Constructing the Estimates: Computational Methods in Burden of Foodborne Diseases

Abstract In the first iteration of the global burden of foodborne diseases, various approaches for imputing missing incidence data at country level were tested and evaluated. In this iteration estimates were presented at subregional level without a time trend. For the second iteration we aimed to have country and year estimates. Literature searches typically do not provide epidemiological parameter estimates for every year-country combination. Therefore, extrapolation or imputation models are essential to construct a complete dataset of epidemiological parameters across space and time. To ensure parsimony and transparency, we adopted a hierarchical meta-regression model as the default method. The default model estimates epidemiological parameters across spatial and temporal dimensions while allowing for adjustments based on additional study-level covariates. The models were implemented in a Bayesian framework to properly account for uncertainty arising from the estimation process. After fitting the hierarchical meta-regression model to the available data, posterior predictive distributions were used to impute incidence values for countries and years with missing data. Countries deemed free from exposure through the food chain were excluded from the imputation model and assigned a zero incidence and mortality. After imputation, the responsible taskforce and experts reassessed the data and estimates critically to ensure reliable results. Following we assembled all pieces into the disease model. We estimated the incidence, mortality, YLD, YLL and DALYs for each country-year. Due to the varying complexities across hazards, including differences in data availability, biological plausibility, and study quality, we tailored several elements of the modelling process. This included adapting the default model structure and parameters, adjusting for specific control variables, reviewing the studies selected for inclusion and estimating some countries by the global level.