Evidence map›Paper›PMID 34223016›Full record

ArticleJAC-antimicrobial resistance2020

How to measure hospital antibiotic consumption: comparison of two methods from data surveillance in France.

Florence Stordeur, Katiuska Miliani, Ludivine Lacavé, Anne-Marie Rogues, Catherine Dumartin, Serge Alfandari, Pascal Astagneau, François L'Hériteau, ATB-Raisin 2012 steering committee and the ENP 2012 steering committee

Abstract read
In one paragraph

Article in JAC-antimicrobial resistance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Florence StordeurCentre hospitalier intercommunal de Poissy St Germain, 10 rue du champ Gaillard, 78100 Poissy, France.ORCID 0000-0001-6527-8845
Katiuska MilianiCentre d'appui pour la prévention des infections associées aux soins (CPias) Ile-de-France, Paris, France.
Ludivine LacavéAssistance Publique - Hôpitaux de Paris (AP-HP), délégation à la recherche clinique et à l'innovation (DRCI), Paris, France.
Anne-Marie RoguesUniversité Bordeaux, Inserm, Bordeaux Population Health Research Center, Team Pharmacoepidemiology, UMR 1219, F-33000 Bordeaux, France.
Catherine DumartinUniversité Bordeaux, Inserm, Bordeaux Population Health Research Center, Team Pharmacoepidemiology, UMR 1219, F-33000 Bordeaux, France.
Serge AlfandariCentre Hospitalier de Tourcoing, 59208 Tourcoing, France.
Pascal AstagneauCentre d'appui pour la prévention des infections associées aux soins (CPias) Ile-de-France, Paris, France.
François L'HériteauCentre d'appui pour la prévention des infections associées aux soins (CPias) Ile-de-France, Paris, France.
ATB-Raisin 2012 steering committee and the ENP 2012 steering committee

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAntibiotic use (ABU) surveillance in healthcare facilities (HCFs) is essential to guide stewardship. Two methods are recommended: antibiotic consumption (ABC), expressed as the number of DDD/1000 patient-days; and prevalence of antibiotic prescription (ABP) measured through point prevalence surveys. However, no evidence is provided about whether they lead to similar conclusions.

objectivesTo compare ABC and ABP regarding HCF ranking and their ability to identify outliers.

methodsThe comparison was made using 2012 national databases from the antibiotic surveillance network and prevalence study. HCF rankings according to each method were compared with Spearman's correlation coefficient. Analyses included the ABU from entire HCFs as well as according to type, clinical ward and by antibiotic class and specific molecule.

resultsA total of 1076 HCFs were included. HCF rankings were strongly correlated in the whole cohort. The correlation was stronger for HCFs with a higher number of beds or with a low or moderate proportion of acute care beds. ABU correlation between ABC or ABP was globally moderate or weak in specific wards. Furthermore, the two methods did not identify the same outliers, whichever HCF characteristics were analysed. Correlation between HCF ranking varied according to the antibiotic class.

conclusionsBoth methods ranked HCFs similarly overall according to ABC or ABP; however, major differences were observed in ranking of clinical wards, antibiotic classes and detection of outliers. ABC and ABP are two markers of ABU that could be used as two complementary approaches to identify targets for improvement.

Identifiers

PMID34223016
PMCPMC8210307

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