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Health inequalities in outpatient neurological conditions across a large UK urban population: a retrospective
Keira Markey1,2, Robyn Hamilton3, Rohan Ahmed1
1Department of Neurology, Northern Care Alliance NHS Trust Manchester Centre for Clinical Neuroscience, Salford, England, UK.
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.
