Evaluation of the Trends of Stomach Cancer Incidence in Districts of Iran From 2000-2010: Application of a Random Effects Markov Model Publisher Pubmed



Zayeri F¹ ; Mansouri A¹ ; Sheidaei A¹ ; Rahimzadeh S² ; Rezaei N² ; Modirian M² ; Khademioureh S² ; Baghestani AR¹ ; Farzadfar F² Show Affiliations
Source: Asian Pacific Journal of Cancer Prevention Published:2016

Abstract

Background: Stomach cancer is the fifth most common cancer and the third leading cause of death among cancers throughout the world. Therefore, stomach cancer outcomes can affect health systems at the national and international levels. Although stomach cancer mortality and incidence rates have decreased in developed countries, these indicators have a raising trend in East Asian developing countries, particularity in Iran. In this study, we aimed to determine the time trend of age-standardized rates of stomach cancer in different districts of Iran from 2000 to 2010. Materials and Methods: Cases of cancer were registered using a pathology-based system during 2000-2007 and with a population-based system since 2008 in Iran. In this study, we collected information about the incidence of stomach cancer during a 10 year period for 31 provinces and 376 districts, with a total of 49,917 cases. We employed two statistical approaches (a random effects and a random effects Markov model) for modeling the incidence of stomach cancer in different districts of Iran during the studied period. Results: The random effects model showed that the incidence rate of stomach cancer among males and females had an increasing trend and it increased by 2.38 and 0.87 persons every year, respectively. However, after adjusting for previous responses, the random effects Markov model showed an increasing rate of 1.53 and 0.75 for males and females, respectively. Conclusions: This study revealed that there are significant differences between different areas of Iran in terms of age-standardized incidence rates of stomach cancer. Our study suggests that a random effects Markov model can adjust for effects of previous responses.