S546 Impact of Insurance Affordability on Access to Colorectal Cancer Screening
Bibliographic record
Abstract
Introduction: Colorectal cancer poses a significant global health burden as one of the leading causes of cancer-related morbidity and mortality worldwide. Early detection through regular screening is essential in reducing the incidence and mortality rates associated with the disease. However, despite the proven benefits of screening, access remains a critical concern, particularly for vulnerable populations. This study aims to examine the association between insurance affordability and compliance with age-appropriate colorectal screening. Methods: Using the National Health Interview Survey data from 2017 to 2021,we sampled adults aged 50 and above, both with and without health insurance, and those who have and have not undergone age-appropriate colorectal cancer screening within this demographic. We conducted a chi square analysis of the categorical variables and further performed a Fisher's Exact Test to explore the correlation between insurance status and colorectal screening rates. Results: A total of 12,428 adults were included in this study. Our analysis revealed a statistically significant association between insurance affordability and colorectal screening (p = 0.026). 89.3% of uninsured age-appropriate adults had colon cancer screening, while 97.9% of insured age-appropriate adults had colon cancer screening. This suggests a higher likelihood of age-appropriate colorectal screening among insured individuals compared to their uninsured counterparts. Conclusion: The results suggest that insurance status significantly impacts colorectal screening rates, with insured individuals, particularly those with private insurance, being more likely to undergo screening. The findings highlight a significant disparity in colorectal screening rates based on insurance status, underscoring the importance of insurance affordability in ensuring access to preventive health services. Policies aimed at increasing insurance coverage and affordability may play a crucial role in enhancing colorectal screening and improving public health 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".