Grant: Project Grant

Due to the enormous human and health system burden of dementia combined with limited prospects for treatment of the disease, prevention strategies have largely focused on modifying risk factors to prevent or slow progression from mild cognitive impairment (MCI) to dementia. A fundamental challenge in the development of effective dementia prevention strategies is developing a better understanding of nature and scope of modifiable risk factors associated with the development of MCI in order to act early and prevent or delay the development of dementia. In 2020, the Lancet Commission (LC) released an influential report that highlighted the importance of dementia prevention and that identified a set of modifiable risk factors. It concluded that the population attributable fraction (PAF) for these factors was almost 40%. Not unsurprisingly, this report had a substantial impact on global and national strategies for dementia prevention, including prevention efforts in Canada. However, there are concerns over the accuracy of estimates provided by the report. The Canadian Longitudinal Study of Aging (CLSA) data provides an opportunity to provide better estimate. This study will use the CLSA and statistical and machine learning tools to address the following objectives: 1.Calculate PAF for dementia prevention in Canada using LC risk estimates, CLSA data on prevalence of LC modifiable risk factors and two approaches to PAF calculation. 2.Estimate relative risk for individual modifiable risk factors for development of MCI at three and six years and their PAF population using CLSA data. 3.Estimate relative risk for combinations of modifiable risk factors for development of MCI at three and six years and their PAF population using CLSA data. 4.Create an interactive tool with stakeholders that uses PAF and relative risk estimates to inform community-level and individual-level dementia and MCI prevention strategies.