National Mental Health, Dementia and Neurology Intelligence

Network

overview and an example of profile use

NMHDNIN November 2015

About the NMHDNIN • Launched June 2014; we cover mental health, dementia & neurology

• One of 5 Health Intelligence Networks led by PHE

• Bring together various holders of information, national charities, NHS partners, researchers and key users of health intelligence

• Collaborative work across stakeholders

• High quality information and analysis

• Driven by need and innovation

our core purpose• To operate as a network across the system partners and deliver for all

• Support the delivery of national policy and enable local intelligence-driven improvement

• Provide useful and impactful data & intelligence tools to local public health system, NHS commissioners and local authorities to:• Identify and measure inequality• Understand the whole system (from prevention to recovery)• Investigate new models of working, and relate activity/outcomes to cost

• Disseminate & support local use of intelligence products

• Measure impact & continuously improve through end user testing

Our productsData intelligence:

– Dementia data briefing– Neurology data briefing– Measuring children’s mental wellbeing – good indicator guide – Psychosis data briefing (in development)

Joint products:– Atlas of variation 3.0– Perinatal mental health needs assessment (with ChiMat)

Profiling tools:– There are 7 (see next slide)– Used to assess variation and help local areas benchmark

3

Using profiling tools to help understand variation

An example from the National Mental Health Intelligence Network

Profile tool structure and set up:

using the Severe Mental Illness tool

 

Data is grouped in topic domains. In the SMI tool there are 6, 5 unique domains and a sixth, pathway domain, which selects key data items from the other 5.

Data is presented at a number of geographies. This is dictated by availability, not all data items are presented for all geographies

Data can be grouped into geographical and statistical neighbours. Options for this tool are shown.

The blue-orange-blue system is used to show significant difference from the benchmark value but not make judgement on that difference, which is the role of the tool user.

Each data item is flagged for it’s quality and validity for being used in the tool. For instance Domestic Abuse data is flagged amber because values are by police authority rather than local authority, and it’s a measure of incidents attended by police rather than all incidents

Mapping and benchmarking

 

Age/sex specific suicide rates

Mapping data by significant difference to the England value provides a visual overview. In this example mapping male age specific suicide rates appears to show that the areas that show rates significantly above the England value differ by the age group under consideration. This information may be vital if local areas are planning age and gender specific targeted preventive interventions

Benchmarking optionsRegion Deprivation

CCG Clusters ONS Clusters

This slide demonstrates how to find the best available benchmarking group when considering SMI identified in primary care in Norwich.

There appears little point in comparing to geographical neighbours as the Norwich value is clearly higher.

When considering other options, comparing to Regional Centres using the ONS cluster method shows that the Norwich value is close to the cluster group average and two thirds of the other areas included are also significantly above England’s value.

Benchmarking by ONS Clusters: Primary Care

This slide shows a number of measure of care of patients with SMI in primary care. Norwich can be compared against a meaningful benchmarking group and it’s relative position observed in that context.

Reviewing data items in this way may allow local areas to identify areas of care worthy of further consideration.

It should be noted that there is always value in testing national data sets against local data and knowledge to ensure what is presented genuinely reflects the local position.

An example pathway:

using depression and IAPT services and considering the most deprived

and least deprived cohorts 

Depression: primary care  

Estimated prevalence of mixed anxiety and depressive disorder is generally higher in more deprived areas compared to more affluent. There are 5 local authorities in the deprived cohort with rates above Hertfordshire, the local authority with highest prevalence rate among the more affluent cohort.

Although prevalence of depression is estimated to be higher in deprived areas, the rate of identification in primary care is similar between the most affluent and most deprived areas. There is greater variation in identification across the more deprived areas.

Review of depression is an indicator of care provision. The proportion of newly identified patients with depression who are reviewed appears to be higher in more affluent areas. In fact more than half of the affluent areas have a higher proportion reviewed within the time period compared to Birmingham Crosscity, the CCG with the highest rate among the most deprived group.

Use of IAPT services 

Estimated prevalence of common mental health disorders is generally higher in more deprived areas compared to more affluent areas. More than half the CCGs in the most deprived cohort have higher estimated rates than Herts Valley CCG, which is highest among the more affluent cohort.

It appears that generally greater numbers of people are entering IAPT treatment in more deprived areas compared to more affluent areas.

However, when considering numbers entering treatment as a proportion of those estimated to have anxiety or depression the picture changes, with more affluent areas being significantly above the England value and more deprived areas being significantly below.

The proportion of patients being seen within 28 days appears generally higher in more affluent areas. Over half of affluent CCGs have more than 90% of patients seen within 28 days, only two CCGs in the more deprived cohort can say the same.

Patient ‘reliable improvement’ rates also appear to be generally higher in more affluent areas. Over half of affluent CCGs have improvement rates that are higher than North Manchester, the CCG with the highest ‘reliable improvement’ rate among the more deprived cohort of CCGs.

A simple and high level assessment but differences across deprivation groups can be observed:

In Primary Care there may be variation in identification and care of people with depression

In IAPT services there may be variation in access to, waits for, and outcomes for people with CMHD

Conclusions

View the profiling tools at: http://fingertips.phe.org.uk/profile-group/mental-health

For more information please contact us by email at:mhdnin@phe.gov.uk