Identification of Barriers to Influenza Vaccination in Patients with Chronic Obstructive Pulmonary Disease: Analysis of the 2012 Behavioral Risk Factors Surveillance Survey
Bibliographic record
Abstract
INTRODUCTION: Patients with chronic obstructive pulmonary disease (COPD) are at increased risk for influenza-related morbidity and mortality. Influenza vaccination is known to decrease influenza incidence, severity, hospitalizations, and mortality. Identification of barriers to influenza vaccination among patients with COPD may aid in efforts to increase vaccination rates. This study aims to identify predictors of influenza vaccination in COPD patients. METHODS: This study used data from the 2012 Behavioral Risk Factor Surveillance System (BRFSS). Participants with self-reported COPD and receiving an influenza vaccination in the prior 12 months were identified. Independent predictors of the exposure were identified by estimating a parsimonious logistic regression model of influenza vaccination. All analyses were performed using weighted data. RESULTS: The final study sample consisted of 36,811 COPD participants, with 48.5% of COPD patients reporting having been vaccinated and 51.5% reporting being unvaccinated. A total of 15 independent predictors of influenza vaccination in COPD patients were identified. Negative predictors included predisposing factors (younger age, male gender, household children, black or non-white/non-Hispanic/non-black race/ethnicity, lower education level, heavy alcohol use, current tobacco use) and enabling factors that reflect access to medical care (insurance status, ability to afford care, having a recent check-up). Positive predictors of influenza vaccination included need factors (chronic comorbidities), being a military veteran, or being a former smoker. CONCLUSIONS: This analysis identifies multiple predictors of influenza vaccination in persons with COPD. Identification of at risk-groups provides the foundation for development of focused efforts to improve influenza vaccination rates in patients with COPD.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".