All Indicators > Indicator SH1: Psychological morbidity

Additional Details

Mental ill health is a condition that can severely impact on the quality of life of those suffering from it and those immediately around them. It may also lead to other forms of deprivation such as unemployment or homelessness. Individuals may find themselves in a downward spiral that may be difficult to break out of.

Creating a small area measure of psychological morbidity is not straightforward. There are no standard small area measures covering England that are ready to use. Survey approaches, using standard measures, would require very large sample sizes and do not yet exist. This suggests an approach using information that is already collected in support of administrative processes. However there are problems with the use of administrative records. These datasets are likely to lead to definitions of mental illness which are particular to the administrative process from which they are drawn. These will not necessarily fit exactly with what is required for the Health Poverty Index.

A further problem when using administrative data to measure mental health is the way the organisation of local services and different practices within and between organisations affect the type of treatment an individual receives. This may lead to groups of individuals, identical in terms of their mental health, coming in contact with some services in some areas and not in others. Some General Practitioners, for example, may be less eager to use drugs in the treatment of depression than others. A count therefore of those receiving a prescription for the treatment of depression may differ between areas with identical numbers of people suffering from depression.

Given these problems it is clear that single mental health indicators that are derived from administrative data should be used with caution: each indicator is likely to vary around what might be thought of as the ‘true’ state of mental health in a small area. A fairly simple method to reduce this bias is to combine a number of indicators that are believed to measure the same underlying ‘true’ state. As the number of indicators is increased, the influence of under or over-recording bias should be reduced. This will be true as long as the bias does not result from an area effect that influences all the different administrative systems, leading to biases in the same direction. By choosing indicators from independent administrative data sources this problem should be minimised. The bias in the overall indicator, therefore, should be lower than that in any single indicator.

The datasets that were used are from prescribing data, secondary care data, mortality data where the cause of death is recorded as suicide, and health related benefit administrative data. Because each of the datasets covers a slightly different group of psychiatric conditions, it was only possible to produce an estimate for a sub-group of these conditions. The sub-group chosen was people aged under 60 suffering mood (affective) disorders and neurotic, stress-related, and somatoform disorders. Together these represent a large proportion of all those suffering mental ill health.

Component SH1_1: Suicide

Additional details

Although suicide is not a direct measure of mental ill health, it is highly associated with depression which is implicated in a majority of suicide cases. Unlike the other measures it is more independent of organisational practises; therefore it may suffer less from biases relating to local practise. However numbers are small and so the precision of the measure may be poor.

The International Classification of Diseases Version 10 (ICD-10) codes used to extract data on deaths from suicide were X60-X84 and Y10-Y34 (excluding Y33.9 where the Coroner’s verdict was pending).

For ethnic estimation a Super Output Areas (SOA) level weighting function was created to model incidence for individuals in ethnic groups within Local Authorities.

Component SH1_2: Benefits for mental health conditions

Additional details

The rate of sickness and disability in an area can be measured using information on receipt of particular benefits. Incapacity Benefit (IB) and Severe Disablement Allowance (SDA) are benefits paid to individuals of working age who are unable to work because of ill health. IB is a non means-tested benefit paid to people who are incapable of work due to ill health and who have paid sufficient National Insurance contributions. SDA is a non means-tested benefit paid to people who are incapable of work through illness and have not paid sufficient National Insurance contributions to qualify for IB.

Both of these benefit datasets are coded for medical conditions. The Department for Work and Pensions matched medical diagnoses to the International Classification of Diseases Version 10 (ICD-10) codes. The ICD-10 codes used to classify individuals claiming IB or SDA for mental health conditions were F3–F4.

Using the working age population as a denominator, a standardised rate of mental ill health amongst those aged 16 to 59 was calculated.

For ethnic estimation an SOA level weighting function was created to model access for individuals in ethnic groups within Local Authorities.

Component SH1_3: Prescribing for anxiety/depression

Additional details

This indicator uses information on drug prescribing to estimate levels of mental health. Because information on the conditions for which various types of drugs are prescribed as well as the typical dosages are known, it is possible to estimate the number of patients within a particular General Practitioner’s (GP) practice who are suffering from mental health problems. The mental health problems examined here are depression and anxiety. This is measured using prescriptions for all drugs with the British National Formulary codes 4.1.2 (anxiolytics) and 4.3 (anti-depressant drugs).

Unfortunately prescription data is not held at individual level and therefore a two-stage methodology was adopted to calculate area rates. This method assumes that those with mental ill health take the national Average Daily Quantity (Prescribing Support Unit) of a specific drug on every day of the year. While these assumptions may not fit very well in individual cases, they are more likely to hold across the ‘average’ for the practice population. The practice rates are then distributed to geographical areas through knowledge of practice population distribution. This process will tend to ‘spatially smooth’ the area rates where practice populations are heterogeneous. In effect the small area rate will move towards a larger area ‘moving average’. However although this does mean high or low rates will tend to move towards the local average, it also reduces the impact of individual GP prescribing behaviour that might be introducing bias because the small area rate will be a combination of a number of different practices.

For ethnic estimation an SOA level weighting function was created to model access for individuals in ethnic groups within Local Authorities.

Component SH1_4: Psychiatric admissions

Additional details

This indicator uses hospital inpatient data to estimate the proportion of the population suffering severe mental health problems relating to depression and anxiety. A count is made of all those who have had at least one in-stay admission to hospital in any one year.

The International Classification of Diseases Version 10 (ICD-10) codes used to extract data on admissions for anxiety and depression were:

• Mood (affective) disorders: F30 – F39
• Neurotic, stress-related and somatoform disorders: F40 – F48

The indicator is therefore an annual count of those suffering at least one severe mental health episode in a year. Where an individual spent the whole year in hospital they will be counted as one in the annual count and they will be attributed to the area they were resident in when first admitted. A standardised rate is calculated using the residential population in the area as a denominator.

There are two significant issues with this indicator as a measure of an underlying rate of mental health. First, the admission of an individual into hospital may be influenced not only by the severity of their condition but also by factors arising from an interaction between primary, social and secondary care. If for example there has been a failure of adequate primary care in an area, individuals who might have remained within primary care in another area, may be admitted into secondary care. The second problem with this indicator is small numbers. This means that the estimate of the underlying risk of admission in some small areas has low precision. Combining a number of years together can reduce the small number problem. In this case 3 years of data were combined.

For indicators derived from the Hospital Episode Statistics (HES) the estimates are based on the relationship between all hospital stays, and those recorded for a specific condition of interest. Detail is added from census data to depict the spatial distribution of individuals in ethnic groups. All estimates are statistically smoothed to reduce noise within the distribution, enabling the underlying trend to be highlighted. For more details see the discussion paper. <link>