Case Study Data Sources

A study to document the outcomes of epilepsy treatment, conducted by Hirsch and Van Den Eeden (1997), illustrates some of the challenges associated with collecting burden of illness data. The traditional clinical measure of seizure frequency is no longer considered appropriate as the sole measure of outcome of treatment or surgical intervention. The additional variables to document the burden of illness that were found illustrates the gap between the type of data desired and what is available. Hitherto, quality of life had been assessed in epilepsy patients using no fewer than 12 different instruments (both disease-specific and general). The economic impact of epilepsy had previously been assessed at a national level and in a few small studies.

These authors wanted to describe the overall disease impact for patients with chronic epilepsy, using a retrospective cross-sectional design in a managed care organization. Multiple data sources were required, since no single data base served as a repository for the various types of data required, and included administrative databases, medical charts, pharmacy databases, outpatient databases, hospitals, laboratories, outside services, memberships, etc. They found that all the identified socio-demographic variables were available in at least one automated database, as were two of the clinical variables, and 26 of the economic variables. None of the humanistic variables were available in any database.

In this case, about half of the data desired was available electronically, most of which was related to health as heavily weighted toward economic information. To gather the remaining desired data the investigators needed to collect prospectively humanistic as well as some additional clinical variables (Hirsch and Van Den Eeden 1997). It is quite typical that clinical data available electronically is often not complete and therefore not very useful, and that humanistic data is missing completely from the databases held by Health Maintenance Organizations.

When setting out to document the burden of illness, it is critical to ensure that the patients in the databases really are patients with the disease. In come cases, the ICD-9 codes are known to be inaccurate regarding patient capture, and means other than electronic data bases must be used. One advantage of using clinical trial patients is the certainty of having patients with the condition of interest—the trade-off being a concern for the generalizability of information to the larger population.

Pharmacoeconomic baseline data should not be considered in isolation, but as one aspect of data that must be considered as a part of the whole. Once the burden of illness information is collected and analysed, the development team must move to plan for ways to measure and document the clinical, economic and humanistic impact of the new pharmaceutical entity or other intervention.

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