Population level mortality data is often subject to substantial reporting errors due to misclassification of cause of death, misclassification of death status, or age reporting errors. Accuracy of error-prone data sources can be assessed by comparing such data to gold standard data for the same population-period. We present Bayesian methods for assessing the extent of reporting errors across different population-periods and generalizing those to settings where gold-standard data are lacking. Firstly, we investigate misclassification errors of maternal cause of death reporting in civil registration vital statistics data. We use a Bayesian hierarchical bivariate random-walk model to estimate country-year specific sensitivity and specificity in countries with at least one period where vital registration data overlaps with gold standard data. For countries without gold standard data, we developed a sequential approach, in which fixed global estimates of sensitivity and specificity are used. Additionally, we propose a new approach to incorporate temporal structure of misclassification parameters. Secondly, we investigate misreporting of adult mortality in sibling survival history data. Sibling survival histories data suffers from reporting errors due to respondent misreporting of birth year and age at death of their maternal siblings. We perform an exploratory analysis of data collected in Malawi and propose a candidate parametrization for reporting errors in cohort survival probabilities by 5-year age groups. We introduce parameters to capture age-group specific age-at-death errors and birth year reporting errors and define the data generating processes that relate sibling survival data to true survival probabilities while accounting for reporting errors. This framework allows for the estimation of age-group specific survival probabilities in settings where only error-prone sibling survival history data is available
