Research into Cost-effectiveness of Population Testing in the General Population

Dr Ranjit Manchanda, who is The Eve Appeal's research lead and acting Principal Investigator (PI) on the GCaPPS trial - shares his vision for future research into the cost-effectiveness of population-based testing for ovarian cancer.

Women carrying BRCA1 and BRCA2 alterations are at high risk of developing ovarian and breast cancer. The traditionally approach to genetic testing for identifying women carrying BRCA1 and BRCA2 alterations has involved a family history driven approach. This involves testing individuals from high-risk families or those with a strong family history of cancer. This is usually undertaken through specialised cancer genetic clinics following face-to-face genetic counselling.

However, this approach misses >50% of individuals who carry a BRCA1/BRCA2 gene alteration and are at risk. Our research team is at the forefront of exploring the novel alternative of population testing where testing for BRCA1 and BRCA2 gene alterations is offered to anyone over the age of 18 years irrespective of their family history of cancer.

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Population based testing can overcome the limitations of a family history based approach. However this would also involve testing many more individuals to identify those at risk than current policy. Therefore this has additional cost implications for the health system. A health-economic assessment is thus crucial for evaluating costs and health benefits for positioning public health policy for genetic testing. In a health economic analysis in the GCaPPS study, we have previously shown that population testing for BRCA1 and BRCA2 alterations in the Jewish population saves lives, reduces breast and ovarian cancer incidence and is also cost saving for the NHS.

However, this approach has not been assessed in a non-Jewish general population. In our current ongoing research we are evaluating the health economic implications for population testing of all (non-Jewish) general population women for a number of ovarian and breast cancer causing gene alterations.

We are developing complex decision analysis mathematical models to do this. We hypothesise that such an approach of offering genetic testing for multiple cancer causing genes to anyone who wants it is likely to be cost-effective and will further reduce cancer incidence and save lives compared to current policy.