Suchergebnisse

ABSTRACT
ObjectivesAs estimated there are about 5.3 million who suffer from Alzheimer's disease in United States. The incidence is increasing as the population is aging. Due to the increasing trend of Alzheimer's disease, there is a lot of discussion on prevention efforts or slowing the incidence. Also, models that could predict individual risk of cognitive impairment are needed to assist in prevention efforts.
In general dementia development has been associated with growth in various vascular, lifestyle and other risk factors. Epidemiological research provides evidence of some vascular, lifestyle and psychological risk factors that are modifiable and protective of disease incidence either independently or while interacting with other factors. However, as reported by National Institute of Aging, it is not yet clear whether health or lifestyle factors can prevent Alzheimer's disease.
The objective of this research project is to adopt a system dynamics modeling approach to study the interaction of several key factors including vascular, lifestyle and psychological aspects over the life course of individuals, to gain further understanding of Alzheimer's disease incidence and evaluate prevention strategies. Both datasets of 'Alzheimer's Disease Neuroimaging Initiative (ADNI)' and 'Health and Retirement Study (HRS)' will be used for model development and validation.
ApproachA system dynamics approach is an optimal choice for addressing the goal of this proposal because different key factors interact over time and make Alzheimer's disease incidence a complex problem. Furthermore, system dynamics approaches focus on understanding the relationship between the structure of a system and the resulting dynamic behaviors generated through multiple interacting feedback loops. Such an approach could be invaluable in studying dynamic problems arising in complex health, social, economic, or ecological systems.
ResultsFor the purpose of the proposal, the following stages are planned:1. Develop a system dynamics simulation model at individual level that predicts the Alzheimer's disease incidence over the life course, and aggregates individual level models to predict population level trends
2. Calibrate the resulting simulation model based upon longitudinal data trends employed from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Both cohorts with Alzheimer's disease and control subjects from this database will be used to fine-tune the simulation model.
ConclusionThe final validated model would be used to test different hypotheses and evaluate various strategies and/or their combinations to help evaluate the efficacy of prevention strategies on Alzheimer's disease incidence and its growth.

AbstractIn this research, we broaden the scope of system dynamics formulations by building on a previously proposed approach to bridge fuzzy logic with dynamic modeling. Our methodology illustrates how to formulate fuzzy dynamic variables in a meaningful way. We highlight several modeling challenges, including the selection of a fuzzification and defuzzification method, their implementation in a system dynamics formulations and the validation of the results. We use a physician prescription decision‐making model substructure as an example, and apply the fuzzy rule‐based inference system to determine how a patient is categorized as "low‐risk," "average‐risk" or "high‐risk." We emphasize various interpretation challenges and suggest careful selection of the fuzzy operators and defuzzification method, to ensure that the defuzzified values behave reasonably in a dynamic context.Copyright © 2020 System Dynamics Society

Fresh water demand is rising due to factors such as population growth, economic development, and land use changes. At the same time, climate change is rendering the water supply even more uncertain for the future. Due to recurring water restrictions and increasing water-related fees triggered by droughts and water shortages, there is a widespread, growing discomfort with respect to future water availability. Among key stakeholders and local policy makers, this has led to an increased interest in modeling the availability of water resources, with the aim of developing and implementing the appropriate water resource infrastructure and management strategies. This paper examines the Washington metropolitan area (WMA) water supply system and uses a system dynamics approach as a planning tool to make an exploratory assessment of the adequacy of the study area's water supply system to meet future water demand under the influence of substantial droughts and climate change. This assessment finds that the study area is self-sufficient under normal climate conditions during the entire planning horizon but that it will be strained under moderately severe droughts. On the basis of the temperature, streamflow and precipitation projections made by climate change models specific to the WMA region, climate change is expected to improve the water supply reliability. However, climate change has uncertainty associated with it. One of the four climate models for the Potomac River basin projects a decrease in the precipitation and streamflow, which may result in a reduction in the water supply and the system's reliability. Regulating the price and the system losses are valuable tools that can be leveraged. But these policy interventions require stakeholder participation (price regulation) and capital investments (reduction of distribution losses). Finally, system reliability can also be improved by increasing water supplies. ; Public domain authored by a U.S. government employee

Purpose: We aimed to help community stakeholders develop a shared understanding of the opioid crisis through qualitative system dynamics (SD) modeling to inform local strategies for prevention and treatment. Methods: As part of the HEALing Communities Study-New York State, we used secondary qualitative data from community stakeholder interviews and coalition meeting notes to develop qualitative SD models that elucidate the interdependencies and feedback structures underlying the opioid epidemic in each community. Results: The synthesized model revealed multiple balancing and reinforcing feedback loops that influenced the adoption and reach of evidence-based practices to reduce opioid overdose and fatality. Conclusion: SD modeling is a novel approach to helping community stakeholders to see the inter-connectedness of actors, factors and sectors and the need for multiple mutually reinforcing strategies to avert opioid overdose and fatality. Social workers could play a key role in linking actions across sectors in such a complex system.