MétaCan
Menu
← Back to cohort
Record W4411846569 · doi:10.3899/jrheum.2025-0314.53

Insights into Insurance for Vaccine Coverage Patients on Specialty Medications

2025· article· en· W4411846569 on OpenAlexaffvenueabout
Abdullahi Ahmed Mohamed, Stephen Williams, Aurore Fifi‐Mah

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineVaccinationInternal medicinePopulationFamily medicineRheumatologyInfluenza vaccineImmunologyEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.287
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueThe Journal of Rheumatology→Same topicRheumatoid Arthritis Research and Therapies→French-language works237,207→