ArticleInfection ecology & epidemiology2016
Molecular characterization of HCV in a Swedish county over 8 years (2002-2009) reveals distinct transmission patterns.
Article in Infection ecology & epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatitis C virus (HCV) is a major public health concern and data on its molecular epidemiology in Sweden is scarce. We carried out an 8-year population-based study of newly diagnosed HCV cases in one of Sweden's centrally situated counties, Södermanland (D-county). The aim was to characterize the HCV strains circulating, analyze their genetic relatedness to detect networks, and in combination with demographic data learn more about transmission.
methodsMolecular analyses of serum samples from 91% (N=557) of all newly notified cases in D-county, 2002-2009, were performed. Phylogenetic analysis (NS5B gene, 300 bp) was linked to demographic data from the national surveillance database, SmiNet, to characterize D-county transmission clusters. The linear-by-linear association test (LBL) was used to analyze trends over time.
resultsThe most prevalent subtypes were 1a (38%) and 3a (34%). Subtype 1a was most prevalent among cases transmitted via sexual contact, via contaminated blood, or blood products, while subtype 3a was most prevalent among people who inject drugs (PWIDs). Phylogenetic analysis revealed that the subtype 3a sequences formed more and larger transmission clusters (50% of the sequences clustered), while the 1a sequences formed smaller clusters (19% of the sequences clustered), possibly suggesting different epidemics.
conclusionWe found different transmission patterns in D-county which may, from a public health perspective, have implications for how to control virus infections by targeted interventions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.