Modeling the Risks Factors Associated with Infant Mortality in Rwanda from 2011 to 2015: Analysis of Rwanda Demographic and Health Survey (RDHS) 2014/2015

Research Article

J Pediatr & Child Health Care. 2019; 4(1): 1026.

Modeling the Risks Factors Associated with Infant Mortality in Rwanda from 2011 to 2015: Analysis of Rwanda Demographic and Health Survey (RDHS) 2014/2015

Biracyaza E* and Habimana S

¹Department of Community Health, University of Rwanda, Kigali-Rwanda

*Corresponding author: Emmanuel Biracyaza, Department of Community Health, School of Public Health, College of Medicine and Health Sciences, University of Rwanda, Kigali-Rwanda

Received: February 05, 2019; Accepted: March 20, 2019; Published: March 27, 2019


Objective: The study aimed at building a model of infant mortality and its associated risk factors in Rwanda from 2011 to 2015.

Method: Cross-sectional survey was conducted using data from 2014/2015 Rwanda Demographic and Health Survey. Target population was women aged 15-49 years from sampled households. All 492 of the clusters selected were surveyed for 2014/2015 RDHS. STATA version 13 was used to analyse the statistical data.

Results: Infants from rural areas were 1.54 times more likely to die than in urban areas. Infants born to women aged 14-19 and 20-29 had [OR=1.23(0.62- 2.42; OR=1.23(0.78-1.33)] respectively, were at high risk of death compared to other age groups. Males had 1.15 times higher risk of dying than females. Infants who were not breastfed were 1.98 times more likely to die compared to those who were breastfed. The women aged 30-34 had [OR=0.69(0.54-0.89)], 35-39 was [OR=0.43(0.4-0.46)], 40-44 was [OR=0.06(0.057-0.062)] and 45-49 [OR=0.22(0.21-0.23)]. IM was higher in males [OR=1.15(1.07-1.23)].

Conclusion: The factors associated with IM were grouped into community, ecological, socio-economic and proximate factors and identified that each group consists of multifactor that influence the infant mortality rate.

Keywords: Infant mortality; Modeling; Logistic regression; Proximate


Infant Mortality (IM) is the death of children before they reach one-year-old [1]. Globally, infant mortality is the public health concern [2]. Around 4.6 million deaths occur annually during infancy, 99% of which occurs are from Lower Middle Income Countries (LMICs) [3]. Infant mortality rate is considered to be an important indicator of socioeconomic welfare of nations. Preceding studies indicate that 10 million infants die each year in developing countries, an estimated 10-20% of all infants die before their first birthday, and black infants continue to die at twice the rate of white infants [4]. Infant mortality is regarded as highly sensitive indicators of population and child health [5]. In LMIC, a large number of births take place outside of health facilities, usually at home and unattended by formally trained doctors or midwives [6]. Preceding studies indicated that IM was hindered by epidemics of HIV/AIDS, malaria, war and conflicts in most of the nation [7].

Infant mortality is associated with community, socio-economic, maternal, infant factor, proximate and delivery factors that are the burdens of public health [8]. Past studies indicate that IM was caused by prematurity factors, congenital causes, injury, other infections, infant infections, maternal conditions, Sudden Infant Death Syndrome, lack of oxygen to the fetus and infant during delivery [9]. Maternal age was found to the factor of IM used as a proxy for physiological, maternal psychological maturity and experience in child-care. Young mothers below 20 years normally tend to have biological, emotional, social and economic problems that frustrate their entire childbearing process. Maternal age affects the survival of a child during the first year of life [10]. Young mothers are less experienced at childcare because they are socially and economically disadvantaged [11-13]. Health knowledge features prominently in the literature as a potential mechanism by which education is associated with higher use of health services [14]. IM was assumed to occur due to birth complications, congenital anomalies, physiological problems and gestational immaturity. Injuries and other factors related to environment, nutrition and infectious diseases account for post-neonatal mortality. Diseases including gastroenteritis and pneumonia are common causes of IM in underdeveloped nations [15]. In developed countries, these deaths are less likely to occur, given the ability to control infectious diseases and to monitor nutritional needs. There is the possibility of accidents, assaults or homicides as the cause of infant deaths [16]. The infant deaths was attributed to SIDS constituting of smoking, lack of breastfeeding and lack of safe sleeping environment [17,18].

Past studies indicated that the relationship between short afterbirth intervals and high infant and child mortality was established. Very long intervals (at least 5 years in length) and shorter birth interval are the predictors of IM [19]. They also showed that IM was associated with the post-neonatal where the variations in mortality by maternal age were larger. Marital status is an important proxy measure of factors traditionally related to post-neonatal mortality, such as socio-economic influence and other circumstances not reflected by education [11]. IM happens in different stage such as very-natal mortality, neonatal mortality, early post-neonatal mortality, late postneonatal mortality and the last stage is the infant mortality where the death happens before celebrating the first birthday [20].

Infant Mortality Rate (IMR) was 85% prior to the Genocide against the Tutsis, and increased dramatically in the aftermath of the tragic events of 1994, reaching a peak of 10.7% in 2000. Since that time it has fallen dramatically to 8.5% by 2005, 6.2% in 2008, and 5% in 2010. To achieve the Millennium Development Goal target of 2.8% reduction [21]. IM in Rwanda has fallen gradually from 12.3% in 1966 to 3.11% in 2015. Community Health Workers (CHWs) contribute to reduction of the financial, infrastructural, and geographical barriers to accessing health care by providing effective and efficient basic health care services at the community level to pregnant women. In 2012, IMR was 48.6% and was higher among boys (53%) than girls (44%). However, IMR has decreased a lot and more quickly during the last decade: from 139% in 2002 to 48.6‰ in 2012. IMR fell gradually from 123% in 1966 to 31.1% in 2015 [22]. Rwanda has made substantial progress nationwide in decreasing the infant mortality rate. This study aims at building the model of infant mortality and its associated risks factors in Rwanda from 2011-2015.

Methods and Materials

Study design

Cross-sectional survey was conducted on Rwandans using 2014/2015 Rwanda Demographic and Health Survey. Target population was women aged 15-49 years from sampled households.

All 492 of the clusters selected were surveyed for 2014/2015 RDHS. Among these households, 30,058 completed the Household Questionnaire including 6,069 (20%) from urban and 23,989 (80%) resided in rural. The composite sample analysis, which accounted for the sampling weight due to multistage stratified sampling, was used in the surveys to gain accurate estimations of standard errors and confidence intervals.

Statistical analysis

Analytical analyses were performed using STATA version 13. The logistic regression model was performed because of its appropriateness in analyzing the effect of several risk factors on a dichotomy variable and significant level used was 5% and 95% confidence interval. Coefficient of determination was used for evaluating the effectiveness of a regression model. The multiple logistic regression method and regression equation were computed. Chi-square test was computed to compare the associated risk factors and demonstrate the association between the factors and infant mortality.

As the present study was based on secondary analysis of existing RDHS data, the investigators of this study did not seek approval from an Institutional Review Board. RDHS consisted of data that are standardized and they may be used by anyone in the empirical studies. Explanatory factors were grouped into 3 categories based on the model including socio-economic, community and proximate determinants (Figure 1).

Citation: Biracyaza E and Habimana S. Modeling the Risks Factors Associated with Infant Mortality in Rwanda from 2011 to 2015: Analysis of Rwanda Demographic and Health Survey (RDHS) 2014/2015. J Pediatr & Child Health Care. 2019; 4(1): 1026.