DETERMINANTS OF COVID-19 SEVERITY AND OUTCOME AMONG NORTHERN SASKATCHEWAN FIRST NATIONS
Bibliographic record
Abstract
Background: Severe acute respiratory syndrome due to Coronavirus-2 (SARS-CoV-2) remains a global public health concern. Demographic and medical factors like vaccination status have been reported to influence the disease burden and outcome. Indigenous populations have been reported to be disproportionately affected by COVID -19; however, the impact of COVID-19 on Indigenous people in Canada remains understudied. The objectives of the study are to: 1) describe the characteristics of COVID-19 cases among on-reserve northern Saskatchewan First Nations people for the period March 2020 to December 2022; and 2) determine the association of demographic and medical factors with various indicators of COVID-19 severity and outcomes. Methods: We accessed de-identified data of 8,428 laboratory-confirmed COVID-19 cases during the period March 2020–December 2022. We conducted univariate, bivariate, and multivariate analyses to describe COVID-19 in this population and to determine the of association between various characteristics and COVID-19 severity. Characteristics of interest were demographic, clinical, and vaccine related. Three indicators of severity were included: hospitalization, admittance to an intensive care unit, and death. Results: Even though they account for <5% of the population, northern Saskatchewan First Nations on-reserve reported 5.6% of the total number of COVID-19 cases in the province. Over 90% of cases were under 65 years old. More than 53% of cases had no COVID-19 vaccination history at the time of infection. The most common clinical symptoms reported among COVID-19 patients in the study were cough, fever, loss of taste, and loss of smell. We observed that people 65 years and older were more likely to be hospitalized with severe COVID-19, despite having less than 10% of the infection rate of younger individuals. This finding is similar to other studies in Canada and other parts of the world. More hospitalization and deaths were associated with males than females. Hospitalization, ICU admission, and death were higher among unvaccinated persons when compared to those who were vaccinated. Similarly, hospitalization was higher among individuals who were vaccinated for >12 months before onset of infection. Like in other studies, the presence of symptoms, and co-existing medical conditions were significantly associated with increased odds of hospitalization. The risk of dying from COVID-19 was higher in people >65 years, males, and those with co-existing medical conditions. The risk of dying from COVID-19 iii was lowered following vaccination with two or more doses of COVID-19 vaccine when compared to those who received one dose of the vaccine or those who were not vaccinated. Conclusion: The implication of this study finding is that prioritizing vulnerable populations during COVID-19 and in subsequent public health emergencies and providing them with relevant interventions will reduce the burden of disease among these groups of individuals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".