Adverse childhood experiences, non-response and loss to follow-up: Findings from a prospective birth cohort and recommendations for addressing missing data

Adverse childhood experiences have wide-ranging impacts on population health but are inherently difficult to study. Retrospective self-report is commonly used to identify exposure but adult population samples may be biased by non-response and loss to follow-up. We explored the implications of missing data for research on child abuse and neglect, domestic violence, parental mental illness and parental substance use. Using 15 waves of data collected over 28 years in a population-based birth cohort, the Australian Temperament Project, we examined the relationship between retrospective self-reports of adverse childhood experiences and parent- and cohort-responsiveness at other time points. We then compared prevalence estimates under complete case analysis, inverse probability-weighting using baseline auxiliary variables, multiple imputation using baseline auxiliary variables, multiple imputation using auxiliary variables from all waves, and multiple imputation using additional measures of participant responsiveness. Retrospective self-reports of adverse childhood experiences were strongly associated with non-response by both parents and cohort members at all observable time points. Biases in complete case estimates appeared large and inverse probability-weighting did not reduce them. Multiple imputation increased the estimated prevalence of any adverse childhood experiences from 30.0% to 36.9% with only baseline auxiliary variables, 39.7% with a larger set of auxiliary variables and 44.0% when measures of responsiveness were added. Close attention must be paid to missing data and non-response in research on adverse childhood experiences as data are unlikely to be missing at random. Common approaches may greatly underestimate their prevalence and compromise analysis of their causes and consequences. Sophisticated techniques using a wide range of auxiliary variables are critical in this field of research, including, where possible, measures of participant responsiveness.Funding for this analysis was supported by a PhD scholarship from the University of South Australia, and the South Australian Health Economics Collaborative (funded by the South Australian Department of Health)

The EU and Asia within an evolving global order: what is Europe? Where is Asia?

The papers in this special edition are a very small selection from those presented at the EU-NESCA (Network of European Studies Centres in Asia) conference on "the EU and East Asia within an Evolving Global Order: Ideas, Actors and Processes" in November 2008 in Brussels. The conference was the culmination of three years of research activity involving workshops and conferences bringing together scholars from both regions primarily to discuss relations between Europe and Asia, perceptions of Europe in Asia, and the relationship between the European regional project and emerging regional forms in Asia. But although this was the last of the three major conferences organised by the consortium, it in many ways represented a starting point rather than the end; an opportunity to reflect on the conclusions of the first phase of collaboration and point towards new and continuing research agendas for the future

Exploring the data divide through a social practice lens : A qualitative study of UK cattle farmers

Appropriate management decisions are key for sustainable and profitable beef and dairy farming. Data-driven technologies aim to provide information which can improve farmers’ decision-making practices. However, data-driven technologies have resulted in the emergence of a “data divide”, in which there is a gap between the generation and use of data. Our study aims to further understand the data divide by drawing on social practice theory to recognise the emergence, linkages, and reproduction of youngstock data practices on cattle farms in the UK. Eight focus groups with fifteen beef and nineteen dairy farmers were completed. The topics of discussion included data use, technology use, disease management in youngstock, and future goals for their farm. The transcribed data were analysed using reflexive thematic analysis with a social practice lens. Social practice theory uses practices as the unit of analysis, rather than focusing on individual behaviours. Practices are formed of three elements: meaning (e.g., beliefs), materials (e.g., objects), and competencies (e.g., skills) and are connected in time and space. We conceptualised the data divide as a disconnection of data collection practices and data use and interpretation practices. Consequently, we were able to generate five themes that represent these breaks in connection.Our findings suggest that a data divide exists because of meanings that de-stabilise practices, tensions in farmers’ competencies to perform practices, spatial and temporal disconnects, and lack of forms of feedback on data practices. The data preparation practice, where farmers had to merge different data sources or type up handwritten data, had negative meanings attached to it and was therefore sometimes not performed. Farmers tended to associate data and technology practices with larger dairy farms, which could restrict beef and small-scale dairy farms from performing these practices. Some farmers suggested that they lacked the skills to use technologies and struggled to transform their data into meaningful outputs. Data preparation and data use and interpretation practices were often tied to an office space because of the required infrastructure, but farmers preferred to spend time outdoors and with their animals. There appeared to be no normalisation of what data should be collected or what data should be analysed, which made it difficult for farmers to benchmark their progress. Some farmers did not have access to discussion groups or veterinarians who were interested in data and therefore could not get feedback on their data practices.These results suggest that the data divide exists because of three types of disconnect: a disconnect between elements within a practice because of tensions in competencies or negative meanings to perform a practice; a disconnect between practices because of temporal or spatial differences; and a break in the reproduction of practices because of lack of feedback on their practices. Data use on farms can be improved through transformation of practices by ensuring farmers have input in the design of technologies so that they align with their values and competencies

A qualitative survey approach to investigating beef and dairy veterinarians’ needs in relation to technologies on farms

Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians’ perceptions of data and technologies on farms. The aim of this study was to understand veterinarians’ experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers’ behaviour, and ensure sustainability of the beef and dairy industries

A guide to evaluating linkage quality for the analysis of linked data.

Linked datasets are an important resource for epidemiological and clinical studies, but linkage error can lead to biased results. For data security reasons, linkage of personal identifiers is often performed by a third party, making it difficult for researchers to assess the quality of the linked dataset in the context of specific research questions. This is compounded by a lack of guidance on how to determine the potential impact of linkage error. We describe how linkage quality can be evaluated and provide widely applicable guidance for both data providers and researchers. Using an illustrative example of a linked dataset of maternal and baby hospital records, we demonstrate three approaches for evaluating linkage quality: applying the linkage algorithm to a subset of gold standard data to quantify linkage error; comparing characteristics of linked and unlinked data to identify potential sources of bias; and evaluating the sensitivity of results to changes in the linkage procedure. These approaches can inform our understanding of the potential impact of linkage error and provide an opportunity to select the most appropriate linkage procedure for a specific analysis. Evaluating linkage quality in this way will improve the quality and transparency of epidemiological and clinical research using linked data

Quantification of play behaviour in farmed calves using automated ultra-wide band location data and its association with age, weaning and health status

Play behaviour can act as an indicator of positive animal welfare. Previous attempts to predict play behaviour in farmed calves are limited because of the classification methods used, which lead to overestimation, and the short time periods that calves are observed. The study aimed to automatically classify and quantify play behaviour in farmed calves using location data from ultra-wide band sensors and to investigate factors associated with play behaviour. Location data were collected from 46 calves in three cohorts for a period of 18 weeks. Behavioural observations from video footage were merged with location data to obtain a total of 101.36 h of labelled data. An AdaBoost ensemble learning algorithm was implemented to classify play behaviour. To account for overestimation, generally seen in low-prevalence behaviours, an adjusted count technique was applied to the outputs of the classifier. Two generalized linear mixed models were fitted to investigate factors (e.g. age, health) associated with duration of play and number of play instances per day. Our algorithm identified play behaviour with > 94% accuracy when evaluated on the test set with no animals used for training, and 16% overestimation, which was computed based on the predicted number of samples of play versus the number of samples labelled as play on the test set. The instances and duration of play behaviour per day significantly decreased with age and sickness, whilst play behaviour significantly increased during and after weaning. The instances of play also significantly decreased as mean temperature increased. We suggest that the quantification method that we used could be used to detect and monitor other low prevalence behaviours (e.g. social grooming) from location data, including indicators of positive welfare

Social and ethical implications of data and technology use on farms: a qualitative study of Swedish dairy and pig farmers

IntroductionLivestock farmers are being increasingly encouraged to adopt digital health technologies on their farms. Digital innovations may have unintended consequences, but there tends to be a pro-innovation bias in previous literature. This has led to a movement towards “responsible innovation,” an approach that questions the social and ethical challenges of research and innovation. This paper explores the social and ethical issues of data and technologies on Swedish dairy and pig farms from a critical perspective.MethodsSix focus groups were conducted with thirteen dairy and thirteen pig farmers. The data were analysed using reflexive thematic analysis and a digital critical health lens, which focuses on concepts of identity and power.Results and discussionThe analysis generated four themes: extending the self, sense of agency, quantifying animals, and managing human labour. The findings suggest that technologies can change and form the identities of farmers, their workers, and animals by increasing the visibility of behaviours and bodies through data collection. Technologies can also facilitate techniques of power such as conforming to norms, hierarchical surveillance, and segregation of populations based on data. There were many contradictions in the way that technology was used on farms which suggests that farmers cannot be dichotomised into those who are opposed to and those that support adoption of technologies. Emotions and morality played an important role in the way animals were managed and technologies were used by farmers. Thus, when developing innovations, we need to consider users’ feelings and attachments towards the technologies. Technologies have different impacts on farmers and farm workers which suggests that we need to ensure that we understand the perspectives of multiple user groups when developing innovations, including those that might be least empowered