The effects of deprivation, age, and regional differences in COVID-19 mortality from 2020 to 2022: a retrospective analysis of public provincial data
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease (COVID-19) quickly spread around the world after its initial identification in Wuhan, China in 2019 and became a global public health crisis. COVID-19 related hospitalizations and deaths as important disease outcomes have been investigated by many studies while less attention has been given to the relationship between these two outcomes at a public health unit level. In this study, we aim to establish the relationship of counts of deaths and hospitalizations caused by COVID-19 over time across 34 public health units in Ontario, Canada, taking demographic, geographic, socio-economic, and vaccination variables into account. METHODS: We analyzed daily data of the 34 health units in Ontario between March 1, 2020 and June 30, 2022. Associations between numbers of COVID-19 related deaths and hospitalizations were explored over three subperiods according to the availability of vaccines and the dominance of the Omicron variant in Ontario. A generalized additive model (GAM) was fit in each subperiod. Heterogeneity across public health units was formulated via a random intercept in each of the models. RESULTS: Mean daily COVID-19 deaths increased quickly as daily hospitalizations increased, particularly when daily hospitalizations were less than 20. In all the subperiods, mean daily deaths of a public health unit was significantly associated with its population size and the proportion of confirmed cases in subjects over 60 years old. The proportion of fully vaccinated (2 doses of primary series) people in the 60 + age group was a significant factor after the availability of the COVID-19 vaccines. The deprivation index, a measure of poverty, had a significantly positive effect on COVID-19 mortality after the dominance of the Omicron variant in Ontario. Quantification of these effects was provided, including effects related to public health units. CONCLUSIONS: The differences in COVID-19 mortality across health units decreased over time, after adjustment for other covariates. In the last subperiod when most public health protections were released and the Omicron variant dominated, the least advantaged group might suffer higher COVID-19 mortality. Interventions such as paid sick days and cleaner indoor air should be made available to counter lifting of health protections.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".