Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Timely initiation of breastfeeding and associated factors among mothers having children less than two years of age in sub-Saharan Africa: A multilevel analysis using recent Demographic and Health Surveys data

  • Achamyeleh Birhanu Teshale ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    achambir08@gmail.com

    Affiliation Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

  • Getayeneh Antehunegn Tesema

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

Timely initiation of breastfeeding and associated factors among mothers having children less than two years of age in sub-Saharan Africa: A multilevel analysis using recent Demographic and Health Surveys data

  • Achamyeleh Birhanu Teshale, 
  • Getayeneh Antehunegn Tesema
PLOS
x

Abstract

Background

Despite the significant advantages of timely initiation of breastfeeding (TIBF), many countries particularly low- and middle-income countries have failed to initiate breastfeeding on time for their newborns. Optimal breastfeeding is one of the key components of the SDG that may help to achieve reduction of under-five mortality to 25 deaths per 1000 live births.

Objective

To assess the pooled prevalence and associated factors of timely initiation of breastfeeding among mothers having children less than two years of age in sub-Saharan Africa.

Methods

We used pooled data from the 35 sub-Saharan Africa (SSA) Demographic and Health Surveys (DHS). We used a total weighted sample of 101,815 women who ever breastfeed and who had living children under 2 years of age. We conducted the multilevel logistic regression and variables with p<0.05, in the multivariable analysis, were declared significantly associated with TIBF.

Results

The pooled prevalence of TIBF in SSA was 58.3% [95%CI; 58.0–58.6%] with huge variation between countries, ranging from 24% in Chad to 86% in Burundi. Both individual and community level variables were associated with TIBF. Among individual-level factors; being older-aged mothers, having primary education, being from wealthier households, exposure to mass media, being multiparous, intended pregnancy, delivery at a health facility, vaginal delivery, single birth, and average size of the child at birth were associated with higher odds of TIBF. Of community-level factors, rural place of residence, higher community level of ANC utilization, and health facility delivery were associated with higher odds of TIBF.

Conclusion

In this study, the prevalence of TIBF in SSA was low. Both individual and community-level factors were associated with TIBF. The authors recommend interventions at both individual and community levels to increase ANC utilization as well as health facility delivery that are crucial for advertising optimal breastfeeding practices such as TIBF.

Background

Breastfeeding is one of the effective interventions that can reduce 55% to 87% of neonatal mortality and morbidity, particularly due to infections like diarrhea, neonatal sepsis, and pneumonia [15]. Globally, optimal breastfeeding can avoid the deaths of more than 800,000 under-fives annually. In lower and middle-income countries, an estimated 13% of all child deaths can be prevented if optimal breastfeeding is practiced [6]. Timely initiation of breastfeeding (TIBF) is giving breast milk to the newborn within one hour of birth [5]. This enables the newborn to take colostrum, which stimulates milk production and promote oxytocin release. In addition, taking colostrum helps the newborn to get protective factors such as antibodies [7]. TIBF can also facilitate bonding between the mother and her baby, reduce the incidence of postpartum hemorrhage, and ensure longer breastfeeding duration [8, 9]. Furthermore, TIBF reduces about 22% of neonatal deaths [1].

Despite the major public health implication of TIBF, many countries (especially low and middle-income countries) failed to initiate breastfeeding promptly for their newborns [10, 11]. Every year, in the world, about half of the newborns do not get breast milk in the first hour after delivery [12]. In sub-Saharan Africa (SSA), the prevalence of TIBF is 52.83% ranging from 17% in Guinea to 95% in Malawi [13, 14].

Studies conducted elsewhere revealed that factors such as maternal age, maternal education, wealth status, maternal occupation, place of birth, antenatal care (ANC) visit, mode of delivery, pregnancy intention, size of the child at birth, and place of residence are associated with TIBF [10, 1521].

By 2030, the Sustainable Development Goal (SDG) aimed to reduce under-five mortality to 25 deaths per 1000 live births and one of the best strategy to achieve this plan is through increasing optimal feeding habits among children [22]. Besides, the 2010 Global Burden of Disease (GBD) identified suboptimal breastfeeding practice as the top three leading contributors of disease in Sub Saharan Africa [23]. The number of studies undertaken in sub-Saharan Africa did not involve the community-level factors related to TIBF. Therefore, this study aimed to assess the pooled prevalence and associated factors of timely initiation of breastfeeding in sub-Saharan Africa. The findings of this study could help policymakers to make a wise decision regarding optimal breastfeeding practices such as TIBF.

Methods

Data source

We used pooled data from the 35 SSA countries Demographic and Health Surveys (DHS), which were conducted from 2008–2019. All these surveys used a stratified two-stage cluster sampling technique. The most recent DHS data was selected for analysis from each country specifically for those countries that have more than one surveys. For our study, we used kids data set with a total weighted sample of 101,815 women who ever breastfeed and who had living children under 2 years of age.

Variables of the study

Dependent variable.

The outcome variable was timely initiation of breastfeeding and it is giving breast milk to the newborn within one hour of birth. It was measured based on maternal report and coded as 1 "if the mother initiated breast milk within 1 hour" and 0 "otherwise".

Independent variables.

Both individual and community level independent variables were incorporated in this study. The individual-level factors used in this study were; maternal age, maternal education, maternal occupation, marital status, household wealth status, mass media exposure, parity, pregnancy intention, ANC visit, place of delivery, mode of delivery, type of birth, size of the child at birth, and sex of the child. The Six community-level variables included were; place of residence, community-level media exposure, community level of women education, community poverty level, community level of ANC utilization, and community level of delivery at a health facility. The community-level factors (community level media exposure, community level of women education, community level of ANC utilization, community level of poverty, and community level of delivery at a health facility) were generated by aggregating individual-level factors, as these factors were not directly found from surveys (Table 1).

thumbnail
Table 1. Categories/Description of independent variables.

https://doi.org/10.1371/journal.pone.0248976.t001

Data management and statistical analysis

Appending data, extraction, re-coding, and statistical analysis were performed using Stata version 14 software. Sample weight was applied to adjust over or under sampling and we used the SVY command to account for the complex survey design and generalizability [24]. Because of the hierarchical nature of the DHS data, we conducted the multilevel analysis. While doing the multilevel analysis, we fitted four models: the null model (with only the outcome variable), Model I (containing individual-level factors only), model II (fitted with community-level factors only), and Model III (fitted with both individual and community-level factors). To examine clustering and the extent to which community-level factors explain the unexplained variance of the null model, the Intraclass correlation coefficient (ICC), a proportional change in variance (PCV), and median odds ratio (MOR) were checked. Model fitness was checked by deviance and the model with the lowest deviance was used as the best-fitted model. Variance inflation factor (VIF) was used to assess Multicollinearity and there was no Multicollinearity between independent variables, with a mean VIF of 1.81 (the minimum and the maximum VIF was 1.01 and 3.96 respectively). The bivariable analysis was used to select eligible variables for multivariable analysis (variables with a p-value <0.20 were eligible). Then, in the multivariable analysis, adjusted odds ratio (AOR) with 95% Confidence interval (CI) were reported, and variables with p<0.05 in the multivariable analysis were declared to be significantly associated with TIBF.

Ethical consideration

Since this is a secondary DHS data analysis, ethical approval was not required. However, from the DHS online archive (www.dhsprogram.com), we requested the DHS datasets, obtained permission to access, and download the data files.

Results

Sociodemographic characteristics of respondents and newborns

The majority of the study participants were from Benin followed by the Democratic Republic of Congo (S1 Table). The median age of the respondents was 27 (IQR = 22–32) years. Most (59.54%) of the participants had some formal education and 48.19% of respondents were multiparous. Greater than half (53.64%) of respondents had four or more ANC visits and more than two-thirds (69.92%) of the respondents gave birth at the health facility. The majority (95.13%) of women gave birth through the vagina and 98.30% of the mother gave a single birth. Regarding place of residence, most (69.11%) of women were rural dwellers (Table 2).

thumbnail
Table 2. Sociodemographic characteristics of respondents and newborns.

https://doi.org/10.1371/journal.pone.0248976.t002

Prevalence of timely initiation of breastfeeding in sub-Saharan Africa

The pooled prevalence of timely initiation of breastfeeding in SSA was 58.3% [95%CI; 58.0–58.6%] with huge variation between countries, ranging from 24% [95%CI; 23–25%] in Chad to 86% [95%CI; 85–87%] in Burundi (Fig 1).

thumbnail
Fig 1. Forest plot showing the pooled prevalence of TIBF in SSA countries.

https://doi.org/10.1371/journal.pone.0248976.g001

Factors associated with timely initiation of breastfeeding in sub-Saharan Africa

Fixed effect analysis.

We used the final model (Model III) to assess the factors associated with TIBF in SSA. All independent variables were eligible for multivariable analysis since all had a p-value <0.20. In the multivariable multilevel analysis, both individual and community-level variables were associated with TIBF. The odds of TIBF was higher among mothers whose age was 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years as compared to those mothers whose age was young (15–19 years). The odds of TIBF was 1.25 [AOR = 1.25; 95%CI: 1.19–1.31] times higher among mothers who had primary education compared to those who had no formal education. Those mothers who were from households with third, fourth, and fifth wealth quantiles had 1.12 [AOR = 1.12; 95%CI: 1.05–1.20], 1.17 [AOR = 1.17; 95%CI: 1.08–1.25], and 1.36 [AOR = 1.36; 95%CI: 1.24–1.49] times higher odds of TIBF as compared to those from households with first wealth quantile. Mothers who have been exposed to mass media had 12% [AOR = 0.88; 95%CI: 0.83–0.92] lower odds of TIBF as compared to their counterparts. The odds of TIBF was 1.15 [AOR = 1.15; 95%CI: 1.09–1.22] times higher among multiparous women as compared to Primiparous women. Regarding pregnancy intention, mothers whose pregnancy was unintended had 7% [AOR = 0.93; 95%CI: 0.89–0.98] lower odds of TIBF as compared to those whose pregnancy was intended. Looking at the place and mode of delivery, mothers who were delivered at the health facility and those who were delivered through cesarean section had 1.73 [AOR = 1.73; 95%CI: 1.64–1.83] times higher and 72% [AOR = 0.28; 95%CI: 0.25–0.31] lower odds of TIBF respectively as compared to their counterparts. The odds of TIBF was 27% [AOR = 0.73; 95%CI: 0.63–0.83] lower among mothers who gave multiple births as compared to those who gave a single birth. Mothers who gave small and large-sized babies had 23% [AOR = 0.77; 95%CI: 0.73–0.81] and 17% [AOR = 0.83; 95%CI: 0.79–0.87] lower odds of TIBF respectively as compared to those who gave the average-sized baby. Among community-level factors, mothers from the rural area had 1.43 [AOR = 1.43; 95%CI: 1.33–1.53] times higher odds of TIBF as compared with those from urban areas. Mothers from communities with higher community levels of ANC utilization and health facility delivery had 1.08 [AOR = 1.08; 95%CI: 1.02–1.14] and 1.10 [AOR = 1.10; 95%CI: 1.03–1.17] times higher odds of TIBF respectively as compared with their counterparts (Table 3).

thumbnail
Table 3. Multivariable multilevel analysis for factors associated with TIBF in SSA.

https://doi.org/10.1371/journal.pone.0248976.t003

Random effect analysis.

Table 4 revealed the random effect analysis. The ICC and the MOR in the null model support the presence of significant variations of TIBF between clusters and countries. For example, the higher MOR value (1.37) in the null model indicates that if we randomly choose women from two different clusters, a woman from a cluster with higher rates of TIBF had 1.37 times higher odds of TIBF as compared to a woman who came from a cluster with lower rates of TIBF. Furthermore, the higher PCV in the final model revealed most of the variations of TIBF were attributable to both individual and community-level factors. Moreover, as shown in Table 4, the best-fitted model was the final model (model III) since it had the lowest deviance (Table 4).

thumbnail
Table 4. Random effect analysis and model fitness for assessing factors associated with TIBF in SSA.

https://doi.org/10.1371/journal.pone.0248976.t004

Discussion

This study aimed to assess timely initiation of breastfeeding and associated factors in SSA using multilevel analysis. The pooled prevalence of TIBF in SSA was 58.3%. When we compared with different individual studies, this figure is in line with studies conducted in Ethiopia and Western Nepal [15, 25]. The finding is higher than the findings from SSA, Pakistan, India, and Bangladesh [14, 2628]. The prevalence found in this study is lower than studies conducted in Ethiopia [29] and Nepal [30]. The divergence of this finding from other studies may be attributable to discrepancies in access to health facilities between countries. The other possible reason may be due to the variations in sociodemographic features, and socio-cultural practices between countries. The difference in sample size (since most of the studies were based on a single country) and study period might be the other possible explanation of the discrepancy between our findings and other studies’ findings.

In the multilevel multivariable analysis, both individual and community-level factors were associated with TIBF. Being in the older age group had higher odds of TIBF as compared with the younger age group. This is in concordance with studies done in Tanzania [17]. This is because older mothers might have experience in everything during previous pregnancies and childbirths and more likely to be exposed to information regarding optimal breastfeeding practices [31]. Mothers who had formal education were more likely to start breastfeeding timely for their newborns. This is consistent with studies done in Nigeria and Tanzania [16, 17, 30]. This may be because education plays an important role in shifting mothers’ views and behaviors about breastfeeding, maximizing ANC follow-up and raising the probability of delivery at health institutions [32, 33]. The impact of education may also be explained by the possibility that educated mothers would readily receive and comprehend health promotion messages such as infant feeding styles [34, 35].

Consistent with studies conducted elsewhere [1820], mothers from third, fourth, and fifth household wealth quantiles had higher odds of TIBF as compared with mothers from the first household wealth quantile. This might be because mothers from wealthy households have easy access to education and maternal health care services such as institutional delivery services that enforce the practice of TIBF [36, 37].

Mothers who have been exposed to mass media had lower odds of TIBF as compared to their counterparts. This might be due to the aggressive advertising of infant formula feedings, milk substitutes, teats, and bottles in different media recently [38, 39]. This may also mean that these media (radio, television, and newspaper) were not readily available and sufficient to encourage appropriate breastfeeding practices. However, the authors suggest further inquiry in this regard.

The study at hand also revealed that multiparous women were more likely to start breastfeeding timely as compared to Primiparous mothers. This is in agreement with the findings of different studies conducted in Saudi Arabia, Nigeria, Ethiopia, and low and middle-income countries [15, 19, 4042]. This could be because if a mother has more birth experience, it is more probable that the next baby would be put in the breast within 1 hour of birth, as through her successive pregnancies and deliveries the mother will be exposed to information on appropriate breastfeeding practices [43].

Delivery at the health facility was associated with higher odds of TIBF, in this study. This is in concordance with studies done in Ethiopia [21, 29], Tanzania [44], and Nepal [30]. This is an expected finding since many of the Health Care Centers and Hospitals already have certified midwives or any other qualified professionals available to enable and assist the mother to start breastfeeding early during childbirth.

Mode of delivery was another factor that was associated with TIBF in this study. Mothers who gave birth by cesarean section had lower odds of TIBF as compared to those who gave birth vaginally. This is in line with studies done in Ethiopia [21, 45], Nigeria [19], Turkey [46], Saudi Arabia [47], Lebanon [48], Brazil [49], and India [28]. The possible explanation is both the newborn delivered by cesarean section and the mothers who gave birth by cesarean section typically remain under various obstetric-related health conditions such as general anesthesia effect, pain, and fatigue [50]. This finding can also be attributed to long postoperative care, which delays mother-baby contact [51]. This result suggests that midwives should be aware of the negative relationship between cesarean delivery and breastfeeding initiation to mitigate delayed breastfeeding initiation in mothers with cesarean delivery.

Mothers with unintended pregnancies were less likely to initiate breastfeeding within 1 hour. This is in agreement with a study done in Ethiopia [52]. The possible reason may be mothers with unintended pregnancies are less likely to utilize maternal health services and due to this, they might not gain information regarding appropriate breastfeeding practices [53]. Moreover, women who experienced mistimed pregnancy might lose support from their families or partners for good healthcare-seeking behaviors of their children [54].

In this study, the type of birth was significantly associated with TIBF. Mothers with multiple births were less likely to initiate breastfeeding within 1 hour. This finding is supported by a study conducted in low and middle-income countries [41]. The possible explanation is mothers with multiple pregnancy are more likely to give birth by a cesarean section [55], which in turn increase delayed initiation of breastfeeding.

Congruent with studies conducted in low and middle-income countries [41], Namibia [56], and Zimbabwe [42], being mothers with small and large-sized babies at birth was associated with lower odds of TIBF as compared to mothers who gave an average-sized baby. The reason behind this might be, in most cases, babies with abnormal weights might be separated from their mothers for longer periods after delivery as they may suffer from other comorbid conditions that need intervention [57]. This separation results in the babies not being able to access breast milk early.

Moreover, community-level factors were associated with TIBF. Mothers from rural areas had higher odds of TIBF. This is a surprising finding that is in line with a study done in Zimbabwe [42] and Malawi [58]. The plausible explanation is the extended health extension program in remote areas to encourage women of reproductive age to utilize maternal health services [59], which in turn helps them to gain information regarding optimal breastfeeding practices. However, it is an unusual finding and we recommend a further investigation in this regard.

Antenatal care and institutional delivery are the best opportunity to promote and educate mothers on essential healthy behaviors like appropriate newborn feeding practices [60]. The study at hand also revealed that, the higher number of women who had ANC visits and who gave birth at the health facility in a community, the more likely to develop a norm that encourages appropriate breastfeeding practices for the newborns such as TIBF. This suggests that improving community participation to increase maternal and child health service utilization can improve optimal breastfeeding practices, such as early breastfeeding initiation [21, 61].

The current study was conducted in SSA by using a multilevel analytical approach, which identifies factors associated with TIBF at both individual and community levels. Moreover, the results are representative of the entire SSA countries because we used appropriate analysis techniques such as weighting and multilevel analysis to get appropriate statistical estimates. Therefore, this study could help policymakers and responsible bodies to plan appropriate strategies and implement interventions. Despite the use of the nationally-representative data of each country, the study was not without limitation. Since the outcome was assessed based on the maternal report, there is a possibility of recall bias. Besides, the DHS did not collect some information such as maternal beliefs and knowledge towards breastfeeding so there may be residual confounding. In addition, we are unable to do a three level analysis, to account the country level heterogeneity, due to the convergence problem. Moreover, since it was a cross-sectional study, we are unable to show the cause and effect relationship between TIBF and independent variables.

Conclusion

The prevalence of TIBF in SSA was low, considering the huge variation between countries and according to the WHO recommendation that all babies should benefit from breastfeeding early in life. Both individual and community level variables were associated with TIBF. Therefore, emphasis should be given to young women, women with poor socioeconomic status, mothers with lower parity, mothers who delivered by cesarean section, women who gave birth at home, and mothers who gave multiple births to plan appropriate strategies and implement interventions.

Supporting information

S1 Table. The 35 SSA countries used for analysis and their sample size.

https://doi.org/10.1371/journal.pone.0248976.s001

(DOCX)

Acknowledgments

We would like to acknowledge the MEASURE DHS program, which helps us to access and use the data sets.

References

  1. 1. Edmond KM, Kirkwood BR, Amenga-Etego S, Owusu-Agyei S, Hurt LS. Effect of early infant feeding practices on infection-specific neonatal mortality: an investigation of the causal links with observational data from rural Ghana. The American journal of clinical nutrition. 2007;86(4):1126–31. pmid:17921392
  2. 2. World Health Organization. Collaborative Study Team on the Role of Breastfeeding on the Prevention of Infant Mortality. Effect of breastfeeding on infant and child mortality due to infectious diseases in less developed countries: a pooled analysis. Lancet. 2000 Feb;355(9202):451–5.
  3. 3. Darmstadt GL, Bhutta ZA, Cousens S, Adam T, Walker N, De Bernis L, Lancet Neonatal Survival Steering Team. Evidence-based, cost-effective interventions: how many newborn babies can we save?. The Lancet. 2005 Mar 12;365(9463):977–88.
  4. 4. Lamberti LM, Walker CL, Noiman A, Victora C, Black RE. Breastfeeding and the risk for diarrhea morbidity and mortality. BMC public health. 2011 Dec;11(3):1–2. pmid:21501432
  5. 5. WHO. Breastfeeding-early initiation: World Health Organization; 2012 [updated 2012]. http://www.who.int/elena/titles/early_breastfeeding/en/ Accessed 5 April 2013.
  6. 6. WHO. 10 facts on breastfeeding. Geneva: World Health Organization; 2012. www.who.int/features/factfiles/breastfeeding/en/.
  7. 7. Organization WH. Guiding principles for feeding infants and young children during emergencies: World Health Organization; 2004.
  8. 8. Brandtzaeg P. Mucosal immunity: integration between mother and the breast-fed infant. Vaccine. 2003;21(24):3382–8. pmid:12850345
  9. 9. Goldman AS. Modulation of the gastrointestinal tract of infants by human milk. Interfaces and interactions. An evolutionary perspective. The Journal of nutrition. 2000;130(2):426S–31S. pmid:10721920
  10. 10. Patel A, Bucher S, Pusdekar Y, Esamai F, Krebs NF, Goudar SS, et al. Rates and determinants of early initiation of breastfeeding and exclusive breast feeding at 42 days postnatal in six low and middle-income countries: a prospective cohort study. Reproductive Health. 2015;12(S2):S10. pmid:26063291
  11. 11. Unicef. The State of the World’s Children 2015: Reimaging the Future: Innovation for Every Child. Executive Summary: Unicef; 2015.
  12. 12. Organization WH. Reaching the every newborn national 2020 milestones: country progress, plans and moving forward. 2017.
  13. 13. Bee M, Shiroor A, Hill Z. Neonatal care practices in sub-Saharan Africa: a systematic review of quantitative and qualitative data. Journal of Health, Population and Nutrition. 2018;37(1):1–12. pmid:29661239
  14. 14. Issaka AI, Agho KE, Renzaho AM. Prevalence of key breastfeeding indicators in 29 sub-Saharan African countries: a meta-analysis of demographic and health surveys (2010–2015). BMJ open. 2017;7(10).
  15. 15. Ekubay M, Berhe A, Yisma E. Initiation of breastfeeding within one hour of birth among mothers with infants younger than or equal to 6 months of age attending public health institutions in Addis Ababa, Ethiopia. International breastfeeding journal. 2018;13(1):4. pmid:29410699
  16. 16. Yahya WB, Adebayo SB. Modelling the Trend and Determinants of Breastfeeding Initiation in Nigeria. Child Development Research. 2013;2013:1–9.
  17. 17. Victor R, Baines SK, Agho KE, Dibley MJ. Determinants of breastfeeding indicators among children less than 24 months of age in Tanzania: a secondary analysis of the 2010 Tanzania Demographic and Health Survey. BMJ open. 2013;3(1). pmid:23299109
  18. 18. Mihrshahi S, Kabir I, Roy SK, Agho KE, Senarath U, Dibley MJ, et al. Determinants of infant and young child feeding practices in Bangladesh: secondary data analysis of Demographic and Health Survey 2004. Food and nutrition bulletin. 2010;31(2):295–313. pmid:20707235
  19. 19. Berde AS, Yalcin SS. Determinants of early initiation of breastfeeding in Nigeria: a population-based study using the 2013 demograhic and health survey data. BMC Pregnancy and Childbirth. 2016;16(1):32. pmid:26852324
  20. 20. Tilahun G, Degu G, Azale T, Tigabu A. Prevalence and associated factors of timely initiation of breastfeeding among mothers at Debre Berhan town, Ethiopia: a cross-sectional study. International breastfeeding journal. 2016;11(1):27. pmid:27729937
  21. 21. Belachew A. Timely initiation of breastfeeding and associated factors among mothers of infants age 0–6 months old in Bahir Dar City, Northwest, Ethiopia, 2017: a community based cross-sectional study. International breastfeeding journal. 2019;14(1):5. pmid:30651748
  22. 22. UNICEF. Progress for every child in the SDG era. New York; 2018. https://www.unicef.org/media/56516/file.
  23. 23. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The lancet. 2012;380(9859):2224–60. pmid:23245609
  24. 24. The DHS Program. Sampling and Weighting with DHS Data. Sep 2015. Avialable at https://blog.dhsprogram.com/sampling-weighting-at-dhs/
  25. 25. Sreeramareddy CT, Joshi HS, Sreekumaran BV, Giri S, Chuni N. Home delivery and newborn care practices among urban women in western Nepal: a questionnaire survey. BMC pregnancy and childbirth. 2006;6(1):27. pmid:16928269
  26. 26. Hanif HM. Trends in breastfeeding and complementary feeding practices in Pakistan, 1990–2007. International Breastfeeding Journal. 2011;6(1):1–7.
  27. 27. Haider R, Rasheed S, Sanghvi TG, Hassan N, Pachon H, Islam S, et al. Breastfeeding in infancy: identifying the program-relevant issues in Bangladesh. International Breastfeeding Journal. 2010;5(1):21. pmid:21118488
  28. 28. Patel A, Banerjee A, Kaletwad A. Factors associated with prelacteal feeding and timely initiation of breastfeeding in hospital-delivered infants in India. Journal of Human Lactation. 2013;29(4):572–8. pmid:23427115
  29. 29. Alebel A, Dejenu G, Mullu G, Abebe N, Gualu T, Eshetie S. Timely initiation of breastfeeding and its association with birth place in Ethiopia: a systematic review and meta-analysis. International breastfeeding journal. 2017;12(1):44. pmid:29026432
  30. 30. Adhikari M, Khanal V, Karkee R, Gavidia T. Factors associated with early initiation of breastfeeding among Nepalese mothers: further analysis of Nepal Demographic and Health Survey, 2011. International breastfeeding journal. 2014;9(1):21. pmid:25493094
  31. 31. DiGirolamo AM, Grummer-Strawn LM, Fein S. Maternity care practices: implications for breastfeeding. Birth. 2001;28(2):94–100. pmid:11380380
  32. 32. Yaya S, Bishwajit G, Ekholuenetale M, Shah V, Kadio B, Udenigwe O. Factors associated with maternal utilization of health facilities for delivery in Ethiopia. International Health. 2018;10(4):310–7. pmid:29447358
  33. 33. Dankwah E, Zeng W, Feng C, Kirychuk S, Farag M. The social determinants of health facility delivery in Ghana. Reproductive health. 2019;16(1):101. pmid:31291958
  34. 34. McKinn S, Linh DT, Foster K, McCaffery K. Distributed health literacy in the maternal health context in Vietnam. HLRP: Health Literacy Research and Practice. 2019;3(1):e31–e42. pmid:31294305
  35. 35. Jiang H, Li M, Wen LM, Hu Q, Yang D, He G, et al. Effect of short message service on infant feeding practice: findings from a community-based study in Shanghai, China. JAMA pediatrics. 2014;168(5):471–8. pmid:24639004
  36. 36. Tey N-P, Lai S-l. Correlates of and barriers to the utilization of health services for delivery in South Asia and Sub-Saharan Africa. The Scientific World Journal. 2013;2013. pmid:24288482
  37. 37. Arba MA, Darebo TD, Koyira MM. Institutional delivery service utilization among women from rural districts of Wolaita and Dawro zones, southern Ethiopia; a community based cross-sectional study. PloS one. 2016;11(3):e0151082. pmid:26986563
  38. 38. Organization WH. Marketing of breast-milk substitutes: National implementation of the international code, status report 2018. 2018.
  39. 39. Vinje KH, Phan LTH, Nguyen TT, Henjum S, Ribe LO, Mathisen R. Media audit reveals inappropriate promotion of products under the scope of the International Code of Marketing of Breast-milk Substitutes in South-East Asia. Public Health Nutrition. 2017;20(8):1333–42. pmid:28294089
  40. 40. El Gilany A, Sarraf B, Al Wehady A. Factors associated with timely initiation of breastfeeding in AI-Hassa province, Saudi Arabia. EMHJ-Eastern Mediterranean Health Journal, 18 (3), 250–254, 2012. 2012. pmid:22574479
  41. 41. Patel A, Bucher S, Pusdekar Y, Esamai F, Krebs NF, Goudar SS, et al. Rates and determinants of early initiation of breastfeeding and exclusive breast feeding at 42 days postnatal in six low and middle-income countries: a prospective cohort study. Reproductive Health. 2015 Dec 1;12(S2):S10. pmid:26063291
  42. 42. Mukora-Mutseyekwa F, Gunguwo H, Mandigo RG, Mundagowa P. Predictors of early initiation of breastfeeding among Zimbabwean women: secondary analysis of ZDHS 2015. Maternal Health, Neonatology and Perinatology. 2019;5(1):2. pmid:30675366
  43. 43. Rollins NC, Bhandari N, Hajeebhoy N, Horton S, Lutter CK, Martines JC, et al. Why invest, and what it will take to improve breastfeeding practices? The lancet. 2016;387(10017):491–504.
  44. 44. Exavery A, Kanté AM, Hingora A, Phillips JF. Determinants of early initiation of breastfeeding in rural Tanzania. International breastfeeding journal. 2015;10(1):27. pmid:26413139
  45. 45. Mekonen L, Seifu W, Shiferaw Z. Timely initiation of breastfeeding and associated factors among mothers of infants under 12 months in South Gondar zone, Amhara regional state, Ethiopia; 2013. International breastfeeding journal. 2018;13(1):17. pmid:29743932
  46. 46. Örün E, Yalçin SS, Madendag Y, Üstünyurt-Eras Z, Kutluk S, Yurdakök K. Factors associated with breastfeeding initiation time in a Baby-Friendly Hospital. The Turkish journal of pediatrics. 2010;52(1):10. pmid:20402061
  47. 47. Dorgham LS, Hafez SK, Kamhawy H, Hassan W. Assessment of initiation of breastfeeding, prevalence of exclusive breast feeding and their predictors in Taif, KSA. Life Sci J. 2014;11(1):1–9.
  48. 48. Batal M, Boulghourjian C, Abdallah A, Afifi R. Breast-feeding and feeding practices of infants in a developing country: a national survey in Lebanon. Public health nutrition. 2006;9(3):313–9.
  49. 49. Vieira TO, Vieira GO, Giugliani ERJ, Mendes CM, Martins CC, Silva LR. Determinants of breastfeeding initiation within the first hour of life in a Brazilian population: cross-sectional study. BMC Public Health. 2010;10(1):760.
  50. 50. Chen C, Yan Y, Gao X, Xiang S, He Q, Zeng G, et al. Influences of cesarean delivery on breastfeeding practices and duration: a prospective cohort study. Journal of Human Lactation. 2018;34(3):526–34. pmid:29365288
  51. 51. Regan J, Thompson A, DeFranco E. The influence of mode of delivery on breastfeeding initiation in women with a prior cesarean delivery: a population-based study. Breastfeeding Medicine. 2013;8(2):181–6. pmid:23186385
  52. 52. Gebremeskel SG, Gebru TT, Gebrehiwot BG, Meles HN, Tafere BB, Gebreslassie GW, et al. Early initiation of breastfeeding and associated factors among mothers of aged less than 12 months children in rural eastern zone, Tigray, Ethiopia: cross-sectional study. BMC research notes. 2019;12(1):671. pmid:31639055
  53. 53. Cheng D, Schwarz EB, Douglas E, Horon I. Unintended pregnancy and associated maternal preconception, prenatal and postpartum behaviors. Contraception. 2009;79(3):194–8. pmid:19185672
  54. 54. Miller BC, Benson B, Galbraith KA. Family relationships and adolescent pregnancy risk: A research synthesis. Developmental review. 2001;21(1):1–38.
  55. 55. Kim B-Y. Factors that influence early breastfeeding of singletons and twins in Korea: a retrospective study. International breastfeeding journal. 2016;12(1):1–10. pmid:28074106
  56. 56. Ndirangu MN, Gatimu SM, Mwinyi HM, Kibiwott DC. Trends and factors associated with early initiation of breastfeeding in Namibia: analysis of the demographic and health surveys 2000–2013. BMC pregnancy and childbirth. 2018 Dec 1;18(1):171. pmid:29769063
  57. 57. Ng S-K, Olog A, Spinks AB, Cameron CM, Searle J, McClure RJ. Risk factors and obstetric complications of large for gestational age births with adjustments for community effects: results from a new cohort study. BMC Public Health. 2010;10(1):1–10. pmid:20687966
  58. 58. Walters CN, Rakotomanana H, Komakech JJ, Stoecker BJ. Maternal determinants of optimal breastfeeding and complementary feeding and their association with child undernutrition in Malawi (2015–2016). BMC public health. 2019;19(1):1503. pmid:31711452
  59. 59. Lewin S, Munabi-Babigumira S, Glenton C, Daniels K, Bosch-Capblanch X, Van Wyk BE, et al. Lay health workers in primary and community health care for maternal and child health and the management of infectious diseases. Cochrane database of systematic reviews. 2010(3). pmid:20238326
  60. 60. Biks GA, Tariku A, Tessema GA. Effects of antenatal care and institutional delivery on exclusive breastfeeding practice in northwest Ethiopia: a nested case–control study. International breastfeeding journal. 2015;10(1):30.
  61. 61. Lassi ZS, Das JK, Salam RA, Bhutta ZA. Evidence from community level inputs to improve quality of care for maternal and newborn health: interventions and findings. Reproductive health. 2014;11(S2):S2.