Tuberculosis and Increased Incidence of Cardiovascular Disease: Cohort Study Using United States and United Kingdom Health Records
Bibliographic record
Abstract
BACKGROUND: Limited evidence suggests elevated risks of cardiovascular disease (CVD) among people diagnosed with tuberculosis (TB) disease, though studies have not adjusted for preexisting CVD risk. We carried out a cohort study using 2 separate datasets, estimating CVD incidence in people with TB versus those without. METHODS: Using data from the United States (Veterans Health Administration) and the United Kingdom (Clinical Practice Research Datalink) for 2000-2020, we matched adults with incident TB disease and no CVD history 2 years before TB diagnosis (US, n = 2121; UK, n = 15 820) with up to 10 people without TB on the basis of age, sex, race/ethnicity and healthcare practice. Participants were followed beginning 2 years before TB diagnosis and for 2 years subsequently. The acute period was defined as 3 months before/after TB diagnosis. TB, CVD, and covariates were identified from electronic routinely collected data (primary and secondary care; mortality). Poisson models estimated incident rate ratios for CVD events in people with TB compared to those without. RESULTS: CVD incidence was consistently higher in people with TB, including during the baseline period (pre-TB) and particularly in the acute period: incident rate ratios were US, 3.5 (95% confidence interval, 2.7-4.4), and UK, 2.7 (2.2-3.3). Rate ratios remained high after adjusting for differences in preexisting CVD risk: US, 3.2 (2.2-4.4); UK, 1.6 (1.2-2.1). CONCLUSIONS: Increased CVD incidence was observed in people with TB versus those without, especially within months of TB diagnosis, persistent after adjustment for differences in preexisting risk. Enhancing CVD screening and risk management may improve long-term outcomes in people with TB.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".