Evidence map›Paper›PMID 40481580›Full record

ArticlePopulation health metrics2025

Methods used to construct disability indicators in linked administrative datasets: a systematic scoping review.

Zoe Aitken, Sarah Walmsley, Glenda M Bishop, Samia Badji, Nicola Fortune

Abstract readScoping Review
In one paragraph

Article in Population health metrics, 2025. 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. 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

5 authors.

Zoe AitkenMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia. zoe.aitken@unimelb.edu.au.ORCID 0000-0002-5413-2450
Sarah WalmsleyMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia.ORCID 0009-0008-3420-3902
Glenda M BishopMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia.ORCID 0000-0002-2736-0415
Samia BadjiCentre for Health Economics, Monash Business School, Monash University, Melbourne, VIC, 3145, Australia.ORCID 0000-0001-7352-0232
Nicola FortuneMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia.ORCID 0000-0003-1489-5709

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this scoping review, we aimed to examine evidence on methods used to construct disability indicators in linked administrative datasets and describe the approaches used to assess the validity of the indicators.

methodsMedline (Ovid) and Embase (Ovid) were searched for studies published between January 2010 and June 2023. Original, peer-reviewed studies that aimed to construct a disability indicator using linked administrative data sources were included. Studies identifying any types of disability were included, but not those which defined the target population in terms of specific health conditions. We produced a narrative synthesis of findings related to disability indicator construction methods and validation approaches.

resultsThirty-six relevant studies were included, with 30 of those identifying a cohort of people with intellectual and/or developmental disability. Health data sources were most commonly used for indicator construction, with 33 of the studies using at least one health data source. Disability and education sector data sources were also commonly used. Diagnostic codes were used for disability identification in 34 of the 36 studies; 16 used diagnostic codes alone and 18 used diagnostic codes along with other information. A subgroup of 19 studies had a primary aim to create a disability cohort or estimate disability prevalence. Thirteen of these 19 studies compared their estimated prevalence rates with previously published estimates. Only five studies conducted testing to investigate the extent to which their derived disability indicator captured the intended target population. DISCUSSION: We found a paucity of evidence on methods for identifying a target population of people with diverse disabilities. In the existing literature, diagnostic information is relied upon heavily for disability identification, likely due to a lack of other types of disability-relevant information in administrative data sources. Use of derived disability indicators within linked data holds potential to advance research regarding people with disability. It is crucial, however, to conduct and report validation testing to understand the strengths and limitations of the indicators and inform their use for specific purposes.

Indexed as

Disability EvaluationPersons with DisabilitiesDatabases, FactualDatasets as TopicHumansAdministrative dataDisabilityDisability indicatorLinked dataScoping review

Identifiers

PMID40481580
PMCPMC12144692

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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.