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|Title:||Community cardiovascular disease risk from cross-sectional general practice clinical data: a spatial analysis|
Del Fante, P.
van Weel, C.
|Citation:||Preventing Chronic Diseases: Public health research, practice, and policy, 2015; 12(E26):140379-1-140379-9|
|Publisher:||National Center for Chronic Disease Prevention and Health Promotion|
|Nasser Bagheri, Bridget Gilmour, Ian McRae, Paul Konings, Paresh Dawda, Peter Del Fante, Chris van Weel|
|Abstract:||INTRODUCTION: 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. CONCLUSION: 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.|
|Keywords:||Humans; Cardiovascular Diseases; Obesity; Body Mass Index; Medical Records; Cluster Analysis; Risk Assessment; Risk Factors; Longitudinal Studies; Cross-Sectional Studies; Smoking; Residence Characteristics; Social Class; Poverty Areas; Adult; Aged; Middle Aged; South Australia; Female; Male; Overweight; Health Status Disparities; General Practice; Spatial Analysis|
|Appears in Collections:||Public Health publications|
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