Mild Health States Research Articles

Comparison of Generic, Condition-Specific, and Mapped Health State Utility Values for Multiple Myeloma Cancer

ObjectiveResource allocation informed by cost-utility analysis requires that the benefits be comparable across patient groups and interventions. One option is to recommend the use of one generic utility measure, but this raises the issue of comparability when the preferred measure is inappropriate or unavailable. Many cancer trials do not include generic measures such as the EuroQol five-dimensional (EQ-5D) questionnaire and instead include condition-specific measures and use these to generate utility estimates. We analyze the comparability of generic, condition-specific, and mapped utility values for a multiple myeloma cancer patient data set. MethodsGeneric EQ-5D, condition-specific EORTC-8D, and EQ-5D utility values mapped from the EORTC QLQ-C30 were compared by using psychometric and statistical analysis to determine discrimination across severity groups, responsiveness, and agreement. ResultsGeneric, condition-specific, and mapped utility estimates were responsive over time and show discriminative validity. The EQ-5D had higher responsiveness and detected a greater change across severity groups and treatment periods than did the EORTC-8D but has a higher proportion of responses at full health (12.8%). Differences in the EQ-5D and the EORTC-8D were due at least in part to differences in the classification system. Mapped EQ-5D estimates had a smaller SD and do not reflect the severe range of health states reported by using the EQ-5D. ConclusionsOur findings suggest that condition-specific EORTC-8D or mapped EQ-5D utility estimates are broadly comparable to directly obtained EQ-5D utilities for a multiple myeloma patient data set. However, EORTC-8D estimates captured changes in quality of life for patients in mild health states that were not captured by the EQ-5D, but estimated lower utility gains than did the use of the EQ-5D directly.

Measuring Alzheimer Disease Progression with Transition Probabilities: Estimates from NACC-UDS

Estimate the probabilities, for Alzheimer's disease (AD) patients, of transitioning between stages of disease severity (mild, moderate, severe, dead) and care settings (community, institutional). Data were compiled by the National Alzheimer Coordinating Center. The main analyses were limited to 3,852 patients who were 50 years old, diagnosed with possible/probable AD and had at least two center visits. A multinomial logistic model accounting for patient and center level correlation was used to calculate transition probabilities between stages of the Clinical Dementia Rating (CDR). Separately we calculated the probabilities of being institutionalized based on CDR stage. Both analyses controlled for baseline age, time between visits, sex, marital status, whether white, whether Hispanic and number of years of education. The annual probabilities of dying for patients in mild, moderate and severe health states were 5.5%, 21.5% and 48.0%, respectively, while the annual probabilities for institutionalization were 1.2%, 3.4% and 6.6%, respectively. The majority of mild and moderate patients remain in the same health state after one year, 77.4% and 50.1% respectively. Progressing patients are most likely to transition one stage, but 1.3% of mild patients become severe in one year. Some patients revert to lower severity stages, 7% from moderate to mild. Transition probabilities to higher CDR stages and to institutionalization are lower than those published previously, but the probability of death is higher. These results are useful for understanding AD progression and can be used in simulation models to evaluate costs and compare new treatments or policies.

Estimation of a Valuation Function for a Diabetes Mellitus-Specific Preference-Based Measure of Health

Preference-based measures of health (PBMH) provide 'preference' or 'utility' weights that enable the calculation of QALYs for the economic evaluations of interventions. The Diabetes Utility Index (DUI) was developed as a brief, self-administered, diabetes mellitus-specific PBMH that can efficiently estimate patient-derived health state utilities. To describe the development of the valuation function for the DUI, and to report the validation results of the valuation function. Multi-Attribute Utility Theory (MAUT) was used as the framework to develop a valuation function for the DUI. Twenty of 768 possible health states of the DUI classified as anchor states, single-attribute level states including corner states, and marker states were selected and described for preference elicitation interviews. Visual analogue scale and standard gamble (SG) exercises were used to measure preferences from individuals with diabetes recruited from primary care and community settings in and around Morgantown, WV, USA for the 20 health states defined by combinations of DUI attributes and severity levels. Data collected in the interviews were used to develop a valuation function that calculates utilities for the DUI health states and calculates attribute-level utilities. A validation survey of the valuation function was conducted in collaboration with the West Virginia University (WVU) Diabetes Institute. A total of 100 individuals with diabetes were interviewed and their preferences for various DUI health states measured. From data generated in the interviews, a DUI valuation function was developed on a scale where 1.00 = perfect health (PH) and 0.00 = the all worse 'pits' state, and adjusted to yield utilities on the conventional scale 1.00 = PH and 0.00 = dead. A total of 396 patients with diabetes who received care at WVU clinics completed a DUI mail validation survey (response rate = 33%). Clinical data consisting of International Classification of Diseases, 9th edition, diagnosis codes and glycosylated haemoglobin (HbA(1c)) values for the respondents were merged with their responses to the DUI. The utilities calculated by the scoring function of the DUI compared favourably to cardinal SG utilities for three DUI health states for which both assessments were available. The DUI utility function slightly underestimated actual SG utilities for mild and moderate health states (mean absolute difference = 0.05). There was a small but significant correlation between DUI utility scores and average past year HbA(1c) values (r = -0.30; p < 0.001). Respondents with two or more complications had significantly lower DUI utilities than those with no complications (p < 0.001) or one complication (p = 0.015). Insulin users had significantly lower DUI utilities than non-users (p < 0.001), and those with HbA(1c) values <7% had significantly higher DUI utilities than those with HbA(1c) values of >or=7% (p < 0.001). No significant association was found between DUI scores and age or sex. These results show evidence of the feasibility and validity of the DUI. Further research is suggested to demonstrate the generalizability of these findings, to study the responsiveness of the DUI, and to examine the clinical meaningfulness of DUI change scores.

Assessment of health state utilities for attention-deficit/hyperactivity disorder in children using parent proxy report

This study used standard gamble (SG) utility interviews to assess parent preferences for health states of childhood attention-deficit/hyperactivity disorder (ADHD). Health state utilities are needed to calculate quality-adjusted life years (QALYs), a critical outcome measure in cost-effectiveness studies of new treatments. Parents (n = 43) of children diagnosed with ADHD completed SG utility interviews, rating their child's current health and 11 hypothetical health states describing untreated ADHD and ADHD treated with a stimulant or non-stimulant. Parents completed questionnaires on their children's symptoms and health-related quality of life (HRQL). Parents' SG rating of their child's current health state (mean of 0.74 on a utility scale ranging from 0 to 1) was significantly correlated with inattentive, hyperactive, and overall ADHD symptoms (r = 0.37, 0.36, and 0.40 respectively; p < 0.05) and psychosocial HRQL domains. Hypothetical health state utilities ranged from 0.48 (severe untreated ADHD) to 0.88 (effective and tolerable non-stimulant treatment). Comparisons between health states found expected differences between untreated mild, moderate, and severe ADHD health states. When both treatments were effective and tolerable, parents preferred the non-stimulant health state over the stimulant health state (p < 0.03). Results suggest that parent SG interviews are a feasible and useful method for obtaining utility scores that can be used in cost-effectiveness models of ADHD treatment.

Caring externalities in health economic evaluation: how are they related to severity of illness?

In health economic evaluations, altruistic preferences in the form of caring externalities, i.e. that people care about others’ health, is usually not taken into account. In this study we examined how people value their own and others’ health. This pilot study was carried out by letting people answer willingness to pay (WTP) questionnaires where internal WTP (own health) and altruistic WTP (others’ health) were isolated and examined. A common method used in health economic evaluations is cost-utility analysis, which is based on the maximisation of QALYs. QALY maximisation may be appropriate if altruistic preferences are non-existent or if they are linear in relation to internal preferences (QALYs gained). We found evidence for the existence of altruistic preferences and that these preferences were relatively higher for severe health states (and lower for mild states of health) compared to internal preferences, i.e. when severity of illness increased, the relative increase in caring was higher concerning others than oneself. The difference was statistically significant ( P < 0.001). Our results indicate that more attention and resources should be directed to severe health states, as compared to mild health states, than advocated by internal preferences in order to obtain more efficient resource allocation in the health care sector.