Journal of Modern Mathematics and Statistics
ISSN: OnlineISSN: Print 1994-5388
Abstract
Globally, infant mortality is used as an important indicator for healthcare status hence an important tool for evaluation and planning of public health strategies. Despite of numerous interventions by governments aimed at reducing infant mortality, high rates are still reported in Kenya. A lot of resources are channeled towards its control leading to low productivity hence impacting the household economic welfare and national gross domestic product. The specific objective was to establish risk factors and the spatial variation of infant mortality in Kenya by analyzing the 2014 Kenya Demographic Health Survey data. Logistic regression model was used to determine infant mortality risk in Kenya. Demographic, socioeconomic and environmental factors were found to have significant effect on infant mortality. Maternal age (OR 0.53, 95% CI 0.29-0.66), wealth index (OR 1.50, 95% CI 0.36-0.67), marital status (OR 0.61, 95% CI 0.51-0.73), maternal education level (OR 0.88, 95% CI 0.82-0.93), occupation (OR 0.81, 95% CI 0.75-0.88), region (OR 1.38, 95% CI 1.23-1.55), source of drinking water (OR 0.90, 95% CI 0.84-0.97), the type of toilet facility (OR 1.78, 95% CI 0.30-1.29) and religion (OR 0.78, 95% CI 0.72-0.83) were found to significant co-variates of infant mortality. Infant mortality is high in arid and semi-arid areas and coastal areas due to high prevalence of infectious diseases and inadequate water supply, health facilities and low education levels.
How to cite this article:
Erick Cheruiyot Kirui, Elphas Luchemo and Ayubu Anapapa. Modeling Infant Mortality Risk Factors using Logistic Regression Model and Spatial Analysis
in Kenya.
DOI: https://doi.org/10.36478/jmmstat.2021.16.24
URL: https://www.makhillpublications.co/view-article/1994-5388/jmmstat.2021.16.24