Evidence map›Paper›PMID 31310636›Full record

ArticlePloS one2019

Streamlining the screening cascade for active Hepatitis C in Russia: A cost-effectiveness analysis.

Paul Jülicher, Vladimir P Chulanov, Nikolay N Pimenov, Ekaterina Chirkova, Anna Yankina, Claudio Galli

Open access · goldAbstract read
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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
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

5 citing papers in PubMed, 16 citations in OpenAlex.

  1. Trial
  2. Article
  3. Cost-Effectiveness of Hepatitis C Virus Case Finding and Treatment in Eastern Europe and Central Asia.Liver international : official journal of the International Association for the Study of the Liver · 2025
    Article
  4. Article
  5. Optimizing diagnostic algorithms to advance Hepatitis C elimination in Italy: A cost effectiveness evaluation.Liver international : official journal of the International Association for the Study of the Liver · 2022
    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

6 authors at 4 institutions in 2 countries.

Paul JülicherHealth Economics and Outcomes Research, Abbott Diagnostics, Wiesbaden, Germany.ORCID 0000-0002-6606-1020
Vladimir P ChulanovReference Center for Viral Hepatitis, Central Research Institute of Epidemiology, Moscow, Russia.ORCID 0000-0001-6303-9293
Nikolay N PimenovReference Center for Viral Hepatitis, Central Research Institute of Epidemiology, Moscow, Russia.
Ekaterina ChirkovaReference Center for Viral Hepatitis, Central Research Institute of Epidemiology, Moscow, Russia.
Anna YankinaMedical Communication, Abbott Diagnostics, Khimki, Russia.
Claudio GalliGlobal Medical & Scientific Affairs, Abbott Diagnostics, Rome, Italy.
Central Research Institute of Epidemiology · RUAbbott (Germany) · DECentre for Independent Social Research · RUSechenov University · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveScreening for hepatitis C in Russia is a complex process that involves several visits and stepwise testing, limiting adherence and substantially reducing the yield in the identification of active infections. We aimed to evaluate the cost-effectiveness of different screening algorithms from a health system perspective.

methodsA decision analytic model was applied to a hypothetical adult population eligible to participate in a general screening program for hepatitis C in Russia. The standard pathway (I: Screen for anti-HCV antibodies followed by a nucleic acid test for HCV RNA on antibody positives) was compared to three alternatives (II: Screen for antibodies, a reflexed test for HCV antigen on antibody positives, and RNA on antigen negatives; III: Screen for antibodies, a reflexed test for HCV antigen on antibody positives; IV: Screen for antigen). Each strategy considered a cascade of events (referral, adherence, testing, diagnosis) that must occur for screening to be effective. The primary measure of effectiveness was the number of diagnosed active infections. Calculations followed a health system perspective with costs derived from 2017 reimbursement rates and a willingness-to-pay of 2,000RUB ($82) per diagnosed active infection. Model was tested with deterministic and probabilistic sensitivity analyses.

resultsNon-adherence to screening stages reduced the capture rate of active infections in Strategy I from 79.0% to 40.6%. Strategies II, III, and IV were less affected and identified 69%, 67%, and 104% more infections. Average costs per diagnosed infection were decreased by 41% from 89,599RUB ($3,681) for I to 53,072RUB ($2,180), 53,004RUB ($2,177), and 59,633RUB ($2,450) for II, III, and IV, respectively. With a probability of 97%, Strategy III was most cost-effective with an incremental cost-effectiveness ratio vs. I of -1,373RUB (CI: -5,011RUB to -2,033RUB; $-56; CI: -$206 to -$84). Below a willingness-to-pay of 91,000RUB ($3,738), Strategy IV was not cost-effective. Sensitivity analyses confirmed the robustness of results.

conclusionsTesting strategies for hepatitis C with HCV antigen on HCV antibody positive cases offer a streamlining opportunity for population screening programs. Those shall increase the chances for detecting active infections and are cost-effective over current practice in Russia.

Indexed as

Cost-Benefit AnalysisHepacivirusAlgorithmsAntigens, ViralDecision MakingHepatitis CHepatitis C AntibodiesHumansMass ScreeningModels, StatisticalPatient ComplianceProbabilityQuality-Adjusted Life YearsRNA, ViralRussiaAntigens, ViralHepatitis C AntibodiesRNA, Viral

Identifiers

PMID31310636
PMCPMC6634401
OpenAlexW2958214337

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.