Factors associated with timely receipt of COVID vaccination in patients with cancer.
Notice bibliographique
Résumé
167 Background: In many jurisdictions patients with new hematological cancers, or those receiving hematopoietic stem cell transplant or immunosuppressive agents, were prioritized for COVID vaccination due to increased risk of infection and death. In Ontario, Canada those residing in congregate settings, or regions with high positivity rates or high proportions of essential workers were also prioritized. While vaccine inequities exist, it remains unclear whether they persisted amongst the prioritized cancer population. Methods: We undertook a retrospective, population-based study to evaluate factors associated with COVID vaccination in patients residing in Ontario, Canada, >18 years of age, and diagnosed with cancer between 01/2010 and 09/2020. Factors associated with time from vaccine approval to full vaccination (two doses) and third doses were evaluated using multivariable Cox proportional hazards regression models. Results: The cohort consisted of 356,535 patients; as of 30 January 2022 of which 86.8% had received at least two doses. Compared to patients with more remote diagnoses (> 1 year), newly diagnosed patients rate of vaccination was lower (HR: 0.89, 95%CI: 0.88-0.91, p < 0.01) and a greater proportion were unvaccinated (13.6% vs 11.8%; p < 0.01). Conversely, rate of vaccination was higher in patients treated with systemic therapy in the last 6 months (HR: 1.04, 95%CI: 1.03-1.05, p < 0.01). Rate of vaccination was 25% lower in recent (HR:0.74,95% CI: 0.72-0.76, p < 0.01) and non-recent immigrants (HR: 0.80, 95% CI: 0.79-0.81, p < 0.01), and a greater proportion remained unvaccinated, compared to those who were Canadian-born (20.1 and 16.6% vs 10.9%; p < 0.01). Compared to the most advantaged quintiles, quintiles with the lowest socioeconomic status (14.5% vs 9.4%; p < 0.01), or highest residential instability (13.3% vs 10.8%; p < 0.01), material deprivation (10.5% vs 9.6%; p < 0.01), or ethnic concentration quintiles (13.7% vs 10.4%; p < 0.01) had higher proportions of unvaccinated patients. Rate of vaccination was 20% lower in patients with the lowest socioeconomic status (HR: 0.83, 95% CI: 0.81-0.84, p < 0.01) and those with highest material deprivation (HR: 0.80, 95% CI: 0.79-0.82, p < 0.01) relative to more advantaged groups. Similar trends were observed for receipt of third doses in the eligible cohort. Conclusions: Despite direct government funding of COVID vaccines and distribution policies aimed a prioritizing high-risk populations marginalized patients with cancer were less likely to be vaccinated than other cancer patients. Differences in receipt of vaccination are likely due to the interplay between systemic barriers to access (low trust, transportation barriers, work schedules), and cultural/ social influences impacting uptake. Future efforts should work directly members of high-risk communities to understand how to improve vaccine delivery among these communities.
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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».