Agreement between individual and neighborhood income measures in patients with colorectal cancer in Canada
Bibliographic record
Abstract
INTRODUCTION: With increasing interest in income-related differences in cancer outcomes, accurate measurement of income is imperative. Misclassification of income can result in wrong conclusions as to the presence of income inequalities. We determined misclassification between individual- and neighborhood-level income and their association with overall survival among colorectal cancer (CRC) patients. METHODS: The Canadian Census Health and Environment Cohorts were used to identify CRC patients diagnosed from 1992 to 2017. We used neighborhood income quintiles from Statistics Canada and created individual income quintiles from the same data sources to be as similar as possible. Agreement between individual and neighborhood income quintiles was measured using cross-tabulations and weighted kappa statistics. Cox proportional hazards and Lin semiparametric hazards models were used to determine the effects of individual and neighborhood income independently and jointly on survival. Analyses were also stratified by rural residence. RESULTS: A total of 103 530 CRC patients were included in the cohort. There was poor agreement between individual and neighborhood income with only 17% of respondents assigned to the same quintile (weighted kappa = 0.18). Individual income had a greater effect on relative and additive survival than neighborhood income when modeled separately. The interaction between individual and neighborhood income demonstrated that the most at risk for poor survival were those in the lowest individual and neighborhood income quintiles. Misclassification was more likely to occur for patients residing in rural areas. CONCLUSION: Cancer researchers should avoid using neighborhood income as a proxy for individual income, especially among patients with cancers with demonstrated inequalities by income.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".