Factors associated with dietary diversity and length-for-age z-score in rural Ethiopian children aged 6–23 months: A novel approach to the analysis of baseline data from the Sustainable Undernutrition Reduction in Ethiopia evaluation

Infants and young children need diets high in nutrient density and diversity to meet the requirements of rapid growth and development. Our aim was to evaluate sociodemographic, agricultural diversity, and women’s empowerment factors associated with child dietary diversity and length-for-age z-score (LAZ) in children 6–23 months using data collected as part of the Sustainable Undernutrition Reduction in Ethiopia (SURE) evaluation study baseline survey in May–June 2016. We here present a novel analysis using directed acyclic graphs (DAGs) to represent our assumptions about the causal influences between the factors of interest and the outcomes. The causal diagrams enabled the identification of variables to be included in multivariable analysis to estimate the total effects of factors of interest using ordinal logistic/linear regression models. We found that child dietary diversity was positively associated with LAZ with children consuming 4 or more food groups having on average an LAZ score 0.42 (95% CI [0.08, 0.77]) higher than those consuming no complementary foods. Household production of fruits and vegetables was associated with both increased child dietary diversity (adjusted OR 1.16; 95% CI [1.09, 1.24]) and LAZ (adjusted mean difference 0.05; 95% CI [0.005, 0.10]). Other factors positively associated with child dietary diversity included age in months, socio-economic status, maternal education, women’s empowerment and dietary diversity, paternal childcare support, household food security, fruit and vegetable cultivation, and land ownership. LAZ was positively associated with age, socio-economic status, maternal education, fruit and vegetable production, and land ownership. Evaluation of the SURE intervention uses a quasi‐experimental study design to determine the impact on child minimum acceptable diet (MAD) and stunting. This study analyses data from the SURE baseline survey completed in May–July 2016 and comprising 1,848 children 6–23 months of age in 36 intervention districts (Oromia: n = 18; Amhara: n = 8; Tigray: n = 4; and SNNP: n = 6) and 36 comparison districts selected by region in equal proportion. Complete details are available in the SURE evaluation study protocol (Moss et al., 2018). At baseline 4,980 children 0–47 months (761 children 0–5 months; 1,848 children 6–23 months; 2,371 children 24–47 months) were selected from 4,299 households. Sample size calculations were based on detecting a change at endline in LAZ/height‐for‐age z‐score (HAZ) score and MAD attributable to the intervention. Detectable differences of 0.15 HAZ and of 4% MAD were calculated for intracluster correlation coefficients of 0.03 with 80% power with a significance level of 5%; and differences of 0.21 HAZ and 6% MAD were calculated for intracluster correlation coefficients of 0.08 with 80% power with a significance level of 5%. Kebeles were selected at study outset using probability proportional to size sampling from lists and population data provided by district officials. Gotes (sub‐kebeles) were selected by simple random sampling (paper in hat) during data collection. A complete listing of all households with children under 47 months in the gote was conducted, and 15 were selected using systematic random sampling. Resident children 0–47 months within a selected household were listed in the following age groups: 0–5 months, 6–23 months, and 24–47 months. Where only one child for any or all age categories was present, all eligible children were selected (up to three children). Where multiple children from a single age category were present, one child was randomly selected per age group by the computer‐assisted personal interview programme. This study uses data from children sampled who were between 6 and 23 months of age only as this is the age group for which WHO complementary feeding indicators apply and for whom dietary data were collected. The household questionnaire (see Data S1) comprised modules on child feeding and care practices, child anthropometry and haemoglobin, household characteristics including food security, mother’s dietary diversity, agricultural practices including diversity of food production, and women’s empowerment. Child anthropometric measurements comprising length were taken using a portable measuring board (UNICEF Supply Division, Copenhagen, 2016). Data collectors were trained in anthropometric measurement for 5 days and completed a standardisation exercise prior to survey deployment. Survey training on the questionnaire, operating procedures, and piloting was completed in 12 days for a total of 17 days of training. Child LAZs were generated using the WHO growth standards (WHO Multicentre Growth Reference Study Group & de Onis, 2006). Scores of <−6 or >6 LAZ were excluded for biological implausibility as per WHO guidelines (World Health Organisation, 2006). Dietary diversity was first generated for children as a score ranging from 0 to 7 food groups as defined by WHO indicators for IYCF (WHO et al., 2010). We then combined 4–7 food groups consumed into a single category to create a five‐category child dietary diversity outcome variable: 0, 1, 2, 3, and 4–7 food groups consumed. Women’s minimum dietary diversity was generated as a scale variable ranging from 0 to 10 food groups as defined by FAO/FANTA (Food and Agriculture Organisation & USAID’s Food and Nutrition Technical Assistance III Project (FANTA), 2016). A scale variable from 0 to 27 based on the Household Food Insecurity Access Score was also used (Coates, Swindale, & Bilinsky, 2007). All dietary data were cleaned by comparing 24‐hr recall foods first entered in computer‐assisted personal interview questionnaire as free text with final food group assignments made by data collectors. A household wealth index was created using principle components analysis applied to proxy indicators of household socio‐economic status, namely, ownership of consumer goods, electricity, livestock (non‐food producing), source of water, type of toilet, and type of materials used for floor, roof, and walls. We created tertiles and checked internal validity by assessing ownership of consumer goods and housing characteristics by socio‐economic status tertile. Maternal education, land ownership, and ownership of livestock producing animal sources foods such as cows or sheep were excluded from the index due to known effects on nutrition outcomes that we wished to explore independently. Variables for household food production were constructed from crops grown, animals reared, and resulting food types produced by the household within the past major and minor growing seasons (1‐year reference period). We constructed a variable for fruit and vegetable production as the sum of all distinct types of fruit and vegetable crops grown in the past year. The score ranged from 0 (i.e., no fruit and vegetable crops grown) to a maximum of 21 as reported by the household. A variable for animal source food production was constructed as the sum of all such food types produced on a scale of 0–10 including eggs, meats, milks and other dairy, and fish. Descriptive statistics were generated for child, mother, and household risk factors and outcomes. Continuous variables were summarised using means and standard deviations or medians and interquartile ranges (IQRs) for nonnormally distributed variables. Categorical variables were summarised using numbers and percentages. We hypothesised pathways of impact between risk factors and dietary diversity and linear growth in children. To represent these pathways, a DAG was developed using DAGitty software (Textor, van der Zander, Gilthorpe, Liśkiewicz, & Ellison, 2016; see Figure 1). DAGs represent a set of assumptions about the causal relationships between variables that are made by researchers based on evidence and logical reasoning and provide a basis on which to reduce bias in statistical modelling (Sauer & Vanderweele, 2013; Shrier & Platt, 2008). Recent evidence on agricultural production and women’s empowerment factors supported construction of our diagram (Cunningham, Ruel, Ferguson, & Uauy, 2015; Hirvonen & Hoddinott, 2016; Ruel & Alderman, 2013). Occurrence of fever and diarrhoea in the past 2 weeks were hypothesised to influence child feeding practices but not growth due to the limited reference period. Directed acyclic graph (DAG) mapping causal relationships. DAGitty web‐based software was used to develop the diagram and to determine the minimal adjustment variable set to estimate total effect when regressing each explanatory risk factor on the outcome of interest—in this study, dietary diversity or length‐for‐age z‐score (Textor et al., 2016) Based on ancestor variables for each individual association, we used the DAGitty software to identify the unique set of covariates to be included in a multivariate model of that association. Unlike stepwise regression, covariates were not forced into a single multivariate model thus avoiding the table 2 fallacy (i.e., presentation of multiple adjusted effect estimates from a single model in a single table, often misunderstood to represent effects of the same direct causal type and therefore misinterpreted; Westreich & Greenland, 2013). We also decided a priori to add age and sex into all multivariate models, and age squared into multivariate model for LAZ. Associations between factors and child dietary diversity were investigated using univariate and multivariate ordinal logistic regression modelling. Associations between factors and LAZ were investigated using univariate and multivariate linear regression modelling. Robust standard errors were used to account for clustering at the kebele level. All analyses were conducted using Stata/IC (version 15). The baseline protocol was approved by the Scientific and Ethical Review Committee at EPHI (Ref number: SERO‐54‐3‐2016) and by the LSHTM ethics committee (Ref number: 10937) prior to data collection. By approval of both institutional review boards, informed written consent was given by caregivers of young children and witnessed by the local health extension worker.