Sandip Mandal1, Nimalan Arinaminpathy2. 1. Public Health Foundation of India, New Delhi, India. 2. Public Health Foundation of India, New Delhi, India Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College, London, UK nim.pathy@imperial.ac.uk.
Abstract
BACKGROUND: TB in India presents the challenges of a complex disease in a complex healthcare system. Mathematical models, offering a framework for capturing such complexities, have proven useful in exploring strategies for the control of TB. As the use of such techniques develops in future, it is important to understand what aspects of the healthcare system are most critical for models to faithfully capture. METHODS: We ask what type of intervention should be prioritized for the control of TB, amongst: improved diagnosis of TB per visit to a healthcare provider; improved treatment success; and increased identification of TB cases in the community? Using simple mathematical models, calibrated to the national TB epidemic in India, we explore how the relative importance of each of these interventions is affected by different assumptions for the patient pathway in careseeking, thus outlining aspects of the healthcare system that may matter most for the transmission dynamics of TB. RESULTS: We illustrate that, under a range of plausible parameter assumptions, it is possible to generate conditions under which a case-finding intervention would be prioritized over improvement of diagnosis and treatment, and vice versa. Key data needs include: the proportion of patients not contacting the healthcare system, and the mean patient delay before first seeking care. CONCLUSIONS: For mathematical models addressing strategic priorities for TB control, it is important to adequately quantify the dynamics of careseeking. We outline ways in which these data gaps may be addressed, and questions for future work.
BACKGROUND: TB in India presents the challenges of a complex disease in a complex healthcare system. Mathematical models, offering a framework for capturing such complexities, have proven useful in exploring strategies for the control of TB. As the use of such techniques develops in future, it is important to understand what aspects of the healthcare system are most critical for models to faithfully capture. METHODS: We ask what type of intervention should be prioritized for the control of TB, amongst: improved diagnosis of TB per visit to a healthcare provider; improved treatment success; and increased identification of TB cases in the community? Using simple mathematical models, calibrated to the national TB epidemic in India, we explore how the relative importance of each of these interventions is affected by different assumptions for the patient pathway in careseeking, thus outlining aspects of the healthcare system that may matter most for the transmission dynamics of TB. RESULTS: We illustrate that, under a range of plausible parameter assumptions, it is possible to generate conditions under which a case-finding intervention would be prioritized over improvement of diagnosis and treatment, and vice versa. Key data needs include: the proportion of patients not contacting the healthcare system, and the mean patient delay before first seeking care. CONCLUSIONS: For mathematical models addressing strategic priorities for TB control, it is important to adequately quantify the dynamics of careseeking. We outline ways in which these data gaps may be addressed, and questions for future work.