1384-P: COVID-19 Vaccination Uptake among 3,779,733 Adults With and Without Diabetes
Bibliographic record
Abstract
Introduction & Objective: Public health measures, especially vaccination, are important for COVID-19 risk reduction. Population level vaccine uptake in people with diabetes is understudied. We assessed COVID-19 vaccine uptake among adults with and without diabetes and the factors associated with vaccination. Methods: This retrospective, population-based cohort study included adults (18+ years) living in Alberta, Canada on December 14, 2020, the date of vaccine availability. Diabetes status was determined using a validated algorithm between April 1, 2002 and December 14, 2020. All individuals were followed until March 31, 2022. The primary outcome was full vaccination as defined by the manufacturer. Descriptive statistics were used to compare vaccination status among people with and without diabetes and multivariate logistic regression assessed the factors associated with full vaccination adjusting for age group, sex, diabetes status, material deprivation, urban/rural residence, and comorbidities (Charlson Comorbidity Index (CCI) score). Results: We identified 3,779,733 adults (49.7% female, mean age 46.8 ± 17.8 years) with 9.8% having diabetes. Overall, 75.5% of the total cohort were fully vaccinated. A higher proportion of people with diabetes, compared to those without, were fully vaccinated (83.9% vs. 74.6%, p<0.001). The presence of diabetes (aOR 1.48; 95% CI 1.46-1.49), female sex (aOR 1.20; 95% CI 1.91-1.21), and CCI score >1 (aOR 1.67; 1.66-1.68) were associated with being fully vaccinated, whereas the age group of ≥ 75 years (aOR 0.84; 95% CI 0.83-0.86), rural residence (aOR 0.60; 95% CI 0.60-0.60) and higher material deprivation (aOR 0.88; 95% CI 0.87-0.89) were associated with lower vaccination. Conclusions: People with diabetes had high COVID-19 vaccination uptake and were more likely than the general population to be fully vaccinated. We also identified several key factors associated with lower uptake highlighting the need for understanding facilitators of vaccination. Disclosure S. Butalia: None. B.R. Shah: None. R.J. Sigal: Research Support; Lexicon Pharmaceuticals, Inc., Bayer Inc., Novo Nordisk. J.L. Benham: None. B. Wicklow: None. C.H. Yu: None. K. Dasgupta: None. S. Amed: None. C. Constantinescu: Research Support; Pfizer Inc. Other Relationship; Sta HealthCare Communications, Canadian Research Network. M. Chu: None. P. Kaul: None. Funding Canadian Institutes of Health Research (CIHR) 483868
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".