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Background: The magnitude of childhood anemia was increased from time to time. Thus, Even if the Ethiopian government applied tremendous efforts, anemia in children continues as a major public health problem. There is limited evidence on the spatial variation of and determinant factors of childhood anemia at the national level. Therefore, this study aimed to explore spatial distribution and determinants of anemia among children aged 6 to 59 months in Ethiopia. Method: A stratified two-stage cluster sampling technique was used in Ethiopian Demographic Health Survey 2016 data. In this study 8602 children aged 6–59 months were included. Bernoulli model was used to explore the presence of purely spatial clusters of Anemia in children in age 6–59 months using Sat scan. ArcGIS version 10.3 was used to know the distribution of anemia cases across the country. A mixed-effects Logistic regression model was used to identify determinant factors of anemia. Results: The finding indicates that the spatial distribution of childhood anemia was non-random in the country with Moran’s I: 0.65, p < 0.001. The SaT scan analysis identified a total of 180 significant primary clusters located in the Somali and Afar regions (LLR = 14.47, P-value< 0.001, RR = 1.47). Age of child 12–23 months (AOR = 0, 68, 95%CI: 0.55, 0.85), 24–35 months (AOR = 0.38, 95%CI: 0.31, 0.47), and36–47 months (AOR = 0.25, 95%CI, 0.20, 0.31), working mother (AOR = 0.87, 95%CI: 0.76, 0.99), anemic mother (AOR = 1.53, 95%CI, 1.35, 1.73), had fever in the last 2 weeks (AOR = 1.36,95%CI:1.13, 1.65), moderate stunting (AOR = 1.31,95%CI: 1.13, 1.50),Severely stunting (AOR = 1.82,95%CI: 1.54, 2.16), religion, wealth index, and number of under-five children in the household were statistically significant associated with childhood anemia. Conclusion: Spatial variation of childhood anemia across the country was non-random. Age of the child, wealth index, stunting, religion, number of under-five children in the household, fever in the last 2 weeks, anemic mother, and working status of the mother were determinants of childhood anemia. Therefore, interventions should be a priority concern for high-risk (hot spot) areas regarding allocation of resources and improved access to health facilities, and to reduce the consequence of anemia among the generation policymakers and concerned bodies should be implemented these specific determinant factors.
The Ethiopian Demographic and Health Survey (EDHS) is a community-based cross-sectional study conducted from 18 January to 27 June 2016. The study was conducted in Ethiopia (3o-14o N and 33o – 48°E), situated at the eastern horn of Africa (Fig. 2). The country covers 1.1 million square kilometers and has a great geographical diversity, which ranges 4550 m above sea level down to the Afar depression to 110 m below sea level [26]. There are nine regional states and two city administrations subdivided into 68 zones, 817 districts, and 16,253 kebeles (lowest local administrative units of the country in the administrative structure of the country) [7]. The source of the data for this study was the Ethiopian Demographic and Health Survey (EDHS) 2016 (N = 7794) and used to assess the spatial variation and determinants of childhood anemia among children age 6–59 months in Ethiopia. Map of Study Area EDHS used a two-stage cluster sampling technique. Since Ethiopia has 9 regional states and 2 administrative cities. Administratively, regions in Ethiopia are divided into zones, and zones, into administrative units called woreda. Each woreda is further subdivided into the lowest administrative unit, called kebeles. During the 2007 census, each kebele was subdivided into census enumeration areas (EA), which were convenient for the implementation of the census [7]. A stratified two-stage cluster sampling procedure was employed where EA is the sampling unit for the first stage and households for the second stage. In 2016 EDHS, a total of 645 EAs (202 in urban areas and 443 in rural areas) were selected with probability proportional to EA size (based on the 2007 housing and population census) and with independent selection in each sampling stratum. Of this 18,008 households were included. A total of 8602 children were interviewed. But in the present study, a total of 7794 children the age of 6–59 months were included in the analysis. The source population was all births from reproductive-age women within 5 years before the survey in Ethiopia and all births from reproductive-age women in the selected enumeration areas within 5 years before the survey were the study population. Birth’s from reproductive age women within 5 years before the survey within enumeration areas with missed global positioning system (GPS) cells were excluded for spatial analysis. The outcome variable for this study was anemia, which was dichotomized as anemic and not anemic. Individuals are considered to be not anemic was defined as Adjusted concentration of blood hemoglobin greater than or equal to 11 mg/dl and those individuals with less than 11 mg/dl were anemic [1]. Anemia status was determined based on hemoglobin concentration in blood adjusted to the altitude. The independent variables were classified as: socio-demographic factors, nutritional factors, clinical factors, and service-related factors. The socio-demographic factors were the sex of a child, age of child, residence, educational status of the mother, maternal age, husband’s educational status, the religion of mother, wealth index, working status of the mother, and a number of children in the household. The nutritional factors were stunting status of a child, wasting status of child, and size of child at birth. The clinical factors were also maternal anemic status, diarrhea in a child in the last 2 weeks, fever in a child in the last 2 weeks, and cough in a child in the last 2 weeks and the service-related factors were taking of vitamin A in the last 6 months, taking of iron pills or sprinkles or syrup and taking of drugs for intestinal parasites in the last 6 months. Descriptive and summary statistics were done using STATA version 14 after extraction and edition of data from EDHS 2016 child data set. Since EDHS data had hierarchical and clustering nature, the assumption of independence among observations was violated. This implies a need to consider the between-cluster variability by using advanced models. The goodness of fit test was checked using Intraclass correlation (ICC) and deviance [27]. So logistic regression (non-anemic child = 0, anemic child = 1), and GLMM (generalized linear mixed model) were fitted. Then the GLMM was selected based on the result of Akaikie Information Criteria (AIC) and Bayesian information criteria (BIC). The model with the smallest AIC value was chosen. Variables having a p-value up to 0.2 in the bi-variable analysis were selected to fit the model in the multi-variable analysis. Finally, a p-value less than 0.05 in the multivariable model of mixed-effects logistic regression was used to select variables that had a statistically significant association with anemia. ArcGIS version 10.3 was used for Moran’s I analysis. The Global Moran’s I spatial statistic measures were used to measure spatial autocorrelation by taking the total data set and producing a single output value that ranges from − 1 to+ 1. Global Moran’s I value closes to − 1 which indicates dispersed childhood anemia, whereas Moran’s I value closest to + 1 indicted clustered childhood anemia and the Moran’s I value is 0 which indicates randomly distributed childhood anemia. Moran’s I (P-value < 0.05) indicates the presence of spatial autocorrelation. Getis-OrdGi* statistics were computed to measure how spatial autocorrelation varies over the study location by calculating GI* statistics for each area. Z-score is computed to determine the statistical significance of clustering, and the p-value is computed for the significance [28]. Statistical output with high GI* indicates “hotspot” whereas low GI* means a “cold spot [29–31]. It is very difficult and expensive in terms of resources and time to collect reliable data in all areas of the country to know the burden of a certain event. Therefore, part of a certain area can be predicted by using observed data using a method called interpolation. The spatial interpolation technique is used to predict childhood anemia in the un-sampled areas in the country based on sampled EAs. There are different deterministic and geostatistical interpolation methods. Among those methods, ordinary Kriging and empirical Bayesian Kriging are considered the best method since it incorporates the spatial autocorrelation and it statistically optimizes the weight [32]. The ordinary Kriging spatial interpolation method was used for this study for predictions of childhood anemia in unobserved areas of Ethiopia. Spatial scan statistical analysis was employed to test for the presence of statistically significant spatial hotspots/clusters of childhood anemia using Kuldorff’s SaT Scan version 9.6 software. The spatial scan statistic uses a scanning window that moves across the study area. Children with anemia were taken as cases and without it as controls to fit the Bernoulli model. The numbers of cases in each location had Bernoulli distribution and the model required data for cases, controls, and geographic coordinates. The default maximum spatial cluster size of < 50% of the population was used, as an upper limit, which allowed both small and large clusters to be detected and ignored clusters that contained more than the maximum limit. For each potential cluster, a likelihood ratio test statistic and p-value were used to determine if the number of observed childhood anemia within the potential cluster was significantly higher than expected or not. The primary and secondary clusters are identified and assigned p-values and ranked based on their likelihood ratio test, based on 999 Monte Carlo replications.
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