Evidence map›Paper›PMID 42206099›Full record

ArticleBMJ neurology open2026

Health inequalities in outpatient neurological conditions across a large UK urban population: a retrospective observational study using automated coding.

Keira Markey, Robyn Hamilton, Rohan Ahmed, Joyutpal Das, Chris Douglass, Goran Nenadic, Karim Webb, James B Lilleker, David Rog, Monty Silverdale and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ neurology 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.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Keira MarkeyDepartment of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.
Robyn HamiltonData and Analytics Data Science Team, Northern Care Alliance NHS Trust, Salford, England, UK.
Rohan AhmedDepartment of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.
Joyutpal DasDepartment of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.
Chris DouglassDepartment of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.
Goran NenadicDepartment of Computer Science, The University of Manchester, Manchester, England, UK.
Karim WebbData and Analytics Data Science Team, Northern Care Alliance NHS Trust, Salford, England, UK.
James B LillekerDepartment of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.
David RogDepartment of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.
Monty SilverdaleDepartment of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.
Rajiv MohanrajDepartment of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.ORCID https://orcid.org/0000-0002-2559-148X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To use automated coding to identify broad neurological diagnoses and link to sociodemographic data. Design: Retrospective observational study. Setting: Tertiary outpatient neurology services covering Greater Manchester and East Cheshire. Participants: All adult patients attending neurology appointments between 1 January 2018 and 1 November 2024, covering a population of 3.3 million. Outcome measures: To extract and correctly code outpatient neurological diagnoses from semistructured clinical letters and to identify sociodemographic differences. Results: Successfully extracted diagnostic data were coded and linked to sociodemographic data for 125 273 unique neurology outpatients. Headache (16.1%, n=26 631) and epilepsy (14.3%, n=24 880) were the most common diagnoses observed. Higher rates were seen from the highest social deprivation for females with functional neurological disorder (age-standardised rate ratio (ASRR) (95% CI) 1.78 (1.73 to 1.83)), headache (ASRR (95% CI) 1.64 (1.61 to 1.68)) and males with epilepsy (ASRR (95% CI) 1.36 (1.32 to 1.39)). Females from lower social deprivation were observed at higher rates with demyelination/inflammation (ASRR (95% CI) 1.34 (1.23 to 1.45)). Ethnicity was missing for 16.5% (n=17 523), but Asian, black and mixed ethnicities had lower rates of clinic attendance compared with white. Conclusions: Automated coding of outpatient neurology data can reveal diagnostic patterns and health disparities, providing insights not previously available at scale. These data offer a powerful tool to support service planning, resource allocation and population-level research.

Indexed as

CLINICAL NEUROLOGYEPIDEMIOLOGYHEALTH POLICY & PRACTICE

Identifiers

PMID42206099
PMCPMC13202089

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