Evaluation of the two non-consecutive 24-h recall instrument for pan-European food consumption surveys

Background: The comparability of food consumption data originating from national nutritional surveys in Europe is currently hampered because of different methodologies used. Therefore, experts in the European Food Consumption Survey Method (EFCOSUM) consortium proposed to use two non-consecutive 24-h recalls for standardised dietary monitoring in European countries. Aim: Within the European Food Consumption Validation (EFCOVAL) consortium (www.efcoval.eu), this thesis aimed to evaluate the data collected with two non-consecutive 24-h recalls using EPIC-Soft for comparisons of dietary intake in adults between countries in future pan-European surveys. Methods: To evaluate the bias in protein and potassium intake as well as the ranking of individuals according to their fish and fruit & vegetable intake collected with two non-consecutive 24-h recalls, we developed a validation study within EFCOVAL. The study included biomarker data of 600 subjects from five European centres in Belgium, the Czech Republic, France, the Netherlands, and Norway. To gain further insight into the determinants of the accuracy of the method by using multilevel analysis, we combined EFCOVAL data from one day with similar data from twelve other centres participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study. Then, we used the EFCOVAL data for assessing the impact of different modes of administration (telephone vs. face-to-face), recall days (1st vs. 2nd) and days of the week (weekdays vs. weekend) on the bias in protein and potassium intake. Finally, data from the Netherlands was used to explore the usefulness of collecting individual dietary data with the 24-h recalls for estimating dietary exposure to flavouring substances. Results: On average, men and women underreported protein intake by 8% in the EFCOVAL study. Underreporting of potassium intake was 7% in men and 4% in women. The coefficient of variation of bias in observed protein and potassium intake between centres ranged from 4 to 7%. The prevalence of subjects with adequate protein and potassium intake according to the observed data at the lower and upper end of the usual intake distribution agreed fairly well ( Conclusion: Two non-consecutive 24-h recalls using EPIC-Soft provides sufficiently valid and suitable data for comparing dietary intake across European populations. </p

Cost, effectiveness and cost-effectiveness of home-based palliative care: integrative literature review

info:eu-repo/semantics/publishedVersio

Predicting urinary creatinine excretion and its usefulness to identify incomplete 24h urine collections

Studies using 24 h urine collections need to incorporate ways to validate the completeness of the urine samples. Models to predict urinary creatinine excretion (UCE) have been developed for this purpose; however, information on their usefulness to identify incomplete urine collections is limited. We aimed to develop a model for predicting UCE and to assess the performance of a creatinine index using para-aminobenzoic acid (PABA) as a reference. Data were taken from the European Food Consumption Validation study comprising two non-consecutive 24 h urine collections from 600 subjects in five European countries. Data from one collection were used to build a multiple linear regression model to predict UCE, and data from the other collection were used for performance testing of a creatinine index-based strategy to identify incomplete collections. Multiple linear regression (n 458) of UCE showed a significant positive association for body weight (ß = 0·07), the interaction term sex × weight (ß = 0·09, reference women) and protein intake (ß = 0·02). A significant negative association was found for age (ß = - 0·09) and sex (ß = - 3·14, reference women). An index of observed-to-predicted creatinine resulted in a sensitivity to identify incomplete collections of 0·06 (95 % CI 0·01, 0·20) and 0·11 (95 % CI 0·03, 0·22) in men and women, respectively. Specificity was 0·97 (95 % CI 0·97, 0·98) in men and 0·98 (95 % CI 0·98, 0·99) in women. The present study shows that UCE can be predicted from weight, age and sex. However, the results revealed that a creatinine index based on these predictions is not sufficiently sensitive to exclude incomplete 24 h urine collections

Design aspects of 24 h recall assessments may affect the estimates of protein and potassium intake in dietary surveys

Objective: To evaluate the impact of different modes of administration (face-to-face v. telephone), recall days (first v. second), clays of the week (weekday v. weekend) and interview clays (1 d later v. 2 d later) on bias in protein and K intakes collected with 24 h dietary recalls (24-HDR). Design: Two non-consecutive 24-HDR (collected with standardised EPIC-Soft software) were used to estimate protein and K intakes by a face-to-face interview at the research centres and a telephone interview, and included all days of the week. Two 24 h urine collections were used to determine biomarkers of protein and K intake. The bias in intake was defined as the ratio between the 24-HDR estimate and the biomarker. Setting: Five centres in Belgium, Czech Republic, France, the Netherlands and Norway in the European Food Consumption Validation (EFCOVAL) study. Subjects: About 120 adults (aged 45-65 years) per centre. Results: The bias in protein intake in the Czech Republic and Norway was smaller for telephone than face-to-face interviews (P=0.01). The second 24-HDR estimates of protein intake in France and K intake in Belgium had a larger bias than the first 24-HDR (P = 0.01 and 0.04, respectively). In the Czech Republic, protein intake estimated during weekends and K intake estimated during weekdays had a larger bias than during other days of the week (P = 0.01). In addition, K intake collected 2 d later in the Czech Republic was likely to be overestimated. Conclusions: The biases in protein and K intakes were comparable between modes of administration, recall days, days of the week and interview days in some, but not all, study centres

Dietary exposure to flavouring substances: from screening methods to detailed assessments using food consumption data collected with EPIC-Soft software

This study aimed to compare different methods of assessing dietary exposure to flavourings in the context of a stepwise approach. The dietary exposure to four flavourings - raspberry ketone, glycyrrhizinic acid, coumarin, and caffeine - was determined. When dietary exposure exceeded the safety limits, the need for more detailed assessment using less aggregated data was judged necessary. First, screening methods - maximized survey-derived daily intake (MSDI), single-portion exposure technique (SPET), and modified theoretical added maximum daily intake (mTAMDI) - were applied. Next, individual food consumption data were used for creating models with different levels of detail to identify the foods: a model based on food groups and models based on food items. These were collected from 121 Dutch adults using a standardized 2 24-h dietary recall (EPIC-Soft) in the European Food Consumption Validation (EFCOVAL) study. Three food item models were developed: without improvements of the flavouring descriptor built in the software; with improvements; and with use of non-specified flavour descriptors. Based on the results of at least one of the three screening methods, refined assessment was necessary for raspberry ketone, glycyrrhizinic acid, and caffeine. When applying the food group model, the need for refinement was indicated for the four flavourings. When applying the food item models, only glycyrrhizinic acid and caffeine presented dietary exposure above the safety limits. In the raspberry ketone case, dietary exposure increased when improvements in food description were considered. The use of non-specified flavour descriptors hardly changed the results. The collection of detailed food consumption data at the individual level is useful in the dietary exposure assessment of these flavouring

Dietary exposure to flavouring substances: from screening methods to detailed assessments using food consumption data collected with EPIC-Soft software

This study aimed to compare different methods of assessing dietary exposure to flavourings in the context of a stepwise approach. The dietary exposure to four flavourings - raspberry ketone, glycyrrhizinic acid, coumarin, and caffeine - was determined. When dietary exposure exceeded the safety limits, the need for more detailed assessment using less aggregated data was judged necessary. First, screening methods - maximized survey-derived daily intake (MSDI), single-portion exposure technique (SPET), and modified theoretical added maximum daily intake (mTAMDI) - were applied. Next, individual food consumption data were used for creating models with different levels of detail to identify the foods: a model based on food groups and models based on food items. These were collected from 121 Dutch adults using a standardized 2 24-h dietary recall (EPIC-Soft) in the European Food Consumption Validation (EFCOVAL) study. Three food item models were developed: without improvements of the flavouring descriptor built in the software; with improvements; and with use of non-specified flavour descriptors. Based on the results of at least one of the three screening methods, refined assessment was necessary for raspberry ketone, glycyrrhizinic acid, and caffeine. When applying the food group model, the need for refinement was indicated for the four flavourings. When applying the food item models, only glycyrrhizinic acid and caffeine presented dietary exposure above the safety limits. In the raspberry ketone case, dietary exposure increased when improvements in food description were considered. The use of non-specified flavour descriptors hardly changed the results. The collection of detailed food consumption data at the individual level is useful in the dietary exposure assessment of these flavouring

Amazon methane budget derived from multi-year airborne observations highlights regional variations in emissions

Atmospheric methane concentrations were nearly constant between 1999 and 2006, but have been rising since by an average of ~8 ppb per year. Increases in wetland emissions, the largest natural global methane source, may be partly responsible for this rise. The scarcity of in situ atmospheric methane observations in tropical regions may be one source of large disparities between top-down and bottom-up estimates. Here we present 590 lower-troposphere vertical profiles of methane concentration from four sites across Amazonia between 2010 and 2018. We find that Amazonia emits 46.2 ± 10.3 Tg of methane per year (~8% of global emissions) with no temporal trend. Based on carbon monoxide, 17% of the sources are from biomass burning with the remainder (83%) attributable mainly to wetlands. Northwest-central Amazon emissions are nearly aseasonal, consistent with weak precipitation seasonality, while southern emissions are strongly seasonal linked to soil water seasonality. We also find a distinct east-west contrast with large fluxes in the northeast, the cause of which is currently unclear

CO2 emissions in the Amazon: are bottom-up estimates from land use and cover datasets consistent with top-down estimates based on atmospheric measurements?

Amazon forests are the largest forests in the tropics and play a fundamental role for regional and global ecosystem service provision. However, they are under threat primarily from deforestation. Amazonia's carbon balance trend reflects the condition of its forests. There are different approaches to estimate large-scale carbon balances, including top-down (e.g., CO2 atmospheric measurements combined with atmospheric transport information) and bottom-up (e.g., land use and cover change (LUCC) data based on remote sensing methods). It is important to understand their similarities and differences. Here we provide bottom-up LUCC estimates and determine to what extent they are consistent with recent top-down flux estimates during 2010 to 2018 for the Brazilian Amazon. We combine LUCC datasets resulting in annual LUCC maps from 2010 to 2018 with emissions and removals for each LUCC, and compare the resulting CO2 estimates with top-down estimates based on atmospheric measurements. We take into account forest carbon stock maps for estimating loss processes, and carbon uptake of regenerating and mature forests. In the bottom-up approach total CO2 emissions (2010 to 2018), deforestation and degradation are the largest contributing processes accounting for 58% (4.3 PgCO2) and 37% (2.7 PgCO2) respectively. Looking at the total carbon uptake, primary forests play a dominant role accounting for 79% (−5.9 PgCO2) and secondary forest growth for 17% (−1.2 PgCO2). Overall, according to our bottom-up estimates the Brazilian Amazon is a carbon sink until 2014 and a source from 2015 to 2018. In contrast according to the top-down approach the Brazilian Amazon is a source during the entire period. Both approaches estimate largest emissions in 2016. During the period where flux signs are the same (2015–2018) top-down estimates are approximately 3 times larger in 2015–2016 than bottom-up estimates while in 2017–2018 there is closer agreement. There is some agreement between the approaches–notably that the Brazilian Amazon has been a source during 2015–2018 however there are also disagreements. Generally, emissions estimated by the bottom-up approach tend to be lower. Understanding the differences will help improve both approaches and our understanding of the Amazon carbon cycle under human pressure and climate change

Biomarker-based evaluation of two 24-h recalls for comparing usual fish, fruit and vegetable intakes across European centers in the EFCOVAL Study

Background/Objectives: A standardized methodology is important to enable consistent monitoring of dietary intake across European countries. For this reason, we evaluated the comparability of the assessment of usual food intake collected with two non-consecutive computerized 24-h dietary recalls (24-HDRs) and a food propensity questionnaire (FPQ) among five European centers. Subjects/Methods: Two 24-HDRs using EPIC-Soft (the software developed to conduct 24-HDRs in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study) were performed to determine fish, fruit and vegetable (FV) consumed by 600 adults in Belgium (BE), the Czech Republic (CZ), France (FR), the Netherlands (NL) and Norway (NO) in a validation study. An FPQ was used to identify non-consumers. Information from the 24-HDRs and FPQ were used to estimate individual usual food intake by the Multiple Source Method (MSM). Blood samples were drawn to determine fatty acids in phospholipids and serum carotenoids as biomarkers of fish, and FV intake, respectively. Results: The pooled correlation between usual fish intake and eicosapentaenoic acid plus docosahexaenoic acid in phospholipids was 0.19 in men and 0.31 in women (P for heterogeneity >0.50) and center-specific correlations ranged between 0.08 (CZ) and 0.28 (BE and NO) in men, and between 0.19 (BE) and 0.55 (FR) in women. For usual FV intake, the pooled correlation with serum carotenoids was 0.31 in men and 0.40 in women (P for heterogeneity >0.10); the center-specific correlations varied between 0.07 (NO) and 0.52 (FR) in men, and between 0.25 (NL) and 0.45 (NO) in women. Conclusion: Two standardized 24-HDRs using EPIC-Soft and an FPQ appeared to be appropriate to rank individuals according to their fish and FV intake in a comparable way among five European centers