1446Forgoing needed medical care among long-term survivors of childhood cancer: Racial/ethnic-insurance disparities
Bibliographic record
Abstract
Abstract Background Among adult childhood cancer survivors, the relationship between race/ethnicity and health insurance status, as a contributor to disparities in healthcare utilization, is poorly understood. Methods We examined racial/ethnic-related disparities by insurance status in “forgoing needed medical care in the last year due to finances” using 3,964 adult childhood cancer survivors (3310 non-Hispanic/Latinx White, 562 non-Hispanic/Latinx Black, and 92 Hispanic/Latinx) participating in the St. Jude Lifetime Cohort Study (SJLIFE). Multivariable logistic regression analyses, guided by Andersen’s Healthcare Utilization Model, were adjusted for “predisposing” (age, sex, childhood cancer diagnosis, cancer treatment, surgery, and treatment era) and “need” (perceived health status) factors. Additional adjustment for income/education and chronic health conditions was considered. Results The risk of forgoing care was highest among non-Hispanic/Latinx Blacks and lowest among Hispanics/Latinxs for each insurance status. Among privately-insured survivors, relative to non-Hispanic/Latinx Whites, non-Hispanic/Latinx Blacks were more likely to forgo care (adjusted OR: 1.82, 95% CI: 1.30–2.54): this disparity remained despite additional adjustment for income/education (adjusted OR: 1.43, 95% CI: 1.01–2.01). In contrast, publicly-insured survivors, regardless of race/ethnicity, had similar risk of forgoing care as privately-insured non-Hispanic/Latinx Whites. All uninsured survivors had high risk of forgoing care. Additional adjustment for chronic health conditions did not alter these results. Conclusions The findings of this study show that provision of public insurance to all childhood cancer survivors may diminish racial/ethnic disparities in forgoing care that exist among the privately-insured and reduce the risk of forgoing care among uninsured survivors to that of privately-insured non-Hispanic/Latinx Whites. Key messages Providing publicly funded health insurance coverage to childhood cancer survivors can reduce disparities in forgoing medical 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".