Long-term survival with sickle cell disease: a nationwide cohort study of Medicare and Medicaid beneficiaries
Bibliographic record
Abstract
To our knowledge, we report the first population-based period life table, the expected lifetime survival for Medicare and Medicaid beneficiaries with sickle cell disease (SCD), and the disparities in survival by insurance types in the United States. We constructed a retrospective cohort of individuals with diagnosed SCD receiving common care (any real-world patterns of care except transplant) based on nationwide Medicare and Medicaid claim data (2008-2016), covering beneficiaries in all 50 states. We analyzed lifetime survival probabilities using Kaplan-Meier curves and projected life expectancies at various ages for all, stratified by sex and insurance types. Our analysis included 94 616 individuals with SCD that have not undergone any transplant. Life expectancy at birth was 52.6 years (95% confidence interval: 51.9-53.4). Compared with the adults covered by Medicaid only, those covered by Medicare for disabilities or end-stage renal disease and those dually insured by Medicare and Medicaid had significantly worse life expectancy. Similarly, for beneficiaries aged ≥65 years, these 2 insurance types were associated with significantly shorter life expectancy than those enrolled in Medicare old age and survivor's insurance. Our study underscores the persistent life expectancy shortfall for patients with SCD, the burden of premature mortality during adulthood, and survival disparities by insurance status.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".