A comparative analysis of the influence of contraceptive use and fertility desire on the length of the second birth interval in four countries in sub-Saharan Africa | BMC Women’s Health

Design and implementation of the study

Surveys (DHS) conducted in four sub-Saharan African countries: DR Congo DHS (2013/14 DRCDHS), Ethiopia DHS (2016 EDHS), Nigeria DHS (2018 NDHS), South Africa DHS (2016 SADHS). DHS is cross-sectional in design and provides population-based health indicators to help policy makers and program managers design and evaluate programs and strategies to improve the health of a country’s population. The data, collected by trained field workers, contains self-reported information on the sexual and reproductive health history of the women sampled.

Currently, the four countries have varying population sizes: DR Congo (90 million), Ethiopia (115 million), Nigeria (206 million) and South Africa (60 million); these constitute the largest population in the central, eastern, western and southern regions of SSA [1], respectively. It should be noted that these countries are among the nine countries of the world which are expected to host more than 50% of the increase in the world population by 2050, with the exception of South Africa. [4]. The growth rates and the TFR in DR Congo are respectively 3.5 and 6.2; Ethiopia, 2.7 and 4.3; Nigeria, 2.5 and 5.3; and South Africa, 1.1 and 2.3 [1]. Although South Africa’s fertility rate remains the lowest in sub-Saharan Africa, its observed fertility decline is still above replacement level. In addition, these countries are classified as low middle income countries by the World Bank. Nigeria and South Africa are the two main economic countries on the continent classified as middle income countries; Ethiopia and DR Congo are classified as low income countries.

In these countries, the DHS employs a two-stage stratified cluster sampling technique using the frame containing the enumeration areas (EAs). In the first step, the clusters (otherwise known as EAs) are selected using a probability proportional to size approach per stratum. At the second stage, households are selected as secondary sampling units using systematic cluster sampling. Women of childbearing age who reside in the respective countries were the study participants. A detailed description of the sampling plan and strategies has already been reported [32,33,34,35].

Study population and variables

Of the 18,827 15,683 41,821,814 women aged 15 to 49 who participated in DRCDHS 2014, 2016 EDHS, 2018 NDHS, 2016 SADHS respectively, 13,884, 10,114, 29,296, 6039 had at least one birth and were included in the current study. These were participants who had reported at least one single birth at the time of the survey.

Result variable The dependent variable of interest was the time between the first and second childbirth for women in the selected countries. Women who had not had a second childbirth at the time of the survey were right-censored and coded 0; otherwise, 1 in the analysis.

Independent variables The main independent variables were contraceptive use and fertility desire. Contraceptive use was derived from questions asking women to indicate “if they have ever used anything or tried to delay or avoid getting pregnant” and their “current use of contraceptives by type of method”; this was categorized as “never-, past or current use” of any means to delay / stop pregnancy / childbirth. Fertility desire, derived from questions asking women to indicate whether they wanted more children, was reclassified as “more, unwilling or undecided”. Based on existing empirical studies [16, 36], the other covariates taken into account for the study were ethnicity, religion (Christian, Islam, traditionalist / other), education (none, primary, secondary, tertiary), wealth index (low, medium , high), age (principal component analysis contained in the women’s recoding file, was grouped into low, middle and high wealth quintiles. This is an indirect measure of the socio-economic status of the household due to the lack of information on household income. The term ethnic origin denoted the self-reported ethnic group in each of the countries except South Africa where information on skin color was provided. Additionally, information on religion was not available in the 2016 SADHS.

Statistical data analysis

Survival analysis methods were used for the analysis. The “time to failure” for women who had a second birth was the SBI. The “censored time” for women without a second birth was the time elapsed since the first birth and the date of interview. The Kaplan-Meier survival method was used to describe the time between women and the second birth, while the log-rank test was used to examine the association between the duration of SBI and individual explanatory variables. The semi-parametric Cox proportional risk regression (CPH) was then used to assess the effect of contraceptive use and fertility desire on SBI among other controlled variables, in each of the selected countries.

Model expression The CPH model can be written in the form:

$$ h left ({t_ {i}} right) = h_ {0} left (t right) ell ^ {{ sum nolimits_ {j = 1} ^ {p} {b_ {j} x_ {ji}}}} , mathop { longrightarrow} limits ^ {imlying} , ln frac {{h left ({t_ {i}} right)}} {{h_ {0} left (t right)}} = mathop sum limits_ {j = 1} ^ {p} b_ {j} x_ {ji} $$


or ({b} _ {j} )—Jth coefficients of the explanatory variable Xj, p– number of explanatory variables, ({h} _ {0} left (t right) )– reference risk function such as ({ mathrm {h} left (t right) / h} _ {0} left (t right) )—Indicates the risk ratio (HR) and the conditional probability of experiencing a second childbirth in a short time interval (t, t + ∆t) having survived until the time t is

$$ h left (t right) = mathop {{ text {lim}}} limits _ { Delta t to 0} left {{ frac {{P left ({t le T le t + Delta t left | T right rangle t} right)}} { Delta t}} right } $$


Usually, the relationship between the functions of risk, H



$$ S left (t right) = mathop smallint limits_ {t} ^ { infty} f left (y right) dy = P left ({T> t} right) = 1 – F left (t right) $$


$$ F left (t right) = mathop smallint limits_ {0} ^ {t} f left (y right) dy = P left ({T


The F


Yes mI is the number of women who were at risk of having a second childbirth, censored women included, before Isurvival time (tI) and II is the number of women who had a second birth in tI, then Eq. (7) below estimates the survival functions.

$$ s left (t right) = mathop prod limits_ {i = 1} ^ {m} left {{ frac {{n_ {i} – l_ {i}}} {{n_ { i}}}} right } mathrel backepsilon t_ {m}


or m is the number of different failure times (i.e., the experience of a second birth).

The unadjusted CPH model was used to explain the association between each of the major independent variables, including other covariates and the SBI. Using the Wald test to assess the significance of the interaction between key variables (contraceptive use and fertility desire), the statistical significance of the interaction term was not uniform across the countries studied (this was not has not been presented). Thus, after confirming the non-violation of the proportional risk assumption, two adjusted CPH models were fitted. Model 1 only constitutes the key independent variables and model 2 includes all significant variables (p

Hazard ratios (HR), including their 95% confidence intervals, are shown. The exponentials of the coefficients (bj which indicates changes in the expected time to the second birth due to a unit change in the jth predictor) suggest the risk trend at the second birth; thus, HR> 1 indicates a higher risk and HR

Ethical considerations

Ethical approval for the parental study was obtained from the National Ethics Committee in the respective countries and from the ICF Institutional Review Committee. Details of the ethics approval were reported earlier [32,33,34,35]. The analysis for the present study used a secondary dataset, freely available for use in the public domain, which does not require ethical approval. In the meantime, the Demographic and Health Surveys Program has authorized the use of the dataset for this analysis.

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