Evidence map›Paper›PMID 24400036›Full record

ArticlePloS one2013

Type-specific human papillomavirus biological features: validated model-based estimates.

Iacopo Baussano, K Miriam Elfström, Fulvio Lazzarato, Anna Gillio-Tos, Laura De Marco, Francesca Carozzi, Annarosa Del Mistro, Joakim Dillner, Silvia Franceschi, Guglielmo Ronco

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

15 citing papers in PubMed, 29 citations in OpenAlex.

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  11. [Trajectory modeling for estimating the trend of human papillomavirus infection status among men who have sex with men].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2018
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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

10 authors at 6 institutions in 3 countries.

Iacopo BaussanoInternational Agency for Research on Cancer, Lyon, France.
K Miriam ElfströmDepartment of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Fulvio LazzaratoUnit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, Turin, Italy.
Anna Gillio-TosUnit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, Turin, Italy.
Laura De MarcoUnit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, Turin, Italy.
Francesca CarozziInstitute for Cancer Study and Prevention (ISPO), Florence, Italy.
Annarosa Del MistroMolecular Oncological and Diagnostic Immunology, Venetian Oncology Institute (IOV), Padova, Italy.
Joakim DillnerDepartment of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Silvia FranceschiInternational Agency for Research on Cancer, Lyon, France.
Guglielmo RoncoUnit of Cancer Epidemiology, Centre for Cancer Prevention, Turin, Italy.
University of Turin · ITCentre International de Recherche sur le Cancer · FRKarolinska Institutet · SENational Centre for Disease Prevention and Control · ITPiedmont Reference Center for Epidemiology and Cancer Prevention · ITVeneto Institute of Molecular Medicine · IT

Funding

World Health Organization 001
6 · The paper itself

Abstract

Infection with high-risk (hr) human papillomavirus (HPV) is considered the necessary cause of cervical cancer. Vaccination against HPV16 and 18 types, which are responsible of about 75% of cervical cancer worldwide, is expected to have a major global impact on cervical cancer occurrence. Valid estimates of the parameters that regulate the natural history of hrHPV infections are crucial to draw reliable projections of the impact of vaccination. We devised a mathematical model to estimate the probability of infection transmission, the rate of clearance, and the patterns of immune response following the clearance of infection of 13 hrHPV types. To test the validity of our estimates, we fitted the same transmission model to two large independent datasets from Italy and Sweden and assessed finding consistency. The two populations, both unvaccinated, differed substantially by sexual behaviour, age distribution, and study setting (screening for cervical cancer or Chlamydia trachomatis infection). Estimated transmission probability of hrHPV types (80% for HPV16, 73%-82% for HPV18, and above 50% for most other types); clearance rates decreasing as a function of time since infection; and partial protection against re-infection with the same hrHPV type (approximately 20% for HPV16 and 50% for the other types) were similar in the two countries. The model could accurately predict the HPV16 prevalence observed in Italy among women who were not infected three years before. In conclusion, our models inform on biological parameters that cannot at the moment be measured directly from any empirical data but are essential to forecast the impact of HPV vaccination programmes.

Indexed as

Models, TheoreticalHuman papillomavirus 16Human papillomavirus 18HumansPapillomavirus Infections

Identifiers

PMID24400036
PMCPMC3882251
OpenAlexW2059818812

What OpenQuestion holds

Textmetadata
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.