ArticlePreventing chronic disease2015
Community cardiovascular disease risk from cross-sectional general practice clinical data: a spatial analysis.
Article in Preventing chronic disease, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Optimal Antihypertensive Systolic Blood Pressure: A Systematic Review and Meta-Analysis.Hypertension (Dallas, Tex. : 1979) · 2024Pooled it
- Intensive Blood Pressure Treatment Goals: Evidence for Cardiovascular Protection From Observational Studies and Clinical Trials.American journal of hypertension · 2022Pooled it
- Combining Geospatial Analysis with Dementia Risk Utilising General Practice Data: A Systematic Review.The journal of prevention of Alzheimer's disease · 2018Pooled it
- Age-Dependent Geographic Convergence of Overdose and Cardiovascular Emergency Medical Services Calls: A Spatial Point Process Analysis.Preventing chronic disease · 2026Article
- Cardiovascular Diseases and Metabolic Medications in the Lebanese Population: A Post Hoc Analysis from a Nationwide Cross-Sectional Study.Pharmacy (Basel, Switzerland) · 2024Article
- Associations between dimensions of the social environment and cardiometabolic risk factors: Systematic review and meta-analysis.SSM - population health · 2024Review
- Spatial analysis of 10-year predicted risk and incident atherosclerotic cardiovascular disease: the CoLaus cohort.Scientific reports · 2024Article
- Spatiotemporal distribution and sociodemographic and socioeconomic factors associated with primary and secondary syphilis in Guangdong, China, 2005-2017.PLoS neglected tropical diseases · 2021Article
- Geographic and Sociodemographic Disparities in Cardiovascular Risk in Burkina Faso: Findings from a Nationwide Cross-Sectional Survey.Risk management and healthcare policy · 2021Article
- Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities.Social psychiatry and psychiatric epidemiology · 2019Article
- Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma.BMC health services research · 2018Article
- Review
- Using Geographical Convergence of Obesity, Cardiovascular Disease, and Type 2 Diabetes at the Neighborhood Level to Inform Policy and Practice.Preventing chronic disease · 2017Article
- Degree of Agreement between Cardiovascular Risk Stratification Tools.Arquivos brasileiros de cardiologia · 2017Observational
- Nationwide Geospatial Analysis to Identify Variations in Primary Cardiovascular Risk in Ethiopia.Journal of primary care & community healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCardiovascular 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.
methodsWe 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.
resultsThe 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.
conclusionThis 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.
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