Social determinant of health and COVID-19 vaccine uptake among US cancer survivors.
Notice bibliographique
Résumé
369 Background: According to the National Comprehensive Cancer Network Guidelines, all individuals with cancer survivors should receive COVID vaccination. However, the extent to which social determinants of health (SDoH) influence the uptake of COVID-19 vaccines in this specific population remains an area of exploration. We aim to examine the potential impact of SDoH on COVID-19 vaccine uptake in cancer survivors. Methods: Cancer survivors with known COVID-19 vaccination status and SDoH information including food insecurity, housing insecurity and transportation barriers were extracted from the 2022 Behavioral Risk Factor Surveillance System (BRFSS). We employed chi-square tests to compare baseline demographics and COVID-19 vaccine uptake by SDoH factors. Logistic regression model was used to calculate the association between SDoH factors and COVID-19 vaccine uptake among cancer survivors, after adjusting for potential confounders. All analyses were weighted. The significance level was set at 2-sided p<0.05. Results: A total of 13025 cancer survivors were included (weighted N=4,620,447) with 85.9% reporting having at least 1 dose of COVID-19 vaccine in the past. After adjusting for confounders, we found that cancer survivors experiencing food insecurity (odds ratio [OR]:0.63, 95% confidence interval [CI]: 0.44-0.90), housing insecurity (OR: 0.63; 95%CI: 0.42-0.96), transportation insecurity (OR: 0.51; 95%CI: 0.31-0.81) and any of the above barriers (OR: 0.58; 95%CI: 0.41-0.82) were significantly less likely to have received at least one dose of the COVID-19 vaccine. Conclusions: 15% cancer survivors reported never having received COVID-19 vaccine. Additionally, we found that having food, housing and transportation insecurity is independently associated with decreased COVID-19 vaccine uptake among US cancer survivors. Prior studies have shown that cancer survivors who with COIVD infections had a higher mortality and hospitalization rate than non-cancer population. These findings emphasize the importance of addressing SDoH in public health efforts to ensure equitable access to vaccines, particularly for a population at increased risk. SDoH Factors COVID-19 Vaccine Uptake (weighted % 95%CI) p-values OR 95%CI* Food insecurity No 88.9 (87.6-90.2) <0.0001 Ref Yes 75.4 (70.4-80.5) 0.63 (0.44-0.90) Housing insecurity No 88.4 (87.1-89.6) <0.0001 Ref Yes 71.2 (63.9-78.5) 0.63 (0.42-0.96) Transportation insecurity No 87.6 (86.3-88.9) <0.01 Ref Yes 71.4 (61.7-81.0) 0.51 (0.31-0.81) Any of above No 89.4 (88.1-90.7) <0.0001 Ref Yes 76.1 (71.8-80.3) 0.58 (0.41-0.82) *Model adjusted for age, sex, race/ethnicity, marital status, education, income, insurance status, heavy drinking, smoking, BMI, number of comorbidities, had routine check-up last year, self-reported health status, US region, flu vaccine status, whether had COVID infection, type of cancer, and current cancer treatment status.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,000 | 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 ».