Data visualisation to support obesity policy: case studies of data tools for planning and transport policy in the UK

A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation

BACKGROUND

Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people's weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE).

METHODS

Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley's K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic.

RESULTS

We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley's K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results.

DISCUSSION

The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.

Interactive Visualization Applications in Population Health and Health Services Research: Systematic Scoping Review

BACKGROUND

Simple visualizations in health research data, such as scatter plots, heat maps, and bar charts, typically present relationships between 2 variables. Interactive visualization methods allow for multiple related facets such as numerous risk factors to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big health care data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support.

OBJECTIVE

The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods, and tools being used in population health and health services research (HSR) and their subdomains in the last 15 years, from January 1, 2005, to March 30, 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals, and co-design of applications.

METHODS

We adapted standard scoping review guidelines with a peer-reviewed search strategy: 2 independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sectors. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and HSR, as well as their subdomains such as epidemiologic surveillance, health resource planning, access, and use and costs among diverse clinical and demographic populations.

RESULTS

In this companion review to our earlier systematic synthesis of the literature on visual analytics applications, we present findings in 6 major themes of interactive visualization applications developed for 8 major problem categories. We found a wide application of interactive visualization methods, the major ones being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality, and studying medication use patterns. The data sources included mostly secondary administrative and electronic medical record data. In addition, at least two-thirds of the applications involved participatory co-design approaches while introducing a distinct category, embedded research, within co-design initiatives. These applications were in response to an identified need for data-driven insights into knowledge generation and decision support. We further discuss the opportunities stemming from the use of interactive visualization methods in studying global health; inequities, including social determinants of health; and other related areas. We also allude to the challenges in the uptake of these methods.

CONCLUSIONS

Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and HSR. Such applications are being fast used by academic and health care agencies for knowledge discovery, hypotheses generation, and decision support.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/14019.

Developing effective data visualization tools for nutrition: reflections on the design of a Nutrition Scorecard in Nigeria

There has been a growing number of nutrition data visualization tools (DVTs) to monitor progress towards targets and encourage action. However, there are few documented examples of how to go about designing effective DVTs for nutrition-related audiences. In this Open Letter, we summarize reflections from collaborative efforts between the Nigeria Governors' Forum (NGF) and the Data for Decisions to Expand Nutrition Transformation project (DataDENT) in 2019-2021 to design a sub-national nutrition scorecard that aims to hold Nigeria's 36 Governors accountable to nutrition commitments. Our reflections add to an emerging body of work advocating for DVT design processes to develop a specific theory of change for how the DVT will influence target groups and achieve aims. Once the target audience is identified, it is important to create a strong engagement strategy to ensure that the DVT promotes constructive action. We also highlight the importance of identifying actionable indicators through participatory processes. We hope that these insights about collaborative DVT design can be applied by countries and institutions who want to develop similar tools to advance the nutrition agenda in their context.

Translating health information into policy-making: A pragmatic framework

Knowledge translation (KT) is increasingly acknowledged to have the potential to improve policy-making. The value of health information (HI), as part of the KT context, is now also increasingly understood. This paper aims to identify existing tools for the translation of HI into policy-making and to develop a related framework facilitating future application of these identified tools. Updating and building upon a scoping review undertaken for the Health Evidence Network (HEN) Synthesis Report No. 54, commissioned by the World Health Organization (WHO) Regional Office for Europe in 2017, a literature search was conducted using the same databases (PubMed and Scopus) and the same keywords as in the WHO/HEN scoping review. All papers elaborating on tools enhancing the use of HI in policy-making were included. Of the 2549 records screened, 17 publications were included in this study. This review identified four different types of tools: 1) Visualisation and modelling tools, 2) Information packaging and synthesis tools, 3) Communication and dissemination tools and 4) Information linkage and exchange tools. The distinctions between these are fluid as different tools can be combined or incorporated into one another to complement each other. Our framework shows that communication/dissemination or linkage tools are crucial to effectively inform policy decisions through HI. This study helps to understand and guide the processes of KT of HI.

Stakeholder experiences of using online spatial data visualisation tools for local public health decision support: A qualitative study

The distribution of food outlets within towns and cities and the provision of active travel infrastructure have been associated with health behaviours that can contribute to obesity risk. Decision-makers describe a lack of local data and research evidence as a barrier to policy adoption to improve the public's health. Online spatial data visualisation tools created by researchers can help to bridge this gap. We explored stakeholder experiences of using such tools for decision-support, with a focus on facilitators and barriers to use. We conducted 16 qualitative interviews with Public Health, Planning and Transport Planning professionals, who had used two recently-developed tools. Participants described the importance of tools being open access; their use in “story-telling”, particularly to non-experts; and more broadly their use even when imperfect. They expressed that ‘robustness’ of underpinning data was important, however this was not easily defined. Participants employed personal heuristics, principally based on endorsement and developer credibility, to determine suitability for use. We present key learning points for future developers to maximise engagement and impact.

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We conducted 16 interviews with Planners, Transport planners & Public Health professionals in England.

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These stakeholders were users of online spatial data visualisation tools, who deemed them acceptable.

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Robustness of underpinning data was an important precursor to use but was not easily defined.

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Participants assessed developer endorsement and credibility as a marker of quality, which affected use.

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Barriers and facilitators to use should be considered by developers to increase engagement & impact.

Overweight and obesity of school-age children in El Salvador according to two international systems: a population-based multilevel and spatial analysis

BACKGROUND

The World Health Organization (WHO-2007) and the International Obesity Task Force (IOTF-Cole) systems assess child weight status. However, derived estimations often differ. We aimed to a) compare the prevalence of overweight and obesity, b) analyze individual and contextual factors associated with child weight using multilevel analysis and c) explore the spatial distribution of overweight and obesity using both classification systems.

METHODS

We used data from the 2015/2016 National School Height and Weight Census in El Salvador. Information on 111,991 children aged 6.0-9.9 years attending the first grade was analyzed. Body mass index Z-score (BMIZ), overweight and obesity were defined with both classification systems. Weighted kappa was used to measure agreement. Child, school and municipal potential determinants of BMIZ were examined by multilevel analysis. Municipal spatial clustering of overweight and obesity was tested using Moran's Index and Getis-ord Gi* statistics.

RESULTS

The combined prevalence of overweight and obesity was higher according to the WHO system than the IOTF (30.4% vs 23.1%). The weighted kappa was 0.83. Boys, children attending urban schools, children attending private schools, and children residing in municipalities with high human development index had higher BMIZ than their counterparts. The Moran's indexes were positives and significant. Clusters of high prevalence (above the national prevalence) of overweight and obesity were found in 29 municipalities using the WHO and IOTF systems. For obesity, 28 and 23 municipalities in clusters of high prevalence were detected using the WHO and IOTF criteria, respectively.

CONCLUSIONS

Overweight and obesity is high among school-age children in El Salvador. The prevalence of overweight and obesity was higher when using the WHO system, as compared to the IOTF system. Irrespective of the classification system, the multilevel and spatial analysis derived similar interpretations. These results support the need for national preventive interventions with targeting strategies to reduce overweight and obesity in school-age children.

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.

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.

Biomarker data visualisation for decision making in clinical trials

OBJECTIVE

To understand how visualization of biomarker data is used for decision making in clinical trials, and identify problems with and suggest improvements to this process.

METHODS

We carried out semi-structured interviews with 18 professionals involved in various aspects of developing or using visualizations of biomarker data for decision making in clinical trials. We used an inductive thematic analysis to identify implicit and explicit ideas within the data captured from the interviews.

RESULTS

We identified 6 primary themes, including: how visualizations were used in clinical trials; the importance of having a clear understanding of the underlying data; the purpose or use of the visualization, and the properties of the visualizations themselves. The results show that participants' 'trust' in the visualization depends on access to the underlying data, and that there is currently no standard or straightforward way to support this access.

CONCLUSIONS

Incorporating information about data provenance into biomarker-related visualizations used for decision making in clinical trials may increase users' trust, and therefore facilitate the decision making process.