Using network analysis to detect associations between suspected painful health conditions and behaviour in dogs

Pain associated with chronic health conditions in non-human animals is an important animal welfare issue. To identify animals in pain and develop an understanding of the mechanisms by which pain affects behaviour, it is therefore important to establish the direct behavioural effects of painful health conditions. We reanalyse data from a cross-sectional survey that considered the presence or absence of a painful condition in dogs and quantified their affective predispositions using the Positive and Negative Activation Scale (PANAS). By applying ideas from network theory, we conceptualise pain as a stressor that exerts direct effects on a network of interacting behavioural variables, and subsequently estimated a network model of conditional dependence relations. Painful health conditions were positively conditionally associated with age (posterior mean partial correlation, ρ = 0.34; standard deviation [SD]=0.05), and negatively conditionally associated with the item 'your dog is full of energy' (ρ = -0.14; SD=0.06). In turn, the energy item was conditionally associated with other PANAS items which were marginally associated with pain, such as items representing ease of excitability and persistence in play. This suggests these marginal effects might be indirectly mediated via the energy item. Further, utilising the posterior predictive distribution we estimated that the median conditional probability (95% credible interval) of a painful health condition given an answer of 'strongly agree' on the energy item was 0.08 (0.05, 0.11), which increased to 0.32 (0.09, 0.58), given a response of 'strongly disagree'. This provides a potentially clinically useful interpretation of the conditional dependencies detected in the network.