Community cardiovascular disease risk from cross-sectional general practice clinical data: A spatial analysis

Nasser Bagheri, Bridget Gilmour, Ian McRae, Paul Konings, Paresh Dawda, Peter Del Fante, Chris van Weel

Research output: Contribution to journalOther Journal Articlepeer-review

17 Citations (Scopus)

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.

Original languageEnglish
Article number140379
Pages (from-to)1-9
Number of pages9
JournalPreventing chronic disease
Volume12
Issue number2
DOIs
Publication statusPublished - 26 Feb 2015
Externally publishedYes

Fingerprint

Dive into the research topics of 'Community cardiovascular disease risk from cross-sectional general practice clinical data: A spatial analysis'. Together they form a unique fingerprint.

Cite this