Gender-Affirming Care in a Public Health Payer System: A Cost–Utility Analysis of Top Surgery
Bibliographic record
Abstract
BACKGROUND: Gender-affirming surgery can improve the mental and physical health of transgender and gender-diverse (TGD) adults. Examination of these effects through economic evaluation has yet to be conducted in a publicly funded health care system. METHODS: A Markov model was used to inform the cost-utility analysis over a 20-year time horizon. Uncertainty was assessed using probabilistic sensitivity analysis. The time horizon was varied in a scenario analysis. The analysis adopted the Ontario public payer perspective, with costs reported in 2024 Canadian dollars (CAD $1 = US $0.86 using Organisation for Economic Co-operation and Development 2022 purchasing power parities). Costs, utilities, and probability states were derived from health authority reports and the literature. The cohort included TGD adults in Ontario who desire top surgery. Top surgery was compared with no surgery. Costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio were the primary outcome measures. Top surgery would be considered cost-effective if the incremental cost-effectiveness ratio was below the typical willingness-to-pay threshold of $50,000 per QALY (discounted at 1.5% per annum). RESULTS: The cohort consisted of 1000 TGD individuals, representative of current Ontario top surgery waitlists. Top surgery was dominant (incremental cost -$2857, incremental effectiveness 0.59 QALY) compared with no surgery over a 20-year time horizon. Results were robust, with surgical intervention being cost-effective in 58% (dominant in 52%) of the 10,000 Monte Carlo simulations. CONCLUSIONS: The findings suggest that top surgery is a cost-effective intervention at conventional willingness-to-pay thresholds. There may be a system-level benefit to providing access to care for waitlisted 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".