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Development and evaluation of complex multicomponent interventions in public health 

Development and evaluation of complex multicomponent interventions in public health
Development and evaluation of complex multicomponent interventions in public health

Rona Campbell

and Chris Bonell

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date: 05 July 2022

This chapter examines the issues to be considered when developing and evaluating complex public health interventions and signposts where more detailed guidance can be found. It starts by considering what complexity means in this context including the contribution that systems theory has made to recent thinking about complexity and fidelity of intervention delivery. When developing complex interventions we suggest: (i) reading quantitative and qualitative research on similar interventions, preferably within systematic reviews; (ii) consulting stakeholders, including those that the intervention is intended to benefit, to help ensure its relevance, acceptability and ownership; (iii) considering using theory to inform the intervention design and developing a logic model to make explicit the mechanisms by which it will effect change; (iv) assessing whether the intervention could operate at more than one level (from individual through to policy) to increase its chances of success; and (v) reflecting on issues of equity and how the intervention could diminish and not widen health inequalities. In terms of evaluation, we recommend: (i) taking a phased approach where studies of efficacy or effectiveness build on early studies of feasibility and pilots; (ii) being clear as to whether a simple ‘does it work?’ answer is required or if a more nuanced ‘does it work and for whom and in what circumstance?’ is needed (we would advocated the latter as being a better way of building the public health evidence base but recognise that it requires more research effort and financial resource); (iii) using a randomised controlled design to answer a question about efficacy or effectiveness if possible, but using other designs such as non-randomised natural experiments or time-series studies where this is not feasible; (iv) embedding process evaluations within evaluations and employing both quantitative and qualitative methods; and (v) considering how a new intervention with evidence of effectiveness can be scaled up to ensure a population level impact that can be maintained.

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