Addressing Cardiovascular Disease Among Populations Disproportionately Impacted in the United States

Cardiovascular disease (CVD) causes the most deaths in the United States and is disproportionately impacting certain groups more than others. A gap in the research exists when focusing on national data for those who bear the highest burden of CVD amongst people with hypertension when cardiovascular morbidity and mortality are examined. There is also a need to investigate the relationships between key indicators for CVD health disparities in people with hypertension. The purpose of this quantitative study is to investigate the cumulative effect of key health disparities indicators such as race, age, gender, education, and income using national-level surveillance data to determine if there are significant differences in CVD morbidity and mortality outcomes among people with hypertension. Systems theory is the theoretical foundation for this research study. Two major research questions seek to determine if there are significant differences in the selected CVD morbidity and CVD mortality outcomes in hypertensive subpopulations who are ages 30 years and older who experience health disparities, and the best group in the United States for hypertension when 3 or more of the health disparities indicators intersect. Data were provided by NHANES between 1999–2010. The results of multivariate analysis show that there are significant differences among people with hypertension in morbidity and mortality CVD outcomes when three or more health disparity indicators intersect. Positive social change can result when the findings of this study are used to address health disparities in CVD and hypertension

Application of Structured Decision Making to Wildlife Management in Montana

Good decision-making is essential to conserving wildlife populations. Whereas there may be multiple ways to address a problem, perfect solutions rarely exist. Managers are therefore tasked with identifying optimal decisions that will best achieve desired outcomes. Structured decision making (SDM) is a method of decision analysis used to identify the most effective, efficient, and realistic optimal decisions while accounting for values and priorities of the decision maker. The stepwise process includes identifying the management problem, defining objectives for solving the problem, developing alternative approaches to achieve the objectives, and formally evaluating which alternative is most likely to accomplish the objectives. The SDM process can be more effective than informal decision-making because it provides a transparent way to quantitatively evaluate decisions for addressing multiple management objectives while incorporating science, uncertainty, and risk tolerance. We illustrate the application of this process to management needs, including an SDM-based decision tool developed to identify optimal decisions for proactively managing risk of pneumonia epizootics in bighorn sheep (Ovis canadensis). Pneumonia epizootics are a major challenge for managers, including in terms of knowing how or when to manage risk. The decision tool facilitates analysis of alternative decisions for how to manage herds based on predictions from a risk model, herd-specific objectives, and predicted costs and benefits of each alternative. Managers can be confident resulting decisions are most effective, efficient, and realistic because they explicitly account for important considerations managers implicitly weigh when making decisions, including competing management objectives, uncertainty in potential outcomes and risk tolerance

Carnivore Territoriality: Simulating Economic Selection of Territories

We are developing theoretical models of territorial behavior of carnivores. This work will be useful for predicting the abundance of wolf (Canis lupus) territories in Montana and Idaho. Coupled with a patch occupancy model, it will provide more accurate estimates of abundance of wolves in each state. Ultimately, our work will also provide a better understanding of territorial behavior of a large carnivore. We are simulating the territory selection process for carnivores choosing patches on a landscape based on benefits of prey, where prey distribution ranges from overdispersed to highly clumped. Simulated carnivores will also consider hypothesized costs of patch ownership, including travel, competition, and mortality risk. In each simulation, carnivores will acquire patches for a territory as economically as possible based on these benefits and costs. Simulating various combinations of these hypothesized benefits and costs of patch ownership will provide predictions of territorial behavior. We can then compare these predictions to the territories of real wolves to determine which model is most predictive of actual wolf behavior. Starting with a model for benefits of prey and costs of travel, we found that prey distribution may influence mean size, quality, and fragmentation of simulated territories. Based on these preliminary results, we might expect differences in size or quality of territories in regions with different prey communities. Most importantly, this work provides a foundation from which we will build more complex models of territorial behavior of carnivores

Proactive Management of Pneumonia Epizootics in Bighorn Sheep in Montana—Project Update

Pneumonia epizootics are a major challenge for effective management of bighorn sheep (Ovis canadensis). Approximately half of the herds in Montana have suffered die-offs since the 1980s, many of which were pneumonia events. A set of models that identify risk of pneumonia and the best management decisions given that risk would be of great value for proactive management of pneumonia epizootics. Our first objective is to design and test a risk model that will help predict a herd’s risk of pneumonia. We hypothesize that various factors increase risk through pathogen exposure, pathogen spread, and disease susceptibility. Analysis of these factors comparing herds with and without recent pneumonia histories using Bayesian logistic regression will allow us to design a risk model. Our second objective is to develop a proactive decision model that incorporates estimates of pneumonia risk to help evaluate costs and benefits of alternative proactive actions appropriate to those estimates. We will use a Structured Decision Making framework, which provides a deliberative, transparent, and defensible decision-making process that is particularly valuable in complex decision-making environments such as wildlife disease management. Together the resulting risk and decision models, to be completed this year, will help managers estimate pneumonia risk and identify the best management action based on both the severity of each herd’s predicted risk and costs and benefits of competing management alternatives. Ultimately, this project will demonstrate the development and application of risk and decision models for proactive wildlife health programs in Montana Fish, Wildlife and Parks

A Risk Model for Proactive Management of Pneumonia Epizootics in Bighorn Sheep

Pneumonia epizootics are a major challenge for management of bighorn sheep (Ovis canadensis). Risk factors associated with the disease are poorly understood, making pneumonia epizootics hard to predict; such epizootics are thus managed reactively rather than proactively. We developed a model that identifies risk factors and addresses biological questions about risk. Using Bayesian logistic regression with repeated measures, we found that private land, weed control using domestic sheep or goats, pneumonia history, and herd density were associated with risk of pneumonia in 43 herds in Montana that experienced 22 epizootics out of 637 herd years from 1979–2013. Within high-risk areas occupied by herds, risk increased with greater amounts of private land and use of domestic sheep or goats for weed control. Herds had >10 times greater odds of having a pneumonia epizootic if they or neighboring herds within high-risk areas had a history of pneumonia. Risk greatly increased when herds were at high density, with nearly 15 times greater odds of pneumonia compared to herds at low density. Number of federal sheep and goat allotments, proximity to nearest herds, ram:ewe ratios, normality of winter and spring precipitation, and herds with native versus mixed or reintroduced origin were not associated with increased risk. We conclude that factors associated with risk of pneumonia are complex and may not always be from the most obvious sources. The ability to identify high risk herds will help determine where to focus management efforts and what risk factors most affect each herd, facilitating more effective, proactive management