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Background: Poor childhood nutritional status has lifetime effects and food insecurity is associated with dietary practices that can impair nutritional status. Objectives: We assessed concurrent and subsequent associations between food insecurity and height-for-age z scores (HAZs) and body mass index-for-age z scores (BMI-Zs); evaluated associations with transitory and chronic food insecurity; and tested whether dietary diversity mediates associations between food insecurity and nutritional status. Methods: We used data from the Young Lives younger cohort composed of children in Ethiopia (n = 1757), India (n = 1825), Peru (n = 1844), and Vietnam (n = 1828) recruited in 2002 (round 1) at ~1 y old, with subsequent data collection at 5 y in 2006 (round 2) and 8 y in 2009 (round 3). Results: Children from food-insecure households had significantly lower HAZs in all countries at 5 y (Ethiopia, -0.33; India, -0.53; Peru, -0.31; and Vietnam, -0.68 HAZ; all P < 0.001), although results were attenuated after controlling for potential confounders (Ethiopia, -0.21; India, -0.32; Peru, -0.14; and Vietnam, -0.27 HAZ; P < 0.01). Age 5 y food insecurity predicted the age 8 y HAZ, but did not add predictive power beyond HAZ at age 5 y in Ethiopia, India, or Peru. Age 5 y food insecurity predicted the age 8 y BMI-Z even after controlling for the 5 y BMI-Z, although associations were not significant after the inclusion of additional confounding variables (Ethiopia, P = 0.12; India, P = 0.29; Peru, P = 0.16; and Vietnam, P = 0.51). Chronically food-insecure households had significantly lower HAZs than households that were consistently food-secure, although BMI-Zs did not differ by chronic food-insecurity status. Dietary diversity mediated 18.8-30.5% of the association between food security and anthropometry in Vietnam, but mediated to a lesser degree (8.4-19.3%) in other countries. Conclusions: In 4 countries, food insecurity at 5 y of age was associated with both HAZ and BMI-Z at age 8 y, although the association was attenuated after adjusting for other household factors and anthropometry at age 5 y, and remained significant only for the HAZ in Vietnam.
This study used data from the Young Lives (YL) younger cohort, a cohort study of ∼8000 children in Ethiopia, India, Peru, and Vietnam. The YL study team recruited ∼2000 children aged ∼1 y from each country in 2002 (round 1) with subsequent data collection at age 5 y (round 2; Ethiopia, October 2006–January 2007; India, January–July 2007; Peru, October 2006–August 2007; and Vietnam, December 2006–April 2007) and age 8 y (round 3; Ethiopia, October 2009–January 2010; India, August 2009–March 2010; Peru, July 2009–January 2010; and Vietnam, September 2009–January 2010). Children’s ages at each round ranged from 6 to 18 mo (round 1), 4.5 to 5.5 y (round 2), and 7.5 to 8.5 y (round 3). The YL team used multistage sampling designs with the first stage consisting of a selection of 20 sentinel sites. Sampling was pro-poor; for example, in Ethiopia, the most food-insecure areas were the sampling universe. In Peru, the richest 5% of districts were excluded from the sample. Although poor clusters were moderately oversampled, the final samples provided diverse representation of social, geographic, and demographic groups. The sample in India consisted only of households from Andhra Pradesh (since split into Andhra Pradesh and Telangana), whereas the 3 other countries used nationwide samples. The YL team randomly selected ∼100 households with children aged 6–18 mo in each cluster. Additional study methods are described elsewhere (28), and are provided at http://www.younglives.org.uk (29). From age 1 y to age 8 y, the YL cohort lost between 1.5% and 5.7% of the age 1 y sample to attrition (Ethiopia, 114/1999; India, 81/2011; Peru, 106/2052; and Vietnam, 36/2000). From the complete age 8 y dataset, children were excluded for this analysis if they were missing the dependent variables, anthropometry at 5 y (2006) or 8 y (2009) (Ethiopia, 128/1885; India, 105/1930; Peru, 102/1946; and Vietnam 136/1964). The University of Oxford Ethics Committee and the Peruvian Nutritional Research Institute institutional review board approved YL study protocols. Approval for these analyses was obtained from the University of Pennsylvania and Boston University. Written parental consent was obtained at each round, and verbal child assent was obtained in round 3. Height was measured with the use of locally made stadiometers with standing plates and moveable head boards accurate to 1 mm. HAZ was calculated with the use of WHO 2006 standards for children 0–59 mo (30) and WHO 2007 standards for older children (31). Weight was measured with the use of calibrated digital balances (Soehnle) with 100 g precision. BMI-Zs also were calculated with the use of WHO growth curves. All anthropometrists were trained and used techniques according to WHO guidelines (32, 33). Birth dates were taken from children’s health cards when available, and mothers’ reports otherwise. YL collected information on consumption of 11 food groups at age 5 y and 15 food groups at age 8 y. Food groups were combined into the following 7 categories at age 5 y: 1) starches (cereals, roots, and tubers), 2) meat (meat and fish), 3) eggs, 4) legumes and nuts, 5) dairy, 6) fruit and vegetables, and 7) fats and oils. At age 8 y, vitamin A–rich fruits and vegetables were added as an additional food category. Because there is no standard dietary diversity tool for children of this age, after reviewing food groupings used by other researchers (34, 35), we chose to aggregate the questions at age 5 y to 7 food groups; with the addition of questions about vitamin A–rich foods at age 8 y, we aggregated the questions at age 8 y into 8 food groups. We assessed individual dietary diversity by asking the caregiver what food items each child had eaten the previous day, and then summing the number of food groups reported. Different questions were used to capture food insecurity across rounds. At both rounds, respondents were asked about food insecurity in the previous 12 mo. For age 5 y, YL adapted questions from the HFSM (36) with the use of formative research to create a YL adaptation (37) focused on quantitative indicators of food insecurity (food shortage, fewer meals, and smaller portions). At age 8 y, YL used the HFIAS (38), which includes additional domains such as, “In the past 12 mo, did you ever worry that your household would run out of food?” and “Were you or any household member not able to eat the kinds of foods you want because of lack of money?” At age 5 y, caregivers were asked whether households experienced various aspects of food insecurity, whereas at age 8 y, respondents were asked to quantify how frequently this occurred (rarely, sometimes, always or nearly always). We coded the age 8 y responses with the use of the HFIAS coding algorithm; households classified as moderately or severely food-insecure were considered food-insecure. After comparing the specific questions (Table 1), we determined that positive responses at age 5 y to any of the food-security questions except eating less-preferred foods captured households that had to limit food quantity, which is most comparable to HFIAS moderate and severe food-insecurity at age 8 y. Thus, households at age 5 y responding positively to any food-insecurity questions other than eating less-preferred foods were considered food-insecure. Chronic food-insecurity was assessed by comparing food-insecurity status at ages 5 y and 8 y; households that were food-insecure at both ages were considered chronically food-insecure. Households that were food-secure at both times were classified as food-secure, and households that were food-insecure at one but not both time points were classified as transitorily food-insecure. Proportion of households reporting food insecurity in the 12 mo before interview, Young Lives younger cohort1 Other measures included community wealth [measured by indexes of asset ownership, housing quality, and service access for other YL households in the same communities (39, 40)], monetary value of all household expenditures in the preceding 2 wk (household consumption), whether interviewed in a food-scarce month, maternal ages, maternal heights, maternal schooling, paternal schooling, and child ages and sex. In an analysis of associations of age 5 y food security with age 8 y anthropometry, we controlled for age 5 y anthropometry to isolate associations of food security with growth at age 8 y that were not acting through growth at age 5 y. We used Stata (version 12.0, 2011) for all analyses. We employed multiple imputation methods with 15 replications (41) with the use of the ice command to impute the following missing covariates: maternal height (n = 279), maternal age (n = 64), rural residence at age 5 y (n = 3), rural residence at age 8 y (n = 3), interviewed in a scarce month at age 5 y (n = 87), interviewed in a scarce month at age 8 y (n = 108), community wealth (n = 2), and food security at age 8 y (n = 12). We used multivariable regressions for HAZ and BMI-Z to examine associations between food insecurity and nutritional status. Results were considered statistically significant at P < 0.05. We assessed dietary diversity mediation in 2 stages. First, we assessed whether the 3 Baron and Kenny criteria (42) were met: 1) food insecurity was a significant predictor of anthropometric measures, 2) dietary diversity was significantly associated with food insecurity, and 3) when dietary diversity and food insecurity were both included in models predicting anthropometric measures, dietary diversity was significant and the food-insecurity coefficient was smaller than when dietary diversity was not included. Second, when these criteria were met, we assessed mediation levels and calculated P values for Sobel–Goodman tests of mediation.
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