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

University of Twente, Department of Psychology, Health and Technology, PO Box 217, 7500 AE Enschede, The Netherlands. .
Cost Effectiveness and Resource Allocation (Impact Factor: 0.87). 03/2012; 10(1):3. DOI: 10.1186/1478-7547-10-3
Source: PubMed


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.
Data sources were searched for publications before May 2011.
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.
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.

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Available from: Rilana Prenger, Oct 04, 2015
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    • "First, many intervention studies have a relatively short follow-up period of 6 months or less. Delayed behavior change can occur after a study period ends, which may lead to biased (cost-)effectiveness results (Prenger et al., 2012, 2013; Smith et al., 2007; Wagner and Goldstein, 2004). When people attempt to change habitual behaviors, the likelihood of relapsing to their old habit after a while is high. "
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    ABSTRACT: 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.
    Health Economics 12/2014; DOI:10.1002/HEC.3119 · 2.23 Impact Factor
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    ABSTRACT: 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.
    BMC Health Services Research 07/2015; 15(266). DOI:10.1186/s12913-015-0908-y · 1.71 Impact Factor