Suchergebnisse

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. RESULTS: For depression, individuals incurred a loss of AU$1062 million in income in 2015, projected to increase to AU$1539 million in 2030 (45% increase). The government is projected to incur costs comprising a 22% increase in social security payments and a 45% increase in lost taxes as a result of depression through its impact on PLYs. CONCLUSIONS: Effectiveness of mental health programmes should be judged not only in terms of healthcare use but also quality of life and economic well-being. DECLARATION OF INTEREST: None.

Abstract Background Little is known about the effects of personal and other characteristics of care recipients on the behaviour of carers. The aim of this study is to examine the association between the main chronic (disabling) condition of care recipients and the likelihood of their (matched) primary carers aged 15–64 years being out of the labour force. Methods We conducted a retrospective analysis of cross-sectional data from the Australian Bureau of Statistics 2009 Survey of Disability, Ageing and Carers (SDAC) for people aged 15–64 years. We estimated the rates of exit from the labour force for primary carers and non-carers; rates of chronic disease occurrence for care recipients living with their main carers; odds ratios of primary carers being out of the labour force associated with the main chronic condition of their care recipient who lives with them. Results From the 2009 SDAC, we identified 1,268 out of 37,186 eligible participants who were primary carers of a care recipient who lived with them. Of these, 628 (49.5%) were out of the labour force. Most common diseases of care recipients were: back problems (12%); arthritis and related disorders (10%); diseases of the nervous system (such as multiple sclerosis, epilepsy, cerebral palsy) (7.4%); and conditions originating in the perinatal period or congenital malformations, deformations and chromosomal abnormalities (5.1%). When adjusted for age, sex, education and whether have a long term chronic condition of informal carers, the five conditions of care recipients associated with the highest odds of their carers being out of the labour force were: head injury/acquired brain damage; neoplasms, blood diseases, disorders of the immune system; leg/knee/foot/hip damage from injury/accident; dementia, Parkinson's disease, Alzheimer's disease; and diseases of the musculoskeletal system and connective tissue (osteoporosis). Conclusions This study identifies the type of conditions that have the greatest impact on the labour force participation of informal carers – previously unavailable information for Australia. Australia, like most developed countries, is facing several skills shortages and an ageing population. These governments will need to adopt novel and more wholistic approaches to increase the labour force participation of diverse groups. Informal carers are one such group.

AbstractData from children suggest that the prevalence of child labour in the cocoa industry in Côte d'Ivoire is a worrying 38%. However, while surveying children has the potential to reduce sensitivity biases such as social desirability bias, it presents significant ethical dilemmas and may also be associated with other reporting biases, making accurate estimates of child labour difficult. To address this, we collected data from 1741 cocoa producers to assess whether parents could provide more accurate estimates using indirect survey methods. We compared direct questioning with a list experiment and a novel non-verbal method ('colorbox'). We found higher prevalence rates of child labour using indirect elicitation methods, but lower than those obtained from children's surveys. Qualitative in-depth interviews revealed motivations for underreporting, including fear of legal consequences and mistrust of stakeholders. Indirect methods alone are not sufficient to correct for underreporting when child labour is collected from parents. Future research should prioritise direct data collection from children and address ethical concerns to obtain more accurate estimates of child labour.

Aims: To assess the labour force participation and quantify the economic status of older Australian workers with multiple health conditions. Background: Many older people suffer from multiple health conditions. While multiple morbidities have been highlighted as an important research topic, there has been limited research in this area to date, particularly on the economic status of those with multiple morbidities. Methods: Cross sectional analysis of Health&WealthMOD, a microsimulation model of Australians aged 45 to 64 years. Results: People with one chronic health condition had 0.59 times the odds of being employed compared to those with no condition (OR 0.59, 95% CI: 0.49, 0.71), and those with four or more conditions had 0.14 times the odds of being employed compared to those with no condition (OR 0.14, 95% CI: 0.11, 0.18). People with one condition received a weekly income 32% lower than those with no health condition, paid 49 % less tax, and received 37% more in government transfer payments; those with four or more conditions received a weekly income 94% lower, paid 97% less in tax and received over 2,000% more in government transfer payments per week than those with no condition. Conclusion: While having a chronic health condition is associated with lower labour force participation and poorer economic status, having multiple conditions compounds the affect – with these people being far less likely to be employed and having drastically lower incomes.

Abstract Background The costs of arthritis to the individuals and the state are considerable. Methods Cross-sectional analysis of the base population of Health&WealthMOD, a microsimulation model of 45 to 64 year old Australians built on data from the Australian Bureau of Statistics' Survey of Disability, Ageing and Carers and STINMOD, an income and savings microsimulation model. Results Individuals aged 45 to 64 years who had retired early due to arthritis had a median value of AU$260 in total weekly income whereas those who were employed full time were likely to average more than five times this. The large national aggregate impact of early retirement due to arthritis includes AU$9.4 billion in lost GDP, attributable to arthritis through its impact on labour force participation. When looking at the ongoing impact of being out of the labour force those who retired from the labour force early due to arthritis were estimated to have a median value of total savings by the time they are 65 of as little as $300 (for males aged 45–54). This is far lower than the median value of savings for those males aged 45–54 who remained in the labour force full time, who would have an estimated $339 100 of savings at age 65. Conclusions The costs of arthritis to the individuals and the state are considerable. The impacts on the state include loss of productivity from reduced workforce participation, lost income taxation revenue, and increased government support payments – in addition to direct health care costs. Individuals bear the economic costs of lost income and the reduction of their savings over the long term.

Background: The costs of arthritis to the individuals and the state are considerable. Methods: Cross-sectional analysis of the base population of Health&WealthMOD, a microsimulation model of 45 to 64 year old Australians built on data from the Australian Bureau of Statistics' Survey of Disability, Ageing and Carers and STINMOD, an income and savings microsimulation model. Results: Individuals aged 45 to 64 years who had retired early due to arthritis had a median value of AU$260 in total weekly income whereas those who were employed full time were likely to average more than five times this. The large national aggregate impact of early retirement due to arthritis includes AU$9.4 billion in lost GDP, attributable to arthritis through its impact on labour force participation. When looking at the ongoing impact of being out of the labour force those who retired from the labour force early due to arthritis were estimated to have a median value of total savings by the time they are 65 of as little as $300 (for males aged 45–54). This is far lower than the median value of savings for those males aged 45–54 who remained in the labour force full time, who would have an estimated $339 100 of savings at age 65. Conclusions: The costs of arthritis to the individuals and the state are considerable. The impacts on the state include loss of productivity from reduced workforce participation, lost income taxation revenue, and increased government support payments – in addition to direct health care costs. Individuals bear the economic costs of lost income and the reduction of their savings over the long term.

Abstract Background Long term illness has far reaching impacts on individuals, and also places a large burden upon government. This paper quantifies the indirect economic impacts of illness related early retirement on individuals and government in Australia in 2009. Methods The output data from a microsimulation model, Health&WealthMOD, was analysed. Health&WealthMOD is representative of the 45 to 64 year old Australian population in 2009. The average weekly total income, total government support payments, and total taxation revenue paid, for individuals who are employment full-time, employed part-time and not in the labour force due to ill health was quantified. Results It was found that persons out of the labour force due to illness had significantly lower incomes ($218 per week as opposed to $1167 per week for those employed full-time), received significantly higher transfer payments, and paid significantly less tax than those employed full-time or part-time. This results in an annual national loss of income of over $17 billion, an annual national increase of $1.5 billion in spending on government support payments, and an annual loss of $2.1 billion in taxation revenue. Conclusions Illness related early retirement has significant economic impacts on both the individual and on governments as a result of lost income, lost taxation revenue and increased government support payments. This paper has quantified the extent of these impacts for Australia.

Background: Mental health conditions have the ability to interrupt an individual's ability to participate in the labour force, and this can have considerable follow on impacts to both the individual and the state. Method: 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 was used to quantify the personal cost of lost income and the cost to the state from lost income taxation, increased benefits payments and lost GDP as a result of early retirement due to mental health conditions in Australians aged 45-64 in 2009. Results: Individuals aged 45 to 64 years who have retired early due to depression personally have 73% lower income then their full time employed counterparts and those retired early due to other mental health conditions have 78% lower incomes. The national aggregate cost to government due to early retirement from these conditions equated to $278 million (£152.9 million) in lost income taxation revenue, $407 million (£223.9 million) in additional transfer payments and around $1.7 billion in GDP in 2009 alone. Conclusions: The costs of mental health conditions to the individuals and the state are considerable. While individuals has to bear the economic costs of lost income in addition to the burden of the conditions itself, the impact on the state is loss of productivity from reduced workforce participation, lost income taxation revenue, and increased government support payments--in addition to direct health care costs.

Abstract Background Mental health conditions have the ability to interrupt an individual's ability to participate in the labour force, and this can have considerable follow on impacts to both the individual and the state. Method 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 was used to quantify the personal cost of lost income and the cost to the state from lost income taxation, increased benefits payments and lost GDP as a result of early retirement due to mental health conditions in Australians aged 45-64 in 2009. Results Individuals aged 45 to 64 years who have retired early due to depression personally have 73% lower income then their full time employed counterparts and those retired early due to other mental health conditions have 78% lower incomes. The national aggregate cost to government due to early retirement from these conditions equated to $278 million (£152.9 million) in lost income taxation revenue, $407 million (£223.9 million) in additional transfer payments and around $1.7 billion in GDP in 2009 alone. Conclusions The costs of mental health conditions to the individuals and the state are considerable. While individuals has to bear the economic costs of lost income in addition to the burden of the conditions itself, the impact on the state is loss of productivity from reduced workforce participation, lost income taxation revenue, and increased government support payments - in addition to direct health care costs.

Background: Long term illness has far reaching impacts on individuals, and also places a large burden upon government. This paper quantifies the indirect economic impacts of illness related early retirement on individuals and government in Australia in 2009. Methods: The output data from a microsimulation model, Health&WealthMOD, was analysed. Health&WealthMOD is representative of the 45 to 64 year old Australian population in 2009. The average weekly total income, total government support payments, and total taxation revenue paid, for individuals who are employment full-time, employed part-time and not in the labour force due to ill health was quantified. Results: It was found that persons out of the labour force due to illness had significantly lower incomes ($218 per week as opposed to $1167 per week for those employed full-time), received significantly higher transfer payments, and paid significantly less tax than those employed full-time or part-time. This results in an annual national loss of income of over $17 billion, an annual national increase of $1.5 billion in spending on government support payments, and an annual loss of $2.1 billion in taxation revenue. Conclusions: Illness related early retirement has significant economic impacts on both the individual and on governments as a result of lost income, lost taxation revenue and increased government support payments. This paper has quantified the extent of these impacts for 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. Setting: Australian population aged 45–64 years in 2015, 2020, 2025 and 2030. Outcome measures: Lost PLYs, lost income, extra welfare payments, lost taxation revenue, lost GDP. Results: 18 100 people are out of the labour force due to diabetes in 2015, increasing to 21 400 in 2030 (18% increase). National costs consisted of a loss of $A467 million in annual income in 2015, increasing to $A807 million in 2030 (73% increase). For the government, extra annual welfare payments increased from $A311 million in 2015 to $A350 million in 2030 (13% increase); and lost annual taxation revenue increased from $A102 million in 2015 to $A166 million in 2030 (63% increase). A loss of $A2.1 billion in GDP was projected for 2015, increasing to $A2.9 billion in 2030 attributable to diabetes through its impact on PLYs. Conclusions: Individuals incur significant costs of diabetes through lost PLYs and lost income in addition to disease burden through human suffering and healthcare costs. The government incurs extra welfare payments, lost taxation revenue and lost GDP, along with direct healthcare costs ; Full Text

Objectives: 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. Design, setting and participants: 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. Main outcome measures: Lost PLYs due to chronic disease; resultant lost income, lost taxes and increased welfare payments in 2015, 2020, 2025 and 2030. Results: We projected 380 000 (6.4%) people aged 45–64 years with lost PLYs in 2015, increasing to 462 000 (6.5%) in 2030—a 22% increase in absolute numbers. Those with lost PLYs experience the largest reduction in income than any other group in each year compared to those employed full time without a chronic disease, and this income gap widens over time. The total economic loss due to lost PLYs consisted of lost income modelled at $A12.6 billion in 2015, increasing to $A20.5 billion in 2030—a 62.7% increase. Additional costs to the government consisted of increased welfare payments at $A6.2 billion in 2015, increasing to $A7.3 billion in 2030—a 17.7% increase; and a loss of $A3.1 billion in taxes in 2015, increasing to $A4.7 billion in 2030—a growth of 51.6%. Conclusions: There is a need for greater investment in effective preventive health interventions which improve workers' health and work capacity. ; Full Text

Background Diabetes is a costly and debilitating disease. The aim of the study is to quantify the individual and national costs of diabetes resulting from people retiring early because of this disease, including lost income; lost income taxation, increased government welfare payments; and reductions in GDP. Methods A purpose-built microsimulation model, Health&WealthMOD2030, was used to estimate the economic costs of early retirement due to diabetes. The study included all Australians aged 45–64 years in 2010 based on Australian Bureau of Statistics' Surveys of Disability, Ageing and Carers. A multiple regression model was used to identify significant differences in income, government welfare payments and taxation liabilities between people out of the labour force because of their diabetes and those employed full time with no chronic health condition. Results The median annual income of people who retired early because of their diabetes was significantly lower (AU$11 784) compared to those employed full time without a chronic health condition who received almost five times more income. At the national level, there was a loss of AU$384 million in individual earnings by those with diabetes, an extra AU$4 million spent in government welfare payments, a loss of AU$56 million in taxation revenue, and a loss of AU$1 324 million in GDP in 2010: all attributable to diabetes through its impact on labour force participation. Sensitivity analysis was used to assess the impact of different diabetes prevalence rates on estimates of lost income, lost income taxation, increased government welfare payments, and reduced GDP. Conclusions Individuals bear the cost of lost income in addition to the burden of the disease. The Government endures the impacts of lost productivity and income taxation revenue, as well as spending more in welfare payments. These national costs are in addition to the Government's direct healthcare costs.