The Influence of Poverty and Rurality on Colorectal Cancer Survival by Race/Ethnicity: An Analysis of SEER Data with a Census Tract-Level Measure of Persistent Poverty
Bibliographic record
Abstract
Purpose: Because of shared mechanisms such as decreased access to health care, rurality and poverty may act synergistically to decrease colorectal cancer (CRC) survival. Methods: We conducted a retrospective cohort analysis of SEER data (22 registries) with census tract-level measures of poverty/rurality for the period 2006–2015. Multivariable Cox proportional hazard regressions were applied to examine the independent and intersectional associations of persistent poverty and rurality on 5-year cause-specific CRC survival across five racial/ethnic groups. Results: Among 532,868 CRC patients, non-Hispanic Blacks (NHB) demonstrated lower 5-year survival probability (64.2% vs. 68.3% in non-Hispanic Whites [NHW], 66.5% in American Indian/Alaska Natives [AI/AN], 72.1% in Asian/Pacific Islanders, and 68.7% in Hispanic groups) (p-value < 0.001). In adjusted analysis, CRC patients living in rural areas with poverty were at a 1.2–1.6-fold increased risk of CRC death than those who did not live in these areas in five racial/ethnic groups. In particular, AI/AN patients living in rural areas with poverty were 66% more likely to die from CRC (95% CI, 1.32, 2.08). Conclusions: CRC patients who live in rural or poverty areas in SEER areas in the U.S. have a poorer survival compared with those who do not live in such areas regardless of race/ethnicity. Significantly greater risk of CRC death was observed in AI/ANs. Impact: Patient navigators, community education or screening, and other health care system interventions may be helpful to address these disparities by socioeconomic status, race, and geographic residence. Multi-level interventions aimed at institutional racism and medical mistrust may also be helpful.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 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".