Factors Associated With Suboptimal Colorectal Cancer Screening in US Immigrants
Bibliographic record
Abstract
BACKGROUND: Our objectives were to: (1) compare colorectal cancer screening (CRCS) among US born citizens (USBs), naturalized citizens (NACs), and noncitizens (NOCs) and (2) evaluate clinical factors and potential barriers associated with CRCS in these populations. METHODS: Screening-eligible patients were identified from the 2007 California Health Interview Survey. Up-to-date CRCS was defined as a fecal occult blood test within 1 year, a sigmoidoscopy within 5 years, or a colonoscopy within 10 years. Using logistic regression, we determined the effects of immigrant status on CRCS. Stratified analyses based on location of residence, health insurance status, and English proficiency were conducted. RESULTS: A total of 30,434 average-risk adults aged 50 years or older completed the survey. Only 67% of USBs, 61% of NACs, and 46% of NOCs underwent CRCS. Advanced age, male sex, high-income earners, nonsmokers, and those who were married or visited their physicians frequently were more likely to receive CRCS (all P < 0.05). Compared with USBs, both NACs and NOCs showed decreased odds of CRCS (odds ratio 0.88, 95% confidence interval, 0.74-1.06 and odds ratio 0.68, 95% confidence interval, 0.53-0.88, respectively; P = 0.011). Stratified analyses revealed that the associations between immigrants and decreased CRCS were more prominent for those who lived in rural areas, lacked insurance, or were not English proficient. CONCLUSIONS: CRCS remains suboptimal, especially in new US immigrants. Improving health care access and mitigating language barriers may minimize this disparity.
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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 | 0.001 | 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) | 0.001 | 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".