Four in ten married women demands satisfied by modern contraceptives in high fertility sub-Saharan Africa countries: a multilevel analysis of demographic and health surveys

A community-based cross-sectional survey was conducted between January 2010 and December 2018 among reproductive-age women in high fertility countries in SSA. The survey was conducted in Niger, Democratic of Republic Congo, Mali, Chad, Angola, Burundi, Nigeria, Gambia, and Burkina Faso. These countries were selected because they are the top ten countries with high fertility rates in SSA, with fertility rates above 5.0, a value that is higher than the rate of 4.44 in Africa and 2.47 in worldwide [31]. Despite Somalia being one of the top ten high fertility countries, the country has no DHS data and excluded from the analysis.

The data for these countries were obtained from the official database of the DHS program, https://​dhsprogram.​com after authorization was granted via online request by explaining the purpose of our study. We used the woman’s individual record (IR file) data set and extracted the dependent and independent variables. DHS is a nationally representative household survey that is conducted across low and middle-income countries every five years. ICF implements the DHS Program, which aims to collect, analyze, and disseminate data about population, health, and nutrition, as well as use these data for planning, policy-making, and program management.

It has been an essential data source on issues of reproductive health in low and middle income countries as it gathers data on a number of reproductive health issues [32]. A two-stage stratified sampling technique was used; a total weighted sample of 43,745 married/ in union reproductive age women were included in the study (Fig. 1).

Demand satisfied for modern contraceptives among married or in union reproductive age (15–49 years) women was the dependent variable. As part of this measure, women who reported using modern contraceptive methods like female sterilisation, male sterilisation, pill, intrauterine device (IUD), injectables, implants, male condom, female condom, emergency contraceptives or lactation amenorrhea methods were considered as demand satisfied by modern methods. Demand satisfied was computed using the revised definition of demand satisfied for modern contraceptives in the Demographic and Health Survey (DHS). The calculations were carried out using modern family planning methods as nominators, which are considered as met needs. Unmet needs include those who need modern contraceptive methods for spacing and/or limiting but cannot obtain them, as well as those who use traditional methods. We used the total demanded as the denominator (sum of met and unmet needs). The demand satisfied rate is equal to \(\frac{met need \times 100}{unmet need+met need}\) [33]. Independent variables grouped under the individual, household and community level were included. The individual level factors were age, occupation, educational level, wealth index, media exposure, number of children, knowledge about family planning. Accordingly, age was grouped as 15–24, 25–34, and 35–49. Occupation was coded as working and not working. No formal education, primary education, secondary and higher education were the categories for highest educational level for the mother and her husband. In DHS wealth index was developed by principal component analysis using durable assets ownership, housing characteristics and access to utilities. Finally the wealth index was recoded as poorest, poorer, middle, richer, and richest. Less than five and five and above were categories for household members, number of children in the household was categorized as 0, 1–3, 4–6, and above 6, sex of household head was grouped as male or female. Knowledge about family planning was coded as ‘yes’ for those women who knows about family planning and otherwise ‘no’. Those women who were either reading newspapers/magazine, or listening radio and watching television less than once a week/at least once a week were considered as having media exposure whereas those women who had not either reading magazine/ newspaper or listening radio/ television at all was considered as having not media exposure. The community level variables were place of residence, countries, community level education, community level poverty, community level media exposure, and distance to the health facility. Thus, place of residence (urban, rural), countries, distance to the health facility (big problem, not big problem) variables were analyzed based on their categorization in the DHS [34‐37]. The community level poverty, community level education and community level media exposure were generated by aggregating the individual level factors independently at cluster level and finally, were categorized as high if the proportion is ≥ 50% and low if the proportion is < 50% based on the national median value since these were not normally distributed [38].

A multilevel logistic regression model was used to identify the association between the individual and community level factors with demand satisfied for modern contraceptive methods. STATA version 14 command “melogit” was used in fitting the models. The data was weighted (v005/1000000) throughout the analysis to ensure that the DHS sample was a representative sample and to obtain reliable estimates and standard errors before data analysis.

The first step was a graphical representation of the demand for modern contraceptives among reproductive age women. Overall, a total weighted sample of 44,052 reproductive age women were included in this study.

The second step was a bivariable analysis that calculated the proportion of demand satisfied for modern contraceptives across the independent variables with their p-values. All the variables having a p-value less than 0.2 in bivariable analysis were used for multivariable analysis. For the multivariable analysis, adjusted odds ratios with 95% confidence intervals and a p-value of less than 0.05 were used to identify statistically significant factors associated with demand satisfied for modern contraceptives. In the final step of the analysis, a multilevel logistic regression analysis comprising fixed effects and random effects was conducted.

The results of the fixed effects of the model were presented as adjusted odds ratio (AOR) while the random effects were assessed with intra-class correlation coefficient (ICC) [39]. Accordingly, four models were fitted; null model (model 0) which shows the variations in the demand satisfied on modern contraceptives in the absence of any independent variables. Model II adjusted for the individual-level variables, Model III adjusted for the community level variables, and model IV adjusted for both individual and community level variables [39, 40]. Correspondingly, model fitness was done using the deviance (-2 log likelihood). Variance inflation factor (VIF) was used to check for multicollinearity among independent variables in which the result showed no multicollinearity (mean value for the final model = 1.38).

In this study a total weighted sample of 43,745 reproductive age women were participated. The majority (42.99%) of the study participants were in the age group of 25–34 years with a mean age of 31.14 ± 7.85 years. Of the study participants, 44.56% of the women had no formal education. Most (70.12%) of the women had work. Among the women surveyed, 87.93% of the household head were males. Of the study participants, 62.11% were from five and more household members. More than half (54.32%) of the women were from community having low proportion of education. With regard to community level poverty, most (88.16%) were from communities having low proportion of poverty (Table 1).

The overall magnitude of demand satisfied for modern contraceptive methods in the high fertility countries in sub Saharan Africa was 39.53% (95%CI: 39.06, 39.98). The highest (48.93%) mDFPS methods was in Burundi and Chad scoring the lowest (19.6%) prevalence (Fig. 2).

The null model in the random effects, revealed a statistically significant difference in the likelihood of demand satisfied for modern contraceptive methods with a community variance of 49.3%. Additionally, the intra-class correlation coefficient (ICC) in the null model showed that 13.0% of the total variability of demand satisfied for modern contraceptive accounted for differences among clusters. Further evidence of variation in the demand satisfied for modern contraceptive techniques was provided by the median odds ratio. Accordingly the odds of demand satisfied for modern contraceptives was 1.24 times higher among women of higher cluster of demand satisfied for modern contraceptives than women within lower cluster of demand satisfied for modern contraceptives. In terms of model comparison, model three was selected as a final model since it has the lowest deviance (48,857.554) (Table 2).

Table 2 demonstrates binary logistic regression for factors associated with mDFPS. After adjusting for individual and community related factors of demand satisfied for modern contraceptive methods, age of the women, women and husband education, husband residence, media exposure, wealth index, the number of children from the individual level factors and distance to the health care facility, urban residence, community poverty and country from the community level variables were statistically, significant associated factors with demand satisfied for modern contraceptive.

The odds of demand satisfied for modern contraceptive methods among women aged 25–34 and 35–49 years had 1.34 (AOR: 1.34, 95%CI: 1.26, 1.42) and 1.28 (AOR: 1.28, 95%CI: 1.20, 1.38) times higher as compared to women aged 15–24 years, respectively.

The likelihood of demand satisfied for modern contraceptive was 1.35 (AOR: 1.35, 95%CI: 1.27, 1.44) times and two times (AOR: 2.05, 95%CI: 1.90, 2.21) higher among women who had educated primary and secondary and higher education, respectively. Similarly, the odds of demand satisfied for modern contraceptive was 1.26 (AOR: 1.26, 95%CI: 1.18, 1.35) and 1.54 (AOR: 1.54, 95%CI: 1.43, 1.66) times higher among women whose husband had educated primary and secondary education than those women whose husband had no formal education, respectively.

The odds of demand satisfied to use modern contraceptives among women who lives with their husband was nearly two times higher as compared to their counterparts (AOR: 1.75, 95%CI: 1.60, 1.91). The likelihood of demand satisfied for modern contraceptives was 1.22 (AOR: 1.22, 95%CI: 1.15, 1.29) times higher among women who had media exposure as compared to women who had no media exposure (AOR: 1.22, 95%CI: 1.15, 1.29).

The odds of demand satisfied was 1.1 (AOR: 1.1, 95%CI: 1.02, 1.19), 1.18 (AOR: 1.18, 95%CI: 1.08, 1.28), 1.37(AOR: 1.37, 95%CI: 1.26, 1.49), and 1.72(AOR: 1.72, 95%CI: 1.56, 1.89) times higher among poorer, middle, richer, and richest households as compared to the poorest wealth quintile households.

The odds of demand satisfied for modern contraceptives among women who had 4–6 and above 6 children was reduced by 52% (AOR: 0.48, 95%CI: 0.43, 0.55) and 61% (AOR: 0.39, 95%CI: 0.29, 0.59) as compared to those women who had below 4 children, respectively.

Women who perceived distance to the health facility as not a big problem had 1.11 times higher odds (AOR: 1.11, 95%CI: 1.04, 1.15) of demand satisfied than their counterparts.

Urban resident women had 1.18 times higher odds (AOR: 1.18, 95%CI: 1.10, 1.27) of demand satisfied to use modern contraceptives than rural resident women.

Women from high community poverty had 15% less odds (AOR: 0.85, 95%CI: 0.74, 0.97) of demand satisfied for modern contraceptive methods as compared to women from low community poverty.

Demand satisfied for modern contraceptive methods was 73% (AOR: 0.27, 95%CI: 0.20, 0.32), 26% (AOR: 0.74, 95%CI: 0.66, 0.89), 43% (AOR: 0.57, 95%CI: 0.50, 0.65), 69% (AOR: 0.31, 95%CI: 0.27, 0.36), 37%(AOR: 0.63, 95%CI: 0.48, 0.82), 52% (AOR: 0.48, 95%CI: 0.43, 0.55) less odds and 1.3 times higher odds (AOR: 1.30, 95%CI: 1.00, 1.70) in Angola, Burkina Faso, Democratic Republic Congo, Gambia, Nigeria, Chad and Burundi as compared to Niger, respectively (Table 2).

The utilization of nationally representative data, large sample size, and analysis of the individual and community level factors were the key strengths. In order to find a more accurate result, multilevel-modeling technique accounting the survey data hierarchical nature was employed. The current study is not generalisable to all women in SSA. Moreover, the study was unable to explore cultural norms and values. The study also have limitations, because the DHS data are cross-sectional no temporal link between the independent variables and the dependent variable is demonstrated.