Association of environmental and socioeconomic indicators with serious mental illness diagnoses identified from general practitioner practice data in England: A spatial Bayesian modelling study

Background: The evidence is sparse regarding the associations between serious mental illnesses (SMIs) prevalence and environmental factors in adulthood as well as the geographic distribution and variability of these associations. In this study, we evaluated the association between availability and proximity of green and blue space with SMI prevalence in England as a whole and in its major conurbations (Greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle). Methods and findings: We carried out a retrospective analysis of routinely collected adult population (≥18 years) data at General Practitioner Practice (GPP) level. We used data from the Quality and Outcomes Framework (QOF) on the prevalence of a diagnosis of SMI (schizophrenia, bipolar affective disorder and other psychoses, and other patients on lithium therapy) at the level of GPP over the financial year April 2014 to March 2018. The number of GPPs included ranged between 7,492 (April 2017 to March 2018) to 7,997 (April 2014 to March 2015) and the number of patients ranged from 56,413,719 (April 2014 to March 2015) to 58,270,354 (April 2017 to March 2018). Data at GPP level were converted to the geographic hierarchy unit Lower Layer Super Output Area (LSOA) level for analysis. LSOAs are a geographic unit for reporting small area statistics and have an average population of around 1,500 people. We employed a Bayesian spatial regression model to explore the association of SMI prevalence in England and its major conurbations (greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle) with environmental characteristics (green and blue space, flood risk areas, and air and noise pollution) and socioeconomic characteristics (age, ethnicity, and index of multiple deprivation (IMD)). We incorporated spatial random effects in our modelling to account for variation at multiple scales. Conclusions: Our study provides further evidence on the significance of socioeconomic associations in patterns of SMI but emphasises the additional importance of considering environmental characteristics alongside socioeconomic variables in understanding these patterns. In this study, we did not observe a significant association between green space and SMI prevalence, but we did identify an apparent association between green spaces with a lake and SMI prevalence. Deprivation, higher concentrations of air pollution, and higher proportion of ethnic minorities were associated with higher SMI prevalence, supporting a social-ecological approach to public health prevention. It also provides evidence of the significance of spatial analysis in revealing the importance of place and context in influencing area-based patterns of SMI. In a cross-sectional study, Joana Cruz and colleagues study the relationship between environmental and socioeconomic factors and prevalence of serious mental illness in England between 2014 and 2018.Why was this study done?: Serious mental illness (SMI), which includes schizophrenia, bipolar affective disorder, or psychosis, affects 335 million people worldwide.In England, the economic cost of SMI was estimated as £2.82 billion in 2019.The presence of environmental risk factors (e.g., air pollution concentration near the residential area) has been implicated in increased prevalence of SMI, but less is known about the relationship between residential green and blue space and prevalence of SMI.We wanted to know if the prevalence of SMI in England was associated with environmental conditions near the patient’s residential area, accounting for area-level socioeconomic characteristics. What did the researchers do and find?: We developed an innovative mathematical model that included the average prevalence of patients diagnosed with schizophrenia, bipolar affective disorder and other psychoses, and other patients on lithium therapy at General Practitioner Practice (GPP) level for the period April 2014 to March 2018.The model included environmental variables near the patient’s residential area (i.e., area of woodland, public green space, distance to nearest public green space, distance to rivers or ponds within green spaces, distance to noise, distance to areas of high flood risk, and annual mean concentration of air pollution—particulate matter 2.5).The model also included socioeconomic variables that may be associated with SMI prevalence: percentage of ethnic minorities, age, and Index of Multiple Deprivation 2015.At national level, higher SMI prevalence was associated with greater distance from green spaces with lakes, higher levels of air pollution, and closeness to roads with high noise levels, although the significance and directionality of these associations varied between conurbations. What do these findings mean?: Greater distance from green spaces with lakes is associated with higher prevalence of SMI, highlighting the complex interplay between green and blue spaces in urban neighbourhoods.Closer proximity to noise and air pollution is associated with higher prevalence of SMI, and these patterns are more pronounced among neighbourhoods with relatively higher measures of deprivation and in areas with more ethnic minorities.The patterning of the associations between green and blue space and prevalence of SMI varied between urban areas, suggesting that local variation is an important factor in understanding the impact of environment on mental health.