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Estimating Risk of Mortality from Cardiovascular Diseases using Negative Binomial Regression | OMICS International| Abstract
ISSN: 2161-1165

Epidemiology: Open Access
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  • Case Report   
  • Epidemiol 2013, Vol 3(2): 127
  • DOI: 10.4172/2161-1165.1000127

Estimating Risk of Mortality from Cardiovascular Diseases using Negative Binomial Regression

Nikhita Charkha*, Aditi Ghatge, Payal Sharma, Vahida Z Attar and Patil AB
Department of Computer Science and Information Technology, College of Engineering, , Pune, India
*Corresponding Author : Nikhita Charkha, Department of Computer Science and Information Technology, College of Engineering, Pune, India, Email: [email protected]

Received Date: Apr 13, 2013 / Accepted Date: Jul 03, 2013 / Published Date: Jul 06, 2013

Abstract

Studies have demonstrated that exposure to moderate-to-high levels of ionizing radiations can cause most forms of cancer, leukemia, cardiovascular diseases, stroke, etc., especially at young ages. Hence, study of exposure to radiation has been the subject of intense epidemiological investigation. For experimentation purpose, we have used Hiroshima and Nagasaki atomic bomb survivor data, 1950-2003, Br Med J. 2010, 340:193. We have found this count cohort data to be over dispersed, which has led us to experiment negative binomial regression modelling on it, as it is over-dispersed in itself. We have estimated risk of cardiovascular data w.r.t. dose-category of radiation. As per our model, we have found that doses above 1.25 Gy are associated with an elevated risk of cardiovascular diseases, but the degree of risk at lower doses is unclear.

Keywords: AIC (Akaike Information Criterion); Cohort data; Epidemiology; Over-dispersion; Regression; Relative risk

Citation: Charkha N, Ghatge A, Sharma P, Attar VZ, Patil AB (2013) Estimating Risk of Mortality from Cardiovascular Diseases using Negative Binomial Regression. Epidemiol 3:127. Doi: 10.4172/2161-1165.1000127

Copyright: © 2013 Charkha N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided theoriginal author and source are credited.

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