Chronic Disease System Dynamics References

From SystemsWiki

Jump to: navigation, search

There have been a number of applications of SD to chronic illness. These applications have typically modeled a population of patients with a particular chronic condition. The models are built around flows of patients who develop a chronic condition and move through various stages of the illness. Their movement among these stages occurs at rates that depend on environmental factors and demographic characteristics and are subject to a variety of interventions. A common lesson coming from these applications has been the value of comprehensive strategies that include preventive programs and produce better outcomes at a potentially lower cost than therapeutic interventions alone.

Contents

Dental Care and Oral Health

Dental care and oral health was an early focus of SD work in chronic illness.

Work done by Gary Hirsch for the Division of Dental Health of the Bureau of Health Manpower in the US Department of Health Education and Welfare began with an initial focus on supply of and requirements for dentists and other dental personnel. However, the presence of feedback relationships among the supply of personnel and availability of care, nature of care given (preventive vs. symptomatic), oral health status of a population and prevalence of dental disease, and workload of dental practices was noted early in the work. It became apparent that the number of dentists and dental personnel needed really should depend on the nation’s goals for the oral health of its citizens rather than some arbitrary norms.

This realization caused the focus to shift from projecting nominal demand to modeling oral health and its relationship to the availability of dental care. The model showed that having a larger number of dentists available (than the level suggested by dentist-to-population ratios and linear projection methods used at the time) could change the nature of care provided and result in significantly improved oral health(1).

The work led to a larger effort for the Division of Dentistry by Hirsch, Michael Goodman, and Tom Bergan at Pugh-Roberts Associates (now part of PA Consulting). The model they developed had an elaborate oral health sector that disaggregated a population by age and “care-seeking behavior” (preventive, symptomatic, extreme symptomatic) and tracked changes in various oral health measures such as prevalence of decayed, missing and filled teeth in response to different manpower policies(2). Changes in manpower availability caused shifts in the distribution of a population among these care-seeking groups and in the nature of the workload faced by dental practices. The model was used to project the impact of a number of different dental manpower policies for the US. The model was also adapted for use in dental manpower planning in the State of Minnesota(3) and by a company designing dental insurance plans for employers(4).

Other work in dental care concerned the Dutch dental health care system and was done by Bronckhorst et al(5).

Cardiovascular Disease

An effort in the State of Indiana was the first of several that focused on cardiovascular disease. Gary Hirsch together with Richard Myers of the Indiana State Health Planning Agency developed a model to project the prevalence of heart disease and stroke in the state and evaluate the potential impact of different programs for reducing the costs and mortality due to these diseases. The model represented multiple stages through which people move as they develop cardiovascular disease from predisposing conditions such as hypertension to undetected and detected sub-acute illness, acute incidents such as heart attacks and strokes, and rehabilitation and recovery after such attacks. The model was used to evaluate a number of different programs that might be brought to bear. Simulations with the model illustrated the value of comprehensive programs that combine hypertension screening and treatment and preventively oriented lifestyle change programs with improved emergency services and acute treatment and rehabilitation of people to reduce disability and prevent recurrence of acute episodes(6).

Another model developed by Hirsch and Goodman together with Drs. William Luginbuhl and Ben Forsyth from the University of Vermont Division of Health Sciences used a similar structure to examine the impact of investing more resources in prevention and rehabilitation rather than more elaborate technologies for treating acute myocardial infarction(7). The effort, funded by the Kaiser Family Foundation, grew out of a perception by Drs. Luginbuhl and Forsyth that cost-benefit analyses of new medical technologies took too narrow a view in simply comparing the costs of expensive technologies to the value of life-years gained. These analyses failed to consider much less expensive ways of achieving the same outcomes. The model utilized data from the literature to project how prevention and rehabilitation could lower heart disease costs in the US much more effectively than new technologies that only marginally extend people’s lives once are in the later stages of the disease.

A third effort by Dr. Wilbert Wils and Hirsch applied a similar model to Ischemic Heart Disease in the Dutch population. It was part of a larger health care scenarios project sponsored by the Ministry of Health Care and the Environment in the Netherlands(8). That work examined a number of different scenarios including several based on different assumptions about changing lifestyles. The scenarios also examined the effect of potential advances in the technology of treating heart disease. These included the use of “clot-busting” drugs and balloon angioplasty to clear blockages, better medications for treating angina pectoris, improved emergency care, and other measures for improving the lifestyles of patients who already have heart disease. The results were consistent with the other cardiovascular disease models and showed the Health Ministry that the country would be facing increasingly high costs if it relied primarily on therapeutic interventions at the expense of preventive strategies.

There have also been other efforts focused on other aspects of cardiovascular disease. Work by Hirsch and Dr. Jack Homer on chronic illness included an examination of heart failure in a community in Washington State in the US. (See discussion below under diabetes.) Mehl-Madrona modeled the factors affecting the potential for an MI in individual patients and used the model interactively to help modify patients’ risk of an MI(9). Petersen used an SD approach for planning heart disease treatment capacity in Denmark(10). Work by Oga and Uehara also used a model of the causes of cardiovascular diseases to help people understand the need to control their weight and thereby reduce the prevalence and cost of those diseases(11).

Renal Disease

The team of Hirsch, Goodman, Luginbuhl, and Forsyth also examined end-stage renal disease (ESRD) in the US. This chronic illness had been selected for special coverage by the Medicare program in the US and there was concern about how large the population with ESRD and the cost of the program might grow. The model they developed examined the potential size of the ESRD population that would result from improvements in the care of these patients that reduced mortality rates and the effects of expanded access to and improved outcomes of kidney transplantation on the number of people requiring dialysis. The other focus of this effort was to demonstrate the use of the model to help identify data required for better decision-making(12).

Other work focused on renal disease included a model developed by Bolger and Davies for planning renal services in a District Health Authority in the UK(13) and the use of a model by Davies and Flowers for planning the purchase of renal replacement services(14). Georganztas et al developed a model of renal care dynamics for better understanding the economics of renal care, tradeoffs between organ transplantation and dialysis, and business opportunities in renal care for biotechnology firms(15).

Diabetes

Diabetes is another chronic illness that has gotten a fair amount of attention from SD practitioners. The earliest work relevant to diabetes was done by Foster who modeled the physiology of blood sugar regulation(16,17).

The more recent work has dealt with populations of diabetic patients. An effort in Whatcom County, Washington, by Hirsch and Homer in collaboration with Dr. Marc Pierson and Mary Minniti from the Peacehealth System, used a population model of diabetes to evaluate the impact of programs being developed in that community. The modeling work was funded by a grant from the Robert Wood Johnson Foundation and supported a coalition of physician groups, nurses, a hospital, and an insurer concerned with improving the treatment of chronic illness.

The model tracked the flow of patients through several stages as they developed diabetes and its complications and moved between having their blood sugar levels under control and out-of-control. The model helped to show the how preventive and treatment interventions being contemplated in Whatcom County could reduce illness prevalence, deaths, and costs due to diabetes and demonstrated that the right combination of programs could reduce both mortality and cost. As indicated earlier, the same approach was used to evaluate programs for heart failure in Whatcom County(18,19).

Another major effort focused on diabetes was done for the Division of Diabetes Translation of the US Centers for Disease Control. It featured development of a similar flow model through an extensive series of meetings with people at the Federal, State, and local levels in the US. In addition to policy analyses, the model was also used as the basis for a number of “Diabetes Action Labs” that enabled state and local policymakers to interact with the model and draw conclusions relevant to their own state(20).

Work by Georgantzas and his colleagues examined the effects of using the drug Rezulin on the treatment of diabetic patients in a Veterans’ Integrated Services Network in New York City. Rezulin treats diabetes by lowering insulin resistance and helps to extend the lives of diabetics. The model helped to project the overall cost savings and health benefits of using this drug on the VA population(21). David Todd and his colleagues in New Zealand used an SD framework and flow model to help improve outcomes in diabetes and cardiovascular disease(21a). The framework links outcomes, indicators, and a flow model that tracks a population as people move through a continuum from wellbeing to advanced disease and death.

Other Chronic Illnesses

Dr. Peter Boggs and Dr. Ali Mashayekhi developed a model of asthma treatment that focused on the tendency to rely on short-term remediation of symptoms. The model showed that such a short-term treatment can have adverse consequences and makes long-term, effective treatment much more difficult. The model was used to examine different treatment strategies and their impacts and has helped both patients and physicians improve their management of asthma(22).

There were two efforts that dealt with cancer. Barry Richmond developed a structural model of cancer that demonstrated how cancer develops(23). Michael Fett created a model representing the multiple stages of breast cancer that could be used to examine the Australian breast cancer screening program(24).

Chronic Illness in General

Rather than focusing on specific diseases, some SD work has dealt with general aspects of chronic illnesses and their prevention and treatment.

A health care Microworld developed by the New England Health Care Assembly and Innovation Associates had a significant community health status component built around a chronic illness model. That model tracks a population of 100,000 people at different ages as they move through increasingly severe stages of chronic illness. Users of the Microworld can use a variety of interventions (e.g., chronic maintenance care and rehabilitation programs) to influence movement among these stages and rates of acute episodes resulting from chronic illness. Programs designed to reduce social, behavioral, and environmental risk factors (e.g., social services and job training) can also be used to affect development of chronic illness. In addition, users have the option of combining various delivery system investments with health interventions to examine which strategies yield the best results in terms of health status and financial performance(25,26).

Veazie and Johnson developed two models to examine the effects of different management strategies on potential outcomes of chronic illness. (They used diabetes as an example, but their emphasis seems to be on strategies for chronic illness in general.) One model examined the impact of interventions that might reduce variability in physician practice patterns. The other examined the value of specialization of treatment for particular categories of patients and concluded that it was more important to match treatment to psychosocial characteristics such as compliance behavior rather than choosing strategies that might be clinically optimal(27,28).

Future Work in Chronic Illness

There are several directions in which SD work in chronic illness might develop further. For example, Hirsch and Homer presented a paper that discusses the need to examine how the performance of chronic illness programs relates to the capacity for delivering those programs and potential problems that may occur when capacity is inadequate. Models that relate capacity to outcomes can help providers develop strategies that assure sufficient capacity(29). There are other individual chronic illnesses that might benefit from a systemic understanding afforded by an SD model and approach.

One additional challenge is to model several conditions that overlap in that they share the same risk factors and are often found in the same patients (e.g., heart disease and diabetes). Models that consider multiple chronic illnesses together can help to identify the true leverage of risk factor reduction programs (e.g., those aimed at reducing obesity) that can help prevent several illnesses at once. Such models of multiple illnesses can also reflect the interactions among them (e.g., depression causing diabetic patients to neglect self-care) and help providers develop multi-faceted strategies for multiple illnesses. Interactions among risk factors in causing chronic illness is another area that can benefit from more emphasis. Work by Homer and Bobby Milstein from the US Centers for Disease Control on "syndemics" is a worthwhile start on understanding these kinds of interactions(30).

References

1. Hirsch, G. B. and W. Killingsworth (1975). "A New Framework for Projecting Dental Manpower Requirements." Inquiry 12(2): 126-142.

2. Hirsch, G. B., T. A. Bergan, and M. R. Goodman (1975). Examining Alternatives for improving the Nation's Oral Health: A System Dynamics Model of the Dental Care Delivery System, Division of Dentistry, Bureau of Health Manpower DHEW.

3. Albertini, T. and D. Born (1977). Assessing Alternatives for Improving Dental Care Through Computer Simulation. 105th Annual Meeting American Public Health Assoc.

4. Hooper, S. and D. Leverett (1978). Projecting Dental Care Costs: The Pugh-Roberts Model vs. the Actuarial Approach. 106th Annual Meeting of the American Public Health Assoc.

5. Bronkhorst, E. M., G. J. Truin, et al. (1990). Using Complex System Dynamic Models : An Example Concerning the Dutch Dental Health Care System. System Dynamics ‘90: Proceedings of the 1990 International Systems Dynamics Conference, Chestnut Hill, Mass., System Dynamics Society.

6. Hirsch, G. B. and R. Myers (1975). Designing Strategies for Particular Health Problems: The Indiana Cardiovascular Disease Model, Indiana Health Planning and Development Agency

7. Luginbuhl, W. H., B. R. Forsyth, et al. (1981). "Prevention and Rehabilitation as a Means of Cost Containment: The Example of Myocardial Infarction." Journal of Public Health Policy 2(2): 103-115.

8. Hirsch, G. B. and W. Wils (1984). Cardiovascular Disease in the Dutch Population: A Model Based Approach to Scenarios. Conference on Health Care Scenarios, Ministry of Health and the Environment, The Hague, Netherlands.

9. Mehl-Madrona, L. (1997). System Dynamics as an Interactive Patient Education Tool for Preventing Coronary Artery Disease and Myocardial Infarction. 15th International System Dynamics Conference: "Systems Approach to Learning and Education into the 21st Century", Istanbul, Turkey, Bogazici University Printing Office. [1]

10. Petersen, L. O. (2000). How Should The Capacity For Treating Heart Disease Be Expanded? 18th International Conference of the System Dynamics Society, Bergen, Norway, System Dynamics Society. (abstract only at [2] )

11. Oga, H. and T. Uehara (2003). An Application of System Dynamics to an Obesity Prevention Program : Simulation of the Risk Reduction of Cardiovascular Disease and the Savable Medical Expenses. Proceedings of the 21st International Conference of the System Dynamics Society, New York City, USA, System Dynamics Society. (abstract only at [3] )

12. Hirsch, GB, MR Goodman, BR Forsyth, and WH Luginbuhl, (1977) Using a Model to Define Data Needed for Policymaking: An Application to End-Stage Renal Disease

13. Bolger, P. G. and R. Davies (1992). "Simulation Model for Planning Renal Services in a District Health Authority." BMJ 305: 605-608.

14. Davies, R. and J. Flowers (1995). "The Growing Need for Renal Services: The Use of Simulation Modelling to Aid Purchasing for Renal Replacement Therapy." OR Insight 8: 6-11.

15. Georgantzas, N. C., A. Batista, et al. (2000). Renal Care Dynamics. 18th International Conference of the System Dynamics Society, Bergen, Norway, System Dynamics Society. (abstract only at [4] )

16. Foster, R. O. (1970). The Dynamics of Blood-Sugar Regulation, M. I. T. Masters Thesis (available on The MIT System Dynamics Group Literature Collection CD)

17. Foster, R. O., J. R. Guyton, et al. (1973). A System Dynamics Model of Glucose Homeostasis. Proceedings of the Conference on Regulation and Control in Physiological Systems, Rochester NY.

18. Homer, J. B., G. B. Hirsch, et al. (2003). Models for Collaboration: How System Dynamics Helped a Community Organize Cost- Effective Care for Chronic Illness. Proceedings of the 21st International Conference of the System Dynamics Society, New York City, USA, System Dynamics Society. [5]

19. Homer, J. B., G. B. Hirsch, et al. (2004). "Models for Collaboration : How System Dynamics Helped a Community Organize Cost-Effective Care for Chronic Illness." System Dynamics Review 20(3): 199-222.

20. Homer, J. B., A. P. Jones, et al. (2004). The CDC's Diabetes Systems Modeling Project : Developing a New Tool for Chronic Disease Prevention and Control. 22nd International Conference of the System Dynamics Society, Oxford, England, System Dynamics Society. [6]

21. Georgantzas, N. C., K. Trilling, et al. (1998). Rezulin: Effective and Efficient Drug for Diabetes? 16th International Conference of the System Dynamics Society, Quebec '98, Quebec City, Canada, System Dynamics Society. [7]

21a. See the Leading for Outcomes web site [8]and another called Let's Beat Diabetes [9]

22. Mashayekhi, A. N. (2002). Management of Asthma Treatment. Proceedings of the 20th International Conference of the System Dynamics Society, Palermo, Italy, System Dynamics Society. [10]

23. Richmond, B. M. (1977). Toward a Structural Theory of Cancer, MIT System Dynamics Group (D-2718 available on MIT System Dynamics Group Literature Collection CD)

24. Fett, M. J. (1999). Developing Simulation Dynamic Models of Breast Cancer Screening. 17th International Conference of the System Dynamics Society and 5th Australian & New Zealand Systems Conference, Wellington, New Zealand, The System Dynamics Society. [11]

25. Hirsch, G. B. and C. S. Immediato (1998). Design of Simulators to Enhance Learning: Examples from a Health Care Microworld. 16th International Conference of the System Dynamics Society Quebec '98, Quebec City, Canada, System Dynamics Society. [12]

26. Hirsch, G. B. and C. S. Immediato (1999). "Microworlds and Generic Structures as Resources for Integrating Care and Improving Health." System Dynamics Review 15(3): 315-330.

27. Johnson, P., P. Veazie, et al. (2000). Physician Decisions As A Source Of Variation In Chronic Disease Outcomes. 18th International Conference of the System Dynamics Society, Bergen, Norway, System Dynamics Society. [13]

28. Veazie, P. and P. Johnson (2001). Treatment Policies for the Management of Chronic Illness : Is Specialization Always Better? The 19th International Conference of the System Dynamics Society, Atlanta, Georgia, System Dynamics Society. [14]

29. Hirsch, G. B. and J. B. Homer (2004). Modeling the Dynamics of Health Care Services for Improved Chronic Illness Management. 22nd International Conference of the System Dynamics Society, Oxford, England, System Dynamics Society. [15]

30. Homer, J. B. and B. Milstein (2002). Communities with Multiple Afflictions: A System Dynamics Approach to the Study and Prevention of Syndemics. Proceedings of the 20th International Conference of the System Dynamics Society, Palermo, Italy, System Dynamics Society. [16]

Acknowledgement

This page was originally written by Gary Hirsch for the HPSIG wiki

Questions & Comments to Geoff McDonnell
Personal tools