This research augments the preliminary national catalogue of preference-based scores developed by Sullivan et al.8
by providing EQ-5D index scores associated with a wide range of chronic ICD-9 codes. The preliminary catalogue of chronic conditions was categorized by QPC and CCC codes.8
The current research adds to the existing catalogue and provides analysts with a standardized and flexible source of nationally representative preference-based scores associated with a variety of chronic ICD-9 codes that can be used in QALY estimation for cost-effectiveness analysis.
Availability of nationally representative, community-based preference scores overcomes one of the most important barriers to appropriate estimation of cost-effectiveness.1
An “off-the-shelf” catalogue of preference-based scores derived from a theory-based method provides a standardized and consistent source of preference weights.1
The preference-based scores for chronic ICD-9 codes reported in this research are based on the EQ-5D, a theory-based, previously validated health status classification system that meets all of the criteria established by the PCEHM. Specific examples of how to incorporate these estimates in cost-effectiveness analyses are provided elsewhere (see the online appendix at the journal's Web site, mdm.sagepub.com
The current list of “off-the-shelf” EQ-5D index scores for ICD-9 codes differs from the previous catalogue for CCC and QPCs. First, the ICD-9 codes in this research provide more refined classification of distinct conditions and may be more appropriate if CCC codes are too generic for the desired clinical conditions. For example, CCC 089 “Blindness and Other Vision Defects” had no associated disutility in previous research,8
which prima facie contradicts clinical expectation. However, CCC 089 is a combination of 4 distinct ICD-9 codes (367 “Disorders of Refraction,” 368 “Visual Disturbance,” 369 “Blindness and Low Vision,” and V41 “Problems with Specific Functions”). In comparison, ICD-9 369 “Blindness and Low Vision” provides a more clinically homogeneous condition classification and has an associated disutility of −0.0498 (). Hence, while CCC 089 and ICD-9 369 appear similar in name, there is a significant difference in the specificity of clinical categorization. We recommend that analysts familiarize themselves with the ICD-9 to CCC crosswalk to determine the appropriateness of a given condition categorization.23
In addition, ICD-9 codes may correspond more practically to available cost data for use in cost-effectiveness models. Estimation of the cost of a given condition in analyses of existing data (i.e., claims data) is often based on ICD-9 codes. In this case, the provision of EQ-5D index scores associated with ICD-9 codes would provide researchers with the flexibility of having scores that more directly correspond to the costs of the condition. Second, the 10 QPCs reported in the previous research are based on a different question frame. For the QPCs reported previously, patients were asked if they had ever
been diagnosed with the condition of interest (e.g., stroke),8
whereas ICD-9 and CCC condition classifications are based on reports of experiencing a given condition within a given year. Hence, ICD-9 436 “Cerebral Vascular Attack (CVA)” corresponds to an individual who, at some point within the year that the EQ-5D was administered, experienced a CVA (), and CCC 109 “Acute Cerebrovascular Disease” from the preliminary catalogue8
corresponds to an individual who, at some point within the year, reported any 1 of 6 three-digit ICD-9 codes (430, 431, 432, 433, 434, or 436). In contrast, QPC “Stroke” from the previous research8
corresponds to an individual who has previously had a stroke (not necessarily within the past year).
The decision to use broader QPC or CCC codes v. more clinically homogeneous ICD-9 codes in QALY estimation will depend on the requirements of the end-user. However, where a broader approach to the clinical condition of interest is desired (e.g., the generic QPC “Coronary Heart Disease”), or when the sample size of individuals reporting a specific ICD-9 code is too small to ensure accurate estimation of EQ-5D indexscores, the CCC or QPC estimates provided in the previous research may be more appropriate.
Administration of the EQ-5D in MEPS could not be correlated to the exact timing of reported conditions. Both were reported within a given year. Hence, the current research has restricted the scope of the analysis to chronic ICD-9 codes (lasting ≥1 year), as classified by Hwang et al.16
Some of the ICD-9 codes used in this research may not have a chronic impact for all individuals reporting the condition. There were 5 ICD-9 condition coefficients from the regression results that were positive and were omitted. The following 4 were statistically significant and positive: ICD-9 199 malignant neoplasm, 367 disorders of refraction, 600 hyperplasia of prostate, and other skin hypertrophy/atrophy. Positive disutility from chronic conditions is not consistent with clinical expectation. It is possible that some of these conditions were not truly chronic and their impact was not captured in the EQ-5D index score when elicited, that there are unobserved variables that are positively associated with EQ-5D index scores for these conditions, and/or that the EQ-5D health status instrument is not sensitive to these conditions. Condition coefficients that were not statistically significant should be used with extreme caution, incorporating appropriate assessment of the greater uncertainty in the estimates.
The ability of survey respondents to report accurate condition data may be a source of inaccuracy for the conditions listed, and this bias may be exacerbated in blacks and Hispanics. Although the ICD-9 codes were verified and error rates for professional coders did not exceed 2.5% in MEPS,10
they were based on self-report. Previous research has shown that blacks, whites, and Hispanics differ in reporting of disease labeling, levels of illness, and disability, and there is evidence that self-reported conditions may be underreported in general.24–26
As discussed, MEPS data do not contain information on severity of disease. Future research examining the impact of disease severity on preference scores for different conditions is needed. In addition, future research validating the condition estimates in this general population is necessary.
The construct validity, reliability, and responsiveness of the EQ-5D index have been documented extensively in both general and specific disease populations. Although considered a major advantage of the EQ-5D, the parsimony in the items and levels of the questionnaire may result in a potential lack of discrimination. Previous research has suggested that EQ-5D index scores based on the US scoring algorithm exhibit strong ceiling effects.8
This characteristic has necessitated the use of censored regression methods to derive unbiased estimates. The minimum possible decrement in the EQ-5D index score is 0.140 for the US weights. The inability to quantify a health state between 0.86 and 1.0 may lead to lack of discrimination for mild health states. This has also been suggested as a limitation of the UK scoring algorithm for the EQ-5D.27–29
Similar to the QWB and the SF-6D, the scoring function of the EQ-5D is based on a linear additive model, which assumes no interactions in preferences between attributes and may be a limitation of the EQ-5D index. In contrast, the HUI relies on a multiplicative functional form.30
In addition, the US scoring algorithm uses TTO values to estimate preferences based on a MAVF rather than standard gamble (SG)—derived utilities based on a multiattribute utility function (MAUF). Although not free of criticism,1,31,32
the SG results in utilities that are more consistent with the inherent uncertainty in health decisions required by Von Neuman Morganstern expected utility theory.33,34
Despite these limitations, there is no consensus that other preference-based health status classification systems are superior to the EQ-5D index.1,29,35,36
Each has its own advantages and limitations, and the EQ-5D is the only theory-based health status classification system with a scoring function based on US general population preferences that is currently available in a nationally representative data set.
Although consensus guidelines exist on the appropriate use of preference-based HRQL scores, inappropriate use continues to be widespread. The wide variability and inconsistencies across estimates of preference-based HRQL scores in prior cost-effectiveness analyses3,7
underscore the need for consistency in reference-case cost-effectiveness evaluations. This need for consistency lead the PCEHM to call for a national catalogue of preference-based HRQL scores1
as an important step toward promoting comparability of cost-effectiveness analyses. Although there are limitations in the current research, it provides an important addition to the preliminary catalogue developed by Sullivan et al.8
and moves toward the consistency called for by the PCEHM. Perhaps the availability of accessible EQ-5D index scores associated with chronic ICD-9 codes will provide analysts with the flexibility and breadth of “off-the-shelf” preference-based HRQL scores needed to improve the consistency of QALY estimation in cost-effectiveness analysis. Additional studies are necessary to validate these scores within condition-specific populations.