Insights into Insurance for Vaccine Coverage Patients on Specialty Medications
Notice bibliographique
Résumé
Objectives There is a growing usage of biologics and targeted synthetic disease-modifying antirheumatic drugs (tsDMARDs) for the management of inflammatory arthritis. These medications, while effective at managing rheumatic disease, may also increase the risk of vaccine-preventable illnesses such as influenza, COVID-19, pneumonia, and shingles. The vaccination rate in the general population is low. We wanted to evaluate the rate of vaccination of patients with inflammatory arthritis seen in our clinic and identify barriers to immunization. Here we focused on type of insurance coverage for vaccines. Methods Vaccination and insurance information of patients seen at the South Health Campus Rheumatology Clinic was collected and analyzed from the period of January 1, 2023, to January 1, 2024. All patients in this study were aged 18 or older, had a diagnosis of inflammatory arthritis, were on a biologic or a tsDMARD, and had some form of insurance. Patients were allocated into groups based on insurance type (Public/Government, Private, and 2 Insurances). Descriptive statistics were used for data analysis. Results 216 patient charts were reviewed (Table 1).[1] Full vaccination in the public insurance group were as follows: Influenza (81/132, 61.4%), COVID-19 (99/132, 75.0%), Prevnar 13 or 20 (75/132, 56.8%), Pneumovax 23 (106/132, 80.3%), and Shingrix (41/132, 31.1%). Full vaccination in private insurance group were as follows: Influenza (50/103, 48.5%), COVID-19 (74/103, 71.8.0%), Prevnar 13 or 20 (59/103, 57.3), Pneumovax 23 (59/103, 57.3), and Shingrix (34/103, 33.0%). Table 1: Vaccination Rates by Insurance Type Conclusion Overall, there were several important findings. The group with the highest vaccination rate across all categories were those possessing 2 or more insurance plans. Higher rates of influenza and Pneumovax 23 vaccination were seen in the public insurance group, but this is likely due to a majority of this group being older than 65 (age required for free Pneumovax coverage) and the general trend of increased influenza vaccination in older adults.[2] Prevnar and Shingrix vaccination rates were seemingly similar for public and private groups, however sub-group analysis presented an important finding. Patients possessing employer insurance had higher vaccination rates (Prevnar 13/20-68%, Shingrix-46%) than any other private or public insurance sub-group. The difference in vaccination rate is likely due to improved vaccine cost coverage, increased formulary size, and health spending accounts associated with these plans. This information will help us advocate for publicly provided coverage for other essential vaccine such as Shingrix (as of July 2024, Prevnar 20 is publicly funded in Alberta). [1.] Bass AR. Arthritis Care & Research 2023;75(3):449-64. [2.] Gilmour H. Health Rep 2024;35(1):14-24.
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 | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».