Menu
Menu
Menu
Menu
Background: Young mothers aged 15 to 24 years are particularly at higher risk of adverse health outcomes during childbirth. Delivery in health facilities by skilled birth attendants can help reduce this risk and lower maternal and perinatal morbidity and mortality. This study assessed the determinants of health facility delivery among young Nigerian women. Methods: A nationally representative population data extracted from the 2018 Nigeria Demographic and Health Survey of 5,399 young women aged 15–24 years who had had their last birth in the five years before the survey was analysed. Data was described using frequencies and proportions. Bivariate and multivariate analyses were carried out using Chi-Square test and multilevel mixed effect binary logistic regression. All the analysis were carried out using STATA software, version 16.0 SE (Stata Corporation, TX, USA). Results: Of the total sampled women in the 2018 NDHS, 5,399 (12.91%) formed our study population of young women 15 -24 years who had their last birth in the preceding five years of the survey. Only 33.72% of the young mothers utilized health facility for delivery. Women educated beyond the secondary school level had 4.4 times higher odds of delivering at a health facility compared with women with no education (AOR 4.42 95%, CI 1.83 – 10.68). Having fewer children and attending more antenatal visits increased the odds of health facility delivery. With increasing household wealth index, women were more likely to deliver in a health facility. The odds of health facility delivery were higher among women whose partners had higher than secondary level of education. Women who lived in communities with higher levels of female education, skilled prenatal support, and higher levels of transportation support were more likely to deliver their babies in a health facility. Conclusion: Strategies to promote institutional delivery among young mothers should include promoting girl child education, reducing financial barriers in access to healthcare, promoting antenatal care, and improving skilled birth attendants and transportation support in disadvantaged communities.
Women recode data extracted from the Nigerian Demographic and Health Survey 2018 was analysed. The 2018 NDHS is the sixth Demographic and Health Survey conducted in Nigeria since 1990 [29]. Data collection took place from 14 August 2018 to 29 December 2018 [4]. The survey was cross-sectional and provides estimates of demographic and health indicators [29]. The Population and Housing Census of the Federal Republic of Nigeria (NPHC), conducted in 2006 was the sampling frame used for the 2018 NDHS [29]. The primary sampling unit (PSU)/cluster for the 2018 NDHS is defined on the basis of enumeration areas (EAs) from the 2006 census. A nationally representative sample of respondents were interviewed in the 6 geographical zones, 36 states and the Federal Capital Territory (FCT) [29]. Stratified sampling in two stages was used to select respondents [4]. The 37 states were separated into urban and rural areas such that in total, there were 74 sampling strata. In the first stage, 1,400 EAs were selected with probability proportional to EA size. In the second stage, 30 households were selected in each cluster by an equal probability systematic sampling. A sample of 41,821 women aged 15–49 in 40,427 households participated in the survey. This study is however limited to 5,399 women aged 15 – 24 years who had recent live birth in the preceding five years of the survey. The dependent and independent variables examined in this study with their descriptions are presented in Table Table11. Description of study variables A health facility delivery was when the most recent childbirth took place in a government hospital, government health center, government health post, other public sector, private hospital/clinic or other private facility When a delivery took place in a respondent’s home, other home, or other places, it was not a health facility delivery ▪ Utilized health facility for delivery ▪ Did not utilize health facility for delivery ▪ 15 – 19 years ▪ 20 – 24 years Women not married were defined as those never in union and those that were formerly in union/living with a man Married women were defined as women currently in union/living with a man ▪ Not married ▪ Married ▪ No education ▪ Primary ▪ Secondary ▪ Higher ▪ currently working ▪ Not currently working ▪ Pregnancy wanted ▪ Pregnancy not wanted ▪ 1 ▪ 2–3 ▪ 4–7 ▪ No ANC visits ▪ less than four visits ▪ at least four visits ▪ A big problem ▪ Not a big problem Household wealth index in the NDHS is divided into five equal categories; poorest, poorer, middle, richer, richest In this study, we recoded wealth index into 3 categories with ‘poor’ comprising of poorest and poorer, ‘middle’ comprising of middle and ‘rich’ comprising of richer and richest ▪ Poor ▪ Middle ▪ Rich Mass media exposure was generated from exposure to television, radio and newspaper Mass media exposure was defined as ‘exposed’ for those with access to at least one of television, radio or newspaper, and ‘no exposure’ for those who had no access to any of these ▪ No exposure ▪ Exposed This refers to whether respondent participates in decision on her healthcare This variable was derived from the variable—person who usually decides on respondent’s health care A respondent participates if the decision is made by respondent alone, or respondent and partner A respondent does not participate when the decision is made by her partner alone, or someone else ▪ Participates: ▪ Does not participate ▪ No education ▪ Primary ▪ Secondary ▪ Higher ▪ Currently employed ▪ Not currently employed ▪ Low ▪ Medium ▪ High ▪ Low ▪ Medium ▪ High ▪ Low ▪ Medium ▪ High ▪ Low ▪ Medium ▪ High Ethnic diversity refers to the concentration of different ethnic groups in a community It was defined as the proportion of women from different ethnic groups in the primary sampling unit The value ranges from 0 to 100. A value of 0 (low) reflects a mono-ethnic community, whereas a value of 100 (high) relects that the community is multi-ethnic in nature ▪ Low ▪ Medium ▪ High ▪ Urban ▪ Rural ▪ Northcentral ▪ Northeast ▪ Northwest ▪ Southeast, ▪ South-south ▪ Southwest Community level poverty, community level women’s education, community level of skilled prenatal support, community level of transportation support and ethnic diversity were computed by aggregating individual characteristics at the cluster level (primary sampling unit), dividing the measure into tertiles and categorizing as low, medium and high. Similar procedure has been widely applied to derive community variables in DHS datasets [21–23] Weighted data analysis was done using STATA software, version 16.0 SE (Stata Corporation, TX, USA). Three levels of analysis were carried out. First, descriptive analysis was done to determine the distribution of respondents in terms of individual characteristics and community levels characteristics. Second, bivariate analysis was done to determine the association between the given characteristics and place of delivery using Chi-square to test the statistical significance. Third, multilevel logistic regression analysis was used to account for the hierarchical nature of the DHS data. We estimated four models. The first model being an empty model, contained no covariates but decomposed the total variance into individual and community components. The second model included individual characteristics only. The third model included only the community level variables, while the fourth model included both the individual and community levels variables. Odds ratios were used to present the results of fixed effect in addition with the confidence interval (95%). Intra cluster correlation (ICC) was used to explain the results of random effect. Model goodness of fit was checked using BIC, multi-collinearity was confirmed through application of Variance Inflation Factor (VIF) and the variable – marital status—was dropped from the regression analysis due to multi-collinearity. The mathematical statement of the multilevel mixed effect binary logistic regression model is as follows: Empty Model (Model 0): The model expresses the similarity in the health facility delivery among young mothers across the communities. Other models that contain explanatory variables: Where:πij is the log of odds of delivery outside of health facility (1-πij) is the log of odds of health facility deliveryβ0 is log odds of the interceptβ1, … βn are changes in level of health facility delivery due to individual and community-level factors X1ij… Xnij are independent variables of individuals and communities U0j are random errors at community levelseij is the error term or residuals Being a secondary data, we registered and obtained permission to download the requested datasets from the measure DHS website. The data were handled with confidentiality. The 2018 NDHS survey protocol was approved by the National Health Research Ethics Committee of Nigeria (NHREC) and the ICF Institutional Review Board. Written informed consents were obtained from all participants. All methods were performed in accordance with the Declaration of Helsinki.
N/A
