Evidence map›Paper›PMID 26196165›Full record

ArticleEmerging infectious diseases2015

Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France.

Cédric Abat, Hervé Chaudet, Philippe Colson, Jean-Marc Rolain, Didier Raoult

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2015. 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
–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

21 citing papers in PubMed.

  1. Rapid and Simple Approaches for Diagnosis ofPolish journal of microbiology · 2022
    Review
  2. Control of common viral epidemics but not of SARS-CoV-2 through the application of hygiene and distancing measures.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2022
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2020
    Article
  9. Comparison of mortality associated with respiratory viral infections between December 2019 and March 2020 with that of the previous year in Southeastern France.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2020
    Article
  10. SARS-CoV-2: fear versus data.International journal of antimicrobial agents · 2020
    Article
  11. Review
  12. Article
  13. No global increase in resistance to antibiotics: a snapshot of resistance from 2001 to 2016 in Marseille, France.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2019
    Article
  14. Review
  15. Extremely and pandrug-resistant bacteria extra-deaths: myth or reality?European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2018
    Review
  16. Using MALDI-TOF MS typing method to decipher outbreak: the case of Staphylococcus saprophyticus causing urinary tract infections (UTIs) in Marseille, France.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2017
    Article
  17. Article
  18. Article
  19. The Point-of-Care Laboratory in Clinical Microbiology.Clinical microbiology reviews · 2016
    Review
  20. 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

5 authors.

Cédric Abat
Hervé Chaudet
Philippe Colson
Jean-Marc Rolain
Didier Raoult

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infectious diseases are a major threat to humanity, and accurate surveillance is essential. We describe how to implement a laboratory data-based surveillance system in a clinical microbiology laboratory. Two historical Microsoft Excel databases were implemented. The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary β-lactam resistance phenotypes for 15 selected bacterial species. The first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing. From May 21, 2013, through June 4, 2014, BALYSES and MARSS enabled the detection of 52 abnormal events for 24 bacterial species, leading to 19 official reports. This system is currently being refined and improved.

Indexed as

Decision Making, Computer-AssistedEpidemiological MonitoringDisease NotificationDisease OutbreaksDrug Resistance, BacterialFranceHumansLaboratoriesMicrobiologyantimicrobial resistancebacteriadata collectionearly warningepidemiologymicrobiologypublic healthsyndromic surveillance

Identifiers

PMID26196165
PMCPMC4517727

What OpenQuestion holds

Textmetadata
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.