A model-based investigation into urban-rural disparities in tuberculosis treatment outcomes under the Revised National Tuberculosis Control Programme in India.

• Main Authors: Himanshu Singh, Varun Ramamohan
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0228712

In this study, we investigate differences in tuberculosis (TB) treatment outcomes between urban and rural India and estimate their impact on epidemiological outcomes such as TB incidence, prevalence and mortality using a mathematical model of TB transmission dynamics. Publicly available district-level treatment outcomes data for new and previously treated TB cases was analyzed in conjunction with census data providing the proportion of urban population in each district to determine the effect of urbanity/rurality on treatment outcomes. Districts were grouped in clusters based on the proportion of urban population in each district, wherein the clusters were identified by applying machine learning methods. Regression analyses revealed that average treatment success rates among both new and previously treated cases decline with increase in the proportion of urban population in a district cluster, with substantially sharper declines in treatment success rates with degree of urbanity observed for previously treated cases. The impact of differences in treatment outcomes on epidemiological outcomes was estimated using a dynamic transmission model developed for this purpose. For example, the cluster with highest treatment success rates is projected to have an average of 3.2% fewer deaths per 100,000 population in comparison with the national average across 2019-24, and the cluster with the lowest treatment success rates has an average of 4.5% more deaths per 100,000 in comparison with the national average. We anticipate that these disparities in TB treatment outcomes and epidemiology between urban and rural India may motivate investigations into the associated causes and their redressal.