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

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 (a = 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 (R2 = 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.

The study was conducted in Nigeria, Africa’s most populous country. Persistent high levels of fertility (TFR = 5.5), infant mortality (68 per 1000 live births) and maternal mortality (550 per 100,000 women) have been reported in the country in recent times [19,26]. The country has six geo-political zones in which there are 36 states and local government areas. Nigeria is a multi-ethnic country but its major tribes are Hausa/Fulani, Igbo and Yoruba. The main religions of the people are Muslim and Christianity, which are commonly practised in the northern and southern parts of the country respectively. The public healthcare system in Nigeria is managed by the government and is characterized by inadequate health workers, lack of essential drugs and poor equipment supply. In most situations, patients buy their drugs themselves and the harsh economic conditions have prevented people, particularly the poor, from accessing health facilities in Nigeria. In this situation, pregnant women, nursing mothers and children who are most susceptible to diseases, infections and morbidity are most affected. Nigerian demographic and health survey data were used [27]. In this cross-sectional design population-based study, a multi-stage cluster design approach was used to select women of reproductive age. It was a nationally representative sample. The sampling procedures thus allowed for the data to be analysed to examine health, social and demographic related issues. Complete information about the sampling procedure is available at the measuredhs website for interested readers. The data were recorded based on the birth status of women and thus women who had birth in the last five years were separated from other women in order to examine their infant and childhood mortality experience. This also provides an avenue for the examination of morbidity prevalence among the children and the healthcare-seeking behaviour for them and that of their mothers. In the current study, mothers who had given birth in the 5 years preceding the survey were investigated and such women had to have complete information on the variables that were used in creating the key independent variable. Consequently, the sample size for this study was 31,155. The dependent variable was infant mortality, which was based on the survival status of children before reaching the end of the first 12 months of life. Thus, if the child was alive at age one, he or she was assigned a code 0, and 1 if dead. The main independent variable was Intra-Demographic Birth Risk Assessment Scheme (IDBRAS) and this was created from the three demographic variables that mainly put women and children at health risk during pregnancy and childbirth. These are: age of the woman at the birth of the reference child (coded as 10–19, 20–29, 30–39 and 40–49), the child’s birth order (coded as 1, 2–3, 4–6 and 7+) and the preceding birth interval of the child (coded as first birth, <24 months, 24–35 months, 36–47 months and 48+). The coding was based on the pattern used in the demographic health and survey’s report. In each category of these three variables, preliminary percentages of infant deaths were obtained to assign weights to them. The category with the smallest percentage was used to divide the percentage obtained for the other categories by the same variable, pi 2 (intra). For instance, regarding birth order, the percentages of infant death were 7.4, 5.8, 6.4 and 9.2% among the 1st, 2–3, 4–6 and 7+ birth order respectively. In this case, the birth order with smallest percentage was 2–3 months. Therefore, 5.8% was used to divide the others to obtain the intra-infant death risk ratio (1.276, 1.000, 1.103 and 1.586). For maternal age at birth of the child, the percentages of infant death were 8.5, 6.3, 6.7 and 8.8% among women aged <20, 20–29, 30–39 and 40–49 years respectively, thus generating intra-infant death risk ratios (1.349, 1.000, 1.063 and 1.397). The percentages of infant deaths were 10.2, 6.3, 4.9 and 5.1% for women who left 0–23, 24–35, 36–47 and 48+ birth intervals respectively. In this case, the intra-infant death risk ratios were 2.082, 1.286, 1.000 and 1.041 respectively. Consequently, the total maximum infant death risk ratio was obtained for all these three main variables as 1.586 + 1.397 + 2.082 = 5.065 and the minimum was 1 + 1 + 1 = 3.000. The mathematical equations relating to the derivation of IDBRAS are as shown in Equations (1–4) below. Total minimum infant death risk ratio = 3.000 where MOR is the measure of risk. The ‘i’ represents the categories in each of the variables used for the computation of IDBRAS and j represents the categories of the outcome variable, i.e. infant mortality (No = 1, Yes = 2). Thus j = 1 if the response is No and j = 2 if Yes. The cut-off points 50 and 75% were based on second and third quartiles respectively. A woman with IDBRAS of 75% and above is considered as high risk, 50–74.99% as medium and low if otherwise. There are a number of factors that may potentially confound the relationship between IDBRAS and infant mortality. These are the socio-economic characteristics of the women, which included region of residence, education, place of residence, household wealth, religion, ethnicity and marital status, and environmental characteristics such as cooking fuel, sources of drinking water and toilet facility and health facility access factors at the time of the child’s pregnancy and delivery (tetanus injection during pregnancy, number of ANC visits, place of delivery, prenatal attendant and delivery assistant). To control for possible confounding effects, variables representing these factors were used in multivariate analyses. In order to avoid multicollinearity, a phenomenon where predictor variables are highly correlated, multicollinearity assessment was performed before inclusion of such variables in the regression model. Descriptive statistics were used to describe the data across the variables. A Chi-square model and analysis of variance were used to test association between IDBRAS and socio-economic factors. Cox regression was used to examine the relationship between infant mortality and IDBRAS amidst other factors. At this level of analysis, five models were generated. The first model is the unadjusted model, which involved only two variables, infant survival status variable and one independent variable. In the second model, the relationship between infant mortality and IDBRAS was adjusted with the inclusion of socio-economic factors such as region, education, place of residence, household wealth, region, religion and marital status. In the third model, health facility utilization factors were introduced into the equation to examine how their inclusion affected the strength of the relationship between IDBRAS and infant mortality. The child’s related factors were used in the fourth model, while the fifth model is the full model which includes all variables found to be statistically significant in infant mortality in the first model. The Cox regression procedure is useful for modelling the time to a specified event, based upon the values of given covariates. For each child in the study, time (t) starts with a value of zero at birth and is right censored at the first 12 months of life. Meanwhile, a child who is alive and has not reached the age of 12 months at the time of the study is censored, including those whose survival status is unknown. Thus, the cases are those who died between ages zero and 1 year. The indicators of child survival in the analysis are the survival status of the child (alive = 0 or death = 1) and the time (t) from age 0 to the timing of death; t depends on a characteristics vector, X i(X 1, X 2,…, Xn). The basic model offered by the Cox regression assumes that the time to event (infant mortality) and the covariates are related through the following equation: where hi(t) is the hazard rate for the i th case at time t; h 0(t) is the baseline hazard at time t; p is the number of covariates; βj is the value of the j th regression coefficient; Xij is the value of the i th case of the j th covariate. The hazard function is a measure of the potential for the event mortality to occur at a particular time t (any time in or before the first 12 months of life), given that the event is yet to occur. Larger values of the hazard function indicate greater potential for the event to occur. The baseline hazard function measures this potential independently of the covariates. The shape of the hazard function over time is defined by the baseline hazard, for all cases of infant mortality. The covariates determine the overall magnitude of the function. The value of the hazard is equal to the product of the baseline hazard and a covariate effect. While the baseline hazard is dependent upon time, the covariate effect is the same for all time points. Thus, the ratio of the hazards for any two cases at any time period is the ratio of their covariate effects. This is the proportional hazards assumption. Infant survivorship probabilities were estimated using the Brass 1-parameter logit system. The system used information on the proportion of children dead (D(i)) to a cohort of women and average parity (P(i)). A set of multipliers ζ(i) = a(i) + b(i)*{P(1)/P(2)} + c(i)*{P(2)/P(3)} were used (a(i), b(i) and c(i) are multiplier coefficients selected from West model life tables) to obtain the probability of dying q(x) = ζ(i)*D(i) and this estimate was adjusted using the equation Y(x) = α+βY(s) given by where α and β are constants and β = 1. The logit of the observed is Y(x) and that of the standard is Y(s) with their corresponding survivorship probabilities, l(x) and l(s). In this study, the Brass African standard was used [28]. The estimated probability of dying was thereafter converted to the infant mortality rate using the equation The hazard function h(t) and survival function S(t) are mathematically related as; Ethical approval for this study was obtained by the data originators from the Nigeria National Ethics Committee (NHREC/2008/07), functioning under the Ministry of Health. Informed consent was obtained from the respondents at the time of data collection and they were assured of the confidentiality and anonymity of the information they provided. Each consented participant was made to sign an appropriate agreement form before the commencement of the interview.

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