An analysis of substantiated complaints made about incidents of poor livestock welfare, in Victoria, Australia.
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ABSTRACT:
SUBMITTER: Williams N
PROVIDER: S-EPMC10502162 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:If livestock at risk of poor welfare could be identified using a risk assessment tool, more targeted response strategies could be developed by enforcement agencies to facilitate early intervention, prompt welfare improvement and a decrease in reoffending. This study aimed to test the ability of an Animal Welfare Risk Assessment Tool (AWRAT) to identify livestock at risk of poor welfare in extensive farming systems in Australia. Following farm visits for welfare- and non-welfare-related reasons, participants completed a single welfare rating (WR) and an assessment using the AWRAT for the farm just visited. A novel algorithm was developed to generate an AWRAT-Risk Rating (AWRAT-RR) based on the AWRAT assessment. Using linear regression, the relationship between the AWRAT-RR and the WR was tested. The AWRAT was good at identifying farms with poor livestock welfare based on this preliminary testing. As the AWRAT relies upon observation, the intra- and inter-observer agreement were compared in an observation study. This included rating a set of photographs of farm features, on two occasions. Intra-observer reliability was good, with 83% of Intra-class Correlation Coefficients (ICCs) for observers ≥ 0.8. Inter-observer reliability was moderate with an ICC of 0.67. The AWRAT provides a structured framework to improve consistency in livestock welfare assessments. Further research is necessary to determine the AWRAT's ability to identify livestock at risk of poor welfare by studying animal welfare incidents and reoffending over time.
| S-EPMC11418070 | biostudies-literature
Project description:The objective of this study was to identify factors more commonly observed on farms with poor livestock welfare compared to farms with good welfare. Potentially, these factors may be used to develop an animal welfare risk assessment tool (AWRAT) that could be used to identify livestock at risk of poor welfare. Identifying livestock at risk of poor welfare would facilitate early intervention and improve strategies to promptly resolve welfare issues. This study focuses on cattle, sheep and goats in non-dairy extensive farming systems in Australia. To assist with identifying potential risk factors, a survey was developed presenting 99 factors about the farm, farmers, animals and various aspects of management. Based on their experience, key stakeholders, including veterinarians, stock agents, consultants, extension and animal welfare officers were asked to consider a farm where the welfare of the livestock was either high or low and rate the likelihood of observing these factors. Of the 141 responses, 65% were for farms with low welfare. Only 6% of factors had ratings that were not significantly different between high and low welfare surveys, and these were not considered further. Factors from poor welfare surveys with median ratings in the lowest 25% were considered potential risks (n = 49). Considering correlation, ease of verification and the different livestock farming systems in Australia, 18 risk factors relating to farm infrastructure, nutrition, treatment and husbandry were selected. The AWRAT requires validation in future studies.
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Project description:BackgroundFor most rural households in sub-Saharan Africa, healthy livestock play a key role in averting the burden associated with zoonotic diseases, and in meeting household nutritional and socio-economic needs. However, there is limited understanding of the complex nutritional, socio-economic, and zoonotic pathways that link livestock health to human health and welfare. Here we describe a platform for integrated human health, animal health and economic welfare analysis designed to address this challenge. We provide baseline epidemiological data on disease syndromes in humans and the animals they keep, and provide examples of relationships between human health, animal health and household socio-economic status.MethodWe designed a study to obtain syndromic disease data in animals along with economic and behavioral information for 1500 rural households in Western Kenya already participating in a human syndromic disease surveillance study. Data collection started in February 2013, and each household is visited bi-weekly and data on four human syndromes (fever, jaundice, diarrhea and respiratory illness) and nine animal syndromes (death, respiratory, reproductive, musculoskeletal, nervous, urogenital, digestive, udder disorders, and skin disorders in cattle, sheep, goats and chickens) are collected. Additionally, data from a comprehensive socio-economic survey is collected every 3 months in each of the study households.FindingsData from the first year of study showed 93% of the households owned at least one form of livestock (55%, 19%, 41% and 88% own cattle, sheep, goats and chickens respectively). Digestive disorders, mainly diarrhea episodes, were the most common syndromes observed in cattle, goats and sheep, accounting for 56% of all livestock syndromes, followed by respiratory illnesses (18%). In humans, respiratory illnesses accounted for 54% of all illnesses reported, followed by acute febrile illnesses (40%) and diarrhea illnesses (5%). While controlling for household size, the incidence of human illness increased 1.31-fold for every 10 cases of animal illness or death observed (95% CI 1.16-1.49). Access and utilization of animal source foods such as milk and eggs were positively associated with the number of cattle and chickens owned by the household. Additionally, health care seeking was correlated with household incomes and wealth, which were in turn correlated with livestock herd size.ConclusionThis study platform provides a unique longitudinal dataset that allows for the determination and quantification of linkages between human and animal health, including the impact of healthy animals on human disease averted, malnutrition, household educational attainment, and income levels.
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Project description:BackgroundSpatiotemporal modelling techniques allow one to predict injury across time and space. However, such methods have been underutilised in injury studies. This study demonstrates the use of statistical spatiotemporal modelling in identifying areas of significantly high injury risk, and areas witnessing significantly increasing risk over time.MethodsWe performed a retrospective review of hospitalised major trauma patients from the Victorian State Trauma Registry, Australia, between 2007 and 2019. Geographical locations of injury events were mapped to the 79 local government areas (LGAs) in the state. We employed Bayesian spatiotemporal models to quantify spatial and temporal patterns, and analysed the results across a range of geographical remoteness and socioeconomic levels.ResultsThere were 31,317 major trauma patients included. For major trauma overall, we observed substantial spatial variation in injury incidence and a significant 2.1% increase in injury incidence per year. Area-specific risk of injury by motor vehicle collision was higher in regional areas relative to metropolitan areas, while risk of injury by low fall was higher in metropolitan areas. Significant temporal increases were observed in injury by low fall, and the greatest increases were observed in the most disadvantaged LGAs.ConclusionsThese findings can be used to inform injury prevention initiatives, which could be designed to target areas with relatively high injury risk and with significantly increasing injury risk over time. Our finding that the greatest year-on-year increases in injury incidence were observed in the most disadvantaged areas highlights the need for a greater emphasis on reducing inequities in injury.
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Project description:Remedial sport hunting of predators is often used to reduce predator populations and associated complaints and livestock depredations. We assessed the effects of remedial sport hunting on reducing cougar complaints and livestock depredations in Washington from 2005 to 2010 (6 years). The number of complaints, livestock depredations, cougars harvested, estimated cougar populations, human population and livestock populations were calculated for all 39 counties and 136 GMUs (game management units) in Washington. The data was then analyzed using a negative binomial generalized linear model to test for the expected negative relationship between the number of complaints and depredations in the current year with the number of cougars harvested the previous year. As expected, we found that complaints and depredations were positively associated with human population, livestock population, and cougar population. However, contrary to expectations we found that complaints and depredations were most strongly associated with cougars harvested the previous year. The odds of increased complaints and livestock depredations increased dramatically (36 to 240%) with increased cougar harvest. We suggest that increased young male immigration, social disruption of cougar populations, and associated changes in space use by cougars - caused by increased hunting resulted in the increased complaints and livestock depredations. Widespread indiscriminate hunting does not appear to be an effective preventative and remedial method for reducing predator complaints and livestock depredations.
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Project description:In 2016, the gambling habits of a sample of 3361 adults in the state of Victoria, Australia, were surveyed. It was found that a number of factors that were highly correlated with self-reported gambling frequency and gambling problems were not significant predictors of gambling frequency and problem gambling. The major predictors of gambling frequency were the degree to which family members and peers were perceived to gamble, self-reported approval of gambling, the frequency of discussing gambling offline, and the participant's Canadian Problem Gambling Severity Index (PGSI) score. Age was a significant predictor of gambling frequency for certain types of gambling (e.g. buying lottery tickets). Approximately 91% of the explainable variance in the participant's PGSI score could be explained by just five predictors: Positive Urgency; Frequency of playing poker machines at pubs, hotels or sporting clubs; Participation in online discussions of betting on gaming tables at casinos; Frequency of gambling on the internet, and Overestimating the chances of winning. Based on these findings, suggestions are made as to how gambling-related harm can be reduced.
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