Evidence map›Paper›PMID 42369823›Full record

ReviewJAMIA open2026

Accuracy of administrative data in ascertaining health conditions: a systematic review.

Alexander C Campbell, Jessica Tyler, Rebecca R Shuttleworth, Lindsay A Pearce, Jessica A Heerde, Rohan Borschmann, Susan M Sawyer, David B Preen, Stuart A Kinner, Lucas Calais-Ferreira

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

10 authors.

Alexander C CampbellCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3053, Australia.ORCID https://orcid.org/0000-0003-4092-7436
Jessica TylerCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3053, Australia.
Rebecca R ShuttleworthCentre for Adolescent Health, Murdoch Children's Research Institute and Royal Children's Hospital, Melbourne, Victoria, 3052, Australia.ORCID https://orcid.org/0009-0005-8015-7634
Lindsay A PearceCentre for Adolescent Health, Murdoch Children's Research Institute and Royal Children's Hospital, Melbourne, Victoria, 3052, Australia.ORCID https://orcid.org/0000-0003-0293-7211
Jessica A HeerdeCentre for Adolescent Health, Murdoch Children's Research Institute and Royal Children's Hospital, Melbourne, Victoria, 3052, Australia.ORCID https://orcid.org/0000-0002-5597-019X
Rohan BorschmannCentre for Adolescent Health, Murdoch Children's Research Institute and Royal Children's Hospital, Melbourne, Victoria, 3052, Australia.ORCID https://orcid.org/0000-0002-0365-7775
Susan M SawyerCentre for Adolescent Health, Murdoch Children's Research Institute and Royal Children's Hospital, Melbourne, Victoria, 3052, Australia.ORCID https://orcid.org/0000-0002-9095-358X
David B PreenSchool of Population and Global Health, The University of Western Australia, Perth, Western Australia, 6009, Australia.ORCID https://orcid.org/0000-0002-2982-2169
Stuart A KinnerCentre for Adolescent Health, Murdoch Children's Research Institute and Royal Children's Hospital, Melbourne, Victoria, 3052, Australia.ORCID https://orcid.org/0000-0003-3956-5343
Lucas Calais-FerreiraCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3053, Australia.ORCID https://orcid.org/0000-0001-6153-8080

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To conduct a systematic review of studies assessing the accuracy of International Classification of Diseases (ICD) and Diagnostic and Statistical Manual of Mental Disorders (DSM) codes in administrative data for ascertaining health conditions when compared to a reference standard. Materials and Methods: We searched MEDLINE, Embase, and PsycINFO, and reported study characteristics and accuracy measures, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We synthesized information about primary and validation sources of administrative data used in this literature and visually described accuracy measures by ICD chapter. Results: Our review included 280 studies; only two were conducted in low or middle-income countries. The majority of studies used hospital records as the primary administrative data source (52.1%; Discussion: The assessed literature is heavily skewed towards data from high-income countries. The variance we observed in accuracy measures, particularly for sensitivity and PPV, indicates that we need more evidence on and ongoing monitoring of the accuracy of ICD/DSM codes in administrative data, which are now widely used in population-based epidemiologic studies and therefore highly relevant for public health policymaking. Conclusion: Health conditions ascertained using ICD/DSM have moderate to high accuracy in identifying true positives and high to very high accuracy in identifying true negatives.

Indexed as

accuracyadministrative datadata linkageepidemiologyhealth diagnosesInternational Classification of Diseases

Identifiers

PMID42369823
PMCPMC13297001

What Socratic 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 Socratic graph.