Intra-demographic birth risk assessment scheme and infant mortality in Nigeria

Abstract

Background: Infant mortality (IM) is high in Nigeria. High-risk birth can limit a newborn’s survival chances to the first year of life. The approach used in investigating the relationship between high-risk birth and IM in this study is yet to be documented in Nigeria. Objectives: The Intra-Demographic Birth Risk Assessment Scheme (IDBRAS) was generated and its relationship with IM was examined. Methods: 2013 Nigeria demographic and health survey data were used. Mothers who gave birth in the 5 years before the survey were investigated (n = 31,155). IDBRAS was generated from information on maternal age at childbirth, parity and preceding birth interval and was disaggregated into low, medium and high. Data were analysed using the Cox proportional hazard and Brass 1-parameter models (α = 0.05). Results: Infant mortality rate was 88.4, 104.7 and 211.6 per 1000 live births among women with low, medium and high level of IDBRAS respectively. The rate of increase of reported infant deaths between low and high IDBRAS was 0.1932 (R 2 = 0.5326; p < 0.001). The prevalence of medium- and high-risk birth was 24.6 and 4.2% respectively. The identified predictors of IM were place of residence, marital status and size of the child at birth. The hazard ratio of IM was higher among women with medium (HR = 1.35; 95% CI = 1.22–1.48, p < 0.001) and high IDBRAS (HR = 1.73; 95% CI = 1.48–2.02, p < 0.001) than among those with low IDBRAS. Controlling for other correlates barely changed this pattern. Conclusions: The risk and level of IM increased as the level of IDBRAS increases in Nigeria. IDBRAS was an important predictor of IM. Maintaining a low level of IDBRAS will facilitate a reduction in IM rate in Nigeria.

Preventable Multiple High-Risk Birth Behaviour and Infant Survival in Nigeria

“…High parity also in uences the chances of child survival as most mothers with many children tend to belong to lower socioeconomic status, have low education status and live in less favourable lifestyle [22]. The ndings from previous studies on the association between each of the three factors used for the creation of HrBBs and infant survival was consistent with that of the combined HrBBs demonstrated in the current study [9,26].…”

“…Other covariates that were considered important according to literature and were included in the analyses include: household/environmental characteristics (regions, residence, ethnicity, religion, wealth quintile, type of cooking fuel, source of drinking water and type of toilet facility), child/mother characteristics (child's sex, birth size, mother's education, employment, marital status, desired for child), and health service-related characteristics (contraceptive use, decision-making involvement, number of ANC visits, tetanus injection received, breastfeeding initiation, health facility perception, place of delivery, prenatal attendant, type of delivery, and birth attendant) [26].…”

“…In a high family size situation, the harsh socioeconomic condition in Nigeria could limit an individual's ability to provide basic needs for the family, particularly for infants who are the most vulnerable to morbidity and mortality [7,13]. Moreover, some women exhibit more than one of these HrBBs at the same time which increases their risk of having children that are prone to death before the age of one year [11,25,26].…”

Spatio‐temporal patterns and determinants of infant mortality in Nigeria

“…They also identified birth size, birth interval and caesarian section delivery as key predictors of IMR in rural areas and poverty, birth size, male gender, birth interval, maternal obesity and caesarian section delivery as key predictors of IMRs in urban areas. Similar studies include those by Adebayo and Fahmeir (2004), Adebowale (), Adebowale and Udjo (), and Ahonsi ().…”

Spatio-temporal patterns of under 5 mortality in Nigeria

“…A significant number of studies have been carried out on under 5 mortality in Nigeria such as studies by Caldwell and McDonald,1982 ; Ojikutu, 2008 ; Antai, 2011 ; Bamgboye, Clement, Adejuwolo, & Duro, 2012 ; Kayode, Adekanmbi, & Uthman, 2012 ; Akinyemi, Bamgboye, & Ayeni, 2013 ; Izugbara, 2014 ; Abu, Madu, & Ajaero, 2015 ; Chuckwu and Okonkwo, 2015 ; Ezeh, Agho, Dibley, Hall, & Page, 2015 Bako, Maiwada, Abubakar, & Akwo, 2016 ; Adebowale (2017) ; Adewemimo et al (2017) among others. However, these studies are largely determinant studies focused on investigating differentials in and risk factors of under 5 mortality without statistically assessing spatial/geographical patterns of under 5 mortality while the few studies that have done so did not examine spatial clustering across states in Nigeria over time ( Adebayo et al, 2004 , Uthman et al, 2012 ).…”