Objectives: To estimate (1) productive life years (PLYs) lost because of chronic conditions in Australians aged 45-64 years from 2010 to 2030, and (2) the impact of this loss on gross domestic product (GDP) over the same period.
Design, Setting And Participants: A microsimulation model, Health&WealthMOD2030, was used to project lost PLYs caused by chronic conditions from 2010 to 2030. The base population consisted of respondents aged 45-64 years to the Australian Bureau of Statistics Survey of Disability, Ageing and Carers 2003 and 2009. The national impact of lost PLYs was assessed with Treasury's GDP equation.
Main Outcome Measures: Lost PLYs due to chronic disease at 2010, 2015, 2020, 2025 and 2030 (ie, whole life years lost because of chronic disease); the national impact of lost PLYs at the same time points (GDP loss caused by PLYs); the effects of population growth, labour force trends and chronic disease trends on lost PLYs and GDP at each time point.
Results: Using Health&WealthMOD2030, we estimated a loss of 347,000 PLYs in 2010; this was projected to increase to 459,000 in 2030 (32.28% increase over 20 years). The leading chronic conditions associated with premature exits from the labour force were back problems, arthritis and mental and behavioural problems. The percentage increase in the number of PLYs lost by those aged 45-64 years was greater than that of population growth for this age group (32.28% v 27.80%). The strongest driver of the increase in lost PLYs was population growth (accounting for 89.18% of the increase), followed by chronic condition trends (8.28%).
Conclusion: Our study estimates an increase of 112 000 lost PLYs caused by chronic illness in older workers in Australia between 2010 and 2030, with the most rapid growth projected to occur in men aged 55-59 years and in women aged 60-64 years. The national impact of this lost labour force participation on GDP was estimated to be $37.79 billion in 2010, increasing to $63.73 billion in 2030.
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http://dx.doi.org/10.5694/mja15.00132 | DOI Listing |
BJPsych Open
May 2019
Associate Professor, University Centre for Rural Health, School of Public Health, The University of Sydney, Australia.
Background: The impact of mental disorders has been assessed in relation to longevity and quality of life; however, mental disorders also have an impact on productive life-years (PLYs).
Aims: To quantify the long-term costs of Australians aged 45-64 having lost PLYs because of mental disorders.
Method: The Survey of Disability, Ageing and Carers 2003, 2009 formed the base population of Health&WealthMOD2030 - a microsimulation model integrating output from the Static Incomes Model, the Australian Population and Policy Simulation Model, the Treasury and the Australian Burden of Disease Study.
BMC Public Health
June 2019
National Centre for Social and Economic Modelling, University of Canberra, Canberra, ACT, Australia.
Background: Most studies measure the impact of ischemic heart disease (IHD) on individuals using quality of life metrics such as disability-adjusted life-years (DALYs); however, IHD also has an enormous impact on productive life years (PLYs). The objective of this study was to project the indirect costs of IHD resulting from lost PLYs to older Australian workers (45-64 years), government, and society 2015-2030.
Methods: Nationally representative data from the Surveys of Disability, Ageing and Carers (2003, 2009) were used to develop the base population in the microsimulation model (Health&WealthMOD2030), which integrated data from established microsimulation models (STINMOD, APPSIM), Treasury's population and workforce projections, and chronic conditions trends.
BMC Public Health
May 2018
University Centre for Rural Health, School of Public Health, The University of Sydney, Lismore, NSW, Australia.
Background: While the direct (medical) costs of arthritis are regularly reported in cost of illness studies, the 'true' cost to indivdiuals and goverment requires the calculation of the indirect costs as well including lost productivity due to ill-health.
Methods: Respondents aged 45-64 in the ABS Survey of Disability, Ageing and Carers 2003, 2009 formed the base population. We projected the indirect costs of arthritis using Health&WealthMOD2030 - Australia's first microsimulation model on the long-term impacts of ill-health in older workers - which incorporated outputs from established microsimulation models (STINMOD and APPSIM), population and labour force projections from Treasury, and chronic conditions trends for Australia.
BMJ Open
January 2017
National Centre for Social and Economic Modelling, University of Canberra, Canberra, Australian Capital Territory, Australia.
Objectives: To project the number of people aged 45-64 years with lost productive life years (PLYs) due to diabetes and related costs (lost income, extra welfare payments, lost taxation revenue); and lost gross domestic product (GDP) attributable to diabetes in Australia from 2015 to 2030.
Design: A simulation study of how the number of people aged 45-64 years with diabetes increases over time (based on population growth and disease trend data) and the economic losses incurred by individuals and the government. Cross-sectional outputs of a microsimulation model (Health&WealthMOD2030) which used the Australian Bureau of Statistics' Survey of Disability, Ageing and Carers 2003 and 2009 as a base population and integrated outputs from two microsimulation models (Static Incomes Model and Australian Population and Policy Simulation Model), Treasury's population and labour force projections, and chronic disease trends data.
Pain
December 2016
University Centre for Rural Health, School of Public Health, The University of Sydney, Lismore, New South Wales, Australia.
This study projected the indirect costs of back problems through lost productive life years (PLYs) from the individual's perspective (lost disposable income), the governmental perspective (reduced taxation revenue, greater welfare spending), and the societal perspective (lost gross domestic product, GDP) from 2015 to 2030, using Health&WealthMOD2030-Australia's first microsimulation model on the long-term impacts of ill-health. Quantile regression analysis was used to examine differences in median weekly income, welfare payments, and taxes of people unable to work due to back problems with working full-time without back problems as comparator. National costs and lost GDP resulting from missing workers due to back problems were also projected.
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