To investigate the spatial distribution of self-harm incidence rates, their socioeconomic correlates and sex/age differences using data on self-harm presentations to emergency departments from The Manchester Self-Harm Project (2003–2013).

Smoothed standardised incidence ratios for index self-harm episodes (n = 14 771) and their associations with area-level socioeconomic factors across 258 small areas (median population size = 1470) in the City of Manchester municipality were estimated using Bayesian hierarchical models.

Higher numbers and rates of self-harm were found in the north, east and far southern zones of the city, in contrast to below average rates in the city centre and the inner city zone to the south of the centre. Males and females aged 10–24, 25–44 and 45–64 years showed similar geographical patterning of self-harm. In contrast, there was no clear pattern in the group aged 65 years and older. Fully adjusted analyses showed a positive association of self-harm rates with the percentage of the unemployed population, households privately renting, population with limiting long-term illness and lone-parent households, and a negative association with the percentage of ethnicity other than White British and travel distance to the nearest hospital emergency department. The area-level characteristics investigated explained a large proportion (four-fifths) of the variability in area self-harm rates. Most associations were restricted to those aged under 65 years and some associations (e.g. with unemployment) were present only in the youngest age group.

The findings have implications for allocating prevention and intervention resources targeted at high-risk groups in high incidence areas. Targets for area-based interventions might include tackling the causes and consequences of joblessness, better treatment of long-term illness and consideration of the accessibility of health services.

We aimed to spatially describe mental illness prevalence in England at small-area geographical level, as measured by prevalence of depression, severe mental illness (SMI) and antidepressant prescription volume in primary care records, and how much of their variation was explained by deprivation, social fragmentation and sociodemographic characteristics.

Information on prevalence of depression and SMI was obtained from the Quality and Outcomes Framework (QOF) administrative dataset for 2015/16 and the national dispensing dataset for 2015/16. Linear regression models were fitted to examine ecological associations between deprivation, social fragmentation, other sociodemographic characteristics and mental illness prevalence.

Mental illness prevalence varied within and between regions, with clusters of high prevalence identified across England. Our models explained 33.4–68.2% of variability in prevalence, but substantial variability between regions remained after adjusting for covariates. People in socially cohesive and socially deprived areas were more likely to be diagnosed with depression, while people in more socially fragmented and more socially deprived areas were more likely to be diagnosed with SMI.

Our findings suggest that to tackle mental health inequalities, attention needs to be targeted at more socially deprived localities. The role of social fragmentation warrants further investigation, and it is possible that depression remains undiagnosed in more socially fragmented areas. The wealth of routinely collected data can provide robust evidence to aid optimal resource allocation. If comparable data are available in other countries, similar methods could be deployed to identify high prevalence clusters and target funding to areas of greater need.