Browsing by Subject "forecasting"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Forecasting the Future of Cardiovascular Disease in the United States
    (2011-03) Heidenreich, Paul A; Trogdon, Justin G; Khavjou, Olga A; Butler, Javed; Dracup, Kathleen; Ezekowitz, Michael D; Finkelstein, Eric Andrew; Hong, Yuling; Johnston, S Claiborne; Khera, Amit; Lloyd-Jones, Donald M; Nelson, Sue A; Nichol, Graham; Orenstein, Diane; Wilson, Peter WF; Woo, Y Joseph; American Heart Association Advocacy Coordinating Committee; Stroke Council; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Council on Arteriosclerosis; Thrombosis and Vascular Biology; Council on Cardiopulmonary; Critical Care; Perioperative and Resuscitation; Council on Cardiovascular Nursing; Council on the Kidney in Cardiovascular Disease; Council on Cardiovascular Surgery and Anesthesia, and Interdisciplinary Council on Quality of Care and Outcomes Research
    Background—Cardiovascular disease (CVD) is the leading cause of death in the United States and is responsible for 17% of national health expenditures. As the population ages, these costs are expected to increase substantially. Methods and Results—To prepare for future cardiovascular care needs, the American Heart Association developed methodology to project future costs of care for hypertension, coronary heart disease, heart failure, stroke, and all other CVD from 2010 to 2030. This methodology avoided double counting of costs for patients with multiple cardiovascular conditions. By 2030, 40.5% of the US population is projected to have some form of CVD. Between 2010 and 2030, real (2008$) total direct medical costs of CVD are projected to triple, from $273 billion to $818 billion. Real indirect costs (due to lost productivity) for all CVD are estimated to increase from $172 billion in 2010 to $276 billion in 2030, an increase of 61%. Conclusions—These findings indicate CVD prevalence and costs are projected to increase substantially. Effective prevention strategies are needed if we are to limit the growing burden of CVD.
  • Thumbnail Image
    ItemOpen Access
    How well can we predict climate migration? A review of forecasting models
    (Frontiers in Climate, 2023-01-01) Schewel, K; Dickerson, S; Madson, B; Nagle Alverio, G
    Climate change will have significant impacts on all aspects of human society, including population movements. In some cases, populations will be displaced by natural disasters and sudden-onset climate events, such as tropical storms. In other cases, climate change will gradually influence the economic, social, and political realities of a place, which will in turn influence how and where people migrate. Planning for the wide spectrum of future climate-related mobility is a key challenge facing development planners and policy makers. This article reviews the state of climate-related migration forecasting models, based on an analysis of thirty recent models. We present the key characteristics, strengths, and weaknesses of different modeling approaches, including gravity, radiation, agent-based, systems dynamics and statistical extrapolation models, and consider five illustrative models in depth. We show why, at this stage of development, forecasting models are not yet able to provide reliable numerical estimates of future climate-related migration. Rather, models are best used as tools to consider a range of possible futures, to explore systems dynamics, to test theories or potential policy effects. We consider the policy and research implications of our findings, including the need for improved migration data collection, enhanced interdisciplinary collaboration, and scenarios-based planning.
  • Thumbnail Image
    ItemOpen Access
    Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival.
    (Front Public Health, 2014) Arbeev, Konstantin G; Akushevich, Igor; Kulminski, Alexander M; Ukraintseva, Svetlana V; Yashin, Anatoliy I
    Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements became known as the "joint models" (JM). An important point to consider in analyses of such data in the context of studies on aging, health, and longevity is how to incorporate knowledge and theories about mechanisms and regularities of aging-related changes that accumulate in the research field into respective analytic approaches. In the absence of specific observations of longitudinal dynamics of relevant biomarkers manifesting such mechanisms and regularities, traditional approaches have a rather limited utility to estimate respective parameters that can be meaningfully interpreted from the biological point of view. A conceptual analytic framework for these purposes, the stochastic process model of aging (SPM), has been recently developed in the biodemographic literature. It incorporates available knowledge about mechanisms of aging-related changes, which may be hidden in the individual longitudinal trajectories of physiological variables and this allows for analyzing their indirect impact on risks of diseases and death. Despite, essentially, serving similar purposes, JM and SPM developed in parallel in different disciplines with very limited cross-referencing. Although there were several publications separately reviewing these two approaches, there were no publications presenting both these approaches in some detail. Here, we overview both approaches jointly and provide some new modifications of SPM. We discuss the use of stochastic processes to capture biological variation and heterogeneity in longitudinal patterns and important and promising (but still largely underused) applications of JM and SPM to predictions of individual and population mortality and health-related outcomes.