Incidence of Herpes Zoster in Patients With Rheumatoid Arthritis in the United States: A Retrospective Cohort Study
Bibliographic record
Abstract
Objective To estimate the incidence of herpes zoster (HZ) in patients with rheumatoid arthritis (RA) compared with the general population in the USA. Methods This retrospective, longitudinal cohort study used data from an administrative claims database containing both commercial and Medicare Advantage Part D data, with a data period from October 2015 to February 2020. Patients were aged ≥ 18 years and divided into 2 cohorts: patients with RA and patients without RA. Diagnosis and procedure codes were used to identify HZ cases and calculate incidence rates (IRs) of HZ in the 2 cohorts. Data were stratified by age group (ie, 18-49, 18-29, 30-39, 40-49, 50-64, and ≥ 65 yrs) and RA therapy type. IR ratios (IRRs), adjusted by cohort baseline characteristics, were estimated using generalized linear models to compare the incidence of HZ between cohorts. Results The overall IR of HZ was higher in the RA cohort (21.5 per 1000 person-years [PY]; N = 67,650) than in the non-RA cohort (7.6 per 1000 PY; N = 11,401,743). The highest IRs in both cohorts were observed in the age group of ≥ 65 yrs (23.4 and 11.4 per 1000 PY in the RA cohort and non-RA cohort, respectively). The overall adjusted IRR of HZ was 1.93 (95% CI 1.87-1.99,P< 0.001) for the RA cohort compared with the non-RA cohort. In the RA cohort, the highest IRs by medication class were observed in patients using corticosteroids and those using Janus kinase inhibitors. Conclusion These results highlight the increased incidence of HZ in patients with RA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".