Suicide rates for young Aboriginal and Torres Strait Islander people: the influence of community level cultural connectedness.

OBJECTIVES: To examine associations between community cultural connectedness indicators and suicide mortality rates for young Aboriginal and Torres Strait Islander people. STUDY DESIGN: Retrospective mortality study. SETTING, PARTICIPANTS: Suicide deaths of people aged 10-19 years recorded by the Queensland Suicide Register, 2001-2015. MAIN OUTCOME MEASURES: Age-standardised suicide death rates, by Indigenous status, sex, and age group; age-standardised suicide death rates for young First Nations people by area level remoteness and Index of Relative Socioeconomic Advantage and Disadvantage, and by cultural connectedness indicators (at statistical area level 2): cultural social capital index score, community Indigenous language use, and reported discrimination. RESULTS: The age-specific suicide rate was 21.1 deaths per 100 000 persons/year for First Nations young people and 5.0 deaths per 100 000 persons/year for non-Indigenous young people (rate ratio [RR], 4.3; 95% CI, 3.5-5.1). The rate for Aboriginal and Torres Strait Islander young people was higher in areas with low levels of cultural social capital (greater participation of community members in cultural events, ceremonies, organisations, and community activities) than in areas classified as having high levels (RR, 1.8; 95% CI, 1.2-2.7), and also in communities with high levels of reported discrimination (RR, 2.7; 95% CI, 1.7-4.3). Associations with proportions of Indigenous language speakers and area level socio-economic resource levels were not statistically significant. CONCLUSION: We found that suicide mortality rates for Aboriginal and Torres Strait Islander young people in Queensland were influenced by community level culturally specific risk and protective factors. Our findings suggest that strategies for increasing community cultural connectedness at the community level and reducing institutional and personal discrimination could reduce suicide rates.

Intellectual disability and autism: socioeconomic impacts of informal caring, projected to 2030.

BACKGROUND: Intellectual disability and autism spectrum disorder (ASD) influence the interactions of a person with their environment and generate economic and socioeconomic costs for the person, their family and society. AIMS: To estimate costs of lost workforce participation due to informal caring for people with intellectual disability or autism spectrum disorders by estimating lost income to individuals, lost taxation payments to federal government and increased welfare payments. METHOD: We used a microsimulation model based on the Australian Bureau of Statistics' Surveys of Disability, Ageing and Carers (population surveys of people aged 15-64), and projected costs of caring from 2015 in 5-year intervals to 2030. RESULTS: The model estimated that informal carers of people with intellectual disability and/or ASD in Australia had aggregated lost income of AU$310 million, lost taxation of AU$100 million and increased welfare payments of AU$204 million in 2015. These are projected to increase to AU$432 million, AU$129 million and AU$254 million for income, taxation, and welfare respectively by 2030. The income gap of carers for people with intellectual disability and/or ASD is estimated to increase by 2030, meaning more financial stress for carers. CONCLUSIONS: Informal carers of people with intellectual disability and/or ASD experience significant loss of income, leading to increased welfare payments and reduced taxation revenue for governments; these are all projected to increase. Strategic policies supporting informal carers wishing to return to work could improve the financial and psychological impact of having a family member with intellectual disability and/or ASD. DECLARATION OF INTEREST: None.

The indirect costs of ischemic heart disease through lost productive life years for Australia from 2015 to 2030: results from a microsimulation model.

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. RESULTS: We projected that 6700 people aged 45-64 were out of the labour force due to IHD in 2015, increasing to 8100 in 2030 (21 increase). National costs consisted of a loss of AU$273 (US$263) million in income for people with IHD in 2015, increasing to AU$443 ($US426) million (62% increase). For the government, extra welfare payments increased from AU$106 (US$102) million in 2015 to AU$143 (US$138) million in 2030 (35% increase); and lost income tax revenue increased from AU$74 (US$71) million in 2015 to AU$117 (US$113) million in 2030 (58% increase). A loss of AU$785 (US$755) million in GDP was projected for 2015, increasing to AU$1125 (US$1082) million in 2030. CONCLUSIONS: Significant costs of IHD through lost productivity are incurred by individuals, the government, and society. The benefits of IHD interventions include not only improved health but also potentially economic benefits as workforce capacity.

The long-term economic impacts of arthritis through lost productive life years: results from an Australian microsimulation model.

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. All costs of arthritis were expressed in real 2013 Australian dollars, adjusted for inflation over time. RESULTS: We estimated there are 54,000 people aged 45-64 with lost PLYs due to arthritis in 2015, increasing to 61,000 in 2030 (13% increase). In 2015, people with lost PLYs are estimated to receive AU$706.12 less in total income and AU$311.67 more in welfare payments per week than full-time workers without arthritis, and pay no income tax on average. National costs include an estimated loss of AU$1.5 billion in annual income in 2015, increasing to AU$2.4 billion in 2030 (59% increase). Lost annual taxation revenue was projected to increase from AU$0.4 billion in 2015 to $0.5 billion in 2030 (56% increase). We projected a loss in GDP of AU$6.2 billion in 2015, increasing to AU$8.2 billion in 2030. CONCLUSIONS: Significant costs of arthritis through lost PLYs are incurred by individuals and government. The effectiveness of arthritis interventions should be judged not only on healthcare use but quality of life and economic wellbeing.

Lost productive life years caused by chronic conditions in Australians aged 45-64 years, 2010-2030.

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.

Depression prevention, labour force participation and income of older working aged Australians: A microsimulation economic analysis.

OBJECTIVE: Depression has economic consequences not only for the health system, but also for individuals and society. This study aims to quantify the potential economic impact of five-yearly screening for sub-syndromal depression in general practice among Australians aged 45-64 years, followed by a group-based psychological intervention to prevent progression to depression. METHOD: We used an epidemiological simulation model to estimate reductions in prevalence of depression, and a microsimulation model, Health&WealthMOD2030, to estimate the impact on labour force participation, personal income, savings, taxation revenue and welfare expenditure. RESULTS: Group therapy is estimated to prevent around 5,200 prevalent cases of depression (2.2%) and add about 520 people to the labour force. Private incomes are projected to increase by $19 million per year, tax revenues by $2.4 million, and transfer payments are reduced by $2.6 million. CONCLUSION: Group-based psychological intervention to prevent depression could result in considerable economic benefits in addition to its clinical effects.

The impact of chronic conditions of care recipients on the labour force participation of informal carers in Australia: which conditions are associated with higher rates of non-participation in the labour force?

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.

The economic impact of diabetes through lost labour force participation on individuals and government: evidence from a microsimulation model.

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.

Economic impact of informal caring for a person with arthritis in Australia from 2015 to 2030: a microsimulation approach using national survey data.

OBJECTIVES: To estimate the economic burden of informal caregivers not in the labour force (NILF) due to caring for a person with arthritis in Australia, with projections of these costs from 2015 to 2030. DESIGN: Static microsimulation modelling using national survey data. SETTING: Australia nationwide survey. PARTICIPANTS: Participants include respondents to the Survey of Disability, Ageing and Carers who are informal carers of a person who has arthritis as their main chronic condition and non-carers. OUTCOME MEASURES: Estimating the economic impact and national aggregated costs of informal carers NILF to care for a person with arthritis and projecting these costs from 2015 to 2030 in 5-year intervals. RESULTS: On a per-person basis, when adjusted for age, sex and highest education attained, the difference in average weekly total income between informal carers and non-carers employed in the labour force is $A1051 (95% CI: $A927 to $A1204) in 2015 and projected to increase by up to 22% by 2030. When aggregated, the total national annual loss of income to informal carers NILF is estimated at $A388.2 million (95% CI: $A324.3 to $A461.9 million) in 2015, increasing to $A576.9 million (95% CI: $A489.2 to $A681.8 million) by 2030. The national annual tax revenue lost to the government of the informal carers NILF is estimated at $A99 million (95% CI: $A77.9 to $A126.4 million) in 2015 and is projected to increase 49% by 2030. CONCLUSION: Informal carers NILF are economically worse off than employed non-carers, and the aggregated national annual costs are substantial. The future economic impact of informal carers NILF to care for a person with arthritis in Australia is projected to increase, with the estimated differences in income between informal carers and employed non-carers increasing by 22% from 2015 to 2030.

The Healthcare and Societal Costs of Familial Intellectual Disability.

Most of the studies on the cost of intellectual disability are limited to a healthcare perspective or cohorts composed of individuals where the etiology of the condition is a mixture of genetic and non-genetic factors. When used in policy development, these can impact the decisions made on the optimal allocation of resources. In our study, we have developed a static microsimulation model to estimate the healthcare, societal, and lifetime cost of individuals with familial intellectual disability, an inheritable form of the condition, to families and government. The results from our modeling show that the societal costs outweighed the health costs (approximately 89.2% and 10.8%, respectively). The lifetime cost of familial intellectual disability is approximately AUD 7 million per person and AUD 10.8 million per household. The lifetime costs to families are second to those of the Australian Commonwealth government (AUD 4.2 million and AUD 9.3 million per household, respectively). These findings suggest that familial intellectual disability is a very expensive condition, representing a significant cost to families and government. Understanding the drivers of familial intellectual disability, especially societal, can assist us in the development of policies aimed at improving health outcomes and greater access to social care for affected individuals and their families.

Individual and national financial impacts of informal caring for people with mental illness in Australia, projected to 2030.

BACKGROUND: Mental illness has a significant impact not only on patients, but also on their carers' capacity to work. AIMS: To estimate the costs associated with lost labour force participation due to the provision of informal care for people with mental illness in Australia, such as income loss for carers and lost tax revenue and increased welfare payments for government, from 2015 to 2030. METHOD: The output data of a microsimulation model Care&WorkMOD were analysed to project the financial costs of informal care for people with mental illness, from 2015 to 2030. Care&WorkMOD is a population-representative microsimulation model of the Australian population aged between 15 and 64 years, built using the Australian Bureau of Statistics Surveys of Disability, Ageing and Carers data and the data from other population-representative microsimulation models. RESULTS: The total annual national loss of income for all carers due to caring for someone with mental illness was projected to rise from AU$451 million (£219.6 million) in 2015 to AU$645 million (£314 million) in 2030 in real terms. For the government, the total annual lost tax revenue was projected to rise from AU$121 million (£58.9 million) in 2015 to AU$170 million (£82.8 million) in 2030 and welfare payments to increase from AU$170 million (£82.8 million) to AU$220 million (£107 million) in 2030. CONCLUSIONS: The costs associated with lost labour force participation due to the provision of informal care for people with mental illness are projected to increase for both carers and government, with a widening income gap between informal carers and employed non-carers, putting carers at risk of increased inequality.

Findings from the Kids in Communities Study (KiCS): A mixed methods study examining community-level influences on early childhood development.

There is increasing international interest in place-based approaches to improve early childhood development (ECD) outcomes. The available data and evidence are limited and precludes well informed policy and practice change. Developing the evidence-base for community-level effects on ECD is one way to facilitate more informed and targeted community action. This paper presents overall final findings from the Kids in Communities Study (KiCS), an Australian mixed methods investigation into community-level effects on ECD in five domains of influence-physical, social, governance, service, and sociodemographic. Twenty five local communities (suburbs) across Australia were selected based on 'diagonality type' i.e. whether they performed better (off-diagonal positive), worse (off-diagonal negative), or 'as expected' (on-diagonal) on the Australian Early Development Census (AEDC) relative to their socioeconomic profile. The approach was designed to determine replicable and modifiable factors that were separate to socioeconomic status. Between 2015-2017, stakeholder interviews (n = 146), parent and service provider focus groups (n = 51), and existing socio-economic and early childhood education and care administrative data were collected. Qualitative and quantitative data analyses were undertaken to understand differences between 14 paired disadvantaged local communities (i.e. on versus off-diagonal). Further analysis of qualitative data elicited important factors for all 25 local communities. From this, we developed a draft set of 'Foundational Community Factors' (FCFs); these are the factors that lay the foundations of a good community for young children.

Informal caring for back pain: overlooked costs of back pain and projections to 2030.

This study models the economic costs of informal caring for people with back pain, using a microsimulation model, Care&WorkMOD, from 2015 to 2030. Care&WorkMOD was based on 3 national Australian Surveys of Disability, Ageing and Carers (2003, 2009, 2012) data sets for individuals aged 15 to 64 years. Estimated national income loss due to caring for people with back pain was AU$258 million in 2015, increasing to $398 million in 2030 (54% increase). Lost income tax revenue to the Australian government due to informal care of people with back pain was estimated to be AU$78 million in 2015, increasing to AU$118 million in 2030 (50% increase), and additional welfare payments were estimated to rise from $132 million in 2015 to AU$180 in 2030 (36% increase). Larger growth in lost income, compared with the increase in welfare payments, means that there would be an increasing income gap between those out of the labour force providing informal care and noncarers who are in the labour force, leading to increased inequality. Informal carers are defined as providers of informal, unpaid assistance to someone with a health condition, for at least 6 months. Informal carers of people with back pain who are out of the labour force incur substantial economic costs. Furthermore, back pain is a large economic burden on national governments. Policies addressing back pain prevention and treatment, and supporting carers, may offset government welfare expenditure, while improving the socioeconomic well-being of carers and patients.

Indirect costs of depression and other mental and behavioural disorders for Australia from 2015 to 2030.

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.

Local Housing Characteristics Associated with Early Childhood Development Outcomes in Australian Disadvantaged Communities.

Disadvantaged communities tend to have poorer early childhood development outcomes. Access to safe, secure, and stable housing is a well-known social determinant of health but there is a need to examine key features of neighbourhood housing that reduce early childhood development inequities. The 2012 Australian Early Development Census (AEDC), a population-wide measure of early childhood development, and the Australian Bureau of Statistics Socio-economic Index for Areas Index of Relative Socio-economic Disadvantage were used to select fourteen disadvantaged local communities in five Australian states and territories based on those performing better (off-diagonal), or as expected (on-diagonal) on the AEDC relative to their socio-economic profile. Between 2015-2017, qualitative and quantitative housing data were collected in the local communities. In total, 87 interviews with stakeholders, 30 focus groups with local service providers and parents, and Australian Census dwelling information were analysed. A comparative case study approach was used to examine differences in housing characteristics (e.g., public housing, density, affordability, and tenure) between disadvantaged local communities performing 'better than expected' and 'as expected' on early childhood development. Perceived better housing affordability, objectively measured housing tenure (ownership) and perceived and objectively measured lower-density public housing were housing characteristics that emerged as points of difference for disadvantaged local communities where children had relatively better early childhood development outcomes. These characteristics are potential modifiable and policy sensitive housing levers for reducing early childhood development inequities.

Economic costs of informal care for people with chronic diseases in the community: Lost income, extra welfare payments, and reduced taxes in Australia in 2015-2030.

We estimated the economic costs of informal care in the community from 2015 to 2030, using an Australian microsimulation model, Care&WorkMOD. The model was based on data from three Surveys of Disability, Ageing, and Carers (SDACs) for the Australian population aged 15-64 years old. Estimated national income lost was AU$3.58 billion in 2015, increasing to $5.33 billion in 2030 (49% increase). Lost tax payments were estimated at AU$0.99 billion in 2015, increasing to AU$1.44 billion in 2030 (45% increase), and additional welfare payments were expected to rise from $1.45 billion in 2015 to AU$1.94 in 2030 (34% increase). There are substantial economic costs both to informal carers and the government due to carers being out of the labour-force to provide informal care for people with chronic diseases. Health and social policies supporting carers to remain in the labour force may allow governments to make substantial savings, while improving the economic situation of carers.

The costs of diabetes among Australians aged 45-64†years from 2015 to 2030: projections of lost productive life years (PLYs), lost personal income, lost taxation revenue, extra welfare payments and lost gross domestic product from Health&WealthMOD2030.

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.

Kids in Communities Study (KiCS) study protocol: a cross-sectional mixed-methods approach to measuring community-level factors influencing early child development in Australia.

INTRODUCTION: Healthy childhood development in the early years is critical for later adult health and well-being. Early childhood development (ECD) research has focused primarily on individual, family and school factors, but largely ignored community factors. The Kids in Communities Study (KiCS) will test and investigate community-level influences on child development across Australia. METHODS AND ANALYSIS: Cross-sectional mixed-methods study exploring community-level effects in 25 Australian local communities; selection based on community socioeconomic status (SES) and ECD using the Australian Early Development Census (AEDC), a population measure of child development, to create a local community 'diagonality type', that is, those performing better or worse (off-diagonal), or as expected (on-diagonal) on the AEDC relative to their SES. Data collection includes stakeholder interviews, parent and service provider focus groups, and surveys with general community residents and service providers, mapping of neighbourhood design and local amenities and services, analysis of policy documents, and the use of existing sociodemographic and early childhood education and care data. Quantitative data will be used to test associations between local community diagonality type, and ECD based on AEDC scores. Qualitative data will provide complementary and deeper exploration of these same associations. ETHICS AND DISSEMINATION: The Royal Children's Hospital Human Research Ethics Committee approved the study protocol (#30016). Further ethics approvals were obtained from State Education and Health departments and Catholic archdioceses where required. ECD community-level indicators will eventually be derived and made publically available. Findings will be published in peer-reviewed journals, community reports, websites and policy briefs to disseminate results to researchers, and key stakeholders including policymakers, practitioners and (most importantly) the communities involved.

Economic costs of chronic disease through lost productive life years (PLYs) among Australians aged 45-64†years from 2015 to 2030: results from a microsimulation model.

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.

The indirect costs of back problems (dorsopathies) in Australians aged 45 to 64 years from 2015 to 2030: results from a microsimulation model, Health&WealthMOD2030.

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. We projected that 90,000 people have lost PLYs due to back problems in 2015, increasing to 104,600 in 2030 (16.2% increase). People with lost PLYs due to back problems are projected to receive AU$340.91 less in total income and AU$339.77 more in welfare payments per week than full-time workers without back problems in 2030 and pay no income tax on average. National costs consisted of a loss of AU$2931 million in annual income in 2015, increasing to AU$4660 million in 2030 (60% increase). For government, extra annual welfare payments are projected to increase from AU$1462 million in 2015 to AU$1709 million in 2030 (16.9% increase), and lost annual taxation revenue to increase from AU$671 million in 2015 to $961 million in 2030 (43.2% increase). We projected losses in GDP of AU$10,543 million in 2015, increasing to AU$14,522 million in 2030 due to back problems.