Breast Cancer Diagnosis and Treatment After High-Deductible Insurance Enrollment
Bibliographic record
Abstract
Purpose High-deductible health plans (HDHPs) require substantial out-of-pocket spending and might delay crucial health services. Breast cancer treatment delays of as little as 2 months are associated with adverse outcomes. Methods We used a controlled prepost design with survival analysis to assess timing of breast cancer care events among 273,499 women age 25 to 64 years without evidence of breast cancer before inclusion. Women were included if continuously enrolled for 1 year in a low-deductible ($0 to $500) plan followed by up to 4 years in a HDHP (at least $1,000 deductible) after an employer-mandated switch. Study inclusion was on a rolling basis, and members were followed between 2003 and 2012. The comparison group comprised 2.4 million contemporaneously matched women whose employers offered only low-deductible plans. Measures were times to first diagnostic breast imaging (diagnostic mammogram, breast ultrasound, or breast magnetic resonance imaging), breast biopsy, incident early-stage breast cancer diagnosis, and breast cancer chemotherapy. Outcomes were analyzed by using Cox models and adjusted for age-group, morbidity score, poverty level, US region, index date, and employer size. Results After the index date, HDHP members experienced delays in receipt of diagnostic imaging (adjusted hazard ratio [aHR], 0.95; 95% CI, 0.94 to 0.96), biopsy (aHR, 0.92; 95% CI, 0.89 to 0.95), early-stage breast cancer diagnosis (aHR, 0.83; 0.78 to 0.90), and chemotherapy initiation (aHR, 0.79; 95% CI, 0.72 to 0.86) compared with the control group. Conclusion Women switched to HDHPs experienced delays in diagnostic breast imaging, breast biopsy, early-stage breast cancer diagnosis, and chemotherapy initiation. Additional research should determine whether such delays cause adverse health outcomes, and policymakers should consider selectively reducing out-of-pocket costs for key breast cancer services.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".