Abstract
Background: How neighbourhood characteristics affect the physical safety of people with mental illness is
unclear. Aim: To examine neighbourhood effects on physical victimisation towards people using mental
health services. Methods: 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 NHS mental health services in Southeast London.
Sociodemographic and clinical data, and diagnostic information on use of acute hospital care (from Hospital
Episode Statistics, HES, linked to CRIS), were collected in this group, defined as cases, and concurrently
sampled controls. Multi-level logistic regression models estimated associations (odds ratios, ORs) between
neighbourhood-level fragmentation, crime, income deprivation, and population density, and physical
victimisation. Results: 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 one standard deviation
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: OR: 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, 1.10)
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. Conclusions: 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.
unclear. Aim: To examine neighbourhood effects on physical victimisation towards people using mental
health services. Methods: 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 NHS mental health services in Southeast London.
Sociodemographic and clinical data, and diagnostic information on use of acute hospital care (from Hospital
Episode Statistics, HES, linked to CRIS), were collected in this group, defined as cases, and concurrently
sampled controls. Multi-level logistic regression models estimated associations (odds ratios, ORs) between
neighbourhood-level fragmentation, crime, income deprivation, and population density, and physical
victimisation. Results: 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 one standard deviation
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: OR: 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, 1.10)
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. Conclusions: 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.
| Original language | English |
|---|---|
| Journal | BJPsych Open |
| Publication status | Accepted/In press - 3 Jun 2020 |
