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Prevalence of Common Mental Disorders in (i) Adults and (ii) Children

Basic Facts

Stage of development


Potential or current usage

Data extracts have been carried out in 4 practices in relation to this measure, using a query designed by Compass.

Brief overview of the measure

General description

This measure was developed to provide an indication of the extent of common mental health disorders in primary care. The first step in improving the delivery of services to people with common mental disorders (CDM) is to appropriately identify them so that services can be tailored to better meet their needs. This measure is part of a suite of five Wellington School of Medicine (WSoM) mental health indicators (common mental disorders and depression).

Rationale for selection

The appropriate identification of common mental disorders (CMD) and management of depression in primary health care is a current national priority . These disorders have been the subject of a recent national guideline which supports risk assessment, the appropriate pharmacological and non-pharmacological management of depression, and follow-up for patients with CMD in primary care.[3] This measure set will be expanded in the future to include children and young people, and the management of substance use disorder.

Type of measure


Application and interpretation of the measure

Stated intent of the measure

This measure will provide a better understanding of the amount of people presenting to primary care with CMD and therefore the extent of primary care activity necessary. This is useful for planning and resource allocation. The measure is also intended to improve identification and coding of people presenting in general practice with common mental disorders.

Caveats - Considerations

There is an underlying assumption that for the measure results to be accurate and meaningful there is appropriate screening, identification, and diagnosis of CMD. There is currently wide variation in how consultations are READ coded within practices, Even within practices there are wide variations in how individual practitioners code consultations. Furthermore, there may be a certain degree of under reporting and coding because of continuing stigma against mental disorder and potential implications for insurance or legal issues. This may also occur at the patient’s request. Thus, data from this measure is not expected to provide robust prevalence data at a practice level. As a result, comparisons between practices is not possible at this stage. Additionally, data from this measure would not be able to be meaningfully compared with regional or national prevalence data. For this measure to be useful it is highly recommended that practices have an explicit and standardised approach to READ coding common mental disorders.

Links to other measures

Risk assessment of people with common mental disorders with a focus on depression Prescription of selective serotonin re-uptake inhibitors for the management of common mental disorders Follow up of patients with common mental disorders

Level of health care delivery/setting

A single primary care facility/ practice level. Inter practice variation between health professionals possible for internal peer review.

Target population

Adults (18-65 years old), or Children (below the age of 18 years).

Stratification by vulnerable populations

If proxy available (i.e. query by quintile), then stratification by ethnicity or socio economic status may be of interest. Stratifying by age may also be useful.

Associated incentives

Use of this measure is linked to: • MOPS • Cornerstone

Possible sources of bias or confounding

It is clear there is wide variation in both the use of particular READ codes and also how commonly READ coding occurs at all. Common mental health disorders are often identified in consultations where some other issue is more likely to be coded e.g. chronic pain, terminal illness, chronic illness. The extent to which multiple READ codes are attached to a single person or a patient encounter and the extent to which people are willing to attach CMD READ codes to an encounter will affect this measure. In calculating this measure, non funded patients are excluded, therefore if a practice has a large number of casual patients, this has the potential to skew results.

Calculation of the measure

Output of calculation

Percentage of patients with a diagnosis of a common mental disorder.

Numerator description

Patients attending the funded practice during the period and with either a CMD recorded within the period or with a CMD recorded prior to the period and marked as long term

Numerator exclusions

When an active decision is made, either as a result of patient request or health professional choice, not to record a diagnosis of CMD in the patient record.

Denominator description

Funded patients.

Denominator exclusions

Non-funded patients.

Time period

One year

Criteria/standard for optimal performance

Ideally all consultations for CMD should be coded. This is a base level criteria to determine the level of coding consultations occurring.

Data source

Primary care electronic practice management systems.

Method of extraction

Common mental disorders include a wide variety of conditions including anxiety, depression, substance use disorder and specific phobic disorders. As these conditions span the whole of the mental disorders READ code hierarchy, high level codes (excluding the codes for ‘mental retardation’) are provided below. These are the codes that should be standard for recording CMD, the core READ codes. Where a specified code is suffixed with the wild card symbol (*) all codes directly below that code in the hierarchy should also be included. Core READ codes E0* E1* E2* Eu* Ey* Ez* Suggested Read code(s) for future use Due to the breadth of the Read code mental disorders hierarchy, and the difficulty of coding common mental disorders with precision, the development of a comprehensive ‘standard’ set of codes is beyond the scope of this work. We would suggest that for a mixed anxiety and depressive disorder the preferred code is: 1465. H/O: depression 6891. Depression screen E0013 Presenile dementia+depression E0021 Senile dementia + depression E112. Endogenous depression first E112. Endogenous depression - first E1120 Single major depression-unspec E1121 Single major depression-mild E113. Endogenous depression-recurr. E1130 Recurr.major depression-unspec E1133 Recurr.major depression-severe E1137 Recurrent depression E11z2 Masked depression E135. Agitated depression E2003 Anxiety with depression E204. Neurotic (reactive) depression E204. Postnatal depression E2B.. *Depression E2B0. Postviral depression E2B1. Chronic depression V79.0 Special screening for MD and developmental handicaps, E204.11 Postnatal depression Eu32y.11 [X]Atypical depression E1137.00 Recurrent depression E204.00 Neurotic (reactive) depression E1121.00 Single major depression-mild E1130.00 Recurr.major depression-unspec E0013.00 Presenile dementia+depression Eu412.11 [X]Mild anxiety depression Eu32z.14 [X] Reactive depression NOS E2B0.00 Postviral depression E112.13 Endogenous depression first E1120.00 Single major depression-unspec Eu33.13 [X]Recur epis/react depression Eu32z.11 [X]Depression NOS Eu341.13 [X]Neurotic depression E112.11 Agitated depression 6891.00 Depression screen E112.14 Endogenous depression E2B1.00 Chronic depression E130.11 Psychotic reactive depression E11z2.00 Masked depression Eu530.11 [X]Postnatal depression NOS 1465.00 H/O: depression E113.11 Endogenous depression-recurr. E2003.00 Anxiety with depression E204.z0 Reactive depression E135.00 Agitated depression E1133.00 Recurr.major depression-severe E2B:.00 Depression annual review E113z.00 Recurr. major depression NOS Eu530.12 [X]Postpartum depression NOS E112.12 Endogenous depression - first We note practices currently use a range of other READ codes to record CMD, depending on their particular needs. Below we include some of these READ codes currently being used that may or may not be directly related to CMD across practices. Other READ codes 1B1A.11 Amnesia symptom 1B1A.12 Memory loss symptom 1B1A.13 Memory disturbance 1B1X.00 Sexual symptom It is strongly suggested that standardisation of data entry occurs within a practice before use of this measure. Technology is currently available to carry out data extractions from free-text clinical notes. This remains relatively expensive and problematic, yet it is still encouraged.

Key issues and challenges for data management

Coding variability Feasibility testing across four practices identified wide variations in how individual practitioners code consultations. This is due to the fact that although READ codes are a set of standardised codes that are agreed by an international body and most modern Patient Management Systems support such coding systems, general practices have also developed their own individualised coding schemes. If the practice is going to establish guidelines for coding it would be useful to first identify what the agreed practice codes are, investigate whether there are other useful codes being used, and then set up a screening template for staff that mapped back to the agreed code set and monitor progress. Long term condition coding Once a condition has been recorded as long term in the patient management system it may be unlikely for clinicians to re-classify or re-code each presentation the patient makes relating to that condition., Yet the chances are it will be discussed at least briefly within most consultations with that patient. Consideration needs to be given to this long term nature of CMD when coding and analyzing results on this indicator. Identifying children The measure specifies children, however, feasibility testing across the four practices focused on adults. It is therefore not possible to comment on children. Suggestions for practice activity Possible options for quality improvement activities within a practice may be to investigate the practice population that has had a CMD read code recorded and marked long term in the last 10 years as well as the practice population that has had a CMD read code recorded and NOT marked long term in the last year. - See more at: https://www.hqmnz.org.nz/measures/mental-health/prevalence-of-common-mental-disorders-in-i-adults-and-ii-children#sthash.2Bj7WUJT.dpuf

Appraisal of the measure

Availability of evidence to support application of the measure

Measure is formulated on and underpinned by evidence from a published systematic review, meta-analysis, or other peer-reviewed synthesis of clinical evidence relating to the area of focus., The measure has been reviewed using the Sieve Tool and a report is available.

Evidence of feasibility and reliability of implementation

Validity - The measure has been demonstrated to be valid (i.e. it measures what it purports to).

Development approach

Measure defined and feasibility of implementation has been tested. This measure is part of a suite of 11 measures the WSoM developed[1]. The process followed to develop this set of measures is summarized below: 1. Priority areas for measure development were identified in consultation with the College, MoH, PPP and the wider primary health care sector. 2. A measure development template was devised, based on a measure appraisal tool (the sieve). 3. The template was populated and specifications for each measure were refined through discussions. 4. Generic implementation plans were developed. Compass field tested indicator on a sample of four practices[2].

Other items

Links to educational activities


Owner details

Reference number


Date of entry to library

2012-07-30 11:13:58

Owner (Organisation name)

Royal New Zealand College of General Practitioners

Owner (Email contact)


Creator (Organisation name)

Primary Health Care Quality Research Unit, Wellington School of Medicine and Health Sciences, University of Otago

Creator (Email contact)