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2 "Abbas Motevalian"
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Original Article
Trends in gastrointestinal cancer incidence in Iran, 2001-2010: a joinpoint analysis
Mehdi Darabi, Mohsen Asadi Lari, Seyed Abbas Motevalian, Ali Motlagh, Shahram Arsang-Jang, Maryam Karimi Jaberi
Epidemiol Health. 2016;38:e2016056.   Published online December 5, 2016
DOI: https://doi.org/10.4178/epih.e2016056
  • 17,836 View
  • 337 Download
  • 28 Web of Science
  • 17 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
The main purpose of this study was to evaluate changes in the time trends of stomach, colorectal, and esophageal cancer during the past decade in Iran.
METHODS
Cancer incidence data for the years 2001 to 2010 were obtained from the cancer registration of the Ministry of Health. All incidence rates were directly age-standardized to the world standard population. In order to identified significant changes in time trends, we performed a joinpoint analysis. The annual percent change (APC) for each segment of the trends was then calculated.
RESULTS
The incidence of stomach cancer increased from 4.18 and 2.41 per 100,000 population in men and women, respectively, in 2001 to 17.06 (APC, 16.7%) and 8.85 (APC, 16.2%) per 100,000 population in 2010 for men and women, respectively. The corresponding values for colorectal cancer were 2.12 and 2.00 per 100,000 population for men and women, respectively, in 2001 and 11.28 (APC, 20.0%) and 10.33 (APC, 20.0%) per 100,000 in 2010. For esophageal cancer, the corresponding increase was from 3.25 and 2.10 per 100,000 population in 2001 to 5.57 (APC, 12.0%) and 5.62 (APC, 11.2%) per 100,000 population among men and women, respectively. The incidence increased most rapidly for stomach cancer in men and women aged 80 years and older (APC, 23.7% for men; APC, 18.6% for women), for colorectal cancer in men aged 60 to 69 years (APC, 24.2%) and in women aged 50 to 59 years (APC, 25.1%), and for esophageal cancer in men and women aged 80 years and older (APC, 17.5% for men; APC,15.3% for women) over the period of the study.
CONCLUSIONS
The incidence of gastrointestinal cancer significantly increased during the past decade. Therefore, monitoring the trends of cancer incidence can assist efforts for cancer prevention and control.
Summary

Citations

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Method
Assessing measurement error in surveys using latent class analysis: application to self-reported illicit drug use in data from the Iranian Mental Health Survey
Kazem Khalagi, Mohammad Ali Mansournia, Afarin Rahimi-Movaghar, Keramat Nourijelyani, Masoumeh Amin-Esmaeili, Ahmad Hajebi, Vandad Sharif, Reza Radgoodarzi, Mitra Hefazi, Abbas Motevalian
Epidemiol Health. 2016;38:e2016013.   Published online April 10, 2016
DOI: https://doi.org/10.4178/epih.e2016013
  • 17,219 View
  • 260 Download
AbstractAbstract PDF
Abstract
Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.
Summary

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