The Impact of Health Policy on Total Ankle Arthroplasty Prices in the United States
Bibliographic record
Abstract
BACKGROUND: Total ankle arthroplasty (TAA) is increasingly used due to advancements in surgical technology leading to promising results. Although TAA may have a higher upfront complication rate, its long-term benefits, including lower rates of adjacent joint arthritis and subsequent surgeries, may enhance its cost-effectiveness relative to ankle arthrodesis. Notable regional variability in TAA prices exists, influenced in part by state-level political dynamics and healthcare regulations. This study investigates how state-level political affiliation, certificate of need (CON) laws, and Medicaid expansion affect TAA pricing across the United States, with a specific focus on North Carolina. METHODS: Data were sourced from the Turquoise Health Database, covering TAA prices since 2021. The unit of analysis was at the hospital level, with price defined as the negotiated hospital facility fee for TAA procedures (current procedural terminology code 27702), exclusive of physician fees. Multivariable regression analyses assessed relationships between TAA prices and factors, including CON regulations, Medicaid expansion, political affiliation, and socioeconomic variables like the area deprivation index. Political affiliation was assessed using both a composite score integrating five indicators of state political control and the Cook Partisan Voting Index for a more granular approach. RESULTS: States with CON regulations showed lower TAA prices, with average savings of $1,650. Medicaid expansion correlated with higher prices, with an average increase of $1,690. The composite political score showed minimal effect, although the Cook Partisan Voting Index indicated higher prices in Republican-leaning states. In North Carolina, higher area deprivation index scores correlated with reduced TAA prices by $15,331.50, potentially due to competitive market pressures or reliance on government payers. CONCLUSION: CON laws may reduce costs, whereas Medicaid expansion correlates with higher prices. Political affiliation shows minimal influence, with Republican affiliation weakly associated with higher prices. These findings provide insights for policymakers aiming to balance cost control and access to TAA. LEVEL OF EVIDENCE: Level IV.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".