Bibliographic record
Abstract
CONTEXT: Reporting on health disparities is fundamental for meeting the goal of reducing health disparities. One often overlooked challenge is determining the best way to report those disparities associated with multiple attributes such as income, education, sex, and race/ethnicity. This article proposes an analytical approach to summarizing social disparities in health, and we demonstrate its empirical application by comparing the degrees and patterns of health disparities in all fifty states and the District of Columbia (DC). METHODS: We used the 2009 American Community Survey, and our measure of health was functional limitation. For each state and DC, we calculated the overall disparity and attribute-specific disparities for income, education, sex, and race/ethnicity in functional limitation. Along with the state rankings of these health disparities, we developed health disparity profiles according to the attribute making the largest contribution to overall disparity in each state. FINDINGS: Our results show a general lack of consistency in the rankings of overall and attribute-specific disparities in functional limitation across the states. Wyoming has the smallest overall disparity and West Virginia the largest. In each of the four attribute-specific health disparity rankings, however, most of the best- and worst-performing states in regard to overall health disparity are not consistently good or bad. Our analysis suggests the following three disparity profiles across states: (1) the largest contribution from race/ethnicity (thirty-four states), (2) roughly equal contributions of race/ethnicity and socioeconomic factor(s) (ten states), and (3) the largest contribution from socioeconomic factor(s) (seven states). CONCLUSIONS: Our proposed approach offers policy-relevant health disparity information in a comparable and interpretable manner, and currently publicly available data support its application. We hope this approach will spark discussion regarding how best to systematically track health disparities across communities or within a community over time in relation to the health disparity goal of Healthy People 2020.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".