Dr K Govindasamy (University of Pretoria, Faculty of Veterinary Science, Department of Tropical Diseases)
Dr K Govindasamy (University of Pretoria, Faculty of Veterinary Science, Department of Tropical Diseases) - Primary Author
Dr B.N. Harris (University of Pretoria, Faculty of Health Sciences, School of Health Systems and Public Health )
Prof D.A. Abernathy (University of Pretoria, Faculty of Veterinary Science, Department for Wildlife Studies)
Prof E. Etter (University of Pretoria, Faculty of Veterinary Science, Department of Production Animals)
Prof P. Thompson (University of Pretoria, Faculty of Veterinary Science, Department of Production Animals)
Background: Brucellosis is a neglected zoonotic disease impacting global development and health. The elimination of brucellosis in livestock to prevent human brucellosis is embedded in complex socio-economic, epidemiological, public and animal health systems. Risk factors for bovine brucellosis are determined using multivariable logistic regression models. However, these models fail to separate the influence of distal determinants or the complex interrelations between multiple factors that result in brucellosis and the effects thereof at the human-cattle-farm interface.
Aim/Objectives: To model the complex interactions between proximate and distal determinants for bovine brucellosis and the effect of these on herd production variables and cattle handler symptoms of brucellosis.
Materials/Methods: Cattle farms participating in the provincial state veterinary services bovine brucellosis control program, between 2014 and 2016 were eligible for recruitment into the study. Farms were classified as either a case or control farms, where a cattle herd with 2 or more serological cattle reactors on RBT and confirmatory CFT > 60IU/ml was considered a case herd, and a control herd was one with a history of no serological reaction. Farms were also categorized into a zoonotic case farm, if one or more of the cattle handlers tested seropositive to the ELISAIgG test, or a zoonotic control farm, if no cattle handler tested seropositive on the ELISAIgG. A case control study design, using structured questionnaires and a combination of on-field and telephone interviews, was used on these farms to determine herd level risk factors for bovine brucellosis. A cross sectional sero-survey of cattle handlers on all case farms and a random selection of control farms was conducted from March to November 2016. Risk factors for four outcomes: farm study status, farm parcel status, zoonotic study status and herd symptoms status were systematically selected through univariate analysis for inclusion into a multilevel mixed-effects logistic regression model, with the group variable being farm parcel status. Variables with Odds Ratios p < 0.2 were retained. All variables selected through the univariate analysis were related to each other in a causal loop diagram, using Vensim PLE (2019), to generate a hypothetical causal web. A GSEM path model was then constructed to test this model using STATA 14.
Results: Human and herd management risk factors identified in this study, reveal that movement of cattle into a herd (p = 0.034), the presence of antelope on a farm (p = 0.010), and government sponsored farms (p = 0.004) are significantly associated to case farms. Herds were significantly clustered by farm parcel status (LR test vs. logistic model: chibar2(01) = 11.10; p >= chibar2 = 0.0004). Exposure of one or more cattle handler on a farm (ElisaIgG seropositive), was significantly associated to the presence of goats on the farm (p = 0.043). The GSEM pathway model of interaction between distal and proximate determinants is described (log likelihood = - 714.220).
Discussion: This complex causal web of brucellosis at the human-cattle-farm interface can be used to illuminate options for multi-faceted, multi-stakeholder, collaborative awareness and risk mitigation strategy.