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

Abstract

ObjectiveTo explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams.Study designCross-sectional geospatial analysis using routinely collected general practitioner electronic record data.Sample and settingTower Hamlets, an inner-city district of London, UK, characterised by high socioeconomic and ethnic diversity and high prevalence of non-communicable diseases.MethodsThe 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.ResultsUsable 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.ConclusionsProducing 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.

Mapping Inequality in London: A Different Approach

“…A correlation of the two poverty measures gave a highly significant relationship of 0.73. Similar findings were shown in Noble et al (2012).…”

“…Since these landmark studies, maps have been used in public policy research. This has been seen in their use for examining the relationship between poverty and environmental risks (Holton, 2004), health (for example Noble et al, 2012) or just simply the spatial patterning of deprivation in major reports (for example CPAG, 2012). The visual nature of maps means that they are good at raising public awareness due to the improvement in data presentation when compared to other dissemination approaches.…”

Research into practice: understanding ethnic differences in healthcare usage and outcomes in general practice

“…4 Large routine healthcare data sets are uniquely valuable, in that they offer sufficient power to study individual ethnic groups, sex differences, and trends across generations.…”

“…For example, maps created by our group in 2012 illustrating 'hot spots' for diabetes risk in Tower Hamlets highlight not only the uniformly elevated risk for South Asian individuals across the borough but also reveal striking similarities to poverty maps created in the late 19th century, with areas of high diabetes risk overlapping areas of high deprivation. 4 An example illustrating the effects of cultural beliefs on health comes from our work evaluating the childhood immunisation arm of a major quality improvement programme involving the formation of GP networks in Tower Hamlets. We found that a borough-wide target of 95% for first mumps, measles, and rubella (MMR) by 2 years of age was achievable.…”

Quantifying the risk of type 2 diabetes in East London using the QDScore: a cross-sectional analysis

“…The methods have been previously described. 11,12 In summary, a cross-sectional analysis was undertaken in August 2011 of all adults aged 25-79 years without diabetes. Individuals with a Read Code for diabetes were excluded.…”