ReviewBMJ open2026
Evaluation of diversity characteristics in a large mental healthcare data platform and their use in research publications: a cross-sectional review.
Review in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
The trial behind it
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundReporting diversity characteristics is required for more inclusive, equitable and policy-relevant research.
objectivesTo evaluate the reporting of diversity characteristics in publications using a large mental healthcare electronic health record (EHR)-derived research data resource, and to compare reporting of these characteristics in publications with their availabilities in the underlying dataset.
designCross-sectional review of Clinical Record Interactive Search (CRIS)-derived publications and assessment of diversity characteristic availability within the underlying EHR-derived database.
settingThe South London and Maudsley (SLaM) National Health Service (NHS) Foundation Trust Biomedical Research Centre Case Register, accessed via the CRIS platform, representing secondary mental healthcare delivered to a geographic catchment area covering four boroughs in south London. METHODS AND ANALYSIS: All CRIS-derived publications were reviewed to ascertain reporting of protected characteristics, as defined in the UK Equality Act 2010, alongside additional diversity-related characteristics. The availability of each characteristic within CRIS was assessed from records available up to 15 April 2026. Descriptive statistics were used to summarise reporting and data availability.
resultsA total of 362 publications were evaluated. The mean number of diversity characteristics reported per publication was 4.0, and no publication reported more than 10 characteristics. Age (89.3%), sex (87.3%) and ethnicity (80.4%) were the most frequently reported characteristics. Socioeconomic status was reported in 42.5% of publications, while marriage and civil partnership (35.0%) and disability (27.0%) were reported in a smaller proportion of studies. All remaining characteristics were reported in less than 10% of publications.Data availability within CRIS was highest for sex (99.9%) and age (99.8%), followed by socioeconomic status, geographic location and homelessness (all 97.5%) and ethnicity (86.5%). However, several characteristics were reported far less frequently than they were available in the dataset, particularly geographic location (6.6% reported despite 97.5% availability) and homelessness (5.8% reported despite 97.5% availability).
conclusionsReporting of diversity characteristics in this case study for EHR-based mental health research was uneven and did not fully reflect availability in the source data. While age, sex and ethnicity are commonly reported, several other protected and diversity-related characteristics are rarely used by researchers despite their availability, and other characteristics remain challenging to capture. As routine EHR-derived datasets increasingly inform mental health research and policy, greater attention to recording, accessibility and reporting of diversity characteristics is required to support more inclusive and representative research.
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