Comparative- and Cost-Effectiveness of Population Strategies to Improve Diet and Reduce Cancer

It is well known that cancer is a leading cause of US deaths and healthcare expenditures, causing 575,000 deaths, $60.9 billion in direct medical costs, and $15.5 billion in indirect morbidity costs annually. It is also known that poor diet contributes strongly to specific cancers.

Improving diet could reduce the US cancer burden. Although nutritional strategies that target individual behavior can be effective in improving diet for some people, population-based policies may have a more wide-reaching effect and have a more sustained impact. From previous projects, we have identified key policy strategies that are promising in improving the public's diet. However, both the impact and costs and cost-effectiveness of such strategies on cancer outcomes are largely unknown.

Our research seeks to fill that knowledge gap by asking the following research questions:

To understand the effectiveness of the selected nutrition policies, we are building both a Comparative Risk Assessment (CRA) model and a Diet Cancer Outcome Model (DiCOM) that evaluate the change in cancer incidence and mortality, disability-adjusted life years (DALYs) averted, and quality-adjusted life years (QALYs) gained based on the selected nutrition policies. We will compare the projections from these two models over 5, 10, 15, and 20 years.

We will estimate the cost of cancer care based on established evidence on direct medical costs (hospitalizations, treatment, prescriptions, etc.) as well as indirect costs associated with lost years of life and lost productivity. We will then use these costs to determine the cost-effectiveness of the selected nutritional policies.

Our work will provide, for the first time, the effectiveness estimates of well-defined population strategies to improve diet and reduce the cancer burden in the US, which are crucial to informing dietary health policy-making and evidence-based cancer prevention efforts. 


Project leader: Dr. Fang Zhang