Evidence map›Paper›PMID 33115422›Full record

ArticleBMC medical research methodology2020

Accuracy of reporting of Aboriginality on administrative health data collections using linked data in NSW, Australia.

Michael A Nelson, Kim Lim, Jason Boyd, Damien Cordery, Allan Went, David Meharg, Lisa Jackson-Pulver, Scott Winch, Lee K Taylor

Abstract read
In one paragraph

Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

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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

9 authors.

Michael A NelsonCentre for Epidemiology and Evidence, NSW Ministry of Health, Sydney, NSW, Australia. Michael.Nelson1@health.nsw.gov.au.ORCID 0000-0002-7990-9166
Kim LimCentre for Epidemiology and Evidence, NSW Ministry of Health, Sydney, NSW, Australia.
Jason BoydBureau of Health Information, Chatswood, NSW, Australia.
Damien CorderySystem Information and Analytics, NSW Ministry of Health, Sydney, NSW, Australia.
Allan WentSystem Information and Analytics, NSW Ministry of Health, Sydney, NSW, Australia.
David MehargFaculty of Medicine and Health, Sydney School of Health Sciences and the Poche Centre for Indigenous Health, University of Sydney, Sydney, NSW, Australia.
Lisa Jackson-PulverDeputy Vice-Chancellor, Indigenous Strategy and Services, University of Sydney, Sydney, Australia.
Scott WinchScott Winch, World Vision, Sydney, Australia.
Lee K TaylorCentre for Epidemiology and Evidence, NSW Ministry of Health, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAboriginal people are under-reported on administrative health data in Australia. Various approaches have been used or proposed to improve reporting of Aboriginal people using linked records. This cross-sectional study used self-reported Aboriginality from the NSW Patient Survey Program (PSP) as a reference standard to assess the accuracy of reporting of Aboriginal people on NSW Admitted Patient (APDC) and Emergency Department Data Collections (EDDC), and compare the accuracy of selected approaches to enhance reporting Aboriginality using linked data.

methodsTen PSP surveys were linked to five administrative health data collections, including APDC, EDDC, perinatal, and birth and death registration records. Accuracy of reporting of Aboriginality was assessed using sensitivity, specificity, and positive and negative predictive values (PPVs and NPVs) and F score for the EDDC and APDC as baseline and four enhancement approaches using linked records: "Most recent linked record", "Ever reported as Aboriginal", and two approaches using a weight of evidence, "Enhanced Reporting of Aboriginality (ERA) algorithm" and "Multi-stage median (MSM)".

resultsThere was substantial under-reporting of Aboriginality on APDC and EDDC records (sensitivities 84 and 77% respectively) with PPVs of 95% on both data collections. Overall, specificities and NPVs were above 98%. Of people who were reported as Aboriginal on the PSP, 16% were not reported as Aboriginal on any of their linked records. Record linkage approaches generally increased sensitivity, accompanied by decrease in PPV with little change in overall F score for the APDC and an increase in F score for the EDDC. The "ERA algorithm" and "MSM" approaches provided the best overall accuracy.

conclusionsWeight of evidence approaches are preferred when record linkage is used to improve reporting of Aboriginality on administrative health data collections. However, as a substantial number of Aboriginal people are not reported as Aboriginal on any of their linked records, improvements in reporting are incomplete and should be taken into account when interpreting results of any analyses. Enhancement of reporting of Aboriginality using record linkage should not replace efforts to improve recording of Aboriginal people at the point of data collection and addressing barriers to self-identification for Aboriginal people.

Indexed as

Data CollectionMedical Record LinkageAdolescentAdultAgedAustralian Aboriginal and Torres Strait Islander PeoplesChildChild, PreschoolCross-Sectional StudiesFemaleHumansInfantInfant, NewbornMaleMiddle AgedNew South WalesAboriginal healthAdministrative dataData linkageIdentificationIndigenous healthLinked dataRecord linkageReporting

Identifiers

PMID33115422
PMCPMC7594479

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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.