Who are those guys? Using anonymized patient-level intelligence to size and characterize an undiagnosed population: anonymized patient-level intelligence can be used to provide more accurate insights into the prediabetic population.

In the film, "Butch Cassidy and the Sundance Kid," Butch Cassidy frets over the mysterious posse pursuing them and repeatedly asks Sundance, "Who are those guys?" More than one pharmaceutical company brand team is surely asking the same question--this time with respect to patients with prediabetes. How many patients are on the brink of developing type 2 diabetes? What are their demographics and clinical characteristics? "Who are those guys?"

Many researchers and clinicians alike are reasoning that drug treatments could help avert the disease in people who appear to be at risk. Currently, no drug has been approved for such prophylactic use, but clinical trials are proving that the idea has merit, and many endocrinologists have begun prescribing certain diabetic treatments for their patients with prediabetes.

With that said, controversy is brewing within the medical and regulatory communities over the merits of pharmacotherapy for this segment of the diabetes population. * Many experts believe that lifestyle modifications alone are sufficient to treat this population. However, given that many patients are reluctant to modify certain behaviors, other experts maintain that lifestyle modifications in conjunction with pharmacotherapy represent the best possible treatment option. Despite the controversy, manufacturers with antidiabetic drugs on the market or in development, as well as those considering agents that treat diseases that may give rise to diabetes, would do well to better understand the size and characteristics of the population with prediabetes, in order to evaluate potential unmet treatment needs and inform their research and development efforts. That is easier said than done. Prediabetes is not a condition that is recognized with its own diagnosis code; therefore, sizing and characterizing this subpopulation of patients using traditional market intelligence sources clearly presents challenges.

The Study Options

Until aggregated patient-level data became available, companies could attempt to quantify and understand such an amorphous patient group using either of two options. The first is primary research with physicians. What this approach gains in customization, it may lose in accuracy, as the results are subject to potential bias owing to small sample sizes and the natural limitations of physician recall. The second option is epidemiology studies used in conjunction with diabetes prescription data. Using this method, the difference between the population estimate of patients with type 2 diabetes and those treated for it could be indicative of the numbers who actually have prediabetes. The assumptions are that physicians seeing patients with prediabetes may simply code them as diabetic for lack of a better way to record their condition, and the percentage of patients who are diagnosed with type 2 diabetes and treated with diet and exercise has been quantified. Even if these assumptions are sound, this method is challenging since it requires piecing information together from disparate sources.

Anonymized patient-level intelligence, however, offers a more robust foundation for this type of research, because it includes:

* Actual patient-based observations that reflect real-world patterns of patient care

* Coverage of the full continuum of health care interactions across time, allowing for ongoing tracking and measurement and

* Capture of more comprehensive information on patient segments, such as prescriptions, diagnoses, procedures, lab results, and hospitalizations--all of which are distilled into a meaningful context of care

An Alternative Approach to Evaluating the Population With Prediabetes

Although no ideal method is available to assess the prediabetes market with 100% accuracy, it is possible to arrive at a more accurate estimate using anonymized patient-level intelligence. Not all patients will have something in their prediagnosis medical records to suggest they have prediabetes, but many patients will have actual evidence of the condition. It is therefore possible to capitalize on the fact that this information source traces patients' history over time and encompasses the full continuum of care to examine their previous medical history leading up to a diabetes diagnosis. By looking at a variety of clinical markers, it is possible to gauge how many people were likely to have had prediabetes at any point in time, and to determine how many may have received treatment.

For example, one could look back several years from the point of the type 2 diabetes diagnosis, examining this intelligence source in six-month intervals for patients who were diagnosed with impaired glucose tolerance, which is a prediabetic state. This segment of patients could be sized and then profiled to determine how many are using diabetes therapies before a diagnosis of type 2 diabetes. Figure 1 reveals more than 90% of patients diagnosed with impaired glucose tolerance are not receiving a therapy indicated for the treatment of type 2 diabetes.

[FIGURE 1 OMITTED]

Anonymized patient-level intelligence can also be used to segment and characterize patients with prediabetes according to demographics, clinical profiles, and patterns of care to reveal potential treatment needs in this subpopulation. For example, Figure 2 assessed the presence of comorbidities leading up to the diagnosis of type 2 diabetes, and reveals that for some patients with diabetes, warning signs, in the form of a hypertension or hyperlipidemia diagnosis, were present as many as three years in advance. This indicates a potential opportunity to better manage this population of patients. By enabling companies to better understand this patient population, such information allows manufacturers of antidiabetic therapies, as well as therapies that treat hypertension and hyperlipidemia, to justify and inform additional clinical investments in an attempt to better serve the treatment needs of this group.

[FIGURE 2 OMITTED]

* Firms study drugs to help avert diabetes. Wall Street Journal October 13, 2006; B1.

Mr. Carroll is Director, Performance Management, at IMS Health, and Ms. Tirrell is Product Marketing Manager at PharMetrics, a unit of IMS.

For more information about the anonymized patient-level information offerings of IMS and PharMetrics, please call (617) 393-8484 or E-mail: apld@us.imshealth.com.