Faktorer kopplade till katters omplaceringstid : en analys av verksamhetsdata från ett svenskt katthem

Sammanfattning

The domesticated cat (Felis catus) is one of the most popular pets in the world. Due to a number of reasons such as a high demand, uncontrolled reproduction, and the large population of free‑roaming cats, a lot of cats are homeless. Abandoned and homeless cats are one of the most common ways a cat gets into a cat shelter. No-kill shelters have a policy of not euthanizing cats because they are shy, feral or have behavioral problems, which can lead to a very long Length of Stay (LOS) for some individuals. A longer LOS has been found to be linked with a negative relationship on welfare. There have also been discrepancies in the results found from shelter to shelter in the past. Due to these two things, it is instrumental to find out what factors might minimize the LOS for shelter cats in a Swedish context. This study therefore investigates factors associated with LOS among cats adopted from a Swedish no‑kill shelter under a four year period. The dataset included 1,193 cats and includes the variables of age at adoption, sex, coat pattern, coat color, breed, and intake type, all measured individually against the LOS. Because LOS was strongly right‑skewed with substantial variation and extreme values, all analyses were conducted using non-parametric tests. Although descriptive figures suggested visual differences between five of the groups (age, sex, coat pattern, breed, and intake type), only breed and intake type were supported by the statistical analyses. The results were as follows: LOS and breed (H =15,50, P = 0,0004), LOS and intake type (H = 240,4, p <0,001), LOS and age (rho = –0,003, p = 0,91), LOS and sex (U = 174,623, p = 0,59), and LOS and coat pattern (H = 7,46, p = 0,28). Overall, this study contributes new insights into shelter cats in a Swedish context and underscores the need for future research looking into behavioral and health‑related variables, as well as multi‑shelter comparisons, to help get a better understanding of the LOS predictors.