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Many public health programmes require individuals to comply with particular behaviours that are novel to them, for example, acquiring new eating habits, accepting immunizations or taking a new medication. In particular, mass drug administration programmes only work to reduce the prevalence of a disease if significant proportions of the target population take the drug in question. In such cases, knowledge of the factors most likely to lead to high levels of compliance is crucial to the programme's success. Existing models of compliance tend to either address interpersonal, organizational or psychological causes independently. Here, the authors present a formal method for analysing relevant factors in the situational context of the compliant behaviour, identifying how these factors may interact within the individual. This method was developed from semantic network analysis, augmented to include environmental and demographic variables to show causal linkages-hence the name 'causal chain mapping'. The ability of this method to provide significant insight into the actual behaviour of individuals is demonstrated with examples from a mass drug administration for lymphatic filariasis in Alor District, Indonesia. The use of this method is likely to help identify key components influencing compliance, and thus make any public health programme reliant on the adoption of novel behaviours more effective.

Causal chain mapping: a novel method to analyse treatment compliance decisions relating to lymphatic filariasis elimination in Alor, Indonesia