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Background: Despite the high rate of teenage pregnancy in Nigeria and host of negative medical, social and economic consequences that are associated with the problem, relatively few studies have examined socioeconomic inequality in teenage pregnancy. Understanding the key factors associated with socioeconomic inequality in teenage pregnancy is essential in designing effective policies for teenage pregnancy reduction. This study focuses on measuring inequality and identifying factors explaining socioeconomic inequality in teenage pregnancy in Nigeria. Methods: This is a cross sectional study using individual recode (data) file from the 2018 Nigeria Demographic Health Survey. The dataset comprises a representative sample of 8,423 women of reproductive age 15 – 19 years in Nigeria. The normalized Concentration index (Cn) was used to determine the magnitude of inequalities in teenage pregnancy. The Cn was decomposed to determine the contribution of explanatory factors to socioeconomic inequalities in teenage pregnancy in Nigeria. Results: The negative value of the Cn (-0.354; 95% confidence interval [CI] = -0.400 to -0.308) suggests that pregnancy is more concentrated among the poor teenagers. The decomposition analysis identified marital status, wealth index of households, exposure to information and communication technology, and religion as the most important predictors contributing to observed concentration of teenage pregnancy in Nigeria. Conclusion: There is a need for targeted intervention to reduce teenage pregnancy among low socioeconomic status women in Nigeria. The intervention should break the intergenerational cycle of low socioeconomic status that make teenagers’ susceptible to unintended pregnancy. Economic empowerment is recommended, as empowered girls are better prepared to handle reproductive health issues. Moreover, religious bodies, parents and schools should provide counselling, and guidance that will promote positive reproductive and sexual health behaviours to teenagers.
The study area is Nigeria, with an estimated population of 198 million in 2018 [20]. About 70 percent of the population resides in rural areas while only about 30 percent lives in urban areas [21]. With 32.4 percent of the population below the age of 18 years and over 23% adolescents/teenagers [22, 23], Nigeria has a large youth population. Administratively, the country is divided into six geopolitical zones viz., North-Central, North-East, North-West, South-East, South-West, and South-South. Of the six geopolitical zones in Nigeria, southern states had the highest youth literacy rate while northern states had the least youth literacy rate [24]. Approximately 21.3 percent of youths, aged 15–19 had never been to school [24]. The dataset for the analysis comprises women of reproductive age of 15–19 years in the six geopolitical zones of Nigeria. Data were obtained from the latest Nigeria Demographic Health Survey (NDHS), conducted between August 14, 2018 and December 29, 2018. DHS is conducted every five years with common questionnaires and/or variables that are generalizable to over 90 low- and middle-income countries [13]. The NDHS data is a representative of Nigerian population with a response rate of 99%. The study used Individual (women’s) Recode data file that collected information on women’s background characteristics, reproductive history, household asset ownership, etc. The NDHS uses a multistage sampling procedure, standardized tools and well-trained interviewers to collect reliable data on maternal and child health. The details of the survey are explained elsewhere [13]. The sample size for the study was limited to 8,423 women (currently or ever pregnant) of reproductive age 15–19 years in Nigeria. As per DHS recommendation, sample weight was applied to get the representative sample size. The sample focused on the variable ‘currently or ever pregnant’ and “teenage current age” rather than “teenage age at first birth”. The outcome variable in the study is teenage pregnancy. The variable is a dummy variable coded 1 if a teenager (aged 15–19 years) currently or ever pregnant, 0 otherwise. The socioeconomic status of a teenager was measured using wealth index as an indicator of socioeconomic status. Since information on individuals’ expenditure or income are often difficult to collect [25–27], the NDHS constucts a wealth index, as a measure of SES, using easy-to-collect data on a household ownership of selected assets (e.g., car, televisions and bicycles), materials used in housing construction, type of water access, and sanitation facilities [26]. A principal component analysis (PCA) technique was used to construct households’ wealth index scores based on the aforementioned information collected in the survey [13]. The first principal component of a set of variables captures the largest amount of information that is common to all the variables [25–27]. Households’ wealth index scores were used to categorise individuals into five SES quintile, starting with the poorest to the richest. In line with previous literature [2, 3, 6, 12], the following variables were used as predictors of teenage pregnancy:, teenage education level, marital status, religion, occupation, place of residence, geopolitical zone, wealth index quintiles, and exposure to information and communication technology (ICT) (frequency of watching television and use of internet). Table Table11 presents description of variables used in the study. Description of variables used in the study We used the concentration index (C) to measure socioeconomic inequality in teenage pregnancy. The C is measured based on the Concentration curve, which plots the cumulative share of health variables in horizontal axis against the cumulative share of population in ascending order of SES in the vertical axis. Twice the area between the Concentration curve and line of perfect equality (i.e., 45-degree line) indicate the magnitude of the C. If the Concentration curve lies above (or below) the line of perfect equality, it suggests that health outcome is concentrated among the poor (or rich). The C was calculated using a convenient regression method as follows [28, 29]: where σr2 is the variance of the fractional rank, h is the healthcare variable of interest (i.e., teenage pregnancy) of i th teenage girl, μ is the mean of the health variable of interest, h, for the whole population, and ri=1N is the fractional rank of the i th teenage girl in the distribution of socioeconomic position, with i=1 for the poorest and i=N for the richest teenager. The C is calculated as the ordinary least squares (OLS) estimate of β [29, 30]. The C ranges from -1 to + 1, for continuous health outcomes. Since our health outcome variable of interest is binary, the minimum and maximum of the C are not between -1 and + 1 and depend on μ [31]. The C can be normalized by multiplying the estimated C by 11-μ to overcome this issue. We used the normalized Concentration index (Cn) to quantify socioeconomic inequalities in teenage pregnancy. If the value of the Cn is zero, it suggests that there is no socioeconomic inequality in health outcomes. A negative (or positive) value of the Cn indicates a higher concentration of the health variable among the poor (or rich) [28]. A higher value of the Cn corresponds to higher socioeconomic inequality in health. In order to identify the contribution of each explanatory variable to socioeconomic inequality in teenage pregnancy, we decomposed the Cn using the Wagstaff, et al. approach [29]. Assume that we have a linear regression model to link our outcome variable (i.e., teenage pregnancy) h, to a set of k explanatory factors, xκ such as: where α is the intercept and β denotes parameter that measure the relationship between each explanatory factor x and the teenage pregnancy, and ε is error term. A Wagstaff, E Van Doorslaer and N Watanabe [29] showed that the C of h can be decomposed into the contribution of determinants that explain the teenage pregnancy as follows: where, x¯ k is the mean of xk, and Ck denotes the C forxk, a contributing factor. The GCε denotes the generalized C of the error term,εi. Equation 3 shows that the overall inequality in the teenage pregnancy has two components. The first term (βkxk¯μ)CK denotes the contribution of factor k to socioeconomic inequality in the teenage pregnancy. It constitutes the deterministic or explained component of the teenage pregnancy of the C. The second term GCεμ represents the unexplained component [28]. Based on Eq. 3, the product of the elasticity of each factor and its Ck gives the contribution of that factor to the inequality. The negative (or positive) contribution of a predictor to the Cn suggests that the socioeconomic distribution of the predictor and the association between the predictor and the teenage pregnancy leads to an increase in the concentration of teenage pregnancy among the poor (or rich). A zero value of either elasticity or the Ck leads to the zero contribution of the factor to C [28]. Applying the A Wagstaff [31] normalization approach to the decomposition of the C can yield: The dataset was weighted using the sampling weight provided in the NDHS to obtain estimates that are representative of all teenagers in Nigeria. Logit model estimation and marginal effects were conducted before the decomposition analysis. Chi-square was used to test associations between explanatory factors and teenage pregnancy. The predictors of teenage pregnancy were considered statistically significant at p < 0.05. All data analyses were conducted using Stata/SE-13 software [32].
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