Patterns of influenza vaccination coverage in the United States from 2009 to 2015
Bibliographic record
Abstract
BACKGROUND: Globally, influenza is a major cause of morbidity, hospitalization and mortality. Influenza vaccination has shown substantial protective effectiveness in the United States. METHODS: We investigated state-level patterns of coverage rates of seasonal and pandemic influenza vaccination, among the overall population (six months or older) in the U.S. and specifically among children (aged between 6 months and 17 years) and the elderly (aged 65 years or older), from 2009/10 to 2014/15, and associations with ecological factors. We obtained state-level influenza vaccination rates from national surveys, and state-level socio-demographic and health data from a variety of sources. We employed a retrospective ecological study design, and used both linear models and linear mixed-effect models to determine the levels of ecological association of the state-level vaccinations rates with these factors, both with and without region as a factor for the three populations. RESULTS AND CONCLUSIONS: Health-care access has a robust, positive association with state-level vaccination rates across all populations and models. This highlights a potential population-level advantage of expanding health-care access. We also found that prevalence of asthma in adults is negatively associated with mean influenza vaccination rates in the elderly populations.
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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 | 0.000 | 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) | 0.001 | 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".