331. Quantifying severity of chronic conditions in English Primary Care using the Clinical Practice Research Datalink
- Principal Investigator: Evan Kontopantelis
- 4 July 2016 to 5 July 2019
- Project No: 332
- Funding round: FR 12
- Clinical trials and databases
Despite full computerisation since the early 2000s, UK GP records do not grade disease severity or the burden of having multiple co-existing health conditions (comorbidity) – information that could be used to better understand and address patient needs. This proposal aims to develop the appropriate analytical methods, through a focus on type-2 diabetes (T2DM) and coronary heart disease (CHD), using Clinical Practice Research Datalink data, and to extend to future healthcare needs.
We will use data from over 650 UK general practices, linked to hospital data, to improve diagnoses for patients with T2DM and CHD from simple diagnoses (have or not have the condition) to a more detailed description that incorporates disease severity and comorbidities. The information will include all available diagnoses, risk factors, treatments and referrals (hospitals/specialist services). We expect to be able to generalise the process and learning gained to other chronic conditions. We aim to describe the progression of conditions, identifying stages of disease severity/multimorbidity and the time it takes to progress from one stage of severity to the next.
The information on available diagnoses will include at least 16 well-recorded chronic conditions (without assessing severity) including: asthma, atrial fibrillation, cancer, chronic kidney disease, chronic obstructive pulmonary disease, dementia, depression, epilepsy, coronary heart disease, hypertension, hypothyroidism, learning disability, osteoarthritis, osteoporosis, severe mental illness and stroke. These will allow us to better understand the comorbidity burden in T2DM/CHD.
Exploring a range of severity/comorbidity measures with data from 2006-2015, we will evaluate how well these models/methods may be used for service planning, including detailed analyses by region, gender and age-group. In addition, we will apply our models to ‘forecast’ needs for years 2020-2030 and consider the implications for service planning and policy. We expect the tools and findings produced to be useful for both direct patient care and policy-making.
Evan Kontopantelis (Manchester) with co-applicants Chris Salisbury (Bristol), Christian Mallen and Carolyn Chew-Graham (Keele), David Reeves, Harm van Marwijk and Darren Ashcroft (Manchester), Nadeem Qureshi and Stephen Weng (Nottingham), Tim Holt and Rafael Perera (Oxford), Mamas Mamas (Keele Cardiovascular Research Group), Martin Rutter (Manchester Diabetes Centre), Iain Buchan (Farr Institute, Manchester), Niels Peek (MRC Health eResearch Centre), Lamiece Hassan (Patient and Public Involvement Engagement Researcher, MRC eResearch Centre.
Amount awarded: £319,861