Comparison between generalized linear modelling and additive Bayesian network; identification of factors associated with the incidence of antibodies against Leptospira interrogans sv Pomona in meat workers in New Zealand

Abstract

BackgroundAdditive Bayesian Network (ABN) is a graphical model which extends Generalized Linear Modelling (GLM) to multiple dependent variables. The present study compares results from GLM with those from ABN analysis used to identify factors associated with Leptospira interrogans sv Pomona (Pomona) infection by exploring the advantages and disadvantages of these two methodologies, to corroborate inferences informing health and safety measures at abattoirs in New Zealand (NZ). Methodology and findingsIn a cohort study in four sheep slaughtering abattoirs in NZ, sera were collected twice a year from 384 meat workers and tested by Microscopic Agglutination with a 91% sensitivity and 94% specificity for Pomona.The study primarily addressed the effect of work position, personal protective equipment (PPE) and non-work related exposures such as hunting on a new infection with Pomona. Significantly associated with Pomona were “Work position” and two “Abattoirs” (GLM), and “Work position” (ABN). The odds of Pomona infection (OR, [95% CI]) was highest at stunning and hide removal (ABN 41.0, [6.9–1044.2]; GLM 57.0, [6.9–473.3]), followed by removal of intestines, bladder, and kidneys (ABN 30.7, [4.9–788.4]; GLM 33.8, [4.2–271.1]). Wearing a facemask, glasses or gloves (PPE) did not result as a protective factor in GLM or ABN. Conclusions/SignificanceThe odds of Pomona infection was highest at stunning and hide removal. PPE did not show any indication of being protective in GLM or ABN. In ABN all relationships between variables are modelled; hence it has an advantage over GLM due to its capacity to capture the natural complexity of data more effectively.