Cost minimization analysis of line probe assay for detection of multidrug-resistant tuberculosis in Arkhangelsk region of Russian Federation

Bogdanova, E. N., Mariandyshev, A. O., Balantcev, G. A., Eliseev, P. I., Nikishova, E. I., Gaida, A. I., Enarson, D., Detjen, A, Dacombe, R., Phillips, P. P. J., Squire, S. B. and Gospodarevskaya, E. 2019, Cost minimization analysis of line probe assay for detection of multidrug-resistant tuberculosis in Arkhangelsk region of Russian Federation, PLOS One, vol. 14, no. 1, pp. 1-15, doi: 10.1371/journal.pone.0211203.

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Title Cost minimization analysis of line probe assay for detection of multidrug-resistant tuberculosis in Arkhangelsk region of Russian Federation
Author(s) Bogdanova, E. N.
Mariandyshev, A. O.
Balantcev, G. A.
Eliseev, P. I.
Nikishova, E. I.
Gaida, A. I.
Enarson, D.
Detjen, A
Dacombe, R.
Phillips, P. P. J.
Squire, S. B.
Gospodarevskaya, E.ORCID iD for Gospodarevskaya, E.
Journal name PLOS One
Volume number 14
Issue number 1
Article ID e0211203
Start page 1
End page 15
Total pages 15
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2019-01-29
ISSN 1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Summary BACKGROUND: The development of new diagnostic tools allows for faster detection of both tuberculosis (TB) and multidrug-resistant (MDR) TB and should lead to reduced transmission by earlier initiation of anti TB therapy. The research conducted in the Arkhangelsk region of the Russian Federation in 2012-14 included economic evaluation of Line Probe Assay (LPA) implementation in MDR-TB diagnostics compared to existing culture-based diagnostics of Löwenstein Jensen (LJ) and BacTAlert. Clinical superiority of LPA was demonstrated and results were reported elsewhere. STUDY AIM: The PROVE-IT Russia study aimed to report the outcomes of the cost minimization analysis. METHODS: Costs of LPA-based diagnostic algorithm (smear positive (SSm+) and for smear negative (SSm-) culture confirmed TB patients by Bactec MGIT or LJ were compared with conventional culture-based algorithm (LJ-for SSm- and SSm+ patients and BacTAlert-for SSm+ patients). Cost minimization analysis was conducted from the healthcare system, patient and societal perspectives and included the direct and indirect costs to the healthcare system (microscopy and drug susceptibility test (DST), hospitalization, medications obtained from electronic medical records) and non-hospital direct costs (patient's travel cost, additional expenses associated with hospitalization, supplementary medicine and food) collected at the baseline and two subsequent interviews using the WHO-approved questionnaire. RESULTS: Over the period of treatment the LPA-based diagnostic corresponded to lesser direct and indirect costs comparing to the alternative algorithms. For SSm+ LPA-based diagnostics resulted in the costs 4.5 times less (808.21 US$) than LJ (3593.81 US$) and 2.5 times less than BacTAlert liquid culture (2009.61 US$). For SSm- LPA in combination with Bactec MGIT (1480.75 US$) vs LJ (1785.83 US$) showed the highest cost minimization compared to LJ (2566.09 US$). One-way sensitivity analyses of the key parameters and threshold analyses were conducted and demonstrated that the results were robust to variations in the cost of hospitalization, medications and length of stay. CONCLUSION: From the perspective of Russian Federation healthcare system, TB diagnostic algorithms incorporating LPA method proved to be both more clinically effective and less expensive due to reduction in the number of hospital days to the correct MDR-TB diagnosis and treatment initiation. LPA diagnostics comparing conventional culture diagnostic algorithm MDR-TB was a cost minimizing strategy for both patients and healthcare system.
Language eng
DOI 10.1371/journal.pone.0211203
Field of Research MD Multidisciplinary
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, Bogdanova et al.
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Document type: Journal Article
Collections: Faculty of Health
Population Health
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