A rank based social norms model of how people judge their levels of drunkenness whilst intoxicated

Background: A rank based social norms model predicts that drinkers’ judgements about their drinking will be based on the rank of their breath alcohol level amongst that of others in the immediate environment, rather than their actual breath alcohol level, with lower relative rank associated with greater feelings of safety. This study tested this hypothesis and examined how people judge their levels of drunkenness and the health consequences of their drinking whilst they are intoxicated in social drinking environments. Methods: Breath alcohol testing of 1,862 people (mean age = 26.96 years; 61.86 % male) in drinking environments. A subset (N = 400) also answered four questions asking about their perceptions of their drunkenness and the health consequences of their drinking (plus background measures). Results: Perceptions of drunkenness and the health consequences of drinking were regressed on: (a) breath alcohol level, (b) the rank of the breath alcohol level amongst that of others in the same environment, and (c) covariates. Only rank of breath alcohol level predicted perceptions: How drunk they felt (b 3.78, 95 % CI 1.69 5.87), how extreme they regarded their drinking that night (b 3.7, 95 % CI 1.3 6.20), how at risk their long-term health was due to their current level of drinking (b 4.1, 95 % CI 0.2 8.0) and how likely they felt they would experience liver cirrhosis (b 4.8. 95 % CI 0.7 8.8). People were more influenced by more sober others than by more drunk others. Conclusion: Whilst intoxicated and in drinking environments, people base judgements regarding their drinking on how their level of intoxication ranks relative to that of others of the same gender around them, not on their actual levels of intoxication. Thus, when in the company of others who are intoxicated, drinkers were found to be more likely to underestimate their own level of drinking, drunkenness and associated risks. The implications of these results, for example that increasing the numbers of sober people in night time environments could improve subjective assessments of drunkenness, are discussed

All-Wales licensed premises intervention (AWLPI): a randomised controlled trial to reduce alcohol-related violence

Background: Alcohol-related violence in and in the vicinity of licensed premises continues to place a considerable burden on the United Kingdom’s (UK) health services. Robust interventions targeted at licensed premises are therefore required to reduce the costs of alcohol-related harm. Previous evaluations of interventions in licensed premises have a number of methodological limitations and none have been conducted in the UK. The aim of the trial was to determine the effectiveness of the Safety Management in Licensed Environments intervention designed to reduce alcohol-related violence in licensed premises, delivered by Environmental Health Officers, under their statutory authority to intervene in cases of violence in the workplace.<p></p> Methods/Design: A national randomised controlled trial, with licensed premises as the unit of allocation. Premises were identified from all 22 Local Authorities in Wales. Eligible premises were those with identifiable violent incidents on premises, using police recorded violence data. Premises were allocated to intervention or control by optimally balancing by Environmental Health Officer capacity in each Local Authority, number of violent incidents in the 12 months leading up to the start of the project and opening hours. The primary outcome measure is the difference in frequency of violence between intervention and control premises over a 12 month follow-up period, based on a recurrent event model. The trial incorporates an embedded process evaluation to assess intervention implementation, fidelity, reach and reception, and to interpret outcome effects, as well as investigate its economic impact.<p></p> Discussion: The results of the trial will be applicable to all statutory authorities directly involved with managing violence in the night time economy and will provide the first formal test of Health and Safety policy in this environment. If successful, opportunities for replication and generalisation will be considered.<p></p&gt

Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM)-based texture measures

The severity of sustained injury resulting from assault-related violence can be minimized by reducing detection time. However, it has been shown that human operators perform poorly at detecting events found in video footage when presented with simultaneous feeds. We utilize computer vision techniques to develop an automated method of violence detection that can aid a human operator. We observed that violence in city centre environments often occur in crowded areas, resulting in individual actions being occluded by other crowd members. Measures of visual texture have shown to be effective at encoding crowd appearance. Therefore, we propose modelling crowd dynamics using changes in crowd texture. We refer to this approach as Violent Crowd Texture (VCT). Real-world surveillance footage of night time environments and the violent flows dataset were tested using a random forest classifier to evaluate the ability of the VCT method at discriminating between violent and non-violent behaviour. Our method achieves ROC values of 0.98 and 0.91 on our own real world CCTV dataset and the violent flows dataset respectively

Violent behaviour detection using local trajectory response

Surveillance systems in the United Kingdom are prominent, and the number of installed cameras is estimated to be around 1.8 million. It is common for a single person to watch multiple live video feeds when conducting active surveillance, and past research has shown that a person’s effectiveness at successfully identifying an event of interest diminishes the more monitors they must observe. We propose using computer vision techniques to produce a system that can accurately identify scenes of violent behaviour. In this paper we outline three measures of motion trajectory that when combined produce a response map that highlights regions within frames that contain behaviour typical of violence based on local information. Our proposed method demonstrates state-of-the-art classification ability when given the task of distinguishing between violent and non-violent behaviour across a wide variety of violent data, including real-world surveillance footage obtained from local police organisations

The Enduring Effects of Parental Alcohol, Tobacco, and Drug Use on Child Well-being: A Multilevel Meta-Analysis

The effects of psychoactive substance abuse are not limited to the user, but extend to the entire family system, with children of substance abusers being particularly at risk. This meta-analysis attempted to quantify the longitudinal relationship between parental alcohol, tobacco, and drug use and child well-being, investigating variation across a range of substance and well-being indices and other potential moderators. We performed a literature search of peer-reviewed, English language, longitudinal observational studies that reported outcomes for children aged 0 to 18 years. In total, 56 studies, yielding 220 dependent effect sizes, met inclusion criteria. A multilevel random-effects model revealed a statistically significant, small detriment to child well-being for parental substance abuse over time (r = .15). Moderator analyses demonstrated that the effect was more pronounced for parental drug use (r = .25), compared with alcohol use (r = .13), tobacco use (r = .13), and alcohol use disorder (r = .14). Results highlight a need for future studies that better capture the effect of parental psychoactive substance abuse on the full breadth of childhood well-being outcomes and to integrate substance abuse into models that specify the precise conditions under which parental behavior determines child well-being

The effectiveness of an intervention to reduce alcohol-related violence in premises licensed for the sale and on-site consumption of alcohol: a randomized controlled trial

Background and Aims: Premises licensed for the sale and consumption of alcohol can contribute to levels of assault-related injury through poor operational practices that, if addressed, could reduce violence. We tested the real-world effectiveness of an intervention designed to change premises operation, whether any intervention effect changed over time, and the effect of intervention dose. Design: A parallel randomized controlled trial with the unit of allocation and outcomes measured at the level of individual premises. Setting: All premises (public houses, nightclubs or hotels with a public bar) in Wales, UK. Participants: A randomly selected subsample (n=600) of eligible premises (that had one or more violent incidents recorded in police-recorded crime data; n=837) were randomized into control and intervention groups. Intervention and comparator: Intervention premises were audited by Environmental Health Practitioners who identified risks for violence and provided feedback by varying dose (informal, through written advice, follow-up visits) on how risks could be addressed. Control premises received usual practice. Measurements: Police data were used to derive a binary variable describing whether, on each day premises were open, one or more violent incidents were evident over a 455-day period following randomization. Findings: Due to premises being unavailable at the time of intervention delivery 208 received the intervention and 245 were subject to usual practice in an intention-to-treat analysis. The intervention was associated with an increase in police recorded violence compared to normal practice (hazard ratio=1.34, 95% confidence interval=1.20–1.51). Exploratory analyses suggested that reduced violence was associated with greater intervention dose (follow-up visits). Conclusion: An Environmental Health Practitioner-led intervention in premises licensed for the sale and on-site consumption of alcohol resulted in an increase in police recorded violence

An open-data, agent-based model of alcohol related crime

The allocation of resources to challenge city centre violent crime traditionally relies on historical data to identify hot-spots. The usefulness of such data-driven approaches is limited when historical data is scarce or unavailable (e.g. planning of a new city) or insufficiently representative (e.g. does not account for novel events, such as Olympic Games). In some cities, crime data is not systematically accumulated at all. We present a graph-constrained agent based simulation model of alcohol-related violent crime that is capable of predicting areas of likely violent crime without requiring any historical data. The only inputs to our simulation are publicly available geographical data, which makes our method immediately applicable to a wide range of tasks, such as optimal city planning, police patrol optimisation, devising alcohol licensing policies. In experiments, we evaluate our model and demonstrate agreement of our model's predictions on where and when violence will occur with real-world violent crime data. Analyses indicate that our agent based model may be able to make a significant contribution to attempts to prevent violence through deterrence or by design

Association of violence with urban points of interest

The association between alcohol outlets and violence has long been recognised, and is commonly used to inform policing and licensing policies (such as staggered closing times and zoning). Less investigated, however, is the association between violent crime and other urban points of interest, which while associated with the city centre alcohol consumption economy, are not explicitly alcohol outlets. Here, machine learning (specifically, LASSO regression) is used to model the distribution of violent crime for the central 9 km2 of ten large UK cities. Densities of 620 different Point of Interest types (sourced from Ordnance Survey) are used as predictors, with the 10 most explanatory variables being automatically selected for each city. Cross validation is used to test generalisability of each model. Results show that the inclusion of additional point of interest types produces a more accurate model, with significant increases in performance over a baseline univariate alcohol-outlet only model. Analysis of chosen variables for city-specific models shows potential candidates for new strategies on a per-city basis, with combined-model variables showing the general trend in POI/violence association across the UK. Although alcohol outlets remain the best individual predictor of violence, other points of interest should also be considered when modelling the distribution of violence in city centres. The presented method could be used to develop targeted, city-specific initiatives that go beyond alcohol outlets and also consider other locations