Background: Accurate national information on dietary intakes, including heterogeneity among individuals, is critical to inform health implications and policy priorities. In low- and middle-income countries, household expenditure surveys constitute the major source of food data, but with uncertain validity for individual-level intakes.
Objective: To investigate how individualized dietary consumption estimated from household survey data compared with individual-level 24-hr dietary recalls (24hR); and to assess potential heterogeneity by method for individualizing household intakes, dietary indicator, and individual characteristics (age, sex, education, religion, household income).
Methods: We evaluated data from the 2011-2012 Bangladesh Household Integrated Survey (BIHS), which included household-level consumption data (5,503 households) and individual-level dietary data based on 24hR from these households (22,173 participants). Household and 24hR estimates were standardized and harmonized for 33 dietary indicators, including 9 food groups, total energy, 8 macronutrients, and 15 micronutrients. Individual consumption was estimated from household data using two approaches, the Adult Male Equivalent (AME) and per capita (PC) approach. For each dietary indicator, differences in household vs. individual mean estimates were evaluated overall and by strata of individual characteristics, using Spearman's correlations and univariate and multivariate linear regression models.
Results: Individualized household estimates overestimated individual intakes from 24hR for all dietary factors using either estimation method (P<0.001 for each), except for starchy vegetables (AME: P = 0.15; PC: P = 0.85). For foods, overestimation ranged from 4% for seafood to about 240% for fruits, and for nutrients from 11% for carbohydrates and poly-unsaturated fats to 55% for vitamin C, with similar overestimation for the AME and the PC method. By strata, overestimation was modestly higher in men vs. women, in children (0-10y) vs. adolescents (11-19y) and adults (20-44y, ≥45y), among adults of higher (≥6y) vs. lower (<6y) education, in Muslims vs. other religions (Christians, Hindus), and for the lowest vs. all other income groups. This overestimation was notably higher in young children (0-5y) vs. all other age groups and in the lowest vs. all other income groups. Underestimation was rarely observed, for example for milk intake (-56%) in young children (0-5y). The PC approach did not capture heterogeneity in validity of estimation of different dietary factors by age, mainly in children (0-5y, 6-10y). Spearman's correlations between individualized household estimates and 24hR data were higher for the AME (0.30-0.70) than PC (0.20-0.50) approach. Findings were similar with and without multivariate regression, with proportions of variance (R2) in 24hR intakes explained by the AME being generally greater than PC estimates, yet still low to modest.
Conclusions: In this national survey, established methods for estimating individual level intakes from household surveys produce overestimation of intakes of nearly all dietary indicators, with significant variation depending on the dietary factor and modest variation depending on individual characteristics. These findings suggest a need for new methods to estimate individual-level consumption from household survey estimates.