Evidence map›Paper›PMID 34914010›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2022

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

Paolo Candio, Koen B Pouwels, David Meads, Andrew J Hill, Laura Bojke, Claire Williams

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Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Paolo CandioCentre for Economics of Obesity, Institute of Applied Health Research, University of Birmingham, Birmingham, B15 2TT, UK. p.candio@bham.ac.uk.ORCID http://orcid.org/0000-0003-1521-088X
Koen B PouwelsHealth Economics Research Centre, University of Oxford, Oxford, UK.
David MeadsLeeds Institute of Health Sciences, University of Leeds, Leeds, UK.
Andrew J HillLeeds Institute of Health Sciences, University of Leeds, Leeds, UK.
Laura BojkeCentre for Health Economics, University of York, York, UK.
Claire WilliamsHealth Economics Research Centre, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeRecent methodological reviews of evaluations of behaviour change interventions in public health have highlighted that the decay in effectiveness 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 effectiveness, this framework inherently lends itself to strong model simplifications. 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 offering a practical guide on how to model decay in effectiveness using a continuous-time Markov chain (CTMC)-based approach.

methodsA 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 effects and improving model transparency.

conclusionsThe use of CTMC should be considered in evaluations where decay in effectiveness 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.

Indexed as

Public HealthCost-Benefit AnalysisHumansMarkov ChainsUncertaintyDecision-makingEffect decayMathematical modellingPublic healthStructural uncertainty

Identifiers

PMID34914010
PMCPMC9395462

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.