Insurance status and cancer disparities in adolescents and young adults age 15 to 39: National Cancer Data Base, 1998-2003.
Bibliographic record
Abstract
6011 Background: Since the mid-1970s there has been little progress in improving cancer survival for adolescents and young adults (AYAs, defined by the National Cancer Institute as individuals 15 to 39 years of age), in contrast to the substantial improvements for children and older adults. The association between insurance status and cancer disparities (stage at diagnosis, survival, and treatment) in AYA patients has not been examined in a national sample. Methods: The National Cancer Data Base, a national hospital-based cancer registry, was used to examine insurance status and cancer disparities in 177,359 AYA cancer patients. Cases were diagnosed during 1998−2003 and followed for vital status through 2008. We examined the association between insurance status and stage at diagnosis, stage-specific survival, and receipt of the most frequently used stage-specific treatments for common AYA sites (thyroid, female breast, non-Hodgkin lymphoma, and acute myeloid leukemia). Results: Insurance status was strongly related to cancer disparities in AYA patients. For example, compared to patients with private insurance, uninsured patients were 1.71 times more likely to be diagnosed at stage IV (95% confidence interval [CI], 1.63−1.79), had higher risk of death within every stage [stage I, hazard ratio (HR) (95% CI), 2.43 (2.13−2.76); stage II, 1.87 (1.70−2.06); stage III, 1.55 (1.43−1.68); stage IV, 1.39 (1.31−1.48); and unstaged (leukemias, CNS tumors, etc.), 1.67 (1.58−1.76), and for the four sites listed above, were less likely to receive the most frequently used stage-specific treatments. Insurance status remained a strong independent predictor for each of these outcomes in multivariate models adjusting for patient, hospital, and tumor factors. Conclusions: In a large national sample, insurance status was a strong independent predictor of cancer disparities in AYA patients. The Affordable Care Act of 2010 should facilitate the acquisition of adequate health insurance by AYA, who are the most under- and uninsured age group in the US population, and should contribute to remediation of such disparities.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".