Exploring respiratory health data through the RTP Toolkit
As part of the scaling and preparation stage of the Respiratory Transformation Partnership (RTP), the team at Health Innovation Oxford and Thames Valley has collated a range of publicly available data to provide a snapshot of respiratory health across health systems in England.
The RTP Toolkit brings key information together in one place, including data on age, sex, ethnicity and deprivation, Short-Acting Beta-2 Agonist (SABA) prescribing in primary care, and analysis of asthma and COPD Quality and Outcomes Framework (QOF) measures. The toolkit provides a snapshot in time to support RTP scaling and preparation activity during autumn and winter 2026/27.
Users can explore the data at different geographical levels, filtering by region and Integrated Care Board (ICB), and where the data allows, by sub-ICB and Primary Care Network (PCN). For transparency and context, each page sets out the time period covered by the data and relevant clinical definitions. We have also included a comprehensive list of the data sources used to develop the toolkit, alongside external links for those wishing to explore the data in greater depth.
COPD resources
NRAP COPD Biologics dataset and collection spreadsheet
We are pleased to share the agreed COPD Biologics dataset for use by providers. The dataset has been developed through the National Respiratory Audit Programme (NRAP), with input and consensus from expert stakeholders. It is intended to support consistent collection of quality and access metrics, and to help services monitor the uptake and use of biologic therapies for COPD. To support consistent local data capture, we have also provided a data collection spreadsheet for services to use. The spreadsheet is for local use only and will enable teams to begin collecting the agreed metrics in a standardised way.
NHSE and the RTP programme will continue to provide updates as this work progresses and we look forward to supporting teams in delivering effective, high-quality care. Should you have any questions regarding the dataset, please email hin-rtp@healthinnovationoxford.org
View and download the NRAP COPD Biologics dataset
View and download the NRAP metrics collection spreadsheet
Commissioning strategies for the treatment of chronic obstructive pulmonary disease
The COPD commissioning strategies and business case guidance for COPD biologics have been published on the NHSE website.
Access the commissioning strategies
Innovative care model case studies: Learning for Chronic Obstructive Pulmonary Disease (COPD) services
The innovative care model case studies (learning for COPD services) have been developed by NHS England to help local systems explore new and innovative service models that support the adoption of innovative treatments and pathways. They present a range of real-world examples, from both respiratory and non-respiratory settings, that illustrate practical approaches to redesigning care pathways and implementing new therapies.
Each case study highlights:
- Drivers for change
- How the model works and how it was implemented
- Outcomes, benefits and key learnings
- Workforce requirements, costs and potential savings.
Together, they offer transferable principles, evidence and data that can be applied and adapted to local contexts, helping systems shape and model their own services.
View and download case studies
Biological therapies for severe asthma and COPD
Asthma + Lung UK have developed a resource on biologics. Learn more about how they work, how they are used, which biologics are available, and possible side effects.
COPD Biologics – Frequently Asked Questions
These FAQs have been developed in response to discussions with systems and regions to support the implementation of COPD biologics. They address common questions and areas where further clarification has been requested, and we hope they will provide a useful resource for systems as they progress implementation locally. The FAQs will continue to be updated as implementation progresses and further questions and learning emerge.