Estimation of infection prevalence from correlated binomial samples

Infection prevalence in a population often is estimated from grouped binary data expressed as proportions. The groups can be families, herds, flocks, farms, etc. The observed number of cases generally is assumed to have a Binomial distribution and the estimate of prevalence is then the sample proportion of cases. However, the individual binary observations might not be independent--leading to overdispersion. The goal of this paper was to demonstrate random-effects models for the estimation of infection prevalence from data which are correlated and in particular, to illustrate a nonparametric random-effects model for this purpose. The nonparametric approach is a relatively recent addition to the random-effects class of models and does not appear to have been discussed previously in the veterinary epidemiology literature. The assumptions for a logistic-regression model with a nonparametric random effect were outlined. In a demonstration of the method on data relating to Salmonella infection in Irish pig herds, the nonparametric method resulted in the classification of herds into a small number of distinct prevalence groups (i.e. low, medium and high prevalence) and also estimated the relative frequency of each prevalence category in the population. We compared the estimates from a logistic model with a nonparametric distribution for the random effects with four alternative models: a logistic-regression model with no random effects, a marginal model using a generalised estimating equation (GEE) and two methods of fitting a Normally distributed random effect (the GLIMMIX macro and the NLMIXED procedure both in SAS). Parameter estimates from random-effects models are not readily interpretable in terms of prevalences. Therefore, we outlined two methods for calculating population-averaged estimates of prevalence from random-effects models: one using numerical integration and the other using Monte Carlo simulation.     

Pharmacokinetics of enrofloxacin in neonatal kittens

OBJECTIVE: To determine the pharmacokinetics of enrofloxacin in neonatal kittens and compare the pharmacokinetics of enrofloxacin in young and adult cats. ANIMALS: 7 adult cats and 111 kittens (2 to 8 weeks old). PROCEDURE: A single dose of 5 mg of enrofloxacin/kg was administered to adults (i.v.) and kittens (i.v., s.c., or p.o.). Plasma concentrations of enrofloxacin and its active metabolite, ciprofloxacin, were determined. RESULTS: The half-life of enrofloxacin administered i.v. in 2-, 6-, and 8-week-old kittens was significantly shorter and its elimination rate significantly greater than that detected in adults. The apparent volumes of distribution were lower at 2 to 4 weeks and greater at 6 to 8 weeks. This resulted in lower peak plasma concentration (Cmax) at 6 to 8 weeks; however, initial plasma concentration was within the therapeutic range after i.v. administration at all ages. Compared with i.v. administration, s.c. injection of enrofloxacin in 2-week-old kittens resulted in similar Cmax, half-life, clearance, and area under the curve values. Enrofloxacin administered via s.c. injection was well absorbed in 6- and 8-week-old kittens, but greater clearance and apparent volume of distribution resulted in lower plasma concentrations. Oral administration of enrofloxacin resulted in poor bioavailability. CONCLUSIONS AND CLINICAL RELEVANCE: In neonatal kittens, i.v. and s.c. administration of enrofloxacin provided an effective route of administration. Oral administration of enrofloxacin in kittens did not result in therapeutic drug concentrations. Doses may need to be increased to achieve therapeutic drug concentrations in 6- to 8-week-old kittens.     

A review of bacterial pathogens in Ctenocephalides felis in New Zealand

The cat flea, Ctenocephalides felis, is the recognised vector of Bartonella henselae, B. clarridgeiae and Rickettsia felis. Although these Gram-negative bacteria were only described in the last decade, they are already known to cause a variety of diseases in people, particularly children and the immunosuppressed. Such diseases include cat-scratch disease, bacillary angiomatosis, endocarditis, bacteraemia, encephalopathy, neuroretinitis, osteomyelitis and peliosis hepatis. Although most infections in cats and dogs appear to be subclinical, recent studies have provided growing evidence that the bartonellas can also cause serious problems in pets, including hepatitis, endocarditis, central nervous system (CNS) signs, lymphadenopathy, uveitis, cataracts and reproductive failure. In 2004, DNA of B. henselae, B. clarridgeiae and R. felis was demonstrated in cat fleas from New Zealand and pets and their owners in the country are thus at risk of infection. While flea control programmes have traditionally been advocated by veterinarians to prevent pruritis and tapeworms in pets, they should now also be recommended to prevent infections with the new flea-borne bacterial pathogens. To raise awareness of the organisms amongst veterinarians and animal health workers, this review describes: the biology of the organisms; clinical and laboratory features of infections in cats, dogs and people; diagnosis; and possible treatments and control of infections with these organisms.     

Solvent-mediated disruption of bovine casein micelles at alkaline pH

The disruption of casein micelles at alkaline pH was investigated using turbidity measurements. The rate and extent of disruption of casein micelles at alkaline pH (8.0-11.0) increased with pH. Furthermore, the extent of alkaline disruption increased with increasing temperature (5-40 degrees C). Preheating milk for 10 min at 90 degrees C did not influence the extent of alkaline disruption of casein micelles, suggesting that whey proteins do not influence the alkaline disruption process. Levels of ionic calcium and serum calcium and phosphate decreased in a logarithmic fashion with increasing pH, indicating precipitation of calcium phosphate onto the casein micelles. A mechanism for alkaline disruption of casein micelles is proposed, in which increasing the milk pH improves the solvent quality for the caseins, thereby leading to the disruption of casein micelles into their constituent nanoclusters; increases in the net-negative charge on the caseins on increasing pH may contribute to micellar dissociation.