Impact of COVID-19 on diagnosis of tuberculosis, multidrug-resistant tuberculosis, and on mortality in 11 countries in Europe, Northern America, and Australia. A Global Tuberculosis Network study
Bibliographic record
Abstract
OBJECTIVES: Although evidence is growing on the overall impact of the COVID-19 pandemic on tuberculosis (TB) services, global studies based on national data are needed to better quantify the extent of the impact and the countries' preparedness to tackle the two diseases. The aim of this study was to compare the number of people with new diagnoses or recurrence of TB disease, the number of drug-resistant (DR)-TB, and the number of TB deaths in 2020 vs 2019 in 11 countries in Europe, Northern America, and Australia. METHODS: TB managers or directors of national reference centers of the selected countries provided the agreed-upon variables through a validated questionnaire on a monthly basis. A descriptive analysis compared the incidence of TB and DR-TB and mortality of the pre-COVID-19 year (2019) vs the first year of the COVID-19 pandemic (2020). RESULTS: Comparing 2020 vs 2019, lower number of TB cases (new diagnosis or recurrence) was notified in all countries (except USA-Virginia and Australia), and fewer DR-TB notifications (apart from France, Portugal, and Spain). The deaths among TB cases were higher in 2020 compared to 2019 in most countries with three countries (France, The Netherlands, USA-Virginia) reporting minimal TB-related mortality. CONCLUSIONS: A comprehensive evaluation of medium-term impact of COVID-19 on TB services would benefit from similar studies in multiple settings and from global availability of treatment outcome data from TB/COVID-19 co-infected patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".