A Bayesian approach to correct the under-count of cancer registry statistics before population-based cancer registry program

Underestimation is a common problem in cancer registries in developing countries. This study introduces a Bayesian approach as a method for correcting undercounts in cancer data, before population-based cancer registry program

The current study is a secondary study performed on data from the cancer registry system. Our analysis focused on utilizing data before the establishment of the population-based cancer registry program in Iran. We employed the Bayesian approach to correct undercounting from 2005 to 2010. The ratio of pathology to population-based in the cancer registry data of Golestan province for four age groups and each year was used as the initial value in the Bayesian method.

The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.
The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 per 100,000 and after Bayesian correction was 137.17 per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 per 100,000 for women and 136.49 per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men.

The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. The findings of this research highlight the appropriateness of using the Bayesian method to correct underestimation in cancer registries and underscore its significance for future studies.