Previously treated latent tuberculosis infection is associated with less severe acute COVID-19: a cohort study
Bibliographic record
Abstract
INTRODUCTION: There is significant potential for respiratory infections, such as tuberculosis (TB) and COVID-19, to overlap but little is known about such co-infection. We aimed to study the impact of active TB and latent TB on the incidence of severe COVID-19 in a large cohort of individuals in a setting of low TB endemicity. METHODS: Clinical data of patients admitted to hospital with acute SARS-CoV-2 were merged with a database of patients with a history of previous or current active TB, latent TB or healthy controls. We assessed the incidence of COVID-19 in these groups, length of hospital stay, admission to the intensive care unit (ICU) and in-hospital mortality. RESULTS: COVID-19 incidence among individuals with current active TB was 6.2% (12/194) and previous active TB 0.67% (30/4496). In contrast, the incidence in previously treated latent TB was 0.09% (4/4542) and among TB contacts 0.24% (34/13 391). There were similar rates of ICU admission and mortality among individuals with COVID-19 and current active TB, TB contacts and other patients. No individuals with previously treated latent TB and COVID-19 were admitted to the ICU or died. CONCLUSIONS: Individuals with a history of latent TB seem to be at reduced risk of severe COVID-19 and have better outcomes than those with active TB and even uninfected controls. Further studies are required to understand the mechanistic basis of this observation.
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".