COST-EFFECTIVENESS OF PULSE OXIMETRY SCREENING FOR CRITICAL CONGENITAL HEART DEFECTS IN ONTARIO
Bibliographic record
Abstract
Abstract BACKGROUND Critical congenital heart defects (CCHDs) are a leading cause of morbidity and mortality in newborns, and late diagnosis is associated with mortality and worse outcomes. Many jurisdictions in the USA and elsewhere have implemented routine pulse oximetry screening (POS) for CCHD, which the Canadian Paediatric Society has recently endorsed. Cost-effective analyses in USA and Europe support this approach, but the geographical setting of Ontario in relation to its vast yet sparsely populated regions presents unique challenges with regard to POS implementation. OBJECTIVES To estimate the cost-effectiveness of POS for CCHD in the context of its implementation in Ontario, Canada. DESIGN/METHODS A cost-effectiveness analysis using a Markov model was conducted inputting values derived from an extensive review of literature, and using relevant local databases. The base-case was a 24-hour clinically stable infant born in Ontario. The model employed the healthcare payer (ministry of health) perspective and a life-time horizon. A number of mutually exclusive health states were created, representative of the natural course of CCHDs. The strategies compared were routine pulse oximetry screening versus no screening. Outcome measures, all discounted 1.5%, were quality-adjusted life months (QALMs), lifetime costs, and incremental cost-effectiveness ratios. An a priori threshold of CAD$4,166.67 per QALM (equivalent to CAD$50,000 per quality adjusted life year) was used. Probabilistic sensitivity analysis was conducted using multiple simulations of the model within expected range of variables included in the model. RESULTS The incremental cost of performing POS was estimated to be $27.27 per individual, with a gain of 0.02455 QALMs (Table 1). This yielded an incremental cost-effectiveness ratio (ICER), [Δ Cost / Δ QALMs] of CAD$1,110.79, well below the pre-determined threshold for cost-effectiveness. A probabilistic sensitivity analysis estimated a 93% chance of routine implementation of POS of being cost-effective, with majority of simulated ICERs lying below the threshold of acceptability (Figure 1). CONCLUSION Routine implementation of POS for CCHD is expected to be cost-effective with a high degree of certainty. Further validation of this model may be conducted following implementation to confirm these findings based on local population data.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".