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\u27 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

Ecosystem dynamics and management after forest die-off : A global synthesis with conceptual state-and-transition models

Broad-scale forest die-off associated with drought and heat has now been reported from every forested continent, posing a global-scale challenge to forest management. Climate-driven die-off is frequently compounded with other drivers of tree mortality, such as altered land use, wildfire, and invasive species, making forest management increasingly complex. Facing similar challenges, rangeland managers have widely adopted the approach of developing conceptual models that identify key ecosystem states and major types of transitions between those states, known as “state-and-transition models” (S&T models). Using expert opinion and available research, the development of such conceptual S&T models has proven useful in anticipating ecosystem changes and identifying management actions to undertake or to avoid. In cases where detailed data are available, S&T models can be developed into probabilistic predictions, but even where data are insufficient to predict transition probabilities, conceptual S&T models can provide valuable insights for managing a given ecosystem and for comparing and contrasting different ecosystem dynamics. We assembled a synthesis of 14 forest die-off case studies from around the globe, each with sufficient information to infer impacts on forest dynamics and to inform management options following a forest die-off event. For each, we developed a conceptual S&T model to identify alternative ecosystem states, pathways of ecosystem change, and points where management interventions have been, or may be, successful in arresting or reversing undesirable changes. We found that our diverse set of mortality case studies fit into three broad classes of ecosystem trajectories: (1) single-state transition shifts, (2) ecological cascading responses and feedbacks, and (3) complex dynamics where multiple interactions, mortality drivers, and impacts create a range of possible state transition responses. We integrate monitoring and management goals in a framework aimed to facilitate development of conceptual S&T models for other forest die-off events. Our results highlight that although forest die-off events across the globe encompass many different underlying drivers and pathways of ecosystem change, there are commonalities in opportunities for successful management intervention.Peer reviewe