Browsing by Author "Nyanjara, Sarah"

Now showing 1 - 2 of 2

A data analytic module for nutrition screening of children under five years in Tanzania
(NM-AIST, 2016-04) Nyanjara, Sarah
Malnutrition is one of the major health problems in the world, and more prevalent in developing countries. The World Health Organisation (WHO) estimates that malnutrition is associated to half of all deaths of children under five years. In Tanzania it is estimated that 43,000 children die and the government loses up to Tsh. 700 billion annually due to the effects associated with malnutrition. Due to its unbearable consequences several international organisation have initiated collective measures to combat malnutrition. Nutrition screening, the process of identifying whether the child is malnourished or not, is a proven and important step towards malnutrition eradication. Regular and effective nutrition screening allows early malnutrition or risk of malnutrition to be identified and proper treatment and malnutrition management to be effected. The whole process of nutrition screening and malnutrition management in Tanzania still requires a lot of improvements. The studies show that the nutrition screening is not effective and it is mostly manual. The process includes the use of papers (clinic card) in recording anthropometrics measurement and that Mid Upper Arm Circumference (MUAC) is the only tool used for nutrition screening despite the already identified shortcomings. In this study, a nutrition data analytic module for nutrition screening for children under five years is proposed and developed. The system will have the ability to store child records after the child is registered, perform nutrition screening to identify if the child is well nourished or malnourished, and establish the current state of malnourished child in order to allow appropriate action to be taken by a health worker. Additionally the progress of malnourished child will be established to allow proper malnutrition management. The module has the ability to create reports which will help researchers, stakeholders and other users to get nutrition related data and information and malnutrition trends. This study puts in place a nutritional data analytic module for nutrition screening. This will facilitate effective nutrition screening of children and hence improve children nutrition health.
Integrated machine learning based quality measurement model for maternal, neonatal and child health services in Tanzania
(NM-AIST, 2022-08) Nyanjara, Sarah
The high maternal and neonatal mortality rate has remained a challenge for most developing countries. Scholars link the high death occurrences to the poor quality of health services provided to pregnant women and children. It is further revealed that most deaths could be prevented if women and children could access high-quality maternal, neonatal and child health services. Quality measurement, a process of using data to evaluate healthcare plans and performance, is essential in improving the quality of health services and reducing mortality rates. However, most developing countries and Tanzania lack effective approaches to measure and report the quality of Maternal, Neonatal and Child Health services provided. The Lack of an effective quality measurement approach limits the quality measurement processes and may jeopardize the quality measurement results. Additionally, failure to establish the quality of health services hampers healthcare plans and governance of healthcare supplies and other resources. The available quality measurement approaches require trained data collectors, dedicated datasets and the physical presence of quality measurement personnel at each health facility; therefore, labour intensive and resource inefficient. This study proposed and developed an integrated machine learning-based quality measurement model for maternal, neonatal and child health services in Tanzania. The study employed a machine learning technique, a K-means clustering algorithm, and a dataset selected from the national health information system and data warehouse: “District Health Information System (DHIS 2)”. The developed model clustered the Maternal, Neonatal and Child Health (MNCH) dataset into two groups (clusters), and cluster analysis was performed to discover the knowledge about the quality of health services in each cluster formed. The study also performed model validation to establish the usefulness of the developed integrated machine learning-based model for quality measurement in MNCH. This study brings to the body knowledge an integrated machine learning-based quality measurement model for maternal, neonatal and child health services and a list of important indicators for quality measurement, the essential inputs for an effective quality measurement process. The current quality measurement model requires only data to measure the quality of health services readily available in DHIS 2, making the quality measurement model resource-efficient and ideal for quality measurement in resource-constrained countries such as Tanzania.