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• Volume 13, Issue 4
• Ethiopia National Food and Nutrition Survey to inform the Ethiopian National Food and Nutrition Strategy: a study protocol
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• Meseret Woldeyohannes 1 ,
• http://orcid.org/0000-0001-9114-1412 Meron Girma 1 ,
• Alemnesh Petros 1 ,
• Alemayehu Hussen 1 ,
• Aregash Samuel 1 ,
• Danial Abera Dinssa 1 ,
• Feyissa Challa 1 ,
• Arnaud Laillou 2 ,
• Stanley Chitekwe 3 ,
• Kaleab Baye 4 ,
• Ramadhani Noor 3 ,
• Anne Sophie Donze 3 ,
• Getachew Tollera 1 ,
• Mesay Hailu Dangiso 1 ,
• Lia Tadesse 5 ,
• Meseret Zelalem 5 ,
• http://orcid.org/0000-0002-7155-4815 Masresha Tessema 1
• 1 Food Science and Nutrition Research Directorate , Ethiopian Public Health Institute , Addis Ababa , Addis Ababa , Ethiopia
• 2 UNICEF, Dakar, Senegal , Dakar , Senegal
• 3 UNICEF Ethiopia , Addis Ababa , Ethiopia
• 4 Addis Ababa University , Addis Ababa , Ethiopia
• 5 Ethiopia Ministry of Health , Addis Ababa , Ethiopia
• Correspondence to Dr Masresha Tessema; dr.masresha.tessema{at}gmail.com

Introduction Ethiopia has made significant progress in reducing malnutrition in the past two decades. Despite such improvements, a substantial segment of the country’s population remains chronically undernourished and suffers from micronutrient deficiencies and from increasing diet-related non-communicable diseases such as diabetes, hypertension and cancer. This survey aims to assess anthropometric status, dietary intake and micronutrient status of Ethiopian children, women and adolescent girls. The study will also assess coverage of direct and indirect nutrition-related interventions and map agricultural soil nutrients. The survey will serve as a baseline for the recently developed Ethiopian Food System Transformation Plan and will inform the implementation of the National Food and Nutrition Strategy.

Methods and analysis As a population-based, cross-sectional survey, the study will collect data from the 10 regions and 2 city administrations of Ethiopia. The study population will be women of reproductive age, children aged 0–59 months, school-aged children and adolescent girls. A total of 16 596 households will be surveyed, allowing the generation of national and regional estimates. A two-stage stratified cluster sampling procedure will be used to select households. In the first stage, 639 enumeration areas (EAs) will be selected using probability-proportional-to-size allocation. In the second stage, 26 eligible households will be selected within each EA using systematic random selection. Primary outcomes include coverage of direct and indirect nutrition interventions, infant and young child feeding (IYCF) practices, food insecurity, dietary intakes, mental health, anthropometric status, micronutrient status and soil nutrient status.

Ethics and dissemination The protocol was fully reviewed and approved by the Institutional Review Board of the Ethiopian Public Health Institute (protocol no: EPHI-IRB-317–2020). The study is based on voluntary participation and written informed consent is required from study participants. The findings will be disseminated via forums and conferences and will be submitted for publication in peer-reviewed journals.

• EPIDEMIOLOGY
• NUTRITION & DIETETICS
• PUBLIC HEALTH

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjopen-2022-067641

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Strengths and limitations of this study

The survey covers a large geographical area, collecting data on anthropometric status, 24 hours recall quantitative dietary intakes and the determination of micronutrient status in the same participants or household, while also capturing data on the food system in Ethiopia.

The study aims to improve understanding of nutritional problems across multiple facets—from agricultural soil to people to the environment in Ethiopia.

Inherent to the cross-sectional design of the study, the findings of this study cannot be used to establish cause and effect.

The study design prevents us from considering seasonal differences in nutritional outcomes and determinants.

Introduction

Globally, one in every three persons is affected by one of more forms of malnutrition. 1 Women and children are particularly vulnerable to malnutrition due to increased physiological nutrient needs required to support fetal and child growth. 2 Nutritional deprivation during early life impairs growth and development, leading to poor school performance, reduced productivity and loss of earnings in later life. 3 Consequently, the first 1000 days of life, from conception to the child’s second year of life, were recognised as a critical window of opportunity to effectively prevent malnutrition. 3 4 Adolescence is also identified as a second window of opportunity to correct nutritional inadequacies and adversities faced in early life, but little is known about this life stage.

Despite significant progress over the past two decades, the burden of malnutrition in Ethiopia remains high. 5–7 Nationally, 37% of Ethiopian children under 5 years of age are stunted, 7 and 22% of women of reproductive age (WRA) are chronically undernourished (body mass index (BMI) <18.5 kg/m 2 ). 5 Only 14% of children under 2 years of age consumed the minimum number of recommended food groups. 5 Furthermore, micronutrient deficiencies coexist with chronic energy deficiency. 8 This along with the ongoing nutrition transition, characterised by shifts in diets, 9 is further complicating the nutrition landscape by increasing the prevalence of overweight and non-communicable diseases. 5 Nearly a fifth (16%) of Ethiopian adults are estimated to be hypertensive, and 3% are diabetic. 10 Therefore, addressing not only undernutrition but all forms of malnutrition is critical.

The Sustainable Development Goals (SDGs) recognise the importance of nutrition, primarily driven by the need to mitigate its detrimental consequences. Further, the 2012 World Health Assembly identified global targets to be achieved by 2025 that aim to reduce stunting, anaemia, low birth weight and childhood obesity. These targets are used to track progress in SDG 2: zero hunger. 11 Recognising the importance of good nutrition, the Government of Ethiopia has made ending malnutrition a national priority. Ethiopia started implementing its first National Nutrition Program in 2008. 12 The second phase of this programme (2011–2016) was a multisectoral programme aimed at accelerating progress in reducing malnutrition. 13 Moreover, Ethiopia’s first Food and Nutrition Policy was endorsed in 2018, 14 followed the National Food and Nutrition Strategy 15 which was launched in 2021 to provide a framework for the operationalisation of the policy. Acceleration of progress in the reduction of malnutrition requires the design and implementation of direct and indirect nutrition interventions that can be implemented at scale. To this end, understanding the various factors contributing to the different forms of malnutrition is critical.

Multiple factors operating at the immediate, underlying and basic levels contribute to malnutrition. 2 Inadequate dietary intake and poor health are immediate determinants. 2 Household food security, child care practices, access to health services and healthy environments are underlying determinants. 16 Structural and contextual factors such as economic structures, and political, environmental, social and cultural factors are the basic determinants of malnutrition. 2 The contribution of these factors varies across different contexts, and target groups, but studies capturing all these factors in a single survey are scant. The lack of timely and comprehensive information on nutritional status across critical life stages and their determinants is a bottleneck that is preventing Ethiopia from designing effective interventions. Up-to-date and comprehensive data on the coverage of direct and indirect nutrition interventions delivered across various implementing sectors of the National Food and Nutrition Strategy are not yet available. This is unfortunate as such data could inform the implementation of the strategy, but it can also serve as a baseline against which progress can be tracked.

Therefore, this study aims to provide the first ever comprehensive information on the nutritional status of different populations in Ethiopia to support evidence-based implementation of the National Food and Nutrition Strategy.

The overall goal of this study will be to produce nationally and regionally representative estimates on anthropometric status, coverage of nutrition interventions, dietary intakes, and micronutrient status for children, adolescent girls and WRA in Ethiopia.

Specific objectives include:

Assess the coverage of direct and indirect nutrition interventions.

Assess food consumption patterns and nutrient intake of children aged 6–59 months and WRA.

Assess the micronutrient status of children (vitamin A, anaemia, iron, iodine and zinc), adolescent girls and WRA (vitamin A, vitamin D, anaemia, iron, iodine, zinc, folate, vitamin B 12 )

Assess the anthropometric status of children under 5 years of age, school-age children (6–12 years), adolescent girls and WRA.

Assess the geographical distribution of soil micronutrient status in the Ethiopian agricultural soil.

Methods and analysis

Study design.

This study is a nationally and subnationally (regionally) representative cross-sectional survey that will characterise dietary intake, micronutrient status and access to nutrition-related services for different target populations. Given that soil nutrient content can influence micronutrient content of foods and hence affect nutrient intake, the soil nutrient composition will also be analysed. The study will have four main components. The first component will assess nutrition-specific and nutrition-sensitive indicators (NSS) for all target groups (children aged 0–59 months and WRA, school-age children and adolescent girls) using semistructured questionnaires. The second component will measure quantitative dietary intake for children aged 6–59 months and WRA (15–49 years). The third component of the survey will collect biomarker samples from all children (6–59 months), school-age children (6–12 years), adolescent girls (10–19 years) and WRA (15–49 years). The final component of the study will measure micronutrients in agricultural soils. The study data will be collected from July 2021 to December 2023.

Ethiopia has an estimated population size of 120 million and is the second most populous country in Africa. 17 The majority of its population resides in rural areas (70%). 17 Agriculture accounts for 40% of the country’s gross domestic product. 17 Children aged 15 years and younger make up 40% of the Ethiopian population in 2021. 18 Ethiopia is administratively divided into 10 regions and 2 city administrations. This study will be conducted in all the regions and city administrations of the country. Figure 1 provides a geographical representation of the study areas.

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Map showing study enumeration areas (EAs) across regions.

Participants

The target population of this study are (1) WRA aged 15–49 years, (2) Children aged 0–59 months, (3) School-age children aged 6–12 years, (4) Adolescent girls aged 10–19 years and (5) Household heads.

Sample size calculations

Sample size was estimated to guarantee adequate precision to generate national and regional estimates for selected indicators for each study target group. Indicators used for each target group are shown in online supplemental table S1 . The required number of households and target groups was calculated using a single population proportion formula at the regional level. We used region-specific prevalence estimates for indicators, a 5% margin-of-error, a design effect of 1.5, a household response rate of 95% and an individual response rate of 80%. The initial sample size was then adjusted for region-specific average household size and percentage of the target population from the total population. An indicator that provides the maximum number of households was used to estimate the final sample size for each region. Regional sample sizes were summed up to derive the total (national) sample size. Based on these calculations, the total sample size for the overall survey was 16 596 households ( online supplemental table S2 ).

Supplemental material

For WRA, dietary and biomarker data will be collected in half of the selected households within each enumeration area (EA). This selection will yield a total sample size of 7386 WRA (50% of the expected 14 772 WRA). The sample size needed to assess dietary intakes and micronutrient status of WRA was calculated using the prevalence of inadequate zinc intake, which yielded the largest sample size. 8 19

Sampling procedures

A two-stage stratified cluster sampling procedure will be used to select households. In the first stage, 639 EAs, 257 urban and 382 rural, will be selected using probability-proportional-to-size allocation. We will use the 2018 Ethiopia Population and Housing Census EAs sampling frame to select EAs (the primary sampling units). The Central Statistical Agency prepared the EAs sampling frame. An EA typically contains 100–150 households. EA maps will be used to delineate the boundaries of the selected EA. In the second stage of sampling to identify eligible households, all households with the EA will be listed. A household will be eligible for selection if at least one of the study target groups are residents (de jure) or stayed at the household the night before the interview (de factor).

Twenty-six (26) eligible households will be selected within each EA using systematic random selection. All target groups will be eligible for the NSS interview in the selected households. All children aged 6–59 months will also be eligible for dietary assessment. Women residing in 13 households (out of 26 households) who will be selected randomly will be eligible for dietary assessment. Biomarker samples will be collected for all children under 5 years of age, school-age children and adolescents in the selected households. Similar to dietary assessment, biomarker samples will be collected for women residing in half of the selected households ( figure 2 ).

Sampling frame for the Ethiopia National Food and Nutrition Survey to inform the Ethiopian National Food and Nutrition Strategy.

Coverage of direct and indirect nutrition interventions

A structured questionnaire will be used to determine the coverage of direct and indirect nutrition interventions provided to children aged 6–59 months, WRA and adolescent girls. Direct nutrition interventions included vitamin A supplementation, iron supplementation, zinc supplementation, growth monitoring and promotion, nutrition counselling services, and food fortification. Water, sanitation and hygiene, coverage of food or cash assistance programme, women empowerment, and mental health will be some nutrition-sensitive indicators considered in this study ( table 1 ). We will use standard indicator definitions proposed by the Data for Decisions to Expand Nutrition Transformation project (DataDENT) to assess coverage of nutrition programmes.

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Nutrition direct and indirect interventions coverage

Anthropometric status

Using standardised procedures, anthropometric measurements, including weight, height/length and mid-upper arm circumference, will be taken for all study target populations. 20 Anthropometric indices (weight-for-height z-scores, length/height-for-age z-scores, weight-for-age z-scores, BMI-for-age z-scores) will be calculated using the WHO 2006 child growth standards and the WHO 2007 child growth reference data. Stunting (length/height-for-age z-scores below −2 SD), wasting (weight-for-height z-scores below −2 SD) and Mid-Upper Arm Circumference (MUAC), underweight (weight-for-age z-scores below −2 SD), thinness (BMI-for-age z-scores below −2 SD) and BMI will be the primary anthropometric outcomes of interest.

IYCF practices

IYCF practices will be assessed using the new WHO and UNICEF recommended 17 indicators to evaluate IYCF practices. 21

Food insecurity

The Food Insecurity Experience Scale (FIES) will be used to assess household food security. 22 The FIES consists of eight questions that assess household experience related to adequate food access. Experience questions range from worrying about getting enough food to not eating for a whole day.

In addition to these outcome indicators, information on the sociodemographic characteristic of households, child health, maternal health, employment status and household agricultural practices will be collected using structured questionnaires.

Mental health of women

Common mental health disorders will be assessed using the WHO Self-reporting Questionnaire which consists of 20 questions. Women will be classified as having a common mental health disorder if the row score will be greater or equal to 6 out of 20. 23

Assessment of dietary intakes of children and WRA

We will measure dietary intake for children aged 6–59 months and WRA. A 1 day quantitative multiple-pass 24-hour recall will be conducted to assess dietary intakes. The interactive multiple-pass 24-hour recall interview consists of four steps designed to enhance memory. 24 All days of the week will be proportionately represented during the dietary survey to account for the day of the week effects on food intake. To account for the day-to-day variability of dietary intake within individuals, a second non-consecutive day 24-hour recall (repeat) will be collected (within 2–10 days of the first recall) on a randomly selected subsample of WRA and children. The number of repeats needed is determined by allocating for each region 50 repeats, which is then multiplied by a design effect of 1.5 and a 10% non-response rate. The number of repeats will be rounded up to 1244 recalls for each target group to ensure that the minimum number of repeats (n=83) needed from each region would be collected. Detailed non-standard recipe ingredient data will be collected for all mixed dishes that were prepared at home.

We will use 15 food groups to assess the dietary intakes of women (15–49 months) and children aged 24–59 months. These food groups were: (1) Cereals and their products, (2) Starchy roots and tubers, and their products, (3) Pulses and their products, (4) Vegetables and their products, (5) Fruits and their products, (6) Meat and poultry their products, (7) Eggs and their products, (8) Fish, shellfish and their products, (9) Milk and milk products, (10) Fats and oils, (11) Nuts and seeds, (12) Sugar and sweetened products, (13) Beverages, (14) Spices and condiments, and (15) Miscellaneous. For children aged 6–23 months, we will use the updated WHO, UNICEF food groups: (1) Breast milk, (2) Grains, roots and tubers, (3) Pulses, nuts and seeds, (4) Dairy products, (5) Flesh foods (meats, fish, poultry, organ meats), (6) Eggs, (7) Vitamin-A rich fruits and vegetables, and (8) Other fruits and vegetables. These food groups were adapted from the FAO/WHO Global Individual Food consumption data Tool food groups. 25

Dietary assessment presurvey work

We carried out presurvey work to aid dietary data collection following recommendations set by the Intake: Centre for Dietary Assessment. 26 27 An initial step will be to develop a food and ingredient list containing a comprehensive list of food items, mixed dishes and ingredients expected to be consumed by the study target groups. The food list will be generated using data from the first 2011 Ethiopian National Food Consumption Survey. 19 Other common foods consumed across the regions in Ethiopia will be derived from the 2016 Household Income and Expenditure Surveys, 28 the Ethiopian Food Composition Tables, and dietary intake data from other recent dietary assessment surveys conducted by the Ethiopian Public Health Institute (EPHI). Portion size estimation methods suitable for large-scale studies will be preselected for use in the survey following intake recommendations. 29 The selected methods will be direct measurement of actual foods consumed, standard unit: size and number, proxy measurement using play dough, water, rice, and maize flour, and finally using food price to estimate the amount of food consumed. Portion size estimation methods will be assigned for all foods included in the food list.

Assessment of micronutrient status

Blood specimens will be collected from the study population to determine serum retinol, ferritin, soluble transferrin receptor (sTfR), zinc, folate, vitamin B 12 , red blood cell (RBC) folate and 25-hydroxyvitamin D. Additionally, markers of inflammation, alpha(1)-acid glycoprotein (AGP), high-sensitivity C reactive protein (hsCRP) will also be measured. We will also analyse parasites from stool specimens. All laboratory analyses will be performed at the EPHI Clinical chemistry, and Food Science and Nutrition Laboratories. Both laboratories participate in an external quality assessment scheme and are accredited by the Ethiopian National Accreditation Office. Collection, storage and analytical procedures for blood, urine, stool and salt samples are described below. The details of each biomarker analysis are described in online supplemental materials 1–11 .

Blood sample collection and analysis

Venous blood samples (5–7 mL) will be collected using vacutainer tubes following standard operating procedures. 30 Trace mineral-free vacutainer tubes will be used to collect blood for trace metal analysis. After collection, blood samples will be allowed to clot for 30 min in cold boxes (<8°C). Samples will then be centrifuged at 3000 rpm (revolution per minute) for 10 min. The separated serum will be aliquoted and stored in −20°C portable freezers in the field. Samples will then be transported to EPHI and stored at −80°C until analysis. Haemoglobin will be measured in the field using Hemocue (Hb 301, Hemocue AB, Angelholm, Sweden). 31 , 31 If the haemoglobin values are below WHO cut-off point(11 g/dL), the phlebotomist will send whole blood samples to the EPHI laboratory to identify haemoglobinopathies using the electrophoresis method. 32 Malaria test will be conducted onsite using Bioline Malaria Ag P.f rapid diagnostic test kits (RDT) for Plasmodium . 33 Serum sTfR, AGP, hsCRP, folate, RBC folate, vitamin B 12 and ferritin will be measured using Cobas 6000 analyzer (Roche Diagnostics GmbH, Mannheim, Germany). Serum retinol will be measured using the high-performance liquid chromatography method, 33 and serum zinc and selenium will be measured using a microwave plasma atomic emission spectrometers analyser.

Stool and urine sample collection and analysis

Stool samples will be collected using stool cups and stored in 10% formalin to preserve the parasite until analysis. 34 A portion of each stool sample will be used to detect direct ova, larvae and cysts of intestine parasites using formal ether concentration technique. 35 Urine samples will be collected from WRA and school-age children using 60 mL urine cup containers. Samples will be stored at −20°C. Urinary iodine excretion will be assessed by Sandell Kolthoff reaction at EPHI Laboratory using Shimadzu 1800 UV-Vis spectroscopy. 36

Salt collection and analysis

Salt samples will be collected from households with WRA for whom dietary data will be collected. At least 25 grams (one coffee cup) of salt will be collected to determine iodine content using the iodometric titration method. 37

Assessment of nutrients in the soil

Soil samples will be collected from three households in each EA. Zigzag or cross-sampling method will be used to collect 10–20 subsamples (0–30 cm depth) constituting one composite sample. Subsamples will be collected at a separation distance of 5 m. After thoroughly mixing the composite samples, 1 kg soil sample will be transferred to polyethylene bags. The collected soil samples will be air-dried in wooden trays and disaggregated using a ceramic mortar and pestle (soil grinder) at the EPHI soil laboratory. Samples will then pass through a 6 mm sieve of stainless steel screens to remove debris and homogenise the soil sample. The sieved fraction will be further pulverised to pass through a 1 mm sieve for the micronutrient analysis. Soil zinc, iron, copper and manganese will be determined following standard procedures. 38 Micronutrient content will be determined using inductively coupled plasma-optical emission spectroscopy after extraction with diethylene triamine penta acetic acid. Additional variables that affect the mobility of micronutrients in the soil and their uptake into crops will also be measured. These variables include soil reaction (pH), electrical conductivity, organic matter, total nitrogen and soil organic carbon content. Data collectors will also record topography, slope, cropping history, type and fertiliser application information. Table 2 provides a summary of procedures for each of the four components of the survey by study target groups.

Summary of data collection procedures for each of the four components of the survey

Data quality assurance and analysis

Training of trainers on components of the survey will be held before training the data collectors and supervisors. After 15 days of training on methodological procedures, questionnaires and quality assurance, the questionnaires will be tested in a pilot group (in EAs not included in the actual survey), and adapted based on the received feedback from the survey team. The questionnaires (including the food list) were translated into local languages (Amharic, Oromifa, Tigrigna, Somali and Afar) and back-translated to English to ensure the quality of the translation. The data collectors’ measurements will be standardised to ensure that the interobserver variability is within tolerable limits. Supervisors received additional training on teamwork and on monitoring and supervising the data collection process. All data collection tools are programmed using open-source software (ODK) ( online supplemental file 12 ). Data quality checks will be included during ODK programming to prevent data recording errors. These include restricted responses, filter insert choices, skip patterns and defaults. During data collection daily data tracking forms will be completed to track completed surveys for each study component to prevent missing data. High frequency checks will be identified prior to the surveys, and error tracking forms will be designed to track data quality in real time. These checks included completeness checks, target group tracking and duplicate ID checks. Random field supervision visits will also be made to check data quality. Every day, collected data will be sent to the EPHI central server and imported into statistical software programs as comma-separated values files. For laboratory analysis, a quality control chart will be used to ensure the internal and external quality control materials are in the acceptable range.

The primary data analysis will focus on computing frequencies and percentages for categorical variables and summary statistics (like means, medians SD, IQR) for summarising continuous variables. Sample weights will be constructed based on the selection probabilities of EAs, eligible households and non-response rates. All analyses will also be adjusted for the survey design. Additional subgroup analysis will be computed for variables with adequate sample sizes for each category. The Biomarkers Reflecting Inflammation and Nutrition Determinants of Anemia working group’s regression correction approach will be used to account for inflammation in the study of all micronutrients status using the biomarkers CRP and AGP. Geostatistical analyses will be employed to determine the spatial patterns of micronutrient distribution in the soil and blood samples. The wealth index will be constructed using principal component analysis. 39 The Rasch model will be used to construct the FIES. 22 All analyses will be done using STATA V.16 and Aeronautical Reconnaissance Coverage Geographic Information System(ArcGIS). Anthropometric indices will be calculated using the WHO Anthro software for children under 5 years of gae and WHO AnthroPlus software for adolescents.

Patient and public involvement

Ethics and dissemination.

The study protocol is approved by the Institutional Review Board of the EPHI (protocol no: EPHI-IRB-317–2020). Written informed consent will be obtained from each respondent and participants may withdraw at any time ( online supplemental material 13 ). Confidentiality of all collected data will be given high priority during each stage of data handling. Individual names and personal information of respondents will be kept confidential and data sets will be kept anonymous for analysis.

The study’s findings will be disseminated through several communication channels, including stakeholder workshops, various local and international conferences, and technical reports. Additionally, the findings will be submitted for publication in peer-reviewed journals.

This comprehensive, nationally representative survey will for the first time characterise simultaneously the dietary intake and micronutrient status of Ethiopian children, adolescent girls and WRA. Besides, the study assesses key drivers of malnutrition including soil nutrient composition, as well as coverage of direct and indirect nutrition interventions. The survey will provide key insights informing the implementation of Ethiopia’s National Food and Nutrition Strategy.

High-quality and timely data are critical to assess the burden of nutritional problems, identify vulnerable populations and priority actions, track the implementation of nutrition programmes, and assess impact. 40 41 Ethiopia conducted its first ever food consumption survey in 2011 19 and its micronutrient survey in 2015. 42 Both surveys were collected at different times, which made it difficult to link the two surveys. The causes of malnutrition are numerous and often interconnected, and addressing this problem requires a comprehensive and multisectoral approach. One of the key challenges in addressing malnutrition is the lack of data on multiple indicators from various sectors. This data is essential for understanding the underlying causes of malnutrition and developing effective strategies to address it. For example, data on soil nutrient levels is critical for understanding the nutritional quality of the crops that are grown, while data on diets and micronutrient status is essential for understanding the nutritional status of individuals and populations. Exposure to direct and indirect nutrition interventions is also important in addressing malnutrition. By collecting data on multiple indicators from various sectors, policymakers and program implementer can develop evidence-based strategies to address malnutrition and improve the health and wellbeing of populations. In this regard, this survey is uniquely positioned to integrate data from multiple domains to support evidence-based decision making for improved diets, nutrition and overall well-being.

This study will allow us to evaluate progress relative to previous food consumption and micronutrient surveys, but, more importantly, will serve as a baseline against which future progress related to the implementation of the National Food and Nutrition Strategy will be evaluated. Furthermore, the current survey will also serve as a baseline for the Ethiopian Food System Transformation Plan by capturing the majority of indicators used for monitoring food systems-related progress, thus filling information gaps that could have impeded successful implementation of the National Food and Nutrition Strategy. By establishing 13 strategic objectives, the National Food and Nutrition Strategy is intended to be aligned with the strategic directions of the Food and Nutrition Policy. Each strategy direction includes initiatives, actions and key performance indicators, as well as leading and collaborating sectors. The key performance indicators should be evaluated to determine the progress of each implementing sector’s achievement. The current survey will provide up-to-date national and subnational information on the current food and nutrition situation in Ethiopia for different target populations as well as provide a comprehensive list of indicators that are pertinent to the implementation of the policy. 40 In addition, this study will provide information on context-specific determinants for prioritising direct and indirect actions that can be implemented across sectors taking into account the specific needs of different target populations.

Additionally, effective multisectoral interventions that address the immediate and underlying determinants of malnutrition must be implemented in order to accelerate the reduction of malnutrition in all its forms. 40 These interventions need to address context-specific determinants to reduce malnutrition effectively. 40 The lack of timely and disaggregated information on the determinants of malnutrition is a bottleneck to preventing malnutrition, particularly among the most vulnerable target populations. This study will also provide information on the coverage and quality of interventions which can be used to contextualise National Food and Nutrition Strategy monitoring frameworks, monitor implementation and track progress towards global and local targets.

Although this study will provide regionally and nationally representative estimates for key indicators and critical life stages, it has several limitations. Inherent to the cross-sectional design of the study, the findings of this study cannot be used to establish cause and effect. Additionally, the design prevents us from considering seasonal differences in nutritional outcomes and determinants. This study also relies on self-reported data, which are subject to recall bias. Notwithstanding the abovementioned limitations, this study is uniquely designed to combine the assessment of anthropometric status, 24-hour recall quantitative dietary intakes and the determination of micronutrient status in the same participants, while at the same time capturing data on the food system. Additionally, the study will be evaluating micronutrients in agricultural soil, which will expand our understanding of factors that influence nutrition. To the best of our knowledge, this will be among—if not—the first study to simultaneously collect these variables from the same household. This could contribute to a better understanding of nutritional problems across multiple facets—from soil to people to the environment. In the past, nutrition programmes implemented in Ethiopia have relied on information provided from small-scale studies and population-based surveys such as the Ethiopia Demographic and Health Surveys. 5–7 43 44 Although these data sources provide some information to track progress and tailor interventions, they only provide data on a limited number of nutrition indicators and do not measure dietary intakes and assess biomarker status. This study will fill these data gaps by providing information on comprehensive indicators that show the burden and spatial distribution of micronutrient deficiencies and shifts in dietary patterns. Additionally, this study will provide information on emerging determinants such as mental health and intake of nutrients such as folate and B 12 that have not been included in previous studies. Finally, the inclusion of adolescent girls and school-age children will provide vital information on nutritional indicators for these target groups, which are often not included in other nationally representative surveys. This survey will also provide information on the coverage of direct interventions implemented in the health sectors and indirect interventions implemented in the agriculture, Water, Sanitation and Hygiene (WASH), education and social protection sectors for whom scant data exist. Hence, this study will provide valuable information that will guide the implementation of strategic actions for the reduction of malnutrition in Ethiopia.

Ethics statements

Patient consent for publication.

Consent obtained directly from patient(s)

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2
• Data supplement 3
• Data supplement 4
• Data supplement 5
• Data supplement 6
• Data supplement 7
• Data supplement 8
• Data supplement 9
• Data supplement 10
• Data supplement 11
• Data supplement 12
• Data supplement 13
• Data supplement 14
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• Published: 11 July 2019

Prevalence of malnutrition and associated factors among under-five children in Ethiopia: evidence from the 2016 Ethiopia Demographic and Health Survey

• Abay Kassa Tekile   ORCID: orcid.org/0000-0001-9505-2804 1 ,
• Ashenafi Abate Woya 1 &
• Garoma Wakjira Basha 1  

BMC Research Notes volume  12 , Article number:  391 ( 2019 ) Cite this article

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The aim of this study was to assess the risk factors for malnutrition among children aged 0–59 months in Ethiopia. The analyzed data were obtained from the 2016 EDHS and 9495 under-5 years’ children were considered in this analysis. The data was extracted, edited and analyzed by using SPSS Version 23.0. Both bivariate and multivariable binary logistic regression model was used to identify the determinants of children malnutrition.

The prevalence of stunting, wasting, and underweight were 38.3%, 10.1%, and 23.3%, respectively. About 19.47% of children were both stunted and underweighted, and only 3.87% of children had all the three conditions. Among the factors that considered in this study, age of a child, residence region, mothers’ education level, mothers’ BMI, household wealth index, sex of a child, family size, water and toilet facility were significantly associated with malnutrition in Ethiopia. The authors concluded that malnutrition among under-five children was one of the public health problems in Ethiopia. Therefore, the influence of these factors should be considered to develop strategies for reducing malnutrition in Ethiopia.

Introduction

Malnutrition among under-5 year children is a common public health problem and it is one of the main reasons for the death of children in developing countries [ 1 ]. As of the World Health Organization report, about 35% of under-five children’s death is associated with malnutrition in the world [ 2 ]. There are 165 million stunted, 99 million under-weighted, and 51 million wasted children globally [ 3 ].

The prevalence of stunting has decreased from 58% in 2000 to 44% in 2011 in Ethiopia. The prevalence of wasting is changed from 12% in 2000 to 10% in 2011. The prevalence of underweight has consistently decreased from 41% in 2000 to 29% in 2011 [ 4 ]. In Tanzania, a high prevalence of underweight (46.0%), stunting (41.9%) and wasting (24.7%) are observed in 2017. In addition, 33% of children are both stunted and underweight, 21% of children are underweight and wasted, and 12% of children are stunted and wasted [ 5 ]. In Ethiopia, more than one-third of a child deaths are associated with malnutrition [ 6 ]. Moreover, the proportion of malnutrition is higher among anemic children compared to those of non-anemic [ 7 ].

Different researchers conducted a study on malnutrition among under-five children in different parts of the country. These studies were mainly focused on the prevalence and determinants of malnutrition among under-five children, but they gave little attention for exploring the relationship between under-five malnutrition and child anemia. So, the main aim of this study was to explore the major factors of malnutrition and its association with anemia by using updated data from the 2016 EDHS.

Study design and sampling

The 2016 Ethiopia Demographic and Health Survey data was used for this study. The 2016 EDHS used a two-stage stratified sampling design to select households. In the first stage, there were 645 enumeration areas (202 in urban and 443 in rural areas) based on the 2007 Ethiopia Population and Housing Census (PHC). A total of 18,008 households were considered, of which 16,650 (98% of response rate) households were eligible. The women were interviewed by distributing questio-ners and information on their birth history and 9495 under-five children were considered for this study [ 8 ].

Measurements

The dependent variable for this study was the malnutrition status of under-5 year children (stunting, underweight and wasting). Children whose height-for-age Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population are considered as stunted. If the weight-for-age Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population then the child is underweight. Children whose weight for height Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population are considered as wasted [ 8 ]. Socio-demographic, socio-economic and health-related variables were considered as independent variables in this study.

Statistical data analysis

The data were extracted, edited, and analyzed by using SPSS version 23 for Windows. Then a weighted analysis was conducted using the same sampling weight given for each region in Ethiopia DHS to compensate for the unequal probability of selection between the strata [ 8 , 12 ]. Bivariate logistic regression was performed and variable with P-value less than 0.25 were transported into multivariable binary logistic regression analysis to identify the determinant of malnutrition of under-five children. Finally, variables with P-values < 0.05 in the multivariable logistic regression model were taken as statistically significant.

Samples of 9495 under-five children were considered in this research. The weighted prevalence of stunting, underweight, and wasting were 38.3%, 23.3%, and 10.1%, respectively. About 66% of interviewed mothers had no education and only 2% of them attended higher education. About 44% of children were found between 0 to 24 months and more than half (51.1%) were males. Only 11% of the respondents were from urban areas and 32% were in the rich wealth index. Around 20% of children’s mother were underweighted (having body mass index less than 18.5) (Additional file 1 ).

Determinants of stunting

Among the factors that considered in this study, child’s age, residence region, mothers’ education level, wealth index, child sex, toilet facility, size of a child, mothers’ BMI and number of children per household were associated with stunting. Compared to children of 0–24 months, the odds of stunting among children in the age group of 25–47 months were 2.645 times higher. The child in the age group of 48–59 months was 1.763 times higher. Compared with children in Tigray region, the risk of being stunted was decreased by 32%, 33%, and 60%, among children living in Afar, Oromia, and Somali regions, respectively. The risk of being stunted among children whose mothers attended primary education was 0.87 times less compared to children whose mother did not attend education. The risk of being stunted among children whose mothers attended secondary and higher education were 0.606 and 0.453 times less compared to children whose mother did not attend education respectively. Compared to male children, the probability of being stunted among female children was decreased by 16%. Compared to children living in households with poor economic status, the odds of being stunted among children living in households with medium and rich economic status were decreased by 20% and 31%, respectively.

Children born with small size were 1.509 times more likely to be stunted than children born larger (AOR = 1.509; 95% CL 1.332–1.709) and children who had born with medium size were 1.189 times more likely to be stunted than children born larger (AOR = 1.062; 95% CL 1.062–1.331). Children born to underweight mothers (BMI < 18.5) were 2.163 (AOR: 2.163, 95% CI 1.750, 2.673) times more likely to be stunted compared to those born to overweight mothers (Table  1 ).

Determinants of under-weight

Age of child, sex of a child, mothers’ education level, mothers’ BMI, region, household wealth index, water facility, toilet facility, size of child and a number children were associated with under-weight (P < 0.05). The risk of being underweighted was 1.748, 1.837 times more likely among children that were aged 24–47, and 48–59 months than those aged 0–24 months. Compared to Tigray region, the odds of being under-weighted was 0.741, 0.664 and 0.393 times lower among children from Oromia, Gambella and Addis Ababa respectively.

The risk of being underweight for children whose mother attend primary, secondary and higher education were 0.771, 0.645, and 0.551 times lower than children whose mothers who did not attend formal education. Children from a household with middle and rich economic status were 0.794 and 0.565 times less likely to be under-weighted compared to children living in a household with poor household economic status.

Female children were 0.856 times less likely to be under-weighted as compared to male children. Children who were born with small size were 1.898 times more likely to be under-weighted than children born larger (AOR = 1.898; 95% CL 1.653–2.180) and children who had born with medium size were 1.324 times more likely to be under-weighted than children born larger (AOR = 1.324; 95% CL 1.164–1.507). Children born to underweight mothers (BMI < 18.5) were 3.162 (AOR: 3.162, 95% CI: 2.410, 4.148) times more likely to be underweight compared to Children born to overweight mother (Table  2 ).

Determinants of wasting

Results of multivariable binary logistic regression model showed that the age of a child, sex of a child, mothers’ education level, household wealth index, a region of residence, water facility, and family size were significantly associated with wasting. Children of the rich household were less likely to be wasted compared to children living in a household with poor household economic status. The risk of being wasted was 0.52 and 0.607 times lower among children of 25–47 and 48–59 months than those 0–24 months. Compared to children from the Tigray region, the odds of being wasted of children from Somali region was 1.671 times higher. The odds of being wasted in SNNPR and Addis Ababa were 0.365 and 0.338 times lower compared to Tigray region respectively. The odds of being wasted was 0.778 times lower among female children than male children (AOR = 0.778, 95% CI 0.681, 0.889). The odds of being wasted was 1.223 times higher among children who lived in household members of 6–10 than children who had lived in household members of 1–5 (AOR = 1.223, 95% CI 1.066, 1.403) (Additional file 2 ).

Associations between children’s anemia and malnutrition

This study showed that among stunted, underweighted, and wasted children, 61%, 64.3%, and 68.2% were anemic respectively. Moreover, the percentages of stunting, wasting, and underweighting were higher among anemic children as compared to no-anemic children. Stunted children were 1.222 times more likely to be anemic compared to those of not stunted (AOR: 1.222, 95% CI 1.101, 1.356). Underweighted children were 1.222 times more likely to be anemic compared to those of not underweight (AOR: 1.222, 95% CI 1.077, 1.386). Wasted children were 1.557 times more likely to be anemic compared to those of not wasted (AOR: 1.557, 95% CI 1.315, 1.844) (Table  3 ).

In this study, the prevalence of malnutrition and associated factors in Ethiopia was assessed. The prevalence of stunting, underweighting, and wasting were 38.3%, 23.3%, and 10.1% respectively. These prevalence were relatively lower than the previous study conducted in Ethiopia [ 9 , 10 ] and in Tanzania [ 5 ], but it was higher than the study conducted in Nairobi, Kenya [ 11 ].

In this study, as the age of a child increase, the probability of a child to be stunted and underweighted will be increased. This finding was in line with the studies that conducted in Ethiopia, in which poor nutritional status of children was associated with the old age of children [ 12 , 13 , 14 ]. In all the three forms of malnutrition (stunting, underweight and wasting), the risk of malnutrition was less prevalent among females than males. This finding was consistent with previous findings [ 15 , 16 , 17 ]. This study revealed that the levels of malnutrition had a significant regional variation ranging from 14.6% in Addis Ababa to over 46.7% in Amhara regions of the country. This finding is similar with [ 10 , 18 ].

Children whose mothers had primary and above educational level were significantly less likely to be stunted and underweighted as compared to children whose mothers had never attended formal education. This finding was consistent with the study conducted in Ethiopia [ 10 ] and Bangladesh [ 19 ] which showed that as mothers’ educational level increase, the likelihood of the children to be stunted and underweighted will be decreased. Mothers with BMI less than 18.5 (underweight) were more likely to have stunted, underweighted and wasted children as compared to overweighted mothers. This finding is similar with other previously conducted studies [ 10 , 19 , 20 ].

As of this study, children who were smaller at birth were more likely to be stunted and underweighted. This finding was supported by study conducted previously in SNNPR, Ethiopia. [ 13 ]. A similar study that conducted in Dale Woreda, Southern Ethiopia showed that the larger the family size, the poorer nutritional status of children would be resulted [ 17 ]. In the current study, anemia and malnutrition of children were highly associated with that anemic children were more likely to be malnutrition as compared to non-anemic [ 7 ].

Conclusions

The prevalence of stunting was still high in Ethiopia. The key determinants of malnutrition in Ethiopia were the child age, maternal education, region, household wealth status, religion, sex of child, number of children, a child size, water and toilet facility. The influence of these factors should be considered to develop strategies for reducing malnutrition in Ethiopia. Finally, improving living standards of children is important to get a better health care, reduces child malnutrition, and child mortality.

Limitations of the study

This study was based on cross-sectional study design. Thus, the authors did not see the seasonal variation of malnutrition status and establish causal relationship. There were some missing values for some variables in the dataset. Therefore, the authors fail to consider some important factors which could affect the interpretation of the results.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request (in SPSS code).

Abbreviations

Statistical Package for Social Science

crude odds ratio

adjusted odds ratio

Ethiopian Demographic and Health Survey

Central Statistical Agency

body mass index

World Health Organization

Southern Nations, Nationalities and People Region

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Acknowledgements

We would like to thank Statistics department staff of Bahir Dar University for their valuable suggestion and encourage. We also acknowledge Ethiopia Central Statistical Agency for giving us permission to use the data for our study.

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AKT designed the current study, edited, analyzed the data and interpreted the results, and wrote the manuscript. AAW and GWB participated in the data analysis, manuscript writing, and acted as second reviewer. All authors read and approved the final manuscript.

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Characteristics of the Study Participants (EDHS, 2016). Descriptive statistics of study variables.

Additional file 2.

Results of multivariable logistic regression to identify the significant determinants related to wasting.

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Tekile, A.K., Woya, A.A. & Basha, G.W. Prevalence of malnutrition and associated factors among under-five children in Ethiopia: evidence from the 2016 Ethiopia Demographic and Health Survey. BMC Res Notes 12 , 391 (2019). https://doi.org/10.1186/s13104-019-4444-4

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Nutrition & Food Science

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This study aims to examine the differences within individuals and clusters in nutritional status and identify socioeconomic factors in the nutritional status of under-five children in Ethiopia.

Design/methodology/approach

A weighted sub-sample of 5,270 under-five children was drawn from the under-five children data set of the Ethiopian 2019 Mini Demographic and Health Survey. Multilevel modeling was used to look at the association between the nutritional status of children with predictors.

The proportion of stunting, underweight and wasting among under-five children were 39.3%, 28.6% and 16.3%, respectively. The observed Global Moran Index’s value for child malnutrition (stunting, wasting and underweight) prevalence in Ethiopia were I = 0.204 for stunting, I = 0.152 for wasting and I = 0.195 for underweight at p = 0.000 was statistically significant indicating that spatial variability of malnutrition of under-five children across survey clusters and regions was observed. Moreover, the result of heterogeneity between clusters obtained for stunting, underweight and wasting was significant providing evidence of variation among regional clusters concerning the status of nutrition of under-five children. Child’s age in months, breastfeeding, family educational level, wealth index, place of residence, media access and region were highly significantly associated with childhood malnutrition. The inclusion of the explanatory variables has shown a significant impact on the variation in malnutrition among regions.

Practical implications

Enhance education, expanding the activities regarding nutritional and health services using media, health extension workers, and health institutions.

Originality/value

The study provides the malnutrition situation status of Ethiopian country when the survey carried out.

• Pattern analysis
• Malnutrition
• Under-five children

Acknowledgements

The authors are grateful to all individuals and institutions for providing the necessary information for this work.

Funding : The project was funded by Ambo University.

Data Sharing Statements : The data can be available from the corresponding author based on your request with the email address of “ [email protected] ”.

Lemessa, R. , Aga, G. , Tafese, A. and Senbeto, T. (2023), "Malnutrition in Ethiopia: pattern analysis and associated factors among under-five children", Nutrition & Food Science , Vol. 53 No. 3, pp. 564-577. https://doi.org/10.1108/NFS-12-2021-0393

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Severe acute malnutrition and associated factors among children under-five years: A community based-cross sectional study in Ethiopia

Associated data.

Data will be made available upon a reasonable request.

Despite consistent efforts to reduce child undernutrition, severe acute malnutrition (SAM) continues to be a serious obstacle to child survival and development in Ethiopia. This study aimed to identify severe acute malnutrition and associated factors among children aged 6–59 months in Ethiopia.

A cross-sectional study was undertaken with 384 under-five children from February to March, 2020 in Ethiopia. A mid-upper arm circumference (MUAC) tape, weight scale, height board (standing) and recumbent length measurements (for children <24 months) were measured. To determine the variables associated with SAM, adjusted odds ratio was computed using multivariable analysis and p < 0.05 was declared as significant.

The prevalence of acute undernutrition was 26%; 18% and 8% of the children were moderately and severely undernourished, respectively. Family size (≥5 members) (AOR: 3.71, 95% CI: 1.55–8.89), younger age group (6–11 months) (AOR: 4.80, 95% CI: 1.61–14.31) and history of diarrhea in the two weeks prior to the survey (AOR: 5.36, 95% CI: 1.97–14.61) were independently associated with SAM in the study population.

Large family size, child age, diarrheal and household insecurity were important determinants of SAM among children. Therefore, aligning social protection programmes and improving health related interventions along with improving optimal breastfeeding, prevention and control of child morbidity, and strengthening family planning services are recommended to reduce child SAM.

Factors; SAM; Under-five children; Seqota district; Ethiopia.

1. Introduction

.Globally, severe acute malnutrition (SAM) is a significant contributor to disease burden and associated with higher risk of child mortality. In 2018, SAM affected approximately 17 million under-five children worldwide [ 1 ]; of these, more than three-fourths lived in low income countries.

SAM is defined as mid-upper arm circumference (MUAC) measurement <11.5 cm or severe wasting (low weight-for-height/length) (<−3SD) or the presence of pitting nutritional edema in children 6–59 months old [ 2 ]. Children with SAM usually have weakened immunity and are more vulnerable to infectious diseases [ 3 ]. SAM is life-threatening causing globally about one million under-five deaths every year [ 4 ]. Due to a weakened immune system, children with SAM have been estimated to have nine-fold higher risk of death compared with normal [ 5 ].

Despite remarkable progress, the majority of the SAM cases were found in Africa where 4.4 million children suffered from SAM [ 1 ]. Furthermore, SAM contributes from 9 to 46% under-five mortality in the region [ 6 , 7 , 8 ]. Ethiopia has demonstrated significant progress in reducing undernutrition over the past three decades, but it remains as a threat for child growth and survival. As per the 2019 Mini Ethiopia Demographic and Health Survey (MEDHS), 7% of children under the age of five were wasted with 1% suffering from severe wasting [ 9 ]. Regional variation of SAM also has been found, ranging from 6% in Somali to 0.4% in Addis Ababa. In Amhara region, 3% of the under-five children suffered from SAM [ 10 ]. In Ethiopia, about 45% of mortality among under five-year children is directly or indirectly associated to undernutrition [ 11 ] and SAM accounts for 8% of these deaths [ 12 ]. Several studies observed child age, morbidity, feeding practices [ 13 ], family size and maternal educational status [ 14 ] as determinants of SAM. Additional variation also may be associated with agro-ecological zone, seasonality, and urban versus rural residence [ 15 , 16 ]. Another very important factor associated with child undernutrition is household food insecurity. Food insecurity is widespread in sub-Saharan Africa; in Ethiopia, from 2002 to 2014, nearly 20% of the population required food assistance, partly due to seasonal food shortage and frequent drought-related famine [ 17 ]. As an underlying cause for child undernutrition, food insecurity influences nutritional status of children by compromising the quantity and quality of the child’s diet [ 18 ].

Recognizing irreversible consequences of child undernutrition, the Ethiopian government has been implementing various programmes that aim to reducing childhood undernutrition [ 19 ]. Despite these efforts, the rates of SAM is still prevalent in the country [ 20 ], indicating the need for further investment and action in order to achieve global nutrition targets for 2025 [ 21 ]. Evidence-based health and nutrition findings have a crucial role in improving the level of the problem and associated mortality reduction in children [ 22 ]. This requires identification of contributing factors for the high magnitude of SAM among children. Therefore, the objective of the study was to identify the factors associated with SAM in food-insecure rural areas of north Ethiopia. Findings generated could be helpful to focus significant investments on specific areas to accelerate the reduction of acute nutritional problem [ 23 ].

2. Methods and materials

2.1. study design, setting and participants.

A cross-sectional study was carried out to assess factors associated with SAM among 6-59 months-old children in rural areas of Seqota district, Amhara region, Ethiopia. The study was conducted from February to March, 2020. The region is characterized by a high prevalence of acute undernutrition (7.6%) in under-five children [ 9 ]. The inhabitants are predominantly subsistence farmers.

2.2. Sample size determination and sampling procedure

The required sample size was calculated by using a single population proportion formula, n = Z 2 α/2 ∗P(1 − P)/d 2 , where n is the number of samples required, with the assumptions of a 5% significance level (i.e., Z α/2 = 1.96), 5% margin of error (d = 0.05), and prevalence of severe malnutrition of 16.3% (P = 0.163) was used from previous study [ 24 ]. The estimated sample size was 240 and was increased to 396 to allow ∼10% non-response rate and a design effect of 1.5.

Of the total 25 kebeles (the lowest administrative unit in Ethiopia) in the district, 15 (60%) were selected by a lottery method. A list of all the mothers with children aged 6–59 months was completed. The number of mother-child pairs to be selected was proportionally allocated to the 15 kebeles in the district based on the total number of the households with 6–59-months old children in each kebele. The study participants were then selected using systematic random sampling method from the sampling frame.

2.3. Dependent variable

The dependent variable for this study was severe acute malnutrition (SAM) among 6–59 months old children. SAM was considered for children with MUAC <11.5 cm and moderate acute malnutrition (MAM) was MUAC ≥ 11.5 cm and <12.5 cm [ 3 ].

2.4. Data collection instruments and study variables

Data were collected using a structured interviewer-administered questionnaire. All the questions were pre-tested in non-sampled neighboring kebeles. Data collectors were recruited from the study area and surrounding communities and one week intensive training was provided by the author. Major data such as socio-demographic and economic related characteristics, feeding practices, morbidity status of child, health-seeking behaviour and practices of mother, hygiene and sanitation related variables were collected.

Anthropometric measurements such as height and weight were measured by standard equipment and procedure [ 25 ], in duplicates with minimal clothes and without chappal/shoes. Child length was measured to the nearest 0.1 cm using a recumbent (infantometer) for <24 month children and standing height board (Stadiometer) for >24 month, and weight was measured to the nearest 0.1 kg using a calibrated SECA electronic balance. A mid upper arm circumference (MUAC) was measured using MUAC measuring tape from the child’s left arm to the nearest 1 mm. All anthropometric measurements were taken by the author and two supervisors.

Household food insecurity status was assessed using the household food insecurity access scale (HFIAS) [ 26 ] during the four weeks preceding the survey. The tool consisted of nine questions that represent generally increasing level of severity of food insecurity and four frequency of occurrence. The nine generic occurrence questions relate to three domains of food insecurity. The first generic question relates to anxiety and uncertainty about the household food supply; the next three generic questions relate to insufficient diet quality; and the rest five generic questions relate to insufficient food intake and its physical consequences.

2.5. Data processing and analysis

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 22. Based on responses given to the nine severity questions and frequency of occurrence over the past 30 days, households were assigned a score that ranges from 0 to 27. A higher HFIAS score is indicative of poorer access to food and greater household food insecurity. The lower the score, the less food insecurity (access) a household experienced. Finally, households with HFIAS score of 0–1 were classified as food secure, 2 and above were considered as food insecure. Descriptive data were analyzed using the complex survey analysis approach on the basis of poststratification weights specified for kebeles . Descriptive statistics were presented as frequencies and percentages along with the calculation of Pearson’s chi-square test to determine associations between predictors and the outcome variable. Anthropometric status of children, the Emergency Nutrition Assessment for Standardized Monitoring and Assessment of Relief and Transitions (ENA for SMART, 2011) software (Action against Hunger, Canada and US Agency for International Development) was used to convert the weight and length or height for age into WHO z-scores. All characteristics associated with the outcome variable in chi-square test were included in the multivariable analysis and significance was considered at p < 0.05 level. A multi-collinearity diagnostic test was applied between the independent variables before logistic regression was applied. Criteria were set out to be a variance inflation factor (VIF) value of >10.

2.6. Ethical approval and consent to participate

Hawassa University Institutional Review Board, Ethiopia reviewed and approved this study (approval number: IRB/178/10). The purpose of the study was explained in a formal letter to district administration and then written consent was obtained from the Seqota district health office. Informed verbal consent was obtained from all participants. Counseling was given for the mothers/caregivers whose children were identified as SAM.

In this study, 384 under-five children were included to assess the predictors of SAM. The mean (SD) age of the mothers was 28.3 (±5.5) years, and 90% were married. Over two-third (68%) of mothers were illiterate while only 32% had some education. Of the 384 children, 59% were males, and the mean age of all children was 22.7 (±13.0) months. Nearly half (47.1%) of the studied children were in the 23–59 months age and the male-to-female ratio was 1.46. Average family size was 4.85 (±1.61) and mean family size was larger for SAM children ( Table 1 ). More than half (52.9%) of the households had no toilet facility, and fewer than a third (27.9%) of households used protected sources of drinking water. About 48% of the households had less than a half hectare of farm land, and 52% of the households were food insecure. Nearly a quarter (24.5%) and two thirds (65%) of mothers had ≥4 and ≤3 antenatal care (ANC) services during their pregnancy of the current child, respectively (Data not shown). More than two thirds of children (68.8%) started breastfeeding within 1 h after birth.

Table 1

Sociodemographic characteristics of households, mothers and children in north Ethiopia (n = 384).

Prevalence of acute undernutrition based on MUAC criteria was 26% (95% CI: 22.1–30.7), of which 18% (95% CI: 14.1–22.1) and 8% (95% CI: 6.0–11.5) were moderately and severely undernourished, respectively. The proportion of SAM based on the definition of WHZ < −3 were 7% (95% CI: 4.4–9.9). Around 45% (95% CI: 38.9–50.3) of the children were stunted and 24% (95% CI: 20.1–28.9) were underweight.

Table 2 shows the mean for MUAC, weight-for-length/height z-score, length/height-for-age z-score and weight-for-age z-score for SAM and non-SAM children. The mean ± SD for MUAC of SAM and the non-SAM group was 10.6 cm ± 1.2 versus 13.1 ± 0.79, respectively. Similarly, the mean ± SD of weight-forage z-score of SAM cases and non-SAM group was -2.18 ± 1.83 versus -1.23 ± 1.11 (p < 0.001), respectively. Multicollinearity was checked by using the variance inflation factor (VIF). Nevertheless, no multicollinearity between independent variables was found. In multivariable logistic regression analysis adjusted for all associated factors family size (≥5 members), younger child age, recent diarrheal illness and household food insecurity remained significant determinants of SAM among children ( Table 3 ).

Table 2

Mean (standard deviation) anthropometric measurements and Z-scores by SAM of the studied children, north Ethiopia (n = 384).

Table 3

Multivariable analysis for SAM with independent variables (maternal, household and child characteristics) among children 6–59 months in north Ethiopia (n = 384).

1 = reference category, COR = Crude odds ratio, AOR = Adjusted odds ratio, statistically significant at p < 0.05.

Households with family size (≥5 members) significantly associated with an increased likelihood of having SAM (AOR = 3.71; 95% CI: 1.55–8.88). The odds of SAM were 3.42 times higher among children of households experiencing food insecurity (AOR = 3.42, 95% CI: 1.15–10.17) compared to children in the food secure households compared to 24–59 months old children, increased odds of SAM were found among children 6–11 months (AOR = 4.80, 95% CI: 1.61–14.31) and 12–23 months (AOR = 4.07, 95% CI: 1.30–12.74). Finally, it was also found that recent diarrheal illness associated with SAM among children (AOR = 5.36; 95% CI: 1.97–14.61).

4. Discussion

This study investigated prevalence of SAM and associated factors among children aged 6–59 months in rural areas of Seqota district of Ethiopia. It was found that only 8% of the studied children were severely undernourished. The finding was similar with other studies conducted in Ethiopia [ 27 ] and Sudan [ 28 ], but higher than the SAM rates reported from Nepal (5.8%) and (4.1%) [ 29 , 30 ], from the recent 2019 MEDHS (2.9%) [ 9 ], other rates from SNNPR (3.3%) [ 31 ] and Oromia (2.3%) [ 32 ] regions in Ethiopia. The high burden of SAM may be explained in terms of suboptimal child feeding practices and chronic food insecurity in the study district as food insecurity compromises quality and quantity of children’s diets.

Four characteristics were associated with SAM in Chi-square test and in the bivariate analysis but did not remain significant in the multivariable analysis. These factors were: initiation of breast feeding, lack of maternal education, small farm land size and use of unprotected water. Most literature supports each of these factors as contributors to prevalence of SAM; however, there are at least two reasons they may not have remained in present study adjusted models. The sample size may have been insufficient for the number of factors tested or the sample variability may have been limiting because all the kebeles had been identified for the Productive Safety Net Programme. The findings of a multivariable analysis showed that family size, household food insecurity, child age and history of diarrhea in the 2 weeks preceding the survey were significantly associated with SAM.

Congruent with previous literatures [ 33 , 34 , 35 ], the present study found that households with five or more members were statistically associated with higher odds of SAM. This is due to the fact that higher the family sizes may compromise intra-household food allocation [ 36 ] and will be the load to the mothers to provide nutritious diets to all the household members and children. Moreover, in higher family members, it is difficult for the parents to provide optimal caring that each child is expected to get from parents, putting them at a higher risk of being undernourished. This result is in contradicting with the studies done in India, and Nepal [ 30 , 37 ]. This could be due to socio-economic and food insecurity status in the study population.

In this study, children from the food insecure households were more likely to be undernourished. Several studies found that household food insecurity had a statistically significant effect on the nutritional status of the children [ 38 , 39 ]. This finding is plausible as food insecurity limits food availability and compromises quality of child’s diet, could not meet the nutrient requirements of a child to sustain needs for growth and development. This is unfortunate; quality diet particularly animal-source foods are correlated with lower risk of undernutrition [ 40 ]. This finding is in contrast with those studies from Bangladesh and elsewhere in Ethiopia [ 41 , 42 ].

It was observed that age had a significant effect on SAM of the children. The findings of this study are in agreement with other countries like Senegal, Mozambique, and Nepal [ 43 , 44 , 45 ]. This might from the fact that younger children have low stomach capacity as compared to the older children, and at the same time they are in transition from predominant breast milk based-diet to family foods. This finding suggests the need for increased frequency of feeding for the younger children compared to the older age group. Moreover, the complementary feeding time is a particularly vulnerable period because energy and micronutrient requirements are high for increased physical and cognitive development [ 46 ]. Optimal feeding practice during the first 1000 days of life helps to prevent SAM and growth failure [ 47 ]. However, inadequate feeding and an early introduction of family foods for children in Ethiopia are widespread [ 48 ], and that could be a possible reason why SAM was higher among younger age group compared to older children.

The results from this study showed a strong association between recent diarrheal morbidity in a child and SAM. Diarrheal infection plays a major role in the etiology of SAM as it results in reduced food intake, decrease in absorption of nutrients and increase in catabolism of nutrient reserves. Although the causal relationship between diarrheal infection and SAM is unclear in this study, previous literatures have long been documented a vicious circle [ 49 , 50 ]. This finding is consistent to studies from Chad and South Africa [ 38 , 51 ].

There are some limitations in this study. First, sample size might be considered inadequate to accommodate more than four predictors and the strength of associations between SAM and breast feeding and maternal education may have been underestimated. Second, the cross-sectional nature of the present study does not account for seasonal variations nor does it allow causal inferences to be made. A further limitation is that the present findings came from a single district and thus cannot be generalized to other districts of the Ethiopia. Nevertheless, this study is one of a few studies in Ethiopia to investigate the determinants of SAM in rural households among the most vulnerable 6–59 months age group. The three important factors determined in this study may be targets for nutrition interventions to reduce undernutrition among study population and other similar settings in Ethiopia. These are important findings that can have implications for design and for directing important nutrition interventions that aim to reducing SAM and future research initiatives.

5. Conclusion

This study highlights that prevalence of SAM was higher compared with other studies. Although the government of Ethiopia has declared its commitment to end undernutrition by 2030 [ 23 ] through government commitment programme, the findings from this study show that much remains to be done within the remaining years. Large family size, child age, diarrheal illness and household food insecurity were significantly associated with SAM among children 6–59 months. This is an important finding in a PSNP area that can have implications in the development and implementation of interventions to tackle SAM. Although further detailed studies are warranted to establish the causal association between aforementioned factors focusing on SAM with bilateral oedema, and moderate acute malnutrition of children, aligning social protection programmes and improving health services along with improving breast feeding practices, prevention and control of child illness, and strengthening family planning services are recommended to reduce child SAM.

Declarations

Author contribution statement.

Anchamo Anato: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This work was supported by Strengthen PSNP4 Institutions and Resilience (SPIR)/Development Food Security Activity (DFSA) project.

Data availability statement

Declaration of interest’s statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

The author acknowledges the mothers and children who participated in the study.

Appendix A. Supplementary data

The following is the supplementary data related to this article:

• Open access
• Published: 28 April 2023

Spatial distribution and associated factors of severe malnutrition among under-five children in Ethiopia: further analysis of 2019 mini EDHS

• Daniel Gashaneh Belay 1 , 2 ,
• Dagmawi Chilot 3 , 4 ,
• Adugnaw Zeleke Alem 2 ,
• Fantu Mamo Aragaw 2 &
• Melaku Hunie Asratie 5  

BMC Public Health volume  23 , Article number:  791 ( 2023 ) Cite this article

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Malnutrition is both a significant cause and a result of poverty and deprivation. In developing nations, child malnutrition is still the main public health issue. Severe malnutrition affects every system of the body and leads to medical instability. The assessment of the burden of severe malnutrition is important for ready-to-use therapeutic foods and preparing therapy for these conditions. Therefore, this study aimed to assess the prevalence and spatial distribution of severe malnutrition and the factors associated with it.

Data from the 2019 Mini-EDHS (Ethiopian Demographic and Health Surveys) with stratified sampling techniques were used. The data were weighted using sample weight to restore the data's representativeness and provide accurate statistical estimations. A total of 5,006 weighted samples of children under the age of five were used to analyze the study. A multilevel binary logistic regression model was built, and a cutoff P-value of 0.05 was used. The wag staff normalized concentration index and curve as well as spatial analysis were used.

The prevalence of severe malnutrition practice among under five years children in Ethiopia was 14.89% (95%CI: 13.93%, 15.91%), and ranges from 4.58% in Addis Ababa to 25.81% in the Afar region. Women with secondary and above education status as compared to uneducated [AOR = 0.17; 95%CI;[0.06, 0.48], high community women's education as compared to low [AOR = 0.54; 95%CI; 0.36, 0.78], women from richest household as compared to poorest [AOR = 0.63; 95%CI; 0.26, 0.94] and living in Oromia region as compared to Tigray [AOR = 0.33: 95%CI; 0.15, 0.74] were preventive factors. Whereas children 24–59 months of age as compared to under six months [AOR = 1.62; 95%CI; 1.50, 1.75], and being multiple births as compared to single [AOR = 5.34; 95%CI; 1.36,2 1.01] have significant risk factors for severe malnutrition. There was a pro-poor distribution of severe malnutrition among under-five children in Ethiopia with a concentration index of -0.23 [95%CI: -0.27, -0.19]. Severe malnutrition has significant spatial variation over regions in the country where the entire Afar, Eastern Amhara, Southern, and eastern Tigray regions were severely affected (RR = 1.72, P -value < 0.01).

Conclusion and recommendations

The prevalence of severe malnutrition in Ethiopia is relatively high as compared to other studies and most of them were severe chronic malnutrition. Having an educated mother/caregiver, and living in a cluster with high community women's education were preventive factors for severe malnutrition in children. Whereas having an unmarried mother/caregiver, old age of the child, plurality of birth, and having double children in the family have a positive association with it. Moreover, it was disproportionately concentrated in poor households (pro-poor distribution).

The spatial distribution of childhood severe malnutrition was not random. Regions like Tigray, Afar, Eastern parts of Amhara, and Somalia regions should be considered priority areas for nutritional interventions for reducing severe malnutrition. Equity-focused nutritional interventions could be needed to curb the wealth-related inequalities of childhood severe malnutrition.

Peer Review reports

Malnutrition is a consequence of the consumption of dietary nutrients either insufficiently or exclusively by especially children [ 1 ]. It causes about half of all child deaths globally and significantly hinders the growth of children who survive [ 2 ]. Malnutrition at the early stages of life can increase the risk of infections, morbidity, and mortality together with decreased mental and cognitive development [ 3 , 4 ]. In addition, it lowers work productivity and academic achievement and increases the chance of developing chronic diseases later in life [ 5 ].

Worldwide, the prevalence of different forms of malnutrition such as stunting (height-for-age), wasting (weight-for-height), and underweight (weight-for-age) in under-five children are still high [ 6 , 7 ]. In 2020, nearly 149.2 million children under 5 suffer from stunting, 45.4 million children under 5 were wasted globally [ 8 ].

Child malnutrition continues to be the leading public health problem in developing countries [ 9 , 10 ]. Africa is unlikely to meet the world health Organization (WHO) target of reducing global stunting by 40% in 2025 [ 11 , 12 ]. The WHO 2021 report shows that the number of children with stunting is declining in all regions of the world except Africa [ 13 ]. From this, under five malnutrition is predominantly prevalent in sub-Saharan Africa (SSA) region [ 9 , 10 ]. A meta-analysis study showed that the prevalence of stunting, wasting, and being underweight in SSA were 32.2%, 7.1%, and 16.3%, respectively, in 2016 [ 14 ].

Ethiopia is one of the largest populated nations in sub-Saharan Africa and the country with the fifth-highest rate of under-five mortality worldwide in 2018 [ 15 ]. In Ethiopia, malnutrition is a leading cause of child illness and death [ 16 ]. The prevalence of stunting, being underweight, and wasting were 38.3%, 23.3%, and 10.1%, respectively in Ethiopia [ 17 ].

Severe malnutrition is, defined as where the respective anthropometric measurements z-scores are below -3 SD [ 18 ]. Children with severe malnutrition have an increased risk of serious illness and death, primarily from acute infectious diseases [ 19 ]. The assessment of the burden of severe malnutrition is important for ready-to-use therapeutic foods and preparing therapy for these conditions. Therefore, the purpose of this study was to explore the spatial distributions and to identify the wealth-related inequalities and other variables associated with severe malnutrition.

Study setting, and period

The recent mini Ethiopian Demographic and Health Survey (EDHS, 2019) which was conducted from March 21, 2019, to June 28, 2019, was used to conduct this study [ 20 ]. Ethiopia is an East African country located 3 0 -14 0 N and 33 0 – 48 0 E with 1.1 million Sq. km coverage. It is the second most populous country in Africa and is federally decentralized into nine regions and two city administrations [ 21 ].

Source and study population

All under-five children preceding five years of the survey period in Ethiopia were the source population. Whereas, under-five children preceding five years of the survey period in the selected Enumeration Areas (EAs) were our study population. For this study, mothers who had more than one kid, the questionnaires and anthropometric measurements were taken about their most recent child during the two years before the study [ 22 ]. The study excludes children who were not weighed or measured, and whose weight and height values were not recorded [ 23 ]. Finally, the unweighted 5,121 samples (5,006 weighted samples) were used.

Sampling technique

A stratified two-stage cluster sampling method was utilized. Every eleven regions were stratified by dividing them into urban and rural areas. In total, 21 sampling strata have been created. Enumeration Areas (EAs) were the sampling units for the first stage of sampling. To ensure that survey precision was comparable across regions, sample allocation was done through an equal allocation where 25 EAs were selected from eight regions. However, 35 EAs were selected from each of the three larger regions: Amhara, Oromia, and the Southern Nations, Nationalities, and Peoples’ Region (SNNPR).In the first stage, a total of 305 EAs (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size (based on the 2019 EPHC frame) [ 21 , 24 ].

Study variables

The outcome variable of this study was severe malnutrition among under-five children. For this study children classified under severe malnutrition, if she/he have either of the three severe malnutrition i.e. severely stunted (height-for-age), severely wasted (weight-for-height), and severely underweight (weight-for-age), with z-score below -3 standard deviations (SD) on the respective anthropometric calculations. The z-scores were calculated using software based on the WHO Anthro program and the macros for statistical packages [ 21 ].

The individual-level factors include socio-demographic and socio-economic characteristics such as; the age of the mother, head of the household, marital status, maternal education, drinking water source, and household wealth status were included. Child-related factors such as the sex and age of the child, the plurality of birth, birth order, and the number of under five in the family are all taken. Health service utilization-related factors such as place of delivery and ANC visit were also considered. The community-level factors include; community level women's education, place of residence, and region were considered. The percentage of women with at least a primary education was another way to gauge the education of women at the community level. It was coded as "0" for low (communities with less than 50% of women having at least a primary education) and "1" for high (communities with more than 50% of women having at least a primary education) (at cluster level) [ 25 ].

Data source, processing, and analysis

The DHS kid's records (KR) datasets were obtained in STATA format from the most recent mini-2019 EDHS. For the analysis, the data was cleaned, combined, and coded to yield useful variables. The data were then weighted using sampling weight for probability sampling and non-response to restore the representativeness. To produce both descriptive and analytical statistics, STATA 14 was employed.

Model building for multi-level analysis

Given the hierarchical nature of the DHS data, the assumptions of the conventional logistic regression model may not hold. A multilevel regression analysis is therefore required. This led to the fitting of four modes, the first of which was employed to gauge the extent of severe malnutrition variation within the cluster. The second model regresses individual-level variables, while third model uses community-level factors with the outcome variable. The final model jointly matched variables at the personal and community levels with the severe malnutrition (Model 4). Model comparisons were made using the deviance test, and the model with the lowest deviance value (model 4) was chosen as the best-fit model.

Parameter estimation method

The generalized linear mixed model (GLMM) was employed for this study, in which the linear predictor comprises both random and fixed effect analyses. The relationships and strength between severe malnutrition and independent variables were shown using adjusted odds ratios and 95% confidence intervals in the fixed effects measure of association [ 26 , 27 , 28 ].

Where, \(\pi ij\) : the probability of having severe malnutrition,  \(1-\pi ij\) : the probability of not having severe malnutrition, \(\beta\) 1xij are individual and community level variables for the i th individual in group j. The ß’s are fixed coefficients indicating a unit increase in X can cause a ß unit increase in the probability of severe malnutrition. While the ß0 is the intercept that is the effect on severe malnutrition when the effect of all explanatory variables is absent. The uj shows the random effect (effect of the cluster on the mother’s decision to provide severe malnutritio) for the j th cluster. The clustered data nature and the within and between cluster variations were taken into account assuming each cluster has a fixed coefficient (β) and a different intercept (β0) [ 26 , 28 , 29 ].

The measure of variation or random effects was estimated by the median odds ratio (MOR), Intra Class Correlation Coefficient (ICC), and Proportional Change in Variance (PCV).

The ICC which reveals the variation of severe malnutrition between clusters is calculated as;  \(ICC=\frac{VA}{VA+3.29}*100\%\) . Based on this; there were 18% variations of severe malnutrition due to cluster differences among under-five children. The MOR is defined as the median value of the odds ratio of severe malnutrition between the area at the highest risk and the area at the lowest risk when randomly picking out two clusters. MOR = exp.[√(2 × VA) × 0.6745], or \({{\mathrm{MOR}=e}^{0.95}}^{\sqrt{VA}}\) [ 26 , 27 , 28 ]. In our study, the median odds ratio between the higher and lower-risk areas of severe malnutrition among clusters was 2.25 [95%CI:2.18, 2.33] in the null model. The PCV reveals the variation in severe malnutrition among children under five explained by factors. The PCV is calculated as; \(PCV=\frac{Vnull-VA}{V null}*100\%\) where; Vnull = variance of the initial model, and VA = variance of the model with more terms [ 26 , 27 , 28 ]. Moreover, in this study, about 55.56% of the variation in severe malnutrition in under five children was explained by the final model (model four). Likelihood and deviance were used for model comparison and the model with the highest likelihood and the lowest deviance (model 4) was considered the best fit model. There was no multicollinearity between independent variables in all models based on the Variance Inflation Factors (VIF) results (Table 1 ).

Wealth-related inequalities of severe malnutrition

To examine the socioeconomic inequalities of severe malnutrition, the concentration index and graph approach were used [ 30 , 31 ]. The concentration curve was applied to identify whether there was socioeconomic inequality in severe malnutrition or if it was more pronounced in one group [ 31 , 32 ].

The concentration curve would be a 45 0 line indicating the absence of inequity while, the concentration curve laying above and below the equality line (45 0 ) indicated that severe malnutrition is disproportionately concentrated between poor and rich, respectively [ 33 ].

The concentration index is twice the area between the concentration curve and the diagonal line [ 32 , 34 ]. It ranges from − 1 to + 1 and the sign indicates the direction of the relationship between the health variable (severe malnutrition) and the distribution of living standards (wealth status).

In our study, there was a pro-poor distribution of severe malnutrition among under-five children in Ethiopia with a concentration index of -0.23 [95%CI: -0.27, -0.19] (Fig.  1 ).

Wealth-related inequalities of severe malnutrition among under-five children in Ethiopia

Spatial analysis of severe malnutrition among under-five children in Ethiopia

To assess the spatial distribution of severe malnutrition among under-five children in Ethiopia, Global Moran’s I statistic spatial autocorrelation measure was used [ 35 ]. Whereas a spherical semivariogram ordinary kriging type spatial interpolation technique was used to predict severe malnutrition among under-five children in Ethiopia for unsampled areas based on sampled clusters. The proportion of severe malnutrition among under-five children in each cluster was taken as an input for spatial prediction.

Using Kuldorff's SaTScan version 9.6 software, Bernoulli-based model spatial scan statistics were used to pinpoint the locations of statistically significant clusters for severe malnutrition among children under the age of five [ 36 ]. The scanning window that moves across the study area in which children who had severe malnutrition were taken as cases and those children who had not severe malnutrition were taken as controls to fit the Poisson model.

Mothers or caregivers and children socio demographic characteristics.

A total weighted sample of 5,006 under-five children was included in this study. More than half (54.07%) of mothers of children were in the age group of 25–34 years with a median age of 28 (IQR: 8) years. More than half of women (53.72%) had no formal education. Moreover, half (52.06%) of children were delivered at home.

Nearly three-fifths (59.83%) of the children were found in the age group from 24–59 months with a median age of 28 (IQR: 30) months. Almost all (97.82%) of the child are single birth. Three-fourths (74.96%) of the children lived in rural and 40% of the children are from the Oromia region (Table 2 ).

Prevalence of severe malnutrition among under-five children in Ethiopia

The prevalence of severe malnutrition among under-five children in Ethiopia was 14.89% (95%CI: 13.93%, 15.91%), and ranges from 4.58% in Addis Ababa to 25.81% in Afar. Of which, most of them were chronic severe malnutrition i.e. severe stunting (12.62%), others were severely underweight (7.19%) and severe wasting (1.16%) (Fig.  2 ).

The bar graph shows the prevalence of severe malnutrition among under-five children in Ethiopia

Multi-level analysis of the determinants of severe malnutrition among under-five children in Ethiopia

The plurality of births, region, age of the child, marital status of the mother/caregiver, wealth index of the household, and education status of women were all significant variables in the chosen model (model 4).

Secondary and above-educated women were 83% less likely to have children with severe malnutrition than women with no formal education [AOR = 0.17; 95%CI;[0.06, 0.48]. Moreover, children who were from high-community education clusters were 46% less likely to have severe malnutrition than those from low-community women's education [AOR = 0.54; 95%CI; 0.36, 0.78].

The odds of having severe malnutrition among children from not currently married mothers were 2.69 times higher as compared to a child from a married one [AOR = 2.69; 95%CI; 1.35, 5.36].

Children who come from the richest wealth family were 37% less likely to have severe malnutrition than the poorest family [AOR = 0.63; 95%CI; 0.26, 0.94].

Being multiple births and having two under-five children in the family were 5 and 2 times more likely to have severe malnutrition than those who were singleton birth and single child [AOR = 5.34; 95%CI; 1.36,2 1.01] and [AOR = 2.01; 95%CI; 1.35, 2.99] respectively.

The odds of severe malnutrition among children aged 6–23 and 24–59 months were 1.93 and 1.62 times higher than children who were found 0–5 months age [AOR = 1.93; 95%CI; 1.27, 2.93] and [AOR = 1.62; 95%CI; 1.50, 1.75] respectively. Children who live in the Oromia region were 66% less likely to have severe malnutrition than those who live in Tigray [AOR = 0.33: 95%CI; 0.15, 0.74] (Table 3 ).

The spatial autocorrelation result of severe malnutrition among under-five children in Ethiopia showed significant spatial variation over regions in the country and was found to be clustered with Global Moran's Index value: 0.384988 with ( p  < 0.01) . Severe malnutrition had more prevalent in Afar, Amhara and Tigray regions and ranges from 36.37% to 57.89% (Fig.  3 ).

Spatial distribution of severe malnutrition among under-five children in Ethiopia

The incremental autocorrelation result showed that there is one significant peak distance at 227.328 km; 7.09 (distances; Z-score) for severe malnutrition were most pronounced using 10 distance bands. Severe malnutrition is more common and hot spotted in Tigray, Afar, Eastern parts of Amhara, and Somalia regions, and ranges from 5.26% to 57.89% (Fig.  4 ).

Hotspot analysis of severe malnutrition among under-five children in Ethiopia

The SaTscan analysis of severe malnutrition among under-five children in Ethiopia showed that 61 primary clusters and 27 secondary clusters were detected for having severe malnutrition. The primary clusters were centered at 11.818783 N, and 39.955788 E with a 279.39 km radius. These were located in the entire Afar, Eastern Amhara, Southern, and eastern Tigray regions. Children who were found in the primary window were 1.72 times more likely to have severe malnutrition than out in-window regions (RR = 1.72, P -value < 0.01) (Table 4 and Fig.  5 ).

Sat Scan analysis of severe malnutrition among under-five children in Ethiopia

The Kriging interpolation methods of predicting severe malnutrition among under-five children in Ethiopia showed that high-risk areas predicted severe malnutrition ranging from 25.84% to 32.28% and are located in Northern and Eastern parts of Amhara, Western parts of Afar, and most parts of the Somali regions. Whereas the lower predicted area was seen in, Addis Ababa, Dire Dawa, and Gambella regions ranging from 0% to 6.46% (Fig.  6 ).

Kriging interpolations of severe malnutrition among under-five children in Ethiopia

Discussions

The world is not on course to meet the targets for malnutrition in 2030. In 2018, globally only 12.2%, 3%, and 3% of 2030 targets for stunting, underweight, and wasting were met respectively [ 15 ]. Malnutrition is both a significant cause and a result of poverty and deprivation [ 15 ]. Severe malnutrition affects every system of the body and leads to medical instability [ 37 ]. In this study, the prevalence of severe malnutrition among under-five children in Ethiopia was 14.89% (95%CI: 13.93%, 15.91%). Similarly, a report from 2018, by UNICEF shows that the prevalence of severe wasting and severe and moderate stunting in Ethiopia were 3% and 38% respectively [ 15 ]. A study also showed that the prevalence of severe malnutrition was highest within countries in East Africa [ 14 ]. The global rates of severe malnutrition also remain high and around 16.6 million children under 5 were estimated to suffer from severe wasting in 2018 [ 15 ]. This high prevalence might be due to population growth, and socio-economic status of the country [ 38 ]. Cultural beliefs and knowledge paradigms about under five nutrition might have also influence on child feeding practices [ 30 , 31 ].

In our study, there was a pro-poor distribution of severe malnutrition among under-five children in Ethiopia. The multilevel analysis result also showed that children who come from the richest wealth family were less likely to have severe malnutrition. In line with a study conducted in 47, developing countries showed that stunting and wasting disproportionately affected the poor [ 32 ]. The UNICEF, 2019 report also says that stunting is an accurate reflection of inequality in societies [ 15 ]. Moreover, a study showed that minimum acceptable diet (MAD) intake among children was disproportionately concentrated in rich households (pro-rich) [ 33 ]. It is expected that children’s from a family of higher income can feed frequent and diversified foods as their families could be more likely to afford to purchase it [ 34 ].

In this study women with secondary and above education status have fewer odds of having children with severe malnutrition. Moreover, children who were from high-community women's education clusters were less likely to have severe malnutrition than those from low-community women's education. This could be due to that educated women are more likely to have access to health messages, and can easily comprehend and translate that information into practice and promote optimal child nutrition [ 25 , 33 ].

The odds of having severe malnutrition among children from not currently married mothers were higher as compared to a child from a married one. Supported by a study that shows children from currently not married women were less likely to have access to MAD [ 25 , 31 , 39 ]. This might be due to that not married mother lack of support from families or communities, which causes poor infant-feeding practices [ 39 , 40 ].

In this study, multiple births and two under-five children in the family were more likely to have severe malnutrition than their counterparts. A study in Nigeria showed that children of multiple births are more likely to be stunted [ 41 ]. Mothers with twins or triplets were twice as likely to choose bottle feeding [ 42 ]. In children of multiple births, inadequate breastfeeding and competition for nutritional intake occur more frequently [ 41 ].

In this study, the odds of having severe malnutrition becomes increase as the age of the child increased. This is might be due to bottle feeding and toothing becoming more common in this age group [ 39 , 43 ] which eventually leads to diarrhea, vomiting, and infections [ 43 ].

The spatial outputs of this study state that severe malnutrition is more common in Tigray, Afar, Eastern parts of Amhara, and Somalia regions. Moreover, in multilevel analysis children who live in the Oromia region were less likely to have severe malnutrition than those who live in the Tigray region. Other studies also showed that there were spatial variations in malnutrition [ 44 , 45 ]. This is because these parts of Ethiopia are experienced higher drought frequency than others [ 46 ]. Having poor access to healthcare services and feeding practices depending on their way of life in pastoral regions (Afar and Somali) has also contributed [ 47 ].

Conclusion and recommendation

Generally, the prevalence of severe malnutrition in Ethiopia is relatively high as compared to other studies and most of them were severe chronic malnutrition. Having educated women, and living in a cluster with high community women's education were preventive factors for severe malnutrition children. Whereas having an unmarried mother/caregiver, being old age of the child, the plurality of birth, and having 2 under-five children in the family have a positive association with it. Moreover, severe malnutrition among under-five children was disproportionately concentrated in poor households (pro-poor distribution).

Strength and limitation

Some of the strengths of this study include the use of recent nationally representative large sample data, the utilization of wealth-related inequalities, and the spatial distribution analysis. Due to the cross-sectional nature of the data, recall and social desirability bias may be present.

Availability of data and materials

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Daniel Gashaneh Belay

Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

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Addis Ababa University, College of Health Sciences, Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), Addis Ababa, Ethiopia

Dagmawi Chilot

Department of Human Physiology, University of Gondar, College of Medicine and Health Science, School of Medicine, Gondar, Ethiopia

Department of Women’s and Family Health, School of Midwifery, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

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The conception of the work, design of the work, acquisition of data, analysis, and interpretation of data was done by DGB, DC, AZA, FMA, and MHA. Data curation, drafting of the article, revising it critically for intellectual content, validation and final approval of the version to be published was done by DGB, DC, AZA, and MHA. All authors read and approved the final manuscript.

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Belay, D.G., Chilot, D., Alem, A.Z. et al. Spatial distribution and associated factors of severe malnutrition among under-five children in Ethiopia: further analysis of 2019 mini EDHS . BMC Public Health 23 , 791 (2023). https://doi.org/10.1186/s12889-023-15639-2

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Prevalence of Malnutrition and Associated Factors among Children in Rural Ethiopia

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Prevalence of malnutrition and associated factors among under-five children in Ethiopia: evidence from the 2016 Ethiopia Demographic and Health Survey

Affiliations.

• 1 Statistics Department, Science College, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia. [email protected].
• 2 Statistics Department, Science College, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia.
• PMID: 31296269
• PMCID: PMC6624874
• DOI: 10.1186/s13104-019-4444-4

Objective: The aim of this study was to assess the risk factors for malnutrition among children aged 0-59 months in Ethiopia. The analyzed data were obtained from the 2016 EDHS and 9495 under-5 years' children were considered in this analysis. The data was extracted, edited and analyzed by using SPSS Version 23.0. Both bivariate and multivariable binary logistic regression model was used to identify the determinants of children malnutrition.

Results: The prevalence of stunting, wasting, and underweight were 38.3%, 10.1%, and 23.3%, respectively. About 19.47% of children were both stunted and underweighted, and only 3.87% of children had all the three conditions. Among the factors that considered in this study, age of a child, residence region, mothers' education level, mothers' BMI, household wealth index, sex of a child, family size, water and toilet facility were significantly associated with malnutrition in Ethiopia. The authors concluded that malnutrition among under-five children was one of the public health problems in Ethiopia. Therefore, the influence of these factors should be considered to develop strategies for reducing malnutrition in Ethiopia.

Keywords: Ethiopia; Stunting; Under-five children; Underweight; Wasting.

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Conflict of interest statement

The authors declared that they have no competing interest.

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Assessment of nutritional status and associated factors among adolescent girls in Afar, Northeastern Ethiopia: a cross-sectional study

• Gebrehiwot Hadush 1 ,
• Oumer Seid 2 &
• Abel Gebre Wuneh 3  

Journal of Health, Population and Nutrition volume  40 , Article number:  2 ( 2021 ) Cite this article

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A body of evidences showed that adolescent undernutrition is a serious public health problem in developing countries including Ethiopia. Adolescence period is the last chance for curbing the consequences of undernutrition and breaking the intergenerational cycle of malnutrition and poor health. Despite this fact, they have been considered as a low-risk group for poor health and nutrition problems than the young children or the very old. This study aimed to assess prevalence of nutritional status and associated factors among adolescent girls in Afar, Northeastern Ethiopia, 2017.

A school-based cross-sectional study design was conducted among 736 adolescent girls from February15 to March 05, 2017 in Afar, Northeastern Ethiopia, 2017. Multi-stage sampling technique was used to select study participants. A pretested and structured interviewer-administered questionnaire and anthropometric measurements was used to collect the data. The collected data were entered in to Epi Data version 3.1 and exported to SPSS version 20.0 for further statistical analysis. Body Mass Index for age (thinness) and height for age (stunting) was used to assess undernutrition of adolescent girls by using the new 2007 WHO Growth Reference. Data were analyzed using bivariate and multivariable logistic regression. The degree of association between dependent and independent variables were assessed using odds ratio with 95% confidence interval, and variables with p value < 0.05 were considered significant.

The study revealed that the prevalence of thinness and stunting were 15.8% (95% CI 13.3–18.5%) and 26.6% (95% CI 23.5–29.9%), respectively. Being at an early adolescent age (AOR = 2.89, 95% CI 1.23–6.81) for thinness and being at an early adolescent age (AOR = 1.96, 95% CI 1.02–3.74), household food insecure (AOR = 2.88, 95% CI 1.15–7.21), menstruation status (AOR = 2.42, 95% CI 1.03–5.71), and availability of home latrine (AOR = 3.26, 95% CI 1.15–4.42) for stunting were the independent predictors among the adolescent girls.

Conclusions

The prevalence of thinness and stunting is above the public health importance threshold level. Thus, Multi-sector-centered nutrition interventions to improve nutritional status of disadvantaged adolescent girls through providing comprehensive nutritional assessment and counseling services at community, school, and health facility levels, and creating household’s income-generating activities are recommended before they reach conception to break the intergenerational cycle effect of malnutrition.

World Health Organization (WHO) defined adolescence as a period of life ranging from 10 to 19 years old which is the transition from dependent childhood to independent adulthood [ 1 ]. Worldwide, there are about 1.2 billion adolescents, representing more than 18% of the global population. Nearly 90% of them live in developing countries, and approximately 600 million are female [ 2 ]. In Ethiopia, 20–26% of the population are adolescents [ 3 ].

Adolescence is a period of rapid growth and development by which up to 45% of skeletal growth takes place, and 15 to 25% of adult height is achieved [ 4 ]. Throughout this period, risk of nutrition inadequacies and other health issues are of great concern due to rapid growth in stature, muscle mass, and fat mass. As a result of these serious nutritional challenges, adolescents would be negatively affected by this rapid growth spurt as well as their health as adults [ 5 ].

Malnutrition in all its forms, particularly undernutrition including underweight for age, too short for age (stunted), too thin relative to height (wasted), and functionally deficient in vitamins and minerals, is a global issue, but in the developing countries, it is catastrophic [ 6 , 7 ]. Adolescents are in a vulnerable group for malnutrition and its consequences, because it is a dynamic period of physical growth and mental development. Undernutrition starts before birth, goes into adolescence and adult life, and can span into generations and results in short stature, lean body mass, and is associated with deficiencies in muscular strength. In addition, it can reduce resistance to infection and other debilitating conditions that reduce productivity [ 8 , 9 , 10 ].

A body of evidence showed that globally, adolescent undernutrition is a serious public health problem in both developed and developing countries, but is disproportionally keeping sever in developing countries, especially in Asia (32–65%) and Africa (4–30%), making them more vulnerable to low productivity, poor health, and early deaths. In Sub-Saharan Africa, the prevalence of adolescent undernutrition is 15–58%, which is higher from other African countries [ 7 , 11 , 12 , 13 ]. According to WHO, the recommended indicator to assess the nutrition status of adolescents are thinness (low body mass index for age) and stunting (low height for age), where the former is a result of mainly acute (short term), and the latter shows chronic (long term) nutritional deficiency [ 14 , 15 , 16 ].

Evidences showed that adolescent girls in many contexts are a marginalized and disempowered group and consequently face diminished opportunities and choices [ 17 ]. They are a nutritionally vulnerable group for their high requirements for growth, their eating patterns and lifestyles, and their risk-taking behaviors; their susceptibility to environmental influences and hard physical work, as commonly observed in low-income countries, may impose additional physiological stress and nutritional requirements in adolescence. In certain cultures, from infancy onwards including adolescents, girls are at particularly high risk because of gender discrimination [ 9 , 18 , 19 , 20 ]. On top of this, adolescents have been considered a low-risk group for poor health and nutrition and often receive little attention. This results in lack of information regarding the nutritional status of adolescents especially from the developing world [ 20 , 21 ].

There are few studies done in Ethiopia regarding the level of adolescent undernutrition in the country. The Ethiopian nutrition baseline report revealed that the prevalence of stunting and thinness in adolescent girls was 23 and 14%, respectively [ 22 ]. The other community-based studies done in different parts of Ethiopia such as Somali, Oromia, and Tigray indicated that the prevalence of both stunting and thinness were high in some rural parts of the country which were 22.9% stunted and 11.5% thin [ 23 ], 27.5% thin and 15.6% stunted [ 24 ], and 21.4% thin and 26.5% stunted [ 4 ], respectively.

Even though the few existing studies done on the nutritional status of adolescent girls in some parts Ethiopia and other developing world [ 10 , 14 , 19 , 20 ] indicated that adolescent undernutrition is a major public health problem in developing countries including our country, data on adolescent girl’s nutritional status in pastoral societies are scarce. Despite the fact that having adequate evidences and information on nutritional status among adolescent girls do have a paramount step for intervention programs to break the intergenerational cycle of malnutrition, to the best of our knowledge, there was no previous study with this objective on adolescent girls in Afar regional state where pastoral communities live particularly in the study area. Therefore, this study aimed to assess the prevalence of nutritional status (thinness, stunting) and associated factors among adolescent girls in Megale district, Afar regional state, Northeastern Ethiopia.

Study design and setting

This study employed a school-based cross-sectional study design from February 15, to March 05, 2017 in selected schools found at Megale district, Afar National Regional State, North East Ethiopia. The district is located at a distance 325 km away to the west of the regional capital, Semera and 765 km northern east of the capital of Ethiopia, Addis Ababa. The district is typically rural and organized into 8 administrative kebeles (the smallest administrative units), and the community is characterized by pastoral livelihood.

According to the Megale district health office report, the total population of the district, in the year 2016, is estimated to be 34,692 (19,220 males and 15,473 females) and children aged 6 months to 59 years old are 3962. Currently, the district has 21 governmental primary schools in the academic year of 2016/2017. The district has 3 health centers, 7 health posts, one private drug store, and one pharmacy. The topography is 60% mountainous, 20% flat, and 20% inclined. The annual rainfall is 500–600 ml, and the temperature is 35–40 °C [ 25 ].

Study populations

All adolescent girls (10–19 years) found in the governmental primary school of Megale district were the targets for the study, where the study population consisted of a sample of all regular adolescent girls found residing in the randomly selected governmental primary schools during the study period. Those adolescent girls who had physical deformity that hinder height measurements, self-reported pregnancy status, and residents of the study area for less than 6 months in the family at the time of interview were excluded from the study.

Sample size and sampling procedure

The required sample size for the first objective of this study (to determine the prevalence of thinness and stunting) was determined using a single population proportion with the following assumptions: The level of confidence ( α ) 95% ( Z 1-α /2 = 1.96), margin of error ( d ) 5%, design effect of 2 and the proportions ( p ) of adolescents’ girls who had thinness and stunting were 22.9 and 11.5% respectively taken from previous study done in Somalia region, Ethiopia [ 23 ], and the higher prevalence (22.9%) was taken and calculated using z 2 × p × q / d 2 . Therefore, the final sample size by considering the non-response rate of 10% was 298.

The required sample size for the second objective of this study (for the factors associated with thinness and stunting) was determined using Open Epi menu online software program with the following assumptions: The level of confidence (α) is taken to be 95%, power 80; and ratio (unexposed: exposed) was taken only once (Table 1 ).

At the end, out of two objectives, the prevalence (290) and associated factors (379), the largest sample 379 was used for this study. Considering a design effect of 2 (379 × 2 = 758), the final sample size calculated was 758.

Study participants were selected by multistage random sampling method. First, out of the twenty one governmental primary schools (grade 4–grade 8), eight schools were selected randomly. Second, the total sample size was allocated in to each randomly selected school using proportion to population size (PPS). Finally, after taking a list of an identification number for each adolescent girl student in the randomly selected schools from each school’s administrators (from their roster) as a sampling frame (list of students between 10 and 19 years), study participants were selected using simple random sampling technique randomly by computer-generated random numbers.

Data collection tools and process

A structured questionnaire was developed from the Ethiopian national nutrition survey report for the national nutrition program of Ethiopia [ 22 ] and other relevant literatures and contextualized to the local situation. The questionnaire was composed of sociodemographic and economic factors, health- and environment-related factors, dietary habits, and anthropometrics. Concerning the dietary diversity, individuals were asked about their past 24-h dietary recall method (from sunrise to sunrise), while for the dietary food frequency, individuals were asked about their past 7 days of food frequency practice using the WHO nine food groups. The minimum dietary diversity score of four or more out of the nine groups of foods was considered as adequate [ 26 ].

Anthropometric measurements such as body weight and height were measured, the former by using a weighing scale in light clothing with no jackets or coats, shoes, and additional clothing to the nearest 0.1 kg on a new calibrated portable scale and the latter by using a portable stadiometer with no shoes; shoulders, buttocks, and heels touching the vertical stand; and the head in Frankfurt position to the nearest 0.1 cm, respectively. Mid upper arm circumference (MUAC) was measured by marking midway between shoulder tip and the elbow tip on the vertical axis of the upper arm with the arm bent at right angle and between the lateral and medial surface of the left arm. Four diploma female nurses as data collectors and two BSc nursing professionals as supervisors were recruited. For each participant from the eight primary schools, direct face-to-face interviews were conducted during their break time before noon.

Data quality control

English version questionnaire was translated into the local language, “Afaraff”, and then back to English to maintain its consistency. Pretest was conducted among 37 students (5% of the sample) in a non-selected school in the district for necessary modification. A two-day training was given to the data collectors and supervisors before the actual data collection. Continuous supervision was done by the supervisors and the principal investigator on a daily basis.

Statistical analysis

All raw data with the exception of anthropometric data were entered and cleaned in EPI data software version 3.1 and then exported to SPSS for analysis; whereas the anthropometric data were entered and converted to height-for-age and BMI-for-age Z scores by using the Antro Plus software. Adolescent girls with BMI-for-age below −2 Z scores and height-for-age below −2 Z scores of the 2007 WHO reference population were classified as thin and stunted, respectively [ 27 ]. Descriptive statistical measures such as percentage, mean, and standard deviation of variables were computed to summarize the data.

Binary logistic regression model was used to assess the association between the two dependent and independent variables using odds ratio with 95% confidence interval. To identify independent variables which have statistically significant association with the outcome variable (thinness and stunting), first, bivariate analysis was computed for each independent variable, and the outcome variables and crude odds ratio (COR) and 95% confidence interval (CI) were obtained.

Then, all variables observed to be significant in the bivariate logistic analysis (at p value < 0.25) were subsequently included in the multivariable logistic regression model to identify the independent predictor variable after controlling the effects of confounders and adjusted odds ratio (AOR) with 95% CI was calculated. Multicollinearity between the independent variables was checked using standard error and excluded the variables that had standard error of > 2, and Goodness of fit was checked by the Hosmer & Lemeshow test with p value > 0.05. All tests were two-sided, and p values of less than 0.05 were considered to be predictive for each outcome variable. Results were described and presented using narrative text, graphs, and tables.

Operational definitions

Adolescents are individuals in the age group of 10–19 years of age. It is categorized as early (adolescents in the age group of 10–13 years of age), middle (adolescents in the age group of 14–16 years of age), and late adolescents (adolescents in the age group of 17–19 years of age) [ 28 ].

Stunting is if the height-for-age Z score is found to be below −2 SD of the 2007 WHO growth reference. Severe stunting is diagnosed if it is below −3 SD [ 27 , 29 ].

Thinness is if the BMI-for-age Z score < −2 SD of the WHO growth reference 2007. Severe thinness is diagnosed if it was below −3 SD [ 27 , 29 ].

Body mass index (BMI) is defined as weight in kilograms divided by height in meters squared = Weight (kg)/Height (m 2 )—normal weight if 18.5 kg/m 2 < BMI < 25 kg/m 2 , underweight if BMI < 18.5 kg/m 2 , and overweight if BMI > 25 kg/m 2 [ 27 , 29 ].

Mid upper arm circumference (MUAC) < 18 cm is classified as severe acute malnutrition, MUAC of 18–21 as moderate acute malnutrition, and MUAC > 21 is classified as normal [ 27 , 29 ].

Household food security was assessed using the four-item module, and the sum of affirmative responses to the six questions in the module was taken. The food security status of households with raw score 0–1 was described as food secure and food insecure [ 23 ].

Adequate dietary diversity score is defined as adolescent girls with dietary diversity score of the median and above the median values ( > 4 food groups), whereas inadequate dietary diversity score is when adolescent girls with dietary diversity score is below the median value (< 4 food groups) [ 26 ].

Ethical considerations

Ethical clearance was obtained from Mekelle University, College of Health Sciences, Research and Community Service Unit Ethical Review Committee. A support letter was also obtained from Afar regional education Bureau, Megale district health and education offices and kebele administrations. Again, informed consent was obtained from the commandant of the schools, participant, participant’s parent/ guardian before being enrolled, and they were assured about the confidentiality of the information. The aims of study and any possible risk of the study were explained to study participants using their own local language.

Demographic and socioeconomic characteristics

A total of 736 adolescent girls participated in this study with a response rate of 97.4%. The mean ± SD age of study participants were 14.28 ± 2.79 years where around two fifth, 286 (38.9%), of them were in the early adolescence period, while 178 (24.2%) were in the late adolescence period. Slightly below three fourths, 270(73.4%), of participants were rural residents. Majority, 714 (97.0%) and 712 (96.7%), of the participants were Muslims in religion and Afar in ethnicity, respectively. Moreover, 628(85.3%) of them were single, while the remaining 108 (14.7%) were currently married (Table 2 ).

The educational distribution of the students’ parents showed that 704 (95.7%) and 674 (91.6%) of their mothers and fathers did not attend formal education, whereas the least percent have joined college or university, 14(1.9%) for mothers and 12 (1.6%) for fathers. Regarding the occupation of parents, majority of the fathers’ occupation were pastoral/herding livestock, 514 (69.8%) and followed by government employee, 168 (22.8%). Majority of mothers’ occupation were housewife, 672 (91.3%) and followed by government employee, 58 (7.9%). Around 702 (95.4%) of households were headed by males and 34 (4.6%) were by females (Table 2 ).

Health and household environment-related characteristics

Slightly below one third, 168 (22.8%) of the participants reported that they have a home latrine, and 162 (96.4%) of them were using a latrine. Concerning school latrine utilization, 154 (20.9%) of the participants do not use the school latrine. Regarding the source of drinking water, 276 (37.5%) of them obtained from a protected or safe water source. Again in terms of waste disposal method, 702 (95.4%) of the participants use the open-field waste disposal method (Table 3 ). About 206 (28.0%) of adolescent girls started menstruation, and the mean ± SD age of menarche was 13.86 + 1.84 years. Moreover, 112 (15.2%) had history of illness in the past 2 weeks prior to the data collection.

Dietary intake-related characteristics

Eating behavior and dietary diversity score of adolescent girls.

Based on the 24-h dietary recalls, the overall proportion of adolescent girls with minimum dietary diversity score (at least consumed four food groups out of nine food groups) was 98 (13.3%). The dietary diversities consumed out of nine food groups were 640 (87.0%), 80 (10.9%), and 16 (2.1%), for low, medium, and high scores, respectively (Table 4 ).

Among the participants, 736 (100 %) consumed starchy staple food (cereals) followed by milk & milk products 452 (61.4%), flesh meat 232 (31.5%), and legumes/nuts 190 (25.8%). Consumption of dark green leafy vegetables, vitamin a-rich fruits and vegetables, and animal source foods (like organ meat, others fruits and vegetables, and eggs) were relatively low (Fig. 1 ).

Types of food groups consumed over a 24-h period by school adolescents girls in Megale district, Afar Regional state, Northeastern Ethiopia, April, 2017 ( n = 736)

Past 7-day food frequency of adolescent girls

Based on the 7-day food frequency report, 736 (100%) of them consumed starchy staple food (cereals), three or more times per week, followed by milk & milk products 604 (82.1%) and legumes/nuts 330 (44.8%), whereas dark-green leafy vegetables, vitamin-A rich fruits and vegetables, and animal source foods (like organ, flesh meat, & eggs) were relatively least consumed (Table 5 ).

Prevalence of thinness and stunting of adolescent girls

The mean ± SD overall height and weight of the participants was 145.8 ± 10.3 cm and 39.1 ± 9.3 kg, respectively. In this study, the overall prevalence of thinness (BAZ < − 2 SD) was 116 (15.8%) (95% CI 13.3–18.5%), the overall prevalence of stunting (HAZ < − 2 SD) was 196 (26.6%) (95% CI 23.5–29.9%), while the prevalence of overweight was 6 (0.8%). The prevalence of severe thinness (BAZ < − 3 SD) and stunting (HAZ < − 3 SD) were 3.8 and 7.6%, respectively. The nutritional status of the adolescent girls according to the body mass index (BMI) showed that 198 (26.9%) of them were underweight. Moreover, according to their mid upper arm circumference (MUAC), 336 (45.7%) of the adolescent girls were found to have moderate acute malnutrition (MUAC 18–21 cm) (Table 6 ).The anthropometric measurements indicated that early age of adolescent girls were more stunted 102 (13.9%) and thin 74 (10.1%) than late adolescents 40 (5.4%) and 16 (2.2%), respectively (Fig. 2 ).

Overall anthropometric status of school adolescent girls in Megale district, Afar Regional state, Northeastern Ethiopia, April, 2017 ( N = 736)

Factors associated with thinness and stunting of adolescent girls

Factors associated with thinness.

In the first logistic regression model, the variables significantly associated with adolescent girls’ thinness were being early adolescent age, eating snacks, grade level, marital status, menarche, and dietary diversity have association at p value < 0.25. In the final multivariable analysis after examining the effect of confounders, the independent predicators for thinness were being early adolescent age (AOR = 2.89, 95% CI 1.23–6.81). The odds of thinness were around 2.89 times higher among adolescent girls who were early adolescent girls than those who were late adolescents. However, the other determinant factors did not show an association with thinness in multivariable analysis (Table 7 ).

Factors associated with stunting

In the first logistic regression model, the variables significantly associated with adolescent girls’ stunting were being early adolescent age, menarche, availability of home latrine, household food insecurity, grade level, family monthly income level, dietary diversity, source of water, and eating snack. Finally, those variables were taken to the final multivariable logistic regression to identify the variables significantly associated with stunting after controlling the effect of confounders. Hence, in the multivariable logistic regression analysis models, being early adolescent age (AOR = 1.96, 95% CI 1.02–3.74), household food insecure (AOR = 2.88, 95% CI 1.15–7.21), menstruation status (AOR = 2.42, 95% CI 1.03–5.71), and availability of home latrine (AOR = 3.26, 95% CI 1.15–4.42) were the independent predictors for stunting.

The odds of stunting were around 1.96 times higher among adolescent girls who were early adolescent girls than those who were of late adolescent age. Those adolescent girls whose households were food insecure were around 2.88 times more likely to get stunted as compared with those whose households were food secure, and those who had not had home latrine were 3.26 times more likely to get stunted as compared with those who had home latrine. Adolescent girls who did not start menstruation were 2.42 times more likely to be stunted as compared with adolescent girls who started menstruation (Table 8 ).

Discussions

Adolescents have specific health and development needs, and many of them face challenges that hinder their well-being especially on adolescent girls such as adverse reproductive outcomes, pregnancy outcomes, and birth weight [ 26 , 30 ]. Despite this fact, many studies in Ethiopia are still carried out focusing on the vulnerable groups like infant, pregnant and lactating women, and limited on adolescent girls. Hence, this study aimed to assess prevalence of nutritional status and associated factors among adolescent girls in primary schools of Megale district, Afar region, North East Ethiopia.

This study revealed that the overall prevalence of thinness among the adolescent girls was 15.8% (95% CI 13.3–18.5%), and this finding is almost similar using the same cutoff point with study done in Asembo and Mumias, Kenya (15.6%) [ 31 ], Kavre District, Nepal (14.94%) [ 32 ], Burkina Faso (13.7%) [ 33 ], and west Bengal (16%) [ 34 ]. It is consistent with the prevalence reported in Addis Ababa (13%) and Mekele (14%) [ 26 , 35 ] but lower than the study done in Adwa town (21.4%) [ 4 ], Ambo (27.5%) [ 36 ], and Eastern Tigray, Ethiopia (33.7%) [ 8 ]. Again, it is much lower when we compared with the study done in Kolar District, Garhwal, India, rural community of Tigray, Ethiopia, and Northern Nigeria where 54.8, 43.47, 58.3, and 58.7% of the adolescent girls were thin [ 30 , 37 , 38 , 39 ], respectively, but higher than study conducted in Tamale Metropolis, Ghana (10%) [ 40 ].

Other studies conducted in Addis Ababa city, Ethiopia (6.2%) [ 41 ] and Tunisia (1.3%) [ 42 ] have been reported much lower prevalence than the current study. These findings indicated that thinness is a major public health problem in majority of Ethiopian and other communities. The possible explanation for this difference could be due to difference in the study group and urban–rural difference between the study subjects and settings. Unlike this study, some studies done in Tunisia considered adolescents the middle and late stages which are less likely to be thin because of less possibility of height growth than early adolescents. The other possible variation could also be due to socioeconomic and cultural difference in dietary habit and care practices of study populations.

The overall prevalence of stunting in this study was also found to be 26.6% (95% CI 23.5–29.9%). This finding was consistent with other studies done in the rural community of Tigray, Ethiopia which reported that prevalence of stunting were 26.5% [ 39 ]. It is also consistent with study done in Nepal (21.08%) [ 32 ] and Seychelles (23%) [ 43 ]. Other previous studies in adolescent Ethiopians girls also reported that much lower levels of stunting. These include studies in Somali, Ethiopia (11.5%) [ 23 ], Adama zone (15.6%) [ 24 ], and Adwa Ethiopia (12.1%) [ 4 ].

Nonetheless, in northern Ethiopia, the prevalence of childhood chronic malnutrition is very high which may have an impact on the level of adolescent stunting [ 44 ]. A number of studies in other African countries including Burkina Faso (8.8%) [ 33 ] and Kenya (12.1%) [ 31 ] have been reported a lower prevalence of stunting. However, a high prevalence of stunting in adolescent girls has been reported in Bangladesh (32%) [ 45 ] and Garhwali, India (30.43%) [ 30 ].The variation could be due to socioeconomic and cultural difference in food access, nutrition information, dietary habit, and care practices of the communities.

In this study among the variables moved to the final multivariable logistic regression analysis model, being of early adolescent age was found to be the independent predictor for thinness. Hence, the odds of thinness were around 2.89 times higher among adolescent girls who were in the early stage of adolescents than those who were in late adolescent age. This might be due to the increased growth spurt during the early adolescent stage as compared to late adolescent stage with a sudden increase of height in the early adolescents than late adolescents. Findings from Tigray, Ethiopia [ 4 , 39 , 46 ] and Belgaum and Karnataka, India [ 47 , 48 ] have reported similar results with the present study.

Regarding stunting, the odds of stunting was around 1.96 times higher among adolescent girls who were in the early stage of adolescent period than those who were late adolescents. This finding is consistent with other studies conducted in five districts of Amhara region, Ethiopia [ 49 ], the baseline national nutrition survey [ 22 ], and rural community of Tigray, Ethiopia [ 39 ], which showed that prevalence and severity of stunting have been found to decrease with age. This might be due to the fact that inadequate nutrient intake besides increased requirement during early adolescent’s faster growth period and those early adolescents might be more affected by undernutrition than the older adolescents in the current study. However, a contradict finding that has been reported from Somalia region, Ethiopia was the present result [ 23 ].

The odds of stunting among adolescent girls who did not start menstruation early were 2.42 times more likely to be stunted as compared with adolescent girls who started menstruation late. This result is in line with the findings of studies done in Adwa, Ethiopia [ 4 ], Goba town, Ethiopia [ 50 ], and Western Kenya [ 31 ] which indicated a negative association between stunting with sexual maturity. This might be explained by the fact that starting menstruation coincides with the adolescent growth spurt. Delay in menstruation in stunted adolescents shows the opportunity for catch-up growth as stunting delay menarche [ 4 , 39 ].

The odds of stunting among adolescent girls who had no home latrine were 3.26 times more likely to be stunted as compared with those adolescent girls who had had home latrine. This might be explained by the fact that those who have home latrine may have used it properly and they could not be affected by communicable diseases easily; as a result, they become healthy. Whereas those who do not have home latrine, they may defecate in the opened field and may be easily affected by communicable diseases; as a result growth will be interrupted and leads to stunting. Previous studies done in the rural community of Tigray, Ethiopia [ 39 ] and Tehuledere District, Ethiopia [ 51 ] showed that lack of home latrine was a predictor of stunting in adolescents.

The odds of stunting among adolescent girls from food-insecure households were 2.88 times more likely to be stunted than adolescent girls from food secured households. This indicate that the presence of chronic food insecurity leads to stunting because of chronic undernutrition and might be one of the important determinant of chronic nutritional insult in adolescent girls. The finding was in agreement with other studies conducted in Mini EDHS report and Tigray, Ethiopia [ 4 , 44 ] and five districts of Amhara region, Ethiopia [ 49 ] where food insecurity is negatively associated with the linear growth of adolescents.

Limitations of the study

The study involved a single cross-sectional design. Hence, causal inference might not be strong.

Recall and reporting bias might also affect for dietary diversity & food frequency questions. Therefore, further studies combined both quantitative and qualitative approach might be necessary for better understanding of undernutrition in the community.

This study revealed that the overall prevalence of thinness and stunting were found to be 15.8% (95% CI 13.3–18.5%) and 26.6% (95% CI 23.5–29.9%) in the study area, respectively. This result indicated that thinness and stunting among the adolescent girls are public health problems in the study area according to the WHO, cutoff values for public health significance.

The independent predictor significantly associated with thinness was being early adolescents’ age while the independent predictors significantly associated with stunting were being early adolescents’ age, household food insecure, menstruation status, and availability of home latrine. A comprehensive strategy such as nutrition education, improving household economy through income-generating activities, personal and environmental hygiene practices are recommended. Interventions are also needed to improve the nutritional status of disadvantaged adolescent girls through providing comprehensive and routine nutritional assessment and counseling services at community, school, and health facility levels before they reach conception period to break the intergenerational cycle effect of malnutrition.

Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request.

Abbreviations

Adjusted odds ratio

achelor of Science

Mid upper arm circumference

Proportion to population size

Standard deviation

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Acknowledgements

Authors would like to thank Mekelle University, Megale district Administrative Office, Megale district, Education Office and respective school administrations, study participants, data collectors, and supervisors for their cooperation in the study.

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GH has designed the study and was involved in data collection. GH, SO, and AG have cleaned and analyzed the data, interpreted the results, and drafted the manuscript. All authors have critically reviewed the manuscript. The authors read and approved the final manuscript.

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Hadush, G., Seid, O. & Wuneh, A.G. Assessment of nutritional status and associated factors among adolescent girls in Afar, Northeastern Ethiopia: a cross-sectional study. J Health Popul Nutr 40 , 2 (2021). https://doi.org/10.1186/s41043-021-00227-0

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Determinants of severe acute malnutrition among children aged 6—23 months in bahir dar city public hospitals, Northwest Ethiopia, 2020: a case control study

• Tigist Gebremaryam 1 ,
• Desalegne Amare 2 ,
• Tilksew Ayalew 2 ,
• Agimasie Tigabu 3 &
• Tiruye Menshaw 4  

BMC Pediatrics volume  22 , Article number:  296 ( 2022 ) Cite this article

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Severe acute malnutrition is a major problem among developing countries and it is one of the major causes of mortality and morbidity in Ethiopia. The impact is more severe among children aged 6–23 months. Severely malnourished children are nine times more likely to die than healthy children. Identification of the determinants of severe acute malnutrition under the age of two years can significantly reduce the burden of child morbidity and mortality. Therefore, this study was aimed to assess determinants of severe acute malnutrition among children aged 6–23 months at Bahir Dar city public hospitals, Northwest Ethiopia, 2020.

Institutional-based unmatched case–control study was conducted among a total sample size of 201 children (67 cases and 134 controls) in Felege Hiwot Comprehensive Specialized Hospital and Tibebe Ghion Specialized teaching hospital, from February 2020–March 2020. Children diagnosed with severe acute malnutrition were considered as cases and children with other problems were control groups. The study participants were randomly selected from pediatrics units in the two specialized hospitals. Data were collected using a structured pretested questionnaire through interviews and anthropometric measurements. The data were entered into Epi data version 3.1 and exported to SPSS software version 23 for analysis. Variables with ( p  < 0.25) in the bivariable analysis were entered into multivariable logistic regression. For multivariable analysis, a backward method was selected with a 95% confidence interval. Statistical significance was declared at P  < 0.05.

In this study , 67 cases and 134 controls of children with their mothers had participated with an overall response rate of 100%. Family size > 5 [(AOR = 3.89, 95% CI:(1.19, -12.70)], average perceived birth weight [(AOR = 0.048, 95% CI: 0.015, -0.148)] and large perceived birth weight [(AOR = 0.023, 95% CI:(0.002, -0.271)], introduction of complementary feeding before six months [(AOR = 6.21, 95% CI: (1.44, -26.76)] and dietary diversity score < 5 groups [(AOR = 9.20, 95% CI; 3.40, -19.83)were significant factors associated with severe acute malnutrition.

In this study, dietary diversity, family size, perceived birth weight, and initiation of complementary feeding were significantly associated with severe acute malnutrition. Therefore, emphasis should be given to improving infant and young child feeding practices, especially timely initiation of complementary feeding and dietary diversity.

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Malnutrition is broadly categorized as undernutrition and overnutrition. Undernutrition is a great concern in many developing countries including Ethiopia; it mainly occurs when an individual does not get enough food that consists of sufficient and balanced food groups. Undernutrition in children is classified as wasting (acute), stunting (chronic), or underweight [ 1 ].

Severe acute malnutrition (SAM) or severe wasting is defined by low Weight for Length (WFL) less than -3 standard deviation (SD) of the median World Health Organization (WHO) growth standards, and/or a low mid-upper-arm-circumference (MUAC) < 11.5 cm and/or presence of bilateral pitting edema [ 2 ]. SAM is the most extreme and visible form of acute undernutrition, resulting from acute food shortages, inadequate or poor quality of diet, a recent bout of illness, repeated infection, inappropriate child care or feeding practices, or a combination of these factors, which results in a child who loses weight rapidly and does not gain enough weight relative to his/her height [ 3 ].

Wasting in children is a life-threatening result of poor nutrient intake and/or disease. Children suffering from wasting have weakened immunity, are susceptible to long-term developmental delays, and face an increased risk of death, particularly in cases of severe wasting. These children require urgent treatment and care to survive. In 2019, 47.0 million children under 5 were wasted of which 14.3 million were severely wasted [ 4 ].

SAM can occur in all age groups, but the impact is more severe among children aged 6–23 months, as this period is extremely important for the child's optimal growth and development, due to this mental and physical damages that are difficult to reverse can occur due to nutritional deficiencies [ 5 ]. Brain and nervous system development begins early in pregnancy and is largely complete when the child reaches the age of two. The timing, severity, and duration of nutritional deficiencies during this period affect brain development, physical growth, and overall health of the child [ 6 ]. Globally, 83% of children less than two years are not getting enough nutritious food appropriate for their age, this denies the infant and young children the nutrients they need at the most critical time in their physical and mental development [ 7 ]. Infants and young children, especially, up to two years of age have the highest nutrient needs than any time in life. But many young children do not reach their full developmental potential; the main reason is that they are receiving too little food which may be inadequate, or too late /too early that is not provided timely [ 8 ].

The burden of diseases and death due to SAM in children is higher and more serious than any other cause. Breastfeeding with complementary feeding could reduce mortality among under-five children by 19% [ 9 ]. Complementary feeding is a time of transition from exclusive breastfeeding to family foods, typically covering the period from 6 to 23 months of age. It is times when malnutrition starts in many infants and young children and contributes to the high prevalence of malnutrition in children less than two years of age worldwide [ 10 ].

Worldwide, an estimated 16 million children under the age of 5 are affected by severe acute malnutrition with more risk to death than well-nourished children. These deaths are the direct result of malnutrition itself, as well as the indirect result of childhood illnesses like diarrhea and pneumonia that malnourished children are too weak to survive [ 11 ]. Globally, around 50 million children are acutely malnourished of which 17 million are severely wasted. Undernutrition is estimated to be associated with around half that is 45% of child deaths annually [ 12 ]. The first two years of a child’s life are especially important, as optimal nutrition during this period lowers morbidity and mortality, reduces the risk of chronic disease, and is the basis for better growth and development [ 13 ].

SAM in children is a major public health problem in developing nations, including sub-Saharan Africa [ 14 ]. Results from the 2019 Ethiopian mini Demography and Health Survey (EMDHS) show that in Ethiopia 37% of children under 5 are stunted, 12% are severely stunted, 7% are wasted, and 1% is severely wasted, 21% of all children are underweight, and 6% are severely underweight. There are also regional variations. Amhara regions are highly affected by child stunting 41.3% and 7.6% are wasted, including 1.7% who are severely wasted which is greater than the national level [ 15 ].

The National Nutrition Program of Ethiopia in 2016 was planned to reduce all forms of malnutrition; Stunting to 26.3%, Wasting to 4.9%, and Under-weight to 13.3%, and to increase the proportion of children 6–23 months received minimum dietary diversity to 56% in 2019/20. But, all plans are not achieved. So it is important to address determinants associated with SAM for better intervention [ 16 ].

The United Nations Children’s Emergency Fund (UNICEF), in its malnutrition framework, three major determinants that cause child undernutrition, is: basic or structural, underlying, and immediate determinants. Inadequate dietary intakes with diseases are considered the immediate causes. Poor socioeconomic status, household food insecurity, inadequate sanitation, and inaccessible health care are considered as underlying causes. Cultural barriers, political and socio-economic structures, and religious ideologies are basic-level causes (1).

Globally different literature revealed that socio-economic factors such as income, residence, occupation, education, maternal age, and family size [ 17 ] have influenced the occurrence of SAM. Community determinants such as lack of maternal and child health services, lack of adequate and safe water supply, and lack of improved environmental sanitation are other determinants [ 18 , 19 ]. Child-related determinants such as a child’s gender, age, weight at birth, and dietary diversity [ 20 ] have been identified as individual-level determinants for SAM. Determinants associated with SAM may differ among Regions and Communities, as well as over time. The association of socio-demographic, maternal, and child health-related determinants that influence occurrences of SAM is not investigated in children under the age of two years in the study area. There are significant gaps in information on the association of SAM with complementary feeding practices in terms of: time of initiation, minimum dietary diversity and minimum meal frequency, child’s age, sex, perceived birth weight, mother’s BMI, mothers education, maternal marital status, family size, residence, source of drinking water, maternal ANC follow up, non-exclusive breastfeeding and child morbidity, vaccination status. Thus, this study was conducted to identify determinants of SAM among children aged 6–23 months at Bahir Dar city public hospitals, Northwest Ethiopia.

Materials and methods

Study area and period.

The study was conducted in Bahir Dar city public hospitals; Felege Hiwot Comprehensive Specialized Hospital (FHCSH) and Tibebe Ghion Specialized teaching hospital (TGSTH), Amhara Region, Ethiopia, from February 2020 to March 2020. Bahir Dar city is the capital of the Amhara region, located approximately 565 km northwest of Addis Ababa (the capital city of Ethiopia). The city administration has 3 hospitals, 10 health centers, and 134 private health institutions. FHSCH is situated in Bahir Dar city kebele 13. TGSTH is located 10 km south of the city center. According to information obtained from administrative offices of these hospitals, they provide different services in the outpatient department, inpatient department, and operation room theater department. TGSTH and FHCSH serve more than 3.5 million and 5 million populations in their catchment area respectively. FHCSH has 14 physicians, 4 pediatricians. 33 nurses, and 61 beds in pediatrics unit with total annual under two admission of more than 988 of which more than 350 was due to SAM. TGSTH has 3 physicians, 2 pediatricians, 21 nurses, and 55 beds in pediatrics unit with total annual less than two admissions of more than 847 of which more than 300 were diagnosed with SAM.

Study design and population

Institutional based unmatched case control study was conducted among cases (children 6–23 months of age with SAM) and controls (children 6–23 months of age without SAM) admitted in the pediatric ward of Bahir Dar city public hospitals of Northwest Ethiopia. All children 6–23 months of age with SAM or without SAM who were admitted to the pediatric ward were our study populations.

Presence of one of the three clinical signs and criteria. Children with clinical signs of possible SAM according to WHO’S Integrated Management of Neonatal and Childhood Illness (IMNCI) guidelines, defined as children aged 6–23 months with severe acute malnutrition whose MUAC < 11.5 cm, or WLZ < -3 SD or with bilateral pitting edema (based on pediatrician’s assessment) and who were admitted to pediatrics unit of the two public hospitals of Bahir Dar city.

Children age 6–23 months with normal nutritional status (without bilateral pitting edema or MUAC > 12.5 cm, or WLZ ≥ -2 SD), who did not fulfill SAM criteria and who were admitted with other health problems other than SAM in the two public hospitals of Bahir Dar city. The diagnosis includes history taking, clinical manifestations (objective findings) and anthropometric measurements.

Sample size determination and sampling procedure

The sample size was determined using Epi-Info version 7. The double population proportion exposure difference formula was used by using major determinant variables (not a model by health extension program, not exclusively breastfed, not given colostrum, be bottle-fed and have illness during the last two weeks before the survey) from another study. Considering not exclusively breastfed (children introduced other diets before six months of age compared to those who did not) as independent predictor exposure variable since it gave the maximum sample size. From that study, the proportion of lack of exclusive breastfeeding is 43.7% among cases and 21% among controls [ 21 ]. Controls to case ratio of 2:1 were recruited to achieve 84% power at 5% significance level. Adding 10% non-response rate, the total sample size was 201 children with 67 cases and 134 controls. Cases were selected randomly among children in the age of 6–23 months admitted due to SAM in the therapeutic feeding units during the study period. Once a case child was admitted, his/her mother/caretaker was interviewed immediately and then the first two controls were selected by simple random method on the same day and in the same hospital.

Data collection tools and quality assurance

The data collection tool was first developed in English and was translated to local language (Amharic) and was translated back to English to maintain consistency. The review was made by Amharic, English language experts and health professionals for consistency of language translation. A pretest was conducted on 5% of the total sample size out of the study area in Debre tabor general hospital, which is found in the south Gondar zone, before actual data collection and necessary adjustment was made on the tool. Two days training was given for data collectors and supervisors about anthropometric measurements and techniques of interview. A questionnaire was adapted from the World Health Organization instrument for stepwise surveillance for child malnutrition (WHO STEPS) [ 22 ] and by reviewing different literatures which were related to determinants of SAM.

Data were collected by four trained BSc nurses and two supervisors through face to face interview of the index mothers using pretested structured and interviewer administered questionnaires and anthropometric measurements [2] of the children(weight, length and MUAC).After measuring the weight and length of the child, we compute the WLZ index. (Mothers were interviewed about their socio-demographic characteristics, maternal determinants, environmental determinants and child related determinants in each pediatrics unit in the two public specialized hospitals. The outcome variable was SAM of the children.

Measurements

Weight: weight measurements were taken to the nearest 0.1 kg using an electronic digital weight scale. The scale was checked before each weighing to ensure that the mark returned to zero. The children were weighted with light clothing and no shoes. The parent or caregiver stands on the scale first, without the child. Then measure the adult with the child and record immediately. Weights were taken in kilograms. Each child was weighed twice.

Measurement of supine or recumbent length was taken to the nearest 0.1 cm using a portable calibrated board. The sole of the baby's feet were held firmly against the wall at the zero point while the length was marked off on the chart at the crown of the head. Length of the child was measured in recumbent position without shoes.

To improve measurements accuracy we have used appropriate instruments and the data collectors were trained and know how to use these instruments properly and also by repeated measurement.

After anthropometric measurement of the children, WHO z-scores were computed.

To use the charts to classify children’s nutritional status: 1. Find the correct table for the child age (6–23 months) and sex (boy or girl). Measure children aged 6–23 months or less than 87 cm long lying down (length). 2. Find the figure closest to the child’s length in the left column. 3. Find the range that contains the child’s weight. 4. The label at the top of the column with the range containing the child’s weight tells the child’s nutritional status to classify as Cases (child with SAM) or controls (child without SAM).

To measure dietary diversity, we adopted the WHO and UNICEF Infant and Young Children Feeding guidelines (IYCF) as an internationally acceptable guideline [2]. Dietary diversity scores were estimated via a 24-h recall method by categorizing the food items into eight major food groups. The food groups assessed were; Grains, roots or tubers; Vitamin A-rich fruit and vegetables; other fruits and vegetables; Flesh foods (Meat, poultry, fish and seafood); Eggs; Legumes, Pulses or nuts, dairy products (milk and milk products) and breast milk. If a child consumed at least one food item from a food group throughout the previous day, the group was assigned a value of one (1) for that child, and zero (0) if not consumed. The group scores are then summed up to obtain dietary diversity score, which ranges from zero to eight, whereby zero represents the non-consumption of any of the food items in the food groups, and eight represents the highest level of diet diversification. The MDD (minimum dietary diversity) was attained if a child had consumed five or more food groups out of the eight food groups over the previous day.

To assure the quality of the data, a properly designed, pretested questionnaire was used. Training was given for the data collectors and supervisors for two days.

Variable definitions

Children with severe acute malnutrition whose MUAC ≤ 11.5 cm, WLZ < -3 SD or with bilateral pitting edema

Children with normal nutritional status (without bilateral pitting edema or their MUAC > 12.5 cm, their WLZ ≥ -2 SD)

Complementary feeding

The process of starting to give additional foods and fluids to the child in addition to breast milk

Minimum dietary diversity

Children 6–23 months of age who consumed foods and beverages from at least five out of eight defined food groups during the previous day. A child with a dietary diversity score (DDS) of less than five is classified as having poor dietary diversity; if DDS greater than or equal to five, it is considered having good dietary diversity.

Perceived birth weight

A child whose body weight less than 1500 g at its birth is considered as Very small, A child whose body weight less than 2500 g at its birth considered as Small, A child whose body weight between 2500 g-4000 g at its birth considered as Average, A child whose body weight greater than 4000 g at its birth considered as Large.

A child who completes all EPI scheduled vaccine considered as fully vaccinated, a child who doesn’t complete all EPI scheduled vaccine considered as not vaccinated.

Exclusive breastfeeding

Breastfeeding while giving no other food or liquid, not even water for infants up to the age of 6 months.

• Severe acute malnutrition

A child whose weight for length is below -3 SD of the median WHO reference values

Data processing and analysis

Data was checked for completeness and consistency and then it was cleaned, coded and entered using Epi data version 3.1 and it was exported to SPSS software version 23 for analysis. WLZ was calculated and compared using the WHO 2006 Growth Reference Standard. A child who’s WLZ less than − 3 Standard Deviation (SD) from the reference population was classified as severe wasting (2). Descriptive statistics were used to describe the study population in relation to relevant variables. The association between SAM and exposure variables was analyzed by using binary logistic regression analysis. The model fitness was tested by the Hosmer–Lemeshow goodness-of-fit tests. A chi-square and odds ratio (OR) with 95% CI were used to assess the relationship between factors associated with the occurrence of child SAM. Then variables that had association in the bivariable model ( p  < 0.25) were entered and analyzed by a multivariable logistic regression model to identify the independent effect of different determinants for the occurrence of SAM. Statistical significance was declared at P  < 0.05.

Socio-demographic characteristics of the respondents

A total of 201 Children (67 cases and 134 controls) admitted in the pediatric ward were included in the study with a response rate of 100%. According to this study, the mean age of the children was 13.4 (SD ± 5.3) months. The mean age of mothers was 28.5 (SD ± 6.4) years. Most of the participants were from rural areas (55.2% cases and 48.5% controls) and more than half 37(55.2%) of cases and 71(53%) of controls were males. Most of the study participants (80%) were orthodox Christian religion followers. Concerning marital status of mothers, 58(86.6%) of cases and 115(85.8%) controls were married (Table 1 ).

Maternal related determinants of SAM

This study revealed that the proportion of women who gave their first birth less than twenty years was higher in cases 37(55.2%) than controls 29 (21.6%). Most of the mothers in 38(56.7%) of cases and 121(90.3%) of controls had ever got ANC service during their pregnancy of the current child. The proportion of women who had attended ANC service less than three times is higher in cases 30(78.9%) than controls 50(41.3%). Regarding place of delivery more than half of women in cases 40(59.7%) and more than ninety percent of controls 126(94%) had given birth at health institution (Table 2 ).

Environmental related determinants of SAM

In this study, the families who had access to protected water sources for drinking 46(68.7%) were cases and 106(79.1%) were controls. Concerning cooking fuel, 15(22.4%) cases and 63(47%) controls used modern fuel. Households who have toilets were 34(50.7%) in cases and 109(81.3) in controls. Regarding methods of hand washing 32(47.76%) cases and 50(37.31%) controls were to wash their hands with water only (Table 3 ).

Child related determinants of SAM

In this study, the proportion of children with small perceived birth weight was higher in cases 48(71.6%) than controls 9(6.7%). Similarly the proportion of children who were not vaccinated at all was higher in cases 40(59.7%) than controls 27(20.1%). More than half of cases 47(70.1%) and less than one fourth of control 23(17.2%) not have been exclusively breast fed. Regarding the initiation of complementary feeding 44(65.7%) of cases and 19(14.2%) of controls started complementary feeding before six months. The proportion of children with dietary diversity scored less than five food groups was higher in cases 58(86.5%) than controls 29(21.6) based on 24 h dietary recall. Regarding morbidity factors of children 23(50.0%) cases and 2(11.1%) controls had a history of diarrhea and also 10(21.7%) cases and 9(50.0%) controls had a history of fever in the last two weeks (Table 4 ).

Determinants of severe acute malnutrition (SAM)

The multivariable logistic regression result showed that, large family size [(AOR = 3.89, 95% CI:(1.19, -12.70)], larger perceived weight at birth [(AOR = 0.023, 95% CI: (0.002, -0.271))], poor dietary diversity, [(AOR = 9.20, 95% CI; 3.40, -19.83) and initiation of complementary feeding before six month [(AOR = 6.21, 95% CI;(1.44,-26.76))] were significantly associated with SAM.

Family size was found to be significantly associated with SAM. Children from households with large family size > 5 were 3.89 times more likely to be affected by SAM [(AOR = 3.89, 95% CI; (1.19, -12.70))] as compared to children from households with smaller family size (≤ 5).

Perceived birth weight was found to be significantly associated with SAM. The odds of having SAM was 95% lower if the child had average perceived weight at birth [(AOR = 0.048, 95% CI; 0.015, -0.148)]. Similarly, the odds of having SAM was 98% lower if the child had a large perceived weight at birth [(AOR = 0.023, 95% CI;(0.002, -0.271)].

Dietary diversity was found to be significantly associated with the risk of SAM. Children who had poor dietary diversity (< 5 food groups) [(AOR = 9.20, 95% CI; (3.40, -19.83))] were 9.20 times more likely to be acutely malnourished as compared to children with good dietary diversity (≥ 5 food groups).

Time for the introduction of complementary feeding was significantly associated with the risk of SAM. The introduction of complementary feeding before six month [(AOR = 6.21, 95% CI: (1.44, -26.76))] increases the risk of SAM by 6.21 times as compared to complementary feeding started at six months (Table 5 ).

This case control study assessed determinants of SAM among 6–23 months children at Bahir Dar city public hospitals. In this study, the determinants identified for SAM were family size, perceived birth weight of the child at birth, introduction of complementary feeding and dietary diversity score.

Family size was found to be a significant predictor of SAM. Those children from households with large family size were found to be at significant risk of being acutely malnourished as compared to children from households with smaller family size. This finding is comparable with studies conducted in south omo [ 23 ], Karat [ 24 ]; which revealed that children who live in family size greater than five were more likely to develop acute malnutrition as compared with family size of less than three This is also in line with a study conducted in Ahmedabad India, children who live in family size greater than or equal to six were more likely to develop acute malnutrition than their counterpart [ 25 ]. This is due to the fact that as the family member increases the quality and time of care given to the child decreases which leads to SAM. Since large family members increase the burden of the scarce household resources to provide nutritious food to all family members and there is more competition for available food [ 26 ]. But, this result is contradicting to a study conducted in Patna India, which revealed that large household size is a protective factors against malnutrition in children [ 27 ].This is due to socio-economic and cultural difference, in our setup when families are large and their resources are limited, the available food is shared by all members, reducing the amount individuals get and care given to the children decreased [ 26 ].

Perceived birth weight of average and large were protective factors of SAM. Accordingly, children with average perceived weight at birth were 95% and children with large perceived weight at birth were 98% less likely to be malnourished as compared to children perceived as small at birth. This is consistent with a study conducted in Myanmar further analysis of 2015–16 DHS, which revealed the risks of undernutrition were higher among children perceived to have low birth size compared with children of average and above perceived birth size [ 19 ]. Another study in Malaysia also showed that households with low birth weight were at higher odds of having malnourished children as compared to average and above birth weight [ 28 ]. This might be due to maternal malnutrition during pregnancy which results in birth weight less than the average and child with malnutrition. In addition children born of well-nourished mothers are less likely to be wasted due to mother’s adequate intake of nutrients such as protein, energy, vitamin and minerals during pregnancy; such nutrients are important for the fetus to obtain average and large weight at birth [ 29 ].In other word nutritional status before and during pregnancy influences maternal and child outcomes. Optimal child development requires adequate nutrient intake, provision of supplements and prevention of disease. Maternal malnutrition leads to poor fetal growth and low birth weight [ 30 ].

Dietary diversity was found to be a significant factor of SAM. In this study children who had poor dietary diversity scores (< 5 food groups) were more likely to be acutely malnourished as compared with children with good dietary diversity scores (≥ 5 food groups). Similar findings were also documented in other studies done in Dabat [ 31 ] and Ghana [ 32 ]. This is due to the fact that poor dietary diversity is an indicator of poor quality of diet and nutrient intake of children and it negatively affects the nutritional status of children. During early life, the growth and development of the body are dependent on adequate supply of all essential nutrients. Providing nutrient-rich foods in sufficient quantity and quality starting from six months of age is one strategy to reduce child malnutrition. Providing good dietary diversity is also important to develop the immune system and prevent infections. So that poor dietary diversity may expose the child to infection due to low immunity, which may lead to severe acute malnutrition [ 24 ].

Time for the introduction of complementary feeding was found to be an important determinant of SAM. In this study the introduction of complementary feeding before six month increases the risk of acute malnutrition as compared to complementary feeding started at six months. This finding is also supported by other studies which are done in India [ 25 ], and Nepal [ 33 ]; initiation of complementary feeding before or after 6 months was found to be at risk of SAM. This is due to the early introduction of complementary food and is associated with an increased risk of gastro-intestinal and other infections. When complementary foods are started before six months, there is a reduction in breast milk consumption, which can lead to a reduction of immunity. When there is low immunity it can lead to infection and finally it results in SAM [ 26 ].

This study has the following strengths. Data were collected through face to face interviews which could be able to reduce information bias. To minimize recall bias the recalling period was made shorter for some variables. Efforts were made to choose the controls as randomly as possible. The study has the following limitations; since the questions relied on the memory of the mothers/ caretakers, this might introduce recall bias. There might also be selection bias because controls were selected from health facilities. Matching has a potential benefit in preventing confounding so this study could have limitations on addressing it. SAM tends to be seasonal, but we didn’t account for seasonal variation, this study has limitations on addressing seasonal variation.

This study shows that, among children admitted in pediatrics units of the two hospitals, children who have family size of greater than five, birth weight perceived to be small at birth, complementary feeding started before six months, and dietary diversity score less than five were independent determinants of SAM among children 6–23 months. Therefore, Professionals working in child health service should provide simple and easy to understand information to the mother/caretaker on child caring practice and nutritional information including timely initiation of complementary feeding and appropriate diet diversity. Make family planning methods and information available for mother’s to manage family size. Interventions should be given during ANC follow up to improve maternal nutrient intake include supplementation with iron, folic acid or multiple micronutrients and provision of food and other supplements where necessary to prevent a child’s low birth weight. Future researches on child SAM are recommended to conduct community based longitudinal study integrating with qualitative study design on prospective dietary assessment.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

Adjusted Odds Ratio

Confidence Interval

Crude Odds Ratio

Dietary Diversity Score

Exclusive Breast Feeding

Felege Hiwot Comprehensive Specialized Hospital

Infant and Young Child Feeding

Minimum Dietary Diversity

Mid Upper Arm Circumference

Severe Acute Malnutrition

Standard Deviation

Statistical Package for Social Science

Stepwise Surveillance for child malnutrition

Tibebe Ghion Specialized teaching hospital

United Nations International Children’s Emergency Fund

World Health Organization

Weight for Length Z-score

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Acknowledgements

First of all, our special thanks & deepest gratitude go to Bahir Dar University for financial support to undertake this research work. We would also like to extend our heartfelt thanks to the study participants, data collectors and supervisor who participated in the study. We are also thankful for administrators of the hospitals, and head nurses of the pediatrics unit in the respective Hospitals.

The study was funded by Bahir Dar University. The views presented in the article are of the author and do not necessarily express the views of the funding organization. Bahir Dar University was not involved in the design of the study, data collection, analysis and interpretation.

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Conceptualization: Tigist Gebremaryam Bihonegn. Data curation: Tigist Gebremaryam Bihonegn (lead author).Formal analysis: Tigist Gebremaryam Bihonegn, Desalegne Amare Zelellw, Tilksew Ayalew Abtie. Methods: Tigist Gebremaryam Bihonegn, Desalegne Amare Zelellw, Tilksew Ayalew Abtie, Agimasie Tigabu Demelash and Tiruye Menshaw Tiruneh. Software: Tigist Gebremaryam Bihonegn., Agimasie Tigabu Demelash and Tiruye Menshaw Tiruneh. Supervision: Desalegne Amare Zelellw, Tilksew Ayalew Abtie. Writing – original draft: Tigist Gebremaryam Bihonegn, Desalegne Amare Zelellw, Tilksew Ayalew Abtie. Writing – review & editing: Tigist Gebremaryam Bihonegn, Desalegne Amare Zelellw, Tilksew Ayalew Abtie, Agimasie Tigabu Demelash and Tiruye Menshaw Tiruneh. All authors read and approved the final manuscript.

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Ethical clearance was obtained from Bahir Dar University, college of Health science ethical review board (Ref No. 0031/2020). The responsible bodies at pediatrics unit were told about the purpose of the study and written informed consent was obtained from participants to confirm willingness. Informed consent was obtained, and the participants were aware of the study purpose, risks, and benefits. They were notified that they have the right to refuse or terminate at any point of the interview. Confidentiality of the information was secured throughout the study process. The study did not involve any invasive procedures and reporting of any response for intervention. The study posed a low or no risk to the study participants. Accordingly, all eligible mothers were informed about the purpose of the study, and an interview was held only with those who agreed to give informed consent to participate. Informed consent was obtained from the parents/caregivers of the children before the interview. Informed consent for illiterate mothers and their children were obtained from their legal representatives. The right of a participant to withdraw from the study at any time, without any precondition was disclosed. Moreover, the confidentiality of information was guaranteed by using code numbers rather than personal identifiers. All methods were performed based on the relevant guidelines.

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Gebremaryam, T., Amare, D., Ayalew, T. et al. Determinants of severe acute malnutrition among children aged 6—23 months in bahir dar city public hospitals, Northwest Ethiopia, 2020: a case control study. BMC Pediatr 22 , 296 (2022). https://doi.org/10.1186/s12887-022-03327-w

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Received : 24 October 2020

Accepted : 28 April 2022

Published : 20 May 2022

DOI : https://doi.org/10.1186/s12887-022-03327-w

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