A comparison of different models for assessing variations in the sero-prevalence of infectious bovine rhinotracheitis by farm, area and district in Kenya

Abstract

The relative variability of the sero-prevalence of antibodies to infectious bovine rhinotracheitis (IBR) due to cow, farm, and agroecological area levels were investigated for three contrasting districts in Kenya: Samburu, an arid and pastoral area; Kiambu, a tropical highland area; and Kilifi, a typical tropical coastal area. Cattle were selected by two-stage cluster sampling and visited once between August 1991 and 1992. Data on animal, farm, and area factors were analyzed using Schall's algorithm and ML n (multi-level, n-level), two generalized mixed-model programs suitable for multi-level analysis. Most variation in IBR sero-prevalence was from farm-to-furm. This was reflected by the many farm-level fixed effects (farm size, disease control measures and type of breeding) significant in models both ignoring and accounting for single variance components (clustering) at farm, area, and district levels. Area-to-area and district-to-district variations were noted but the area and district variance components were one-third and one-fifth the size of the farm variance components for both methods. As farm-to-farm variation differed markedly by farm size and district, models in ML n were extended to allow for multiple farm-level variance components by these categories. For each, sero-prevalence of IBR increased with age and was significantly decreased on small-sized zero-grazing farms. These models, particularly the model with different farm variance components by districts, fit the data better and highlighted well that there was considerable farm-to-farm variation—differing by district—and that the available farm-level fixed effects did not predict IBR sero-prevalence well.