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BMC Med. 2019 Aug 6;17(1):155. doi: 10.1186/s12916-019-1384-8.

Informing decision-making for universal access to quality tuberculosis diagnosis in India: an economic-epidemiological model.

Author information

1
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., E6529, Baltimore, MD, 21205, USA. hsohn6@jhu.edu.
2
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., E6529, Baltimore, MD, 21205, USA.
3
Division of Infectious Disease, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
4
Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
5
Department of Epidemiology & Biostatistics & McGill International TB Centre, McGill University, Montreal, QC, H3A 1A2, Canada.
6
Manipal McGill Centre for Infectious Diseases, Manipal Academy of Higher Education, Manipal, India.

Abstract

BACKGROUND:

India and many other high-burden countries have committed to providing universal access to high-quality diagnosis and drug susceptibility testing (DST) for tuberculosis (TB), but the most cost-effective approach to achieve this goal remains uncertain. Centralized testing at district-level hub facilities with a supporting sample transport network can generate economies of scale, but decentralization to the peripheral level may provide faster diagnosis and reduce losses to follow-up (LTFU).

METHODS:

We generated functions to evaluate the costs of centralized and decentralized molecular testing for tuberculosis with Xpert MTB/RIF (Xpert), a WHO-endorsed test which can be performed at centralized and decentralized levels. We merged the cost estimates with an agent-based simulation of TB transmission in a hypothetical representative region in India to assess the impact and cost-effectiveness of each strategy.

RESULTS:

Compared against centralized Xpert testing, decentralization was most favorable when testing volume at decentralized facilities and pre-treatment LTFU were high, and specimen transport network was exclusively established for TB. Assuming equal quality of centralized and decentralized testing, decentralization was cost-saving, saving a median $338,000 (interquartile simulation range [IQR] - $222,000; $889,000) per 20 million people over 10 years, in the most cost-favorable scenario. In the most cost-unfavorable scenario, decentralized testing would cost a median $3161 [IQR $2412; $4731] per disability-adjusted life year averted relative to centralized testing.

CONCLUSIONS:

Decentralization of Xpert testing is likely to be cost-saving or cost-effective in most settings to which these simulation results might generalize. More decentralized testing is more cost-effective in settings with moderate-to-high peripheral testing volumes, high existing clinical LTFU, inability to share specimen transport costs with other disease entities, and ability to ensure high-quality peripheral Xpert testing. Decision-makers should assess these factors when deciding whether to decentralize molecular testing for tuberculosis.

KEYWORDS:

Cost-benefit analysis; Diagnostic techniques and procedures; Systems analysis; Tuberculosis

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