Abstract
RATIONALE: Tuberculosis (TB) is characterized by a subclinical phase (symptoms absent or not considered abnormal); prediagnostic phase (symptoms noticed but diagnosis not pursued); and clinical phase (care actively sought). Diagnostic capacity during these phases is limited. OBJECTIVES: To estimate the population-level impact of TB case-finding strategies in the presence of subclinical and prediagnostic disease. METHODS: We created a mathematical epidemic model of TB, calibrated to global incidence. We then introduced three prototypical diagnostic interventions: increased sensitivity of diagnosis in the clinical phase by 20% ("passive"); early diagnosis during the prediagnostic phase at a rate of 10% per year ("enhanced"); and population-based diagnosis of 5% of undiagnosed prevalent cases per year ("active"). MEASUREMENTS AND MAIN RESULTS: If the subclinical phase was ignored, as in most models, the passive strategy was projected to reduce TB incidence by 18% (90% uncertainty range [UR], 11-32%) by year 10, compared with 23% (90% UR, 14-35%) for the enhanced strategy and 18% (90% UR, 11-28%) for the active strategy. After incorporating a subclinical phase into the model, consistent with population-based prevalence surveys, the active strategy still reduced 10-year TB incidence by 16% (90% UR, 11-28%), but the passive and enhanced strategies' impact was attenuated to 11% (90% UR, 8-25%) and 6% (90% UR, 4-13%), respectively. The degree of attenuation depended strongly on the transmission rate during the subclinical phase. CONCLUSIONS: Subclinical disease may limit the impact of current diagnostic strategies for TB. Active detection of undiagnosed prevalent cases may achieve greater population-level TB control than increasing passive case detection.