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HealthBound Game - Work In Progress

"HealthBound" used courtesy of Associates & Wilson.

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HealthBound Game
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Contact Info
Syndemics Prevention Network
4770 Buford Hwy, NE
MS K-67
Atlanta, GA 30341-3717

E-mail: cdcinfo@cdc.gov

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Frequently Asked Questions

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All FAQs PDF Icon (PDF-211 Kb)

Who Created the Game and Why?
CDC developed the Health Protection Game for those wanting to experience, for themselves, the possibility of transforming the troubled U.S. health system. It is intended to be used—with a trained facilitator—as a resource for multi-stakeholder visioning, strategy design, and leadership development.

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How will the Game be Used?
CDC plans to use—and continually refine—the game in support of the Healthiest Nation initiative (
http://www.healthiestnation.org). The game provides an explicit basis for Wayfinding Dialogues in which stakeholders across the country consider what they can do to help steer a course toward a healthier, more equitable, and more prosperous future.

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Who are the Intended Players?
Anyone who aspires to lead change either on a national scale or in their own communities may benefit by first testing and refining their ideas in this realistic, but simplified version of the U.S. health system.

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What Can Players Learn?
The game supports both cognitive and experiential learning. It teaches essential lessons about how the health system works and establishes a productive frame for finding a viable way forward. Players may play out popular proposals, explore new ideas, rule out ineffective strategies, and gather support for more promising scenarios. But simulating interventions and seeing their effects is only part of the experience. Even deeper insight comes from learning why and how our complicated health system behaves the way it does. Players may interact with and learn from the game—and more importantly, from each other as teammates or competitors.

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What Factors are Included in the Game?
The game’s simulator tracks the entire U.S. population and its movement among states of health, risk behavior, environmental exposures, and socioeconomic advantage or disadvantage. Disadvantage erodes people’s health by making life more stressful; it also makes it harder for people to choose healthier behaviors and exposes them to more hazardous environments, leaving them more vulnerable to an array of afflictions that increase aggregate disease prevalence. The disadvantaged also have worse access to health care than do the advantaged, due to less insurance coverage and less sufficiency of primary care providers to meet patient demand. Greater disease prevalence combined with worse access to care means that the disadvantaged experience greater morbidity and mortality per capita than the advantaged do. Another factor affecting health outcomes is the quality of care delivered, reflecting the extent to which providers take the time to listen carefully to their patients and do a better job of diagnosis, counseling, and care. Quality of care may be improved by encouraging adoption of guidelines for best practice, but the incentive for such adoption is hindered if insurance reimbursement rates are not adequate.

Figure 1 shows the main features of the health system that are included in the game’s design. It is a broad summary of the game’s causal structure, which actually contains several hundred interacting elements. Two facts are immediately obvious: (1) all parts of the system—so often considered separately—are causally connected; and (2) there are more processes at work—and more intervention options available—than one might infer from many discussions of health care reform.

Figure 1 Major Causal Relationships in the Health Protection Game (version 4d)

Larger map PDF Icon  (PDF-99 Kb)

The main values (or outcomes) at stake in any scenario for change are shown in red and bolded (i.e., health status, health equity, and health care costs). Purple italics indicate the broad classes of potential intervention (e.g., players may choose to expand insurance coverage, reduce behavioral risks, or alter any of the other italicized factors, either alone or in combination). Blue arrows indicate same-direction effects (e.g., more environmental hazards lead to more disease and injury). Green arrows indicate opposite-direction effects (e.g., greater sufficiency of primary care providers leads to less use of specialists and hospitals for non-urgent care).

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What Factors are Left Out of the Game?
A number of factors were excluded from the game on the premise that our health system would remain troubled even if certain ongoing trends were somehow frozen or eliminated. These include the adoption of new technologies, the "tug of war" over billing between insurers and providers, population growth and aging, the rise of defensive medicine, globalization of the medical marketplace, the medicalization of common ailments through direct-to-consumer advertising, increasing regulations on tobacco use, and trends in employment, transportation, recreational options, and food options. We have previously shown how some of these factors can create instability in the health system and cause costs to grow (see Homer, Hirsch, Milstein 2007). But, for the game, we defined a system starting in a dynamic equilibrium, with all outcome variables sitting close to where they were in real life around the year 2003—and unchanging. Players must identify the most powerful drivers of system behavior and use that knowledge to move from an initially undesirable state toward one that is healthier, more equitable, and more cost-effective. This setup—where many features are intentionally held constant—allows us to rest the game on processes that are less transitory and lets players better understand the results of their decisions.

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What Information Sources Were Used?
The game integrates data and findings from earlier studies on factors affecting health system performance (see below). Because of its broad sweep, most variables are defined at a high level of aggregation. For example, the game does not consider individual types of disease or injury, but rather combines them all into a single measure of prevalence based on national surveys like NHIS and NHANES. Such aggregate metrics have been shown to be reliable predictors of health service utilization and health outcomes. In general, quantification of the prototype model is based on a variety of publicly available data from the Census, Vital Statistics, national health surveys, the National Health Expenditures database, and studies from the professional literature on health care utilization and programmatic impact. We expect to refine some concepts and estimates as we gather more information from research studies and from subject matter experts, but aggregated representations will always be necessary to make the analysis tractable and consonant with available data.

Below is a list of the main data sources and influential references that were used to formulate the current model. A detailed Reference Guide is being developed to provide a narrative explanation of the model’s structure and numerical assumptions.

Databases

  • Behavioral Risk Factor Surveillance System (BRFSS)
  • National Ambulatory Medical Care Survey (NAMCS)
  • National Health and Nutrition Examination Survey (NHANES)
  • National Hospital Discharge Survey (NHDS)
  • National Health Interview Survey (NHIS)
  • National Health Expenditure Accounts
  • National Vital Statistics Reports
  • U.S. Census

Selected References

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Bodenheimer T, Chen E, Bennett HD. Confronting the growing burden of chronic disease: can the U.S. health care workforce do the job? Health Affairs 2009;28(1):64-74.

Cohen JT, Neumann PJ, Weinstein MC. Does preventive care save money? health economics and the presidential candidates. New England Journal of Medicine 2008;358(7):661-663.

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Hirsch G, Homer J, McDonnell G, Milstein B. Achieving health care reform in the United States: toward a whole-system understanding. 23rd International Conference of the System Dynamics Society; Boston, MA; July 17-21, 2005. Available at <http://www.systemdynamics.org/conf2005/proceed/papers/HIRSC406.pdf>.

Hirsch G, Immediato CS. Microworlds and generic structures as resources for integrating care and improving health. System Dynamics Review 1999;15(3):315-330.

Hirsch GB, Immediato CS. Design of simulators to enhance learning: examples from a health care microworld. International Conference of the System Dynamics Society; Quebec City; July, 1998. Available at < http://systemdynamics.org/conferences/1998/PROCEED/00018.PDF>.

Hoffman C, Rice D, Sung H-Y. Persons with chronic conditions: their prevalence and costs. Journal of the American Medical Association 1996;276(18):1473-1479.

Homer J, Hirsch G, Minniti M, Pierson M. Models for collaboration: how system dynamics helped a community organize cost-effective care for chronic illness. System Dynamics Review 2004;20(3):199-222.

Homer J, Hirsch G. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3):452-458.

Homer J, Hirsch G, Milstein B. Chronic illness in a complex health economy: the perils and promises of downstream and upstream reforms. System Dynamics Review 2007;23(2/3):313–343.

Homer J, Milstein B, Wile K, Trogdon J, Huang P, Labarthe D, Orenstein D. Simulating and evaluating local interventions to improve cardiovascular health. Preventing Chronic Disease 2009 (under review).

Iglehart JK. Medicare, graduate medical education, and new policy directions. New England Journal of Medicine 2008;359(6):643-650.

Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press, 2001.

Institute of Medicine. The future of the public's health in the 21th century. Washington, DC: National Academy Press, 2002.

Kahn R, Robertson RM, Smith R, Eddy DM. The impact of prevention on reducing the burden of cardiovascular disease. Circulation 2008;118(5):576-585.

Kaiser Family Foundation, Health Research and Educational Trust. Employer health benefit survey; September 14, 2005. <http://www.kff.org/insurance/7315.cfm>.

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Lambrew JM, Podesta JD, Shaw TL. Change in challenging times: a plan for extending and improving health coverage. Health Affairs 2005;Web Exclusive:W5-119-W5-132.

Lantz PM, House JS, Lepkowski JM, et al. Socioeconomic factors, health behaviors, and mortality. Journal of the American Medical Association 2007; 279(21):1703-1708.

Lantz PM, Lichtenstein RL, Pollack HA. Health policy approaches to population health: the limits of medicalization. Health Affairs 2007;26(5):1253-1257.

Larme AC, Pugh JA. Evidence-based guidelines meet the real world: the case of diabetes care. Diabetes Care 2001;24(10):1728-1733.

Levi J, Segal LM, Juliano C. Prevention for a healthier America: investments in disease prevention yield significant savings, stronger communities. Washington, DC: Trust for America's Health; July, 2008. <http://healthyamericans.org/reports/prevention08/

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McKinlay JB, Marceau LD. Upstream healthy public policy: lessons from the battle of tobacco. International Journal of Health Services 2000;30(1):49 - 69.

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Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; April 15, 2008. <http://www.cdc.gov/syndemics/monograph/index.htm>.

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Sterman JD. Learning from evidence in a complex world. American Journal of Public Health 2006;96(3):505-514.

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Thorpe KE. Reframing the debate over health care reform: the role of system performance and affordability. Health Affairs 2007;26(6):1560-1562.

Thorpe KE, Florence CS, Howard DH, Joski P. The rising prevalence of treated disease: effects on private health insurance spending. Health Affairs 2005:W5-317-325.

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Yach D, Hawkes C, Gould CL, Hofman KJ. The global burden of chronic diseases: overcoming impediments to prevention and control. Journal of the American Medical Association 2004;291(21):2616-2622.

Zahran HS, Kobau R, Moriarty DG, Zack MM, Holt J, Donehoo R. Health-related quality of life surveillance--United States, 1993-2002. MMWR Surveillance Summaries 2005;54(SS-4):1-35. Available at <http://www.cdc.gov/mmwr/preview/mmwrhtml/ss5404a1.htm>

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Where Can I Get Additional Information?
Contact the lead developers:

Bobby Milstein
Centers for Disease Control and Prevention
BMilstein@cdc.gov

Jack Homer
Homer Consulting
JHomer@comcast.net

Gary Hirsch
Independent Consultant
GBHirsch@comcast.net

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For problems or questions regarding this web contact: syndemics@cdc.gov


Page last reviewed: January 30, 2008
Page last modified: January 30, 2008

Content source: Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion

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