title: Community cardiovascular disease risk from cross-sectional general practice clinical data: a spatial analysis.
abstract: BACKGROUND
  Cardiovascular disease (CVD) continues to be a leading cause of illness and death among adults worldwide. The objective of this study was to calculate a CVD risk score from general practice (GP) clinical records and assess spatial variations of CVD risk in communities.
  
  
  METHODS
  We used GP clinical data for 4,740 men and women aged 30 to 74 years with no history of CVD. A 10-year absolute CVD risk score was calculated based on the Framingham risk equation. The individual risk scores were aggregated within each Statistical Area Level One (SA1) to predict the level of CVD risk in that area. Finally, the pattern of CVD risk was visualized to highlight communities with high and low risk of CVD.
  
  
  RESULTS
  The overall 10-year risk of CVD in our sample population was 14.6% (95% confidence interval [CI], 14.3%-14.9%). Of the 4,740 patients in our study, 26.7% were at high risk, 29.8% were at moderate risk, and 43.5% were at low risk for CVD over 10 years. The proportion of patients at high risk for CVD was significantly higher in the communities of low socioeconomic status.
  
  
  CONCLUSIONS
  This study illustrates methods to further explore prevalence, location, and correlates of CVD to identify communities of high levels of unmet need for cardiovascular care and to enable geographic targeting of effective interventions for enhancing early and timely detection and management of CVD in those communities.
authors.affiliation: Australian Primary Health Care Research Institute, ANU College of Medicine, Biology and Environment, Building 63, Corner of Mills and Eggleston Roads, Australian National University, ACTON 0200, Canberra, Australia. Email: nasser.bagheri@anu.edu.au.
authors.forename: Nasser
authors.initials: N
authors.lastname: Bagheri
authors.affiliation: Australian Primary Health Care Research Institute, Australian National University, Australia.
authors.forename: Bridget
authors.initials: B
authors.lastname: Gilmour
authors.affiliation: Australian Primary Health Care Research Institute, Australian National University, Australia.
authors.forename: Ian
authors.initials: I
authors.lastname: McRae
authors.affiliation: Australian Primary Health Care Research Institute, Australian National University, Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Konings
authors.affiliation: Australian Primary Health Care Research Institute, Australian National University, Australia.
authors.forename: Paresh
authors.initials: P
authors.lastname: Dawda
authors.affiliation: Healthfirst Network, Adelaide, Australia.
authors.forename: Peter
authors.initials: P
authors.lastname: Del Fante
authors.affiliation: Australian Primary Health Care Research Institute, Australian National University, Australia, and Department of Primary and Community Care, Radboud University Medical Centre, Nijmegen, the Netherlands.
authors.forename: Chris
authors.initials: C
authors.lastname: van Weel
mesh_terms: D000328:Adult; D000368:Aged; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D005260:Female; D058006:General Practice / Q000706:statistics & numerical data*; D054624:Health Status Disparities; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008499:Medical Records / Q000706:statistics & numerical data*; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology; D050177:Overweight / Q000453:epidemiology; D011204:Poverty Areas; D012111:Residence Characteristics; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012907:Smoking / Q000453:epidemiology; D012923:Social Class; D013021:South Australia / Q000453:epidemiology; D062206:Spatial Analysis