Hidden Cost of Rheumatoid Arthritis (RA): Estimating Cost of Comorbid Cardiovascular Disease and Depression Among Patients with RA
Bibliographic record
Abstract
OBJECTIVE: To examine resource utilization and direct healthcare cost associated with comorbid cardiovascular disease (CVD) and depression among patients with prevalent rheumatoid arthritis (RA) based on analyses of retrospective healthcare claims data. METHODS: The index date was set as the first observed claim with an RA diagnosis. Patients were required to be >or=18 years of age, to have received RA-related treatment during the pre-index period, and to have 12-month pre- and post-index data. Based on pre-index utilization, patients were classified into 4 diagnosis groups: RA alone, RA+CVD, RA+depression, and RA+CVD+depression. Analyses focused on annual differences in costs between patients with RA alone and those with CVD and/or depression. A generalized linear model was applied to control for demographic and clinical characteristics and to estimate cohort-specific adjusted mean annual healthcare cost. RESULTS: Of 10,298 patients, 8,916 had RA alone (86.6%), 608 had RA+CVD (5.9%), 716 had RA+depression (7.0%), and 58 had RA+CVD+depression (0.5%). All patients with CVD and/or depression incurred significantly higher followup costs compared with patients with RA alone. Adjusted annual mean healthcare costs were highest for RA+CVD (US$14,145), followed by RA+CVD+depression ($13,513), RA+depression ($12,225), and RA alone ($11,404). Although patients with CVD and/or depression had a greater rate of RA-related hospitalization, adjusted RA-related healthcare costs did not reflect any statistically significant differences as compared to the RA-alone cohort. CONCLUSION: A significant proportion (13.4%) of patients with prevalent RA have comorbid CVD and/or depression. The presence of these conditions significantly affects annual healthcare costs as well as specific RA-related utilization patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".