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Dog bites cannot be studied experimentally as exposing a volunteer to a bite or provoking a dog to bite would be unethical. As bite incidents are relatively rare, collecting data through real-time observations is not feasible. Therefore, dog bite data is gathered through general population surveys e. The hospital admission datasets are often large, but the data does not systematically include information about the circumstances of the bite Some of the data, e. As only a fraction of bites warrant a visit to a hospital 31 , 32 , 33 , hospital derived-data does not represent all types of bites and bites that do not warrant medical attention have been under studied Data collected by reviewing veterinary referral cases is also biased to those who are willing to pay for behavioural referral and it is plausible that this data over-represents large dogs as owners tolerate aggression in smaller dogs for longer Surveys and questionnaires regarding being bitten often rely on convenience sampling, which may lead to a self-selection bias.
Detailed interviews with dog bite victims or witnesses of dog bites are an alternative to the above methods 26 , 35 , however the sample size is typically small. Video sharing platforms, such as YouTube, offer an opportunity to address some of the above issues.
YouTube has been used to study sequential behaviours and human-dog interactions within the context in which they occur e. YouTube provides a chance to observe the interactions leading to a bite directly, in a naturalistic context. This is important as bite education strategies are often structured around the ladder of aggression This theory proposes that dog behaviours before a bite escalate gradually in the time immediately before the bite or over the years , with some behaviours like lip licking or head turning , being shown earlier in time than other behaviours like growling or teeth-barring This study has the following aims: 1 to summarise the contexts in which dog bites occur and to describe victim and dog characteristics using YouTube videos of bites, 2 to describe human and dog behaviour preceding a bite, 3 to examine factors that predict the perceived severity of a bite using variables extracted from YouTube videos, and 4 to evaluate YouTube as a novel method of collecting data about dog bites.
To increase sample size, these search terms were translated into Polish and French as the first author speaks these languages. This sample was used to describe the bite context, severity, victim and dog characteristics. Fifty-six videos from this sample showed the behaviour of a dog and a person in detail from the beginning of an interaction until a bite and were included in analysis of pre-bite behaviour.
Bite severity is usually approximated by asking if a bite required medical attention or by inspecting the wound 39 , When constructing this measure, the importance of puncture wounds was emphasised, because bites that result in a puncture have been the basis of previous bite severity scales 39 , We assumed that the puncture did not occur when it was not possible to ascertain whether a bite broke the skin.
Dog head shaking whilst biting was highlighted as it can lead to further lacerations of existing wounds The duration of the bite was included as bites that are longer could be more traumatic.
A cut-off point for bite duration was set at one second because most bites observed here were less than that. Where a video showed multiple bites of different severity, the most extreme scores for variables a, b and c were included to calculate the total score. Perceived severity is defined as 1 :. Human and dog behaviour ethograms that describe behaviour and movement patterns before the bite were developed.
In addition, the following behaviours were included: locomotory behaviours direction in relation to the person and pace , body, tail and ear posture as these are associated with negative affect in dogs 42 , body position, vocalisation and the type of contact that a dog made with a person gentle or intensive. To describe human behaviour preceding bites, the following behaviours were included: macro-movements near the dog i. We also noted the site of contact on the body and body part used during contact for both person and a dog.
The videos were coded from beginning of each clip or a beginning of a human-dog interaction if a dog and person were not both in the video at the beginning until the first bite. The ethograms were applied via scan sampling. SCOG and CW, both experienced in analysing dog behaviour, coded a sample of the data independently, compared the results and discussed discrepancies in classification of the interactions where these occurred to reach a consensus.
For both intra- and inter-rater reliability a threshold of 0. All statistical analyses were conducted using R To summarize the behavior before the bite, videos across all contexts were pooled and a percentage of occurrence within a given time frame before the bite was provided. To understand the association between bite severity score and context, victim and dog characteristics, we used a hierarchical regression model. The distribution of the bite severity scores was checked and data were assumed gamma distributed, as on visual inspection the data fit the gamma model better than models for positive integers, e.
Bite severity scores were the dependent variable in these models and were modelled using a log-link as a function of: bite context, the duration of the interaction in seconds, dog size, victim sex, victim age, the anatomical location of the bite, and whether the human or dog initiated the interaction.
The model was hierarchical because varying intercept parameters were included for different bite contexts, and those intercepts were constrained by a common distribution. This approach reflected that the bite contexts are not completely independent of one another but are a subset of possible categorisations.
This allowed partial-pooling of bite severity estimates across contexts, which often results in more accurate predictions 45 , particularly when the number of data points per hierarchical group e.
We used model selection to assess whether all of the predictor variables were necessary for predicting bite severity. The baseline model included the bite contexts, the duration of the interaction and dog size, since these variables were considered a priori important for predicting bite severity. Thirteen additional models were computed including all combinations of the remaining predictor variables noted above.
The best fitting model was recomputed with bite contexts as a fixed effect rather than a varying effect, to assess whether a hierarchical model was necessary. Models were assessed using the widely applicable information criterion WAIC , a Bayesian information criterion that evaluates the out-of-sample predictive accuracy of a model relative to other possible models. Information criteria are preferable to classical measures of model fit e. R 2 because they guard against under- and over-fitting to the data Prior distributions on regression parameters were broad except for predictor variable coefficients, which had normally distributed priors with means of 0 and standard deviations of 1, further guarding against spurious results in addition to the model selection.
As all videos were in the public domain, ethical approval from the University Ethics Committee was not required.
Videos were used in accordance with YouTube regulations and laws. Three hundred and sixty-two bites were observed in videos. Almost half of bites Male victims were more numerous across all bite contexts and children and infants were more numerous than adults. There were more big dogs compared to medium and small dogs in this sample.
Victims initiated more interactions than dogs Bites to limbs were more frequent than bites to any other location. The severity score of most bites did not exceed 5, however The proportion of videos where dogs were seen holding their body awkwardly or in a low position and showing a non-neutral ear carriage increased before the bite. There was no clear pattern of changes in tail carriage and high body posture before a bite. Yawning and shake off were observed sporadically and lip licking, paw raises and sniffing did not follow any clear pattern Fig.
There was an increase in the proportion of dogs growling and a decrease in dogs being silent or barking before the bite. Pain-related vocalisations were rare. Closer in time to the bite, more dogs were coded as restrained and fewer were coded as standing. There was no clear pattern regarding play bows, sitting and laying down. As the bite became closer, there was more of fast pace locomotory behaviours and less jumping and slow pace locomotory behaviours.
There was no clear pattern regarding dogs making a gentle contact before the bite and there was a clear spike in a proportion of dogs making an intensive contact immediately before the bite, which reflects the moment of a bite. There was no clear pattern to all other non-contact behaviours.
Patterns of changes in human behaviour petting, restraining and standing over the dog preceding the bite. Hugging, hitting, pushing and pulling did not follow any clear pattern. Kissing, hitting with an object, kicking and pulling hair were not observed or were rare. There was no clear trend regarding changes of pace of movement in time before the bite. Normal talk and silence were observed proportionally less often closer in time to the bite.
Thus, all predictors appeared important to predicting severity. Across bite contexts, the mean bite severity score was estimated as 5. Due to the varying numbers of videos in each category, differences among contexts were pooled closer to the overall mean compared to the raw data.
The benign and play contexts have the most influence due to having the largest sample sizes. Estimated bite severity in each context. Sample sizes are shown next to each parameter. Regression model estimates are pooled towards the overall mean dashed vertical line when contexts have relatively low sample size e.
Among the fixed-effect predictor variables, bite severity scores increased by an average of 1. Estimated differences in bite severity between categorical predictor variables. Estimates in black exclude zero, indicating a significantly non-zero difference; estimates in grey overlap zero. In this study, we used YouTube videos to explore dog bites to humans.
The most common breeds and types of dogs found in our sample i. German Shepherds, Chihuahuas, Labrador Retrievers and Pit bulls reflect those previously identified in studies of dog bites 10 , 11 , 12 , 13 , 15 , 16 , 17 , Chihuahuas are rarely mentioned in studies that use hospital admissions, possibly because their small size makes them less likely to cause serious injury.
In addition to this, we hypothesize that bites by a small dog may be perceived as comical and thus more often uploaded online. It is also unclear if the breeds observed here are more likely to bite or to be more commonly owned 13 , 15 , In our study, male victims were over-represented.
This trend has been noticed in previous publications 1 , 5 , 12 , 13 , 15 but not to the same extent. It is therefore plausible that clips showing men are more often shared online. Our study included a similar proportion of adults to children and infants as those previously reported 12 , 16 , 17 , 49 , with children and infants being considerably more common victims than adults. Here, most bites were to the limbs, followed by bites to the face and neck area. Bites to face and neck area were more common among children and infants, which is also consistent with earlier reports 1 , 10 , 11 , A variety of bite contexts were represented in our sample.
Bites during play and benign interactions were particularly common, as reported before 10 , 14 , 16 , 18 , 24 , Play bites as well as behaviours that we labelled as benign interactions may be perceived as a normal part of human-dog behavioural repertoires and thus more frequently permitted and easier to film than other bite contexts.
In contrast to Reisner et al. The dissimilarity could arise due to differences in studied population: our sample consisted of all age groups, whilst Reisner et al. Alternatively, it could be due to these contexts being unlikely to be filmed. Here we followed the classification used by Reisner et al. This terminology can, however, be misleading.
Dogs may dislike being petted on top of their heads 49 , 51 , although we did not see a clear increase in tactile contact with head and neck areas before the bite. Displacement and appeasement behaviours as well as postural changes and vocalisations were included in this analysis as they are often discussed as preceding a bite and taught as a part of bite-prevention education Closer to the time of the bite, dogs were more often coded as holding their body low or in an awkward position and their ears were more often observed to be in a non-neutral position.
The postural changes have been linked with dogs experiencing acute distress in response to a fear-inducing stimuli 42 and changes in ear carriage have been observed during training that involved painful stimuli It is plausible that these changes were detected here as some interactions leading to a bite may be painful or cause a distress to a dog.
However, not all dogs in the videos show these changes and we also did not observe any clear changes in tail carriage pattern. Following from the ladder of aggression theory 38 , behaviours such as lip licking, head turning are expected to escalate and be replaced with behaviours like snapping or growling in time before the bite.
Head turning and full body turning as well as staring, stiffening, snapping, growling and frowning were observed proportionally more often in a build up to a bite, with head turning and staring dropping immediately before the bite, as would be expected from the ladder of aggression theory. However, as the increase in these behaviours is gradual, a person may not recognise their presence until later, if at all.
Other behaviours included in the ladder of aggression like lip licking, paw lifting and sniffing did not follow a clear pattern and sniffing and paw lifts were rarely observed. However, these behaviours may have escalated over a longer period of time, for instance a dog may have shown some of the behaviours from the lower steps of the ladder during previous interactions with a person, which would not have been captured in the video studied. Alternatively, these behaviours may not fit the pattern of behaviour progression proposed by the ladder of aggression theory.
Overall, the postural changes were observed more often than other behaviours included in the ladder of aggression. Previous studies linked some of these behaviours lip licking, paw lifts, head turns and yawning with acute stress and pain 42 , 53 , emotional conflict 52 and as a response to human facial expressions linked with a negative emotional valence 54 which may be specific to some, but not all contexts in which bites occur.
Other behaviours that did not result in contact and other tactile behaviours did not follow any clear pattern. The high frequency of petting and restraining behaviours makes prevention advice challenging, as these types of contacts are likely to occur when a person is familiar with the dog and interacts with a dog on daily basis, in a routine, habitual fashion.
This results shows that dog owner education should emphasise the idea of all interactions with a dog, and in particular tactile interactions like petting, should be mutually consensual, i.
In addition, restraining a dog e. It indicates the importance of teaching low-stress handling methods This could be because there may be more similarities between bite contexts than differences, making the distinction between contexts difficult. Bites in the context of benign and unpleasant interactions and resting were less severe, which reflects previous research The bootstrap analysis also indicated that when a dog initiated the interactions vs.
Bites in the context of benign interactions and unpleasant interactions may be more inhibited as the victim involved is likely to be more familiar with the dog. It is also plausible that the dog-initiated interactions in general may include more offensive aggression, whereas the human-initiated interactions may reflect more of the defensive aggression Different motivation to aggress could explain differences in severity as, in cases of defensive aggression, a dog may strive to warn off, which may result in a lesser severity of bite.
Again, this could be due to a small sample size or the way the severity measure was derived. In general, there may be numerous interactive effects between predictor variables that were not possible to explore in this study due to limited sample size and differences in number of videos in each context.
The analysis showed that severity of a bite was correlated with the duration of a video, regardless of the context of interactions or other predictor variables. This could be because a person who was attacked for longer received more serious injuries or because longer attacks simply score higher severity mark. Moreover, not all variables which are often cited in literature as risk factors for bites were used as predictor variables in our model as it was not always possible to discern them from the video.
We also did not include a breed as a predictor variable due to documented problems in recognising a breed based on visual characteristics 30 and a small number of dogs in each breed category.
Using YouTube to study dog bites enabled us to carry out observations of bites of diverse severity, in naturalistic settings and across a range of contexts. The benefit of this approach is that permits studying human and dog behaviours preceding bites, which is not possible with other retrospective methods. However, the sample generated through YouTube search is subject to some biases as the frequency of bites in a given context and the victim and dog characteristics could reflect the likelihood with which these interactions are filmed and the self-selection bias for uploading videos online.
The quality of videos and editing styles varied across the sample which meant that we could not collect a fine level of detail from each video. Small sample size meant that the analysis of body language had to be restricted to simple descriptive statistics, which is a further limitation of this method. Moreover, our analysis is limited because the bite severity score reflects the perception of severity as observed in the video, as it is impossible to assess the full extent of each injury. It is plausible that as puncture wounds may be more difficult to identify in some videos, we sometimes under-estimated the severity.
The same score on severity could, for example, reflect different elements of the bite; videos in the context of play may have a high severity score due to number of bites in a video whereas bites in the context of territorial aggression could have a high score due to puncture wounds and tearing movement of the dog, whereas in the reality, the later would cause more damage. In summary, this study used a novel approach to analyse human-dog interactions in naturalistic contexts.
We found that despite potential biases of this sample, the demographic characteristics of the victims and dogs seen in YouTube bite videos reflect those found in previous publications. Prevention messages could emphasise the risk of leaning over a dog and simply advise avoiding contact with a dog when possible or in doubt for instance, when interacting with an unfamiliar dog.
In a lead up to a bite, changes in dog body posture were more obvious than changes in appeasement and displacement behaviours. Winter, J. Benson, L. Dog and cat bites to the hand: treatment and cost assessment.
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Injury 27 , 89—91 Rezac, P. Human behavior preceding dog bites to the face. Mannion, C. Dog bites and maxillofacial surgery: what can we do? Custody is something that frightens him, he is honest about that. Get in touch with our news team by emailing us at webnews metro. For more stories like this, check our news page. Follow Metro. Got a story for Metro. Share this article via facebook Share this article via twitter Share this article via messenger Share this with Share this article via email Share this article via flipboard Copy link.