The long economic shadow of a cancer diagnosis during adolescence or young adulthood
Bibliographic record
Abstract
The period of adolescence and young adulthood is characterized by numerous critical life course transitions such as from secondary to postsecondary education and workforce entry and from living with parents to living independently and often having children of their own. A cancer diagnosis during this period can have an important negative impact on long-term physical (1) and mental (2) health. However, despite vulnerability to the short- and long-term consequences of cancer on socioeconomic health and well-being, little is known about financial outcomes in survivors of adolescent and young adult (AYA) cancers (3). In this issue of the Journal, Siebinga and colleagues contribute important data to this topic in the article, “Financial outcomes of adolescent and young adult cancer survivors: a longitudinal population-based registry study” (4). The authors leverage a novel data linkage of newly diagnosed patients in the Netherlands Cancer Registry to their personal income data from Statistics Netherlands to chart the changes in income and employment levels between the period prior to a cancer diagnosis and the period following diagnosis in 2434 AYAs with cancer in comparison with 9736 AYA without a history of cancer. Relative to the comparison group, survivors of AYA cancer suffered an average decrease of 8.5% in their annual earnings, a disparity that did not disappear during the subsequent 5 years of follow-up. Further, the proportion of employed AYAs with cancer decreased by 8% after diagnosis, approximately 4 times greater than the decrease observed in the comparison group over the same time frame. Estimates from Siebinga et al. (4) likely understate the magnitude of economic losses associated with a cancer diagnosis among AYAs in the Netherlands for several reasons. First, the study examined longer-term survivors who had lived at least 5 years following diagnosis. It is well-established that care at the end-of-life is intensive (5,6), and disruptions in employment and associated income loss are likely even higher for patients who did not survive 5 years following their diagnosis. Second, a cancer diagnosis and treatment can have adverse economic consequences for the members of a patient’s family, including spouses or partners, parents, children, and siblings, who provide informal care (7,8). For employed informal family caregivers, taking care of a patient with cancer may result in missed work hours, decreased productivity or challenges in performing usual duties, lower pay, switching from full-time to part-time work, or retiring from the workforce earlier than expected, which further reduces family income and increases financial hardship. Young adults diagnosed with cancer may have children in the home; a recent study showed that minor children with a parental cancer history were more likely to have school absences and problems with medical care unaffordability compared with children without a parental cancer history (9). Other research has shown that having a sibling diagnosed with cancer during childhood is associated with financial hardship in adulthood (8), suggesting the potential for lasting consequences for members of households forced to make economic tradeoffs when caring for a patient with cancer. Finally, although income from employment is an important component of household financial security and well-being, it does not fully reflect other components of financial security, such as home ownership and retirement savings. Working-age adults with a cancer history are more likely to report fewer assets, greater debt, and lower net worth than their counterparts without a cancer history (10). This article has many strengths, among which the ability to link AYAs in the national cancer registry to their tax and employment data is particularly notable. Because the Netherlands Cancer Registry captures all patients diagnosed with a malignancy, this was a true population-based sample. Many jurisdictions are unable to do this type of research given the logistical, technological, and privacy barriers to linking cancer registry data to administrative databases. Similar work has been undertaken in Sweden, where parents of children with cancer (captured in the Swedish Childhood Cancer Registry) were linked to their income and employment data in the Longitudinal Integration Database for Health Insurance and Labor Market Studies (11). The authors observed that parents of children with cancer have a statistically significant decrease in their income at the time of their child’s cancer diagnosis and that it takes 3 years for fathers and 6 years for mothers to catch up in annual earnings with matched parents without children with cancer. Similar linkages are currently being conducted in Canada using the Canadian Cancer Registry and Revenue Canada tax files held by Statistics Canada—notably, these data include information on household and individual income. The impact of the current article is somewhat hindered by lack of availability of key data elements likely associated with the ability of cancer survivors to maintain employment and income. First, the investigators did not have access to specific chemotherapy exposures or radiation fields and doses, nor did they have data about the development of chronic physical or psychological health conditions. This limited the authors’ capacity to determine who was at greatest risk for financial hardship given the established impact of treatment exposures on the development of physical, psychological, and psychosocial late effects (12). Information about cancer type and stage is useful, but insufficient, to define risk in a specific patient. Second, the investigators were able to report on individual income but not on that of the patient’s partner or their entire household. Such information would provide a deeper understanding of the broader impact of cancer diagnoses and how other household members adjust their own work to compensate for the loss of income in a partner. Third, survivors were followed for approximately 5 years from diagnosis. Therefore, it is not possible to say whether their income or employment eventually caught up to that of the control population in later years. Finally, no data were provided on differences between those AYAs who were studying compared with those who were employed at the time of their cancer diagnosis nor on whether they were still living with parents or had moved out of their childhood home. As per the authors, some of these data are available and will be reported in future manuscripts. Are these findings from the Netherlands generalizable to other countries? Unlike the Netherlands, a country with universal health insurance coverage and a strong social safety net, millions of people in the United States lack health insurance coverage and worker protections, such as paid sick leave. More than 40% of working cancer survivors younger than age 40 years lack paid sick leave in the United States (13) and consequently face lost income for the weeks (and potentially months) needed for completing cancer treatment. Absences from work because of cancer diagnosis and treatment for patients and informal caregivers may result in job loss and loss of access to employer-based health insurance coverage, the most common type of coverage for working-age adults, further compounding household financial hardship. The Family and Medical Leave Act ensures that eligible employees can take unpaid, job-protected leave for specified medical reasons, but not all employers are covered by the Family and Medical Leave Act and not all eligible employees can afford to take unpaid leave from work. Individuals without health insurance coverage may be responsible for the entirety of the cost of their cancer care, but even individuals with health insurance coverage face substantial out-of-pocket spending of more than $6000 (in 2020 US$) in the first year following diagnosis alone (14). Accumulating evidence has demonstrated that cancer survivors are more likely to report extreme financial vulnerability, including medical debt (15), bankruptcy (15,16), food insecurity (17), and housing instability, than their counterparts without a cancer history. Thus, the adverse economic consequences of cancer diagnosis and treatment are likely greater among young adult cancer survivors in the United States than in the Netherlands. As recognition of the risk for financial hardship among AYA cancer survivors grows, the oncology community is challenged with identifying interventions to reduce this burden. As with any late effect, the first challenge is to ensure that health-care providers assess for these outcomes in all patients and that this assessment occurs throughout the cancer journey, not just at diagnosis or in survivorship. There are several screening tools for financial hardship and health-related social needs, although these have not been broadly implemented into cancer clinics (18). In the United States, financial navigators play an increasingly prominent role in helping cancer patients deal with a range of financial and insurance challenges and connecting them with available community resources. However, these resources are mostly concentrated in cancer centers within the acute cancer clinic rather than in survivor clinics, and pediatric centers and community-based practices that treat many younger AYA patients may not have access to such resources. Finally, advocacy efforts targeted at improving access to affordable cancer care, addressing health-related social needs of patients and families, and enhancing social safety nets are being championed by governmental and nongovernmental organizations (18,19). Although the incredible burden of developing cancer as an AYA can never be completely alleviated, the identification of financial hardship as an outcome in critical need of effective intervention is an important step forward. No new data were generated or analyzed for this editorial. Paul Craig Nathan, MD, MSc (Conceptualization; Writing – original draft; Writing – review & editing) and K. Robin Yabroff, PhD (Conceptualization; Writing – original draft). No funding was used for this editorial. PCN has no disclosures. KRY has served on the Flatiron Health Equity Advisory Board; all honoraria are donated to her employer, the American Cancer Society. KRY, a JNCI Deputy Editor and co-author of this editorial, was not involved in the decision to publish the editorial.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".