The Individual and Social Determinants of COVID-19 Diagnosis in Ontario, Canada: A Population-Wide Study
Bibliographic record
Abstract
Abstract Background Optimizing the public health response to reduce coronavirus disease 2019 (COVID-19) burden necessitates characterizing population-level heterogeneity of COVID-19 risks. However, heterogeneity in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing may introduce biased estimates depending on analytic design. Methods We explored the potential for collider bias and characterized individual, environmental, and social determinants of testing and diagnosis using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those diagnosed, we used separate analytic designs to compare predictors of: 1) individuals testing positive versus negative; 2) symptomatic individuals only testing positive versus testing negative; and 3) individuals testing positive versus individuals not testing positive (i.e., testing negative or not being tested). Analyses included tests conducted between March 1 and June 20, 2020. Results Of a total of 14,695,579 individuals, 758,691 were tested for SARS-CoV-2, of whom 25,030 (3.3%) tested positive. The further the odds of testing from the null, the more variability observed in the odds of diagnosis across analytic design, particularly among individual factors. There was less variability in testing by social determinants across analytic designs. Residing in areas with highest household density (adjusted odds ratio [aOR]: 1.86; 95%CI: 1.75-1.98), highest proportion of essential workers (aOR: 1.58; 95%CI: 1.48-1.69), lowest educational attainment (aOR: 1.33; 95%CI: 1.26-1.41), and highest proportion of recent immigrants (aOR: 1.10; 95%CI: 1.05-1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design. Interpretation Where testing is limited, risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation, and structural racism.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".