How well can we measure chronic pain impact in existing longitudinal cohort studies? Lessons learned.

Multiple large longitudinal cohorts provide opportunities to address questions about predictors of pain and pain trajectories, even when not anticipated in design of the historical databases. This focus article uses two empirical examples to illustrate the processes of assessing the measurement properties of data from large cohort studies to answer questions about pain. In both examples, data were screened to select candidate variables that captured the impact of chronic pain on self-care activities, productivity and social activities. We describe a series of steps to select candidate items and evaluate their psychometric characteristics in relation to the measurement of pain impact proposed. In UK Biobank, a general lack of internal consistency of variables selected prevented the identification of a satisfactory measurement model, with lessons for the measurement of chronic pain impact. In the English Longitudinal Study of Ageing, a measurement model for chronic pain impact was identified, albeit limited to capturing the impact of pain on self-care and productivity but lacking coverage related to social participation. In conjunction with its supplementary material, this focus article aims to encourage exploration of these valuable prospectively collected data; to support researchers to make explicit the relationships between items in the databases and constructs of interest in pain research; and to use empirical methods to estimate the possible biases in these variables. PERSPECTIVE: This focus article outlines a theory-driven approach for fitting new measurement models to data from large cohort studies, and evaluating their psychometric properties. This aims to help researchers develop an empirical understanding of the gains and limitations connected with the process of re-purposing the data stored in these datasets.

The impact of the COVID-19 pandemic on a cohort of Labrador retrievers in England.

BACKGROUND: The COVID-19 pandemic is likely to have affected the welfare and health of dogs due to surges in adoptions and purchases, changes in the physical and mental health and financial status of dog owners, changes in dogs' lifestyle and routines and limited access to veterinary care. The aims of this study were to investigate whether COVID-19 restrictions were associated with differences in Labrador retrievers' lifestyle, routine care, insurance status, illness incidence or veterinary attendance with an illness, who were living in England and enrolled in Dogslife, an owner-based cohort study. Longitudinal questionnaire data from Dogslife that was relevant to the dates between the 23rd of March and the 4th of July 2020, during COVID-19 restrictions in England, were compared to data between the same dates in previous years from 2011 to 2019 using mixed regression models and adjusted chi-squared tests. RESULTS: Compared with previous years (March 23rd to July 4th, 2010 to 2019), the COVID-19 restrictions study period (March 23rd to July 4th 2020) was associated with owners reporting increases in their dogs' exercise and worming and decreases in insurance, titbit-feeding and vaccination. Odds of owners reporting that their dogs had an episode of coughing (0.20, 95% CI: 0.04-0.92) and that they took their dogs to a veterinarian with an episode of any illness (0.58, 95% CI: 0.45-0.76) were lower during the COVID-19 restrictions compared to before. During the restrictions period, owners were less likely to report that they took their dogs to a veterinarian with certain other illnesses, compared to before this period. CONCLUSIONS: Dogslife provided a unique opportunity to study prospective questionnaire data from owners already enrolled on a longitudinal cohort study. This approach minimised bias associated with recalling events prior to the pandemic and allowed a wider population of dogs to be studied than is available from primary care data. Distinctive insights into owners' decision making about their dogs' healthcare were offered. There are clear implications of the COVID-19 pandemic and associated restrictions for the lifestyle, care and health of dogs.

Surveillance of a vomiting outbreak in dogs in the UK using owner-derived and internet search data.

BACKGROUND: In early 2020, the Small Animal Veterinary Surveillance Network reported evidence of an outbreak of acute prolific vomiting in dogs in the UK. The aims of this study were to investigate whether there was evidence for a vomiting outbreak in Dogslife and Google Trends data and to describe its characteristics. METHODS: Incidence of Dogslife vomiting reports and the Google search index for 'dog vomiting' and 'puppy vomiting' between December 2019 and March 2020 was compared to the respective data from the same months in previous years. Risks for dogs vomiting and factors influencing veterinary attendance in Dogslife were identified using multivariable logistic regression. RESULTS: This study confirmed a vomiting outbreak was evident in UK dogs between December 2019 and March 2020 using data from Dogslife and Google Trends. The odds of a vomiting incident being reported to Dogslife was 1.51 (95% CI: 1.24-1.84) in comparison to previous years. Dogslife data identified differences in owner-decision making when seeking veterinary attention and identified factors associated with dogs at higher odds of experiencing a vomiting episode. CONCLUSION: Owner-derived data including questionnaires and internet search queries should be considered a valid, valuable source of information for veterinary population health surveillance.

Is it time to stop sweeping data cleaning under the carpet? A novel algorithm for outlier management in growth data.

All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage across different domains. A decision-making algorithm that modified or deleted growth measurements based on a combination of pre-defined cut-offs and logic rules was designed. Five data cleaning methods for growth were tested with and without the addition of the algorithm and applied to five different longitudinal growth datasets: four uncleaned canine weight or height datasets and one pre-cleaned human weight dataset with randomly simulated errors. Prior to the addition of the algorithm, data cleaning based on non-linear mixed effects models was the most effective in all datasets and had on average a minimum of 26.00% higher sensitivity and 0.12% higher specificity than other methods. Data cleaning methods using the algorithm had improved data preservation and were capable of correcting simulated errors according to the gold standard; returning a value to its original state prior to error simulation. The algorithm improved the performance of all data cleaning methods and increased the average sensitivity and specificity of the non-linear mixed effects model method by 7.68% and 0.42% respectively. Using non-linear mixed effects models combined with the algorithm to clean data allows individual growth trajectories to vary from the population by using repeated longitudinal measurements, identifies consecutive errors or those within the first data entry, avoids the requirement for a minimum number of data entries, preserves data where possible by correcting errors rather than deleting them and removes duplications intelligently. This algorithm is broadly applicable to data cleaning anthropometric data in different mammalian species and could be adapted for use in a range of other domains.