Journal of Health, Medicine and Nursing www.iiste.org ISSN 2422-8419 An International Peer-reviewed Journal Vol.42, 2017 151 Why are Neonates Dying? Socioeconomic and Proximate Determinants of Neonatal Mortality among Stable Low-Birth- Weight (LBW) Infants in Kenya Kennedy J. Muthoka* PhD student: College of Health Sciences, JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY (JKUAT), Nairobi, Kenya Prof. Simon Karanja School of Public Health, JKUAT, Nairobi, Kenya Dr. Drusilla Makworo School of Nursing, JKUAT, Nairobi, Kenya Dr. Yeri Kombe Kenya Medical Research Institute (KEMRI), Nairobi, Kenya The research is financed by the corresponding author as part of his PhD studies in JKUAT Abstract Background: Neonatal mortality rates are very high in Kenya, like the rest of Sub-Saharan Africa. The sustainable development goals aim to reduce the current 21 neonatal deaths per 1,000 live births to below 12 deaths per 1,000 live births. The rate of decline in Neonatal mortality in many countries is very slow compared to other childhood mortality rates. The objective of this study was to determine the socioeconomic and proximate determinants of neonatal mortality in Kenya. Methodology: A cohort study was carried out at Pumwani Maternity hospital, Thika Level 5 hospital and Machakos Level 5 hospital in Kenya with a sample of 343 stable LBW infants (≤2000g). Informed by the concepts of the Mosley and Chen (1984) analytical framework, several socioeconomic and proximate characteristics were included in the study. Cross tabulations and multiple logistic regression analyses were done to determine the relationships between the determinants and neonatal mortality. Results: The mean birth weight was 1492.6 g (SD=275.3) and mean gestational age was 30.3 weeks. Most infants (59.8%, N=343) were female. Incidence of neonatal mortality was 8.5% (n=340). Household income, birth complications, birth weight, gestational age and multiple births were significant determinants of neonatal mortality among the LBW infants weighing ≤2000 grams. Conclusion and recommendations: The findings affirm the Mosley and Chen (1984) analytical framework on determinants of neonatal survival. The study provides useful information on determinants of neonatal mortality that is relevant to the Kenyan context and applicable to other low income countries. Keywords: neonatal survival; neonatal mortality; socioeconomic determinants; proximate determinants; low- birth-weight infants 1. INTRODUCTION The neonatal period is only the first 28 days of life and yet is the most vulnerable time for a child’s survival [1,2,3,4]. Goal 3 of the United Nations Sustainable Development Goals call for an end to preventable deaths of newborns and children by 2030. All countries should aim at reducing neonatal mortality to below 12 per 1,000 live births [5]. Neonatal mortality has been declining globally, falling from 33 deaths per 1,000 live births in 1990 to 21 deaths per 1,000 live births in 2012. However, this represents a slow decline of 37 percent compared to the 47 percent in the under-five mortality rate. Pre-term birth has been shown as the largest direct cause of neonatal mortality [6]. Low-birth-weight (less than 2500 g) has a causal relationship with neonatal mortality. Globally, LBW contributes to 60% - 80% of all neonatal deaths [3,4,7,8,9]. In Kenya, all childhood mortality rates have declined between 2003 and 2014. Neonatal mortality however has shown the slowest decline rate of only 33 percent. The neonatal mortality was 22 deaths per 1,000 live births between 2009 and 2014 [10,11]. This was 1.4 times higher than the post neonatal rate. The neonatal mortality rate has distribution disparities with neonatal mortality being 24 percent higher in urban areas than in rural areas. Nairobi, the capital city of Kenya, has the highest neonatal mortality (39 deaths per 1,000 live births). Data from Kenya show that wealthier families experience highest neonatal mortality rates compared to poorer families [10,11]. Mosley and Chen (1984) [12] developed an analytical framework for analyzing determinants of child survival in developing countries. According to the model, impact on mortality is influenced by socioeconomic determinants (independent variables) that operate through a certain set of proximate determinants (intermediate