Im writing to add a few thoughts to P. M. Bartons comments about the
similarities between SD and Markov Process models for reflecting the
progression of chronic illlnesses in populations. I agree with him that the
two are similar when used to model naturally-occurring disease processes and
also incremental effects of interventions such as new technologies that
reduce mortality rates.
SD models can offer some real advantages when modeling the effects of
different care delivery strategies. In SD models, mortality rates and rates
of flow between disease states can be made variable and dependent on such
things as the relative workloads carried by different components of the
health care system and the care delivery patterns that result. The feedback
loops among illness states, workloads, and care delivery created by these
linkages can help to explain how certain care delivery strategies "lock in"
disease patterns.
In the US, for example, an emphasis on high-tech interventions for people who
are already quite sick leaves limited resources for more cost-effective
primary care. This pattern of care delivery produces both higher costs and
higher mortality rates than in other countries that ration high-tech
resources and emphasize primary care. Inclusion of these feedbacks can also
help health care planners and managers identify high leverage interventions
that reduce the burden of illness and make more resources available for
primary care, reducing the burden of illness, and so on...
These feedback structures were an important addition to a Microworld
developed by my colleagues and me to help health care providers understand
the impacts of different strategies for improving community health status.
Much of the underlying model is "Markov-like" in reflecting transitions among
disease states and age groups, but the feedback loops provided an ability to
differentiate among strategies that would not have been possible with a
Markov Process model alone.
There are additional feedback loops that shed light on the relationship
between care delivery and illness prevalence. One set of these involve
care-seeking behavior. In a set of dental manpower models, colleagues and I
were able to show how simply matching manpower to current needs would lock in
existing illness and care-seeking patterns (largely in response to symptoms)
while small surpluses created the potential for encouraging more
preventively-oriented careseeking behavior and reducing illness prevalence
over time.
In a model of heroin addiction, the communitys definition of the problem (as
medical vs. criminal) was seen by us as a key determinant of rates of flow
between treatment programs and either a drug-free state or return to
addiction. The communitys definition of the problem is the result of the
communitys experience with heroin addiction and related problems such as
addict crime. In the US at least, emphasis on a criminal view has helped to
lock in addiction as a serious problem and get in the way of dealing with it
effectively.
These kinds of feedbacks seem important for understanding illness and care
delivery and a real contribution of SD models.
Gary Hirsch
GBHirsch@aol.com