Laura A Hanson|Elizabeth A Zahn|Sommer R Wild|Dörte Döpfer|James Scott|Cláudia Stein
Estimating global mortality from potentially foodborne diseases: an analysis using vital registration data
St. Olaf College|St. Olaf College|St. Olaf College|University of Wisconsin–Madison|Colby College|World Health Organization Regional Office for Europe
foodborne diseases fbd comprise large part global mortality burden yet true extent impact remains unknown present study utilizes multiple regression first attempt use nonhealth variables predict potentially fbd mortality country level vital registration vr data used build multiple regression model incorporating nonhealth variables addition traditionally used health indicators model subsequently used predict fbd mortality rates countries world health organization classifications amra amrb eura eurb statistical modeling strongly supported inclusion nonhealth variables multiple regression model predictors potentially fbd mortality six variables included final model percent irrigated land average calorie supply animal products meat production metric tons adult literacy rate adult hivaids prevalence percent deaths age 5 caused diarrheal disease interestingly nonhealth variables robust predictors mortality health variables also remained significant adding additional health variables analysis mortality rate predictions model ranged 026 deaths per 100000 netherlands 1565 deaths per 100000 honduras reported mortality rates potentially fbd vr data lie within 95 prediction interval majority countries 3739 comparison possible nonhealth variables appear strong predictors potentially fbd mortality country level may powerful tool effort estimate global mortality burden fbd views expressed document solely authors represent views world health organization