Original Articles
Vol. 21 No. 1 (2026)
Spatial distribution and predictive risk of perpetuation of non-typhoidal salmonellosis in poultry farms and human communities: meta-analysis of data from Nigeria
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Received: 31 May 2024
Accepted: 23 November 2024
Accepted: 23 November 2024
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To gain insight into the common pathogenic, bacterial zoonosis represented by Salmonella infections in poultry and humans, we acted to determine salmonellosis prevalence in poultry and humans in Nigeria mapping hotspots. Using multi-sourced data, we conducted a meta-analysis to determine national and sub-national prevalence of salmonellosis in poultry from 2000 until 2020. Bayesian spatial joint modelling was used to map Non-Typhoidal Salmonella (NTS) infections in humans and poultry using climatic and demographic predictor variables. With the overall prevalence in poultry at 31.6%, the highest state-level prevalence rates were seen in Ogun (70.2%), Lagos (61.8%), Zamfara (58.2%) and Bauchi (57.1%). The North-West, South-West and South-South regions of Nigeria have the highest regional-level prevalence in poultry amounting to 38.5%, 36.9% and 33.6%, respectively. Thirteen states have higher than the average national prevalence (31.6%). While we found a negative association between NTS in humans and in poultry, the prevalence of diarrhoea in humans positively predicted salmonellosis in poultry. Not surprisingly, poultry populations positively predicted salmonellosis in other poultry populations. Higher numbers of human cases were predicted in the North, with more poultry cases in the South and in some North-Eastern states. The observed human NTS-poultry salmonellosis correlation is counterfactual to logic and plausibility as high poultry density and contamination in poultry are expected to predict human infection. The outcome pointed to under-reporting linked to self-treatment, under-testing in the public health and veterinary laboratory and lack of uniform primary healthcare services, particularly in under-served areas of Nigeria. Salmonellosis continues to be a serious burden, and provision of better health data is needed.
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Supporting Agencies
Federal Ministry of Agriculture and Rural Development Nigeria, National Primary Health Care Development Agency, Nigeria, Usmanu Danfodiyo University, Sokoto, Federal University of Technology, MinnaHow to Cite
Spatial distribution and predictive risk of perpetuation of non-typhoidal salmonellosis in poultry farms and human communities: meta-analysis of data from Nigeria. (2026). Geospatial Health, 21(1). https://doi.org/10.4081/gh.2026.1316
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