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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 7101343, 7 pages
Research Article

Changes in Obesity Odds Ratio among Iranian Adults, since 2000: Quadratic Inference Functions Method

1Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
2Center for Diseases Control, Ministry of Health and Medical Education, Tehran, Iran
3Department of Physiology, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
4Department of Biostatistics, School of Public Health and Institute of Public Health Research, Tehran University of Medical Sciences, Tehran, Iran

Received 19 April 2016; Accepted 14 September 2016

Academic Editor: Ezequiel López-Rubio

Copyright © 2016 Enayatollah Bakhshi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Monitoring changes in obesity prevalence by risk factors is relevant to public health programs that focus on reducing or preventing obesity. The purpose of this paper was to study trends in obesity odds ratios (ORs) for individuals aged 20 years and older in Iran by using a new statistical methodology. Methods. Data collected by the National Surveys in Iran, from 2000 through 2011. Since responses of the member of each cluster are correlated, the quadratic inference functions (QIF) method was used to model the relationship between the odds of obesity and risk factors. Results. During the study period, the prevalence rate of obesity increased from 12% to 22%. By using QIF method and a model selection criterion for performing stepwise regression analysis, we found that while obesity prevalence generally increased in both sexes, all ages, all employment, residence, and smoking levels, it seems to have changes in obesity ORs since 2000. Conclusions. Because obesity is one of the main risk factors for many diseases, awareness of the differences by factors allows development of targets for prevention and early intervention.