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Background: A sizable cross-sectional studies demonstrated a low dietary diversity in Southern Ethiopia. However, its seasonal trend has not been well studied in areas where nutrient-poor enset (false banana (Ensete ventricosum)) foods are major staple. Moreover, there is scarcity of information on seasonal nature of anthropometric status of mother–child pairs (MCP) from the same areas in Southern Ethiopia. Therefore, the present study aimed to investigate the dietary diversity and anthropometric status of MCP in postharvest dry and lean wet seasons and identify factors associated with anthropometric status. Methods: The dietary intake and anthropometric data were collected from 578 households (578 mothers and 578 children) January–June 2017. The study compared data of the two seasons using McNemar’s test for dichotomous, Wilcoxon signed-rank test for non-normally distributed, and paired samples t-test for normally distributed continuous data. Logistic regression was conducted to identify risk factors for malnutrition. In addition, Spearman’s Rho test was used to determine correlations between maternal and child variables. Results: Over 94% of the mothers did not fulfil the minimum diet diversity score in both seasons. The meal frequency and pulses/legumes intake significantly declined in lean wet season; however, dark green leaves consumption increased. Meat, poultry, and fish consumption dropped to almost zero in the lean wet season. The dietary diversity and anthropometric status of the MCP were correlated. Weight-for-age (WAZ) and weight-for-height (WHZ) of children significantly declined in the lean wet season. In the same way, maternal mid upper arm circumference (MUAC), body weight, and body mass index (BMI) dropped (p < 0.001) in this season. Being pregnant and a lactating mother, poverty, and the ability to make decisions independently predicted maternal undernutrition (low MUAC). On the other hand, maternal undernutrition and education were associated with child underweight. Conclusions: The results demonstrated that the dietary diversity of MCP is low in both postharvest dry and lean wet seasons. This suggests the need for continuous nutrition intervention to improve the dietary diversity. In addition, the anthropometric status of MCP declines in lean wet season. This may provide some clue for policy targeting on improving nutritional status of mothers and children in rural Southern Ethiopia.
This study was conducted in Shebedino and Hula Districts from Sidama Zone in Southern Nations Nationalities and Peoples Region (SNNPR). The SNNPR contains 56 ethnic groups reflecting extreme diversity with unique social and cultural identities [37]. The region is known for enset production and enset food products are amongst its typical traditional foods and staples. Sidama is one among the 15 zones in this region. According to 2017 population projection, Sidama Zone has a total population of nearly 3.1 million [38]. Coffee, enset, and maize are amongst the major crops in this zone. This is a community-based panel study. Dietary diversity and anthropometric status of MCPs were assessed. Data collection was conducted in postharvest dry season (January to first week of February 2017) and lean wet season (June 2017). Reproductive age mothers and 24–59-month-old children were eligible for this study. In order to reduce the loss to follow-up, temporary-resident mothers and children were excluded from this study. Furthermore, the mothers and children who were severely sick during the survey periods were excluded as sickness alters the dietary intake pattern. For this study, a sample size of 492 was calculated using a single population proportion formula. In the calculation, 0.05 degree of precision, 1.5 effect design, and 0.27 expected prevalence of BMI < 18.5 kg/m2 were considered [31]. Finally, 10% of basic sample size was added to compensate for the loss to follow-up. To obtain a sufficient sample size for additional analysis, a total of 625 MCP were enrolled in postharvest dry season. A two-stage sampling technique was employed. Firstly, two enset growing districts (Shebedino and Hula) were selected from Sidama Zone. Secondly, five kebeles (kebele is the smallest administrative unit of district) were randomly selected using probability proportional to size (PPS). Then, the sample size was distributed to each kebele using PPS. Accordingly, 239 MCP was sampled from Hula District (Worare 155 MCP and Chirone 84 MCP) and 386 MCP from Shebedino District (Fura 126 MCP, Howolso 124 MCP, and Dila Gumbe 136 MCP). The MCP was randomly selected based on the sampling frame prepared from house-to-house listing of households with 24–59-month-old children. From the initially registered total, 578 MCP completed the study. However, 47 left the study due to delivery, migration, death, and severe illness during the lean wet season data collection. For body weight measurement, portable digital scale (Seca 770, Hanover, Germany) was used. Body weight was measured with light wear to minimize weight effect of over coats. Mothers’ height was measured with dissembling plastic height measuring board with a sliding head bar. A Shorr sliding length board was used to measure standing height of children. The study participants were requested to take off their shoes to minimize its effect on height measurement. Mid upper arm circumference (MUAC) of mothers was measured using MUAC tape. Height and MUAC were measured to the nearest 0.1 cm and weight to the nearest 0.01 kg. Measurements were taken in duplicate and averaged. When the variation was above maximum tolerable difference (0.5 kg in weight, 1.0 cm in height, and 0.5 cm in MUAC), a third measurement took place. In order to assess the seasonal trends of anthropometric status, z-scores of weight-for-age (WAZ) and weight-for-height (WHZ) were computed for children. Next, BMI of the mothers was calculated as weight in kilograms divided by the square of their height in meters [39]. To identify factors associated with anthropometric status of the MCP, the children with WAZ < −2 SD were categorized as underweight while the ones with WAZ ≥ −2 SD as normal [39]. As the BMI was not appropriate for maternal categorization due to the weight gain of pregnant women, MUAC was used. Accordingly, the mothers were categorized as undernourished when MUAC < 22 cm for non-pregnant/non-lactating, and normal when MUAC ≥ 22 cm [40]. Pregnant and lactating mothers with MUAC < 23 cm were classified undernourished, and normal when MUAC ≥ 23 cm [40,41]. Household’s wealth status was assessed collecting data on variables related to ownership of valuable assets, type of living house, and possession of improved water and sanitation facilities. For principal component analysis (PCA), 18 variables (types of roof and floor, number of sleeping rooms, availability of window, availability of separate kitchen, owning cow/ox, sheep, goat, transport animals (horse/mule and/or donkey), source of cooking fuel, cleaning drinking water, saving account, radio, mobile, chair, bed, land size, and electricity) were used in this study. The categorical variables were recoded into dichotomous indicators (0, bad; 1, improved). Then, PCA was analyzed to produce common factor scores. The first factor, which explained 66.30% of the variation, was taken to characterize the households’ wealth status. Finally, the households were ranked as five wealth quintiles (highest, second, middle, fourth, and lowest) [32]. The household food insecurity access scale (HFIAS) status indicator questionnaire was used to determine the degree of food insecurity (access) in the households. The HFIAS questionnaire contained nine main questions. Each main question was followed by a sub-question on frequency of occurrence of a condition in the past four weeks (1, rarely; 2, sometimes; 3, often). Based on the HFIAS indicators, the households were categorized into four groups (food secure, mild, moderately, and severely food insecure) [42]. In order to capture the usual intake, dietary consumption data were collected using 24 h dietary recalls in days other than fasting and holidays. In addition, mothers and children who were severely sick at the time of the survey were excluded to avoid effect of illness on the usual dietary pattern. Food models, food charts, and local units were used to facilitate mothers’ recall. Mothers’ DDS was constructed based on 10 food groups (grains, white roots, and tubers; pulses (beans, peas, and lentils); nuts and seeds; dairy; meat, poultry and fish; eggs; dark green leafy vegetables; other vitamin A-rich fruits and vegetables; other vegetables; and other fruits) [3]. Children’s DDS was created based on 7 food groups (grains, roots, and tubers; legumes and nuts; dairy products (milk, yogurt, cheese); flesh foods (meat, fish, poultry, and liver/organ meats); eggs; vitamin-A rich fruits and vegetables; and other fruits and vegetables) [6]. Food groups were assigned to 1 if any food item within the group consumed, otherwise 0, if not eaten in the last 24 h. Negligible food items with intake less than 15 g/day were not considered in the current study. A cutoff point of ≥5 was used to determine minimum dietary diversity score (MDDS-W) for mothers [3]. On the other hand, a cutoff point of ≥4 was used to compute minimum dietary diversity score of children (MDDS-C) [6]. A structured and semi-structured questionnaire was prepared first in English, then translated to Amharic. Enumerators with educational background of health, nutrition, and agriculture who have experience of data collection were recruited. Data collectors were trained by the principal investigator for one week. Similar enumerators collected the lean wet season data after a refreshing training. The data collection instruments were pre-tested on 5% of the sample size in other kebele for the necessary adjustment. Anthropometric measurements were taken by the nutritionists from Hawassa University. Data collection was supervised by the principal investigator. Before data entry, data templates were prepared, and variables were coded. Following, data were entered into SPSS version 20 (IBM Corporation, Armonk, NY, USA) for analysis. Children’s WHZ and WAZ were analyzed using WHO Anthro version 3.2.2 (World Health Organization, Geneva, Switzerland). Data normality was checked with Kolmogorov–Smirnov test. To compare the postharvest dry and lean wet seasons, Wilcoxon signed-rank test was used for non-normally distributed data (DDS, animal source food consumption (ASF), meal frequency, enset food intake, MUAC, and BMI). Additionally, a paired samples t-test and McNemar’s test were employed to compare normally distributed continuous (WAZ and WHZ) and dichotomous data (food group consumption), respectively. Logistic regression was conducted to identify association between the outcomes (maternal undernutrition and child underweight) and independent (socioeconomic and dietary intake related) variables. Univariate logistic regression was conducted to identify independent variables, which were candidates for multivariate multiple logistic regression. The independent variables with p-value < 0.25 were entered into the final logistic regression model to adjust for confounders. Multi-collinearity of the variables was checked with the variance inflation factor and standard error with respective cutoff points of <10 and <2. The Hosmer and Lemeshow test was used to check for goodness of fit for the final model. Crude and adjusted odds ratios were reported with 95% confidence interval to show the strength of association between the outcomes and predictors. The cutoff point for statistical significance test was a p-value of 0.05. Ethical clearance was received from Institutional Review Board of Hawassa University, Ethiopia; and Ethik-Kommission, Landesäztekammer Baden-Württemberg, Germany (F-2016-127). Supplementary permission was obtained from health administrative offices of the study districts. Written informed consent was received from mothers. Information was kept confidential by providing pseudonymous codes.
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