PROJECT SUMMARY / ABSTRACT Significance: Approximately 10.6 million people fell ill with tuberculosis (TB) in 2021. However, only 6.4 million of these individuals were diagnosed and linked to care. A quarter of the 4.2 million undiagnosed TB cases were in India. To help find the missing millions, the World Health Organization recommends investigating household contacts of individuals with active TB. This intervention is usually implemented after index patient diagnosis. Considering India’s high TB recurrence rate of approximately 13%, the government has also introduced TB screening among treatment-completed patients. However, evidence around the effectiveness of screening in the post-treatment period is scarce and optimal strategies for implementation have not yet been identified. Specific Aims: Post-treatment household contact investigation is being implemented in Maharashtra, India under the TB Aftermath non-inferiority trial and includes screening of the index patient (n=1076) and their household contacts. This proposal assesses the overall effectiveness of post-treatment household contact investigation and will generate policy-relevant evidence for optimizing the intervention in India and other similar high TB burden settings. We will (1) determine the effect of the intervention compared to control sites that represent standard of care, (2) evaluate implementation of the intervention compared to the standard of care, and (3) develop a model for predicting TB recurrence to help target the intervention among high-risk households. Approach: We will conduct a segmented regression analysis of TB recurrences detected in the 36 months before and 36 months after post-treatment household contact investigation was introduced under TB Aftermath. We will use routine TB data and compare trends in TB Aftermath sites (n=6) to control sites (n=6) selected using propensity score matching. We will evaluate implementation based on the RE-AIM framework and will use mixed methods. For the qualitative component, we will leverage existing TB Aftermath data (n=100 in-depth interviews with patients, contacts, and staff) and will conduct 20 additional interviews with staff at the control sites. We will use a convergent design to merge qualitative and quantitative findings (descriptive statistics on implementation outcomes). Lastly, we will build and validate a predictive model for recurrence using TB Aftermath data (n=1076 participants). We will leverage existing RePORT India cohort data for external validation. By combining rigorous epidemiologic and implementation research methods and engaging with key stakeholders, our team is poised to thoughtfully translate optimized post-treatment household contact investigation into policy and practice. Fellowship Information: The proposed study will serve as the dissertation for Ms. Samyra Cox, an Infectious Disease Epidemiology PhD candidate at the Johns Hopkins Bloomberg School of Public Health. We propose a dedicated mentor...