Targeted policy intervention for reducing red meat consumption: conflicts and trade-offs.

BACKGROUND: There are a range of policies and guidelines focused on meat consumption which aim to tackle health and environmental issues. Policies are often siloed in nature and propose universal limits on consumption. Despite this, there will be a number of conflicts and trade-offs between interest groups. This study explores secondary impacts associated with guidelines issued by the World Cancer Research Fund and assesses the utility of a targeted policy intervention strategy for reducing red meat consumption. METHODS: We used highly detailed consumption data of over 5,000 individuals from the National Diet and Nutrition Survey. We firstly compared individual consumption against the policy guidelines to identify demographic groups most likely to consume above recommended levels. We then synthetically modified the food diary data to investigate the secondary impacts of adherence to the recommendations by all individuals. We assessed changes in overall consumption, nutrient intake (iron, zinc, vitamin B12, vitamin B3, fat and saturated fat) and global warming potential. We also projected future impacts under various population projections. RESULTS: We found that certain demographic groups are much more likely to exceed the recommendations and would therefore benefit from a targeted intervention approach. Our results provide a baseline for which the impacts of any meat substitute diets can be assessed against. Whilst secondary health benefits may be realised by reducing intake of certain nutrients (e.g. fats), negative impacts may occur due to the reduced intake of other nutrients (e.g. iron, zinc). Reduced overall consumption is likely to have implications for the wider meat industry whilst complementary impacts would occur in terms of reduced greenhouse gas emissions. Impacts will be counteracted or maybe even reversed by any substitute products, highlighting the need to carefully consider the suitability and impacts of meat-replacements. CONCLUSION: The future structure of the meat industry will depend on how conflicts and trade-offs are addressed and how more holistic policy ideas are implemented. This research provides a framework for using demographic and consumption data to reduce negative trade-offs and improve policy effectiveness.

A foresight whole systems obesity classification for the English UK biobank cohort.

BACKGROUND: The number of people living with obesity or who are overweight presents a global challenge, and the development of effective interventions is hampered by a lack of research which takes a joined up, whole system, approach that considers multiple elements of the complex obesity system together. We need to better understand the collective characteristics and behaviours of those who are overweight or have obesity and how these differ from those who maintain a healthy weight. METHODS: Using the UK Biobank cohort we develop an obesity classification system using k-means clustering. Variable selection from the UK Biobank cohort is informed by the Foresight obesity system map across key domains (Societal Influences, Individual Psychology, Individual Physiology, Individual Physical Activity, Physical Activity Environment). RESULTS: Our classification identifies eight groups of people, similar in respect to their exposure to known drivers of obesity: 'Younger, urban hard-pressed', 'Comfortable, fit families', 'Healthy, active and retirees', 'Content, rural and retirees', 'Comfortable professionals', 'Stressed and not in work', 'Deprived with less healthy lifestyles' and 'Active manual workers'. Pen portraits are developed to describe the characteristics of these different groups. Multinomial logistic regression is used to demonstrate that the classification can effectively detect groups of individuals more likely to be living with overweight or obesity. The group identified as 'Comfortable, fit families' are observed to have a higher proportion of healthy weight, while three groups have increased relative risk of being overweight or having obesity: 'Active manual workers', 'Stressed and not in work' and 'Deprived with less healthy lifestyles'. CONCLUSIONS: This paper presents the first study of UK Biobank participants to adopt this obesity system approach to characterising participants. It provides an innovative new approach to better understand the complex drivers of obesity which has the potential to produce meaningful tools for policy makers to better target interventions across the whole system to reduce overweight and obesity.

A data flow process for confidential data and its application in a health research project.

BACKGROUND: The use of linked healthcare data in research has the potential to make major contributions to knowledge generation and service improvement. However, using healthcare data for secondary purposes raises legal and ethical concerns relating to confidentiality, privacy and data protection rights. Using a linkage and anonymisation approach that processes data lawfully and in line with ethical best practice to create an anonymous (non-personal) dataset can address these concerns, yet there is no set approach for defining all of the steps involved in such data flow end-to-end. We aimed to define such an approach with clear steps for dataset creation, and to describe its utilisation in a case study linking healthcare data. METHODS: We developed a data flow protocol that generates pseudonymous datasets that can be reversibly linked, or irreversibly linked to form an anonymous research dataset. It was designed and implemented by the Comprehensive Patient Records (CPR) study in Leeds, UK. RESULTS: We defined a clear approach that received ethico-legal approval for use in creating an anonymous research dataset. Our approach used individual-level linkage through a mechanism that is not computer-intensive and was rendered irreversible to both data providers and processors. We successfully applied it in the CPR study to hospital and general practice and community electronic health record data from two providers, along with patient reported outcomes, for 365,193 patients. The resultant anonymous research dataset is available via DATA-CAN, the Health Data Research Hub for Cancer in the UK. CONCLUSIONS: Through ethical, legal and academic review, we believe that we contribute a defined approach that represents a framework that exceeds current minimum standards for effective pseudonymisation and anonymisation. This paper describes our methods and provides supporting information to facilitate the use of this approach in research.

Identifying the effect of retail brands on private residential rental prices in Great Britain.

This study extends our understanding of the influence of proximity to retail grocery provision on housing rental prices. To achieve this, extensive data on the size and location of retail outlets are combined with neighbourhood rental values for small areas across a two year period, together with varied contextual data for each area. In order to control the influence of many confounding variables in the determination of housing rentals, the technique of propensity score matching is applied. This provides a sophisticated means for the comparison between areas where there is substantial natural variation, rather than manageable controls. For a variety of types of retail brands, only a significant relationship is found between the proximity of a Premium retail outlet and the housing rental value. The findings of this research allow local planning officers to further understand the impact of planning applications on the potential for gentrification and the affordability of neighbouring housing. Supplementary Information: The online version contains supplementary material available at 10.1007/s10901-021-09904-2.

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.

Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: "Active 9 to 5", "Active", "Morning Movers", "Get up and Active", "Live for the Weekend", "Moderates", "Leisurely 9 to 5", "Sedate" and "Inactive". These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use.

Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach.

Consumer food environments have transformed dramatically in the last decade. Food outlet prevalence has increased, and people are eating food outside the home more than ever before. Despite these developments, national spending on food control has reduced. The National Audit Office report that only 14% of local authorities are up to date with food business inspections, exposing consumers to unknown levels of risk. Given the scarcity of local authority resources, this paper presents a data-driven approach to predict compliance for newly opened businesses and those awaiting repeat inspections. This work capitalizes on the theory that food outlet compliance is a function of its geographic context, namely the characteristics of the neighborhood within which it sits. We explore the utility of three machine learning approaches to predict non-compliant food outlets in England and Wales using openly accessible socio-demographic, business type, and urbanness features at the output area level. We find that the synthetic minority oversampling technique alongside a random forest algorithm with a 1:1 sampling strategy provides the best predictive power. Our final model retrieves and identifies 84% of total non-compliant outlets in a test set of 92,595 (sensitivity = 0.843, specificity = 0.745, precision = 0.274). The originality of this work lies in its unique and methodological approach which combines the use of machine learning with fine-grained neighborhood data to make robust predictions of compliance.

Geodemographic Patterns of Meat Expenditure in Great Britain.

The future of the meat industry will require the management of important trade-offs between economic, environmental and health aspects of both humans and animals. Understanding the patterns and trends of meat expenditure and consumption is crucial for assessing the current resilience of the system and for economic, planning, health and environmental applications. Here, we show how the technique of geodemographic classification, combined with fine scale expenditure estimates can be used to explore temporal and spatial patterns of meat expenditure in Great Britain between 2008 and 2017. Whilst the expenditure patterns of some food categories such as sausages remained relatively consistent, others such as lamb show a trend towards a reduced proportion of expenditure and increased inequality of purchases. Short term changes in expenditure patterns also occurred, potentially due to product specific price variability, price elasticities or zoonotic disease scare. Environmental attitudes, financial constraints and the prominence of communities who do not eat meat for religious or cultural reasons are likely to be driving the differences between geodemographic groups. The methodology and results could be a valuable tool for policy makers in the meat industry and beyond.

A dynamic microsimulation model for epidemics.

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.

Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study?

COVID-19 is a disease that has been shown to have outcomes that vary by certain socio-demographic and socio-economic groups. It is increasingly important that an understanding of these outcomes should be derived not from the consideration of one aspect, but by a more multi-faceted understanding of the individual. In this study use is made of a recent obesity driven classification of participants in the United Kingdom Biobank (UKB) to identify trends in COVID-19 outcomes. This classification is informed by a recently created obesity systems map, and the COVID-19 outcomes are: undertaking a test, a positive test, hospitalisation and mortality. It is demonstrated that the classification is able to identify meaningful differentials in these outcomes. This more holistic approach is recommended for identification and prioritisation of COVID-19 risk and possible long-COVID determination.

Understanding Barriers to Novel Data Linkages: Topic Modeling of the Results of the LifeInfo Survey.

BACKGROUND: Novel consumer and lifestyle data, such as those collected by supermarket loyalty cards or mobile phone exercise tracking apps, offer numerous benefits for researchers seeking to understand diet- and exercise-related risk factors for diseases. However, limited research has addressed public attitudes toward linking these data with individual health records for research purposes. Data linkage, combining data from multiple sources, provides the opportunity to enhance preexisting data sets to gain new insights. OBJECTIVE: The aim of this study is to identify key barriers to data linkage and recommend safeguards and procedures that would encourage individuals to share such data for potential future research. METHODS: The LifeInfo Survey consulted the public on their attitudes toward sharing consumer and lifestyle data for research purposes. Where barriers to data sharing existed, participants provided unstructured survey responses detailing what would make them more likely to share data for linkage with their health records in the future. The topic modeling technique latent Dirichlet allocation was used to analyze these textual responses to uncover common thematic topics within the texts. RESULTS: Participants provided responses related to sharing their store loyalty card data (n=2338) and health and fitness app data (n=1531). Key barriers to data sharing identified through topic modeling included data safety and security, personal privacy, requirements of further information, fear of data being accessed by others, problems with data accuracy, not understanding the reason for data linkage, and not using services that produce these data. We provide recommendations for addressing these issues to establish the best practice for future researchers interested in using these data. CONCLUSIONS: This study formulates a large-scale consultation of public attitudes toward this kind of data linkage, which is an important first step in understanding and addressing barriers to participation in research using novel consumer and lifestyle data.

Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles.

Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns. Results identified seven dietary purchase patterns that we named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders -who may buy as an individual or for a household- is less than the adult reference intake, but this varies according to dietary purchase pattern. In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.

Food safety vulnerability: Neighbourhood determinants of non-compliant establishments in England and Wales.

This paper utilises logistic regression to identify ecological determinants of non-compliant food outlets in England and Wales. We consider socio-demographic, urbanness and business type features to better define vulnerable populations based on the characteristics of the area within which they live. We find a clear gradient of association between deprivation and non-compliance, with outlets in the most deprived areas 25% less likely (OR = 0.75) to meet hygiene standards than those in the least deprived areas. Similarly, we find outlets located in conurbation areas have a lower probability of compliance (OR = 0.678) than establishments located in rural and affluent areas. Therefore, individuals living in these neighbourhoods can be considered more situationally vulnerable than those living in rural and non-deprived areas. Whilst comparing compliance across business types, we find that takeaways and sandwich shops (OR = 0.504) and convenience retailers (OR = 0.905) are significantly less likely to meet hygiene standards compared to restaurants. This is particularly problematic for populations who may be unable to shop outside their immediate locality. Where traditional food safety interventions have failed to consider the prospect of increased risk based on proximity to unsafe and unhygienic food outlets, we re-assess the meaning of vulnerability by considering the type of neighbourhoods within which non-compliant establishments are located. In-lieu of accurate foodborne illness data, we recommend prioritised inspections for outlets in urban and deprived areas. Particularly takeaways, sandwich shops and small convenience retailers.

Evidence from big data in obesity research: international case studies.

BACKGROUND/OBJECTIVE: Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered. METHODS AND RESULTS: Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle. CONCLUSIONS: The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.

The SIPHER Consortium: Introducing the new UK hub for systems science in public health and health economic research.

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER ( Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium's vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing.

Creating a long-term future for big data in obesity research.

Big data are part of the future in obesity research. The ESRC funded Strategic Network for Obesity has together generated a series of papers, published in the International Journal for Obesity illustrating various aspects of their utility, in particular relating to the large social and environmental drivers of obesity. This article is the final part of the series and reflects upon progress to date and identifies four areas that require attention to promote the continued role of big data in research. We additionally include a 'getting started with big data' checklist to encourage more obesity researchers to engage with alternative data resources.

Local level estimates of food, drink and tobacco expenditure for Great Britain.

We present expenditure estimates for 106 product categories across Great Britain for the years 2008-2016. Estimates are at the Local Authority District level (n = 380) and the categories cover all food, drink and tobacco commodities. Reliable, local level expenditure estimates are crucial for understanding broader market trends, assessing economic stability and for projections. This is especially important for commodities such as alcohol, tobacco and unhealthy foods due to their role in the prevalence of non-communicable diseases. There has been relatively little research into local area spatial patterns of expenditure, with existing estimates often of insufficient resolution for informing planning decisions. We use spatial microsimulation to create an archive of expenditure datasets. This was achieved by linking socio-demographic foundations with detailed datasets on individual expenditure. Whilst initially developed to aid investigations into sociodemographic trends in the meat industry, the data have reuse potential in a number of disciplines, including public health, economics, retail geography and environmental management. The framework could be applied to other regions with appropriate data.

Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map.

BACKGROUND: Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed. METHODS: Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out. RESULTS: A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research. CONCLUSIONS: Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.

Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions.

The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probability density functions that are generated by the stochastic dynamical systems and observed experimentally.