Differential Patterns by Area-Level Social Determinants of Health in Coronavirus Disease 2019 (COVID-19)–Related Mortality and Non–COVID-19 Mortality: A Population-Based Study of 11.8 Million People in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Social determinants of health (SDOH) have been associated with coronavirus disease 2019 (COVID-19) outcomes. We examined patterns in COVID-19-related mortality by SDOH and compared these patterns to those for non-COVID-19 mortality. METHODS: Residents of Ontario, Canada, aged ≥20 years were followed from 1 March 2020 to 2 March 2021. COVID-19-related death was defined as death within 30 days following or 7 days prior to a positive COVID-19 test. Area-level SDOH from the 2016 census included median household income; proportion with diploma or higher educational attainment; proportion essential workers, racially minoritized groups, recent immigrants, apartment buildings, and high-density housing; and average household size. We examined associations between SDOH and COVID-19-related mortality, and non-COVID-19 mortality using cause-specific hazard models. RESULTS: Of 11 810 255 individuals, we observed 3880 COVID-19-related deaths and 88 107 non-COVID-19 deaths. After accounting for demographics, baseline health, and other area-level SDOH, the following were associated with increased hazards of COVID-19-related death (hazard ratio [95% confidence interval]: lower income (1.30 [1.04-1.62]), lower educational attainment (1.27 [1.07-1.52]), higher proportions essential workers (1.28 [1.05-1.57]), racially minoritized groups (1.42 [1.08-1.87]), apartment buildings (1.25 [1.07-1.46]), and large vs medium household size (1.30 [1.12-1.50]). Areas with higher proportion racially minoritized groups were associated with a lower hazard of non-COVID-19 mortality (0.88 [0.84-0.92]). CONCLUSIONS: Area-level SDOH are associated with COVID-19-related mortality after accounting for demographic and clinical factors. COVID-19 has reversed patterns of lower non-COVID-19 mortality among racially minoritized groups. Pandemic responses should include strategies to address disproportionate risks and inequitable coverage of preventive interventions associated with SDOH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".