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Community cardiovascular disease risk from cross-sectional general practice clinical data: a spatial analysis
Nasser Bagheri1, Bridget Gilmour2, Ian McRae2
1Australian 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.
Preventing Chronic Disease
|February 27, 2015
Summary
This study calculated cardiovascular disease (CVD) risk scores from general practice records, finding higher risks in low socioeconomic communities. This highlights unmet needs for targeted CVD care interventions.
Area of Science:
- Public Health
- Epidemiology
- Health Services Research
Background:
- Cardiovascular disease (CVD) remains a primary global health burden.
- General practice (GP) data offers a valuable resource for population health assessment.
Purpose of the Study:
- To calculate 10-year absolute CVD risk scores using GP clinical records.
- To analyze spatial variations in CVD risk across communities.
- To identify areas with high unmet needs for cardiovascular care.
Main Methods:
- Utilized GP clinical data from 4,740 adults (30-74 years) without prior CVD.
- Calculated 10-year absolute CVD risk scores via the Framingham risk equation.
- Aggregated individual risk scores by Statistical Area Level One (SA1) for spatial analysis and visualization.
Main Results:
- Overall 10-year CVD risk was 14.6% in the study population.
- 26.7% of individuals were classified at high risk for CVD.
- High CVD risk was significantly more prevalent in communities with lower socioeconomic status.
Conclusions:
- Spatial analysis of CVD risk identifies communities requiring targeted cardiovascular care.
- Geographic targeting of interventions can enhance early CVD detection and management.
- This methodology aids in addressing disparities in cardiovascular health outcomes.
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