Incorporating patient reporting patterns to evaluate spatially targeted TB interventions.

Area of Science:

• Epidemiology
• Public Health
• Mathematical Modeling

Background:

• Tuberculosis (TB) exhibits significant geographic heterogeneity, necessitating targeted interventions for maximum impact.
• Standard TB notification data may not fully represent this geographic variation.
• Analyzing patient reporting patterns, where individuals seek diagnosis away from their residence, can refine intervention targeting.

Purpose of the Study:

• To assess the impact of geographic targeting for TB interventions.
• To investigate how patient reporting patterns influence the effectiveness of TB control strategies.
• To improve the utilization of TB notification data for targeted public health efforts.

Main Methods:

• Utilized demographic and TB report data from Dhaka North and South City Corporations.
• Developed and calibrated a TB transmission model using patient-level data.
• Assessed intervention impact under four distinct patient reporting pattern assumptions.

Main Results:

• Geographically targeted interventions showed variable impact based on reporting pattern assumptions.
• In Dhaka North City Corporation, targeted active case finding reduced TB incidence by 1.20 (weak correlation) to 2.45 (perfect correlation) compared to untargeted methods.
• Similar reductions were observed in Dhaka South City Corporation (1.03 vs. 2.08).

Conclusions:

• Patient movement for diagnosis significantly influences ward-level TB transmission.
• Accurate understanding of patient reporting patterns enhances estimates for targeted intervention impact.
• High-quality, patient-level data is essential for optimizing TB intervention strategies.