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      Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning

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          Abstract

          Objective

          To explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams.

          Study design

          Cross-sectional geospatial analysis using routinely collected general practitioner electronic record data.

          Sample and setting

          Tower Hamlets, an inner-city district of London, UK, characterised by high socioeconomic and ethnic diversity and high prevalence of non-communicable diseases.

          Methods

          The authors used type 2 diabetes as an example. The data set was drawn from electronic general practice records on all non-diabetic individuals aged 25–79 years in the district (n=163 275). The authors used a validated instrument, QDScore, to calculate 10-year risk of developing type 2 diabetes. Using specialist mapping software (ArcGIS), the authors produced visualisations of how these data varied by lower and middle super output area across the district. The authors enhanced these maps with information on examples of locality-based social determinants of health (population density, fast food outlets and green spaces). Data were piloted as three types of geospatial map (basic, heat and ring). The authors noted practical, technical and information governance challenges involved in producing the maps.

          Results

          Usable data were obtained on 96.2% of all records. One in 11 adults in our cohort was at ‘high risk’ of developing type 2 diabetes with a 20% or more 10-year risk. Small-area geospatial mapping illustrated ‘hot spots’ where up to 17.3% of all adults were at high risk of developing type 2 diabetes. Ring maps allowed visualisation of high risk for type 2 diabetes by locality alongside putative social determinants in the same locality. The task of downloading, cleaning and mapping data from electronic general practice records posed some technical challenges, and judgement was required to group data at an appropriate geographical level. Information governance issues were time consuming and required local and national consultation and agreement.

          Conclusions

          Producing small-area geospatial maps of diabetes risk calculated from general practice electronic record data across a district-wide population was feasible but not straightforward. Geovisualisation of epidemiological and environmental data, made possible by interdisciplinary links between public health clinicians and human geographers, allows presentation of findings in a way that is both accessible and engaging, hence potentially of value to commissioners and policymakers. Impact studies are needed of how maps of chronic disease risk might be used in public health and urban planning.

          Article summary

          Article focus
          • To explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams.

          Key messages
          • Creating small-area geospatial maps of risk of type 2 diabetes is feasible using routinely collected data from electronic general practice records.

          • Maps complement a traditional statistical approach to public health data, requiring different ways of processing and presenting information.

          • Such maps may be of use to commissioners and public health planners who seek to make sense of vast amounts of routine health information.

          Strengths and limitations of this study
          • The study uses routinely collected local individual patient data to generate high-quality small-area maps of disease risk across an entire district.

          • Quality and completeness of the data set from which the geospatial maps were derived was high.

          • A potential limitation of our study is the uniqueness of the local IT context. In order for the method used here to be successfully reproduced by others, a number of conditions need to be met.

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          Most cited references19

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          The built environment and obesity: a systematic review of the epidemiologic evidence.

          We completed a systematic search of the epidemiologic literature on built environment and obesity and identified 63 relevant papers, which were then evaluated for the quality of between-study evidence. We were able to classify studies into one of two primary approaches for defining place and corresponding geographic areas of influence: those based on contextual effects derived from shared pre-determined administrative units and those based on individually unique geographic buffers. The 22 contextual papers evaluated 80 relations, 38 of which did not achieve statistical significance. The 15 buffer papers evaluated 40 relations, 24 of which did not achieve statistical significance. There was very little between-study similarity in methods in both types of approaches, which prevented estimation of pooled effects. The great heterogeneity across studies limits what can be learned from this body of evidence. Copyright 2009 Elsevier Ltd. All rights reserved.
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            Risk models and scores for type 2 diabetes: systematic review

            Objective To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. Design Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. Results 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. Conclusion Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.
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              Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt.

              The American "stroke belt" has contributed to the study of stroke. However, U.S. geographic patterns of diabetes have not been as specifically characterized. This study identifies a geographically coherent region of the U.S. where the prevalence of diagnosed diabetes is especially high, called the "diabetes belt." In 2010, data from the 2007 and 2008 Behavioral Risk Factor Surveillance System were combined with county-level diagnosed diabetes prevalence estimates. Counties in close proximity with an estimated prevalence of diagnosed diabetes ≥11.0% were considered to define the diabetes belt. Prevalence of risk factors in the diabetes belt was compared to that in the rest of the U.S. The fraction of the excess risk associated with living in the diabetes belt associated with selected risk factors, both modifiable (sedentary lifestyle, obesity) and nonmodifiable (age, gender, race/ethnicity, education), was calculated. A diabetes belt consisting of 644 counties in 15 mostly southern states was identified. People in the diabetes belt were more likely to be non-Hispanic African-American, lead a sedentary lifestyle, and be obese than in the rest of the U.S. Thirty percent of the excess risk was associated with modifiable risk factors, and 37% with nonmodifiable factors. Nearly one third of the difference in diabetes prevalence between the diabetes belt and the rest of the U.S. is associated with sedentary lifestyle and obesity. Culturally appropriate interventions aimed at decreasing obesity and sedentary lifestyle in counties within the diabetes belt should be considered. Published by Elsevier Inc.
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                Author and article information

                Journal
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2012
                15 February 2012
                15 February 2012
                : 2
                : 1
                : e000711
                Affiliations
                [1 ]Centre for Primary Care and Public Health, Barts and The London School of Medicine and Dentistry, London, UK
                [2 ]Department of Primary Care and Public Health, Imperial College London, London, UK
                Author notes
                Correspondence to Dr Douglas Noble; d.noble@ 123456qmul.ac.uk
                Article
                bmjopen-2011-000711
                10.1136/bmjopen-2011-000711
                3282296
                22337817
                7207913b-ad73-488c-834d-9b322e326713
                © 2012, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

                History
                : 2 December 2011
                : 27 January 2012
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