Abstract
INTRODUCTION: Risk prediction models aim to identify those at high risk to receive targeted interventions. We aimed to identify the proportion of future dementia cases that would be missed by a high-risk screening program. METHODS: We identified validated dementia risk prediction models from systematic reviews. We applied these to European Prospective Investigation of Cancer Norfolk, a large population-based cohort of 30,387 individuals with 29 years of linked healthcare data. RESULTS: A maximum of 16.0% (14.7,17.2) and 31.9% (30.2,33.5) of cases arose from the highest risk decile and quintiles, respectively. For every 1000 people considered to be at high risk, a maximum of 235 (215, 255) developed dementia. DISCUSSION: Seven in every 10 cases of dementia arose from people at normal risk, and eight in every 10 people at high risk did not develop dementia. Individual-level prevention approaches targeted at high-risk groups are unlikely to produce large reductions in disease incidence at the population level. HIGHLIGHTS: Dementia, a significant public health challenge, is not an inevitability of aging; risk reduction is possible. Several dementia risk prediction models have been validated in the general population, and these aim to identify people at high risk of the disease who can then be targeted with primary prevention interventions. An alternative prevention approach is to focus on interventions that reduce risk across the population, irrespective of risk status. In our study, seven out of every ten people who developed dementia during 29 year follow-up were classed as 'normal-risk' (rather than 'high risk') at baseline. Eight out of every ten people who were at high risk at baseline did not go on to develop dementia. Even if effective, dementia risk reduction efforts based upon targeted high-risk approaches are unlikely to reduce incidence of disease at the population level.