Socio-Economic Determinants of Low Birth Weight in Kenya: An Application of Logistic Regression Model
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 6, November 2015, Pages: 438-445
Received: Sep. 10, 2015; Accepted: Sep. 21, 2015; Published: Oct. 12, 2015
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Edwine Benson Atitwa, Department of Computer and Statistics, Moi University, Eldoret, Kenya
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Babies born with Low-birth weight are at increased risk for serious health problems which are accompanied by disabilities and even death. The purpose of this study was to determine socio-economic factors that lead to low birth weight of children in Kenya. Data used was from Kdhs 2003 and the significant effect of socio-economic determinants on low birth weight was examined using logistic regression analysis data is categorical and continuous in nature, where predictor variables being socio-economic determinants and birth weight being dependent variable. Results indicate that out of six socio-economic factors involved in the study, four (Religion, Time Wanted Pregnancy, Marital Status and Economic Status) revealed some significant effects on the children with low birth weight. Therefore Socio-economic determinants have a significant effect on Low birth weight which suggests a strong negative associated with infant survival in Kenya independent of other risk factors. The logistic function revealed a statistically significant association between the birth weight, Religion, Time Wanted Pregnancy, Marital Status and Economic Status. Predicted probability is 11.4% low birth weight. Researcher recommends that respondents should avoid conceiving unexpectedly since it was associated with high low birth weight. Also to effectively enhance normal birth weight in Kenya, then expectant mothers should keenly focus on the socio-economic determinants by avoiding marital problems like divorce.
Socio-Economics, Birth Weight, Predictor Variables, Logistic Regression Model
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Edwine Benson Atitwa, Socio-Economic Determinants of Low Birth Weight in Kenya: An Application of Logistic Regression Model, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 6, 2015, pp. 438-445. doi: 10.11648/j.ajtas.20150406.14
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