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Re-calibration of a Framingham Risk Equation for a Rural Population in India
  1. Clara Kayei Chow1,
  2. Rohina Joshi2,
  3. David S Celermajer3,
  4. Anushka Patel2,
  5. Bruce C Neal2
  1. 1 Population Health Research Institute, McMaster University, Canada;
  2. 2 The George Institute for International Health, Australia;
  3. 3 University of Sydney, Australia
  1. E-mail: cchow{at}


Background: Coronary heart disease (CHD) risk estimation tools are a simple means of identifying those at high risk in a community and hence a potential 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, we re-calibrate a Framingham risk equation to estimate CHD risks in a population from rural India and examine the sensitivity of the method to information resources. Recent surveys of this population have found high levels of cardiovascular risk factors (CVRFs), particularly metabolic risk factors and a high proportion of mortality due to cardiovascular diseases.

Methods: We estimated the proportion of a rural Indian population at high risk of CHD using 3 risk estimation equations. 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%(9.6-11.1) for men and 5.3%(4.9–5.7) for women using the Framingham equation; 10.7%(9.9-11.5) for men and 4.2%(3.9–4.5) for women using the local recalibration; and 18.9%(17.7-20.1) for men and 8.2%(7.6–8.8) for women using the national recalibration.

Conclusion: These findings indicate that in India, equations rec-alibrated 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.

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