How neighbourhood characteristics affect the physical safety of people with mental illness is unclear.

To examine neighbourhood effects on physical victimisation towards people using mental health services.

We developed and evaluated a machine-learning-derived free-text-based natural language processing (NLP) algorithm to ascertain clinical text referring to physical victimisation. This was applied to records on all patients attending National Health Service mental health services in Southeast London. Sociodemographic and clinical data, and diagnostic information on use of acute hospital care (from Hospital Episode Statistics, linked to Clinical Record Interactive Search), were collected in this group, defined as ‘cases’ and concurrently sampled controls. Multilevel logistic regression models estimated associations (odds ratios, ORs) between neighbourhood-level fragmentation, crime, income deprivation, and population density and physical victimisation.

Based on a human-rated gold standard, the NLP algorithm had a positive predictive value of 0.92 and sensitivity of 0.98 for (clinically recorded) physical victimisation. A 1 s.d. increase in neighbourhood crime was accompanied by a 7% increase in odds of physical victimisation in women and an 13% increase in men (adjusted OR (aOR) for women: 1.07, 95% CI 1.01–1.14, aOR for men: 1.13, 95% CI 1.06–1.21, P for gender interaction, 0.218). Although small, adjusted associations for neighbourhood fragmentation appeared greater in magnitude for women (aOR = 1.05, 95% CI 1.01–1.11) than men, where this association was not statistically significant (aOR = 1.00, 95% CI 0.95–1.04, P for gender interaction, 0.096). Neighbourhood income deprivation was associated with victimisation in men and women with similar magnitudes of association.

Neighbourhood factors influencing safety, as well as individual characteristics including gender, may be relevant to understanding pathways to physical victimisation towards people with mental illness.