TY  - JOUR
T1  - Modeling Infant Mortality Risk Factors using Logistic Regression Model and Spatial Analysis
in Kenya
AU - Cheruiyot Kirui, Erick AU - Luchemo, Elphas AU - Anapapa, Ayubu 
JO  - Journal of Modern Mathematics and Statistics
VL  - 15
IS  - 2
SP  - 16
EP  - 24
PY  - 2021
DA  - 2001/08/19
SN  - 1994-5388
DO  - jmmstat.2021.16.24
UR  - https://makhillpublications.co/view-article.php?doi=jmmstat.2021.16.24
KW  - Mortality
KW  -logistic regression
KW  -spatial analysis
KW  -odds ratio
KW  -Kenya
AB  - 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.
ER  -