Medical Decision Making

Decisions about medical treatments and the settings of health programs are not purely technical, but also involve issues of value such as the evaluation of trade-offs between quality of life (morbidity) and quantity of life (mortality). The most commonly used measure of outcome in such cases is the quality-adjusted life year (QALY). The authors show that QALYs, being a health status index, do not stem directly from the individual's utility function and thus only partly reflect the individual's true preferences. This might lead to the choice of the nonpreferred alternative due to the misrepresentation of the individual's preferences. Two examples illustrate this claim. An alternative measure of outcome, the healthy-years equiv alent (HYE), is described. This measure stems directly from the individual's utility function and thus fully reflects his/her preferences. It combines outcomes of both morbidity and mortality and thus can serve as common unit of measure for all programs, allowing com parisons across programs. Different ways of measuring the HYE are discussed. Key words: utility theory; economic evaluation; cost-effectiveness analysis. (Med Decis Making 1989;9:142-149)

Background. A common feature of most reviews or catalogues of health utilities has been their focus on adult health states or derivation of values from adult populations. More generally, utility measurement in or on behalf of children has been constrained by several methodological concerns. The objective of this study was to conduct the first comprehensive systematic review and meta-analysis of primary utility data for childhood conditions and descriptors, and to determine the effects of methodological factors on childhood utilities. Methods. The review followed PRISMA guidelines. PubMed, Embase, Web of Science, PsycINFO, EconLit, CINAHL and Cochrane Library were searched for primary studies reporting health utilities for childhood conditions or descriptors using direct or indirect valuation methods. The Paediatric Economic Database Evaluation (PEDE) Porject was also searched for cost-utility analyses with primary utility values. Mean or median utilities for each of the main samples were catalogued, and weighted averages of utilities for each health condition were estimated, by valuation method. Mixed-effects meta-regression using hierarchical linear modeling was conducted for the most common valuation methods to estimate the utility decrement for each health condition category relative to general childhood population health, as well as the independent effects of methodological factors. Results. The literature searches resulted in 272 eligible studies. These yielded 3,414 utilities when all sub-groups were considered, covering all ICD-10 chapters relevant to childhood health, 19 valuation methods, 12 respondent types, 8 modes of administration, and data from 36 countries. A total of 1,191 utility values were obtained when only main study samples were considered, and these were catalogued by health condition or descriptor, and methodological characteristics. 1,073 mean utilities for main samples were used for fixed-effects meta-analysis by health condition and valuation method. Mixed-effects meta-regressions estimated that 53 of 76 ICD-10 delineated health conditions, valued using the HUI3, were associated with statistically significant utility decrements relative to general population health, whereas 38 of 57 valued using a visual analog scale (VAS) were associated with statistically significant VAS decrements. For both methods, parental proxy assessment was associated with overestimation of values, whereas adolescents reported lower values than children under 12 y. VAS responses were more heavily influenced by mode of administration than the HUI3. Conclusion. Utilities and their associated distributions, as well as the independent contributions of methodological factors, revealed by this systematic review and meta-analysis can inform future economic evaluations within the childhood context.

In this study, the authors demonstrate how mixed logit analysis of discrete choice experiment (DCE) data can provide information about unobserved preference heterogeneity. Their application investigates unobserved heterogeneity in men’s preferences for benign prostatic hyperplasia (BPH) treatment. They use a DCE to elicit preferences for seven characteristics of BPH treatment: time to symptom improvement, sexual and nonsexual treatment side effects, risks of acute urinary retention and surgery, cost of treatment, and reduction in prostate size. They investigate the importance of these characteristics and the trade-offs men are willing to make between them. Preferences are elicited from a sample of 100 men attending an outpatient clinic in Ireland. The authors find all treatment characteristics are significant determinants of treatment choice. There is significant preference heterogeneity in the population for four treatment characteristics: time to symptom improvement, treatment reducing prostate size, risk of surgery, and sexual side effects. The importance of preference heterogeneity at the policy level within the context of shared decision making is discussed.

Background. There is an increasing movement towards the release of hospital “report-cards.” However, there is a paucity of research into the abilities of the different methods to correctly classify hospitals as performance outliers.Objective.To examine the ability of risk-adjusted mortality rates computed using conventional logistic regression and random-effects logistic regression models to correctly identify hospitals that have higher than acceptable mortality.Research Design.Monte Carlo simulations.Measures.Sensitivity, specificity, and positive predictive value of a classification as a high-outlier for identifying hospitals with higher than acceptable mortality rates.Results.When the distribution of hospital-specific log-odds of death was normal, random-effects models had greater specificity and positive predictive value than fixed-effects models. However, fixed-effects models had greater sensitivity than random-effects models.Conclusions.Researchers and policy makers need to carefully consider the balance between false positives and false negatives when choosing statistical models for determining which hospitals have higher than acceptablemortality in performance profiling.

There is a growing interest in the use of Bayesian methods for profiling institutional performance. In the literature, several studies have compared different frequentist methods for classifying hospitals as performance outliers. The purpose of this study was to compare 4 different Bayesian methods for classifying hospitals as outcomes outliers, using 30-day hospital-level mortality rates for a cohort of acute myocardial infarction patients as a test case. The 1st Bayesian method involved determining the probability that a hospital’s mortality rate for an average patient exceeded a specified threshold. The 2nd method involved ranking hospitals according to their mortality rate for an average patient. The 3rd method involved determining the probability that a hospital’s standardized mortality ratio exceeded a specified threshold. The 4th method involved ranking hospitals according to their standardized mortality ratio. In most of the scenarios examined, there was only marginal agreement between the different methods. In only 4 of 19 comparisons, was there good agreement between the different methods (0.40 kappa 0.75). Methods based on ranking institutions were relatively insensitive to differences between hospitals. These inconsistencies raise questions about the choice of methods for classifying hospital performance, and they suggest a need for urgent research into which methods are best able to discriminate between institutions and which are most meaningful to decision makers.

Objective. Guidelines on primary prevention of cardiovascular disease (CVD) emphasize identifying high-risk patients for more intensive management, but patients' misconceptions of risk hamper implementation. Insight is needed into the type of patients that general practitioners (GPs) encounter in their cardiovascular prevention activities. How appropriate are the risk perceptions and worries of patients with whom GPs discuss CVD risks? What determines inappropriate risk perception? Method. Cross-sectional study in 34 general practices. The study included patients aged 40 to 70 years with whom CVD risk was discussed during consultation. After the consultation, the GPs completed a registration form, and patients completed a questionnaire. Correlations between patients' actual CVD risk and risk perceptions were analyzed. Results. In total, 490 patients were included. In 17% of the consultations, patients were actually at high risk. Risk was perceived inappropriately by nearly 4 in 5 high-risk patients (incorrect optimism) and by 1 in 5 low-risk patients (incorrect pessimism). Smoking, hypertension, and obesity were determinants of perceiving CVD risk as high, whereas surprisingly, diabetic patients did not report any anxiety about their CVD risk. Men were more likely to perceive their CVD risk inappropriately than women. Conclusion. In communicating CVD risk, GPs must be aware that they mostly encounter low-risk patients and that the perceived risk and worry do not necessarily correspond with the actual risk. Incorrect perceptions of CVD risk among men and patients with diabetes were striking.

Background . Many subjects attach equal value to different health care programs in surveys eliciting preferences for resource allocation. It has been suggested that subjects may be prepared to attach different priority if they were asked to evaluate someone else’s decision instead of adopting the role of a social decision maker. This study investigated whether the perspective individuals are asked to adopt affects their priority setting decisions and the likelihood of assigning equal value to health care programs. Methods . 1253 members of an Internet panel were presented a set of clinical vignettes describing preventive health care initiatives and were asked to prioritize among these. They choose between “discrimination,” that is, allocating all resources on the better program, and “equality,” that is, dividing the resources equally between programs while reducing efficiency. Respondents were randomized to either of 4 survey versions that differed in terms of perspective (evaluator vs. decision maker) and expert status (expert vs. layperson) of the role to be adopted. Results . Subjects in the evaluator perspectives were more likely to choose equality over discrimination between patients as compared to those in the social decision-maker perspectives, regardless of expert status (odds ratios 2.09 and 2.03, P < 0.0001). Excess rates of equality choices in the evaluator frames resulted from passive acceptance of equality decisions and active revision of prioritization decisions. Conclusion . Preferences for an equal allocation of resources are strongly affected by decision-making perspective but stable across expert status of the adopted role.

The effect of a patient’s age on the social valuation of health services remains controversial, with empirical results varying in magnitude and implying a different age-value profile. This article employs a new methodology to re-examine these questions. Data were obtained from 2 independent Web-based surveys that administered the Relative Social Willingness to Pay instrument. In the first survey, the age of the patient receiving a life-saving service was varied. Patients were left with either poor mental or physical health. In the second survey, patient age was varied for a service that fully cured the patient’s poor mental or physical health. In total, therefore, 4 sets of age weights were obtained: weights for life-extending services with poor physical or mental health outcomes and weights for quality-of-life improvement for patients in poor mental or physical health. Results were consistent. Increasing age was associated in each case with a monotonic decrease in the social valuation of the services. The decrease in value was quantitatively small until age 60 years. By age 80 years, the social value of services had declined by about 50%. The decline commenced at an earlier age in the context of physical health, although the magnitude of the decrement by age 80 years was unrelated to the type of service. With 1 exception, there was little difference in the valuation of services by the age of the survey respondent. Respondents aged >60 years placed a lower, not higher, value on quality-of-life improvement for elderly individuals than other respondents. There was no difference in the valuation of life-extending services.