Georgina Cherry

Abstract for ISPOR Europe 2022 poster presentation. Social media are seldom explored in animal health despite the potential for insights into pet owners’ perceptions. Owners often seek information and advice online before seeking veterinary care. The aim was to investigate owners’ perceptions of feline allergic skin disease using Social Asset, a proof-of-concept social listening (SL) platform to create a dataset concerning information-seeking behaviours.

Estimating population-level burden, abilities of pet-parents to identify disease and demand for veterinary services worldwide is challenging. The purpose of this study is to compare a feline pruritus survey with social media listening (SML) data discussing this condition. Surveys are expensive and labour intensive to analyse but SML data is freeform and requires careful filtering for relevancy. This study considers data from a survey of owner-observed symptoms of 156 pruritic cats conducted using Pet Parade® and SML posts collected through web-scraping, to gain insights into the characterisation and management of feline pruritus. SML posts meeting a feline body area, behaviour and symptom were captured and reviewed for relevance representing 1299 public posts collected from 2021 to 2023. The survey involved 1067 pet-parents who reported on pruritic symptoms in their cats. Among the observed cats, approximately 18.37% (n=196) exhibited at least one symptom. The most frequently reported symptoms were hair loss (9.2%), bald spots (7.3%) and infection, crusting, scaling, redness, scabbing, scaling, or bumpy skin (8.2%). Notably, bald spots were the primary symptom reported for short-haired cats, while other symptoms were more prevalent in medium and long-haired cats. Affected body areas, according to pet-parents, were primarily the head, face, chin, neck (27%), and the top of the body, along the spine (22%). 35% of all cats displayed excessive behaviours consistent with pruritic skin disease. Interestingly, 27% of these cats were perceived as non-symptomatic by their owners, suggesting an under-identification of itch-related signs. Furthermore, a significant proportion of symptomatic cats did not receive any skin disease medication whether prescribed or over the counter (n=41). These findings indicate a higher incidence of pruritic skin disease in cats than recognized by pet owners, potentially leading to a lack of medical intervention for clinically symptomatic cases. The comparison between the survey and social media listening data revealed bald spots were reported in similar proportions in both datasets (25% in the survey and 28% in SML). Infection, crusting, scaling, redness, scabbing, scaling, or bumpy skin accounted for 31% of symptoms in the survey, whereas it represented 53% of relevant SML posts (excluding bumpy skin). Abnormal licking or chewing behaviours were mentioned by pet-parents in 40% of SML posts compared to 38% in the survey. The consistency in the findings of these two disparate data sources, including a complete overlap in affected body areas for the top 80% of social media listening posts, indicates minimal biases in each method, as significant biases would likely yield divergent results. Therefore, the strong agreement across pruritic symptoms, affected body areas, and reported behaviours enhances our confidence in the reliability of the findings. Moreover, the small differences identified between the datasets underscore the valuable insights that arise from utilising multiple data sources. These variations

Industrial regulation to protect privacy, intellectual property and proprietary information often restricts data sharing ─ an important prerequisite for developing services in the digital economy. Social media data is publicly available for data mining but requires intensive cleaning and harmonisation before analysis. This paper reveals the process of semantic sensing to convert social network tweets into meaningful insights. Our research question is: how to realise semantic sensing for data innovation? We use design science research to develop an artefact-ontology that collects tweets by pet owners talking about their itchy pet into knowledge graphs, including symptoms, location, breed, timestamp and potential cause and converts them into a thematic map of the regional occurrence of symptoms and potential treatment needs, providing vital information for data innovation. The semantic engine can predict potential causes of itching from the tweet, so a Chatbot may contact the pet owner, inviting them to a veterinary screening. Animal health and pharma companies can use this information to position their services. Our theoretical contribution is a process of semantic sensing, which is a vital part of dynamic capability. Although limited to animal health, the results could be transferred to other contexts.

The open data market size is estimated at €184 billion and forecast to reach between €199.51 and €334.21 billion in 2025. In this paper, we conceptualise the semantic data innovation platform, which will be able to answer inter-disciplinary questions via semantic reasoning over open data. We use 750 open animal healthcare datasets to exemplify this work, covering mainly poultry, swine, ruminants, and other livestock, which are complemented by open data from complementary domains, such as geographic location, medicine and virology. We aggregate the domain knowledge (classes) and enable the logical links (properties) between these classes. The prototype encapsulates the complexity of animal healthcare knowledge into ontology, which can answer complex questions using semantic reasoning on the datasets (answer-as-a-service).

An owner's ability to detect changes in the behavior of a dog afflicted with osteoarthritis (OA) may be a barrier to presentation, clinical diagnosis and initiation of treatment. Management of OA also relies upon an owner's ability to accurately monitor improvement following a trial period of pain relief. The changes in behavior that are associated with the onset and relief of pain from OA can be assessed to determine the dog's health-related quality of life (HRQOL). HRQOL assessments are widely used in human medicine and if developed correctly can be used in the monitoring of disease and in clinical trials. This study followed established guidelines to construct a conceptual framework of indicators of HRQOL in dogs with OA. This generated items that can be used to develop a HRQOL assessment tool specific to dogs with OA. A systematic review was conducted using Web of Science, PubMed and Scopus with search terms related to indicators of HRQOL in dogs with osteoarthritis. Eligibility and quality assessment criteria were applied. Data were extracted from eligible studies using a comprehensive data charting table. Resulting domains and items were assessed at a half-day workshop attended by experts in canine osteoarthritis and quality of life. Domains and their interactions were finalized and a visual representation of the conceptual framework was produced. A total of 1,264 unique articles were generated in the database searches and assessed for inclusion. Of these, 21 progressed to data extraction. After combining synonyms, 47 unique items were categorized across six domains. Review of the six domains by the expert panel resulted in their reduction to four: physical appearance, capability, behavior, and mood. All four categories were deemed to be influenced by pain from osteoarthritis. Capability, mood, and behavior were all hypothesized to impact on each other while physical appearance was impacted by, but did not impact upon, the other domains. The framework has potential application to inform the development of valid and reliable instruments to operationalize measurement of HRQOL in canine OA for use in general veterinary practice to guide OA management decisions and in clinical studies to evaluate treatment outcomes.

BACKGROUND Social media are seldom explored in animal health despite the potential for insights into pet owners’ perceptions and information seeking behaviours before and after accessing veterinary care [1]. A study in Feline Pruritus was conducted using social listening to investigate owners’ perceptions of feline allergic skin disease using a thematic analysis technique. OBJECTIVES • To apply thematic analysis to social listening (SL) data and thereby create a unique dataset concerning pet owner perceptions of feline pruritus and online information-seeking behaviours. METHODS • Fifty dynamic (frequently updated) content sources applicable to cats and feline pruritus were chosen, keywords were defined by a veterinary expert panel and organised into topics. • Keywords were augmented by reference to academic literature, a baseline survey of 1000 cat owners in the United States, the addition of synonyms and further iterations using Google Trends analytics keywords and sources. • Six bespoke topic filters were developed: body areas, behaviours, symptoms, disease diagnosis, solutions and treatments. • Content from the selected sources was collected using a social intelligence solution developed by ATC, tagged using both keywords (with stemming) and topic filters. • The data was aggregated, duplicates removed, and sentiment calculated by algorithm. • Content matching topic(s) in the body areas, behaviours and symptoms filters were reviewed manually, relevancy criteria developed, and posts marked relevant if: posted by a pet owner, identifying an itchy cat and not duplicated e.g. previous versions of a post, similar posts or cross posting to different sources. • A sub-set of 493 posts (title and text only) marked relevant and published between 2009 and 2022 were used for reflexive thematic analysis in NVIVO (Burlington, MA) to extract the key themes. RESULTS Qualitative thematic analysis was conducted on 493 relevant posts collected up to 30th May 2022 producing five top level themes: allergy, pruritus, additional behaviours, unusual or undesirable behaviours, diagnosis and treatment. The analytical method used the most recent ‘reflexive thematic analysis’ approach developed by Braun and Clarke [2] and adapted from [3]. The newly developed reflexive thematic analysis approach is not bound to one specific theoretical framework but allows for the flexibility to return to a previous phase, as the analysis develops, guiding the research based on the researcher’s level of interpretation and design of the study. The data was published between 2009 and 2022, met the body areas, behaviours and symptoms topic filters, met the relevancy criteria, had been manually reviewed and marked relevant for feline pruritus. Internet forums and Twitter were the most likely sources of relevant posts: Reddit (198/493), Catsite (110/493), Twitter (97/493) and Quora (59/493). Relevant posts were most frequently from the United States (188/493), United Kingdom (12/493), Canada (9/493), Greece (7/493), Australia (3/493) and Italy (2/493). A single post came from each of 11 countries and 260/493 posts had no location. The total number of responses coded was 493; the total number of themes was 5, total codes was 47 and the total number of references coded was 880. CONCLUSIONS • SL provides unique insights into verbatim owner perceptions on health and veterinary care. • This study shows there is a need for an increased awareness by veterinarians to pet owner frustrations with treatment options to tackle feline pruritus. • The data analysis could be scaled up using machine learning for topic modelling. • The data could enable data-driven decisions such as assessing demand for veterinary services by location and impact on quality of life. • These findings will be validated by comparison with thematic analysis of a direct pet owner survey.