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Registries and condition specific data

Introduction

Learning objectives: You will learn about the three types of data sources, including registries, routine national data collections, and audits.

There are a number of rich sources of information on morbidity and condition specific data. Three important sources are covered in this section and provide an overview of the depth of the data that is potentially available to inform clinical decision-making.

Read the resource text below.

Resource text

Routine morbidity data are gathered at national level through a number of sources, depending on the condition or disease. These are often led by healthcare organisations, professional bodies, universities, and in some cases healthcare charities. Data may be available through condition specific registries for data, national audits, and other national data collections.

Registries

Examples of registries include congenital anomalies, industrial diseases, and diabetes and cancer. The National Cancer Registry, overseen by the Office for National Statistics, collates data from nine regional registries. Cancer registries were set up to collate new cases of cancer and use this information to produce statistics about cancer incidence, prevalence, survival and mortality. In recent years the work of cancer registries has expanded from the monitoring of cancer occurrence to include the analysis of different aspects of cancer prevention, treatment outcomes and care.

There are a number of issues to consider when setting up a registry, including the purpose, clear case definition, a system for reporting new events, who will handle the analyses and financial implications at local and national level.

Registry data can be a rich source of information with patient identifiable data, which is longitudinal and can be updated over time. The data can be used for patient follow up, auditing treatments and outcomes, evaluation of studies, studies of causation and health service planning.

However, registries are expensive to run, and updating the dataset is laborious. There are also confidentiality issues, and in addition, assessing the data completeness and coverage is not a straightforward process, although methods are being developed.

Routine national data collections

There are a number of national data collections, which can be used for routine morbidity data. Reporting periods can vary between monthly, quarterly and annual data collection. Where the data is collected from NHS organisations, they will be available at healthcare organisation level, at either acute and specialist trusts or primary care trusts. The most valuable feature of routine data is their availability at little cost to the researcher. They may be especially helpful in establishing baseline characteristics regarding the health status of the community, in generating hypotheses as a result of sex, age, cohort or geographic variation, and in identifying potential areas requiring further research. However, no one single data set can provide the whole picture of a population's health and its needs.

Although data may be available from a wide range of sources, the data completed by NHS organisations as requested from national bodies, Department of Health etc. requires approval through the Review of Central Returns (ROCR) process. This is managed by the NHS Information Centre. Therefore, it is possible to gain a quick understanding of the data collections that NHS organisations participate in, and their frequency, by viewing the Information Catalogue, which contains details of approximately 600 data collections.

Examples of the type of data collected include:

  • Genito-Urinary Medicine 48 Hour Access Target Monthly Monitoring (GUMAMM) for sexual health data
  • COVER (Cover of Vaccination Evaluated Rapidly), with information about the uptake of immunisation among children aged one, two and five years
  • Adult Screening Programme: Breast Cancer (PCT return) (KC63), including information on the population coverage of breast screening.
  • Accident and Emergency Attendance Commissioning Dataset (CDS), with data for an Accident and Emergency Attendance Episode submitted by Ambulance and independent and voluntary sector.

For mental health data, two particular datasets include Mental Health Minimum Dataset (MHMDS) and the mental health service mapping. The MHMDS brings together administrative and clinical information about people using specialist NHS mental health services for adults and older people. The mental health service mapping gives an overview on services commissioned, measuring progress against the mental health priority and planning framework (PPF), the public service agreement (PSA) and the performance indicator (PI) target.

In addition, the Mental Health Needs Index (MINI) can be used to estimate the need for inpatient mental health services for adults (ages 16-59) by ward and borough. It is calculated using a number of population variables likely to indicate need for access to services, such as deprivation, proportion of economically active adults unemployed, proportion of adults living in households not self contained etc. The MINI provides both predicted admission rates and a ratio of need compared to the England average. The MINI was developed by the Centre for Public Mental Health, which has produced an online tool for accessing information on a ward/borough level.

Audits and other data collections

Another important source of information are audits and ad-hoc collation of data. Audits can be carried out at local or national level and are specific to health conditions or diseases. National audits are an important source of information for measuring patient outcomes and clinical effectiveness. There are a number of national audits managed by the Healthcare Quality Improvement Partnership, covering cancer (e.g. LUCADA for lung cancer), heart disease (e.g. MINAP for myocardial infarction), mental health (e.g. psychological therapies), diabetes and renal audits, as well as child and maternity (e.g. neonatal intensive care).

The main purpose of audit is to improve patient care and outcomes by highlighting areas of concern or where improvements need to be considered. As such, it is important to consider the full audit cycle, which includes making improvements and sustaining improvement as the final stage of the cycle. For more information see the NHS Audit Advice.

There are also a number of organisations or data management systems that aim to combine information from a wide range of sources and make it available for decision-making. Three examples include:

  • Durham University manages the mental health observatory and child health mapping service, providing reports across wide geographical areas.
  • British Health Foundation manages http://www.heartstats.org/, which is a comprehensive and up-to-date source of statistics on the burden, prevention, treatment and causes of heart disease in the UK. There are annual publications that make comparisons across countries, taking into consideration lifestyle risk factors and treatments.
  • EUROCAT provide essential epidemiologic information on congenital anomalies in Europe based on a network of population-based registries in Europe. With publications dating back to 1979, this database can enable historical analysis of trends across Europe.

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