When using systems analysis to understand disease and its outcomes, we need to start by identifying the most important influences on the outcome(s) of interest. Influences are factors or determinants that interact with each other to bring about outcomes, such as disease or the results of disease.d
Let us see how we might identify influences on smoking cessation. Using one-at-a-time reductionist studies, the following interventions have been shown to be effective: smoking cessation programs, prohibitions on smoking in public places, social marketing, and cigarette taxes. In addition, measures of the strength of a relationship such as relative risk obtained from reductionist studies often help us measure the magnitude of the influence that a factor has on an outcome. Thus, the first two steps in systems thinking are often built on data derived using reductionist thinking.
Rather than looking at one intervention at a time, however, systems thinking asks about the best combination of interventions and how they can be used together. Let us assume that smoking cessation programs, prohibition on smoking in public, social marketing, and higher taxes have been identified as the four most important interventions or influences on the rate of cigarette smoking. The question then becomes how they can be effectively and efficiently combined.
Reductionist thinking usually assumes a straight-line or linear relationship between influences, implying that increased levels of an intervention, such as increasing taxes on tobacco, will produce a straight-line decrease in the levels of tobacco use. However, it is possible that small increases in taxes have little effect, while somewhat larger increases have dramatic effects. In addition, reductionist thinking does not look at how the impact of one intervention may be affected by connecting it with other interventions, whereas systems thinking looks at these interactions. Thus, systems thinking would ask questions about how to most effectively utilize cigarette taxes by combining them with other approaches, such as using the taxes to support tobacco education programs, or reduce exposure to other causes of lung cancer, such as radon and asbestos.
The following summarizes the initial steps in a systems analysis:
• Step 1: Identify the key influences or interventions on an outcome such as disease or the outcome of disease.
• Step 2: Indicate the relative strength of the impact of each of the influences or interventions.
• Step 3: Identify how these influences or interventions interact—that is, how they work together or, alternatively, interfere with each other.
These three steps in systems analysis are basic to understanding the structure of a system.
WHAT ADDITIONAL STEPS ARE NEEDED TO COMPLETE A SYSTEMS ANALYSIS?
Increasingly, we are taking the process one step further. We are using systems analysis to better understand how systems function. Systems thinking requires not only an examination of multiple influences and their interaction at one point in time using a static approach but also encourages us to look at how these factors change over time. That is, systems thinking can lead to a dynamic approach. Let us see how this may be accomplished.
Systems analysis attempts to take into account changes in the overall system that occur over time due to changes in one or more of the factors or influences. These changes in a factor or influence are said to provide feedback into the process producing what are called feedback loops. Feedback loops can have either positive or negative impacts on the outcome. For instance, in developing a systems analysis, we might ask: Does the reduction in the percentage of people who smoke due to higher taxes lead to changes over time in social attitudes, which themselves may set the stage for greater enforcement of public smoking regulations? This would be a positive feedback loop. Alternatively, raising cigarette taxes might reduce the money available to individuals to pay for smoking cessation programs if these services are not paid for by health insurance. This would be a negative feedback loop. Systems thinking does not view the impact of interventions as static. Rather, it tries to develop dynamic models, incorporating the feedback processes that reinforce or accelerate the impacts or alternatively dampen or reduce the impact.
Systems analyses encourage us to identify feedback loops, including positive feedback loops that reinforce or accentuate the process and negative feedback loops that dampen or slow down the process. Feedback loops are key to understanding how a system operates or functions. Complex systems, such as the human body, rely heavily on feedback loops in order to maintain stability. When one component gets out of control, such as body temperature or hydration, other components of the system respond to maintain the body within a tolerable range. This requires positive and negative feedback loops. Similarly, communicable disease in a population is controlled to a certain extent by responses or feedback, including voluntary isolation of sick individuals, development of immunity, and, unfortunately, death of affected individuals. Understanding these feedback loops can help us improve on the natural systems that exist while utilizing the positive aspects of existing systems.
Looking at the dynamic nature of systems and the changes that occur over time allows us to identify bottlenecks that limit the effectiveness of systems and leverage points that provide opportunities to greatly improve outcomes. For instance, systems analysis might identify a bottleneck such as the need to train large numbers of clinicians in smoking cessation methods so that they can address the demand for smoking cessation services created by social marketing, increased cigarette taxes, and better drug treatments. A leverage point that might be identified is pregnant women who smoke but are highly motivated to quit due to the severe impact on their offspring.
Thus, the additional steps in systems analysis can be described as follows:
• Step 4: Identify the dynamic changes that may occur in a system by identifying the feedback loops that occur in the system.
• Step 5: Identify bottlenecks that limit the effectiveness of the system.
• Step 6: Identify leverage points that provide opportunities to greatly improve outcomes.