Evidence map›Paper›PMID 31830076›Full record

ArticlePloS one2019

Linking surveillance and clinical data for evaluating trends in bloodstream infection rates in neonatal units in England.

Caroline Fraser, Berit Muller-Pebody, Ruth Blackburn, Jim Gray, Sam J Oddie, Ruth E Gilbert, Katie Harron

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. 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

7 authors.

Caroline FraserUCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.ORCID 0000-0001-7260-7053
Berit Muller-PebodyNational Infection Service, Public Health England, London, United Kingdom.
Ruth BlackburnInstitute of Health Informatics, University College London, London, United Kingdom.
Jim GrayMicrobiology, Birmingham Women's & Children's Hospitals, Birmingham, United Kingdom.
Sam J OddieBradford Neonatology, Bradford Royal Infirmary, Bradford, United Kingdom.
Ruth E GilbertUCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
Katie HarronUCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.ORCID 0000-0002-3418-2856

Funding

Department of Health 03/37/06Department of Health 04/51/01Department of Health 08/13/47Department of Health 12/167/02Medical Research Council MR/S003797/1Wellcome TrustWellcome Trust 103975/A/14/Z
6 · The paper itself

Abstract

objectiveTo evaluate variation in trends in bloodstream infection (BSI) rates in neonatal units (NNUs) in England according to the data sources and linkage methods used.

methodsWe used deterministic and probabilistic methods to link clinical records from 112 NNUs in the National Neonatal Research Database (NNRD) to national laboratory infection surveillance data from Public Health England. We calculated the proportion of babies in NNRD (aged <1 year and admitted between 2010-2017) with a BSI caused by clearly pathogenic organisms between two days after admission and two days after discharge. We used Poisson regression to determine trends in the proportion of babies with BSI based on i) deterministic and probabilistic linkage of NNRD and surveillance data (primary measure), ii) deterministic linkage of NNRD-surveillance data, iii) NNRD records alone, and iv) linked NNRD-surveillance data augmented with clinical records of laboratory-confirmed BSI in NNRD.

resultsUsing deterministic and probabilistic linkage, 5,629 of 349,740 babies admitted to a NNU in NNRD linked with 6,660 BSI episodes accounting for 38% of 17,388 BSI records aged <1 year in surveillance data. The proportion of babies with BSI due to clearly pathogenic organisms during their NNU admission was 1.0% using deterministic plus probabilistic linkage (primary measure), compared to 1.0% using deterministic linkage alone, 0.6% using NNRD records alone, and 1.2% using linkage augmented with clinical records of BSI in NNRD. Equivalent proportions for babies born before 32 weeks of gestation were 5.0%, 4.8%, 2.9% and 5.9%. The proportion of babies who linked to a BSI decreased by 7.5% each year (95% confidence interval [CI]: -14.3%, -0.1%) using deterministic and probabilistic linkage but was stable using clinical records of BSI or deterministic linkage alone.

conclusionLinkage that combines BSI records from national laboratory surveillance and clinical NNU data sources, and use of probabilistic methods, substantially improved ascertainment of BSI and estimates of BSI trends over time, compared with single data sources.

Indexed as

BacteremiaData AccuracyDatabases, FactualElectronic Health RecordsEnglandFemaleGestational AgeHumansInfant, NewbornInfant, Newborn, DiseasesIntensive Care Units, PediatricMaleMedical Record LinkagePopulation Surveillance

Identifiers

PMID31830076
PMCPMC6907823

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.