Income disparities in cardiovascular health across the lifespan
Bibliographic record
Abstract
Using data from the 1999-2014 National Health and Nutrition Examination Survey (n ~ 46,000), this study documents income disparities in the age patterning of cardiovascular conditions across the lifespan in the U.S. The conditions were assessed from laboratory test results, self-reports of medications used to treat specific conditions, and anthropometric measurements, allowing us to capture whether individuals at given ages had developed the various conditions, regardless of previous diagnosis and treatment. We found evidence of large income disparities in the presence of cardiovascular conditions and risk factors for females, smaller disparities in the same conditions for males, and few disparities that increased with age for either gender. Results were very similar when considering disparities by education instead of income. The findings suggest that the widening socioeconomic gradients in health over the lifespan found in many previous studies-which have generally focused on self-rated health, activity limitations, or diagnosed conditions-reflect, at least to some extent, differences in diagnosis, treatment, and management of health conditions rather than age-related differences in developing them. The findings also suggest that preventive healthcare is not an important source of socioeconomic disparities in cardiovascular health in the U.S., at least for men. The observed patterns of income disparities in cardiovascular conditions over the lifespan are more consistent with theories of early life conditions and the imprinting of health endowments and susceptibilities early in life than with cumulative life exposure or stress hypotheses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".