Objective: To investigate the relation of child dietary diversity and household food insecurity along with other socio-demographic with child anthropometric indices in north-central Ethiopia, an area with a high level of food insecurity and inadequate diet quality. Design: A community-based cross-sectional study was used. Settings: The study was conducted in Dessie and Combolcha towns of north-central Ethiopia from April to May 2018. Participants: Randomly selected 512 mother-child pairs with child’s age range of 6–59 months. Results: The mean (± SD) scores of weight-for-height/length, height/length-for-age, weight-for-age, and BMI-for-age Z-scores were 1.35 (± 2.03), − 1.89 (± 1.79), 0.05 (± 1.54), and 1.39 (± 2.06), respectively. From all anthropometric indicators, stunting and overweight/obesity remained the severe public issues hitting 43% and 42% of the children, respectively. In the model, mothers’ age and education and child’s age, sex, and dietary diversity were significantly related with child height-for-age Z-score while place of residence, sex of household head, child’s age, and dietary diversity score were the predictors of child BMI-for-age Z-score in the urban contexts of the study area. Nevertheless, food insecurity was not related to any of the child anthropometric indices. Conclusion: The double burden of malnutrition epidemics (stunting and obesity) coexisted as severe public health concerns in urban settings. Anthropometric statuses of children were affected by multidimensional factors and seek strong integration and immediate intervention of multiple sectors.
We employed a cross-sectional study in Dessie and Combolcha towns from April to May 2018. Dessie and Combolcha cities are found in South Wollo Zone, north-central Ethiopia, with elevations between 1842 and 2550 m above sea level, as Combolcha takes the lower elevation. There is more than 58% of the total rainfall in the summer season, while 18% falls in spring and less than 5% of the total occurring during winter. The uneven distribution of rainfall gives rise to a serious shortage of water during the dry season in the area [15]. Most of the time, small business/self-employment and government salary/wages were still the main livelihood activities for most urban households. Considering variations by town with regard to food security conditions, Dessie had the second-highest poor consumption percentage of households (47%) [16]. All children aged between 6 and 59 months that have resided in the study area for the last 6 months were included in the study sample, while any child with a severe medical problem, lack of household head or caregivers, or physical deformity was excluded from the study. The largest sample (512 mother-child pairs) was taken from a study conducted in Ethiopia by considering maternal education as a predictor for child stunting and overweight [17] with the assumptions that 95% of confidence level, 80% of power, 1.7 the odds of being stunting when the mother is not educated, and 24.3% of child stunting among uneducated mothers. Three sub-cities from Dessie and two kebeles from Combolcha town were selected randomly. We conducted a preliminary census in the selected catchments to identify target participants. The samples allocated to the total populations with the eligible study subjects proportionally, and the younger child was selected if more than one child were found in the household. A predesigned and pre-tested questionnaire was used to interview the study participants to elicit information on family and child socio-demographic characteristics like residence, religion, type of family, education, occupation of parents, socio-economic condition (household expenditure and wealth index), household food insecurity, child feeding characteristics, and anthropometric measurements. The questionnaire was standardized to assure the quality and validity of the data and translated into the local language (Amharic) and was re-translated to English. All assessment team members were able to administer the questionnaires properly; a total of 5 days of rigorous training of enumerators and supervisors was given by the three authors. Before the actual data collection work, data collectors and supervisors carried out role-play practices and they filled the pre-test activities in the community other than target areas. Data collectors were responsible for filling out the data using mobile devices while supervisors checked the completeness and correctness of the filled data before sending it to the researchers. At the end of every data collection day, each questionnaire was examined for completeness and consistency by the supervisors and finally cross-checked by the researchers. A regular adjustment has been made for anthropometric measurements in each circumstance. The household socio-economic status (SES) was parameterized by the principal component analysis (PCA) method using house properties confirmed by the questionnaire: property owned, source of drinking water, type of toilet facility, and type of flooring, wall material, and roof material. The score in the first PCA component was used as an asset index of SES status for each household [18], and households were categorized into tertiles as poor, medium, and rich. The household food security status was assessed using the Household Food Insecurity Access Scale (HFIAS), and households were classified as food secure if it had not experienced any food insecurity conditions or had rarely worried about not having enough food, whereas food-insecure households were categorized as mild, moderate, and severe in accordance with the guidelines [19]. For data validation, Cronbach’s alpha coefficient, which is a measure of the internal consistency of a scale, was used to confirm the reliability of the HFIAS and the household SES measure. An alpha value of more than 0.7 indicated that the measure was acceptable. Child dietary assessment was done based on the procedure recommended by the Food and Agriculture Organization (FAO) [20]. Mothers or caregivers were asked whether the child consumed more than a spoonful of the seven food groups (namely, cereals, tubers and roots, legumes and nuts, vitamin A-rich fruits and vegetables, flesh foods, milk and milk products, eggs, and other fruits and vegetables) within the past 24 h recall. The child food groups were developed based on the food items recommended in the Infant and Young Child Feeding (IYCF) guidelines. The total dietary diversity score was generated with the response of “yes” and “no” for each child. In accordance with the IYCF guidelines, a child’s DDS was categorized as poor and good [21]. Child weight and length/height were taken by following critical and meticulous procedures. Ages were also recorded from immunization cards, direct probing of mothers, or birth certificates. The weight of children was taken using an electronic digital weight scale and recorded in kilograms to the nearest 0.1 kg [22] and with light clothes and no shoes. Two measurements were recorded for each child, and the average result was taken. In every instance of measurement, the scale was checked for its reading and calibration. It was also standardized with 2 kg iron rod before taking the measure. The length/height of the child was also documented twice. The length was measured for children less than 24 months (child unable to stand erectly or < 85 cm) in recumbent position using wood-made sliding length board with the help of two examiners. For children greater than 24 months, height was measured using a sliding height board in Frank fret position and recorded in centimeters to the nearest 0.1 cm [22]. During this procedure, hats and shoes were removed, and the gentle pressing of hair has been made. The data were collected using a mobile data collection tool called Open Data Kit (ODK), and the collected data was directly sent to the KoBo Toolbox account created by the researchers. The daily data collected and submitted by the data enumerators were checked and cleaned by the researchers. Finally, the collected data were exported to STATA version 15 and made ready for data analysis. Standardization of measurements has been carried out, and the coefficient of variation was kept minimal (< 3%) for weight and height measurements. The data were cleaned and prepared for analysis, and STATA version 15 (StataCrop LLC, College Station, TX 77845, USA) was used to present the summary results and inferential statistics. Exploratory data analyses were done to identify missing values, influential outliers, and normality of data for both outcome and explanatory variables. Anthropometric data were exported to WHO Anthro Software version 3.2.2 to generate anthropometric indices for weight-for-length/height Z-score (WHZ), height/length-for-age Z-score (HAZ), weight-for-age Z-score (WAZ), and BMI-for-age Z-score (BAZ). Child nutritional status was determined using the above indices where each of the indices < − 2 SD is categorized as wasted, stunted, underweight, and thin. The child overnutrition was also defined when BAZ score between + 2 and + 3 SD and greater than + 3 SD reflecting the presence of overweight and obesity, respectively. We omitted outliers for WHZ and BAZ when less than − 5 and greater than + 5 and for HAZ and WAZ when the score less than − 6 and greater than + 6, respectively [22]. We fitted a generalized linear model (GLM) to declare the presence of significant associations between anthropometric indices (WHZ, HAZ, WAZ, and BAZ) and different explanatory variables. Maximum likelihood estimation was used to estimate the parameters. We checked the assumption for GLM for independently distributed outcome variables; not more than 20% of the expected cells had less than 5 for goodness-of-fit measures and the presence of the relationship between the transformed response in terms of the link function and the explanatory variables. To assess confounding, factors were included in the model based on biological plausibility and known epidemiological predisposing factors such as socio-demographic characteristics, socio-economic status, food insecurity, and child feeding practices. During data collection, a letter of ethical clearance was collected from the Wollo University, College of Medicine and Health Sciences. Particularly, the institutional health research ethics review committee was consulted about the importance of the research to the community and the harms that would occur during data collection. An official letter has been written for each city administrator and health office where the data were taken. In addition, informed verbal consent was obtained from each client, and confidentiality was maintained by giving codes for each respondent rather than recording their names. Data collectors were informed that clients have full right to discontinue or refuse to participate in the study.
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