Menu
Menu
Menu
Menu
Anaemia in children remains a significant public health threat. Recent numbers from Ethiopia showed that more than two-thirds of children under the age of 2 years were anaemic. This study aimed to investigate the determinants of anaemia throughout Ethiopia over 11 years, making use of the Ethiopian Demographic and Health Survey (EDHS) rounds 2005, 2011 and 2016. The EDHS made it possible to use data on blood tests and detailed questionnaires among infants and young children. Multivariable logistic regression was applied to assess the association of anaemia and different immediate and underlying determinants. A total of 7,324 children aged 6–23 months were included in the analysis, with prevalences of anaemia being 71% in 2005, 61% in 2011 and 72% in 2016. The following determinants were significantly associated with childhood anaemia throughout the entire period: children younger than 1 year, anaemic mothers and those growing up in pastoralist regions. Risk factors such as diet and infections were consistently not significantly associated with anaemia. Given the tremendous adverse health effects of anaemia in young children, urgent action is needed. Hence, this study recommends nationwide multisectoral interventions targeting pastoralist regions, maternal and child health, screening and treatment of risk groups that could reduce the prevalence of anaemia.
National‐based data on the prevalence of anaemia in children were used from the EDHS rounds 2005, 2011 and 2016. The surveys covered all administrative areas in Ethiopia, using a stratified, two‐stage cluster sampling design (CSA & ICF, 2012, 2017; CSA & ORC Macro, 2006). Across the three surveys, a total of 50,470 households were selected, from which 93% were successfully interviewed. This resulted in a total of 22,568 children being tested for anaemia. This study was restricted to children between 6 and 23 months. Consequently, we used the data of 1,290 children in 2005, 2,970 in 2011 and 3,064 in the year 2016. Further information on the methodology can be found elsewhere (CSA and ICF, 2012, 2017; CSA and ORC Macro, 2006). Appendix SA1 displays the variables included at baseline, and Appendix SB1 visually presents the relation of childhood anaemia determinants in the form of a conceptual framework. The determinants have been chosen based on literature search and available data in the EDHSs and were organized in line with the established United States Agency for International Development (USAID) conceptual framework for anaemia (CSA and ICF, 2012, 2017; CSAand ORC Macro, 2006; USAID, 2013). The variables were coded as follows: Blood samples were drawn from a drop of blood taken from the palm side of the end of a finger, and in the case of infants younger than 12 months, blood was taken from the heel prick. Anaemia status was defined as mild, moderate and severe anaemia. For this study’s purpose, this was recoded into a dichotomous variable, having anaemia or not. Any blood haemoglobin count below 11 g/dl was considered anaemic. Participants were asked whether their child was infected with any of the three most common childhood illnesses 2 weeks preceding the survey: fever, diarrhoea and signs of acute respiratory infection. Nutritional deficiencies were identified by asking mothers about the food the children ate the day preceding the survey: flesh foods, including meat, fish, poultry and liver/organ meats; Vitamin A‐rich fruits and vegetables; legumes; milk and dairy. A minimum dietary diversity variable was created to identify whether the child ate at least four out of seven food groups as recommended and defined by the WHO (WHO, 2009). Lastly, mothers were asked whether they were breastfeeding or not. The two variables of latrine facility and drinking water sources were classified into improved or unimproved based on the definitions by the WHO/United Nations Children’s Fund (UNICEF) Joint Monitoring Program (JMP) for Water Supply and Sanitation (WHO, 2017). Slight changes in classifications across the different surveys were taken into account. We made a distinction between one or two births and more than two births, as increased intervals between pregnancies are associated with an increase in haemoglobin level and a decrease in adverse health outcomes (Afeworki, Smits, Tolboom, & van der Ven, 2015; Conde‐Agudelo, Rosas‐Bermudez, Castano, & Norton, 2012). Additionally, the women were asked whether they took the recommended minimum of 90 iron tablets or syrup during the pregnancy of their last born child and whether they received a minimum of four antenatal care visits according to the WHO (Croft, Marshall, & Allen, 2018). Other determinants were identified with the questions whether the child received vitamin A supplements or deworming in the 6 months preceding the interview. Lastly, we checked whether the national routine immunization had been completed in all children at the intended time. The routine immunization schedule in Ethiopia comprises six vaccine‐preventable diseases, measles, diphtheria, pertussis, tetanus and tuberculosis (Federal Ministry of Health, 2015). The nutritional status among children was assessed by applying three indices: height‐for‐age, weight‐for‐height and weight‐for‐age, each providing different information on growth and body composition (CSA & ICF, 2017). To summarize essential attributes of the children and mothers, we included some baseline characteristics: gender and age of the children, anaemia status and age of the mothers and the educational and geographical background of the mothers and their partners. The regions were distributed as follows: Afar, Somali, Gambela and Benishangul‐Gumuz as pastoralist regions (also referred to as pastoralist and emerging regions in Ethiopian context), the regions of Tigray, Amhara, Oromiya and the Southern Nations, Nationalities and People’s Region (SNNPR) represent the agrarian region, and the three cities Harari, Dire Dawa and Addis Ababa were combined (Federal Ministry of Health Ethiopia, n.d.). Also, the wealth index was added as an appropriate measure of a household’s cumulative living standard. This index consists of data on several selected household assets such as water access and sanitation facilities, televisions and bicycles. The DHS distributes the population into five wealth quintiles ranging from the poorest to the wealthiest (CSA & ICF, 2017). The statistical analysis of the data was performed using STATA 15. To ensure a representative sample, we applied complex sample design weightings to all analyses (Croft et al., 2018). Descriptive statistics were used to analyse baseline characteristics to provide an overall picture of the sample. Pearson’s correlation was run to determine the relationship between all predictor variables. Positive correlations were found, but were adjusted for in the following analyses by using the robust estimator (Do Cameron & Miller, 2015). An exploratory cross‐tabulation of anaemia prevalence and its determinants was performed to guide further analysis. We used multivariable logistic regression models to investigate the relationship between anaemia and the selected predictors per year. Independent variables were deleted by using the ‘backward’ elimination principle. For all independent variables, the nonrisk factor was considered the reference category. A significance level of 0.05 was chosen for all analyses. The socioeconomic gradient was investigated using the wealth index and corresponding wealth quintiles. In this study, the Erreygers concentration index (CI) using wealth as a rank variable was computed for each year (Ambel et al., 2017). The CI is a value bounding between −1 and +1. A negative index indicates that poorer households disproportionately bear the burden of anaemia, whereas a positive value indicates that wealthier households are more affected (Bilger, Kruger, & Finkelstein, 2017; Cai, Coyte, & Zhao, 2017). The study received ethical approval by the Health, Ethics & Society of the Faculty of Health, Medicine and Life Sciences at Maastricht University. Ethical clearances for the surveys were provided by the EHNRI Review Board, the National Research Ethics Review Committee, the ORC Macro Institutional Review Board in Calverton, USA, the Institutional Review Board of ICF International and the United States Centers for Disease Control and Prevention.
