Article Text

This article has a correction. Please see:

Download PDFPDF

Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning
  1. Douglas Noble1,
  2. Dianna Smith2,
  3. Rohini Mathur1,
  4. John Robson1,
  5. Trisha Greenhalgh1
  1. 1Centre for Primary Care and Public Health, Barts and The London School of Medicine and Dentistry, London, UK
  2. 2Department of Primary Care and Public Health, Imperial College London, London, UK
  1. Correspondence to Dr Douglas Noble; d.noble{at}


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.

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: and

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.


  • To cite: Noble D, Smith D, Mathur R, et al. Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning. BMJ Open 2012;2:e000711. doi:10.1136/bmjopen-2011-000711

  • Contributors DN led the conceptualisation and management of the project, briefed and supported all researchers, assisted with data analysis and the technical process of creating the maps and led the writing of the paper. DS performed all of the mapping procedures, advised on methodology and revised versions of the manuscript. RM extracted and cleaned quantitative data from electronic general practice records and commented on versions of the manuscript. JR oversaw data extraction from general practice records, led on information governance and revised versions of the manuscript. TG helped conceptualise and manage the study, assisted with interpreting the data and developing the maps and revised versions of the manuscript. JR and TG act as guarantors.

  • Funding The study was funded from small grants from Tower Hamlets and Newham and City & Hackney Primary Care Trusts, and an MRC fellowship (G0802447) for DS. The Primary Care Trusts funded the research in return for a separate report on diabetes risk in East London. The funders had no role in the analysis of data or the content of the final manuscript. The study was exempted from research ethics approval by the Chair of East London and The City Research Ethics Committee, on the grounds it was audit and service development.

  • Competing interests JR was an author of the QDScore.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement No further data to share.

Linked Articles

  • Miscellaneous
    British Medical Journal Publishing Group