Abstract
PurposeLittle is known about estimating utilities for comorbid (or ‘joint’) health states. Several joint health state prediction models have been suggested (for example, additive, multiplicative, best-of-pair, worst-of-pair, etc.), but no general consensus has been reached. The purpose of the study is to explore the relationship between health-related quality of life (HRQoL) and increasing numbers of diagnoses.MethodsWe analyzed a large dataset containing respondents’ ICD-9 diagnoses and preference-based HRQoL (EQ-5D and SF-6D). Data were stratified by the number of diagnoses, and mean HRQoL values were estimated. Several adjustments, accounting for the respondents’ age, sex, and the severity of the diagnoses, were carried out. Our analysis fitted additive and multiplicative models to the data and assessed model fit using multiple standard model selection methods.ResultsA total of 39,817 respondents were included in the analyses. Average HRQoL values were represented well by both linear and multiplicative models. Although results across all analyses were similar, adjusting for severity of diagnoses, age, and sex strengthened the linear model’s performance measures relative to the multiplicative model. Adjusted R2 values were above 0.99 for all analyses (i.e., all adjusted analyses, for both HRQoL instruments), indicating a robust result.ConclusionsAdditive and multiplicative models perform equally well within our analyses. A practical implication of our findings, based on the presumption that a linear model is simpler than an additive model, is that an additive model should be preferred unless there is compelling evidence to the contrary.
Highlights
Economic evaluation of healthcare interventions is typically carried out using quality-adjusted life years (QALYs) as the outcome measure
As an addition to direct empirical comparison of QALY gain of available treatment options, modeling of cost utility is becoming increasingly common, since modeling based on existing data is more flexible and affordable than tailoring clinical tests to every scenario of potential interest. Such modeling rests on extensive use of preexisting recorded values representing the mean healthrelated quality of life (HRQoL) loss associated with particular health conditions—so-called health-state utility values (HSUVs). This has created a demand for values for common ailments, which in turn has spurred on an effort to estimate catalogs of HSUVs associated with specific diagnoses [1, 2]
Without any preconceptions about the preferred functional form, the purpose of this study is to explore the relationship between mean HRQoL and increasing numbers of diagnoses
Summary
Economic evaluation of healthcare interventions is typically carried out using quality-adjusted life years (QALYs) as the outcome measure. The QALY combines length of life and health-related quality of life (HRQoL) in a single metric. As an addition to direct empirical comparison of QALY gain of available treatment options, modeling of cost utility is becoming increasingly common, since modeling based on existing data is more flexible and affordable than tailoring clinical tests to every scenario of potential interest. Such modeling rests on extensive use of preexisting recorded values representing the mean HRQoL loss associated with particular health conditions—so-called health-state utility values (HSUVs). Priority setting in health care is becoming an increasingly important field for policy makers as the medical frontier is advancing ahead of
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