Who
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Population: Singapore citizens and permanent residents with sputum culture–confirmed pulmonary tuberculosis (TB)
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Sample size: 18,693 pulmonary TB cases
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Key characteristics: Subclinical TB patients were older (median age 62–63 years) and predominantly male (~75%). High-risk groups included individuals aged ≥70 years and those with immunocompromising conditions (renal failure, steroid therapy, malignancy, HIV).
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Exclusions: Patients with extrapulmonary TB
What
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Focus: Description of the subclinical TB disease spectrum, identification of risk factors, and evaluation of diagnostic methods to improve TB control.
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Findings:
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Subclinical TB was common: 41.6% met definition 1 (culture-positive with no cough or cough <2 weeks) and 31.6% met definition 2 (culture-positive with no cough).
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Most subclinical cases had abnormal chest X-rays (~96%), and a substantial proportion had high sputum smear positivity, indicating potential infectiousness despite minimal symptoms.
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Subclinical TB was independently associated with older age, male sex, immunocompromising conditions, known TB contact, and positive sputum TB PCR.
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Diagnostic performance of models was moderate (AUC 0.69–0.72).
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Implications: Symptom-based screening alone is insensitive; relying solely on culture confirmation may delay diagnosis and increase transmission.
When
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Study period: January 1, 2004 to December 31, 2023
Where
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Setting: Singapore, using data from the Singapore National TB Registry
Why
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To address gaps in TB control by characterizing subclinical TB, which may be missed by symptom-based screening, particularly in moderate-incidence settings, and to inform more effective screening strategies.
How
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Design: Retrospective registry-based analysis
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Data sources: Mandatory TB notification data, including demographics, clinical presentation, laboratory results, and treatment outcomes
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Definitions: Two operational definitions of subclinical TB based on cough duration/absence
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Analysis: Logistic regression with stepwise forward selection; odds ratios and adjusted odds ratios with 95% confidence intervals; ROC curve analysis to assess model fit
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