Healthcare utilization and costs associated with acute lymphoblastic leukemia in children with and without Down syndrome
Bibliographic record
Abstract
BACKGROUND: Children with Down syndrome (DS) and acute lymphoblastic leukemia (ALL) are at increased risk of treatment-related morbidity and mortality compared to non-DS-ALL, requiring increased supportive care. We examined the healthcare utilization and costs in DS-ALL patients to inform future evaluations of novel therapies. METHODS: A provincial registry identified all children (1-17 years) diagnosed with B-lineage ALL in Ontario, Canada between 2002 and 2012. Detailed demographic, disease, treatment, and outcome data were abstracted. Linkage to population-based health services databases identified all outpatient and emergency department (ED) visits, hospitalizations, and physician billings. Healthcare utilization costs were available for patients diagnosed during 2006-2012 using validated algorithms (2018 Canadian dollars). Healthcare utilization rates and costs were compared between DS and non-DS patients using regression models, adjusting for all covariates. RESULTS: Of 711 patients, 28 (3.9%) had DS. Adjusting for all covariates, children with DS-ALL experienced substantially higher rates of ED visits (rate ratio [RR] 1.5, 95% confidence interval [95% CI]: 1.2-2.0; p = .001) and inpatient days (RR 2.5, 95% CI: 1.4-4.5; p = .002) compared to non-DS children. Outpatient visit rates were similar (RR 1.1, 95% CI: 0.9-1.3; p = .41). Among patients with available cost data (N = 533, DS = 19), median 5-year healthcare utilization cost was $247,700 among DS patients (interquartile range [IQR]: 200,900-354,500) and $196,200 among non-DS patients (IQR: 148,900-280,300; p = .02). In adjusted analyses, DS-associated costs were 50% higher (RR 1.5, 95% CI: 1.2-1.9; p < .002). CONCLUSIONS: Healthcare utilization and treatment costs of DS-ALL patients are substantially higher than those of non-DS-ALL. Our data provide a baseline for future DS-specific cost-effectiveness studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".