Geographic Variation in Influenza Vaccination among US Nursing Home Residents: A National Study
Bibliographic record
Abstract
ABSTRACT Objective Estimates of influenza vaccine use are not available at the county level for U.S. nursing home (NH) residents but are critically necessary to guide implementation of quality improvement programs aimed at increasing vaccination rates. Furthermore, estimates that account for differences in resident characteristics between counties are unavailable. We estimated risk-standardized vaccination rates among short- and long-stay NH residents by U.S. county and identified drivers of geographic variation. Methods We conducted a retrospective cohort study utilizing 100% of 2013-2015 fee-for-service Medicare claims, Minimum Data Set assessments, Certification and Survey Provider Enhanced Reports, and LTCFocUS. We separately evaluated short-stay (<100 days) and long-stay (≥100 days) residents aged ≥65 years old across the 2013-2014 and 2014-2015 influenza seasons. We estimated county-level risk-standardized vaccination rates (RSVRs) via hierarchical logistic regression adjusting for 32 resident-level covariates. We then used multivariable linear regression models to assess associations between county-level NHs predictors and RSVRs. Results The overall study cohort consisted of 2,817,217 residents in 14,658 NHs across 2,798 counties. Short-stay residents had lower RSVRs than long-stay residents (2013-2014: median [IQR], 69.6% [62.8-74.5] vs 84.0% [80.8-86.4]). Counties with the highest vaccination rates were concentrated in the Midwestern, Southern, and Northeast US. Several modifiable facility-level characteristics were associated with increased RSVRs, including higher registered nurse to total nurse ratio and higher total staffing for licensed practical nurses, speech language pathologists, and social workers. Characteristics associated with lower RSVRs included higher percentage of residents restrained, with a pressure ulcer, and NH-level hospitalizations per resident-year. Conclusions Substantial county-level variation in influenza vaccine use exists among short- and long-stay NH residents. Quality improvement interventions to improve vaccination rates can leverage these results to target NHs located in counties with lower risk-standardized vaccine use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".