Medical financial hardship between young adult cancer survivors and matched individuals without cancer in the United States
Bibliographic record
Abstract
BACKGROUND: Young adult cancer survivors face medical financial hardships that may lead to delaying or forgoing medical care. This study describes the medical financial difficulties young adult cancer survivors in the United States experience in the post-Patient Protection and Affordable Care Act period. METHOD: We identified 1009 cancer survivors aged 18 to 39 years from the National Health Interview Survey (2015-2022) and matched 963 (95%) cancer survivors to 2733 control individuals using nearest-neighbor matching. We used conditional logistic regression to examine the association between cancer history and medical financial hardship and to assess whether this association varied by age, sex, race and ethnicity, and region of residence. RESULTS: Compared with those who did not have a history of cancer, young adult cancer survivors were more likely to report material financial hardship (22.8% vs 15.2%; odds ratio = 1.65, 95% confidence interval = 1.50 to 1.81) and behavior-related financial hardship (34.3% vs 24.4%; odds ratio = 1.62, 95% confidence interval = 1.49 to 1.76) but not psychological financial hardship (52.6% vs 50.9%; odds ratio = 1.07, 95% confidence interval = 0.99 to 1.16). Young adult cancer survivors who were Hispanic or lived in the Midwest and South were more likely to report psychological financial hardship than their counterparts. CONCLUSIONS: We found that young adult cancer survivors were more likely to experience material and behavior-related financial hardship than young adults without a history of cancer. We also identified specific subgroups of young adult cancer survivors that may benefit from targeted policies and interventions to alleviate medical financial hardship.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".