The mediating role of SARS-CoV-2 variants between income and hospitalization due to COVID-19: a period-based mediation analysis
Bibliographic record
Abstract
The mechanisms facilitating the relationship between low income and COVID-19 severity have not been partitioned in the presence of SARS-CoV-2 variants of concern (VOCs). To address this, we used causal mediation analysis to quantify the possible mediating role infection with VOC has on the relationship between neighborhood income (exposure) and hospitalization due to COVID-19 among cases (outcome). A population-based cohort of 65 629 individuals residing in British Columbia, Canada, was divided into 3 periods of VOC co-circulation in the 2021 calendar year, whereby each period included co-circulation of an emerging and an established VOC. Each cohort was subjected to g-formula mediation techniques to decompose the relationship between exposure and outcome into total, direct, and indirect effects. In the mediation analysis, the total effects indicated that low income was associated with increased odds of hospitalization across all periods. Further decomposition of the effects revealed that income is directly and indirectly associated with hospitalization. The resulting indirect effect through VOC accounted for approximately between 6% and 13% of the total effect of income on hospitalization. This study underscores, conditional on the analysis, the importance of addressing underlying inequities to mitigate the disproportionate impact on historically marginalized communities by adopting an equity lens as central to pandemic preparedness and response from the onset.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".