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The association between neighbourhood characteristics and physical victimisation in men and women with mental disorders.

ABSTRACT: BACKGROUND:How neighbourhood characteristics affect the physical safety of people with mental illness is unclear. AIMS:To examine neighbourhood effects on physical victimisation towards people using mental health services. METHOD: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. 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 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. 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.

SUBMITTER: Bhavsar V

PROVIDER: S-EPMC7443921 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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The association between neighbourhood characteristics and physical victimisation in men and women with mental disorders.

Bhavsar Vishal V Sanyal Jyoti J Patel Rashmi R Shetty Hitesh H Velupillai Sumithra S Stewart Robert R Broadbent Matthew M MacCabe James H JH Das-Munshi Jayati J Howard Louise M LM

BJPsych open 20200716 4

<h4>Background</h4>How neighbourhood characteristics affect the physical safety of people with mental illness is unclear.<h4>Aims</h4>To examine neighbourhood effects on physical victimisation towards people using mental health services.<h4>Method</h4>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 ...[more]

PMID: 32669154

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Project description:BACKGROUND:Women Veterans have high rates of mental health disorders and other psychosocial factors which may render this population particularly vulnerable to negative health outcomes associated with unintended pregnancy. OBJECTIVE:The objective of our study was to assess the relationship between self-reported mental illness and history of unintended pregnancy among women Veterans. DESIGN AND PARTICIPANTS:Data are from a nationally representative, cross-sectional telephone survey of women Veterans, ages 18-45, who used VA for primary care within 12 months prior to interview (survey completion rate 83%). MAIN MEASURES:Predictors were self-report of any and number of mental health disorders (depression, anxiety, post-traumatic stress disorder, bipolar disorder, or schizophrenia). Outcomes were any and number of unintended pregnancies. Multivariable logistic and negative binomial regression were used to assess relationships between mental illness and unintended pregnancy. To assess women's current risk of unintended pregnancy, we examined associations between any mental health disorder and contraceptive use at last sex among heterosexually active women not desiring pregnancy. KEY RESULTS:Among 2297 women Veterans, 1580 (68.8%) reported a history of at least one mental health disorder, with 20.1, 21.6, and 27.0% reporting one, two, or three or more conditions, respectively. Any history of unintended pregnancy was reported by 1315 women (57.3%); 28.3% reported one, 15.6% reported two, and 13.4% reported three or more. Compared to women with no mental illness, women with any mental health disorder were more likely to report any unintended pregnancy (60.3 vs. 50.5%; adjusted OR 1.40; 95% CI 1.15, 1.71) and to have experienced greater numbers of unintended pregnancies (adjusted incidence rate ratio 1.29; 95% CI 1.15, 1.44). Increasing numbers of mental health disorders were associated with greater numbers of unintended pregnancies. Contraceptive use and method efficacy at last sex did not differ by mental health status. CONCLUSIONS:Women Veterans with mental health disorders are more likely to have experienced any and greater numbers of unintended pregnancies than Veterans without mental health disorders.

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Dataset Information

The association between neighbourhood characteristics and physical victimisation in men and women with mental disorders.

Publications

The association between neighbourhood characteristics and physical victimisation in men and women with mental disorders.

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