Anemia and associated factors among 6 to 59 months age children attending health facilities in Kombolcha town, Northeast Ethiopia: a facility-based cross-sectional study

The application of machine learning approaches to determine the predictors of anemia among under five children in Ethiopia

Health professionals need a strong prediction system to reach appropriate disease diagnosis, particularly for under-five child with health problems like anemia. Diagnosis and treatment delay can potentially lead to devastating disease complications resulting in childhood mortality. However, the application of machine learning techniques using a large data set provides scientifically sounded information to solve such palpable critical health and health-related problems. Therefore, this study aimed to determine the predictors of anemia among under-5 year's age children in Ethiopia using a machine learning approach. A cross-sectional study design was done using the Ethiopian Demographic and Health Survey 2016 data set. A two-stage stratified cluster sampling technique was employed to select the samples. The data analysis was conducted using Statistical Package for Social Sciences/SPSS version 25 and R-software. Data were derived from Ethiopian Demographic and Health Survey. Boruta algorism was applied to select the features and determine the predictors of anemia among under-5 years-old children in Ethiopia. The machine learning algorism showed that number of children, distance to health facilities, health insurance coverage, youngest child's stool disposal, residence, mothers' wealth index, type of cooking fuel, number of family members, mothers' educational status and receiving rotavirus vaccine were the top ten important predictors for anemia among under-five children. Machine-learning algorithm was applied to determine the predictors of anemia among under- 5 year's age children in Ethiopia. We have identified the determinant factors by conducting a feature importance analysis with the Boruta algorithm. The most significant predictors were number of children, distance to health facility, health insurance coverage, youngest child's stool disposal, residence, mothers' wealth index, and type of cooking fuel. Machine learning model plays a paramount role for policy and intervention strategies related to anemia prevention and control among under-five children.

Prevalence and factors related to anaemia in children aged 6-59 months attending a quaternary health facility in Maputo, Mozambique

Globally, anaemia prevails as a public health issue, being also a concern in Mozambique, where about two-thirds of children 6-59 months of age are affected by this condition. We carried out this study to estimate anaemia prevalence and evaluate structural determinants and haematological parameters association among children aged 6-59 months attending pediatric inpatient and outpatient services in a Quaternary Health Facility in Maputo City Province, Mozambique. We collected data from 637 inpatients or outpatients who attended pediatric consultations at the Maputo Central Hospital. The overall rate of anaemia in children aged 6-59 months was 62.2% (396/637), with 30.9% moderate anaemia (197/637), 23.9% mild anaemia (152/637), and 7.4% severe anaemia (47/637). Among our study participants, critical factors for anaemia were those concerning the age group, child´s caregiver schooling, malaria and size of the liver.