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

Mathur, Rohini, Noble, Douglas, Smith, Dianna, Greenhalgh, Trisha and Robson, John (2012) Quantifying the risk of type 2 diabetes in East London using the QDScore: a cross-sectional analysis. British Journal of General Practice, 62 (603), 663-670. (doi:10.3399/bjgp12X656793).

Abstract

Background: risk scores calculated from electronic patient records can be used to predict the risk of adults developing diabetes in the future.

Aim: to use a risk-prediction model on GPs’ electronic health records in three inner-city boroughs, and to map the risk of diabetes by locality for commissioners, to guide possible interventions for targeting groups at high risk.

Design and setting: cross-sectional analysis of electronic general practice records from three deprived and ethnically diverse inner-city boroughs in London.

Method: a cross-sectional analysis of 519 288 electronic primary care records was performed for all people without diabetes aged 25–79 years. A validated risk score, the QDScore, was used to predict 10-year risk of developing type 2 diabetes. Descriptive statistics were generated, including subanalysis by deprivation and ethnicity. The proportion of people at high risk (?20% risk) per general practice was geospatially mapped.

Results: data were obtained from 135 out of 145 general practices (91.3%); 1 in 10 people in this population were at high risk (?20%) of developing type 2 diabetes within 10 years. Of those with known cardiovascular disease or hypertension, approximately 50% were at high risk. Male sex, increasing age, South Asian ethnicity, deprivation, obesity, and other comorbidities increased the risk. Geospatial mapping revealed hotspots of high risk.

Conclusion: individual risk scores calculated from electronic records can be aggregated to produce population risk profiles to inform commissioning and public health planning. Specific localities were identified (the ‘East London diabetes belt’), where preventive efforts should be targeted. This method could be used for other diseases and risk states, to inform targeted commissioning and preventive research

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Contributors

Author: Dianna Smith

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