Modelling decay in effectiveness for evaluation of behaviour change interventions: a tutorial for public health economists

Journal article

Background and purpose Recent methodological reviews of evaluations of behaviour change interventions in public health have highlighted that the decay in efectiveness over time has been mostly overlooked, potentially leading to suboptimal decision-making. While, in principle, discrete-time Markov chains—the most commonly used modelling approach—can be adapted to account for decay in efectiveness, this framework inherently lends itself to strong model simplifcations. The application of formal and more appropriate modelling approaches has been supported, but limited progress has been made to date. The purpose of this paper is to encourage this shift by ofering a practical guide on how to model decay in efectiveness using a continuous-time Markov chain (CTMC)-based approach. Methods A CTMC approach is demonstrated, with a contextualized tutorial being presented to facilitate learning and uptake. A worked example based on the stylized case study in physical activity promotion is illustrated with accompanying R code. Discussion The proposed framework presents a relatively small incremental change from the current modelling practice. CTMC represents a technical solution which, in absence of relevant data, allows for formally testing the sensitivity of results to assumptions regarding the long-term sustainability of intervention efects and improving model transparency. Conclusions The use of CTMC should be considered in evaluations where decay in efectiveness is likely to be a key factor to consider. This would enable more robust model-based evaluations of population-level programmes to promote behaviour change and reduce the uncertainty surrounding the decision to invest in these public health interventions

Authors: Candio, P; Pouwels, KB; Meads, D; Hill, AJ; Bojke, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: European Journal of Health Economics
• Volume: 23
• Issue: 7
• Pages: 1151-1157
• Publication date: 2021-12-16
• DOI: 10.1007/s10198-021-01417-7
• EISSN: 1618-7601
• ISSN: 1618-7598
• Language: English
• Keywords: Transparency (behavior); Machine learning; Computer science; Psychological intervention; Stylized fact; Medicine; Economics; Risk analysis (engineering); Markov chain; Management science