COVID-19 and its impact on life expectancy among First Nations people in Alberta, Canada from 2019 to 2022: a population-based study
Bibliographic record
Abstract
Background: COVID-19 differentially impacted First Nations people due to pre-existing social, racial, and systemic inequities. This study explores life expectancy of First Nations people and the perspectives of First Nations health staff on the observed trends in life expectancy during the pandemic in Alberta, Canada. Methods: All First Nations people in Alberta from a First Nations-identifying dataset (1999-2022) were included in this descriptive study (n = 164,964 in 2017, 49.7% male, 50.3% female, mean age 29.4 years [standard deviation 1.46; range 26.9-31.9 years]). In partnership with First Nations health managers, knowledge users, and using appropriate methods for First Nations people, we examined how the pandemic affected life expectancy in First Nations vs non-First Nations people. First Nations health managers were asked their opinions on observed trends in mortality during the pandemic through individual conversations. Findings: Since 1999, First Nations people consistently had lower life expectancies compared to non-First Nations. In 2019-2021, life expectancy dropped 7.3 years (70.4 [95% confidence interval (CI) 69.48-71.24] to 63.1 [CI 62.28-64.02]) for First Nations people vs 1.4 years (82.8 [CI 82.65-82.92] to 81.4 [CI 81.22-81.51]) for non-First Nations people. Mortality was highest in young adult First Nations females relative to non-First Nations females (∼11-fold increase) during COVID-19. First Nations people had lower life expectancy across most age categories. The 20-50 year age groups had some of the highest relative mortality rates. Health managers observed similar life expectancy trends, perceiving that the pandemic exacerbated existing issues, including delayed access to care, poisonings (i.e., accidental or intentional overdoses), and pre-existing health conditions. Interpretation: First Nations people in Alberta had a substantial drop in life expectancy during the pandemic compared to non-First Nations people. From the perspective of First Nations health managers, the pandemic indirectly exacerbated existing social and health issues, possibly contributing to the trend in life expectancy. Significant initiatives and resources will need to be deployed to assist First Nations communities to close these gaps. Funding: Canadian Institutes of Health research (#PJT-178219).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".