Discrepancies among ecological, household, and individual data on fruits and vegetables consumption in Brazil

Claro, Rafael Moreira; Jaime, Patrícia Constante; Lock, Karen; Fisberg, Regina Mara; Monteiro, Carlos Augusto

doi:10.1590/s0102-311x2010001100018

Cited by 13 publications

(13 citation statements)

References 20 publications

(28 reference statements)

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“…Data from such dietary surveys are also considered to more closely reflect actual consumption than data collected by other means (Kroes et al, 2002;Pomerleau et al, 2003;Claro et al, 2010). Data from such dietary surveys are also considered to more closely reflect actual consumption than data collected by other means (Kroes et al, 2002;Pomerleau et al, 2003;Claro et al, 2010).…”

Section: Food Consumption Data Sourcesmentioning

confidence: 99%

Guidance on the EU Menu methodology

2014

EFS2

135

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The availability of detailed, harmonised and high-quality food consumption data for use in dietary exposure assessments is a long-term objective of EFSA. In 2009, the EFSA guidance on "General principles for the collection of national food consumption data in the view of a pan-European dietary survey" was published, and a pan-European food consumption survey, also known as the "EU Menu", was launched. Based on the 2009 EFSA guidance, two EU Menu feasibility pilot studies and two methodological projects, EFSA has updated the former guidance document to cover the EU Menu methodology and therefore facilitate the collection of more harmonised food consumption data from all European Union Member States by the year 2020. This guidance has been developed by the EFSA Evidence Management Unit (DATA) and the EU Menu Working Group with Advisory Function, and has been endorsed by the EFSA Network on Food Consumption Data. It provides recommendations for the collection of more harmonised food consumption data among the EU Member States for use in dietary exposure assessments of food-borne hazards and nutrient intake estimations under the remit of EFSA's scientific panels. Food consumption information should be collected for two non-consecutive days. The 24-hour food diary method, followed by a computer-assisted personal or telephone interview (CAPI/CATI), should be used to collect data from infants and children. For all other age groups, the 24-hour dietary recall CAPI/CATI method should be used. The reported foods should be described in accordance with the EFSA FoodEx2 food classification system. A short food propensity questionnaire should be used to collect information on the consumption of some less frequently eaten foods and the consumption frequencies of food supplements. Information on the weight, height and physical activity levels of participants should also be collected in the survey.

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Section: Food Consumption Data Sourcesmentioning

confidence: 99%

Guidance on the EU Menu methodology

2014

EFS2

135

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“…In addition, FAO estimates do not account for waste from cooking, spoilage, or plate waste; meals not eaten at home; home farming or production, which can be common in lower-income nations; and food reaching the household from nonretail markets. Analyses of a limited number of countries and food groups suggested that FAO estimates can dramatically overestimate national dietary consumption ( 5 – 7 ). For instance, in the United States, mean total energy intake was estimated to be 2081 kcal/d in the 2009–2010 NHANES ( 8 ) but 3688 kcal/d by using the corresponding FAO estimate ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

Assessing global dietary habits: a comparison of national estimatesfrom the FAO and the Global Dietary Database

Gobbo

Khatibzadeh

Imamura

et al. 2015

The American Journal of Clinical Nutrition

118

107

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Background: Accurate data on dietary habits are crucial for understanding impacts on disease and informing policy priorities. Nation-specific food balance sheets from the United Nations FAO provided the only available global dietary estimates but with uncertain validity.Objectives: We investigated how FAO estimates compared with nationally representative, individual-based dietary surveys from the Global Dietary Database (GDD) and developed calibration equations to improve the validity of FAO data to estimate dietary intakes.Design: FAO estimates were matched to GDD data for 113 countries across the following 9 major dietary metrics for 30 y of data (1980–2009): fruit, vegetables, beans and legumes, nuts and seeds, whole grains, red and processed meats, fish and seafood, milk, and total energy. Both absolute and percentage differences in FAO and GDD mean estimates were evaluated. Linear regression was used to evaluate whether FAO estimates predicted GDD dietary intakes and whether this prediction varied according to age, sex, region, and time. Calibration equations were developed to adjust FAO estimates to approximate national dietary surveys validated by using randomly split data sets.Results: For most food groups, FAO estimates substantially overestimated individual-based dietary intakes by 74.5% (vegetables) and 270% (whole grains) while underestimating beans and legumes (−50%) and nuts and seeds (−29%) (P < 0.05 for each). In multivariate regressions, these overestimations and underestimations for each dietary factor further varied by age, sex, region, and time (P < 0.001 for each). Split–data set calibration models, which accounted for country-level covariates and other sources of heterogeneity, effectively adjusted FAO estimates to approximate estimates from national survey data (r = 0.47–0.80) with small SEs of prediction (generally 1–5 g/d).Conclusions: For all food groups and total energy, FAO estimates substantially exceeded or underestimated individual-based national surveys of individual intakes with significant variation depending on age, sex, region, and time. Calibration models effectively adjusted the comprehensive, widely accessible FAO data to facilitate a more-accurate estimation of individual-level dietary intakes nationally and by age and sex.

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“…Up to now, most global analyses have evaluated only single dietary factors or have used data on crude household expenditure or national food supply estimates that do not adequately capture individuals’ actual consumption levels 5 , 6 . Moreover, types of foods consumed and diet-related diseases are often unevenly distributed within populations and it is therefore essential to collect data on specific demographic groups to understand the impact of diets on diseases.…”

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confidence: 99%