Testing lags and emerging COVID-19 outbreaks in federal penitentiaries: A view from Canada
Bibliographic record
Abstract
ABSTRACT Objectives To provide the first known comprehensive analysis of COVID-19 outcomes in a federal penitentiary system. We examined the following COVID-19 outcomes within federal penitentiaries in Canada and contrasted them with estimates for the overall population in the penitentiaries’ respective provincial jurisdictions: testing, prevalence, the proportion recovered, and fatality. Methods Data for prisons were obtained from the Correctional Service of Canada and, for the general population, from the Esri COVID-19 Canadian Outbreak Tracking Hub. Data were retrieved between March 30 and April 21, 2020, and are accurate to this date. Penitentiary-, province- and sex-specific frequency statistics for each outcome were calculated. Results Data on 50 of 51 penitentiaries (98%) were available. Of these, 72% of penitentiaries reported fewer tests per 1000 population than the Canadian general population average (16 tests/1000 population), and 24% of penitentiaries reported zero tests. Penitentiaries with high levels of testing were those that already had elevated COVID-19 prevalence. Five penitentiaries reported an outbreak (at least one case). Hardest hit penitentiaries were those in Quebec, Ontario, and British Columbia, with some prisons reporting COVID-19 prevalence of 30% to 40%. Of these, two were women’s prisons. Female prisoners were over-represented among cases (31% of cases overall, despite representing 5% of the total prison population). Conclusion Increased sentinel or universal testing may be appropriate given the confined nature of prison populations. This, along with rigorous infection prevention control practices and the potential release of prisoners, will be needed to curb current outbreaks and those likely to come. GRAPHICAL SUMMARY Between 20% and 57% fewer tests per 1000 population have been conducted in federal prisons in Saskatchewan, New Brunswick, Nova Scotia and Alberta than in the general population of those provinces. Though Alberta, Manitoba, Saskatchewan, New Brunswick and Nova Scotia are reporting lower counts of COVID-19 cases, these are also the regions reporting the lowest levels of testing. Case incidence has been highest in federal prisons in Quebec, Ontario, and British Columbia, where a total of five prisons are experiencing outbreaks (1 or more cases). These regions are those reporting the highest levels of testing – higher than the testing levels in the general population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".