Patient Heterogeneity and Comparative Effectiveness Research

According to Kathleen Lohr, the most pressing issues for comparative effectiveness research (CER) include: 1) how to conduct CER for heterogeneous patient populations and 2) ways to implement longitudinal investigations to capture long-term health outcomes.  Today I will focus on measuring patient heterogeneity.  Although it makes sense to take into account differences across patients, measuring this in practice can be difficult.

A paper by Kaplan et al. (2010) discusses a number of ways to measure patient heterogeneity in practice.  The article discusses six categories of patient characteristics.  Below I present each and–where appropriate–discuss how the authors attempt to measure differences in patient characteristics.

  • Immutable characteristics: These are intrinsic factors that the patient cannot change such as demographic characteristics or genetic factors.  The authors use age, gender and race as well as education in their study.  Although one could of course get more education, few elderly receive advanced degrees.  A more appropriate measure might be highest education reached by age 30 (which would not change over time after age 30), but because this measure would likely be very similar to education overall, current education can be used as a substitute.
  • Health Profile.  This category represents the patient’s current health condition.  This can be represented by factors such as a patient’s disease burden, mental/physicial functioning and other measures.  The authors use the patient’s Total Illness Burden Index (TIBI).  A paper by Willson et al (2000) uses a Comprehensive Severity Index (CSI) to measure patient illness severity over time.  To determine physician functioning, the authors use a 10 item physical function scale (PFI-10) on the Short Form 36 of the TIBI.  Other studies have used the Functional Independence Measure (FIM) to estimate patient physical functioning. To measure the patient’s mental health and depression, the authors used a modified version of the Center for Epidemiological Studies Depression Scale (CES-D).
  • Personality Profile Measures.  A patient’s personality can affect health outcomes as well.  In Kaplanet al.  (2010) study, the authors measure whether the patient has a passive orientation to health and health care according to the Provider Dependent Health Care Orientation (PDHCO) measure.  The scale measures whether patients take a passive approach to disease management and the author claims that this measure “has been linked with poor transitions in physical functioning over time.”
  • Behavioral Profile.  This includes disease management skills and health habits (e.g., smoking, diet, exercise).
  • Medical/Treatment Context.  The category measures differences across patients in the relationship with their physician, the setting where care is given, and continuity of care.
  • Life Context.  The goal of this final category is to measure whether the patient experience a stressful life event or whether they have a significant social support system.

In the paper, the authors examine whether diabetes treatment improves glycemic control (i.e., HbA1c levels).  Only the first three categories of patient information are included in the regressions.  Unsurprisingly, the strongest predictor of differential benefits from treatment is generally the health profile.  The TIBI index has the strongest effect on how treatment affects patient outcomes, but the PF-10 physical functioning disability measure is also influential in a statistically significant manner.   Patient adherence to drug regimens, unsurprisingly, also improves the affect of treatment.

Although the finding that individual patient characteristics affects treatment benefits is not surprising, Kaplan and co-authors present researchers with some tools to summarize these differences for a number of different patient characteristic categories.

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