Improving TB Control through Case Finding, Screening and Management: System Dynamics Modeling and Evaluation of a TB Detection Program in Bihar, India

Abstract

Tuberculosis (TB) is a major issue facing today’s world. Millions of people each year are affected by the disease and it is ranked as the second leading cause of death from an infectious disease worldwide, after HIV. The latest statistics estimate that in 2011 there were almost 9 million new cases of TB and 1.4 million deaths due to TB. Shortcourse regimens of first-line drugs that can cure 90% of cases have been available since the 1980’s and substantial progress has been made since then to deliver treatment to the areas where it is needed most. However, challenges remain for TB control. Amongst the most pressing issues is case finding the patients who are in need of treatment. Case finding is extremely important as the majority of people infected with TB are unaware that they have the disease. Further compounding the problem is many of these people live in remote or poverty-stricken areas and are difficult to locate or may not wish to be found. To be successful, a rural TB control program must dedicate resources to either encourage the infected population to self-submit themselves for diagnosis, referred to as passive case finding (PCF) or the organization must physically track down all the cases of TB, referred to as active case finding (ACF), whilst preparing for the rising marginal cost of finding each additional case. Few studies currently exists which approach this problem with a mathematical model, and none which compare case finding methods and allow decision makers to input budget changes and simulate the outcomes on community TB control. The approach taken in this research, in partnership with a rural TB control program in Bihar, India, seeks to fill in this gap. Through on-site visits, constant communication with program personnel and utilizing historic program data, vital insights were gained which were then applied to the multi-layered mathematical systems dynamics model which allow various case-finding methods to be compared and evaluated. Through use of the tool, new information on the interaction of complex variables is realized and focused on investment to assist policy-makers in making decisions.

Date of Award	Dec 2013
Original language	American English
Supervisor	Scott Kennedy (Supervisor)