Impact of the COVID-19 pandemic on tuberculosis program performance in Alberta, Canada: a population-based evaluation
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic caused major disruptions to essential tuberculosis (TB) services globally. We evaluated the performance of the TB program in Alberta, Canada, in 2 periods - before and during the pandemic - to estimate the impact of those disruptions. METHODS: We applied 10 program performance indicators and their related targets and compared them by period. The performance indicators included a measure of decline in the age- and sex-adjusted incidence by population group, the proportion of recently arrived immigrants screened on time, and 5 case management and 3 close contact management indicators. We measured performance targets by time period and clinic type - outpatient versus virtual. We used interrupted time series analysis to estimate the impact of the COVID-19 pandemic response on timeliness of immigrant screening. RESULTS: Since 2009, the rate of disease has declined in the Canadian-born but not the foreign-born population. However, the rate of disease by population group was not different prepandemic versus during the pandemic. Program performance was not negatively affected by the pandemic 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, associated with improvements to the proportion of referrals assessed (91.7% v. 96.6%, relative risk [RR] 0.949, 95% confidence interval [CI] 0.936-0.962); contacts assessed (81.7% v. 90.0%, RR 0.908, 95% CI 0.875-0.943), and contacts completing treatment of infection (90.4% v. 97.1%, RR 0.931, 95% CI 0.886-0.979). Among patients with TB disease, monitoring of treatment response was suboptimal, whereas other targets were met or nearly met. Virtual clinic performance tended to be worse during the pandemic than the outpatient clinics. INTERPRETATION: COVID-19-related disruptions were not as substantial in the Alberta TB program as elsewhere, likely because of its centralized operational model and protection of its staff from secondment. However, no progress has been made toward reducing TB incidence. Better resourcing of prevention activity and a more responsive information system should be considered.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".