The economic impact of diabetes through lost labour force participation on individuals and government: evidence from a microsimulation model (original) (raw)
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BMJ open, 2017
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. 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. Australian population aged 45-64 years in 2015, 2020, 2025 and 2030. Lost PLY...
BMJ open, 2016
To project the number of older workers with lost productive life years (PLYs) due to chronic disease and resultant lost income; and lost taxes and increased welfare payments from 2015 to 2030. Using a microsimulation model, Health&WealthMOD2030, the costs of chronic disease in Australians aged 45-64 were projected to 2030. The model integrates household survey data from the Australian Bureau of Statistics Surveys of Disability, Ageing and Carers (SDACs) 2003 and 2009, output from long-standing microsimulation models (STINMOD (Static Incomes Model) and APPSIM (Australian Population and Policy Simulation Model)) used by various government departments, population and labour force growth data from Treasury, and disease trends data from the Australian Burden of Disease and Injury Study (2003). Respondents aged 45-64 years in the SDACs 2003 and 2009 formed the base population. Lost PLYs due to chronic disease; resultant lost income, lost taxes and increased welfare payments in 2015, 2020,...
2011
Health of the ageing population has the potential to place fiscal pressure on future Australian governments. The aim of this research was to build a dynamic microsimulation model of the Australian health system to evaluate the impact of an ageing population on government health expenditure, as well as consider the moderating effects of the population's health status and health behaviour profiles. As a modelling platform, the Australian Population and Policy Simulator (APPSIM) was used to provide the basefile and inform general socioeconomic parameter inputs across time. This allowed the consideration of health from a wider socioeconomic perspective including factors such as education, income and labour force status. Further, the module was developed to consider health risk behaviours, as well as general health status and their resultant impacts on health service usage and expenditure. Generalised linear models were used for both the baseline imputation and the transitioning through time of the individual's health characteristics. Development of equations to inform the transition of an individual's health status and obesity status used both socioeconomic explanatory variables and lagged dependant variables, due to the strong persistence of health characteristics. Validation to look at the quality of the health module has included comparative analysis with external data. Both cross-sectional and longitudinal comparisons have been used in the validation. To demonstrate the capacity of the health module and its ability to evaluate possible policy levers, scenarios around shifts in population levels primarily of obesity and secondarily of physical activity have been completed. In comparison with the baseline simulations, which project an ageing population and allows the probability equations of the model to operate as is, scenarios that increase obesity have substantial effect on the population health profile and associated health expenditure. Also, there are substantial gains to be made with respect to the population health profile with decreases in the prevalence of inadequate physical activity. Under the assumptions of this modelling, change in physical activity offered more potential to improve health due to it acting on health both directly but also on health through obesity, than only changing obesity levels within the population. The health module that has been developed offers a framework from which relevant policy levers can be examined. It also provides a starting point for the development of more sophisticated relationships associated with the health system within a dynamic microsimulation model. Research Council (under grant LP0883041), and by the linkage partner to the grant, the Department of Health and Ageing. I also acknowledge the financial support and access to facilities provided by the National Centre for Social and Economic Modelling at the University of Canberra. I wish to gratefully acknowledge Laurie Brown, the chair of my supervisory panel, and Alan Duncan, my secondary supervisor, for their academic advice, patience, encouragement and guidance in the completion of this PhD. Thanks also go to Ann Harding and Anthony King for their advice as part of my supervisory panel. Simon Kelly needs special mention for his assistance in the original coding of the health module in c# and his ongoing guidance in teaching me how to further develop the code.
International Journal of Microsimulation, 2014
Policymakers in Australia, like in most OECD countries, have recognised the importance of early retirement due to ill health on individuals and families, as well as on the budget balance when planning for the health needs of an ageing population. In order to understand these effects, a unique microsimulation model, called Health&WealthMOD2030, was built to estimate the impacts of early retirement due to ill health on labour force participation, personal and household income, economic hardship (poverty), and government taxation revenue, spending and GDP in the years 2010, 2015, 2020, 2025 and 2030. This paper describes the construction of Health&WealthMOD2030. The model captures the long term projections of demographic change, changing labour force participation patterns, real wages growth and trends in major illnesses affecting the older working age population. The base population of Health&WealthMOD2030 are the individuals aged 45-64 years with information on their work force status and health from the Australian Bureau of Statistics’ Surveys of Disability, Ageing and Carers (SDAC) 2003 and 2009. Projected estimates of income, taxation, income support payments, savings and superannuation from the National Centre for Social and Economic Modelling (NATSEM’s) dynamic microsimulation model Australian Population and Policy Simulation Model (APPSIM) were synthetically matched with the base population. Health&WealthMOD2030 project forward the economic impacts of early retirement from ill health to 2030. This will fill substantial gaps in the current Australian evidence of health conditions that will keep older working age Australians out of the labour market over the long- term.
2012
Spinal disorders can reduce an individual's ability to participate in the labor force, and this can lead to considerable impacts on both the individual and the state. This study was aimed to quantify the personal cost of lost income and the cost to the state from lost income taxation, increased benefits payments, and lost gross domestic product (GDP) as a result of early retirement because of spinal disorders in Australians aged 45 to 64 years in 2009. This was done using cross-sectional analysis of the base population of Health&WealthMOD, a microsimulation model built on data from the Australian Bureau of Statistics' Survey of Disability, Ageing and Carers, and STINMOD, an income and savings microsimulation model. Linear regression models were used to examine the relationship between spinal disorders, labor force participation, income, taxation, and government support payments. It was found that individuals aged 45 to 64 years who have retired early because of spinal disorders have significantly lower income (79% less; 95% confidence interval [CI], -84.7, -71.1; p<.0001), pay significantly less taxation (100% less; 95% CI, -100.0, 99.9; p<.0001), and receive significantly more in government support payments (21,000% more; 95% CI, 12,767.0, 35,336.4; p<.0001) than those employed full time with no health condition. Individuals who have retired early because of spinal disorders have a median value of total weekly income of only AU$310, whereas those who are employed full time are likely to receive four times this. This has a large national aggregate impact, with AU$4.8 billion lost in annual individual earnings, AU$622 million in additional welfare payments, AU$497 million lost in taxation revenue for governments, and AU$2.9 billion in lost GDP: all attributable to spinal disorders through their impact on labor force participation. Although the individual has to bear the economic costs of lost income in addition to the burden of the condition itself, the state experiences the impacts of loss of productivity from reduced workforce participation, lost income taxation revenue, and increasing government support payments.