Evidence map›Paper›PMID 30695043›Full record

ArticlePloS one2019

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

E N Bogdanova, A O Mariandyshev, G A Balantcev, P I Eliseev, E I Nikishova, A I Gaida, D Enarson, A Detjen, R Dacombe, P P J Phillips and 2 more

Abstract readComparative Study
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

E N BogdanovaNorthern Arctic Federal University, Arkhangelsk, Russian Federation.ORCID 0000-0001-9610-4709
A O MariandyshevNorthern State Medical University, Arkhangelsk, Russian Federation.
G A BalantcevNorthern Arctic Federal University, Arkhangelsk, Russian Federation.
P I EliseevNorthern State Medical University, Arkhangelsk, Russian Federation.
E I NikishovaArkhangelsk Clinical Antituberculosis Dispensary, Arkhangelsk, Russian Federation.
A I GaidaArkhangelsk Clinical Antituberculosis Dispensary, Arkhangelsk, Russian Federation.
D EnarsonThe International Union Against Tuberculosis and Lung Disease, Paris, France.
A DetjenThe International Union Against Tuberculosis and Lung Disease, Paris, France.
R DacombeLiverpool School of Tropical Medicine, Liverpool, United Kingdom.
P P J PhillipsMRC Clinical Trials Unit at UCL, London, United Kingdom.
S B SquireLiverpool School of Tropical Medicine, Liverpool, United Kingdom.
E GospodarevskayaLiverpool School of Tropical Medicine, Liverpool, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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

aimThe PROVE-IT Russia study aimed to report the outcomes of the cost minimization analysis.

methodsCosts 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.

resultsOver 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.

conclusionFrom 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.

Indexed as

AdultAlgorithmsBacteriological TechniquesCosts and Cost AnalysisFemaleHealth Care CostsHealth ExpendituresHumansMaleMiddle AgedReagent Kits, DiagnosticRussiaSurveys and QuestionnairesTuberculosis, Multidrug-ResistantReagent Kits, Diagnostic

Identifiers

PMID30695043
PMCPMC6350971

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.