The use of negative binomial modelling in a longitudinal study of gastrointestinal parasite burdens in Canadian dairy cows   总被引：3，自引：0，他引：3       下载免费PDF全文

Ane Ndtvedt  Ian Dohoo  Javier Sanchez  Gary Conboy  Luc DesCteaux  Greg Keefe  Ken Leslie    John Campbell 《Canadian journal of veterinary research》2002,66(4):249-257

The epidemiology of bovine gastrointestinal nematodes was investigated through a 1-year (October 1999 to September 2000) longitudinal study in 38 Canadian dairy herds from 4 different provinces (Prince Edward Island, Quebec, Ontario, Saskatchewan). For each herd, fecal egg counts from 8 randomly selected animals were performed on a monthly or quarterly basis. Larval cultures were performed once, to determine the species breakdown of the parasites. All producers were interviewed regarding herd management practices. The observed fecal egg counts were low in this study, with a range from 0 to 419 nematode eggs per 5 g of feces. The mean count was 9.8 and the median was 1. Standard transformations failed to normalize the data, which followed an over-dispersed Poisson distribution. A zero inflated negative binomial model was applied to assess factors that would influence the fecal egg counts. Identified associations were: egg counts were lowest in the winter and highest in the late spring; first-lactation cattle had higher counts than older cows; if manure was spread mechanically on pastures used by lactating cattle the egg counts were higher; and if manure was spread on heifer-pastures, the adult cows had lower counts. In herds where pasture use was more extensive, the cattle had higher fecal egg counts. The difference in pasture exposure was found to be a main contributor to an observed difference in fecal egg counts among herds in the 4 provinces.  相似文献   

Bias—Is it a problem,and what should we do?     

Ian R. Dohoo 《Preventive veterinary medicine》2014

Observational studies are prone to two types of errors: random and systematic. Random error arises as a result of variation between samples that might be drawn in a study and can be reduced by increasing the sample size. Systematic error arises from problems with the study design or the methods used to obtain the study data and is not influenced by sample size. Over the last 20 years, veterinary epidemiologists have made great progress in dealing more effectively with random error (particularly through the use of multilevel models) but paid relatively little attention to systematic error. Systematic errors can arise from unmeasured confounders, selection bias and information bias. Unmeasured confounders include both factors which are known to be confounders but which were not measured in a study and factors which are not known to be confounders. Confounders can bias results toward or away from the null. The impact of selection bias can also be difficult to predict and can be negligible or large. Although the direction of information bias is generally toward the null, this cannot be guaranteed and its impact might be very large. Methods of dealing with systematic errors include: qualitative assessment, quantitative bias analysis and incorporation of bias parameters into the statistical analyses.  相似文献