Urban-rural divide in COVID-19 infection and vaccination rates in healthcare workers in British Columbia, Canada
Bibliographic record
Abstract
Introduction: Healthcare workers (HCWs) play a critical role in responding to the COVID-19 pandemic. Early in the pandemic, urban centres were hit hardest globally; rural areas gradually became more impacted. We compared COVID-19 infection and vaccine uptake in HCWs living in urban versus rural locations within, and between, two health regions in British Columbia (BC), Canada. We also analysed the impact of a vaccine mandate for HCWs. Methods: We tracked laboratory-confirmed SARS-CoV-2 infections, positivity rates and vaccine uptake in all 29,021 HCWs in Interior Health (IH) and all 24,634 HCWs in Vancouver Coastal Health (VCH), by occupation, age and home location, comparing to the general population in that region. We then evaluated the impact of infection rates as well as the mandate on vaccination uptake. Results: While we found an association between vaccine uptake by HCWs and HCW COVID-19 rates in the preceding 2-week period, the higher rates of COVID-19 infection in some occupational groups did not lead to increased vaccination in these groups. By 27 October 2021, the date that unvaccinated HCWs were prohibited from providing healthcare, only 1.6% in VCH compared with 6.5% in IH remained unvaccinated. Rural workers in both areas had significantly higher unvaccinated rates compared with urban dwellers. Over 1800 workers, comprising 6.7% of rural HCWs and 3.6% of urban HCWs, remained unvaccinated and set to be terminated from their employment. While the mandate prompted a significant increase in uptake of second doses, the impact on the unvaccinated was less clear. Conclusions: As rural areas often suffer from under-staffing, loss of HCWs could have serious impacts on healthcare provision as well as on the livelihoods of unvaccinated HCWs. Greater efforts are needed to understand how to better address the drivers of rural-related vaccine hesitancy.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".