Evidence map›Paper›PMID 30381794›Full record

ArticleBig data & society2017

Challenges in administrative data linkage for research.

Katie Harron, Chris Dibben, James Boyd, Anders Hjern, Mahmoud Azimaee, Mauricio L Barreto, Harvey Goldstein

Abstract read
In one paragraph

Article in Big data & society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
159citing papers in PubMed, 5 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

159 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  14. Attitudes towards data access and sharing health data for research: a case study of Australian data custodian perspectives.Health information management : journal of the Health Information Management Association of Australia · 2026
    Article
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  16. A national atlas to improve the study of Canadians living with long COVID (post-COVID-19 condition).Canadian journal of public health = Revue canadienne de sante publique · 2026
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99 more citing papers are in PubMed but not listed here.

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

7 authors.

Katie HarronDepartment of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK.
Chris DibbenInstitute of Geography and the Lived Environment, University of Edinburgh, Edinburgh, UK.
James BoydCentre for Population Health Research, Curtin University, Perth, Australia.
Anders HjernCHESS Karolinska Institutet, Stockholm University, Stockholm, Sweden.
Mahmoud AzimaeeInstitute for Clinical Evaluative Sciences, Toronto, Canada.
Mauricio L BarretoCenter of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundacção Oswaldo Cruz, CEP 41745-715 Salvador-Bahia, Brazil.
Harvey GoldsteinGraduate School of Education, University of Bristol, Bristol, UK, and UCL Great Ormond Street Institute of Child Health, London, UK.

Funding

Wellcome Trust 103975
6 · The paper itself

Abstract

Linkage of population-based administrative data is a valuable tool for combining detailed individual-level information from different sources for research. While not a substitute for classical studies based on primary data collection, analyses of linked administrative data can answer questions that require large sample sizes or detailed data on hard-to-reach populations, and generate evidence with a high level of external validity and applicability for policy making. There are unique challenges in the appropriate research use of linked administrative data, for example with respect to bias from linkage errors where records cannot be linked or are linked together incorrectly. For confidentiality and other reasons, the separation of data linkage processes and analysis of linked data is generally regarded as best practice. However, the 'black box' of data linkage can make it difficult for researchers to judge the reliability of the resulting linked data for their required purposes. This article aims to provide an overview of challenges in linking administrative data for research. We aim to increase understanding of the implications of (i) the data linkage environment and privacy preservation; (ii) the linkage process itself (including data preparation, and deterministic and probabilistic linkage methods) and (iii) linkage quality and potential bias in linked data. We draw on examples from a number of countries to illustrate a range of approaches for data linkage in different contexts.

Indexed as

data accuracy administrative dataData linkageepidemiological studiesmeasurement errorrecord linkageselection bias

Identifiers

PMID30381794
PMCPMC6187070

What OpenQuestion holds

Textmetadata
LicenceTDM
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.