Can States Simultaneously Improve Health Outcomes and Reduce Health Outcome Disparities?
Bibliographic record
Abstract
INTRODUCTION: Reducing racial health disparities is often stated as a population health goal, but specific targets for such improvement are seldom set. It is often assumed that improving overall health outcomes will be linked to disparity reduction, but this is not necessarily the case. METHODS: We compared the annual change from 1999 through 2013 in combined-race (black and white) mortality with the annual change in absolute and relative racial mortality disparities for US states. RESULTS: Median annual improvement in combined-race mortality was 1.08% per year. Annual overall mortality rate reductions ranged from 0.24% per year in Oklahoma to 1.83% per year in Maryland. For disparities, the median for the black-white absolute gap was 3.60% per year, and the median for the relative black-to-white ratio was 1.19% per year. There was no significant correlation between the combined-race measure and either the absolute (0.03) or relative disparity measure reductions (-0.17). CONCLUSION: For mortality in US states over a recent period, improvement in the population mean and disparity reduction do not usually occur together. The disparity reduction rates observed may provide realistic guidance for public and private policy makers in setting goals for reducing population health disparity and creating investment priorities. As a starting point for discussion, the observed national median annual percentage improvement of 1.1 per year combined, 3.6% per year absolute gap reduction, and 1.2% per year relative gap reduction would be modest and reasonable goals.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".