Patterns and association of vaccination among adults with a history of cancer in the USA: a population-based study
Bibliographic record
Abstract
Aim: To assess the association of vaccination status among adults with history of cancer in a population-based cohort in the USA. Materials & methods: National Health Interview Survey datasets (2008–2018) have been accessed and information about the patterns and associations of the following vaccinations were collected (influenza vaccination, pneumococcal vaccination, hepatitis B vaccination, hepatitis A vaccination and shingles vaccination). Association of different sociodemographic variables with each of the above types of vaccination was studied through multivariable logistic regression analysis. Results: Private health insurance (vs no private insurance) was associated with higher percentages of recommended vaccination (influenza vaccination: 65 vs 59.7%; pneumococcal vaccination: 74.9 vs 68.8%; hepatitis B vaccination: 22.9 vs 19.3%; hepatitis A vaccination: 10.1 vs 8.6%; shingles vaccination: 33.8 vs 26.7%; p < 0.001 for all comparisons). Within multivariable logistic regression analyses, African American race, lower education and lower income were associated with less probability of adherence to recommended vaccination (for influenza vaccination; odds ratio (OR) for black race vs white race: 0.785; 95% CI: 0.717–0.859; OR for ≤high school vs >high school education: 0.763; 95% CI: 0.723–0.805; OR for income ≤US$45,000 vs >US$45,000: 0.701; 95% CI: 0.643–0.764). Conclusion: There is evidence of socio-economic disparities in adherence to recommended vaccination among this cohort of cancer survivors in the USA. More efforts need to be done to ensure that recommended vaccination is being delivered to all cancer survivors in need (including enhancing coverage and awareness to under-represented groups of the society).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".