Healthcare Utilization and Cost of Herpes Zoster Infection in Patients With Rheumatoid Arthritis: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatoid arthritis (RA) have an increased risk of developing herpes zoster (HZ) compared to the general population. We aimed to measure healthcare utilization (HCU) and related costs of HZ among patients with RA, from the public payer's perspective. METHODS: Adult patients with RA diagnosed with HZ between 2008 and 2020 were matched by sex, age, and date of HZ infection to (1) patients with RA without HZ, (2) the non-RA population with HZ, and (3) the non-RA population without HZ. Unadjusted gamma distribution models and generalized estimating equations were used to compare HCU costs and the number of clinical events (CEs), including hospital admissions and emergency department and physician visits, in patients with RA with HZ to each matched cohort. RESULTS: We identified 15,573 patients with RA diagnosed with HZ and a similar number for each of the 3 matched cohorts. From year 1 to year 10, mean total cost ranged from CAD $13,507 to CAD $17,120 for the RA with HZ cohort compared to CAD $12,651 to CAD $14,534 in the RA without HZ cohort. Physician billing and inpatient hospital costs were the largest drivers of increased costs for all cohorts. Compared to patients with RA with HZ, each matched cohort experienced a significantly lower mean number of total CEs, with the highest difference in total CEs 1 year following an HZ infection. CONCLUSION: HCU and related costs were higher in patients with RA with HZ compared to patients with RA without HZ and non-RA populations with and without HZ. Treatment strategies that minimize the risk of HZ and encourage patients to keep up to date with vaccinations should be considered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".