SARS-CoV-2 testing and COVID-19–related primary care use among people with citizenship, permanent residency, and temporary immigration status: an analysis of population-based administrative data in British Columbia
Bibliographic record
Abstract
OBJECTIVES: Having temporary immigration status affords limited rights, workplace protections, and access to services. There is not yet research data on impacts of the COVID-19 pandemic for people with temporary immigration status in Canada. METHODS: We use linked administrative data to describe SARS-CoV-2 testing, positive tests, and COVID-19 primary care service use in British Columbia from January 1, 2020 to July 31, 2021, stratified by immigration status (citizen, permanent resident, temporary resident). We plot the rates of people tested and confirmed positive for COVID-19 by week from April 19, 2020 to July 31, 2021 across immigration groups. We use logistic regression to estimate adjusted odds ratios of a positive SARS-CoV-2 test, access to testing, and primary care among people with temporary status or permanent residency, compared with people who hold citizenship. RESULTS: A total of 4,146,593 people with citizenship, 914,089 people with permanent residency, and 212,215 people with temporary status were included. Among people with temporary status, 52.1% had "male" administrative sex and 74.4% were ages 20-39, compared with 50.1% and 24.4% respectively among those with citizenship. Of people with temporary status, 4.9% tested positive for SARS-CoV-2 over this period, compared with 4.0% among people with permanent residency and 2.1% among people with citizenship. Adjusted odds of a positive SARS-CoV-2 test among people with temporary status were almost 50% higher (aOR 1.42, 95% CI 1.39, 1.45), despite having half the odds of access to testing (aOR 0.53, 95% CI 0.53, 0.54) and primary care (aOR 0.50, 95% CI 0.49, 0.52). CONCLUSION: Interwoven immigration, health, and occupational policies place people with temporary status in circumstances of precarity and higher health risk. Reducing precarity accompanying temporary status, including regularization pathways, and decoupling access to health care from immigration status can address health inequities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".