Medical Financial Hardship in Survivors of Adolescent and Young Adult Cancer in the United States
Bibliographic record
Abstract
BACKGROUND: Cancer and its treatment can result in lifelong medical financial hardship, which we aimed to describe among adult survivors of adolescent and young adult (AYA) cancers in the United States. METHODS: We identified adult (aged ≥18 years) survivors of AYA cancers (diagnosed ages 15-39 years) and adults without a cancer history from the 2010-2018 National Health Interview Surveys. Proportions of respondents reporting measures in different hardship domains (material [eg, problems paying bills], psychological [eg, distress], and behavioral [eg, forgoing care due to cost]) were compared between groups using multivariable logistic regression models and hardship intensity (cooccurrence of hardship domains) using ordinal logistic regression. Cost-related changes in prescription medication use were assessed separately. RESULTS: A total of 2588 AYA cancer survivors (median = 31 [interquartile range = 26-35] years at diagnosis; 75.0% more than 6 years and 50.0% more than 16 years since diagnosis) and 256 964 adults without a cancer history were identified. Survivors were more likely to report at least 1 hardship measure in material (36.7% vs 27.7%, P < .001) and behavioral (28.4% vs 21.2%, P < .001) domains, hardship in all 3 domains (13.1% vs 8.7%, P < .001), and at least 1 cost-related prescription medication nonadherence (13.7% vs 10.3%, P = .001) behavior. CONCLUSIONS: Adult survivors of AYA cancers are more likely to experience medical financial hardship across multiple domains compared with adults without a cancer history. Health-care providers must recognize this inequity and its impact on survivors' health, and multifaceted interventions are necessary to address underlying causes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".