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High maternal mortality ratio in sub-Saharan Africa (SSA) has been linked to inadequate medical care for pregnant women due to limited health facility delivery utilization. Thus, this study, examined the association between age at first childbirth and health facility delivery among women of reproductive age in Nigeria. Methods The study used the most recent secondary dataset from Nigeria’s Demographic and Health Survey (NDHS) conducted in 2018. Only women aged15-49 were considered for the study (N = 34,193). Bi-variate and multivariable logistic regression models were used to examine the association between age at first birth and place of delivery. The results were presented as crude odds ratios and adjusted odds ratios (aOR) with corresponding 95% confidence intervals (CIs). Statistical significance was set at p<0.05. Results The results showed that the prevalence of health facility deliveries was 41% in Nigeria. Women who had their first birth below age 20 [aOR = 0.82; 95%(CI = 0.74–0.90)] were less likely to give birth at health facilities compared to those who had their first birth at age 20 and above. Conclusion Our findings suggest the need to design interventions that will encourage women of reproductive age in Nigeria who are younger than 20 years to give birth in health facilities to avoid the risks of maternal complications associated with home delivery. Such interventions should include male involvement in antenatal care visits and the education of both partners and young women on the importance of health facility delivery.
This study involved a cross-sectional analysis of secondary data from NDHS conducted in 2018. We utilized data from the birth records (NGIR7ADT) in the women’s file. According to Corsi and Neuman [26], the Demographic Health Survey (DHS) is a nationally representative survey usually carried out to estimate sociodemographic, health, and other health-related indicators. The survey collects information on several indicators, including the place of childbirth and age at first childbirth. The survey is conducted in over 85 LMICs globally [26]. The 2018 NDHS employed a two-stage stratified sampling technique to recruit the respondents for the survey. A detailed explanation of the sampling procedures can be found in Aliaga and Ruilin [27] study and the 2018 NDHS report [6]. The 2018 NDHS dataset consisted of 41,821 individual women aged 15–49. 40,666 occupied households were selected for the sample, and 40,427 were interviewed successfully, resulting in a response rate of 99% [6]. Out of this, 34,193 of the respondents with complete cases on the variables of interest in this study were included in the final analysis. We relied on strengthening the reporting of observational studies in Epidemiology in writing this manuscript [28]. The main outcome variable was the place of delivery. NDHS asks questions on place of delivery within five years preceding the survey, and the response options received were respondent’s home, other homes, government hospital, government health center, government health post, other public sectors, private hospital/clinic, other private sectors, and other health facilities. Those who responded respondent’s home and other homes were classified as “home delivery,” while the remaining responses were grouped as “health facility delivery”. This classification was informed by previous studies [29–32]. The main explanatory variable was the age at first childbirth. This was assessed using the variable “age of respondent at first childbirth”. The response options ranged from 12 years to 48 years. The age at first childbirth was then created by recoding the responses into “below 20 years” and “20 years and above”. This categorization was informed by previous findings on age at first birth in Nigeria [6,25] and elsewhere [33]. A total of thirteen covariates were studied. The covariates were selected based on their significant association with health facility delivery [29,30,34,35] and their availability in the DHS dataset. The covariates include maternal age, place of residence, parity, level of education, wealth quintile, current working status, marital status, religion, visit to the health facility in the past 12 months, permission to get medical help, distance to the health facility, region, and exposure to media. For this study, several of the covariates were recoded. The pre-existing coding for the place of residence, distance to the health facility, permission to get medical help, region, and visited health facility last 12 months were maintained and used in the final analysis. Maternal age was recoded into “15–24”, “25–34”, and “35+”. The level of education was re-classified into “no education”, “primary”, and “secondary or higher”. Wealth index was recoded as “poor”, “middle’, and “rich” by recoding poorer wealth quantile and poorest wealth quantile as “poor”, richer wealth quantile and richest wealth quantile as “Rich”, Middle wealth quantile still retains the categorisation as “Middle”. This categorization has been utilized elsewhere as well [36,37]. Also, marital status was recoded as “single”, “married”, and “others”. Parity was re-grouped into “1–3”, “4–6”, and “7+”. The women’s current working status was ground into “yes” and “no”. Religious affiliation of the women was recoded as “Christian”, “Islam” and traditional and other religious were recategorized as “others”. Exposure to media was created from three (3) variables (frequency of watching television, frequency of reading newspaper/magazine, and frequency of listening to the radio). All the three variables had the same response options (not at all less than once a week and at least once a week). The responses were recoded as “No = not at all” and “Yes” (less than once a week and at least once a week). Later, an index called the media exposure variable was created. In the current study, media exposure was defined as a woman with exposure in all three variables (watching television, reading newspaper/magazine, and listening to the radio). Data were analyzed using Stata version 16.0 (Stata Corporation, College Station, TX, USA). A descriptive analysis was first performed to determine the proportion of health facility delivery, with the result presented using percentages. A Pearson chi-square test analysis was later carried out to examine the relationship between health facility and age at first birth and the studied covariates. Also, bivariate and multivariable binary logistic regression analyses were performed to determine the association between health facility and age at first birth and selected covariates. The results were presented using crude odds ratio [38] and adjusted odds ratio (aOR) at 95% confidence intervals (CIs). The level of statistical significance was set at p<0.05 in the chi-square and regression analyses. A multicollinearity test was conducted using the Variance Inflation Factor (VIF), and we found no evidence of multicollinearity among the studied variables. During the analysis, the women’s sample weights (v005/1,000,000) were applied to obtain unbiased estimates. According to the DHS guidelines and the survey command (SVY) in Stata, it was used to adjust the data’s complex sampling structure in both the chi-square and regression analyses. This was a secondary analysis of data, and therefore no ethical approval was required since the data is available in the public domain. Further information about the DHS data usage and ethical standards is available at http://goo.gl/ny8T6X. The authors of this manuscript did not collect the data, we sought permission from the MEASURE DHS website, access to the latest dataset of Nigeria was provided after our intent for the request was assessed and approved on the 20th of March 2021. The DHS surveys are ethically accepted by the ORC Macro Inc. Ethics Committee and the Ethics Boards of partner organizations in different countries, such as the Ministries of Health. The interviewed women gave either written or verbal consent during each of the surveys and followed Helsinki’s declaration of ethical principles.
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