| Sumario: | Most health management programs, such as disease management or health promotion/wellness interventions, implement targeted interventions for an identified high-risk group, leaving the remaining non-managed lower-risk population as controls. This is problematic from an outcomes perspective because individuals initially identified by their high-risk scores will inevitably have lower average scores on remeasurement, even in the absence of a health management program. This statistical phenomenon is called regression to the mean (RTM). This article presents actual examples of RTM, describes the classic method for estimating the impact of RTM in a pre-post study, and provides suggestions for designing health management program evaluations to mitigate the effects of RTM.
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