Changes in some health and lifestyle behaviours are significantly associated with changes in gambling behaviours: Findings from a longitudinal New Zealand population study.

Although a large number of studies have investigated associations between risky gambling behaviours and health, lifestyle and social factors, research has not focused on changes in these factors and associations with changes in gambling risk level. This study utilised existing data from the four waves of the longitudinal New Zealand National Gambling Study to examine associations between changes in substance use, mental and physical health, and quality of life and deprivation with changes in gambling risk level over time. A Markov chain transition model was used to perform these analyses using data from participants who had completed all four waves (11,080 data transitions). Although changes in various covariates were associated with changes in all gambling risk levels, the highest number of significant factors was for transitioning into risky gambling from non-problematic gambling, including development, or continuation, of several negative health and lifestyle factors that may possibly be alleviated by transitioning out of risky gambling. These findings highlight the importance of screening for gambling behaviours when assisting people with substance use, health issues, or social situations or conditions in order to provide appropriate and effective social, health and treatment supports for people whose gambling behaviour increases over time.

Predicting veal-calf trading events in France.

Global trade has been ranked as one of the top five drivers of infectious disease threat events. More specifically, livestock trade is known to increase the speed at which infectious diseases circulate and to facilitate their dissemination over large distances Therefore, predicting animal movements arising from trade is crucial for assessing epidemic risk and the impact of preventive measures. In this study, we developed a statistical framework for predicting trading events using predictors accessible from routinely collected data. We focused on veal calves, a category of animals with significant commercial value; the dataset considered the veal calf trade in France between January 2011 and June 2019. A subset of farms with consistent trade behaviour over time was built to be used throughout the study. To predict sale or purchase event occurrences, our predictive framework was based on random forests as a binary classification tool, an approach that allows a large number of potential predictors. We explored the robustness of model predictions with respect to the delay in data acquisition and prediction lag time. Overall, sales were more accurately predicted than purchasing events. Unsurprisingly, a delay in data acquisition led to a decrease in the performance of indicators, whereas prediction lag time had little impact. Sale-related predictors mostly reflected past trading events, whereas purchase-related predictors were associated with past trading events, farm management and general farm characteristics. The model outputs also suggested that the veal calf trading network is driven by sales rather than by purchases. Regardless of the length of the delay in data acquisition and prediction lag, the random forest approach fitted on data with municipality as trading unit and a 28-day trading period provided better performance scores (F1-score, positive predictive value and negative predictive value) than scenarios with finer temporal and spatial aggregation units. Predicted trade events can therefore be used to reconstruct the entire veal calf trading network and transfers between selling and purchasing units for each period. This predicted network could be further used to simulate the spread of pathogens via animal trade.