Abstract
ABSTRACT
Background
Although most infants have their birth weights measured, not all newborns have this opportunity, particularly those in low and middle-income countries. This study evaluates neonatal, infant and child mortality among infants who were not weighed at birth compared with peers weighed at birth and to those identified as having low birth weight.
Methods
A cross-sectional observational study of 308 414 African children in 33 countries from the most recent nationally representative Demographic Health Surveys was conducted between 2011 and 2022. Mixed effect Cox regression was used to estimate HRs and 95% CIs, with adjustments made for socioeconomic and proximate determinants of child survival.
Findings
In total, 116 717 (37.84%) infants did not have their birth weight measured. Compared with infants with a measured birth weight, infants without a measured birth weight were at least three and half-times more likely to die within the initial 28 days of life (HR: 3.51; 95% CI 3.18 to 3.88), and approximately two times likely to die by their first birthday (HR: 2.15; 95% CI 2.01 to 2.30) or by their fifth birthday (HR: 1.89; 95% CI 1.78 to 2.00), after adjusting for confounding factors. The disparity in survival rates was consistent between infants who were born at health facilities but were weighed or not weighed at birth. Those unweighed at birth also had a higher risk of death at 28 days of life (HR: 1.76; 95% CI 1.53 to 2.02), by the first birthday (HR: 1.33; 95% CI 1.21 to 1.47) and by the fifth birthday (HR: 1.25; 95% CI 1.14 to 1.37) than infants identified as having low birth weights.
Interpretation
Infants whose birth weight is not measured face a higher mortality risk before age 5 compared with those with measured birth weights. The absence of birth weight measurement may indicate a higher risk profile for newborns and their vulnerability to mortality.
Keywords: Child health, Delivery of Health Care
WHAT IS ALREADY KNOWN ON THIS TOPIC
Measuring birth weight shortly after birth can help identify newborns who may require immediate medical intervention and support.
Although most infants have weights measured at birth, about a fifth to one-third of the newborns in Africa do not have their birth weight measured at all.
However, the health and survival of infants with unmeasured birth weight have not been extensively studied in research focusing on infant health.
WHAT THIS STUDY ADDS
This novel study offers valuable insights into the survival outcomes of infants who are not weighed at birth.
The analysis provides robust evidence on the vulnerability of these infants, demonstrating a risk that is greater than even those classified as having low birth weight.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Lack of measured birth weight may indicate an infant’s risk profile and vulnerability to mortality.
Enhancing institutional delivery services and ensuring access to essential resources, such as functional weighing scales, may reduce the number of unweighed newborns.
Policies and interventions ensuring that newborn weights are measured soon after birth can initiate timely healthcare for high-risk infants, potentially reducing infant and child mortality rates in sub-Saharan Africa.
Introduction
Birth weight is an indicator of fetal development during pregnancy and newborn health. While adequate birth weight is typically associated with good health and a strong start in life, low birth weights (<2500 g) can increase the likelihood of developmental abnormalities and short-term to long-term health issues.1 Measuring and recording birth weight is a simple and cost-effective intervention that can help identify newborns who are at risk of complications and require immediate and follow-up care.2 Birth weight measurement may also serve as an indicator of the quality of care provided during childbirth.3 Although most infants have their weights measured at birth, not all newborns have this opportunity, particularly those in low and middle-income countries (LMICs) where 60% of infants are not weighed at birth or have birth weight recorded at birth.4 5
Sub-Saharan Africa has the highest child mortality rate globally, with 1 in 13 children dying before their fifth birthday.6 7 The high mortality has been attributed to poverty, infections, inadequate nutrition, harmful cultural practices and limited access to healthcare.8 9 Despite widespread efforts to enhance institutional deliveries, which are typically carried out by skilled professionals and result in improved maternal and neonatal outcomes,10 approximately 20% of births in Sub-Saharan Africa occur at home and without the assistance of skilled professionals.10,12 Home births can pose various risks, such as the likelihood of neonatal infections and birth asphyxia. Also, in many cases, the weight of the newborn is not measured.13 While birth weight measurement is often not performed at unskilled home births, some infants born in healthcare facilities may not be weighed, such as when weighing scales are not available or not functioning properly.2
It is unknown whether child survival differs between infants who are weighed at birth and those who are not weighed at birth in Sub-Saharan Africa. Furthermore, it is unknown if child survival differs between infants who are not weighed at birth and those who are low birth weight. This study aimed to investigate these issues. The research findings would offer insight into the survival of infants without a measured birth weight and help understand where investments could be made in strengthening support for high-risk infants. To the best of our knowledge, this study is the first to examine the survival of children whose weight was not measured at birth in Sub-Saharan Africa, while also contrasting the survival outcomes of low birth weight infants and those with unmeasured birth weights. The findings of this research have the potential to influence policy and practice interventions through efforts that ensure that all newborn weights are measured shortly after birth, contribute to improving the overall health and well-being of children living in low-resource settings through prompt initiation of healthcare pathways and advance progress towards achieving Sustainable Development Goal 3.
Methods
Study design and setting
In this cross-sectional observational study, we performed a secondary analysis of the most recent Demographic Health Survey (DHS) from each of the 33 sub-Saharan African countries conducted between 2011 and 2022. Sub-Saharan Africa is a region home to approximately 1.1 billion people and faces significant challenges in healthcare infrastructure and access, including a shortage of trained health professionals and inadequate maternal and neonatal care. The region’s diverse sociocultural landscape also influences health behaviours, particularly in maternal and child health. As such, sub-Saharan Africa remains a critical focus for research on healthcare disparities, child survival and the effectiveness of health system interventions.
Data sources
The DHS are nationally representative surveys that provide data on a range of population and health-related indicators. These surveys use standardised questionnaires to gather high-quality information on various topics, including maternity, birth and child health, from different countries worldwide. The questionnaire and datasets are publicly accessible for research purposes from the DHS Program (https://dhsprogram.com/), which oversees the Demographic and Health Surveys.14 Approval to analyse the data for this study was obtained from the DHS Program, and no additional ethical approval was required. Infant mortality rates derived from the DHS data analysed were relatively consistent with the most recent 2022 World Bank national-level estimates across countries (online supplemental table S1).15
Participants
The study population comprised all livebirths within 5 years of the survey and whose weight was either measured or not measured at birth. We excluded children whose parents or guardians were unsure whether their birth weight was measured or not (24 006; 6.71%), those whose birth weight was measured but could not be confirmed from birth card or maternal recall (22 471; 6.28%), children with birth weight outside the plausible range of 500–6500 g (38; 0.0001%). Furthermore, to mitigate the risk of reverse causation, whereby infants were not weighed due to intrapartum death or death immediately after birth, we excluded 2641 children (0.74%) who died on the day of birth (day 0). Due to data limitations, it was not possible to determine the exact time (hours or minutes) of weighing in relation to death on the day of birth. In total, data of 308 414 children were analysed (online supplemental figure S1). Online supplemental table S2 describes the contributions of each sub-Saharan African country to the pooled analysis sample.
Procedure
Explanatory variable
The primary explanatory variable was ‘measured birth weight’, which indicated whether a child’s weight was measured at birth or not by the birth attendant. This variable was categorised as ‘yes’ if the birth weight was measured or ‘no’ if it was unmeasured.
Outcome of interest
The primary outcomes of interest were neonatal, infant and under-5 mortality based on the survey. Neonatal mortality was defined as the death of an infant within the first 28 days of life.16 In addition, we defined infant mortality as the death of an infant before their first birthday and under-5 mortality as the death of a child before their fifth birthday.6 17 The secondary outcome of interest is the contrast in survival outcomes between low birth weight infants and those with unmeasured birth weights.
Covariates
We identified potential confounders, including socioeconomic and proximate determinants of child survival.18 To account for socioeconomic factors, we utilised proxies such as maternal education, the household wealth index (a quintile measure of socioeconomic status, with the lowest quintile indicating the ‘poorest’ and the highest quintile indicating the ‘richest’ households), place of residence and subgeographical regions of Sub-Saharan Africa (online supplemental table S3). Furthermore, we considered proximate factors, such as maternal age, parity, preceding birth interval, location of birth, skilled attendant at birth, mode of delivery, child’s assigned sex at birth and the number of births (singleton/multiple). In addition, we controlled for the year of survey.
Statistical analysis
We estimated the neonatal, infant and under-5 mortality rates and presented the characteristics of the study population. Sampling weights were applied to adjust for the differences in the probability of participant selection. We classified the occurrence of child mortality as an ‘event’, and those who had not experienced mortality at the time of the survey interview were right censored. Survival time was defined as the interval between birth and the occurrence of child mortality or the date of the interview for children who were still alive during the survey. We used a life table analysis to describe the number of survivors per 1000 live births until the first birthday and the Kaplan-Meier curve with log-rank test to compare the survival function between infants with and without measured birth weights. The Cox proportional hazards model was employed to estimate the association of unmeasured birth weight with the risk of child mortality, while simultaneously adjusting for socioeconomic and proximate determinants of child survival. We employed multilevel modelling, treating sub-Saharan countries, primary sampling units (enumeration areas) and households as random variables. This approach was adopted because of the hierarchical structure of the data, in which children were nested within these three levels. All analyses were conducted using the Stata software (V.16.1, Stata Corp LP, College Station, Texas).
Additional analysis
As data on antenatal care was only collected for the most recent births, we examined the influence of this confounder in the subset of the study population who were latest births. Furthermore, to isolate the impact of birth weight measurement on child survival more effectively, we contrasted infants who were delivered in health facilities, with and without a measured birth weight. To better understand the potential effect of recall bias on the connection between birth weight measurement and child survival, we performed a sensitivity analysis that compared infants who were not weighed at birth with those whose birth weight could be directly obtained from the birth card. In addition, we investigated whether the hazards of child mortality were higher in children with unmeasured birth weights than in those with known low birth weights (<2500 g). Finally, we analysed mortality risks among infants with unmeasured birth weights, disaggregated by region.
Results
The average duration from birth to completion of survey among participants is 1.87±1.41 years. Of the 308 414 children, approximately 56% were born in health facilities and 35% were delivered at home. About 62% of infants had a healthcare professional present during delivery, 33% were delivered by a traditional birth attendant and 5% had no one present during the delivery (table 1). A total of 191 697 (62.16%) children had their weight measured at birth, whereas 116 717 (37.84%) did not have their birth weight measured. Mostly, children who were not weighed at birth had a mother who had not received an education. They were born at home, with no skilled attendant present, were from the poorest households and resided in rural areas.
Table 1. Basic description of the study population*.
| Variable | Total frequency, n (%) | Birth weight not measured, n (%) |
|---|---|---|
| Maternal age (years) | ||
| 15–19 | 18 379 (6.02) | 6856 (6.05) |
| 20–24 | 68 208 (22.36) | 24 196 (21.34) |
| 25–29 | 82 901 (27.17) | 30 692 (27.07) |
| 30–34 | 64 701 (21.21) | 23 619 (20.84) |
| 35–39 | 44 905 (14.72) | 16 828 (14.84) |
| 40–44 | 20 077 (6.58) | 8211 (7.24) |
| 45–49 | 5918 (1.94) | 2959 (2.61) |
| Maternal education | ||
| None | 116 560 (38.21) | 69 541 (61.35) |
| Primary | 99 154 (32.50) | 31 048 (27.39) |
| Secondary and higher | 89 376 (29.29) | 12 772 (11.27) |
| Preceding birth interval | ||
| None (first birth) | 67 027 (21.97) | 17 756 (15.66) |
| < 24 months | 46 341 (15.19) | 23 089 (20.37) |
| ≥ 24 months | 191 722 (62.84) | 72 515 (63.97) |
| Location of birth | ||
| Public health facilities | 170 967 (56.04) | 15 320 (13.51) |
| Private/NGO health facilities | 27 823 (9.12) | 2941 (2.59) |
| Home and others | 106 299 (34.84) | 95 100 (83.89) |
| Attendant at birth† | ||
| Healthcare professional | 190 463 (62.45) | 22 244 (19.63) |
| TBA and others | 99 276 (32.55) | 77 405 (68.31) |
| No one | 15 236 (5.00) | 13 659 (12.05) |
| Mode of delivery | ||
| Vaginal | 288 194 (94.46) | 112 515 (99.25) |
| Caesarean section | 16,895 (5.54) | 846 (0.75) |
| Assigned sex at birth | ||
| Male | 154 258 (50.56) | 57 025 (50.30) |
| Female | 150 831 (49.44) | 56 335 (49.70) |
| Wealth index | ||
| Poorest | 69 413 (22.75) | 37 913 (33.44) |
| Poorer | 66 252 (21.72) | 30 784 (27.16) |
| Middle | 61 186 (20.06) | 23 285 (20.54) |
| Richer | 57 669 (18.90) | 15 636 (13.79) |
| Richest | 50 569 (16.58) | 5742 (5.06) |
| Place of residence | ||
| Urban | 98 968 (32.44) | 16 023 (14.13) |
| Rural | 206 121 (67.56) | 97 337 (85.87) |
| SSA subregion | ||
| Central | 71 077 (23.30) | 28 208 (24.88) |
| East | 107 024 (35.08) | 29 906 (26.38) |
| West | 116 399 (38.15) | 54 470 (48.05) |
| Southern | 10 590 (3.47) | 776 (0.68) |
Weighted frequency; per cent in column.
Missing data (weighted), n=114.
NGO, non-governmental organisations; SSA, sub-Saharan Africa; TBA, traditional birth attendant.
Of the 5245 neonates who died between the first and 28th day after birth, 2300 (43.85%) were weighed at birth, while 2945 (56.15%) were not weighed at birth. A total of 1669 (31.82%) neonatal deaths occurred on day 1 and 770 (14.68%) occurred on day 2 after birth. The cumulative mortality rates of children until their first birthday, as presented in online supplemental table S4, were 29 (95% CI 28 to 30) per 1000 live births for those with measured birth weights, compared with 55 (95% CI 54 to 57) per 1000 live births for those whose weights were not measured at birth (p<0.001). The under-5 mortality rate among infants weighed at birth was 48 per 1000 live births (95% CI 47 to 49), whereas for those not weighed, it was 98 per 1000 live births (95% CI 96 to 101). Figure 1 presents the cumulative survival probabilities for children with measured and unmeasured birth weights, revealing a trend towards worse survival outcomes for those whose weight was not measured at birth. In particular, there was a striking decrease in the probability of surviving from the first month for children whose birth weight was not measured.
Figure 1. Kaplan-Meier curve of survival probabilities. Log rank p<0.0001.
After accounting for socioeconomic and proximate determinants of child survival and the survey time, infants without a measured birth weight were found to be more than three and half times likely to die within the initial 28 days of life (HR: 3.51; 95% CI 3.18 to 3.88), and approximately two times likely to die by their first birthday (HR: 2.15; 95% CI 2.01 to 2.30) or by their fifth birthday (HR: 1.89; 95% CI 1.78 to 2.00) (figure 2). Additionally, the hazard risks were consistent among the latest births, for which we were able to control for history of antenatal care history (online supplemental figure S2). In the restricted analysis comparing infants born in health facilities with and without a measured birth weight, those who were not weighed at birth showed a fourfold higher risk of neonatal mortality (HR: 4.54; 95% CI 4.05 to 5.09), and approximately twofold and half-fold higher risk of infant mortality (HR: 2.74; 95% CI 2.53 to 2.97) and under-5 mortality (HR: 2.40; 95% CI 2.23 to 2.58) (figure 3). In the sensitivity analysis that compared infants who were not weighed at birth with only those whose birth weight could be directly obtained from the birth card, the magnitude of the risk was higher at 28 days (HR: 8.28; 95% CI 7.23 to 9.49), by the first birthday (HR: 4.93, 95% CI 4.50 to 5.40) and by the fifth birthday (HR: 4.23; 95% CI 3.90 to 4.59) among those not weighed at birth (online supplemental figure S3).
Figure 2. Mixed effects Cox regression model of unmeasured birth weight referent to measured birth weight.a aAdjusted for maternal age, education, parity, preceding birth interval, location of birth, skilled attendant at birth, mode of delivery, child’s assigned sex at birth, number of births (singleton/multiple), household wealth index, place of residence, subgeographical regions and the year of survey. Random variables: SSA country, primary sampling unit and household. SSA, sub-Saharan Africa.
Figure 3. Mixed effect Cox regression model comparing survival of unweighed infants born at health facilities (referent to counterparts weighed at birth).a aAdjusted for maternal age, education, parity, preceding birth interval, skilled attendant at birth, mode of delivery, child’s assigned sex at birth, number of births (singleton/multiple), household wealth index, place of residence, subgeographical regions and the year of survey. Random variables: SSA country, primary sampling unit and household. BW, birth weight; SSA, sub-Saharan Africa.
When we examined the mortality risks between children whose birth weight was not measured and those with low birth weight, the results revealed that the risk of death was 76% higher within the initial 28 days of life (95% CI 1.53 to 2.02), 33% higher by the first birthday (95% CI 1.21 to 1.47) and 25% higher by the fifth birthday (95% CI 1.14 to 1.37) for those whose weight at birth was not measured, after adjusting for confounders (figure 4). The analysis of mortality risks among infants with unmeasured birth weights, disaggregated by region, showed directional consistency with the main findings, with the strongest association observed in West Africa (online supplemental figure S4).
Figure 4. Mixed effects Cox regression model comparing survival of infants with unmeasured birth weight and low birth weight infants.a aAdjusted for maternal age, education, parity, preceding birth interval, location of birth, skilled attendant at birth, mode of delivery, child’s assigned sex at birth, number of births (singleton/multiple), household wealth index, place of residence, subgeographical regions and the year of survey. Random variables: SSA country, primary sampling unit and household. BW, birth weight; SSA, sub-Saharan Africa.
Discussion
Over a third of the children analysed were not assessed for their weight at birth, and these children experienced worse survival outcomes than their counterparts who were measured at birth. The decrease in the likelihood of survival for children who were not weighed at birth was particularly pronounced during the first month of life and persisted throughout their early years until the fifth birthday, even after accounting for the socioeconomic and proximate determinants of child survival. The disparity in survival rates was consistent between infants who were born at health facilities but were weighed or not weighed at birth. In addition, low birth weight infants had a better survival than those whose weight was not measured at birth. This association underscores that failing to measure birth weight may signal underlying vulnerabilities of newborns to mortality.
The observed prevalence of unmeasured birth weights and their preponderance among home births is comparable to findings in Bihar, India.13 Various factors have been suggested to impact the choice to give birth at home, such as the accessibility of healthcare facilities and their operating hours, medical expenses and the negative actions of healthcare providers.19,21 The measurement of birth weight has traditionally been a standard clinical practice, as it helps healthcare professionals to identify infants who may require special attention or support and to make informed decisions regarding care and potential interventions.2 22 The absence of birth weight information can lead to inaccurate evaluation of newborn health and the possible need for intervention, such as extra warm and feeding support.2 23 In addition, failure to measure an infant’s weight at birth can make it difficult to track their growth progress over time and compare it to healthy growth standards. This may lead to a delay in the diagnosis and treatment of conditions such as failure to thrive or malnutrition.
The observation that survival is the lowest when the first month of life is consistent with global trends.24 Specifically, our findings showed that infants who were not weighed were at higher risk of neonatal death. Measuring an infant’s weight at birth can potentially trigger various healthcare interventions that could improve survival. Approximately 99% of low birth weight infants are born in LMICs and account for about 60%–80% of neonatal deaths worldwide.25 26 Our research findings showed that infants who did not have a measured birth weight experienced worse survival outcome than low birth weight infants. Although some of these infants without a measured birth weight may indeed have low birth weight, as a group, they tend to have poorer survival outcomes. This observation highlights the significance of measuring birth weight shortly after birth and boosting institutional births in LMICs, as it increases the likelihood that birth weight will be measured and that a child would have appropriate medical intervention.
The fact that birth weight is not measured may be indicative of the quality of healthcare services, readiness to offer support during childbirth and broader weaknesses in the health system. Overall, the health system requires greater investment. Given that most unweighed babies in LMICs are delivered at home, community-based interventions that would make births at home safe are critical.19 It is essential that skilled professionals attend home births and take necessary measures to ensure the safety of the newborn, including accurately measuring their weight. Public health interventions, including free outreach programmes, such as the deployment of mobile baby-weighing scales and home visits by healthcare workers in areas where home birth is more prevalent, have the potential to substantially enhance the survival of children in African communities through prompt and appropriate support and clinical interventions.
The fourfold and half-fold increase in neonatal mortality among children born in facilities who did not have their birth weight measured compared with colleagues further lends credence to the importance of measuring birth weight as a standard clinical practice. Given that some infants who were unweighed at birth were delivered in healthcare facilities, it is important for these facilities to comply with appropriate standards, including the availability of essential physical resources such as weighing scales, to improve the quality of care provided to small and sick newborns.3 Most infants who were not weighed at birth came from the poorest households and rural areas, highlighting family factors, including poverty, and broader systemic issues affecting quality healthcare access in sub-Saharan Africa,19 27 and the need to address them to meet Sustainable Development Goal (SDG)-3 targets.
The strength of this study lies in its analysis of a large, contemporary and representative sample of under-5 children from 33 sub-Saharan African countries. Reliable survey sampling methods, consistent questionnaires and manuals, and standardised data collection processes across countries strengthen the validity of the results. We acknowledge that the data available did not contain information on gestational age and complications during pregnancy and childbirth, such as pre-eclampsia and obstetric haemorrhage, which could be correlated with the absence of birth weight measurement. It is possible that infants with unmeasured birth weight, especially those born in health facilities, represent complicated births and high-risk newborns who are more likely to die. However, this likelihood was diminished by omitting newborns who did not survive their day of birth (day 0). We recognise the potential for recall bias in cases where the measurement of birth weight could not be verified directly from the birth card. However, the consistency of the findings in the sensitivity analysis, which compared infants with unmeasured birth weight to those whose birth weight was obtained directly from the birth card, is reassuring against substantial recall bias. We appreciate the potential influence of residual confounders (such as early childhood illnesses and nutrition) on infant death, for which data could not be obtained.
Conclusion
Infants who were not weighed at birth had lower survival rates than those whose birth weight was measured regardless of whether they had low birth weight. A disparity in survival outcomes persists among children born in health facilities, depending on whether their birth weight was measured or not. This finding underscores that the absence of birth weight measurement, often reflecting systemic issues such as limited access to skilled care, inadequate resources or complications like prematurity, may signal underlying vulnerabilities in both the newborn’s health and the quality of the health system, serving as a proxy for increased mortality risk. Strengthening health systems, increasing institutional births and implementing policies and interventions that ensure that all newborn weights are measured shortly after birth can contribute to prompt initiation of healthcare pathways for high-risk infants and the potential of lowering child mortality rates in sub-Saharan Africa.
Supplementary material
Footnotes
Funding: This work was supported by Coventry University’s Research Excellence Development Fund (14271-26 grant to AA). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Provenance and peer review: Not commissioned; externally peer-reviewed.
Handling editor: Naomi Clare Lee
Patient consent for publication: Not applicable.
Data availability free text: Data are publicly accessible from the DHS Program (https://dhsprogram.com/)
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Ethics approval: The datasets used in this study are deidentified and publicly available, and approval to use them was obtained from the DHS Program, which oversees Demographic and Health Surveys and no additional ethical clearance was required.
Data availability statement
Data are available in a public, open access repository.
References
- 1.Kim HY, Cho GJ, Ahn KH, et al. Short-term neonatal and long-term neurodevelopmental outcome of children born term low birth weight. Sci Rep. 2024;14:2274. doi: 10.1038/s41598-024-52154-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Gladstone ME, Salim N, Ogillo K, et al. Birthweight measurement processes and perceived value: qualitative research in one EN-BIRTH study hospital in Tanzania. BMC Pregnancy Childbirth. 2021;21:232. doi: 10.1186/s12884-020-03356-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.World Health Organization Standards for improving the quality of care for small and sick newborns in health facilities. 2020
- 4.World Health Organization UNICEF-WHO low birthweight estimates: levels and trends 2000-2015. 2019
- 5.Marete I, Ekhaguere O, Bann CM, et al. Regional trends in birth weight in low- and middle-income countries 2013-2018. Reprod Health. 2020;17:176. doi: 10.1186/s12978-020-01026-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.UNICEF Under-five mortality. 2023. [07-Feb-2024]. https://data.unicef.org/topic/child-survival/under-five-mortality/ Available. Accessed.
- 7.World Health Organization Children: improving survival and well-being. 2020. [07-Feb-2024]. https://www.who.int/news-room/fact-sheets/detail/children-reducing-mortality Available. Accessed.
- 8.Ester PV, Torres A, Freire JM, et al. Factors associated to infant mortality in Sub-Saharan Africa. J Public Health Afr. 2011;2:e27. doi: 10.4081/jphia.2011.e27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Rahman MM, Alam K, Khanam R. Socio-economic factors affecting high infant and child mortality rates in selected African countries: does globalisation play any role? Global Health. 2022;18:69. doi: 10.1186/s12992-022-00855-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Hernández-Vásquez A, Chacón-Torrico H, Bendezu-Quispe G. Prevalence of home birth among 880,345 women in 67 low- and middle-income countries: A meta-analysis of Demographic and Health Surveys. SSM Popul Health. 2021;16:100955. doi: 10.1016/j.ssmph.2021.100955. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Regassa LD, Tola A, Weldesenbet AB, et al. Prevalence and associated factors of home delivery in Eastern Africa: Further analysis of data from the recent Demographic and Health Survey data. SAGE Open Med. 2022;10:20503121221088083. doi: 10.1177/20503121221088083. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Wan S, Jin B, Kpordoxah MR, et al. A descriptive analysis of the coverage of newborn care services among women who delivered in health facilities in 17 sub-Saharan African countries. BMC Pregnancy Childbirth. 2023;23:256. doi: 10.1186/s12884-023-05592-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kumar GA, George S, Akbar M, et al. Implications of the availability and distribution of birth weight on addressing neonatal mortality: population-based assessment from Bihar state of India. BMJ Open. 2022;12:e061934. doi: 10.1136/bmjopen-2022-061934. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.The DHS Program . ICF; 2022. [04-Oct-2023]. Demographic and health surveys (DHS)https://dhsprogram.com/data/ Available. accessed. [Google Scholar]
- 15.World Bank Group Mortality rate, infant (per 1,000 live births) - Sub-Saharan Africa. 2022. [09-Apr-2025]. https://data.worldbank.org/indicator/SP.DYN.IMRT.IN?locations=ZG&utm_source Available. Accessed.
- 16.Pathirana J, Muñoz FM, Abbing-Karahagopian V, et al. Neonatal death: Case definition & guidelines for data collection, analysis, and presentation of immunization safety data. Vaccine (Auckl) 2016;34:6027–37. doi: 10.1016/j.vaccine.2016.03.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Centers for Disease Control and Prevention Infant mortality. 2022. [06-Feb-2024]. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/infantmortality.htm Available. Accessed.
- 18.Mosley WH, Chen LC. An Analytical Framework for the Study of Child Survival in Developing Countries. Popul Dev Rev. 1984;10:25. doi: 10.2307/2807954. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Montagu D, Yamey G, Visconti A, et al. Where do poor women in developing countries give birth? A multi-country analysis of demographic and health survey data. PLoS One. 2011;6:e17155. doi: 10.1371/journal.pone.0017155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Campbell OMR, Graham WJ, Lancet Maternal Survival Series steering group Strategies for reducing maternal mortality: getting on with what works. Lancet. 2006;368:1284–99. doi: 10.1016/S0140-6736(06)69381-1. [DOI] [PubMed] [Google Scholar]
- 21.UNICEF . New York City: UNICEF; 2005. Accelerating child survival and development: a results-based approach in high under-five mortality areas: final report to CIDA. [Google Scholar]
- 22.Norris T, Seaton SE, Manktelow BN, et al. Updated birth weight centiles for England and Wales. Arch Dis Child Fetal Neonatal Ed. 2018;103:F577–82. doi: 10.1136/archdischild-2017-313452. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.World Health Organization WHO recommendations on newborn health. 2017
- 24.UNICEF Neonatal mortality. 2023. [06-Mar-2024]. https://data.unicef.org/topic/child-survival/neonatal-mortality/ Available. Accessed.
- 25.Andrews KM, Brouillette DB, Brouillette RT. In: Encyclopedia of infant and early childhood development. Haith MM, Benson JB, editors. Academic Press; 2008. Mortality, infant; pp. 343–59. [Google Scholar]
- 26.Wardlaw T, Blanc A, Zupan Z, et al. United Nations Children’s Fund and World Health Organization; 2004. Low birthweight: country, regional and global estimates. [Google Scholar]
- 27.Oleribe OO, Momoh J, Uzochukwu BS, et al. Identifying Key Challenges Facing Healthcare Systems In Africa And Potential Solutions. Int J Gen Med. 2019;12:395–403. doi: 10.2147/IJGM.S223882. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available in a public, open access repository.