Evidence map›Paper›PMID 39380579›Full record

ArticleClinical epidemiology2024

Recording of Alcohol Use Disorder in Electronic Health Records: Developing a Recommended Codelist for Research.

Sarah Cook, David Osborn, Arti Maini, Ravi Parekh, Shamini Gnani, Thomas Beaney, Ana Luisa Neves, Sonia Saxena, Jennifer K Quint

Abstract read
In one paragraph

Article in Clinical epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  6. Review
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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.

Sarah CookSchool of Public Health, Imperial College London, London, UK.ORCID 0000-0003-1250-2967
David OsbornDivision of Psychiatry, University College London, London, UK.ORCID 0000-0003-2519-1539
Arti MainiSchool of Public Health, Imperial College London, London, UK.
Ravi ParekhSchool of Public Health, Imperial College London, London, UK.ORCID 0000-0003-0219-4956
Shamini GnaniSchool of Public Health, Imperial College London, London, UK.
Thomas BeaneySchool of Public Health, Imperial College London, London, UK.
Ana Luisa NevesSchool of Public Health, Imperial College London, London, UK.
Sonia SaxenaSchool of Public Health, Imperial College London, London, UK.ORCID 0000-0003-3787-2083
Jennifer K QuintSchool of Public Health, Imperial College London, London, UK.ORCID 0000-0003-0149-4869

Funding

Wellcome Trust
6 · The paper itself

Abstract

Purpose: Electronic health records (EHR) are valuable resources for health research; however, their use is challenging. A validated alcohol use disorder (AUD) codelist for UK primary care is needed to improve population-based research in this patient group. We aimed to develop an AUD codelist for use in the Clinical Practice Research Datalink (CPRD) Aurum database, a UK EHR primary-care database. Methods: The CPRD code browser was searched using keywords related to alcohol use using a previously developed search strategy. The resulting codes were categorised as AUD if they were: a) diagnostic of AUD, b) indicated alcohol withdrawal, or c) indicated chronic alcohol-related harm (physical or mental). Codes related to alcohol use but not used to define AUD were also classified into relevant categories (alcohol status, acute harm, and alcohol screening). All codes were categorised independently by at least two reviewers (one person reviewed all codes and five reviewers (all practising GPs) each reviewed a subset of codes (100-200 codes each). Disagreements in categorisation were discussed by at least three coders and a consensus was reached. The reliability of categorisation was assessed using kappa statistics. Results: In total, 556 potential codes related to alcohol use were identified. The Kappa for reliability between coders was moderate for both AUD (0.72) and across all categories (0.62), with substantial variability between coders (AUD: 0.33-0.97; all categories 0.36-0.74). In the final codelist, 138 codes were included as indicating AUD: 38 codes identified which indicated diagnosis of AUD, 14 indicating withdrawal plus 85 codes indicating chronic alcohol-related harm (41 physical health and 44 mental health). Conclusion: Many codes are used in primary care to record alcohol use and associated harms, and there is substantial variability in how clinicians categorise them. While future work formally validating the codelist against gold standard clinical reviews and qualitative work with General Practitioners is needed for a deeper understanding of coding processes, we have documented here the process used for the development of an AUD codelist within primary care which can be used as a reference for future research.

Indexed as

alcohol use disorderclinical practice research datalinkelectronic health recordsprimary care

Identifiers

PMID39380579
PMCPMC11460355

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Registered trials

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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.