Cancer Screening Among U.S. Medicaid Enrollees with Chronic Comorbidities or Residing in Long-Term Care Facilities
Bibliographic record
Abstract
BACKGROUND: Ensuring appropriate cancer screenings among low-income persons with chronic conditions and persons residing in long-term care (LTC) facilities presents special challenges. This study examines the impact of having chronic diseases and of LTC residency status on cancer screening among adults enrolled in Medicaid, a joint state-federal government program providing health insurance for certain low-income individuals in the U.S. METHODS: We used 2000-2003 Medicaid data for Medicaid-only beneficiaries and merged 2003 Medicare-Medicaid data for dually-eligible beneficiaries from four states to estimate the likelihood of cancer screening tests during a 12-month period. Multivariate regression models assessed the association of chronic conditions and LTC residency status with each type of cancer screening. RESULTS: LTC residency was associated with significant reductions in screening tests for both Medicaid-only and Medicare-Medicaid enrollees; particularly large reductions were observed for receipt of mammograms. Enrollees with multiple chronic comorbidities were more likely to receive colorectal and prostate cancer screenings and less likely to receive Papanicolaou (Pap) tests than were those without chronic conditions. CONCLUSIONS: LTC residents have substantial risks of not receiving cancer screening tests. Not performing appropriate screenings may increase the risk of delayed/missed diagnoses and could increase disparities; however, it is also important to consider recommendations to appropriately discontinue screening and decrease the risk of overdiagnosis. Although anecdotal reports suggest that patients with serious comorbidities may not receive regular cancer screening, we found that having chronic conditions increases the likelihood of certain screening tests. More work is needed to better understand these issues and to facilitate referrals for appropriate cancer screenings.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".