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Implementing a Primary-Tertiary Shared Care Model for Scaling Up Genetic Medicine

NSW Health Pathology

  • Translational Research Grants Scheme
Date Funded:
  • 1 July, 2022
Chief Investigator/s:
  • Dr. David Sullivan
  • Dr. Mitchell Sarkies

Project Summary

Implementing a general practice and medical specialist shared-care model for genetic medicine to improve familial hypercholesterolemia (FH) detection.

The main researchers for this project are Dr David R Sullivan and Dr Mitchell Sarkies.

What is the issue for NSW?

FH represents an exemplary Tier 1 condition for the implementation and scaling of genetic medicine. Fewer than 10% of the >50,000 people in NSW at risk of FH have been identified, which is concerning as it increases their risk of cardiovascular disease by more than 100-fold in younger age groups. If untreated, it advances the onset of cardiovascular disease by 20 to 40 years. While the benefits of detecting FH through genetic testing and cascade screening are well established, the ideal model for implementation at scale in Australia has not yet been established. FH is too common to rely on tertiary referral centres to manage all patients, especially for those with only routine management needs. Furthermore, the waitlists at the limited number of specialist lipid clinics are long, approaching 12 months or beyond.

What does the research aim to do and how?

This research aims to implement a primary-tertiary shared care model for FH, to determine how existing tertiary health services can support the delivery of genetic medicine at scale. A mixed methods implementation science study with a health economics evaluation will be conducted over four stages. It will seek to understand the barriers and enablers to implementation before tailoring the model and implementation strategy support package for local clinician and patient needs.

The project will assess:

  • Acceptability, appropriateness and feasibility of the shared care model
  • Adoption of the model measured by the count of people tested for FH
  • Fidelity to the model measured by the count of people detected with FH.