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

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.