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Case-load, associated characteristics and outcomes of small for gestational age (SGA) neonates admitted to a tertiary hospital neonatal unit in Kigali, Rwanda: a cross-sectional study

Case-load, associated characteristics and outcomes of small for gestational age (SGA) neonates admitted to a tertiary hospital neonatal unit in Kigali, Rwanda: a cross-sectional study

Raban Dusabimana1,2, Jaeseok Choi1,2, Fedine Urubuto1,2, Faustine Agaba2, Raissa Teteli3, Muzungu Kumwami2, Cliff O’Callahan2,4,5,6, Peter Cartledge2,5,6,&


1School of Medicine, University of Rwanda, Kigali, Rwanda, 2Department of Pediatrics, University Teaching Hospital of Kigali (CHUK), Kigali, Rwanda, 3Department of Pediatrics, Harmony clinic, Kigali, Rwanda, 4Department of Pediatrics, Middlesex Health and University of Connecticut, Storrs, Connecticut, USA, 5Department of Emergency Medicine, Yale University, New Haven, Connecticut, USA, 6Human Resources for Health (HRH) Program, Ministry of Health, Kigali, Rwanda



&Corresponding author
Peter Cartledge, Univerisity Teaching Hospital of Kigali (CHUK), KN4 Ave, Kigali, Rwanda




Introduction: small for gestational age (SGA) is defined as birth weight less than the 10th percentile with a population prevalence of 17% in sub-Saharan Africa. Mortality and morbidity are worse in SGA neonates and there are long term implications from fetal growth restriction. Objective: the goal of this study was to evaluate and report the case-load, associated characteristics and outcomes of neonates admitted with SGA at the largest tertiary public hospital in Kigali, Rwanda.


Methods: a prospective, cross-sectional, observational study was performed. We defined SGA as birth weight <10th percentile by gender according to the Alexander reference population. Eligible infants were identified through the neonatal registry.


Results: of 1184 admitted neonates, 38% were SGA. Mortality in these SGA neonates (16%) was higher than appropriate for gestational age neonates (AGA, 13.4%) (AOR=2.03, CI: 1.1-3.5, p=0.011). SGA neonates, compared to their AGA peers, had a more extended hospital stay, and displayed faster postnatal growth.


Conclusion: the case-load of SGA neonates in this reference hospital setting is high. The poorer outcomes in SGA neonates speaks to the need for: i. continued improvements in antenatal care throughout the health system to decrease the prevalence of small for gestation age and therefore case-load and ii. Optimisation of direct care for SGA neonates in order to minimise negative outcomes.



Introduction    Down

Small for gestational age (SGA) is defined as birth weight less than the 10th percentile or less than -2 standard deviations (SDs) by reference population [1-4]. Fetal growth restriction is found in term and preterm neonates, and in both groups has important adverse effects on future survival, health, growth and development [5-8]. Being born SGA is associated with fetal distress in labor and perinatal mortality and morbidity [9-11]. SGA neonates are associated with both early and late complications. Early complications include raised mortality, infection, hypoglycaemia, and more extended hospital stay [12-16] SGA neonates have a post-natal weight catch-up period from 6-months to 2-years before most attain normal growth [17,18]. However, 10% persist with short stature throughout their childhood [19]. To be born with SGA, increases the risks of insulin resistance, obesity, hyperlipidaemia and other metabolic disorders in later life contributing to poorer health outcomes in later adult life [1,20,21]. Demographic surveys have demonstrated that Rwanda has reduced neonatal mortality significantly between 1994 and 2014, from 44 to 20 neonates per 1000 live births [22]. However, no published data are available in Rwanda for the subgroup of SGA neonates who are admitted to neonatal units.

Objective: the goal of this study was to evaluate and report the case-load, associated characteristics and outcomes of neonates admitted with SGA at the largest tertiary public hospital in Kigali, Rwanda through the use of an electronic database (registry).



Methods Up    Down

Study design: this was a cross-sectional, descriptive study where the reporting has been verified in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist [23].


Location: this study was carried out in the neonatal unit at Kigali University Teaching Hospital (Centre Hospitalier Universitaire de Kigali, CHUK), located in Kigali (the capital city of Rwanda). CHUK is the largest, public, tertiary referral hospital in Rwanda, and serves as a teaching hospital for the University of Rwanda (UR). The hospital has approximately 2000 deliveries annually and is a referral centre for high-risk pregnancies and deliveries. The neonatology unit has approximately 560 admissions annually (with approximately 20-30 cot capacity). The majority of admissions are neonates born in the hospital (“in-born”). Some neonates transferred from District Hospitals (“out-born”) are admitted but typically these are admitted to the general pediatric department due to bed capacity and space constraints for both neonatology and maternity units [24].


Participants: inclusion criteria were all neonates admitted in the neonatal unit who had the Rwandan Neonatal Database Collection Form (RNDCF) completed prospectively and had all three data-points available to identify SGA, namely; birth weight, sex, and gestation. Exclusion criteria were cases based on retrospective data or duplicated in the neonatal database.


The neonatal database/registry (data collection and management): the CHUK neonatal registry was established in 2013 and the description of its development and use has previously been described in the literature [24-27]. A robust newborn admission record (NAR), was introduced between July and August 2012 and facilitates data-collection. Data from the paper data-collection tool is inputted into the electronic Rwanda Neonatal Database (NDB), held in a password-protected Microsoft Access database. Pediatric residents and paediatricians on-duty for the neonatal unit complete the data-collection.


Data protection and confidentiality: all data were maintained in a secure area in a password protected database. Names or other patient identification information was not disclosed or accessible publicly.


Data quality: to assess the quality of recruitment of admitted newborns, the nursing admission book was cross-checked with the NDB at the end of the study period to retrospectively identify basic demographic information on any missing cases (Figure 1). The RNDCF was designed with a tick-box to document if a pathology or risk was present; however, multiple negative options for each outcome (i.e. No or Unknown) were not completed during data-collection [24].


Variables and outcomes: the primary outcome of this study was to assess the case-load of SGA in neonates admitted to the newborn unit. Secondary outcomes were risk factors associated with SGA birth, morbidity, mortality and confounding factors. Outcomes included the length of stay (LOS), growth rate (g/kg/day to the point of discharge), admission temperature, episodes of hypoglycaemia, and episodes of infection.


How were outcomes defined? SGA neonates were defined using the Alexander reference population as those neonates with birth weight less than 10th percentile by sex [4]. The length of stay was defined as the number of days from the date of admission to the date of discharge or death. The growth rate (g/kg/day) was defined as the weight gained or lost from the date of admission to the date of discharge divided by the length of stay and the birth weight of the neonate.


Sample size (power calculation): the primary outcome of this study was to assess the case-load of SGA of neonates admitted to the neonatal unit and not prevalence in all births. Annual admission rates are approximately 560 per year. Assuming a case-load of 24% [2] with a confidence limit of 5%, a sample size of 188 neonates is required for each year of study (4 years). A total sample of 752 was therefore required. This was surpassed by the prospective data available in the NDB.


Statistical analysis: the data was analyzed using Statistical Package for the Social Sciences (SPSS) version 24.0. Comparison of means was undertaken with independent, two-sided, t-tests. Comparison of categorical variables was undertaken with bivariate analysis with Chi-square test and description of unadjusted odds ratios (OR). This was followed by multivariate logistical regression and presentation of Adjusted Odds Ratios (AOR) with 95% confidence intervals. All risk factors were included in the multivariate analysis. All outcomes, along with the significantly associated characteristics (p<0.05), were included in the multivariate analysis of outcomes.


Ethical approval and consent to participate: informed consent was waived based on this being a data review with no patient interaction. The research protocol was reviewed and granted approved by the the CHUK Research Ethical Committee (Ref: EC/CHUK/300/2017) on 17th March 2017. Subjects did not receive any incentive for this study, and there were no significant physical, legal, emotional, financial and/or social risks to the subjects identified during this study.



Results Up    Down

Participants: a total of 2535 neonates were available in the NDB for the study period. Of these, 1351 neonates were excluded due to duplication of cases (n=292), SGA status unknown (n=236), and retrospectively data-inputted neonates (n=823). A total of 1184 neonates were therefore included in the analysis (Figure 1).


Data quality: the data were collected prospectively, however, not all data-points were available in all eligible neonates (Figure 1). For example, in order to calculate weight gain in g/kg/day, three variables were required and data not always available, namely; birth weight (n=43), discharge weight (n=316), admission date (n=12) and discharge date (n=155).


Case-load of SGA and baseline data: 444 (38%) of the 1184 included neonates were SGA (Table 1). There was no significance between the SGA and AGA groups with regards to sex or place of birth.


Characteristics associated with SGA: on bivariate analysis, characteristics associated with SGA were gestation and maternal hypertension (Table 2). On multivariate logistical regression analysis, the risk factors associated with SGA were prematurity, maternal hypertension, and place of birth. Mild prematurity (32-37 weeks) had the strongest association with SGA (AOR: 4.0, p<0.001).


Outcomes in SGA neonates: on bivariate analysis SGA was not associated with mortality (OR=1.25, CI: 0.9 to 1.7, p=0.18) but was associated with admission hypothermia (OR=1.4, CI: 1.0 to 1.9, p=0.02) (Table 3). Multivariate analysis revealed that mortality (AOR=2.01, CI: 1.1-3.5, p=0.01) and hyperglycaemia (AOR=3.7, CI: 1.0-13.1, p=0.03) were positively associated with SGA, while infections, hypoglycaemia, hypothermia, and need for respiratory support were not significantly associated.


Weight gain and length of stay: Infants who die during admission will have their length of stay and weight gain impacted by their death, they were therefore removed from the analysis of weight gain and length of stay. Surviving SGA infants were found to have faster weight gain during their entire stay, and a longer length of stay compared to surviving AGA neonates (Table 4).



Discussion Up    Down

The goal of this study was to evaluate and report the case-load, associated factors and outcomes of neonates admitted with SGA. We demonstrated that SGA neonates made up a large proportion (38%) of the neonatal case-load. In 2012, an estimated 5.6 million infants born in sub-Saharan Africa were SGA, with a population prevalence of 17% [7,10,28]. The case-load in our centre is therefore double the likely population prevalence. A likely reason for this increased case-rate is that CHUK is one of the specialised centres in Rwanda with the capacity and expertise to manage high risk pregnancies. Hence, reflecting the health needs of these infants have after birth and the burden of care they provide on health facilities and their families.


Associated characteristics: in our cohort of admitted neonates, the characteristics associated with SGA birth were: prematurity, maternal hypertension, and place of birth. Mild prematurity (32-37 weeks gestation) had the strongest association with SGA (AOR: 4.0, p<0.001). It is well known that conditions which cause uteroplacental insufficiency such as maternal hypertension result in prematurity and increase the risk of SGA [5,29]. The prevalence of SGA in the term infant group may, potentially, be biased by the prevalence of infants with Hypoxic Ischemic Encephalopathy (HIE) in this group. Identifying associated factors, such as maternal hypertension, highlight the importance of maternal health, improving pre-conception and antenatal care, in order to reduce the case-burden of SGA and its associated outcomes in settings such as Rwanda.


Outcomes: completely preventing SGA delivery is unrealistic, therefore understanding the outcomes in these neonates is important. Outcomes for SGA neonates reported in the literature demonstrate increased risk of death, hypothermia and hypoglycaemia [11,15,16,30]. Knowing that there is a high case-load, is pertinent to local health system planning. We identified that the mortality rate was significantly higher in SGA vs. AGA neonates (AOR=2.03, p=0.011), a rate consistent with others studies [9,11,28,31-34]. This further highlights the need for prevention, through good antenatal care. It also highlights the importance of recognising these infants and optimisation of care. Hyperglycaemia was to be more significance on multivariate analysis, and further study is needed to explore this and determine whether the significance holds or a clinical explanation is revealed. Our subsequent clinical observation is that this situation has been largely eliminated in our nursery due to the introduction of syringe pumps and more careful monitoring and choice of fluids. Consistent with previous reports, this study demonstrates no difference in early and late-onset infection rate between SGA and AGA neonates and conflicts with reports from India where SGA status increases sepsis risk [11,35,36].


Weight gain and length of stay: weight gain and length of stay in surviving neonates were stratified by gestational group. Consistent with other recent studies, the length of stay was significantly higher for SGA neonates [11,32,33]. This large dataset was able to demonstrate a significantly increased rate of growth throughout the hospital stay for the SGA infants compared to their AGA peers and is consistent with evidence from US and Swedish population-based studies demonstrating more rapid growth of SGA infants in the first 6 months of life, reflecting catch up growth [17,18]. Feeding in SGA infants is often conservative due to fears related to necrotising enterocolitis, a significant complication of prematurity. In resource-limited settings length of stay places high cost on families, places extra burden on stretched nursing teams and places infants at risk of nosocomial infections. Therefore, feeding strategies need to be identified that optimise growth and minimise length of stay.


Study strengths: there are several strengths to this description of SGA case-load. It is a large sample size prospectively collected by physicians into a well-described database. The data reflects neonates born over a long period of time, minimizing short term differences in medical providers or their care, and the process of eliminating incomplete records from the database resulted in a dataset that is still quite large. This public referral teaching hospital is likely representative of many centers in LMIC principal cities where high risk obstetric and neonatal cases are transferred in from within the surrounding metropolitan area and the more distant district hospitals.


Data collection and analysis leading to systems improvements at the clinical level is imperative in LMIC neonatal centers. One powerful result of this study is demonstrating that a database can be created, maintained, and used to generate relevant data, even in a nascent neonatal unit in an LMIC teaching site in a large public teaching hospital. The data collection form and the electronic database are easy to develop, but this effort also shows that there must be institutional support [24].


Limitations: the limitations of our neonatal registry have been fully described, in particular it is limited by the data-quality [24]. Related to this study, the database requires the co-operation and effort of residents who are encouraged to complete the data-collection process on daily rounds during the neonate´s admission in order to increase the capture of pertinent variables in real time. However, our experience suggests that many forms are completed at the point of discharge, therefore data points are not always available or completed. It is well described in the literature that hypertensive disorder of pregnancy, smoking, maternal SGA are factors associated with SGA, however, our limited variable set precluded the ability to determine the association, and potential confounding, with several important antecedent maternal and demographic characteristics, such as smoking status, maternal age, history of personal and previous delivery SGA status, and intrapartum risks that have been linked to the delivery of an SGA infant [5]. Other limitations include the fact that CHUK is not representative of the many smaller district, regional, and private hospitals in LMIC settings and can not generate a national rate for any particular variable. No long term follow-up monitoring was performed and, therefore, the ability to study growth and development over a prolonged period are not available from our cohort. We used the US-based Alexander reference in order to classify the newborns as SGA or AGA, and the cut-offs would likely be different if there were a similar reference based on data from LMIC settings.



Conclusion Up    Down

To conclude, we have identified a large case-load and mortality associated with SGA birth in our public referral teaching neonatal unit in Rwanda. Continued efforts are required to prevent fetal growth restriction by improving maternal care at all levels of the Rwandan health system, from the village community health workers and local Health Centers to the District hospitals. This should be concurrent with optimisation of direct care for SGA neonates to minimise negative outcomes.

What is known about this topic

  • An estimated 5.6 million infants born in sub-Saharan Africa were born small for gestational age (SGA) in 2012, with a population prevalence of 17%;
  • Factors associated with SGA birth include; advanced maternal age, primigravida state, maternal tobacco smoking, parental SGA, maternal hypertension, uteroplacental insufficiency, previous SGA neonates, low social-economic status, and maternal age <20-years;
  • The mortality rate increases steadily with decreasing birth weight, and mortality is more common in male than females.

What this study adds

  • The case-rate of SGA in admitted neonates is high (38%) in a Rwandan tertiary hospital;
  • Mortality rate was significantly higher in SGA vs. AGA neonates (AOR=2.03, p=0.011).



Competing interests Up    Down

The authors declare no competing interest.



Authors' contributions Up    Down

The study was undertaken as the undergraduate thesis of the Principal Investigator (RD). RD was supported and supervised by the three authors (PC, FA & CO). CO and RT created the database. RD, JC and FU contributed to data-collection. RT was engaged in the project conception and early data-collection. MK, FU, and RD assisted the data-collection and analysis. RD and PC undertook the analysis. All authors were significant contributors in writing the manuscript. All authors read and approved the final manuscript.



Acknowledgments Up    Down

The authors are grateful to the University of Rwanda pediatric residents who collect and enter data for the NDB.



Tables and figures Up    Down

Table 1: baseline data of SGA and AGA neonates

Table 2: risk factors for SGA

Table 3: outcomes in SGA neonates

Table 4: weight gain and length of stay in surviving neonates (SGA versus AGA neonates)

Figure 1: consort diagram of participants



References Up    Down

  1. Cho WK, Suh BK. Catch-up growth and catch-up fat in children born small for gestational age. Korean Journal of Pediatrics. 2016;59(1):1-7. PubMed | Google Scholar

  2. 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):e1001292. PubMed | Google Scholar

  3. Francis A, Hugh O, Gardosi J. Customized vs INTERGROWTH-21st standards for the assessment of birthweight and stillbirth risk at term. Am J Obstet Gynecol. 2018;218(2):S692-S699. PubMed | Google Scholar

  4. Alexander GR, Himes JH, Kaufman RB, Mor J, Kogan M. A United States national reference for fetal growth. Obstet Gynecol. 1996 Feb;87(2):163-8. PubMed | Google Scholar

  5. Royal College of Obstetricians and Gynaecologist. The Investigation and Management of the Small for Gestational Age Fetus. Green-top Guideline. 2013; 13.

  6. Muhihi A, Sudfeld CR, Smith ER, Noor RA, Mshamu S, Briegleb C et al. Risk factors for small-for-gestational-age and preterm births among 19,269 Tanzanian newborns. BMC Pregnancy Childbirth. 2016;16:1-12. PubMed | Google Scholar

  7. Black RE. Global Prevalence of Small for Gestational Age Births. In: Nestle Nutrition Institute Workshop Series. 2015;81:1-7. PubMed | Google Scholar

  8. Christian P, Lee SE, Angel MD, Adair LS, Arifeen SE, Ashorn P et al. Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries. Int J Epidemiol. 2013;42(5):1340-1355. PubMed | Google Scholar

  9. García-Basteiro AL, Quintó L, Macete E, Bardají A, González R, Nhacolo A et al. Infant mortality and morbidity associated with preterm and small-for-gestational-age births in Southern Mozambique: a retrospective cohort study. PLoS One. 2017;12(2):1-14. PubMed | Google Scholar

  10. Lee ACC, Kozuki N, Cousens S, Stevens GA, Blencowe H, Silveira MF et al. Estimates of burden and consequences of infants born small for gestational age in low and middle income countries with INTERGROWTH-21 st standard: analysis of CHERG datasets. BMJ. 2017;358:1-11. PubMed | Google Scholar

  11. Hasthi UR, Ashwani N, Kumar CS, Chejeti SR. Morbidity and Mortality Patterns in Small for Gestational Age versus Appropriate for Gestational Age Preterm Neonates Admitted in Level II Neonatal Intensive Care Unit: a Observational Study. Int J Sci Study. 2017;4(10):133-136. Google Scholar

  12. Maso G, Jayawardane MAMM, Alberico S, Piccoli M, Senanayake HM. The implications of diagnosis of small for gestational age fetuses using European and South Asian growth charts: an outcome-based comparative study. Sci World J. 2014 Jan 27;2014:474809. PubMed | Google Scholar

  13. United Nations Children´s Fund and World Health Organization.Low Birthweight: Country, regional and global estimates. Geneva: World Health Organization. 2004.

  14. Duman N, Kumral A, Gülcan H, Özkan H, Duman N, Kumral A et al. Outcome of very-low-birth-weight infants in a developing country?: a prospective study from the western region of Turkey Outcome of very-low-birth-weight infants in a developing country: a prospective study from the western region of Turkey. J Matern neonatal Med. 2015;13(1):54-58. PubMed | Google Scholar

  15. Mejri A, Dorval VG, Nuyt AM, Carceller A. Hypoglycemia in term newborns with a birth weight below the 10th percentile. Paediatr Child Health (Oxford). 2010;15(5):271-275. PubMed | Google Scholar

  16. Flamant C, Gascoin G. Devenir précoce et prise en charge néonatale du nouveau-né petit pour l´âge gestationnel. J Gynecol Obstet Biol la Reprod. 2013;42(8):985-995. PubMed | Google Scholar

  17. Albertsson-Wikland K, Karlberg J. Natural growth in children born small for gestational age with and without catch-up growth. Acta Pædiatrica. 1994 Apr;399:64-70. PubMed | Google Scholar

  18. Hediger ML, Overpeck MD, Maurer KR, Kuczmarski RJ, McGlynn A, Davis WW. Growth of Infants and Young Children Born Small or Large for Gestational Age. Arch Pediatr Adolesc Med. 1998;152(12):7-10. PubMed | Google Scholar

  19. Clayton PE, Cianfarani S, Czernichow P, Johannsson G, Rapaport R, Rogol AD. Consensus statement: Management of the child born small for gestational age through to adulthood: a consensus statement of the international societies of pediatric endocrinology and the growth hormone research society. J Clin Endocrinol Metab. 2007;92(3):804-810. PubMed | Google Scholar

  20. Torche F, Echevarría G. The effect of birthweight on childhood cognitive development in a middle-income country. Int J Epidemiol. 2011;40(4):1008-1018. PubMed | Google Scholar

  21. Barker DJ, Gluckman PD, Godfrey KM, Harding JE, Owens JA, Robinson JS. Fetal nutrition and cardiovascular disease in adult life. Lancet. 1993 Apr 10;341(8850):938-41. PubMed | Google Scholar

  22. National institute of Statistics of Rwanda, Ministry of Finance and Economic Planning/Rwanda, Ministry of Health/Rwanda, The DHS Program/ Rockville M. Rwanda Demographic and Health Survey. Kigali. National Institute of Statistics of Rwanda. 2014. Google Scholar

  23. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Lancet. 2007 Oct 20;370(9596):1453-7. PubMed | Google Scholar

  24. Choi J, Urubuto F, Dusabimana R, Kumwami M, Agaba F, Teteli R et al. Establishing a neonatal database in a tertiary hospital in Rwanda-an observational study. Paediatr Int Child Health. 2019;39(4):265-274. PubMed | Google Scholar

  25. Urubuto F, Agaba F, Choi J, Dusabimana R, Kumwami M, Conard C et al. Prevalence, risk factors and outcomes of neonatal hypothermia at admission at a tertiary neonatal unit , Kigali , Rwanda a cross-sectional study. J Matern Neonatal Med. 2019;1-8. PubMed | Google Scholar

  26. Cartledge P, Iratubona, Dusabimana, Choi, Agaba, Teteli et al. Hypothermia prevalence and risk factors in admitted neonates and impact on outcomes at a tertiary neonatal unit, Rwanda: a crosssectional study. Arch Dis Child. 2019;104(Supplement 2):A107. Google Scholar

  27. Cartledge P, Dusabimana R, Choi J, Iratubona F, Agaba A, Teteli R et al. Characteristics and outcomes of small for gestational age (SGA) neonates at a tertiary hospital neonatal unit in Rwanda: a cross-sectional study. Arch Dis Child. 2019;104(Supplement 2):A110-A111. Google Scholar

  28. Katz J, Lee ACC, Kozuki N, Lawn JE, Cousens S, Blencowe H et al. Mortality risk in preterm and small-for-gestational-age infants in low-income and middle-income countries: A pooled country analysis. Lancet. 2013;382(9890):417-425. PubMed | Google Scholar

  29. Willacy H. Small for Gestational Age Babies. Egt Med Inf Syst Ltd. 2013;1-4.

  30. Mullany LC, Katz J, Khatry SK, LeClerq SC, Darmstadt GL, Tielsch JM. Neonatal hypothermia and associated risk factors among newborns of southern Nepal. BMC Med. 2010 Jul 8;8:43. PubMed | Google Scholar

  31. Katz J, Wu LA, Mullany LC, Coles CL, Lee ACC, Kozuki N et al. Prevalence of small-for-gestational-age and its mortality risk varies by choice of birth-weight-for-gestation reference population. PLoS One. 2014;9(3):1-9. PubMed | Google Scholar

  32. Sharma P, McKay K, Rosenkrantz TS, Hussain N. Comparisons of mortality and pre-discharge respiratory outcomes in small-for-gestational-age and appropriate-for-gestational-age premature infants. BMC Pediatr. 2004;4:9. PubMed | Google Scholar

  33. Marzouk A, Filipovic-Pierucci A, Baud O, Tsatsaris V, Ego A, Charles MA et al. Prenatal and post-natal cost of small for gestational age infants: A national study. BMC Health Serv Res. 2017;17(1):221. PubMed | Google Scholar

  34. Tsai LY, Chen YL, Tsou KI, Mu SC. The impact of small-for-gestational-age on neonatal outcome among very-low-birth-weight infants. Pediatr Neonatol. 2015;56(2):101-107. PubMed | Google Scholar

  35. Bartels DB, Schwab F, Geffers C, Poets CF, Gastmeier P. Nosocomial infection in small for gestational age newborns with birth weight <1500 g: A multicentre analysis. Arch Dis Child Fetal Neonatal Ed. 2007;92(6):F449-F453. PubMed | Google Scholar

  36. Hofer N, Edlinger S, Resch B. Comparison of risk for early-onset sepsis in small-for-gestational-age neonates and appropriate-for-gestational-age neonates based on lower levels of white blood cell, neutrophil, and platelet counts. Pediatr Neonatol. 2014;55(4):323-325. PubMed | Google Scholar