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SP12. Providing Gender Affirming Care In A Public Health Payer System: Health Policy Implications From A Cost-utility Analysis Of Top Surgery

2024· article· en· W4395053920 on OpenAlexaffabout
Chantal R. Valiquette, Jessica E. Morgan, Sarah Rae, Rebecca Hancock, Brian Chan, Peter C. Coyte, Kathleen Armstrong, Beverley M. Essue, Daniel Grace, Mitchell H. Brown

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsHealth careHealthcare systemPublic healthMedicineNursingBusinessEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Purpose: Gender affirming surgery can have significant positive clinical impacts by improving mental and physical health for transgender and gender diverse (TGD) individuals desiring surgery. Top surgery (i.e., chest reconstructive mastectomy or augmentation) is a safe and commonly performed surgery for TGD individuals. While a previous study has conducted economic evaluations of gender affirming care from the perspective of a United States insurance provider, focused examination of the cost and utility impacts of gender affirming surgical care have yet to be appraised in a publicly funded healthcare system. This study aimed to examine these impacts in a Canadian healthcare system contextualized with potential health policy implications. Methods: A cost-utility analysis (CUA) was conducted. The CUA examined incremental cost per quality adjusted life year (QALY), a standardized measure of patient intervention impact, gained through the provision of gender affirming top surgery. A Markov model was used to estimate the health care costs and QALYs gained from top surgery compared to no surgery. Sub-groups with and without hormone therapy were included to represent current estimates of TGD patients’ hormone use. A cohort of 1000 prospective adult TGD patients were used to represent current surgical waitlist estimates. Once they entered the model, patients experienced outcomes over one-year cycles for ten cycles. A half cycle correction was applied to the model. Given provincial funding differences, the analysis used the Ontario public payer perspective with costs reported in 2022 Canadian dollars. Costs, QALYs, and probability states were derived from health authority reports and the literature. A probabilistic sensitivity analysis was conducted to assess robustness of results, applying standard distributions to each variable in the model and running 10,000 Monte Carlo simulations. Results: Top surgery is considered cost-effective compared to no surgery over a ten-year time horizon when considering the impacts of mental health, suicidal ideation, and smoking, using a typical willingness to pay threshold of $50,000/QALY. The ten-year incremental cost effectiveness ratio, measuring intervention costs per QALY, was $-81,183.56 per QALY gained, with a net monetary benefit of $394,050.00. Results were robust with 97.5% of the Monte Carlo simulations finding surgical intervention cost effective in the probabilistic sensitivity analysis. Further top surgery was dominant in 75.4% of these simulations. Conclusions: Economic evaluation findings suggest that when mental and physical health benefits are considered, top surgery is cost-effective when compared to no surgery for adult TGD individuals in Ontario. Considering these additional patient health states can more accurately assess system level impacts of gender affirming surgical care provision. By using a cohort size representative of the current surgical waitlist numbers, these findings suggest there may be system level benefit to prioritizing providing access to care for waitlisted patients. Further research may include evaluation of other provincial health systems and incorporation of patient lived experience to identify outcomes not routinely accounted for in current economic evaluation models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.157
GPT teacher head0.351
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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