Modelling the impact of effective private provider engagement on tuberculosis control in urban India

Abstract

In India, the country with the world’s largest burden of tuberculosis (TB), most patients first seek care in the private healthcare sector, which is fragmented and unregulated. Ongoing initiatives are demonstrating effective approaches for engaging with this sector, and form a central part of India’s recent National Strategic Plan: here we aimed to address their potential impact on TB transmission in urban settings, when taken to scale. We developed a mathematical model of TB transmission dynamics, calibrated to urban populations in Mumbai and Patna, two major cities in India where pilot interventions are currently ongoing. We found that, when taken to sufficient scale to capture 75% of patient-provider interactions, the intervention could reduce incidence by upto 21.3% (95% Bayesian credible interval (CrI) 13.0–32.5%) and 15.8% (95% CrI 7.8–28.2%) in Mumbai and Patna respectively, between 2018 and 2025. There is a stronger impact on TB mortality, with a reduction of up to 38.1% (95% CrI 20.0–55.1%) in the example of Mumbai. The incidence impact of this intervention alone may be limited by the amount of transmission that has already occurred by the time a patient first presents for care: model estimates suggest an initial patient delay of 4–5 months before first seeking care, followed by a diagnostic delay of 1–2 months before ultimately initiating TB treatment. Our results suggest that the transmission impact of such interventions could be maximised by additional measures to encourage early uptake of TB services.

Optima TB: A tool to help optimally allocate tuberculosis spending

“…A number of TB epidemiological modelling tools exist to support country-level decision-making, including TIME Impact, AuTuMN, SEARO, VI, and EMOD among others [ 12 – 14 , 16 , 96 ]. While most comprehensive models nowadays include the study of MDR-TB cases [ 12 – 14 , 16 , 96 ], only a few have an explicit structure able to capture co-morbidities or high-risk groups [ 12 , 14 , 96 ] and, of them, only Optima TB and AuTuMN [ 12 ] explicitly analyse XDR cases.…”

“…A number of TB epidemiological modelling tools exist to support country-level decision-making, including TIME Impact, AuTuMN, SEARO, VI, and EMOD among others [ 12 – 14 , 16 , 96 ]. While most comprehensive models nowadays include the study of MDR-TB cases [ 12 – 14 , 16 , 96 ], only a few have an explicit structure able to capture co-morbidities or high-risk groups [ 12 , 14 , 96 ] and, of them, only Optima TB and AuTuMN [ 12 ] explicitly analyse XDR cases. More specifically, TIME and AuTuMN enable users to run a variety of epidemic scenarios, and TIME Impact can be combined with the OneHealth Tool to produce detailed costings of TB interventions and guide NTP implementation by assessing health system components such as infrastructure or human resource needs.…”

Building a tuberculosis-free world: The Lancet Commission on tuberculosis

“…Further, enhanced private sector engagement in concert with other strategies to close gaps in the care cascade, such as targeted active case-finding interventions, optimisation of diagnostic networks, and improved adherence support strategies, could lead to substantial reductions in tuberculosis mortality over the next 30 years. 142,221 Tackling drug resistance Projections show that, over the next decade, at least 6 million people will develop drug-resistant tuberculosis. Without improvements in treatment provision and success for drug-resistant tuberculosis, many of these people will die from tuberculosis, with many transmitting their drug-resistant infections to others.…”

Predicting the impact of patient and private provider behavior on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modeling study

“…Further studies are required to quantify the incentives for early screening and referral based on the public health benefit that might accrue from overall reduction in diagnostic delay. Second, our model needs to be embedded in transmission models to accurately translate the reduction in diagnostic delay into a reduction in TB disease burden in terms of prevalence and incidence [36].…”