Neonatal Mortality and Its Associated Factors in Gisenyi Hospital, Rubavu District, Rwanda: A Cross-Sectional Study

Neonatal Mortality and Its Associated Factors in Gisenyi Hospital, Rubavu District, Rwanda: A Cross-Sectional 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 Neonatal Mortality and Its Associated Factors in Gisenyi Hospital, Rubavu District, Rwanda: A Cross-Sectional Study Innocent Ntirushwamaboko, Ruton Hinda, Bibiane Uwamahoro, Aline Umubyeyi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7056008/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Neonatal mortality remains a critical global health issue, particularly in sub-Saharan Africa, where preventable causes are prevalent. In Rwanda the neonatal mortality still accounts for a substantial portion and exceeds the SDG target. This study aims to determine the prevalence and associated factors among neonates admitted at the neonatology department at Gisenyi district hospital in western Rwanda. Methods The hospital-based cross-sectional study used a retrospective descriptive review of 753 neonates and their mothers' records systematically sampled between May 1st, 2024, and June 30th 2024. The quantitative data on sociodemographic, obstetric, and clinical characteristics variables were extracted from maternal and neonatal clinical charts and registers. Data were double entered in a pretested data collection tool, cleaned and analyzed using STATA 17. Logistic regression analyses using odds rations with 95% confidence interval (C. I) were applied to assess the association between factors associated with neonatal mortality. The adjusted odds ratios(AoR) has been done to assess other neonatal mortality determinants variables. Data was analysed using statistical software, Stata version 17.0 Results There were 136 ,18% (95% CI: 15.3–27.2%) among them 421, 55.2% were male. Mothers associated factors were mothers aged 25–34 years (aOR = 7.97; 95% CI: 1.7–35.70). Unemployed mothers had 2.5 times higher odds (aOR = 2.52; 95% CI: 1.08–5.87), and public (aOR = 2.26; 95% CI: 1.82–6.27). Multi-gravida mothers (aOR = 5.89; 95% CI: 3.42–10.13). Zero antenatal care visits (aOR = 0.27; 95% CI: 0.12–0.58) and fewer visits (1–2 visits, aOR = 0.33; 95% CI: 0.17–0.64), neonates born before 32 weeks of gestational age (aOR = 2.90; 95% CI: 1.76–4.80), The neonates admitted within 24 hours (aOR = 6.17; 95% CI: 2.16–17.67). Hypothermic neonates (aOR = 2.02; 95% CI: 1.28–3.19) and the Apgar scores ≤ 3 (aOR = 10.24; 95% CI: 2.71–38.75) were strongly associated with higher mortality. Conclusion In this study, the neonatal mortality remains alarmingly high, driven by both maternal and neonatal risk factors. More than 30% recorded deaths were due to prematurity complications Strengthening antenatal care utilization, early identification of high-risk pregnancies, and improving the management of preterm and low Apgar score neonates are essential steps toward reducing preventable neonatal deaths as main associated factors. Maternal & Fetal Medicine Associated factors neonatal mortality newborns Prevalence Rubavu Rwanda Figures Figure 1 Introduction Neonatal mortality, as defined by the World Health Organization (WHO), refers to deaths among live-born infants within the first 28 completed days of life. It is further categorized into early neonatal deaths (occurring within the first seven days) and late neonatal deaths (from day 8 to day 28 of life) 1 . Despite global progress in reducing child mortality, neonatal deaths remain a major public health challenge, particularly in low- and middle-income countries. Globally, approximately 4.8 million children under five died in 2023, with 2.3 million of these deaths occurring during the neonatal period 2 , 3 . Sub-Saharan Africa carries the highest burden, accounting for over 1.2 million (40%) neonatal deaths annually, equivalent to around 13,000 deaths per day or nearly nine deaths every minute 4 . The region also has the highest neonatal mortality rate (NMR) at 27 deaths per 1,000 live births, followed by Central and Southern Asia with an NMR of 21 per 1,000 live births 5 . According to the Millennium Development Goal Gap Analysis, nearly two-thirds of neonatal deaths in sub-Saharan Africa are attributed to preventable causes 6 – 9 . Rwanda has made notable progress in reducing under-five mortality, declining from 44 deaths per 1,000 live births in 2000 to 19 per 1,000 in 2020 10 . However, this figure remains above the Sustainable Development Goal (SDG) target of 12 per 1,000 live births by 2030 11 . Achieving this goal requires intensified efforts to identify and address factors contributing specifically to neonatal mortality. Neonatal outcomes are influenced by a complex interplay of maternal, neonatal, and health system-related factors. A 2024 facility-based study in Somalia identified several factors associated with neonatal mortality, including neonatal sex, antenatal care (ANC) attendance, tetanus toxoid immunization, mode of delivery, sepsis, tetanus, pneumonia, breastfeeding challenges, and prematurity 12 , 13 . Similarly, research from Indonesia revealed that maternal education, occupation, decision-making autonomy regarding healthcare, quality of antenatal care, and delivery complications significantly influenced neonatal survival 14 , 15 . A study conducted in Ghana also emphasized the importance of ANC attendance, neonatal sex, and immediate skin-to-skin contact as predictors of neonatal mortality, many of which are preventable with improved health system practices 16 . Rubavu District, located in Rwanda’s Western Province, is served by a Gisenyi hospital that acts as the primary referral center for surrounding health facilities. While national policies and programs targeting neonatal health are in place, local data on neonatal mortality and its contributing factors remain scarce. According to the Rwanda Biomedical Center (2024), more localized evidence is needed to guide targeted and context-specific interventions. This study aimed to determine the prevalence of neonatal mortality and identify associated factors among neonates admitted to Gisenyi District Hospital. The findings are expected to provide critical insights for healthcare providers, policymakers, and program implementers to strengthen neonatal care services in the district and contribute to the national efforts toward achieving the SDG targets. Materials and methods Study design This is a hospital-based cross-sectional study and collected the quantitative data through retrospective descriptive review of 753 neonatal medical records of admitted neonates to the neonatology department from May 1st 2024, and June 30th 2024. Study setting Study setting The study was conducted at the neonatal department of Gisenyi district hospital located in Rubavu District, Western Province of Rwanda. The hospital lies along the shores of Lake Kivu and situated approximately 2 kilometers from the Lacoriniche Border Post and about 153.6 kilometers from Rwanda’s capital city, Kigali. Gisenyi Hospital is one public hospital that provide the neonatal care services to the whole Rubavu district populations (approximately 403,662 district total population), and hospital act as referral to both public and private peripheral health facilities or surrounding communities that refer neonates requiring advanced care. With reference to the hospital records, Gisenyi hospital neonatology department on average, admits 1,500 neonates annually equal to 125 per month and approximately 31 neonates per week.Of the admitted neonates 64% are born at Gisenyi hospital and 6% referred from the surrounding communities. Study population The study population included neonates who were admitted to the neonatology department at Gisenyi District Hospital during the study period. Only neonates admitted who survived for at least 28 days. Furthermore, only records of mothers who delivered live-born at a gestational age of 28 weeks and above, either within the hospital (inborn) or referred (out born) between May 2019 and June 2024, were considered an eligible participants and included in the final dataset. Study measures Any neonate admitted to the neonatology department who died during the neonatal period was considered as neonatal death. Social demographic characteristics of mothers whose neonates were admitted to neonatology, including mothers' age, address (Urban vs Rural), profession, possession of health insurance, and marital status. Pregnancy and gynecological characteristics such as gestation at delivery time, mode of delivery for current child, gravidity, parity. Antenatal care visits. Other variables of interest include the admitted neonates' records at delivery time such as appearance, pulse, grimace, activity, and respiratory (Apgar) score at 10min, birth weight categories, age of neonate at death in days, gender, neonatal morbidities such as respiratory distress syndrome(RDS) and neonatal sepsis. Data collection Using a pretested developed data extraction tool and modified from a similar study 17 data were collected by a trained team consisting of five professional nurses and one medical doctor. Before data collection, all team members underwent a comprehensive four-day training to ensure familiarity with the study objectives, data abstraction tools, and ethical considerations. Neonatal characteristics were extracted from individual neonatal medical record charts, while maternal data were obtained from hospital delivery registers, maternal admission files, and medical charts. The data abstraction process for each participant took approximately 15 to 20 minutes. To maintain data quality and consistency, the principal investigator conducted daily reviews of completed data forms. This included cross-checking entries for completeness, accuracy, and internal consistency, with any discrepancies addressed promptly through consultation with the data collection team. Statistical analysis All collected data were initially entered into Microsoft Excel 2016 for cleaning, coding, and validation, and subsequently exported to STATA version 17 for statistical analysis. Descriptive statistics were used to summarize the sociodemographic, obstetric, and clinical characteristics of the neonates admitted to neonatology and their mothers. Frequencies and percentages were calculated to determine the overall prevalence of neonatal mortality. To identify factors associated with neonatal mortality, a binary outcome coded as 1 for deceased neonates and 0 for survivors, a multivariable logistic regression analysis was performed. Before model building, multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF), and variables with a VIF ≥ 10 were excluded. A stepwise selection approach was used to construct the final model, retaining variables with statistical relevance and clinical plausibility. Adjusted odds ratios (aORs) with corresponding 95% confidence intervals (CIs) and p-values were reported to indicate the strength and significance of associations. A p-value of < 0.05 was considered statistically significant. Results Socio-demographic characteristics of participants In this study, 753 neonates admitted to the neonatology department participated. Most mothers were aged 24 years and below (74.7%), and 82.2% resided in rural areas. A significant proportion had only primary-level education (55.8%), while 22.4% had no formal education. 84.2% were farmers and legally married mothers (64%). Additionally, 93.5% had medical insurance (Table 1 ). Table 1 The mothers' socio-demographic characteristics whose newborns were admitted to neonatology, 2019–2024 Characteristic Frequency, n = 753 Percentage, % Mother’s age 24 years and below 563 74.7 25–34 Years 152 20.2 35 years and above 38 5.1 Mother’s professional (n = 753) Farmer 634 84.2 Business owner 44 5.9 Unemployed 41 5.4 Public servant 28 3.7 Vocational work 6 0.8 Mother’s highest level of education (n = 753) No formal education 169 22.4 Primary level 420 55.8 Secondary level 141 19.2 University level and above 20 2.7 Mother’s marital status (n = 753) Legal married 482 64 Single 199 26.2 Cohabited 72 9.5 Medical insurance (n = 753) No 49 6.5 Yes 704 93.5 Residence for study participants (n = 753) Rural 618 82.2 Urban 135 17.8 Obstetrics and gynecological characteristics of mothers of neonates admitted to Gisenyi Hospital Nearly half of the mothers were primigravida (49.6%), and 46.4% were Primipara. Most deliveries were vaginal (71%), with cesarean sections accounting for 28.1%. A significant proportion of mothers (35.7%) delivered preterm, with 22.3% giving birth before 32 weeks of gestation. Regarding antenatal care, 15.1% of mothers had no ANC visits, while nearly half (49.1%) attended 3–4 antenatal care visits (Table 2 ). Table 2 Obstetrics and gynecological characteristics of mothers whose newborns were admitted at Gisenyi DH hospital Characteristic Frequency, n = 753 Percentage, % Gravidity; (n = 753) Multi-gravida 380 50.4 Primi-gravida 373 49.6 Parity (n = 753) Multi-para 404 53.6 Primi-para 349 46.4 Mode of delivery (n = 753) Vaginal delivery 535 71 Cesarean 211 28.1 Assisted vaginal delivery 7 0.9 Place for delivery (n = 753) Hospital 484 64.2 Health center 223 29.6 Home delivery 46 6.11 Gestational age (n = 753) 37 weeks 333 44.3 Number of ANCs done (n = 753) No ANC 113 15.1 1–2 ANC 224 29.6 3–4 ANC 370 49.1 5 ANC and above 46 6.2 Characteristics of neonates at admission to the Neonatology department, Gisenyi district hospital. Most neonates (79.6%) were admitted within the first 24 hours of life, and 84.2% were received in the unit within that time. Low birth weight was common, affecting 45.4% of neonates, and 55.7% were born preterm (before 37 weeks of gestation). Hypothermia was observed in 41.7% of neonates at admission. Although most neonates (84.2%) had Apgar scores ≥ 7 at 10 minutes, the neonatal mortality rate remained notable at 18.1% (Table 3 ). Table 3 Characteristics of neonates at admission to neonatology department Characteristic Frequency, n = 753 Percentage, % Age of neonates at admission (n = 753) 0–24 hours 600 79.6 1–7 days 25 3.4 8–28 days 128 17.0 Gender of neonate (n = 753) Male 421 55.2 Female 332 44.8 Body temperature at admission (n = 753) Hypothermia ( 37.5°C) 44 5.9 Gestational age (n = 753) 37 weeks 333 44.3 Birth weight (n = 753) Low (< 2500mg) 342 45.4 Normal (2500mg-4000mg) 377 50.1 Overweight (above 4000mg) 34 4.6 Apgar scores first (10 Minutes) ≤ 3 Apgar score 12 1.6 4–6 Apgar score 107 14.2 ≥ 7 Apgar score 634 84.2 Time to admission to neonatology unit (n = 753) Admitted within 24 hours 634 84.2 Admitted after 24 hours 119 15.8 Neonate discharge outcome (n = 753) Alive 617 81.9 Died 136 18.1 Prevalence of neonatal mortality, cause of admission, and cause of death at discharge among neonates admitted to Gisenyi District Hospital The neonatal mortality rate was 18% (95% Confidence Interval: 15.3–27.2%), indicating that nearly one in five admitted newborns did not survive during the study period. The most common cause of neonatal admission to the neonatology unit was complications related to prematurity, accounting for 38.4% of all admissions. Notably, prematurity complications also emerged as the leading cause of neonatal mortality at discharge, contributing to 30.2% of the deaths. This underscores the urgent need for improved case management and enhanced quality of care for preterm infants. Interestingly, while 13.3% of neonatal deaths were attributed to neonatal pneumonia, there were no recorded cases of neonatal admission primarily due to pneumonia. This discrepancy suggests the possibility of nosocomial (hospital-acquired) infections, emphasizing the need to strengthen infection prevention and control (IPC) practices within the neonatal care environment (Fig. 1 ). Multivariable factors associated with neonatal mortality among admitted neonates to the neonatology unit. The mother's socio-demographic factors are associated with neonatal mortality Neonates born to mothers aged 25–34 years were nearly eight times more likely to die compared to those born to mothers aged 35 years and above (aOR = 7.97; 95% CI: 1.7–35.70; p = 0.005). In terms of occupation, unemployed mothers had about 2.5 times higher odds of neonatal mortality (aOR = 2.52; 95% CI: 1.08–5.87; p = 0.030), while those working as public servants had 2.3 times higher odds (aOR = 2.26; 95% CI: 1.82–6.27; p = 0.010) compared to farmers. Interestingly, single and cohabiting mothers had 91% (aOR = 0.09; p < 0.001) and 93% (aOR = 0.07; p < 0.001) lower odds of experiencing neonatal death, respectively, compared to legally married mothers. Other variables such as education level, place of residence, and medical insurance status showed no statistically significant association with neonatal mortality (Table 4 ). Table 4 Multivariable logistic regression of maternal social demographic factors associated with neonatal mortality Characteristics cOR (95%CI) P-value aOR (95%CI) P-value Mother’s age group 35 years old and above 1.00 1.00 ≤ 24 years old 2.93(0.69–12.44) 0.143 2.69(0.61–11.80) 0.188 25–34 years old 10.20(2.36–44.02) < 0.001 7.97(1.7–35.70) 0.005* Mother’s occupation Farmer 1.00 1.00 Business owner 1.71(0.84–3.51) 0.137 2.08(0.92–4.70) 0.076 Unemployed 2.13(1.05–4.31) 0.035 2.52(1.08–5.87) 0.030* Public servant 2.44(1.07–5.54) 0.033 2.26(1.82–6.27) 0.010* Vocational work 1.03(0.11–8.91) 0.978 1.12(0.12–10.27) 0.916 Mother’s education University level and above 1.00 1.00 No formal education 0.87(0.29–2.54) 0.800 0.62(0.16–2.29) 0.478 Primary level 0.59(0.21–1.73) 0.338 0.72(0.44–1.184) 0.198 Secondary level 0.57(0.18–1.72) 0.320 0.57(0.29–1.09) 0.093 Mother’s marital status Legal married 1.00 1.00 Single 0.08(0.03–0.19) < 0.001 0.09(0.04–0.23) < 0.001* Cohabited 0.07(0.01–0.32) < 0.001 0.07(0.01–0.33) < 0.001* Medical insurance Yes 1.00 1.00 No 2.01(0.78–5.17) 0.147 2.04(0.73–5.66) 0.171 Residence for study participants Urban 1.00 1.00 Rural 2.01(0.78–5.17) 0.147 1.36(0.75–2.48) 0.305 cOR; crude Odds Ratio, aOR; adjusted Odds Ratio, *P ≤ 0.05 at multivariable analysis Gynecology-obstetrical factors higher odds of mortality compared to those born to primi-gravida mothers (aOR = 5.89; 95% CI: 3.42–10.13; p < 0.001). Similarly, neonates born to multi-para mothers (those with multiple deliveries) had about twice the odds of neonatal death compared to those born to primi-para mothers (aOR = 2.01; 95% CI: 1.19–3.36; p = 0.008). Gestational age also played a critical role, with neonates born before 32 weeks having almost three times higher odds of death compared to full-term neonates (aOR = 2.90; 95% CI: 1.76–4.80; p < 0.001). The number of antenatal care (ANC) visits was also a significant factor, with no ANC visits associated with 73% lower odds of neonatal survival (aOR = 0.27; 95% CI: 0.12–0.58; p < 0.001), and fewer ANC visits (1–2 visits) associated with 67% lower odds (aOR = 0.33; 95% CI: 0.17–0.64; p < 0.001). Other variables, such as mode of delivery and place of delivery, did not show a significant impact on neonatal mortality (Table 5 ). Table 5 Multivariable logistic regression of the gynecology-obstetrical factors associated with neonatal mortality(n = 753) Characteristics cOR (95%CI) P-value aOR (95%CI) P-value Gravidity Primi-gravida 1.00 1.00 Multi-gravida 2.29(1.55–3.39) 0.001 5.89(3.42–10.13) < 0.001* Parity Primi-para 1.00 1.00 Multi-para 1.11 (0.76–1.62) 0.565 2.01(1.19–3.36) 0.008* Mode of delivery Vaginal delivery 1.00 1.00 Cesarian 1.12(0.12–9.54) 0.923 0.95(0.55–1.63) 0.867 Assisted vaginal delivery 1.41(0.16–11.86) 0.750 0.96(0.65–14.23) 0.981 Place for delivery Hospital 1.00 1.00 Home delivery 0.78(0.61–1.40) 0.723 1.18(0.73–3.04) 0.717 Health center 0.92(0.34–1.81) 0.572 1.18(0.46–3.02) 0.510 Gestational age > 37 weeks 1.00 1.00 < 32 weeks 4.18(2.62–6.64) < 0.001 2.90(1.76–4.80) < 0.001* 33–37 weeks 1.56(0.96–2.51) 0.068 1.38(0.83–2.29) 0.213 Number of mother’s antenatal care visit done 5 ANC and above 1.00 1.00 No ANC 0.26(0.12–0.56) < 0.001 0.27(0.12–0.58) < 0.001* 3–4 ANC 0.01(0.0–0.03) < 0.001 0.00(0.00-0.04) < 0.001* 1–2 ANC 0.31(0.16–0.58) < 0.001 0.33(0.17–0.64) < 0.001* cOR; crude Odds Ratio, aOR; adjusted Odds Ratio, *P ≤ 0.05 at multivariable analysis The admitted Neonate-related factors associated with their mortality, n = 753 Neonates admitted within the first 24 hours of life had over six times higher odds of mortality compared to those admitted between 8 and 28 days (aOR = 6.17; 95% CI: 2.16–17.67; p < 0.001). Neonates with hypothermia at admission had twice the odds of neonatal mortality compared to those with normothermia (aOR = 2.02; 95% CI: 1.28–3.19; p = 0.002). In contrast, hyperthermia was not significantly associated with mortality (aOR = 2.79; 95% CI: 0.78–9.93; p = 0.113). Apgar scores at 10 minutes were a strong predictor of mortality, with neonates scoring ≤ 3 having over 10 times the odds of death compared to those with an Apgar score ≥ 7 (aOR = 10.24; 95% CI: 2.71–38.75; p < 0.001), and those scoring 4–6 having three times higher odds (aOR = 3.07; 95% CI: 1.50–6.28; p = 0.002). Neonates who stayed in the hospital for 7 days or more had 3.45 times higher odds of mortality compared to those who stayed less than 7 days (aOR = 3.45; 95% CI: 1.33–8.95; p = 0.010). Other factors such as gender, birth weight, and admission time showed no significant association with neonatal mortality (Table 6 ). Table 6 Multivariable logistic regression of the neonate-related factors associated with neonatal mortality Characteristics cOR (95%CI) P-value aOR (95%CI) P-value Neonate’s age group at admission to the neonatology Unit 8–28 days 1.00 Ref 1.00 Ref 0–24 hours 7.03(2.82–17.54) < 0.001 6.17(2.16–17.67) < 0.001* 1–7 days 1.00(0.11-9.00) 0.994 1.17(0.11–11.78) 0.891 Gender of admitted neonate Male 1.00 Ref 1.00 Ref Female 1.15(0.79–1.67) 0.441 1.13(0.48–1.34) 0.562 Body temperature at admission to the neonatology unit Normothermia (36°C-37.5°C) 1.00 Ref 1.00 Ref Hypothermia (< 36°C) 2.98(2.01–4.41) 37.5°C) 1.06(0.40–2.84) 0.894 2.79(0.78–9.93) 0.113 Birth weight before admission to the neonatology unit Normal (2500mg-4000mg) 1.00 Ref 1.00 Ref Low (< 2500mg) 1.72(1.16–2.56) 0.005 0.75(0.44–1.30) 0.315 Overweight (above 4000mg) 1.43(0.64–3.12) 0.379 0.60(0.23–1.58) 0.305 Apgar scores at first 10 Minutes after birth ≥ 7 Apgar score 1.00 ref 1.00 Ref ≤ 3 Apgar score 5.04(1.66–15.29) 0.004 10.24(2.71–38.75) < 0.001* 4–6 Apgar score 2.20(1.14–4.23) 0.18 3.07(1.50–6.28) 0.002* Admission time Admitted within 24 hours 1.00 ref 1.00 Ref Admitted after 24 hours 1.74(0.96–3.15) 0.065 1.12(0.53–2.36) 0.747 Hospital stay Below 7 days 1.00 Ref 1.00 Ref 8 days and above 5.82(2.54–13.29) < 0.001 3.45(1.33–8.95) 0.010* cOR; crude Odds Ratio, aOR; adjusted Odds Ratio, *P ≤ 0.05 at multivariable analysis Discussion The neonatal mortality prevalence of 18% observed in this study is alarmingly high and significantly exceeds both global and regional estimates. According to the World Health Organization (2023), the global neonatal mortality rate stood at approximately 17 deaths per 1,000 live births, 18 while Sub-Saharan Africa recorded a higher rate of about 27 per 1,000 live births, yet both figures remain markedly lower than the 180 deaths per 1,000 live births reported here 4 . This discrepancy likely reflects the hospital-based nature of the study, where the sample comprises neonates already at elevated risk due to clinical complications necessitating admission. Similar facility-based studies in comparable low-resource settings have reported lower but still concerning mortality rates; for instance, a study in Ethiopia found a rate of 20% 19 , while one in Uganda reported 17% 20 . The strong association between maternal age and neonatal mortality draws attention to the often-overlooked risks within the 25–34 age bracket, which is typically considered a lower-risk reproductive age group 21 .The significantly higher odds of mortality among neonates born to women in this age group, compared to older mothers, may reflect disparities in birth preparedness, care-seeking behaviors, or undetected obstetric complications 22 , 23 . Contrary to traditional assumptions that older maternal age confers greater perinatal risk, some studies, particularly those from resource-limited settings, suggest that older mothers may benefit from accumulated maternal experience, improved self-efficacy in navigating health services, and greater household decision-making power 24 , 25 . A study done in Nigeria has revealed that an employment status emerged as another key determinant, with higher mortality among neonates of unemployed and public servant mothers compared to those of farmers 26 . While public employment often implies better socioeconomic positioning, it may also come with higher stress levels, longer working hours, or reduced time for postnatal care. Conversely, farming mothers may benefit from extended familial support networks and the proximity to home-based caregiving environments 27 - 28 . These findings mirror those from Sub-Saharan Africa, where maternal employment, especially in non-flexible formal sectors, has been associated with suboptimal early neonatal outcomes 29 . Interestingly, the data revealed that single and cohabiting mothers had lower odds of neonatal death than their legally married counterparts. This counterintuitive result may suggest that unmarried mothers, especially in the context of strong community-based maternal support, may receive more focused care or engage more consistently with health systems due to perceived vulnerability 30 . Alternatively, cultural and legal marital norms may obscure underlying socioeconomic or gender dynamics that compromise care access in formally married households 28 . Parity and gravidity remain well-established risk factors in perinatal health literature. The elevated risk of neonatal mortality among neonates born to multigravida and multiparous mothers is consistent with findings from similar low-income settings. These risks may stem from uterine fatigue, reduced placental efficiency, or complacency in seeking skilled birth care among women with previous childbirth experience 31 , 32 . Additionally, repeated pregnancies without optimal spacing may deplete maternal nutritional reserves, adversely affecting neonatal outcomes 33 , 34 . Globally the prematurity continues to be a dominant contributor to neonatal deaths 35 , and this study reaffirms its devastating impact in a district hospital setting. The markedly higher mortality risk for neonates born before 32 weeks of gestation aligns with WHO reports, which highlight that extremely preterm births have limited survival prospects in low-resource facilities due to inadequate respiratory support, infection control, and thermal regulation 36 . The timing of admission also proved critical; early admission (within 24 hours of life) likely signals critical neonatal distress at birth, which may overwhelm the limited capacity of district-level neonatal units 37 . In Rwanda, the Antenatal care utilization remains one of the strongest predictors of neonatal survival 38 . In this study, the reduced survival odds among mothers with no or limited ANC visits underscore systemic gaps in early detection of high-risk pregnancies. This reinforces WHO's recommendation of at least eight ANC contacts during pregnancy to facilitate timely identification and management of complications 39 .In many rural settings, barriers such as transport, cultural beliefs, and low health literacy limit ANC engagement, placing neonates at elevated risk even before birth 40 . In present study, the thermal instability at admission, especially hypothermia, emerged as another critical determinant. Neonatal hypothermia is both a marker and a mediator of poor outcomes, often reflecting inadequate postnatal thermal care, especially among low birth weight and premature neonates 41 . Although hyperthermia was not statistically significant in this analysis, its presence may still signal underlying infection, which, in resource-limited settings, is a frequent but often underdiagnosed contributor to neonatal mortality 42 . Apgar scores at 10 minutes served as a powerful prognostic marker 43 .The gradient of mortality risk across Apgar score categories reinforces the value of immediate postnatal assessment and resuscitation efforts 44 . In settings like Gisenyi DH, where neonatal resuscitation capacity may be constrained, low Apgar scores could reflect delayed or ineffective interventions at birth. Investment in neonatal resuscitation training and equipment remains a low-cost, high-impact strategy that could significantly reduce early neonatal deaths. Finally, the increased odds of mortality among neonates who stayed in the hospital for 7 days or more could be indicative of nosocomial complications, late-onset infections, or underlying congenital conditions that required prolonged management 45 . It also highlights a subgroup of neonates who initially survived the critical early window but later succumbed to complications, emphasizing the need for robust infection control, nutritional support, and close monitoring throughout the neonatal period 46 This study had one key limitation. As a hospital-based study conducted at Gisenyi District Hospital, the findings may not fully represent neonatal mortality trends in the general population, especially in rural or home-birth settings. However, Gisenyi DH is the main referral hospital in the region and receives a high volume of neonatal admissions from various catchment areas, thereby offering a reasonably broad picture of facility-based neonatal outcomes. Conclusion Despite significant improvements in the health system and rapid declines in neonatal mortality in Rwanda, the findings of this study revealed a high neonatal mortality rate of 18% at the hospital, highlighting a critical burden within the facility-based neonatal care system and case management. Several maternal, neonatal, and clinical factors were significantly associated with this mortality, including younger maternal age (25–34 years), lack of antenatal care, preterm birth (< 32 weeks), low Apgar scores, early neonatal admission (within 24 hours), and neonatal hypothermia. These findings underscore the multifaceted nature of neonatal mortality and emphasize the urgent need for comprehensive and well-informed policies, guidelines, and system-level improvements. with targeted interventions across the continuum of maternal and newborn care that address the key risk factors identified in this study, especially in rural areas. Health workers at both primary and referral levels should be trained to identify the neonatal cause of death danger signs, and the management of preterm births early. Additionally, community health workers and local leaders should intensify maternal health education campaigns to promote timely antenatal care and facility-based deliveries Study strengths and limitations This study encompasses inborn and outborn multicenter-level data from all public health facilities that referred their neonates to the Gisenyi district Hospital neonatology department. Therefore, it reflects the district's public health system structure in neonate case management. The identified factors associated with neonatal deaths (Maternal and neonatal factors) and the general recommendations derived from this study were relevant and applicable to all public health facilities. Despite that, the study utilized only medical records of neonates and their mothers found at the hospital, which may not capture all factors influencing neonatal mortality. Consequently, the results might not be entirely representative of the community, as deaths in the community were not counted, and their cause was not assessed. This limitation could lead to an underrepresentation of the total number of deaths within the district. Given the rising number of premature neonatal deaths, there is a pressing need for a prospective, community-based study to thoroughly examine maternal factors. Abbreviations ANC Antenatal care visit AoR Adjusted odds ratio Apgar score Appearance (Skin color), pulse (heart rate), grimace (reflex irritability), activity (muscle tone), and respiratory (breathing rate and efforts) GDH Gisenyi district hospital NMR Neonatal mortality rate RDS Respiratory distress syndrome SDG Sustainable Development Goals STATA Statistical software for data science WHO World Health Organization Declarations Acknowledgement The authors would like to express their gratitude to the University of Rwanda, particularly to the College of Medicine and Health Sciences and the School of Public Health, for their ethical review process. We also extend our gratitude to the Ministry of Health, the Rwanda Biomedical Centre, and the Africa Field Epidemiology Network-Rwanda for their invaluable assistance during the pre-data collection process through research site preparation and connecting data collectors with hospital administration. Our special appreciation is directed to the research committee at Gisenyi District Hospital for granting access to the neonatal health records, as well as to the nurses and doctors involved in data collection, which made this research possible. We are also thankful for the support and guidance from our supervisors and mentors at the University of Rwanda, in the field epidemiology program, during the writing of the manuscript. Authors contributions Innocent NTIRUSHWAMABOKO conceived the study's idea, designed the methodology, developed the questionnaire, analyzed the data, and wrote the manuscript. Hinda Ruton supervised the study, contributed to the study design and results interpretation. Bibiane Uwamahoro assisted with the study design and the interpretation of the results. Associate Professor Aline Umubyeyi was provided the constructive feedback on the manuscript and critically reviewed the manuscript, supported the study design, assisted in data analysis and results interpretation. Funding The author(s) received no financial support for the research, authorship, and /or publication of this manuscript Data Availability Statement The original contributions presented in this study are included in the article and/or the Supplementary Material. The dataset used in this study is stored securely and is available upon reasonable request. Interested parties are encouraged to contact the corresponding author for access or further inquiries. Ethical consideration This study utilized secondary data from human participants and received approval from the University of Rwanda's Human Research Institutional Review Board (IRB) under Reference Number CMHS/IRB/458/2024 of May 2025. Furthermore, ethical clearance was granted by the Ethical Committee of the Rwanda Ministry of Health and the Gisenyi District Hospital research committee, which permitted the study investigator to access both neonatal and maternal data. Both research committees were assured that the collected data would remain anonymous, all data securely stored and accessed only by authorized study personnel. Informed consent was obtained as requested by the ethics committee. The need for consent was waived for the study, including newborns and their mothers, under hospital data access approval with reference No: 053/2024 of 07/06/2024 and University of Rwanda Human Research Institutional Review Board data access approval letter N o 130/UR-CMHS/SPH/2024. This research was conducted under the Declaration of Helsinki. Consent for publication Not applicable Competing interests The author(s) declared no potential conflicts of interest related to this manuscript's research, authorship, or publication. Authors details 1 Department of Epidemiology and Biostatistics, School of Public Health, University of Rwanda, Kigali, Rwanda 2 Department of Epidemiology and Biostatistics, School of Public Health, University of Rwanda, Kigali, Rwanda 2 Department of Epidemiology and Biostatistics, School of Public Health, University of Rwanda, Kigali, Rwanda 3 Department of Epidemiology and Biostatistics, School of Public Health, University of Rwanda, Kigali, Rwanda. References World Health Organization. World health sWORLD HEALTH ORGANIZATION - World health statistics 2024. ISBN 9789240094703. tatistics 2024 (2024) Group U (2024) N. I. Levels & Trends in Langlois EV, Gasparri G, Khosla R (2025) The future at risk: Tackling newborn and child mortality amidst a global health crisis. PLOS Glob public Heal 5:e0004519 Moges N et al (2024) Burden of early neonatal mortality in Sub-Saharan Africa. A systematic review and meta-analysis. PLoS ONE 19:1–14 Ahmed KY et al (2024) Population modifiable risk factors associated with neonatal mortality in 35 sub-Saharan Africa countries: analysis of data from demographic and health surveys. eClinicalMedicine 73:1–12 Extension KP (2023) The Occurrence and Correlating Elements of Newborn Mortality in Jinja District, International Network Organization for Scientific Research The Occurrence and Correlating Elements of Newborn Mortality in Jinja District. Uganda Mbito Chindoro Mongo Usman F, Imam A, Farouk ZL, Dayyabu AL (2019) Newborn mortality in sub-saharan africa: Why is perinatal asphyxia still a major cause? Ann Glob Heal 85:1–6 Lawn JE, Zupan J, Begkoyian G, Knippenberg R (2006) Chapter 27. Newborn Survival. Dis. Control Priorities Dev. Ctries. (2nd Ed. 531–550 10.1596/978-0-8213-6179-5/chpt-27 Bezie MM et al (2025) Impact of health facility delivery and antenatal care on neonatal mortality in Sub-Saharan Africa: a propensity score matching analysis. BMC Pregnancy Childbirth 25 Hirschhorn LR, Sayinzoga F, Beyer C, Donahoe K, Binagwaho A (2018) Exemplars in Under-5 Mortality: Rwanda Case Study. 1–98 Vesel L et al (2023) Acceleration towards the Sustainable Development Goal targets for maternal health and child mortality Report by the Director-General. Lancet 621:1–12 Ali ME, Hassan YO, Ahmed MAM, Mohamud LB (2025) Neonatal Mortality and Associated Factors at a Tertiary-Level Neonatal Intensive Care Unit in Mogadishu, Somalia: A Retrospective Study. Pediatr Heal Med Ther 16:93–102 Ali IA, Inchon P, Suwannaporn S, Achalapong J (2024) Neonatal mortality and associated factors among newborns in Mogadishu, Somalia: a multicenter hospital-based cross-sectional study. BMC Public Health 24:1–15 Andriani H, Rachmadani SD, Natasha V, Saptari A (2021) Continuity of maternal healthcare services utilisation in Indonesia: analysis of determinants from the Indonesia Demographic and Health Survey. Fam Med community Heal 9 Aji RS, Efendi F, Kurnia ID, Tonapa SI, Chan C-M (2021) Determinants of maternal healthcare service utilisation among Indonesian mothers: A population-based study. F1000Research 10, 1124 Adongo EA, Ganle JK (2023) Predictors of neonatal mortality in Ghana: evidence from 2017 Ghana maternal health survey. BMC Pregnancy Childbirth 23:556 Merdassa E, Id R, Tumtu MI, Gebre DS (2019) Predictors, causes, and trends of neonatal mortality at Nekemte Referral Hospital, east Wollega Zone, western Ethiopia (2010–2014). Retrospective cohort study. 1–13 Mohamed HA, Shiferaw Z, Kedir A, Id R, Id AK (2022) Neonatal mortality and associated factors among neonates admitted to neonatal intensive care unit at public hospitals of Somali Regional State, Eastern Ethiopia : A multicenter retrospective analysis. 22:1–16 Mitiku HD (2021) Neonatal mortality and associated factors in Ethiopia: a cross-sectional population-based study. BMC Womens Health 21:1–9 Aguma N, Ekak S, Emetu L, Ojok S, Akera P (2025) Prevalence and factors associated with neonatal mortality at the neonatal intensive care unit at St. Mary’s Hospital Lacor, Northern Uganda. BMC Pediatr 25:403 Kim YN, Choi DW, Kim DS, Park EC, Kwon JY (2021) Maternal age and risk of early neonatal mortality: a national cohort study. Sci Rep 1–9. 10.1038/s41598-021-80968-4 Vieira E, Oliveira CN et al (2025) Live births and deaths of neonates born to adolescent mothers: analysis of trends and associations from a population study in a region of a middle-income country. BMC Pregnancy Childbirth 25:184 Ochieng Arunda M, Agardh A, Larsson M, Asamoah BO (2022) Survival patterns of neonates born to adolescent mothers and the effect of pregnancy intentions and marital status on newborn survival in Kenya, Uganda, and Tanzania, 2014–2016. Glob Health Action 15:2101731 Glick I, Kadish E, Rottenstreich M (2021) Management of Pregnancy in Women of Advanced Maternal Age: Improving Outcomes for Mother and Baby. Int J Womens Health 13:751–759 Mehari M-A et al (2020) Advanced maternal age pregnancy and its adverse obstetrical and perinatal outcomes in Ayder comprehensive specialized hospital, Northern Ethiopia, 2017: a comparative cross-sectional study. BMC Pregnancy Childbirth 20:60 Akinyemi JO, Solanke BL, Odimegwu CO (2018) Maternal Employment and Child Survival During the Era of Sustainable Development Goals: Insights from Proportional Hazards Modelling of Nigeria Birth History Data. 84:15–30 Xiao Y et al (2024) The mediating effect of family support in the relationship between socio – economic status and postpartum depressive symptoms. BMC Public Health. 10.1186/s12889-024-20849-3 Town C, May SA, Thompson E, Caroline J (2024) Proceedings of the 2023 International Maternal Newborn Health Conference. 1–156 10.1186/s12919-024-00289-y Mkono N, Chirande L, Moshiro R, Noorani M (2024) Factors associated with exclusive breast feeding among mothers in formal employment in Dar es Salaam, Tanzania: a cross-sectional study. BMJ Open 14:e091993 Yan J (2024) Does Marriage Still Make a Difference in Infant Health? East Econ J 51:246–267 Garces A et al (2020) Association of parity with birthweight and neonatal death in five sites: The Global Network’s Maternal Newborn Health Registry study. Reprod Health 17:1–8 Tamir TT (2024) Neonatal mortality rate and determinants among births of mothers at extreme ages of reproductive life in low and middle income countries. Sci Rep 14:12596 Essilfie G, Kofinti RE, Asmah EE (2024) Reducing stunting and underweight through mother’s birth spacing: evidence from Ghana. BMC Pregnancy Childbirth 24:624 Dewey KG, Cohen RJ (2007) Does birth spacing affect maternal or child nutritional status? A systematic literature review. Matern Child Nutr 3:151–173 Id AR et al (2024) Preterm birth and neonatal mortality in selected slums in and around Dhaka City of Bangladesh: A cohort study. 1–14. 10.1371/journal.pone.0284005 Darmstadt GL et al (2023) New World Health Organization recommendations for care of preterm or low birth weight infants: health policy. eClinicalMedicine 63 Kyasimire L, Tibaijuka L, Ochora M, Kayondo M, Kumbakumba E (2024) Clinical profiles, incidence and predictors of early neonatal mortality at Mbarara Regional Referral Hospital, south – western Uganda. BMC Pediatr. 10.1186/s12887-024-05014-4 Uwimana G et al (2023) Association between adequacy of antenatal care and neonatal outcomes in Rwanda: a cross-sectional study design using the Rwanda demographic and health surveys. BMC Health Serv Res 23:1–11 Rahman O, Rauf A, Ulfa Y, Siddiqi NA (2025) Association of quality antenatal care and completion of eight or more antenatal care visits with skilled delivery care utilization among pregnant women in Bangladesh: A nationwide population – based study. 1–15. 10.1371/journal.pone.0322725 Lee S et al (2024) Compliance with the WHO recommended 8 + antenatal care contacts schedule among postpartum mothers in eastern Uganda: A cross-sectional study. PLoS ONE 19:e0314769 Mohamed SOO et al (2021) Outcomes of neonatal hypothermia among very low birth weight infants: a Meta-analysis. Matern Heal Neonatol Perinatol 7:1–9 Garvey M (2024) Neonatal Infectious Disease: A Major Contributor to Infant Mortality Requiring Advances in Point-of-Care Diagnosis. 1–14 Shukla VV et al (2021) Predictive Ability of 10-Minute Apgar Scores for Mortality and Neurodevelopmental Disability. 10.1542/peds.2021-054992 Mense L et al (2025) Assessing the postnatal condition: the predictive value of single items of the Apgar score Raturi A (2024) Neonatal Sepsis: Aetiology, Pathophysiology, Diagnostic Advances and Management Strategies. 10.1177/11795565241281337 Brotherton HC (2021) Early Kangaroo mother care for mild-moderately unstable neonates < 2000g in The Gambia Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7056008","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481206786,"identity":"afe3e2f2-1240-4aa0-9676-48e076904048","order_by":0,"name":"Innocent Ntirushwamaboko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACNhR2gsF/ORDrwAMitEhAtFQwG4O1JBBhG0QLwxnmxAYQF58WPunegw9+VNypM5+R/uzBwza29Plhhx8CbbGT023A4TCZc8mGPWeeScjcyDE3SGzjyd14O80AqCXZ2OwADi0SOWbSjG2HJSQkctgkEtskcjfOTgBpOZC4Da+WfyAt6c+AWgzSDWenfyBCSwNIS4KZRMKZhAR56RyCthgb9hw7LDmD5w1QS8UBww3SOQUHEgxw+0V+Ro7hgx81h/kl2NOfSf4wOCAvPzt984cPFXZyuLRgAgOwSgNilYPtbSBF9SgYBaNgFIwEAADf+lwLKWNIYwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-6357-8992","institution":"University of Rwanda","correspondingAuthor":true,"prefix":"","firstName":"Innocent","middleName":"","lastName":"Ntirushwamaboko","suffix":""},{"id":481206787,"identity":"4b708065-d439-46df-8fe8-640e2f071eb0","order_by":1,"name":"Ruton Hinda","email":"","orcid":"https://orcid.org/0000-0003-0538-275X","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Ruton","middleName":"","lastName":"Hinda","suffix":""},{"id":481206788,"identity":"1aab7fa8-2562-47ad-a2c5-498d010a0909","order_by":2,"name":"Bibiane Uwamahoro","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Bibiane","middleName":"","lastName":"Uwamahoro","suffix":""},{"id":481206789,"identity":"3763a4e2-ff9b-4f39-ac68-39d8039e591a","order_by":3,"name":"Aline Umubyeyi","email":"","orcid":"https://orcid.org/0000-0002-0960-5220","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Aline","middleName":"","lastName":"Umubyeyi","suffix":""}],"badges":[],"createdAt":"2025-07-06 06:02:47","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7056008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7056008/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86240666,"identity":"09d40ccb-8d68-455f-ac7f-985d117bfa00","added_by":"auto","created_at":"2025-07-08 10:36:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36811,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMedical cause of admission to neonatology and Cause of death at discharge, 2019-2024\u003c/em\u003e\u003c/p\u003e","description":"","filename":"NeonatalcauseofadmissionvsNeonatalcauseofdeath.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7056008/v1/e4ec1e32035bbb965bf883a6.jpg"},{"id":86241546,"identity":"098c067b-0f8f-4ba1-a714-8f3a1df49ba6","added_by":"auto","created_at":"2025-07-08 10:44:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1285089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7056008/v1/e4783934-d7f6-40e4-bcb2-485457f65e3f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eNeonatal Mortality and Its Associated Factors in Gisenyi Hospital, Rubavu District, Rwanda: A Cross-Sectional Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeonatal mortality, as defined by the World Health Organization (WHO), refers to deaths among live-born infants within the first 28 completed days of life. It is further categorized into early neonatal deaths (occurring within the first seven days) and late neonatal deaths (from day 8 to day 28 of life) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite global progress in reducing child mortality, neonatal deaths remain a major public health challenge, particularly in low- and middle-income countries. Globally, approximately 4.8\u0026nbsp;million children under five died in 2023, with 2.3\u0026nbsp;million of these deaths occurring during the neonatal period \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Sub-Saharan Africa carries the highest burden, accounting for over 1.2\u0026nbsp;million (40%) neonatal deaths annually, equivalent to around 13,000 deaths per day or nearly nine deaths every minute \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The region also has the highest neonatal mortality rate (NMR) at 27 deaths per 1,000 live births, followed by Central and Southern Asia with an NMR of 21 per 1,000 live births \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. According to the Millennium Development Goal Gap Analysis, nearly two-thirds of neonatal deaths in sub-Saharan Africa are attributed to preventable causes \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRwanda has made notable progress in reducing under-five mortality, declining from 44 deaths per 1,000 live births in 2000 to 19 per 1,000 in 2020 \u003csup\u003e10\u003c/sup\u003e. However, this figure remains above the Sustainable Development Goal (SDG) target of 12 per 1,000 live births by 2030 \u003csup\u003e11\u003c/sup\u003e. Achieving this goal requires intensified efforts to identify and address factors contributing specifically to neonatal mortality. Neonatal outcomes are influenced by a complex interplay of maternal, neonatal, and health system-related factors. A 2024 facility-based study in Somalia identified several factors associated with neonatal mortality, including neonatal sex, antenatal care (ANC) attendance, tetanus toxoid immunization, mode of delivery, sepsis, tetanus, pneumonia, breastfeeding challenges, and prematurity \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Similarly, research from Indonesia revealed that maternal education, occupation, decision-making autonomy regarding healthcare, quality of antenatal care, and delivery complications significantly influenced neonatal survival \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A study conducted in Ghana also emphasized the importance of ANC attendance, neonatal sex, and immediate skin-to-skin contact as predictors of neonatal mortality, many of which are preventable with improved health system practices \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRubavu District, located in Rwanda\u0026rsquo;s Western Province, is served by a Gisenyi hospital that acts as the primary referral center for surrounding health facilities. While national policies and programs targeting neonatal health are in place, local data on neonatal mortality and its contributing factors remain scarce. According to the Rwanda Biomedical Center (2024), more localized evidence is needed to guide targeted and context-specific interventions.\u003c/p\u003e\u003cp\u003eThis study aimed to determine the prevalence of neonatal mortality and identify associated factors among neonates admitted to Gisenyi District Hospital. The findings are expected to provide critical insights for healthcare providers, policymakers, and program implementers to strengthen neonatal care services in the district and contribute to the national efforts toward achieving the SDG targets.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy design\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This is a hospital-based cross-sectional study and collected the quantitative data through retrospective descriptive review of 753 neonatal medical records of admitted neonates to the neonatology department from May 1st 2024, and June 30th 2024.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study was conducted at the neonatal department of Gisenyi district hospital located in Rubavu District, Western Province of Rwanda. The hospital lies along the shores of Lake Kivu and situated approximately 2 kilometers from the Lacoriniche Border Post and about 153.6 kilometers from Rwanda\u0026rsquo;s capital city, Kigali.\u003c/p\u003e\u003cp\u003eGisenyi Hospital is one public hospital that provide the neonatal care services to the whole Rubavu district populations (approximately 403,662 district total population), and hospital act as referral to both public and private peripheral health facilities or surrounding communities that refer neonates requiring advanced care.\u003c/p\u003e\u003cp\u003eWith reference to the hospital records, Gisenyi hospital neonatology department on average, admits 1,500 neonates annually equal to 125 per month and approximately 31 neonates per week.Of the admitted neonates 64% are born at Gisenyi hospital and 6% referred from the surrounding communities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study population included neonates who were admitted to the neonatology department at Gisenyi District Hospital during the study period. Only neonates admitted who survived for at least 28 days. Furthermore, only records of mothers who delivered live-born at a gestational age of 28 weeks and above, either within the hospital (inborn) or referred (out born) between May 2019 and June 2024, were considered an eligible participants and included in the final dataset.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAny neonate admitted to the neonatology department who died during the neonatal period was considered as neonatal death. Social demographic characteristics of mothers whose neonates were admitted to neonatology, including mothers' age, address (Urban vs Rural), profession, possession of health insurance, and marital status. Pregnancy and gynecological characteristics such as gestation at delivery time, mode of delivery for current child, gravidity, parity. Antenatal care visits. Other variables of interest include the admitted neonates' records at delivery time such as appearance, pulse, grimace, activity, and respiratory (Apgar) score at 10min, birth weight categories, age of neonate at death in days, gender, neonatal morbidities such as respiratory distress syndrome(RDS) and neonatal sepsis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing a pretested developed data extraction tool and modified from a similar study\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e data were collected by a trained team consisting of five professional nurses and one medical doctor. Before data collection, all team members underwent a comprehensive four-day training to ensure familiarity with the study objectives, data abstraction tools, and ethical considerations.\u003c/p\u003e\u003cp\u003eNeonatal characteristics were extracted from individual neonatal medical record charts, while maternal data were obtained from hospital delivery registers, maternal admission files, and medical charts. The data abstraction process for each participant took approximately 15 to 20 minutes. To maintain data quality and consistency, the principal investigator conducted daily reviews of completed data forms. This included cross-checking entries for completeness, accuracy, and internal consistency, with any discrepancies addressed promptly through consultation with the data collection team.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll collected data were initially entered into Microsoft Excel 2016 for cleaning, coding, and validation, and subsequently exported to STATA version 17 for statistical analysis. Descriptive statistics were used to summarize the sociodemographic, obstetric, and clinical characteristics of the neonates admitted to neonatology and their mothers. Frequencies and percentages were calculated to determine the overall prevalence of neonatal mortality. To identify factors associated with neonatal mortality, a binary outcome coded as 1 for deceased neonates and 0 for survivors, a multivariable logistic regression analysis was performed. Before model building, multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF), and variables with a VIF\u0026thinsp;\u0026ge;\u0026thinsp;10 were excluded. A stepwise selection approach was used to construct the final model, retaining variables with statistical relevance and clinical plausibility. Adjusted odds ratios (aORs) with corresponding 95% confidence intervals (CIs) and p-values were reported to indicate the strength and significance of associations. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSocio-demographic characteristics of participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, 753 neonates admitted to the neonatology department participated. Most mothers were aged 24 years and below (74.7%), and 82.2% resided in rural areas. A significant proportion had only primary-level education (55.8%), while 22.4% had no formal education. 84.2% were farmers and legally married mothers (64%). Additionally, 93.5% had medical insurance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe mothers' socio-demographic characteristics whose newborns were admitted to neonatology, 2019\u0026ndash;2024\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency, n\u0026thinsp;=\u0026thinsp;753\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage, %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s age\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24 years and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 Years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35 years and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s professional (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness owner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic servant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s highest level of education (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.4\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\u003e420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.8\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\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity level and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s marital status (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegal married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCohabited\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMedical insurance (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\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\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence for study participants (n\u0026thinsp;=\u0026thinsp;753)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.2\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eObstetrics and gynecological characteristics of mothers of neonates admitted to Gisenyi Hospital\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNearly half of the mothers were primigravida (49.6%), and 46.4% were Primipara. Most deliveries were vaginal (71%), with cesarean sections accounting for 28.1%. A significant proportion of mothers (35.7%) delivered preterm, with 22.3% giving birth before 32 weeks of gestation. Regarding antenatal care, 15.1% of mothers had no ANC visits, while nearly half (49.1%) attended 3\u0026ndash;4 antenatal care visits (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eObstetrics and gynecological characteristics of mothers whose newborns were admitted at Gisenyi DH hospital\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency, n\u0026thinsp;=\u0026thinsp;753\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage, %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGravidity; (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-gravida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimi-gravida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eParity (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-para\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimi-para\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMode of delivery (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVaginal delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssisted vaginal delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePlace for delivery (n\u0026thinsp;=\u0026thinsp;753)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.2\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGestational age (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;32 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33\u0026ndash;37 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;37 weeks\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNumber of ANCs done (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2 ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;4 ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 ANC and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacteristics of neonates at admission to the Neonatology department, Gisenyi district hospital.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMost neonates (79.6%) were admitted within the first 24 hours of life, and 84.2% were received in the unit within that time. Low birth weight was common, affecting 45.4% of neonates, and 55.7% were born preterm (before 37 weeks of gestation). Hypothermia was observed in 41.7% of neonates at admission. Although most neonates (84.2%) had Apgar scores\u0026thinsp;\u0026ge;\u0026thinsp;7 at 10 minutes, the neonatal mortality rate remained notable at 18.1% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of neonates at admission to neonatology department\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency, n\u0026thinsp;=\u0026thinsp;753\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage, %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAge of neonates at admission (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;24 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u0026ndash;28 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGender of neonate (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.2\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eBody temperature at admission (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothermia (\u0026lt;\u0026thinsp;36\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormothermia (36\u0026deg;C-37.5\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperthermia (\u0026gt;\u0026thinsp;37.5\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGestational age (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;32 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33\u0026ndash;37 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;37 weeks\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eBirth weight (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow (\u0026lt;\u0026thinsp;2500mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (2500mg-4000mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight (above 4000mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eApgar scores first (10 Minutes)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;3 Apgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;6 Apgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;7 Apgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTime to admission to neonatology unit (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmitted within 24 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmitted after 24 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNeonate discharge outcome (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrevalence of neonatal mortality, cause of admission, and cause of death at discharge among neonates admitted to Gisenyi District Hospital\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe neonatal mortality rate was 18% (95% Confidence Interval: 15.3\u0026ndash;27.2%), indicating that nearly one in five admitted newborns did not survive during the study period.\u003c/p\u003e\u003cp\u003eThe most common cause of neonatal admission to the neonatology unit was complications related to prematurity, accounting for 38.4% of all admissions. Notably, prematurity complications also emerged as the leading cause of neonatal mortality at discharge, contributing to 30.2% of the deaths. This underscores the urgent need for improved case management and enhanced quality of care for preterm infants. Interestingly, while 13.3% of neonatal deaths were attributed to neonatal pneumonia, there were no recorded cases of neonatal admission primarily due to pneumonia. This discrepancy suggests the possibility of nosocomial (hospital-acquired) infections, emphasizing the need to strengthen infection prevention and control (IPC) practices within the neonatal care environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariable factors associated with neonatal mortality among admitted neonates to the neonatology unit.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe mother's socio-demographic factors are associated with neonatal mortality\u003c/p\u003e\u003cp\u003eNeonates born to mothers aged 25\u0026ndash;34 years were nearly eight times more likely to die compared to those born to mothers aged 35 years and above (aOR\u0026thinsp;=\u0026thinsp;7.97; 95% CI: 1.7\u0026ndash;35.70; p\u0026thinsp;=\u0026thinsp;0.005). In terms of occupation, unemployed mothers had about 2.5 times higher odds of neonatal mortality (aOR\u0026thinsp;=\u0026thinsp;2.52; 95% CI: 1.08\u0026ndash;5.87; p\u0026thinsp;=\u0026thinsp;0.030), while those working as public servants had 2.3 times higher odds (aOR\u0026thinsp;=\u0026thinsp;2.26; 95% CI: 1.82\u0026ndash;6.27; p\u0026thinsp;=\u0026thinsp;0.010) compared to farmers. Interestingly, single and cohabiting mothers had 91% (aOR\u0026thinsp;=\u0026thinsp;0.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 93% (aOR\u0026thinsp;=\u0026thinsp;0.07; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) lower odds of experiencing neonatal death, respectively, compared to legally married mothers. Other variables such as education level, place of residence, and medical insurance status showed no statistically significant association with neonatal mortality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression of maternal social demographic factors associated with neonatal mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s age group\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35 years old and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;24 years old\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.93(0.69\u0026ndash;12.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.69(0.61\u0026ndash;11.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 years old\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.20(2.36\u0026ndash;44.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.97(1.7\u0026ndash;35.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s occupation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness owner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.71(0.84\u0026ndash;3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.08(0.92\u0026ndash;4.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.13(1.05\u0026ndash;4.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.52(1.08\u0026ndash;5.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.030*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic servant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.44(1.07\u0026ndash;5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.26(1.82\u0026ndash;6.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03(0.11\u0026ndash;8.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12(0.12\u0026ndash;10.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s education\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity level and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87(0.29\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62(0.16\u0026ndash;2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.478\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\u003e0.59(0.21\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72(0.44\u0026ndash;1.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.198\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\u003e0.57(0.18\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.57(0.29\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s marital status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegal married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08(0.03\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09(0.04\u0026ndash;0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003eCohabited\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07(0.01\u0026ndash;0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07(0.01\u0026ndash;0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMedical insurance\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e2.01(0.78\u0026ndash;5.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04(0.73\u0026ndash;5.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eResidence for study participants\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.01(0.78\u0026ndash;5.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36(0.75\u0026ndash;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ecOR; crude Odds Ratio, aOR; adjusted Odds Ratio, *P\u0026thinsp;\u0026le;\u0026thinsp;0.05 at multivariable analysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGynecology-obstetrical factors higher odds of mortality compared to those born to primi-gravida mothers (aOR\u0026thinsp;=\u0026thinsp;5.89; 95% CI: 3.42\u0026ndash;10.13; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, neonates born to multi-para mothers (those with multiple deliveries) had about twice the odds of neonatal death compared to those born to primi-para mothers (aOR\u0026thinsp;=\u0026thinsp;2.01; 95% CI: 1.19\u0026ndash;3.36; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Gestational age also played a critical role, with neonates born before 32 weeks having almost three times higher odds of death compared to full-term neonates (aOR\u0026thinsp;=\u0026thinsp;2.90; 95% CI: 1.76\u0026ndash;4.80; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The number of antenatal care (ANC) visits was also a significant factor, with no ANC visits associated with 73% lower odds of neonatal survival (aOR\u0026thinsp;=\u0026thinsp;0.27; 95% CI: 0.12\u0026ndash;0.58; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and fewer ANC visits (1\u0026ndash;2 visits) associated with 67% lower odds (aOR\u0026thinsp;=\u0026thinsp;0.33; 95% CI: 0.17\u0026ndash;0.64; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other variables, such as mode of delivery and place of delivery, did not show a significant impact on neonatal mortality (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression of the gynecology-obstetrical factors associated with neonatal mortality(n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eGravidity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimi-gravida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-gravida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.29(1.55\u0026ndash;3.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.89(3.42\u0026ndash;10.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eParity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimi-para\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-para\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11 (0.76\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.01(1.19\u0026ndash;3.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMode of delivery\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVaginal delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12(0.12\u0026ndash;9.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95(0.55\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssisted vaginal delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41(0.16\u0026ndash;11.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96(0.65\u0026ndash;14.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePlace for delivery\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78(0.61\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18(0.73\u0026ndash;3.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.717\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92(0.34\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18(0.46\u0026ndash;3.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eGestational age\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;37 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;32 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.18(2.62\u0026ndash;6.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.90(1.76\u0026ndash;4.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e33\u0026ndash;37 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.56(0.96\u0026ndash;2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38(0.83\u0026ndash;2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNumber of mother\u0026rsquo;s antenatal care visit done\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 ANC and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.26(0.12\u0026ndash;0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27(0.12\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e3\u0026ndash;4 ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01(0.0\u0026ndash;0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00(0.00-0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e1\u0026ndash;2 ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31(0.16\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33(0.17\u0026ndash;0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ecOR; crude Odds Ratio, aOR; adjusted Odds Ratio, *P\u0026thinsp;\u0026le;\u0026thinsp;0.05 at multivariable analysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eThe admitted Neonate-related factors associated with their mortality, n\u0026thinsp;=\u0026thinsp;753\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003cp\u003eNeonates admitted within the first 24 hours of life had over six times higher odds of mortality compared to those admitted between 8 and 28 days (aOR\u0026thinsp;=\u0026thinsp;6.17; 95% CI: 2.16\u0026ndash;17.67; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Neonates with hypothermia at admission had twice the odds of neonatal mortality compared to those with normothermia (aOR\u0026thinsp;=\u0026thinsp;2.02; 95% CI: 1.28\u0026ndash;3.19; p\u0026thinsp;=\u0026thinsp;0.002). In contrast, hyperthermia was not significantly associated with mortality (aOR\u0026thinsp;=\u0026thinsp;2.79; 95% CI: 0.78\u0026ndash;9.93; p\u0026thinsp;=\u0026thinsp;0.113). Apgar scores at 10 minutes were a strong predictor of mortality, with neonates scoring\u0026thinsp;\u0026le;\u0026thinsp;3 having over 10 times the odds of death compared to those with an Apgar score\u0026thinsp;\u0026ge;\u0026thinsp;7 (aOR\u0026thinsp;=\u0026thinsp;10.24; 95% CI: 2.71\u0026ndash;38.75; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and those scoring 4\u0026ndash;6 having three times higher odds (aOR\u0026thinsp;=\u0026thinsp;3.07; 95% CI: 1.50\u0026ndash;6.28; p\u0026thinsp;=\u0026thinsp;0.002). Neonates who stayed in the hospital for 7 days or more had 3.45 times higher odds of mortality compared to those who stayed less than 7 days (aOR\u0026thinsp;=\u0026thinsp;3.45; 95% CI: 1.33\u0026ndash;8.95; p\u0026thinsp;=\u0026thinsp;0.010). Other factors such as gender, birth weight, and admission time showed no significant association with neonatal mortality (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression of the neonate-related factors associated with neonatal mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNeonate\u0026rsquo;s age group at admission to the neonatology Unit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u0026ndash;28 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;24 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.03(2.82\u0026ndash;17.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.17(2.16\u0026ndash;17.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e1\u0026ndash;7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00(0.11-9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.17(0.11\u0026ndash;11.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eGender of admitted neonate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15(0.79\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13(0.48\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eBody temperature at admission to the neonatology unit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormothermia (36\u0026deg;C-37.5\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothermia (\u0026lt;\u0026thinsp;36\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.98(2.01\u0026ndash;4.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.02(1.28\u0026ndash;3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperthermia (\u0026gt;\u0026thinsp;37.5\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06(0.40\u0026ndash;2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.79(0.78\u0026ndash;9.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eBirth weight before admission to the neonatology unit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (2500mg-4000mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow (\u0026lt;\u0026thinsp;2500mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.72(1.16\u0026ndash;2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75(0.44\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight (above 4000mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.43(0.64\u0026ndash;3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60(0.23\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eApgar scores at first 10 Minutes after birth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;7 Apgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;3 Apgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.04(1.66\u0026ndash;15.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.24(2.71\u0026ndash;38.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e4\u0026ndash;6 Apgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.20(1.14\u0026ndash;4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.07(1.50\u0026ndash;6.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAdmission time\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmitted within 24 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmitted after 24 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.74(0.96\u0026ndash;3.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12(0.53\u0026ndash;2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHospital stay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow 7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8 days and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.82(2.54\u0026ndash;13.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.45(1.33\u0026ndash;8.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ecOR; crude Odds Ratio, aOR; adjusted Odds Ratio, *P\u0026thinsp;\u0026le;\u0026thinsp;0.05 at multivariable analysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe neonatal mortality prevalence of 18% observed in this study is alarmingly high and significantly exceeds both global and regional estimates. According to the World Health Organization (2023), the global neonatal mortality rate stood at approximately 17 deaths per 1,000 live births,\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e while Sub-Saharan Africa recorded a higher rate of about 27 per 1,000 live births, yet both figures remain markedly lower than the 180 deaths per 1,000 live births reported here \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This discrepancy likely reflects the hospital-based nature of the study, where the sample comprises neonates already at elevated risk due to clinical complications necessitating admission. Similar facility-based studies in comparable low-resource settings have reported lower but still concerning mortality rates; for instance, a study in Ethiopia found a rate of 20% \u003csup\u003e19\u003c/sup\u003e, while one in Uganda reported 17% \u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe strong association between maternal age and neonatal mortality draws attention to the often-overlooked risks within the 25\u0026ndash;34 age bracket, which is typically considered a lower-risk reproductive age group\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.The significantly higher odds of mortality among neonates born to women in this age group, compared to older mothers, may reflect disparities in birth preparedness, care-seeking behaviors, or undetected obstetric complications \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Contrary to traditional assumptions that older maternal age confers greater perinatal risk, some studies, particularly those from resource-limited settings, suggest that older mothers may benefit from accumulated maternal experience, improved self-efficacy in navigating health services, and greater household decision-making power \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA study done in Nigeria has revealed that an employment status emerged as another key determinant, with higher mortality among neonates of unemployed and public servant mothers compared to those of farmers\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. While public employment often implies better socioeconomic positioning, it may also come with higher stress levels, longer working hours, or reduced time for postnatal care. Conversely, farming mothers may benefit from extended familial support networks and the proximity to home-based caregiving environments\u003csup\u003e27\u003c/sup\u003e-\u003csup\u003e28\u003c/sup\u003e. These findings mirror those from Sub-Saharan Africa, where maternal employment, especially in non-flexible formal sectors, has been associated with suboptimal early neonatal outcomes \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterestingly, the data revealed that single and cohabiting mothers had lower odds of neonatal death than their legally married counterparts. This counterintuitive result may suggest that unmarried mothers, especially in the context of strong community-based maternal support, may receive more focused care or engage more consistently with health systems due to perceived vulnerability \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Alternatively, cultural and legal marital norms may obscure underlying socioeconomic or gender dynamics that compromise care access in formally married households\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eParity and gravidity remain well-established risk factors in perinatal health literature. The elevated risk of neonatal mortality among neonates born to multigravida and multiparous mothers is consistent with findings from similar low-income settings. These risks may stem from uterine fatigue, reduced placental efficiency, or complacency in seeking skilled birth care among women with previous childbirth experience \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, repeated pregnancies without optimal spacing may deplete maternal nutritional reserves, adversely affecting neonatal outcomes \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGlobally the prematurity continues to be a dominant contributor to neonatal deaths\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, and this study reaffirms its devastating impact in a district hospital setting. The markedly higher mortality risk for neonates born before 32 weeks of gestation aligns with WHO reports, which highlight that extremely preterm births have limited survival prospects in low-resource facilities due to inadequate respiratory support, infection control, and thermal regulation \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The timing of admission also proved critical; early admission (within 24 hours of life) likely signals critical neonatal distress at birth, which may overwhelm the limited capacity of district-level neonatal units\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eIn Rwanda, the Antenatal care utilization remains one of the strongest predictors of neonatal survival\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In this study, the reduced survival odds among mothers with no or limited ANC visits underscore systemic gaps in early detection of high-risk pregnancies. This reinforces WHO's recommendation of at least eight ANC contacts during pregnancy to facilitate timely identification and management of complications\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e .In many rural settings, barriers such as transport, cultural beliefs, and low health literacy limit ANC engagement, placing neonates at elevated risk even before birth \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn present study, the thermal instability at admission, especially hypothermia, emerged as another critical determinant. Neonatal hypothermia is both a marker and a mediator of poor outcomes, often reflecting inadequate postnatal thermal care, especially among low birth weight and premature neonates \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Although hyperthermia was not statistically significant in this analysis, its presence may still signal underlying infection, which, in resource-limited settings, is a frequent but often underdiagnosed contributor to neonatal mortality\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eApgar scores at 10 minutes served as a powerful prognostic marker\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.The gradient of mortality risk across Apgar score categories reinforces the value of immediate postnatal assessment and resuscitation efforts\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In settings like Gisenyi DH, where neonatal resuscitation capacity may be constrained, low Apgar scores could reflect delayed or ineffective interventions at birth. Investment in neonatal resuscitation training and equipment remains a low-cost, high-impact strategy that could significantly reduce early neonatal deaths.\u003c/p\u003e\u003cp\u003eFinally, the increased odds of mortality among neonates who stayed in the hospital for 7 days or more could be indicative of nosocomial complications, late-onset infections, or underlying congenital conditions that required prolonged management\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. It also highlights a subgroup of neonates who initially survived the critical early window but later succumbed to complications, emphasizing the need for robust infection control, nutritional support, and close monitoring throughout the neonatal period\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThis study had one key limitation. As a hospital-based study conducted at Gisenyi District Hospital, the findings may not fully represent neonatal mortality trends in the general population, especially in rural or home-birth settings. However, Gisenyi DH is the main referral hospital in the region and receives a high volume of neonatal admissions from various catchment areas, thereby offering a reasonably broad picture of facility-based neonatal outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDespite significant improvements in the health system and rapid declines in neonatal mortality in Rwanda, the findings of this study revealed a high neonatal mortality rate of 18% at the hospital, highlighting a critical burden within the facility-based neonatal care system and case management. Several maternal, neonatal, and clinical factors were significantly associated with this mortality, including younger maternal age (25\u0026ndash;34 years), lack of antenatal care, preterm birth (\u0026lt;\u0026thinsp;32 weeks), low Apgar scores, early neonatal admission (within 24 hours), and neonatal hypothermia. These findings underscore the multifaceted nature of neonatal mortality and emphasize the urgent need for comprehensive and well-informed policies, guidelines, and system-level improvements. with targeted interventions across the continuum of maternal and newborn care that address the key risk factors identified in this study, especially in rural areas. Health workers at both primary and referral levels should be trained to identify the neonatal cause of death danger signs, and the management of preterm births early. Additionally, community health workers and local leaders should intensify maternal health education campaigns to promote timely antenatal care and facility-based deliveries\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy strengths and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study encompasses inborn and outborn multicenter-level data from all public health facilities that referred their neonates to the Gisenyi district Hospital neonatology department. Therefore, it reflects the district's public health system structure in neonate case management. The identified factors associated with neonatal deaths (Maternal and neonatal factors) and the general recommendations derived from this study were relevant and applicable to all public health facilities. Despite that, the study utilized only medical records of neonates and their mothers found at the hospital, which may not capture all factors influencing neonatal mortality. Consequently, the results might not be entirely representative of the community, as deaths in the community were not counted, and their cause was not assessed. This limitation could lead to an underrepresentation of the total number of deaths within the district. Given the rising number of premature neonatal deaths, there is a pressing need for a prospective, community-based study to thoroughly examine maternal factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAntenatal care visit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAoR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdjusted odds ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eApgar score\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAppearance (Skin color), pulse (heart rate), grimace (reflex irritability), activity (muscle tone), and respiratory (breathing rate and efforts)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGDH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGisenyi district hospital\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeonatal mortality rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRespiratory distress syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSustainable Development Goals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTATA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical software for data science\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the University of Rwanda, particularly to the College of Medicine and Health Sciences and the School of Public Health, for their ethical review process. We also extend our gratitude to the Ministry of Health, the Rwanda Biomedical Centre, and the Africa Field Epidemiology Network-Rwanda for their invaluable assistance during the pre-data collection process through research site preparation and connecting data collectors with hospital administration. Our special appreciation is directed to the research committee at Gisenyi District Hospital for granting access to the neonatal health records, as well as to the nurses and doctors involved in data collection, which made this research possible. We are also thankful for the support and guidance from our supervisors and mentors at the University of Rwanda, in the field epidemiology program, during the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInnocent NTIRUSHWAMABOKO\u0026nbsp;conceived the study\u0026apos;s idea, designed the methodology, developed the questionnaire, analyzed the data, and wrote the manuscript. Hinda Ruton supervised the study, contributed to the study design and results interpretation. Bibiane Uwamahoro\u0026nbsp;assisted with the study design and the interpretation of the results. Associate Professor Aline Umubyeyi was\u0026nbsp;provided the constructive feedback on the manuscript and\u0026nbsp;critically reviewed the manuscript, supported the study design, assisted in data analysis and results interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and /or publication of this manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study are included in the article and/or the Supplementary Material. The dataset used in this study is stored securely and is available upon reasonable request. Interested parties are encouraged to contact the corresponding author for access or further inquiries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical consideration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized secondary data from human participants and received approval from the University of Rwanda\u0026apos;s Human Research Institutional Review Board (IRB) under Reference Number CMHS/IRB/458/2024 of May 2025. Furthermore, ethical clearance was granted by the Ethical Committee of the Rwanda Ministry of Health and the Gisenyi District Hospital research committee, which permitted the study investigator to access both neonatal and maternal data. Both research committees were assured that the collected data would remain anonymous, all data securely stored and accessed only by authorized study personnel. Informed consent was obtained as requested by the ethics committee. The need for consent was waived for the study, including newborns and their mothers, under hospital data access approval with reference No: 053/2024 of 07/06/2024 and University of Rwanda Human Research Institutional Review Board \u0026nbsp; data access approval letter N\u003csup\u003eo\u003c/sup\u003e130/UR-CMHS/SPH/2024. This research was conducted under the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest related to this manuscript\u0026apos;s research, authorship, or publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Epidemiology and\u0026nbsp;Biostatistics, School of Public Health, University of Rwanda, Kigali, Rwanda\u003csup\u003e2\u003c/sup\u003eDepartment of Epidemiology and Biostatistics, School of Public Health, University of Rwanda, Kigali, Rwanda \u003csup\u003e2\u003c/sup\u003eDepartment of Epidemiology and\u0026nbsp;Biostatistics, School of Public Health, University of Rwanda, Kigali, Rwanda\u003csup\u003e3\u003c/sup\u003eDepartment of Epidemiology and Biostatistics, School of Public Health, University of Rwanda, Kigali, Rwanda.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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In Rwanda the neonatal mortality still accounts for a substantial portion and exceeds the SDG target. This study aims to determine the prevalence and associated factors among neonates admitted at the neonatology department at Gisenyi district hospital in western Rwanda.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe hospital-based cross-sectional study used a retrospective descriptive review of 753 neonates and their mothers' records systematically sampled between May 1st, 2024, and June 30th 2024. The quantitative data on sociodemographic, obstetric, and clinical characteristics variables were extracted from maternal and neonatal clinical charts and registers. Data were double entered in a pretested data collection tool, cleaned and analyzed using STATA 17. Logistic regression analyses using odds rations with 95% confidence interval (C. I) were applied to assess the association between factors associated with neonatal mortality. The adjusted odds ratios(AoR) has been done to assess other neonatal mortality determinants variables. Data was analysed using statistical software, Stata version 17.0\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThere were 136 ,18% (95% CI: 15.3\u0026ndash;27.2%) among them 421, 55.2% were male. Mothers associated factors were mothers aged 25\u0026ndash;34 years (aOR\u0026thinsp;=\u0026thinsp;7.97; 95% CI: 1.7\u0026ndash;35.70). Unemployed mothers had 2.5 times higher odds (aOR\u0026thinsp;=\u0026thinsp;2.52; 95% CI: 1.08\u0026ndash;5.87), and public (aOR\u0026thinsp;=\u0026thinsp;2.26; 95% CI: 1.82\u0026ndash;6.27). Multi-gravida mothers (aOR\u0026thinsp;=\u0026thinsp;5.89; 95% CI: 3.42\u0026ndash;10.13). Zero antenatal care visits (aOR\u0026thinsp;=\u0026thinsp;0.27; 95% CI: 0.12\u0026ndash;0.58) and fewer visits (1\u0026ndash;2 visits, aOR\u0026thinsp;=\u0026thinsp;0.33; 95% CI: 0.17\u0026ndash;0.64), neonates born before 32 weeks of gestational age (aOR\u0026thinsp;=\u0026thinsp;2.90; 95% CI: 1.76\u0026ndash;4.80), The neonates admitted within 24 hours (aOR\u0026thinsp;=\u0026thinsp;6.17; 95% CI: 2.16\u0026ndash;17.67). Hypothermic neonates (aOR\u0026thinsp;=\u0026thinsp;2.02; 95% CI: 1.28\u0026ndash;3.19) and the Apgar scores\u0026thinsp;\u0026le;\u0026thinsp;3 (aOR\u0026thinsp;=\u0026thinsp;10.24; 95% CI: 2.71\u0026ndash;38.75) were strongly associated with higher mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn this study, the neonatal mortality remains alarmingly high, driven by both maternal and neonatal risk factors. More than 30% recorded deaths were due to prematurity complications Strengthening antenatal care utilization, early identification of high-risk pregnancies, and improving the management of preterm and low Apgar score neonates are essential steps toward reducing preventable neonatal deaths as main associated factors.\u003c/p\u003e","manuscriptTitle":"Neonatal Mortality and Its Associated Factors in Gisenyi Hospital, Rubavu District, Rwanda: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 10:36:50","doi":"10.21203/rs.3.rs-7056008/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6a8f9b7-dba1-4e5b-8a4c-771126bf2adc","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51096306,"name":"Maternal \u0026 Fetal Medicine"}],"tags":[],"updatedAt":"2025-07-10T08:48:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 10:36:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7056008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7056008","identity":"rs-7056008","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}