Social Deprivation, Inequality, and the Neighborhood-Level Incidence of Psychotic Syndromes in East London

Abstract

Although urban birth, upbringing, and living are associated with increased risk of nonaffective psychotic disorders, few studies have used appropriate multilevel techniques accounting for spatial dependency in risk to investigate social, economic, or physical determinants of psychosis incidence. We adopted Bayesian hierarchical modeling to investigate the sociospatial distribution of psychosis risk in East London for DSM-IV nonaffective and affective psychotic disorders, ascertained over a 2-year period in the East London first-episode psychosis study. We included individual and environmental data on 427 subjects experiencing first-episode psychosis to estimate the incidence of disorder across 56 neighborhoods, having standardized for age, sex, ethnicity, and socioeconomic status. A Bayesian model that included spatially structured neighborhood-level random effects identified substantial unexplained variation in nonaffective psychosis risk after controlling for individual-level factors. This variation was independently associated with greater levels of neighborhood income inequality (SD increase in inequality: Bayesian relative risks [RR]: 1.25; 95% CI: 1.04–1.49), absolute deprivation (RR: 1.28; 95% CI: 1.08–1.51) and population density (RR: 1.18; 95% CI: 1.00–1.41). Neighborhood ethnic composition effects were associated with incidence of nonaffective psychosis for people of black Caribbean and black African origin. No variation in the spatial distribution of the affective psychoses was identified, consistent with the possibility of differing etiological origins of affective and nonaffective psychoses. Our data suggest that both absolute and relative measures of neighborhood social composition are associated with the incidence of nonaffective psychosis. We suggest these associations are consistent with a role for social stressors in psychosis risk, particularly when people live in more unequal communities.

Income inequality and mental illness-related morbidity and resilience: a systematic review and meta-analysis

“…Eight articles reported results from multi-country studies 12,27,29,[31][32][33][34][35] ; ten studies were conducted in the USA 28,30,[36][37][38][39][40][41][42][43] ; three studies were carried out in the UK [44][45][46] ; two articles reported results from a study carried out in Brazil 47,48 ; one study was conducted in Australia 49 , one in Mexico 50 , one in Spain 51 , and one in South Africa 26 (Table 1).…”

“…One article included university students 35 , one included 11, 13 and 15-year-old students 31 and one sampled women with infant children 40 . Twenty-two articles assessed psychiatric diagnoses 12,[26][27][28][29][30]32,[34][35][36][37][39][40][41][42][43][44][45][46][47] , whereas three assessed psychological distress/symptoms or mental well-being/functioning 31,33,49 . One article assessed both psychiatric diagnosis and psychological symptoms 38 , and one article reported use of mental health services 48 .…”

“…In the quality assessment (Appendix 2), ten studies 12,[26][27][28]32,35,39,40,42,45 scored two or less; the remaining 17 scored 3 or 4. The main problems identified were use of cross-sectional and ecological study design and absence of missing data assessment.…”

“…Nine articles [26][27][28]31,32,35,40,45,49 reported that the prevalence/incidence of mental health problems was higher in areas with greater income inequality. These studies assessed depression 28,37,41,51 , psychological distress/symptoms 31 and psychosis 26,27,32,45 .…”

Variation in compulsory psychiatric inpatient admission in England: a cross-classified, multilevel analysis

“…Investment in mental health services, bed capacity and provision of community-based alternatives vary between places, 12 as do patients and the communities in which they live. 13 Elucidating the effects of service setting factors and local area characteristics across large and representative samples, and differentiating these from variation due to differences between patients, may represent the best way to identify intervention targets. Our aims were to describe and model spatial variation in compulsory admissions in England using national patient-level data, and to quantify the extent to which adjusting for patient, local area and service setting characteristics accounted for this variation.…”