Determinants of optimal antenatal care visit among reproductive age women in Low and Middle Income Countries; Evidence from recent demographic health survey: application of a multilevel binary logistic regression model

Abstract

Background

The evaluation of utilization and the factors influencing optimal ANC visits is critical to improving maternal and neonatal health outcomes. The goal of antenatal care is to reduce maternal and perinatal mortality and morbidity. The study's goal is to identify the factors that influence optimal ANC visits among reproductive-age women in low-income countries.

Methods

The study included a total weighted sample of 329,721 women who gave birth during the study period. For model fitness and comparison, the intra-class correlation coefficient, median odds ratio, proportional change in variance, AIC, BIC, and deviance were used. To identify the determinants of optimal ANC visits in LMICs, a multilevel multivariable logistic regression model was fitted. To declare significant determinants of optimal ANC visits, the adjusted odds ratio and its 95% confidence interval were used.

Results

The overall prevalence of optimal ANC visits was 60.1%, and this ranged from 16.8% in Afghanistan to 97.0% in the Dominican Republic. In the Multilevel multivariable logistic regression model; age 20 to 34 (AOR = 1.267; 95%CI: 1.233–1.303)), age above 34 (AOR = 1.342; 95%CI: 1.302–1.384)), primary educated women (AOR = 1.529; 95%CI: 1.496–1.563), secondary educated women (AOR = 2.626; 95%CI: 2.561–2.694), higher educated women (AOR = 4.563; 95%CI: 4.341–4.796), middle wealth index (AOR = 1.033; 95%CI: 1.015–1.052), rich wealth index (AOR = 1.340; 95%CI: 1.284–1.399), having media exposure (AOR = 1.273; 95%CI: 1.251–1.293)), employed women (AOR = 1.252; 95%CI: 1.212–1.293), and being Central America resident (AOR = 5.967; 95%CI: 5.655–6.297) were significantly associated with optimal ANC visits.

Conclusion

Maternal age, maternal education level, family size, number of children, sex of household head, wealth index, marital status, husband/partner education level, husband/partner occupation, maternal occupation, media exposure, place of delivery, and region were all significant predictors of optimal ANC visits in low- and middle-income countries. This discovery assists health care providers and policymakers in implementing appropriate policies and programs to ensure optimal ANC coverage. It is critical to develop strategies to improve antenatal care access and availability.

Background

ANC is a pregnancy-related service provided to pregnant women by health professionals; it is one of the major interventions aimed at preventing neonatal deaths and maintaining women's health during pregnancy [1–3]. The goal of antenatal care is to reduce maternal and perinatal mortality and morbidity [3]. The first intervention on the list is antenatal care, which is provided between conception and the start of labor with the goal of improving pregnancy outcomes and the health of the mother and child [4]. ANC serves as a platform for essential health care functions such as disease prevention, screening and diagnosis, and health promotion [5]. This case entails a series of assessments and appropriate treatments covering three components: monitoring the woman's and fetus' health status; provision of medical and psychosocial interventions; and support and health promotion [4].

Maternal healthcare remains a critical public health issue worldwide. Healthcare services are absolutely necessary for the well-being and survival of mothers and children during pregnancy, childbirth, and post-delivery periods [6]. For decades, the international community has been concerned with improving women's health and reducing maternal and child mortality [7, 8]. The high priority given to maternal and child health care in the Millennium Development Goals (MDGs) and, more recently, the Sustainable Development Goals (SDGs) [9] demonstrates this special interest.

Maternal mortality is one of the leading causes of death among women of reproductive age in developing countries [10]. According to the World Health Organization (WHO), the global maternal mortality rate in 2017 was 211 per 100,000 live births [11]. The United Nations (UN) set a target of less than 70 maternal deaths per 100,000 live births by 2030, with no single country exceeding 140 maternal deaths per 100,000 [12], but meeting this target will be difficult. According to the United Nations Millennium Development Goals [13], every year, at least 500,000 women and girls die unnecessarily as a result of complications during pregnancy, childbirth, or the six weeks following delivery. Almost all of these deaths (99%) occur in developing countries [14].

Globally, significant reductions in maternal and child morbidity and mortality have not been achieved, especially in low- and middle-income countries (LMICs) [15]. Despite the fact that ANC services have been linked to lower maternal morbidity and mortality [16, 17], five out of every ten women in low- and middle-income countries (LMICs) do not receive adequate ANC services [18, 19]. Sub-Saharan Africa was responsible for an estimated two-thirds of all maternal deaths in 2015, with a maternal mortality ratio (MMR) twice that of HIC, and recent data suggest that geographic disparities in maternal health are widening [20]. Common causes of maternal mortality in LMICs are classified as direct and indirect [21, 22]. Hemorrhages, hypertensive disorders, eclampsia, sepsis, abortion complications, and obstructed labor are direct causes [23, 24], while severe anemia, HIV/AIDS complications, and severe malaria are indirect causes [25].

Furthermore, the World Health Organization (WHO) has advocated for a variety of approaches to preventing pregnancy-related issues. The WHO's FANC (focused antenatal care) model, for example, emphasizes that a pregnant woman should receive comprehensive ANC visits as well as screening and treatment for anemia, malaria, HIV/AIDS, and tetanus [26]. Furthermore, the WHO recently recommended new ANC guidelines, as well as numerous other changes to the FANC model. A pregnant woman should have at least eight ANC visits, beginning with the first visit at 12 weeks' gestational age and continuing with visits to a skilled health care provider at 20, 26, 30, 34, 36, 38, and 40 weeks' gestational age [27].

The evaluation of utilization and the factors influencing optimal ANC visits is critical to improving maternal and neonatal health outcomes. Many factors associated with the use of optimal ANC visits have been reported in various studies around the world, including the mother's age, region, having a living child, women's educational level, place of residence, household wealth status, family size, sex of the household head, marital status, husband or partner, mother's occupation, current working status of the mother, place of delivery, the husband's education, and access to mass media [28–30]. Similar factors have been identified as institutional delivery determinants [31–33].

[34] were the only studies conducted in LMICs. While the authors looked at optimal ANC visits at the LMIC level, they aggregated the countries into sub-regions, failing to account for variations in prevalence and determinants of optimal ANC visits based on the regions of the countries included in their analysis. As a result, their research did not take into account regional policies that may influence the prevalence and determinants of optimal ANC visits. With ANC visits being a major determinant of maternal mortality, it is critical to use appropriate statistical techniques of analysis to distinguish the source of variation, the fundamental features of variation in the occurrence of optimal ANC visits, and the factors of optimal ANC visits. Because unsuitable results lead to confusion between deduction and involvement. However, there is no suggestion that studies done at the LMIC level can be used to determine the main factors of optimal ANC visit prevalence by taking the regional effect into account using the mixed effect model method. As a result, this study will examine the determinants of optimal ANC visits among reproductive-age women in LMICs using data from a recent demographic health survey and a multilevel binary logistic regression model. As a result, this study will provide policymakers and the general public with data that will aid in reducing the high prevalence of maternal and neonatal mortality in LMICs, which contributes to the global burden.

Methods

Results

Characteristics of dependent and independent variables

In 54 countries, 329,721 households were included. Overall, the prevalence of health-care delivery was 60.1%, ranging from 16.8% in Afghanistan to 97.0% in the Dominican Republic (Table 1). Pakistan had the most women (19,787 (0.06%), while Tajikistan had the fewest (992 (0.003%)). More than two-thirds, 219,798 (66.7%), of the women were from rural areas. More than one-third of the women, 113,982 (34.6%), were uneducated, and 222,921 (67.6%) were delivered in a health facility. More than half of women, 182,568 (55.4%), have access to the media, and 151,595 (46.0%) are from low-income households (Table 2).

Determinants of optimal ANC visits among reproductive age women

The random effects model results

The multilevel logistic regression model III had the lowest AIC, BIC, largest log-likelihood ratio, and lowest deviance because the models were nested. The ICC value for the empty model was 16.6% (p-value less than 0.001), indicating that cluster variability explained about 16.6% of the total variability of optimal antenatal care visits in LMICs, while individual variation explained the remaining 83.4% of the total variability. Furthermore, the MOR for an optimal ANC visit was 2.16 (95% CI: 1.52-2.81) in the null model, indicating that there is variation between communities (clustering) because the MOR is higher than the reference (MOR = 1). This revealed that there is significant heterogeneity in optimal ANC visits across communities. In the full model, the community variance (community variance = 0.124; P-value less than 0.001) remained significant but decreased (model adjusted for both individual and community-level factors). Even after accounting for some contextual risk factors, approximately 3.63% of the total variance in health facility delivery that can be attributed to contextual-level factors remained significant. The proportional change in variance (PCV) in this model was 81.07%, indicating that both community and individual level variables explained 81.07% of the community variance observed in the null model (Table 3).

Table 3. Multivariable multilevel logistic regression analysis of both individual and community-level factors associated with optimal ANC visits in LMICs.

Table 3. Multivariable multilevel logistic regression analysis of both individual and community-level factors associated with optimal ANC visits in LMICs (Continued).

Table 3. Multivariable multilevel logistic regression analysis of both individual and community-level factors associated with optimal ANC visits in LMICs (Continued).

The fixed effects model results

Model III, the best-fit model, had a lower AIC, BIC, deviance, and a higher log-likelihood ratio test. As a result, the fixed effects were interpreted using Model III, which was adjusted for both individual and community-level factors. As a result, in the multilevel multivariable analysis, maternal age, maternal education level, family size, number of children, sex of household head, wealth index, marital status, husband/partner education level, husband/partner occupation, maternal occupation, media exposure, place of delivery, and region were significant determinants of place of delivery in LMICs.

After controlling for other individual and community-level factors, the odds of an optimal ANC visit were 1.267 (AOR = 1.267; 95%CI: 1.233-1.303) and 1.342 (AOR = 1.342; 95%CI: 1.302-1.384) times higher among women aged 20 to 34 and greater than 34, respectively, compared to women under the age of 20. Our findings show that women's educational status is a significant positive determinant of optimal ANC visits.  Women with primary, secondary, and higher education were 1.529 times (AOR = 1.529; 95%CI: 1.496-1.563), 2.626 times (AOR = 2.626; 95%CI: 2.561-2.694), and 4.563 times (AOR = 4.563; 95%CI: 4.341-4.796) more likely than women with no formal education. Not only maternal education level but also husband or partner education level were important determinants of optimal ANC visits. Husbands/partners with primary, secondary, and higher education were 1.233 times (AOR = 1.233; 95%CI: 1.205-1.263), 1.568 times (AOR = 1.568; 95%CI: 1.530-1.607), and 1.742 times (AOR = 95%CI: 1.675-1.812) more likely to have optimal ANC visits, respectively. The likelihood of optimal ANC visits was lower among reproductive-age women with more than 8 family members (AOR = 0.907; 95%CI: 0.876-0.939) when compared to less than 4 family members. The likelihood of optimal ANC visits among reproductive-age women having 2 to 3 and more than 3 children was lower by 0.792 (AOR = 0.792; 95%CI: 0.778–0.807) and 0.763 (AOR = 0.763; 95%CI: 0.736-0.791) as compared to women having only one child, respectively. The sex of the household head is an important determinant of optimal ANC visits. Households led by female heads were 1.107 times (AOR = 1.107; 95%CI: 1.081-1.133) more likely to have optimal ANC visits than households led by male heads. The odds of optimal ANC visits among women from households with middle and rich wealth status were 1.033 times (AOR = 1.033, 95%CI: 1.015–1.052) and 1.340 times (AOR = 1.340; 95%CI: 1.284–1.399), respectively, as compared to those from households with poor wealth status. The odds of optimal ANC visits among reproductive-age women who are married and living with a partner were 1.167 (AOR = 1.167; 95%CI: 1.090-1.250) and 1.239 (AOR = 1.239; 95%CI: 1.156-1.329) times higher as compared to separated women, respectively. The likelihood of optimal ANC visits among reproductive-age women whose husband or partner has an occupation was 1.103 (AOR = 1.103; 95% CI: 1.060-1.147) times higher as compared to reproductive-age women whose husband or partner has no occupation. The odds of optimal ANC visits for employed women were 1.252 (AOR = 1.252; 95% CI: 1.212-1.293) times higher as compared to unemployed women. The odds of optimal ANC visits among women who had media access were 1.273 (AOR = 1.272; 95% CI: 1.251-1.293) times higher as compared to women who had no media access. The likelihood of optimal ANC visits among women who use health facility delivery was 2.437 (AOR = 2.437; 95% CI: 2.394–2.481) times higher as compared to women who use home delivery (Table 3).

Discussion

We investigated the prevalence and determinants of optimal ANC visits among reproductive-age women using data from the DHS of 54 LMICs and multilevel binary logistic regression analysis. The prevalence of optimal ANC visits was 60.1% overall. The result was significantly higher than in East Africa [37], Ethiopia [38, 39], Sub-Saharan Africa [36], and Jordan [40]. This result is also lower than those obtained in a systematic review and meta-analysis in Ethiopia [41], Ghana [42], Liberia [43], and Angola [44]. The systematic review and meta-analysis studies could explain this disparity; there is a sample size issue as well as a quality issue for articles that include the meta-analysis. Other studies are limited to a single country and are not representative of other regions. In terms of coverage of basic maternal health interventions such as antenatal care, the African Region has significant intraregional disparities [36]. According to the multilevel logistic regression analysis, maternal age, maternal education level, family size, number of children, sex of household head, wealth index, marital status, husband/partner education level, husband/partner occupation, maternal occupation, media exposure, place of delivery, and region were significant determinants of optimal ANC visits in LMICs.

Women and their partners with higher levels of education have a higher likelihood of receiving optimal ANC visits. This finding is consistent with previous research [3, 36, 45-49]. This is because education improves health-care utilization and increases knowledge about specific issues. Women's empowerment through education, household wealth, and decision-making increases their use of maternal health care services [50].

According to our findings, household wealth status is a significant predictor of optimal ANC visits. Women from the wealthiest families had significantly more ANC visits than women from the poorest families. In line with this finding, studies from Pakistan [51], Sub-Saharan Africa [52], Bangladesh [28, 30, 47], Indonesia [53], and India [54] discovered that women from the wealthiest families were more likely than women from poorer families to receive optimal ANC services. This could be because economic growth encourages health-care utilization and the ability to afford medical and non-medical costs associated with ANC services during pregnancy [55-58]. As a result of the findings, wealth status appears to be an important factor in determining optimal ANC visits. Because of their low socioeconomic status, they have less money to pay for transportation to a health facility to receive ANC services. Another possibility is that women from wealthy families have better education and access to the media than women from poor families.

We discovered that pregnant women with more children and a larger family size had a higher likelihood of receiving optimal ANC visits than pregnant women with fewer children and a smaller family size. It is comparable to previous research from Ethiopia [38, 59], Ghana [60], and Rwanda [61]. A larger family's lack of time and resources, as well as their self-confidence developed from previous pregnancy and childbirth, could be reasons for not using recommended ANC services [62-64].

In this study, married women and women who live with their partners were more likely to have optimal ANC visits than separated women. This research is related to studies [65, 66]. Separated women face a lack of or a reduced level of psychosocial support and relationship stability, according to theories that link marital status, pregnancy, and birth readiness. Unmarried pregnant women may be unplanned and/or unwanted. On the contrary, because illegitimate births are still stigmatized in many countries, social acceptance of separated status is low. As a result, separated women may differ fundamentally from married women in terms of empowerment, self-isolation, and motivation to seek health care [67-69].

According to our findings, working women and women whose husband or partner works had a higher likelihood of using optimal ANC visits than nonworking women and husbands or partners who do not work. This is supported by research from Nigeria [48], and Sub-Saharan Africa [70]. Nonworking women are unable to attend proper ANC visits and do not use health facility delivery due to financial constraints. Furthermore, women aged 20-34 and over 34 years were more likely than those aged under 20 to use optimal ANC visits. This finding corroborated previous research from Uganda [71], Ethiopia [72], and Sub-Saharan Africa [46].

In this study, access to media was also an important factor in health facility utilization. Individuals who had access to the media (reading newspapers or magazines, listening to the radio, and watching television) had a higher chance of using ANC than those who did not. This finding is in agreement with studies [49, 61, 70, 73]. Individuals with access to local media were also more likely to seek medical attention [74]. This simple fact may explain how individuals can quickly obtain various health messages, information about maternal health risk factors, and institutional delivery promotion via multiple radio or television programs [75]. According to the findings of this study, broadcasting the importance of health facility delivery on television, radio, and newspapers may help LMICs achieve maternal and child health-related goals [76]. According to the findings of this study, women who use health facility delivery have significantly higher optimal ANC visits than women who use home delivery. This finding was supported by the study from Sub-Saharan Africa [36]. One possible explanation is that women in health-care settings learned the value of ANC follow-up education. As a result, during ANC follow-up, women will exhibit behavioral changes toward health-care delivery.

Women with a female household head had a higher tendency to initiate optimal ANC visits than those with a male household head. This finding is consistent with the study's findings [70]. Seeking permission from others, especially those of the opposite gender, is a disincentive to early ANC initiation [77]. As a result, independent decision-making may be required for women to access and obtain optimal health care at ANC. Finally, these findings will help the government and stakeholders plan, design, and implement appropriate interventions, as well as address barriers to improving utilization of health facilities, thereby contributing to the reduction of maternal mortality in LMICs.

Strength and limitation of the study

The study's findings are supported by large datasets from 54 LMICs. The information was gathered using a standard, internationally accepted methodological procedure. The findings are representative of all included countries and generalizable to women in LMICs due to the survey's representative nature. The DHS survey year variation may have an impact on this result. The data was gathered using self-reports from mothers within the five years preceding the survey, which could be a source of recall and misclassification bias.

Conclusion

In low-income countries, maternal age, maternal education level, family size, number of children, gender of household head, wealth index, marital status, husband/partner education level, husband/partner occupation, maternal occupation, media exposure, place of delivery, and region were found to be significant predictors of optimal ANC visits. This discovery aids health care providers and policymakers in putting in place appropriate policies and programs to ensure optimal ANC coverage. It is critical to develop strategies to improve access to and availability of antenatal care. In this study, women with a low family wealth index were less likely to attend optimal ANC visits. Financial assistance that allows mothers from low-income families to cover the costs of ANC services may be beneficial. Health campaigns targeting uneducated women and their husbands or partners are critical for raising awareness about the importance of attending at least four antenatal care services.

Abbreviations

ANC: Antenatal Care; AOR: Adjusted Odds Ratio; CI: Confidence Interval; DHS: Demographic Health Survey; FANC: Focal Antenatal Care; ICC: Intra-class Correlation Coefficient; HIV/AIDS: Human Immunodeficiency Virus/Acquired Immunodeficiency Disease Syndrome; LLR: Log-likelihood Ratio; MGDs: Millennium Development Goals; LMICs: Low and Middle Income Countries; MMR: Maternal Mortality Ratio; MOR: Median Odds Ratio; Ref.: Reference Category; SBA: Skilled Birth Attendance; SDG: Sustainable Development Goal; UN: United Nation; WHO: World Health Organization.

Declarations

Authors’ contributions

YA wrote the proposal, analyzed the data, and did the manuscript writing. AA accredited the proposal with revisions, analysis of the data, and manuscript writing. Both authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

This study was built on the analysis of publicly available secondary data with all identifiers removed. The Institutional Review Board (IRB) of ICF Macro at Fairfax, Virginia, in the USA, reviewed and approved the MEASURE DHS Project Phase 3. The 2010–2018 DHSs are considered under that approval. The IRB of ICF Macro complied with the United States Department of Health and Human Services requirements for the "Protection of Human Subjects" (45 CFR 46). ICF Macro permitted the authors to use the data. Most importantly, the informed consent statement emphasizes that participation is voluntary; that the respondent may refuse to answer any question, decline any biomarker test, or terminate participation at any time; and that the respondent's identity and information will be kept strictly confidential. In addition, written informed consent was obtained from a parent or guardian for participants under 16 years old. The full details of the ethical approvals can be found at http://dhsprogram.com, and the data can be found at https://www.dhsprogram.com/data/dataset_admin/login_main.cfm. We confirm that all methods were carried out in accordance with the relevant guidelines and regulations.

Availability of data and materials

The data used in this article were available on http://dhsprogram.com.

Funding

The authors have no support or funding to report.

Acknowledgments

The authors are grateful to the DHS program for giving us permission to use the data for our purpose. The manuscript was edited and proofread for language by Assafaw Kelebu (MSc.), Department of English Language and Literature, Mekdela Amba University.

Consent for publication

Not applicable

References

1. Darmstadt, G.L., et al., Evidence-based, cost-effective interventions: how many newborn babies can we save? The Lancet, 2005. 365(9463): p. 977-988.
2. Organization, W.H., The World health report: 2005: make every mother and child count. 2005: World Health Organization.
3. Titaley, C.R., M.J. Dibley, and C.L. Roberts, Factors associated with underutilization of antenatal care services in Indonesia: results of Indonesia Demographic and Health Survey 2002/2003 and 2007. BMC public health, 2010. 10(1): p. 1-10.
4. Islam, M.A. and T. Tabassum, Does antenatal and post-natal program reduce infant mortality? A meta-analytical review on 24 developing countries based on demographic and health survey data. Sexual & Reproductive Healthcare, 2021. 28: p. 100616.
5. Organization, W.H., WHO recommendations on antenatal care for a positive pregnancy experience. 2016: World Health Organization.
6. Dahab, R. and D. Sakellariou, Barriers to accessing maternal care in low income countries in Africa: a systematic review. International journal of environmental research and public health, 2020. 17(12): p. 4292.
7. Alkema, L., et al., Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Maternal Mortality Estimation Inter-Agency Group. The lancet, 2016. 387(10017): p. 462-474.
8. Wardlaw, T., et al., UNICEF Report: enormous progress in child survival but greater focus on newborns urgently needed. Reproductive health, 2014. 11(1): p. 1-4.
9. Adewuyi, E.O., Y. Zhao, and R. Lamichhane, Risk factors for infant mortality in rural and urban Nigeria: evidence from the national household survey. Scandinavian journal of public health, 2017. 45(5): p. 543-554.
10. Awasthi, M.S., et al., Utilization of antenatal care services in Dalit communities in Gorkha, Nepal: a cross-sectional study. Journal of pregnancy, 2018. 2018.
11. Organization, W.H., Trends in maternal mortality: 1990-2015: estimates from WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. 2015: World Health Organization.
12. Sanhueza, A., I. Espinosa, and O.J. Mújica, Leaving no one behind: a methodology for setting health inequality reduction targets for Sustainable Development Goal 3. Revista Panamericana de Salud Pública, 2021. 45: p. e63.
13. Nations, U., The millennium development goals report. New York: United Nations, 2015.
14. Agus, Y. and S. Horiuchi, Factors influencing the use of antenatal care in rural West Sumatra, Indonesia. BMC pregnancy and childbirth, 2012. 12(1): p. 1-8.
15. Bauserman, M., et al., Maternal mortality in six low and lower-middle income countries from 2010 to 2018: risk factors and trends. Reproductive health, 2020. 17(3): p. 1-10.
16. Wolde, H.F., A.T. Tsegaye, and M.M. Sisay, Late initiation of antenatal care and associated factors among pregnant women in Addis Zemen primary hospital, South Gondar, Ethiopia. Reproductive health, 2019. 16(1): p. 1-8.
17. Phommachanh, S., et al., Improvement of quality of antenatal care (ANC) service provision at the public health facilities in Lao PDR: perspective and experiences of supply and demand sides. BMC pregnancy and childbirth, 2019. 19(1): p. 1-13.
18. Finlayson, K. and S. Downe, Why do women not use antenatal services in low-and middle-income countries? A meta-synthesis of qualitative studies. PLoS medicine, 2013. 10(1): p. e1001373.
19. Awotunde, O.T., et al., Pattern of antenatal care services utilization in a mission hospital in Ogbomoso South-west Nigeria. Journal of Advances in Medical and Pharmaceutical Sciences, 2019. 21(2): p. 1-11.
20. Kassebaum, N.J., et al., Global, regional, and national levels of maternal mortality, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 2016. 388(10053): p. 1775-1812.
21. Alvarez, J.L., et al., Factors associated with maternal mortality in Sub-Saharan Africa: an ecological study. BMC public health, 2009. 9(1): p. 1-8.
22. Karthik, L., et al., Protease inhibitors from marine actinobacteria as a potential source for antimalarial compound. PloS one, 2014. 9(3): p. e90972.
23. Ronsmans, C. and W.J. Graham, Lancet Maternal Survival Series steering group. Maternal mortality: who, when, where, and why, 2006.
24. Rwabilimbo, A.G., et al., Trends and factors associated with the utilisation of antenatal care services during the Millennium Development Goals era in Tanzania. Tropical Medicine and Health, 2020. 48(1): p. 1-16.
25. Say, L., et al., Global causes of maternal death: a WHO systematic analysis. The Lancet global health, 2014. 2(6): p. e323-e333.
26. Tunçalp, Ӧ., et al., WHO recommendations on antenatal care for a positive pregnancy experience-going beyond survival. Bjog, 2017. 124(6): p. 860-862.
27. Organization, W.H., WHO recommendations on intrapartum care for a positive childbirth experience. 2018: World Health Organization.
28. Chanda, S.K., et al., Factors associating different antenatal care contacts of women: A cross-sectional analysis of Bangladesh demographic and health survey 2014 data. PLoS One, 2020. 15(4): p. e0232257.
29. Kabir, R. and H. Khan, Utilization of Antenatal care among pregnant women of Urban Slums of Dhaka City, Bangladesh. IOSR Journal of Nursing and Health Science, 2013. 2(2).
30. Rahman, A., et al., Trends, determinants and inequities of 4+ ANC utilisation in Bangladesh. Journal of Health, Population and Nutrition, 2017. 36(1): p. 1-8.
31. Kabir, M.R., et al., Factors associated with antenatal and health facility delivery care in selected areas of Subornochor upazila, Noakhali, Bangladesh. Clinical Epidemiology and Global Health, 2020. 8(3): p. 983-988.
32. Kamal, S.M., C.H. Hassan, and G.M. Alam, Determinants of institutional delivery among women in Bangladesh. Asia Pacific Journal of Public Health, 2015. 27(2): p. NP1372-NP1388.
33. Xiao, X., Z.-C. Wu, and K.-C. Chou, A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites. PloS one, 2011. 6(6): p. e20592.
34. Arroyave, L., et al., Inequalities in antenatal care coverage and quality: an analysis from 63 low and middle-income countries using the ANCq content-qualified coverage indicator. International journal for equity in health, 2021. 20(1): p. 1-10.
35. Adde, K.S., K.S. Dickson, and H. Amu, Prevalence and determinants of the place of delivery among reproductive age women in sub–Saharan Africa. Plos one, 2020. 15(12): p. e0244875.
36. Tessema, Z.T., et al., Determinants of completing recommended antenatal care utilization in sub-Saharan from 2006 to 2018: evidence from 36 countries using demographic and health surveys. BMC Pregnancy and Childbirth, 2021. 21(1): p. 1-12.
37. Raru, T.B., et al., Association of Higher Educational Attainment on Antenatal Care Utilization Among Pregnant Women in East Africa Using Demographic and Health Surveys (DHS) from 2010 to 2018: A Multilevel Analysis. International Journal of Women's Health, 2022. 14: p. 67.
38. Yehualashet, D.E., et al., Determinants of optimal antenatal care visit among pregnant women in Ethiopia: a multilevel analysis of Ethiopian mini demographic health survey 2019 data. Reproductive Health, 2022. 19(1): p. 1-8.
39. Hailu, G.A., et al., Quality of antenatal care and associated factors in public health centers in Addis Ababa, Ethiopia, a cross-sectional study. PLOS ONE, 2022. 17(6): p. e0269710.
40. Hijazi, H.H., et al., Determinants of antenatal care attendance among women residing in highly disadvantaged communities in northern Jordan: a cross-sectional study. Reproductive health, 2018. 15(1): p. 1-18.
41. Tekelab, T., et al., Factors affecting utilization of antenatal care in Ethiopia: a systematic review and meta-analysis. PloS one, 2019. 14(4): p. e0214848.
42. Sakeah, E., et al., Determinants of attending antenatal care at least four times in rural Ghana: analysis of a cross-sectional survey. Global health action, 2017. 10(1): p. 1291879.
43. Blackstone, S.R., Evaluating antenatal care in Liberia: evidence from the demographic and health survey. Women & Health, 2019. 59(10): p. 1141-1154.
44. Rosário, E.V.N., et al., Determinants of maternal health care and birth outcome in the Dande Health and Demographic Surveillance System area, Angola. PloS one, 2019. 14(8): p. e0221280.
45. Andegiorgish, A.K., et al., Determinants of antenatal care use in nine sub-Saharan African countries: a statistical analysis of cross-sectional data from Demographic and Health Surveys. BMJ open, 2022. 12(2): p. e051675.
46. Adedokun, S.T. and S. Yaya, Correlates of antenatal care utilization among women of reproductive age in sub-Saharan Africa: evidence from multinomial analysis of demographic and health surveys (2010–2018) from 31 countries. Archives of Public Health, 2020. 78(1): p. 1-10.
47. Bhowmik, K.R., S. Das, and M.A. Islam, Modelling the number of antenatal care visits in Bangladesh to determine the risk factors for reduced antenatal care attendance. PloS one, 2020. 15(1): p. e0228215.
48. Adewuyi, E.O., et al., Prevalence and factors associated with underutilization of antenatal care services in Nigeria: A comparative study of rural and urban residences based on the 2013 Nigeria demographic and health survey. PloS one, 2018. 13(5): p. e0197324.
49. Fagbamigbe, A.F., O. Olaseinde, and V. Setlhare, Sub-national analysis and determinants of numbers of antenatal care contacts in Nigeria: assessing the compliance with the WHO recommended standard guidelines. BMC Pregnancy and Childbirth, 2021. 21(1): p. 1-19.
50. Chopra, I., S.K. Juneja, and S. Sharma, Effect of maternal education on antenatal care utilization, maternal and perinatal outcome in a tertiary care hospital. International Journal of Reproduction, Contraception, Obstetrics and Gynecology, 2019. 8(1): p. 248.
51. Noh, J.-W., et al., Factors associated with the use of antenatal care in Sindh province, Pakistan: A population-based study. PloS one, 2019. 14(4): p. e0213987.
52. Tessema, Z.T., G.A. Tesema, and L. Yazachew, Individual-level and community-level factors associated with eight or more antenatal care contacts in sub-Saharan Africa: evidence from 36 sub-Saharan African countries. BMJ open, 2022. 12(3): p. e049379.
53. Laksono, A.D., R. Rukmini, and R.D. Wulandari, Regional disparities in antenatal care utilization in Indonesia. PLoS One, 2020. 15(2): p. e0224006.
54. Kumar, G., et al., Utilisation, equity and determinants of full antenatal care in India: analysis from the National Family Health Survey 4. BMC pregnancy and childbirth, 2019. 19(1): p. 1-9.
55. Ataguba, J.E.-O., A reassessment of global antenatal care coverage for improving maternal health using sub-Saharan Africa as a case study. PloS one, 2018. 13(10): p. e0204822.
56. Arsenault, C., et al., Equity in antenatal care quality: an analysis of 91 national household surveys. The Lancet Global Health, 2018. 6(11): p. e1186-e1195.
57. Gebreyohannes, Y., et al., Improving antenatal care services utilization in Ethiopia: an evidence–based policy brief. Int J Health Econ Policy, 2017. 2: p. 111-117.
58. Makate, M. and C. Makate, The evolution of socioeconomic status-related inequalities in maternal health care utilization: evidence from Zimbabwe, 1994–2011. Global health research and policy, 2017. 2(1): p. 1-12.
59. Tsegaye, B. and M. Ayalew, Prevalence and factors associated with antenatal care utilization in Ethiopia: an evidence from demographic health survey 2016. BMC Pregnancy and Childbirth, 2020. 20(1): p. 1-9.
60. Afaya, A., et al., Women’s knowledge and its associated factors regarding optimum utilisation of antenatal care in rural Ghana: A cross-sectional study. Plos one, 2020. 15(7): p. e0234575.
61. Sserwanja, Q., et al., Factors associated with utilization of quality antenatal care: a secondary data analysis of Rwandan demographic health survey 2020. BMC health services research, 2022. 22(1): p. 1-10.
62. Krugu, J.K., et al., Beyond love: a qualitative analysis of factors associated with teenage pregnancy among young women with pregnancy experience in Bolgatanga, Ghana. Culture, health & sexuality, 2017. 19(3): p. 293-307.
63. Christofides, N.J., et al., Risk factors for unplanned and unwanted teenage pregnancies occurring over two years of follow-up among a cohort of young South African women. Global health action, 2014. 7(1): p. 23719.
64. Yaya, S., et al., Predictors of skilled birth attendance among married women in Cameroon: further analysis of 2018 Cameroon Demographic and Health Survey. Reproductive Health, 2021. 18(1): p. 1-12.
65. Gitonga, E., Determinants of focused antenatal care uptake among women in tharaka nithi county, Kenya. Advances in Public Health, 2017. 2017.
66. Odusina, E.K., et al., Noncompliance with the WHO’s recommended eight antenatal care visits among pregnant women in sub-Saharan Africa: a multilevel analysis. BioMed Research International, 2021. 2021.
67. Ahinkorah, B.O., Non-utilization of health facility delivery and its correlates among childbearing women: a cross-sectional analysis of the 2018 Guinea demographic and health survey data. BMC health services research, 2020. 20(1): p. 1-10.
68. Lowe, M., D.-R. Chen, and S.-L. Huang, Social and cultural factors affecting maternal health in rural Gambia: an exploratory qualitative study. PloS one, 2016. 11(9): p. e0163653.
69. Nyblade, L., et al., Stigma in health facilities: why it matters and how we can change it. BMC medicine, 2019. 17(1): p. 1-15.
70. Seidu, A.-A., et al., Type of occupation and early antenatal care visit among women in sub-Saharan Africa. BMC Public Health, 2022. 22(1): p. 1-12.
71. Sserwanja, Q., L.M. Mutisya, and M.W. Musaba, Exposure to different types of mass media and timing of antenatal care initiation: insights from the 2016 Uganda Demographic and Health Survey. BMC women's health, 2022. 22(1): p. 1-8.
72. Arefaynie, M., et al., Number of antenatal care utilization and associated factors among pregnant women in Ethiopia: zero-inflated Poisson regression of 2019 intermediate Ethiopian Demography Health Survey. Reproductive Health, 2022. 19(1): p. 1-10.
73. Aboagye, R.G., et al., Intimate partner violence and timely antenatal care visits in sub-Saharan Africa. Archives of public health, 2022. 80(1): p. 1-11.
74. Mekonnen, Z.A., et al., Multilevel analysis of individual and community level factors associated with institutional delivery in Ethiopia. BMC research notes, 2015. 8(1): p. 1-9.
75. Yebyo, H., M. Alemayehu, and A. Kahsay, Why do women deliver at home? Multilevel modeling of Ethiopian National Demographic and Health Survey data. PLoS One, 2015. 10(4): p. e0124718.
76. Setu, S.P., et al., Individual and Community-Level Determinants of Institutional Delivery Services among Women in Bangladesh: A Cross-Sectional Study. International Journal of Clinical Practice, 2022. 2022.
77. Dako-Gyeke, P., et al., The influence of socio-cultural interpretations of pregnancy threats on health-seeking behavior among pregnant women in urban Accra, Ghana. BMC pregnancy and childbirth, 2013. 13(1): p. 1-12.