Comparison of Statistical Approaches to Evaluate Factors Associated with Metabolic Syndrome

Details

• Personal Author:
  Andrew M ; Burchfiel C ; Charles L ; Fekedulegn D ; Hartley T ; Violanti J
• Description:
  In statistical analyses, metabolic syndrome as a dependent variable is often utilized in a binary form (presence/absence) where the logistic regression model is used to estimate the odds ratio as the measure of association between health-related factors and metabolic syndrome. Since metabolic syndrome is a common outcome the interpretation of odds ratio as an approximation to prevalence or risk ratio is questionable as it may overestimate its intended target. In addition, dichotomizing a variable that could potentially be treated as discrete may lead to reduced statistical power. In this paper, the authors treat metabolic syndrome as a discrete outcome by defining it as the count of syndrome components. The goal of this study is to evaluate the usefulness of alternative generalized linear models for analysis of metabolic syndrome as a count outcome and compare the results with models that utilize the binary form. Empirical data were used to examine the association between depression and metabolic syndrome. Measures of association were calculated using two approaches; models that treat metabolic syndrome as a binary outcome (the logistic, log-binomial, Poisson, and the modified Poisson regression) and models that utilize metabolic syndrome as discrete/count data (the Poisson and the negative binomial regression). The method that treats metabolic syndrome as a count outcome (Poisson/negative binomial regression model) appears more sensitive in that it is better able to detect associations and hence can serve as an alternative to analyze metabolic syndrome as count dependent variable and provide an interpretable measure of association. [Description provided by NIOSH]
• Subjects:
  Cardiovascular Diseases Cardiovascular System Occupational Health Safety
• Keywords:
  Analytical Processes BCOPS Cardiovascular Disease Metabolic Disorders Statistical Analysis
• ISSN:
  0748-450X
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  New York ; OSHA Region 2 ; OSHA Region 3 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  HELD - Health Effects Laboratory Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  365-373
• Volume:
  12
• Issue:
  5
• NIOSHTIC Number:
  nn:20036803
• Citation:
  J Clin Hypertens 2010 May; 12(5):365-373
• Contact Point Address:
  Desta Fekedulegn, PhD, Biostatistics and Epidemiology Branch, National Institute for Occupational Safety and Health, HELD, BEB, MS 4050, 1095 Willowdale Rd., Morgantown, WV 26505
• Email:
  djf7@cdc.gov
• Federal Fiscal Year:
  2010
• NORA Priority Area:
  Services ; Public Safety
• Performing Organization:
  State University of New York at Buffalo
• Peer Reviewed:
  True
• Source Full Name:
  Journal of Clinical Hypertension
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:b2562e59641f24a855f3e55a2422cb2b9d0f7bbdf7727bd11f88b85cbf42c5e87e7e9ee3787c4c30ae3f126aa0471285316b0832887f9d40d522b98f7c67e132
• Download URL:
  https://stacks.cdc.gov/view/cdc/188071/cdc_188071_DS1.pdf
• File Type:
  [PDF - 232.69 KB ]