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Monitoring population diet quality and nutrition status with household consumption and expenditure surveys: suggestions for a Bangladesh baseline

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Abstract

Increasing understanding of nutrition and the changing nature of malnutrition have increased interest in diet quality. Owing largely to the apparent lack of national and sub-national level data about diet and diet quality, however, we have little knowledge about dietary patterns, and little understanding of how agriculture, trade, food industry and health policy may be used to improve diet quality. This paper reviews the growing use of household consumption and expenditure surveys (HCES)— multi-purpose surveys that are routinely conducted in roughly 120 countries—to address this dietary information gap. By virtue of their being household-based and sub-nationally representative, HCES can get beyond the limitations of the long-standing, traditional data source used to inform food policy—FAO Food Balance Sheets –to provide an understanding of sub-national and household level food consumption patterns. To date, nutritionists have had little knowledge about or involvement with HCES. The confluence of a constellation of factors has now created an opportunity for familiarizing nutritionists with HCES. This paper provides an introduction to and overview of HCES, a description of the activities spawning this opportunity and a case study of Bangladesh. The paper demonstrates how HCES may be used to calculate baseline indicators, which may be used to articulate specific goals and targets, assess progress, identify gaps and prioritize actions, and thereby provide a platform for increasing accountability and commitment to improving nutrition.

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Notes

  1. General introductions to HCES and key issues in their use for food and nutrition analysis include: an HCES primer (Fiedler et al. 2012b); a guide to using HCES to measure food security (Smith and Subandoro 2007); a guide and criteria for selecting among HCES, FBS, 24HR and Food Frequency methods for obtaining data to design and monitor fortification programs (Coates et al. 2012); a description of how to use HCES together with food composition table data to develop estimates of nutrient availability (Bermudez et al. 2012); a comparative analysis of the costs and technical requirements of HCES and 24HR (Fiedler et al. 2013b); general reviews of HCES that also lay out a global strategy for strengthening HCES for undertaking food and nutrition analyses, including specific topics which would greatly benefit from input from the nutrition community (Fiedler et al. 2012a; Fiedler 2013); and an assessment of the relevance and reliability of HCES based on a review of 100 countries’ questionnaires (Smith et al. 2014)

  2. See Fiedler (2013) for a chronology of the major developments.

  3. MECOVI is the Spanish for Merjoramiento de las Encuestas y la Medición de las Condiciones de Vida” (Improvement of Surveys and the Measurement of Living Conditions, a Latin American and Caribbean regional program.

  4. Household surveys (but not exclusively HCES) are the source for 27 of the MDGs (UNSG 2015).

  5. That is not the case in Bangladesh, however, which regularly conducts an agricultural census; the last, in 2008.

  6. Between 1980 and 2009, per capita gross national income increased more than 5-fold (measured in international dollars; i.e., purchasing power parity (World Bank 2013).

  7. Coverage of the vitamin A supplementation program grew steadily from 1996 to 2007 when it peaked at 88%, before slipping to 60% in 2011. ORS usage rates have followed a parallel trend, falling from 81% to 60%.

  8. While the Bangladesh HIES collects data specifically on consumption, many other countries’ HCES collect data on a mixture of consumption and purchases, thereby introducing additional potential distortions in apparent consumption estimates; namely, the possibility that foods consumed during the recall period were purchased prior to it, and that food purchased during the recall period was consumed after it. For a discussion of the size and possible distorting effects of food stocks on HCES-based usual intake estimates see Fiedler et al. 2009; Gibson and Kim 2011; Beegle et al. 2012.

  9. There is growing evidence that this provides a reasonable proxy (Sununtnasuk and Fiedler 2016; Coates et al. 2016; Rambeloson et al. 2012), although the external validity of the few existing studies remains questionable.

  10. The ultimate disposition of “stored” is not known at the time of data collection. The household might subsequently use it for consumption, sell it or use it for seed or feed. One of the close-ended disposition categories is “consumed by the household” which can be directly compared to the households’ consumption module-reported quantity of the same food in order to provide a better understanding of the links between production and diets, as well as to be provide an assessment of the internal validity of the HCES database.

  11. No minimum consumption amount was used to qualify any of the food group categories so as to be “counted”.

  12. The unconditional mean consumption level is the total amount of consumption of the item in question divided by the total number of households, regardless of whether or not they consumed the item. The conditional mean consumption level is the total amount of consumption of the item in question divided by the number of households that consumed the item.

  13. Reporting the subnational prevalence rates is important for understanding the distribution of malnutrition and for identifying priorities and designing programs. For example, while the prevalence rate of inadequate intake of calcium in Dhaka is equal to the nationwide rate, which would seem to suggest that Dhaka is not a particularly important division to be concerned about in the fight against calcium inadequacy. Dhaka, however, contains one-third of all Bangladeshis who have inadequate calcium intakes, suggesting it is a critically important division.

  14. In countries—like Bangladesh—that conduct agricultural censuses, a similar type of analysis to this one could be done with the census data. As noted earlier, in many low- and middle- income countries, the HCES is the best available data on the agricultural sector, which is why we have included its analysis in addition to the food consumption module in this paper. Moreover, the questionnaires used in the HCES agriculture modules are very similar, sometimes identical, to the agriculture census survey questionnaire.

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Authors and Affiliations

  1. The International Dietary Data Expansion (INDDEX) Project; Poverty, Health and Nutrition Division and Agriculture for Nutrition and Health (A4NH), International Food Policy Research institute, 20006 K Street NW, Washington, DC, USA

    John L. Fiedler

  2. HarvestPlus, International Food Policy Research institute, 20006 K Street NW, Washington, DC, USA

    Keith Lividini

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  1. John L. Fiedler
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  2. Keith Lividini
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Correspondence to John L. Fiedler.

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Fiedler, J.L., Lividini, K. Monitoring population diet quality and nutrition status with household consumption and expenditure surveys: suggestions for a Bangladesh baseline. Food Sec. 9, 63–88 (2017). https://doi.org/10.1007/s12571-016-0631-5

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  • DOI: https://doi.org/10.1007/s12571-016-0631-5

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