A Map of Racial and Ethnic Disparities in Influenza Vaccine Uptake in the Medicare Fee-for-Service Program
Bibliographic record
Abstract
INTRODUCTION: Despite improved understanding of the risks of influenza and better vaccines for older patients, influenza vaccination rates remain subpar, including in high-risk groups such as older adults, and demonstrate significant racial and ethnic disparities. METHODS: This study considers demographic, clinical, and geographic correlates of influenza vaccination among Medicare Fee-for-Service (FFS) beneficiaries in 2015-2016 and maps the data on a geographic information system (GIS) at the zip code level. RESULTS: Analyses confirm that only half of the senior beneficiaries evidenced a claim for receiving an inactivated influenza vaccine (IIV), with significant disparities observed among black, Hispanic, rural, and poorer beneficiaries. More extensive disparities were observed for the high-dose (HD) vaccine, with its added protection for older populations and confirmed economic benefit. Most white beneficiaries received HD; no non-white subgroup did so. Mapping of the data confirmed subpar vaccination in vulnerable populations with wide variations at the zip code level. CONCLUSION: Urgent and targeted efforts are needed to equitably increase IIV rates, thus protecting the most vulnerable populations from the negative health impact of influenza as well as the tax-paying public from the Medicare costs from failing to do so.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".