Rates of influenza vaccination in older adults and factors associated with vaccine use: A secondary analysis of the Canadian Study of Health and Aging
Bibliographic record
Abstract
BACKGROUND: Influenza vaccination has been shown to reduce morbidity and mortality in the older adult population. In Canada, vaccination rates remain suboptimal. We identified factors predictive of influenza vaccination, in order to determine which segments of the older adult population might be targeted to increase coverage in influenza vaccination programs. METHODS: The Canadian Study of Health and Aging (CSHA) is a population-based national cohort study of 10263 older adults (>/= 65) conducted in 1991. We used data from the 5007 community-dwelling participants in the CSHA without dementia for whom self-reported influenza vaccination status is known. RESULTS: Of 5007 respondents, 2763 (55.2%) reported having received an influenza vaccination within the previous 2 years. The largest predictive factors for flu vaccination included: being married (57.4 vs. 52.6%, p = 0.0007), having attained a higher education (11.0 vs. 10.3 years, p < 0.0001), smoking (57.1% vs. 52.9%, p = 0.0032), more alcohol use (57.9% of those who drank more vs. 53.2% of those who drank less, p = 0.001), poorer self-rated health (54.1% of those with good self-rated health vs. 60.6% of those with poor self-rated health, p = 0.0006), regular exercise (56.8% vs. 52.0%, p = 0.001), and urban living (55.8% vs. 51.0%, p = 0.03). While many other differences were statistically significant, most were small (e.g. mean age 75.1 vs. 74.6 years for immunized vs. unimmunized older adults, p = 0.006, higher Modified Mini Mental Status Examination score (89.9 vs. 89.1, p < 0.0001), higher comorbidity (2.7 vs. 2.3 comorbidities, p < 0.0001). Residents of Ontario were more likely (64.6%) to report vaccination (p < 0.0001), while those living in Quebec were less likely to do so (48.2%, p < 0.0001). Factors retaining significance in a multivariate analysis included older age, higher education, married status, drinking alcohol, smoking, engaging in regular exercise, and having higher comorbidity. CONCLUSIONS: The vaccination rate in this sample, in whom influenza vaccination is indicated, was low (55.2%). Even in a publicly administered health care setting, influenza vaccination did not reach an important proportion of the elderly population. Whether these differences reflect patient preference or access remains to be determined.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".