Dutch Tariff for the Five-Level Version of EQ-5D.

BACKGROUND: In 2009, a new version of the EuroQol five-dimensional questionnaire (EQ-5D) was introduced with five rather than three answer levels per dimension. This instrument is known as the EQ-5D-5L. To make the EQ-5D-5L suitable for use in economic evaluations, societal values need to be attached to all 3125 health states. OBJECTIVES: To derive a Dutch tariff for the EQ-5D-5L. METHODS: Health state values were elicited during face-to-face interviews in a general population sample stratified for age, sex, and education, using composite time trade-off (cTTO) and a discrete choice experiment (DCE). Data were modeled using ordinary least squares and tobit regression (for cTTO) and a multinomial conditional logit model (for DCE). Model performance was evaluated on the basis of internal consistency, parsimony, goodness of fit, handling of left-censored values, and theoretical considerations. RESULTS: A representative sample (N = 1003) of the Dutch population participated in the valuation study. Data of 979 and 992 respondents were included in the analysis of the cTTO and the DCE, respectively. The cTTO data were left-censored at -1. The tobit model was considered the preferred model for the tariff on the basis of its handling of the censored nature of the data, which was confirmed through comparison with the DCE data. The predicted values for the EQ-5D-5L ranged from -0.446 to 1. CONCLUSIONS: This study established a Dutch tariff for the EQ-5D-5L on the basis of cTTO. The values represent the preferences of the Dutch population. The tariff can be used to estimate the impact of health care interventions on quality of life, for example, in context of economic evaluations.

Dealing With Missing Behavioral Endpoints in Health Promotion Research by Modeling Cognitive Parameters in Cost-Effectiveness Analyses of Behavioral Interventions: A Validation Study.

Cost-effectiveness analyses (CEAs) of behavioral interventions typically use physical outcome criteria. However, any progress in cognitive antecedents of behavior change may be seen as a beneficial outcome of an intervention. The aim of this study is to explore the feasibility and validity of incorporating cognitive parameters of behavior change in CEAs. The CEA from a randomized controlled trial on smoking cessation was reanalyzed. First, relevant cognitive antecedents of behavior change in this dataset were identified. Then, transition probabilities between combined states of smoking and cognitions at 6 weeks and corresponding 6 months smoking status were obtained from the dataset. These rates were extrapolated to the period from 6 to 12 months in a decision analytic model. Simulated results were compared with the 12 months' observed cost-effectiveness results. Self-efficacy was the strongest time-varying predictor of smoking cessation. Twelve months' observed CEA results for the multiple tailoring intervention versus usual care showed € 3188 had to be paid for each additional quitter versus € 10,600 in the simulated model. The simulated CEA showed largely similar but somewhat more conservative results. Using self-efficacy to enhance the estimation of the true behavioral outcome seems a feasible and valid way to estimate future cost-effectiveness.

Exploring Outcomes to Consider in Economic Evaluations of Health Promotion Programs: What Broader Non-Health Outcomes Matter Most?

BACKGROUND: Attention is increasing on the consideration of broader non-health outcomes in economic evaluations. It is unknown which non-health outcomes are valued as most relevant in the context of health promotion. The present study fills this gap by investigating the relative importance of non-health outcomes in a health promotion context. METHOD: We investigated the relative importance of ten non-health outcomes of health promotion programs not commonly captured in QALYs. Preferences were elicited from a sample of the Dutch general public (N = 549) by means of a ranking task. These preferences were analyzed using Borda scores and rank-ordered logit models. RESULTS: The relative order of preference (from most to least important) was: self-confidence, insights into own (un)healthy behavior, perceived life control, knowledge about a certain health problem, social support, relaxation, better educational achievements, increased labor participation and work productivity, social participation, and a reduction in criminal behavior. The weight given to a particular non-health outcome was affected by the demographic variables age, gender, income, and education. Furthermore, in an open question, respondents mentioned a number of other relevant non-health outcomes, which we classified into outcomes relevant for the individual, the direct social environment, and for society as a whole. CONCLUSION: The study provides valuable insights in the non-health outcomes that are considered as most important by the Dutch general population. Ideally, researchers should include the most important non-health outcomes in economic evaluations of health promotion.

A comparison of time-varying covariates in two smoking cessation interventions for cardiac patients.

The aim of the study was to explore the time-varying contribution of social cognitive determinants of smoking cessation following an intervention on cessation. Secondary analyses were performed on data from two comparable randomized controlled trials on brief smoking cessation interventions for cardiac in- and outpatients. Cox regression with time-varying covariates was applied to examine the predictive cognitions for smoking cessation over time. Both samples showed self-efficacy and intention-to-quit to be strong time-varying indicators of smoking cessation during the full 1-year follow-up period, and during the post-treatment phase in particular. Less consistently, time-varying cons of quitting and social influence were also found to be associated with smoking cessation, depending on the sample and type of intervention. Self-efficacy and intention-to-quit were the major covariates and positively related to smoking cessation over time among cardiac patients, in line with social-cognitive theories. Interestingly, both cognitive constructs appeared to act with some delay. Apparently, smoking cessation is a lengthy process in which the interplay between self-efficacy (and intention indirectly) and quitting behavior will largely determine long-term maintenance of abstinence. The presented time-varying analyses seem a valid and feasible way to underpin trajectories of cognitions in datasets with a limited number of time intervals.

Cost-effectiveness of an intensive smoking cessation intervention for COPD outpatients.

INTRODUCTION: To determine the cost-effectiveness of a high-intensity smoking cessation program (SmokeStop Therapy; SST) versus a medium-intensity treatment (Minimal Intervention Strategy for Lung patients [LMIS]) for chronic obstructive pulmonary disease outpatients. METHODS: The cost-effectiveness analysis was based on a randomized controlled trial investigating the effectiveness of the SST compared with the LMIS with 12-month follow-up. The primary outcome measure was the cotinine-validated continuous abstinence rate based on intention to treat. A health care perspective was adopted, with outcomes assessed in terms of (incremental) additional quitters gained, exacerbations prevented, and hospital days prevented. Health care resource use, associated with smoking cessation, was collected at baseline and 12 months after the start of the interventions. Monte Carlo simulations were performed to evaluate the robustness of the results. RESULTS: The average patient receiving SST generated €581 in health care costs, including the costs of the smoking cessation program, versus €595 in the LMIS. The SST is also associated with a lower average number of exacerbations (0.38 vs. 0.60) and hospital days (0.39 vs. 1) per patient and a higher number of quitters (20 vs. 9) at lower total costs. This leads to a dominance of the SST compared with the LMIS. CONCLUSIONS: The high-intensive SST is more cost-effective than the medium-intensive LMIS after 1 year. This is associated with cost savings per additional quitter, prevented exacerbations, and hospital days at lower or equal costs.

The role of cognition in cost-effectiveness analyses of behavioral interventions.

BACKGROUND: Behavioral interventions typically focus on objective behavioral endpoints like weight loss and smoking cessation. In reality, though, achieving full behavior change is a complex process in which several steps towards success are taken. Any progress in this process may also be considered as a beneficial outcome of the intervention, assuming that this increases the likelihood to achieve successful behavior change eventually. Until recently, there has been little consideration about whether partial behavior change at follow-up should be incorporated in cost-effectiveness analyses (CEAs). The aim of this explorative review is to identify CEAs of behavioral interventions in which cognitive outcome measures of behavior change are analyzed. METHODS: Data sources were searched for publications before May 2011. RESULTS: Twelve studies were found eligible for inclusion. Two different approaches were found: three studies calculated separate incremental cost-effectiveness ratios for cognitive outcome measures, and one study modeled partial behavior change into the final outcome. Both approaches rely on the assumption, be it implicitly or explicitly, that changes in cognitive outcome measures are predictive of future behavior change and may affect CEA outcomes. CONCLUSION: Potential value of cognitive states in CEA, as a way to account for partial behavior change, is to some extent recognized but not (yet) integrated in the field. In conclusion, CEAs should consider, and where appropriate incorporate measures of partial behavior change when reporting effectiveness and hence cost-effectiveness.

Moving beyond a limited follow-up in cost-effectiveness analyses of behavioral interventions.

BACKGROUND: Cost-effectiveness analyses of behavioral interventions typically use a dichotomous outcome criterion. However, achieving behavioral change is a complex process involving several steps towards a change in behavior. Delayed effects may occur after an intervention period ends, which can lead to underestimation of these interventions. To account for such delayed effects, intermediate outcomes of behavioral change may be used in cost-effectiveness analyses. The aim of this study is to model cognitive parameters of behavioral change into a cost-effectiveness model of a behavioral intervention. METHODS: The cost-effectiveness analysis (CEA) of an existing dataset from an RCT in which an high-intensity smoking cessation intervention was compared with a medium-intensity intervention, was re-analyzed by modeling the stages of change of the Transtheoretical Model of behavioral change. Probabilities were obtained from the dataset and literature and a sensitivity analysis was performed. RESULTS: In the original CEA over the first 12 months, the high-intensity intervention dominated in approximately 58% of the cases. After modeling the cognitive parameters to a future 2nd year of follow-up, this was the case in approximately 79%. CONCLUSION: This study showed that modeling of future behavioral change in CEA of a behavioral intervention further strengthened the results of the standard CEA. Ultimately, modeling future behavioral change could have important consequences for health policy development in general and the adoption of behavioral interventions in particular.

The effects of mindfulness-based stress reduction therapy on mental health of adults with a chronic medical disease: a meta-analysis.

OBJECTIVES: The objective of this study was to examine the effectiveness of mindfulness-based stress reduction (MBSR) on depression, anxiety and psychological distress across populations with different chronic somatic diseases. METHODS: A systematic review and meta-analysis were performed to examine the effects of MBSR on depression, anxiety, and psychological distress. The influence of quality of studies on the effects of MBSR was analyzed. RESULTS: Eight published, randomized controlled outcome studies were included. An overall effect size on depression of 0.26 was found, indicating a small effect of MBSR on depression. The effect size for anxiety was 0.47. However, quality of the studies was found to moderate this effect size. When the studies of lower quality were excluded, an effect size of 0.24 on anxiety was found. A small effect size (0.32) was also found for psychological distress. CONCLUSIONS: It can be concluded that MBSR has small effects on depression, anxiety and psychological distress in people with chronic somatic diseases. Integrating MBSR in behavioral therapy may enhance the efficacy of mindfulness based interventions.