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Trend of Gastric Cancer after Bayesian Correction of Misclassification Error in Neighboring Provinces of Iran

Ahmad Reza Baghestani, Mohamad Amin Pourhoseingholi, Sara Ashtari, Hadis Najafimehr, Luca Busani, Mohammad Reza Zali

Background: Some errors may occur in the disease registry system. One of them is misclassification error in cancer registration. It occurs because some of the patients from deprived provinces travel to their adjacent provinces to receive better healthcare without mentioning their permanent residence. The aim of this study was to re-estimate the incidence of gastric cancer using the Bayesian correction for misclassification across Iranian provinces. Materials and Methods: Data of gastric cancer incidence were adapted from the Iranian national cancer registration reports from 2004 to 2008. Bayesian analysis was performed to estimate the misclassification rate with a beta prior distribution for misclassification parameter. Parameters of beta distribution were selected according to the expected coverage of new cancer cases in each medical university of the country. Results: There was a remarkable misclassification with reference to the registration of cancer cases across the provinces of the country. The average estimated misclassification rate was between 15% and 68%, and higher rates were estimated for more deprived provinces. Conclusion: Misclassification error reduces the accuracy of the registry data, in turn causing underestimation and overestimation in the assessment of the risk of cancer in different areas. In conclusion, correcting the regional misclassification in cancer registry data is essential for discerning high-risk regions and making plans for cancer control and prevention. [GMJ.2019;8:e1223] 


Bayesian Analysis; Gastric Cancer; Incidence

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