When are person tradeoffs valid?

Deriving disability weights for the Netherlands: findings from the Dutch disability weights measurement study

BACKGROUND

The aims of this study were to establish national disability weights based on the health state preferences of a Dutch general population sample, examine the relation between results and respondent's characteristics, and compare disability weights with those estimated in the European disability weights study.

METHODS

In this cross-sectional study, a web-based survey was administered to a general population 18-75 years from the Netherlands. The survey included paired comparison questions. Paired comparison data were analysed using probit regression and located results onto the 0-to-1 disability weight scale using non-parametric regression. Bootstrapping was used to estimate 95% uncertainty intervals (95%UI). Spearman's correlation was used to investigate the relation of probit regression coefficients between respondent's characteristics.

RESULTS

3994 respondents completed the questionnaire. The disability weights ranged from 0.007 (95%UI: 0.003-0.012) for mild distance vision impairment to 0.741 (95% UI: 0.498-0.924) for intensive care unit admission. Spearman's correlation of probit coefficients between sub-groups based on respondent's characteristics were all above 0.95 (p < 0.001). Comparison of disability weights of 140 health states that were included in the Dutch and European disability weights study showed a high correlation (Spearman's correlation: 0.942; p < 0.001); however, for 76 (54.3%) health states the point estimate of the Dutch disability weight fell outside of the 95%UI of the European disability weights.

CONCLUSIONS

Respondent's characteristics had no influence on health state valuations with the paired comparison. However, comparison of the Dutch disability weights to the European disability weights indicates that health state preferences of the general population of the Netherlands differ from those of other European countries.

Quality- and productivity-adjusted life years: From QALYs to PALYs and beyond

We develop a unified framework for the measurement and valuation of health and productivity. Within this framework, we characterize evaluation functions allowing for compromises between the classical quality-adjusted life years (QALYs) and its polar productivity-adjusted life years (PALYs). Our framework and characterization results provide a new normative basis for the economic evaluation of health care interventions, as well as occupational health and safety policies, aimed to impact both health and productivity of individuals.

Disability Weights Measurement for 228 Causes of Disease in the Korean Burden of Disease Study 2012

Disability weight for each disease plays a key role in combining years lived with disability and years of life lost in disability adjusted life year. For the Korean Burden of Disease 2012 study, we have conducted a re-estimation of disability weights for causes of disease by adapting the methodology of a recent Global Burden of Disease study. Our study was conducted through a self-administered web-based survey using a paired comparison (PC) as the main valuation method. A total of 496 physicians and medical college students who were attending in third or fourth grade of a regular course conducted the survey. We applied a probit regression on the PC data and computed the predicted probabilities of each cause of disease from the coefficient estimates of the probit regression. We used 'being dead (1)' and 'full health (0)' as anchor points to rescale the predicted probability of each cause of disease on a scale of 0 to 1. By this method, disability weights for a total of 228 causes of disease were estimated. There was a fairly high correlation between the disability weights of overlapping causes of disease from this study and a previous South Korean study despite the differences in valuation methods and time periods. In conclusion, we have shown that disability weights can be estimated based on a PC by including 'full health' and 'being dead' as anchor points without resorting to a person trade-off. Through developments in the methodology of disability weights estimation from this study, disability weights can be easily estimated and continuously revised.

How Many Alternatives Can Be Ranked? A Comparison of the Paired Comparison and Ranking Methods

OBJECTIVES

To determine the feasibility of converting ranking data into paired comparison (PC) data and suggest the number of alternatives that can be ranked by comparing a PC and a ranking method.

METHODS

Using a total of 222 health states, a household survey was conducted in a sample of 300 individuals from the general population. Each respondent performed a PC 15 times and a ranking method 6 times (two attempts of ranking three, four, and five health states, respectively). The health states of the PC and the ranking method were constructed to overlap each other. We converted the ranked data into PC data and examined the consistency of the response rate. Applying probit regression, we obtained the predicted probability of each method. Pearson correlation coefficients were determined between the predicted probabilities of those methods. The mean absolute error was also assessed between the observed and the predicted values.

RESULTS

The overall consistency of the response rate was 82.8%. The Pearson correlation coefficients were 0.789, 0.852, and 0.893 for ranking three, four, and five health states, respectively. The lowest mean absolute error was 0.082 (95% confidence interval [CI] 0.074-0.090) in ranking five health states, followed by 0.123 (95% CI 0.111-0.135) in ranking four health states and 0.126 (95% CI 0.113-0.138) in ranking three health states.

CONCLUSIONS

After empirically examining the consistency of the response rate between a PC and a ranking method, we suggest that using five alternatives in the ranking method may be superior to using three or four alternatives.

Cost-Value Analysis and the SAVE: A Work in Progress, But an Option for Localised Decision Making?

BACKGROUND

Cost-value analysis aims to address the limitations of the quality-adjusted life-year (QALY) by incorporating the strength of public concerns for fairness in the allocation of scarce health care resources. To date, the measurement of value has focused on equity weights to reflect societal preferences for the allocation of QALY gains. Another approach is to use a non-QALY-based measure of value, such as an outcome 'equivalent to saving the life of a young person' (a SAVE).

OBJECTIVE

This paper assesses the feasibility and validity of using the SAVE as a measure of value for the economic evaluation of health care technologies.

METHODS

A web-based person trade-off (PTO) survey was designed and implemented to estimate equivalent SAVEs for outcome events associated with the progression and treatment of early-stage breast cancer. The estimated equivalent SAVEs were applied to the outputs of an existing decision analytic model for early breast cancer.

RESULTS

The web-based PTO survey was undertaken by 1094 respondents. Validation tests showed that 68 % of eligible responses revealed consistent ordering of responses and 32 % displayed ordinal transitivity, while 37 % of respondents showing consistency and ordinal transitivity approached cardinal transitivity. Using consistent and ordinally transitive responses, the mean incremental cost per SAVE gained was £ 3.72 million.

CONCLUSION

Further research is required to improve the validity of the SAVE, which may include a simpler web-based survey format or a face-to-face format to facilitate more informed responses. A validated method for estimating equivalent SAVEs is unlikely to replace the QALY as the globally preferred measure of outcome, but the SAVE may provide a useful alternative for localized decision makers with relatively small, constrained budgets-for example, in programme budgeting and marginal analysis.

A new axiomatic approach to the evaluation of population health

In this paper we explore the implications of normative principles for the evaluation of population health. We formalize those principles as axioms for social preferences over distributions of health for a given population. We single out several focal population health evaluation functions, which represent social preferences, as a result of combinations of those axioms. Our results provide new rationale for popular theories in health economics, such as the unweighted aggregation of quality-adjusted life years (QALYs) or healthy years equivalents (HYEs) and generalizations of the two, aimed to capture concerns for distributive justice, without resorting to controversial assumptions on individual preferences.

How to Aggregate Health? Separability and the Effect of Framing

Background. Unweighted summation or quality-adjusted life year (QALY) utilitarianism is the most common way to aggregate health benefits in a cost-effectiveness analysis. A key qualitative principle underlying QALY utilitarianism is separability: those individuals unaffected by a policy choice should not influence the policy choice. Separability also underlies several of the alternatives for QALY utilitarianism that have been proposed. Objectives. To test separability and to test whether the support for separability is affected by the framing of the choice questions. Methods. In 2 experiments, 345 student subjects (162 in the first experiment, and 183 in the second experiment) were asked to select 1 of 2 possible treatments, with each treatment resulting in a different distribution of health across individuals. The only aspect that varied across choice questions was the state of the patients whose health was unaffected by the act of choosing a policy. In each experiment, we used 2 frames. In the implicit frame, it was implied but not plainly expressed what outcomes the treatments had in common. In the explicit frame, common outcomes of the 2 treatments were directly stated. The 2 experiments differed in the way the explicit frame was presented (verbal v. numerical). Results. The support for separability was significantly greater in the explicit frame. The proportion of violations in the implicit frame was 44% in Experiment 1 and 31% in Experiment 2, while in the explicit frame, the proportion of violations was 28% in Experiment 1 and 8% in Experiment 2. Conclusions. Framing affected the support for separability, raising issues as to whether it is possible to achieve a canonical representation of social choices.