The impact of survey characteristics on the measurement of food consumption

Conforti, Piero; Grünberger, Klaus; Troubat, Nathalie

doi:10.1016/j.foodpol.2017.08.011

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…It should continue to improve given the current work program that is focused on strengthening the overall quality of HCES data and the measurement of food consumption. 25,28,68…”

Section: Discussionmentioning

confidence: 99%

Household‐level consumption data can be redistributed for individual‐level Optifood diet modeling: analysis from four countries

Knight

Woldt

Sethuraman³

et al. 2021

Annals of the New York Academy of Sciences

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A barrier to using Optifood linear programming (LP), which identifies nutrient gaps and supports population‐specific food‐based recommendation (FBR) development, is the requirement for dietary intake data. We investigated whether Household Consumption and Expenditure Surveys (HCESs) could be used instead of individual‐level 24‐h recalls (24HRs). The 24HR data from 12‐ to 23‐month‐old breastfeeding children in rural Kenya, Uganda, Guatemala, and Bangladesh were paired with HCES food consumption data from similar areas (n = 8) and time periods. HCES food intakes (g/week) were estimated using adult male equivalents, adjusted for breastfeeding. Paired HCES‐ and 24HR‐defined LP inputs and outputs were compared using percentage agreement. Mean overall percentage agreements were 42%, 63%, and 80%, for food, food subgroup, and food‐group model parameters, respectively. HCES food lists were on average 1.3 times longer than 24HR. Similar nutrient gaps (77–100% agreement), food sources of nutrients (71–100% agreement), and FBRs (80–100% agreement) were identified. The results suggest that HCES data can be used in Optifood analyses for 12‐ to 23‐month‐old children, despite recognized challenges of using it to estimate dietary intakes of young children compared with older age groups. Further analyses, however, are required for different age groups and locations to confirm expectations that it would perform equally well.

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“…It should continue to improve given the current work program that is focused on strengthening the overall quality of HCES data and the measurement of food consumption. 25,28,68…”

Section: Discussionmentioning

confidence: 99%

Household‐level consumption data can be redistributed for individual‐level Optifood diet modeling: analysis from four countries

Knight

Woldt

Sethuraman³

et al. 2021

Annals of the New York Academy of Sciences

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“…Both retrospective and prospective assessment methods are also subject to some measure of social desirability bias (the latter by way of respondent “self-monitoring”) [ 38 ], however, research is lacking on the importance of this bias in assessing diet and household food consumption in Mongolia. Efforts to improve the accuracy of household food consumption measurements are ongoing and have considered such cognitive and survey design [ 7 , 39 ]. In Mongolia, recent analysis by Troubat and colleagues suggests that the HSES-HH’s diary instrument could be satisfactorily substituted with a less costly consumption recall combined with measurement of changes in household foods stocks and acquisitions [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Methods for Estimating Dietary Food and Nutrient Intakes and Intake Densities from Household Consumption and Expenditure Data in Mongolia

et al. 2018

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Household consumption and expenditure surveys are frequently conducted around the world and they usually include data on household food consumption, but their applicability to nutrition research is limited by their collection at the household level. Using data from Mongolia, this study evaluated four approaches for estimating diet from household surveys: direct inference from per-capita household consumption; disaggregation of household consumption using a statistical method and the “adult male equivalent” method, and direct prediction of dietary intake. Per-capita household consumption overestimated dietary energy in single- and multi-person households by factors of 2.63 and 1.89, respectively. Performance of disaggregation methods was variable across two household surveys analyzed, while the statistical method exhibited less bias in estimating intake densities (per 100 kcal) of most dietary components in both of the surveys. Increasingly complex prediction models explained 54% to 72% of in-sample variation in dietary energy, with consistent benefits incurred by inclusion of basic dietary measurements. In conclusion, in Mongolia and elsewhere, differences in how household and dietary measurements are recorded make their comparison challenging. Validity of disaggregation methods depends on household survey characteristics and the dietary components that are considered. Relatively precise prediction models of dietary intake can be achieved by integrating basic dietary assessment into household surveys.

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“…Conforti et al [5] performed a study on "The Impact of Survey Characteristics on the Measurement of Food Consumption within Households" in 2017. The research found out that food acquisition was always much higher than food consumption.…”

Section: Literature Surveymentioning

confidence: 99%

Food Recommendation using Classifier and Modified Apriori Algorithm

Dasgupta*¹,

Karmakar²

2019

IJITEE

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In today’s world, computer technologies have advanced a lot. One of its greatest gifts to the world is Artificial Intelligence. Natural Language Processing (NLP) and Machine Learning (ML) are two of its subdomains. In this paper, modified versions of two common NLP and ML algorithms have been used to classify food reviews and provide suitable recommendations from them. Currently, reviews can be classified into positive and negative reviews, but it becomes difficult when one review says positive about item A and negative about item B. Moreover, the current Apriori algorithm doesn’t consider the feedbacks from customers (reviews). Modified classifier algorithm and consequently, modified Apriori algorithm has been used to classify each statement part by part and provide recommendations, not just on previous purchases but also using the reviews about above-mentioned purchases. The algorithms can be used for purposes other than food analysis also – wherever purchases and reviews are involved. For e.g., e-commerce companies can use the algorithms to predict and recommend suitable items a user may be interested in.

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The impact of survey characteristics on the measurement of food consumption

Cited by 14 publications

References 16 publications

Household‐level consumption data can be redistributed for individual‐level Optifood diet modeling: analysis from four countries

Household‐level consumption data can be redistributed for individual‐level Optifood diet modeling: analysis from four countries

Comparison of Methods for Estimating Dietary Food and Nutrient Intakes and Intake Densities from Household Consumption and Expenditure Data in Mongolia

Food Recommendation using Classifier and Modified Apriori Algorithm

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