Pond-Level Risk Factors Associated with Columnaris Disease on Mississippi Commercial Catfish Farms

A large commercial catfish enterprise encompassing over 500 food fish ponds from five farms covering multiple counties in the Mississippi Delta was included in this analysis of columnaris risk factors. A gram-negative bacterium, Flavobacterium columnare, is the cause of columnaris disease and is considered the second-most prevalent bacterial disease in farm-raised catfish. The objective of this study was to determine if pond-level risk factors reported by farm personnel were associated with columnaris disease mortalities. To identify risk factors affecting susceptibility of farm-raised channel catfish Ictalurus punctatus to columnaris disease, a Catfish Management database was developed. Logistic regression was used to model the relationships between probability of columnaris in ponds and risk factors examined. Generalized linear mixed models incorporating hierarchically structured random effects of ponds and one or more fixed-effects risk factors were fitted. In the screening process, each risk factor was evaluated in the basic model as a single fixed-effects factor, and if associated with the outcome (P <= 0.20), was retained for development of multivariable models. Two multivariable logistic regression models were constructed from data collected at the pond level by producers. The first was constructed from data in which water quality was not considered. Pond depth and reduced feed consumption for a 14-d period prior to disease outbreaks measured on a per hectare basis were significantly (P <= 0.05) associated with columnaris disease. The second, in which water quality variables were also considered, pond depth, reduced feed consumption, shorter intervals from stocking to disease outbreaks, and total ammonia nitrogen were significantly (P <= 0.05) associated with columnaris occurrence. This study showed some commonly recorded production variables were associated with columnaris disease outbreaks and, if monitored, could help identify at risk ponds before disease outbreaks occur.