Zoonoses and public health · 2026
Bayesian Estimation of True Prevalence and Associated Risk Factors for Leptospira spp. Among Slaughterhouse Workers and Slaughtered Cattle in the Bahr El Ghazal Region of South Sudan.
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Onafruo David, Klein Jörn, Kostoulas Polychronis, Erume Joseph, Kokas Ikwap, Jubara Ambrose, Sánchez Romano Javier, Odoch Terence, Kitale Estella, Marin Peter, Sabbath Esther, Kankya Clovice
Аннотация
INTRODUCTION: Leptospirosis is a major but under-reported zoonotic disease, and epidemiological data from South Sudan remain limited. This study estimated the true prevalence of Leptospira spp. exposure and identified associated risk factors among slaughterhouse workers and slaughtered cattle in Western Bahr El Ghazal.
METHODS: A cross-sectional study was conducted in major slaughterhouses. Serum samples from workers and cattle were tested using the microscopic agglutination test (MAT). Bayesian hierarchical models were used to adjust for diagnostic test imperfections and to estimate true prevalence. Structured questionnaires captured occupational and animal-level risk factors for analysis within the Bayesian framework.
RESULTS: The estimated true prevalence was 10% in slaughterhouse workers and 85% in slaughtered cattle, indicating a high zoonotic exposure risk. Among workers, flaying, inconsistent use of protective equipment, and handling higher numbers of carcasses per day were significantly associated with seropositivity. In slaughtered cattle, exposure varied by breed, age, and sex. The model further indicated a 78% probability that a randomly selected slaughterhouse was affected and a 65% probability that infection levels among workers remained below 5%.
CONCLUSIONS: This study provides the first Bayesian-based estimates of leptospiral exposure in slaughterhouse settings in Western Bahr El Ghazal. The findings underscore the need to improve occupational safety, strengthen surveillance, and apply One Health approaches to reduce zoonotic transmission. Despite limitations, including lack of environmental data, the Bayesian framework proved effective for generating robust prevalence estimates in a resource-limited setting. Expanded geographic coverage and incorporation of environmental assessments are recommended to inform targeted leptospirosis control strategies.
METHODS: A cross-sectional study was conducted in major slaughterhouses. Serum samples from workers and cattle were tested using the microscopic agglutination test (MAT). Bayesian hierarchical models were used to adjust for diagnostic test imperfections and to estimate true prevalence. Structured questionnaires captured occupational and animal-level risk factors for analysis within the Bayesian framework.
RESULTS: The estimated true prevalence was 10% in slaughterhouse workers and 85% in slaughtered cattle, indicating a high zoonotic exposure risk. Among workers, flaying, inconsistent use of protective equipment, and handling higher numbers of carcasses per day were significantly associated with seropositivity. In slaughtered cattle, exposure varied by breed, age, and sex. The model further indicated a 78% probability that a randomly selected slaughterhouse was affected and a 65% probability that infection levels among workers remained below 5%.
CONCLUSIONS: This study provides the first Bayesian-based estimates of leptospiral exposure in slaughterhouse settings in Western Bahr El Ghazal. The findings underscore the need to improve occupational safety, strengthen surveillance, and apply One Health approaches to reduce zoonotic transmission. Despite limitations, including lack of environmental data, the Bayesian framework proved effective for generating robust prevalence estimates in a resource-limited setting. Expanded geographic coverage and incorporation of environmental assessments are recommended to inform targeted leptospirosis control strategies.