Prevalence and maternal risk factors of small for gestational age infants born at a regional centre in Australia: a retrospective study

Introduction

Low birth weight (LBW) refers to an infant with a birth weight below 2,500 g, irrespective of gestational age, gender, ethnicity, or other concurrent clinical conditions (1,2). This classification encompasses constitutionally small, premature, small for gestational age (SGA), and intrauterine growth restriction (IUGR) infants. Infants categorised as SGA are born at a weight more than two standard deviations below the mean or less than the 10th percentile for their gestational age. IUGR is categorised by a fetal growth rate below the normal range for the general population and the growth potential of a specific infant, resulting in the birth of SGA infants (1,3).

Weight at birth is a critical indicator of an infant’s overall health and well-being. Recent research has shown a strong correlation between growth and neurodevelopmental outcomes, highlighting the necessity of closely monitoring a child’s growth (4). In both preterm and term-born children, SGA significantly impacts cognitive outcomes in childhood, in contrast to those born with appropriate weight for their gestational age (4-7). SGA newborns face notable risks that extend from infancy into adulthood, including not only neurodevelopmental issues but also conditions that may arise later in life, such as hypertension, diabetes, and metabolic syndrome. Accurate identification of these newborns is essential for predicting their long-term outcomes (7,8).

SGA is a global public health concern linked to short- and long-term complications (9). The authors could not find Australia-wide data for infants born SGA, specifically among moderate to late preterm infants. Additionally, the national data focuses on low-birth-weight prevalence rather than SGA. In Australia, the proportion of low-birth-weight infants from 2006 to 2017 remained relatively stable, ranging from 6.1% to 6.7%. Among all preterm LBW babies, the percentage varied between 56 and 58%, whereas for full-term babies, it ranged from 1.9% to 2.2% (5). In Organisation for Economic Co-operation and Development (OECD) countries, the percentage of low-birth-weight babies was 6.4% in 2021 (9).

Multiple antenatal risk factors can cause SGA. These risks can be prenatal or perinatal, including fetal infection, malformation, prematurity, maternal smoking and maternal obesity (10,11). Other factors that may contribute to adverse pregnancy outcomes include extreme maternal age (12,13), multiple pregnancies (14), infection (15), chronic medical conditions (16), obstetric complications (17), drug and alcohol consumption (18), and low socioeconomic status (19). Identifying the number of infants born with SGA and examining the underlying factors contributing to SGA is crucial to minimising the adverse effects of neurocognitive delay, morbidity, and mortality. Therefore, it is imperative to recognise the significance of this issue and take appropriate measures to mitigate its consequences.

Aim

This study investigates the prevalence of SGA infants and their risk factors from literature among infants admitted to the postnatal ward (PNW) and special care nursery (SCN) at a regional healthcare facility. The study focused on infants born at or after 32 weeks gestation from 2017 to 2019. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-556/rc).

Methods

This retrospective observational study was conducted at a regional hospital in Victoria, Australia, between January 2017 and December 2019. The SCN unit at the regional hospital provides care for infants born at or after 32 weeks gestation, with exceptions only in cases where delivery of a preterm infant less than 32 weeks gestation was unavoidable. SGA infants are determined based on the birth weight below the 10^th percentile using the Fenton Growth Chart, which provides an assessment of birth weight based on gestational age (20).

A review of the relevant literature was conducted through systematic searches across Medline, PubMed, Scopus, CINAHL, and Google Scholar. In this study, eleven risk factors known in the literature were reviewed and selected for investigation (10). These are maternal age (under 18 and over 35), multiple pregnancies (more than 1), obstetric complications, chronic maternal complications, maternal infections, smoking, substance use, alcohol use, maternal body mass index (BMI) (<18.5 and >24 kg/m²), low socioeconomic status, and prematurity (32–36⁺⁶ weeks). Socioeconomic status was assessed by using the postcode map of the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD), which is part of the Socio-Economic Indexes for Areas (SEIFA) produced by the Australian Bureau of Statistics. This index offers a relative measure of socioeconomic advantages and disadvantages for regions within Australia (21). Gestational diabetes mellitus (GDM) was excluded as a risk factor due to insufficient data and lack of oral glucose tolerance test results. Preterm infants <32 weeks gestational age were excluded from the final analysis.

The data on risk factors associated with SGA infants were retrieved from electronic and physical patient records maintained by the hospital’s medical records department. The data sources included the maternity ward birth register, Special Care Nursery admission register, electronic records including BOS maternity and BOS infant [Birthing Outcome System, Management Consultant And Technology Services (MCATS), Melbourne, Australia], and electronic medical records (EMR) (Sunrise™, Altera Digital Health, Niagara Falls, NY, USA). When the information was unavailable in the EMR, data were sourced from physical medical records and outpatient clinic electronic software, Genie^® (Practice Management Software, Magentus, Brisbane, Australia).

Statistical analysis

All data were collected and recorded onto a Microsoft Office Excel File, for cleaning, then imported to SPSS 24, for analysis and summation. Descriptive statistics were employed to report frequency and proportions. Additionally, bivariate correlation analysis was used to explore any potential correlation between variables and assess the strength and direction of such a relationship.

Ethics approval

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Human Research Ethics Committee of Latrobe Regional Health (No. 2021-37HREA), and individual consent for this retrospective analysis was waived.

Results

Descriptive information

During the study period, the regional centre that delivers infants at or after 32 weeks of gestation recorded a total of 2,546 live births. Out of these, 103 SGA infants were born at the regional centre; all but three were born at or after 32 weeks gestation, and those three were excluded from the study. The three-year prevalence of SGA infants born at or after 32 weeks for 2017–2019 was estimated to be 4.0%. Tables 1,2 provide the demographic characteristics of these SGA infants and their mothers.

Maternal risk factors for SGA

Low socioeconomic status (99%) emerged as the most prevalent risk factor among the eleven identified in the literature, followed by smoking (64.3%) and elevated maternal BMI (52.6%). Table 3 provides a detailed overview of the frequency and proportion of all the risk factors examined.

In all cases of SGA infants, at least one risk factor was identified, with the majority having two or more risk factors (Figure 1). The Pearson bivariate correlational analysis results showed that SGA infants’ birth weight had a significant but small negative correlation with the number of associated risk factors [r(98)=−0.209, P=0.04]. As expected, there was a significant and large positive correlation between birth weight and gestational age [r(98)=0.552, P<0.001]. The analysis suggests that the correlation between birth weight and maternal age was non-significant [r(98)=0.047, P=0.64].

Figure 1 Distribution of the identified number of risk factors in the study sample.

Discussion

Our study evaluated the prevalence of SGA infants and related risk factors identified from the existing literature. Among the SGA infants in our cohort, nearly two-thirds were born at term gestation or later (mean gestation 37.3 weeks), and premature infants fell into the moderate to late preterm category. This differs from national prevalence rates, which usually include SGA infants among LBW infants across all gestational periods, including early and very early preterm infants, significantly increasing the proportion of SGA (22). Consequently, the prevalence of SGA in our study appears lower than the national average. However, there is a notable lack of data in the literature on the prevalence of SGA in regional areas, and our study provides valuable insight into the prevalence of SGA and its risk factors in regional Australia.

We found a strong and clear positive correlation between birth weight and gestational age. The outcome is consistent with the well-established fact that premature birth poses the highest risk for the development of SGA (23). Thus, it is crucial to minimise all risk factors that can affect the length of gestation to prevent the development of SGA. Also, our study has a higher prevalence of SGA among female infants, which is consistent with national data. Nevertheless, the current literature does not provide a definitive correlation between gender and SGA (24).

Our study identified three important risk factors prevalent among our cohort of SGA babies: low socioeconomic status, smoking, and high BMI. This research focussed on a cohort of infants of mothers residing in suburban areas of a region characterised by a low score on the IRSAD, reflecting a relative lack of resources and opportunities available to the population in this region (21). Consequently, low-income families often find it challenging to meet their essential needs, including access to healthcare and education, which can lead to unhealthy gestation and fetal development. This lack of adequate care poses a substantial risk to both the child and the mother. Early interventions during pregnancy could potentially mitigate some of the risks of SGA during fetal development. However, research indicates that low socioeconomic status is not an independent risk factor for SGA infants; rather it is a contributing cofactor. This study of maternal cohort data revealed that nearly all mothers (99.0%) were exposed to multiple additional risk factors alongside low socioeconomic status (19,25).

Research indicates that smoking during pregnancy is a major risk factor for giving birth to an SGA baby (26). Mothers who smoke cigarettes face a considerably higher chance of delivering an SGA infant, with the level of risk increasing in relation to the duration of smoking during pregnancy (27). In 2017, although the Australian Institute of Health and Welfare (AIHW) reported national prevalence of LBW was 6.7%, this rate was significantly higher among mothers who smoked during pregnancy, at 13%, compared to only 6% among mothers who did not smoke (5). It is particularly concerning that smoking is prevalent among the group of mothers in this study, which notably increases the likelihood of SGA births (11). Further, it was not possible to thoroughly investigate the smoking habits of the maternal cohort due to a lack of data, including active or passive exposure and duration of exposure, which are significant factors in determining the severity of the risk.

Elevated maternal BMI can negatively impact the growth and function of the placenta, as well as fetal circulation (28). This can lead to impaired fetal growth, which may persist into childhood and adulthood (29). High BMI during pregnancy is associated with an increased risk of obstetric complications, premature birth and LBW newborns (11). Therefore, effectively managing maternal obesity during pregnancy is essential for the health and well-being of both the mother and the child. This management should encompass a balanced diet and exercise plan along with diligent monitoring of maternal and fetal health throughout the pregnancy. Additionally, it should also include adequate birthing plans to avoid complications for mothers with obesity during both caesarean and vaginal deliveries.

Placental insufficiency is a significant contributor to SGA infants among mothers, with a high proportion of our cohort (41.7%) experiencing this condition. Various factors can lead to placental insufficiency, including inadequate maternal nutrition due to low socioeconomic status, abnormal placental development, or defects in the placenta caused by the anti-phospholipid syndrome (30,31). Mothers should adopt preventive measures to avoid placental insufficiency and ensure healthy growth for their newborns. Recommended interventions include quitting smoking and maintaining adequate blood pressure and blood glucose control during pregnancy (28).

Among the chronic maternal risk factors identified in this cohort, poorly managed hypertension (34.7%) and asthma (18.2%) were the most prevalent. Chronic hypertension is widely recognised as a contributing factor to SGA, and when inadequately managed, it elevates the risk of insufficient placental blood flow in SGA infants (10). Research indicates that maternal asthma, particularly when treated with steroids, increases the likelihood of SGA infants (32).

Mothers who have previously given birth to SGA infants may experience a fourfold increase in the risk of having another SGA infant in subsequent pregnancies (10). Our study supports this finding, revealing that nearly one-third of mothers in our cohort had a history of previous SGA births. Notably, around 1 in 5 mothers (21.9%) reported having little or no antenatal care. Identifying this cohort of mothers with previous SGA births and implementing carefully planned antenatal care is essential to ensure favourable outcomes in future pregnancies (33).

Limitations of the study

This study involves an analysis of retrospective data. It includes limited number of risk factors and is not a comprehensive analysis of all known risk factors associated with SGA. The risk factors discussed in this study only reflect those documented in the medical records available to the authors.

Regrettably, there is a paucity of current documented literature on the prevalence of SGA infants at or beyond 32 weeks, making it challenging to compare our findings with existing data. Nationally, the prevalence rates available are of LBW infants and encompass all gestational periods, including early and very early preterm infants.

We did not examine GDM as a risk factor due to insufficient data and the unavailability of oral glucose tolerance test results. It is worth noting that individuals from low socioeconomic backgrounds are more likely to have low screening rates for GDM, which can lead to complications for both the mother and neonate during and after pregnancy.

The study’s methodological limitation is the absence of a non-SGA control group, limiting causal and correlational analyses of maternal risk factors. Ethical and sample-size constraints also precluded the use of statistical tests to assess the synergistic effects of multiple risk factors. Future research should incorporate larger samples with non-SGA controls for detailed multivariate and interaction analyses.

Conclusions

We assessed the prevalence of infants classified as SGA in a regional centre, focussing on those born at or after 32 weeks of gestation in an area where existing data is limited. SGA infants were more frequently observed in mothers with lower socioeconomic status, those who smoked, and those with elevated body mass index (BMI) and the majority of them had multiple risk factors. Implementing lifestyle changes through public health interventions, such as education and surveillance aimed at reducing obesity and smoking, could help reduce the incidence of SGA and mitigate its potential long-term adverse outcomes. Additionally, further research is needed to compare the prevalence of SGA across regional Australia and to investigate the associated risk factors in greater detail.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-556/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-556/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-556/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-556/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Human Research Ethics Committee of Latrobe Regional Health (No. 2021-37HREA), and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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