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Factors influencing exclusive breastfeeding in Sub-Saharan Africa: analysis of demographic and health surveys

BMC Public Health volume 25, Article number: 1790 (2025) Cite this article

Abstract

Background

Low rates of exclusive breastfeeding (EBF) contribute to high child mortality rates. This study aimed to identify the prevalence and factors influencing EBF in SSA using nationally representative data from the Demographic Health Survey program (DHS).

Methods

A cross-sectional study design using the most recent DHS data of 25 SSA countries from 2010 to 2023. A total of 17,431 women aged 15–49 years with infants below six months were included. Pearson’s chi-square and multivariable logistic regression tests were used to test the association and predictors of EBF at a 95% confidence interval (CI) at a significance level of p ≤ 0.05.

Results

The pooled prevalence of EBF across 25 Sub-Saharan African countries was 49%, ranging from 83% in Burundi to 19% in Gabon. Multivariable logistic regression revealed that older infants aged 2–3 months (AOR = 0.511, 95% CI: 0.458–0.570) and 4–5 months (AOR = 0.176, 95% CI: 0.156–0.198) had significantly lower odds of EBF compared to newborns (0–1 month). Overweight infants (AOR = 0.800, 95% CI: 0.647–0.987) and those experiencing fever (AOR = 0.805, 95% CI: 0.704–0.921) or diarrhea (AOR = 0.799, 95% CI: 0.683–0.935) were also less likely to be exclusively breastfed. Mothers with higher education (AOR = 0.700, 95% CI: 0.516–0.948) had reduced EBF odds. Wealthier households demonstrated higher odds (middle: AOR = 1.411, 95% CI: 1.185–1.681; richer: AOR = 1.539, 95% CI: 1.254–1.889; richest: AOR = 1.455, 95% CI: 1.119–1.892). Rural, the East (AOR = 2.588, 95% CI: 2.162–3.099) or West Africa (AOR = 3.018, 95% CI: 2.464–3.697) significantly increased EBF odds compared to urban areas and Central Africa, respectively.

Conclusion

This study highlights key factors influencing EBF in 25 Sub-Saharan African countries. The findings provide complex interplay of individual and community factors influencing EBF. To improve EBF, targeted interventions should focus on: supporting mothers of older infants, integrating counseling into child health services, addressing wealth gaps, and implementing region-specific strategies. Strengthening health systems and community engagement is crucial to overcome barriers and promote equitable breastfeeding practices.

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Introduction

Exclusive breastfeeding (EBF) is the feeding practice where infants are exclusively fed with breastmilk without anything else for the first 6 months of life [1]. It benefits both the mother and child as it protects the child from childhood illness, enhances child’s cognitive capacity and reduces the risk of overweight and obesity in adulthood; lactating women also benefit from reduced risk of cardiovascular disease, breast and ovarian cancer and type 2 diabetes and ameliorate child spacing [2,3,4]. Breastmilk contains bioactive compounds like antibodies, probiotics, and microbiome responsible for specific and nonspecific immunity against infectious (diarrheal and respiratory diseases), chronic diseases and obesity from the first few months throughout breastfeeding period and later in life [5,6,7,8,9].

A recent study conducted in Cameroon revealed that children who were exclusively breastfed had lower prevalence of malaria parasite (16.2%) than non-breastfed children (61.3%) [10]. According to UNICEF, WHO, and World Bank report of 2023, children born in SSA are 18 times more likely to die compared to those born in Australia and New Zealand. Also, 4 in 5 under-five child deaths occurred in SSA and Southern Asia, and only 3 in 5 live births occurred in these regions in 2022 [10, 11]. According to this report SSA and Southern Asia contributed to 57% (2.8 million) and 26% (1.3 million) of global under five mortalities, respectively [11]. These two regions carried unacceptably high newborn mortality rate accounting for 46% (1.1 million) in SSA and 34% (0.8 million) in Southern Asia [11].

It is estimated that 16% of children die each year due to inadequate breastfeeding globally [2]. Research also showed that non-breastfed children were 14 times more likely to die from childhood illness and 8 times more likely to die from infectious disease than exclusively breastfed infants from 0 to 5 months [12]. In 2023, the global prevalence of EBF was 48% among infants aged 0–5 months which is close to the WHO target of 50% by 2025 [2], which was shifted to the 2030 global target that 70% of children will be exclusively breastfed up to 6 months [13]. If this universal breastfeeding recommendation is successfully met, 820,000 lives of children will be saved globally and 20,000 lives of children will be saved in LMICs [14].

A study conducted in 57 LMICs through 2010–2018, showed that the global prevalence of breastfeeding was 51.9% for early initiation of breastfeeding while EBF under 6 months, 4–5 months accounted for 45.7% and 32% respectively [15]. It was also shown that upper middle-income countries (UMICs) had poor performance of EBF in children less than 6 months (38.4%) than LMICs (47.4%) [15]. However, in SSA, the prevalence of EBF in children under 6 months was still low accounting for 34% in the period of 2000 to 2018 compared to the WHO target, and the prevalence of early initiation of breastfeeding was estimated at 47%, respectively [16].

In SSA, inappropriate EBF has led to the 55–75% of child deaths and this mortality contributed to the loss of 6% of GDP in SSA countries. However, variability in prevalence (early initiation and under 6 months children) exist in SSA countries, ranging from 5% in Chad to 86% in Rwanda [16]. The said variability is attributable to multiple factors influencing breastfeeding practices. These include but not limited to sociodemographic factors like age, education, marital status, occupation, wealth index, region of residence, sex of the child, and parity; health facilities utilization such as antenatal care (ANC) visits, place and mode of delivery; environmental and cultural factors [17,18,19,20,21,22,23,24].

The low prevalence of EBF (34%) is an alarming public health concern that requires robust interventions to save lives of children and economy in SSA as it has the highest child mortality (55–75%) attributable to poor EBF [16]. To the best of our knowledge, there is limited research about the drivers of inappropriate EBF in SSA. It is in this regard our study aims to find out the factors influencing EBF in SSA using a nationally representative data from the Demographic Health Survey (DHS) program.

Study objectives

To assess the prevalence and predictors of EBF in SSA using DHSs.

Methods and materials

Study design and data source

A cross-sectional study design using the most recent DHS data of 25 SSA countries from 2010 to 2023. This data set is a representative survey usually conducted in an interval of 5- years in low - and middle-income countries (LMICs) globally [25]. Respondents of these surveys are recruited using a multistage random sampling method and samples are generally representative at the national, residential (urban-rural) and regional (counties, cities, etc.) levels. This comprehensive data collection strategy used by the DHS programme has been detailed in the DHS website (https://www.dhsprogram.com/Methodology/Survey-Types/DHS-Methodology.cfm) and in a previous study [25].

Study population and sample

The study included 17,431 (weighted sample = 17000) youngest living children aged below 6 months and living with their mother and their respective mothers. The data were obtained from the DHS website’s children record (labelled KR file) [26]. Supplementary Table 1 shows the distribution of the samples by countries.

Study variables

The outcome variable for this study was EBF of infants less than 6 months of age. EBF is a feeding practice where infants are exclusively fed with breastmilk without anything else for the first 6 months of life [1]. The EBF was coded as 0 “No EBF” and 1 “EBF”. The guide to recoding the EBF variable is described in literature [27].

The independent variables for the study were classified into child factors, maternal factors and community level factors. The child factors only focused on the youngest child age 0–5 months living with the mother. These include child age, child’s sex, birth weight, birth order, child had fever or diarrhoea two weeks prior to the survey. Maternal factors included maternal age, religion, marital status, parity, birth interval, maternal educational level, maternal occupation, wealth index, age at ANC registration, number of ANC visits, frequency of listening to radio, frequency of reading newspaper/magazine, frequency of watching television, and overall exposure to any form of traditional media. These variables were considered because of their statistically significant relationships with EBF factors in previous studies [28,29,30].

The community level factors include place of residence, community level of education, community level of poverty, community media exposure, and SSA sub-region. The DHS do not collect data that can directly be described as community level data except for place of residence. Therefore, the community-level data were generated using the individual level responses within a cluster and the average used for categorization [31]. For instance, suppose in one cluster, the average years of education is 8 years. If a woman’s education level is below 8 years, she is coded as part of a “low-education” community. If her education level is 8 years or higher, she is coded as part of a “high-education” community. Again, let’s say the average wealth index in a community is 2.5 on a scale of 1 to 5, where a lower score indicates lower poverty. If a woman’s wealth index is 2.6 or higher, she is in a “high wealth” community. If her index is below 2.5 or lower, she falls into a “low-wealth” community.

Data analysis

The study analysis used multi-step approach including a univariate, bivariate and multivariate analysis. In the first step of the analysis, frequencies and proportions were used to summarize the prevalence of EBF among the demographic groups. Pearson’s chi-square test was used test the association between EBF at a 95% confidence interval (CI) at a significance level of p ≤ 0.05.

In the third step, binary logistic regression was used to identify the individual predictor variables of EBF in the form of crude odds ratio (cOR). Adjusted Odds Ratio (aOR) was used to adjusted for confounders in the study. Findings from the aOR, significant at 95% CI, were reported.

The data was analysed using STATA MacOS version 18, and the figures and tables were created using Microsoft Excel. In the analysis, the ’svy’ command in STATA was used to allow for adjustments for the cluster-sampling design of the data collection and weight. This was also done to account for selection bias in the study.

Missing data

By default STATA uses pairwise deletion methods to control missing data at random. We used this method to address the missing data in the study.

Model diagnostics

Due to the complexity of the study sampling design, the hierarchical nature of the fitted model, and the categorical nature of the outcome variable, the collinearity of the model was assessed using generalized variance inflation factor (GVIF). To obtain the GVIF of the independent variables, first the variance inflation factor was obtained through post-estimation result [32]. The GVIF was then computed using:

$$GVIF = VI{F^{[1/(2*df)]}}$$
(1)

where df: degree of freedom = 18.

Supplementary Table 2 present the GVIF. Number of ANC visits was removed due to collinearity.

Hosmer Lemeshow (HL) test was used to test the goodness-of-fit for the logistic regression model. The HL results shows that the model was fit (chi- square χ2 = 1.45 and p-value = 0.16). Likelihood Ratio (LR) test (LR chi2 = 2613.29 and p < 0.0001) explains a statistically significant model.

Results

Prevalence of exclusive breastfeeding

The pooled prevalence of EBF in SSA was 49% [95% CI: 42 − 55%,], with the highest EBF prevalence recorded in Rwanda (79% [95% CI: 74 – 82%]) and Burundi (83% [95% CI: 79 – 82%]). The lowest EBF prevalence was observed in Gabon (19% [95% CI: 16 – 23%]) and Guinea (25% [95% CI: 21 – 30%]). Evidence of a significant heterogeneity was observed (I2 = 98.71%, p < 0.0001), suggesting a very high variability of prevalence of EBF between each country. This is presented in Fig. 1.

Fig. 1
figure 1

Pooled prevalence of exclusive breastfeeding in SSA

Full size image

Table 1 presents the prevalence of EBF by demographic characters and the factors associated with EBF. Several child-related factors were significantly associated with EBF, except for the sex of the child (p = 0.910) and birth order (p = 0.099). One of the strongest predictors of EBF was child age (p < 0.0001), as the prevalence of EBF decreased significantly as the child grew older. Among 0–1 month-old infants, 64.8% were exclusively breastfed, while among 4–5 month-old infants, only 27.8% were exclusively breastfed.

Birth weight also influenced EBF practices (p < 0.0001). Underweight babies (49.0%) had a higher prevalence of EBF than overweight babies (43.4%), while normal-weight infants had the highest prevalence at 53.1%. Child illness was another significant factor (p < 0.0001). Infants who had fever (36.8%) or diarrhea (3.9%) had much lower EBF rates than those without these illnesses (48.4% and 48.8%, respectively).

Most maternal factors were significantly associated with EBF, except for maternal age (p = 0.793), marital status (p = 0.324), parity (p = 0.208), and birth interval (p = 0.116). Maternal education was a strong predictor, where women with higher education (2%) had the lowest EBF rate, whereas those with primary education (16.9%) had the highest prevalence. Maternal occupation also influenced EBF, as women working in agriculture (54.2%) and manual labor (51.7%) had significantly higher EBF rates, while professional women (41.0%) and those not working (45.5%) had lower EBF rates. Media exposure played a role as well, with women who listened to the radio almost every time (59.7%) having a higher EBF prevalence than those who did not listen at all (46.7%) (p < 0.001). Similarly, reading newspapers and watching TV frequently were also associated with increased EBF rates. This is presented in Table 1 continue.

Table 1 Prevalence and factors associated with EBF in SSA
Full size table
Table 1 Continue prevalence and factors associated with EBF
Full size table

Community factors were significantly associated with EBF (p < 0.0001 for all variables). Urban mothers (40.2%) had a lower EBF prevalence compared to rural mothers (50.6%). Community education level also played a role, with lower community education levels associated with higher EBF (48.0%), compared to higher community education (43.5%). Community poverty level showed that women in poorer communities (48.8%) had higher EBF rates than those in wealthier communities (45.1%). The SSA sub-regions also showed significant variation in EBF prevalence. West Africa had the highest EBF prevalence (63.6%), while Central Africa had the lowest (37.9%). This is presented in Table 2.

Table 2 Prevalence and factors associated with EBF (Community-level factors)
Full size table

Multivariable logistic regression

Table 3 presents the multivariable logistic regression analysis of factors influencing EBF in this study. The odds of EBF decline significantly as the child ages. Compared to infants aged 0–1 months, those aged 2–3 months have 48.9% lower odds of EBF (aOR: 0.511, 95% CI: 0.458–0.570), while infants aged 4–5 months have 82.4% lower odds (aOR: 0.176, 95% CI: 0.156–0.198). Religion was found to be signficant predictor of EBF, with mothers belonging to the Islamic faith (aOR: 0.732, 95%CI: 0.634–0.844) or nor no religion (aOR: 0.632, 95% CI: 0.489–0.818) having lower odds of EBF compared to mothers in the Christian faith. Maternal education plays a role, with mothers who have higher education (aOR: 0.700, 95% CI: 0.516–0.948) showing lower odds of EBF compared to those with no education. Compared to the poorest households, those in the middle (aOR: 1.411, 95% CI: 1.185–1.681), richer (aOR: 1.539, 95% CI: 1.254–1.889), and richest (aOR: 1.455, 95% CI: 1.119–1.892) quintiles have significantly higher odds of EBF. Early ANC registration improves EBF odds. Mothers who registered at < 4 months (aOR: 1.608, 95% CI: 1.320–1.960), 4–5 months (aOR: 1.550, 95% CI: 1.277–1.882), or 8 + months (aOR: 1.625, 95% CI: 1.093–2.415) have higher odds of EBF than those with no ANC. Whilst reading newspapers/magazines weekly increases EBF odds (aOR: 1.114, 95% CI: 1.003–1.238), watching TV weekly decreased the odds of EBF (aOR: 0.816, 95% CI: 0.721–0.924).

Regarding community and regional factors, our findings also show that rural residence increases EBF odds by 39.5% (aOR: 1.395, 95% CI: 1.191–1.633) compared to urban areas. High community poverty reduces EBF odds (aOR: 0.814, 95% CI: 0.697–0.950). East Africa (aOR: 2.588, 95% CI: 2.162–3.099) and West Africa (aOR: 3.018, 95% CI: 2.464–3.697) have 2.5-3 times higher odds of EBF than Central Africa. Southern Africa shows a modest increase (aOR: 1.270, 95% CI: 1.057–1.525).

Table 3 Multivariable logistic regression model
Full size table

Discussion

This study assesses the prevalence and predictors of EBF among 25 countries SSA. The analysis reveals that the pooled prevalence of EBF in SSA is 49%. The age of child, birthweight, child having fever diarrhoea were found to be significant predictors of EBF. Also, maternal factors like religion, maternal level of education, wealth index, gestational age at ANC registration, number of ANC visits, and media exposure were found to be significant predictors of EBF. Community level factors; place of residence, community level of poverty, and the sub-region were also found to be significantly associated with the prevalence of EBF.

Among the countries studied, Burundi (83%) and Rwanda (79%) reported the highest EBF prevalence. In contrast, Gabon (19%) and Guinea (25%) reported the lowest EBF. These results indicate a significant heterogeneity among the countries, as shown by heterogeneity value of 98.71% and a p-value of less than 0.0001. This high heterogeneity value suggests a substantial variation in EBF prevalence rates across the different countries in the region. The significant heterogeneity implies that the differences in EBF prevalence are not due to random chance but are likely influenced by various country-specific factors, such as cultural practices, healthcare infrastructure, socioeconomic conditions, and policy implementations related to breastfeeding. Moreover, regional variations indicated that mothers in East and West Africa had higher odds of practicing EBF compared to those in Central Africa, pointing to regional differences in breastfeeding practices, healthcare infrastructure, and cultural influences.

Age of child was found to be negatively associated with EBF, with older children less likely to be EBF. This finding corresponds with several earlier studies [33,34,35]. This finding could be explained as the age of the infant increases mothers often initiate early complementary feeding. Some mothers hold the view that breast milk alone may not meet the nutritional and water need of the growing infant. Additionally, working mothers may resume work as the infant grow older. This could result in mothers not having enough time to breastfeed the infant.

The study also found that mothers with overweight children were less likely to give EBF. Studies have demonstrated that children born to overweight or obese mothers are at a higher risk of being overweight themselves [36]. Other researchers further argued that overweight mothers had lower rates of breastfeeding [37]. Our study finding therefore suggest an interplay between maternal weight and child breastfeeding status. Therefore, addressing maternal obesity and supporting breastfeeding initiation and continuation could be pivotal strategies in promoting healthier weight outcomes for both mothers and their children.

The study also found that infants who had fever or diarrhoea were less likely to be on EBF. This finding aligns with studies conducted in Ethiopia [33], and Egypt [38]. When a child has a fever, they may experience a decreased appetite, fatigue, or discomfort, which can make breastfeeding less frequent or less efficient. Similarly, diarrhoea can lead to dehydration, which might discourage breastfeeding as the child may experience stomach discomfort or nausea, making them less likely to feed. Mothers of such infants may perceive that breastmilk is not sufficient to give energy to the child and also build their immunity against other infections.

The study found that religion significantly influenced EBF practices in SSA. Muslim mothers and those with no religion were less likely to practice EBF compared to Christians. This aligns with previous studies suggesting that cultural and religious norms may shape infant feeding practices [39, 40]. Some Muslim communities favor mixed feeding due to traditional beliefs or early complementary feeding practices. Further research is needed to explore how religious teachings and community norms influence breastfeeding behaviors in different SSA contexts.

Maternal education showed higher education reduced the likelihood of EBF in our study. This contrasts with an earlier study that higher education typically promotes better health practices [17]. A possible explanation is that educated mothers in SSA have, over the years, gained active workforce participation. This may limit their ability to exclusively breastfeed due to work constraints. Alternatively, they may have greater exposure to formula marketing, influencing feeding choices. This finding suggests that workplace breastfeeding support policies are crucial, particularly for educated working mothers.

Maternal occupation also played a key role, with agricultural workers more likely to practice EBF compared to non-working mothers. This may be due to flexible work environments that allow mothers to breastfeed while working, unlike formal sector jobs with rigid schedules. Conversely, clerical and part-time workers had lower EBF rates, likely due to job demands and lack of breastfeeding-friendly workplaces. These findings highlight the need for maternity protection policies and on-site childcare support to enable EBF among working mothers.

The wealth index revealed an unexpected pattern. Increased wealth index improved EBF among mothers. This suggests that wealth linearly improve EBF practices. This corresponds with studies from other regions where wealth positively correlates with EBF [33]. Wealthy mothers may have access to quality ANC and PNC that has been found to improve EBF [41].

Gestational age at ANC registration was also found to be significant predictor of EBF in our study. Mothers who registered ANC before the fourth month were more likely to give EBF, compared to mothers who did not register for ANC. This finding corresponds with earlier studies [42, 43]. In contrast to this finding, an earlier study reported no significant relationship between EBF and ANC timing [44]. Early exposure to maternal and child health education during ANC visits helps mothers understand the importance of exclusive breastfeeding, thereby promoting healthier breastfeeding practices [42]. Early registration could also provide opportunities for addressing potential breastfeeding challenges, leading to greater confidence and success in initiating and maintaining EBF.

Mothers who listen to radio, watch television or read newspaper/magazine at least once a week or almost every time had high odds for EBF compared to mother who do media not at all. An Indian study reported that women who are exposed to radio, television or newspaper were at a high odds of practicing optimum Infant and Young Child Feeding practices (IYCF), including EBF [45]. Media exposure provides information and awareness on the importance of EBF, influencing maternal behavior. The implication is that increasing media campaigns about breastfeeding can promote EBF. Therefore, public health initiatives should focus on using media to educate and support breastfeeding practices, targeting wider audience reach through frequent and accessible channels.

The study found that several community-level factors significantly predicted EBF. For instance, rural mothers were more likely to practice EBF compared to their urban counterparts. Rural mothers are more likely to engage in informal or agricultural work, which allows for greater flexibility in breastfeeding compared to urban mothers in rigid formal employment. Limited access to and affordability of commercial formula in rural settings further reinforce reliance on breastfeeding. Urban mothers, meanwhile, face higher exposure to formula promotion, workplace constraints, and perceived social stigma around public breastfeeding, all of which may discourage EBF. This urban-rural divide highlights how structural and cultural environments shape infant feeding practices. This study’s findings suggest that rural mothers are more likely to practice EBF due to limited access to alternatives.

The study also found that community level of poverty increased EBF rate. This finding could be related to the urban-rural divide discussed in the previous paragraph. Most rural areas have reduced level of wealth. Also, communities with high level of poverty could also present a reduced access to formula, thereby increasing their reliance on EBF. Interventions promoting EBF should therefore be targeted at urban and wealthier communities. This would address the availability and convenience of alternatives like formula feeding. Additionally, programs should leverage rural communities’ strong breastfeeding traditions and provide support to educated mothers, encouraging continued EBF despite work or societal pressures.

Strengths and limitations

There was adequate statistical power because of the large dataset used in this study. The study’s use of nationally representative data from 25 SSA nations’ Demographic and Health Surveys improves the findings’ generalizability. Regression models were rigorously examined in order to guarantee the reliability and validity of the study’s analysis. Because of the cross-sectional nature of the research design, it is challenging to determine causal links. There is also a possibility of recall bias, as the data collection necessitated that the participant recalls prior events.

Conclusion

This study highlights the prevalence and predictors of EBF across 25 countries in SSA. The pooled prevalence of EBF in SSA was found to be 49%, with significant variations across countries, suggesting the influence of cultural, healthcare, and socioeconomic factors. The study identified several individual and community-level predictors of EBF, including child age, birthweight, child health (fever, diarrhoea), maternal education, wealth index, and media exposure. Community factors, such as place of residence, poverty levels, and education, also significantly affected EBF practices.

The study’s findings underscore the importance of early ANC registration, media exposure, and community support in promoting EBF. It also highlights the need for tailored interventions addressing urban and wealthier communities, where the availability of formula and work pressures may discourage EBF. Furthermore, rural communities, which often have stronger breastfeeding traditions, can serve as a valuable source for promoting breastfeeding practices with the right support. The heterogeneity in EBF prevalence across SSA calls for region-specific strategies that take into account cultural, economic, and healthcare differences. Therefore, interventions to promote EBF should be multifaceted, addressing maternal, child, and community-level factors, while emphasizing the role of media and education in supporting breastfeeding practices across the region.

Data availability

Data for this study is freely available in the Demographic and Health Survey website (https://www.dhsprogram.com/Data/).

Abbreviations

ANC:

Antenatal care

aOR:

adjusted odds ratio

cOR:

crude odds ratio

CI:

confidence interval

DHS:

Demographic Health Survey

EBF:

Exclusive Breastfeeding

GVIF:

Generalized Variance Inflation Factor

IYCF:

Infant and Young Child Feeding

LMIC:

Low-and-Middle Income Country

PNC:

Postnatal care

SSA:

sub-Saharan Africa

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Acknowledgements

We wish to acknowledge the Demographic Health Survey Program for permitting us to use the data for our study.

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Upper West Regional Health Directorate, Wa, Ghana

    Munawar Harun Koray

  2. The Technical University of Kenya, Nairobi, Kenya

    James Njuguna Wanjiru

  3. African Methodist Episcopal University, Monrovia, Liberia

    Johnson Socrates Kerkula

  4. Rugarama Health Centre, Kigali, Rwanda

    Theophile Dushimirimana

  5. Rally Time Hospital, Grand Cess, Liberia

    Sudue Epaphroditus Mieh

  6. National Public Health Institute of Liberia, Monrovia, Liberia

    Tanya Curry

  7. University of Makeni (UNIMAK), Makeni, Sierra Leone

    Lucas K. Kanu

  8. Southern Medical University, Guangzhou, China

    John Mugisha & Lucas K. Kanu

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  1. Munawar Harun Koray
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Contributions

Conceptualization - MHK, TD; Methodology – MHK, TC & SEM; Data Curation: JSK, JNW & LKK; Formal analysis – MHK, TD & SEM; Writing of Original draft – JNW, LKK & TD; Writing review & editing – LKK, TC & SEM; Supervision & Validation – JSK & JM.

Corresponding author

Correspondence to Munawar Harun Koray.

Ethics declarations

Ethics approval and consent to participate

This research made use of the DHS database, a five-year survey carried out in LMIC. Following subject registration and submission via their website, ICF International approved access and usage of the dataset. The DHS website has thorough information on the approach and ethical issues. Since the used data for this study were secondary data and publicly accessible, individual consent to participate in the study was not required. The study obtained DHS Programme approval. All data were carefully managed guaranteeing privacy all through the processing and analysis stages.

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Not applicable.

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The authors declare no competing interests.

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Koray, M.H., Wanjiru, J.N., Kerkula, J.S. et al. Factors influencing exclusive breastfeeding in Sub-Saharan Africa: analysis of demographic and health surveys. BMC Public Health 25, 1790 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-23045-z

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BMC Public Health

ISSN: 1471-2458

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