Individual-Level and Neighborhood-Level Income Measures
Bibliographic record
Abstract
BACKGROUND: Census-based measures of income often are used as proxies for individual-level income. Yet, the validity of such area-based measures relative to 'true' individual-level income has not been fully characterized. OBJECTIVES: The objectives of this study were (1) to determine whether area-based measures of household income are a suitable proxy for self-reported household income and (2) to assess whether these measures are associated with outcomes in a cardiac disease cohort. RESEARCH DESIGN: We used a prospective cohort from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) cardiac catheterization registry. SUBJECTS: A total of 4372 patients having undergone cardiac catheterization and who also completed a 1-year follow-up questionnaire on self-reported income level were studied. MEASURES: Our measurements were survival to 2.5 years after catheterization and health-related quality of life (EuroQoL). RESULTS: Agreement between the 2 income measures generally was poor (unweighted Kappa = 0.07), particularly for the low-income patients. Despite this poor agreement, both income measures were positively associated with survival and EuroQoL scores. An outcome analysis that simultaneously considered individual level income and area-based income revealed that low-income individuals have poorer survival and lower quality of life scores if they live in low income neighborhoods, but not if they live in high income neighborhoods. CONCLUSIONS: The area-based estimates of household income in these data demonstrate poor agreement with self-reported household income at the level of individual patients, particularly for low-income patients. Despite this, both income measures appear to be prognostically relevant, perhaps because individual and neighborhood income measure different constructs.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".