Impact of the COVID-19 pandemic on tuberculosis program performance in Alberta, Canada, A population-based evaluation
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic imposed major disruptions to essential tuberculosis (TB) services globally. We evaluated the performance of the TB program in Alberta, Canada by comparing two periods - before and during the pandemic - to estimate the local impact of those disruptions. METHODS: Ten program performance indicators and their related targets were applied and compared by period. These include a measure of decline in the age- and sex-adjusted incidence by population group, proportion of recently-arrived immigrants screened on time, five case management and three close contact management indicators. Performance targets were measured by time period and clinic type – outpatient vs virtual. The latter did not see patients face-to-face. An interrupted time series analysis estimated the COVID-19 impact on timeliness of immigrant screening. RESULTS: The rate of disease by population group was not remarkably different, pre-pandemic vs pandemic. Over a more extended period of time, the rate in the Canadian-born but not the foreign-born, declined. Local program performance was not negatively affected by COVID-19 in general, but there was a large reduction in immigration and in turn the number of immigrants referred for screening (37.6%) and contacts identified for assessment (71.8%) during the pandemic, resulting in improvements to the proportion of referrals assessed (91.7% vs 96.6%, relative risk and 95% confidence interval 0.949 (0.936-0.962)), contacts assessed (81.7% vs 90.0%, 0.908 (0.875-0.943)), and contacts completing treatment of infection (90.4% vs 97.1%, 0.931 (0.886-0.979)). Among patients with TB disease, monitoring of treatment response was suboptimal while other targets were met or nearly met. Virtual clinic performance tended to be worse during the pandemic. INTERPRETATION: COVID-19 related disruptions were not as significant in the Alberta TB program as elsewhere with multiple likely explanations.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".