The impact of neighbourhood crime on mental health: A systematic review and meta-analysis

Baranyi, Gergő; Marco, Martín Hernán Di; Russ, Tom C.; Dibben, Chris; Pearce, Jamie

doi:10.1016/j.socscimed.2021.114106

Cited by 61 publications

(29 citation statements)

References 93 publications

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“…The socioeconomic associations with SMI prevalence that we identified largely confirm what has been described previously [77]. High SMI risk is associated with being unemployed, having low income, a low level of education [77][78][79], and living in areas with high crime rates [80]. SMI prevalence is higher in more deprived parts of England [81].…”

Section: Plos Medicinesupporting

confidence: 85%

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

Cruz

Aragon

et al. 2022

PLoS Med

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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. Across England, the environmental characteristics associated with higher SMI prevalence at LSOA level were distance to public green space with a lake (prevalence ratio [95% credible interval]): 1.002 [1.001 to 1.003]), annual mean concentration of PM2.5 (1.014 [1.01 to 1.019]), and closeness to roads with noise levels above 75 dB (0.993 [0.992 to 0.995]). Higher SMI prevalence was also associated with a higher percentage of people above 24 years old (1.002 [1.002 to 1.003]), a higher percentage of ethnic minorities (1.002 [1.001 to 1.002]), and more deprived areas. Mean SMI prevalence at LSOA level in major conurbations mirrored the national associations with a few exceptions. In Birmingham, higher average SMI prevalence at LSOA level was positively associated with proximity to an urban green space with a lake (0.992 [0.99 to 0.998]). In Liverpool and Manchester, lower SMI prevalence was positively associated with road traffic noise ≥75 dB (1.012 [1.003 to 1.022]). In Birmingham, Liverpool, and Manchester, there was a positive association of SMI prevalence with distance to flood zone 3 (land within flood zone 3 has ≥1% chance of flooding annually from rivers or ≥0.5% chance of flooding annually from the sea, when flood defences are ignored): Birmingham: 1.012 [1.000 to 1.023]; Liverpool and Manchester: 1.016 [1.006 to 1.026]. In contrast, in Leeds, there was a negative association between SMI prevalence and distance to flood zone 3 (0.959 [0.944 to 0.975]). A limitation of this study was because we used a cross-sectional approach, we are unable to make causal inferences about our findings or investigate the temporal relationship between outcome and risk factors. Another limitation was that individuals who are exclusively treated under specialist mental health care and not seen in primary care at all were not included in this analysis. 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.

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Section: Plos Medicinesupporting

confidence: 85%

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

Cruz

Aragon

et al. 2022

PLoS Med

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“…(the higher risks to mental health of not being satisfied with the safety of the residence area, lower family and community relational support, sense of belonging and identity, and trust in public institutions), literature supports the importance and capacity of public space to contribute to the mental health gains of the population. Adequate street lighting and urban furniture location (e.g., communal seating), clean and maintained spaces (e.g., without accumulated trash), the presence of nature, public art, heritage/patrimonial and identity elements, and the existence of undegraded buildings without signs of vandalism, are all features that can have positive effects on mental health and reduce the fear of crime [65,[127][128][129][130], injuries [131], and the perception of unsafe neighbourhoods [132]. Several studies also suggest that an environment that enables walking safety can improve the mental health of the population by increasing the practice of physical activity of different age groups [133][134][135][136][137][138].…”

Section: Cdf 1 Public Space Qualitymentioning

confidence: 99%

Strategic Assessment of Neighbourhood Environmental Impacts on Mental Health in the Lisbon Region (Portugal): A Strategic Focus and Assessment Framework at the Local Level

2022

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Scientific evidence shows that each place/environment generates specific conditions with associated impacts on the mental health and well-being of the population. A holistic, multilevel and integrated environmental approach to mental health enhances the understanding of this phenomena, supporting the local decision-making processes to improve spatial planning of neighbourhood environments. The aim of this study is to develop a strategic assessment framework, based on four municipalities in the Lisbon Region (Portugal), that explores policy and planning initiatives capable of generating favourable neighbourhood environmental conditions for mental health while also detecting risks. Using baseline results of significant statistical associations between individuals’ perceptions of their neighbourhood environment and their mental health in the Lisbon Region, a Strategic Focus on Environmental and Mental Health Assessment framework (SEmHA) was built, by applying the methodology ‘Strategic Thinking for Sustainability’ in Strategic Environmental Assessment, developed by Partidário in 2012. Taking into account the promotion of the population’s mental health, four critical decision factors of neighbourhood environments were identified: (1) public space quality (e.g., improving sense of place), (2) physical environment quality (e.g., low levels of noise exposure), (3) professional qualification and creation of economic activities (e.g., attracting new economic activities), and (4) services and facilities (e.g., improving access to health and education services). The proposed strategic focus and assessment framework contributes to ensuring that interventions in neighbourhood environments truly achieve community mental health benefits and reduce inequalities, thus helping policy makers to assess impacts at the local level.

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“…Crime rates are an important neighborhood characteristic that can cause distress on individuals' mental well-being ( Dustmann and Fasani, 2016 ) and has been linked with various children's developmental outcomes ( Sharkey, 2010 , Baranyi et al, 2021 ). However, the impact of crime within the context of other neighborhood variables and how these impact neural mechanisms during children’s development is less clear.…”

Section: Physical Environment Assessmentsmentioning

confidence: 99%

Adolescent Brain Cognitive Development (ABCD) study Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data

Fan

Marshall

Smolker

et al. 2021

Developmental Cognitive Neuroscience

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The impact of neighbourhood crime on mental health: A systematic review and meta-analysis

Cited by 61 publications

References 93 publications

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

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

Strategic Assessment of Neighbourhood Environmental Impacts on Mental Health in the Lisbon Region (Portugal): A Strategic Focus and Assessment Framework at the Local Level

Adolescent Brain Cognitive Development (ABCD) study Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data

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