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

Markey, Keira and Hamilton, Robyn and Ahmed, Rohan and das, joyuptal and Douglass, Chris and nenadic, Goran and Webb, Karim and Lilleker, James and Rog, David and Silverdale, Monty and Mohanraj, Rajiv (2026) Health inequalities in outpatient neurological conditions across a large UK urban population : a retrospective observational study using automated coding. BMJ Neurology Open. ISSN 2632-6140 (In Press)

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Abstract

Objectives To utilise 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 1st January 2018 and 1st November 2024, covering a population of 3.3 million. Outcome measures To extract and correctly code outpatient neurological diagnoses from semi-structured 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 commonest 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[1·73-1·83]), headache (ASRR[95% CI]: 1·64[1·61-1·68]) and males with epilepsy (ASRR[95% CI]: 1·36[1·32-1·39]). Females from lower social deprivation were observed at higher rates with demyelination/inflammation (ASRR[95% CI]: 1·34[1·23-1·45]). Ethnicity was missing for 16·5% (n=17,523), but Asian, Black, and Mixed ethnicities had lower rates of clinic attendance compared to 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.