Spatial analysis of prevalence of mental illness in primary care in England and its association with deprivation and social fragmentation at small-area level.

Conference: 
SAPC ASM 2018 - London
Talk Code: 
P2.61
Presenter: 
Christos Grigoroglou
Co-authors: 
Luke Munford, Roger T. Webb, Nav Kapur, Darren M. Ashcroft, Evangelos Kontopantelis
Author institutions: 
University of Manchester

Problem

Indicators of poor mental health across the UK suggest widening mental health inequalities. Primary care is at the forefront of modern healthcare, and may enable a reduction in inequalities in mental health. In this study, we aimed to spatially describe the mental illness prevalence in England at a small-area geographical level, as measured by the prevalence of depression, serious mental illness (SMI) and antidepressant prescription volume in primary care records, and also explore the role of deprivation, social fragmentation and other sociodemographic characteristics in explaining prevalence rates.

Approach

Outcome information was obtained from the Quality and Outcomes framework (QOF) primary care administrative dataset for 2015/16 and the national prescribing data 2015/2016 and were assigned to a low-geography level in England. Linear regression models were used to examine the association between the outcomes of interest and deprivation, social fragmentation, demographic and regional characteristics. The spatial clustering of each outcome, for the whole of England and within each geographical region, was quantified with Moran’s I.

Findings

Mental illness prevalence varied within and between regions, with clusters of high prevalence identified across England. At the regional level, there was a modest association of prevalence of SMI with deprivation and fragmentation, and weak associations between prevalence of depression and antidepressant prescription volume with deprivation and fragmentation. Our models explained 33% to 68% of the variability in the three outcome measures, but large variability across regions remained after adjusting for covariates. Mental illness prevalence was strongly associated with social fragmentation, rurality and age while it was modestly associated with deprivation.

Consequences

To tackle mental health inequalities attention should be brought to those areas that suffer from worst fragmentation. The wealth of routinely data can provide informative and robust evidence that will aid optimal resource allocation. If comparable data are available in other countries, similar methods could be deployed to identify high prevalence clusters and to target funding to areas of greater need.

Submitted by: 
Christos Grigoroglou
Funding acknowledgement: 
A NIHR SPCR PhD studentship supported the time and facilities of CG. No specific funding was received for this study. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.