Association of Medicaid expansion with five-year survival after cancer diagnosis.
Bibliographic record
Abstract
11006 Background: Medicaid expansion is associated with improvements in early detection, access to treatment, and increased 2-year cancer survival. However, the association between Medicaid expansion and longer-term survival outcomes in newly diagnosed cancer patients remains understudied. Methods: Patients aged 18-59 years newly diagnosed with first primary cancers in 2007-2008 and 2014-2015 living in 25 states (AZ, AR, CA, CO, CT, DE, HI, IL, IA, KY, MD, MI, MN, NV, NH, NJ, NM, NY, ND, OH, OR, RI, WA, WV) that expanded Medicaid in 2014 and 12 states (AL, FL, GA, MS, MO, NC, OK, SC, TN, TX, WI, WY) that had not expanded Medicaid by the end of 2020 were obtained from the Cancer Incidence in North America (CiNA) Survival dataset compiled by the North American Association of Central Cancer Registries. Cases were stratified by cancer type, race and ethnicity, census-tract poverty level, and rurality. Difference-in-differences analysis was used to examine the association of Medicaid expansion with changes in 5-year observed overall and cause-specific survival (CSS) based on multivariable flexible parametric survival models adjusted for age group, sex, race and ethnicity, census tract–level poverty, rurality, state, and year of diagnosis. Results: A total of 1,256,349 individuals were diagnosed with cancer in Medicaid expansion (N = 698,870) and non-expansion states (N = 557,479) during the study period. The 5-year overall survival increased from 65.0% to 73.4% in expansion states and from 61.0% to 70.0 in non-expansion states, leading to a non-significant net increase of 0.21 percentage points (95%CI: -0.11,0.53) in expansion states after adjusting for sociodemographic factors. Increases in observed and cause-specific survival were greatest in expansion states for cancers of the pancreas (observed: 1.86ppt, 95%CI: 0.33ppt – 3.39ppt; CSS: 2.33ppt, 95%CI: 0.51ppt – 4.14ppt), colon and rectum (observed: 1.55ppt, 95%CI: 0.45ppt – 2.65ppt; CSS: 1.64ppt, 95%CI: 0.53ppt – 2.76 ppt), and lung (observed: 1.19ppt, 95%CI: 0.32ppt – 2.07ppt; CSS: 1.16ppt, 95%CI: 0.09ppt – 2.24ppt). The net increase associated with Medicaid expansion was also prominent among non-Hispanic Black patients (observed: 1.25ppt, 95%CI: 0.30ppt – 2.19ppt; CSS:0.80ppt, 95%CI: -0.10ppt – 1.70ppt), people living in the most deprived area (observed: 1.21ppt, 95%CI: -0.14ppt – 2.56ppt; CSS:1.45ppt, 95%CI: 0.16ppt – 2.75ppt), and rural communities (observed: 2.30ppt, 95%CI: -0.30ppt – 4.88ppt; CSS:2.40ppt, 95%CI: -0.03ppt – 4.83ppt). Conclusions: Medicaid expansion was associated with greater increases in 5-year observed and cause-specific survival for Non-Hispanic Black individuals, individuals living in the most deprived area, and rural communities. These findings reinforce the importance of Medicaid expansion in reducing disparities in cancer survival outcomes.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".