Decomposing Racial and Ethnic Disparities in Nursing Home Influenza Vaccination
Bibliographic record
Abstract
OBJECTIVES: Quantify how observable characteristics contribute to influenza vaccination disparities among White, Black, and Hispanic nursing home (NH) residents. DESIGN: Retrospective cohort. SETTING AND PARTICIPANTS: Short- and long-stay U.S. NH residents aged ≥65 years. METHODS: We linked Minimum Data Set (MDS) and Medicare data to LTCFocUS and other facility data. We included residents with 6-month continuous enrollment in Medicare and an MDS assessment between October 1, 2013, and March 31, 2014. Residents were classified as short-stay (<100 days in NH) or long-stay (≥100 days in NH). We fit multivariable logistic regression models to assess the relationships between 27 resident and NH-level characteristics and receipt of influenza vaccination. Using nonlinear Oaxaca-Blinder decomposition, we decomposed the disparity in influenza vaccination between White versus Black and White versus Hispanic NH residents. Analyses were repeated separately for short- and long-stay residents. RESULTS: Our study included 630,373 short-stay and 1,029,593 long-stay residents. Proportions vaccinated against influenza included 67.2% of White, 55.1% of Black, and 54.5% of Hispanic individuals among short-stay residents and 84.2%, 76.7%, and 80.8%, respectively among long-stay residents. Across 4 comparisons, the crude disparity in influenza vaccination ranged from 3.4 to 12.7 percentage points. By equalizing 27 prespecified characteristics, these disparities could be reduced 37.7% to 59.2%. Living in a predominantly White facility and proxies for NH quality were important contributors across all analyses. Characteristics unmeasured in our data (eg, NH staff attitudes and beliefs) may have also contributed significantly to the disparity. CONCLUSIONS AND IMPLICATIONS: The racial/ethnic disparity in influenza vaccination was most dramatic among short-stay residents. Intervening on factors associated with NH quality would likely reduce these disparities; however, future qualitative research is essential to explore potential contributors that were unmeasured in our data and to understand the degree to which these factors contribute to the overall disparity in influenza vaccination.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".