The Problem

Following the successful eradication of smallpox and impressive progress in the elimination of polio in the Americas, in 1988 the World Health Assembly committed to the global eradication of wild polioviruses by the year 2000. By 2000, the Global Polio Eradication Initiative (GPEI) had significantly reduced the global circulation of wild polioviruses. However, in 2002–3, faced with insufficient funding to continue intense vaccination everywhere, the GPEI focused its vaccination efforts. At the time, wild polioviruses continued to circulate in six countries, but many other countries remained vulnerable to importation. Political and logistical challenges led to outbreaks and exportations, and between 2004 and 2006 wild polioviruses appeared again in previously polio-free African and Asian countries.

Toward the end of 2005, a debate began about abandoning the goal of eradication. How could the world continue to justify the significant use of resources (both financial and human) on polio, particularly with the number of cases globally already so low and so many other disease control and health services programs in need of resources?

The Solution

The dynamic disease outbreak model represents a more complicated version of the standard SIR model used in a popular System Dynamics textbook (Sterman, 2000). However, in Polio, we must deal with different types of imperfect immunity (i.e., from historic or recent exposure to polioviruses – including the oral poliovirus vaccine and/or vaccination with the inactivated poliovirus vaccine – as well as a latent period and routine or supplemental immunization rates). Modifying and expanding our existing model allowed us to determine that it was not possible to “effectively control” (i.e., achieve low cases) at a low cost. This means that control either implies high costs and low cases, or low costs and high cases, but not low costs and low cases.

However, our most significant insight came from exploring the dynamics of the economic investment in eradication. After watching the GPEI deal with the reintroductions of wild polioviruses in previously polio-free countries between 2004 and 2006, we recognized that reducing vaccination led the stock of susceptible individuals to build up and ultimately to outbreaks after some delay. Responding to the outbreaks requires reinvesting in intensive vaccination, which after some delay contains the outbreak and reduces or eliminates the circulation of the virus. With success comes a perception that the high level of investment compared to the low incidence is no longer justified. If policymakers succumb to the resulting pressure to reduce vaccination spending, this creates a situation in which populations again become vulnerable to new outbreaks.

“If policymakers succumb to the resulting pressure to reduce vaccination spending, this creates a situation in which populations again become vulnerable to new outbreaks.”

To capture this behavior, we constructed the negative feedback loop shown here, which we called  “wavering”. We incorporated this feedback loop into our dynamic disease model and tailored the model to two populous northern Indian states in which wild poliovirus still circulates. We explored two options: (1) vaccinate intensively until eradication;  and (2) vaccinate intensively only if the costs per incident case remain below a certain acceptable level, but reduce the vaccination intensity otherwise  (i.e.,  a “control” option with the possibility of wavering).

Outcomes

This application of System Dynamics highlights the systemic causes of overruns and emphasizes the importance of understanding the complex physical and social systems within which large projects operate. We, fortunately, saw the wavering commitment loop when no one else seemed to see it, and we went beyond just seeing the loop to build and use a model that provided answers to critical questions at the time the decision makers could use them (and needed them and asked us). In the presentation to the stakeholders, we showed the results to tell the dynamic story in the simplest possible way (i.e., by comparing a firm commitment to a wavering commitment showing the cumulative costs and cases).

We did not focus on explaining the model itself to attempt to walk the decision makers through the equations or diagrams. Instead, we focused on communicating the key insights based on what they already knew (e.g., the 2002–3 reduction in vaccination led to big outbreaks and high costs). However, we anticipated and received (as anticipated) some criticism from economists who did not recognize in the model a traditional health economic analysis, but these were relatively limited.

Do you want to know more?

• Using System Dynamics to Develop Policies That Matter: Global Management of Poliomyelitis and Beyond
• Economic analysis of the global polio eradication initiative
• Economic benefits of the global polio eradication initiative estimated at $40-50 billion
• Modeling Global: Policy for Managing Polioviruses: An Analytical Journey
• Eradication versus control for poliomyelitis: an economic analysis

The Official Website

Lexidyne, LLC specializes in helping clients understand and leverage key cause–and–effect relationships using the principles of System Dynamics. Combining strong facilitation skills with powerful simulation tools and over 100 years of collective System Dynamics experience, the Lexidyne team offers consulting solutions to government agencies, non–profit organizations, and several Fortune 500 companies. Headquartered in Colorado, Lexidyne works with clients across the country and around the globe.

The Issue You Tackled

Pharmaceutical companies face many complexities guiding a new drug through the development process toward the launch of the product — a complicated endeavor involving numerous milestones and a large investment of human and financial resources.

The efforts of the Brand Plan team result in a comprehensive look at the disease marketplace, the competitive landscape, currently available and pipeline treatment options, the assessment of the unmet medical needs in the market, and other information designed to inform decision makers about the conditions into which a new compound might be introduced. From a marketing standpoint, however, the key outcome of the Brand Planning process is the concept of brand positioning. Brand positioning helps establishing a series of product strategies created to leverage the collective knowledge of the disease market and effectively use resources to increase uptake of the new product. The strategies are often categorized by areas of target influence, such as patient and or physician segmentation, impact on the regulatory environment, effect on pricing/reimbursement, publication strategy, etc.

The typical brand planning process can be hindered in four key ways:

1. Misapplication of product analogs
2. Failure to leverage the institutional knowledge of cross-functional team members
3. Inherent limitations of static approaches
4. Maintaining consistent assumptions when evaluating alternative strategic options

Often there is a lack of integration between forecasts and product strategies. In the prelaunch timeframe however, rigorously testing the effects of possible strategies is impossible without an operational way to evaluate the expected outcome of strategic marketing decisions.

What You Actually Did

This updated Second Edition of Pharmaceutical Product Branding Strategies details how marketers, forecasters, and brand planners can achieve optimal success by building internally consistent simulation models to project future behavior of patients, physicians, and R&D processes. By introducing the reader to the complexities facing many pharmaceutical firms, specifically issues around cross-functional coordination and knowledge integration, this guide provides a framework for dynamic modeling of interest to several pharmaceutical markets, including epidemiology, market definitions, compliance/persistency, and revenue generation in the context of patient flows or movements.

Using clear terminology, Pharmaceutical Product Branding Strategies provides a solid framework for dynamic modeling to help marketers, forecasters, and brand planners to successfully:

• Predict the behavior of patients, physicians, and R&D processes
• Build a successful brand management strategy
• Target a wider audience with your product
• This didactic guide discusses the complexities faced by many pharmaceutical firms and explains how dynamic modeling effectively addresses these problems in a systematic way. The positive effects of this method are supported by articles from recent business publications, literature reviews, definitions sections, and collectable market-level data. Dynamic modeling is also compared and contrasted with other existing techniques to give the reader background information and context before initiating a plan.

Strategies highlighted as part of a strong brand planning formula include:

• Cross-functional coordination and knowledge integration to assess the patient’s needs
• Diffusion, segmentation quantification, and ultimate calibration to encourage doctors to adopt your product
• Choice models, conjoin analysis,, competitive sets, data collection/estimation, and market calibration to create an “attractive” treatment for consumers
• Integration of three basic analysis platforms (patient dynamics doctor adoption, and treatment attractiveness) to sell your brand.

A “typical” disease market model would follow the following evolution (Doctor Adoption and Portfolio Model examples follow similar processes):

1. Begin by leveraging the system dynamics principles of stocks and flows to establish epidemiological projections for specific disease markets
2. Often times a combination of modeling approaches are used within this framework

   a.System Dynamics models provide a clear aggregate view of epidemiology dynamics.

   b.While Agent Based models support the inclusion of more extensive segmentation and discrete dynamics.

3. Each patient’s journey can be virtualized based on probabilities that are tied to conditional probabilities related to their micro-demographic epidemiology status.
4. Once robust epidemiological underpinnings have been established, we analyze longitudinal treatment data to distill segmented therapy dynamics that are incorporated into the simulation structure thus creating opportunity for strategic insights.

The Results

Numerous pharmaceutical companies have adopted this dynamic modeling approach to evaluate disease markets, doctor adoption, and the R&D pipeline process. Over 100 models have been implemented across numerous indications both in US markets and internationally.

These models have been instrumental in creating optimal strategic initiatives and have enhanced the forecasting process. These models also often serve as the repository for analytics from “big data” as well as institutional team knowledge about the disease area and provide a transparent shared view of data driven dynamics and market assumptions. The “what if” scenario testing capability of the models provides these organizations with a tool to test marketing strategies and evaluate hypothetical changes in future market evolution dynamics, allowing these organizations to understand implications of an uncertain future.

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Publications

Did You Know?

System Dynamics Forrester Award

The Jay Wright Forrester Award recognizes the authors of the best contribution to the field of System Dynamics in the preceding five years. In 2010, the award was presented to Mark Paich, Corey Peck, and Jason Valant of Lexidyne, LLC for their winning work Pharmaceutical Product Branding Strategies: Simulating Patient Flow and Portfolio Dynamics, published by Informa Healthcare; 2nd edition March 2009. More information on this book can be found at this link.

The citation and winners’ speech (delivered at the award ceremony in Seoul) has been published in full in the System Dynamics Review.
(Jul 2010)

Diabetes mellitus is a growing health problem worldwide. In the United States, the number of people with diabetes has grown since 1990 at a rate much greater than that of the general population; it was estimated at 20.8 million in 2005. Total costs of diabetes in the United States in 2002 were estimated at 2 billion.

Health planners in the National Center for Chronic Disease Prevention and Health Promotion of the Centers for Disease Control and Prevention used system dynamics simulation modeling to gain a better understanding of diabetes population dynamics and to explore implications for public health strategy. A model was developed to explain the growth of diabetes since 1980 and portray possible futures through 2050.

The model simulations suggest four characteristic dynamics of the diabetes population.

First, it shows obesity’s role in driving the growth of prediabetes and diabetes prevalence.

Second, the model quantifies the “backing up” phenomenon (in which reduced outflow from a population stock causes a buildup in that stock) that may undercut the benefits of management and control efforts. Third, management and control efforts alone are unable to reduce diabetes prevalence in the long term. Fourth, there are significant delays between primary prevention efforts and downstream improvements in diabetes outcomes.

Do you want to know more?

Jones A.P., Homer J.B., Murphy D.L., Essien, J.D.K., Milstein B., Seville D.A. (2006) Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 96: 488-494