Deprioritized and disrupted: tuberculosis care in the shadow of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic significantly disrupted tuberculosis (TB) care worldwide, undermining years of progress in TB prevention and control. This Perspective offers a comparative analysis of how TB services were affected in a high-income, low-burden country (Canada) versus two low- and middle-income, high-burden countries (India and Nigeria). Drawing on secondary data and global surveillance reports, the article highlights key disruptions across the TB care cascade, including delays in diagnosis, reduced case detection, and the collapse of community-based treatment models like DOTS. In Canada, digital transitions partially mitigated the impact, though access was unequal. In contrast, India and Nigeria faced widespread diagnostic interruptions, compounded by preexisting infrastructure gaps and limited digital access. The comparison reveals how underlying health system strength and digital readiness shaped national responses and recovery trajectories. Crucially, the pandemic exposed policy inertia and the deprioritization of routine infectious disease care in crisis contexts. This article calls for a global rethink of public health preparedness that centers on equity, continuity of essential services, and support for high burden settings. By analyzing divergent country experiences, this Perspective contributes actionable insights for strengthening TB programs and public health systems during future pandemics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".