Evidence map›Paper›PMID 28623916›Full record

ArticleBMC medical informatics and decision making2017

Using record linkage to validate notification and laboratory data for a more accurate assessment of notifiable infectious diseases.

Faye J Lim, Christopher C Blyth, Avram Levy, Parveen Fathima, Nicholas de Klerk, Carolien Giele, Hannah C Moore

Abstract readValidation Study
In one paragraph

Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Elevated CD3PeerJ · 2018
    Article
  5. 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.

Faye J LimWesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, The University of Western Australia, PO Box 855, West Perth, WA, 6872, Australia. Janice.Lim@telethonkids.org.au.
Christopher C BlythWesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, The University of Western Australia, PO Box 855, West Perth, WA, 6872, Australia.
Avram LevyPathWest Laboratory Medicine WA, QE2 Medical Centre, Locked Bag 2009, Nedlands, WA, 6906, Australia.
Parveen FathimaWesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, The University of Western Australia, PO Box 855, West Perth, WA, 6872, Australia.
Nicholas de KlerkWesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, The University of Western Australia, PO Box 855, West Perth, WA, 6872, Australia.
Carolien GieleCommunicable Disease Control Directorate, Western Australian Department of Health, PO Box 8172, Perth Business Centre, Perth, 6879, Australia.
Hannah C MooreWesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, The University of Western Australia, PO Box 855, West Perth, WA, 6872, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfectious disease burden is commonly assessed using notification data. Using retrospective record linkage in Western Australia, we described how well notification data captures laboratory detections of influenza, pertussis and invasive pneumococcal disease (IPD).

methodsWe linked data from the Western Australian Notifiable Infectious Diseases Database (WANIDD) and the PathWest Laboratory Database (PathWest) pertaining to the Triple I birth cohort, born in Western Australia in 1996-2012. These were combined to calculate the number of unique cases captured in each dataset alone or in both datasets. To assess the impact of under-ascertainment, we compared incidence rates calculated using WANIDD data alone and using combined data.

resultsOverall, there were 5550 influenza, 513 IPD (2001-2012) and 4434 pertussis cases (2000-2012). Approximately 2% of pertussis and IPD cases and 7% of influenza cases were solely recorded in PathWest. Notification of influenza and pertussis cases to WANIDD improved over time. Overall incidence rates of influenza in children aged <5 years using both datasets was 10% higher than using WANIDD data alone (IRR = 1.1, 95% CI = 1.1-1.2).

conclusionsThis is the first time WANIDD data have been validated against routinely collected laboratory data. We anticipated all cases would be captured in WANIDD but found additional laboratory-confirmed cases that were not notified. Studies investigating pathogen-specific infectious disease would benefit from using multiple data sources.

Indexed as

Databases, FactualDisease NotificationMedical Record LinkageChildChild, PreschoolHumansInfluenza, HumanPneumococcal InfectionsWestern AustraliaWhooping CoughDisease notificationRecord linkageValidation studies

Identifiers

PMID28623916
PMCPMC5473994

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Registered trials

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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.