Georgina Tarrant

Background: This study investigates the challenges faced by women in livestock production across Sub-Saharan Africa (SSA) by analysing online discourse from social media platforms.

Methods: Using social media listening (SML) tools and Large Language Model (LLM)-assisted analysis, we processed approximately 84,000 posts, with 8,048 posts retained after relevancy filtering.

Results: Four principal themes emerged: the marginalisation of women’s roles in livestock farming; structural barriers such as limited access to resources and persistent gender inequalities; disease management and veterinary service challenges; and the impact of training and empowerment initiatives. Key findings highlight that women encounter significant financial barriers, including exclusion from traditional credit systems, while emerging fintech solutions offer potential to address funding gaps. Digital divides further restrict women’s access to information and services, with online narratives predominantly representing English-speaking regions with better internet connectivity, such as Nigeria and Kenya, and underrepresenting francophone and conflict-affected areas. Despite these obstacles, women demonstrate strong community-based animal health knowledge and effective farm management when structural barriers are reduced. The study also identifies grassroots adaptation strategies to climate change that are often absent from formal reports. Our methodology enabled the identification of informal disease management practices and peer-to-peer knowledge exchange, complementing traditional research approaches.

Conclusions: These insights underscore urgent policy priorities, including targeted interventions to improve women’s access to veterinary services, finance, digital technology, and training. Success stories from community-based training and technology-enabled finance access provide promising models for scaling effective interventions. This scalable and cost-effective methodology offers potential for broader agricultural research applications, though digital access disparities necessitate multilingual approaches and complementary data sources.

Background:
Antibiotic resistance increasingly threatens the interconnected health of humans, animals, and the environment. While misuse of antibiotics is a known driver, environmental factors also play a critical role. A balanced One Health approach—including the environmental sector—is necessary to understand the emergence and spread of resistance.

Methods:
We systematically searched English-language literature (1990–2021) in MEDLINE, Embase, and Web of Science, plus grey literature. Titles, abstracts, and keywords were screened, followed by full-text reviews using a structured codebook and dual-reviewer assessments.

Results:
Of 13,667 records screened, 738 met the inclusion criteria. Most studies focused on freshwater and terrestrial environments, particularly associated with wastewater or manure sources. Evidence of research has predominantly focused on Escherichia coli and Pseudomonas spp., with a concentration on ARGs conferring resistance to sulphonamides (sul1–3), tetracyclines (tet), and beta-lactams. Additionally, the People’s Republic of China has produced a third of the studies—twice that of the next country, the United States—and research was largely domestic, with closely linked author networks.

Conclusion:
Significant evidence gaps persist in understanding antibiotic resistance in non-built environments, particularly in marine, atmospheric, and non-agricultural set65 tings. Stressors such as climate change and microplastics remain notably under-explored. There is also an urgent need for more research in low-income regions, which face higher risks of antibiotic resistance, to support the development of targeted, evidence-based interventions.

Zoonotic diseases pose significant challenges to public health, creating substantial societal and economic burdens. Current surveillance systems rely primarily on laboratory-confirmed cases and statutory notifications, which may underestimate true disease prevalence. This study investigates the feasibility of using routine electronic health records from the Clinical Practice Research Datalink (CPRD) as an alternative or complementary approach to zoonotic disease surveillance in the United Kingdom.

The objective was to compare incidence of zoonotic diseases reported by Public Health England (PHE) with new diagnoses observed in the CPRD and to assess the potential of routine healthcare records for epidemiological monitoring.

A comprehensive retrospective cohort study was conducted over a ten-year period (2009–2019), examining ten notifiable zoonotic diseases. Data were sourced from the CPRD, including primary care records, Hospital Episode Statistics, and Office for National Statistics death certification. Age-sex-standardised annual incidence was calculated using Poisson regression.

The study analysed 10,441 new zoonotic disease cases in CPRD over 152 million person-years, compared to 32,167 cases reported by PHE over 631 million person-years. Overall, there was good correspondence between CPRD and PHE incidence data (R-square: 0.571). Lyme disease emerged as the most common zoonotic disease in CPRD (3.67 incident cases per 100,000 person-years) while pasteurellosis was underreported.

The CPRD demonstrates potential as a complementary surveillance tool for zoonotic diseases. The study reveals both strengths and limitations of routine healthcare records in epidemiological monitoring, highlighting the need for integrated, multi-source approaches to disease surveillance including data-linkage with animal health records.

This research provides critical insights for developing more comprehensive zoonotic disease monitoring strategies.

Introduction: Pet owner compliance with oral medication administration represents a significant challenge in veterinary medicine, yet limited real-world data exists on the experiences and barriers faced by dog owners during at-home “pilling” processes.

Methods: This study employed social media listening (SML) to analyze 4,787 relevant posts from X, Reddit, Facebook, blogs, and forums discussing oral medication administration to dogs. Large language models (LLMs) were used for data cleaning, relevancy filtering and analysis. Topic modeling, sentiment and thematic analyses were conducted to identify key themes and challenges.

Results: Analysis revealed significant anxiety and fear associated with medication administration, with 12% of posts mentioning anxiety and additional fear-related terms appearing in 1-3% of posts. Only 7.6% of posts discussed soft/chewable medications, which showed positive sentiment and preference. Geographic analysis showed posts predominantly from English-speaking countries (US 70.3%, UK 17.8%). Five major themes emerged from X, Reddit, blogs and forums: general medication/veterinary experiences, pill types and practices, pill delivery methods, specific medications/preventatives, product reviews/natural remedies. Financial concerns were prominent, with pet owners describing medication costs as barriers to optimal care. Successful pilling strategies included hiding pills in peanut butter, cheese or meat products; crushing pills and mixing with food, and using distraction techniques.

Discussion: The study identified key barriers to compliance including financial constraints, fear and anxiety, mistrust of veterinary advice and practical administration challenges. Pet owners showed higher adherence when treatments visibly improved quality of life or addressed chronic conditions. Chewable formulations were preferred but raised concerns about accidental overdosing. The methodology demonstrated that SML combined with AI analysis effectively captures real-world pet owner experiences.

Conclusions: This novel approach revealed that dog owners face significant psychological and practical barriers when administering oral medications. Chewable formulations may improve compliance, though proper storage and education are essential. The study provides veterinarians with evidence-based successful pilling strategies reported by pet owners and highlights the need for better communication about treatment benefits, financial planning options and alternative delivery methods to improve medication adherence.

Non-steroidal anti-inflammatory drugs (NSAIDs) are used to manage pain and inflammation in pets, particularly for chronic conditions like osteoarthritis (OA) and acute post-surgical pain. Despite their efficacy, concerns regarding adverse effects, affordability and long-term safety drive pet owners to discuss their experiences online. This study employed social media listening (SML) to analyse online discussions about NSAIDs, leveraging large language models (LLMs) to classify posts and identify themes. In total, 15,921 posts were collected from X, Reddit, Facebook, blogs, forums, reviews and Threads. After pre-processing and zero-shot relevancy filtering, 9,434 posts were analysed using non-negative matrix factorization (NMF) and LLM-driven thematic extraction. Key findings reveal pet owners frequently compare NSAID efficacy and safety, with carprofen, meloxicam and grapiprant discussed most. Gastrointestinal and renal issues emerged as concerning side effects, leading to increased interest in multimodal pain management, including cannabidiol (CBD), gabapentin, hydrotherapy and acupuncture. The high cost of NSAIDs drives demand for generic alternatives. This study underscores the value of SML and AI-driven data analysis in understanding real-world pet owner experiences. These insights can inform veterinary professionals and pharmaceutical companies, guiding strategies to improve pet owner education and medication accessibility, more effectively supporting owners' concerns and advancing veterinary pain management approaches.

Introduction: Chronic kidney disease (CKD) is a common and progressive condition in dogs characterized by irreversible damage to one or both kidneys over an extended period leading to gradual decline in kidney function. Early diagnosis is crucial to improve quality of life and increase survival through medical interventions. Methods: This study investigated pet owner understanding of this condition using insights gained by comparing pet owner survey responses with bulk harvested social media discussions on canine CKD. We combined structured survey data (n = 132) with social media analysis spanning multiple platforms to understand owner perceptions of disease characteristics, clinical sign reporting, and pet owner experiences. Results: Both data sources highlighted increased urination and water consumption as primary pet owner concerns, with these clinical signs showing moderate positive correlation (Pearson correlation coefficient of r = 0.66). Although not explicitly investigated within the survey, social media data demonstrated pain as a significant concerning clinical sign and revealed the emotional toll of end-of-life care considerations. Further analysis also demonstrated significant associations between CKD diagnosis in dogs and both animal age (p < 0.001) and female gender (p = 0.006), while breed group and weight showed no significant correlations. Discussion: The complementary nature of structured surveys and social media analysis provided richer understanding of pet owner experiences, understanding and management of CKD. This combined methodological approach offers a model for investigating other chronic conditions in veterinary medicine.

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.