Anthropometric failures and its associated factors among preschool-aged children in a rural community in southwest Ethiopia

Background In 2019, 144 million under-five-year-old children were stunted, and 47 million were wasted globally. In Ethiopia, approximately 350,000 children are estimated to die each year. Preschool aged children need focused attention because this age group not only has special needs, but also forms the platform for growth and development of all children. Under nutrition among preschool children is the result of a complex interplay of diverse elements, such as birth weight, household access to food, availability and use of drinking water. This study aimed at determining the anthropometric failures and associated factors using composite indictors. Methods A community-based cross-sectional study design was used among randomly selected 588 caregivers with pre-school aged children. Under-nutrition of pre-school aged children was computed by using the composite index of anthropometric failure. A multi-stage sampling technique followed by a systematic random sampling technique was used to select study participants. Structured questionnaires were used to collect data. WHO Anthro software was used to calculate height for age, weight for age and weight for height. The overall prevalence of anthropometric failure (CIAF). Both bivariable and multivariable binary logistic regressions were used to identify factors associated with under-nutrition. Results The overall prevalence of under-nutrition among pre-school children was 50.8%, which was significantly associated with being a female (AOR = 1.51, CI: 1.076, 2.12), being from a large family (AOR = 1.78, CI: 1.19, 2.663), having acute respiratory infection (AOR = 1.767, CI: 1.216, 2.566), lack of improved source of drinking water (AOR = 1.484 CI: 1.056, 2.085) and poor dietary diversity score (AOR = 1.5, CI: 1.066, 2.112). Conclusions The study area has a high prevalence of CIAF in pre-school aged children. The CIAF was found to be significantly associated with the sex of the child, family size, ARI within the last two weeks, and dietary diversity score. To promote the use of family planning and the prevention of infectious diseases, health education is required. The government should adapt CIAF as a metric for assessing children’s nutritional status. From March to April 2019, a community-based cross-sectional study was conducted among pre-school aged children in rural areas of Ilu Abba Bor Zone, Southwest Ethiopia. The Ilu Abba Bor Zone is 600 kilometers away from the country’s capital city. The Zone is divided into 14 districts, 13 of which are rural and one administrative town. The zone’s estimated total population was 934,783, with 153,585 children under the age of five and 100,209 children aged two to five years. It is located at 70 27’40 “N to 90 02’ 10” N latitude and 340 52’12 “E to 410 34’55” longitude in the western part of the area. There are three major climatic zones in the zone: temperate rainy, rainy, and dry arid climates. Rainfall occurs twice a year in the zone. The highest annual rainfall total reaches 2400 mm, while the lowest annual rainfall total is about 100 mm. In most highland areas of Ilu Abba Bora, the highest mean annual temperature ranges from 26°C to 10.6°C. The sample size was calculated using a G-Power model with the following assumptions: 52.5 percent estimated prevalence of stunting among children aged 24 to 59 months, 2.07 odds ratio of acute respiratory tract infection [22], 80 percent power, and a 5% margin of error. As a result, the sample size is calculated to be n = 256. Then, when calculating the final sample size, the design effect of 2 and 15% non-response rate were taken into account. As a result, the total sample size was 588. To find caregivers with preschool-aged children, researchers used a multi-stage sampling technique followed by a systematic random sampling technique. Four districts were selected at random from a total of fourteen districts in the first round. In the second level, kebeles (Ethiopia’s smallest administrative units) were chosen from the districts that had been chosen. Simple random sampling was used to choose twenty-two of the kebeles. Pre-school aged children (2–5 years) were classified and registered in each kebele after the kebeles were chosen. Following the registration of preschool-aged children, proportional allocation was used to pick a sample from each kebele using a systematic random sampling technique. If a caregiver/household had more than one child, a child was chosen at random via a lottery system. A second visit was made if the mothers/caregivers were not present at the time of data collection. If that didn’t work, the residents of the neighborhood were considered. Data were collected using a structured interviewer questionnaire. It was adapted from Ethiopia demographic health survey 2016 [23]. To maintain consistency, the questionnaire was first written in English and then translated into the local language (Afan Oromo). It was then translated back into English by an expert translator. Before the actual data collection, a pre-test of the questionnaire was conducted on 5% of the total samples from outside the research area to assess the acceptability and applicability of the tools and procedures. As data collectors, 12 degree graduate nurses were recruited, and six degree graduate public health officers were in charge of supervision. Both data collectors and supervisors received rigorous training over three days. To avoid within-examiner error, all anthropometric measurements were taken by investigators, qualified supervisors, and data collectors. Before performing the measurement, the weight scale was set to zero with no item on it and put on a level surface. The data collection was supervised by a principal investigator and trained supervisors. They supervised and checked each questionnaire for completeness, irregularities, inconsistencies, and unusual answers, making immediate corrections. During data entry, computer frequencies were used to search for missing variables, outliers, and other errors. The original questionnaire was revised to correct any errors discovered at this time. The questionnaire includes questions about socio demographic and economic factors, water and hygiene habits, maternal and child health, and child feeding habits. Face-to-face interviews were conducted at the mothers’/caregivers’ homes by trained data collectors. The age of the child was calculated using the child’s date of birth and the date of the interview. Where the exact date of birth was not registered or known with certainty, the caregiver was asked to guess based on a local events calendar. By subtracting the date of birth from the date of data collection, the child’s age was calculated using a precise day. Using repeated hours of dietary recall, the dietary intake of children was measured by asking caregivers (two week days and one weekend days). Every day of the week was described, including fasting and feasting days. The children’s caregivers were asked to remember all their child ate or drank during the 24-hour period of study. The Dietary Diversity Score (DDS) of the study respondents was calculated using the data from the 24-hour recall, according to the FAO guidelines for calculating household and individual dietary diversity [24]. Dietary diversity was calculated as the sum of scores in each of the seven food groups, with a scale of 0 to 7. The minimum dietary diversity (MDD) indicator was calculated using at least four of the seven food groups mentioned below: (1) staples (cereals/grains, roots and tubers); (2) dairy products; (3) animal/flesh foods (4) legumes and nuts; (5) vitamin A-rich fruits and vegetables; (6) eggs and (7) other fruits and vegetables [24]. The Ethiopia Demographic and Health Survey factors were used to create the Household Wealth Index, which is focused on household ownership of fixed assets, services, housing characteristics, and other factors [4]. Standard anthropometric procedures were used to measure the children’s height and weight [25]. All randomly selected children who had no evidence of physical impairment (such as physical defects or grossly deformed), malnourished and edematous conditions were eligible for the weight and height measurements. The children’s heights were measured using a portable stadiometer. All of the children were told to take off their shoes and stand erect, with their heels, knees, buttocks, shoulders, and heads in contact with the stadiometer’s wall and their eyes straight ahead (Frankfurt plane) so that their line of sight was perpendicular to their bodies. To the nearest 0.1cm, the height was measured. A portable digital scale was used to measure the weight (Seca, Germany Model). To the nearest 0.1kg, the weight was registered. It was regularly calibrated against a known weight. During the training, a standardization exercise was conducted to capture technical measurement error before the actual anthropometric data collection (TEM). The children wore light clothing and removed their shoes for the procedure. Height for age Z-scores (HAZ), Weight for height Z-scores (WHZ), and Weight for age Z-scores (WAZ) were calculated using the height and weight measurements. In height for age, height for weight, and weight for age, moderately stunted, wasted, and underweight children had Z-scores of-3 to-2 SD. Stunted, wasting, and underweight children with Z-scores of less than-3SD are classified as severely stunted, wasting, and underweight [26]. The composite index of anthropometric failure was used to calculate the overall prevalence of under-nutrition in pre-school children (CIAF). The Nandy et al model was used to divide CIAF into seven (7) groups. Group A (no failure), group B (waste only), group C (waste and underweight), group D (stunting, wasting, and underweight), group E (stunting and underweight), group F stunting only), and group Y (underweight only) (Table 1) [27]. *adapted from Nandy et al, 2005. Data was checked, cleaned, coded and entered into Epi-data 3.1 version and then it was exported to SPSS (version 21.0) for further analysis. Recoding and transforming of some variables were performed. Descriptive statistics such as frequencies and percentages for discrete data were calculated. Bivarable logistic regression was performed to identify potential candidate variables and each variable with a p-value less than 0.25 was interred into multivariable logistic regression analysis to determine the factors significantly associated with under-nutrition of preschool children. Results with a P-value less than 0.05 were taken as statically significant and an Odds Ratio with 95% confidence interval (CI) was used to measure the level of association. Before the inclusion of predictors in the final model, standard error was used to assess for multicollinearity amongst predictor variables, and any variable with SE ≥0.2 was removed from the study. The goodness of fit for the final regression models was checked by the Hosmer-Lemeshow goodness of fit test with a p value of ≥0.05 considered a good fit. For anthropometric data analysis, anthropometric indices were calculated by WHO Anthro software 3.2.2 using WHO child growth references. The z-scores of (< −2SD) were calculated to determine HAZ, WHZ and WAZ category of stunting, wasting and underweight, respectively. Finally, CIAF was computed from the above three anthropometric indices. Any child who has one of the six different types of anthropometric measurements is classified as having CIAF. Ethical approval was provided by the Jimma University Ethical Review Board. The letter was sent to the Ilu Abba Bora Zonal Health Department, as well as each selected district and kebele, in order to obtain consent. The mothers/caregivers of the children were given the requisite information about the study’s intent and procedures. Mothers/caregivers who agreed to participate in the intervention were requested to sign a written informal consent. All children who showed clinical symptoms of extreme malnutrition or a medical condition were referred to local health facilities for further evaluation and care.