Social determinants of health (SDOH) and late mortality among survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS).
Bibliographic record
Abstract
10021 Background: Neighborhood-level SDOH may increase disparities in adverse cancer-related outcomes. The US CDC-constructed Social Vulnerability Index (SVI) reflects 4 SDOH domains (socioeconomic status [SES]; household composition; minority status/language; housing/transportation) and captures the vulnerability of underserved communities. The impact of neighborhood-level SDOH on late mortality among survivors of childhood cancer is not known. Methods: Analyses included 5-year survivors in the US diagnosed in 1970-1999 participating in the CCSS, a multi-institutional retrospective cohort study. We evaluated geocoded SVI quintiles (Q1 to Q5, from least to most vulnerable) based on residential addresses and personal SES factors including income, education level, and health insurance status collected at CCSS baseline. The impact of SVI and personal-level SES on all-cause and cause-specific mortality rates were evaluated using cumulative incidence and relative rates (RRs) from piecewise exponential regression models adjusted for age, sex, diagnosis age, and treatments. Results: Among 20,261 survivors with geocode data (mean age at cancer diagnosis and baseline evaluation, 7y and 24y respectively, with a mean follow up of 17y), 2,439 survivors died. All-cause late mortality was greater in survivors living in more vulnerable areas (Q5 vs. Q1, at 20y: 14.7% vs. 10.8%, P<0.001). We observed a dose-response relationship between worsening SVI and the all-cause mortality rate (Q5 vs. Q1 RR 1.52, 95% CI 1.32-1.76, P trend <0.001) as well as for mortality rates due to specific health causes (Table). Among the SDOH domains, neighborhood SES (Q5 vs. Q1 RR 1.68, 95% CI 1.45-1.95) showed the strongest association with all-cause mortality followed by household composition (RR 1.43, 95% CI 1.24-1.66). Notably, these findings remained largely consistent after adjusting for personal-level SES as well as in analyses stratified by income and insurance coverage. Conclusions: Living in socially vulnerable neighborhoods during young adulthood is associated with a ~50% increased risk for late mortality among survivors of childhood cancer and is largely unaffected by favorable personal-level SES. Policies and interventions targeting neighborhood-level SDOH during the transition to survivorship care are needed to reduce mortality risk in this population. Adjusted RRs and 95% confidence intervals for overall and cause-specific mortality. SVI All cause Subsequent neoplasm cause Cardiovascular cause Other health causes Q2 1.00 (0.88 - 1.14) 0.90 (0.74 - 1.11) 1.09 (0.76 - 1.55) 1.09 (0.85 - 1.39) Q3 1.16 (1.02 - 1.32) 1.03 (0.84 - 1.27) 1.18 (0.82 - 1.70) 1.24 (0.97 - 1.59) Q4 1.24 (1.08 - 1.42) 1.12 (0.90 - 1.39) 1.29 (0.88 - 1.90) 1.44 (1.11 - 1.86) Q5 1.52 (1.32 - 1.76) 1.35 (1.07 - 1.69) 1.54 (1.02 - 2.33) 1.83 (1.38 - 2.42) SVI Q1 (least vulnerable) is the referent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".