Bias in protein and potassium intake collected with 24-h recalls (EPIC-Soft) is rather comparable across European populations

Purpose: We investigated whether group-level bias of a 24-h recall estimate of protein and potassium intake, as compared to biomarkers, varied across European centers and whether this was influenced by characteristics of individuals or centers. Methods: The combined data from EFCOVAL and EPIC studies included 14 centers from 9 countries (n = 1,841). Dietary data were collected using a computerized 24-h recall (EPIC-Soft). Nitrogen and potassium in 24-h urine collections were used as reference method. Multilevel linear regression analysis was performed, including individual-level (e.g., BMI) and center-level (e.g., food pattern index) variables. Results: For protein intake, no between-center variation in bias was observed in men while it was 5.7% in women. For potassium intake, the between-center variation in bias was 8.9% in men and null in women. BMI was an important factor influencing the biases across centers (p <0.01 in all analyses). In addition, mode of administration (p = 0.06 in women) and day of the week (p = 0.03 in men and p = 0.06 in women) may have influenced the bias in protein intake across centers. After inclusion of these individual variables, between-center variation in bias in protein intake disappeared for women, whereas for potassium, it increased slightly in men (to 9.5%). Center-level variables did not influence the results. Conclusion: The results suggest that group-level bias in protein and potassium (for women) collected with 24-h recalls does not vary across centers and to a certain extent varies for potassium in men. BMI and study design aspects, rather than center-level characteristics, affected the biases across center

DYNAMO-HIA–A Dynamic Modeling Tool for Generic Health Impact Assessments

Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty

Background: Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases. Methods. Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000. Results: Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank. Conclusion: Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences

Early transcriptional response in the jejunum of germ-free piglets after oral infection with virulent rotavirus

Germ-free piglets were orally infected with virulent rotavirus to collect jejunal mucosal scrapings at 12 and 18 hours post infection (two piglets per time point). IFN-gamma mRNA expression was stimulated in the mucosa of all four infected piglets, indicating that they all responded to the rotavirus infection. RNA pools prepared from two infected piglets were used to compare whole mucosal gene expression at 12 and 18 hpi to expression in uninfected germ-free piglets (n = 3) using a porcine intestinal cDNA microarray. Microarray analysis identified 13 down-regulated and 17 up-regulated genes. Northern blot analysis of a selected group of genes confirmed the data of the microarray. Genes were functionally clustered in interferon-regulated genes, proliferation/differentiation genes, apoptosis genes, cytoskeleton genes, signal transduction genes, and enterocyte digestive, absorptive, and transport genes. Down-regulation of the transport gene cluster reflected in part the loss of rotavirus-infected enterocytes from the villous tips. Data mining suggested that several genes were regulated in lower- or mid-villus immature enterocytes and goblet cells, probably to support repair of the damaged epithelial cell layer at the villous tips. Furthermore, up-regulation was observed for IFN-γ induced guanylate binding protein 2, a protein that effectively inhibited VSV and EMCV replication in vitro (Arch Virol 150:1213–1220, 2005). This protein may play a role in the small intestine’s innate defense against enteric viruses like rotavirus