1107 Economic evaluation of hydroxychloroquine use in an international inception cohort
Bibliographic record
Abstract
Background While there is overwhelming evidence for the beneficial role of hydroxychloroquine (HCQ) in SLE, little is known about its economic impact. We estimated annual direct, indirect, and total costs (DC, IC, TC) associated with HCQ use. Methods A subset of patients from the Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) inception cohort were assessed annually between 2014 and 2019 for health resource use, lost work-force/non-work-force productivity and concurrent HCQ use. Resource use was costed using 2021 Canadian prices and lost productivity using Statistics Canada age-and-sex specific wages. At each assessment, HCQ dose over the past year and weight were documented and patients were stratified into 1 of 3 HCQ dosage groups: non-users (0 mg/kg/day), low-intensity users (≤ 5 mg/kg/day), or high-intensity users (>5 mg/kg/day). Costs associated with HCQ dose were calculated by averaging all observations within each dosage group. Multiple random effects linear regressions adjusted for the possible confounding of age at diagnosis, sex, race/ethnicity, disease duration, geographic region, education, alcohol use, and smoking on the association between annual DC and IC and HCQ dose. A possible mediating effect of disease damage (SLICC/ACR DI) on these associations was also investigated. Results 661 patients (89.4% female, 59.3% non-Caucasian race/ethnicity, mean age and mean disease duration at the start of economic assessments was 42.1 years and 9.5 years, respectively) were followed over a mean of 2.8 years. Across 1536 annual assessments, 36.1% of observations were provided by HCQ non-users, 43.1% by low-intensity users (mean dosage 3.4 mg/kg/day), and 20.8% by high-intensity users (mean dosage 5.9 mg/kg/day). Annual adjusted DC were higher in non-users ($9599) versus low-intensity users ($6344) and high-intensity users ($6333) (table 1). When disease damage was included in the regression, there were no significant differences in DC between dosage groups. While unadjusted IC were higher in non-users ($37,610) versus low-intensity users ($32,480) and high-intensity users ($31,418), adjusted IC did not differ. Adjusted TC were higher in non-users ($46,157) versus low-intensity users ($39,257) and high-intensity users ($37,634). Conclusion SLE patients reported higher adjusted annual DC and TC during periods of HCQ non-use versus periods of use, regardless of the intensity of use. There was no additional cost savings in those using high intensity dosages. The cost-savings effect of HCQ could potentially be partially mediated through reduced damage. In addition to its well-established therapeutic potential, there may be an economic imperative for HCQ use in SLE patients.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".