Evidence map›Paper›PMID 34312655›Full record

ArticleThe Journal of antimicrobial chemotherapy2021

Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points.

Robin Bruyndonckx, Samuel Coenen, Niels Adriaenssens, Ann Versporten, Dominique L Monnet, Herman Goossens, Geert Molenberghs, Klaus Weist, Niel Hens, ESAC-Net study group

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of antimicrobial chemotherapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed, 11 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

10 authors at 3 institutions in 2 countries.

Robin BruyndonckxLaboratory of Medical Microbiology, Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.ORCID 0000-0002-4217-2869
Samuel CoenenLaboratory of Medical Microbiology, Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.ORCID 0000-0002-1238-8052
Niels AdriaenssensLaboratory of Medical Microbiology, Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.
Ann VersportenLaboratory of Medical Microbiology, Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.
Dominique L MonnetDisease Programmes, European Centre for Disease Prevention and Control, Stockholm, Sweden.
Herman GoossensLaboratory of Medical Microbiology, Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.
Geert MolenberghsInteruniversity Institute for Biostatistics and statistical Bioinformatics (I-BIOSTAT), Data Science Institute, Hasselt University, Hasselt, Belgium.
Klaus WeistDisease Programmes, European Centre for Disease Prevention and Control, Stockholm, Sweden.
Niel HensInteruniversity Institute for Biostatistics and statistical Bioinformatics (I-BIOSTAT), Data Science Institute, Hasselt University, Hasselt, Belgium.
ESAC-Net study group
University of Antwerp · BEEuropean Centre for Disease Prevention and Control · SEHasselt University · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis tutorial describes and illustrates statistical methods to detect time trends possibly including abrupt changes (referred to as change-points) in the consumption of antibiotics in the community.

methodsFor the period 1997-2017, data on consumption of antibacterials for systemic use (ATC group J01) in the community, aggregated at the level of the active substance, were collected using the WHO ATC/DDD methodology and expressed in DDD (ATC/DDD index 2019) per 1000 inhabitants per day. Trends over time and presence of common change-points were studied through a set of non-linear mixed models.

resultsAfter a thorough description of the set of models used to assess the time trend and presence of common change-points herein, the methodology was applied to the consumption of antibacterials for systemic use (ATC J01) in 25 EU/European Economic Area (EEA) countries. The best fit was obtained for a model including two change-points: one in the first quarter of 2004 and one in the last quarter of 2008.

conclusionsAllowing for the inclusion of common change-points improved model fit. Individual countries investigating changes in their antibiotic consumption pattern can use this tutorial to analyse their country data.

Indexed as

Anti-Bacterial AgentsDrug UtilizationEuropeHumansAnti-Bacterial Agents

Identifiers

PMID34312655
PMCPMC8314099
OpenAlexW3187062000

What OpenQuestion holds

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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.