Phase-Specific Healthcare Costs Associated With Giant Cell Arteritis in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To estimate the additional healthcare system costs associated with giant cell arteritis (GCA) in the 1-year prediagnosis and postdiagnosis periods and over long-term follow-up compared to individuals with similar demographics and comorbidities without GCA. METHODS: We performed a population-based study using health administrative data. Newly diagnosed cases of GCA (between 2002 and 2017 and aged ≥ 66 years) were identified using a validated algorithm and matched 1:6 to comparators using propensity scores. Follow-up data were accrued until death, outmigration, or March 31, 2020. The costs associated with care were determined across 3 phases: the year before the diagnosis of GCA, the year after, and ongoing costs thereafter in 2021 Canadian dollars (CAD). RESULTS: The cohort consisted of 6730 cases of GCA and 40,380 matched non-GCA comparators. The average age was 77 (IQR 72-82) years and 68.2% were female. A diagnosis of GCA was associated with an increased cost of CAD $6619.4 (95% CI 5964.9-7274.0) per patient during the 1-year prediagnostic period, $12,150.3 (95% CI 11,233.1-13,067.6) per patient in the 1-year postdiagnostic phase, and $20,886.2 (95% CI 17,195.2-24,577.2) per patient during ongoing care for year 3 onward. Increased costs were driven by inpatient hospitalizations, physician services, hospital outpatient clinic services, and emergency department visits. CONCLUSION: A diagnosis of GCA was associated with increased healthcare costs during all 3 phases of care. Given the substantial economic burden, strategies to reduce the healthcare utilization and costs associated with GCA are warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".