Effect of race and ethnicity on influenza vaccine uptake among older US Medicare beneficiaries: a record-linkage cohort study
Bibliographic record
Abstract
BACKGROUND: Seasonal influenza vaccine (SIV) uptake among US adults aged 65 years or older remains suboptimal and stagnant. Further, there is growing concern around racial and ethnic disparities in uptake. We aimed to assess racial and ethnic disparities in overall SIV and in high-dose vaccine (HDV) uptake among Medicare beneficiaries during the 2015-16 influenza season and sought to identify possible mediators for observed disparities. METHODS: We did a historical record-linkage cohort study using Medicare (a US national health insurance programme) databases, which included all older adults (≥65 years) enrolled in Medicare during the study period (July 1, 2015, to June 30, 2016). We excluded beneficiaries of Medicare Part C (managed care offered by private companies), and residents of long-term care facilities. The primary outcome was SIV receipt during the study period, classified into receipt of HDV and standard-dose vaccines (SDVs, representing all other SIVs). SIV uptake probabilities were estimated using competing-risk survival analysis methods. Mediation analyses were done to investigate potential mediators of the association between race and ethnicity and uptake. FINDINGS: During the study period, of 26·5 million beneficiaries in the study cohort, 47·4% received a SIV, 52·7% of whom received HDV. Compared with white beneficiaries (49·4%), Hispanic (29·1%), Black (32·6%), and Asian (47·6%) beneficiaries were less likely to be vaccinated and, when vaccinated, were less likely to receive HDV (37·8% for Hispanic people, 41·1% for Black people, and 40·3% for Asian people, compared with 53·8% of white people who received HDV). Among those vaccinated, after accounting for region, income, chronic conditions, and health-care use, minority groups were 26-32% less likely to receive HDV, relative to white people (odds ratio [OR] 0·68 [95% CI 0·68-0·69] for Black people; OR 0·71 [0·71-0·72] for Asian people; and OR 0·74 [0·73-0·74] for Hispanic people). INTERPRETATION: Substantial racial and ethnic disparities in SIV uptake among Medicare beneficiaries aged 65 years or older are evident. New legislative, fiscal, and educational strategies are urgently needed to address these inequities. FUNDING: Sanofi Pasteur.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".