HC Management of Chronic Diseases

Welcome to the HC Management of Chronic Disease Working Group.

Chronic Diseases are defined as diseases that have a long course of illness, rarely resolve spontaneously and are generally not cured by medication or prevented by vaccine. These are diseases that can be controlled but not cured. Although chronic diseases are among the most common and costly health problems, they are also among the most preventable and many can be effectively controlled. Examples of chronic diseases include: Alzheimer's Disease, Asthma, Cancer, Diabetes, Heart Disease, Obesity, and Glacoma.

Chronic diseases are the leading causes of death and disability in the United States accounting for more than 70% of all deaths in the U.S - 1.7 million annually. These diseases also cause major limitations in daily living for almost 1 out of 10 Americans or about 25 million people (CDC).

This is not unique to the US, chronic diseases are the leading cause of mortality in the world accounting for 63% of all deaths (WHO). Out of the 36 million people who died from chronic disease in 2008, 9 million were under 60 and 90% of these premature deaths occurred in low- and middle-income countries (WHO).

Chronic diseases also have a significant economic impact. Healthy People 2010 reported that more than 75% of the $2 trillion spent annually in US medical care is due to chronic conditions. In addition to direct costs in health care, chronic diseases place a significant burden on the economy, through limitations in daily activities, loss in productivity and loss of days of work.

Prevention is believed to be effective in reducing the effect of chronic conditions; in particular, early detection results in less severe outcomes. The World Health Organization has estimated that if the major risk factors for chronic disease were eliminated, at least 80% of all heart disease, stroke, and type 2 diabetes would be prevented, and more than 40% of cancer cases would be prevented (Power of Prevention, 2009). These challenges provide a rich opportunity for the use of statistics and operations research to model chronic diseases and inform the development of effective chronic disease prevention and management policies.

Common OR Modeling Tools that have been applied to modeling of chronic diseases include various Stochastic Modeling, for example Markov Decision Processes & Partially Observable (Hidden) MDP have been applied extensively. Multi-agent Models are beginning to be explored and various forms of Survival Analysis have been applied for modeling disease progression.An implicit Requirements for utilizing these modeling tools are:
1. Metrics for Measuring a “Good” Decision,
2. Methods for Capturing Disease Progression, and
3. Methods for Defining and Characterizing the “state”

Each of these comes with its own unique challenges. Some of which include:
1. The need to capture patient preference and outcomes - which implies the need for the development of Behavioral Models, e.g., Discrete Choice Models
2. Data Analysis to capture the disease dynamics which requires time series and other longitudinal modeling of data that is often sparse or intermittent, quantitative and qualitative, with missing observations.
3. Data is often from Various Sources

The goal of this working group is to bring together researchers from operations research, mathematics and statistics to develop models to begin to address the challenges of Chronic Disease Management and Prevention.