The Impact of High-deductible Health Plans on Men and Women
Bibliographic record
Abstract
BACKGROUND: Prior studies show that men are more likely than women to defer essential care. Enrollment in high-deductible health plans (HDHPs) could exacerbate this tendency, but sex-specific responses to HDHPs have not been assessed. We measured the impact of an HDHP separately for men and women. METHODS: Controlled longitudinal difference-in-differences analysis of low, intermediate, and high severity emergency department (ED) visits and hospitalizations among 6007 men and 6530 women for 1 year before and up to 2 years after their employers mandated a switch from a traditional health maintenance organization plan to an HDHP, compared with contemporaneous controls (18,433 men and 19,178 women) who remained in an health maintenance organization plan. RESULTS: In the year following transition to an HDHP, men substantially reduced ED visits at all severity levels relative to controls (changes in low, intermediate, and high severity visits of -21.5% [-37.9 to -5.2], -21.6% [-37.4 to -5.7], and -34.4% [-62.1 to -6.7], respectively). Female HDHP members selectively reduced low severity emergency visits (-26.9% [-40.8 to -13.0]) while preserving intermediate and high severity visits. Male HDHP members also experienced a 24.2% [-45.3 to -3.1] relative decline in hospitalizations in year 1, followed by a 30.1% [2.1 to 58.1] relative increase in hospitalizations between years 1 and 2. CONCLUSIONS: Initial across-the-board reductions in ED and hospital care followed by increased hospitalizations imply that men may have foregone needed care following an HDHP transition. Clinicians caring for patients with HDHPs should be aware of sex differences in response to benefit design.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".