NHS Research Time Award

1) Using routinely collected data to determine healthcare resource use in HD

Aims:

Develop a platform to extract health care resource usage for patients with HD

Objectives:

1. Learn how to apply current algorithms for data extraction
2. Determine current coverage and coding for HD patients
3. Test and verify linkage methods in HD patients in SAIL

Locations: Centre for Trials Research, Cardiff University. Secure Anonymised Information Linkage (SAIL) databank, Swansea University

Deliverables: Able to extract, clean and analyse routinely collected data independently

In addition to determining clinical effectiveness, it is of paramount importance to determine whether treatments are cost effective. Current methods of determining health resource usage are either prospective and dependent on researchers collecting data from patients directly or retrospective and based on patients’ recall of resource use. These approaches are either onerous and costly, or vulnerable to recall bias and impaired cognition: a particular problem in HD. An alternative approach has been to extract data on resource use from large datasets of routinely collected NHS data. The SAIL databank uses data from GP records in Wales and covers 100% of secondary care episodes and 80% of GP care episodes: learning to use this resource to collect healthcare resource use will prove invaluable in future clinical trails in HD.

2) Piloting novel psychiatric outcomes in HD patients

Aims:

Develop a pilot battery of novel psychiatric outcomes for use in clinical trials

Objectives:

1. Modify and update current task battery
2. Recruit HD patients and healthy controls from the IMAGE- CLARITY-HD study
3. Establish face and biological validity using current gold-standard psychiatric assessments and imaging sequences from IMAGE- CLARITY-HD

Locations: BRAIN unit, Cardiff University. Cardiff University Brain Research Imaging Centre.

Deliverables: 10 HD patients recruited with pilot data

Current assessments of psychiatric disorders are based on diagnostic interview or self-report, however these approaches are limited by clinician bias or misinterpretation, participant cognitive impairment or lack of insight and have no translational potential. My doctoral work took an alternative approach: I developed tasks measuring cognitive processes that underlie neuropsychiatric disorders and found several core processes contributing to depression and apathy in HD2,6 (references from Section C). However post-doctoral work showed that only one assessment reliably measured psychiatric dysfunction longitudinally1. Professor Rosser is the PI of the CLARITY-HD and Co-I on IMAGE CLARITY-HD: a multi-modal imaging study determining imaging changes throughout the natural history of HD and the association between imaging changes and current cognitive assessments. I will develop and pilot novel cognitive tasks measuring processes I have previously shown to be associated with depression and apathy in HD (effort for reward, option generation and learning from negative outcome) in participants already recruited to IMAGE CLARITY-HD in order to develop pilot data for a future funding application, and link task performance with imaging measures from IMAGE CLARITY-HD.

3) Health Economic Outcomes: Current Assessment and Future Direction

Aims:

Develop collaboration and further knowledge base on resource use assessment

Objectives:

1. Develop links with Swansea University Health Economics Department
2. Further knowledge base of resource use assessmentDevelop a strategy for creating a novel resource use assessment for HD

Locations: Swansea University Centre for Health Economics

Deliverables: Complete courses in Health Economics, able to conduct Delphi process

In addition to the novel approach outlined in 1), I am keen to deepen my understanding around the current methods of resource use assessment. With this knowledge and the support of colleagues in Swansea, I will study methods of creating a novel HD-specific instrument in order to compare this with the data extracted from routine NHS datasets in a future funding application.