Evidence map›Paper›PMID 29844989›Full record

ArticlePeerJ2018

The global burden of HIV-1 drug resistance in the past 20 years.

Maurizio Zazzi, Hui Hu, Mattia Prosperi

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

21 citing papers in PubMed, 32 citations in OpenAlex.

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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

3 authors at 2 institutions in 2 countries.

Maurizio ZazziDepartment of Medical Biotechnologies, University of Siena, Italy.
Hui HuDepartment of Epidemiology, University of Florida, United States of America.
Mattia ProsperiDepartment of Epidemiology, University of Florida, United States of America.
University of Florida · USUniversity of Siena · IT

Funding

HIV Dynamic Modelling for Identification of Transmission Epicenters (HIV-DYNAMITE)R21AI138815 · NIAID · UNIVERSITY OF FLORIDA · PI PROSPERI, MATTIA, SALEMI, MARCO · 2018 to 2019
$419k
NIAID NIH HHS R21 AI138815
6 · The paper itself

Abstract

Genotypic drug resistance testing has been an integral part of the clinical management of HIV patients for almost 20 years, not only assisting treatment choices but also informing drug development. Accurate estimations on the worldwide circulation of drug resistance are difficult to obtain, particularly in low/middle-income countries. In this work, we queried two of the largest public HIV sequence repositories in the world-Los Alamos and Stanford HIVdb-to derive global prevalence, time trends and geodemographic predictors of HIV drug resistance. Different genotypic interpretation systems were used to ascertain resistance to reverse transcriptase and protease inhibitors. Continental, subtype-specific (including circulating recombinant forms) stratification as well as analysis on drug-naïve isolates were performed. Geographic information system analysis correlated country-specific drug resistance to sociodemographic and health indicators obtained from the World Bank. By looking at over 33,000 sequences worldwide between 1996 and 2016, increasing drug resistance trends with non-B subtypes and recombinants were found; transmitted drug resistance appeared to remain stable in the last decade. While an increase in drug resistance is expected with antiretroviral therapy rollout in resource-constrained areas, the plateau effect in areas covered by the most modern drug regimens warns against the downgrading of the resistance issue.

Indexed as

Antiretroviral treatmentDrug resistanceGenotypic interpretation systemHIV/AIDSHIV subtype

Identifiers

PMID29844989
PMCPMC5971836
OpenAlexW2803131802

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.