Survival of newborns and determinants of their mortality in Burundi: A prospective cohort study at Kamenge Teaching Hospital

Survival of newborns and determinants of their mortality in Burundi: A prospective cohort study at Kamenge Teaching Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Survival of newborns and determinants of their mortality in Burundi: A prospective cohort study at Kamenge Teaching Hospital Jean Claude Ndayishimiye, Arnaud IRADUKUNDA, Ornella MASIMBI, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4337583/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background Despite the free healthcare services policy for pregnant women and children under five since 2006, the neonatal mortality rate remains high in Burundi. In 2019, two newborns out of one hundred live births died. This study sought to determine the neonatal survival time and identify factors associated with neonatal mortality in Burundi. Methods We conducted a prospective cohort study to collect data among newborns delivered in the maternity ward of Kamenge Teaching Hospital. Our cohort recruitment was opened over 3 months between October and December 2020 and then the last recruited followed up till January 27, 2021. Kaplan Meier curve and logistic regression were used to analyze data. Results Out of 885 live births followed up at Kamenge Teaching Hospital, 30 died. This resulted in a neonatal mortality rate of 34 per 1,000 live births. Nearly 40% of deaths occurred during the first 24 hours of life and 90% within the first week of life. The main causes of neonatal deaths were preterm complications (60%), birth asphyxia (13.33%), neonatal infections (13.33%), and congenital malformations (13.33%). Factors associated with neonatal mortality were preterm delivery (AOR: 9.01, 95%CI 2.05–39.52), less than four ANC (AOR: 6.64, 95%CI 1.46–30.21), Apgar score at five minutes below 6 (AOR: 10.83, 95%CI 2.61–44.93), and Caesarean delivery section (AOR: 2.47, 95%CI 1.00–6.07). Conclusion Neonatal mortality is relatively high while it is mostly due to preventable factors. The government of Burundi should prioritize comprehensive ANC services to improve maternal and neonatal health. Survival analysis Neonatal mortality Determinants Kamenge Teaching Hospital Burundi Figures Figure 1 Figure 2 1. Introduction The neonatal period is the most vulnerable time for child survival ( 1 ) Click or tap here to enter text.. Children faced the highest risk of dying in their first 28 days of life, and the neonatal period accounted for a large proportion of child deaths ( 2 ). Globally, 2.4 million neonates died before reaching their first 28 days of life in 2019, which represented 47% of all deaths among children under five years, a figure up from 1990 when it was 40% ( 1 ). Although the improvement was made among children under five years, the decline in mortality from 1990 to 2019 has been slower for newborns than for children under five years who have exceeded the neonatal period ( 3 ). Since 1990, the global under-five mortality rate (U5MR) declined by almost 60 per cent from 93 per 1,000 live births to 38 per 1,000 live births in 2019. However, the neonatal mortality rate only declined by 52 percent dropping from 37 percent to 17 percent in 2019 ( 4 ). Therefore, the neonatal mortality rate accounted for a large and growing proportion of all under-five mortality rates. Neonatal mortality remained a challenge in low- and middle- income countries (LMICs) where there are inadequate health systems and a critical shortage of healthcare providers who can adequately manage and provide quality care ( 5 , 6 ). The Sustainable Development Goals (SDGs) agenda highlighted the importance of a continued momentum towards improving newborn health by setting, under SDG3, targets aiming for all countries to reduce neonatal mortality to less than 12 per 1,000 live births by 2030 ( 7 , 8 ). Neonatal mortality varied considerably from place to place, country to country, and within each country itself ( 1 ). The neonatal mortality rate was 3‰ in high-income countries (HIC), with considerable differences in Low-income countries (LIC), where it was 27 per 1,000 live births in 2019. Most newborns’ deaths occurred in low-and middle-income countries (LMICs), with the south-Asia and Sub-Saharan Africa (SSA) accounting for more than 80% of all neonatal deaths ( 2 , 5 ). The SSA had the highest neonatal mortality rate (NMR), with 27 neonatal deaths per 1,000 live births ( 1 , 2 ). In the East African Community (EAC) sub-region, Burundi has the second highest NMR of 21‰ after the South-Sudan with 39‰ ( 1 ). The causes of neonatal mortality are mostly preventable by high-quality maternal and newborn healthcare services. Evidence showed that 16 million newborn’s deaths should be averted by 2030 if LIC reduced their NMR to the same rate as the HIC ( 2 ). Hence, neonatal mortality is considered as one of the sensitive indicators of the quality maternal and newborn healthcare services ( 9 ). Evidence from previous studies conducted in South Asia and sub-Saharan Africa showed perinatal asphyxia, neonatal infections, and preterm complications as the leading causes of neonatal deaths ( 10 – 14 ). Those neonatal mortality causes are mostly preventable. They include both clinical, therapeutic, socio-economic, and demographic factors ( 15 – 17 ) For instance, home delivery, multiple gestation, non-use of family planning, poor to moderate health services delivery, parents deceased associated neonatal mortality ( 18 ). In addition, congenital anomalies, Apgar score below 7 at the first minute of life, low birth weight, less than 4 ANC, and preterm birth predicted neonatal mortality ( 19 ).Although progress was made in Burundi, the neonatal mortality rate remained relatively high where two out of one hundred newborns died ( 1 ). Despite the free health care and services policy for pregnant women and children under five introduced in 2006 ( 20 ) and the performance-based incentives ( 21 ), the neonatal mortality rate remained below political expectations. With a forecast of an annual reduction rate of 2.7% (2017–2030), Burundi risks missing the SDG target to reduce the neonatal mortality rate to less than 12 per 1,000 live births by 2030 ( 22 ). The recent demographic and health survey (DHS) 2016–2017 concluded an NMR of 23‰ with variations according to residence area ( 23 ). The probability of dying between the first and the 28th day of life was significantly higher in urban areas compared to rural areas (30‰ versus 22‰). For previous studies conducted in rural areas, one showed an NMR of 50‰ and the main causes were neonatal infections, preterm complications, birth asphyxia, and respiratory distress ( 24 , 25 ). However, these two studies were retrospective surveys using neonatal hospitalization-based data, which should have overestimated the NMR. Furthermore, in another study to investigate inequalities in neonatal mortality using a national sample, Yaya et al. (2020) found significant disparities according to newborn’s sex, mother’s education level, wealth index, sub-national region, and residence areas ( 26 ). Therefore, this study aimed to determine the neonatal survival time and identify factors affecting neonatal mortality at Kamenge Medical Teaching Center, an urban-based hospital. 2. Methods 2-1. Study design, setting, and follow-up We conducted a prospective cohort study on all live births delivered in the maternity ward of Kamenge Teaching Hospital (KTH) from 1 st October to 31 st December 2020. Kamenge Teaching Hospital is located in the north-east of the economic capital, which has the highest proportion of women who delivered in a health facility and assisted by a trained healthcare provider (23). It was selected because it is both a tertial and unique public teaching hospital, providing maternal and neonatal care services including, antenatal, postnatal, and emergency obstetrical and neonatal care services free of charge. All live births were included in the study and followed up until their first 28 days of life. The cohort recruitment was open for 3 months from 1 st October to 31 st December 2020 and then the last recruited followed up till the end of the 28 first days of life on January 27, 2021. For all newborns admitted into the neonatology service, we followed them up from the cohort entry up to the occurrence of an event (death) or until their 28 days of life. For not hospitalized or discharged newborns within their first 28 days of life, we assessed the neonatal status by contacting (by cell phone) their mothers and/or the heads of the household who consented to participate in the study. They were contacted on the first day of life and every seven days. For each call, the following question was asked: “How is the newborn doing?” 2.2 Study population We included all live births delivered in the maternity ward of Kamenge Teaching Hospital from 1 st October to 31 st December 2020. Newborns admitted into the neonatology unit but delivered outside of the maternity ward of KTH were excluded. Also, newborns who’s their mothers did not consent to participate in the study were excluded. In the period of study, a cohort of 885 newborns was followed up. However, we were able to follow up only 722 newborns till their 28 days of life while 30 died during the follow up period (Details in Figure 1). 2.3 Data collection Data were prospectively collected using a structured questionnaire delivered from the WHO neonatal verbal autopsy standards (27). The questionnaire was also similar to the obstetrical record “Patrogram” and was initially developed in French and translated into Kirundi, the local language. Two nurses and three midwives working in the maternity ward of KTH were recruited and trained to collect data during the period after childbirth. We followed up newborns up to the occurrence of the event (neonatal death) or up to their 28 days of life. The trained nurses and midwives, after a woman’s informed consent to participate in the study, were responsible for collecting data by interviewing each mother within 12 hours after giving birth. Besides, clinical information for the mother and the newborn was collected using the obstetrical record “Patrogram”. Afterward, we followed up newborns into two alternatives. For newborns admitted into hospitalization, we collected data from the neonatology entry/exit records for the first day of life and every 7 days. When the death occurred, the date and cause were recorded by physicians working in the neonatology unit and then collected. For newborns not hospitalized or discharged within their first 28 days of life, we assessed the neonatal conditions and survival status using a cell phone contacting their mothers and/or the heads of the family every 7 days. For each call, the following question was asked: “how is doing the newborn?”. No newborn among those whom we were able to reach their mothers or heads of family died at home. Those whom we were not able to reach because of cell phone communication issues, were excluded from the study. 2.4 Definition and management of variables The selection of independent variables was based on evidence from previous studies and the WHO neonatal verbal autopsy standards. The variables used included socio-demographic characteristics (maternal age, residence, mother’s education level, mother’s profession), pregnancy-related characteristics (gestational age, gravidity, parity, ANC number, Ultrasound number, perinatal complications), delivery conditions (delivery mode, birth type, birth date), and neonatal clinical and measurements (birth weight, sex, Apgar score, neonatal survival status). We defined a mother’s profession based on the mother’s reported occupation, and we categorized women “without profession”, the farmer/ housewife or student while women who reported as government employees, private sector employees or business owners were categorized into women “with profession”. Mother’s age was classified according to the World Health Organization (WHO) and International Federation of Gynecology and Obstetrics definition of high-risk pregnancy related to maternal age. We then categorized maternal age as below 20 years (teenage pregnancy), between 20 and 35 years and over 35 years (late pregnancy) (28). However, during analysis due to few observations of teenage pregnancies, we recoded the maternal age into two categories by merging the two extreme categories (less than 20 and greater than 35 years). Similarly, due to few no education level observations, educational status was categorized into none and primary education level and secondary education and above (high school to university level). The mother’s residence was classified as rural and urban. We defined parity as the number of children previously born to a woman, excluding miscarriages or abortions, but including stillbirths and fetal deaths. We classified parity into first parous (the current birth), pauci-parity (2-3), and over 3 as multiparity. We defined gravidity as the number of pregnancies delivered or not a woman has had in her life. We categorized it into first gravidity (current birth), pauci-gravidity (2-3), and over 3 pregnancies as a multi-gravidity. We categorized perinatal complications into yes or no, which are considered present if the mother had either an obstetric haemorrhage, premature rupture of membranes, preeclampsia and its complications, puerperal fever, and other emergency obstetrical conditions. Pregnancy was defined as well-monitored if the mother attended at least four antenatal care (ANC) and/or at least three ultrasounds according to the WHO fan model of antenatal care recommendations (29). The reference to four ANC was based on the fact that Burundi has not yet implemented the new model (WHO-2016) recommending at least eight ANC (29). According to the WHO, we categorized the birth gestational age into preterm birth (< 37 weeks of gestation) and term birth (≥37 weeks of gestation) (30,31). Birth weight was categorized according to the WHO and United Nations Children’s Fund classification as low birth weight (< 2500 grams) and normal birth weight (≥ 2500 grams) (32). The sex of the newborn was male and female. An Apgar score less or equal to five (≤5) at five minutes of birth defines bad conditions for the newborn. We classified the mode of delivery into vaginal delivery and caesarean section. The birth type was categorized into single birth and multiple births, which means twin, triple, and above. Mothers who gave multiple births were counted separately for each birth for a sake analysis. 2.5 Outcome variable Neonatal mortality was defined as the death of newborns in their 28 first days of life. The neonatal mortality rate (NMR) was defined as a number of deceased newborns per 1,000 live births during the period of the study. Early neonatal mortality (ENMR) was defined as the probability of dying within the 7 days of life while the late neonatal mortality rate (LNMR), on the other side, was defined as the probability of dying between 7 days and before 28 days of life. The newborn death was categorized into “yes” if the newborn died and “no” otherwise. 2.6 Data analysis Neonatal mortality was the event of interest and coded “1” if the death occurred and “0” if the newborn was censored. The time spent by each newborn in the study was calculated by subtracting the date of death, lost to follow-up, and or the end of the neonatal period from the date of birth. We imported, cleaned, recorded, and analyzed data into STATA Version 17.0. For descriptive statistics, frequencies and percentages were used. The Kaplan-Meier survival curve was used to estimate neonatal survival time. For statistical analysis, logistic regression was used to calculate unadjusted odds ratios (ORs) and adjusted odds ratios (AORs) to identify potential factors associated with neonatal mortality. In the bivariate analysis, variables with a significance level (p<0.15) were included in the multivariate analysis (33). To identify which factors, impact neonatal mortality, the final analysis was made by successive stepwise backward elimination to calculate adjusted ORs. 3. Results 3.1 Response rate and characteristics of newborns The descriptive characteristics of the newborns are presented in table 1. Eight hundred eighty-five newborns were eligible to the followed up (Details in Table 1). However, we were able to follow up on 752 newborns till the end of the neonatal period or the occurrence of an event (death). For consistent data analysis, we assigned maternal characteristics (for mothers who gave multiple births) to each newborn. The majority of newborns (more than 95%) were single births and had a good Apgar score at five minutes. Plus, about 85% of neonates were born without perinatal complications, at term, and with normal birth weight. About three-quarters of neonates (73%) were born from mothers aged 20-35 years and from urban areas. Regarding pregnancy monitoring, nearly 45% and 54% of newborns were born from pregnancies well monitored by at least 3 ultrasounds and more than 3 ANC, respectively. About half of newborns were born by caesarean section, from mothers with a profession, and mothers with at least a secondary education level. The female-to-male ratio was 1:1.07. Table. 1 : Characteristics of newborns at Kamenge Teaching Hospital, October-December 2020, (N=752). Variables Frequency Percentage (%) Maternal age (years) 20-35 556 73.93 35 196 26.06 Profession of the mother Yes 440 58.51 Non 312 41.48 Residence of the mother Urban 556 73.93 Rural 196 26.06 Education of the mother Secondary/university 418 55.58 None/primary 334 44.41 Gravidity 1 201 26.72 2-3 287 38.16 >3 264 35.10 Parity 1 225 29.92 2-3 298 39.62 >3 229 30.45 Perinatal complications Yes 84 11.17 No 668 88.82 Ultrasound ≥3 337 44.81 <3 415 55.18 ANC ≥4 404 53.72 <4 348 46.27 Gestational age ≥37WG 646 85.90 <37WG 106 14.09 Sex of the newborn Male 389 51.72 Female 363 48.27 Birth weight ≥2500grams 643 85.50 <2500grams 108 14.36 Apgar score at five min 0-5 21 2.79 6-10 731 97.20 Delivery mode Vaginal 354 47.07 Caesarean 398 52.92 Birth type Single 718 95.47 Multiple 34 4.52 ANC: Antenatal care; WG: Weeks of gestation 3-2. Neonatal Survival Analysis We followed up on newborns for 23,792 neonate days. In the study, 30 newborn deaths occurred which made the NMR 34‰. Among all, 27 died in the first week of their life, of which 12 occurred during the first 24 hours. Therefore, the early neonatal mortality rate (ENMR) represented 90% of the overall neonatal mortality while 40% occurred within the first 24 hours of life. At the end of the neonatal period, the cumulative survival probability was 96.59% and 722 neonates were alive, 30 died, and 133 lost to follow-up (Details in Table 2). The main causes of neonatal mortality were preterm complications (60%), birth asphyxia (13.33%), neonatal infections (13.33%), and abnormal congenital (13.33%). The graph of Kaplan-Meier indicates that neonatal mortality was quicker during the first seven days of life and, therefore, a lower probability of neonatal survival. From the eighth day of life, the graph fell slowly, indicating a lower proportion of neonates were dying (Details in Figure 2). Table 2: Neonatal survival analysis during the follow-up time at Kamenge Teaching Hospital, October-December 2020, (N=885) Time interval Total Deaths Lost CSP (%) 95%CI 0-1 st day 885 12 0 98.64 [0.97 - 0.99] 2-7 th day 873 15 23 96.95 [0.95 - 0.97] 8-14 th day 835 2 17 96.72 [0.95 - 0.97] 15-21 st day 816 0 46 96.72 [0.95 - 0.97] 22-28 th day 770 1 47 96.59 [0.95 - 0.97] 28 th day 722 0 722 96.59 [0.95 - 0.97] Note: CSP: Cumulative survival probability 3.3 Results of logistic regression analysis The results of the multivariate analysis are presented in Table 3. After successive stepwise backward elimination, the multivariate final model revealed four variables associated with neonatal mortality: gestational age, ANC visits number attended during pregnancy, delivery mode, and Apgar score at five minutes (). The highest risk was observed among neonates delivered before 37 weeks of gestation, and thus the odds of neonatal mortality among preterm neonates were 9 times higher compared to term neonates (AOR: 9.01, 95%CI 2.05 - 39.52). The risk of neonatal mortality decreased with the number of ANC attendance, neonates born from pregnancies monitored by less than four ANC were 7 times more likely to die (AOR: 6.64, 95%CI 1.46 - 30.21) than those born from pregnancies monitored by at least four ANC. Neonates who had an Apgar score of 5 and below at five minutes were 11 times more likely to die (AOR: 10.83, 95%CI 2.61 - 44.93) than those with an Apgar score above 5. Furthermore, neonates born by caesarean had 2.5 times (AOR: 2.47, 95%CI 1.00 - 6.07) higher risk of dying compared to neonates delivered by vaginal mode. Table 3: Bivariate and Multivariate analysis of determinants of neonatal mortality at Kamenge Teaching Hospital, October-December 2020 Bivariate Multivariate Variables cOR 95% CI P aOR 95%CI p Maternal age 20-35 1 35 0.94 [0.37 - 2.35] 0.904 Profession Yes 1 No 1.64 [0.79 - 3.42] 0.183 Residence Urban 1 Rural 1.22 [0.55 - 2.72] 0.617 Education ≥Secondary 1 ≤Primary 2.23 [1.04 - 4.75] 0.038 Gravidity 3 2.82 [1.07 - 7.38] 0.035 Parity 3 2.29 [0.89 - 5.93] 0.085 Perinatal complications Yes 9.46 [4.43 - 20.18] <0.001 No 1 Ultrasound ≥3 1 <3 2.76 [1.17 - 6.52] 0.020 ANC visits ≥4 1 1 <4 17.58 [4.15 – 74.38] <0.001 6.64 [1.46 - 30.21] 0.014 Gestational age ≥37WG 1 1 <37WG 31.21 [12.39 – 78.62] <0.001 9.01 [2.05 - 39.52] 0.004 Sex Male 1 Female 0.70 [0.33 - 1.48] 0.357 Birth weight ≥2500 grams 1 <2500 grams 16.81 [7.46 - 37.86] <0.001 2.80 [0.67 - 11.63] 0.155 Apgar score at 5 minutes 0-5 8.82 [2.99 - 25.99] <0.001 10.83 [2.61 - 44.93] 0.001 6-10 1 1 Delivery mode Vaginal 1 1 Caesarean 2.13 [0.96 - 4.72] 0.061 2.47 [1.00 - 6.07] 0.049 Birth type Single(ref) 1 Multiple 2.47 [0.71 - 8.60] 0.154 Note: ANC: Antenatal care, WG: Weeks of gestation Discussion The NMR at Kamenge Teaching Hospital was 34 per 1,000 live births. This rate is relatively high compared to other studies conducted in developing countries (13,34,35) as well as the Burundi national rate of 23‰ according to the Third demographic and health survey (DHS) (2016-2017) (23). It is also higher than the Burundi NMR estimated at 21‰ by the United Nations Inter-Agency Group for Child Mortality Estimation in 2019 (1). This divergence could be explained in terms of study methods; most studies were retrospective or cross-sectional which could have underestimated the NMR. Furthermore, the NMR for this study remained very high compared to other studies conducted in developed countries (36–38). Nevertheless, the rate of 34‰ was comparable to the NMR found in certain studies carried out in some developing countries (15–17,39).The NMR in the emergency obstetric and neonatal care facilities of the three Bujumbura rural district hospitals in 2011 was 50‰. This was explained by the fact that the centers received only parturients referred for pregnancy complications and/or complicated delivery, which were likely to play a role (24). In this study, preterm complications were the leading causes of neonatal mortality, representing 60% of all causes. Therefore, the neonatal mortality rate of 34‰ can be explained by the low ANC services which constitute a platform for screening, diagnosis, and prevention of diseases including preterm birth. On the other hand, in this low-and middle-income country, insufficiency or even inadequate infrastructure and materials necessary for the management of premature births can be a reason. Another reason could be that KTH, a tertiary referral institution, is often cloudy because many babies are delivered after that transfer from all over the country due to complicated pregnancies. Regarding neonatal survival time, this study revealed that most neonatal deaths (90%) occurred during the first week of life and 40% in the first 24 hours of life. This distribution of neonatal death by age is consistent with the neonatal mortality incidence found in other studies from the literature review (15,17,35,40) and the study carried out in Burundi (25). A prospective cohort study in the Tigray region, northern Ethiopia, reported that most neonates died within their first week of life due to complications occurring during pregnancy and childbirth. Poor quality of prenatal care, delayed complications’ identification, and management of complications during pregnancy and childbirth could also be reasons according to the same study (17). In this study, this survival time could be explained by the precocity of preterm complications as the main cause of neonatal deaths, and on the other hand, by poor ANC attendance among pregnant women. The first 24 hours and the first week of life are therefore a critical period for the survival of neonates. It is becoming clear that policies and programs that target neonates during the first week of life are more than urgent needed to improve infant survival. In this study, the leading causes of neonatal mortality were preterm complications (60%), birth asphyxia (13.33%), neonatal infections (13.33%), and congenital malformations (13.33%). These results are consistent with WHO reports on causes of neonatal mortality as well as findings from other studies (10,12,15,38), but the only difference resided in magnitude. We noted a high neonatal mortality related to preterm complications (60%). Contrary to studies conducted in some developed countries, the leading cause of neonatal mortality was congenital malformations at more than 25% (36,37). Even if tocolysis, steroids, and preventive antibiotics are part of the obstetrical guidelines for premature delivery management in Burundi, premature birth management remained the most important challenge. The study showed a low proportion of neonatal mortality related to birth asphyxia and neonatal infections compared to WHO estimations and other studies (10,11,34,41–43). These results may reflect the contribution of universal health coverage of intrapartum and postpartum birth care as well as the improvement of obstetrical care during labor and childbirth to reduce neonatal deaths related to birth asphyxia. Regarding neonatal infections, the practice of hygienic childbirth, maternal prophylaxis, and neonatal antibiotics for neonates with a high risk of infections may have been protective. Nevertheless, the diagnosis of neonatal infections was made by clinical criteria accompanied by indirect tests including C-reactive protein and complete blood count. Without a certainty diagnosis by blood culture, they may have been false diagnostics. For neonatal mortality related to congenital malformations, similar trends have been observed in most other studies (10,12,38,44). Most neonatal deaths related to malformations are not preventable. Therefore, there is a need to improve technics to diagnose antenatal chromosomal abnormalities to reduce neonatal mortality related to congenital malformations. The study found a negative correlation between term birth and neonatal mortality. In comparison with term births, we found that the risk of neonatal mortality was 9 times for preterm births. Our results are in line with those recorded in other studies but with little difference in terms of magnitude (10,13,17,34). For instance, preterm births associated 5.8 and 3 times a risk of neonatal mortality for studies conducted in Pakistan and Ghana, respectively. In Burundi’s context, this would be linked to a lack of preterm prevention strategies and effective management of preterm births. Preterm neonates are unable to adapt to extra-uterus life, prenatal care improvement is therefore crucial in reducing preterm births. This study also showed that neonatal mortality is linked to the number of ANC attended during pregnancy. The risk of neonatal mortality was 7 times higher for babies born from pregnancies followed up by less than 4 ANC compared to those born from pregnancies followed up at least by 4 ANC. Our results are similar to those found by McCurdy et al. (2011) in sub-Saharan Africa and a study conducted in Ethiopia (45). The reason could be that a well-monitored pregnancy makes possible to quickly detect the risk factors and to have the necessary health information to carry out effective interventions. In addition, four or more ANC visits was found to be the most of effective interventions to reduce neonatal mortality in SSA (45). The Apgar score below 6 at five minutes associated a neonatal mortality risk of 11 times compared to newborns with an Apgar score at five minutes above 5. This relationship corroborates other studies (19,46,47) finding low Apgar score at five minutes strongly associated with the risk of neonatal mortality. For instance, regardless gestational age, Apgar score less than 6 associated with an increased early neonatal, late neonatal, and infant mortality (47). The Apgar score is used to assess the newborn’s respiratory, tonicity, skin coloration, and cardiovascular functioning conditions at birth, five, and ten minutes, this relationship could be explained by the inadequate care for neonates with birth asphyxia. Furthermore, neonates born by caesarean section had 2.5 times higher risk of dying compared to neonates delivered by vaginal mode. This is consistent with findings from studies conducted in Pakistan (10). However, contradictory results have found the vaginal section associated with a high risk of neonatal mortality (48,49). For instance, the vaginal birth mode associated six higher times of neonatal mortality among newborns delivered at the El Fateh-Suka clinic in Burkina Faso (48). Surprisingly, this study found a high rate of Caesarean section (52.92%) compared to the global births rate by Caesarean section of 15% to minimize maternal and neonatal mortalities (50). The high rate of Caesarean section delivery and the associated high risk of neonatal mortality at KTH could be explained by its status of a tertiary referral hospital mostly for complicated pregnancies jeopardizing the newborn and maternal health. Strengths and limitations This study has both political and clinical importance. For policies, it provides direct insights into the determinants of neonatal mortality to take related actions. Establishing neonatal survival time and identifying neonatal mortality causes allow clinicians to have information on when and how to act more to benefit maternal and newborn health. However, 15% of neonates were lost to follow up and this should have underestimated the neonatal mortality rate once there have been neonatal death among them. We are aware that our study is very minimal contributory, but we gained pride in our contribution to opening another related research later. For example, a qualitative study investigating why pregnant women do not attend free of charge ANC services should specify which specific interventions or strategies may contribute to reducing neonatal mortality. Policy implications Findings from this study feed into the Burundian context. Therefore, they could help public health policy decision-makers to identify better neonates who are most risked to die within their first 28 days of life. Knowing neonatal mortality risk factors helps women and health care providers to adopt pregnancy related behaviors for a positive pregnancy experience. When planning, characteristics including gestational age, ANC number, and Apgar score should be considered. Conclusion In conclusion, the neonatal mortality rate is relatively high at Kamenge Teaching Hospital. Overall, 90% of neonatal deaths occurred during the first week of life with 40% during the first 24 hours. In general, the leading causes of neonatal mortality were preterm complications, birth asphyxia, neonatal infections, and congenital malformations. Furthermore, the gestational age, ANC attendance, Caesarean section, and Apgar score at five minutes were significantly associated with neonatal mortality. Neonatal mortality due to preterm complications, birth asphyxia, and neonatal infections can be prevented through comprehensive ANC services. Therefore, there is a need for the government of Burundi to successfully implement the new “WHO-2016 recommendation on antenatal care for a positive pregnancy experience” for maternal and neonatal health improvement. Abbreviations ANC: Antenatal care; DHS: Demographic and health survey; EAC: East African community; ENMR; Early neonatal mortality rate; HIC: High-income countries; KTH: Kamenge Teaching Hospital; LMICs: Low-and middle-income countries; LIC: Low-income countries; LNMR: Late neonatal mortality rate; NMR: Neonatal mortality rate; SSA: Sub-Saharan Africa; SDGs: Sustainable development goals: U5MR: Under-five mortality rate; WHO: World Health Organization. Declarations Ethics approval An ethical approval from the Ethics Review Committee of the Faculty of Medicine, University of Burundi was given for this study. The declaration of Helsinki carried out all methods. Consent for publication. Not applicable Availability of data and materials. The datasets generated during the current study are available from the corresponding author. Competing interests. The authors declare no competing interests. Funding. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions All authors contributed toward drafting and critically revising the paper and agreed to be accountable for all the aspects of the work. Specifically, JCN conceptualized and designed the study. AI and JCN followed up newborns, analysed data, and wrote the first draft. AI and ENO provided technical support. AA, ENO, and MO proofread the manuscript for improvement. All authors read and approved the final version of this manuscript for submission. Acknowledgments The authors are grateful to the KTH managers for access to data. References Hug L, Lee S, Liu Y, Mishra A, Sharrow D, You D, et al. Levels and trends in child mortality: report 2020, estimates developed by the United Nations Inter-agency Group for Child Mortality Estimation. 2020. World Health Organization. Newborns: improving survival and well-being [Internet]. 2020 [cited 2024 Jan 28]. Available from: https://www.who.int/news-room/fact-sheets/detail/newborns-reducing-mortality WHO. World health statistics 2022: monitoring health for the SDGs, sustainable development goals. [Internet]. World Health Organization, editor. Geneva; 2022. Available from: http://apps.who.int/bookorders. UNICEF. Pour chaque enfant, une chance de vivre : L’urgence de mettre fin à la mortalité néonatale [Internet]. 2018 [cited 2024 Jan 28]. Available from: https://reliefweb.int/report/world/pour-chaque-enfant-une-chance-de-vivre-l-urgence-de-mettre-fin-la-mortalit-n-onatale Hug L, Alexander M, You D, Alkema L. National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis. Lancet Glob Health [Internet]. 2019 Jun 1 [cited 2023 Feb 12];7(6):e710. Available from: /pmc/articles/PMC6527519/ Alkema L, New JR, Pedersen J, You D, Bastian P, Wu J, et al. Child mortality estimation 2013: An overview of updates in estimation methods by the United Nations Inter-Agency Group for Child Mortality Estimation. Vol. 9, PLoS ONE. Public Library of Science; 2014. United Nations. The Sustainable Development Goals [Internet]. 2020 [cited 2024 Jan 28]. Available from: https://sdgs.un.org/goals Every Woman Every Child. the-global-strategy-for-women-s-children-s-and-adolescents-health-2016-2030. 2015; World Health Organization. Monitoring emergency obstetric care: a handbook. Geneva; 2009. Jehan I, Harris H, Salat S, Zeb A, Mobeen N, Pasha O, et al. Neonatal mortality, risk factors and causes: A prospective population-based cohort study in urban Pakistan. Bull World Health Organ. 2009 Feb;87(2):130–8. Ahmed I, Ali SM, Amenga-Etego S, Ariff S, Bahl R, Baqui AH, et al. Population-based rates, timing, and causes of maternal deaths, stillbirths, and neonatal deaths in south Asia and sub-Saharan Africa: a multi-country prospective cohort study. Lancet Glob Health. 2018 Dec 1;6(12):e1297–308. Masaba BB, Mmusi-Phetoe RM. Neonatal survival in sub-sahara: A review of kenya and south africa. J Multidiscip Healthc. 2020;13:709–16. Seid SS, Ibro SA, Ahmed AA, Olani Akuma A, Reta EY, Haso TK, et al. Causes and factors associated with neonatal mortality in Neonatal Intensive Care Unit (NICU) of Jimma University Medical Center, Jimma, South West Ethiopia. Pediatric Health Med Ther. 2019 May;Volume 10:39–48. McKinnon B, Harper S, Kaufman JS, Bergevin Y. Socioeconomic inequality in neonatal mortality in countries of low and middle income: A multicountry analysis. Lancet Glob Health. 2014;2(3):e165–73. Wolde HF, Gonete KA, Akalu TY, Baraki AG, Lakew AM. Factors affecting neonatal mortality in the general population: Evidence from the 2016 Ethiopian Demographic and Health Survey (EDHS)-multilevel analysis. Vol. 12, BMC Research Notes. BioMed Central Ltd.; 2019. Al Kibria GM, Khanam R, Mitra DK, Mahmud A, Begum N, Moin SMI, et al. Rates and determinants of neonatal mortality in two rural sub-districts of Sylhet, Bangladesh. PLoS One. 2018 Nov 1;13(11). Mengesha HG, Wuneh AD, Lerebo WT, Tekle TH. Survival of neonates and predictors of their mortality in Tigray region, Northern Ethiopia: Prospective cohort study. BMC Pregnancy Childbirth. 2016 Aug 2;16(1). Gupta N, Hirschhorn LR, Rwabukwisi FC, Drobac P, Sayinzoga F, Mugeni C, et al. Causes of death and predictors of childhood mortality in Rwanda: A matched case-control study using verbal social autopsy. BMC Public Health. 2018 Dec 17;18(1). De Souza S, Duim E, Nampo FK. Determinants of neonatal mortality in the largest international border of Brazil: A case-control study. BMC Public Health. 2019 Oct 16;19(1). Meessen B, Hercot D, Noirhomme M, Ridde V, Tibouti A, Tashobya CK, et al. Removing user fees in the health sector: A review of policy processes in six sub-Saharan African countries. Vol. 26, Health Policy and Planning. 2011. Gage A, Bauhoff S. The effects of performance-based financing on neonatal health outcomes in Burundi, Lesotho, Senegal, Zambia and Zimbabwe. Health Policy Plan. 2021 Apr 1;36(3):332–40. Mejía-Guevara I, Zuo W, Bendavid E, Li N, Tuljapurkar S. Age distribution, trends, and forecasts ofunder-5 mortality in 31 sub-saharan africancountries: A modeling study. PLoS Med. 2019 Mar 1;16(3). Ministère à la Présidence chargé de la Bonne Gouvernance et du Plan Ministère de la Santé Publique et de la Lutte contre le Sida Institut de Statistiques et d’Études Économiques du Burundi Bujumbura B. Burundi Troisième Enquête Démographique et de Santé. 2016. Zuniga I, Van den Bergh R, Ndelema B, Bulckaert D, Manzi M, Lambert V, et al. Characteristics and mortality of neonates in an emergency obstetric and neonatal care facility, rural Burundi. Public Health Action. 2013 Dec 24;3(4):276–81. Moise IK. Causes of morbidity and mortality among neonates and children in post-conflict burundi: A cross-sectional retrospective study. Children. 2018 Sep 1;5(9). Yaya S, Zegeye B, Ahinkorah BO, Ameyaw EK, Seidu AA, Shibre G. Time trends, geographical, socio-economic, and gender disparities in neonatal mortality in Burundi: evidence from the demographic and health surveys, 2010–2016. Archives of Public Health. 2020 Dec 1;78(1). World Health Organization. Verbal autopsy standards: The 2012 WHO verbal autopsy instrument [Internet]. 2012. Available from: www.who.int World Health Organization. Adolescent pregnancy [Internet]. 2023 [cited 2024 Jan 29]. Available from: https://www.who.int/news-room/fact-sheets/detail/adolescent-pregnancy World Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience. 2016. Ohuma EO, Moller AB, Bradley E, Chakwera S, Hussain-Alkhateeb L, Lewin A, et al. National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis [Internet]. www.thelancet.com. 2023. Available from: www.thelancet.com The WHO ACTION Trials Collaborators. Antenatal Dexamethasone for Early Preterm Birth in Low-Resource Countries. New England Journal of Medicine [Internet]. 2020 Dec 24;383(26):2514–25. Available from: http://www.nejm.org/doi/10.1056/NEJMoa2022398 WHO Immediate KMC Study Group. Immediate “Kangaroo Mother Care” and Survival of Infants with Low Birth Weight. New England Journal of Medicine [Internet]. 2021 May 27;384(21):2028–38. Available from: http://www.nejm.org/doi/10.1056/NEJMoa2026486 Zhang Z. Model building strategy for logistic regression: Purposeful selection. Ann Transl Med. 2016 Mar 1;4(6). Welaga P, Moyer CA, Aborigo R, Adongo P, Williams J, Hodgson A, et al. Why Are Babies Dying in the First Month after Birth? A 7-Year Study of Neonatal Mortality in Northern Ghana. PLoS One. 2013 Mar 19;8(3). Kananura RM, Tetui M, Mutebi A, Bua JN, Waiswa P, Kiwanuka SN, et al. The neonatal mortality and its determinants in rural communities of Eastern Uganda. Reprod Health. 2016 Feb 16;13(1). Koshida S, Yanagi T, Ono T, Tsuji S, Takahashi K. Possible prevention of neonatal death: A regional population-based study in Japan. Yonsei Med J. 2016 Mar 1;57(2):426–9. Wang XL, Wang J, Yuan L, Shi WJ, Cao Y, Chen C. Trend and causes of neonatal mortality in a level III children’s hospital in Shanghai: a 15-year retrospective study. World Journal of Pediatrics. 2018 Feb 1;14(1):44–51. Allanson ER, Muller M, Pattinson RC. Causes of perinatal mortality and associated maternal complications in a South African province: Challenges in predicting poor outcomes. BMC Pregnancy Childbirth. 2015 Feb 15;15(1). Mugo NS, Agho KE, Zwi AB, Damundu EY, Dibley MJ. Determinants of neonatal, infant and under-five mortality in a war-affected country: Analysis of the 2010 Household Health Survey in South Sudan. BMJ Glob Health. 2018 Jan 1;3(1). Sauvegrain P, Carayol M, Piedvache A, Guéry E, Bréart G, Bucourt M, et al. Understanding high rates of stillbirth and neonatal death in a disadvantaged, high-migrant district in France: A perinatal audit. Acta Obstet Gynecol Scand. 2020 Sep 1;99(9):1163–73. Eshete A, Abiy S. When Do Newborns Die? Timing and Cause-Specific Neonatal Death in Neonatal Intensive Care Unit at Referral Hospital in Gedeo Zone: A Prospective Cohort Study. International Journal of Pediatrics (United Kingdom). 2020;2020. Bazzano AN, Var C, Wilkosz D, Duggal R, Oberhelman RA. Neonatal deaths in Cambodia: Findings from a community-based mortality review. Vol. 12, BMC Research Notes. BioMed Central Ltd.; 2019. Koffi AK, Kalter HD, Kamwe MA, Black RE. Verbal/social autopsy analysis of causes and determinants of under-5 mortality in Tanzania from 2010 to 2016. J Glob Health. 2020 Dec 1;10(2). Aggarwal AK, Kumar P, Pandit S, Kumar R. Accuracy of WHO Verbal Autopsy Tool in Determining Major Causes of Neonatal Deaths in India. PLoS One. 2013 Jan 30;8(1). McCurdy RJ, Kjerulff KH, Junjia Z. Prenatal care associated with reduction of neonatal mortality in Sub-Saharan Africa: Evidence from demographic and health surveys. Acta Obstet Gynecol Scand. 2011 Jul;90(7):779–90. Li F, Wu T, Lei X, Zhang H, Mao M, Zhang J. The Apgar Score and Infant Mortality. PLoS One. 2013 Jul 29;8(7). Iliodromiti S, MacKay DF, Smith GCS, Pell JP, Nelson SM. Apgar score and the risk of cause-specific infant mortality: A population-based cohort study. The Lancet. 2014 Nov 15;384(9956):1749–55. Nagalo K, Dao F, Tall FH, Yé D. Morbidité et mortalité des nouveau-nés hospitalisés sur 10 années à la clinique el fateh-suka (Ouagadougou, Burkina Faso). Pan African Medical Journal. 2013; 14:153. Sidi-Yakhlef A, Boukhelif M, Kech Z. Determinants of neonatal mortality in Maghnia in the far west of Algeria. Vol. 01, Algerian Journal of Health Sciences. 2019. Betrán AP, Merialdi M, Lauer JA, Bing-Shun W, Thomas J, Look P Van, et al. Caesarean section Rates of caesarean section: analysis of global, regional and national estimates [Internet]. Vol. 21, Paediatric and Perinatal Epidemiology. 2007. Available from: http://www.measuredhs. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4337583","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299181161,"identity":"007cd998-36fe-4a87-ae2f-d316b1f3f38c","order_by":0,"name":"Jean Claude Ndayishimiye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACxgYGBomPfySY7duBLAYDC+K0SM5ssGE34DkA0iJBnE3SvA1p/AYSCSA2EVqY288evM2747C0ueTzqxt+FEgw8Ld3J+B3WE9esuXcM4eNLWfnlN3sATpM4szZDfi1zOAxk3jDdjiZ4XZO2g0eoBYDiVwitPCwHa5vuHkm7eYfYrVI8ralMRvcYD92mzhbenKMLWecsWGW7Mlhuy1jIMFD0C+G7WcMb3yokGDmZz/+7OabPzZy/O29BLQ0wJk8BmASr3IQkEcw2R8QVD0KRsEoGAUjEwAAzfNH9YsrFTQAAAAASUVORK5CYII=","orcid":"","institution":"University of Burundi","correspondingAuthor":true,"prefix":"","firstName":"Jean","middleName":"Claude","lastName":"Ndayishimiye","suffix":""},{"id":299181163,"identity":"53076a57-386e-4813-bf96-4577f7ba4abc","order_by":1,"name":"Arnaud IRADUKUNDA","email":"","orcid":"","institution":"University of Burundi","correspondingAuthor":false,"prefix":"","firstName":"Arnaud","middleName":"","lastName":"IRADUKUNDA","suffix":""},{"id":299181165,"identity":"f8be1af6-3edb-4c5d-b2b6-b04c5acb2daa","order_by":2,"name":"Ornella MASIMBI","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ornella","middleName":"","lastName":"MASIMBI","suffix":""},{"id":299181168,"identity":"75c9bb1c-c4a2-4298-a858-36bf135ca122","order_by":3,"name":"Alain Ahishakiye","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Alain","middleName":"","lastName":"Ahishakiye","suffix":""},{"id":299181173,"identity":"9381a01a-61ef-4812-8f62-a552caa3e58d","order_by":4,"name":"Emmanuel Nene ODJIDJA","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"Nene","lastName":"ODJIDJA","suffix":""}],"badges":[],"createdAt":"2024-04-28 11:00:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4337583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4337583/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25468-0","type":"published","date":"2025-11-24T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56196749,"identity":"8e516dfb-3c73-475f-9cc1-fcb79ec7f489","added_by":"auto","created_at":"2024-05-09 18:13:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32293,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of followed neonates at KTH, October-December 2020\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4337583/v1/023fef1d9994f86a6939f54e.png"},{"id":56196771,"identity":"c19965e5-4d0c-4001-962a-7f2a2f8e1ef2","added_by":"auto","created_at":"2024-05-09 18:13:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28690,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier survival curve for neonates born at Kamenge Teaching Hospital, October-December 2020 (N=885).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4337583/v1/255a18e7d069c357bc0ab69f.png"},{"id":97179632,"identity":"56c91b43-942c-4ee1-9d14-51bc886f1822","added_by":"auto","created_at":"2025-12-01 16:16:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":863065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4337583/v1/e1590a50-5735-477d-9f24-02b79f8430c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Survival of newborns and determinants of their mortality in Burundi: A prospective cohort study at Kamenge Teaching Hospital","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe neonatal period is the most vulnerable time for child survival (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Click or tap here to enter text.. Children faced the highest risk of dying in their first 28 days of life, and the neonatal period accounted for a large proportion of child deaths (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Globally, 2.4\u0026nbsp;million neonates died before reaching their first 28 days of life in 2019, which represented 47% of all deaths among children under five years, a figure up from 1990 when it was 40% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although the improvement was made among children under five years, the decline in mortality from 1990 to 2019 has been slower for newborns than for children under five years who have exceeded the neonatal period (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Since 1990, the global under-five mortality rate (U5MR) declined by almost 60 per cent from 93 per 1,000 live births to 38 per 1,000 live births in 2019. However, the neonatal mortality rate only declined by 52 percent dropping from 37 percent to 17 percent in 2019 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, the neonatal mortality rate accounted for a large and growing proportion of all under-five mortality rates. Neonatal mortality remained a challenge in low- and middle- income countries (LMICs) where there are inadequate health systems and a critical shortage of healthcare providers who can adequately manage and provide quality care (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The Sustainable Development Goals (SDGs) agenda highlighted the importance of a continued momentum towards improving newborn health by setting, under SDG3, targets aiming for all countries to reduce neonatal mortality to less than 12 per 1,000 live births by 2030 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNeonatal mortality varied considerably from place to place, country to country, and within each country itself (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The neonatal mortality rate was 3\u0026permil; in high-income countries (HIC), with considerable differences in Low-income countries (LIC), where it was 27 per 1,000 live births in 2019. Most newborns\u0026rsquo; deaths occurred in low-and middle-income countries (LMICs), with the south-Asia and Sub-Saharan Africa (SSA) accounting for more than 80% of all neonatal deaths (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The SSA had the highest neonatal mortality rate (NMR), with 27 neonatal deaths per 1,000 live births (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In the East African Community (EAC) sub-region, Burundi has the second highest NMR of 21\u0026permil; after the South-Sudan with 39\u0026permil; (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The causes of neonatal mortality are mostly preventable by high-quality maternal and newborn healthcare services. Evidence showed that 16\u0026nbsp;million newborn\u0026rsquo;s deaths should be averted by 2030 if LIC reduced their NMR to the same rate as the HIC (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Hence, neonatal mortality is considered as one of the sensitive indicators of the quality maternal and newborn healthcare services (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Evidence from previous studies conducted in South Asia and sub-Saharan Africa showed perinatal asphyxia, neonatal infections, and preterm complications as the leading causes of neonatal deaths (\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Those neonatal mortality causes are mostly preventable. They include both clinical, therapeutic, socio-economic, and demographic factors (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) For instance, home delivery, multiple gestation, non-use of family planning, poor to moderate health services delivery, parents deceased associated neonatal mortality (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In addition, congenital anomalies, Apgar score below 7 at the first minute of life, low birth weight, less than 4 ANC, and preterm birth predicted neonatal mortality (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).Although progress was made in Burundi, the neonatal mortality rate remained relatively high where two out of one hundred newborns died (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite the free health care and services policy for pregnant women and children under five introduced in 2006 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and the performance-based incentives (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), the neonatal mortality rate remained below political expectations. With a forecast of an annual reduction rate of 2.7% (2017\u0026ndash;2030), Burundi risks missing the SDG target to reduce the neonatal mortality rate to less than 12 per 1,000 live births by 2030 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The recent demographic and health survey (DHS) 2016\u0026ndash;2017 concluded an NMR of 23\u0026permil; with variations according to residence area (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The probability of dying between the first and the 28th day of life was significantly higher in urban areas compared to rural areas (30\u0026permil; versus 22\u0026permil;). For previous studies conducted in rural areas, one showed an NMR of 50\u0026permil; and the main causes were neonatal infections, preterm complications, birth asphyxia, and respiratory distress (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). However, these two studies were retrospective surveys using neonatal hospitalization-based data, which should have overestimated the NMR. Furthermore, in another study to investigate inequalities in neonatal mortality using a national sample, Yaya et al. (2020) found significant disparities according to newborn\u0026rsquo;s sex, mother\u0026rsquo;s education level, wealth index, sub-national region, and residence areas (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Therefore, this study aimed to determine the neonatal survival time and identify factors affecting neonatal mortality at Kamenge Medical Teaching Center, an urban-based hospital.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2-1. Study design, setting, and follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a prospective cohort study on all live births delivered in the maternity ward of Kamenge Teaching Hospital (KTH) from 1\u003csup\u003est\u003c/sup\u003e October to 31\u003csup\u003est\u003c/sup\u003e December 2020. Kamenge Teaching Hospital is located in the north-east of the economic capital, which has the highest proportion of women who delivered in a health facility and assisted by a trained healthcare provider (23). It was selected because it is both a tertial and unique public teaching hospital, providing maternal and neonatal care services including, antenatal, postnatal, and emergency obstetrical and neonatal care services free of charge.\u003c/p\u003e\n\u003cp\u003eAll live births were included in the study and followed up until their first 28 days of life. The cohort recruitment was open for 3 months from 1\u003csup\u003est\u003c/sup\u003e October to 31\u003csup\u003est\u003c/sup\u003e December 2020 and then the last recruited followed up till the end of the 28 first days of life on January 27, 2021. \u0026nbsp;For all newborns admitted into the neonatology service, we followed them up from the cohort entry up to the occurrence of an event (death) or until their 28 days of life. For not hospitalized or discharged newborns within their first 28 days of life, we assessed the neonatal status by contacting (by cell phone) their mothers and/or the heads of the household who consented to participate in the study. They were contacted on the first day of life and every seven days. For each call, the following question was asked: \u0026ldquo;How is the newborn doing?\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included all live births delivered in the maternity ward of Kamenge Teaching Hospital from 1\u003csup\u003est\u003c/sup\u003e October to 31\u003csup\u003est\u003c/sup\u003e December 2020. Newborns admitted into the neonatology unit but delivered outside of the maternity ward of KTH were excluded. Also, newborns who\u0026rsquo;s their mothers did not consent to participate in the study were excluded. In the period of study, a cohort of 885 newborns was followed up. However, we were able to follow up only 722 newborns till their 28 days of life while 30 died during the follow up period (Details in Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were prospectively collected using a structured questionnaire delivered from the WHO neonatal verbal autopsy standards (27). The questionnaire was also similar to the obstetrical record \u0026ldquo;Patrogram\u0026rdquo; and was initially developed in French and translated into Kirundi, the local language. Two nurses and three midwives working in the maternity ward of KTH were recruited and trained to collect data during the period after childbirth. We followed up newborns up to the occurrence of the event (neonatal death) or up to their 28 days of life. The trained nurses and midwives, after a woman\u0026rsquo;s informed consent to participate in the study, were responsible for collecting data by interviewing each mother within 12 hours after giving birth. Besides, clinical information for the mother and the newborn was collected using the obstetrical record \u0026ldquo;Patrogram\u0026rdquo;. Afterward, we followed up newborns into two alternatives. For newborns admitted into hospitalization, we collected data from the neonatology entry/exit records for the first day of life and every 7 days. When the death occurred, the date and cause were recorded by physicians working in the neonatology unit and then collected. For newborns not hospitalized or discharged within their first 28 days of life, we assessed the neonatal conditions and survival status using a cell phone contacting their mothers and/or the heads of the family every 7 days. For each call, the following question was asked: \u0026ldquo;how is doing the newborn?\u0026rdquo;. No newborn among those whom we were able to reach their mothers or heads of family died at home. Those whom we were not able to reach because of cell phone communication issues, were excluded from the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Definition and management of variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selection of independent variables was based on evidence from previous studies and the WHO neonatal verbal autopsy standards. The variables used included socio-demographic characteristics (maternal age, residence, mother\u0026rsquo;s education level, mother\u0026rsquo;s profession), pregnancy-related characteristics (gestational age, gravidity, parity, ANC number, Ultrasound number, perinatal complications), delivery conditions (delivery mode, birth type, birth date), and neonatal clinical and measurements (birth weight, sex, Apgar score, neonatal survival status).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe defined a mother\u0026rsquo;s profession based on the mother\u0026rsquo;s reported occupation, and we categorized women \u0026ldquo;without profession\u0026rdquo;, the farmer/ housewife or student while women who reported as government employees, private sector employees or business owners were categorized into women \u0026ldquo;with profession\u0026rdquo;. Mother\u0026rsquo;s age was classified according to the World Health Organization (WHO) and International Federation of Gynecology and Obstetrics definition of high-risk pregnancy related to maternal age. We then categorized maternal age as below 20 years (teenage pregnancy), between 20 and 35 years and over 35 years (late pregnancy) (28). However, during analysis due to few observations of teenage pregnancies, we recoded the maternal age into two categories by merging the two extreme categories (less than 20 and greater than 35 years). Similarly, due to few no education level observations, educational status was categorized into none and primary education level and secondary education and above (high school to university level).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mother\u0026rsquo;s residence was classified as rural and urban. We defined parity as the number of children previously born to a woman, excluding miscarriages or abortions, but including stillbirths and fetal deaths. We classified parity into first parous (the current birth), pauci-parity (2-3), and over 3 as multiparity. We defined gravidity as the number of pregnancies delivered or not a woman has had in her life. We categorized it into first gravidity (current birth), pauci-gravidity (2-3), and over 3 pregnancies as a multi-gravidity. We categorized perinatal complications into yes or no, which are considered present if the mother had either an obstetric haemorrhage, premature rupture of membranes, preeclampsia and its complications, puerperal fever, and other emergency obstetrical conditions. Pregnancy was defined as well-monitored if the mother attended at least four antenatal care (ANC) and/or at least three ultrasounds according to the WHO fan model of antenatal care recommendations (29). The reference to four ANC was based on the fact that Burundi has not yet implemented the new model (WHO-2016) recommending at least eight ANC (29).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the WHO, we categorized the birth gestational age into preterm birth (\u0026lt; 37 weeks of gestation) and term birth (\u0026ge;37\u0026nbsp;weeks of gestation) (30,31). Birth weight was categorized according to the WHO and United Nations Children\u0026rsquo;s Fund classification as low birth weight (\u0026lt; 2500 grams) and normal birth weight (\u0026ge;\u0026nbsp;2500 grams) (32). The sex of the newborn was male and female. An Apgar score less or equal to five (\u0026le;5) at five minutes of birth defines bad conditions for the newborn. We classified the mode of delivery into vaginal delivery and caesarean section. The birth type was categorized into single birth and multiple births, which means twin, triple, and above. Mothers who gave multiple births were counted separately for each birth for a sake analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Outcome variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeonatal mortality was defined as the death of newborns in their 28 first days of life. The neonatal mortality rate (NMR) was defined as a number of deceased newborns per 1,000 live births during the period of the study. Early neonatal mortality (ENMR) was defined as the probability of dying within the 7 days of life while the late neonatal mortality rate (LNMR), on the other side, was defined as the probability of dying between 7 days and before 28 days of life. The newborn death was categorized into \u0026ldquo;yes\u0026rdquo; if the newborn died and \u0026ldquo;no\u0026rdquo; otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeonatal mortality was the event of interest and coded \u0026ldquo;1\u0026rdquo; if the death occurred and \u0026ldquo;0\u0026rdquo; if the newborn was censored. The time spent by each newborn in the study was calculated by subtracting the date of death, lost to follow-up, and or the end of the neonatal period from the date of birth. We imported, cleaned, recorded, and analyzed data into STATA Version 17.0. For descriptive statistics, frequencies and percentages were used. The Kaplan-Meier survival curve was used to estimate neonatal survival time. For statistical analysis, logistic regression was used to calculate unadjusted odds ratios (ORs) and adjusted odds ratios (AORs) to identify potential factors associated with neonatal mortality. In the bivariate analysis, variables with a significance level (p\u0026lt;0.15) were included in the multivariate analysis (33). To identify which factors, impact neonatal mortality, the final analysis was made by successive stepwise backward elimination to calculate adjusted ORs.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Response rate and characteristics of newborns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe descriptive characteristics of the newborns are presented in table 1. Eight hundred eighty-five newborns were eligible to the followed up (Details in Table 1). However, we were able to follow up on 752 newborns till the end of the neonatal period or the occurrence of an event (death). For consistent data analysis, we assigned maternal characteristics (for mothers who gave multiple births) to each newborn. The majority of newborns (more than 95%) were single births and had a good Apgar score at five minutes. Plus, about 85% of neonates were born without perinatal complications, at term, and with normal birth weight. About three-quarters of neonates (73%) were born from mothers aged 20-35 years and from urban areas. Regarding pregnancy monitoring, nearly 45% and 54% of newborns were born from pregnancies well monitored by at least 3 ultrasounds and more than 3 ANC, respectively. About half of newborns were born by caesarean section, from mothers with a profession, and mothers with at least a secondary education level. The female-to-male ratio was 1:1.07.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e: Characteristics of newborns at Kamenge Teaching Hospital, October-December 2020, (N=752).\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMaternal age (years)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 20-35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e73.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;20/\u0026gt;35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eProfession of the mother\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e58.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Non\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eResidence of the mother\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e73.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eEducation of the mother\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Secondary/university\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; None/primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGravidity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eParity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e39.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePerinatal complications\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e88.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eUltrasound\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eANC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e53.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGestational age\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge;37WG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;37WG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eSex of the newborn\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e48.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eBirth weight\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge;2500grams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;2500grams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eApgar score at five min\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eDelivery mode\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Vaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e47.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Caesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e52.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eBirth type\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e95.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.70860927152318%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.509933774834437%\" valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.78145695364238%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;ANC: Antenatal care; WG: Weeks of gestation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3-2. Neonatal Survival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe followed up on newborns for 23,792 neonate days. In the study, 30 newborn deaths occurred which made the NMR 34\u0026permil;.\u0026nbsp;Among all, 27 died in the first week of their life, of which 12 occurred during the first 24 hours. Therefore, the early neonatal mortality rate (ENMR) represented 90% of the overall neonatal mortality while 40% occurred within the first 24 hours of life. At the end of the neonatal period, the cumulative survival probability was\u0026nbsp;96.59% and 722 neonates were alive, 30 died, and 133 lost to follow-up (Details in Table 2).\u0026nbsp;The main causes of neonatal mortality were preterm complications (60%), birth asphyxia (13.33%), neonatal infections (13.33%), and abnormal congenital (13.33%). The graph of\u0026nbsp;Kaplan-Meier indicates that neonatal mortality was quicker during the first seven days of life and, therefore, a lower probability of neonatal survival. From the eighth day of life, the graph fell slowly, indicating a lower proportion of neonates were dying (Details in Figure 2).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2: Neonatal survival analysis during the follow-up time at Kamenge Teaching Hospital, October-December 2020, (N=885)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eTime interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eLost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eCSP (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0-1\u003csup\u003est\u003c/sup\u003e day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e98.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e[0.97 - 0.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2-7\u003csup\u003eth\u003c/sup\u003e day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e96.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e[0.95 - 0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e8-14\u003csup\u003eth\u003c/sup\u003e day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e96.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e[0.95 - 0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e15-21\u003csup\u003est\u003c/sup\u003e day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e96.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e[0.95 - 0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e22-28\u003csup\u003eth\u003c/sup\u003e day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e96.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e[0.95 - 0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003csup\u003eth\u003c/sup\u003e day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e96.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e[0.95 - 0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eNote: CSP: Cumulative survival probability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Results of logistic regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the multivariate analysis are presented in Table 3. After successive stepwise backward elimination, the multivariate final model revealed four variables associated with neonatal mortality: gestational age, ANC visits number attended during pregnancy, delivery mode, and Apgar score at five minutes (). The highest risk was observed among neonates delivered before 37 weeks of gestation, and thus the odds of neonatal mortality among preterm neonates were\u0026nbsp;9 times higher compared to term neonates (AOR:\u0026nbsp;9.01, 95%CI\u0026nbsp;2.05 - 39.52). The risk of neonatal mortality decreased with the number of ANC attendance, neonates born from pregnancies monitored by less than four ANC were 7 times more likely to die (AOR:\u0026nbsp;6.64, 95%CI\u0026nbsp;1.46 - 30.21) than those born from pregnancies monitored by at least four ANC. Neonates who had an Apgar score of 5 and below at five minutes were 11 times more likely to die (AOR:\u0026nbsp;10.83, 95%CI\u0026nbsp;2.61 - 44.93) than those with an Apgar score above 5. Furthermore, neonates born by caesarean had 2.5 times (AOR:\u0026nbsp;2.47, 95%CI\u0026nbsp;1.00 - 6.07) higher risk of dying compared to neonates delivered by vaginal mode.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3: Bivariate and Multivariate analysis of determinants of neonatal mortality at Kamenge Teaching Hospital, October-December 2020\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.24755700325733%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.732899022801302%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003ecOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMaternal age\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;20-35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;20/\u0026gt;35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.37 - 2.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eProfession\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.79 - 3.42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eResidence\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.55 - 2.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eEducation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;Secondary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026le;Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[1.04 - 4.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGravidity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.76 - 6.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[1.07 - 7.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eParity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.81 - 5.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.89 - 5.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePerinatal complications\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[4.43 - 20.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eUltrasound\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[1.17 - 6.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.020\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eANC visits\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[4.15 \u0026ndash; 74.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\n \u003cp\u003e[1.46 - 30.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGestational age\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;37WG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;37WG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[12.39 \u0026ndash; 78.62] \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\n \u003cp\u003e[2.05 - 39.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eSex\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.33 - 1.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eBirth weight\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;2500 grams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;2500 grams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e16.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[7.46 - 37.86]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.67 - 11.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eApgar score at 5 minutes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[2.99 - 25.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\n \u003cp\u003e[2.61 - 44.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eDelivery mode\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Vaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Caesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.96 - 4.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\n \u003cp\u003e[1.00 - 6.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eBirth type\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Single(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.078175895765472%\" valign=\"bottom\"\u003e\n \u003cp\u003e[0.71 - 8.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.609120521172638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003eNote: ANC: Antenatal care, WG: Weeks of gestation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe NMR at Kamenge Teaching Hospital was 34 per 1,000 live births. This rate is relatively high compared to other studies conducted in developing countries (13,34,35) as well as the Burundi national rate of 23\u0026permil; according to the Third demographic and health survey (DHS) (2016-2017) (23).\u0026nbsp;It is also higher than the Burundi NMR estimated at 21\u0026permil; by the\u0026nbsp;United Nations Inter-Agency Group for Child Mortality Estimation\u0026nbsp;in 2019 (1).\u0026nbsp;This divergence could be explained in terms of study methods; most studies were retrospective or cross-sectional which could have underestimated the NMR. Furthermore, the NMR for this study remained very high compared to other studies conducted in developed countries (36\u0026ndash;38). Nevertheless, the rate of 34\u0026permil; was comparable to the NMR found in certain studies carried out in some developing countries (15\u0026ndash;17,39).The NMR in the emergency obstetric and neonatal care facilities of the three Bujumbura rural district hospitals in 2011 was 50\u0026permil;. This was explained by the fact that the centers received only parturients referred for pregnancy complications and/or complicated delivery, which were likely to play a role (24). In this study, preterm complications were the leading causes of neonatal mortality, representing 60% of all causes. Therefore, the neonatal mortality rate of 34\u0026permil; can be explained by the low ANC services which constitute a platform for screening, diagnosis, and prevention of diseases including preterm birth. On the other hand, in this low-and middle-income country, insufficiency or even inadequate infrastructure and materials necessary for the management of premature births can be a reason. Another reason could be that KTH, a tertiary referral institution, is often cloudy because many babies are delivered after that transfer from all over the country due to complicated pregnancies. Regarding neonatal survival time, this study revealed that most neonatal deaths (90%) occurred during the first week of life and 40% in the first 24 hours of life. This distribution of neonatal death by age is consistent with the neonatal mortality incidence found in other studies from the literature review (15,17,35,40) and the study carried out in Burundi (25). A prospective cohort study in the Tigray region, northern Ethiopia, reported that most neonates died within their first week of life due to complications occurring during pregnancy and childbirth. Poor quality of prenatal care, delayed complications\u0026rsquo; identification, and management of complications during pregnancy and childbirth could also be reasons according to the same study (17). In this study, this survival time could be explained by the precocity of preterm complications as the main cause of neonatal deaths, and on the other hand, by poor ANC attendance among pregnant women. The first 24 hours and the first week of life are therefore a critical period for the survival of neonates. It is becoming clear that policies and programs that target neonates during the first week of life are more than urgent needed to improve infant survival.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, the leading causes of neonatal mortality were preterm complications (60%), birth asphyxia (13.33%), neonatal infections (13.33%), and congenital malformations (13.33%). These results are consistent with WHO reports on causes of neonatal mortality as well as findings from other studies (10,12,15,38), but the only difference resided in magnitude. We noted a high neonatal mortality related to preterm complications (60%). Contrary to studies conducted in some developed countries, the leading cause of neonatal mortality was congenital malformations at more than 25% (36,37). Even if tocolysis, steroids, and preventive antibiotics are part of the obstetrical guidelines for premature delivery management in Burundi, premature birth management remained the most important challenge. The study showed a low proportion of neonatal mortality related to birth asphyxia and neonatal infections compared to WHO estimations and other studies (10,11,34,41\u0026ndash;43). These results may reflect the contribution of universal health coverage of intrapartum and postpartum birth care as well as the improvement of obstetrical care during labor and childbirth to reduce neonatal deaths related to birth asphyxia. Regarding neonatal infections, the practice of hygienic childbirth, maternal prophylaxis, and neonatal antibiotics for neonates with a high risk of infections may have been protective. Nevertheless, the diagnosis of neonatal infections was made by clinical criteria accompanied by indirect tests including C-reactive protein and complete blood count. Without a certainty diagnosis by blood culture, they may have been false diagnostics. For neonatal mortality related to congenital malformations, similar trends have been observed in most other studies (10,12,38,44). Most neonatal deaths related to malformations are not preventable. Therefore, there is a need to improve technics to diagnose antenatal chromosomal abnormalities to reduce neonatal mortality related to congenital malformations.\u003c/p\u003e\n\u003cp\u003eThe study found a negative correlation between term birth and neonatal mortality. In comparison with term births, we found that the risk of neonatal mortality was 9 times for preterm births. Our results are in line with those recorded in other studies but with little difference in terms of magnitude (10,13,17,34). For instance, preterm births associated 5.8 and 3 times a risk of neonatal mortality for studies conducted in Pakistan and Ghana, respectively. In Burundi\u0026rsquo;s context, this would be linked to a lack of preterm prevention strategies and effective management of preterm births. Preterm neonates are unable to adapt to extra-uterus life, prenatal care improvement is therefore crucial in reducing preterm births. This study also showed that neonatal mortality is linked to the number of ANC attended during pregnancy. The risk of neonatal mortality was\u0026nbsp;7 times higher for babies born from pregnancies followed up by less than 4 ANC compared to those born from pregnancies followed up at least by 4 ANC. Our results are similar to those found by McCurdy et al. (2011) in sub-Saharan Africa and a study conducted in Ethiopia (45). The reason could be that a well-monitored pregnancy makes possible to quickly detect the risk factors and to have the necessary health information to carry out effective interventions. In addition, four or more ANC visits was found to be the most of effective interventions to reduce neonatal mortality in SSA (45). The Apgar score below 6 at five minutes associated a neonatal mortality risk of 11 times compared to newborns with an Apgar score at five minutes above 5. This relationship corroborates other studies (19,46,47) finding low Apgar score at five minutes strongly associated with the risk of neonatal mortality. For instance, regardless gestational age, Apgar score less than 6 associated with an increased early neonatal, late neonatal, and infant mortality (47). The Apgar score is used to assess the newborn\u0026rsquo;s respiratory, tonicity, skin coloration, and cardiovascular functioning conditions at birth, five, and ten minutes, this relationship could be explained by the inadequate care for neonates with birth asphyxia.\u0026nbsp;Furthermore, neonates born by caesarean section had 2.5 times higher risk of dying compared to neonates delivered by vaginal mode. This is consistent with findings from studies conducted in Pakistan (10). However, contradictory results have found the vaginal section associated with a high risk of neonatal mortality (48,49). For instance, the vaginal birth mode associated six higher times of neonatal mortality among newborns delivered at the El Fateh-Suka clinic in Burkina Faso (48). Surprisingly, this study found a high rate of Caesarean section (52.92%) compared to the global births rate by Caesarean section of 15% to minimize maternal and neonatal mortalities (50). The high rate of Caesarean section delivery and the associated high risk of neonatal mortality at KTH could be explained by its status of a tertiary referral hospital mostly for complicated pregnancies jeopardizing the newborn and maternal health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has both political and clinical importance. For policies, it provides direct insights into the determinants of neonatal mortality to take related actions. Establishing neonatal survival time and identifying neonatal mortality causes allow clinicians to have information on when and how to act more to benefit maternal and newborn health. However, 15% of neonates were lost to follow up and this should have underestimated the neonatal mortality rate once there have been neonatal death among them. We are aware that our study is very minimal contributory, but we gained pride in our contribution to opening another related research later. For example, a qualitative study investigating why pregnant women do not attend free of charge ANC services should specify which specific interventions or strategies may contribute to reducing neonatal mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFindings from this study feed into the Burundian context. Therefore, they could help public health policy decision-makers to identify better neonates who are most risked to die within their first 28 days of life. Knowing neonatal mortality risk factors helps women and health care providers to adopt pregnancy related behaviors for a positive pregnancy experience. When planning, characteristics including gestational age, ANC number, and Apgar score should be considered.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the neonatal mortality rate is relatively high at Kamenge Teaching Hospital. Overall, 90% of neonatal deaths occurred during the first week of life with 40% during the first 24 hours. In general, the leading causes of neonatal mortality were preterm complications, birth asphyxia, neonatal infections, and congenital malformations. Furthermore, the gestational age, ANC attendance, Caesarean section, and Apgar score at five minutes were significantly associated with neonatal mortality. Neonatal mortality due to preterm complications, birth asphyxia, and neonatal infections can be prevented through comprehensive ANC services. Therefore, there is a need for the government of Burundi to successfully implement the new \u0026ldquo;WHO-2016 recommendation on antenatal care for a positive pregnancy experience\u0026rdquo; for maternal and neonatal health improvement.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANC: Antenatal care; DHS: Demographic and health survey; EAC: East African community; ENMR; Early neonatal mortality rate; HIC: High-income countries; KTH: Kamenge Teaching Hospital; LMICs: Low-and middle-income countries; LIC: Low-income countries; LNMR: Late neonatal mortality rate; NMR: Neonatal mortality rate; SSA: Sub-Saharan Africa; SDGs: Sustainable development goals: U5MR: Under-five mortality rate; WHO: World Health Organization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;An ethical approval from the Ethics Review Committee of the Faculty of Medicine, University of Burundi was given for this study.\u0026nbsp;\u003cstrong\u003eThe declaration of Helsinki carried out all methods.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed toward drafting and critically revising the paper and agreed to be accountable for all the aspects of the work. Specifically, JCN conceptualized and designed the study. AI and JCN followed up newborns, analysed data, and wrote the first draft. AI and ENO provided technical support. AA, ENO, and MO proofread the manuscript for improvement. All authors read and approved the final version of this manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors are grateful to the KTH managers for access to data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHug L, Lee S, Liu Y, Mishra A, Sharrow D, You D, et al. Levels and trends in child mortality: report 2020, estimates developed by the United Nations Inter-agency Group for Child Mortality Estimation. 2020. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Newborns: improving survival and well-being [Internet]. 2020 [cited 2024 Jan 28]. 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Understanding high rates of stillbirth and neonatal death in a disadvantaged, high-migrant district in France: A perinatal audit. Acta Obstet Gynecol Scand. 2020 Sep 1;99(9):1163\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eEshete A, Abiy S. When Do Newborns Die? Timing and Cause-Specific Neonatal Death in Neonatal Intensive Care Unit at Referral Hospital in Gedeo Zone: A Prospective Cohort Study. International Journal of Pediatrics (United Kingdom). 2020;2020. \u003c/li\u003e\n\u003cli\u003eBazzano AN, Var C, Wilkosz D, Duggal R, Oberhelman RA. Neonatal deaths in Cambodia: Findings from a community-based mortality review. Vol. 12, BMC Research Notes. BioMed Central Ltd.; 2019. \u003c/li\u003e\n\u003cli\u003eKoffi AK, Kalter HD, Kamwe MA, Black RE. Verbal/social autopsy analysis of causes and determinants of under-5 mortality in Tanzania from 2010 to 2016. J Glob Health. 2020 Dec 1;10(2). \u003c/li\u003e\n\u003cli\u003eAggarwal AK, Kumar P, Pandit S, Kumar R. Accuracy of WHO Verbal Autopsy Tool in Determining Major Causes of Neonatal Deaths in India. PLoS One. 2013 Jan 30;8(1). \u003c/li\u003e\n\u003cli\u003eMcCurdy RJ, Kjerulff KH, Junjia Z. Prenatal care associated with reduction of neonatal mortality in Sub-Saharan Africa: Evidence from demographic and health surveys. Acta Obstet Gynecol Scand. 2011 Jul;90(7):779\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eLi F, Wu T, Lei X, Zhang H, Mao M, Zhang J. The Apgar Score and Infant Mortality. PLoS One. 2013 Jul 29;8(7). \u003c/li\u003e\n\u003cli\u003eIliodromiti S, MacKay DF, Smith GCS, Pell JP, Nelson SM. Apgar score and the risk of cause-specific infant mortality: A population-based cohort study. The Lancet. 2014 Nov 15;384(9956):1749\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eNagalo K, Dao F, Tall FH, Y\u0026eacute; D. Morbidit\u0026eacute; et mortalit\u0026eacute; des nouveau-n\u0026eacute;s hospitalis\u0026eacute;s sur 10 ann\u0026eacute;es \u0026agrave; la clinique el fateh-suka (Ouagadougou, Burkina Faso). Pan African Medical Journal. 2013; 14:153. \u003c/li\u003e\n\u003cli\u003eSidi-Yakhlef A, Boukhelif M, Kech Z. Determinants of neonatal mortality in Maghnia in the far west of Algeria. Vol. 01, Algerian Journal of Health Sciences. 2019. \u003c/li\u003e\n\u003cli\u003eBetr\u0026aacute;n AP, Merialdi M, Lauer JA, Bing-Shun W, Thomas J, Look P Van, et al. Caesarean section Rates of caesarean section: analysis of global, regional and national estimates [Internet]. Vol. 21, Paediatric and Perinatal Epidemiology. 2007. Available from: http://www.measuredhs.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Survival analysis, Neonatal mortality, Determinants, Kamenge Teaching Hospital, Burundi","lastPublishedDoi":"10.21203/rs.3.rs-4337583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4337583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite the free healthcare services policy for pregnant women and children under five since 2006, the neonatal mortality rate remains high in Burundi. In 2019, two newborns out of one hundred live births died. This study sought to determine the neonatal survival time and identify factors associated with neonatal mortality in Burundi.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a prospective cohort study to collect data among newborns delivered in the maternity ward of Kamenge Teaching Hospital. Our cohort recruitment was opened over 3 months between October and December 2020 and then the last recruited followed up till January 27, 2021. Kaplan Meier curve and logistic regression were used to analyze data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of 885 live births followed up at Kamenge Teaching Hospital, 30 died. This resulted in a neonatal mortality rate of 34 per 1,000 live births. Nearly 40% of deaths occurred during the first 24 hours of life and 90% within the first week of life. The main causes of neonatal deaths were preterm complications (60%), birth asphyxia (13.33%), neonatal infections (13.33%), and congenital malformations (13.33%). Factors associated with neonatal mortality were preterm delivery (AOR: 9.01, 95%CI 2.05\u0026ndash;39.52), less than four ANC (AOR: 6.64, 95%CI 1.46\u0026ndash;30.21), Apgar score at five minutes below 6 (AOR: 10.83, 95%CI 2.61\u0026ndash;44.93), and Caesarean delivery section (AOR: 2.47, 95%CI 1.00\u0026ndash;6.07).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNeonatal mortality is relatively high while it is mostly due to preventable factors. The government of Burundi should prioritize comprehensive ANC services to improve maternal and neonatal health.\u003c/p\u003e","manuscriptTitle":"Survival of newborns and determinants of their mortality in Burundi: A prospective cohort study at Kamenge Teaching Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 18:09:26","doi":"10.21203/rs.3.rs-4337583/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-06T09:56:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-02T05:08:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-02T05:08:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-04-28T10:54:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d74fc6b8-a5b7-406e-8941-b62d464d4193","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:14:00+00:00","versionOfRecord":{"articleIdentity":"rs-4337583","link":"https://doi.org/10.1186/s12889-025-25468-0","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-11-24 15:58:18","publishedOnDateReadable":"November 24th, 2025"},"versionCreatedAt":"2024-05-09 18:09:26","video":"","vorDoi":"10.1186/s12889-025-25468-0","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25468-0","workflowStages":[]},"version":"v1","identity":"rs-4337583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4337583","identity":"rs-4337583","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}