Preference Scores For Health States Research Articles

Assessment of Preference for Hormonal Treatment–Related Health States among Patients with Breast Cancer

Objectives1) To obtain preference scores from patients with breast cancer in Singapore for different stages of breast cancer and hormonal therapy–related adverse effects, and 2) to determine the association of patients’ demographic and clinical characteristics with those preference scores. MethodsA total of 22 health states were used to elicit preference values from 64 patients with breast cancer. At each interview, 14 health states were randomly selected and rated by the patient using the visual analogue scale and standard gamble methods to derive health state preference scores, which were recalibrated to the scale of 0 (death) and 1 (perfect health). ResultsMean adjusted visual analogue scale scores ranged from 0.25 (no recurrence with ischemic cerebrovascular events) to 0.82 (no recurrence with no adverse effects). Mean adjusted standard gamble scores ranged from 0.31 (distant recurrence with chemotherapy-related adverse effects) to 0.80 (no recurrence with no adverse effects). Adverse effects ischemic cerebrovascular events and spine fracture resulted in the greatest decline in health state preference scores. Age, ethnicity, education level, and prior chemotherapy were associated with preference scores. Having children was not found to be associated with the preference scores. ConclusionsTaking into account disease progression and hormonal therapy–related adverse effects as well as their impact on health-related quality of life, this study quantifies patients’ preference for various breast cancer–related health states. The findings offer valuable information for future cost-utility analysis of breast cancer treatments.

Use of Nomograms for Personalized Decision-Analytic Recommendations

A difficulty with applying decision analysis at the bedside is that it generally requires computer software for the calculations, which may render the method impractical. The purpose of this study was to illustrate the feasibility of developing a regression model that approximates the results from a published decision-analytic model for prostate cancer and permits bedside generation of personalized decision-analytic recommendations with a paper nomogram. The authors used the example of radical prostatectomy v. watchful waiting for patients with early-stage prostate cancer. First, they took a published decision analysis and generated recommendations using simulated data where patient baseline factors and preference scores for health states were systematically varied. Multivariable logistic regression was used to identify the parameters with strong associations with the recommendation. A reduced model was fit that excluded other preference scores except for watchful waiting. They compared the recommended management predictive accuracies from the full v. reduced model at the individual patient level for 63 men from another published study. Discrimination was assessed using receiver operating characteristic (ROC) curve analysis. A nomogram was constructed from the covariates in the reduced model. The reduced logistic regression model predicted the recommendations accurately for the 63 patients, with an area under the ROC curve of 0.92. Discrimination was excellent as demonstrated by histograms. The authors demonstrated that logistic regression modeling allows accurate reproduction of decision-analytic recommendations with simplified calculations, which can be accomplished using a graphic nomogram. This approach should facilitate clinical decision analysis at the bedside.

How Bad Is Depression? Preference Score Estimates from Depressed Patients and the General Population

To compare depression health state preference scores across four groups: (1) general population, (2) previous history of depression but not currently depressed, (3) less severe current depression, and (4) more severe current depression. Primary data were collected from 95 general population, 163 primary care, and 83 specialty mental health subjects. Stratified sampling frames were used to recruit general population and patient subjects. Subjects completed cross-sectional surveys. Key variables included rating scale and standard gamble scores assigned to depression health state descriptions developed from the Patient Health Questionnaire-9 (PHQ-9) and SF-12. Each subject completed an in-person interview. Forty-nine subjects completed test/retest reliability interviews. Depressed patient preference scores for three of six SF-12 depression health states were significantly lower than the general population using the rating scale and two of six were significantly lower using standard gamble. Depressed patient scores for five of six PHQ-9 depression health states were significantly lower than the general population using the rating scale and two of six were significantly lower using standard gamble. Depressed patients report lower preference scores for depression health states than the general population. In effect, they perceived depression to be worse than the general public perceived it to be. Additional research is needed to examine the implications for cost-effectiveness ratios using general population preference scores versus depressed patient preference scores.

Health state preference scores for children with permanent childhood hearing loss: a comparative analysis of the QWB and HUI3

The aim of this study was to compare two preference-weighted, caregiver-reported measures of health-related quality of life for children with permanent childhood hearing loss to determine whether cost-effectiveness analysis applied to deaf and hard of hearing populations will provide similar answers based on the choice of instrument. Caregivers of 103 children in Arkansas, USA, with documented hearing loss completed the Quality of Well-Being Scale (QWB) and the Health Utilities Index Mark 3 (HUI3) to describe the health status of their children. Audiology and other clinical measures were abstracted from medical records. Mean scores were compared overall and by degree of hearing loss. Linear regression was used to correlate preference scores with a four-frequency pure-tone average, cochlear implant status, and other factors. Mean preference scores for the QWB and HUI3 were similar (0.601 and 0.619, respectively) although the HUI3 demonstrated a wider range of values (-0.132 to 1.000) compared to the QWB (0.345-0.854) and was more sensitive to mild hearing loss. Both measures correlated with the pure-tone average, were negatively associated with comorbid conditions and positively associated with cochlear implant status. In the best fitting regression models, similar estimates for cochlear implant status and comorbid conditions were obtained from the two measures. Despite considerable differences in the HUI3 and the QWB scale, we found agreement between the two instruments at the mean, but clinically important differences across a number of measures. The two instruments are likely to yield different estimates of cost-effectiveness ratios, especially for interventions involving mild to moderate hearing loss.

Preference-Based EQ-5D Index Scores for Chronic Conditions in the United States

The Panel on Cost-Effectiveness in Health and Medicine has called for an "off-the-shelf" catalogue of nationally representative, community-based preference scores for health states, illnesses, and conditions. A previous review of cost-effectiveness analyses found that 77% did not incorporate community-based preferences, and 33% used arbitrary expert or author judgment. These results highlight the necessity of making a wide array of appropriate, community-based estimates more accessible to cost-effectiveness researchers. To provide nationally representative EQ-5D index scores for chronic ICD-9 codes. The nationally representative Medical Expenditure Panel Survey (MEPS) was pooled (2000-2002) to create a data set of 38,678 adults. Ordinary least squares (OLS), Tobit, and censored least absolute deviations (CLAD) regression methods were used to estimate the marginal disutility of each condition, controlling for age, comorbidity, gender, race, ethnicity, income, and education. Most chronic conditions, age, comorbidity, income, and education were highly statistically significant predictors of EQ-5D index scores. Homoskedasticity and normality assumptions were rejected, suggesting only CLAD estimates are theoretically unbiased. The magnitude and statistical significance of coefficients varied by analytic method. OLS and Tobit coefficients were on average 60% and 143% greater than CLAD, respectively. The marginal disutility of 95 chronic ICD-9 codes as well as unadjusted mean, median, and 25th and 75th percentiles are reported. This research provides nationally representative, community-based EQ-5D index scores associated with a wide variety of chronic ICD-9 codes that can be used to estimate quality-adjusted life-years in cost-effectiveness analyses.

Prospect theory in the valuation of health

Prospect theory is the prominent nonexpected utility theory in the estimation of health state preference scores for quality-adjusted life year calculation. Until recently, the theory was not considered to be developed to the point of implementation in economic analysis. This review focuses on the research and evidence that tests the implementation of prospect theory into health state valuation. The typical application of expected utility theory assumes that a decision maker has stable preferences under conditions of risk and uncertainty. Under prospect theory, preferences are dependent on whether the decision maker regards the outcome of a choice as a gain or loss, relative to a reference point. The conceptual preference for standard gamble utilities in the valuation of health states has led to the development of elicitation techniques. Empirical evidence using these techniques indicates that when individual preferences are elicited, a prospect theory consistent framework appears to be necessary for adequate representation of individual health utilities. The relevance of prospect theory to policy making and resource allocation remains to be established. Societal preferences may not need the same attitudes towards risks as individual preferences, and may remain largely risk neutral.

Health state preference scores of children with spina bifida and their caregivers

Cost-effectiveness evaluations of interventions to prevent or treat spina bifida require quality of life information measured as preference scores. Preference scores of caregivers also may be relevant. This study tested whether the preference scores of children with spina bifida and their caregivers would decrease as disability in the child increased. Families of children aged 0-17 with spina bifida (N = 98) were identified using a birth defect surveillance system in the state of Arkansas. Primary caregivers of children with spina bifida identified other families with an unaffected child (N = 49). Preference scores for child health states were determined using the Health Utilities Index--Mark 2 (HUI2). Caregiver preference scores were determined using the Quality of Well-Being (QWB) scale. Children with spina bifida were categorized into three disability levels according to the location of the child's lesion. Mean preference scores declined for both affected children and the primary caregiver as disability in the child increased. In multivariate analysis, the preference score of the child was a significant and positive predictor of the primary caregiver's preference score. A more modest association was found for caregiver health preference scores by lesion location. The findings can inform cost-effectiveness evaluations of interventions to treat or prevent spina bifida.

An off-the-shelf help list: a comprehensive catalog of preference scores from published cost-utility analyses.

The Panel on Cost-Effectiveness in Health and Medicine recommends an organized collection of preference measure values for health states that can be used in costutility analyses (CUAs). The authors sought to construct a catalog of preference scores from published CUAs, organize the catalog by clinical categories, and identify methods of preference score assessment. The authors systematically searched Medline and other databases to identify original CUAs published through 1997. Information was abstracted on the health state descriptions, corresponding preference scores, method of preference score elicitation, and the source of the estimate. Two hundred twenty-eight CUAs were appraised. The authors found 949 health states and corresponding preference scores. Most frequently, health states pertained to the circulatory system (21.7%), health states were valued by experts (35.8%), and values were derived through community-based preference scores (23.5%). A catalog of preference scores for health states can be constructed. The catalog (http://www.hsph.harvard.edu/organizations/hcra/cuadatabase/ intro.html) may provide a useful reference tool for producers and consumers of CUAs but also underscores the methodologic variation and inconsistencies present in the field.

Multi-attribute preference functions. Health Utilities Index.

Multi-attribute utility theory, an extension of conventional utility theory, can be applied to model preference scores for health states defined by multi-attribute health status classification systems. The type of preference independence among the attributes determines the type of preference function required: additive, multiplicative or multilinear. In addition, the type of measurement instrument used determines the type of preference score obtained: value or utility. Multi-attribute utility theory has been applied to 2 recently developed multi-attribute health status classification systems, the Health Utilities Index (HUI) Mark II and Mark III systems. Results are presented for the Mark II system, and ongoing research is described for the Mark III system. The theory is also discussed in the context of other well known multi-attribute systems. The HUI system is an efficient method of determining a general public-based utility score for a specified health outcome or for the health status of an individual. In clinical populations, the scores can be used to provide a single summary measure of health-related quality of life. In cost-utility analyses, the scores can be used as quality weights for calculating quality-adjusted life years. In general populations, the measure can be used as quality weights for determining population health expectancy.