No abstract

Estimating usual food intake distributions from short-term quantitative measurements is critical when occasionally or rarely eaten food groups are considered. To overcome this challenge by statistical modeling, the Multiple Source Method (MSM) was developed in 2006. The MSM provides usual food intake distributions from individual short-term estimates by combining the probability and the amount of consumption with incorporation of covariates into the modeling part. Habitual consumption frequency information may be used in 2 ways: first, to distinguish true nonconsumers from occasional nonconsumers in short-term measurements and second, as a covariate in the statistical model. The MSM is therefore able to calculate estimates for occasional nonconsumers. External information on the proportion of nonconsumers of a food can also be handled by the MSM. As a proof-of-concept, we applied the MSM to a data set from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Calibration Study (2004) comprising 393 participants who completed two 24-h dietary recalls and one FFQ. Usual intake distributions were estimated for 38 food groups with a proportion of nonconsumers > 70% in the 24-h dietary recalls. The intake estimates derived by the MSM corresponded with the observed values such as the group mean. This study shows that the MSM is a useful and applicable statistical technique to estimate usual food intake distributions, if at least 2 repeated measurements per participant are available, even for food groups with a sizeable percentage of nonconsumers.

[1] This paper presents a multiple linear regression model developed for describing global river export of sediments (suspended solids, TSS) to coastal seas, and approaches for estimating organic carbon, nitrogen, and phosphorous transported as particulate matter (POC, PN, and PP) associated with sediments. The model, with river-basin spatial scale and a 1-year temporal scale, is based on five factors with a significant influence on TSS yields (the extent of marginal grassland and wetland rice, Fournier precipitation, Fournier slope, and lithology), and accounts for sediment trapping in reservoirs. The model generates predictions within a factor of 4 for 80% of the 124 rivers in the data set. It is a robust model which was cross-validated by using training and validation sets of data, and validated against independent data. In addition, Monte Carlo simulations were used to deal with uncertainties in the model coefficients for the five model factors. The global river export of TSS calculated thus is 19 Pg yr À1 with a 95% confidence interval of 11-27 Pg yr À1 when accounting for sediment trapping in regulated rivers. Associated POC, PN, and PP export is 197 Tg yr À1 (as C), 30 Tg yr À1 (N), and 9 Tg yr À1 (P), respectively. The global sediment trapping included in these estimates is 13%. Most particulate nutrients are transported by rivers to the Pacific ($37% of global particulate nutrient export), Atlantic (28-29%), and Indian ($20%) oceans, and the major source regions are Asia ($50% of global particulate nutrient export), South America ($20%), and Africa (12%).

We present a multiple linear regression model developed for describing global river export of dissolved SiO2 (DSi) to coastal zones. The model, with river basin spatial scale and an annual temporal scale, is based on four variables with a significant influence on DSi yields (soil bulk density, precipitation, slope, and area with volcanic lithology) for the predam situation. Cross validation showed that the model is robust with respect to the selected model variables and coefficients. The calculated global river export of DSi is 380 Tg a(-1) (340-427 Tg a(-1)). Most of the DSi is exported by global rivers to the coastal zone of the Atlantic Ocean (41%), Pacific Ocean (36%), and Indian Ocean (14%). South America and Asia are the largest contributors (25% and 23%, respectively). DSi retention in reservoirs in global river basins may amount to 18-19%

SPADE offers new features to existing programs to estimate the habitual intake distribution because it can handle many different types of modeling with the first-shrink-then-add approach.