پر کردن یک مدل اقتصادی با سلامت ارزشهای سودمند: حرکت به سوی بهتر تمرین

Background The methods used to estimate health-state utility values (HSUV) for multiple health conditions can produce very different values. Economic results generated using baselines of perfect health are not comparable with those generated using baselines adjusted to reflect the HSUVs associated with the health condition. Despite this, there is no guidance on the preferred techniques and little research describing the effect on cost per quality adjusted life-year (QALY) results when using the different methods. Methods Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health-state utility profiles (perfect health, no history of CVD, general population) in conjunction with models (minimum, additive, multiplicative) frequently used to approximate scores for health states with multiple health conditions. HSUVs are calculated using the EQ-5D UK preference-based algorithm. Results Assuming a baseline of perfect health ignores the natural decline in quality of life associated with age, overestimating the benefits of treatment. The results generated using baselines from the general population are comparable to those obtained using baselines from individuals with no history of CVD. The minimum model biases results in favor of younger-aged cohorts. The additive and multiplicative models give similar results. Conclusion Although further research in additional health conditions is required to support our findings, our results highlight the need for analysts to conform to an agreed reference case. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation.

A number of agencies, including the National Institute for Health and Clinical Excellence (NICE), require economic evidence to be presented in the form of cost-effectiveness analyses whereby health benefits are quantified by quality adjusted life-years (QALYs) [1]. QALYs are calculated by summing the time spent in a health state weighted by the health-state utility value (HSUV) associated with the health state, thus incorporating both length of survival and HSUVs into a single metric. Classification systems can produce a wide range of values for the same health state and the economic results generated using different systems are not always comparable [1]. Consequently, for submissions in the UK, the Institute advocate a preference for EQ-5D data with HSUVs obtained using UK population weights when available [1]. However, this is not sufficient to ensure consistency across appraisals because there is no guidance on appropriate baseline HSUVs that should be used to quantify the underlying health condition for patients entering the model [1]. If a baseline utility of perfect health (i.e., EQ-5D equals 1) is used to represent the absence of a health condition, the incremental QALYs gained by an intervention are inflated [2] and the results obtained using a baseline of perfect health are not comparable with those obtained when the baseline is adjusted for not having a particular health condition [3]. There is currently no consensus on baseline HSUVs used in economic evaluations. In addition, there is currently no directive on the method that should be used to combine HSUVs for multiple health condi- tions. Analysts are increasingly exploring the benefits of inter- ventions in individuals with several comorbid conditions. For example, HMG-CoA reductase inhibitors (statins) reduce both cardiovascular (CV) risk and rheumatoid arthritis (RA) disease activity; and an economic model exploring the benefits of statins in this population would include health states for patients with a history of both RA and cardiovascular disease (CVD) [4]. Because of strict exclusion criteria preventing patients with comorbidities entering clinical trials, it is unlikely that HSUVs will be available from patients with both health conditions. When HSUVs for the multiple health states are not available, approximate scores are estimated by combining data collected from patients with the individual health conditions. Three methods are frequently used: 1) additive; 2) multiplicative; and 3) minimum models. The additive and multiplicative models assume a constant absolute or proportional effect, respectively, while the minimum model applies a disutility that can vary depending on the baseline utility modeled. Research exploring the appropriateness of the techniques used to combine utility values is inconclusive. The additive and multiplicative models have been shown to produce similar results for individuals with both diabetes and thyroiditis [5]; the multiplicative model pro- duced accurate utilities for several comorbid conditions [6]; and the minimum model was advocated as the preferred methodol- ogy in two other studies [7,8]. Although literature describing minimum requirements for probabilistic analyses is growing [9], research exploring the basic principles involved in using HSUVs in economic models, and the implications for results generated from the models when using different techniques is scarce. The limited research undertaken in this area has explored the appropriateness of different baseline utilities and approximate HSUVs for multiple health conditions in isolation; and there is currently no consensus on the preferred methodologies when the two adjustments are undertaken together. We describe the results of a pilot study in which we explore the effect of using different baseline utility values and different techniques to estimate approximate HSUVs for multiple health conditions in combination. We use an existing economic model and data from the Health Survey for England to investigate the potential effect on policy decision-making using cost per QALY thresholds. The primary objective of the study is to instigate additional research in this area to provide a foundation for better practice in economic evaluations used to inform health care decision-makers in the UK and elsewhere.

Our results reinforce earlier recommendations and, until guide- lines are in place, we would recommend that data from the general population are used as approximate baseline utility mea- sures for individuals without the health condition under consid- eration if the actual data are not available. Although our findings demonstrate the additive and multiplicative models give similar results in CVD, additional research in other health conditions and datasets are required. The underlying principle behind using the same preference- based instrument for all economic evaluations is to enable com- parison across different interventions and health conditions. If this is to be realized, some consensus is needed on the most appropriate methods to populate the economic models. The methods used should be clearly described to inform policy decision-makers who are comparing results generated from dif- ferent evaluations.