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

Rowland, Thomas, Pike, Tom, Reaney-Wood, Sarah , Mills, Daniel and Burman, Oliver (2023) Using network analysis to detect associations between suspected painful health conditions and behaviour in dogs. The Veterinary Journal . ISSN 1090-0233

Full content URL: https://doi.org/10.1016/j.tvjl.2023.105954

Documents
Using network analysis to detect associations between suspected painful health conditions and behaviour in dogs
Pre-proof

Request a copy
[img] PDF
Rowland, T. et al. (2023) Using network analysis to detect associations between suspected painful health conditions and behaviour in dogs. The Veterinary Journal.pdf - Whole Document
Restricted to Repository staff only

9MB
Item Type:Article
Item Status:Live Archive

Abstract

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.

Keywords:Behaviour, Canine, Networks, Pain, Welfare
Subjects:D Veterinary Sciences, Agriculture and related subjects > D390 Veterinary Sciences not elsewhere classified
Divisions:College of Science > School of Life and Environmental Sciences > Department of Life Sciences
Related URLs:
ID Code:53470
Deposited On:17 Feb 2023 16:56

Repository Staff Only: item control page