Adjusting to changes in health: implications for cost-effectiveness analysis

Preference-based measurement of mobility-related quality of life: developing the MobQoL-7D health state classification system

PURPOSE

Generic preference-based measures often lack validity in states of impaired mobility. Our aim was to derive a novel health state classification system from the MobQoL tool, for the purpose of preference-based measurement of states of impaired mobility.

MATERIALS AND METHODS

Data were collected through online and postal surveys. Respondents were recruited through the NHS, charitable organisations and HealthWise Wales. Statistical and psychometric analyses were used to assess the validity and reliability of the MobQoL tool. Exploratory factor analysis and Rasch analysis were used to determine dimensional structure and to select items for the MobQoL health state classification system.

RESULTS

Three hundred and forty-two respondents completed the survey. Respondents had a wide range of different mobility impairments. Nine of the MobQoL items demonstrated adequate validity and reliability. Exploratory factor analysis and Rasch analysis confirmed two sub-scales within the item structure: 1) physical and role functioning, and 2) mental wellbeing. Seven items were found to have adequate model fit and were retained in the final health state classification system, called the MobQoL-7D.

CONCLUSIONS

The MobQoL-7D contains seven dimensions of mobility-related quality of life: accessibility, contribution, pain/discomfort, independence, self-esteem, mood/emotions and anxiety. Population level preference weights are now needed for different states of mobility impairment.IMPLICATIONS FOR REHABILITATIONMobility impairment has distinct impacts on mental wellbeing and physical/role functioning.The MobQoL-7D will facilitate accurate preference-based outcome measurement in populations with impaired mobility, which in turn will promote methods of economic evaluation in this context.MobQoL-7D offers a concise and valid tool for rehabilitation professionals to measure and monitor mobility-related quality of life as part of routine clinical practice.

Patient and general public preferences for health states: A call to reconsider current guidelines

In economic evaluations of health care interventions, benefits are often expressed in terms of Quality-Adjusted Life-Years (QALYs). The QALY comprises length and quality of life into one measure which allows cross-disease comparability. The quality adjustment of the QALY is based on preferences for health states. An important normative choice is the question whose preferences for states of health we wish to capture. The answer to this question is directly related to the normative question regarding the appropriate maximand in health care decisions. Currently, preferences are commonly derived from the general public, rather than from actual patients. This choice, which can have large consequences on final outcomes of economic evaluations, has always been a topic of debate. This paper clarifies and furthers the discussion regarding the appropriate source of preferences for health state valuations, acknowledges the plurality of different perspectives, and argues that health economic guidelines could require analysis of benefit in terms of QALYs based on both patient and general public preferences.

Implications of an economic model of health states worse than dead

We introduce a formal definition of health equivalent to dead into a standard model to develop previously unrecognized insights. We find that the health state viewed as equivalent to dead will depend on an individual's health prognosis, probability of survival, and rate of time preference. Our work on maximum endurable time shows that using QALY scores based on long-run preferences to value health states that last for shorter periods can alter cardinal and ordinal valuations. Simulations show that errors of substantial magnitude in QALY scores can consequently result. We describe situations where biases are likely and identify possible corrections.

Health status and health dynamics in an empirical model of expected longevity

Expected longevity is an important factor influencing older individuals' decisions such as consumption, savings, purchase of life insurance and annuities, claiming of Social Security benefits, and labor supply. It has also been shown to be a good predictor of actual longevity, which in turn is highly correlated with health status. A relatively new literature on health investments under uncertainty, which builds upon the seminal work by Grossman [Grossman, M., 1972. On the concept of health capital and demand for health. Journal of Political Economy 80, 223-255] has directly linked longevity with characteristics, behaviors, and decisions by utility maximizing agents. Our empirical model can be understood within that theoretical framework as estimating a production function of longevity. Using longitudinal data from the Health and Retirement Study, we directly incorporate health dynamics in explaining the variation in expected longevities, and compare two alternative measures of health dynamics: the self-reported health change, and the computed health change based on self-reports of health status. In 38% of the reports in our sample, computed health changes are inconsistent with the direct report on health changes over time. And another 15% of the sample can suffer from information losses if computed changes are used to assess changes in actual health. These potentially serious problems raise doubts regarding the use and interpretation of the computed health changes and even the lagged measures of self-reported health as controls for health dynamics in a variety of empirical settings. Our empirical results, controlling for both subjective and objective measures of health status and unobserved heterogeneity in reporting, suggest that self-reported health changes are a preferred measure of health dynamics.

Optimal assignment of treatments to health states using a Markov decision model: an introduction to basic concepts

Assessing the cost effectiveness of a new health intervention often requires modelling to estimate the impact of the intervention on cost, survival and quality of life over the lifetime of a cohort of patients. Markov modelling is a methodology that is commonly employed to estimate these long-term costs and benefits. As commonly used, these models assume that the patients continue to get the treatments assigned regardless of the change in health states. In this paper, we describe an extension to the Markov modelling approach, called Markov decision modelling. Such a model starts with a set of health states and treatments and optimally assigns treatments to each of the health states. A Markov decision model can be used to identify the optimal treatment strategy not just for the initial disease state, but also as the disease state changes over time. We present a dynamic programming approach to identifying the optimal assignment of treatments, and illustrate this methodology using an example. The Markov decision modelling approach provides an efficient way of identifying optimal assignment of treatments to health states, but, like the standard Markov model, may be of limited use when probabilities of future events depend on past history in a complex fashion. Even with its limitations, Markov decision models offer an opportunity for health economists to inform healthcare decision-makers on how to modify current treatment pathways to incorporate new treatments as they become available.

Good research practices for cost-effectiveness analysis alongside clinical trials: the ISPOR RCT-CEA Task Force report

OBJECTIVES

A growing number of prospective clinical trials include economic end points. Recognizing the variation in methodology and reporting of these studies, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) chartered the Task Force on Good Research Practices: Randomized Clinical Trials-Cost-Effectiveness Analysis. Its goal was to develop a guidance document for designing, conducting, and reporting cost-effectiveness analyses conducted as a part of clinical trials.

METHODS

Task force cochairs were selected by the ISPOR Board of Directors. Cochairs invited panel members to participate. Panel members included representatives from academia, the pharmaceutical industry, and health insurance plans. An outline and a draft report developed by the panel were presented at the 2004 International and European ISPOR meetings, respectively. The manuscript was then submitted to a reference group for review and comment.

RESULTS

The report addresses issues related to trial design, selecting data elements, database design and management, analysis, and reporting of results. Task force members agreed that trials should be designed to evaluate effectiveness (rather than efficacy), should include clinical outcome measures, and should obtain health resource use and health state utilities directly from study subjects. Collection of economic data should be fully integrated into the study. Analyses should be guided by an analysis plan and hypotheses. An incremental analysis should be conducted with an intention-to-treat approach. Uncertainty should be characterized. Manuscripts should adhere to established standards for reporting results of cost-effectiveness analyses.

CONCLUSIONS

Trial-based cost-effectiveness studies have appeal because of their high internal validity and timeliness. Improving the quality and uniformity of these studies will increase their value to decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions.