Original ArticleThree methods tested to model SF-6D health utilities for health states involving comorbidity/co-occurring conditions
Introduction
Cost-effectiveness analysis (CEA) of interventions and regulations that impact health requires a method to quantify changes in health. Both the Panel on Cost-Effectiveness in Health and Medicine (PCEHM) and the Committee to Evaluate Measures of Health Benefits for Environmental, Health, and Safety Regulation (CEMHB) recommended using generic health-related quality of life instruments with preference-based scoring systems to quantify health. The resulting scores, referred to as “health utility,” are constructed so that full health is anchored at 1.0 and death is anchored at 0 [1], [2].
CEA requires an estimate of the impact of delaying or removing a given health condition on health utility. CEA often incorporates previously published health utilities rather than performing primary data analysis [3]. Combining impact estimates from several smaller studies, however, can be complicated by variations in sampling, health utility measurement, and adjustment for important covariates. Large population surveys are an attractive option for generating “condition catalogs,” which report the average difference between health utilities of people with and without selected named health conditions. These surveys sample and represent the experience of large segments of the population and allow for consistent impact estimation across many different health conditions. Although large surveys may include enough people who only report a single health condition to directly estimate the health utility for that condition, there are not enough people who report a combination of two or more conditions to report the observed health utility score for groups with a specific combination of conditions.
CEAs must often address the impacts of several health conditions at the same time because the population of interest has coexisting conditions or the intervention of interest affects several health conditions. In the absence of catalogs of utility impacts of multiple conditions, these impacts must be modeled using single condition catalogs. There is currently no standardized guidance regarding the combination of single condition impacts to model the impact of the combination of those health conditions. Determining the most appropriate method to combine health utility estimates for multiple health conditions is important for both analyses that use health utilities from the literature and analyses that collect primary data. Even when collecting primary data about health utilities, obtaining health utilities for every possible combination of health conditions may be prohibitively expensive or time consuming. It is more feasible to collect health utilities for individual health conditions and then to use a prespecified algorithm to combine these single health utilities when the impact of multiple health conditions on health utility is needed for the analysis.
Both the PCEHM and CEMHB called for consistently estimated catalogs of health utilities that could be used as a source of values for CEA. The CEMHB also specified that, “such research should give special attention to the documentation of comorbid conditions and the development of health-related quality of life values for health states involving multiple impairments” [2]. Thus, condition catalogs could include the impact of health conditions conditioned on presence of other health conditions and demographic variables.
To estimate the impact of a health condition using a sample of persons with the health condition, the PCEHM and CEMBH have suggested comparing the sample's average health utility scores and average health utilities in a national data set adjusted to the same age and sex distribution as the sample. A list of these estimates could then be used to make a condition catalog. These estimates would accurately reflect the impact of single health conditions if all conditions are generally of low prevalence and independently distributed in the population. Low prevalence is necessary so that the age- and sex-adjusted normative scores do not include many individuals with the condition, otherwise the cataloged differences would underestimate the effect of very prevalent health condition on health utility. Without independence, the impact of a single health condition may be overestimated because it tends to cluster with other health conditions.
Neither the low prevalence nor independence assumption holds for many conditions in population-based data sets. Particularly at older ages, there are conditions with very high prevalence, for example, 45% of those 65 and older report having been diagnosed with arthritis [4]. There are also several highly prevalent conditions of interest that cluster together, such as cardiovascular conditions (e.g., stroke, coronary artery disease, and myocardial infarction), immunologically mediated conditions (e.g., asthma, eczema, and allergy), and sexually transmitted infections. The prevalence of chronic conditions in the United States is increasing as life expectancy increases, our population ages, and many diseases that were once fatal have become manageable chronic conditions [5]. Currently, about 80% of US adults 65 years and older report at least one chronic condition, and 50% report at least two [6]. It has been estimated that half of all Americans will have a chronic health condition by 2020 and almost half of these individuals will have two or more such conditions [7].
Available condition catalogs of health utilities have used different methods to adjust for the presence of multiple health conditions when assessing impact of a given condition. The first catalogs ignored comorbidities and simply reported the mean health utility score for those with a particular health condition [8], [9], [10]. The most recent catalogs control for many chronic health conditions in a single regression equation using an additive model that assumes the impact of any given health condition is the same regardless of the presence of other health conditions [11], [12], [13], [14]. This model assumption has not been formally tested. Other research groups, particularly those working with the Health Utilities Index and Disability-Adjusted Life Years, assume that a multiplicative method is most appropriate for combining health utility impacts of multiple health conditions [15], [16]. This method assumes that the impact of any health condition is a constant proportion of underlying health utility. A recent formal test of additive, multiplicative, and minimum methods used directly elicited utilities and found that a minimum method performed best [17]. However, this test used directly elicited utilities from a patient sample, and the results may vary for health utility scores from instruments that use community-based preferences. Previous simulation research has shown that disability-adjusted life expectancy calculated using the additive and multiplicative methods is quite similar except in groups with several chronic health conditions, such as elderly populations [16]. This simulation research used disability-adjusted life expectancy that is different from the quality-adjusted life expectancy calculated using health utilities [18].
In this report, we test three mathematical methods that combine information about single health conditions to estimate health utilities for persons with more than one health condition: the additive, minimum, and multiplicative methods. We conduct this test using data from the Medicare Health Outcomes Survey (HOS). The sampling frame for this data set is a large section of the US population where co-occurring chronic conditions are common, allowing us to evaluate the ability of these methods to predict health utility scores in groups where multiple health conditions are present. We test the three modeling methods using the SF-6D health utility score computed from the SF-36 version 1 questionnaire.
Section snippets
Data
The Medicare HOS (formerly known as the Health of Seniors Survey) is used to measure outcomes in individuals enrolled in Medicare managed care plans. It is mailed and self-administered with baseline and 1-year follow-up surveys. If enrollees do not respond to two mailed questionnaires, the data collection agency attempts to reach them by telephone. Each year, beginning in 1998, 1,000 Medicare beneficiaries were randomly sampled from each Medicare Advantage (MA) managed care plan. There are many
Results
Table 1 includes demographic information about the Medicare HOS data used for model estimation and model testing. Ages range from 22 to 106 years. About a quarter of the sample has less than high school education. Eighty percent of the sample reports at least one of the health conditions. The under-65 group, which includes individuals who receive social security disability benefits as well as individuals who have certain conditions, given Medicare entitlement by act of Congress, has more males
Discussion
This report presents comparisons of three commonly used or suggested methods [11], [12], [13], [14], [15], [16], [17] for combining the impacts from single health conditions to estimate the health utility associated with multiple conditions. For the SF-6D health utility score from the Medicare HOS, the multiplicative method, where the impact of each condition is a constant proportional reduction of overall utility, showed better performance when compared with additive or minimum methods. The
Acknowledgments
The authors would like to acknowledge thoughtful comments from Nancy Sweitzer and Brian Harahan. This project was funded by a dissertation grant from the Agency for Healthcare Quality and Research (1 R36 HS016574) and a grant from the National Institute on Aging (AG020679). The Centers for Medicare and Medicaid Services provided data used in this report. The funding agreements ensured the authors' independence in designing the study, interpreting the data, writing, and publishing the report.
References (30)
- et al.
Trends in the measurement of health utilities in published cost-utility analyses
Value Health
(2006) - et al.
The estimation of a preference-based measure of health from the SF-36
J Health Econ
(2002) - et al.
Measuring change over time: assessing the usefulness of evaluative instruments
J Chronic Dis
(1987) - et al.
Valuing health for regulatory analysis
(2006) - et al.
Racial/ethnic differences in the prevalence and impact of doctor-diagnosed arthritis—United States, 2002
MMWR Morb Mortal Wkly Rep
(2005) Prevalence and costs of chronic disease in a health care system structured for treatment of acute illness
Radiology
(2005)- Centers for Disease Control and Prevention. Healthy aging: preventing disease and improving quality of life among older...
- et al.
Projection of chronic illness prevalence and cost inflation
(2000) - et al.
The Beaver Dam Health Outcomes study: initial catalog of health-state quality factors
Med Decis Making
(1993)