AIM: To describe the Dynamic Analyses to Optimise Ageing (DYNOPTA) project and illustrate its contributions to understanding ageing through innovative methodology, and investigations on outcomes based on the project themes. DYNOPTA provides a platform and technical expertise that may be used to combine other national and international datasets. METHODS: The DYNOPTA project has pooled and harmonised data from nine Australian longitudinal studies to create the largest available longitudinal dataset (n= 50652) on ageing in Australia. RESULTS: A range of findings have resulted from the study to date, including methodological advances, prevalence rates of disease and disability, and mapping trajectories of ageing with and without increasing morbidity. DYNOPTA also forms the basis of a microsimulation model that will provide projections of future costs of disease and disability for the baby boomer cohort. CONCLUSION: DYNOPTA contributes significantly to the Australian evidence base on ageing to inform key social and health policy domains.
AIM: To describe the Dynamic Analyses to Optimise Ageing (DYNOPTA) project and illustrate its contributions to understanding ageing through innovative methodology, and investigations on outcomes based on the project themes. DYNOPTA provides a platform and technical expertise that may be used to combine other national and international datasets. METHODS: The DYNOPTA project has pooled and harmonised data from nine Australian longitudinal studies to create the largest available longitudinal dataset (n= 50652) on ageing in Australia. RESULTS: A range of findings have resulted from the study to date, including methodological advances, prevalence rates of disease and disability, and mapping trajectories of ageing with and without increasing morbidity. DYNOPTA also forms the basis of a microsimulation model that will provide projections of future costs of disease and disability for the baby boomer cohort. CONCLUSION:DYNOPTA contributes significantly to the Australian evidence base on ageing to inform key social and health policy domains.
Authors: Lesley A Ross; Colette Browning; Mary A Luszcz; Paul Mitchell; Kaarin J Anstey Journal: J Am Geriatr Soc Date: 2011-02-02 Impact factor: 5.562
Authors: Kaarin J Anstey; Julie E Byles; Mary A Luszcz; Paul Mitchell; David Steel; Heather Booth; Colette Browning; Peter Butterworth; Robert G Cumming; Judith Healy; Timothy D Windsor; Lesley Ross; Lauren Bartsch; Richard A Burns; Kim Kiely; Carole L Birrell; Gerald A Broe; Jonathan Shaw; Hal Kendig Journal: Int J Epidemiol Date: 2009-01-17 Impact factor: 7.196
Authors: Richard A Burns; Peter Butterworth; Kim M Kiely; Allison A M Bielak; Mary A Luszcz; Paul Mitchell; Helen Christensen; Chwee Von Sanden; Kaarin J Anstey Journal: J Clin Epidemiol Date: 2011-02-02 Impact factor: 6.437
Authors: Richard A Burns; Carole L Birrell; David Steel; Paul Mitchell; Kaarin J Anstey Journal: Soc Psychiatry Psychiatr Epidemiol Date: 2012-08-10 Impact factor: 4.328
Authors: Lesley A Ross; Kaarin J Anstey; Kim M Kiely; Tim D Windsor; Julie E Byles; Mary A Luszcz; Paul Mitchell Journal: J Am Geriatr Soc Date: 2009-08-20 Impact factor: 5.562
Authors: Kaarin J Anstey; Richard A Burns; Carole L Birrell; David Steel; Kim M Kiely; Mary A Luszcz Journal: BMC Neurol Date: 2010-07-21 Impact factor: 2.474