Volume 13, Issue 2 (Vol.13, No.2, Summer 2017)
irje 2017, 13(2): 90-97 |
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Sahebi R, Motevalian S, Sahebi L, Sharifi H. The Intrinsic Estimator Method for Control of Age, Period, Cohort, and Its Comparison with the Descriptive Classic Model. irje 2017; 13 (2) :90-97
URL: http://irje.tums.ac.ir/article-1-5753-en.html
URL: http://irje.tums.ac.ir/article-1-5753-en.html
1- MS of Epidemiology, Tabriz University of Medical Sciences, Tabriz, Iran
2- Associate Professor of Epidemiology, Iran University of Medical Sciences, Tehran, Iran
3- PHD of Epidemiology, Maternal, Fetal and Neonatal Research Center, Tehran University of Medical Sciences, Tehran, Iran
4- Associate Professor of Epidemiology، HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran ,hsharifi@kmu.ac.ir
2- Associate Professor of Epidemiology, Iran University of Medical Sciences, Tehran, Iran
3- PHD of Epidemiology, Maternal, Fetal and Neonatal Research Center, Tehran University of Medical Sciences, Tehran, Iran
4- Associate Professor of Epidemiology، HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran ,
Abstract: (6847 Views)
Background and Objectives: The aim of this study was to compare the intrinsic estimator method and the age, period and cohort accounting model used for identifying age, period, and cohort trends in incidence, prevalence, and mortality rates.
Methods: The data of 2 studies " Age, Period, and Cohort Effects on Alcohol and Drug use Among Students of Tehran University of Medical Sciences from 2006 to 2009" and "The Role of Age, Period, and Cohort Effects on Smoking among the Students of Tehran University of Medical Science "were used. First the age, period and cohort accounting model and then The IE method are explained, their advantages and disadvantages are discussed, and their results are compared.
Results: Both methods showed an increasing, decreasing, and increasing trend for age, cohort, and cohort effect for the prevalence of smoking. IE could predict a preventing effect for an early age factor for smoking. In the descriptive model, a decreasing trend was seen from old cohorts to younger cohorts in nearly all age groups. However, the IE method did not show any cohort effects for alcohol consumption in female studnets.
Conclusion: Currently, the IE method is the best method for solving linear dependence between three variables of age, birth cohort, and period in this type of studies. However, researchers should use it with caution because it has many pitfalls.
Methods: The data of 2 studies " Age, Period, and Cohort Effects on Alcohol and Drug use Among Students of Tehran University of Medical Sciences from 2006 to 2009" and "The Role of Age, Period, and Cohort Effects on Smoking among the Students of Tehran University of Medical Science "were used. First the age, period and cohort accounting model and then The IE method are explained, their advantages and disadvantages are discussed, and their results are compared.
Results: Both methods showed an increasing, decreasing, and increasing trend for age, cohort, and cohort effect for the prevalence of smoking. IE could predict a preventing effect for an early age factor for smoking. In the descriptive model, a decreasing trend was seen from old cohorts to younger cohorts in nearly all age groups. However, the IE method did not show any cohort effects for alcohol consumption in female studnets.
Conclusion: Currently, the IE method is the best method for solving linear dependence between three variables of age, birth cohort, and period in this type of studies. However, researchers should use it with caution because it has many pitfalls.
Type of Study: Research |
Subject:
General
Received: 2017/08/29 | Accepted: 2017/08/29 | Published: 2017/08/29
Received: 2017/08/29 | Accepted: 2017/08/29 | Published: 2017/08/29
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