Factors associated with frequent high-cost individuals with cystic fibrosis and their healthcare utilization and cost patterns
Bibliographic record
Abstract
Cystic fibrosis (CF) is a progressive multi-organ disease with significant morbidity placing extensive demands on the healthcare system. Little is known about those individuals with CF who continually incur high costs over multiple years. Understanding their characteristics may help inform opportunities to improve management and care, and potentially reduce costs. The purpose of this study was to identify and understand the clinical and demographic attributes of frequent high-costing CF individuals and characterize their healthcare utilization and costs over time. A longitudinal study of retrospective data was completed in British Columbia, Canada by linking the Canadian CF Registry with provincial healthcare administrative databases for the period between 2009 and 2017. Multivariable Cox regression models were employed to identify baseline factors associated with becoming a frequent high-cost CF user (vs. not a frequent high-cost CF user) in the follow-up period. We found that severe lung impairment (Hazard Ratio [HR]: 3.71, 95% confidence interval [CI], 1.49-9.21), lung transplantation (HR: 4.23, 95% CI, 1.68-10.69), liver cirrhosis with portal hypertension (HR: 10.96, 95% CI: 3.85-31.20) and female sex (HR: 1.97, 95% CI: 1.13-3.44) were associated with becoming a frequent high-cost CF user. Fifty-nine (17% of cohort) frequent high-cost CF users accounted for more than one-third of the overall total healthcare costs, largely due to inpatient hospitalization and outpatient medication costs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".