Evidence map›Paper›PMID 41047276›Full record

ArticleBMJ open2025

Bridging the gap: development of a methodology for retrieving and harmonising body mass index (BMI) from population-level linked electronic health records.

Michael Jeanne Childs, Sarah J Aldridge, Helen Daniels, Gareth Ivor Davies, Victoria Best, Hoda Abbasizanjani, Ronan Lyons, Ashley Akbari, Fatemeh Torabi

Abstract read
In one paragraph

Article in BMJ open, 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

9 authors.

Michael Jeanne ChildsPopulation Data Science, Swansea University Medical School, Swansea, UK.ORCID http://orcid.org/0000-0001-9086-8898
Sarah J AldridgePopulation Data Science, Swansea University Medical School, Swansea, UK.
Helen DanielsPopulation Data Science, Swansea University Medical School, Swansea, UK.
Gareth Ivor DaviesPopulation Data Science, Swansea University Medical School, Swansea, UK.
Victoria BestPopulation Data Science, Swansea University Medical School, Swansea, UK.
Hoda AbbasizanjaniPopulation Data Science, Swansea University Medical School, Swansea, UK.ORCID http://orcid.org/0000-0002-9575-4758
Ronan LyonsPopulation Data Science, Swansea University Medical School, Swansea, UK.ORCID http://orcid.org/0000-0001-5225-000X
Ashley AkbariPopulation Data Science, Swansea University Medical School, Swansea, UK.ORCID http://orcid.org/0000-0003-0814-0801
Fatemeh TorabiPopulation Data Science, Swansea University Medical School, Swansea, UK Fatemeh.Torabi@Swansea.ac.uk.ORCID http://orcid.org/0000-0002-5853-4625

Funding

Wellcome Trust
6 · The paper itself

Abstract

objectiveThis study aims to develop a methodology to retrieve, harmonise and evaluate the completeness of national body mass index (BMI) data from linked electronic health record (EHR) sources to build a longitudinal research-ready data asset (RRDA).

designA longitudinal study of BMI records spanning 23 years (1 January 2000 to 31 December 2022) from four data sources.

settingThe national BMI RRDA is created within the Secure Anonymised Information Linkage (Databank), encompassing the entire population of Wales, UK. PROCEDURE AND

participantsWe built a methodology that provides a reproducible framework for extracting and harmonising BMI data from four major linked EHRs across two age groups: children and young people (CYP; 2-18 years old) and adults (19 years and older). The methodology is adaptable across different trusted research environments. We evaluated the completeness and retention of records over 1-, 5- and 23-year periods by calculating the proportion of missing data relative to each year's population.

resultsWe retrieved 53.4 million records for 3.2 million individuals across Wales from 1st January 2000 to 31 December 2022. Among these, 3% of CYP and 34% of adults had repeat BMI measurements recorded over periods ranging from 5 to 23 years. Throughout the entire population of Wales during this period, 49% of CYP and 26% of adults had at least one BMI reading recorded, resulting in a missingness rate of 51% for CYP and 74% for adults. Preserving BMI information by retaining the most recently recorded BMI over 1-, 5- and 23-year intervals from 2022 showed coverage rates of 10%, 33% and 68%, respectively, for CYP, and 25%, 51% and 73%, respectively, for adults.

conclusionsOur findings highlight substantial variations in BMI data availability and retention across CYP and adults, as well as time periods within EHR in Wales. Wider adoption of this approach can enhance standardised approaches in using accessible measures like BMI to assess disease risk in population-based studies, strengthening public health initiatives and research efforts.

Indexed as

Body Mass IndexElectronic Health RecordsInformation Storage and RetrievalMedical Record LinkageAdolescentAdultAgedChildChild, PreschoolFemaleHumansLongitudinal StudiesMaleMiddle AgedWalesYoung AdultBody Mass IndexElectronic Health RecordsEPIDEMIOLOGYPUBLIC HEALTH

Identifiers

PMID41047276
PMCPMC12506101

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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.