Evidence map›Paper›PMID 29691919›Full record

ArticlePharmacoepidemiology and drug safety2018

Impact of different assumptions on estimates of childhood diseases obtained from health care data: A retrospective cohort study.

Osemeke U Osokogu, Alexandra Pacurariu, Mees Mosseveld, Peter Rijnbeek, Daniel Weibel, Katia Verhamme, Miriam C J M Sturkenboom

Abstract read
In one paragraph

Article in Pharmacoepidemiology and drug safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Osemeke U OsokoguDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.ORCID 0000-0002-4921-0899
Alexandra PacurariuDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.ORCID 0000-0002-2217-0540
Mees MosseveldDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Peter RijnbeekDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Daniel WeibelDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Katia VerhammeDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.ORCID 0000-0001-8162-4904
Miriam C J M SturkenboomUniversity Medical Center Utrecht, Julius Center, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAccurate estimates of disease incidence in children are required to support pediatric drug development. Analysis of electronic health care records (EHR) may yield such estimates but pediatric-specific methods are lacking. We aimed to understand the impact of assumptions regarding duration of disease episode and length of run-in period on incidence estimates from EHRs.

methodsChildren aged 0 to 17 years (5-17 years for asthma) registered in the Integrated Primary Care Information database between 2002 and 2014 were studied. We tested the impact of the following: maximum duration of disease episode (0, 14, 30, 60, and 90 days) on recurrent diseases (acute otitis media [common] and acute pyelonephritis [rare]); and database run-in period on chronic diseases-asthma (common) and type 1 diabetes (DM) (rare). We calculated incidence rate ratios with 95% confidence intervals and stratified using 1-year age categories.

resultsAltogether, 503 495 children were registered. The incidence of acute otitis media was highest in <2-year-old children; using 30 days disease duration as reference, the rate increased with 8% if the duration was 14 days and decreased with 8% when extended to 60 days. Disease duration did not impact acute pyelonephritis (rare). No run-in (to exclude prevalent cases) versus 24-month run-in period overestimated the incidence rate for asthma and DM by a factor of 2.

conclusionsAnalysis of EHR allows for estimation of disease incidence in children, but assumptions regarding episode length and run-in period impact the incidence estimates. Such assumptions may be routinely explored.

Indexed as

Acute DiseaseAdolescentAsthmaChildChild, PreschoolChronic DiseaseData Interpretation, StatisticalDiabetes Mellitus, Type 1Electronic Health RecordsFemaleHumansIncidenceInfantInfant, NewbornMaleOtitis Mediachildrenincidencemethodologypharmacoepidemiologyprevalence

Identifiers

PMID29691919
PMCPMC6001570

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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.