Recalibration of a Framingham risk equation for a rural population in India
- 1Population Health Research Institute, Hamilton General Hospital, Hamilton, Ontario, Canada
- 2The George Institute for International Health, Camperdown, Sydney, New South Wales, Australia
- 3Royal Prince Alfred Hospital, University of Sydney, Camperdown, Sydney, New South Wales, Australia
- Dr C K Chow, The George Institute for International Health, University of Sydney, PO Box M201, Missenden Road, Sydney, New South Wales 2050, Australia; cchow{at}george.org.au
- Accepted 22 December 2008
- Published Online First 29 January 2009
Abstract
Background: Coronary heart disease (CHD) risk estimation tools are a simple means of identifying those at high risk in a community and hence a potentially cost-effective strategy for CHD prevention in resource-poor countries. Since India has few local data upon which to develop such a tool de novo, in this study a Framingham risk equation has been recalibrated to estimate CHD risks in a population from rural India and the sensitivity of the method to information resources examined. Recent surveys of this population have found high levels of cardiovascular risk factors, particularly metabolic risk factors and a high proportion of mortality due to cardiovascular diseases.
Methods: The proportion of a rural Indian population at high risk of CHD using three risk estimation equations was estimated. The first a published version of the Framingham risk equation, the second a recalibrated equation using local mortality surveillance data and local risk factor data, and the third a recalibrated equation using national mortality data and local risk factor data.
Results: The mean 10-year probability of CHD for adults >30 years was 10.4% (9.6% to 11.1%) for men and 5.3% (4.9% to 5.7%) for women using the Framingham equation; 10.7% (9.9% to 11.5%) for men and 4.2% (3.9% to 4.5%) for women using the local recalibration; and 18.9% (17.7% to 20.1%) for men and 8.2% (7.6% to 8.8%) for women using the national recalibration.
Conclusion: These findings indicate that in India, equations recalibrated to summary national data are unlikely to be relevant to all regions of India and demonstrate the importance of local data collection to enable development of relevant CHD risk tools.
Footnotes
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Funding: CC is supported by an Australian NHMRC/NHFA Public Health postdoctoral fellowship, RJ by an IPRS scholarship and BN and AP by Fellowships from the National Heart Foundation of Australia while doing this work.
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Competing interests: None.
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Ethics approval: Ethics approval was obtained for all data collection in APHRI from both local Indian and Australian ethics committees.
