The Role of Commercial Health Insurance Characteristics in Bariatric Surgery Utilization
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to understand relationships among insurance plan type, out-of-pocket cost sharing, and the utilization of bariatric surgery among commercially insured patients. BACKGROUND: Only 1% of eligible persons undergo bariatric operations, and this underutilization is often attributed to lack of insurance coverage. But even among the insured, underinsurance is now recognized as a major barrier to accessing medical care. The relationships among commercial insurance design, out-of-pocket cost sharing, and elective surgery utilization, particularly in bariatrics, are not well understood. METHODS: Retrospective review of 73,002 commercially insured members of the IBM MarketScan commercial claims database who underwent bariatric surgery from 2014 to 2017. The exposure variables were insurance plan type and out-of-pocket cost sharing. The outcome was utilization of bariatric surgery. We also examined seasonal trends in bariatric surgery utilization stratified by average levels of cost sharing. RESULTS: Utilization of bariatric surgery was higher in plans with lower cost sharing, such as PPOs (20 operations/100,000 enrollees) than in HDHPs (high-deductible health plans, 12.1 operations/100,000 enrollees). Overall, every $1000 increase in cost sharing was associated with 5 fewer bariatric operations per 100,000 insured lives; this association was strongest in plans with high cost sharing (high-deductible and consumer-directed health plans). Members of all plan types had higher surgical utilization in quarter 4 relative to quarter 1 of each year; these seasonal variations were also most pronounced in plans with high cost sharing. CONCLUSIONS: Insurance plan types with higher cost sharing have lower utilization of bariatric surgery. Underinsurance may represent a newly identified barrier to surgical care that should be addressed by advocates and policymakers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".