Factors associated with timely receipt of COVID vaccination in patients with cancer.
Bibliographic record
Abstract
167 Background: In many jurisdictions patients with new hematological cancers, or those receiving hematopoietic stem cell transplant or immunosuppressive agents, were prioritized for COVID vaccination due to increased risk of infection and death. In Ontario, Canada those residing in congregate settings, or regions with high positivity rates or high proportions of essential workers were also prioritized. While vaccine inequities exist, it remains unclear whether they persisted amongst the prioritized cancer population. Methods: We undertook a retrospective, population-based study to evaluate factors associated with COVID vaccination in patients residing in Ontario, Canada, >18 years of age, and diagnosed with cancer between 01/2010 and 09/2020. Factors associated with time from vaccine approval to full vaccination (two doses) and third doses were evaluated using multivariable Cox proportional hazards regression models. Results: The cohort consisted of 356,535 patients; as of 30 January 2022 of which 86.8% had received at least two doses. Compared to patients with more remote diagnoses (> 1 year), newly diagnosed patients rate of vaccination was lower (HR: 0.89, 95%CI: 0.88-0.91, p < 0.01) and a greater proportion were unvaccinated (13.6% vs 11.8%; p < 0.01). Conversely, rate of vaccination was higher in patients treated with systemic therapy in the last 6 months (HR: 1.04, 95%CI: 1.03-1.05, p < 0.01). Rate of vaccination was 25% lower in recent (HR:0.74,95% CI: 0.72-0.76, p < 0.01) and non-recent immigrants (HR: 0.80, 95% CI: 0.79-0.81, p < 0.01), and a greater proportion remained unvaccinated, compared to those who were Canadian-born (20.1 and 16.6% vs 10.9%; p < 0.01). Compared to the most advantaged quintiles, quintiles with the lowest socioeconomic status (14.5% vs 9.4%; p < 0.01), or highest residential instability (13.3% vs 10.8%; p < 0.01), material deprivation (10.5% vs 9.6%; p < 0.01), or ethnic concentration quintiles (13.7% vs 10.4%; p < 0.01) had higher proportions of unvaccinated patients. Rate of vaccination was 20% lower in patients with the lowest socioeconomic status (HR: 0.83, 95% CI: 0.81-0.84, p < 0.01) and those with highest material deprivation (HR: 0.80, 95% CI: 0.79-0.82, p < 0.01) relative to more advantaged groups. Similar trends were observed for receipt of third doses in the eligible cohort. Conclusions: Despite direct government funding of COVID vaccines and distribution policies aimed a prioritizing high-risk populations marginalized patients with cancer were less likely to be vaccinated than other cancer patients. Differences in receipt of vaccination are likely due to the interplay between systemic barriers to access (low trust, transportation barriers, work schedules), and cultural/ social influences impacting uptake. Future efforts should work directly members of high-risk communities to understand how to improve vaccine delivery among these communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".