The data you need: Mental health

This selection of indicators looks at some of the small-area data available related to mental health conditions. These may be useful in identifying areas with higher proportions of vulnerable residents.

All of this neighbourhood data on mental health is available in Local Insight for any area you care about – whether that is wards, parishes or towns. Book a demo to find out more. 

Small Area Mental Health Index (SAMHI)

Source: Place-Based Longitudinal Data Resource (PLDR)

How often updated: Irregular

The SAMHI is a composite annual measure of population mental health for each Lower Super Output Area (LSOA) in England. 

The SAMHI combines data on mental health from multiple sources into a single index including:

• NHS-Mental health related hospital attendances
• GP Patient Survey – Q34 Best describe your own health state today
• Prescribing data – Antidepressants
• QOF – depression 
• DWP – Incapacity benefit and Employment support allowance for mental illness

 A higher score indicates that an area is experiencing high levels of mental health need.

The SAMHI is an attempt to update the Indices of Deprivation Mood and Anxiety Disorders indicator (more on this below). It includes a broader set of data but some data included in the Mood and Anxiety Disorders indicator won’t be available in this index.

Prevalence of mental health conditions

Source: House of Commons Library

How often updated: Irregular

These estimates are based on analysis of 2019/20 data from England’s GP practices published by NHS Digital. NHS Digital also publishes figures on GP practice ‘footprints’ – i.e. the LSOAs in which the patients of each GP practice live. By combining this data with practice-level data on prevalence, it’s possible to estimate variation of health conditions in between neighbourhoods.

The data provides estimates on the prevalence of a number of health conditions including:

• Dementia
• Depression
• Serious mental illness

There are some caveats to be aware of when using this data:

• These figures are only estimates and some divergence between separate areas served by individual GP practices will be lost.
• In attributing GP practice-level data to different areas, weighting adjustments have been made in respect of the relevant age category (e.g. diabetes prevalence is measured for age 17+ only), based on the varying age profiles of different small areas.
• For some conditions, the proportion of people on GP registers is less than the proportion of the people living with the disease. For example: only 67.5% of cases of dementia are estimated to have been diagnosed, and 29% of adults are obese compared with 10% identified on GP registers. The prevalence estimates here represent only those cases diagnosed by a GP.
• These estimates are sensitive to the quality and consistency of data reporting by GPs. People who are not registered at GP practices are not included in the estimates – either in the numerator or the denominator.

Prevalence of loneliness for those aged 65+

Source: Age UK

Next updated: Irregular

Although feeling lonely isn’t in itself a mental health problem, the two are strongly linked with some research suggesting that loneliness is associated with an increased risk of certain mental health problems, including depression, anxiety, low self-esteem, sleep problems and increased stress.

This indicator provides a prediction of the prevalence of loneliness amongst usual residents, living in households, aged 65 and over. 

The prevalence of loneliness indicator uses data from the English Longitudinal Study of Ageing (ELSA) survey, to identify predictors of loneliness in older age. The results from this modelling were applied to data from Census 2011 to predict the prevalence of loneliness across small area geographies such as Output Areas (OAs), Super Output Areas (LSOAs and MSOAs) and local authorities. This starts with a base value, which is then adjusted based on a number of other variables. The result is a final prediction value for each person, and results are displayed as averages for each geographical area. Areas with a value closer to 0 predict a greater prevalence of loneliness amongst those aged 65 and over and living in households compared to areas with a value further away from 0.

As this is a modelled indicator using a larger survey and the census data on household living arrangements to get estimated values at small area level caution should be taken as it does not take into account wider factors that may affect loneliness locally.

Indices of Deprivation Mood and Anxiety Disorders indicator

Source: Ministry of Housing, Communities and Local Government

Timepoint: 2013 – 2018

Next updated: Irregular

This indicator provides a broad measure of levels of mental ill health in the local population. The definition used for this indicator includes mood (affective), neurotic, stress-related and somatoform disorders. The indicator is a modelled estimate based on three separate sources:

• prescribing data
• hospital episodes data
• suicide mortality data. 

Although none of the three sources on their own provide a comprehensive measure of mood and anxiety disorders, used in combination they represent a large proportion of all those suffering mental ill health.  A higher score represents a more deprived area in regards to mood and anxiety disorders. 

In the Indices of Deprivation 2015 (and earlier) this indicator also included a fourth component which was derived from health benefits data from the Department for Work and Pensions. The health benefits data component has been dropped from this indicator for the Indices of Deprivation 2019 due to concerns about the quality of the data on health conditions within the health benefits dataset.

Explore this data

All of this neighbourhood data on mental health and more are available to use in Local Insight.

• More than 1000 small-area socio-economic datasets available
• Create any neighbourhood you like and have data instantly matched to it
• Access data via maps, reports, dashboards and CSV exports.
• Add your own organisational data into the system to compare with the contextual data we pre-load into the system for you.

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