Long-Term Dynamic Financial Impacts Among Adolescents and Young Adults With Cancer: A Longitudinal Matched-Cohort Study
Bibliographic record
Abstract
PURPOSE: Surviving cancer has significant financial implications for adolescents and young adults (AYAs). It is unclear how cancer affects AYA income over time compared with the general population, and how this differs by subtype. METHODS: We performed a population-based retrospective matched-cohort study of AYAs age 15-39 years diagnosed from 1994 to 2013 in Canada's universal health care system. Survivors were 1-to-10 variable-ratio matched to cancer-free comparators in the year before diagnosis on birth year, sex, migration background, geography, family composition, and ±5% of income. Participants were followed until death, second cancer, loss to follow-up, 10 years after diagnosis, or December 31, 2015. The primary outcome was annual total income, inflation-adjusted to 2015 Canadian dollars (CAD). Doubly robust difference-in-difference analyses estimated relative and absolute income changes for survivors versus cancer-free peers. Analyses were conducted for cancer overall and stratified by subtype. RESULTS: A total of 93,325 survivors with an average diagnosis age of 32.0 (standard deviation [SD], 4.9) years were matched to 765,240 cancer-free peers. Mean follow-up was 8.1 (SD, 2.9) years. Cancer led to an average loss of 5.3% (95% CI, 4.3% to 6.4%), or $2,023 CAD (95% CI, $1,706 to $2,340), in total income. Losses varied by subtype, with CNS malignancies experiencing the largest reduction of 28.4% (95% CI, 23.9% to 32.6%). Hematologic, lung, GI, and breast cancer losses ranged from 7.7% to 16.8%. Income reductions were largest in the first 5 years after diagnosis. After 10 years, income losses ranged from 9% to 32% for survivors of hematologic and CNS malignancies. CONCLUSION: Cancer in AYAs leads to decreased income with varying magnitudes by subtype, with the largest burden in the first 5 years after diagnosis. Policy interventions to mitigate income inequalities among survivors can ensure stable financial well-being throughout survivorship.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".