Title

Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: fatal event prediction using the NHANES III Linked Mortality File in proportional hazards modeling

Document Type

Capstone

Lead Author Type

MBI Masters Student

Advisors

Dr. Guenter Tusch, tuschg@gvsu.edu

Embargo Period

8-16-2010

Abstract

The third National Health and Nutrition Examination Survey (NHANES III), conducted from 1988 through 1994, was the seventh in a series of periodic surveys conducted by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). It was designed to provide national estimates of the health and nutritional status of the United States' civilian, non-institutionalized population aged two months and older. Longitudinal studies are possible with a number of the NHANES series for researchers performing analyses using a linked mortality file or other follow-up study (when available) that is connected to a selected series in the NHANES. The purpose of this project was to select a peer-reviewed article that analyzed NHANES data and to perform a similar analysis using more recent data from a different NHANES series to compare the models and their usefulness in another population spanning a different time period. The article chosen as a basis for this project was “Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort” by Thomas A Gaziano, Cynthia R Young, Garrett Fitzmaurice, Sidney Atwood, J Michael Gaziano (Lancet, Volume 371, Issue 9616, March 15-21, 2008, Pages 923-931). This article assesses if a risk prediction method that does not require any laboratory tests could be as accurate as one that requires laboratory information (i.e. blood, urine, etc.). Assessment of patients at high risk for cardiovascular death is an important strategy for prevention, especially in developing countries which account for roughly 80% of all cardiovascular deaths occurring

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