SynthesisMedicine2016
Epidemiological burden estimates for pathologies with a nonconstant risk: an application to HCV in Italy according to age, Metavir score, and genotype: A systematic review and meta-analysis.
Synthesis in Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Intention to Screen for Hepatitis C Among University Students: Influence of Different Communicative Scenarios.Frontiers in psychiatry · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Between western European countries, the hepatitis C virus (HCV) endemic is highest in Italy. The main objective of this paper is to estimate the endemic diffusion of hepatitis C at the national level and by geographical area, with an extrapolation at the regional level and by uniform cohorts of subjects (by sex and year of birth). The secondary objective is a stratification by gravity of the estimated statistical figures to provide an overview of possible targets of the new anti-HCV treatments.PubMed and the Cochrane Library were searched for relevant Italian populations studies regarding HCV prevalence. Random and fixed effect models were used for pooling data. To develop the epidemiological model, a meta-analysis of studies of Italian populations and the explicit consideration of the changes in the etiology of the disease in different cohorts (by year of birth) of population and the impact of effective treatments that have been introduced since the 1990s. A Markovian transition model, which is based on the distribution of HCV+ and HCV Ribonucleic Acid (RNA)+ subjects, provides a plausible assessment of the Italian situation. The Meta-analysis of Observational Studies in Epidemiology recommendations/statements were followed.In 2014, 1569,215 HCV+ subjects (95% credible interval [CrI]: 1202,630-2021,261) were estimated in Italy, with a 2.58% prevalence (95% CrI: 1.98%-3.33%). A total of 828,884 HCV RNA+ subjects (95% CrI: 615,892-1081,123), which is equal to a 1.36% prevalence (95% CrI: 1.01%-1.78%), is higher in southern Italy and the islands (1.9%) than in central-northern Italy (1.1%). The predominance of adult and elderly subjects, with an old or very old infection, inevitably entails a significant number of HCV RNA+ subjects in the advanced stages of the illness. According to our estimates, approximately 400,000 subjects have cirrhosis, decompensated cirrhosis, and hepatocarcinoma, with a median age of 70 years.The model aims to support policymakers to define action plans by providing an estimate of both the emerged infected population and nonemerged infected population by age, gender, gravity, genotype, and geographical area. In the future, the model may contribute to simulation of the costs and outcome of different action strategies that can be adopted by health authorities.
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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.