Foodborne diseases continue to be a major contributor to morbidity and mortality worldwide . The wide range of illnesses and the presence of multiple routes of transmission make computing epidemiological estimates of the burden of FBD a challenging task. Regardless, reliable mortality estimates are crucial to the World Health Organization's strategy to reduce this burden. The present study, part of WHO's Initiative to Estimate the Global Burden of Foodborne Disease, sought to assess of the utility of nonhealth-related variables as predictors of potentially FBD mortality. Through the model building process, numerous insights were gained.
The most significant finding from this analysis is that so-called nonhealth variables improve the estimation of FBD mortality. Specifically, some nonhealth variables have proved to be more prominent predictors of potentially FBD mortality than some of the traditionally used health indictors. As demonstrated by this analysis, only two of the six variables included in the final model were directly related to health. The four nonhealth variables proved to be robust predictors of potentially FBD mortality, remaining significant even with the addition of a number of other traditionally used health indicators. Health indicators do not therefore appear to be confounding variables in this model. As such, we propose that nonhealth variables are likely providing unique and previously unmodeled information in FBD estimations.
While health indicators will always remain strong predictors of overall health outcomes, food-related variables may provide more specific insights into estimating outcomes of potentially FBD. Model building for this analysis included testing nonhealth variables from a variety of categories that were both food and nonfood related. However, all of the nonhealth variables that remained in the final model could be specifically linked to food production or consumption.
Furthermore, the nonhealth variables that appear in the final model make intuitive sense. For example, increased meat production predicts increased FBD mortality, which could be related to unsafe handling practices. A higher per capita average calorie supply from animal products predicts reduced mortality and could be an indication of improved nutrition. Likewise, increased percent irrigated land predicts reduced mortality, perhaps due a higher availability of safe food supplies. Case studies regarding these and other predictive variables may aid in the confirmation and discovery of contributors to the global mortality burden of FBD.
This analysis also provides support for the use of nonhealth variables in predicting potentially FBD mortality in countries lacking VR data. However, nonhealth models should not be used indiscriminately. In this analysis, 42 of the 48 countries were classified in the two lowest mortality levels (A or B). Additionally, the geographic distribution of these countries was skewed heavily in favor of the Americas and Europe regions. We suspect this grouping of countries to be somewhat self-selected. That is, countries with better reporting of FBD tend to be more developed and have demonstrated a lower incidence of FBD. The predictive capabilities of our model are therefore strongest for countries at a similar development level and with comparable FBD incidence. As such, we focused on predicting potentially FBD mortality in AmrA, AmrB, EurA, and EurB region countries.
Recent estimates from county-level studies of FBD mortality were consistent with predictions from our model. A 2011 study from the United States estimated the number of deaths from foodborne infections at approximately 2,612 each year (90% credible interval 1,723-3,819) . This compares favorably with the predicted number of deaths from our model at 3,058 deaths per year with an upper limit of 19,135 deaths per year (rate of 1.01 per 100,000, upper limit 6.32 per 100,000). In the Netherlands, a national study of the burden of FBD concluded that approximately 80 persons die from foodborne infections every year ; our model again predicts well, estimating 43 deaths per year with an upper limit of 326 persons per year. Given the known problems of miscoding and misclassification inherent to CoD registration data, particularly in diseases that are not frequently observed, we assume that VR has underestimated the true incidence of potentially FBD mortality.
The predictive accuracy of nonhealth variables was supported by the validity of the FBD estimates generated by our model. The results of the predictive validity check in Additional file 2: Table A3 were consistent with our final model choice. Specifically, we found that our final model had good out-of-sample predictive validity (i.e., our model yielded smaller prediction errors than did other candidate models when predicting data points outside the original data set). We suspect that factors such as geographic location, cultural practices, and economic status are likely to have a strong effect on which particular nonhealth indicators are predictive of FBD mortality. Therefore it is hoped that further model building efforts will focus on specific regions or development levels. However, the lack of available data in these areas provides an obstacle to this aim, further underscoring the need for improved data collection and reporting in countries with high mortality rates.