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The Introduction highlights the opportunities for a healthier and wealthier society following a transition to a low-carbon economy but also notes the serious consequences of inaction. It outlines the aim of the book to help policy-makers with practical guidance and summarises the various sections of the book including: the technologies available, economic projections for a low-carbon Australian economy and comparisons with two emerging giants – Indonesia and India, the sectoral analysis encompassing cities and their precincts, industry and manufacturing, tranportation and regional environments, land use, forestry and agriculture.
This book is a comprehensive manual for decision-makers and policy leaders addressing the issues around human caused climate change, which threatens communities with increasing extreme weather events, sea level rise, and declining habitability of some regions due to desertification or inundation. The book looks at both mitigation of greenhouse gas emissions and global warming and adaption to changing conditions as the climate changes. It encourages the early adoption of climate change measures, showing that rapid decarbonisation and improved resilience can be achieved while maintaining prosperity. The book takes a sector-by-sector approach, starting with energy and includes cities, industry, natural resources, and agriculture, enabling practitioners to focus on actions relevant to their field. It uses case studies across a range of countries, and various industries, to illustrate the opportunities available. Blending technological insights with economics and policy, the book presents the tools decision-makers need to achieve rapid decarbonisation, whilst unlocking and maintaining productivity, profit, and growth.
Questions have been raised regarding differences in the standards of care that patients receive when they are admitted to or discharged from in-patient units at weekends.
To compare the quality of care received by patients with anxiety and depressive disorders who were admitted to or discharged from psychiatric hospital at weekends with those admitted or discharged during the ‘working week’.
Retrospective case-note review of 3795 admissions to in-patient psychiatric wards in England. Quality of care received by people with depressive or anxiety disorders was compared using multivariable regression analyses.
In total, 795 (20.9%) patients were admitted at weekends and 157 (4.8%) were discharged at weekends. There were minimal differences in quality of care between those admitted at weekends and those admitted during the week. Patients discharged at weekends were less likely to be given sufficient notification (48 h) in advance of being discharged (OR = 0.55, 95% CI 0.39–0.78), to have a crisis plan in place (OR = 0.65, 95% CI 0.46–0.92) or to be given medication to take home (OR = 0.45, 95% CI 0.30–0.66). They were also less likely to have been assessed using a validated outcome measure (OR = 0.70, 95% CI 0.50–0.97).
There is no evidence of a ‘weekend effect’ for patients admitted to psychiatric hospital at weekends, but the quality of care offered to those who were discharged at weekends was relatively poor, highlighting the need for improvement in this area.
Gun violence is a large and growing problem in the United States. Many reformers look towards elections to spur policy change in this area. In this paper, we explore the effects of school shootings on electoral mobilization and election outcomes. We pair data from several sources that measure validated voter registration; validated voter turnout; and the electoral performance of officials at the local, state, and federal levels with regression discontinuity and panel methods. Our effects show that shootings have little to no effect on electoral outcomes in the United States. Our work demonstrates that even when tragic events occur that are squarely in the realm of elected officials’ responsibility, have high levels of issue salience, are highly-covered by the media, draw citizens’ attention, and (perhaps) shift public opinion, these seemingly favorable conditions may not be enough to elicit democratic accountability.
Nested data arise frequently in clinical research. The nesting might be hierarchical, such as patients nested within clinicians, or it might be longitudinal, such as repeated assessments over time nested within individuals. As articulated in this chapter, whenever and however nesting occurs, it is necessary to account for the statistical dependence of observations within units when analyzing the data. Further, it is important to determine the level(s) of the data at which predictors exert their effects. Multilevel models are a particularly popular and useful approach for addressing these issues. We thus describe these models in detail, illustrating the application of multilevel models in clinical research via two examples. The first example considers nesting of siblings within families and demonstrates the importance of separating within- versus between-family effects. The second example focuses on the application of multilevel models with repeated measures to evaluate within-person change over time. Additionally, we provide a brief survey of other approaches to the analysis of nested data (e.g., cluster-robust standard errors, generalized estimating equations, fixed-effects models).