Survival Status and Risk Factors for Neonatal Mortality in a Byumba Level Two Teaching Hospital: A Prospective Cohort Study

Survival Status and Risk Factors for Neonatal Mortality in a Byumba Level Two Teaching Hospital: A Prospective Cohort Study | 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 Status and Risk Factors for Neonatal Mortality in a Byumba Level Two Teaching Hospital: A Prospective Cohort Study HAKIZIMANA Leonard, Charles NSANZABERA, Ephrem NSABIMANA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7436214/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Neonatal mortality remains a major public health issue in Rwanda, contributing to more than 42% of underfive deaths despite overall improvements in child survival. This prospective cohort study aimed to identify key factors influencing neonatal survival among 385 newborns admitted to the neonatology unit at Byumba Level Two Teaching Hospital between May and October 2024. The data were analyzed via SPSS version 28, with survival assessed via Kaplan‒Meier curves and predictors identified via Cox proportional hazards models. The study revealed that 89.9% of neonates survived, whereas 10.1% died during the neonatal period. Several factors were significantly associated with increased mortality risk. Maternal education level was a strong predictor: neonates born to mothers with no formal education faced the highest risk of death (AHR = 19.329; p = 0.002), followed by those whose mothers had primary or secondary education. Limited antenatal care (fewer than three visits) also increased mortality risk (AHR = 3.576; p = 0.005). Other key risk factors included low APGAR scores at one minute (AHR = 4.251; p = 0.003), multiple gestations (AHR = 3.264; p = 0.001), the need for resuscitation at birth (AHR = 2.527; p = 0.014), and the need for respiratory support (AHR = 2.548; p = 0.028). Neonatal infections were the strongest predictor of mortality (AHR = 16.306; p < 0.001). The absence of Kangaroo Mother Care and low birth weight were also associated with poorer outcomes. The findings underscore the need for targeted, evidence-based interventions to improve neonatal survival and inform policy and health planning in Rwanda. Figures Figure 1 Figure 2 Figure 3 1. Introduction The neonatal period, defined as the first 28 days of life, represents the most vulnerable phase for a child’s survival, with heightened risks of prematurity, birth asphyxia, infections, and congenital anomalies (UNICEF, 2023). These risks are further compounded by maternal health challenges, inadequate neonatal care services, and limited access to high-quality healthcare. Addressing these vulnerabilities is essential for achieving Sustainable Development Goal (SDG) 3, which aims to eliminate preventable deaths of newborns and children under five years of age by 2030 (WHO, 2023). Globally, neonatal mortality remains a major public health concern. Neonatal deaths account for approximately 47% of all under-five mortality, with an estimated 2.4 million neonates dying within the first month of life in 2020, corresponding to roughly 6,500 deaths per day (WHO, 2024). Nearly one-third of neonatal deaths occur within the first 24 hours, and nearly three-quarters occur within the first week. About 98% of these deaths take place in low- and middle-income countries, placing a disproportionate burden on sub-Saharan Africa, which averages 27 deaths per 1,000 live births (Global Economy, 2021 ). In Rwanda, despite notable progress in maternal and child health, neonatal mortality decreased only marginally from 20 to 19 per 1,000 live births between 2015 and 2020 (NISR, 2020). The major contributors to neonatal mortality include low birth weight, prematurity, birth asphyxia, infections, congenital anomalies, poor antenatal care, and inadequate delivery services. Effective interventions such as early initiation of breastfeeding, kangaroo mother care (KMC), infection prevention, and specialized neonatal care have been shown to significantly improve survival outcomes. Nevertheless, persistently high mortality rates in many low-resource settings underscore the urgent need for strengthened neonatal healthcare systems. Improving neonatal survival requires evidence-based strategies across the continuum of care, including quality antenatal care, skilled birth attendance, essential newborn care, timely diagnosis, continuous monitoring, efficient referral systems, availability of medical supplies, and adequate staffing of trained healthcare providers (Tekelab, Melku, & Mossie, 2019 ; Kebaya, Mwaniki, & Mbugua, 2018). Byumba Level Two Teaching Hospital, serving a predominantly rural population in Rwanda’s Northern Province, plays a crucial role in providing neonatal services. However, there is limited evidence on survival outcomes and the factors influencing neonatal mortality in this context. This study aims to fill this knowledge gap by assessing the survival status of neonates admitted to Byumba Level Two Teaching Hospital and identifying the maternal, perinatal, and neonatal determinants of mortality. By generating evidence on predictors of neonatal outcomes, this research seeks to inform targeted interventions, strengthen clinical care practices, and guide health policy to reduce preventable neonatal deaths and improve overall neonatal health outcomes in Rwanda. 2. Materials and Methods 2.1. Research Design This study employed a prospective cohort design with a quantitative approach to examine factors influencing neonatal survival among neonates admitted to the neonatal ward at Byumba Level Two Teaching Hospital in Gicumbi District, Rwanda. The study was conducted over a six-month period, from May 2024 to October 2024. A prospective cohort design involves selecting a group of participants and following them over time to observe outcomes as they occur. This design is particularly well-suited for evaluating the relationship between exposures such as maternal, perinatal, and neonatal factors and outcomes, including neonatal survival (Nasa et al., 2021). By collecting data in real time, the prospective approach provides detailed and accurate insights into temporal associations between risk factors and survival outcomes, allowing for stronger evidence of potential predictors of neonatal mortality. 2.2 Research Setting Byumba Level Two Teaching Hospital, located in Gicumbi District in Rwanda’s Northern Province, is one of the country’s oldest healthcare facilities, established in 1947 during the Belgian colonial period. The hospital is currently supported by the Rwandan Ministry of Health and its partners, with a capacity of 249 beds and a workforce of 278 clinical and non-clinical staff as of May 2024. Situated 62 kilometers from Kigali, the hospital primarily serves a predominantly agricultural population. The region experiences a temperate climate with occasional cold temperatures, averaging around 20°C. 2.2 Target Population The population targeted included all neonates admitted to the Neonatology ward, regardless of the reason for their admission between 15th May 2024 and 25th October 2024. The study included neonates with complete medical records who were admitted within the first 28 days of life during the study period. 2.3 Sample Design 2.3.1 Sample size determination A sample is a subset of a population selected to represent it accurately (Carter et al., 2022 ). In this study, key predictors of neonatal survival including APGAR score, hypothermia, early initiation of breastfeeding, antenatal care, and birth weight were considered in calculating the sample size. The calculation was performed using the two-proportion method in Epi Info 3.1 (Dessu et al., 2018; Limaso, Dangisso & Hibstu, 2020 ; Tolossa et al., 2022 ). To ensure adequate statistical power, the largest calculated sample size was selected. Based on the standard two-proportion formula, using outcome proportions of 0.87 among exposed and 0.96 among non-exposed neonates reported by Limaso et al. ( 2020 ), with a 95% confidence level and 80% power, each group required 192 participants, resulting in a total sample of 385 neonates. This sample size was deemed sufficient to reliably assess survival outcomes and associated risk factors (Sharma et al., 2020). 2.3.2 Sampling Technique Systematic sampling was applied. Neonates admitted between 15th May and 25th October 2024 were selected at regular intervals from eligible cases. This approach ensured representativeness, reduced selection bias, and simplified participant selection (Ahmed, 2024 ). 2.4 Data collection methods 2.4.1 Data Collection Tool Data were collected using a structured questionnaire adapted from the WHO Verbal Autopsy checklist and relevant literature (Dessu et al., 2018; Limaso, Dangisso & Hibstu, 2020 ; Tolossa et al., 2022 ). It captured sociodemographic, maternal, healthcare access, and neonatal variables, enabling systematic tracking of newborns from admission to discharge. 2.4.2 Data Collection Procedure Data were collected prospectively through interviews with mothers at admission, combined with clinical assessments of mothers and neonates. Neonates were monitored daily for up to 28 days. Post-discharge, survival status was followed via daily phone calls; unreachable cases were visited weekly by community health workers. Three trained investigators, supervised by one coordinator, conducted data collection. Investigators received three days of intensive training, and questionnaires were coded and cross-checked every 50 records to ensure data quality. 2.4.3 Reliability and Validity Reliability, assessed via Cronbach’s alpha, was acceptable (0.72 pretest; 0.86 pilot). Validity was ensured through adaptation of standardized instruments, expert review (content validity index = 0.8), and a pilot study (15% of sample). 2.5 Data Analysis Data were coded, cleaned, entered into Epi-Data, and analyzed in SPSS v28. Kaplan–Meier curves estimated survival probabilities, with differences tested via log-rank tests. Cox proportional hazards regression identified factors associated with survival. Proportional hazards assumptions were checked with log–minus–log plots and global tests. Both crude and adjusted hazard ratios were reported; p < 0.05 indicated significance. 2.6 Ethical Consideration Ethical approval was obtained from Mount Kenya University’s School of Health Sciences, and hospital authorization was granted. Mothers provided informed consent, were informed of voluntary participation, and assured confidentiality. Personal identifiers were removed, and study data were securely stored. 3. Results and Discussion 4.1 Sociodemographic characteristics Details concerning the sociodemographic characteristics of the study participants among neonates are displayed in Table 4.1 below: Table 4 1: Table 4.1: Sociodemographic characteristics of the participants Variable Frequency Percentage Gender Male 228 59.2 Female 157 40.8 Total 385 100.0 Age Below 19 8 2.1 19–24 77 20.0 25–29 205 53.2 30–34 58 15.1 Above and equal to 35 37 9.6 Total 385 100.0 Education Level None 4 1.0 Primary 50 13.0 Secondary 189 49.1 University 142 36.9 Total 385 100.0 Residence Rural 283 73.5 Urban 102 26.5 Total 385 100.0 Place of Delivery Home 5 1.3 Health Post 14 3.6 Health Center 130 33.8 Ambulance 29 7.5 Hospital 207 53.8 Total 385 100.0 Age at Admission 7 days and low 362 94.0 Above 7 days 23 6.0 Total 385 100.0 Gestation age Premature birth 238 61.8 Normal term birth 147 38.2 Total 385 100.0 Birth weight Low birth weight 230 59.7 Normal weight 155 40.3 Total 385 100.0 Gestational Age of newborn Below 32 weeks (Very preterm) 46 11.9 32–36 weeks (Preterm) 180 46.8 37–42 weeks (Term) 135 35.1 (Above 42 weeks (Post term) 24 6.2 Total 385 100.0 Source : Primary data, 2025 Table 4.1 shows the demographic information of 385 participants. Among the neonates, males represented 59.2% (228), whereas females accounted for 40.8% (157). The majority of mothers were aged between 25 and 29 years (53.2%), followed by those aged 19 to 24 years (20%). The other age groups included 15.1% aged 30–34 years, 9.6% aged 35 years and older, and 2.1% under 19 years. In terms of educational background, 49.1% of the mothers had completed secondary school, 36.9% had attained university education, 13% had only primary education, and 1% had no formal education. The majority of respondents (73.5%) lived in rural regions, whereas 26.5% did so in urban areas. A total of 53.8% of the participants gave birth in a hospital, 33.8% in a health center, 7.5% in an ambulance, 3.6% at a health post, and 1.3% at home, according to the data. According to the data, 94% of the newborns were admitted to the neonatal ward during their first week of life. Among the neonates, 59.7% had low birth weights, and 61.8% were preterm. A total of 11.9% of the babies were born very preterm (before 32 weeks), 46.8% were born preterm (between 32 and 36 weeks), 35.1% were born at term (between 37 and 42 weeks), and 6.2% were born postterm (beyond 42 weeks). 4.2 Presentation of Findings The study results are illustrated via tables and figures aligned with the research objectives. Specifically, Figs. 4.1 , 4.2, and 4.3, along with Tables 4.2 and 4.3, depict the survival outcomes of neonates. Moreover, Tables 4.4 outline the key predictors of neonatal mortality among newborns admitted. 4.2.1 Survival Status of Neonates The primary objective of this study was to assess the survival status of neonates admitted to the neonatology unit at Byumba Level Two Teaching Hospital in Gicumbi, Rwanda. This information is illustrated in Figs. 4.1 , 4.2, and 4.3, as well as Tables 4.2 and 4.3, which collectively present the survival outcomes of the admitted neonates. Figure 4.1 presents the overall neonatal survival outcomes for those admitted to the neonatology unit at Byumba Level Two Teaching Hospital. According to the results, 89.9% of the neonates survived, whereas 10.1% died within the neonatal period. Additionally, a Kaplan‒Meier survival analysis was conducted to evaluate how maternal antenatal care (ANC) influences neonatal survival rates. This method enables a comparative assessment of survival probabilities among neonates on the basis of the level of ANC their mothers receive, emphasizing the relationship between ANC attendance and neonatal health outcomes. Figure 4.2 shows that maternal antenatal care (ANC) attendance is significantly associated with neonatal survival. Kaplan–Meier curves indicate that neonates whose mothers attended three or more ANC visits had higher cumulative survival over 30 days compared with those whose mothers attended fewer than three visits. The ≥ 3 ANC group maintained consistently higher survival, while the < 3 ANC group showed a steeper decline, reflecting increased neonatal mortality. The log-rank test confirmed this difference was statistically significant (Chi-square = 8.499, p = 0.004). Table 4.2 Mean Estimate of Test of Equality of Survival Distribution Among Neonates Admitted to Neonatology Variable Mean estimate 95%CI Chi-square(df) P value Antenatal care Lower Upper 8.499(1) 0.004 =3 ANC Attendance 26.599 27.071 28.028 Source : Primary data, 2025 Table 4.2 presents the mean survival estimates for neonates based on maternal antenatal care (ANC) attendance. The log-rank test showed a significant difference in survival times between neonates whose mothers attended fewer than three ANC visits and those with three or more visits (Chi-square = 8.499, df = 1, p = 0.004). Neonates whose mothers attended ≥ 3 ANC visits had higher mean survival (26.60 days; 95% CI: 27.07–28.03) compared with those with < 3 visits (25.98 days; 95% CI: 25.23–26.73). A Kaplan–Meier analysis also examined neonatal survival in relation to infection severity among admitted neonates, highlighting the impact of infection levels on survival probabilities. Figure 4.3 illustrates the impact of neonatal infections on survival. Kaplan–Meier curves show that neonates without infections had higher survival probabilities over 30 days compared with those who developed infections. The infection group exhibited a steep decline in survival, indicating increased mortality risk, while the uninfected group’s curve remained near 1.0. The log-rank (Mantel–Cox) test confirmed this difference was statistically significant (Chi-square = 92.374, p < 0.001), highlighting neonatal infection as a major determinant of mortality. Table 4.3 Mean Estimate of Test of Equality of Survival Distribution between Presence and Non-Presence of Infection Among Neonates Admitted to Neonatology Variable Mean estimate 95%CI Chi-square(df) P value Neonatal infection Lower Upper 92.374(1) < 0.001 Yes 22.834 21.091 24.567 No 27.777 27.559 28.995 Source : Primary data, 2025 Table 4.3 presents mean survival estimates for neonates based on infection status. The log-rank test showed a significant difference in survival between infected and noninfected neonates (Chi-square = 92.374, df = 1, p < 0.001). Neonates without infection had higher mean survival (27.78 days; 95% CI: 27.56–28.99) compared with those with infection (22.83 days; 95% CI: 21.09–24.57). 4.2.2 Predictors of neonatal mortality among neonates The second goal of the study was to identify the critical variables linked to newborn survival in infants hospitalized at the Byumba Level Two Teaching Hospital's neonatology unit in Rwanda's Gicumbi District. The predictors of neonatal mortality are systematically presented in Table 4.4 , offering a detailed analysis of variables contributing to newborn deaths within this healthcare setting. Table 4.4 Multivariate Cox Regression Analysis of Predictors of Neonatal Mortality among Newborns Admitted to Byumba Level Two Teaching Hospital Variable CHR 95%CI P value AHR 95%CI P value Gender Male 1* Female 2.193 1.159–4.152 0.01 - - - Education Level No education 13.039 2.702–62.922 0.001 19.329 3.033-123.196 0.002 Primary level 3.975 1.480-10.675 0.006 4.365 1.481–12.865 0.008 Secondary level 2.333 0.992–5.488 0.052 4.347 1.741–10.855 0.002 University 1* ANC Attendance >=3 ANC visit 1* < 3 ANC visit 3.160 1.395–7.159 0.006 3.576 1.456–8.780 0.005 Parity Primiparous 1* Multiparous 2.744 0.972–7.742 0.049 0.187 0.056–0.625 0.006 APGAR 1st Minute Above 5 1* Below 5 2.800 1.276–6.144 0.01 4.251 1.627–11.110 0.003 APGAR 5th Minute Above 5 1* Below 5 4.400 2.343–8.261 < 0.001 - - - Multiple Birth No 1* Yes 9.379 4.881–18.024 < 0.001 3.264 1.587–6.714 0.001 Resuscitation at birth No 1* Yes 8.745 4.615–16.570 < 0.001 2.527 1.209–5.281 0.014 Kangaroo Mother Care Yes 1* No 1.231 0.702–3.321 0.02 0.395 0.162-.960 0.040 Respiratory support No 1* Yes 2.790 1.282–6.070 .010 2.548 1.109–5.854 0.028 Neonatal infection during hospitalization No 1* Yes 18.158 8.005–41.190 < 0.001 16.306 6.801–39.094 < 0.001 Birth weight Normal 1* Low 1.712 0.904–3.241 0.01 0.325 0.141–0.750 0.008 Gestation age Term Birth 1* Premature birth 1.470 1.045–2.069 0.027 - - - PROM No 1* Yes 7.290 3.350-15.863 < 0.001 - - - Any comorbidities No 1* Yes 3.311 1.736–6.313 7 days 1* <=7 days 2.942 1.233–7.021 0.01 - - - Breastfeed within 1 hr Yes 1* No 3.827 1.988–7.365 < 0.001 - - - If the antibiotics started on time Yes 1* No 8.473 3.011–23.844 < 0.001 - - - Source : Primary data, 2025 Table 4.4 presents Multivariate Cox Regression Analysis of Predictors of Neonatal Mortalities among Newborns. In bivariate analysis, female neonates had higher crude hazard (CHR = 2.193; 95% CI: 1.159–4.152; p = 0.01) than males, but this was not significant in the multivariate model. Maternal education was strongly protective: compared with mothers with university education, the risk of neonatal death was highest among mothers with no education (AHR = 19.329; 95% CI: 3.033–123.196; p = 0.002), followed by primary (AHR = 4.365; p = 0.008) and secondary education (AHR = 4.347; p = 0.002). ANC attendance was significant: neonates whose mothers attended < 3 visits had higher mortality (AHR = 3.576; 95% CI: 1.456–8.780; p = 0.005). Multiparity showed a protective effect (AHR = 0.187; p = 0.006). Low 1-minute APGAR (AHR = 4.251; p = 0.003), multiple births (AHR = 3.264; p = 0.001), resuscitation at birth (AHR = 2.527; p = 0.014), lack of kangaroo mother care (AHR = 0.395; p = 0.040), and respiratory support (AHR = 2.548; p = 0.028) were also significant predictors. Neonatal infection during hospitalization was the strongest predictor (AHR = 16.306; 95% CI: 6.801–39.094; p < 0.001). Low birth weight also increased mortality risk (AHR = 0.325; p = 0.008). Factors significant in bivariate analysis only included prematurity, PROM, comorbidities, late admission, delayed breastfeeding, and late antibiotic initiation. 4.2.3 Discussion of Findings (a) Demographic characteristics of the respondents In this study, male neonates accounted for 59.2% of admissions, consistent with findings from CHUK, Rwanda (57%) and Ethiopia (60.5%) (Uwiringiyimana et al., 2021 ; Tewabe et al., 2021 ). Male predominance may reflect biological vulnerability to complications such as respiratory distress and infections (Lawn et al., 2014 ), though some studies, like in Nigeria, report a more balanced ratio (Ekwochi et al., 2018 ). Over half of neonates (53.8%) were hospital-born, with smaller proportions in health centers (33.8%), ambulances (7.5%), health posts (3.6%), or at home (1.3%). Nonfacility births, particularly in transit or at home, remain a concern, as seen in Rwanda, Uganda, and Nepal (Tuyisenge et al., 2020 ; Waiswa et al., 2015 ; Khanal et al., 2014 ). Most neonates (94%) were admitted within the first seven days, reflecting the burden of early neonatal complications such as birth asphyxia, prematurity, and sepsis (Mwaniki et al., 2017 ; Worku & Ayele, 2019). High rates of prematurity (61.8%) and low birth weight (59.7%) underscore the need for enhanced maternal care and targeted interventions to reduce neonatal morbidity and mortality, consistent with findings from Rwanda, Tanzania, and India (Mukamurigo et al., 2020 ; Mmbaga et al., 2016 ; Kumar et al., 2018 ). (b) Survival Status of Neonates In this study, 89.9% of neonates survived, while 10.1% died during the neonatal period, reflecting a relatively low neonatal mortality rate (NMR) and suggesting effective neonatal care at Byumba Level Two Teaching Hospital. This rate is comparable to CHUK (11.3%) and slightly lower than Muhima District Hospital (12.5%) (Uwiringiyimana et al., 2021 ; Mukamurigo et al., 2020 ), likely reflecting similar healthcare resources and improved early detection, timely management, and adherence to protocols. Higher NMRs have been reported elsewhere, including Uganda (19.1%), Tanzania (21.3%), India (15.2%), and Ethiopia (17%) (Waiswa et al., 2015 ; Mmbaga et al., 2016 ; Kumar et al., 2018 ; Tewabe et al., 2021 ), often linked to limited neonatal care, delayed treatment, and higher perinatal risks. The comparatively lower rate in Byumba highlights the benefits of Rwanda’s structured and decentralized healthcare system, supported by community programs and increased facility deliveries. Globally, WHO reported an NMR of 17 per 1,000 live births in 2021, with higher rates in sub-Saharan Africa and South Asia (WHO, 2022). While the 10.1% rate exceeds general population averages, it aligns with expectations for hospitalized neonates, emphasizing the importance of early interventions, skilled birth attendance, and essential newborn care. (c) Predictors of neonatal mortality among newborns Neonates with a low APGAR score at 1 minute were 4.25 times more likely to die (AHR = 4.251, p = 0.003), consistent with findings from Nigeria (3.9-fold increase; Udo et al., 2021) and Nepal (AOR = 5.1; Karki et al., 2020 ). This underscores the importance of prompt diagnosis and resuscitation, particularly in resource-limited settings. Neonates who received Kangaroo Mother Care (KMC) demonstrated better survival, with the absence of KMC significantly increasing mortality risk (AHR = 0.395, p = 0.040). Similar benefits of KMC have been reported in Bangladesh, reducing neonatal mortality by 60% (Haider et al., 2021 ), and in South African hospitals, particularly for low-birth-weight infants (Bergh et al., 2017 ), highlighting its role in thermoregulation, breastfeeding support, and maternal–infant bonding. Neonatal infection was the strongest predictor of mortality, with an adjusted hazard ratio of 16.306 (p < 0.001). Comparable findings were reported in Pakistan, where infections accounted for over 30% of neonatal deaths (Zaidi et al., 2019 ), and in Ethiopia, where infected neonates were 12 times more likely to die (Mekonnen et al., 2020 ). Low birth weight also significantly increased mortality risk (AHR = 0.325, p = 0.008), corroborating studies in Kenya (AHR = 4.18; Wanjala et al., 2021 ) and Brazil (Leal et al., 2019 ), emphasizing the need for improved maternal nutrition, comprehensive prenatal care, and skilled delivery services. (d) Study limitations This study, while providing valuable insights into neonatal survival and its predictors, has several limitations. First, it was conducted in a single health facility, which may limit the generalizability of the findings to other hospitals or regions within Rwanda. Second, the reliance on retrospective hospital records may have introduced information bias due to incomplete or inaccurate documentation. Third, certain important maternal and environmental factors—such as maternal education, household income, and quality of prenatal care—were not captured in the analysis, potentially affecting the comprehensiveness of the findings. Finally, the observational study design restricts the ability to draw causal inferences between identified predictors and neonatal outcomes. (e) Study implications The findings of this study have important implications for neonatal health interventions and policies in Rwanda. The high survival rate observed is encouraging but highlights the need to further reduce preventable neonatal deaths by addressing modifiable risk factors. Interventions targeting improved neonatal resuscitation, infection prevention, the promotion of kangaroo mother care (KMC), and specialized care for low-birth-weight infants should be prioritized. Health care providers should be trained to identify and manage neonates at higher risk promptly. Moreover, policymakers should consider strengthening neonatal care services, especially at district and teaching hospitals, and expanding community-level education and follow-up care to improve early neonatal health outcomes nationwide. 4. Conclusions This study, which was conducted at Byumba Level Two Teaching Hospital, assessed neonatal survival and its associated factors among 396 admitted neonates between May and October 2024. The findings revealed a high overall survival rate of 89.9%, with 10.1% mortality during the neonatal period. The key predictors of neonatal death included low APGAR scores at one minute, the absence of kangaroo mother care (KMC), neonatal infections, low birth weight, and inadequate antenatal care attendance. This study emphasizes the importance of improving maternal education, enhancing infection control, promoting KMC, and strengthening early neonatal interventions. On the basis of these findings, recommendations were made for both the Ministry of Health and the hospital, including improved ANC services, community awareness, continuous training for health care workers, and stricter infection prevention protocols. Furthermore, future research is suggested to explore strategies for reducing late-onset neonatal infections and the role of maternal health education in neonatal outcomes and to replicate the study in multiple hospitals for broader applicability. Abbreviations AHR : Adjusted hazard ratio ANC: Antenatal Care AOR: adjusted odds ratio APGAR: A mnemonic that stands for appearance, pulse, grace, activity, and respiration CI: Confidence interval DHS: Demographic Health Survey LBW : Low birth weight (LBW) LMICs: Low and Low Middle-Income Countries NICUs: Neonatal intensive care units NISR : National Institute of Statistics of Rwanda NMR: neonatal mortality rate PROM: Premature Membrane Rupture SDGs : Sustainable development goals UNICEF: United Nations Children's Fund WHO: World Health Organization Declarations Ethical Approval and Consent to Participate This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from Mount Kenya University, and permission for data collection was granted by Byumba Level Two Teaching Hospital. The study was carried out following all relevant guidelines and regulations. Written informed consent was obtained from all participants aged 16 years and above. For participants younger than 16 years, written informed consent was obtained from their parents or legal guardians. Consent for Publication Not applicable Availability of Data and Materials The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions HL conceptualized the study, led the data collection, and drafted the manuscript. Dr. Charles Nsanzabera provided primary guidance on the study design, methodology, and critical manuscript revision. Nsabimana Ephrem contributed to secondary guidance on the study design, methodology and manuscript editing. All the authors read and approved the final manuscript. Acknowledgments The authors gratefully acknowledge the leadership of the hospital and staff of the neonatal unit at Byumba Level Two Teaching Hospital for their invaluable support and cooperation during the conduct of this prospective cohort study on neonatal mortality. Authors’ information HAKIZIMANA Leonard (HL) General Nurse Practitioner & Quality Improvement Officer Master of Public Health (Global Health), Byumba Level Two Teaching Hospital, Gicumbi District, Rwanda; Mount Kenya University, Kigali, Rwanda. Dr. Charles Nsanzabera, PhD Lecturer, Mount Kenya University, Kigali, Rwanda; Research interests include hypertension, cardiovascular risk, nutrition, and public health. 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Lawn JE, Blencowe H, Oza S, You D, Lee AC, Waiswa P, et al. Every Newborn: progress, priorities, and potential beyond survival. Lancet. 2014;384(9938):189-205. doi:10.1016/S0140-6736(14)60496-7. Leal MDC, et al. Neonatal mortality and birth weight in Brazil. Rev Saude Publica. 2019;53:80. Limaso AA, Dangisso MH, Hibstu DT. Neonatal survival and determinants of mortality in Aroresa district, Southern Ethiopia: a prospective cohort study. BMC Pediatr. 2020;20:33. doi:10.1186/s12887-019-1907-7 Mekonnen MY, et al. Predictors of neonatal mortality among neonates admitted to NICUs in Ethiopia. BMC Pediatr. 2020;20(1):568. Mmbaga BT, et al. Causes and predictors of neonatal deaths in Tanzania. BMC Pediatr. 2016;16(1):44. Mugisha C. Newborn Survival Analysis: Neonatal Mortality between 2019 and 2021 in Burundi. Scientific Research. 2025. doi:10.4236/ojs.2025.152009 Mukamurigo J, et al. Prematurity and neonatal outcomes at Muhima District Hospital, Rwanda. Rwanda J Med Health Sci. 2020;3(2):103–110. Mwaniki M, et al. Neonatal morbidity patterns in Kenya. J Perinatol. 2017;37(9):938–944. NISR. 6th Rwanda Demographic and Health Survey, 2019-2020 (RDHS -VI) (Suvey Report 1; p. 619). National institute of statistics of Rwanda; 2020. Available from: https://www.statistics.gov.rw/datasource/demographic-and-health-survey-dhs Seaton SE, Agarwal R, Draper ES, Fenton AC, Kurinczuk JJ, Manktelow BN, Smith LK. Estimated neonatal morbidity and mortality in the UK 2010-2011 and implications for neonatal service provision: A national population-based study. Lancet Child Adolesc Health. 2019;3(7):540-547. doi:10.1016/S2352-4642(19)30107-4 Soofi S, Cousens S, Imdad A, Bhutto N, Ali N, Bhutta ZA. Effect of provision of maternal and newborn health services on neonatal mortality in rural Pakistan: a cluster-randomized trial. Lancet. 2015;386(10008):1516-23. doi:10.1016/S0140-6736(15)00373-8. Tekelab T, Melku M, Mossie A. Neonatal mortality and its predictors among neonates admitted to neonatal intensive care unit at Gondar University Hospital, Northwest Ethiopia: a retrospective cohort study. BMC Pregnancy Childbirth . 2019;19(1):64. doi:10.1186/s12884-019-2201-y. Tekelab T, Melku M, Mossie A. Neonatal mortality and its predictors among neonates admitted to neonatal intensive care unit at Gondar University Hospital, Northwest Ethiopia: a retrospective cohort study. BMC Pregnancy Childbirth. 2019;19(1):64. doi:10.1186/s12884-019-2201-y. Tewabe T, Hailu M, Azanaw J, Melak T, Berhe A. Neonatal mortality and associated factors in Ethiopia: a systematic review and meta-analysis. PLoS One. 2021;16(1):e0246414. doi:10.1371/journal.pone.0246414. Tolossa T, Wakuma B, Mengist B, Fetensa G, Mulisa D, Ayala D, et al. Survival status and predictors of neonatal mortality among neonates admitted to Neonatal Intensive Care Unit (NICU) of Wollega University Referral Hospital (WURH) and Nekemte Specialized Hospital, Western Ethiopia: a prospective cohort study. PLoS ONE. 2022;17(7):e0268744. doi:10.1371/journal.pone.0268744 Tuyisenge, L., et al. (2020). Institutional delivery and neonatal outcomes in Rwanda. BMC Pregnancy and Childbirth, 20(1), 345. UNICEF. Neonatal mortality rate in Rwanda, 2020. Available from: https://data.unicef.org/country/rwa/ United Nations. The Sustainable Development Goals Report 2023. New York: United Nations; 2023. Uwiringiyimana, E., et al. (2021). Neonatal mortality and associated factors at CHUK, Rwanda. Rwanda Medical Journal, 78(3), 22–30. Waiswa, P., et al. (2015). Home vs facility delivery and neonatal outcomes in Uganda. Health Policy and Planning, 30(2), 233–244. Wanjala, S.W., et al. (2021). Low birth weight and neonatal mortality in Kenya: A retrospective cohort study. East African Medical Journal, 98(1), 45–51. WHO. Newborns: improving survival and well-being. Geneva: World Health Organization; 2020. Available from: https://www.who.int/news-room/fact-sheets/detail/newborns-reducing-mortality. World Health Organization. WHO verbal autopsy standards: the 2016 instrument. Geneva: WHO; 2022. Available from: https://www.who.int/publications/i/item/9789241516158 Zaidi, A.K.M., et al. (2019). Burden of neonatal infections in Pakistan. Pediatrics International, 61(12), 1125–1131. Zohrabi, M. (2013). Mixed Method Research: Instruments, Validity, Reliability and Reporting Findings. Theory and Practice in Language Studies , 3 (2), 254–262. https://doi.org/10.4304/tpls.3.2.254-262 Additional Declarations No competing interests reported. Supplementary Files Datacollectiontool.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 23 Sep, 2025 Editor invited by journal 03 Sep, 2025 Submission checks completed at journal 01 Sep, 2025 First submitted to journal 01 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7436214","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525539215,"identity":"739633cb-60f6-4932-90e3-da273c871fdf","order_by":0,"name":"HAKIZIMANA 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1","display":"","copyAsset":false,"role":"figure","size":50427,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival status of neonates admitted to Byumba Level Two Teaching Hospital\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7436214/v1/ce7b104abfcbe4c8ed5885d1.jpg"},{"id":93028208,"identity":"c3efecc2-9528-46aa-beab-dae9fd4fad9b","added_by":"auto","created_at":"2025-10-08 09:52:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78195,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier Survival Curves of Neonates by Maternal Antenatal Care Attendance\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7436214/v1/b5a8ab085dc3a8569cf1aa2f.jpg"},{"id":93029733,"identity":"9edf7af8-e8d4-4c9a-8167-16b62f87d06e","added_by":"auto","created_at":"2025-10-08 10:00:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88771,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Status of Neonates Admitted to Neonatology Modeled by Neonatal Infection\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7436214/v1/17071fd4b6e0ce469cdf0064.jpg"},{"id":93031607,"identity":"d259f9a1-2090-4c0b-a95d-fc4278728b03","added_by":"auto","created_at":"2025-10-08 10:16:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1775945,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7436214/v1/7af00b3c-69ac-4874-b16e-bd61bed2372c.pdf"},{"id":93029735,"identity":"654459d0-974a-4a69-a7df-56eef8fe76f1","added_by":"auto","created_at":"2025-10-08 10:00:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":225970,"visible":true,"origin":"","legend":"","description":"","filename":"Datacollectiontool.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7436214/v1/e08c9609318c4d70f36d5d1a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSurvival Status and Risk Factors for Neonatal Mortality in a Byumba Level Two Teaching Hospital: A Prospective Cohort Study\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe neonatal period, defined as the first 28 days of life, represents the most vulnerable phase for a child\u0026rsquo;s survival, with heightened risks of prematurity, birth asphyxia, infections, and congenital anomalies (UNICEF, 2023). These risks are further compounded by maternal health challenges, inadequate neonatal care services, and limited access to high-quality healthcare. Addressing these vulnerabilities is essential for achieving Sustainable Development Goal (SDG) 3, which aims to eliminate preventable deaths of newborns and children under five years of age by 2030 (WHO, 2023).\u003c/p\u003e\u003cp\u003eGlobally, neonatal mortality remains a major public health concern. Neonatal deaths account for approximately 47% of all under-five mortality, with an estimated 2.4\u0026nbsp;million neonates dying within the first month of life in 2020, corresponding to roughly 6,500 deaths per day (WHO, 2024). Nearly one-third of neonatal deaths occur within the first 24 hours, and nearly three-quarters occur within the first week. About 98% of these deaths take place in low- and middle-income countries, placing a disproportionate burden on sub-Saharan Africa, which averages 27 deaths per 1,000 live births (Global Economy, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Rwanda, despite notable progress in maternal and child health, neonatal mortality decreased only marginally from 20 to 19 per 1,000 live births between 2015 and 2020 (NISR, 2020).\u003c/p\u003e\u003cp\u003eThe major contributors to neonatal mortality include low birth weight, prematurity, birth asphyxia, infections, congenital anomalies, poor antenatal care, and inadequate delivery services. Effective interventions such as early initiation of breastfeeding, kangaroo mother care (KMC), infection prevention, and specialized neonatal care have been shown to significantly improve survival outcomes. Nevertheless, persistently high mortality rates in many low-resource settings underscore the urgent need for strengthened neonatal healthcare systems.\u003c/p\u003e\u003cp\u003eImproving neonatal survival requires evidence-based strategies across the continuum of care, including quality antenatal care, skilled birth attendance, essential newborn care, timely diagnosis, continuous monitoring, efficient referral systems, availability of medical supplies, and adequate staffing of trained healthcare providers (Tekelab, Melku, \u0026amp; Mossie, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kebaya, Mwaniki, \u0026amp; Mbugua, 2018). Byumba Level Two Teaching Hospital, serving a predominantly rural population in Rwanda\u0026rsquo;s Northern Province, plays a crucial role in providing neonatal services. However, there is limited evidence on survival outcomes and the factors influencing neonatal mortality in this context.\u003c/p\u003e\u003cp\u003eThis study aims to fill this knowledge gap by assessing the survival status of neonates admitted to Byumba Level Two Teaching Hospital and identifying the maternal, perinatal, and neonatal determinants of mortality. By generating evidence on predictors of neonatal outcomes, this research seeks to inform targeted interventions, strengthen clinical care practices, and guide health policy to reduce preventable neonatal deaths and improve overall neonatal health outcomes in Rwanda.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Research Design\u003c/h2\u003e\u003cp\u003eThis study employed a prospective cohort design with a quantitative approach to examine factors influencing neonatal survival among neonates admitted to the neonatal ward at Byumba Level Two Teaching Hospital in Gicumbi District, Rwanda. The study was conducted over a six-month period, from May 2024 to October 2024.\u003c/p\u003e\u003cp\u003eA prospective cohort design involves selecting a group of participants and following them over time to observe outcomes as they occur. This design is particularly well-suited for evaluating the relationship between exposures such as maternal, perinatal, and neonatal factors and outcomes, including neonatal survival (Nasa et al., 2021). By collecting data in real time, the prospective approach provides detailed and accurate insights into temporal associations between risk factors and survival outcomes, allowing for stronger evidence of potential predictors of neonatal mortality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Research Setting\u003c/h2\u003e\u003cp\u003eByumba Level Two Teaching Hospital, located in Gicumbi District in Rwanda\u0026rsquo;s Northern Province, is one of the country\u0026rsquo;s oldest healthcare facilities, established in 1947 during the Belgian colonial period. The hospital is currently supported by the Rwandan Ministry of Health and its partners, with a capacity of 249 beds and a workforce of 278 clinical and non-clinical staff as of May 2024. Situated 62 kilometers from Kigali, the hospital primarily serves a predominantly agricultural population. The region experiences a temperate climate with occasional cold temperatures, averaging around 20\u0026deg;C.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Target Population\u003c/h2\u003e\u003cp\u003eThe population targeted included all neonates admitted to the Neonatology ward, regardless of the reason for their admission between 15th May 2024 and 25th October 2024. The study included neonates with complete medical records who were admitted within the first 28 days of life during the study period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sample Design\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Sample size determination\u003c/h2\u003e\u003cp\u003eA sample is a subset of a population selected to represent it accurately (Carter et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, key predictors of neonatal survival including APGAR score, hypothermia, early initiation of breastfeeding, antenatal care, and birth weight were considered in calculating the sample size. The calculation was performed using the two-proportion method in Epi Info 3.1 (Dessu et al., 2018; Limaso, Dangisso \u0026amp; Hibstu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tolossa et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To ensure adequate statistical power, the largest calculated sample size was selected. Based on the standard two-proportion formula, using outcome proportions of 0.87 among exposed and 0.96 among non-exposed neonates reported by Limaso et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with a 95% confidence level and 80% power, each group required 192 participants, resulting in a total sample of 385 neonates. This sample size was deemed sufficient to reliably assess survival outcomes and associated risk factors (Sharma et al., 2020).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Sampling Technique\u003c/h2\u003e\u003cp\u003eSystematic sampling was applied. Neonates admitted between 15th May and 25th October 2024 were selected at regular intervals from eligible cases. This approach ensured representativeness, reduced selection bias, and simplified participant selection (Ahmed, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data collection methods\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Data Collection Tool\u003c/h2\u003e\u003cp\u003eData were collected using a structured questionnaire adapted from the WHO Verbal Autopsy checklist and relevant literature (Dessu et al., 2018; Limaso, Dangisso \u0026amp; Hibstu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tolossa et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It captured sociodemographic, maternal, healthcare access, and neonatal variables, enabling systematic tracking of newborns from admission to discharge.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Data Collection Procedure\u003c/h2\u003e\u003cp\u003eData were collected prospectively through interviews with mothers at admission, combined with clinical assessments of mothers and neonates. Neonates were monitored daily for up to 28 days. Post-discharge, survival status was followed via daily phone calls; unreachable cases were visited weekly by community health workers. Three trained investigators, supervised by one coordinator, conducted data collection. Investigators received three days of intensive training, and questionnaires were coded and cross-checked every 50 records to ensure data quality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Reliability and Validity\u003c/h2\u003e\u003cp\u003eReliability, assessed via Cronbach\u0026rsquo;s alpha, was acceptable (0.72 pretest; 0.86 pilot). Validity was ensured through adaptation of standardized instruments, expert review (content validity index\u0026thinsp;=\u0026thinsp;0.8), and a pilot study (15% of sample).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data Analysis\u003c/h2\u003e\u003cp\u003eData were coded, cleaned, entered into Epi-Data, and analyzed in SPSS v28. Kaplan\u0026ndash;Meier curves estimated survival probabilities, with differences tested via log-rank tests. Cox proportional hazards regression identified factors associated with survival. Proportional hazards assumptions were checked with log\u0026ndash;minus\u0026ndash;log plots and global tests. Both crude and adjusted hazard ratios were reported; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated significance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Ethical Consideration\u003c/h2\u003e\u003cp\u003eEthical approval was obtained from Mount Kenya University\u0026rsquo;s School of Health Sciences, and hospital authorization was granted. Mothers provided informed consent, were informed of voluntary participation, and assured confidentiality. Personal identifiers were removed, and study data were securely stored.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Sociodemographic characteristics\u003c/h2\u003e\u003cp\u003eDetails concerning the sociodemographic characteristics of the study participants among neonates are displayed in Table\u0026nbsp;4.1 below:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e1: Table\u0026nbsp;4.1: Sociodemographic characteristics of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow 19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove and equal to 35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of Delivery\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Post\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Center\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmbulance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at Admission\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7 days and low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove 7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGestation age\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremature birth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal term birth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBirth weight\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birth weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGestational Age of newborn\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow 32 weeks (Very preterm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e32\u0026ndash;36 weeks (Preterm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e37\u0026ndash;42 weeks (Term)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Above 42 weeks (Post term)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource\u003c/b\u003e: Primary data, 2025\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;4.1 shows the demographic information of 385 participants. Among the neonates, males represented 59.2% (228), whereas females accounted for 40.8% (157). The majority of mothers were aged between 25 and 29 years (53.2%), followed by those aged 19 to 24 years (20%). The other age groups included 15.1% aged 30\u0026ndash;34 years, 9.6% aged 35 years and older, and 2.1% under 19 years. In terms of educational background, 49.1% of the mothers had completed secondary school, 36.9% had attained university education, 13% had only primary education, and 1% had no formal education.\u003c/p\u003e\u003cp\u003eThe majority of respondents (73.5%) lived in rural regions, whereas 26.5% did so in urban areas. A total of 53.8% of the participants gave birth in a hospital, 33.8% in a health center, 7.5% in an ambulance, 3.6% at a health post, and 1.3% at home, according to the data. According to the data, 94% of the newborns were admitted to the neonatal ward during their first week of life. Among the neonates, 59.7% had low birth weights, and 61.8% were preterm. A total of 11.9% of the babies were born very preterm (before 32 weeks), 46.8% were born preterm (between 32 and 36 weeks), 35.1% were born at term (between 37 and 42 weeks), and 6.2% were born postterm (beyond 42 weeks).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Presentation of Findings\u003c/h2\u003e\u003cp\u003eThe study results are illustrated via tables and figures aligned with the research objectives. Specifically, Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e, 4.2, and 4.3, along with Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e and 4.3, depict the survival outcomes of neonates. Moreover, Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e outline the key predictors of neonatal mortality among newborns admitted.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Survival Status of Neonates\u003c/h2\u003e\u003cp\u003eThe primary objective of this study was to assess the survival status of neonates admitted to the neonatology unit at Byumba Level Two Teaching Hospital in Gicumbi, Rwanda. This information is illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e, 4.2, and 4.3, as well as Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e and 4.3, which collectively present the survival outcomes of the admitted neonates.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e presents the overall neonatal survival outcomes for those admitted to the neonatology unit at Byumba Level Two Teaching Hospital. According to the results, 89.9% of the neonates survived, whereas 10.1% died within the neonatal period. Additionally, a Kaplan‒Meier survival analysis was conducted to evaluate how maternal antenatal care (ANC) influences neonatal survival rates. This method enables a comparative assessment of survival probabilities among neonates on the basis of the level of ANC their mothers receive, emphasizing the relationship between ANC attendance and neonatal health outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e shows that maternal antenatal care (ANC) attendance is significantly associated with neonatal survival. Kaplan\u0026ndash;Meier curves indicate that neonates whose mothers attended three or more ANC visits had higher cumulative survival over 30 days compared with those whose mothers attended fewer than three visits. The \u0026ge;\u0026thinsp;3 ANC group maintained consistently higher survival, while the \u0026lt;\u0026thinsp;3 ANC group showed a steeper decline, reflecting increased neonatal mortality. The log-rank test confirmed this difference was statistically significant (Chi-square\u0026thinsp;=\u0026thinsp;8.499, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean Estimate of Test of Equality of Survival Distribution Among Neonates Admitted to Neonatology\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChi-square(df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntenatal care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.499(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3ANC attendance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=3 ANC Attendance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: Primary data, 2025\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e presents the mean survival estimates for neonates based on maternal antenatal care (ANC) attendance. The log-rank test showed a significant difference in survival times between neonates whose mothers attended fewer than three ANC visits and those with three or more visits (Chi-square\u0026thinsp;=\u0026thinsp;8.499, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.004). Neonates whose mothers attended\u0026thinsp;\u0026ge;\u0026thinsp;3 ANC visits had higher mean survival (26.60 days; 95% CI: 27.07\u0026ndash;28.03) compared with those with \u0026lt;\u0026thinsp;3 visits (25.98 days; 95% CI: 25.23\u0026ndash;26.73).\u003c/p\u003e\u003cp\u003eA Kaplan\u0026ndash;Meier analysis also examined neonatal survival in relation to infection severity among admitted neonates, highlighting the impact of infection levels on survival probabilities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e illustrates the impact of neonatal infections on survival. Kaplan\u0026ndash;Meier curves show that neonates without infections had higher survival probabilities over 30 days compared with those who developed infections. The infection group exhibited a steep decline in survival, indicating increased mortality risk, while the uninfected group\u0026rsquo;s curve remained near 1.0. The log-rank (Mantel\u0026ndash;Cox) test confirmed this difference was statistically significant (Chi-square\u0026thinsp;=\u0026thinsp;92.374, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting neonatal infection as a major determinant of mortality.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean Estimate of Test of Equality of Survival Distribution between Presence and Non-Presence of Infection Among Neonates Admitted to Neonatology\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChi-square(df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeonatal infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.374(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: Primary data, 2025\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e presents mean survival estimates for neonates based on infection status. The log-rank test showed a significant difference in survival between infected and noninfected neonates (Chi-square\u0026thinsp;=\u0026thinsp;92.374, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Neonates without infection had higher mean survival (27.78 days; 95% CI: 27.56\u0026ndash;28.99) compared with those with infection (22.83 days; 95% CI: 21.09\u0026ndash;24.57).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Predictors of neonatal mortality among neonates\u003c/h2\u003e\u003cp\u003eThe second goal of the study was to identify the critical variables linked to newborn survival in infants hospitalized at the Byumba Level Two Teaching Hospital's neonatology unit in Rwanda's Gicumbi District. The predictors of neonatal mortality are systematically presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e, offering a detailed analysis of variables contributing to newborn deaths within this healthcare setting.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Cox Regression Analysis of Predictors of Neonatal Mortality among Newborns Admitted to Byumba Level Two Teaching Hospital\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.159\u0026ndash;4.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.702\u0026ndash;62.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.033-123.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.480-10.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.481\u0026ndash;12.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.992\u0026ndash;5.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.741\u0026ndash;10.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eANC Attendance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=3 ANC visit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3 ANC visit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.395\u0026ndash;7.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.456\u0026ndash;8.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimiparous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiparous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.972\u0026ndash;7.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.056\u0026ndash;0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPGAR 1st Minute\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.276\u0026ndash;6.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.627\u0026ndash;11.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPGAR 5th Minute\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.343\u0026ndash;8.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMultiple Birth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.881\u0026ndash;18.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.587\u0026ndash;6.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResuscitation at birth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.615\u0026ndash;16.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.209\u0026ndash;5.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKangaroo Mother Care\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.702\u0026ndash;3.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.162-.960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespiratory support\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.282\u0026ndash;6.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.109\u0026ndash;5.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeonatal infection during hospitalization\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.005\u0026ndash;41.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.801\u0026ndash;39.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBirth weight\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.904\u0026ndash;3.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.141\u0026ndash;0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGestation age\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm Birth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremature birth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.045\u0026ndash;2.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePROM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.350-15.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAny comorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.736\u0026ndash;6.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at Admission\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;=7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.233\u0026ndash;7.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBreastfeed within 1 hr\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.988\u0026ndash;7.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIf the antibiotics started on time\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.011\u0026ndash;23.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource\u003c/b\u003e: Primary data, 2025\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e presents Multivariate Cox Regression Analysis of Predictors of Neonatal Mortalities among Newborns. In bivariate analysis, female neonates had higher crude hazard (CHR\u0026thinsp;=\u0026thinsp;2.193; 95% CI: 1.159\u0026ndash;4.152; p\u0026thinsp;=\u0026thinsp;0.01) than males, but this was not significant in the multivariate model. Maternal education was strongly protective: compared with mothers with university education, the risk of neonatal death was highest among mothers with no education (AHR\u0026thinsp;=\u0026thinsp;19.329; 95% CI: 3.033\u0026ndash;123.196; p\u0026thinsp;=\u0026thinsp;0.002), followed by primary (AHR\u0026thinsp;=\u0026thinsp;4.365; p\u0026thinsp;=\u0026thinsp;0.008) and secondary education (AHR\u0026thinsp;=\u0026thinsp;4.347; p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003cp\u003eANC attendance was significant: neonates whose mothers attended\u0026thinsp;\u0026lt;\u0026thinsp;3 visits had higher mortality (AHR\u0026thinsp;=\u0026thinsp;3.576; 95% CI: 1.456\u0026ndash;8.780; p\u0026thinsp;=\u0026thinsp;0.005). Multiparity showed a protective effect (AHR\u0026thinsp;=\u0026thinsp;0.187; p\u0026thinsp;=\u0026thinsp;0.006). Low 1-minute APGAR (AHR\u0026thinsp;=\u0026thinsp;4.251; p\u0026thinsp;=\u0026thinsp;0.003), multiple births (AHR\u0026thinsp;=\u0026thinsp;3.264; p\u0026thinsp;=\u0026thinsp;0.001), resuscitation at birth (AHR\u0026thinsp;=\u0026thinsp;2.527; p\u0026thinsp;=\u0026thinsp;0.014), lack of kangaroo mother care (AHR\u0026thinsp;=\u0026thinsp;0.395; p\u0026thinsp;=\u0026thinsp;0.040), and respiratory support (AHR\u0026thinsp;=\u0026thinsp;2.548; p\u0026thinsp;=\u0026thinsp;0.028) were also significant predictors.\u003c/p\u003e\u003cp\u003eNeonatal infection during hospitalization was the strongest predictor (AHR\u0026thinsp;=\u0026thinsp;16.306; 95% CI: 6.801\u0026ndash;39.094; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Low birth weight also increased mortality risk (AHR\u0026thinsp;=\u0026thinsp;0.325; p\u0026thinsp;=\u0026thinsp;0.008). Factors significant in bivariate analysis only included prematurity, PROM, comorbidities, late admission, delayed breastfeeding, and late antibiotic initiation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Discussion of Findings\u003c/h2\u003e\u003cp\u003e\u003cb\u003e(a) Demographic characteristics of the respondents\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, male neonates accounted for 59.2% of admissions, consistent with findings from CHUK, Rwanda (57%) and Ethiopia (60.5%) (Uwiringiyimana et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tewabe et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Male predominance may reflect biological vulnerability to complications such as respiratory distress and infections (Lawn et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), though some studies, like in Nigeria, report a more balanced ratio (Ekwochi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Over half of neonates (53.8%) were hospital-born, with smaller proportions in health centers (33.8%), ambulances (7.5%), health posts (3.6%), or at home (1.3%). Nonfacility births, particularly in transit or at home, remain a concern, as seen in Rwanda, Uganda, and Nepal (Tuyisenge et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Waiswa et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khanal et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Most neonates (94%) were admitted within the first seven days, reflecting the burden of early neonatal complications such as birth asphyxia, prematurity, and sepsis (Mwaniki et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Worku \u0026amp; Ayele, 2019). High rates of prematurity (61.8%) and low birth weight (59.7%) underscore the need for enhanced maternal care and targeted interventions to reduce neonatal morbidity and mortality, consistent with findings from Rwanda, Tanzania, and India (Mukamurigo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mmbaga et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003e(b) Survival Status of Neonates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, 89.9% of neonates survived, while 10.1% died during the neonatal period, reflecting a relatively low neonatal mortality rate (NMR) and suggesting effective neonatal care at Byumba Level Two Teaching Hospital. This rate is comparable to CHUK (11.3%) and slightly lower than Muhima District Hospital (12.5%) (Uwiringiyimana et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mukamurigo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), likely reflecting similar healthcare resources and improved early detection, timely management, and adherence to protocols. Higher NMRs have been reported elsewhere, including Uganda (19.1%), Tanzania (21.3%), India (15.2%), and Ethiopia (17%) (Waiswa et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mmbaga et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tewabe et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), often linked to limited neonatal care, delayed treatment, and higher perinatal risks. The comparatively lower rate in Byumba highlights the benefits of Rwanda\u0026rsquo;s structured and decentralized healthcare system, supported by community programs and increased facility deliveries. Globally, WHO reported an NMR of 17 per 1,000 live births in 2021, with higher rates in sub-Saharan Africa and South Asia (WHO, 2022). While the 10.1% rate exceeds general population averages, it aligns with expectations for hospitalized neonates, emphasizing the importance of early interventions, skilled birth attendance, and essential newborn care.\u003c/p\u003e\u003cp\u003e\u003cb\u003e(c) Predictors of neonatal mortality among newborns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNeonates with a low APGAR score at 1 minute were 4.25 times more likely to die (AHR\u0026thinsp;=\u0026thinsp;4.251, p\u0026thinsp;=\u0026thinsp;0.003), consistent with findings from Nigeria (3.9-fold increase; Udo et al., 2021) and Nepal (AOR\u0026thinsp;=\u0026thinsp;5.1; Karki et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This underscores the importance of prompt diagnosis and resuscitation, particularly in resource-limited settings. Neonates who received Kangaroo Mother Care (KMC) demonstrated better survival, with the absence of KMC significantly increasing mortality risk (AHR\u0026thinsp;=\u0026thinsp;0.395, p\u0026thinsp;=\u0026thinsp;0.040). Similar benefits of KMC have been reported in Bangladesh, reducing neonatal mortality by 60% (Haider et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and in South African hospitals, particularly for low-birth-weight infants (Bergh et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), highlighting its role in thermoregulation, breastfeeding support, and maternal\u0026ndash;infant bonding.\u003c/p\u003e\u003cp\u003eNeonatal infection was the strongest predictor of mortality, with an adjusted hazard ratio of 16.306 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comparable findings were reported in Pakistan, where infections accounted for over 30% of neonatal deaths (Zaidi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and in Ethiopia, where infected neonates were 12 times more likely to die (Mekonnen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Low birth weight also significantly increased mortality risk (AHR\u0026thinsp;=\u0026thinsp;0.325, p\u0026thinsp;=\u0026thinsp;0.008), corroborating studies in Kenya (AHR\u0026thinsp;=\u0026thinsp;4.18; Wanjala et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Brazil (Leal et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), emphasizing the need for improved maternal nutrition, comprehensive prenatal care, and skilled delivery services.\u003c/p\u003e\u003cp\u003e\u003cb\u003e(d) Study limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study, while providing valuable insights into neonatal survival and its predictors, has several limitations. First, it was conducted in a single health facility, which may limit the generalizability of the findings to other hospitals or regions within Rwanda. Second, the reliance on retrospective hospital records may have introduced information bias due to incomplete or inaccurate documentation. Third, certain important maternal and environmental factors\u0026mdash;such as maternal education, household income, and quality of prenatal care\u0026mdash;were not captured in the analysis, potentially affecting the comprehensiveness of the findings. Finally, the observational study design restricts the ability to draw causal inferences between identified predictors and neonatal outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003e(e) Study implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings of this study have important implications for neonatal health interventions and policies in Rwanda. The high survival rate observed is encouraging but highlights the need to further reduce preventable neonatal deaths by addressing modifiable risk factors. Interventions targeting improved neonatal resuscitation, infection prevention, the promotion of kangaroo mother care (KMC), and specialized care for low-birth-weight infants should be prioritized. Health care providers should be trained to identify and manage neonates at higher risk promptly. Moreover, policymakers should consider strengthening neonatal care services, especially at district and teaching hospitals, and expanding community-level education and follow-up care to improve early neonatal health outcomes nationwide.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study, which was conducted at Byumba Level Two Teaching Hospital, assessed neonatal survival and its associated factors among 396 admitted neonates between May and October 2024. The findings revealed a high overall survival rate of 89.9%, with 10.1% mortality during the neonatal period. The key predictors of neonatal death included low APGAR scores at one minute, the absence of kangaroo mother care (KMC), neonatal infections, low birth weight, and inadequate antenatal care attendance. This study emphasizes the importance of improving maternal education, enhancing infection control, promoting KMC, and strengthening early neonatal interventions. On the basis of these findings, recommendations were made for both the Ministry of Health and the hospital, including improved ANC services, community awareness, continuous training for health care workers, and stricter infection prevention protocols. Furthermore, future research is suggested to explore strategies for reducing late-onset neonatal infections and the role of maternal health education in neonatal outcomes and to replicate the study in multiple hospitals for broader applicability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAHR\u003c/strong\u003e: Adjusted hazard ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANC:\u003c/strong\u003e Antenatal Care\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAOR:\u0026nbsp;\u003c/strong\u003eadjusted odds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPGAR:\u003c/strong\u003e A mnemonic that stands for appearance, pulse, grace, activity, and respiration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI:\u003c/strong\u003e Confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDHS:\u0026nbsp;\u003c/strong\u003eDemographic Health Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLBW\u003c/strong\u003e: Low birth weight (LBW)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLMICs:\u0026nbsp;\u003c/strong\u003eLow and Low Middle-Income Countries\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNICUs:\u0026nbsp;\u003c/strong\u003eNeonatal intensive care units\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNISR\u003c/strong\u003e: National Institute of Statistics of Rwanda\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNMR:\u003c/strong\u003e neonatal mortality rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePROM:\u003c/strong\u003e Premature Membrane Rupture\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSDGs\u003c/strong\u003e: Sustainable development goals\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUNICEF:\u003c/strong\u003e United Nations Children\u0026apos;s Fund\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO:\u003c/strong\u003e World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from Mount Kenya University, and permission for data collection was granted by Byumba Level Two Teaching Hospital. The study was carried out following all relevant guidelines and regulations. Written informed consent was obtained from all participants aged 16 years and above. For participants younger than 16 years, written informed consent was obtained from their parents or legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHL conceptualized the study, led the data collection, and drafted the manuscript. Dr. Charles Nsanzabera provided primary guidance on the study design, methodology, and critical manuscript revision. Nsabimana Ephrem contributed to secondary guidance on the study design, methodology and manuscript editing. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the leadership of the hospital and staff of the neonatal unit at Byumba Level Two Teaching Hospital for their invaluable support and cooperation during the conduct of this prospective cohort study on neonatal mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHAKIZIMANA Leonard (HL)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneral Nurse Practitioner \u0026amp; Quality Improvement Officer\u003c/p\u003e\n\u003cp\u003eMaster of Public Health (Global Health),\u003c/p\u003e\n\u003cp\u003eByumba Level Two Teaching Hospital, Gicumbi District, Rwanda;\u003c/p\u003e\n\u003cp\u003eMount Kenya University, Kigali, Rwanda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDr. Charles Nsanzabera, PhD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLecturer, Mount Kenya University, Kigali, Rwanda;\u003c/p\u003e\n\u003cp\u003eResearch interests include hypertension, cardiovascular risk, nutrition, and public health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNsabimana Ephrem\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLecturer, Mount Kenya University, Kigali, Rwanda; Faculty Member, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed SK. 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Mixed Method Research: Instruments, Validity, Reliability and Reporting Findings. \u003cem\u003eTheory and Practice in Language Studies\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 254\u0026ndash;262. https://doi.org/10.4304/tpls.3.2.254-262\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7436214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7436214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeonatal mortality remains a major public health issue in Rwanda, contributing to more than 42% of underfive deaths despite overall improvements in child survival. This prospective cohort study aimed to identify key factors influencing neonatal survival among 385 newborns admitted to the neonatology unit at Byumba Level Two Teaching Hospital between May and October 2024. The data were analyzed via SPSS version 28, with survival assessed via Kaplan‒Meier curves and predictors identified via Cox proportional hazards models. The study revealed that 89.9% of neonates survived, whereas 10.1% died during the neonatal period. Several factors were significantly associated with increased mortality risk. Maternal education level was a strong predictor: neonates born to mothers with no formal education faced the highest risk of death (AHR = 19.329; p = 0.002), followed by those whose mothers had primary or secondary education. Limited antenatal care (fewer than three visits) also increased mortality risk (AHR = 3.576; p = 0.005). Other key risk factors included low APGAR scores at one minute (AHR = 4.251; p = 0.003), multiple gestations (AHR = 3.264; p = 0.001), the need for resuscitation at birth (AHR = 2.527; p = 0.014), and the need for respiratory support (AHR = 2.548; p = 0.028). Neonatal infections were the strongest predictor of mortality (AHR = 16.306; p \u0026lt; 0.001). The absence of Kangaroo Mother Care and low birth weight were also associated with poorer outcomes. The findings underscore the need for targeted, evidence-based interventions to improve neonatal survival and inform policy and health planning in Rwanda.\u003c/p\u003e","manuscriptTitle":"Survival Status and Risk Factors for Neonatal Mortality in a Byumba Level Two Teaching Hospital: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:51:58","doi":"10.21203/rs.3.rs-7436214/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-30T10:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34715048481855475356217471717030405682","date":"2025-09-30T08:38:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T15:02:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T12:35:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-03T20:59:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-01T22:44:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-09-01T22:41:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87949ffd-da90-4550-9687-07830b421176","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T09:51:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 09:51:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7436214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7436214","identity":"rs-7436214","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}