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The Australian National University
DYNOPTA - Dynamic Analyses to Optimise Ageing
ANU College of Medicine, Biology & Environment



DYNOPTA focuses on four outcomes that significantly contribute to the burden of disease and disability, namely:

  • dementia and cognition
  • mental health
  • sensory disability
  • mobility/activity limitations
DYNOPTA will also develop the first Australian dynamic microsimulation model that can forecast the health and social outcomes of the baby boomer and older cohorts. The simulation modelling will allow researchers to evaluate the impact of modifying risk factors and costs associated with different trajectories of health and ageing.

Guided by an interdisciplinary life course approach to human development and ageing, this project incorporates interdependencies among demographic, behavioural, social, economic and health factors. The outcomes of this project will have significant implications for health promotion and are likely to inform social and medical interventions for healthy ageing in Australia many years into the future.

DYNOPTA objectives

The broad objectives of the program are to:
  • Forecast health and functional status in the context of population ageing over the next 20 years;
  • Identify potential interventions within population target groups and estimate the associated costs and benefits of the extent to which risk reduction may prevent disease in order to compress morbidity and delay mortality, thus increasing social and economic participation;
  • Contribute a state-of-the art, innovative resource for government and researchers, with the potential for wider application in identifying factors that will optimize healthy and productive ageing at both the individual and population levels;
  • Ensure the effective implementation of research findings by engaging with government, consumers and industry.

WHY combine data?

Given the dynamic, time-dependent nature of ageing, longitudinal studies are needed for successful investigations of critical health and social outcomes. Individual longitudinal studies make important contributions to knowledge in the Australian context and contain rich data on particular topics. However, these studies often have only small numbers of people in with specific medical conditions, or combinations of different conditions, and lack the statistical power for effective comparisons among groups with specific characteristics such as very old age, low-prevalence disorders or co-morbidities.