Variable selection and regression analysis for the prediction of mortality rates associated with foodborne diseases

• Published in: Epidemiology and infection Vol. 144; no. 9; pp. 1959 - 1973
• Main Authors: AMENE, E., HANSON, L. A., ZAHN, E. A., WILD, S. R., DÖPFER, D.
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.07.2016
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Animals; Biostatistics - methods; Child; Child, Preschool; Cluster Analysis; Epidemiologic Methods; Feature selection; Female; Fertility; Foodborne Diseases - mortality; Gastroenteritis; Gastrointestinal Infections; Global Health; Humans; Infant; Infant, Newborn; Life expectancy; Male; Middle Aged; Missing data; Morbidity; Mortality; Original Papers; Pregnancy; Survival Analysis; Variables; World Health Organization; Young Adult; United States > US; foodborne diseases; cluster analysis; elastic net; Bayesian hierarchical regression
• ISSN: 0950-2688; 1469-4409; 1469-4409
• DOI: 10.1017/S0950268815003234

The purpose of this study was to apply a novel statistical method for variable selection and a model-based approach for filling data gaps in mortality rates associated with foodborne diseases using the WHO Vital Registration mortality dataset. Correlation analysis and elastic net regularization methods were applied to drop redundant variables and to select the most meaningful subset of predictors. Whenever predictor data were missing, multiple imputation was used to fill in plausible values. Cluster analysis was applied to identify similar groups of countries based on the values of the predictors. Finally, a Bayesian hierarchical regression model was fit to the final dataset for predicting mortality rates. From 113 potential predictors, 32 were retained after correlation analysis. Out of these 32 predictors, eight with non-zero coefficients were selected using the elastic net regularization method. Based on the values of these variables, four clusters of countries were identified. The uncertainty of predictions was large for countries within clusters lacking mortality rates, and it was low for a cluster that had mortality rate information. Our results demonstrated that, using Bayesian hierarchical regression models, a data-driven clustering of countries and a meaningful subset of predictors can be used to fill data gaps in foodborne disease mortality.