Regional Disparities in the Uptake of Differentiated Influenza Vaccines
Bibliographic record
Abstract
Significant racial/ethnic inequities in the uptake of differentiated influenza vaccines (DIVs) have been previously reported, though less is known about regional disparities. We conducted a retrospective longitudinal study (2014/15-2017/18 influenza seasons) among privately insured adults aged 65+ years in the US. The exposure was beneficiary’s area of residence (US Census Bureau division) and outcome was type of influenza vaccine: differentiated (High-Dose [HDV], adjuvanted, recombinant, and cell-based) versus standard-dose egg-based. Among those vaccinated in physician offices, beneficiaries in the East North Central region were twice as likely to receive a DIV vs those in the South Atlantic, whereas those in the East and West South Central were least likely. Disparities became more pronounced in models adjusted for individual and community characteristics, suggesting that crude uptake estimates understate the true magnitude of disparities. Regional disparities remained even in fully adjusted models, pointing to currently poorly understood factors that may include quality of healthcare, client health literacy and engagement, and other political and cultural factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".