open access research feasibility study of geospatial ... · feasibility study of geospatial mapping...

Feasibility study of geospatial mappingof chronic disease risk to inform publichealth commissioning

Douglas Noble,1 Dianna Smith,2 Rohini Mathur,1 John Robson,1

Trisha Greenhalgh1

ABSTRACTObjective: To explore the feasibility of producingsmall-area geospatial maps of chronic disease risk foruse by clinical commissioning groups and publichealth teams.

Study design: Cross-sectional geospatial analysisusing routinely collected general practitioner electronicrecord data.

Sample and setting: Tower Hamlets, an inner-citydistrict of London, UK, characterised by highsocioeconomic and ethnic diversity and highprevalence of non-communicable diseases.

Methods: The authors used type 2 diabetes as anexample. The data set was drawn from electronicgeneral practice records on all non-diabetic individualsaged 25e79 years in the district (n¼163 275). Theauthors used a validated instrument, QDScore, tocalculate 10-year risk of developing type 2 diabetes.Using specialist mapping software (ArcGIS), theauthors produced visualisations of how these datavaried by lower and middle super output area acrossthe district. The authors enhanced these maps withinformation on examples of locality-based socialdeterminants of health (population density, fast foodoutlets and green spaces). Data were piloted as threetypes of geospatial map (basic, heat and ring). Theauthors noted practical, technical and informationgovernance challenges involved in producing themaps.

Results: Usable data were obtained on 96.2% of allrecords. One in 11 adults in our cohort was at ‘highrisk’ of developing type 2 diabetes with a 20% or more10-year risk. Small-area geospatial mapping illustrated‘hot spots’ where up to 17.3% of all adults were at highrisk of developing type 2 diabetes. Ring maps allowedvisualisation of high risk for type 2 diabetes by localityalongside putative social determinants in the samelocality. The task of downloading, cleaning andmapping data from electronic general practice recordsposed some technical challenges, and judgement wasrequired to group data at an appropriate geographicallevel. Information governance issues were timeconsuming and required local and nationalconsultation and agreement.

Conclusions: Producing small-area geospatial mapsof diabetes risk calculated from general practiceelectronic record data across a district-wide populationwas feasible but not straightforward. Geovisualisation

of epidemiological and environmental data, madepossible by interdisciplinary links between publichealth clinicians and human geographers, allowspresentation of findings in a way that is bothaccessible and engaging, hence potentially of value tocommissioners and policymakers. Impact studies areneeded of how maps of chronic disease risk might beused in public health and urban planning.

INTRODUCTIONNon-communicable diseaseNon-communicable diseasesddiabetes,cardiovascular disease, chronic respiratorydisease and cancersdaccount for 60% of

To cite: Noble D, Smith D,Mathur R, et al. Feasibilitystudy of geospatial mappingof chronic disease risk toinform public healthcommissioning. BMJ Open2012;2:e000711.doi:10.1136/bmjopen-2011-000711

< Prepublication history forthis paper is available online.To view these files pleasevisit the journal online (http://dx.doi.org/10.1136/bmjopen-2011-000711).

Received 2 December 2011Accepted 27 January 2012

This final article is availablefor use under the terms ofthe Creative CommonsAttribution Non-Commercial2.0 Licence; seehttp://bmjopen.bmj.com

1Centre for Primary Care andPublic Health, Barts and TheLondon School of Medicineand Dentistry, London, UK2Department of Primary Careand Public Health, ImperialCollege London, London, UK

Correspondence toDr Douglas Noble;[email protected]

ARTICLE SUMMARY

Article focus- To explore the feasibility of producing small-area

geospatial maps of chronic disease risk for useby clinical commissioning groups and publichealth teams.

Key messages- Creating small-area geospatial maps of risk of

type 2 diabetes is feasible using routinelycollected data from electronic general practicerecords.

- Maps complement a traditional statisticalapproach to public health data, requiring differentways of processing and presenting information.

- Such maps may be of use to commissioners andpublic health planners who seek to make senseof 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-areamaps of disease risk across an entire district.

- Quality and completeness of the data set fromwhich the geospatial maps were derived washigh.

- A potential limitation of our study is theuniqueness of the local IT context. In order forthe method used here to be successfullyreproduced by others, a number of conditionsneed to be met.

Noble D, Smith D, Mathur R, et al. BMJ Open 2012;2:e000711. doi:10.1136/bmjopen-2011-000711 1

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global deaths.1 Their cumulative financial burden worldwide in 2008 was estimated to be US$2.35 trillion, andthis burden of disease is projected to greatly increase.2

Almost 350 million people have diabetes, and thenumber expected to die from this cause is predicted todouble between 2005 and 2030.3 In the UK, approxi-mately 4% of the population have diabetes, with 400 newdiagnoses every day; 90% are classified as type 2.4

Prevention of non-communicable diseases is essentialfor long-term reduction in disease burden.5 The 2011United Nations High Level Summit on Non-Communi-cable Disease called for a strengthening of nationalpolicies and health systems, including informationsystems for health planning and management tofacilitate public health interventions.6

Chronic disease risk, health inequalities and socialdeterminants of health are intimately linked.7e9 Therelationship between environmental variables and devel-opment of obesity and type 2 diabetes is significant andcomplex.10 Social and ethnic diversity of urban popula-tions heavily influence chronic disease risk, for example,South Asians are four times more likely to develop dia-betes than the white population and more likely to die ofcomplications.11 12 Prevention may be feasible throughnational, community and individual measures, which mayreduce development of diabetes by 0.5%e75%.13e15

Risk prediction of non-communicable diseaseModels for predicting risk of developing chronic diseasehave become more common.16e18 A recent systematicliterature review revealed 145 such models for type 2diabetes, and while some researchers had speculatedthat their model might be used to identify high-risksectors of the community for public health interven-tions, most risk models and scores were being used onlyin individual clinical encounters (or not at all).19 TheQDScore, a risk model designed for use on generalpractice electronic records, is particularly well suited forproducing population-level risk estimates.17

Clinical commissioning of health servicesThe English National Health Service is undergoingradical restructuring, including transfer of commis-sioning of clinical and public health services to clinicalcommissioning groups (comprising general practi-tioners, secondary care clinicians, nurses and laymembers).20 Part of the public health function willshortly be transferred to local authorities, who aretraditionally responsible for urban planning and envi-ronmental health. Commissioning and local authoritybodies will need health information in an easily acces-sible format in order to plan, procure, monitor, evaluateand coordinate clinical and public health interventionsand neighbourhood initiatives.

GeovisualisationHistorically mapping has often been used in a publichealth context. For example, the well-known maps of

cholera cases around Broad Street pump in Londonin the mid-1800s.21 22 Geovisualisationdthe use ofcomputer-aided graphical methods (Geographic Infor-mation Systems) to visualise geospatial information23disa technique which has begun to be used to help guidehealth service planning, public health interventions andinform the public about disease ‘hot spots’.10 A well-known use of this technique are the maps of obesityproduced by the Center for Disease Control in the USA,which have shown higher prevalence in the southernstates and a shift in prevalence from low (shown in blue)through high (shown in red) over the past 40 years.24

Geospatial mapping of self-reported questionnairedata has shown the USA to have a ‘diabetes belt’ (ie,a band of states with high prevalence of this condition)in the south-east of the country linked to distribution ofthe known risk factors of obesity, inactivity, low socio-economic status and AfricaneAmerican ethnicity.25

Small-area geographical variation in diabetes prevalencehas also been mapped in a single city in Canada usingresearch survey data and links demonstrated with thegeographical distribution of social and environmentaldeterminants including family income, education,aboriginal status and neighbourhood crime.26 In theUK, small-area mapping of coronary heart diseasemorbidity and mortality using multiple data sources (eg,hospital admission statistics and mortality statistics) hasbeen linked to social and environmental risk factors (eg,income and ethnicity) and geographical ‘hot spots’ ofcoronary heart disease demonstrated in localities wherethese risk factors are clustered.27 Data from a UKpopulation-based register of arthritis have been used toidentify geographical clusters of polyarthritis.28

A key aspect of rigour in geovisualisation of disease orrisk of disease is the completeness, accuracy, timeliness,accessibility and granularity of the primary data fromwhich the maps are constructed, and in particular, theextent to which the data are capable of illuminating thefine-grained geographical variability needed to informlocality-based health or environmental interventions.Unlike USA and Canada, the UK has the advantage of

near-universal registration with general practitioners,whose records are at an advanced state of computer-isation.29 Quality of electronically held data is high inmost practices, partly due to a national financial incen-tive scheme for general practice, the Quality andOutcomes Framework, a component of which is chronicdisease management.30 Aggregated data from Qualityand Outcomes Framework returns have been used tomodel estimates of disease prevalence by locality.31

Recent advances in general practice computer systemsinclude remote server ‘cloud’ storage of records, withstaff gaining access via the world wide web rather thanrecords held on practice-based servers. This allowsauthorised staff to undertake complex data searchesacross large numbers of practices, allowing local generalpractice records to be used as the data source forsophisticated mapping of disease or risk factors by small

2 Noble D, Smith D, Mathur R, et al. BMJ Open 2012;2:e000711. doi:10.1136/bmjopen-2011-000711

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geographical area. However, accessing and usingpersonal medical data for this purpose raises significantpractical, technical, ethical and information governancechallenges.To our knowledge, there are no previous studies of

small-area geospatial mapping of either disease or riskfactors using electronic general practice patient recordsas the data source and oriented primarily to an audienceof local health planners. This is important whenconsidering dense urban areas where a street may sepa-rate relatively poor and affluent neighbourhoods.Models estimating disease prevalence often showgreatest discrepancy between observed and expectedprevalence in areas of social complexity, suggesting thatsmall-area mapping may be particularly useful in suchareas.32

Using type 2 diabetes as an example, the purpose ofthis study was to (1) explore the feasibility of producingmaps of population risk of chronic disease, calculatedfrom general practitioner electronic record data; (2)link this information with small-area data on putativesocial and environmental determinants of health and(3) consider the extent to which such information wouldbe useful to clinical commissioners and local authoritiesengaged in neighbourhood regeneration. A particularfocus of this study was to identify the practicalitiesand information governance hurdles around thesecondary uses of general practice data at a time whenlocal general practice led commissioning groups werebeing established.

METHODSSettingThe study was based in Tower Hamlets, an inner-citydistrict in the East End of London, UK, known interna-tionally for its vibrant street life, restaurants and cultureand also for its socioeconomic deprivation and poorhealth outcomes (eg, unemployment and crime rates aretwice the national average, illegal drug use is high, 7%diabetes prevalence is 40% above the national averageand mean life expectancy is 7 years lower than inChelsea, another more affluent district of London,UK).33 Tower Hamlets is home to a large BritishBangladeshi population and to more recent migrantsfrom Africa and to a white British working class popu-lation. The district includes significant pockets ofdeprivation, mainly in high-rise estates, alongsidepockets of affluence such as riverside suburbs in theSouth and parkside ones in the North. Tower Hamletsthus exemplifies the challenges facing providers andcommissioners planning for culturally diverse anddisadvantaged populations in inner-city urban areas.

Data sources, extraction and managementWe used two complementary data sources: postcode withclinical risk factors for individual residents of TowerHamlets, drawn from general practice electronicrecords, and social and environmental determinantsof health, drawn from local authority registers and

nationally available data at lower super output arealevel (relating to around 400 households/1000e1500people) or middle super output area (around 2000households/5000e7200 people).Using the electronic general practice record system,

a cohort was identified comprising all non-diabeticindividuals aged 25e79 years in Tower Hamlets from 35of 36 general practices that used the same computersystem. Data download were carried out on secure N3networks. In order to overcome the information gover-nance hurdle of preventing postcode linking to clinicalvariables, it was necessary to first download clinicalvariables attached to a pseudonymised identifier(n¼163 275d‘data set 1’). And then, postcode wasdownloaded separately attached to the same pseudony-mised identifier (n¼159 353d‘data set 2’). The reduc-tion in numbers was due to two practices that could notshare postcode due to technical reasons. We convertedTower Hamlets postcode districts (n¼8911) to lowersuper output area (n¼130) using an electronic lookuptable.34 Data set 2 (with lower super output area butwithout postcode) was linked using the pseudonymisedidentifier to data set 1. Thus, each individual record inthe final data set comprised a set of individual-levelclinical risk factors plus a lower super output area levelindicator of geographical locality, which could be relatedto local and nationally available statistics.Our local authority data set, extracted at middle super

output area, comprised (1) fast food outlets per capita(n¼371); (2) green spaces per square kilometre and (3)population density per square kilometre. Fast foodoutlets were identified using local authority registry datafor codes X15 ‘takeaway’ and X17 ‘restaurants’. All X17codes were manually reviewed by two researchers andpremises unlikely to serve fast food as a major part oftheir business based on their registration details wereremoved. This step was necessary because large corpo-rate fast food chains such as McDonalds were registeredas ‘restaurants’ rather than ‘takeaways’. Green spaces arequantified at the lower super output area level using theGeneralised Land Use Database from 2005, whichprovides data on the area (in square kilometres) in eachlower super output area dedicated to public green space.This does not include private gardens.35 Populationdensity was defined as the total population size of themiddle super output area divided by the area in squarekilometres. This was calculated from the Office forNational Statistics midyear population estimates for2010, the most recent available.

Estimating diabetes riskFor each individual in the final data set, 10-year risk ofdiabetes was estimated using the QDScore.17 It is basedon 11 variables: age, gender, ethnicity, Townsend scoreof deprivation (based on unemployment, car ownership,owner occupation and overcrowding), family history ofdiabetes, personal history of cardiovascular disease,smoking status, treated hypertension, current cortico-steroid usage and height and weight. If no family history





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is recorded, the QDScore algorithm defaults to none. Ifbody mass index is missing, a substituted value iscomputed based on age, sex, ethnicity, smoking status,presence of treated hypertension and cardiovasculardisease. Missing data on smoking are replaced with non-smoking status. If ethnicity is not stated, the algorithmdefaults to white British.Descriptive statistics and data handling were

performed using STATA V.1036 and Microsoft Excel2007. Quintiles of risk were derived and ‘high risk’defined as $20% risk of developing type 2 diabetes in10 years.

Geospatial mappingWe knew of no previous methodology for describing howchronic disease risk from an entire district’s set ofgeneral practice electronic records should be displayedby lower super output area. Methodological principleswere therefore applied from other relevant research.37

Determining how to display and group data, such asusing deciles versus quintiles or percentage at risk versusmedian risk score (as QDScore was not normallydistributed), required consultation and consensusbuilding with relevant local partners includingacademics, general practitioners and the director ofpublic health. Our final selection of display formatsreflected what these consultees considered the mostmeaningful framings of the data.Three different geospatial mapping techniques were

employed using ArcGIS V.9.238 and Adobe IllustratorV.10. In the ‘basic’ (choropleth style) map, the high-riskpopulation was displayed by lower super output area asa proportion of the denominator (non-diabetic adultsaged 25e79 years). A second basic map was created ofthe Index of Multiple Deprivation score 201039 to allowa visual comparison between high risk of type 2 diabetesand a different indicator of deprivation than that usedwithin the QDScore. Statistical analysis of correlation wasnot performed due to an unquantified degree ofcollinearity between Townsend score, which is used inthe QDScore and Index of Multiple Deprivation. Basicmaps thus presented the data as geographically definedlower super output areas (typically defined by streetblocks) in different shades of colour. A list of generalpractices and hospitals were located using their post-code. They were located in GIS using the centre of eachpostcode. This analysis was performed to demonstratethe potential usefulness of informing local practicegeographical needs assessment.The ‘heat map’ assigned the proportion at high risk to

the population-weighted centroid for each lower superoutput area. A kriging procedure (which uses a globalsemivariogram algorithm) was used to create an inter-polated surface of risk.40 Heat maps thus offereda statistically ‘smoothed’ presentation of the data inwhich the lower super output area blocks were no longervisible.The ‘ring map’ is a relatively new technique, which

allows factors of interest (such as putative environmental

determinants) to be displayed circumferentially arounda map.41 To produce these, we aggregated data to thelevel of middle super output area (n¼31) and presentedas quintiles of risk. The following data were assembledfor each middle super output area: (1) fast food outletsper capita, (2) percentage of non-green space and (3)population density per square kilometre. Using a vali-dated adjustment procedure,41 we divided each of theseinto highest quartile, middle 50% (second and thirdquartiles) and lowest quartile. The ring map thus givesa less granular picture of the geographical distributionof a variable but allows additional mapping of factorsthat might influence this variable in each locality. Hadthe map focused on prevalence of diabetes rather thana risk score we could have included known risk factors.We avoided this, however, to avoid any overlap orcollinearity between the variables of the QDScore.

Management and governanceThis exploratory study was made possible by a number ofkey partnerships. The work was led by DN, a publichealth registrar who had previously worked at TowerHamlets Primary Care Trust and was on an academicattachment with the Centre for Primary Care and PublicHealth within the medical school, with input from theDepartment of Geography.An initiative to improve and maintain data quality of

general practice records across the Primary Care Trusthad been in place for several years, led by the ClinicalEffectiveness Group within the Centre for Primary Careand Public Health. Key relationships and infrastructureincluding data sharing and governance arrangementswere thus already in place to enable Clinical Effective-ness Group staff to securely download and audit datafrom the electronic medical records of 35 of 36 practicesin the district which used the same electronic recordsystem, the Egton Medical Information System (EMIS),and had recently moved to a web-based version of thissystem enabling remote access.17

The study was classed as service ‘audit’ and deemedoutwith its remit by the local National Health ServiceResearch Ethics Committee in January 2011. The localinformation governance group representing the generalpractices at the Primary Care Trust agreed to the studyand advice on data handling, and mapping was alsosought from the National Information GovernanceBoard.

Feasibility assessmentThe tasks of identifying, extracting, manipulating,sharing, summarising and presenting our data, especiallythose derived from the electronic medical records ofa large cohort of general practice patients, presentedcomplex practical, technical and information gover-nance challenges.To capture these, we prospectively collected a data set

comprising documents (protocols, service-level agree-ments, agendas and minutes of meetings) and corre-spondence (letters, emails, notes of telephone calls).





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Those represented in this data set included the NHSResearch and Ethics Board, University Departments,Tower Hamlets Primary Care Trust, local general prac-titioners and public health specialists, and the NationalInformation Governance Board.We analysed this data set by applying a theoretical

framework developed previously to study the complexorganisational, social and political issues involved inintroducing a nationally shared electronic medicalrecord.42 Specifically, we considered: (1) informationgovernance challenges; (2) practical challenges, such asthe ease with which procedures could actually be carriedout and (3) technical challenges including issues of datasecurity, downloading and interoperability.

RESULTSData qualityCompleteness of general practice records in our selectedcohort aged 25e79 years without diabetes (data set 1,n¼163 275) was as follows: age (100%), gender (100%),ethnicity (92.1%), Townsend deprivation score (99.7%),body mass index (76.4%), smoking status (96.3%) andfamily history of diabetes (21.5%). Of the data that wereused in the mapping (n¼157 045) 9.48% of people(n¼14 885) were at high risk of developing type 2 dia-betes within 10 years. This is in addition to the 7% of theadult population of Tower Hamlets already known tohave type 2 diabetes.43

Records could not be generated or were removed if(1) the general practice was not able to share the datafor technical reasons (n¼3922) or patient permissionwas withheld (n¼187), (2) the individual recordcontained no postcode (n¼29) or lower super outputarea was not calculable from the available postcode(n¼275), (3) the geographic location was outside TowerHamlets (n¼1813) or (4) there was a mismatch betweenrecords in set 1 and set 2 (n¼4). This left 157 045records for analysis (96.2%) representing 33 of 36general practices. Reducing the list of restaurants tothose with a major business purpose of takeaway foodresulted in a total sample of 371 outlets.

Mapping resultsThe basic map (figure 1A) illustrates the variation inprevalence of high diabetes risk across lower superoutput areas in Tower Hamlets, with a maximum of17.3% of the non-diabetic population being at high risk(not including the 7% already diabetic). General prac-tices and hospitals are also shown in figure 1A. The areasof highest prevalence for diabetes risk were distributedon either side of the main east-west highway whichtransects the district and corresponds with well-knowndeprived housing estates and high-rise blocks of flats oneither side of this road.A basic map of Index of Multiple Deprivation scores by

lower super output area (figure 1B) showed a near-identical geographical distribution with high diabetesrisk.

The heat map (figure 2) shows the same informationas figure 1A but displayed as a globally smoothed surfaceover the entire geographic area. The prevalence of highdiabetes risk in this smoothed version of the data variedfrom 5.1% to 13.8%. This way of visualising the datadepictsdsomewhat more dramaticallyda high-risk ‘hot’band running west to east through the deprived housingestates and much lower risk ‘cool’ areas in the moreaffluent riverside in the south and parkside in the northof the district. The heat map is free from the visual lowersuper output area administrative boundaries that arecommonly used in maps of the ‘basic’ type. The resultingmap is likely more intuitive for users to interpret due tothe colour scheme, and there are no boundaries todisrupt the visualisation of diabetes risk.The ring map (figure 3) shows prevalence of high

diabetes risk by middle super output area. In thisdepiction of the data, prevalence of diabetes risk rangesfrom 3.8% to 13.7%. Each middle super output area isshown linked to a band of three social and environ-mental indicators, which are often suggested to influ-ence poorer health.44 These are (from the inside out)fast food outlets per head of population, percentage ofnon-green space and population density per squarekilometre.Overall, the ring map provided a striking visual display

of type 2 diabetes risk in the areas that corresponded toknown deprivation, and the ring provided a relativelynew way of displaying social and environmental deter-minants of health at a small area level. The ring providesa dashboard of indicators of wider determinants ofhealth that appeared most useful when locally applied tospecific population groups of 5000e7200 persons. Itdemonstrates the sort of putative environmental deter-minants that public health specialists may want to map aspart of routine health needs assessment to informinterventions at small area level.

Feasibility assessmentAs we had anticipated, the information governancechallenges were substantial and were as time consumingas the technical ones. In order to access the data fromgeneral practice records, permission had to be obtainedfrom both the local information governance committeeof the Primary Care Trust and the National InformationGovernance Board. In addition, because we consideredthat this project had a research element, we were alsorequired to seek advice from the local National HealthService Research Ethics Committee and from theuniversity’s Research and Development Office (whoboth deemed the project ‘audit’). Potentially identifiabledata from patient records had to be handled securelyunder a protocol advised by the National InformationGovernance Board. This kept postcode informationseparate from clinical variables with pseudonymisedconversion to lower super output area.Information governance issues were thus time

consuming and required specialised knowledge andformal permissions, but they were not insurmountable.





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Figure 1 (A) Basic map showing percentage of adult population at high risk of diabetes by lower super output area. (B) Basic mapshowing Index of Multiple Deprivation score by lower super output area.





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Furthermore, the process of establishing a procedure forthe current project built a stock of in-house knowledgeand a network of contacts that would make any subse-quent set of permissions and procedures substantiallyeasier to set up.The practical challenges of undertaking this work were

relatively minor. However, this was probably due toa near-optimal local infrastructure (see ‘Managementand governance’ above). Unusually, we had access toa single electronic database covering an entire PrimaryCare Trust area due to unique data sharing arrange-ments between the local general practices, the PrimaryCare Trust and the university. Furthermore, the qualityand completeness of general practice electronic dataacross the district was higher than average. Thoseseeking to replicate this approach in other parts of theworld may need to undertake groundwork to establisha mechanism for data extraction from multiple differentcomputer systems, underpinned by relationships andpermission for governance, data sharing and dataquality.Technical challenges included downloading and

cleaning the data, which had to be done in several stagesdue to the size of the files and handling of multiplevalues. Conversion of postcode to lower super output

area with lookup tables and secure data pairing proto-cols between data sets 1 and 2 was time consuming.Specialist software was expensive and different versionsused between the clinical effectiveness group and thegeography department were inconvenient and resultedin time spent converting files and reducing lines of data,with older software unable to hold as much data. EMISweb does not keep records of searches performed oncean update is installed (which occurs every 4e6 months),so there is a limited time window for cross-sectionalanalysis.All geographical work was carried out on a 256 bit

NHS encrypted memory stick in the geography labora-tory so that files with lines of patient information werenever used outwith the clinical effectiveness groupexcept on secure memory sticks. This was timeconsuming and prevented regular backup of data, whichhad to be done between two encrypted memory sticksperiodically. The technical process of mapping wasrelatively straightforwarddonce the data had beenprepared, received and decisions made about what mapsto createdas expertise was present within the researchteam to use GIS and Adobe Illustrator. It is unlikelythat without these skills high-quality maps could beproduced.

Figure 2 Heat map showingpercentage of adult population athigh risk of diabetes usinga statistical smoothing technique.





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DISCUSSIONSummary of findingsIn this study, we have shown that it is possible to (1)obtain a near-complete set of de-identified data drawnfrom an entire district’s electronic general practicerecords in an ethnically and socio-economically diverseinner-city district, (2) use a computer algorithm todetermine 10-year risk of type 2 diabetes for individualson this data set and (3) use geospatial mapping tohighlight dramatic variation in diabetes risk by small-area geography and show how social and environmentaldeterminants of health can be effectively displayed and

communicated. Information governance and technicalissues were challenging but surmountable. We concludethat the technique of geospatial mapping, of which wehave explored three different formats, may help to meetthe rapidly growing need for local health intelligence byplanners and commissioners of health services.

Mapping health informationTaking a geospatial view of health information such aspopulation at risk of disease complements a traditionalstatistical approach to such data. Epidemiologists usestatistical tests, arithmetic adjustments and critique

Figure 3 Ring map showing percentage of population at high risk of diabetes, with selected social and environmentalcharacteristics, by middle super output area.





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causality claims and data. By contrast, cartographers usegeospatial visualisation, utilise classing breaks (eg,quintiles) and critique symbolisation.37 These differentparadigms have an important complementary role.Quantitative analysis identifies statistically significanttrends; cartography brings meaning and local relevance.Yet merely converting routine epidemiological data intomaps runs the risk of oversimplifying complex data andmisunderstands the purpose of geovisualisation, which isto represent data spatially. Grouping and classing datafor mapping is an interpretive process, and ‘points ofinterest’ to which the eye is drawn on a map may ormay not correspond to statistically significant relation-ships between variables as determined by traditionalepidemiological approaches.The key aim in health mapping is not to identify

statistically significant relationships but to gain firstinsight, then understanding of the ways in which healthstatus varies over space and to reveal the potential driversbehind this variation. In our research, by identifyingareas of highest prevalence of greater diabetes risk inrelation to small areas, local general practitioners, publichealth specialists and planners can be aware of increasedrisk and possible causes in their locality, so as to targetindividual and population interventions. Such ‘local’information may be unlikely to emerge from statisticalanalyses alone.Although we emphasise small-area geographic analysis,

we recognise that individual health is linked to non-spatial social determinants, and a map of local-level datais most valuable when interpreted in the wider socialcontext. Relative income inequality within the UK is likelyto influence weight (and therefore diabetes) via complexpathways.45 One example is the ‘obesogenic environ-ment’ model, which encompasses local and national,physical and social environments.46 The maps presentedhere are ideally considered with this context in mind.Resources and skills in handling health information in

order to commission new interventions and services maybe limited, particularly where they relate to dualresponsibility of both local authorities and healthproviders for the health of local populations. Geospatialmapping offers one option to address these deficienciesand present diverse information about health and itswider determinants in an accessible format to supportcommissioning and planning expertise. It is possible,though somewhat speculative at this stage, that invest-ment in the skill base needed for this approach mayprove a sound investment in the longer term.

Strengths of this studyThis study is the first in the UK (and possibly world wide)to use routinely collected, local individual patient data togenerate high-quality small-area maps of disease riskacross an entire district. A significant strength of thisstudy was the quality and completeness of the data setfrom which the geospatial maps were derived. Weobtained up-to-date data on over 96% of the target cohort(aged 25e79 years) across the whole of Tower Hamlets

and only one of the data fields (family history of diabetes)contained a significant proportion of missing data.The completeness of data capture in this study was

attributable to a number of things: (1) existing part-nerships between the university and the National HealthService; (2) a 20-year history of using electronic medicalrecords in local general practices, with standard dataentry templates for performance monitoring, audit andneeds assessment; (3) existence of local data sharingagreements and information governance infrastructurefor overseeing the use of electronic personal medicaldata and (4) the fact that 35 of 36 general practices inthe district used the same computer system (EMIS)which was compatible with the chosen diabetes riskalgorithm (QDScore) and 33 of 35 shared postcode.

Limitations of this studyA potential limitation of our study is the uniqueness ofthe local context. In order for the method used here tobe successfully reproduced by others, a number ofconditions need to be met. First, effective data sharingagreements must be in place and a high degree of trust isnecessary between all parties. Second, the general prac-tice records of a whole population need to be accessibleand the quality of relevant data fields on these records(completeness, accuracy and consistency of coding)must be high. Third, our method requires that patientsregistered at a particular general practice live in the samedistrict. This was not the case for 1813 (1.1%) individualsin this study. In some other localities, this discrepancymight be far greater. Fourth, the task of downloadingand cleaning data and geographically mapping diseaserisk required an advanced set of skills and took manyhours of input from a data analyst, public healthspecialist and human geographer. We are some way offa set-up whereby planners or general practitioners cansimply hit the ‘map it’ button on their consoles toproduce maps like the ones illustrated in this paper.

ConclusionsUsing small-area maps to plot risk of chronic disease ata local level is relatively novel. In particular, ring mapshave been used previously by other research teams, butthis technique is still in its infancy.41 It informs visual-isation of important social determinants of health, whichmay generate engagement of people with an interest(including local populations) in research and targetedinitiatives for improvement. However, the use of thistechnique beyond the research environment may belimited by governance and technical factors and by thespecialist skills needed for the data extraction andmapping. The methodology could be refined throughfurther research of potential utility to improve geospatialmapping for public health planning. Further studies offeasibility, impact and cost are needed, as are publishedinformation governance guidance on how to handlepatient-level data for geospatial mapping.

Contributors DN led the conceptualisation and management of the project,briefed and supported all researchers, assisted with data analysis and the





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technical process of creating the maps and led the writing of the paper. DSperformed all of the mapping procedures, advised on methodology and revisedversions of the manuscript. RM extracted and cleaned quantitative data fromelectronic general practice records and commented on versions of themanuscript. JR oversaw data extraction from general practice records, led oninformation governance and revised versions of the manuscript. TG helpedconceptualise and manage the study, assisted with interpreting the data anddeveloping the maps and revised versions of the manuscript. JR and TG act asguarantors.

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

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.

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CorrectionNoble D, Smith D, Mathur R et al. Feasibility study of geospatial mapping of chronic diseaserisk to inform public health commissioning. BMJ Open 2012;2:e000711. In the section‘Strengths of this study’ it was reported that only one data field (family history of diabetes) con-tained a significant proportion of missing data. In fact, this variable is only recorded if positiveand therefore it cannot be said whether or not it has a significant proportion of missing data.

BMJ Open 2012;2:e000711corr1. doi:10.1136/bmjopen-2011-000711corr1

BMJ Open 2012;2:e000711corr1. doi:10.1136/bmjopen-2011-000711corr1 1

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