Evidence map›Paper›PMID 30419045›Full record

ArticlePloS one2018

Use of physician billing claims to identify infections in children.

Jeremiah Hwee, Lillian Sung, Jeffrey C Kwong, Rinku Sutradhar, Karen Tu, Jason D Pole

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
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

11 citing papers in PubMed, 25 citations in OpenAlex.

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  9. Defining appropriate antibiotic prescribing in primary care: A modified Delphi panel approach.Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada · 2020
    Article
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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

6 authors at 3 institutions in 1 country.

Jeremiah HweeDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-2623-7706
Lillian SungDepartment of Pediatrics, Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada.
Jeffrey C KwongDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Rinku SutradharDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Karen TuDepartment of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada.
Jason D PoleDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
University of Toronto · CAUniversity Health Network · CAPediatric Oncology Group · CA

Funding

Canadian Institutes of Health Research
6 · The paper itself

Abstract

While medical records have detailed information, they are limited in reach to the availability and accessibility of those records. On the other hand, administrative data while limited in scope, have a much further reach in coverage of an entire population. However, few studies have validated the use of administrative data for identifying infections in pediatric populations. Pediatric patients from Ontario, Canada aged <18 years were randomly sampled from the Electronic Medical Record Administrative data Linked Database (EMRALD). Using physician diagnoses from the electronic medical record (EMR) as the reference standard, we determined the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes from 2012 to 2014. Diagnosis codes were assessed by infection category (respiratory, skin and soft tissue, gastrointestinal, urinary tract and otitis externa) and for all infections combined. Sensitivity analyses assessed the performance if patients had more than one reason to visit the physician. We analysed 2,139 patients and found 33.3% of all visits were for an infection, and respiratory infections accounted for 67.6% of the infections. When we combined all infection categories, sensitivity was 0.74 (95% CI 0.70-0.77), specificity was 0.95 (95% CI 0.93-0.96), positive predictive value (PPV) was 0.87 (95% CI 0.84-0.90), and negative predictive value (NPV) was 0.88 (95% CI 0.86-0.89). For respiratory infections, sensitivity was 0.77 (95% CI 0.73-0.81), specificity was 0.96 (95% CI 0.95-0.97), PPV was 0.85 (95% CI 0.81-0.88), and NPV was 0.94 (95% CI 0.92-0.95). Similar performance was observed for skin and soft tissue, gastrointestinal, urinary tract, and otitis externa infections, but with lower sensitivity. Performance measures were highest when the patient visited the physician with only one health complaint. We found when using linked EMR data as the reference standard, administrative billing codes are reasonably accurate in identifying infections in a pediatric population.

Indexed as

Administrative Claims, HealthcareDatabases, FactualInsurance Claim ReviewAdolescentChildChild, PreschoolFemaleHumansInfantInfant, NewbornInfectionsMaleOntarioRandom Allocation

Identifiers

PMID30419045
PMCPMC6231686
OpenAlexW2900518013

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.