Programs and Communities

Falls are prevalent among those aged 65 years and older and may result in minor to debilitating injuries in this vulnerable population. Frailty, unsteady gait, and medication side effects all contribute to fall risk as well as dementia, a type of cognitive impairment that disrupts memory and judgment leading to an underestimation of fall risk. Fall prevention evidence suggests that interventions aimed at decreasing fall rates begin with a fall risk assessment and tailored fall prevention measures that promote safety.

To examine the effectiveness of a fall prevention program in dementia care, an evidence-based pilot was conducted in a long-term care facility focused on dementia care. A convenience sample of 16 nurses received a fall prevention education intervention. A fall prevention knowledge instrument measured pre and post-fall prevention knowledge. There was a significant increase in fall risk knowledge from the pre-test (p < .001). The participants then conducted a fall risk assessment of 50 dementia patients using the Morse Fall Scale.

Of the 50 dementia patients, 28 were identified as high risk for falls. The nurses then instituted tailored fall risk prevention measures for those high risk for falls. As a result of the pilot, 40 fall events were noted within a three-month time period, reflecting a significant reduction in falls (p < .001) from the previous year. The institution of a fall prevention program in dementia care incorporating nursing education, a fall risk scale, and measures to promote safety can reduce fall risk in dementia patients.

Hybrid system models - those devised from two or more disparate sub-system models - provide a number of benefits in terms of conceptualization, development, and assessment of dynamical systems. The decomposition approach helps to formulate complex interactions that are otherwise difficult or impractical to express. However, hybrid model development and usage can introduce complexity that emerges from the composition itself.

To improve assurance of model correctness, sub-systems using disparate modeling formalisms must be integrated above and beyond just the data and control level; their composition must have model specification and simulation execution aspects as well. Poly-formalism composition is one approach to composing models in this manner.

This dissertation describes a poly-formalism composition between a Discrete EVent System specification (DEVS) model and a Cellular Automata (CA) model types. These model specifications have been chosen for their broad applicability in important and emerging domains. An agent-environment domain exemplifies the composition approach. The inherent spatial relations within a CA make it well-suited for environmental representations. Similarly, the component-based nature of agents fits well within the hierarchical component structure of DEVS.

This composition employs the use of a third model, called an interaction model, that includes methods for integrating the two model types at a formalism level, at a systems architecture level, and at a model execution level. A prototype framework using DEVS for the agent model and GRASS for the environment has been developed and is described. Furthermore, this dissertation explains how the concepts of this composition approach are being applied to a real-world research project.

This dissertation expands the tool set modelers in computer science and other disciplines have in order to build hybrid system models, and provides an interaction model for an on-going research project. The concepts and models presented in this dissertation demonstrate the feasibility of composition between discrete-event agents and discrete-time cellular automata. Furthermore, it provides concepts and models that may be applied directly, or used by a modeler to devise compositions for other research efforts.

The tables and graphs in this chartbook were created using data collected by Camden Public Schools for the school year 2008-2009. Rutgers Center for State Health Policy obtained de-identified data from the schools and computed a BMI score and a BMI percentile (BMIPCT) for each child. Weight status is defined using the following BMIPCT categories.

BMIPCT

BMIPCT < 85

BMIPCT ~ 85

BMIPCT ~ 95

BMIPCT ~ 97

Weight Status

Not Overweight or Obese

Overweight and Obese

Obese

Very Obese

BMIPCT categories are presented at the city level and in sub-group analysis by age, gender, and race. Aggregate data are also presented at the school level, with notation, where representativeness of the data was a concern.

Tables and graphs on pages 5, 7, 9, and 11 show comparisons with national estimates (National Health and Nutrition Examination Survey, 2007-2008). The national data are representative of all 2-19 year old children in the US.

Each graph and table is accompanied by brief summary statements. Readers are encouraged to review the actual data presented in tables and graphs as there is much more detail.