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There is a gap between the knowledge obtained in dementia research and the practical use of it. This review examines the concept of knowledge translation (KT) and the process of translating research into practice in the field of dementia. KT in dementia practice involves key players including researchers, educators, clinicians, policy-makers, the general public and consumers who act within a multidimensional network, disseminating findings widely to effect changes in community awareness, clinical practice and health policy. Challenges include the volume of dementia research; difficulties in determining who is responsible for KT; the problems of premature KT; the lack of resources to perform KT; the paucity of research about effective KT strategies; and the characteristics of the dementia care workforce. Key features of effective KT include a simple compelling message; use of interpersonal contact and roles; a practical framework with an emphasis on "know-how"; and the provision of resources and support. More effective dementia KT will require a commitment by key players to engage in the process, a better understanding of effective KT strategies, adequate resources, and judicious selection of appropriate evidence-based information.

Background: A computer model was designed to test hypothetical scenarios regarding dementia prevalence in Australia (2001–2040). Methods: The study implemented 3 scenarios: delaying dementia onset, slowing disease progression and, in a previously unpublished experiment, eradicating dementia types. Sensitivity analysis and parameter variation were the main methods of experimentation. Results: The model predicts that delaying dementia onset by 5 years will reduce the 2040 prevalence by 37%. An onset delay of 2 years, introduced in 2010, will reduce the 2040 prevalence by 16%. Slowing disease progression increases the 2040 prevalence by 4–7%. Total eradication of Alzheimer’s disease (currently approximately 50% of all dementia cases) in 2020 will decrease the 2040 prevalence by 42%. Conclusion: Computer modeling of future scenarios and interventions helps health and aged care planners understand the likely challenges society will face with the ageing of the world’s population.

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