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.

Background/Objectives: The aim of this paper was to compare methods to estimate usual intake distributions of nutrients and foods. As 'true' usual intake distributions are not known in practice, the comparison was carried out through a simulation study, as well as empirically, by application to data from the European Food Consumption Validation (EFCOVAL) Study in which two 24-h dietary recalls (24-HDRs) and food frequency data were collected. The methods being compared were the Iowa State University Method (ISU), National Cancer Institute Method (NCI), Multiple Source Method (MSM) and Statistical Program for Age-adjusted Dietary Assessment (SPADE). Subjects/Methods: Simulation data were constructed with varying numbers of subjects (n), different values for the Box-Cox transformation parameter (l BC ) and different values for the ratio of the within-and between-person variance (r var ). All data were analyzed with the four different methods and the estimated usual mean intake and selected percentiles were obtained. Moreover, the 2-day within-person mean was estimated as an additional 'method'. These five methods were compared in terms of the mean bias, which was calculated as the mean of the differences between the estimated value and the known true value. The application of data from the EFCOVAL Project included calculations of nutrients (that is, protein, potassium, protein density) and foods (that is, vegetables, fruit and fish). Results: Overall, the mean bias of the ISU, NCI, MSM and SPADE Methods was small. However, for all methods, the mean bias and the variation of the bias increased with smaller sample size, higher variance ratios and with more pronounced departures from normality. Serious mean bias (especially in the 95th percentile) was seen using the NCI Method when r var ¼ 9, l BC ¼ 0 and n ¼ 1000. The ISU Method and MSM showed a somewhat higher s.d. of the bias compared with NCI and SPADE Methods, indicating a larger method uncertainty. Furthermore, whereas the ISU, NCI and SPADE Methods produced unimodal density functions by definition, MSM produced distributions with 'peaks', when sample size was small, because of the fact that the population's usual intake distribution was based on estimated individual usual intakes. The application to the EFCOVAL data showed that all estimates of the percentiles and mean were within 5% of each other for the three nutrients analyzed. For vegetables, fruit and fish, the differences were larger than that for nutrients, but overall the sample mean was estimated reasonably. Conclusions: The four methods that were compared seem to provide good estimates of the usual intake distribution of nutrients. Nevertheless, care needs to be taken when a nutrient has a high within-person variation or has a highly skewed distribution, and when the sample size is small. As the methods offer different features, practical reasons may exist to prefer one method over the other.

No abstract

To gain insight into pertussis disease dynamics, we studied age-specific long-term periodicity and seasonality of pertussis in The Netherlands. Hierarchical time-series models were used to analyse the monthly reported pertussis incidence in January 1996-June 2006 by age group. The incidence of pertussis showed a slightly increasing long-term trend with highest incidence rates seen in 1996, 1999, 2001 and 2004. For all age groups the annual peak incidence was found in August, except for the 13-18 years age group where the peak occurred in November. Monthly trends in adults showed high correlation with trends in age groups 0-4 years (0.94) and 5-12 years (0.92). We found no evidence for a relationship between annual rises in pertussis and the opening of schools. Concurrent annual fluctuations of pertussis incidence in adults and infants suggest frequent transmission within and between these age groups. Studying trends offers insight into transmission dynamics and may facilitate decisions on future vaccination strategies.