Associations between social determinants of health and COVID-19 outcomes by variants of concern: a Bayesian spatiotemporal analysis
Bibliographic record
Abstract
Background: Socially and economically disadvantaged populations were disproportionately affected by COVID-19, but it remains unclear whether and how variants of concern moderated the associations between social determinants of health and COVID-19 outcomes. Methods: We aggregated cases by week and health region (n=231) from 1 January 2021 to 27 February 2022, covering four major variants of concern waves-Alpha, Gamma, Delta and Omicron-in British Columbia, the third most populated province in Canada. We constructed a region-specific, continuous socioeconomic index (SI) in which a one-unit increase represents a 10% increase in socioeconomic status across the health region. We fitted Bayesian spatiotemporal models on four COVID-19 outcomes separately: cases, hospitalisations, intensive care unit admissions and deaths, adjusting for interaction between SI and variants of concern, percentage male and vaccine coverage. We estimated the marginal rate ratio (RR) between SI and the outcomes by variants of concern. Results: Overall, Alpha, Gamma and Delta heightened the associations between SI and all outcomes. The marginal RR between SI and cases was 0.96 (95% credible interval (CrI): 0.93, 1.00) in the absence of variants of concern, 0.93 (95% CrI: 0.88, 0.97) in the presence of Alpha, 0.93 (95% CrI: 0.88, 0.98) for Gamma and 0.90 (95% CrI: 0.87, 0.93) for Delta. Omicron reversed the direction of SI's association with cases (RR: 1.03, 95% CrI: 0.99, 1.07) and dampened the associations with other outcomes. Conclusions: Variants of concern generally widened the socioeconomic disparities in COVID-19 outcomes. Tailoring and optimising pandemic preparedness and response measures to the specific needs of disadvantaged populations is vital for reducing the additional disease burden experienced by some communities in British Columbia and across Canada.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".