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Article Excerpt
Health state preference scores assign a quantitative measure of value to specific health states constrained by death (given a score of 0) and perfect health (given a score of 1 or 100). The specific health states used in this context can be an individual's current health or a description of a hypothetical health state. Health state preference scores are obtained using a variety of methods (Drummond et al. 1997). Health state preference scores form the basis for calculating quality-adjusted life-years (QALYs). Cost per QALY ratios are increasingly used to inform health care resource allocation decisions (National Institute for Clinical Excellence 2004). However, important methodological issues remain regarding the measurement of health state preferences, including who should be the source of the health state preferences used in cost per QALY calculations.
The Panel on Cost-Effectiveness in Health and Medicine recommended using the general population as the source of health state preferences for the reference case analysis (Gold et al. 1996). The Panel's rationale for making this recommendation was based on fairness and minimizing bias, that is, the general population is blind to its own self-interest (unaware of future health problems) and therefore able to provide a less biased assessment of health state preferences. However, in practice, researchers use many sources to generate health state preferences (Brauer et al. 2006). A recent review of cost-utility analyses published between 1998 and 2001 found that 30.3 percent of preference scores were derived from the community, 23.3 percent from patients, 21.0 percent from clinicians, and 18.7 percent from the authors (Brauer et al. 2006). While distinctions are drawn between utility, value, and preference scores (Gold et al. 1996), for simplicity, this paper will use the term "preference score" for each.
Health state preferences obtained from different groups are often similar but can vary widely (Ubel, Loewenstein, and Jepson 2003). Specifically, health state preference scores obtained from patients who have experienced the condition may differ from preference scores obtained from groups who have not experienced the condition. Individuals with the condition may incorporate a greater range of experiences associated with a health state, may accommodate to their current state of health, or may change the way they rate their health in comparison with others (scale recalibration) (Ubel, Loewenstein, and Jepson 2003; Ubel et al. 2005). Within-group differences also exist. For example, the severity of illness (Badia et al. 1996; Lenert, Treadwell, and Schwartz 1999; Insinga and Fryback 2003) and the length of time since a health event (Adang et al. 1998; Smith et al. 2006) may impact health preference scores.
A number of studies have compared health state preference scores generated by different groups. Some of these studies have found differences based on health experience (Gabriel et al. 1999; Lenert, Treadwell, and Schwartz 1999; De Wit, Busschbach, and De Charro 2000; Postulart and Adang 2000; Insinga and Fryback 2003; Rashidi, Anis, and Marra 2006) while others have not (Balaban et al. 1986; Revicki, Shakespeare, and Kind 1996; Dolders et al. 2006). In general, studies that compare patient and general population health state preferences find that patients assign preference scores to less than perfect health states that are equal to or greater than the preference scores assigned by members of the general population (Sackett and Torrance 1978; Balaban et al. 1986; Froberg and Kane 1989b; De Wit, Busschbach, and De Charro 2000; Dolders et al. 2006). A conclusion that could be drawn from these studies is that using general population health state preferences might result in more favorable cost per QALY ratios than using patient preferences, except in cases of life-saving interventions (Brazier et al. 2005). For example, if the general population assigns a lower preference score than patients to a less than perfect health state, then using general population preferences for an intervention that restores perfect health would result in a larger QALY difference and a more attractive cost per QALY ratio. Conversely, a life-saving intervention for unhealthy patients could appear less cost-effective using general population preference scores because the patient would return to a health state the general population assigned a lower preference score to.
Our study explored whether depression experience influenced depression health state preferences and how this might affect cost per QALY calculations. We chose depression because depression is often misunderstood and stigmatized by the general population (Link et al. 1999; Barney et al. 2006; Perry et al. 2007). The objective of this study was to compare depression health state preferences across four groups: (1) general population, (2) patients with past depression but not currently depressed, (3) patients with mild to moderate depression, and (4) patients with moderate to severe depression.
METHODS
Design
Our study was a cross-sectional, face-to-face survey of individuals sampled from the following recruitment sites: general population, primary care clinics, and specialty mental health clinics. Our recruitment target for the general population sample was 100, and we recruited 9.5. From the clinic sites, we attempted to recruit subjects with a broad range of depression severity (see Table 1). Our recruitment target from the clinic sites was 300, and we recruited 246. We also collected test-retest reliability data within 2 weeks of the baseline interview from 49 randomly selected subjects (15 from the general population and 34 from the clinic sites).
Subjects
Eligibility criteria for all groups included (1) age 18-70 years, (2) able to read and understand English, (3) negative screen for significant cognitive impairment as evidenced by diagnosis of dementia or a score >8 on the Blessed Orientation-Memory-Concentration test, (4) no history of schizophrenia diagnosis, (5) negative screen for bipolar disorder, (6) no life-threatening condition, (7) residence within 60 miles of downtown Little Rock, and (8) access to a telephone. Subjects were compensated US$30 to complete the interview. The University of Arkansas for Medical Sciences (UAMS) Institutional Review Board approved the research protocol.
The general population group was recruited from Central Arkansas (Little Rock and surrounding areas) using a commercially available phone list. The Central Arkansas area was selected because the location corresponded with the clinic sites. The phone list included phone numbers, addresses, age, gender, and ethnicity. Potential subjects were selected from the phone list using a stratified random sampling plan to approximate the age, gender, and ethnicity demographic characteristics of Central Arkansas residents. The general population sampling plan did not include depression severity. Potential participants were mailed a postcard stating that...
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