Integrating CRASH, Hospital, and Roadway Data to Investigate the Effect of Cable Median Barriers on Injury Severity

Executive Summary In median-involved crashes, the odds of a police-reported injury were estimated to be 42% lower on road segments with a cable median barrier (CMB) than on road segments with a concrete median barrier, and the difference was statistically significant [odds ratio 0.58, 95% confidence interval (0.43, 0.78)]. In median-involved crashes, the odds of having an injury severity score of 8 or greater were estimated to be 34% higher on road segments with a CMB than on road segments with a concrete median barrier; however, the difference was not statistically significant [odds ratio 1.34, 95% confidence interval (0.67, 2.66). In median-involved crashes, the odds of having a police-reported injury were estimated to be 48% lower on road segments with a CMB than on road segments with a no median barrier; however, the difference was not statistically significant [odds ratio 0.52, 95% confidence interval (0.20, 1.31). In median-involved crashes, the odds of having an injury severity score of 4 or greater were estimated to be 65% lower on road segments with a CMB than on road segments with a no median barrier; however, the difference was not statistically significant [odds ratio 0.35, 95% confidence interval (0.04, 3.02). Sample size (numbers of vehicles and occupants involved in median-involved crashes for each median barrier type) was smaller than anticipated, resulting in low statistical power to assess differences in injury risk for different median barrier types. The findings raise the possibility that in some cases conclusions based on physician-based injury severity measures differ from conclusions based on police-reported injury severity measures The question of differences in police- vs. physician-reported injury severity measures bears further investigation using approaches that address lessons learned from this pilot study. This study did not address the question of which type of median barrier is most effective at preventing crashes altogether; it only assessed the risk of injury in crashes that occurred and were reported by polic

Local Data Spaces: Leveraging trusted research environments for secure location-based policy research in the age of coronavirus disease-2019

This work explores the use of Trusted Research Environments for the secure analysis of sensitive, record-level data on local coronavirus disease-2019 (COVID-19) inequalities and economic vulnerabilities. The Local Data Spaces (LDS) project was a targeted rapid response and cross-disciplinary collaborative initiative using the Office for National Statistics’ Secure Research Service for localized comparison and analysis of health and economic outcomes over the course of the COVID-19 pandemic. Embedded researchers worked on co-producing a range of locally focused insights and reports built on secure secondary data and made appropriately open and available to the public and all local stakeholders for wider use. With secure infrastructure and overall data governance practices in place, accredited researchers were able to access a wealth of detailed data and resources to facilitate more targeted local policy analysis. Working with data within such infrastructure as part of a larger research project involved advanced planning and coordination to be efficient. As new and novel granular data resources become securely available (e.g., record-level administrative digital health records or consumer data), a range of local policy insights can be gained across issues of public health or local economic vitality. Many of these new forms of data however often come with a large degree of sensitivity around issues of personal identifiability and how the data is used for public-facing research and require secure and responsible use. Learning to work appropriately with secure data and research environments can open up many avenues for collaboration and analysis

A deep learning approach to identify unhealthy advertisements in street view images

While outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements, which may encourage their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool 360 ∘ Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 25,349, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th–18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities. © 2021, The Author(s)

A deep learning approach to identify unhealthy advertisements in street view images

While outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements encouraging their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool 360 degree Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 26,645, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th -- 18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and broadband). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas, and those frequented by students and children carrying excess weight. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities

Archetypes of Footfall Context: Quantifying Temporal Variations in Retail Footfall in relation to Micro-Location Characteristics

The UK retail sector is constantly changing and evolving. The increasing share of online sales and the development of out-of-town retail provision, in conjunction with the 2008–09 economic crisis, have disproportionately impacted high streets and physical retail negatively. Understanding and adapting to these changes is fundamental to the vitality, sustainability and prosperity of businesses, communities and the economy. However, there is a need for better information to support attempts to revitalise UK high streets and retail centres, and advances in sensor technology have made this possible. Footfall provides a commonly used heuristic of retail centre vitality and can be increasingly estimated in automated ways through sensing technology. However, footfall counts are influenced by a range of externalities such as aspects of retail centre function, morphology, connectivity and attractiveness. The key contribution of this paper is to demonstrate how footfall patterns are expressed within the varying context of different retail centre architypes providing both a useful tool for benchmarking and planning; but also making a theoretical contribution to the understanding of retail mobilities. This paper integrates a range of contextual data to develop a classification of footfall sensor locations; producing three representations of sensor micro-locations across Great Britain: chain and comparison retail micro-locations, business and independent micro-locations and value-orientated convenience retail micro-locations. These three groups display distinct daily and weekly footfall magnitudes and distributions, which are attributed to micro-locational differences in their morphology, connectivity and function

Using loyalty card records and machine learning to understand how self-medication purchasing behaviours vary seasonally in England, 2012–2014

This paper examines objective purchasing information for inherently seasonal self-medication product groups using transaction-level loyalty card records. Predictive models are applied to predict future monthly self-medication purchasing. Analyses are undertaken at the lower super output area level, allowing the exploration of ~300 retail, social, demographic and environmental predictors of purchasing. The study uses a tree ensemble predictive algorithm, applying XGBoost using one year of historical training data to predict future purchase patterns. The study compares static and dynamic retraining approaches. Feature importance rank comparison and accumulated local effects plots are used to ascertain insights of the influence of different features. Clear purchasing seasonality is observed for both outcomes, reflecting the climatic drivers of the associated minor ailments. Although dynamic models perform best, where previous year behaviour differs greatly, predictions had higher error rates. Important features are consistent across models (e.g. previous sales, temperature, seasonality). Feature importance ranking had the greatest difference where seasons changed. Accumulated local effects plots highlight specific ranges of predictors influencing self-medication purchasing. Loyalty card records offer promise for monitoring the prevalence of minor ailments and reveal insights about the seasonality and drivers of over-the-counter medicine purchasing in England

Prevalence of psychosis in black ethnic minorities in Britain: analysis based on three national surveys

Purpose A considerable excess of psychosis in black ethnic minorities is apparent from clinical studies, in Britain, as in other developed economies with white majority populations. This excess is not so marked in population surveys. Equitable health service provision should be informed by the best estimates of the excess. We used national survey data to establish the difference in the prevalence of psychosis between black ethnic groups and the white majority in the British general population. Methods Analysis of the combined datasets (N = 26,091) from the British national mental health surveys of 1993, 2000 and 2007. Cases of psychosis were determined either by the use of the Schedules for Clinical Assessment in Neuropsychiatry (SCAN), or from a combination of screening items. We controlled for sex, age, social class, unemployment, design features and other putative confounders, using a Disease Risk Score. Results People from black ethnic minorities had an excess prevalence rate of psychosis compared with the white majority population. The OR, weighted for study design and response rate, was 2.72 (95 % CI 1.3–5.6, p = 0.002). This was marginally increased after controlling for potential confounders (OR = 2.90, 95 % CI 1.4–6.2, p = 0.006). Conclusions The excess of psychosis in black ethnic minority groups was similar to that in two previous British community surveys, and less than that based on clinical studies. Even so it confirms a considerable need for increased mental health service resources in areas with high proportions of black ethnic minority inhabitants

Open data on health-related neighbourhood features in Great Britain

Our study details the creation of a series of national open source low-level geographical measures of accessibility to health-related features for Great Britain. We create 14 measures across three domains: retail environment (fast food outlets, gambling outlets, pubs/bars/nightclubs, off-licences, tobacconists), health services (General Practitioners, pharmacies, dentists, hospitals, leisure centres) and the physical environment (green space and air quality). Using the network analysis process of Routino, postcode accessibility (km) to each of these features were calculated for the whole of Great Britain. An average score for each domain was calculated and subsequently combined to form an overall Index highlighting ‘Access to Healthy Assets and Hazards’. We find the most accessible healthy areas are concentrated in the periphery of the urban cores, whilst the least accessible healthy areas are located in the urban cores and the rural areas. The open data resource is important for researchers and policy makers alike with an interest in measuring the role of spatial features on health

A deep learning approach to identify unhealthy advertisements in street view images

While outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements encouraging their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool 360 Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 25,349, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th - 18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and broadband). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.Comment: 13 pages, 5 figures, 3 table. To appear in Nature Scientific Report