Burden of out-of-pocket spending among high-deductible health plan members with metastatic breast cancer.
Bibliographic record
Abstract
1029 Background: 50% of workers have high-deductible health plans (HDHP) that require major outofpocket (OOP) spending for cancerrelated care. The OOP burden among patients with advanced cancer in HDHPs is unknown. Our objective was to estimate OOP spending for women with metastatic breast cancer (mbc) stratified by health plan type. Methods: Our data source was administrative health insurance claims and enrollment data of members insured though a large national health plan. We included 7142 women age 25-64 with mbc who had at least 6 months enrollment before the diagnosis and at least 12 months followup. We used a time series design and plotted OOP spending stratified by HDHP vs low-deductible plan. Primary outcome measures included: (1) 20042012 calendar trends in total annual OOP spending, (2) monthly total OOP spending in the 6 months before and 24 months after women were diagnosed with mbc, and (3) monthly total OOP spending in the last 6 months of life. Plots were adjusted for age, socioeconomic status, race/ethnicity, and US region of residence, and we then conducted linear regression to assess for statistical significance of trends. Results: In 2004, average annual OOP spending for women with mbc cancer in low-deductible health plans was $1196.2 and increased to $2570 in 2012, a yearly increase of $159.2 (113.2205.2). For women in HDHP average OOP spending in 2004 amounted to $2648 and increased to $3736.4 in 2012, representing an annual increase of $160.4 per year (105.4215.4) Average OOP spending per person month peaked in the month of diagnosis to $1633.8 for women in HDHPs and to $643 among low-deductible plan members. Average OOP spending in the last 6 months of life were $285.7 per person month among low-plan ($1714.2 per 6 months) and $607.3 among HDHP ($3644 per 6 months). Conclusions: To our knowledge, this is the first analysis to estimate OOP spending for women with mbc accounting for enrollment in HDHPs versus low-deductible plans. We found that OOP spending is increasing over time and is high in the last 6 months of life. HDHP members with mbc faced much higher OOP spending than women in traditional plans across all analyses. Findings raise concerns that HDHPs could worsen access to mbc treatments.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".