Mental Health Services Monthly Statistics Final April 2019

These statistics are produced from the Mental Health Services Data Set (MHSDS) and are published monthly.

The MHSDS is a complex relational data set which collects record-level data on NHS-funded specialist mental health, learning disabilities and autism services. As a secondary uses data set it intends to re-use clinical and operational data for purposes other than direct patient care.

The MHSDS is unique in its coverage, because it covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care.

From April 2019 data, the MHSDS is collected via the Strategic Data Collection Service (SDCS) Cloud service using the MHSDS v4.0 Intermediate Database (IDB). Prior to this, MHSDS data was collected via the Bureau Service Portal.

Unlike the Bureau Service Portal, the SDCS Cloud an internet-facing service that does not require an N3 or HSCN connection, making it easier for providers to submit data and therefore enabling better coverage in published statistics. This new service stores MHSDS data in cloud-based infrastructure.

Detailed guidance is available to support providers in making submissions via the SDCS Cloud. This includes detailed technical and user guidance. New providers must complete a defined process to gain access to the SDCS Cloud. It also uses two-factor authentication as the secure method of confirming user identity using a combination of two different factors.

From April 2019 data, the MHSDS data are processed using NHS Digital’s new Data Processing Services (DPS). DPS uses modern technologies and processes to collect, process and access data more efficiently.

Find out more about our Data Processing Services (DPS) here.

The methodology for deriving Clinical Commissioning Group (CCG) was updated for the 2019-20 reporting year. There are two major changes; the removal of the submitted organisation identifier (when OrgCodeGPPrac IS not null then OrgCodeGPPrac) and the removal of the exclusion code for submitted organisation identifier (and OrgCodeGPPrac <> '-1').

The organisation identifier field, submitted by providers, allows unvalidated entry of a CCG codes; and the contents of this field supersede any other CCG information in the dataset when assigning a CCG to a patient.

It was found that the data provided in this field varies greatly and can include a number of non-current CCG values; these values result in the patients being assigned to Unknown CCG. Removing this step means that the CCG assigned to a record is derived within MHSDS from postcode information and, as such, will be a valid current CCG.

Analysis conducted, comparing statistics based on the current methodology with that of the updated methodology produces, shows a significant decrease in the number of unknown CCGs being reported using the updated methodology. Analysis also showed that, when the organisation identifier field was valid, there was little difference between this and the derived CCG.

The data quality of this field is currently not of a standard that improves the measure; the data quality of this field, and other fields like it, will need to be improved before it can be reliably used to assign valid CCGs.

This publication reports the number of children and young people receiving at least two contacts (including indirect contacts) and where their first contact occurs before their 18th birthday and their second contact occurs during the reporting period.

The methodology for this measure was updated for the 2019-20 reporting year with one major change – the inclusion of XenZone/ Kooth data following the approval of NHS England – this change has brought with it a number of small changes to capture their activity.

For XenZone/ Kooth:

• CCG is assigned using the code of commissioner from the field ORGANISATION IDENTIFIER (CODE OF COMMISSIONER) in the Referral Table; differing from the CCG method set out in the prior section of this paper
• the 05 Email and 06 Short Message Service (SMS) - Text Messaging codes from the CONSULTATION MEDIUM USED field are added to the currently used codes; these two codes are not used for any other providers
• two contacts are determined using the service request identifiers supplied by XenZone/ Kooth; differing from the method for all other providers that use person and service request identifiers to ensure the same person is receiving two contacts

The updated code is available in the metadata file.

Important limitations of this methodology

The major limitation of including the XenZone/ Kooth data within the measure is the risk of double counting and the unknown level of double counting.

The MHSDS uses an algorithm using patient information to produce a unique identifier for each patient; this identifier is then used to identify a patient with two contacts with a service. XenZone/ Kooth is a provider of an anonymous online service; this means our usual method of identifying patients (and those that have two contacts) does not function for XenZone/ Kooth submitted data.

As such we may identify a patient as having two contacts and then also identify the same patient as having two contacts with XenZone/ Kooth because we cannot determine that they are the same patient.

There is currently no estimation of the scale of this double counting. NHS Digital are investigating possible methods to estimate or quantify this.

As a result, this measure is to be treated as experimental and used with caution, considering this limitation.

MHSDS data is validated in stages.

Firstly, the data is validated at the point of submission, for each provider. If file-level validation checks are not passed the provider will receive a file-level rejection report. If file-level rejection is passed, the data are successfully submitted but the provider will still receive a report containing details of field-level errors and warnings. These can be investigated, corrected and further submissions can be made within the submission window.

For submitted data, NHS Digital publishes two types of data quality reports in this publication series. By publishing data from both ‘primary’ and ‘refresh’ submissions, providers can review issues identified in a ‘primary’ submission and investigate and resolve them for a ‘refresh’ submission.

The first type of report is a coverage report. This shows the number of records successfully submitted by each provider, for each data table in the MHSDS. When viewed in time-series format in our Power BI reports, this also provides intelligence on the consistency of submissions and enables outliers to be identified.

The second type of report is a ‘VODIM’ report. This classifies each record into five categories; Valid, Other, Default, Invalid, Missing. NHS Digital is expanding the number of items for which VODIM reporting is available. This file also includes a number of Integrity measures. This information is also available as a Power BI report.

These statistics are disseminated via the NHS Digital website.

Monthly statistics are disseminated from data collected in both ‘primary’ and ‘refresh’ submission windows for each month. The ‘primary’ data are designated as provisional and the ‘refresh’ as final data.

The data are also disseminated in the annual Mental Health Bulletin, which contains reporting based on a financial year reporting period and additional analysis where resources permit.

The statistics presented in this publication are currently designated as experimental statistics and, as such, remain under constant review; although major changes to methodologies are made between reporting years in order to preserve time-series. 

The Mental Health Analysis team welcomes any comments or feedback on the publication so please send any such communications to [email protected] with ‘Mental Health Monthly Statistics’ in the subject.