Incidence of mortality and its predictors among preterm neonates admitted to neonatal intensive care unit of Adama Hospital Medical College, Adama, Ethiopia. A retrospective follow-up study.

Incidence of mortality and its predictors among preterm neonates admitted to neonatal intensive care unit of Adama Hospital Medical College, Adama, Ethiopia. A retrospective follow-up 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 Incidence of mortality and its predictors among preterm neonates admitted to neonatal intensive care unit of Adama Hospital Medical College, Adama, Ethiopia. A retrospective follow-up study. Kumsa Kene Arersa, Dube Jara, Rebuma Sorsa, Bati Leta, Shifera Girma Bayeta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6710199/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Preterm birth, or delivery before 37 weeks of gestation, is a significant global health problem, especially in low and middle-income countries like Ethiopia. Preterm infants have a higher risk of mortality despite advances in neonatal care. This study was aimed to assess the incidence of mortality and its predictors among preterm neonates admitted to the neonatal intensive care unit of Adama Hospital Medical College in Ethiopia. Methods A retrospective follow-up study was conducted among 579 preterm neonates from the identified list of neonates admitted to neonatal intensive care unit of Adama Hospital Medical College from January 1, 2021 to December 30, 2023 selected using simple random sampling. Data were checked, cleaned and entered into Epi-info software version 7.2.6.0, and then it was exported to STATA version 17.0 statistical software for analysis. Kaplan-Meier curves and Log-rank test was used to estimate and compare time to death over time. Cox proportional hazards regression model was fitted to identify predictors of mortality. A p-value of < 0.05 was used to declare the statistical significance of the association. Results The study found a mortality rate of 26.3% (95% CI: 22.8, 29.9) and the overall incidence rate was 33.5 deaths per 1000 preterm neonate-days of observation. Maternal medical problems (AHR = 3.1 [95% CI: 1.6, 6.1]), Respiratory distress syndrome (AHR = 2.2 [95% CI:1.5, 3.2]), Perinatal asphyxia (AHR = 2.8 [95% CI 1.1, 7.0]), 5th minute Apgar score less than seven (AHR = 5.4 [95% CI: 3.5, 8.2]) were identified as the predictors of mortality among preterm. Conclusion The incidence of preterm neonatal mortality is relatively high, Maternal medical problems, respiratory distress syndrome, low Apgar scores, and perinatal asphyxia should be prioritized to improve outcomes for preterm infants in this setting. Incidence preterm birth neonatal mortality Censored NICU Ethiopia Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Preterm birth, defined as the delivery of an infant before 37 completed weeks of gestation, is a significant global health challenge. The World Health Organization (WHO) estimates that 15 million infants are born preterm annually ( 1 ), with 90% of these births happening in low- and middle-income countries (LMIC), Particularly in Sub-Saharan Africa and South Asia, which account about 85% of global preterm births ( 2 ). Prematurity is a leading cause of neonatal morbidity and mortality, especially in (LMICs) ( 3 ). The mortality rate vary across countries, with rate as high as 69% in Cameroon ( 4 ), 52% in East Africa ( 5 ) and 21.8%- 34% in Ethiopia ( 6 ). Globally, preterm birth is responsible for 18% of all deaths among under-five children and up to 35% of newborn death ( 7 ). The newborn period, particularly the first day and week of life, is the most vulnerable period for a child’s survival, with the most of neonatal deaths occurring within first 28 days ( 8 ). In developing countries, survival rates for preterm babies born at 24 and 28 weeks of gestation vary significantly, with the survival rates ranging from 50–90% babies, but drops dramatically to less than 10% in low income countries ( 9 ). Preterm neonates face short-term complications, such as respiratory distress syndrome, feeding difficulties, and sepsis, as well as long-term developmental delays, neurologic impairments, and chronic health problems ( 10 , 11 ). Multiple factors contribute to preterm birth, including medical conditions affecting the mother or fetus, genetic influences, environmental exposures, infertility treatments, behavioral and socio-economic factors ( 12 ). The study in East Africa revealed that 52% of neonatal deaths were due to prematurity ( 5 ), with a significant proportion of these deaths occurring within the first 3 days of life ( 13 ). In Ethiopia, study indicated that incidence rate of 62.15 and 19.2 death per 1000 person-day-observations with median survival time from 15–17 days in Mizan Tepi and Addis Ababa, respectively ( 11 , 12 ). Despite the implementation of Various strategies, preterm mortality remains one of global agendas ( 15 ). Neonatal death represents an increasing proportion of under-five child deaths, with the percentage of neonatal death rising from 41% in 2000 46% in 2016 ( 16 , 17 ). The majority of newborn deaths occurred in two regions: Southern Asia (39%) and sub-Saharan Africa (38%). Five countries accounted for half of all new-born deaths: India (24%), Pakistan (10%), Nigeria (9%), the Democratic Republic of the Congo (4%) and Ethiopia (3%) ( 16 ). In Ethiopia, preterm birth is a major contributor to neonatal mortality ( 18 ). The neonatal mortality rate in Ethiopia has displayed a concerning trend over the years, with minimal improvement noted. Reports from the Ethiopian Demographic and Health Survey (EDHS) highlight a potential increase in neonatal mortality rates: 39 per 1000 live births in 2005, 37 in 2011, 29 per 1000 live births in 2016 rising to 33 per 1000 live births in 2019, predominantly attributed to prematurity ( 19 ). Approximately 320,000 premature births occur annually in Ethiopia, resulting in direct deaths of 23,100 children under the age of five ( 20 ). The prevalence of neonatal mortality varies across the regions, with higher rates observed in the Amhara (20.3%) and Oromia (18.8%) ( 21 ). Preterm mortality predictors include prenatal asphyxia, hyaline membrane disease, sepsis, jaundice, low gestational age, respiratory distress syndrome, and initial temperature ( 22 ). Additionally, factors such as antepartum hemorrhage, preeclampsia, eclampsia, multiple pregnancies, premature rupture of membranes, and smoking or substance use during pregnancy contribute to preterm neonatal mortality ( 23 ). The implication of preterm birth and subsequent mortality extend beyond affected infants to their families and society at large. Preterm mortality can lead to emotional distress for the families, increased healthcare expenses, and potential long-term health issues for surviving preterm infants ( 24 ). The financial burden on families and health care facilities is significant, as preterm infants often require specialized medical care in neonatal intensive care units (NICUs) and are more likely to need prolonged hospital stays ( 25 ). Despite efforts to reduce preterm mortality, LMICs like Ethiopia continue to face a high prevalence of preterm births and neonatal mortality, with prematurity being a leading cause of neonatal deaths ( 26 ). There is an urgent need for effective strategies to address this public health issue. This study aims to contribute valuable insights to the existing knowledge by identifying the predictors of preterm mortality. It will also assist healthcare providers and policymakers in developing evidence-based strategies to reduce mortality rates and enhance the overall survival and well-being of preterm infants. The findings of this study will also aid healthcare providers in Adama Hospital Medical College in developing evidence-based strategies to enhance care, reduce mortality rates, and improve the overall quality of neonatal healthcare delivery in their specific context. Therefore, this study was aimed to assess the incidence and predictors of mortality among preterm neonates admitted to neonatal intensive care unit of Adama Hospital Medical College. Methods Study Area and period The study was conducted at Adama Hospital Medical College, located in Adama town, Oromia region from June 1, 2024 to July 30, 2024 which is located approximately 100 km southeast of Ethiopia's capital, Addis Ababa. Serves as a vital healthcare hub for a catchment population exceeding 6 million from five regions: Oromia, Amhara, Afar, Somali, and Dire-Dawa. The hospital has a capacity of 232 beds of which 77 beds are allocated for NICU. There are 8 public health facilities in the town (one hospital, the rest health centers), 9 private hospitals, 6 non-governmental health centers, and 104 private clinics. On a monthly basis, the NICU admits an average of 310 patients, while the hospital as a whole attends to around 1000 patients daily across six medical case teams (OPDs) and various specialty clinics. Functioning as both a referral center and a teaching hospital, Adama Hospital Medical College plays a critical role in healthcare delivery and medical education in the region. Study design Institutional-based retrospective follow-up study was employed. Population: Source population All preterm neonates admitted to the NICU of Adama Hospial Medical College. Study population All preterm neonates that were admitted at NICU of Adama Hospital Medical College from January 1, 2021 to December 30, 2023 and fulfill eligibility criteria. Eligibility criteria Inclusion criteria Preterm neonates admitted to Adama Hospital Medical College NICU from January 2021 to December 2023. Exclusion criteria Neonates with incomplete medical records or missing key data required for analysis was considered under exclusion criteria. Sample size determination All preterm neonates admitted in the NICU from January 2021 to December 2023 were considered with this study. The required sample size was determined using a single population proportion formula n = \(\:\frac{{\left(Z\frac{a}{2}\right)}^{2}\:\text{*}\:\text{p}(1-\text{p})}{{d}^{2}}\) , where n is the sample size; z is the value of standard normal distribution corresponding to a significant level of α of 5%, which is 1.96; d is the margin of error taken as 5% and p is the estimated proportion of preterm neonatal death admitted in the NICU 29.7% taken from previous study the sample size was 352 ( 27 ). For the second objective the sample size was determined using STAT CALC of Epi info for cohort study with the assumptions of 95% CI, power 80% and 22.1% as outcome among unexposed and risk ratio 1.51 by considering sex of the neonate as a predictor variable from previous study done. Based on these assumptions the sample size is 526. The sample size calculated for second objective was larger than the sample size calculated for first objective. By adding 10% non-response rate, final sample size was 579 (Table 1 .) Table 1 Sample size for incidence of mortality and its predictors among preterm neonates admitted in the NICU of Adama Hospital Medical College. Variable CI Power Ratio % of outcome in unexposed AHR Non response rate Total sample size Reference Prenatal asphyxia 95 80 1 18.7 2.91 10% 73 ( 28 ) Sepsis 95 80 1 5.63 3.4 10% 235 ( 29 ) Having 5th minute APGAR score ≤ 5 95 80 1 15.94 1.91 10% 321 ( 30 ) Sex 95 80 1 22.1 1.51 10% 579 ( 27 ) Sampling technique and sampling procedure All newborns admitted in the NICU from January 2021 to December 2023 were included in this study. Neonate’s admission and discharge medical card from NICU was extracted. The 579 charts were selected by computer generated simple random sampling technique with their medical number. Variables Dependent Variable Preterm neonatal mortality Independent Variables Maternal Socio-demographic characteristics (maternal age, residence). Prenatal factors (GA, Gravidity, Parity, ANC follow-up, medical complications (Preeclampsia, Eclampsia, APH, DM)). Intrapartum factors (PROM, place of delivery, weight of infant at delivery, multiple pregnancies, cord prolapse, mode of delivery). Neonatal related factors (sex of neonates, age at admission, Temperature at admission, neonatal complications and Apgar score). Operational definition Event Refers to preterm neonates who were recorded as having died during the follow-up period, as documented in their medical records. Censored Denotes preterm neonates who were admitted to the Neonatal Intensive Care Unit (NICU) but were still alive at the end of the study or were lost to follow-up. This includes cases where neonates were discharged to home, discharged against medical advice, or transferred out to other health institutions, as indicated in their medical records. Time to Death Specifies the exact time at which the death of a preterm neonate occurred within the hospital's waiting time, as documented in the medical records. Follow-up Time Refers to the duration from admission to the NICU until either an event (death) or censorship occurs, as recorded in the medical records. Mortality The rate of death among preterm neonates in the neonatal intensive care unit (NICU) before discharge, expressed as the number of deaths per 1000 live births. Data Collection tools and techniques A structured data abstraction sheet was used to collect data from preterm neonate’s chart and delivery chart. The data abstraction sheet incudes maternal socio-demographic, prenatal, intranatal, and neonatal-related variables with considering mortality of preterm neonates as an outcome. The questionnaire was taken from different kinds of literature and modified to the required variables. Two BSc nurse data collector and one BSc. nurse supervisor were participated during data collection. Data were collected from June 2024 to July 2024. Data quality Assurance To assure quality of data, properly designed data abstraction checklist was used. Two data collector were recruited for data collection. In the same manner, 1 BSc nurse was recruited as supervisor. One day training was given for all of them on the data extraction checklist. Checklist prepared in English version was used to collect necessary information from register. Pretest was conducted on 5% of sample size at Adama General Hospital. The collected data were reviewed daily and was checked for completeness, accuracy, and consistency by supervisor and investigator. Data Processing and Analysis Data were checked, cleaned and entered into Epi-info software version 7.2.6.0, and then it was exported to STATA version 17 statistical software for analysis. Descriptive statistics was used to summarize the characteristics of the study population, including measures such as means, medians, proportions, and standard deviations. Survival analysis techniques, such as Kaplan-Meier curves and Log-rank test was employed to estimate time to death over time. Cox proportional hazards regression analysis was used to identify predictors of neonatal mortality. Schoenfeld residuals test, interaction of each covariate with time and graphical methods was used to check the Cox Proportional Hazard (PH) assumption. Akaike information criteria (AIC) criteria was used to identify model fitness. Goodness of fit of the model was assessed by using cox-snell residual technique. Model was built by stepwise backwards elimination procedure. The potential candidate predictors to the full model was selected by bivariable Cox proportional hazard regression with cut-off point P ≤ 0.25. The multi-collinearity for variables in the final fitted model was checked using variance inflation factor (VIF) with cut-off point mean VIF > 5. Association between predictors and hazard of neonatal death was summarized using adjusted hazard ratio (AHR), and statistical significances was tested at P < 0.05. The incidence was measured with neonate’s days of observation. Results and discussion Socio-demographic characteristics of the mother and neonates Total of five hundred and seventy nine charts were reviewed. The mean (± SD) age of mother was 30 (± 6.54) years. Majority, 393 (67.9%) of mother were in the age group of 20–34 years. The majority, 395 (68.2%) were urban residents. Five hundred forty five (94.1%) of them have ANC follow up and 454 (78.4%) have ANC follow up greater than or equals to four visits. Three hundred sixth-eight (63.5%) of delivery was spontaneous vagina delivery (SVD). The majority, 458 (79.1%) of neonate was born at Adama Hospital Medical College and 537 (92.7%) of pregnancy was singleton. Two hundred sixth nine (46.5%) of mother had medical problem during pregnancy. One hundred forty-eight (25.6%) of mothers have preeclampsia and 22(3.8%) of them have DM during their pregnancy (Table 2 ). Table 2 Maternal-related characteristics of mothers who had preterm neonatal admission at Adama Hospital Medical College NICU, Adama, Ethiopia, from January 1, 2021 to December 30, 2023 Covariates Category Outcome Frequency (%) Death No. (%) Censored No. (%) Maternal age < 20 years 0(0) 34(5.87) 34(5.8) 20–34 years 72(12.43) 321(55.4) 393(67.9) 34 years 80(13.8) 72(12.5) 152(26.3) Residence Urban 89(15.37) 306(52.85) 395(68.2) Rural 63(10.88) 121(20.9) 184(31.8) ANC Yes 146(25.2) 399(68.9) 545(94.1) No 6(1.04) 28(4.8) 34(5.9) Number of ANC Follow up ≥ 4 80(13.8) 374(64.6) 454(78.4) < 4 72(12.4) 53(9.2) 125(21.6) Parity 2 99(17.1) 110( 19 ) 209(36.1) Mode of delivery SVD 96(16.6) 272(46.9) 368(63.5) Caesarean section 56(9.7) 151(26.1) 207(35.8) Instrumental delivery 0(0) 4(0.7) 4(0.7) Place of delivery In born 114(19.7) 344(59.4) 458(79.1) Out born 38(6.6) 83(14.3) 121(20.9) Type of pregnancy Singleton 141(24.4) 396(68.3) 537(92.7) Multiple 11(1.9) 31(5.4) 42(7.3) Maternal Medical problem Yes 136(23.5) 133( 23 ) 269(46.5) No 16(2.8) 294(50.7) 310(53.5) Preeclampsia Yes 79(13.6) 69( 12 ) 148(25.6) No 73(12.6) 358(61.8) 431(74.4) DM Yes 10(1.7) 12(2.1) 22(3.8) No 142(24.5) 415(71.7) 557(96.2) APH Yes 39(6.7) 27(4.7) 66(10.4) No 113(19.5) 400(69.1) 513(88.6) PROM Yes 133(19.5) 161(27.8) 294(50.8) No 19(3.3) 266(45.9) 285(49.2) Cord prolapse Yes 51(8.8) 61(10.5) 112(19.3) No 101(17.4) 366(63.2) 467(80.7) Neonatal related characteristics The majority, 323 (55.8%) of neonates were male and 234 (55.4%) neonates were born with gestational age greater than or equals to 32 weeks. The majority of neonates, 314 (54.2%) were born with low birth weight and 466 (80%) of preterm neonates had fifth minutes APGAR score ≥ 7 (Table 3 .) Table 3 Neonatal characteristics of neonates admitted diagnosed preterm at NICU of Adama Hospital Medical College from January 1, 2021 to December 30,2023 Covariates Category Outcome Total (%) Death No. (%) Censored No. (%) Sex of Neonate Male 83(14.3) 240(41.5) 323(55.8) Female 69(11.9) 187(32.3) 256(44.2) Gestational age Extremely preterm 18(3.1) 6(1.04 24(4.15) Very preterm 73(12.6) 161(27.8) 234(40.4) Moderate to late preterm 61(10.5) 260(44.9) 321(55.4) Birth weight Extremely low birth weight 35(6.04) 10(1.73) 45(7.8) Very low birth weight 68(11.7) 129(22.3) 197( 34 ) Low birth weight 49(8.5) 265(45.8) 314(54.2) Normal birth weight 0(0) 23(3.97) 23(3.97) APGAR score 5’ < 7 103(17.8) 10(1.7) 113(19.5) ≥ 7 49(8.5) 417(72) 466(80.5) PNA Yes 6(0.86) 0(0) 6(0.86) No 146(25.2) 427(73.75) 573(98.96) Respiratory rate Increased 48(8.3) 181(31.3) 229(39.55) Normal 104(17.96) 246(42.5) 350(60.45) Temperature Hypothermic 105(18.1) 246(42.5) 351(60.6) Hyperthermia 7(1.2) 21(3.6) 28(4.8) Normal 40(6.9) 160(27.6) 200(34.5) Heart rate Increased 24(4.15) 75(12.95) 99(17.1) Normal 128(22.1) 352(60.8) 480(82.9) Apnea prematurity, RDS and Sepsis were common cause of mortality among preterm neonates. (Fig. 1 ) Incidence of Mortality Based on the finding the mortality among preterm neonates was 26.3% (95% CI: 22.8, 29.9) at NICU of Adama Hospital Medical College. This overall mortality rate is in line with the study done at Debre Markos Referral Hospital (27.11%) ( 31 ) and selected hospital in Addis Ababa, Ethiopia 29%( 6 ). However, it is lower when compared to study conducted at Nepal (60%) ( 32 ) and higher when compared to the study conducted at Hawassa University Comprehensive Specialized Hospital 14.2% ( 33 ) and Uganda (19.8%). The possible reason for this variation in mortality might be difference in sample size, methodology, study population and quality of care provided. At the end of the follow up the overall incidence of mortality of preterm neonates was found to be 33.54 deaths per 1000 preterm neonate-days of observation. The finding is consistent with EDHS 2019 report, which noted 33 deaths per 1000 live births ( 19 ). However, it is lower than the study conducted at Tikur Anbessa Specialized Hospital, which was 39.1 per-1000 person day ( 27 ), and in a study conducted at selected public hospital in Addis Ababa, which found a mortality rate of 36.4 per 1000 neonatal days observation ( 34 ). Additionally a study at public hospital in Southern Ethiopia reported a mortality rate was 47.7 per 1000 neonatal days observation ( 35 ), while study in Gurage zone public hospitals revealed an overall incidence density rate was 36.9 per 1000 person-day observation ( 28 ). Conversely, the mortality rate was higher than the study conducted at Hawassa University Comprehensive Specialized Hospital (28 per 1000 neonatal day observation) ( 33 ), Debre Tabor Specialized Comprehensive Hospital ( 31.2 per 1000 live births) ( 36 ) and Addis Ababa (19.2 deaths per 1,000 live births). This marked variation may be attributed to difference in sample size, study period and the characteristics of study participants (Fig. 2 ). Time to mortality of preterm neonates Median time to death among preterm neonates was 3 days with minimum of 1 day and maximum of 20 days. Kaplan-Meier preterm neonatal survival probability The graph below indicates that the survival rate preterm neonate decreases as time increases approximately up to 7 days. After 20 days, the survival rate remains relatively constant (Fig. 3 ). Predictors of death among preterm neonates This study revealed that preterm neonates born to mother who had medical problem during pregnancy had higher risk of death when compared to their counterpart. This finding is supported by study conducted at TASH ( 27 ) and Debre Tabor Comprehensive Specialized Hospital ( 36 ). This may be due to the medical complications can leads to impaired blood flow that results in insufficient oxygen and nutrients for the fetus can cause respiratory problems and other developmental issues in neonates. Also it can affect the ability to care for newborn. According to this study the risk of death was higher among neonates born to mother who had APH during the pregnancy when compared to neonates born from mother who didn’t had APH. The finding is in line with the study conducted in Addis Ababa Public Hospitals, Ethiopia ( 34 ), and Iraq ( 37 ). The reason for this may be due to compromization of blood flow and oxygen delivery to the fetus and stress from APH can trigger a systemic response in the mother that affect fetal health. Also the study revealed that the hazard of death among neonates who born to mother who had PROM during delivery was higher when compared to neonates who born from mother who had no PROM during delivery. The finding is consistent with the study conducted at Dilchora Referral hospital, Dire Dawa city, Ethioipa ( 38 ). The reason for this may be they are exposed to infection, respiratory distress and organ immaturity. The study found that the risk of death was higher among neonates born from mother who had ANC follow up less than four visit when compared to neonates whose mother had greater than four ANC follow up. This study is supported by study conducted in Jimma Zone, Southwest Ethiopia ( 39 ). This might be due to inadequate monitoring during pregnancy, delayed intervention due to limited ANC potential complication may go untreated and reduced quality of care. The risk of neonatal death diagnosed with respiratory distress syndrome was higher compared to those without respiratory distress syndrome. This study was consistent to study conducted at Debre Markos Referral Hospital ( 31 ), Northwest part of Ethiopia ( 40 ), Tikur Anbessa Specialized Hospital ( 27 ) and Uganda( 41 ). This might be due to inadequate perinatal care attributed to less use of lung surfactant, inadequate availability of noninvasive and invasive ventilation methods and concomitant overlooked complications of RDS that increase the risk of mortality in preterm neonate. Again the study found that the risk of neonatal mortality was higher among neonates diagnosed to have PNA when compared to those who didn’t have PNA. This study was in line with the study conducted at Hawassa University Comprehensive Specialized Hospital ( 30 ), Gurage zone public hospitals ( 28 ), Nepal ( 32 ) and Uganda ( 41 ). The possible reasons for this might be PNA leads to progressive hypoxemia and hypercapnia resulting in central nervous and other end organ damage. The presence of neonatal encephalopathy is considered as an essential etiologic link that predicts mortality or severe disability. The study also found that the neonates who had fifth minutes Apgar score less than seven have higher risk of death than those who had Apgar score greater than seven. This study is consistent with the study conducted at TASH( 27 ), Hawassa University Comprehensive Specialized Hospital ( 30 ), and Ghana ( 42 ). This may be due to compromised physiological functions, requiring more intensive care and potentially leading to increased mortality. (Table 4 ) Table 4 Predictors of preterm neonatal death admitted to NICU of Adama Hospital Medical College from January 1, 2021 to December 30, 2023 Covariate Category Outcome CHR (95%CI) AHR(95%CI) Dead Censored Maternal medical problem Yes No 136 16 133 294 13.078(7.738,22.104) 1 3.141(1.619,6.092)** 1 Preeclampsia Yes No 95 57 69 358 5.266(3.764, 7.366) 1 1.477(.990,2.204) 1 DM Yes No 16 136 12 415 2.945(1.748, 4.960) 1 1.306(.585,2.914) 1 APH Yes No 39 113 27 400 3.948(2.728, 5.713) 1 2.034(1.298,3.187)** 1 PROM Yes No 133 19 161 266 7.542(4.662, 12.200) 1 2.797(1.607,4.868)** 1 Number of ANC < 4 ≥ 4 72 80 53 374 3.668(2.664, 5.052) 1 2.502(1.719,3.642)** 1 Gestational age Moderate to late preterm Very preterm Extremely preterm 61 73 18 261 161 6 1 1.687(1.201, 2.371) 4.859(2.852, 8.279) 1 .924(.621,1.374) 1.464(.776,2.762) APGAR score ≥ 7 < 7 49 103 417 10 1 13.944(9.808,19.824) 1 5.358(3.506,8.190)** RDS Yes No 67 85 127 300 1.734(1.261, 2.408) 1 2.180(1.510, 3.147)** 1 PNA Yes No 6 146 0 427 4.932(2.168,11.221) 1 2.809(1.125,7.013)** 1 Place of delivery At the same facility Referred from other facility 114 38 344 83 1 1.552(1.073, 2.246) 1 .864(.516,1.448) Place of residence Urban Rural 89 63 306 121 1 1.716(1.238,2.378) 1 1.063(.704,1.605) ** Significant at p-value < 0.05 Limitation of the study Since the data were recorded from secondary sources it is prone to data incompleteness. The incomplete record were excluded that may leads to selection bias. The study is retrospective study and based on the secondary data, first it did not allow inference to be drawn with respect to the temporal relationship among variables and association does not imply causation. Conclusions The incidence of preterm neonatal mortality rate was high. The risk of death increased if the neonates had respiratory distress syndrome, perinatal asphyxia, born from mother who had medical complication during pregnancy, Antepartum hemorrhage, ANC follow up less than four and premature rupture of membrane. To reduce the burden of preterm neonatal mortality the health care provider should give due attentions to neonates diagnosed with RDS, perinatal asphyxia and low APGAR score. More emphasis should be given to prevent and treat complications during pregnancy and delivery. Furthermore, longitudinal prospective cohort study recommended to identify additional factors that determine preterm survival and also see the outcomes of those preterm who were censored in this study. Abbreviations APGAR- Appearance, Grimace, Activity, and Respiration, CI- Confidence Interval , C/S- Caesarean section, DM- Diabetes Mellitus, EDHS-Ethiopian Demographic and Health Survey, IQR-Interquartile Range, LMIC-Low and Middle Income Country, NICU- Neonatal Intensive Care Unit, PROM-Premature Rupture of Membrane, RDS-Respiratory Distress Syndrome, SDG-Sustainable Development Goal, SVD-Spontaneous Vaginal Delivery, TASH-Tikur Anbessa Specialized Hospital , UNICEF-United Nation International Child Education and Fund, WHO-World Health Organization. Declarations Ethical Consideration The ethical clearance was obtained from Institutional review board of Yekatit 12 Hospital medical college. Support letter was obtained from the department of Public health research and publication office. The necessary permission was obtained from Oromia Regional Health Bureau, Public Health and Emergency Management (PHEM) (Approval No. BB/KBTRC/196/1909, dated of 31/ 05/2024 G.C. Since the data were from medical records and no direct contact was made with the patients, informed consent was not obtained from the participants. However, all data were anonymized and handled with strict confidentiality to protect patient privacy. Identifiable personal information was not used in any part of the analysis or reporting. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki. Data availability The data sets generated and analyzed during the current study are not publicly available due to institutional and ethical restrictions concerning patient confidentiality but are available from the corresponding author on reasonable request and with permission. Competing interest The authors have declared that no competing interests exist. Consent to Participate This study was conducted using secondary data with no direct contact with participants. Ethical approval was obtained from Institutional review board of Yekatit 12 Hospital Medical College and necessary permission obtained from Oromia regional Health Bureau, Public Health and Emergency Management (PHEM) which also granted a waiver of informed consent (Approval no. BB/KBTRC/196/1909, dated of 31/ 05/2024 G.C). Therefore, informed consent from participants or their legal guardians was not required. However, all data were anonymized and handled with strict confidentiality to protect patient privacy. Funding The author received no specific funding for this work. Clinical trial number: not applicable Consent to publication: not applicable Authors contribution KKA : Conceptualization, Data curation, Formal analysis and Writing original draft. DJ : Methodology, software, supervision, Validation and visualization. RS : Data curation, methodology, software and supervision. BL : Investigation, Methodology, software and Validation. SGB : Data curation, investigation, methodology and software. Acknowledgment I would like to express my deepest appreciation goes to Yekatit 12 Hospital Medical College for selecting the topic that align with my interests. My sincere gratitude also goes to Adama Hospital Medical College, the data collectors, the study participants, and the dedicated members of the staff. Special thanks to the head of the NICU for their invaluable support in facilitating the data acquisition process. References Quinn J, Munoz FM, Gonik B, Frau L, Cutland C, Mallett-moore T, et al. Preterm birth : Case definition & guidelines for data collection , analysis , and presentation of immunisation safety data q. Vaccine [Internet]. 2016;34(49):6047–56. Available from: http://dx.doi.org/10.1016/j.vaccine.2016.03.045 Hug L, Alexander M, You D, Alkema L. National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis. Lancet Glob Heal [Internet]. The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license; 2019;7(6):e710–20. Available from: http://dx.doi.org/10.1016/S2214-109X(19)30163-9 Id ADC, Moller A, Blencowe H, Johansson EW, Hussain-alkhateeb L, Ohuma EO, et al. Study protocol for WHO and UNICEF estimates of global , regional , and national preterm birth rates for 2010 to 2019. 2021;1–13. Available from: http://dx.doi.org/10.1371/journal.pone.0258751 Ndombo PK, Ekei QM, Tochie JN, Temgoua MN, Angong FTE, Ntock FN, et al. A cohort analysis of neonatal hospital mortality rate and predictors of neonatal mortality in a sub-urban hospital of Cameroon. Ital J Pediatr. Italian Journal of Pediatrics; 2017;43(1):1–8. Marchant T, Willey B, Katz J, Clarke S, Kariuki S, ter Kuile F, et al. Neonatal Mortality Risk Associated with Preterm Birth in East Africa, Adjusted by Weight for Gestational Age: Individual Participant Level Meta-Analysis. PLoS Med. 2012;9(8). Muhe LM, McClure EM, Nigussie AK, Mekasha A, Worku B, Worku A, et al. Major causes of death in preterm infants in selected hospitals in Ethiopia (SIP): a prospective, cross-sectional, observational study. Lancet Glob Heal [Internet]. The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license; 2019;7(8):e1130–8. Available from: http://dx.doi.org/10.1016/S2214-109X(19)30220-7 Walani SR. Global burden of preterm birth. Int J Gynecol Obstet. 2020;150(1):31–3. Wake GE, Chernet K, Aklilu A, Yenealem F, Wogie Fitie G, Amera Tizazu M, et al. Determinants of neonatal mortality among neonates admitted to neonatal intensive care unit of Dessie comprehensive and specialized hospital, Northeast Ethiopia; An unmatched case-control study. Front Public Heal. 2022;10. Draper ES. Evaluating and comparing neonatal outcomes. Arch Dis Child Fetal Neonatal Ed. 2010;95(3):158–9. WHO recommendations on interventions to improve preterm birth outcomes. Available from: www.who.int/reproductivehealth Ravi Mangal Patel, MD Ms. Short and Long-Term Outcomes for Extremely Preterm Infants. J Pediatr. 2016;33(3):318–28. Pennell CE, Jacobsson B, Williams SM, Buus RM, Muglia LJ, Dolan SM, et al. WITHDRAWN: Genetic epidemiological studies of preterm birth: Guidelines for research. Am J Obstet Gynecol. 2006; Sankar MJ, Natarajan CK, Das RR, Agarwal R, Chandrasekaran A, Paul VK. When do newborns die? A systematic review of timing of overall and cause-specific neonatal deaths in developing countries. J Perinatol. Nature Publishing Group; 2016;36(S1):S1–11. Bereka B, Demeke T, Fenta B, Dagnaw Y. Survival Status and Predictors of Mortality Among Preterm Neonates Admitted to Mizan Tepi University Teaching Hospital, South West Ethiopia. Pediatr Heal Med Ther. 2021;Volume 12:439–49. Liu L, Oza S, Hogan D, Perin J, Rudan I, Lawn JE, et al. Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: An updated systematic analysis. Lancet. 2015;385(9966):430–40. Bryce J, Boschi-Pinto C, Shibuya K, Black RE. WHO estimates of the causes of death in children. The lancet. 2005 Mar 26;365(9465):1147-52. WWHO U, Mathers C. Global strategy for women’s, children’s and adolescents’ health (2016–2030). Organization. 2017 May 13;2016(9).HO U, Mathers C. Global strategy for women’s children’s and adolescents’ health (2016–2030). O 2017 M 13;2016(9). Duration of breastfeeding and its correlates in Bangladesh. Berhan Y, Berhan A. Perinatal mortality trends in Ethiopia. Ethiop J Health Sci. 2014;24:29–40. Ethiopian Public Health Institute (EPHI) [Ethiopia] and ICF. 2021. Ethiopia Mini Demographic and Health Survey 2019: Final Report. Rockville, Maryland, USA: EPHI and ICF. EUSAID P, GAPPS A. Profile of preterm and low birth weight prevention and care-Ethiopia.; 2017. Washington, DC: USAID. 2019. Aynalem YA, Shiferaw WS, Akalu TY, Dargie A, Assefa HK, Habtewold TD. The Magnitude of Neonatal Mortality and Its Predictors in Ethiopia : A Systematic Review and Meta-Analysis. Int J Pediatr. 2021;2021:10. Wesenu M, Kulkarni S, Tilahun T. Modeling Determinants of Time-To-Death in Premature Infants Admitted to Neonatal Intensive Care Unit in Jimma University Specialized Hospital. Ann Data Sci. Springer Berlin Heidelberg; 2017;4(3):361–81. Rezaeian A, Rezaeian M, Khatami SF, Khorashadizadeh F, Moghaddam FP. Prediction of mortality of premature neonates using neural network and logistic regression. J Ambient Intell Humaniz Comput [Internet]. Springer Berlin Heidelberg; 2022;13(3):1269–77. Available from: https://doi.org/10.1007/s12652-020-02562-2 Morniroli D, Tiraferri V, Maiocco G, De Rose DU, Cresi F, Coscia A, et al. Beyond survival: the lasting effects of premature birth. Front Pediatr [Internet]. 2023;11(July):1–6. Available from: https://doi.org/10.3389/fped.2023.1213243 Tongo OO, Orimadegun AE, Ajayi SO, Akinyinka OO. The economic burden of preterm/very low birth weight care in Nigeria. J Trop Pediatr. 2009;55(4):262–4. Mekonnen T, Tenu T, Aklilu T, Abera T. Assessment of Neonatal Death and Causes among Admitted Neonates in Neonatal Intensive Care Unit of Mizan Tepi University Teaching Hospital, Bench Maji Zone, South-West Ethiopia, 2018. Clin Mother Child Heal. 2018;15(4). Aynalem YA, Mekonen H, Akalu TY, Gebremichael B, Shiferaw WS. Preterm Neonatal Mortality and its predictors in Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia: a retrospective cohort study. Ethiop J Health Sci. 2021;31(1):43–54. Chekole B, Terefe TF, Tenaw SG, Zewudie BT, GebreEyesus FA, Kassaw A, et al. Survival Status, Length of Stay, and Predictors of Mortality Among Neonates Admitted in the Neonatal Intensive Care Unit of Gurage Zone Public Hospitals. SAGE Open Nurs. 2023;9:1–10. Menalu MM, Gebremichael B. Time to death and its predictors among neonates who were admitted to the neonatal intensive care unit at tertiary hospital, Addis Ababa, Ethiopia: Retrospective follow up study. Front Pediatr. 2022;10:913583. Feleke T, Kawet G. Predictors of Preterm Neonatal Mortality in Hawassa University Comprehensive Specialized Hospital Neonatal Intensive Care Unit , Southern Ethiopia : a Retrospective Cohort Study. Res Sq. 2022;1–23. Abebaw E, Reta A, Kibret GD, Wagnew F. Incidence and Predictors of Mortality among Preterm Neonates Admitted to the Neonatal Intensive Care Unit at Debre Markos Referral Hospital, Northwest Ethiopia. Ethiop J Health Sci. 2021;31(5):937–46. Karmacharya SB, Subedi KU, Agrawal S, Pradhan N, Banwal R, Paudel P. Determinants of Mortality in Preterm Newborns Admitted in a Neonatal Intensive Care Unit: Findings from a Tertiary Level Maternity Hospital in Nepal. J Nepal Paediatr Soc. 2022;42(1):33–8. Taye K. Predictors of neonatal mortality among neonates admitted to the neonatal intensive care unit at Hawassa University Comprehensive Specialized Hospital , Sidama regional state , Ethiopia. 2023;1–21. Birhanu D, Gebremichael B, Tesfaye T, Tadesse M, Belege F, Godie Y, et al. Survival status and predictors of mortality among preterm neonates admitted to neonatal intensive care unit of Addis Ababa public hospitals, Ethiopia, 2021. A prospective cohort study. BMC Pediatr [Internet]. BioMed Central; 2022;22(1):1–12. Available from: https://doi.org/10.1186/s12887-022-03176-7 Huka AE, Oljira L, Weldesenbet AB, Bushra AA, Ahmed IA, Tura AK, et al. Predictors of time to death among preterm neonates admitted to neonatal intensive care units at public hospitals in southern Ethiopia: A cohort study. PLoS One [Internet]. 2023;18(10 10):4–12. Available from: http://dx.doi.org/10.1371/journal.pone.0283143 Minuye Birhane B, Assefa N, Endalamaw A, Yeshambel A, Mengistie B. Predictors of survival among preterm neonates admitted to Debre Tabor Comprehensive Specialized Hospital, Northwest Ethiopia; Implication for the maternal and neonatal health care-services. J Neonatal Nurs [Internet]. Elsevier Ltd; 2023;29(2):368–74. Available from: https://doi.org/10.1016/j.jnn.2022.07.022 Hamadameen A. The maternal and perinatal outcome in antepartum hemorrhage: A cross-sectional study. Zanco J Med Sci. 2018;22(2):155–63. Thomas G, Demena M, Hawulte B, Eyeberu A, Heluf H, Tamiru D. Neonatal Mortality and Associated Factors Among Neonates Admitted to the Neonatal Intensive Care Unit of Dil Chora Referral Hospital, Dire Dawa City, Ethiopia, 2021: A Facility-Based Study. Front Pediatr. 2022;9(February):1–7. Debelew GT, Afework MF, Yalew AW. Determinants and causes of neonatal mortality in jimma Zone, Southwest Ethiopia: A multilevel analysis of prospective follow up study. PLoS One. 2014;9(9). Sinshaw AE, Minuye B, Mengistie B, Yeshambel A, Assefa N. Mortality of preterm neonates and its predictors in the Northwest part of Ethiopia: A retrospective cohort study. Res Sq [Internet]. 2019;1–23. Available from: https://doi.org/10.21203/rs.2.10832/v1 Tibaijuka L, Bawakanya SM, Owaraganise A, Kyasimire L, Kumbakumba E, Boatin AA, et al. Incidence and predictors of preterm neonatal mortality at Mbarara Regional Referral Hospital in South Western Uganda. PLoS One [Internet]. 2021;16(November):1–17. Available from: http://dx.doi.org/10.1371/journal.pone.0259310 Kofi E, Id A, Osarfo J, Id JA, Anane-fenin B, Okai E, et al. Determinants of preterm survival in a tertiary hospital in Ghana : A ten-year review. PLoS One [Internet]. 2021;16(1):1–15. Available from: http://dx.doi.org/10.1371/journal.pone.0246005 Additional Declarations No competing interests reported. Supplementary Files EnglishversionofDataAbstractionsheet.docx Annex 1. English version of Data Abstraction sheet.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 14 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 18 Jun, 2025 Editor invited by journal 28 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 27 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6710199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477629435,"identity":"a7363a1e-a690-4284-b895-03ef030e693f","order_by":0,"name":"Kumsa Kene Arersa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACCTBZYSMHog48IF7LmTRjsJYEorUwth1KbAAxiNIiOSM7+cMPtgPp88MOPwTaYien20BAi7RE7jbJHp47uRtvpxkAtSQbmx0goEUOqIWBR+JZ7sbZCSAtBxK3EaFl88c/BofTDWenfyBOC9BhG6R5Eg4nyEvnEGmLZM/bbdIyB9IMN0jnFBxIMCDCLxLHgQ57+89GXn52+uYPHyrs5AhqgQMDsEoDYpWDgHwDKapHwSgYBaNgRAEAgiBIawEZahcAAAAASUVORK5CYII=","orcid":"","institution":"Jimma University","correspondingAuthor":true,"prefix":"","firstName":"Kumsa","middleName":"Kene","lastName":"Arersa","suffix":""},{"id":477629436,"identity":"17b59882-8f43-4f4f-97fd-1900a9c14836","order_by":1,"name":"Dube Jara","email":"","orcid":"","institution":"Yekatit 12 Hospital Medical College","correspondingAuthor":false,"prefix":"","firstName":"Dube","middleName":"","lastName":"Jara","suffix":""},{"id":477629438,"identity":"53322003-2a14-40f4-879a-d481a24df536","order_by":2,"name":"Rebuma Sorsa","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Rebuma","middleName":"","lastName":"Sorsa","suffix":""},{"id":477629440,"identity":"54411185-469e-40fb-991a-19ea17e0baea","order_by":3,"name":"Bati Leta","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Bati","middleName":"","lastName":"Leta","suffix":""},{"id":477629441,"identity":"e8355cf0-85ec-4e0a-8937-51ecefffcf54","order_by":4,"name":"Shifera Girma Bayeta","email":"","orcid":"","institution":"Family Guidance Association of Ethiopia","correspondingAuthor":false,"prefix":"","firstName":"Shifera","middleName":"Girma","lastName":"Bayeta","suffix":""}],"badges":[],"createdAt":"2025-05-20 18:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6710199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6710199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85746548,"identity":"13b23131-09ea-41d0-8755-a6abd9d3e957","added_by":"auto","created_at":"2025-07-01 09:32:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26238,"visible":true,"origin":"","legend":"\u003cp\u003eCause of Neonatal Mortality among preterm neonates admitted to neonatal intensive care unit of Adama Hospita Medical College from January 1, 2021 to December 30, 2023\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6710199/v1/979d6d979813e7355e391dfb.jpg"},{"id":85747944,"identity":"5d6cf170-e841-410e-a8d7-91941261acf2","added_by":"auto","created_at":"2025-07-01 09:40:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20366,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of outcome of preterm neonates admitted to NICU of Adama Hospital Medical College from January 1, 2021 to December 30, 2023\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6710199/v1/9d46513044743d5ce1c57623.jpg"},{"id":85747943,"identity":"3eb4da9a-d56d-4048-b625-a43e081d3e1e","added_by":"auto","created_at":"2025-07-01 09:40:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27648,"visible":true,"origin":"","legend":"\u003cp\u003eOverall Kaplain-Meier probability of failure estimate of preterm neonates admitted to Neonatal intensive care unit of Adama Hospital Medical College from January 1, 2021 to December 30\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6710199/v1/89c6b890e72eac8c4ec99852.jpg"},{"id":85746553,"identity":"cc79ddfd-1f6d-4bae-8a85-e3963d389842","added_by":"auto","created_at":"2025-07-01 09:32:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55988,"visible":true,"origin":"","legend":"\u003cp\u003eKaplain-Meier probability of failure estimate for selected variables among preterm neonate admitted to Adama Hospital Medical College from January 1, 2021 to December 30, 2023\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6710199/v1/b33c70920f3217e49e224b5b.jpg"},{"id":85749729,"identity":"c6a6699f-c584-42f5-9719-a09979ef2682","added_by":"auto","created_at":"2025-07-01 09:56:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1318432,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6710199/v1/79be6b25-90a4-4241-a161-bab6b34ae645.pdf"},{"id":85746551,"identity":"2caacbd0-6c7b-452d-8eed-7b4cbb165dcf","added_by":"auto","created_at":"2025-07-01 09:32:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17124,"visible":true,"origin":"","legend":"\u003cp\u003eAnnex 1. English version of Data Abstraction sheet.docx\u003c/p\u003e","description":"","filename":"EnglishversionofDataAbstractionsheet.docx","url":"https://assets-eu.researchsquare.com/files/rs-6710199/v1/1816b0880434a3268ab41057.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incidence of mortality and its predictors among preterm neonates admitted to neonatal intensive care unit of Adama Hospital Medical College, Adama, Ethiopia. A retrospective follow-up study.","fulltext":[{"header":"Background","content":"\u003cp\u003ePreterm birth, defined as the delivery of an infant before 37 completed weeks of gestation, is a significant global health challenge. The World Health Organization (WHO) estimates that 15\u0026nbsp;million infants are born preterm annually (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), with 90% of these births happening in low- and middle-income countries (LMIC), Particularly in Sub-Saharan Africa and South Asia, which account about 85% of global preterm births (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Prematurity is a leading cause of neonatal morbidity and mortality, especially in (LMICs) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The mortality rate vary across countries, with rate as high as 69% in Cameroon (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), 52% in East Africa (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and 21.8%- 34% in Ethiopia (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, preterm birth is responsible for 18% of all deaths among under-five children and up to 35% of newborn death (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The newborn period, particularly the first day and week of life, is the most vulnerable period for a child\u0026rsquo;s survival, with the most of neonatal deaths occurring within first 28 days (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In developing countries, survival rates for preterm babies born at 24 and 28 weeks of gestation vary significantly, with the survival rates ranging from 50\u0026ndash;90% babies, but drops dramatically to less than 10% in low income countries (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreterm neonates face short-term complications, such as respiratory distress syndrome, feeding difficulties, and sepsis, as well as long-term developmental delays, neurologic impairments, and chronic health problems (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Multiple factors contribute to preterm birth, including medical conditions affecting the mother or fetus, genetic influences, environmental exposures, infertility treatments, behavioral and socio-economic factors (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The study in East Africa revealed that 52% of neonatal deaths were due to prematurity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), with a significant proportion of these deaths occurring within the first 3 days of life (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In Ethiopia, study indicated that incidence rate of 62.15 and 19.2 death per 1000 person-day-observations with median survival time from 15\u0026ndash;17 days in Mizan Tepi and Addis Ababa, respectively (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the implementation of Various strategies, preterm mortality remains one of global agendas (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Neonatal death represents an increasing proportion of under-five child deaths, with the percentage of neonatal death rising from 41% in 2000 46% in 2016 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe majority of newborn deaths occurred in two regions: Southern Asia (39%) and sub-Saharan Africa (38%). Five countries accounted for half of all new-born deaths: India (24%), Pakistan (10%), Nigeria (9%), the Democratic Republic of the Congo (4%) and Ethiopia (3%) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Ethiopia, preterm birth is a major contributor to neonatal mortality (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The neonatal mortality rate in Ethiopia has displayed a concerning trend over the years, with minimal improvement noted. Reports from the Ethiopian Demographic and Health Survey (EDHS) highlight a potential increase in neonatal mortality rates: 39 per 1000 live births in 2005, 37 in 2011, 29 per 1000 live births in 2016 rising to 33 per 1000 live births in 2019, predominantly attributed to prematurity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Approximately 320,000 premature births occur annually in Ethiopia, resulting in direct deaths of 23,100 children under the age of five (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The prevalence of neonatal mortality varies across the regions, with higher rates observed in the Amhara (20.3%) and Oromia (18.8%) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreterm mortality predictors include prenatal asphyxia, hyaline membrane disease, sepsis, jaundice, low gestational age, respiratory distress syndrome, and initial temperature (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Additionally, factors such as antepartum hemorrhage, preeclampsia, eclampsia, multiple pregnancies, premature rupture of membranes, and smoking or substance use during pregnancy contribute to preterm neonatal mortality (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe implication of preterm birth and subsequent mortality extend beyond affected infants to their families and society at large. Preterm mortality can lead to emotional distress for the families, increased healthcare expenses, and potential long-term health issues for surviving preterm infants (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The financial burden on families and health care facilities is significant, as preterm infants often require specialized medical care in neonatal intensive care units (NICUs) and are more likely to need prolonged hospital stays (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite efforts to reduce preterm mortality, LMICs like Ethiopia continue to face a high prevalence of preterm births and neonatal mortality, with prematurity being a leading cause of neonatal deaths (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). There is an urgent need for effective strategies to address this public health issue. This study aims to contribute valuable insights to the existing knowledge by identifying the predictors of preterm mortality. It will also assist healthcare providers and policymakers in developing evidence-based strategies to reduce mortality rates and enhance the overall survival and well-being of preterm infants. The findings of this study will also aid healthcare providers in Adama Hospital Medical College in developing evidence-based strategies to enhance care, reduce mortality rates, and improve the overall quality of neonatal healthcare delivery in their specific context. Therefore, this study was aimed to assess the incidence and predictors of mortality among preterm neonates admitted to neonatal intensive care unit of Adama Hospital Medical College.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area and period\u003c/h2\u003e \u003cp\u003eThe study was conducted at Adama Hospital Medical College, located in Adama town, Oromia region from June 1, 2024 to July 30, 2024 which is located approximately 100 km southeast of Ethiopia's capital, Addis Ababa. Serves as a vital healthcare hub for a catchment population exceeding 6\u0026nbsp;million from five regions: Oromia, Amhara, Afar, Somali, and Dire-Dawa. The hospital has a capacity of 232 beds of which 77 beds are allocated for NICU. There are 8 public health facilities in the town (one hospital, the rest health centers), 9 private hospitals, 6 non-governmental health centers, and 104 private clinics. On a monthly basis, the NICU admits an average of 310 patients, while the hospital as a whole attends to around 1000 patients daily across six medical case teams (OPDs) and various specialty clinics. Functioning as both a referral center and a teaching hospital, Adama Hospital Medical College plays a critical role in healthcare delivery and medical education in the region.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eInstitutional-based retrospective follow-up study was employed.\u003c/p\u003e\n\u003ch3\u003ePopulation:\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eSource population\u003c/strong\u003e \u003cp\u003eAll preterm neonates admitted to the NICU of Adama Hospial Medical College.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy population\u003c/strong\u003e \u003cp\u003eAll preterm neonates that were admitted at NICU of Adama Hospital Medical College from January 1, 2021 to December 30, 2023 and fulfill eligibility criteria.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInclusion criteria\u003c/h2\u003e \u003cp\u003ePreterm neonates admitted to Adama Hospital Medical College NICU from January 2021 to December 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExclusion criteria\u003c/h2\u003e \u003cp\u003e Neonates with incomplete medical records or missing key data required for analysis was considered under exclusion criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size determination\u003c/h3\u003e\n\u003cp\u003eAll preterm neonates admitted in the NICU from January 2021 to December 2023 were considered with this study. The required sample size was determined using a single population proportion formula n = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\left(Z\\frac{a}{2}\\right)}^{2}\\:\\text{*}\\:\\text{p}(1-\\text{p})}{{d}^{2}}\\)\u003c/span\u003e\u003c/span\u003e, where n is the sample size; z is the value of standard normal distribution corresponding to a significant level of α of 5%, which is 1.96; d is the margin of error taken as 5% and p is the estimated proportion of preterm neonatal death admitted in the NICU 29.7% taken from previous study the sample size was 352 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). For the second objective the sample size was determined using STAT CALC of Epi info for cohort study with the assumptions of 95% CI, power 80% and 22.1% as outcome among unexposed and risk ratio 1.51 by considering sex of the neonate as a predictor variable from previous study done. Based on these assumptions the sample size is 526. The sample size calculated for second objective was larger than the sample size calculated for first objective. By adding 10% non-response rate, final sample size was 579 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSample size for incidence of mortality and its predictors among preterm neonates admitted in the NICU of Adama Hospital Medical College.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% of outcome in unexposed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon response rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal sample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrenatal asphyxia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving 5th minute APGAR score\u0026thinsp;\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\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\n\u003ch3\u003eSampling technique and sampling procedure\u003c/h3\u003e\n\u003cp\u003eAll newborns admitted in the NICU from January 2021 to December 2023 were included in this study. Neonate\u0026rsquo;s admission and discharge medical card from NICU was extracted. The 579 charts were selected by computer generated simple random sampling technique with their medical number.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eDependent Variable\u003c/h2\u003e \u003cp\u003ePreterm neonatal mortality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Variables\u003c/h2\u003e \u003cp\u003eMaternal Socio-demographic characteristics (maternal age, residence).\u003c/p\u003e \u003cp\u003ePrenatal factors (GA, Gravidity, Parity, ANC follow-up, medical complications (Preeclampsia, Eclampsia, APH, DM)).\u003c/p\u003e \u003cp\u003eIntrapartum factors (PROM, place of delivery, weight of infant at delivery, multiple pregnancies, cord prolapse, mode of delivery).\u003c/p\u003e \u003cp\u003eNeonatal related factors (sex of neonates, age at admission, Temperature at admission, neonatal complications and Apgar score).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOperational definition\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEvent\u003c/strong\u003e \u003cp\u003eRefers to preterm neonates who were recorded as having died during the follow-up period, as documented in their medical records.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCensored\u003c/strong\u003e \u003cp\u003eDenotes preterm neonates who were admitted to the Neonatal Intensive Care Unit (NICU) but were still alive at the end of the study or were lost to follow-up. This includes cases where neonates were discharged to home, discharged against medical advice, or transferred out to other health institutions, as indicated in their medical records.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTime to Death\u003c/strong\u003e \u003cp\u003eSpecifies the exact time at which the death of a preterm neonate occurred within the hospital's waiting time, as documented in the medical records.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFollow-up Time\u003c/strong\u003e \u003cp\u003eRefers to the duration from admission to the NICU until either an event (death) or censorship occurs, as recorded in the medical records.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMortality\u003c/strong\u003e \u003cp\u003eThe rate of death among preterm neonates in the neonatal intensive care unit (NICU) before discharge, expressed as the number of deaths per 1000 live births.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData Collection tools and techniques\u003c/h2\u003e \u003cp\u003eA structured data abstraction sheet was used to collect data from preterm neonate\u0026rsquo;s chart and delivery chart. The data abstraction sheet incudes maternal socio-demographic, prenatal, intranatal, and neonatal-related variables with considering mortality of preterm neonates as an outcome. The questionnaire was taken from different kinds of literature and modified to the required variables. Two BSc nurse data collector and one BSc. nurse supervisor were participated during data collection. Data were collected from June 2024 to July 2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData quality Assurance\u003c/h2\u003e \u003cp\u003eTo assure quality of data, properly designed data abstraction checklist was used. Two data collector were recruited for data collection. In the same manner, 1 BSc nurse was recruited as supervisor. One day training was given for all of them on the data extraction checklist. Checklist prepared in English version was used to collect necessary information from register. Pretest was conducted on 5% of sample size at Adama General Hospital. The collected data were reviewed daily and was checked for completeness, accuracy, and consistency by supervisor and investigator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Processing and Analysis\u003c/h2\u003e \u003cp\u003eData were checked, cleaned and entered into Epi-info software version 7.2.6.0, and then it was exported to STATA version 17 statistical software for analysis. Descriptive statistics was used to summarize the characteristics of the study population, including measures such as means, medians, proportions, and standard deviations. Survival analysis techniques, such as Kaplan-Meier curves and Log-rank test was employed to estimate time to death over time. Cox proportional hazards regression analysis was used to identify predictors of neonatal mortality. Schoenfeld residuals test, interaction of each covariate with time and graphical methods was used to check the Cox Proportional Hazard (PH) assumption. Akaike information criteria (AIC) criteria was used to identify model fitness. Goodness of fit of the model was assessed by using cox-snell residual technique. Model was built by stepwise backwards elimination procedure. The potential candidate predictors to the full model was selected by bivariable Cox proportional hazard regression with cut-off point \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.25. The multi-collinearity for variables in the final fitted model was checked using variance inflation factor (VIF) with cut-off point mean VIF\u0026thinsp;\u0026gt;\u0026thinsp;5. Association between predictors and hazard of neonatal death was summarized using adjusted hazard ratio (AHR), and statistical significances was tested at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The incidence was measured with neonate\u0026rsquo;s days of observation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics of the mother and neonates\u003c/h2\u003e \u003cp\u003eTotal of five hundred and seventy nine charts were reviewed. The mean (\u0026plusmn;\u0026thinsp;SD) age of mother was 30 (\u0026plusmn;\u0026thinsp;6.54) years. Majority, 393 (67.9%) of mother were in the age group of 20\u0026ndash;34 years. The majority, 395 (68.2%) were urban residents. Five hundred forty five (94.1%) of them have ANC follow up and 454 (78.4%) have ANC follow up greater than or equals to four visits. Three hundred sixth-eight (63.5%) of delivery was spontaneous vagina delivery (SVD). The majority, 458 (79.1%) of neonate was born at Adama Hospital Medical College and 537 (92.7%) of pregnancy was singleton. Two hundred sixth nine (46.5%) of mother had medical problem during pregnancy. One hundred forty-eight (25.6%) of mothers have preeclampsia and 22(3.8%) of them have DM during their pregnancy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaternal-related characteristics of mothers who had preterm neonatal admission at Adama Hospital Medical College NICU, Adama, Ethiopia, from January 1, 2021 to December 30, 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCensored\u003c/p\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMaternal age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(5.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(12.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e393(67.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152(26.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(15.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e306(52.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e395(68.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(10.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184(31.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eANC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399(68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e545(94.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(5.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of ANC Follow up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374(64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e454(78.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125(21.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(9.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31754.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e370 (63.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209(36.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMode of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272(46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e368(63.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaesarean section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151(26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e207(35.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstrumental delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344(59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e458(79.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOut born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121(20.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e396(68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e537(92.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42(7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaternal Medical problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269(46.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e294(50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310(53.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148(25.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e431(74.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142(24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e415(71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e557(96.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400(69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e513(88.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePROM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e294(50.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266(45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e285(49.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCord prolapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112(19.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366(63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e467(80.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eNeonatal related characteristics\u003c/h2\u003e \u003cp\u003eThe majority, 323 (55.8%) of neonates were male and 234 (55.4%) neonates were born with gestational age greater than or equals to 32 weeks. The majority of neonates, 314 (54.2%) were born with low birth weight and 466 (80%) of preterm neonates had fifth minutes APGAR score\u0026thinsp;\u0026ge;\u0026thinsp;7 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNeonatal characteristics of neonates admitted diagnosed preterm at NICU of Adama Hospital Medical College from January 1, 2021 to December 30,2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCensored\u003c/p\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of Neonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240(41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e323(55.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187(32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e256(44.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGestational age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely preterm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24(4.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery preterm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e234(40.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate to late preterm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e260(44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e321(55.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBirth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely low birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(6.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery low birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129(22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e265(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314(54.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(3.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAPGAR score 5\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113(19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e417(72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e466(80.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e427(73.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e573(98.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRespiratory rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181(31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229(39.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104(17.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246(42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e350(60.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothermic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105(18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246(42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e351(60.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHyperthermia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200(34.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75(12.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99(17.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128(22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e352(60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e480(82.9)\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\u003eApnea prematurity, RDS and Sepsis were common cause of mortality among preterm neonates. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIncidence of Mortality\u003c/h2\u003e \u003cp\u003eBased on the finding the mortality among preterm neonates was 26.3% (95% CI: 22.8, 29.9) at NICU of Adama Hospital Medical College. This overall mortality rate is in line with the study done at Debre Markos Referral Hospital (27.11%) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and selected hospital in Addis Ababa, Ethiopia 29%(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, it is lower when compared to study conducted at Nepal (60%) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and higher when compared to the study conducted at Hawassa University Comprehensive Specialized Hospital 14.2% (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and Uganda (19.8%). The possible reason for this variation in mortality might be difference in sample size, methodology, study population and quality of care provided.\u003c/p\u003e \u003cp\u003eAt the end of the follow up the overall incidence of mortality of preterm neonates was found to be 33.54 deaths per 1000 preterm neonate-days of observation. The finding is consistent with EDHS 2019 report, which noted 33 deaths per 1000 live births (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, it is lower than the study conducted at Tikur Anbessa Specialized Hospital, which was 39.1 per-1000 person day (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), and in a study conducted at selected public hospital in Addis Ababa, which found a mortality rate of 36.4 per 1000 neonatal days observation (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Additionally a study at public hospital in Southern Ethiopia reported a mortality rate was 47.7 per 1000 neonatal days observation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), while study in Gurage zone public hospitals revealed an overall incidence density rate was 36.9 per 1000 person-day observation (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, the mortality rate was higher than the study conducted at Hawassa University Comprehensive Specialized Hospital (28 per 1000 neonatal day observation) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), Debre Tabor Specialized Comprehensive Hospital ( 31.2 per 1000 live births) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and Addis Ababa (19.2 deaths per 1,000 live births). This marked variation may be attributed to difference in sample size, study period and the characteristics of study participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eTime to mortality of preterm neonates\u003c/h2\u003e \u003cp\u003eMedian time to death among preterm neonates was 3 days with minimum of 1 day and maximum of 20 days.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eKaplan-Meier preterm neonatal survival probability\u003c/h2\u003e \u003cp\u003eThe graph below indicates that the survival rate preterm neonate decreases as time increases approximately up to 7 days. After 20 days, the survival rate remains relatively constant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of death among preterm neonates\u003c/h2\u003e \u003cp\u003eThis study revealed that preterm neonates born to mother who had medical problem during pregnancy had higher risk of death when compared to their counterpart. This finding is supported by study conducted at TASH (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and Debre Tabor Comprehensive Specialized Hospital (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This may be due to the medical complications can leads to impaired blood flow that results in insufficient oxygen and nutrients for the fetus can cause respiratory problems and other developmental issues in neonates. Also it can affect the ability to care for newborn.\u003c/p\u003e \u003cp\u003eAccording to this study the risk of death was higher among neonates born to mother who had APH during the pregnancy when compared to neonates born from mother who didn\u0026rsquo;t had APH. The finding is in line with the study conducted in Addis Ababa Public Hospitals, Ethiopia (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), and Iraq (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The reason for this may be due to compromization of blood flow and oxygen delivery to the fetus and stress from APH can trigger a systemic response in the mother that affect fetal health.\u003c/p\u003e \u003cp\u003eAlso the study revealed that the hazard of death among neonates who born to mother who had PROM during delivery was higher when compared to neonates who born from mother who had no PROM during delivery. The finding is consistent with the study conducted at Dilchora Referral hospital, Dire Dawa city, Ethioipa (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The reason for this may be they are exposed to infection, respiratory distress and organ immaturity.\u003c/p\u003e \u003cp\u003eThe study found that the risk of death was higher among neonates born from mother who had ANC follow up less than four visit when compared to neonates whose mother had greater than four ANC follow up. This study is supported by study conducted in Jimma Zone, Southwest Ethiopia (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This might be due to inadequate monitoring during pregnancy, delayed intervention due to limited ANC potential complication may go untreated and reduced quality of care.\u003c/p\u003e \u003cp\u003eThe risk of neonatal death diagnosed with respiratory distress syndrome was higher compared to those without respiratory distress syndrome. This study was consistent to study conducted at Debre Markos Referral Hospital (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), Northwest part of Ethiopia (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), Tikur Anbessa Specialized Hospital (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and Uganda(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). This might be due to inadequate perinatal care attributed to less use of lung surfactant, inadequate availability of noninvasive and invasive ventilation methods and concomitant overlooked complications of RDS that increase the risk of mortality in preterm neonate.\u003c/p\u003e \u003cp\u003eAgain the study found that the risk of neonatal mortality was higher among neonates diagnosed to have PNA when compared to those who didn\u0026rsquo;t have PNA. This study was in line with the study conducted at Hawassa University Comprehensive Specialized Hospital (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), Gurage zone public hospitals (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), Nepal (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and Uganda (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The possible reasons for this might be PNA leads to progressive hypoxemia and hypercapnia resulting in central nervous and other end organ damage. The presence of neonatal encephalopathy is considered as an essential etiologic link that predicts mortality or severe disability.\u003c/p\u003e \u003cp\u003eThe study also found that the neonates who had fifth minutes Apgar score less than seven have higher risk of death than those who had Apgar score greater than seven. This study is consistent with the study conducted at TASH(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), Hawassa University Comprehensive Specialized Hospital (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), and Ghana (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). This may be due to compromised physiological functions, requiring more intensive care and potentially leading to increased mortality. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictors of preterm neonatal death admitted to NICU of Adama Hospital Medical College from January 1, 2021 to December 30, 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCensored\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal medical problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.078(7.738,22.104)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.141(1.619,6.092)**\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.266(3.764, 7.366)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.477(.990,2.204)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.945(1.748, 4.960)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.306(.585,2.914)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.948(2.728, 5.713)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.034(1.298,3.187)**\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161\u003c/p\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.542(4.662, 12.200)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.797(1.607,4.868)**\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of ANC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.668(2.664, 5.052)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.502(1.719,3.642)**\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate to late preterm\u003c/p\u003e \u003cp\u003eVery preterm\u003c/p\u003e \u003cp\u003eExtremely preterm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003cp\u003e73\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e261\u003c/p\u003e \u003cp\u003e161\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.687(1.201, 2.371)\u003c/p\u003e \u003cp\u003e4.859(2.852, 8.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e.924(.621,1.374)\u003c/p\u003e \u003cp\u003e1.464(.776,2.762)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPGAR score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e417\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e13.944(9.808,19.824)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e5.358(3.506,8.190)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127\u003c/p\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.734(1.261, 2.408)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.180(1.510, 3.147)**\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.932(2.168,11.221)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.809(1.125,7.013)**\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt the same facility\u003c/p\u003e \u003cp\u003eReferred from other facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e344\u003c/p\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.552(1.073, 2.246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e.864(.516,1.448)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e306\u003c/p\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.716(1.238,2.378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.063(.704,1.605)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e** Significant at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eLimitation of the study\u003c/h2\u003e \u003cp\u003eSince the data were recorded from secondary sources it is prone to data incompleteness. The incomplete record were excluded that may leads to selection bias. The study is retrospective study and based on the secondary data, first it did not allow inference to be drawn with respect to the temporal relationship among variables and association does not imply causation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe incidence of preterm neonatal mortality rate was high. The risk of death increased if the neonates had respiratory distress syndrome, perinatal asphyxia, born from mother who had medical complication during pregnancy, Antepartum hemorrhage, ANC follow up less than four and premature rupture of membrane.\u003c/p\u003e \u003cp\u003eTo reduce the burden of preterm neonatal mortality the health care provider should give due attentions to neonates diagnosed with RDS, perinatal asphyxia and low APGAR score. More emphasis should be given to prevent and treat complications during pregnancy and delivery. Furthermore, longitudinal prospective cohort study recommended to identify additional factors that determine preterm survival and also see the outcomes of those preterm who were censored in this study.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eAPGAR- Appearance, Grimace, Activity, and Respiration, CI- Confidence Interval , C/S- Caesarean section, DM- Diabetes Mellitus, EDHS-Ethiopian Demographic and Health Survey, IQR-Interquartile Range, LMIC-Low and Middle Income Country, NICU- Neonatal Intensive Care Unit, PROM-Premature Rupture of Membrane, RDS-Respiratory Distress Syndrome, SDG-Sustainable Development Goal, SVD-Spontaneous Vaginal Delivery, TASH-Tikur Anbessa Specialized Hospital , UNICEF-United Nation International Child Education and Fund, WHO-World Health Organization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Consideration\u003c/h2\u003e\n\u003cp\u003eThe ethical clearance was obtained from Institutional review board of Yekatit 12 Hospital medical college. Support letter was obtained from the department of Public health research and publication office. The necessary permission was obtained from Oromia Regional Health Bureau, Public Health and Emergency Management (PHEM) (Approval No. BB/KBTRC/196/1909, dated of 31/ 05/2024 G.C.\u0026nbsp;Since the data were from medical records and no direct contact was made with the patients, informed consent was not obtained from the participants. However, all data were anonymized and handled with strict confidentiality to protect patient privacy. Identifiable personal information was not used in any part of the analysis or reporting. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eData availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe data sets generated and analyzed during the current study are not publicly available due to institutional and ethical restrictions concerning patient confidentiality but are available from the corresponding author on reasonable request and with permission.\u003c/p\u003e\n\u003ch2\u003eCompeting interest\u003c/h2\u003e\n\u003cp\u003eThe authors have declared that no competing interests exist.\u003c/p\u003e\n\u003ch2\u003eConsent to Participate\u003c/h2\u003e\n\u003cp\u003eThis study was conducted using secondary data with no direct contact with participants. Ethical approval was obtained from Institutional review board of Yekatit 12 Hospital Medical College and necessary permission obtained from Oromia regional Health Bureau, Public Health and Emergency Management (PHEM) which also granted a waiver of informed consent (Approval no.\u0026nbsp;BB/KBTRC/196/1909, dated of 31/ 05/2024 G.C). Therefore, informed consent from participants or their legal guardians was not required. However, all data were anonymized and handled with strict confidentiality to protect patient privacy.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe author received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e\n\u003cp\u003eConsent to publication: not applicable\u003c/p\u003e\n\u003ch2\u003eAuthors contribution\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eKKA\u003c/strong\u003e: Conceptualization, Data curation, Formal analysis and Writing original draft. \u003cstrong\u003eDJ\u003c/strong\u003e: Methodology, software, supervision, Validation and visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRS\u003c/strong\u003e: Data curation, methodology, software and supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBL\u003c/strong\u003e: Investigation, Methodology, software and Validation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGB\u003c/strong\u003e: Data curation, investigation, methodology and software.\u003c/p\u003e\n\u003ch2\u003eAcknowledgment\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eI would like to express my deepest appreciation goes to Yekatit 12 Hospital Medical College for selecting the topic that align with my interests. My sincere gratitude also goes to Adama Hospital Medical College, the data collectors, the study participants, and the dedicated members of the staff. Special thanks to the head of the NICU for their invaluable support in facilitating the data acquisition process.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQuinn J, Munoz FM, Gonik B, Frau L, Cutland C, Mallett-moore T, et al. Preterm birth : Case definition \u0026amp; guidelines for data collection , analysis , and presentation of immunisation safety data q. Vaccine [Internet]. 2016;34(49):6047\u0026ndash;56. Available from: http://dx.doi.org/10.1016/j.vaccine.2016.03.045\u003c/li\u003e\n\u003cli\u003eHug L, Alexander M, You D, Alkema L. National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis. Lancet Glob Heal [Internet]. The Author(s). Published by Elsevier Ltd. 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Zanco J Med Sci. 2018;22(2):155\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eThomas G, Demena M, Hawulte B, Eyeberu A, Heluf H, Tamiru D. Neonatal Mortality and Associated Factors Among Neonates Admitted to the Neonatal Intensive Care Unit of Dil Chora Referral Hospital, Dire Dawa City, Ethiopia, 2021: A Facility-Based Study. Front Pediatr. 2022;9(February):1\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eDebelew GT, Afework MF, Yalew AW. Determinants and causes of neonatal mortality in jimma Zone, Southwest Ethiopia: A multilevel analysis of prospective follow up study. PLoS One. 2014;9(9). \u003c/li\u003e\n\u003cli\u003eSinshaw AE, Minuye B, Mengistie B, Yeshambel A, Assefa N. Mortality of preterm neonates and its predictors in the Northwest part of Ethiopia: A retrospective cohort study. Res Sq [Internet]. 2019;1\u0026ndash;23. Available from: https://doi.org/10.21203/rs.2.10832/v1\u003c/li\u003e\n\u003cli\u003eTibaijuka L, Bawakanya SM, Owaraganise A, Kyasimire L, Kumbakumba E, Boatin AA, et al. Incidence and predictors of preterm neonatal mortality at Mbarara Regional Referral Hospital in South Western Uganda. PLoS One [Internet]. 2021;16(November):1\u0026ndash;17. Available from: http://dx.doi.org/10.1371/journal.pone.0259310\u003c/li\u003e\n\u003cli\u003eKofi E, Id A, Osarfo J, Id JA, Anane-fenin B, Okai E, et al. Determinants of preterm survival in a tertiary hospital in Ghana : A ten-year review. PLoS One [Internet]. 2021;16(1):1\u0026ndash;15. Available from: http://dx.doi.org/10.1371/journal.pone.0246005\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Incidence, preterm birth, neonatal mortality, Censored, NICU, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-6710199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6710199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePreterm birth, or delivery before 37 weeks of gestation, is a significant global health problem, especially in low and middle-income countries like Ethiopia. Preterm infants have a higher risk of mortality despite advances in neonatal care. This study was aimed to assess the incidence of mortality and its predictors among preterm neonates admitted to the neonatal intensive care unit of Adama Hospital Medical College in Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A retrospective follow-up study was conducted among 579 preterm neonates from the identified list of neonates admitted to neonatal intensive care unit of Adama Hospital Medical College from January 1, 2021 to December 30, 2023 selected using simple random sampling. Data were checked, cleaned and entered into Epi-info software version 7.2.6.0, and then it was exported to STATA version 17.0 statistical software for analysis. Kaplan-Meier curves and Log-rank test was used to estimate and compare time to death over time. Cox proportional hazards regression model was fitted to identify predictors of mortality. A p-value of \u0026lt;\u0026thinsp;0.05 was used to declare the statistical significance of the association.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study found a mortality rate of 26.3% (95% CI: 22.8, 29.9) and the overall incidence rate was 33.5 deaths per 1000 preterm neonate-days of observation. Maternal medical problems (AHR\u0026thinsp;=\u0026thinsp;3.1 [95% CI: 1.6, 6.1]), Respiratory distress syndrome (AHR\u0026thinsp;=\u0026thinsp;2.2 [95% CI:1.5, 3.2]), Perinatal asphyxia (AHR\u0026thinsp;=\u0026thinsp;2.8 [95% CI 1.1, 7.0]), 5th minute Apgar score less than seven (AHR\u0026thinsp;=\u0026thinsp;5.4 [95% CI: 3.5, 8.2]) were identified as the predictors of mortality among preterm.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe incidence of preterm neonatal mortality is relatively high, Maternal medical problems, respiratory distress syndrome, low Apgar scores, and perinatal asphyxia should be prioritized to improve outcomes for preterm infants in this setting.\u003c/p\u003e","manuscriptTitle":"Incidence of mortality and its predictors among preterm neonates admitted to neonatal intensive care unit of Adama Hospital Medical College, Adama, Ethiopia. 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