Prevalence and factors associated with neonatal mortality at Masaka Regional Referral and Teaching Hospital in central Uganda: A facility based cross sectional study

Prevalence and factors associated with neonatal mortality at Masaka Regional Referral and Teaching Hospital in central Uganda: A facility based 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 Prevalence and factors associated with neonatal mortality at Masaka Regional Referral and Teaching Hospital in central Uganda: A facility based cross sectional study Jacob Otile, Kyle Kercher, Awunor N Simeon, Salaam Mujeeb, Oyella Sheilla, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7821024/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: Globally, approximately 4 million of the 130 million infants born each year die within their first 28 days of life, accounting for nearly 46% of all under five deaths. Neonatal mortality—the death of a live-born infant within the first 28 days—remains a critical public health issue, particularly in Sub-Saharan Africa and Uganda, with rates of 27 and 22 deaths per 1,000 live births, respectively, contributing more than twice the World Health Organization's target of fewer than 12 per 1,000. Despite this burden, data on the prevalence and contributing factors to neonatal mortality remain scarce in many low- and middle-income countries (LMICs), including Masaka Regional Referral and Teaching Hospital (MRRTTH), which hampers the development of effective, evidence-based policies and interventions. The primary objective of this study was to assess the prevalence and key determinants of neonatal mortality, and the secondary objective was to evaluate the patterns of maternal and neonatal healthcare service utilization and their associations with neonatal outcomes at MRRTTH. The study hypothesized that neonatal mortality at MRRTTH mirrors the national average and that there are no significant associations between neonatal mortality and major sociodemographic variables. Methods: A facility-based cross-sectional study of 378 participants was conducted between October and November 2020 at the postnatal ward, neonatal intensive care unit (NICU), and Young Child Infant Clinic of MRRTTH. The mothers and/or caregivers of the neonates were randomly selected. Data were collected via an open data kit (ODK) via a researcher-administered electronic questionnaire. Descriptive statistics and regression analyses were performed via R Studio (version 4.4.1). Results : A total of 20.9% of the participants reported a history of neonatal death from birth asphyxia (44.3%), prematurity (27.8%), neonatal sepsis (17.7%), or other causes (10.1%). The factors associated with increased odds of neonatal mortality included maternal age 30–39 years, p <0.05 * , 95% CI (0.37–3.55), age 40–49 years, p <0.05 ** , 95% CI (0.95–4.51), pregnancy complications p <0.05 *** , 95% CI (0.77–1.91) and the perception of the need for staff recruitment, p<0.05**, 95% CI (0.29–1.55). Conclusion : Neonatal mortality at MRRTH remains high, with birth asphyxia, prematurity, and neonatal sepsis as the primary causes. Key associations include advanced maternal age, pregnancy-related complications, and the perception of inadequate staffing. Clinical trial number : not applicable Neonatal mortality Low- and middle-income countries (LMICs) Cross-sectional study Figures Figure 1 Figure 2 Figure 3 BACKGROUND Globally, an estimated 130 million infants are born each year, with approximately 4 million neonatal deaths occurring within the neonatal period. These deaths account for 46% of under-five (approximately 6500 daily) total deaths. Most of these deaths are preventable ( 1 , 2 ). Neonatal mortality refers to the death of a newborn under 28 days of completed life ( 3 ). Sub-Saharan Africa is responsible for the highest neonatal mortality rate globally, with 27 deaths per 1000 live births, accounting for 57% of the total under-five deaths ( 2 ). In Uganda, neonatal mortality is 22 deaths per 1000 live births, approximately double the WHO target of less than 12 deaths per 1,000 live births ( 4 ). Several factors increase the risk of neonatal mortality, including maternal factors such as delay in initiation of breastfeeding, very young and advanced maternal age, multiparity, short birth intervals, increased maternal body mass index, smoking during pregnancy, and pregnancy-related complications such as maternal diabetes mellitus, preeclampsia/eclampsia, preterm premature rupture of membranes, and antepartum hemorrhage. The neonatal factors included low gestational age, low birth weight, a 5-minute Apgar score less than seven, the need for neonatal intensive care unit admission, premature birth, and birth complications, i.e., birth asphyxia/trauma, neonatal infections and congenital anomalies. ( 2 , 5 – 7 ). Limited data exist for LMICs or MRRTTHs concerning the prevalence and factors associated with neonatal mortality, such as the characteristics and predictors of mortality. Understanding these factors is a critical step for identifying existing gaps and implementing health policies, programs, and interventions aimed at addressing gaps leading to neonatal mortality, improving neonatal outcomes and reducing mortality in Uganda and worldwide ( 8 ). Additionally, while few studies have explored neonatal mortality in similar contexts, the findings remain inconsistent. Some studies report positive effects of interventions such as enhanced antenatal care and skilled birth attendance in reducing neonatal mortality ( 9 ), whereas others indicate that neglecting key factors such as socioeconomic, cultural, and logistical barriers to maternal and newborn care; failing to implement essential preventive practices such as kangaroo mother care; and not promptly identifying and managing sick newborns can contribute to adverse outcomes ( 10 , 11 ). These findings highlight the importance of context-specific data and the need for further research into local neonatal health determinants to better inform health policies and interventions in MRRTTH and hence tailor interventions to address the unique challenges in Masaka. Therefore, we conducted a facility-based cross-sectional study of 378 participants. The primary objective of this study was to assess the prevalence and determinants of neonatal mortality at MRRTTH in Uganda. The secondary objective was to evaluate the patterns of maternal and neonatal healthcare service utilization and perceptions of service delivery, including antenatal care attendance, place and mode of delivery, and postnatal care, and explore their associations with neonatal outcomes at MRRTTH. The study hypothesized that the prevalence of neonatal mortality at MRRTTH aligns with the national average and that no significant associations exist between neonatal mortality and key sociodemographic characteristics. METHODS Study Design and Setting This study was conducted to assess the prevalence and factors associated with neonatal mortality at MRRTH in central Uganda through a facility-based cross-sectional design. The secondary objective was to evaluate the patterns of maternal and neonatal healthcare service utilization and perceptions of service delivery, including antenatal care attendance, place and mode of delivery, and postnatal care, and explore their associations with neonatal outcomes at MRRTTH. We carried out a cross-sectional study of mothers and caretakers from October 29, 2020, to November 30, 2020, at three different units: the postnatal ward, NICU, and young child infant clinic of MRRTTH. The study area, MRRTTH, is within Masaka district, one of the new cities approximately 140 km or 87 miles by road southwest of the capital city Kampala on a highway to Mbarara. The hospital’s catchment area is primarily the districts of Masaka, Rakai, Lyantonde, Lwengo, Ssembabule, Bukomansimbi, Kalungu and Kalangala, with Luganda as the predominant language spoken in these areas. MRRTTH is a 330-bed capacity hospital in the central business district established in 1929. It serves as a teaching hospital for the faculty of health sciences, Habib Medical School, Islamic University in Uganda, an internship site, and a training center for various tertiary health institutions in the region. The Obstetrics and Gynecology Department, which includes the postnatal ward and the Young Child Infant Clinic, is staffed by 4 obstetricians, 5 junior house officers, 2 intern nurses, and 22 midwives. This team manages an average of 600 spontaneous vaginal deliveries and 200 cesarean deliveries each month. The Pediatric Unit, which includes the Neonatal Intensive Care Unit (NICU), is staffed by two pediatricians, one volunteer medical officer, one volunteer nurse, and 7 nurses in the pediatric ward. Additionally, there are 6 junior house officers, with 2 assigned to the NICU. The NICU itself is supported by 8 NICU nurses and 1 intern nurse (MRRTH record, 2025). Study participants This study included mothers or caregivers who met the following inclusion criteria : were present in the postnatal ward, NICU, or young child infant clinic during the study period; provided informed consent to participate; were aged 18 years or older; were directly involved in the care of a neonate; and had or were caring for a neonate aged 0–28 days receiving care at the facility. The exclusion criteria included mothers who did not provide consent, those who were not present in the specified units, those under 18 years of age, deaf mothers, mothers whose neonates were critically ill and who required close attention in the NICU, and those who were unable to speak English or Luganda. Sample size determination: Sample size estimation for a single population proportion: n = (Z² × p × (1 – p))/d² . To adjust for a finite population, the following correction formula was applied: n_adj = n/(1 + (n/N)) . This yielded an adjusted sample size of 358 participants. To account for a 10% nonresponse rate, the final sample size was increased to 394 participants, which was used as the target for recruitment ( 12 ). Procedures Data collection was conducted between October 29 and November 30, 2020. Prior to the start of data collection, fifth-year medical students received training on the use of ODK software, the structure and flow of questionnaires, procedures for obtaining informed consent, and adherence to privacy and ethical guidelines. Using smartphones equipped with ODK, trained assistants administered structured questionnaires through face‒to‒face interviews with mothers or caregivers of neonates. Participants who were mothers and caretakers with neonates in these three units were randomly selected, and data were collected via a researcher-administered electronic questionnaire at ODK. Informed consent was obtained from each participant after they clearly explained the study’s purpose, potential risks, and benefits. The questionnaires were administered in both Luganda and English, the predominant languages spoken and understood by both the participants and the interviewers. The study focused on collecting quantitative data and achieved a 100% response rate. Measures Quality control measures Good clinical practice was observed, ensuring the validity of the data collected by i) pretesting (dry run) questionnaires and simplifying, especially how to ask interviews in the local language Luganda; ii) setting the flow of the questionnaire in such a way that it did not allow skipping without an answer option; and iii) checking for completeness immediately after data collection before another participant was interviewed. Confidentiality was maintained by i) assigning each participant a unique participant identification number entered in the data collection tool after providing informed consent, ii) ensuring that no participants' names were obtained, and iii) keeping other demographic features, such as age, parity, number of children, and pregnancy complications, among others, confidential. Data analysis The data collected were securely stored in a password-protected database and subsequently exported to R Studio (version 4.4.1) for analysis. Data cleaning was performed by checking for completeness and missing values. Descriptive statistics, including proportions and frequencies, were computed for the independent variables. Categorical independent variables, including the sociodemographic and socioeconomic status of mothers or caregivers, were collected. These variables included maternal age, education level, residence, marital status, religion, occupation, monthly income, and number of children. The dependent variable, neonatal mortality, was recorded as a binary outcome (yes/no). Independent variables included sociodemographic factors, obstetric history, immunization records, knowledge of maternal and child health, and postnatal care services. Logistic regression analysis was conducted via three distinct models. Model 1 included demographic variables such as participants’ age, marital status, educational attainment, and antenatal/prenatal care attendance. Model 2 focused on obstetric history, including pregnancy complications, parity, birth spacing, delivery planning, and immunization knowledge. Model 3 assessed perceptions and awareness related to neonatal care services, postnatal care access, hospital staffing needs, recognition of neonatal danger signs, and the child’s immunization status. RESULTS Table 1 Descriptive statistics (n = 378) Neonatal Deaths Variables Yes (79) No (299) Total 1. Demographics Residence Urban 32 (8.5%) 144 (38.1%) 176 (46.6%) Rural 47 (12.4%) 155 (41.0%) 202 (53.4%) Age 15–19 2 (0.5%) 42 (11.1%) 44 (11.6%) 20–29 31 (8.2%) 172 (45.5%) 203 (53.7%) 30–39 33 (8.7%) 72 (19.1%) 105 (27.8%) 40–49 13 (3.4%) 13 (3.4%) 26 (6.8%) Marital Status Single 6 (1.6%) 57 (15.1%) 63 (16.7%) Married 58 (15.3%) 232 (61.4) 290 (76.7%) Separated 4 (1.1%) 3 (0.8%) 7 (1.9%) Divorced 3 (0.8%) 1 (0.3%) 4 (1.1%) Widowed 8 (2.1%) 6 (1.6%) 14 (3.7%) Education No formal Education 1 (0.3%) 5 (1.3%) 6 (1.6%) Primary 29 (7.7%) 90 (23.8%) 119 (31.5%) Secondary 34 (8.9%) 167 (44.2%) 201 (53.1%) Tertiary 15 (3.9%) 37 (9.8%) 52 (13.7%) Occupation Unemployed 44 (11.6%) 163 (43.1%) 207 (54.7%) Formal employment 10 (2.7%) 33 (8.7%) 43 (11.4%) Small scale business 24 (6.4) 101 (26.7) 125 (33.1) Informal Job 1 (0.3%) 2 (0.5%) 3 (0.8%) Regular income Yes 38 (10.1%) 100 (26.5%) 138 (36.6%) No 41 (10.9%) 199 (52.7%) 240 (63.6%) Religion Christian 30(7.9%) 91(24.1%) 121(32%) Catholic 25(6.6%) 101(26.7%) 126(33.3%) Islam 23(6.1%) 94(24.9%) 117(31%) Others 1(0.3%) 13(3.4%) 14(3.7%) Among the 378 participants, the majority resided in rural areas (53.4%), were aged between 20–29 years (53.7%), were married (76.7%), had attained at least secondary school (53.1%) or primary school education (31.5%), were unemployed (54.7%), and lacked a regular source of income (63.6%) (see Table 1 ). Table 2 Participants’ obstetric history (n = 378). Neonatal Deaths Variables Yes (79) No (299) Total 2. Obstetrics History Parity One 13 (3.4%) 111 (29.4%) 124 (32.8%) Two-Four 38 (10.1%) 153 (40.5%) 191 (50.6%) Five and more 28 (7.4%) 35 (9.3%) 63 (16.7%) Age at first pregnancy Less than 20yrs 46 (12.2%) 183 (48.4%) 229 (60.6%) 20–30 years 33 (8.7%) 114 (30.2%) 147 (38.9%) More than 30 0 (0.0%) 2 (0.5%) 2 (0.5%) Child Spacing Primigravida 8 (2.1%) 94 (24.9%) 102 (27.0%) One year 11 (2.9%) 47 (12.4%) 58 (15.3%) Two years 13 (3.4%) 36 (9.5%) 49 (12.9%) Mixed 47 (12.4%) 122 (32.3%) 169 (44.7%) Pregnancy Complications Yes 52 (13.8%) 106 (28.0%) 158 (41.8%) No 27 (7.1%) 193 (51.1%) 220 (58.2%) ANC Attendance Yes 79 (20.9%) 288 (76.2%) 367 (97.1%) No 0 (0.0%) 11 (2.9%) 11 (2.9%) Place of ANC Attendance Health Centre 55 (14.6%) 223 (58.9%) 278 (73.5%) Hospital 22 (5.8%) 64 (16.9%) 86 (22.7%) Private Clinic 2 (0.5%) 6 (1.6%) 8 (2.1%) Traditional Birth Attendant 0 (0.0%) 6 (1.6%) 6 (1.6%) Among the 378 participants, over half (50.6%) had more than one child, and a majority (60.6%) had their first pregnancy before the age of 20 years. Nearly half (44.7%) practiced mixed child spacing methods. Most participants (58.2%) did not report any major or concerning complications during their most recent pregnancy. The vast majority (97.1%) attended at least one antenatal care visit during the most recent pregnancy, and their most recent delivery was predominantly attended by a trained healthcare professional at a health facility, either at a health center (73.5%) or a hospital (22.7%) (see Table 2 above). Figures showing neonatal mortality and causes (n = 378). Among the 378 participants, 20.9% reported a history of neonatal death, whereas 79.1% did not, as shown in Fig. 1. The primary reported causes of neonatal death were birth asphyxia (44.3%), prematurity (27.8%), neonatal sepsis (17.7%), and other causes (10.1%), as shown in Fig. 2 ( 6 , 8 ). Table 3 Linear regression model Model 1 β (Estimate) 95% Lower CI 95% CI Upper P Value Age 30–39 1.96 0.37 3.55 < 0.05 * Age 40–49 2.73 0.95 4.51 < 0.05 ** Married -2.64 -4.99 -0.29 < 0.05 * Model 2 Pregnancy Complications 1.34 0.77 1.91 < 0.05 *** One child -1.88 -2.68 -1.08 < 0.05 *** Two-four children -1.14 -1.79 -0.49 < 0.05 *** Model 3 Staff recruitment 0.92 0.29 1.55 < 0.05 ** *Level of significance = 0.05, CI = β ± Z×SE, CI = Confidence interval, SE = standard error In Model 1 (neonatal death according to maternal demographic data), mothers aged 30–39 and 40–49 years had significantly greater odds of neonatal death than did those aged 20–29 years (β = 1.96, SE = 0.81, z = 2.42, p value < 0.05* and β = 2.73, SE = 0.91, z = 3.00, p value < 0.05**, respectively) ( 6 ). Married participants had significantly lower odds of neonatal death (β = -2.64, SE = 1.20, z = -2.20, p value < 0.05*). ( 13 , 14 ). See Table 3 . In M odel 2 (maternal obstetric history), participants who experienced pregnancy complications had significantly greater odds of neonatal death than those without complications did (β = 1.34, SE = 0.29, z = 4.64, p value < 0.05*** ) ( 15 , 16 ). Compared with those with more than one child, those with only one child had significantly lower odds of experiencing neonatal death (β = -1.88, SE = 0.41, z = -4.62, p value < 0.05***) ( 17 ). Participants with two–four children had significantly lower odds of neonatal death than those with five or more children did (β = -1.14, SE = 0.33, z = -3.41, p value < 0.05*** ) ( 17 ). See Table 3 . In Model 3 , participants who identified hospital staff recruitment as necessary improvement were significantly more likely to report a history of neonatal death (β = 0.92, SE = 0.32, z = 2.84, p value < 0.05**) ( 18 , 19 ). See Table 3 . DISCUSSION This facility–based cross-sectional study was conducted with the primary objective of assessing the prevalence and determinants of neonatal mortality at MRRTTH in Uganda. The secondary objective was to evaluate the patterns of maternal and neonatal healthcare service utilization and perceptions of service delivery, including antenatal care attendance, place and mode of delivery, and postnatal care, and explore their associations with neonatal outcomes at MRRTTH. We randomly selected participants who were mothers and caretakers with neonates in the postnatal ward, NICU, and young child infant clinic of MRRTTH and data collected via a researcher administered an electronic questionnaire at ODK to gain a better understanding of how neonatal mortality is associated with various factors, such as individual demographic characteristics, health service delivery and health-seeking behaviors. Addressing the study objectives, first, our findings highlighted that the neonatal mortality rate remains persistently high at MRRTH. Second, the most common causes of neonatal mortality are birth asphyxia, prematurity, and neonatal sepsis. Finally, factors such as advanced maternal age, pregnancy-related complications, and the perception of inadequate staffing are associated with increased odds of neonatal death ( 7 , 19 ). This finding aligns with other studies indicating that advanced maternal age is associated with pregnancy-related complications such as preeclampsia, gestational diabetes, prematurity, and cesarean section ( 21 – 23 ). Pregnancy complications such as preeclampsia, antepartum hemorrhage, premature rupture of the membrane, infections, and placental issues can compromise placental blood flow, leading to fetal growth restriction, low birth weight, and respiratory distress, which significantly increase the likelihood of premature birth and neonatal mortality rates ( 24 , 25 ). In contrast, lower parity (≤ 4) and being married were associated with a reduced risk of neonatal mortality ( 25 ). The association between high neonatal mortality and insufficient staffing may point to systemic shortcomings in healthcare service delivery, contributing to poor neonatal outcomes ( 27 ). Additionally, implementing timely screening, early detection, and effective management of pregnancy complications may further help improve neonatal survival rates ( 28 ). CONCLUSION This study highlights the persistent burden of neonatal mortality at MRRTH, with birth asphyxia, prematurity, and neonatal sepsis as the primary contributors. Several risk factors, including advanced maternal age, pregnancy complications, and perceived healthcare system limitations, were identified. On the other hand, lower parity and marital status were associated with a reduced risk of neonatal death. To address these findings, a multipronged approach is essential—one that improves staffing levels, enhances early detection and management of pregnancy complications, and expands access to quality perinatal and neonatal care. These interventions are critical for reducing neonatal mortality and improving maternal–newborn health outcomes in Uganda and similar low-resource settings. RECOMMENDATIONS The study recommends strengthening policy interventions at primary, secondary and tertiary healthcare facilities to reduce neonatal mortality in Uganda by strengthening the healthcare workforce, particularly by improving staffing levels and capacity building through provider training, alongside the promotion of timely screening and effective management of pregnancy-related maternal complications. LIMITATIONS This study was conducted at only one regional referral hospital, which may limit the generalizability of the findings to other regions or healthcare facilities with different resources, staffing levels, or population characteristics. Limited control over confounding variables such as socioeconomic status, maternal nutrition, and antenatal care quality could influence neonatal outcomes. Declarations Ethical considerations An approval letter was obtained from both the Islamic University in Uganda (Habib Medical School) administration and the Research and Ethics Committee MRRTH prior to conducting the study. Informed consent was obtained from participants willing to participate in the study. All participants were above the age of 18 years for ease of obtaining full consent. The participants retained the right to withdraw from the study at any stage of the interview without consequence. Their autonomy was upheld, and their dignity and personal integrity were fully respected throughout the research process. Consent for publication. All participants provided an informed consent form at the time of data collection in this study. Availability of data and materials All the data generated or analyzed during this study are included in this published article, and supplementary information files are provided. Competing interest statement The authors declare that they have no competing interests. Funding This project was self-funded by the researchers who were final-year medical students, and the money was utilized to facilitate approval by the research and innovation department of MRRTTH, as well as transport and lunch allowances for during the data collection process, paying information technology to design and set up the questionnaire in ODK. Authors’ contributions JO and KE conceptualized and designed the study. JO and KK managed the data, performed the analysis, and drafted the manuscript. SO, MN, SM, and ANS revised and approved the study design and supervised the data collection. CAE, MH, and KAK were responsible for data collection. All the authors reviewed and approved the final manuscript. Acknowledgments Special thanks to the staff of Masaka Regional Referral and Teaching Hospital, Faculty of Health Sciences, Habib Medical School, Islamic University in Uganda, and Indiana University Bloomington School of Public Health for their invaluable support in making this project a success. References UNICEF. Newborn care. In 2024. Available from: https://data.unicef.org/topic/maternal-health/newborn-care/#:~:text=Deaths%20in%20the%20first%20month%20of%20life%2C%20which , total%20deaths%20among%20children%20under%20five%20in%202021. WHO. Newborn mortality [Internet]. 2024. Available from: https://www.who.int/news-room/fact-sheets/detail/newborn-mortality UNECE, Global. SDG indicators for UNECE countries [Internet]. 2022. 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Otile","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYBACxgYGhgMPGGx4QBwJIrUwMxxIYEgjQQsDAzMDQwLDYQbitTC39x88kFBxXsbgAPPB2zxEOaznMNBhZ27zGBxgS7YmTsuMZIYDiW0gLTxm0iRo+XcOqIX/GylaGg6AbGEjUkvPYYMDCceSeSQPsxlbziFGi2F74+MPH2rs7PmONz+88YYoLQ0wFjMxykFAnliFo2AUjIJRMIIBAL95MbOS62KbAAAAAElFTkSuQmCC","orcid":"","institution":"Islamic University in Uganda","correspondingAuthor":true,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Otile","suffix":""},{"id":527368358,"identity":"40164f29-402e-41d1-8a64-1c83c1e2294f","order_by":1,"name":"Kyle Kercher","email":"","orcid":"","institution":"Indiana University Bloomington","correspondingAuthor":false,"prefix":"","firstName":"Kyle","middleName":"","lastName":"Kercher","suffix":""},{"id":527368359,"identity":"d506421a-12c0-4452-b961-e6743645a8c4","order_by":2,"name":"Awunor N Simeon","email":"","orcid":"","institution":"Islamic University in Uganda","correspondingAuthor":false,"prefix":"","firstName":"Awunor","middleName":"N","lastName":"Simeon","suffix":""},{"id":527368360,"identity":"fcdf7195-dc95-4fc2-b5b4-abde657fe292","order_by":3,"name":"Salaam Mujeeb","email":"","orcid":"","institution":"Islamic University in Uganda","correspondingAuthor":false,"prefix":"","firstName":"Salaam","middleName":"","lastName":"Mujeeb","suffix":""},{"id":527368361,"identity":"b22a14dd-2e06-461b-8312-ce43697cb989","order_by":4,"name":"Oyella Sheilla","email":"","orcid":"","institution":"Masaka Hospital","correspondingAuthor":false,"prefix":"","firstName":"Oyella","middleName":"","lastName":"Sheilla","suffix":""},{"id":527368362,"identity":"ec198295-aff5-44a7-b8af-4a69198d417c","order_by":5,"name":"Mina Nakwuka","email":"","orcid":"","institution":"Islamic University in 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1","display":"","copyAsset":false,"role":"figure","size":47322,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7821024/v1/250e10f254ac5db4ca797253.png"},{"id":93571479,"identity":"dcea83d6-6244-49ba-af92-bce51b249d89","added_by":"auto","created_at":"2025-10-15 09:04:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60290,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7821024/v1/0a1767241f1d502d3820aa32.png"},{"id":93573618,"identity":"840d8ce3-6c2f-4182-884c-49620661cbc7","added_by":"auto","created_at":"2025-10-15 09:12:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131984,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7821024/v1/933c268442dd216245fb0e9f.jpeg"},{"id":93576167,"identity":"2020b971-382e-495b-9dcf-9bee2430d85b","added_by":"auto","created_at":"2025-10-15 09:28:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1231562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7821024/v1/10fdc3a9-c1ae-428d-8e35-a718c4cef752.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and factors associated with neonatal mortality at Masaka Regional Referral and Teaching Hospital in central Uganda: A facility based cross sectional study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eGlobally, an estimated 130\u0026nbsp;million infants are born each year, with approximately 4\u0026nbsp;million neonatal deaths occurring within the neonatal period. These deaths account for 46% of under-five (approximately 6500 daily) total deaths. Most of these deaths are preventable (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Neonatal mortality refers to the death of a newborn under 28 days of completed life (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Sub-Saharan Africa is responsible for the highest neonatal mortality rate globally, with 27 deaths per 1000 live births, accounting for 57% of the total under-five deaths (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Uganda, neonatal mortality is 22 deaths per 1000 live births, approximately double the WHO target of less than 12 deaths per 1,000 live births (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral factors increase the risk of neonatal mortality, including maternal factors such as delay in initiation of breastfeeding, very young and advanced maternal age, multiparity, short birth intervals, increased maternal body mass index, smoking during pregnancy, and pregnancy-related complications such as maternal diabetes mellitus, preeclampsia/eclampsia, preterm premature rupture of membranes, and antepartum hemorrhage. The neonatal factors included low gestational age, low birth weight, a 5-minute Apgar score less than seven, the need for neonatal intensive care unit admission, premature birth, and birth complications, i.e., birth asphyxia/trauma, neonatal infections and congenital anomalies. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Limited data exist for LMICs or MRRTTHs concerning the prevalence and factors associated with neonatal mortality, such as the characteristics and predictors of mortality. Understanding these factors is a critical step for identifying existing gaps and implementing health policies, programs, and interventions aimed at addressing gaps leading to neonatal mortality, improving neonatal outcomes and reducing mortality in Uganda and worldwide (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, while few studies have explored neonatal mortality in similar contexts, the findings remain inconsistent. Some studies report positive effects of interventions such as enhanced antenatal care and skilled birth attendance in reducing neonatal mortality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), whereas others indicate that neglecting key factors such as socioeconomic, cultural, and logistical barriers to maternal and newborn care; failing to implement essential preventive practices such as kangaroo mother care; and not promptly identifying and managing sick newborns can contribute to adverse outcomes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These findings highlight the importance of context-specific data and the need for further research into local neonatal health determinants to better inform health policies and interventions in MRRTTH and hence tailor interventions to address the unique challenges in Masaka.\u003c/p\u003e\u003cp\u003eTherefore, we conducted a facility-based cross-sectional study of 378 participants. The primary objective of this study was to assess the prevalence and determinants of neonatal mortality at MRRTTH in Uganda. The secondary objective was to evaluate the patterns of maternal and neonatal healthcare service utilization and perceptions of service delivery, including antenatal care attendance, place and mode of delivery, and postnatal care, and explore their associations with neonatal outcomes at MRRTTH. The study hypothesized that the prevalence of neonatal mortality at MRRTTH aligns with the national average and that no significant associations exist between neonatal mortality and key sociodemographic characteristics.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eThis study was conducted to assess the prevalence and factors associated with neonatal mortality at MRRTH in central Uganda through a facility-based cross-sectional design. The secondary objective was to evaluate the patterns of maternal and neonatal healthcare service utilization and perceptions of service delivery, including antenatal care attendance, place and mode of delivery, and postnatal care, and explore their associations with neonatal outcomes at MRRTTH. We carried out a cross-sectional study of mothers and caretakers from October 29, 2020, to November 30, 2020, at three different units: the postnatal ward, NICU, and young child infant clinic of MRRTTH. The study area, MRRTTH, is within Masaka district, one of the new cities approximately 140 km or 87 miles by road southwest of the capital city Kampala on a highway to Mbarara. The hospital\u0026rsquo;s catchment area is primarily the districts of Masaka, Rakai, Lyantonde, Lwengo, Ssembabule, Bukomansimbi, Kalungu and Kalangala, with Luganda as the predominant language spoken in these areas.\u003c/p\u003e\u003cp\u003eMRRTTH is a 330-bed capacity hospital in the central business district established in 1929. It serves as a teaching hospital for the faculty of health sciences, Habib Medical School, Islamic University in Uganda, an internship site, and a training center for various tertiary health institutions in the region. The Obstetrics and Gynecology Department, which includes the postnatal ward and the Young Child Infant Clinic, is staffed by 4 obstetricians, 5 junior house officers, 2 intern nurses, and 22 midwives. This team manages an average of 600 spontaneous vaginal deliveries and 200 cesarean deliveries each month. The Pediatric Unit, which includes the Neonatal Intensive Care Unit (NICU), is staffed by two pediatricians, one volunteer medical officer, one volunteer nurse, and 7 nurses in the pediatric ward. Additionally, there are 6 junior house officers, with 2 assigned to the NICU. The NICU itself is supported by 8 NICU nurses and 1 intern nurse (MRRTH record, 2025).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy participants\u003c/h3\u003e\n\u003cp\u003eThis study included mothers or caregivers who met the following \u003cb\u003einclusion criteria\u003c/b\u003e: were present in the postnatal ward, NICU, or young child infant clinic during the study period; provided informed consent to participate; were aged 18 years or older; were directly involved in the care of a neonate; and had or were caring for a neonate aged 0\u0026ndash;28 days receiving care at the facility. \u003cb\u003eThe exclusion criteria\u003c/b\u003e included mothers who did not provide consent, those who were not present in the specified units, those under 18 years of age, deaf mothers, mothers whose neonates were critically ill and who required close attention in the NICU, and those who were unable to speak English or Luganda.\u003c/p\u003e\n\u003ch3\u003eSample size determination:\u003c/h3\u003e\n\u003cp\u003eSample size estimation for a single population proportion: \u003cb\u003en = (Z\u0026sup2; \u0026times; p \u0026times; (1 \u0026ndash; p))/d\u0026sup2;\u003c/b\u003e. To adjust for a finite population, the following correction formula was applied: \u003cb\u003en_adj\u0026thinsp;=\u0026thinsp;n/(1 + (n/N))\u003c/b\u003e. This yielded an adjusted sample size of 358 participants. To account for a 10% nonresponse rate, the final sample size was increased to 394 participants, which was used as the target for recruitment (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eData collection was conducted between October 29 and November 30, 2020. Prior to the start of data collection, fifth-year medical students received training on the use of ODK software, the structure and flow of questionnaires, procedures for obtaining informed consent, and adherence to privacy and ethical guidelines. Using smartphones equipped with ODK, trained assistants administered structured questionnaires through face‒to‒face interviews with mothers or caregivers of neonates. Participants who were mothers and caretakers with neonates in these three units were randomly selected, and data were collected via a researcher-administered electronic questionnaire at ODK.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003ewas obtained from each participant after they clearly explained the study\u0026rsquo;s purpose, potential risks, and benefits. The questionnaires were administered in both Luganda and English, the predominant languages spoken and understood by both the participants and the interviewers. The study focused on collecting quantitative data and achieved a 100% response rate.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eQuality control measures\u003c/h2\u003e\u003cp\u003eGood clinical practice was observed, ensuring the validity of the data collected by i) pretesting (dry run) questionnaires and simplifying, especially how to ask interviews in the local language Luganda; ii) setting the flow of the questionnaire in such a way that it did not allow skipping without an answer option; and iii) checking for completeness immediately after data collection before another participant was interviewed. Confidentiality was maintained by i) assigning each participant a unique participant identification number entered in the data collection tool after providing informed consent, ii) ensuring that no participants' names were obtained, and iii) keeping other demographic features, such as age, parity, number of children, and pregnancy complications, among others, confidential.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe data collected were securely stored in a password-protected database and subsequently exported to R Studio (version 4.4.1) for analysis. Data cleaning was performed by checking for completeness and missing values. Descriptive statistics, including proportions and frequencies, were computed for the independent variables. Categorical independent variables, including the sociodemographic and socioeconomic status of mothers or caregivers, were collected. These variables included maternal age, education level, residence, marital status, religion, occupation, monthly income, and number of children. The dependent variable, neonatal mortality, was recorded as a binary outcome (yes/no). Independent variables included sociodemographic factors, obstetric history, immunization records, knowledge of maternal and child health, and postnatal care services.\u003c/p\u003e\u003cp\u003eLogistic regression analysis was conducted via three distinct models. \u003cb\u003eModel 1\u003c/b\u003e included demographic variables such as participants\u0026rsquo; age, marital status, educational attainment, and antenatal/prenatal care attendance. \u003cb\u003eModel 2\u003c/b\u003e focused on obstetric history, including pregnancy complications, parity, birth spacing, delivery planning, and immunization knowledge. \u003cb\u003eModel 3\u003c/b\u003e assessed perceptions and awareness related to neonatal care services, postnatal care access, hospital staffing needs, recognition of neonatal danger signs, and the child\u0026rsquo;s immunization status.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\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\u003eDescriptive statistics (n\u0026thinsp;=\u0026thinsp;378)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNeonatal Deaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes (79)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo (299)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Demographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e144 (38.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e176 (46.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e155 (41.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e202 (53.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e172 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e203 (53.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e232 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e290 (76.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeparated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167 (44.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e201 (53.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (13.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e163 (43.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e207 (54.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormal employment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall scale business\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (33.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformal Job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegular income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138 (36.6%)\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e199 (52.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e240 (63.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30(7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91(24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121(32%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatholic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25(6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101(26.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126(33.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIslam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23(6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94(24.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117(31%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1(0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13(3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14(3.7%)\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\u003eAmong the 378 participants, the majority resided in rural areas (53.4%), were aged between 20\u0026ndash;29 years (53.7%), were married (76.7%), had attained at least secondary school (53.1%) or primary school education (31.5%), were unemployed (54.7%), and lacked a regular source of income (63.6%) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eParticipants\u0026rsquo; obstetric history (n\u0026thinsp;=\u0026thinsp;378).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNeonatal Deaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes (79)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo (299)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Obstetrics History\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e124 (32.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo-Four\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e153 (40.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e191 (50.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFive and more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35 (9.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at first pregnancy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 20yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e183 (48.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e229 (60.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;30 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e114 (30.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e147 (38.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChild Spacing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimigravida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94 (24.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122 (32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e169 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePregnancy Complications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e158 (41.8%)\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e193 (51.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e220 (58.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eANC Attendance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e288 (76.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e367 (97.1%)\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of ANC Attendance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Centre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55 (14.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e223 (58.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e278 (73.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate Clinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraditional Birth Attendant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (1.6%)\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\u003eAmong the 378 participants, over half (50.6%) had more than one child, and a majority (60.6%) had their first pregnancy before the age of 20 years. Nearly half (44.7%) practiced mixed child spacing methods. Most participants (58.2%) did not report any major or concerning complications during their most recent pregnancy. The vast majority (97.1%) attended at least one antenatal care visit during the most recent pregnancy, and their most recent delivery was predominantly attended by a trained healthcare professional at a health facility, either at a health center (73.5%) or a hospital (22.7%) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e above).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigures showing neonatal mortality and causes (n\u0026thinsp;=\u0026thinsp;378).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the 378 participants, 20.9% reported a history of neonatal death, whereas 79.1% did not, as shown in Fig.\u0026nbsp;1. The primary reported causes of neonatal death were birth asphyxia (44.3%), prematurity (27.8%), neonatal sepsis (17.7%), and other causes (10.1%), as shown in Fig.\u0026nbsp;2 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\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\u003eLinear regression model\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (Estimate)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Lower CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI Upper\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\" colname=\"c1\"\u003e\u003cp\u003eAge 30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge 40\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePregnancy Complications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo-four children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStaff recruitment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e*Level of significance\u0026thinsp;=\u0026thinsp;0.05, CI\u0026thinsp;=\u0026thinsp;β\u0026thinsp;\u0026plusmn;\u0026thinsp;Z\u0026times;SE, CI\u0026thinsp;=\u0026thinsp;Confidence interval, SE\u0026thinsp;=\u0026thinsp;standard error\u003c/h2\u003e\u003cp\u003eIn \u003cb\u003eModel 1\u003c/b\u003e (neonatal death according to maternal demographic data), mothers aged 30\u0026ndash;39 and 40\u0026ndash;49 years had significantly greater odds of neonatal death than did those aged 20\u0026ndash;29 years (β\u0026thinsp;=\u0026thinsp;1.96, SE\u0026thinsp;=\u0026thinsp;0.81, z\u0026thinsp;=\u0026thinsp;2.42, \u003cem\u003ep value\u0026thinsp;\u0026lt;\u0026thinsp;0.05*\u003c/em\u003e and β\u0026thinsp;=\u0026thinsp;2.73, SE\u0026thinsp;=\u0026thinsp;0.91, z\u0026thinsp;=\u0026thinsp;3.00, \u003cem\u003ep value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05**, respectively) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Married participants had significantly lower odds of neonatal death (β = -2.64, SE\u0026thinsp;=\u0026thinsp;1.20, z = -2.20, \u003cem\u003ep value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05*). (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn \u003cb\u003eM\u003c/b\u003e\u003cb\u003eodel 2\u003c/b\u003e (maternal obstetric history), participants who experienced pregnancy complications had significantly greater odds of neonatal death than those without complications did (β\u0026thinsp;=\u0026thinsp;1.34, SE\u0026thinsp;=\u0026thinsp;0.29, z\u0026thinsp;=\u0026thinsp;4.64, \u003cem\u003ep value\u0026thinsp;\u0026lt;\u0026thinsp;0.05***\u003c/em\u003e) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Compared with those with more than one child, those with only one child had significantly lower odds of experiencing neonatal death (β = -1.88, SE\u0026thinsp;=\u0026thinsp;0.41, z = -4.62, \u003cem\u003ep value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05***) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Participants with two\u0026ndash;four children had significantly lower odds of neonatal death than those with five or more children did (β = -1.14, SE\u0026thinsp;=\u0026thinsp;0.33, z = -3.41, \u003cem\u003ep value\u0026thinsp;\u0026lt;\u0026thinsp;0.05***\u003c/em\u003e) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn \u003cb\u003eModel 3\u003c/b\u003e, participants who identified hospital staff recruitment as necessary improvement were significantly more likely to report a history of neonatal death (β\u0026thinsp;=\u0026thinsp;0.92, SE\u0026thinsp;=\u0026thinsp;0.32, z\u0026thinsp;=\u0026thinsp;2.84, \u003cem\u003ep value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05**) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis facility\u0026ndash;based cross-sectional study was conducted with the primary objective of assessing the prevalence and determinants of neonatal mortality at MRRTTH in Uganda. The secondary objective was to evaluate the patterns of maternal and neonatal healthcare service utilization and perceptions of service delivery, including antenatal care attendance, place and mode of delivery, and postnatal care, and explore their associations with neonatal outcomes at MRRTTH. We randomly selected participants who were mothers and caretakers with neonates in the postnatal ward, NICU, and young child infant clinic of MRRTTH and data collected via a researcher administered an electronic questionnaire at ODK to gain a better understanding of how neonatal mortality is associated with various factors, such as individual demographic characteristics, health service delivery and health-seeking behaviors.\u003c/p\u003e\u003cp\u003eAddressing the study objectives, first, our findings highlighted that the neonatal mortality rate remains persistently high at MRRTH. Second, the most common causes of neonatal mortality are birth asphyxia, prematurity, and neonatal sepsis. Finally, factors such as advanced maternal age, pregnancy-related complications, and the perception of inadequate staffing are associated with increased odds of neonatal death (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This finding aligns with other studies indicating that advanced maternal age is associated with pregnancy-related complications such as preeclampsia, gestational diabetes, prematurity, and cesarean section (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Pregnancy complications such as preeclampsia, antepartum hemorrhage, premature rupture of the membrane, infections, and placental issues can compromise placental blood flow, leading to fetal growth restriction, low birth weight, and respiratory distress, which significantly increase the likelihood of premature birth and neonatal mortality rates (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In contrast, lower parity (\u0026le;\u0026thinsp;4) and being married were associated with a reduced risk of neonatal mortality (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The association between high neonatal mortality and insufficient staffing may point to systemic shortcomings in healthcare service delivery, contributing to poor neonatal outcomes (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, implementing timely screening, early detection, and effective management of pregnancy complications may further help improve neonatal survival rates (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study highlights the persistent burden of neonatal mortality at MRRTH, with birth asphyxia, prematurity, and neonatal sepsis as the primary contributors. Several risk factors, including advanced maternal age, pregnancy complications, and perceived healthcare system limitations, were identified. On the other hand, lower parity and marital status were associated with a reduced risk of neonatal death.\u003c/p\u003e\u003cp\u003eTo address these findings, a multipronged approach is essential\u0026mdash;one that improves staffing levels, enhances early detection and management of pregnancy complications, and expands access to quality perinatal and neonatal care. These interventions are critical for reducing neonatal mortality and improving maternal\u0026ndash;newborn health outcomes in Uganda and similar low-resource settings.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRECOMMENDATIONS\u003c/h2\u003e\u003cp\u003eThe study recommends strengthening policy interventions at primary, secondary and tertiary healthcare facilities to reduce neonatal mortality in Uganda by strengthening the healthcare workforce, particularly by improving staffing levels and capacity building through provider training, alongside the promotion of timely screening and effective management of pregnancy-related maternal complications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLIMITATIONS\u003c/h2\u003e\u003cp\u003eThis study was conducted at only one regional referral hospital, which may limit the generalizability of the findings to other regions or healthcare facilities with different resources, staffing levels, or population characteristics.\u003c/p\u003e\u003cp\u003eLimited control over confounding variables such as socioeconomic status, maternal nutrition, and antenatal care quality could influence neonatal outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAn approval letter was obtained from both the Islamic University in Uganda (Habib Medical School) administration and the Research and Ethics Committee MRRTH prior to conducting the study. Informed consent was obtained from participants willing to participate in the study. All participants were above the age of 18 years for ease of obtaining full consent. The participants retained the right to withdraw from the study at any stage of the interview without consequence. Their autonomy was upheld, and their dignity and personal integrity were fully respected throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided an informed consent form at the time of data collection in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated or analyzed during this study are included in this published article, and supplementary information files are provided.\u003c/p\u003e\n\u003ch3\u003eCompeting interest statement\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was self-funded by the researchers who were final-year medical students, and the money was utilized to facilitate approval by the research and innovation department of MRRTTH, as well as transport and lunch allowances for during the data collection process, paying information technology to design and set up the questionnaire in ODK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJO and KE conceptualized and designed the study. JO and KK managed the data, performed the analysis, and drafted the manuscript. SO, MN, SM, and ANS revised and approved the study design and supervised the data collection. CAE, MH, and KAK were responsible for data collection. All the authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial thanks to the staff of Masaka Regional Referral and Teaching Hospital, Faculty of Health Sciences, Habib Medical School, Islamic University in Uganda, and Indiana University Bloomington School of Public Health for their invaluable support in making this project a success.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUNICEF. Newborn care. In 2024. 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Effectiveness of strategies incorporating training and support of traditional birth attendants on perinatal and maternal mortality: meta-analysis. BMJ. 2011;343(dec01 1):d7102\u0026ndash;7102.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoss W, Darmstadt GL, Marsh DR, Black RE, Santosham M. Research Priorities for the Reduction of Perinatal and Neonatal Morbidity and Mortality in Developing Country Communities. J Perinatol. 2002;22(6):484\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTumukunde VS, Katongole J, Namukwaya S, Medvedev MM, Nyirenda M, Tann CJ et al. Kangaroo mother care prior to clinical stabilization: Implementation barriers and facilitators reported by caregivers and healthcare providers in Uganda. Mekonnen AA, editor. PLOS Glob Public Health. 2024;4(7): e0002856.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKish L. Survey sampling. New York: Wiley; 1965.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalayla J, Azoulay L, Abenhaim HA. Maternal Marital Status and the Risk of Stillbirth and Infant Death: A Population-Based Cohort Study on 40 million Births in the United States. Womens Health Issues. 2011;21(5):361\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShapiro GD, Bushnik T, Wilkins R, Kramer MS, Kaufman JS, Sheppard AJ, et al. Adverse birth outcomes in relation to maternal marital and cohabitation status in Canada. Ann Epidemiol. 2018;28(8):503\u0026ndash;e50911.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKyasimire L, Tibaijuka L, Ochora M, Kayondo M, Kumbakumba E, Nantongo J, et al. Clinical profiles, incidence and predictors of early neonatal mortality at Mbarara Regional Referral Hospital, southwestern Uganda. BMC Pediatr. 2024;24(1):542.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoyer CA, Kolars CK, Oppong SA, Bakari A, Bell A, Busingye P. Predictors of stillbirths and neonatal deaths in rural western Uganda. Int J Gynecol Obstet. 2016;134(2):190\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBai J, et al. Parity and pregnancy outcomes. Am J Obstet Gynecol. 2002;186(2):274\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChi PC, Bulage P, Urdal H, Sundby J. Barriers in the Delivery of Emergency Obstetric and Neonatal Care in Post-Conflict Africa: Qualitative Case Studies of Burundi and Northern Uganda. Harris F, editor. PLOS ONE. 2015;10(9): e0139120.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatson SI, Arulampalam W, Petrou S, Marlow N, Morgan AS, Draper E, et al. The effects of a one-to-one nurse-to-patient ratio on the mortality rate in neonatal intensive care: a retrospective, longitudinal, population-based study. Arch Dis Child - Fetal Neonatal Ed. 2016;101(3):F195\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStC Hamilton KE, Redshaw ME, Tarnow-Mordi W. Nurse staffing in relation to risk-adjusted mortality in neonatal care. Arch Dis Child - Fetal Neonatal Ed. 2007;92(2):F99\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Mulhim AA, Abu-Heija A, Al-Jamma F, El-Harith EHA. Pre-Eclampsia: Maternal Risk Factors and Perinatal Outcome. Fetal Diagn Ther. 2003;18(4):275\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUlfsdottir H, Grandahl M, Bj\u0026ouml;rk J, Karlemark S, Ek\u0026eacute;us C. The association between pre-eclampsia and neonatal complications in relation to gestational age. Acta Paediatr. 2024;113(3):426\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVandekerckhove M, Guignard M, Civadier MS, Benachi A, Bouyer J. Impact of maternal age on obstetric and neonatal morbidity: a retrospective cohort study. BMC Pregnancy Childbirth. 2021;21(1):732.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDC Dutta. Dc Dutta\u0026rsquo;s Textbook of Obstetrics 7th edition. 7th ed. Jaypee Brothers Medical Publishers (P) Ltd; 2013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarchant T, Willey B, Katz J, Clarke S, Kariuki S, Kuile FT, et al. Neonatal Mortality Risk Associated with Preterm Birth in East Africa, Adjusted by Weight for Gestational Age: Individual Participant Level Meta-Analysis. Bhutta ZA, editor. PLoS Med. 2012;9(8):e1001292.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarces A, Perez W, Harrison MS, Hwang KS, Nolen TL, Goldenberg RL, et al. Association of parity with birthweight and neonatal death in five sites: The Global Network\u0026rsquo;s Maternal Newborn Health Registry study. Reprod Health. 2020;17(S3):182.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNove A, Friberg IK, De Bernis L, McConville F, Moran AC, Najjemba M, et al. Potential impact of midwives in preventing and reducing maternal and neonatal mortality and stillbirths: a Lives Saved Tool modeling study. Lancet Glob Health. 2021;9(1):e24\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSontag MK, Miller JI, McKasson S, Sheller R, Edelman S, Yusuf C et al. Newborn screening timeliness quality improvement initiative: Impact of national recommendations and data repository. Van Wouwe JP, editor. PLOS ONE. 2020;15(4): e0231050.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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},"keywords":"Neonatal mortality, Low- and middle-income countries (LMICs), Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-7821024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7821024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGlobally, approximately 4 million of the 130 million infants born each year die within their first 28 days of life, accounting for nearly 46% of all under five deaths. Neonatal mortality—the death of a live-born infant within the first 28 days—remains a critical public health issue, particularly in Sub-Saharan Africa and Uganda, with rates of 27 and 22 deaths per 1,000 live births, respectively, contributing more than twice the World Health Organization's target of fewer than 12 per 1,000.\u003c/p\u003e\n\u003cp\u003eDespite this burden, data on the prevalence and contributing factors to neonatal mortality remain scarce in many low- and middle-income countries (LMICs), including Masaka Regional Referral and Teaching Hospital (MRRTTH), which hampers the development of effective, evidence-based policies and interventions. The primary objective of this study was to assess the prevalence and key determinants of neonatal mortality, and the secondary objective was to evaluate the patterns of maternal and neonatal healthcare service utilization and their associations with neonatal outcomes at MRRTTH. The study hypothesized that neonatal mortality at MRRTTH mirrors the national average and that there are no significant associations between neonatal mortality and major sociodemographic variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA facility-based cross-sectional study of 378 participants was conducted between October and November 2020 at the postnatal ward, neonatal intensive care unit (NICU), and Young Child Infant Clinic of MRRTTH. The mothers and/or caregivers of the neonates were randomly selected. Data were collected via an open data kit (ODK) via a researcher-administered electronic questionnaire. Descriptive statistics and regression analyses were performed via R Studio (version 4.4.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 20.9% of the participants reported a history of neonatal death from birth asphyxia (44.3%), prematurity (27.8%), neonatal sepsis (17.7%), or other causes (10.1%). The factors associated with increased odds of neonatal mortality included maternal age 30–39 years, \u003cem\u003ep \u003c/em\u003e\u0026lt;0.05\u003csup\u003e*\u003c/sup\u003e, 95% CI (0.37–3.55), age 40–49 years, \u003cem\u003ep \u003c/em\u003e\u0026lt;0.05\u003csup\u003e**\u003c/sup\u003e, 95% CI (0.95–4.51), pregnancy complications \u003cem\u003ep \u003c/em\u003e\u0026lt;0.05\u003csup\u003e***\u003c/sup\u003e, 95% CI (0.77–1.91) and the perception of the need for staff recruitment, \u003cem\u003ep\u0026lt;0.05**, \u003c/em\u003e95% CI (0.29–1.55).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Neonatal mortality at MRRTH remains high, with birth asphyxia, prematurity, and neonatal sepsis as the primary causes. Key associations include advanced maternal age, pregnancy-related complications, and the perception of inadequate staffing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable\u003c/p\u003e","manuscriptTitle":"Prevalence and factors associated with neonatal mortality at Masaka Regional Referral and Teaching Hospital in central Uganda: A facility based cross sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 08:56:01","doi":"10.21203/rs.3.rs-7821024/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"80cb5f72-594a-4337-925f-1ebe1fd4d01f","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T08:56:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 08:56:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7821024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7821024","identity":"rs-7821024","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}