How much does vaccination reduce the rate of HBV infection in Iranian population? a Bayesian adjustment analysis

The aim of this research was to estimate the changing rate of odds ratio (OR) by varying degrees of hepatitis B virus (HBV) underreporting. Background: Data registering is usually associated with much errors such as misclassification, under-reporting, missing data due to lack of co-operation, error prone factor, and also in medical studies due to inadequate diagnosis of physician or low accuracy of laboratory tests. In the present study, which discuss about the actual impact of vaccination on HBV prevention, exposure and response were prone to various errors. Furthermore, some people in the community possibly infected to virus while were not reported in the count of patients with HBV infected. Methods: This was a Case control study. Cases include patients with HBV who were referred to the gastroenterology and liver disease research center. Control group include patients without HBV who were performed a fatty liver test at Taleghani hospital laboratory. Bayesian approach and Gibbs sampling algorithm were used to estimate OR. Results: According to results, misclassification rate was mild in raw data but with an increase in degree of underreporting for 50 and 500 of unreported cases, o‎r increased about half and more than double, respectively, while sensitivity decreased strikingly. Conclusion: Our analysis asserted that knowing the degree of underreporting is essential to accurately calculate o‎r and sensitivity. In addition, despite varying o‎r in the different samples, overall results were similar according to the pattern of exposure and response association. Key Words: Vaccination, Hepatitis B virus, Misclassification, Underreporting, Bayesian adjustment