A time series model of the occurrence of gastric dilatation-volvulus in a population of dogs

BACKGROUND Gastric dilatation-volvulus (GDV) is a life-threatening condition of mammals, with increased risk in large breed dogs. The study of its etiological factors is difficult due to the variety of possible living conditions. The association between meteorological events and the occurrence of GDV has been postulated but remains unclear. This study introduces the binary time series approach to the investigation of the possible meteorological risk factors for GDV. The data collected in a population of high-risk working dogs in Texas was used. RESULTS Minimum and maximum daily atmospheric pressure on the day of GDV event and the maximum daily atmospheric pressure on the day before the GDV event were positively associated with the probability of GDV. All of the odds/multiplicative factors of a day being GDV day were interpreted conditionally on the past GDV occurrences. There was minimal difference between the binary and Poisson general linear models. CONCLUSION Time series modeling provided a novel method for evaluating the association between meteorological variables and GDV in a large population of dogs. Appropriate application of this method was enhanced by a common environment for the dogs and availability of meteorological data. The potential interaction between weather changes and patient risk factors for GDV deserves further investigation.