A hurdle and negative binomial model approach to analyzing the gender differences in diagnostic imaging utilization under high-deductible health plans
Bibliographic record
Abstract
Introduction: High-Deductible Health Plans (HDHPs) have been increasingly adopted as a cost-containment strategy in healthcare. However, their impact on the utilization of diagnostic imaging services, particularly across different genders, remains underexplored. This study explores how HDHPs enrollment affect imaging utilization rates and usage patterns among patients of different genders, and it examines the associated gender disparities across various imaging modalities. Methods: Using data from the 2010 Thomson-Reuters MarketScan Commercial Database, we conducted a quantitative analysis employing Negative Binomial Regression and Hurdle models. The models assessed the association between HDHPs enrollment and diagnostic imaging utilization, with a focus on gender-based differences in usage patterns. Results: The analysis revealed that males generally utilize diagnostic imaging services less frequently than females. After HDHPs enrollment, overall imaging utilization declined by 7%, with a more pronounced reduction observed among male enrollees. Specifically, the likelihood of initial ultrasound utilization among males dropped by 8.2% more than among females. However, once at least one imaging procedure had been initiated, gender differences in utilization among HDHPs enrollees were no longer significant. Discussion: The findings suggest that HDHPs have a gender-differentiated effects on diagnostic imaging utilization, with males experiencing a more significant reduction, especially in the initial use of diagnostic imaging and in the use of ultrasound services. These results highlight the need for gender-sensitive approaches in health insurance policy design and emphasize the importance of targeted patient education to promote equitable access and resource allocation.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".