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    NCDMod: A microsimulation model projecting chronic disease and risk factors for australian adults

    Access Status
    Fulltext not available
    Authors
    Lymer, S.
    Schofield, D.
    Lee, Crystal
    Colagiuri, S.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Lymer, S. and Schofield, D. and Lee, C. and Colagiuri, S. 2016. NCDMod: A microsimulation model projecting chronic disease and risk factors for australian adults. International Journal of Microsimulation. 9 (3): pp. 103-139.
    Source Title
    International Journal of Microsimulation
    ISSN
    1747-5864
    School
    School of Public Health
    URI
    http://hdl.handle.net/20.500.11937/55137
    Collection
    • Curtin Research Publications
    Abstract

    Chronic disease and the associated risk factors are an ongoing public health concern. They represent a major burden of disease and result in significant health expenditure. The purpose of this paper is to describe the development and attributes of the microsimulation model NCDMod, an Australian, purpose-built model that simulates multiple chronic diseases and associated risk factors. The model offers flexibility in perspective and population of interest. The model projects out to 2025 in 5-year increments using a combination of static and dynamic methods. Transition equations are sourced from the literature and in-house analysis. NCDMod is able to capture the impacts of population ageing and shifts in incidence of specific chronic diseases and risk factors. Results from NCDMod will help to fill gaps in current Australian evidence on the population level impacts of interventions to prevent/delay chronic health conditions particularly diabetes, heart disease and obesity. Further, in combination with Health & WealthMod2030 this model will be able to offer evidence regarding the potential impact of health interventions on the long-term costs (both from an individual and government perspective) of ill health leading to unplanned retirement. In the consideration of disease interventions, the additional workers who may be able to continue to work due to delayed/prevented chronic disease can be modelled.

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