Clinical characteristics and survival of lung cancer patients according to insurance status prior to and post implementation of the Affordable Care Act.
Bibliographic record
Abstract
e18607 Background: Insurance status affects cancer stage at diagnosis, access to treatment and survival in the US. The Affordable Care Act (ACA) was implemented to expand access to health care. The aim of this study was to determine the association of insurance status with clinical characteristics, access to surgical treatment and survival among lung cancer pts pre and post-ACA. Methods: US SEER data was obtained for 18 to 64yo pts diagnosed with lung cancer from 2007 to 2012. Pts ≥65yo were excluded due to unreliable insurance status classification. To account for the introduction of the ACA in the US in 2010, data was analyzed by years 2007-2009 vs 2010-2012. OS was evaluated over a period of 26m and the 50th percentile was estimated. Pearson’s χ2 was used to assess significance of associations with insurance status and diagnosis year, and unadjusted associations were compared using the log-rank test. HRs were estimated using Cox proportional hazards model. Results: 84,549 pts were included. Median age was 58y. Black pts were more represented in the Medicaid (23.1%) and uninsured (21.1%) groups compared to the insured group (12.9%, P < 0.001). Insured pts were less likely to present with distant disease (50.4%) and more likely to receive surgery (27.7%) than pts in the Medicaid (55.3%; 16.7%) or uninsured groups (61.9%; 13.7%; P < 0.001). Median OS was longer in the insured (16m; p < 0.001) compared with uninsured (9m) or Medicaid (10m) groups. In an adjusted Cox regression, pts in the Medicaid and uninsured groups had worse OS relative to the insured group [HR = 1.28 (95%CI: 1.25-1.31); HR = 1.25 (95%CI: 1.22-1.30); P < 0.001]. When the groups pre and post-ACA were compared, the percentage of Medicaid pts increased in the post-ACA vs pre-ACA years (21.7 vs 18.7%; p < 0.0001) and median OS showed a significant improvement in the post-ACA-years (14m) than in the pre-ACA years (13m; p = 0.0002). Conclusions: Among lung cancer pts, those in the Medicaid or uninsured groups were more likely to present with advanced disease, less likely to receive cancer-directed surgery and had worse OS. The number of Medicaid pts increased and survival rates in all insurance groups improved in the years post-ACA.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".