Experiences of the Red River Métis with COVID-19 Policy Decisions: A partnership-based, whole-population linked administrative data study
Bibliographic record
Abstract
ObjectivesIndigenous peoples, like Red River Métis, were greatly affected by COVID-19. Manitoba’s Indigenous COVID-19 vaccine policy initially focused exclusively on First Nations. Red River Métis, one of Canada’s recognized Indigenous peoples, were not prioritized. We examined the health outcomes of these policy decisions. MethodThis retrospective cohort study leveraged data available in the Manitoba Population Research Data Repository. We linked data from the Métis Population Database to whole-population COVID testing and vaccination data, and administrative data on health service use. Restricted mean survival time models tested whether vaccination uptake differed between Red River Métis and all other Manitobans (AOM), adjusting for sociodemographic characteristics and comorbidities. A Susceptible-Exposed-Infected-Recovered-Vaccinated model simulated how prioritizing Red River Métis for vaccination at the same time as First Nations may have altered the pandemic curve. We used estimates to model associated reductions in health service use for Red River Métis. ResultsCumulative prevalence of COVID-19 infection rates were similar between Métis and AOM until May 2021 when rates became higher among Métis. Between May and December 2021, rates of first vaccination were lower among Metis than AOM. Métis were more likely than AOM to be hospitalized due to Covid-19 between March and August 2021 and visit physicians for COVID related reasons from October 2020 to Feb 2021 and November 2021 to March 2022. Simulation analyses showed that prioritizing Red River Métis at the same time as First Nations would have reduced the peak infection rate by 54.6% reducing health service use. ConclusionsUnderstanding the experiences of Red River Métis relative to AOM is critical to identifying public health strategies which close gaps in vaccine uptake and infections. Including Red River Métis with other Indigenous populations may have reduced adverse outcomes and health service use associated with COVID-19.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".