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Published Online: 23 June 2010

Prevalence of Obesity, Type II Diabetes Mellitus, Hyperlipidemia, and Hypertension in the United States: Findings from the GE Centricity Electronic Medical Record Database

Publication: Population Health Management
Volume 13, Issue Number 3

Abstract

This study analyzed GE Centricity Electronic Medical Record (EMR) data to examine the effects of body mass index (BMI) and obesity, key risk factor components of metabolic syndrome, on the prevalence of 3 chronic diseases: type II diabetes mellitus, hyperlipidemia, and hypertension. These chronic diseases occur with high prevalence and impose high disease burdens. The rationale for using Centricity EMR data is 2-fold. First, EMRs may be a good source of BMI/obesity data, which are often underreported in surveys and administrative databases. Second, EMRs provide an ideal means to track variables over time and, thus, allow longitudinal analyses of relationships between risk factors and disease prevalence and progression. Analysis of Centricity EMR data showed associations of age, sex, race/ethnicity, and BMI with diagnosed prevalence of the 3 conditions. Results include uniform direct correlations between age and BMI and prevalence of each disease; uniformly greater disease prevalence for males than females; varying differences by race/ethnicity (ie, African Americans have the highest prevalence of diagnosed type II diabetes and hypertension, while whites have the highest prevalence of diagnosed hypertension); and adverse effects of comorbidities. The direct associations between BMI and disease prevalence are consistent for males and females and across all racial/ethnic groups. The results reported herein contribute to the growing literature about the adverse effects of obesity on chronic disease prevalence and about the potential value of EMR data to elucidate trends in disease prevalence and facilitate longitudinal analyses. (Population Health Management 2010;13:151–161)

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Information & Authors

Information

Published In

cover image Population Health Management
Population Health Management
Volume 13Issue Number 3June 2010
Pages: 151 - 161
PubMed: 20521902

History

Published online: 23 June 2010
Published ahead of print: 3 June 2010
Published in print: June 2010

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Authors

Affiliations

Albert G. Crawford
Thomas Jefferson University School of Population Health, Philadelphia, Pennsylvania.
Christine Cote
Johnson & Johnson, Corporate Office of Science and Technology, Medical Device and Diagnostics New Brunswick, New Jersey.
Joseph Couto
Thomas Jefferson University School of Population Health, Philadelphia, Pennsylvania.
Mehmet Daskiran
Johnson & Johnson, Corporate Office of Science and Technology, Medical Device and Diagnostics New Brunswick, New Jersey.
Candace Gunnarsson
S2Statistical Solutions, Inc., Cincinnati, Ohio.
Kara Haas
Ethicon Endo-Surgery, Inc., New Brunswick, New Jersey.
Sara Haas
S2Statistical Solutions, Inc., Cincinnati, Ohio.
Somesh C. Nigam
Johnson & Johnson, Office of Evidence Based Medicine, Medical Device and Diagnostics New Brunswick, New Jersey.
Rob Schuette
S2Statistical Solutions, Inc., Cincinnati, Ohio.

Notes

Address correspondence to:Albert G. Crawford, Ph.D.Thomas Jefferson University School of Population Health1015 Walnut Street, Suite 115Philadelphia PA 19107E-mail: [email protected]

Author Disclosure Statement

Dr. Haas is employed by Ethicon Endo-Surgery, Inc. Ethicon Endo-Surgery, Inc., funded this study, and it contracted with all other authors listed on this article. Drs Crawford, Cote, Couto, Daskiran, Gunnarsson, Haas, Nigam, and Schuette, and Ms. Haas disclosed no other financial ties or conflicts of interest.

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