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 role of cognition in cost-effectiveness analyses of behavioral interventions

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>Data sources were searched for publications before May 2011.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

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.</p

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 promotio

Data-based decision making for teacher and student learning: a psychological perspective on the role of the teacher

Data-based decision-making has the potential to increase student achievement results. Data-based decision-making can be defined as teachers’ systematic analysis of data sources in order to study and adapt their educational practices for the purpose of maximizing learning results. Teachers must apply the findings from their data use to their personal teaching activities. Therefore, data-based decision-making may be influenced by individual teachers' psychological characteristics. The present study aimed to explore which psychological factors contribute to teachers’ data use in a Dutch primary school context. A questionnaire-based quantitative methodology was employed. We included the following psychological constructs: affective and instrumental attitudes, perceived control, social norms, self-efficacy, collective efficacy, and intentions regarding data use. Results of the path analysis showed that perceived control, instrumental attitude, and intention regarding data use all significantly influenced data use. Additionally, intention was found to be a mediator of the relation between affective attitude and data use. Interventions aimed at data-based decision-making should take these psychological factors into account to increase teachers’ implementation of data-based decision-making for instruction and, consequently, educational quality

A lesson study professional learning network in secondary education

The penultimate case study In chapter 8 explores a networked lesson study model from the Netherlands, and examines its impact on teacher outcomes over three-year period. As Siebrich de Vries and Rilana Prenger explains, the aim of their project was to develop Dutch language teachers' activating and differentiating skills in the domain of rewarding in secondary education. The chapter describes the key aspects as well as the structure of the authors' networked lesson study approach and assesses the relevant processes and influencing factors that led to its success