Laboratory Surveillance of Bovine Brucellosis: Predictors of Rose Bengal Test Positivity in Mpumalanga Province, South Africa
1
Health and Society, Wits University, Parktown, South Africa
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
BACKGROUND:
Bovine brucellosis remains an endemic zoonotic disease in South Africa, posing sustained risks to livestock productivity and public health. Routine laboratory surveillance generates large volumes of diagnostic data; however, these data are seldom analysed to inform risk-based surveillance and outbreak preparedness at sub-provincial level. This study used routine Rose Bengal Test (RBT) laboratory data to identify temporal, seasonal, and spatial predictors of bovine brucellosis detection in Mpumalanga Province.
METHODS:
A retrospective observational analysis was conducted using bovine brucellosis laboratory records from the Mpumalanga Provincial Veterinary Laboratory collected between January 2021 and December 2024. The dataset comprised 568 batch submissions representing 67 974 serum samples. RBT positivity was analysed as the outcome of interest. Temporal (year), seasonal (summer, autumn, winter, spring), and spatial (local municipality area) factors were examined. Descriptive analyses were followed by bivariate, multivariable, and mixed-effects logistic regression models to estimate adjusted odds ratios (AORs) and account for clustering within municipalities.
RESULTS:
Overall RBT positivity was 9.1% (6 182/67 974). Marked inter-annual variation was observed, with significantly higher odds of RBT positivity in 2023 compared with 2021 (AOR = 2.47; 95% CI: 2.27–2.68). Strong seasonal patterns were evident, with increased odds in spring (AOR = 1.80; 95% CI: 1.65–1.97) and reduced odds in autumn and winter relative to summer. Spatial heterogeneity persisted after adjustment, with high-risk municipalities showing higher odds of RBT positivity than low-risk areas (AOR = 1.21; 95% CI: 1.12–1.30). Mixed-effects modelling confirmed significant residual municipality-level variation.
CONCLUSIONS:
Routine laboratory surveillance data can be effectively leveraged to identify outbreak-prone periods and locations for bovine brucellosis. Integrating temporal and spatial risk profiling into surveillance systems supports early warning, targeted monitoring, and more efficient allocation of veterinary and public health resources within a One Health framework.
Bovine brucellosis remains an endemic zoonotic disease in South Africa, posing sustained risks to livestock productivity and public health. Routine laboratory surveillance generates large volumes of diagnostic data; however, these data are seldom analysed to inform risk-based surveillance and outbreak preparedness at sub-provincial level. This study used routine Rose Bengal Test (RBT) laboratory data to identify temporal, seasonal, and spatial predictors of bovine brucellosis detection in Mpumalanga Province.
METHODS:
A retrospective observational analysis was conducted using bovine brucellosis laboratory records from the Mpumalanga Provincial Veterinary Laboratory collected between January 2021 and December 2024. The dataset comprised 568 batch submissions representing 67 974 serum samples. RBT positivity was analysed as the outcome of interest. Temporal (year), seasonal (summer, autumn, winter, spring), and spatial (local municipality area) factors were examined. Descriptive analyses were followed by bivariate, multivariable, and mixed-effects logistic regression models to estimate adjusted odds ratios (AORs) and account for clustering within municipalities.
RESULTS:
Overall RBT positivity was 9.1% (6 182/67 974). Marked inter-annual variation was observed, with significantly higher odds of RBT positivity in 2023 compared with 2021 (AOR = 2.47; 95% CI: 2.27–2.68). Strong seasonal patterns were evident, with increased odds in spring (AOR = 1.80; 95% CI: 1.65–1.97) and reduced odds in autumn and winter relative to summer. Spatial heterogeneity persisted after adjustment, with high-risk municipalities showing higher odds of RBT positivity than low-risk areas (AOR = 1.21; 95% CI: 1.12–1.30). Mixed-effects modelling confirmed significant residual municipality-level variation.
CONCLUSIONS:
Routine laboratory surveillance data can be effectively leveraged to identify outbreak-prone periods and locations for bovine brucellosis. Integrating temporal and spatial risk profiling into surveillance systems supports early warning, targeted monitoring, and more efficient allocation of veterinary and public health resources within a One Health framework.
| eISSN: | 2654-1459 |
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