Feasibility of collecting social determinants of health data in the frontline pediatric acute lymphoblastic leukemia (ALL) trial AALL1731: A report from the Children's oncology group
Bibliographic record
Abstract
Abstract Background : Poverty-exposed children with B-acute lymphoblastic leukemia (B-ALL) are more likely to relapse, and long-term survivors are more likely to suffer chronic health conditions including neurocognitive late effects. Caregivers of these children are twice as likely to experience severe psychological distress during ALL therapy. Sociodemographic data routinely collected on Children's Oncology Group (COG) trials have historically included race, ethnicity, insurance, and ZIP code. These data elements can proxy exposure to adverse social determinants of health (SDOH), including poverty, and identify outcome disparities—but are not modifiable targets for intervention. Whether SDOH data collection is feasible in a large scale, groupwide ALL trial is unknown. We report data from AALL1731, the first COG ALL trial to include systematic collection of parent-reported SDOH as part of an optional, longitudinal correlative study of neurocognitive late effects. Methods : AALL1731 (NCT03914625) enrolled children aged 1-9 years with NCI standard risk B-ALL from 6/2019 to 7/2024. In 9/2020, AALL1731 was amended to include an opt-in, longitudinal, correlative “Household Material Hardship (HMH) and Neurocognitive Late Effects” aim to evaluate the association between SDOH-exposures and neurocognitive late effects. The primary exposure of interest was HMH—defined as food, housing, utility or transportation insecurity. Participants who spoke English, Spanish or French, enrolled at a US or Canadian site, and without Down Syndrome were eligible to opt-in to the correlative study at time of AALL1731 consent. Participation included both parent/guardian completion of a 75-item survey and child neurocognitive testing at 4-timepoints: baseline (before end-induction), start of maintenance, end of therapy, and 1-year post-therapy. Written surveys were self-completed or read-aloud by site personnel with an interpreter as needed. Families were not remunerated for participation. We report the feasibility of baseline SDOH data collection in a groupwide ALL trial, defined as the proportion of correlative study participants with completed baseline surveys and acceptability defined as the proportion of surveys with evaluable HMH data (the primary exposure of interest). Results : As of 6/30/24, 1487/2040 (73%) eligible participants opted-in to the HMH and Neurocognitive Late Effects aim. Of 1487 correlative study participants,1102 (74%) completed the baseline survey a median of 20 days (Q1-Q3: 10-27) from trial consent across 179 sites. Among completed surveys, 1075 (97.5%) had evaluable HMH data. Sociodemographic characteristics were similar for correlative study participants and non-participants: age (median 5.30 vs 5.45 years), sex (female 46% vs 46%), race/ethnicity (Hispanic 25% vs 23%; Asian 4% vs 7%; Black 7% vs 4%; non-Hispanic White 51% vs 46%; unknown 14% vs 20%), and insurance (Medicaid-only 34% vs 29%). Thirty-three percent of respondents reported HMH exposure at baseline—with housing (24%) and food insecurity (16%) being most common. Primary correlative aim analyses and secondary analyses of SDOH and outcomes are pending mature trial data. Conclusions : Collection of baseline parent-reported SDOH data within a COG ALL correlative study is feasible and acceptable during the first month of induction therapy based on high survey completion and minimal data missingness across 179 sites. One in three children were HMH-exposed at trial entry, highlighting the high prevalence of clinically relevant adverse SDOH in a patient population for whom interventions to improve outcomes may be warranted. Notably, a 74% SDOH survey completion rate is lower than previously reported in COG Neuroblastoma and Dana-Farber ALL Consortium trials (88% and 96%), which collected SDOH survey data as stand-alone correlative aims. A brief (11-item), single-timepoint SDOH survey will be evaluated in future COG trials not linked to other correlative data requirements to minimize family time investment and maximize data collection. Systematic integration of SDOH data collection as an expected component of future trial design will support investigation of mechanisms underlying treatment-failure and late effects of therapy and to identify children at risk of inferior outcomes who may benefit from targeted health equity interventions.
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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 | 0.063 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".