The National Bridges to Health Segmentation Dataset

Powering the backbone of national population health data. OBH have partnered with NHS England to place data at the heart of a national strategy to support population health decision making.

Our National Bridges to Health Segmentation Dataset plays a central role in NHSE population health analytics strategy.

Features of the National Bridges to Health Segmentation Dataset

Healthy or generally well population

The dataset puts individuals at the centre, with entire population coverage.

As the entire GP registered population is included, uniquely the dataset includes people who are healthy or generally well, which is crucial for population health management related to primary prevention.

Fully longitudinal and dynamic

Longitudinal at person level including people who have died, been born, movement of populations between GP practices and changes in health states over time.

Data can be used for analysis of progression of MLTCs and incidence analysis.

Clinically curated condition registers

59 curated condition registers curated by a team of clinicians, data analysts, and public health experts. These conditions align with the global consensus for the definition of multiple long term conditions.

Based on regularly reviewed national and international standards and best practice guidelines with over 130 reviewed to date.

Refreshed regularly since 2019, covering a period of 7 years (April 2016 to March 2023), with data available on a monthly basis. Soon to be 8 years to March 2024, largest longitudinal dataset globally.

Understand the health of your population

The National Bridges to Health Segmentation Dataset enables a better understanding of population health needs and trends from a person perspective (as opposed to just a provider perspective), to support forecasting and planning.

It is the first of its kind to link datasets and provide longitudinal condition registers for the population of over 60 million people registered to a GP practice in England.

This means on any given month since 2016, NHS analysts can view every individual’s anonymised health state (segment), conditions they may have (subsegments), and demographic and geographical information.

The dataset can scale, be filtered by population characteristics and be used flexibly, including being joined to further datasets for advanced analysis.

Talk to us

To find out how the National Bridges to Health Segmentation Dataset can help you analyse your local population drop us a line. Our team of clinical and technical experts are ready to help.

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The National Segmentation Dataset will soon be available to
all Integrated Care Systems in England through FDP

The National Bridges to Health Segmentation Dataset will soon be available through the NHS Federated Data Platform, making the dataset available to every ICB in England.

The Segmentation Dataset is highly versatile, supporting a variety of use cases including data driven care coordination, tackling of health inequalities, and evaluation of prevention programmes. Access to this powerful tool empowers ICBs across the country to take their population health management strategies to a new level of sophistication through unprecedented insights into the needs of their population.

Learn more

Delivering high impact analysis

The Segmentation Dataset has been used as the key data source for a number of landmark analyses which have led to peer reviewed publications in high impact medical journals. These analyses were possible due to the unique combination of features of the Segmentation Dataset, such as whole-population coverage, a fully longitudinal view of the health trajectory of each person, and 59 condition registers.

The burden of diabetes-associated MLTCs on years of life spent and lost

Nature Medicine. 2024 Aug 1:1-8.

Prevalence of MLTCs in England: A whole population study of over 60 million people

Journal of the Royal Society of Medicine. 2024 Mar;117(3):104-17.

Associations of type 1 and type 2 diabetes with COVID-19-related mortality

The Lancet Diabetes and Endocrinology. 2020 Oct; 8: 813–22

Supporting the upcoming NHS 10 year plan

A national calculation of HEALTHSPAN®

The Segmentation Dataset allows the calculation of HEALTHSPAN, the first objective, population-level measure of the time individuals spend in good health.

High service user analysis

The Segmentation Dataset links to activity data to identify segment-specific cohorts with multiple emergency admissions or frequent A&E attendance who may benefit from enhanced community support and better care planning.

Insights into service planning and resource allocation

The Segmentation Dataset helps teams analyse changes in expenditure, identify service gaps, and target resources to areas with highest unmet population needs.

Prevention opportunities

The Segmentation Dataset provides data on progression patterns, allowing teams to identify people at risk of developing additional long term conditions, or areas where early intervention could prevent condition deterioration.

Identify health inequalities

The Segmentation Dataset includes demographic and geographic data to identify health disparities across different communities and ethnic groups, and variations in service access.

Quantifying the burden of multiple long term conditions (MLTC)

The Segmentation Dataset is used to design and evaluate major national programmes such as the Diabetes Prevention Programme and identify people with MLTC who may need coordinated care.

Fully longitudinal person-centred data

The Segmentation Dataset contains a set of characteristics and events that support a fully longitudinal view of each person.

Each feature is available for any month since April 2016 in the Dataset period. An individual is attributed to one of six overarching ‘segments’ by determining which subsegment/condition criteria are met.

Learn more about all the features in
our comprehensive reference guide.

A comprehensive set of clinically curated condition registers

The segment and subsegment conditions in the Bridges to Health National Segmentation Dataset are created by aggregating data from multiple national sources including Secondary Uses Services (SUS), Mental Health Data, Community Data, IAPT, and the National Diabetes Audit. These pseudonymised patient-level datasets are processed through sophisticated analytical pipelines that link data using NHS numbers and apply complex business rules involving clinical code clusters like ICD-10, OPCS, and SNOMED.

The resulting categorisation identifies individuals based on specific long-term conditions, aggregated condition groups, and nested conditions. By incorporating socio-demographic factors such as age, sex, ethnicity, and geographical data, the segmentation provides a comprehensive and nuanced view of population health characteristics. The methodology ensures that each subsegment is clinically validated and reflects the complex healthcare needs of different population groups.