Cumulative incidence of household material hardship and income loss as measures of financial toxicity during pediatric acute lymphoblastic leukemia (ALL) treatment: A report from the DFCI ALL 16-001 Trial
Bibliographic record
Abstract
Abstract Introduction: Over 90% of children with acute lymphoblastic leukemia (ALL) will survive their cancer in the context of >2 years of multi-agent chemotherapy. This intensive treatment paradigm may lead to significant treatment-related financial toxicity for families— an outcome highly relevant to long-term child and family well-being. The magnitude and trajectories of financial toxicity are unknown in pediatric oncology, and necessary to inform optimal screening and intervention strategies for children with cancer and their families. Development of new household material hardship ([HMH], housing, food, or utility insecurity) or income loss during cancer treatment are concrete, targetable metrics of financial toxicity. We present results from Dana-Farber Cancer Institute (DFCI) ALL Consortium Trial 16-001, the first pediatric oncology clinical trial to systematically collect longitudinal HMH and household income data as an embedded correlative trial aim. Methods: DFCI 16-001 (NCT03020030) enrolled children ages 1-<22 years with de novoB- or T-cell ALL at 8 US and Canadian centers from 2017-2021. Participants <18 years were eligible to opt-in to the correlative HMH study at time of initial trial consent. Parents/guardians of participants completed surveys within 32-days of trial enrollment and longitudinally at 6-, 12-, and 24-months. Financial toxicity was the primary endpoint of interest for this secondary analysis, defined by two variables: (1) development of any new HMH domain (food, housing, or utility insecurity) at 6-24 months as compared to baseline and (2) catastrophic income loss defined as ≥25% annual household income loss at 6-24 months compared to baseline. Cumulative incidence of new HMH and catastrophic income loss in the presence of competing risks of coming off study (off-protocol therapy, death, relapse) were estimated using the Aalen-Johansen method. A subcohort analysis was conducted among participants who reported no HMH at baseline. Multivariable competing risk models evaluated the association of patient, household, and disease characteristics with risk of developing new HMH. Results: Among 422 participants (15% Hispanic and 7% non-Hispanic Black, 23% single-parent household, 40% annual household income <200% federal poverty level) with evaluable baseline survey data, 115 (27%) reported HMH at baseline. The cumulative incidence of any new HMH was 19.3% (95% CI: 15.9-23.5) at 6-months, 27.7% (95% CI: 23.8-32.4) at 12-months, and 30.0% (95% CI: 25.9-34.7) at 24-months. The cumulative incidence of catastrophic income loss was 20.3% (95% CI: 16.7-24.7) at 6-months, 28.6% (95% CI: 24.5-33.5) at 12-months, and 31.5% (95% CI: 27.2-36.5) at 24-months. Among the subcohort of 307 families with no baseline HMH, the cumulative incidences of any new HMH or catastrophic income loss at 24-months were 24.3% (95% CI: 19.9-29.6) and 27.9% (95% CI: 23.1-33.7), respectively. In multivariable modeling, children who identified as non-Hispanic Black (RR 3.5, 95% CI: 1.7-7.3), lived in a single-parent household (RR 2.1, 95% CI: 1.3-3.2), preferred a non-dominant language (i.e. non-English for US participants and non-English/French for Canadian participants, RR 2.1, 95% CI: 1.2-3.6), or had baseline household income <200% federal poverty level (RR 1.8, 95% CI: 1.1-2.9) were more likely to develop new HMH during treatment. Conclusion: Nearly a third of families of children receiving chemotherapy for ALL develop catastrophic financial toxicity during therapy including new unmet basic needs such as food insecurity, or ≥25% income loss. A majority experience this financial toxicity by 6-months into treatment, identifying a key inflection point for potential intervention. Importantly, nearly 1 in 4 families who had no HMH at the time of their child's diagnosis developed HMH during therapy. These findings stress the clinical importance of longitudinal financial screening over the course of cancer treatment, given the high prevalence of new material needs that arise and the potential to address these needs with family-centered interventions. Future work will assess the impact of early financial toxicity on clinical outcomes in this population and focus on the development of interventions to mitigate the profound financial toxicity impacting families during leukemia care.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".