Multilevel analysis of risk factors for clinical mastitis in dairy cows in Plateau State, Nigeria: a hierarchical mixed-effects logistic regression modelling approach

Mastitis remains a significant health challenge affecting dairy cows, with implications for animal welfare, milk production, and farm profitability. In Nigeria, there are limited large-scale studies that have systematically investigated the multifactorial risk factors for mastitis across diverse production systems and geographical locations. This study aimed to assess the prevalence of mastitis and identify its risk factors using a hierarchical mixed effects logistic regression, block-wise analytical approach. A cross-sectional survey was conducted across 298 dairy farms in North-Central Nigeria. Data were collected on farm demographics, animal characteristics, housing and management practices, water and feeding routines, milking hygiene, and mastitis management. Mastitis was defined at the herd level as the occurrence of at least one farmer-reported case within the current lactation cycle. Because mastitis diagnosis relied on farmer-reported cases rather than laboratory confirmation (e.g., California Mastitis Test, CMT, or somatic cell count, SCC), the possibility of misclassification bias cannot be excluded and should be considered when interpreting the findings. Univariable and multivariable mixed-effects logistic regression models were fitted for each conceptual block of variables, accounting for clustering at the local government area (LGA) level using random intercepts. Model performance was evaluated using likelihood ratio tests, intraclass correlation coefficients (ICCs), and diagnostic plots of residuals. The prevalence of reported mastitis in the current year was 59.4% (95% CI: 53.7–64.9). Final multivariable models revealed significant associations between mastitis and herd size, presence of working bulls, milking hygiene (e.g., teat dipping and use of separate cleaning cloths), and mastitis treatment practices. Notably, large cattle herds had significantly higher odds of mastitis (adjusted OR = 6.56, 95% CI: 1.99–21.62), while post-milking teat dipping (OR = 0.013, 95% CI: 0.000–0.797) was strongly associated with lower odds. The ICC values across models ranged from 0.58 to 0.83, indicating substantial variation at the LGA level. Mastitis is highly prevalent in Nigerian dairy farms, with multivariable risks: herd demographics, management practices, and hygiene behaviours. Interventions promoting evidence-based milking hygiene and targeted herd-level management could substantially reduce mastitis burden. The hierarchical modeling approach provides a comprehensive framework for identifying context-specific risk factors and guiding regionally appropriate control strategies.