Factors associated with timely COVID-19 vaccination in a population-based cohort of patients with cancer
Bibliographic record
Abstract
BACKGROUND: In many jurisdictions, cancer patients were prioritized for COVID-19 vaccination because of increased risk of infection and death. To understand sociodemographic disparities that affected timely receipt of COVID-19 vaccination among cancer patients, we undertook a population-based study in Ontario, Canada. METHODS: Patients older than 18 years and diagnosed with cancer January 2010 to September 2020 were identified using administrative data; vaccination administration was captured between approval (December 2020) up to February 2022. Factors associated with time to vaccination were evaluated using multivariable Cox proportional hazards regression. RESULTS: The cohort consisted of 356 535 patients, the majority of whom had solid tumor cancers (85.9%) and were not on active treatment (74.1%); 86.8% had received at least 2 doses. The rate of vaccination was 25% lower in recent (hazard ratio [HR] = 0.74, 95% confidence interval [CI] = 0.72 to 0.76) and nonrecent immigrants (HR = 0.80, 95% CI = 0.79 to 0.81). A greater proportion of unvaccinated patients were from neighborhoods with a high concentration of new immigrants or self-reported members of racialized groups (26.0% vs 21.3%, standardized difference = 0.111, P < .001), residential instability (27.1% vs 23.0%, standardized difference = 0.094, P < .001), or material deprivation (22.1% vs 16.8%, standardized difference = 0.134, P < .001) and low socioeconomic status (20.9% vs 16.0%, standardized difference = 0.041, P < .001). The rate of vaccination was 20% lower in patients from neighborhoods with the lowest socioeconomic status (HR = 0.82, 95% CI = 0.81 to 0.84) and highest material deprivation (HR = 0.80, 95% CI = 0.78 to 0.81) relative to those in more advantaged neighborhoods. CONCLUSIONS: Despite funding of vaccines and prioritization of high-risk populations, marginalized patients were less likely to be vaccinated. Differences are likely due to the interplay between systemic barriers to access and cultural or social influences affecting uptake.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".