Evidence map›Paper›PMID 31364569›Full record

ArticleEpidemiology and infection2019

Estimating prevalence from dried blood spots without using biological cut-offs: application of a novel approach to hepatitis C virus in drug users in France (ANRS-Coquelicot survey).

L Léon, J Pillonel, M Jauffret-Roustide, F Barin, Y Le Strat

Abstract readMulticenter Study
In one paragraph

Article in Epidemiology and infection, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

5 authors.

L LéonSanté publique France, French national public health agency,F-94415 Saint-Maurice,France.ORCID 0000-0002-0871-3510
J PillonelSanté publique France, French national public health agency,F-94415 Saint-Maurice,France.
M Jauffret-RoustideSanté publique France, French national public health agency,F-94415 Saint-Maurice,France.
F BarinUMR Inserm U1259, Université de Tours, & CNR VIH, CHU Bretonneau,Tours,France.
Y Le StratSanté publique France, French national public health agency,F-94415 Saint-Maurice,France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Seroprevalence estimation using cross-sectional serosurveys can be challenging due to inadequate or unknown biological cut-off limits of detection. In recent years, diagnostic assay cut-offs, fixed assay cut-offs and more flexible approaches as mixture modelling have been proposed to classify biological quantitative measurements into a positive or negative status. Our objective was to estimate the prevalence of anti-HCV antibodies among drug users (DU) in France in 2011 using a biological test performed on dried blood spots (DBS) collected during a cross-sectional serosurvey. However, in 2011, we did not have a cut-off value for DBS. We could not use the values for serum or plasma, knowing that the DBS value was not necessarily the same. Accordingly, we used a method which consisted of applying a two-component mixture model with age-dependent mixing proportions using penalised splines. The component densities were assumed to be log-normally distributed and were estimated in a Bayesian framework. Anti-HCV prevalence among DU was estimated at 43.3% in France and increased with age. Our method allowed us to provide estimates of age-dependent prevalence using DBS without having a specified biological cut-off value.

Indexed as

AdultBayes TheoremCross-Sectional StudiesDried Blood Spot TestingDrug UsersEnzyme-Linked Immunosorbent AssayFemaleFranceHepacivirusHepatitis CHepatitis C AntibodiesHumansMaleMiddle AgedPredictive Value of TestsPrevalenceHepatitis C AntibodiesRNA, ViralCut-offdrug usershepatitis C virusmixture modelprevalence

Identifiers

PMID31364569
PMCPMC6625185

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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.