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.

Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.

The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one 'self' diagnoses and prescribes over the counter medicines for treatment. The self-care movement has important policy implications, perceived to relieve the National Health Service (NHS) burden, increasing patient subsistence and freeing resources for more serious ailments. However, there has been little research exploring how self-medication behaviours vary between population groups due to a lack of available data. The aim of our study is to evaluate how high street retailer loyalty card data can help inform our understanding of how individuals self-medicate in England. Transaction level loyalty card data was acquired from a national high street retailer for England for 2012-2014. We calculated the proportion of loyalty card customers (n ~ 10 million) within Lower Super Output Areas who purchased the following medicines: 'coughs and colds', 'Hayfever', 'pain relief' and 'sun preps'. Machine learning was used to explore how 50 sociodemographic and health accessibility features were associated towards explaining purchasing of each product group. Random Forests are used as a baseline and Gradient Boosting as our final model. Our results showed that pain relief was the most common medicine purchased. There was little difference in purchasing behaviours by sex other than for sun preps. The gradient boosting models demonstrated that socioeconomic status of areas, as well as air pollution, were important predictors of each medicine. Our study adds to the self-medication literature through demonstrating the usefulness of loyalty card records for producing insights about how self-medication varies at the national level. Big data offer novel insights that add to and address issues that traditional studies are unable to consider. New forms of data through data linkage may offer opportunities to improve current public health decision making surrounding at risk population groups within self-medication behaviours.

Developing an openly accessible multi-dimensional small area index of 'Access to Healthy Assets and Hazards' for Great Britain, 2016.

Health geographers have been long concerned with understanding how the accessibility of individuals to certain environmental features may influence health and wellbeing. Such insights are increasingly being adopted by policy makers for designing healthy neighbourhoods. To support and inform decision making, there is a need for small area national level data. This paper details the creation of a suite of open access health indicators, including a novel multidimensional index summarising 14 health-related features of neighbourhoods for Great Britain. We find no association of our overall index with physical health measures, but a significant association to mental wellbeing.