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3 QUESTIONS ON . . . Long-Term Financial Effects of Surviving Cancer as an AYA

2025· article· en· W4414561809 on OpenAlexaboutno aff
Sarah DiGiulio

Bibliographic record

VenueOncology Times · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsCancerCancer treatmentPaymentMEDLINE

Abstract

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Danielle Friedman, MD, MSFor survivors of cancer who underwent diagnosis and treatment as adolescents and young adults (AYA), what are the long-term financial implications? Compared with the general population, this information has previously not been well known. Now, researchers have conducted a population-based, retrospective, matched-cohort analysis of 93,325 cancer survivors diagnosed between ages 15 and 39 (with average diagnosis age of 32) matched to individuals without a history of cancer, and matched for several factors, including birth year, sex, migration background, geography, family composition, and income (within 5%). The primary outcome measured was annual total income adjusted for inflation. The individuals were all diagnosed in Canada, within the country's universal healthcare system. The data were published earlier this year in the Journal of Clinical Oncology (2024; doi.org/10.1200/JCO-24-02121). The data showed that cancer diagnosis led to an average loss of 5.3% in total income. Central nervous system malignancies were associated with the highest income losses (28.4% reduction in income). Hematologic, lung, gastrointestinal, and breast cancer losses were 7.7% to 16.8%. Income losses were largest in the first 5 years after cancer diagnosis but still remained high farther out for certain subgroups. Income losses ranged from 9% to 32% for survivors of hematologic and central nervous system malignancies 10 years after diagnosis. An editorial accompanying the study expands on why the new data is important, and its implications for AYA survivors outside of Canada (J Clin Oncol 2025; doi.org/10.1200/JCO-25-010). “Notably, this study was performed within a universal healthcare system and thus focuses on the indirect costs of cancer, as quantified by income loss over time. These estimates do not account for the considerable direct medical costs of cancer care faced by AYAs in settings without such healthcare provisions,” the authors note in the editorial. One of the editorial's co-authors, Danielle Novetsky Friedman, MD, MS, a pediatrician in Memorial Sloan Kettering's Pediatric Long-Term Follow-Up Program, shared additional thoughts on the research and the implications for patients in the U.S. 1 Why did you and your colleagues write this editorial now? “We wrote the editorial to emphasize that financial toxicity extends well beyond treatment and warrants more attention in adolescence or young adulthood (AYA) cancer care. The study gave us a timely opportunity to call for greater focus on the economic well-being of this vulnerable population. “The research adds to the growing body of evidence showing that a cancer diagnosis in AYAs carries lasting economic consequences that persist well beyond treatment. While earlier studies have comprehensively demonstrated the short-term financial burdens of a cancer diagnosis, this study illustrates the long-term economic effects that persist years after diagnosis, even in a universal healthcare system. “Prior to this study, most of what was known about the financial toxicity of cancer focused on adults or pediatric cancer survivors, especially within non-universal healthcare systems like the U.S. This study extends the existing evidence by demonstrating that even with care covered, a cancer diagnosis can disrupt employment trajectories and long-term earning potential. “This work highlights the need for routine screening for financial distress among survivors. Early and continued discussions of financial health can make a significant difference in helping survivors navigate long-term challenges.” 2 Given that the analysis was conducted in Canadian patients within a universal healthcare system, what conclusions apply to patients in other systems with different cost models, such as the U.S.? “This point is crucial because it shows that healthcare coverage is critical, but still not enough to protect cancer survivors from long-term economic challenges. This shows that even when care is covered, cancer can derail education and career paths, particularly among AYAs with cancer, leading to long-term income loss. “In systems without universal healthcare, these impacts may be even more severe and highlights the need for multi-level interventions.” 3 What are some solutions that might help address these issues? “Several strategies to mitigate these financial long-term effects are currently being evaluated in the research and clinical space, including expanded financial navigation services during and after treatment, financial toxicity ‘tumor boards,’ and expanded educational/vocational reintegration support for cancer survivors. For AYA survivors, these interventions need to be tailored to their developmental stage and unique needs, considering both the immediate and downstream effects of cancer. “I do think the oncology community has a role to play in addressing and ameliorating this issue. There are ongoing efforts to integrate financial toxicity screening into acute and survivorship care as well as advocating for multidisciplinary care models that account for the immediate and long-term costs of care. Fundamentally, however, there must be collaboration with public health and policy leaders to advocate for systemic changes in this area. “Bridgette Thom, PhD, Assistant Professor at the University of North Carolina School of Social Work at UNC-Chapel Hill, and I are currently collaborating on a pilot study aimed at improving AYA survivors' ability to understand and manage the financial aspects of cancer care and build financial resilience among young adult survivors. Future research should track survivors over time and across different healthcare systems, and test interventions like financial counseling and educational re-entry programs.” Sarah DiGiulio is a contributing writer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.298
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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