Economic consequences of cancer in adolescents and young adults
Bibliographic record
Abstract
Cancer among adolescents and young adults (AYAs), defined as those aged 15–39 years, occurs during a critical developmental period as individuals exit educational roles, establish careers, and achieve financial independence. This makes AYAs vulnerable to significant disruptions in their economic trajectories if diagnosed with cancer. Through four related studies, this thesis addresses the knowledge gaps on the economic outcomes of AYAs surviving cancer compared with the general population. The first study presents a systematic review to synthesize published evidence on the socioeconomic outcomes of AYAs surviving cancer. The review identified no evidence of a difference in educational attainment among the limited published studies. However, survivors are more likely to be unemployed, earn less income, and require social assistance for income supplementation. Studies two and three use a population-based difference-in-difference design to estimate the longitudinal financial impacts of cancer among AYAs using the Canadian Cancer Registry linked to personal income tax data. Findings demonstrate that cancer causes a significant income reduction that persists throughout survivorship compared to cancer-free individuals. Income losses are largest during the first five years after diagnosis, and there is significant heterogeneity between disease subtypes. AYA men experience larger income reductions than women, and younger-aged men and older-aged women are most vulnerable to income losses. The fourth study utilizes a linkage of the Canadian Community Health Survey and the Canadian Cancer Registry to examine the mediating role of educational attainment on the relationship between cancer diagnosed during early adulthood and subsequent employment participation. Results demonstrate that cancer survivors have a higher likelihood of unemployment compared to cancer-free individuals, and that educational attainment mediates only a small statistically insignificant proportion of this relationship. Overall, this dissertation demonstrates the substantial socioeconomic burden of cancer among AYAs. Clinical implications of this work include incorporating financial screening and navigation into the clinical care continuum. It also serves as a foundation for decision-makers to develop public health interventions that support employment reintegration and financial assistance programs to mitigate the resultant economic disparities. Future intervention strategies must account for cancer type and developmental life stage to effectively target the most vulnerable AYA populations.
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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 | 0.005 |
| 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 | 0.000 |
| 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".