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Spatial variations in family planning demand to limit childbearing and the demand satisfied with modern methods in sub-Saharan Africa

Reproductive Health volume 19, Article number: 144 (2022) Cite this article

Abstract

Background

There is an increasing demand for family planning to limit childbearing in sub-Saharan Africa (SSA). However, limited studies have quantified the spatial variations. This study examined: (i) the spatial patterns in the demand for family planning to limit childbearing and satisfied with modern methods, and (ii) the correlates of the demand for family planning to limit childbearing satisfied with modern methods in SSA.

Methods

This study analyzed secondary data on 306,080 married/in-union women obtained from Demographic Health Surveys conducted between 2010 and 2019 in 33 sub-Saharan African countries. We conducted exploratory spatial data analysis, with countries as the unit of analysis. We also performed regression analysis to determine the factors associated with demand for family planning to limit childbearing satisfied with modern methods in SSA.

Results

The mean percentage of women who demanded for family planning to limit childbearing by country was 20.5% while the mean prevalence of demand for family planning to limit childbearing satisfied with modern methods by country was 46.5%. There was a significant positive global spatial autocorrelation in the demand for family planning to limit childbearing (global Moran’s I = 0.3, p = 0.001). The cluster map showed the concentration of cold spots (low–low clusters) in western and central Africa (WCA), while hot spots (high–high clusters) were concentrated in eastern and southern Africa (ESA). Also, the demand for family planning to limit childbearing satisfied with modern methods showed significant positive global spatial autocorrelation (global Moran’s I = 0.2, p = 0.004) and concentration of cold spots in WCA. In the final multivariable regression model the joint family planning decision making (β = 0.34, p < 0.001), and antenatal care (β = 13.98, p < 0.001) were the significant factors associated with the demand for family planning to limit childbearing satisfied by modern methods.

Conclusions

There are significant spatial variations in the demand for family planning to limit childbearing and the demand satisfied by modern methods, with cold spots concentrated in WCA. Promoting joint decision making by partners and increasing uptake of antenatal care may improve the demand for family planning to limit childbearing satisfied with modern methods.

Plain Language Summary

In sub-Saharan Africa (SSA), studies have shown that the proportion of married women who want to stop having children has been increasing as well as the proportion using modern contraceptive methods among them. These studies also indicated that this proportion of women are higher in certain regions of Africa than the others. To extend these previous findings, we performed geographical analysis to assess how the proportion of married/in-union women who want to stop having children and the ones using modern methods among them differ geographically. Our findings indicated that neighboring countries where the proportion of married/in-union women who want to stop having children was higher than the overall average were concentrated in eastern and southern Africa (ESA), while neighboring countries in which the proportion of married/in-union women who want to stop having children was lower than the overall average were concentrated in western and central Africa (WCA). Similarly, the results also showed that neighboring countries where the proportion of married/in-union women using modern contraceptive methods among those who want to stop having children was lower than the overall average were concentrated in WCA. Our findings suggest that increasing joint decision making on family planning and uptake of antenatal care in SSA may improve the use of modern contraceptive methods among married/in-union women who want to stop childbearing.

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Background

Contraceptive use allows individuals or couples to delay, space, or limit (stop) childbearing [1]. By preventing unintended pregnancies, contraceptive use contributes to maternal and infant survival, poverty reduction, and economic growth [2]. The use of contraceptives for family planning has been recognized as one of the 10 greatest public health achievements of the twentieth century [3], and it has continued to be featured on the global agenda for economic and social development. For example, improving access to modern contraceptive methods was one of the targets of the Millennium Development Goals (MDGs) which ended in 2015 [4]. The ongoing Sustainable Development Goals (SDGs) also specifies universal access to family planning services by 2030, with the demand for family planning satisfied with modern methods as one of the indicators for monitoring this target [5]. While there is no standardized definition for modern methods [6, 7], they have been found to be more effective than traditional contraceptive methods [8].

Clients seeking to limit childbearing are an important population that require effective contraceptive methods to prevent unintended pregnancies. In sub-Saharan Africa (SSA), where there is a rapid unsustainable population growth, prevention of unintended pregnancies among clients with the intention to limit childbearing may have an impact on fertility rates [9, 10]. Interestingly, evidence suggests increasing demand for family planning to limit childbearing in SSA [11]. For instance, in countries such as Kenya, Lesotho, Malawi, Namibia, Rwanda, and Swaziland, the demand for family planning to limit childbearing was found to exceed the demand for child spacing [12]. However, findings by Van Lith et al. indicated that a considerable proportion of limiters using contraceptives in SSA rely on traditional methods [10]; increasing their risks of having unintended pregnancies.

While evidence from descriptive studies suggests geographic variation in the demand for family planning to limit childbearing and the demand satisfied with modern methods in SSA [11, 12], to our knowledge, no prior studies have quantified these spatial relationships. Despite the growing literature on spatial dimensions of contraceptive use in SSA, available studies have focused on contraceptive prevalence of modern methods among women of reproductive age [13,14,15,16,17,18,19,20,21] or unmet needs [22, 23], with a majority in select countries.

Identifying spatial clusters and gaining insights into shared demographic, health systems, or economic factors by contiguous areas can inform interventions to improve uptake of family planning services among women who are seeking to limit childbearing. Accordingly, this study examined: (i) the spatial patterns in the demand for family planning to limit childbearing and the demand satisfied with modern methods and (ii) the correlates of the demand for family planning to limit childbearing satisfied with modern methods in SSA.

Methods

Data source and study sample

This study analyzed secondary data obtained from 33 Demographic Health Surveys [24] conducted in 33 countries and from other two data repositories (World Bank Open Data [25] and World Health Organization Global Health Observatory Data [26]). The Demographic Health Surveys (DHS) are nationally representative household surveys that gather data on several health-related topics, including family planning, in low- and middle-income countries. The methodology and procedures are standardized, making the surveys in the different countries comparable. The DHS program uses a stratified two-stage probabilistic sampling design [27]. The samples are drawn from an existing sampling frame, usually the latest census frame [27]. The sampling frame is usually stratified by geographic region and by area of residence (urban and rural) within each region [27]. The first stage involves the selection of the primary sampling units (PSU) (usually enumeration areas from population census files), with the probability of selecting a unit proportional to its size within each stratum. The second stage involves selecting a fixed number of households; about 25–30 households per PSU [27]. A detailed description of the DHS design can be found elsewhere [27]. We included 33 countries with a standard DHS conducted within the last 10 years (2010–2019) (Fig. 1). Our study sample was restricted to 306,080 married or in-union (i.e., living with a partner) women of reproductive age (15–49 years) (Table 1).

Fig. 1
figure 1

Countries included in the study by subregion

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Table 1 Description of study sample
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Measures

The DHS program collects data on the contraceptive methods currently being used by women, and report on the met and unmet needs for family planning to limit childbearing. In the survey, women are described as having: (i) met need for limiting if they are using a method of contraception and want no more children; are sterilized; or say they cannot get pregnant when asked about the desire for future children and (ii) unmet need for limiting if they are not using a method of contraception and are pregnant and did not want the current pregnancy at all; postpartum amenorrheic and did not want their last birth at all; or fecund and do not want any more children [27]. We assessed two indicators: demand for family planning to limit childbearing and the demand for family planning to limit childbearing satisfied with modern methods. We defined the demand for family planning to limit childbearing as the percentage of married/in-union women who had met or an unmet need to limit childbearing and the demand for limiting childbearing satisfied with modern methods as the percentage of married/in-union women with demand for family planning to limit childbearing using modern methods. Consistent with the DHS program, modern methods in this study included: pill, intrauterine device, injection, diaphragm, condom, male permanent contraception, female permanent contraception, implants, lactational amenorrhea, female condom, foam and jelly, emergency contraception, and standard day method [27]. For the correlates of the demand for family planning to limit childbearing satisfied with modern methods, we examined the following factors that have been found to influence the uptake of family planning methods in previous literature [28,29,30,31,32,33,34]: individual-level factors (educational attainment, occupation, area of residence, exposure to family planning messages on mass media, household wealth index, distance to health facility, husband/partner’s educational attainment, husband/partner’s occupation, joint family planning decision making, and antenatal care), and country-level factors (out-of-pocket expenditure, gross national income per capita, and density of nurses/midwives) (see Table 2 for the description of the explanatory variables).

Table 2 Description of the explanatory variables
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Statistical analysis

We conducted exploratory spatial data analysis (ESDA) with the countries as the unit of analysis in a geographic coordinate polygon shapefile of SSA [35]. The shapefile has a standard World Geodetic System 1984 (WGS84) which sets its angular units in degrees and Greenwich as the prime meridian (longitude 0 degree). We generated a spatial weights matrix using the distance band method, with the bandwidth set at an arc distance of 3000 km. The connectivity histogram indicated an even distribution of the neighbor cardinality and absence of isolates. The global Moran’s I statistic was used to assess the overall spatial autocorrelation, while the local indicator of spatial association (LISA) was used to identify the specific locations of the clusters. The LISA cluster maps showed the significant locations in four color-coded categories: low–low, high–high, low–high, and high–low. The terms low and high are defined relative to the overall mean of the indicators [36]. A low–low (or cold spot) location signified a country with a low value surrounded by countries with low values, while a high–high (or hot spot) location signified a country with a high value surrounded by countries with high values. A low–high location signified a country with a low value surrounded by countries with high values, while a high–low location signified a country with high value surrounded by countries with low values. The high–high and low–low locations (positive local spatial autocorrelation) are referred to as spatial clusters, while low–high and high–low locations (negative local spatial autocorrelation) are referred to as spatial outliers [36].

We also performed confirmatory spatial data analysis to determine the factors associated with the demand for family planning to limit childbearing satisfied with modern method. We first conducted a univariate ordinary least squares (OLS) regression analysis, and the significant variables were included in the multivariable OLS regression analysis. A backward stepwise approach was used to fit a parsimonious global multivariate model with the least number of statistically significant variables and lowest Akaike information criterion (AIC). The Lagrange Multiplier lag (LM-lag) and Lagrange Multiplier error (LM-error) tests were not significant, hence we did not proceed to conducting spatial regression [36]. In the final model, the condition number was 8.44 (less than 10), indicating the absence of multicollinearity [37]. The Jarque–Bera test for non-normality (p = 0.707) and Breusch–Pagan test for heteroskedasticity (p = 0.389) were not statistically significant. The data analysis was conducted using GeoDa v. 1.14. All analyses were considered statistically significant at p < 0.05.

Results

Descriptive statistics

Table 3 shows the descriptive statistics for all the variables. The mean prevalence of demand for family planning to limit childbearing by country was 20.5%. The percentage of women who demanded for family planning to limit childbearing ranged from 4.3% in Niger to 47.4% in Lesotho (Fig. 2A). The mean prevalence of demand for family planning to limit childbearing satisfied with modern methods by country was 46.5%. The percentage of women who demanded for family planning to limit childbearing satisfied with modern methods ranged from 21.3% in Democratic Republic of Congo to 86.0% in Zimbabwe (Fig. 2B).

Table 3 Descriptive statistics of the outcome and explanatory variables
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Fig. 2
figure 2

A Demand for family planning to limit childbearing (%). B Demand for family planning to limit childbearing satisfied with modern methods (%)

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Exploratory spatial data analyses

There was a significant positive global spatial autocorrelation (global Moran’s I = 0.3, p = 0.001), indicating significant clustering of countries with similar values in the demand for family planning to limit childbearing among married/in-union women. The LISA cluster map showed that the cold spots were concentrated in WCA (Fig. 3A). These spatial clusters of demand for family planning to limit childbearing were made up of 11 neighboring countries (Benin, Burkina Faso, Cote d’Ivoire, Gambia, Guinea, Liberia, Mali, Niger, Nigeria, Senegal, and Sierra Leone) (Fig. 3A). However, there were two spatial outliers (Ghana and Togo) contiguous with the cold spots in WCA. The hot spots were found in ESA. These high–high clusters included seven neighboring countries (Lesotho, Malawi, Namibia, South Africa, Tanzania, Zambia, and Zimbabwe) with values higher than the mean (Fig. 3A). Adjacent to the hot spots were three outliers (Angola, Comoros, and Mozambique), with low demand for family planning to limit childbearing (Fig. 3A).

Fig. 3
figure 3

LISA cluster map. A Demand for family planning to limit childbearing. B Demand for family planning to limit childbearing satisfied with modern methods

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Also, the global spatial autocorrelation in the demand for family planning to limit childbearing satisfied with modern methods was significant and positive (global Moran’s I = 0.2, p = 0.004). The cold spots were concentrated in WCA and included 11 neighboring countries (Benin, Burkina Faso, Cote d’Ivoire, Ghana, Guinea, Liberia, Mali, Niger, Nigeria, Sierra Leone, and Togo) (Fig. 3B). However, there was one outlier of high–low (Senegal) contiguous with the cold spots (Fig. 3B). A hot spot was located in ESA (Malawi) (Fig. 3B), while there were two outliers in the sub-region (Mozambique and Comoros) with low demand for family planning to limit childbearing satisfied with modern contraceptive methods compared with their neighboring countries (Fig. 3B).

Regression analysis

From the 13 potential independent variables, educational attainment, occupation, joint family planning decision, density of nurses/midwives, antenatal care, and out-of-pocket expenditure were significant at the univariate level (Table 4). Out of these variables, joint family planning decision making and antenatal care were selected by the backward stepwise procedure in the multivariate model (Table 5). After adjusting for antenatal care, the model showed that one unit increase in the percentage of joint family planning was associated with 0.34%-point increase in the demand for family planning to limit childbearing satisfied with modern methods (p < 0.001) (Table 5). Similarly, a unit increase in the percentage of women with antenatal care was associated 13.98%-point increase in the demand for family planning to limit childbearing satisfied with modern methods (p < 0.001) (Table 5).

Table 4 Univariate regression analysis of factors associated with the demand for family planning to limit childbearing satisfied with modern methods
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Table 5 Final multivariable regression analysis of factors associated with the demand for family planning to limit childbearing satisfied with modern methods
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Discussion

The understanding of the geographic variations in the use of family planning and its determinants in SSA is important for targeted interventions to achieve the SDG target 3.7 which specifies universal access to sexual and reproductive healthcare services, including family planning by 2030. Accordingly, this study assessed the demand for and correlates of family planning to limit childbearing and the demand for family planning to limit childbearing satisfied with modern methods. The results showed significant global spatial autocorrelation, providing evidence of spatial clustering of the two indicators. On the demand for family planning to limit childbearing, the LISA map showed that cold spots were concentrated in WCA, while hot spots were concentrated in ESA. A similar pattern was observed in the demand for family planning to limit childbearing satisfied with modern methods, particularly with the concentration of cold spots in WCA. Joint family planning decision making and antenatal care were the significant factors associated with demand for family planning to limit childbearing satisfied with modern methods in SSA.

Over the years, the demand for family planning to limit childbearing has been growing in many African countries. Economic reasons, health benefits, high parity, and knowledge of family planning are some of the factors motivating or associated with the desire to limit childbearing in SSA [38,39,40]. However, our findings suggest that the demand for limiting varies geographically in SSA, with high–high clusters concentrated in ESA. Although there has been a long-standing debate on the relative role played by socioeconomic development and increased access to family planning on reproductive behavior in resource-limited countries [41], both factors may have accounted for the observed variation across the countries. Going by the benchmark of ≥ 75% to evaluate the demand for family planning satisfied with modern methods among those who desire to limit childbearing [42], our results suggest that several countries may be underperforming. But with strong political will and concerted efforts, immense progress can be made before 2030.

Similar to the demand for family planning to limit childbearing, the spatial pattern of the demand for family planning to limit childbearing satisfied with modern methods showed a concentration of cold spots in WCA. Prior studies have indeed demonstrated a linear relationship between the demand for family planning and demand satisfied in SSA [43, 44], suggesting that both indicators are perhaps driven by similar factors. Our results are in line with previous findings that have reported lower contraceptive use in WCA compared to ESA [45, 46], perhaps due to poorer access to family planning services. In a study that examined the reasons for contraceptive non-use among married women, the proportion of respondents who cited lack of access (including high cost, lack of source or unawareness of source to procure contraception, source too far away, and preferred method or no method available) were higher in western (9.9%) and central Africa (14.6%) than in eastern Africa (6.9%) [47]. Lower educational attainment among women and approval of family planning in western Africa have also been implicated as limiting factors in the sub-region [45].

Our results indicate that joint family planning decision has a positive effect on the demand for family planning to limit childbearing satisfied with modern methods. In many patriarchal societies in Africa, male partners play an important role regarding contraceptive use by their spouses [48,49,50,51,52]. Compared to limiters who made contraception decision on their own, Olakunde et al. reported that the use of female permanent contraception was higher among those who made joint decision with their partners [53]. While women’s autonomy to decision making regarding their health is important, promoting interspousal communication and male involvement may improve the coverage of modern contraception among women with demand for family planning to limit childbearing. We also found a positive relationship between antenatal care and the demand for family planning to limit childbearing satisfied with modern methods. Antenatal care presents a platform to provide family planning counselling to pregnant women [54]. However, the impact of family planning messages during antenatal care on the use of family planning has varied in literature [55,56,57,58,59], with evidence suggesting that frequency of antenatal care may be a moderating factor [60, 61]. The mode of counselling may also a play an important role, as uptake of family planning has been found to be higher among women who participated in group counselling during antenatal care [55]. Despite the benefits of receiving antenatal care, its uptake, particularly the recommended four or more visits remains suboptimal in SSA, especially in WCA [62, 63]. The barriers affecting antenatal care coverage in SSA are multifaceted and will require interventions at community and health system levels [64]. For women who receive antenatal care, counselling for family planning should be provided at every visit.

The study has some limitations. We included only married/in-union women, thus the findings are not generalizable to all women. The surveys we used in the study were conducted in different years, and the status of contraceptive coverage in some of the countries may have changed. Also, for some of the external variables obtained from World Bank Open Data and World Health Organization Global Health Observatory Data, the most recent available data we used did not correspond with the DHS survey year. Unavailability of information in the surveys also limited the variables considered in this study. We recommend that future spatial analysis should consider lower areal units.

Conclusions

There are significant spatial variations in the demand for family planning to limit childbearing and the demand satisfied by modern methods in SSA, with cold spots (low–low clusters) concentrated in WCA. To improve the demand for family planning to limit childbearing satisfied by modern methods, our findings suggest the need for interventions to promote joint decision making by partners and uptake of antenatal care. As countries in SSA strive to ensure and benefit from universal access to reproductive healthcare services, it is critical that the reproductive needs of women who desire to limit childbearing are met with modern methods.

Data availability

Data used in study are publicly available via https://dhsprogram.com/; https://data.worldbank.org/indicator; and https://www.who.int/data/gho.

Abbreviations

DHS:

Demographic and health survey

ESA:

Eastern and southern Africa

ESDA:

Exploratory spatial data analysis

LIMCs:

Low- and middle-income countries

LISA:

Local indicator of spatial association

OLS:

Ordinary least squares

SDG:

Sustainable development goal

SSA:

Sub-Saharan Africa

WCA:

West and central Africa

References

  1. World Health Organization. Family planning/contraception. 2019. http://www.who.int/mediacentre/factsheets/fs351/en/. Accessed 4 Oct 2020.

  2. Cleland J, Bernstein S, Ezeh A, Faundes A, Glasier A, Innis J. Family planning: the unfinished agenda. Lancet. 2006;368(9549):1810–27.

    Article  PubMed  Google Scholar 

  3. Centers for Disease Control and Prevention. Achievements in public health, 1900–1999: family planning. MMWR Morb Mortal Wkly Rep. 1999;48(47):1073–80.

    Google Scholar 

  4. United Nations. The millennium development goals report 2015. New York; 2015.

  5. United Nations. SDG indicators. 2019. https://unstats.un.org/sdgs/METADATA?Text=&Goal=3&Target=3.7. Accessed 5 Oct 2020.

  6. Hubacher D, Trussell J. A definition of modern contraceptive methods. Contraception. 2015;92(5):420–1.

    Article  PubMed  Google Scholar 

  7. Festin MPR, Kiarie J, Solo J, Spieler J, Malarcher S, Van Look PFA, et al. Moving towards the goals of FP2020—classifying contraceptives. Contraception. 2016;94(4):289–94.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Polis CB, Bradley SEK, Bankole A, Onda T, Croft T, Singh S. Typical-use contraceptive failure rates in 43 countries with demographic and health survey data: summary of a detailed report. Contraception. 2016;94(1):11–7.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Westoff CF. Reproductive intentions and fertility rates. Int Fam Plan Perspect. 1990;16(3):84–96.

    Article  Google Scholar 

  10. Van Lith LM, Yahner M, Bakamjian L. Women’s growing desire to limit births in sub-Saharan Africa: meeting the challenge. Glob Health Sci Pract. 2013;1(1):97–107.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Westoff CF, Bankole A. Trends in the demand for family limitation in developing countries. Int Fam Plan Perspect. 2000;26:56–97.

    Article  Google Scholar 

  12. Westoff CF. Unmet need for modern contraceptive methods. DHS analytical studies. Calverton: ICF International; 2012.

    Google Scholar 

  13. Bolarinwa OA, Tessema ZT, Frimpong JB, Seidu A-A, Ahinkorah BO. Spatial distribution and factors associated with modern contraceptive use among women of reproductive age in Nigeria: a multilevel analysis. PLoS ONE. 2021;16(12):e0258844.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nyarko SH. Spatial variations and socioeconomic determinants of modern contraceptive use in Ghana: a Bayesian multilevel analysis. PLoS ONE. 2020;15(3):e0230139.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Habyarimana F, Ramroop S. Spatial analysis of socio-economic and demographic factors associated with contraceptive use among women of childbearing age in Rwanda. Int J Environ Res Public Health. 2018;15(11):2383.

    Article  PubMed Central  Google Scholar 

  16. Kandala NB, Lukumu FK, Mantempa JN, Kandala JD, Chirwa T. Disparities in modern contraception use among women in the Democratic Republic of Congo: a cross-sectional spatial analysis of provincial variations based on household survey data. J Biosoc Sci. 2015;47(3):345–62.

    Article  PubMed  Google Scholar 

  17. Adebayo AM, Ojo TO, Omotoso BA, Ayodeji OO. Family planning services in a tertiary hospital in a semi-urban area of south-western Nigeria: uptake and determinants of contraceptive use. J Obstet Gynaecol. 2016;36(7):904–8.

    Article  CAS  PubMed  Google Scholar 

  18. Burgert-Brucker CR, Yourkavitch J, Assaf S, Delgado S. Geographic variation in key indicators of maternal and child health across 27 countries in sub-Saharan Africa. DHS spatial analysis reports. Rockville: ICF International; 2015.

    Google Scholar 

  19. Lakew Y, Reda AA, Tamene H, Benedict S, Deribe K. Geographical variation and factors influencing modern contraceptive use among married women in Ethiopia: evidence from a national population based survey. Reprod Health. 2013;10:52.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Tegegne TK, Chojenta C, Forder PM, Getachew T, Smith R, Loxton D. Spatial variations and associated factors of modern contraceptive use in Ethiopia: a spatial and multilevel analysis. BMJ Open. 2020;10(10):e037532.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Okoli ML, Alao S, Ojukwu S, Emechebe NC, Ikhuoria A, Kip KE. Predictive and spatial analysis for estimating the impact of sociodemographic factors on contraceptive use among women living with HIV/AIDS (WLWHA) in Kenya: implications for policies and practice. BMJ Open. 2019;9(1):e022221.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Nyarko SH, Sparks CS, Bitew F. Spatio-temporal variations in unmet need for family planning in Ghana: 2003–2014. Genus. 2019;75:22.

    Article  Google Scholar 

  23. Alaba OO, Olaomi JO, Olubusoye OE. Spatial pattern and determinants of unmet need of family planning in Nigeria. S Afr Fam Pract. 2015;57(5):306–12.

    Article  Google Scholar 

  24. Demographic and health surveys (DHS) program. https://dhsprogram.com/. Accessed 4 Oct 2020.

  25. The World Bank. Data. https://data.worldbank.org/indicator. Accessed 4 Oct 2020.

  26. World Health Organization.The global health observatory. https://www.who.int/data/gho. Accessed 4 Oct 2020.

  27. Croft TN, Marshall AMJ, Allen CK. Guide to DHS statistics. Rockville: ICF; 2018.

    Google Scholar 

  28. Mutumba M, Wekesa E, Stephenson R. Community influences on modern contraceptive use among young women in low and middle-income countries: a cross-sectional multi-country analysis. BMC Public Health. 2018;18:430.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Elfstrom KM, Stephenson R. The role of place in shaping contraceptive use among women in Africa. PLoS ONE. 2012;7(7):e40670.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gakidou E, Vayena E. Use of modern contraception by the poor is falling behind. PLoS Med. 2007;4(2):e31.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Stephenson R, Baschieri A, Clements S, Hennink M, Madise N. Contextual influences on modern contraceptive use in sub-Saharan Africa. Am J Public Health. 2007;97(7):1233–40.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yaya S, Uthman OA, Ekholuenetale M, Bishwajit G. Women empowerment as an enabling factor of contraceptive use in sub-Saharan Africa: a multilevel analysis of cross-sectional surveys of 32 countries. Reprod Health. 2018;15:214.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Asaolu I, Nunõ VL, Ernst K, Taren D, Ehiri J. Healthcare system indicators associated with modern contraceptive use in Ghana, Kenya, and Nigeria: evidence from the performance monitoring and accountability 2020 data. Reprod Health. 2019;16:152.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Ngome E, Odimegwu C. The social context of adolescent women’s use of modern contraceptives in Zimbabwe: a multilevel analysis. Reprod Health. 2014;11:64.

    Article  PubMed  PubMed Central  Google Scholar 

  35. ICPAC Geoportal. Africa—admin level 0. 2017. http://geoportal.icpac.net/layers/geonode%3Aafr_g2014_2013_0. Accessed 6 Oct 2020.

  36. Anselin L. Exploring spatial data with GeoDa TM: a workbook. Urbana: University of Illinois; 2005.

    Google Scholar 

  37. Kim JH. Multicollinearity and misleading statistical results. Korean J Anesthesiol. 2019;72(6):558–69.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kodzi IA, Johnson DR, Casterline JB. T To have or not to have another child: life cycle, health and cost considerations of Ghanaian women. Soc Sci Med. 2012;74(7):966–72.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Machiyama K, Mumah JN, Mutua M, Cleland J. Childbearing desires and behaviour: a prospective assessment in Nairobi slums. BMC Pregnancy Childbirth. 2019;19:100.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Dibaba Y. Factors influencing women’s intention to limit child bearing in Oromia, Ethiopia. Ethiop J Health Dev. 2009;23(1):28–33.

    Article  Google Scholar 

  41. Bongaarts J. The impact of family planning programs on unmet need and demand for contraception. Stud Fam Plan. 2014;45(2):247–62.

    Article  Google Scholar 

  42. Fabic MS, Choi Y, Bongaarts J, Darroch JE, Ross JA, Stover J, et al. Meeting demand for family planning within a generation: the post-2015 agenda. Lancet. 2015;385(9981):1928–31.

    Article  PubMed  Google Scholar 

  43. Bongaarts J, Hardee K. The role of public-sector family planning programs in meeting the demand for contraception in sub-Saharan Africa. Int Perspect Sex Reprod Health. 2017;43(2):41–50.

    Article  PubMed  Google Scholar 

  44. Bongaarts J. The causes of educational differences in fertility in sub-Saharan Africa. Vienna Yearb Popul Res. 2010;8:31–50.

    Article  Google Scholar 

  45. Cleland JG, Ndugwa RP, Zulu EM. Family planning in sub-Saharan Africa: progress or stagnation? Bull World Health Organ. 2011;89(2):137–43.

    Article  PubMed  Google Scholar 

  46. Tsui AO, Brown W, Li Q, Brown W, Li Q. Contraceptive practice in sub-Saharan Africa. Popul Dev Rev. 2017;43(Suppl 1):166–91.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Sedgh G, Hussain R. Reasons for contraceptive nonuse among women having unmet need for contraception in developing countries. Stud Fam Plan. 2014;45(2):151–69.

    Article  Google Scholar 

  48. Shattuck D, Kerner B, Gilles K, Hartmann M, Ng’ombe T, Guest G. Encouraging contraceptive uptake by motivating men to communicate about family planning: the Malawi male motivator project. Am J Public Health. 2011;101(6):1089–95.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Olakunde BO, Sam-Agudu NA, Patel TY, Hunt AT, Buffington AM, Phebus TD, et al. Uptake of permanent contraception among women in sub-Saharan Africa: a literature review of barriers and facilitators. Contraception. 2019;99(4):205–11.

    Article  PubMed  Google Scholar 

  50. Daniele MAS, Ganaba R, Sarrassat S, Cousens S, Rossier C, Drabo S, et al. Involving male partners in maternity care in Burkina Faso: a randomized controlled trial. Bull World Health Organ. 2018;96(7):450–61.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Musalia JM. Gender, social networks, and contraceptive use in Kenya. Sex Roles. 2005;53:835–46.

    Article  Google Scholar 

  52. Blackstone SR, Nwaozuru U, Iwelunmor J. Factors influencing contraceptive use in sub-Saharan Africa: a systematic review. Int Q Community Health Educ. 2017;37(2):79–91.

    Article  PubMed  Google Scholar 

  53. Olakunde BO, Pharr JR, Chien L-C, Benfield RD, Sy FS. Individual- and country-level correlates of female permanent contraception use in sub-Saharan Africa. PLoS ONE. 2020;15(12):e0243316.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Osungbade K, Oginni S, Olumide A. Content of antenatal care services in secondary health care facilities in Nigeria: implication for quality of maternal health care. Int J Qual Health Care. 2008;20(5):346–51.

    Article  PubMed  Google Scholar 

  55. Lori JR, Chuey M, Munro-Kramer ML, Ofosu-Darkwah H, Adanu RMK. Increasing postpartum family planning uptake through group antenatal care: a longitudinal prospective cohort design. Reprod Health. 2018;15:208.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Smith KB, Van Der Spuy ZM, Cheng L, Elton R, Glasier AF. Is postpartum contraceptive advice given antenatally of value? Contraception. 2002;65(3):237–43.

    Article  CAS  PubMed  Google Scholar 

  57. Keogh SC, Urassa M, Kumogola Y, Kalongoji S, Kimaro D, Zaba B. Postpartum contraception in northern Tanzania: patterns of use, relationship to antenatal intentions, and impact of antenatal counseling. Stud Fam Plan. 2015;46(4):405–22.

    Article  Google Scholar 

  58. Coomson JI, Manu A. Determinants of modern contraceptive use among postpartum women in two health facilities in urban Ghana: a cross-sectional study. Contracept Reprod Med. 2019;4:17.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Ayiasi RM, Muhumuza C, Bukenya J, Orach CG. The effect of prenatal counselling on postpartum family planning use among early postpartum women in Masindi and Kiryandongo districts, Uganda. Pan Afr Med J. 2015;21:138.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Adanikin AI, Onwudiegwu U, Loto OM. Influence of multiple antenatal counselling sessions on modern contraceptive uptake in Nigeria. Eur J Contracept Reprod Health Care. 2013;18(5):381–7.

    Article  PubMed  Google Scholar 

  61. Teka TT, Feyissa TR, Melka AS, Bobo FT. Role of antenatal and postnatal care in contraceptive use during postpartum period in western Ethiopia: a cross sectional study. BMC Res Notes. 2018;11:581.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Woldegiorgis MA, Hiller J, Mekonnen W, Meyer D, Bhowmik J. Determinants of antenatal care and skilled birth attendance in sub-Saharan Africa: a multilevel analysis. Health Serv Res. 2019;54(5):1110–8.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Kanyangarara M, Munos MK, Walker N. Quality of antenatal care service provision in health facilities across sub-Saharan Africa: evidence from nationally representative health facility assessments. J Glob Health. 2017;7(2):021101.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Okedo-Alex IN, Akamike IC, Ezeanosike OB, Uneke CJ. Determinants of antenatal care utilisation in sub-Saharan Africa: a systematic review. BMJ Open. 2019;9(10):e031890.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

None.

Funding

No funding was received for this study.

Author information

Author notes

  1. Babayemi O. Olakunde

    Present address: Department of Community Prevention and Care Services, National Agency for the Control of AIDS, 3, Ziguinchor Street, off IBB Way, Wuse Zone 4, Abuja, Nigeria

  2. Babayemi O. Olakunde

    Present address: Center for Translation and Implementation Research, University of Nigeria Nsukka, Enugu, Nigeria

Authors and Affiliations

  1. Department of Environmental and Occupational Health, School of Public Health, University of Nevada, Las Vegas, NV, USA

    Babayemi O. Olakunde, Jennifer R. Pharr & Francisco S. Sy

  2. Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Canada

    Daniel A. Adeyinka

  3. Department of Public Health, National AIDS and STI Control Programme, Federal Ministry of Health, Abuja, Nigeria

    Daniel A. Adeyinka

  4. Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada, Las Vegas, NV, USA

    Lung-Chang Chien

  5. School of Nursing, University of Nevada, Las Vegas, NV, USA

    Rebecca D. Benfield

Authors
  1. Babayemi O. Olakunde
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  6. Francisco S. Sy
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Contributions

BOO and JRP conceived the study. BOO and DAA conducted the analysis. BOO and JRP wrote the first draft. L-CC, RDB, and FSS revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Babayemi O. Olakunde.

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This study was a secondary analysis of publicly available data. It was deemed exempt by the University of Nevada, Las Vegas Institutional Review Board.

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Olakunde, B.O., Pharr, J.R., Adeyinka, D.A. et al. Spatial variations in family planning demand to limit childbearing and the demand satisfied with modern methods in sub-Saharan Africa. Reprod Health 19, 144 (2022). https://doi.org/10.1186/s12978-022-01451-5

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Keywords

Reproductive Health

ISSN: 1742-4755