Persistence of Racial Inequities in Receipt of Influenza Vaccination Among Nursing Home Residents in the United States
Bibliographic record
Abstract
BACKGROUND: We sought to determine if racial differences in influenza vaccination among nursing home (NH) residents during the 2008-2009 influenza season persisted in 2018-2019. METHODS: We conducted a cross-sectional study of NHs certified by the Centers for Medicare & Medicaid Services during the 2018-2019 influenza season in US states with ≥1% Black NH residents and a White-Black gap in influenza vaccination of NH residents (N = 2 233 392) of at least 1 percentage point (N = 40 states). NH residents during 1 October 2018 through 31 March 2019 aged ≥18 years and self-identified as being of Black or White race were included. Residents' influenza vaccination status (vaccinated, refused, and not offered) was assessed. Multilevel modeling was used to estimate facility-level vaccination status and inequities by state. RESULTS: The White-Black gap in influenza vaccination was 9.9 percentage points. In adjusted analyses, racial inequities in vaccination were more prominent at the facility level than at the state level. Black residents disproportionately lived in NHs that had a majority of Blacks residents, which generally had the lowest vaccination. Inequities were most concentrated in the Midwestern region, also the most segregated. Not being offered the vaccine was negligible in absolute percentage points between White residents (2.6%) and Black residents (4.8%), whereas refusals were higher among Black (28.7%) than White residents (21.0%). CONCLUSIONS: The increase in the White-Black vaccination gap among NH residents is occurring at the facility level in more states, especially those with the most segregation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".