title: Quantifying the risk of type 2 diabetes in East London using the QDScore: a cross-sectional analysis.
abstract: BACKGROUND
  Risk scores calculated from electronic patient records can be used to predict the risk of adults developing diabetes in the future.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  Cross-sectional analysis of electronic general practice records from three deprived and ethnically diverse inner-city boroughs  in London.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, London, UK.
authors.forename: Rohini
authors.initials: R
authors.lastname: Mathur
authors.forename: Douglas
authors.initials: D
authors.lastname: Noble
authors.forename: Dianna
authors.initials: D
authors.lastname: Smith
authors.forename: Trisha
authors.initials: T
authors.lastname: Greenhalgh
authors.forename: John
authors.initials: J
authors.lastname: Robson
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D044466:Asian People / Q000706:statistics & numerical data; D002318:Cardiovascular Diseases / Q000453:epidemiology; D015897:Comorbidity; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D005544:Forecasting / Q000379:methods*; D058006:General Practice / Q000706:statistics & numerical data*; D006801:Humans; D008131:London / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D011204:Poverty Areas; D015995:Prevalence; D011634:Public Health / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012959:Socioeconomic Factors; D055815:Young Adult