirut, Lebanon. All Syrian adults and a random sample of adults from low-socioeconomic status neighborhoods were invited to participate in a telephone survey. The main outcome was uptake of COVID-19 vaccine. Predictors of COVID-19 vaccine uptake were assessed using LASSO regression for Lebanese and Syrian nationalities, respectively. Results Of 2,045 participants, 79% were Lebanese, 18% Syrians and 3% of other nationalities. COVID-19 vaccination uptake was higher among Lebanese (85% (95%CI:82-86) compared to Syrians (47% (95% CI:43-51)) (P<0.001); adjusted odds ratio (aOR) 6.8 (95%CI:5.5-8.4). Predictors of uptake of one or more vaccine dose for Lebanese were older age, presence of an older adult in the household, higher education, greater asset-based wealth index, private healthcare coverage, feeling susceptible to COVID-19, belief in the safety and efficacy of vaccines and previous receipt of flu vaccine. For Syrians they were older age, male, completing school or higher education, receipt of cash assistance, presence of comorbidities, belief in the safety and efficacy of vaccines, previous receipt of flu vaccine, and legal residency status in Lebanon. Conclusions These findings indicate barriers for vaccine uptake in Syrian migrants and refugees, including legal residency status. They call for urgent action to enable equitable access to vaccines by raising awareness about the importance of vaccination and the targeting of migrant and refugee populations through vaccination campaigns. Key Messages What is already known on this topic Vaccines are essential to prevent infection and reduce morbidity of infectious diseases, and vulnerable populations may lack access to vaccination campaigns. What this study adds To the best of our knowledge, no studies have examined compared predictors of COVID-19 vaccine uptake and measured the rate of vaccination among between Syrian migrants and refugees and their Lebanese host communities. This study illustrates a clear difference in vaccine uptake between nationalities and developed prediction models among the Syrian and Lebanese that identified differential predictors of COVID-19 vaccine uptake for each population. How this study might affect research, practice or policy These findings indicate barriers for vaccine uptake in Syrian migrants and refugees, including legal status, and calls for urgent action to enable access to vaccines by raising awareness about the importance of vaccination against COVID-19 in vulnerable groups and targeting migrant and refugee populations through vaccination campaigns.
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 | 0,008 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».