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. 2021 Oct 28;16(10):e0258967. doi: 10.1371/journal.pone.0258967

Incidence, risk factors, and feto-maternal outcomes of inappropriate birth weight for gestational age among singleton live births in Qatar: A population-based study

Salma Younes 1, Muthanna Samara 2, Noor Salama 3,4, Rana Al-jurf 5, Gheyath Nasrallah 6, Sawsan Al-Obaidly 7, Husam Salama 8, Tawa Olukade 8, Sara Hammuda 2, Ghassan Abdoh 8, Palli Valapila Abdulrouf 1,9, Thomas Farrell 1,7, Mai AlQubaisi 8, Hilal Al Rifai 8, Nader Al-Dewik 5,10,11,12,*
Editor: Dayana Farias13
PMCID: PMC8553085  PMID: 34710154

Abstract

Background

Abnormal fetal growth can be associated with factors during pregnancy and at postpartum.

Objective

In this study, we aimed to assess the incidence, risk factors, and feto-maternal outcomes associated with small-for-gestational age (SGA) and large-for-gestational age (LGA) infants.

Methods

We performed a population-based retrospective study on 14,641 singleton live births registered in the PEARL-Peristat Study between April 2017 and March 2018 in Qatar. We estimated the incidence and examined the risk factors and outcomes using univariate and multivariate analysis.

Results

SGA and LGA incidence rates were 6.0% and 15.6%, respectively. In-hospital mortality among SGA and LGA infants was 2.5% and 0.3%, respectively, while for NICU admission or death in labor room and operation theatre was 28.9% and 14.9% respectively. Preterm babies were more likely to be born SGA (aRR, 2.31; 95% CI, 1.45–3.57) but male infants (aRR, 0.57; 95% CI, 0.4–0.81), those born to parous (aRR 0.66; 95% CI, 0.45–0.93), or overweight (aRR, 0.64; 95% CI, 0.42–0.97) mothers were less likely to be born SGA. On the other hand, males (aRR, 1.82; 95% CI, 1.49–2.19), infants born to parous mothers (aRR 2.16; 95% CI, 1.63–2.82), or to mothers with gestational diabetes mellitus (aRR 1.36; 95% CI, 1.11–1.66), or pre-gestational diabetes mellitus (aRR 2.58; 95% CI, 1.8–3.47) were significantly more likely to be LGA. SGA infants were at high risk of in-hospital mortality (aRR, 226.56; 95% CI, 3.47–318.22), neonatal intensive care unit admission or death in labor room or operation theatre (aRR, 2.14 (1.36–3.22).

Conclusion

Monitoring should be coordinated to alleviate the risks of inappropriate fetal growth and the associated adverse consequences.

1. Introduction

Gestational age and birth weight are two crucial factors for assessing the fetal growth. Birth weight is a strong determinant of a newborn infant’s survival rate [1, 2]. An appropriate birth weight at gestational age (AGA) is critical when assessing the typical development of a newborn infant. Inappropriate gestational age classification ranges from small-for-gestational age (SGA), referring to birth weight below the 10th percentile, and large-for-gestational age (LGA), referring to birth weight above the 90th percentile [3].

There is a substantial disparity in the prevalence of SGA babies (4.6–15.3%) across Europe [4] and LGA babies (5–20%) in developed countries [5]. These varieties are more apparent in developing countries. According to global estimates, in 2010, 27% of all live births were found to be SGA (over 32 million) in low- and middle-income countries [6], with an SGA prevalence as high as 41.5% in Pakistan and as low as 5.3% in China [7]. There is also a huge deviation in the prevalence of macrosomia (birthweight ≥4000 g) in developing countries, with figures as low as 0.5% in India and as high as 14.9% in Algeria [8]. The variability in the rates of prevalence of SGA and LGA infants is mainly due to socio-environmental factors, population differences, as well as wide variations in the standards applied for assessment in different studies [8, 9].

Size for gestational age is considered as a measure of fetal growth, with SGA regarded an indication of fetal growth restriction and LGA as an indication of rapid fetal development [10, 11]. Risk factors that have been linked to SGA include pre-pregnancy weight, previous history of SGA, smoking, and cardiovascular-associated diseases [12–17]. On the other hand, maternal obesity, diabetes, multipara was found to be linked to higher rates of LGA [12, 15, 17–20].

Babies born SGA or LGA are at high risk of developing increased long-term health complications during the antepartum, intrapartum, and postpartum periods. SGA infants have been shown to develop health complications including birth asphyxia, hypothermia and abnormal neurological development, and are at high risk of mortality [21–28], whereas LGA infants have been shown to develop postpartum hemorrhage and birth injuries [5, 18, 29]. Thus, these newborns often need specialized care to avoid and manage the complications.

Several studies have investigated the risk factors and outcomes associated with birth weight and gestational age separately. However, the concept of defining birth weight in the context of gestational age, referred to as ‘birthweight percentiles’ has been understudied specifically in the Middle East [30]. In addition, most studies to date have focused on low birth weight, and only few reports have described the link between increased birth weight and high mortality risks [31–33] or death in the Neonatal Intensive Care Unit (NICU). While SGA is generally known to be associated with several neonatal outcomes [1–8], LGA is understudied, and comparisons between both groups with AGA in the context of risk factors and outcomes are lacking.

SGA or LGA have traditionally been defined using standards that were based on the weight distribution of infants born in a particular population, rather than describing physiological or healthy growth [34]. In fact, most studies have advocated the continued use of local or customized charts [35, 36]; however, these local charts are only relevant to the population and time from which they were derived and hence make comparison between populations and studies impossible. Recently, The International Fetal and Newborn Growth Consortium for the Twenty-First Century (INTERGROWTH-21st) has described a multinational standard for newborn weight. This research revealed that when women who are not subjected to societal, dietary, medical, or other restrictions on fetal growth, the growth of infants all over the globe is surprisingly comparable [34]. Thus, the INTERGROWTH-21st birth weight standard offers a reliable multinational tool for estimating fetal weight percentiles.

In the present study we aimed to assess the incidence, risk factors and feto-maternal outcomes associated with SGA and LGA births via a population-based retrospective data analysis of singleton live births data retrieved from the PEARL-Peristat Study between April 2017 and March 2018 in Qatar. We examined several demographic and medical confounders to assess the risk for SGA and LGA, while investigating how these confounders are associated with low Apgar score, NICU admission, and mortality. In addition, we explored the relationship between inappropriate birth weight for gestational age and preterm birth, taking into account late preterm and early terms which are rarely investigated in the literature.

2. Methods

2.1. Study design

This was a 12-month retrospective population-based study conducted using registry data from the PEARL-Peristat Study, Qatar. This population-based registry was designed using routinely collected hospital data for parturient women and their offspring. The study was approved by the Hamad Medical Corporation (HMC) Institutional Review Board (IRB), with a waiver of consent.

We included singleton live births at 24+0 weeks gestation and above, whose mothers delivered between April 2017 and March 2018 at the Women’s Wellness and Research Centre (WWRC) in HMC. HMC is the main national hospital, and the main provider of secondary and tertiary healthcare in Qatar. It is also one of the leading hospital providers in the Middle East. HMC consists of four regional hospitals that are widely distributed in different geographical areas of Qatar (Al-Wakra, Al-Khor, Cuban and Women’s Wellness Research Centre hospitals). These hospitals account for the majority of births in the country. In addition, premature babies and those who are admitted to NICU come to these hospitals only. Stillbirths were excluded. A total of 14,641 singleton births were examined.

2.1.1. Neonatal factors

We used the FETALGPSXL tool [37, 38] which takes into account gestational age (days), fetal weight (grams), gender, and maternal ethnic/race group to calculate fetal weight percentiles for births occurring prior to 280 days. This tool provides a simple spreadsheet-based estimated fetal weight percentile calculator and corresponding R software package encompassing 6 fetal growth standards, among which we chose the Intergrowth 21st standard to calculate the percentiles [38]. Accordingly, newborns were categorized into three groups: SGA (defined as birth weight for gestational age below the 10th percentile), AGA (defined as birth weight for gestational age between the 10th and the 90th percentile; reference group), and LGA (defined as birthweight for gestational age above the 90th percentile) [39].

Gestational age (GA) was based on mother’s last menstrual period (LMP), early ultrasound scan (USS) and Ballard scoring [40]. GA was classified in accordance with established international definitions [41]; into preterm (less than 37 weeks’ gestation) and term (at 37 weeks’ gestation and above). For further investigation, GA was further categorized into; extreme to very preterm: < 32 weeks, moderate preterm: 32 to < 34 weeks, late preterm: 34 to < 37 weeks, early term: 37 to < 39, and full term: 39 to < 42). Baby gender was categorized into male, female, and ambiguous. Immediate birth status included an Apgar (Appearance, Pulse, Grimace, Activity, and Respiration) score < 7 at 1 minute, and at 5 minutes. Baby outcome was categorized into discharged alive or in-hospital mortality, while baby disposition was categorized into postnatal ward and NICU or death in Labour Room/ Operation Theatre (LR/OT).

2.1.2. Maternal factors

Maternal age at delivery was grouped into young adults (20–34 years), adolescents (<20 years), and advanced maternal age (> 35 years). Nationality was grouped into Qatari, other Arabs and other nationalities based on the UNESCO list of Arab countries. Consanguinity was coded as yes (the mother and the father are related to each other in any level of relatedness) or no. Educational level was classified into elementary and below, secondary school or high school, college/university or above. Employment status was categorised into employed or unemployed. Smoking status was coded as yes or no, where the mother was asked whether she is a smoker or not.

Women were categorized according to their glycemic status into diabetic and non-diabetic, and further categorized into pregestational diabetics (PGDM), gestational diabetics (GDM) and non-diabetics (no data on Type 1 or 2 were recorded). All pregnant women were screened at the first antenatal care visit using fasting blood glucose and HBA1c- to rule out pre-existing diabetes. Then, 75 grams oral glucose tolerance test (OGTT) was performed between 24–32 weeks’ gestation in low-risk patients and between 16–20 weeks’ gestation in high-risk patients. GDM was diagnosed according to the modified International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria [42], when one or more of the following glucose levels were elevated: fasting plasma glucose level ≥5.1 mmol/L, 1 h plasma glucose level ≥10.0 mmol/L, and 2 h plasma glucose level ≥8.5 mmol/L [42].

Chronic hypertension was coded as yes or no. In addition, for Body Mass Index (BMI) we used pre-pregnancy height and weight and in case they are not available, the early pregnancy (gestational age < = 12) weight and height were used. These measures were taken by the health practitioner (doctor or nurse). Accordingly, mothers’ BMI was categorized into four groups: normal (18.5 to 24.9), underweight (< 18.5), overweight, (25.0 to 29.9) and obese (≥ 30 kg/m2) following NHLBI/WHO guidelines [43, 44].

Parity was classified into nulliparous or parity ≥1. A history of any preterm birth (spontaneous or medically indicated) was coded as yes or no. Pregnancy mode was defined as spontaneous or assisted (including ovulation induction, invitro fertilisation, intracytoplasmic sperm injection, intra uterine insemination, and others). Delivery mode was categorized into vaginal and caesarean.

2.2. Statistical analysis

Statistical analysis was conducted using IBM SPSS 26 software (SPSS Chicago IL, USA). All categorical and binary variables were expressed as numbers and percentages. The overall incidence of SGA and LGA, risk factors, and outcomes were analyzed using Chi Square analysis.

Firstly, logistic regression analysis was performed for risk factors/confounders (demographic and medical factors) and mediators (prematurity and gender) of appropriateness of fetal growth for the GA groups (SGA/LGA vs. AGA). In step one univariate analysis was performed, and the associations were quantified. The statistical significance was set at p<0.05. In step two, multiple logistic regression was performed using all the significant variables (P<0.05) from the univariate analysis as confounders (demographic and medical factors), along with the mediators (prematurity and gender) to investigate associations with SGA and LGA groups.

Secondly, logistic regression was performed to investigate the outcomes of SGA and LGA including Apgar score, NICU/death in LR/OT, and in-hospital mortality. Multiple logistic regression was performed, including all significant confounders (prematurity and gender) from the univariate analysis to investigate the association of SGA/LGA with Apgar score, NICU/death in LR/OT, and in-hospital mortality as outcomes.

We then applied the formula described by Zhang and Yu [45], to compute the relative risk (RR) from the odds ratio (OR) for all logistic regression analyses. Crude and adjusted RRs and their 95% CIs were recorded, with a statistical significance set at p<0.05.

Furthermore, we calculated the population attributable fraction (PAF) % among the different risk factors to determine what percentage of SGA and LGA births might have been prevented if the risk factors had been avoided. For calculating the crude PAFs (cPAFs), we utilized the formula PAF = Pe (RRe − 1)/[1 + Pe (RRe − 1)] [46–48], where Pe is the percentage of people in the population who were exposed to the risk factor and RRe is the crude relative risk in the exposed vs. the unexposed group. For the adjusted PAFs (aPAFs), we used the formula Pd [(aRR—1) ⁄ aRR], in which Pd is the prevalence of exposure among those who were born SGA or LGA, and aRR is the adjusted relative risk in the exposed vs. unexposed group [49–51].

Kaplan-Meier curves were constructed to assess differences in medians, among the three groups (AGA, SGA and LGA) for the outcomes (Apgar score, NICU/death in LR/OT and in-hospital mortality) during the course of 24–40 weeks of gestation. A log rank (Mantel Cox) test was used to assess this difference, with a two-tailed P-value <0.05 regarded as statistically significant.

3. Results

3.1. Characteristics of the study population

A total of 14,641 singleton live births registered in the PEARL database from April 2017 to March 2018 were examined. Of these, 32.45% were overweight mothers and 32.34% were obese. In addition, 31.68% of the mothers had total DM (29.09% GDM and 2.6% PGDM). The maternal characteristics and distribution of the overall study population according to fatal growth are presented in Table 1 and S1 Table. SGA and LGA incidence rates were 6.0% and 15.6%, respectively. In-hospital mortality was 2.5% among SGA infants and 0.3% among LGA infants, while NICU admission or death in LR or OT were 28.9% and 14.9% respectively (Table 1).

Table 1. Characteristics of the study population.

AGA (n = 11,477) SGA (n = 882) LGA (n = 2,282)
n (%) n (%) n (%) p value
Gestational Age 0.000
Preterm 898 (7.8) 181 (20.5) 309 (13.5)
Term 10579 (92.2) 701 (79.5) 1973 (86.5)
Maternal age 0.000
Young adults (20–34 years) 8971 (78.2) 704 (79.8) 1678 (73.5)
Adolescents (<20 years) 252 (2.2) 35 (4) 29 (1.3)
Advanced maternal age (≥35 years) 2254 (19.6) 143 (16.2) 575 (25.2)
Parity 0.000
Nulliparous 3186 (27.8) 379 (43) 341 (14.9)
Parity ≥1 8291 (72.2) 503 (57) 1941 (85.1)
Pregnancy mode 0.211
Spontaneous 11105 (97.3) 851 (97.5) 2221 (97.9)
Assisted 310 (2.7) 22 (2.5) 47 (2.1)
Nationality 0.000
Qatari 3620 (31.5) 308 (34.9) 616 (27)
Other Arabs 4450 (38.8) 261 (29.6) 1072 (47)
Other Nationalities 3404 (29.7) 313 (35.5) 593 (26)
Consanguinity 0.655
No 3064 (66.4) 204 (64.4) 584 (67.2)
Yes 1547 (33.6) 113 (35.6) 285 (32.8)
Education 0.005
Elementary and below 425 (8.6) 27 (7.8) 102 (11)
Secondary/Highschool 1523 (30.8) 127 (36.8) 254 (27.3)
University or above 2992 (60.6) 191 (55.4) 574 (61.7)
Diabetes Status 0.000
No DM 8000 (69.7) 654 (74.1) 1348 (59.1)
GDM 3235 (28.2) 214 (24.3) 810 (35.5)
PGDM 242 (2.1) 14 (1.6) 124 (5.4)
Chronic Hypertension 0.000
No 11335 (98.8) 857 (97.2) 2244 (98.3)
Yes 142 (1.2) 25 (2.8) 38 (1.7)
Pre- or early-pregnancy BMI 0.000
Normal 1494 (33.7) 145 (43) 196 (22.2)
Underweight 128 (2.9) 21 (6.2) 5 (0.6)
Overweight 1445 (32.6) 91 (27) 297 (33.7)
Obese 1364 (30.8) 80 (23.7) 383 (43.5)
Baby gender 0.000
Male 5705 (49.7) 348 (39.5) 1443 (63.2)
Female 5769 (50.3) 534 (60.5) 839 (36.8)
Ambiguous 2 (0) 0 (0) 0 (0)
Chromosomal/Congenital abnormalities 0.000
No 11319 (98.6) 830 (94.1) 2244 (98.3)
Yes 158 (1.4) 52 (5.9) 38 (1.7)
Smoking 0.589
No 8767 (99.1) 664 (98.8) 1708 (99.2)
Yes 77 (0.9) 8 (1.2) 13 (0.8)
Preterm history 0.220
No 10701 (93.2) 814 (92.3) 2108 (92.4)
Yes 776 (6.8) 68 (7.7) 174 (7.6)
Employment status 0.004
Employed 4648 (99.1) 319 (98.8) 874 (97.8)
Unemployed 44 (0.9) 4 (1.2) 20 (2.2)
Delivery mode 0.000
Vaginal 8193 (71.4) 554 (62.8) 1323 (58)
Caesarean 3284 (28.6) 328 (37.2) 959 (42)
Apgar <7 at 1 min 0.000
No 11241 (98.2) 807 (92.2) 2215 (97.3)
Yes 208 (1.8) 68 (7.8) 62 (2.7)
Apgar <7 at 5 mins 0.000
No 11430 (99.8) 867 (99) 2267 (99.6)
Yes 26 (0.2) 9 (1) 9 (0.4)
Baby disposition 0.000
Postnatal ward 10346 (90.2) 627 (71.1) 1941 (85.1)
NICU or died in LR/OT 1130 (9.8) 255 (28.9) 341 (14.9)  
Baby outcome 0.000
Discharged alive 11441 (99.7) 860 (97.5) 2276 (99.7)
Died in hospital 36 (0.3) 22 (2.5) 6 (0.3)
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Abbreviations: DM, diabetes mellitus; GDM, gestational diabetes mellitus; PGDM, pre-gestational diabetes mellitus; AGA, appropriate for gestational age; SGA, small for gestational age; LGA, large for gestational age; Apgar, Appearance, Pulse, Grimace, Activity, and Respiration; NICU, neonatal intensive care unit; LR, labour room; OT, operation theatre.

Bold values denote statistical significance at the p<0.05 level.

There was a significant difference among the groups in the distribution of SGA and LGA in term of gestational age, maternal age, parity, nationality, education, diabetes status, chronic hypertension, early- or pre-pregnancy BMI, baby gender, chromosomal/congenital abnormalities, employment status, delivery mode, Apgar <7 at 1 min, Apgar <7 at 5 mins, baby outcome, and baby disposition (P<0.05). SGA was more likely to occur amongst female preterm babies who were born to adolescent underweight mothers from other nationalities, Qataris, with chronic hypertension and with more chromosomal/congenital abnormalities, with low Apgar score <7 at 1 minute and 5 minutes (p<0.05). On the other hand, LGA was more likely to occur amongst babies with advanced age mothers, parity ≥1, from other Arab origin, with GDM and PGDM and overweight and obese mothers (p<0.05).

3.2. Risk factors associated with inappropriate weight for gestational age

Univariate analysis for SGA as an outcome revealed that preterm birth (cRR, 2.7; 95% CI, 2.32–3.14) and male baby gender (cRR, 0.68; 95% CI, 0.6–0.77) were significantly related to SGA. In addition, SGA was more likely to occur amongst babies of adolescent mothers (cRR, 1.68; 95% CI, 1.22–2.3), with a secondary/high school level of education (cRR, 1.28 (1.03–1.59), chronic hypertension (cRR, 2.13; 95% CI, 1.48–3.07), underweight (cRR, 1.59; 95% CI, 1.04–2.44), or with chromosomal/congenital abnormalities (cRR, 3.55; 95% CI, 2.75–4.58). On the other hand, SGA was less likely to occur amongst mothers with advanced age (0.82 (0.69–0.98), from other Arabs origin (cRR, 0.66; 95% CI, 0.56–0.77), parity ≥1 (cRR, 0.54; 95% CI, 0.47–0.61), GDM (cRR, 0.82; 95% CI, 0.71–0.95), overweight (cRR, 0.67; 95% CI, 0.52–0.86), and obese (cRR, 0.63; 95% CI, 0.48–0.82). In the multivariate analysis, preterm birth (aRR, 2.31; 95% CI, 1.45–3.57) and baby gender (aRR, 0.57; 95% CI, 0.4–0.81) remained significant mediators. In addition, the confounding variables; parity (aRR, 0.66; 95% CI, 0.45–0.93), and overweight mothers (aRR, 0.64; 95% CI, 0.42–0.97) remained significant. The rest of the factors became non-significant in the adjusted model (Table 2).

Table 2. Risk factors associated with SGA and LGA.

Risk factors SGAa LGAb
(n = 882) (n = 2282)
cRR p value cPAF (%) aRR p value aPAF (%) cRR p value cPAF (%) aRR p value aPAF (%)
Gestational Age*
Preterm 2.7 (2.32–3.14) 0.000 12.9 2.31 (1.45–3.57) 0.001 11.641 1.63 (1.47–1.81) 0.000 5.2 1.5 (1.07–2.02) 0.019 4.5
Term Ref 0.0 Ref Ref 0.0 Ref
Maternal age
young adults (20–34 years) Ref 0.0 Ref Ref 0.0 Ref
adolescents (<20 years) 1.68 (1.22–2.3) 0.002 1.9 1.63 (0.72–3.44) 0.240 1.8378 0.65 (0.46–0.93) 0.013 -0.9 0.62 (0.15–2.04) 0.471 -1.0
Advanced maternal age (≥35 years) 0.82 (0.69–0.98) 0.025 -3.7 0.83 (0.5–1.35) 0.465 -3.436 1.29 (1.18–1.4) 0.000 5.7 0.96 (0.74–1.21) 0.707 -1.1
Parity
Nulliparous Ref 0.0 Ref Ref 0.0 Ref
Parity ≥1 0.54 (0.47–0.61) 0.000 -48.6 0.66 (0.45–0.93) 0.018 -29.94 1.96 (1.76–2.19) 0.000 41.7 2.16 (1.63–2.82) 0.000 45.7
Pregnancy Mode
Spontaneous Ref 0.0 Ref 0.0
Assisted 0.93 (0.62–1.4) 0.731 -0.2 0.79 (0.6–1.03) 0.079 -0.6
Nationality
Qatari 0.93 (0.8–1.08) 0.354 -3.7 1.41 (0.94–2.07) 0.104 14.314 0.98 (0.88–1.09) 0.706 -1.0 1.11 (0.82–1.47) 0.500 5.0
Other Arabs 0.66 (0.56–0.77) 0.000 -23.4 0.84 (0.54–1.28) 0.423 -8.531 1.31 (1.19–1.43) 0.000 15.2 1.53 (1.2–1.91) 0.001 22.2
Other Nationalities Ref 0.0 Ref Ref 0.0 Ref
Consanguinity
No Ref 0.0 Ref 0.0
Yes 1.09 (0.87–1.36) 0.445 2.9 0.97 (0.85–1.11) 0.666 -1.0
Education
Elementary and below 1 (0.67–1.47) 0.982 0.0 1.01 (0.53–1.89) 0.976 0.1153 1.2 (0.99–1.45) 0.060 2.5
Secondary/Highschool 1.28 (1.03–1.59) 0.024 8.7 1.38 (0.95–1.96) 0.085 10.916 0.89 (0.77–1.02) 0.086 -3.8
University or above Ref 0.0 Ref Ref 0.0
Diabetes Status
No DM Ref 0.0 Ref Ref 0.0 Ref
GDM 0.82 (0.71–0.95) 0.009 -5.4 0.89 (0.61–1.29) 0.551 -3.106 1.39 (1.28–1.5) 0.000 10.5 1.36 (1.11–1.66) 0.004 10.0
PGDM 0.72 (0.43–1.21) 0.211 -0.8 0.42 (0.1–1.72) 0.237 -2.905 2.35 (2.02–2.73) 0.000 4.8 2.58 (1.8–3.47) 0.000 5.2
Chronic Hypertension
No Ref 0.0 Ref Ref 0.0
Yes 2.13 (1.48–3.07) 0.000 1.5 2.58 (0.78–6.53) 0.116 1.7364 1.28 (0.96–1.7) 0.100 0.4
Pre-or early-pregnancy BMI
Normal Ref 0.0 Ref Ref 0.0 Ref
Underweight 1.59 (1.04–2.44) 0.035 4.7 1.54 (0.78–2.84) 0.212 4.4155 0.32 (0.14–0.77) 0.005 -5.3 0.56 (0.18–1.62) 0.300 -2.0
Overweight 0.67 (0.52–0.86) 0.002 -19.0 0.64 (0.42–0.97) 0.037 -21.55 1.47 (1.24–1.74) 0.000 19.3 1.12 (0.86–1.46) 0.410 6.5
Obese 0.63 (0.48–0.82) 0.000 -20.9 0.65 (0.41–1.02) 0.060 -19.04 1.89 (1.61–2.22) 0.000 31.1 1.15 (0.87–1.5) 0.329 8.5
Baby gender
Male 0.68 (0.6–0.77) 0.000 -18.6 0.57 (0.4–0.81) 0.002 -29.54 1.59 (1.47–1.72) 0.000 23.5 1.82 (1.49–2.19) 0.000 28.5
Female Ref 0.0 Ref Ref 0.0 Ref
Chromosomal/Congenital abnormalities
No Ref 0.0 Ref Ref 0.0
Yes 3.55 (2.75–4.58) 0.000 4.2 2.03 (0.79–4.56) 0.137 2.9858 1.18 (0.88–1.58) 0.290 0.3
Smoking
No Ref 0.0 Ref 0.0
Yes 1.34 (0.69–2.6) 0.396 0.3 0.89 (0.53–1.47) 0.634 -0.1
Preterm history
No Ref 0.0 Ref 0.0
Yes 1.14 (0.9–1.45) 0.282 0.9 1.11 (0.97–1.28) 0.137 0.8
Employment status
Employed Ref 0.0 Ref 0.0 Ref
Unemployed 1.3 (0.5–3.34) 0.591 0.3       1.97 (1.37–2.85) 0.001 1.1 2.37 (1.2–3.81) 0.015 1.3
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Abbreviations; cRR, crude risk ratio; aRR, adjusted risk ratio; cPAF, crude population attributable fraction; aPAF, adjusted population attributagle fraction; CI, confidence interval; Ref, referent; DM, diabetes mellitus; GDM, gestational diabetes mellitus; PGDM, pre-gestational diabetes mellitus; AGA, appropriate for gestational age; SGA, small for gestational age; LGA, large for gestational age; Apgar, Appearance, Pulse, Grimace, Activity, and Respiration; NICU, neonatal intensive care unit; LR, labour room; OT, operation theatre.

For the cPAFs, we utilized the formula PAF = Pe (RRe − 1)/[1 + Pe (RRe − 1)] [43–45] where Pe is the percentage of people in the population who were exposed to the risk factor and RRe is the crude relative risk in the exposed vs. the unexposed group. For the adjusted PAFs (aPAFs), we used the formula Pd ((aRR—1) ⁄ aRR), in which Pd is the prevalence of exposure among those who were born SGA or LGA, and aRR is the adjusted relative risk in the exposed vs. unexposed group [49–51]. The full results are provided in S2 Table.

a adjusted for the risk factors associated with SGA that were significant in the univariate analysis, with p-values <0.05: Gestational age, maternal age, parity, nationality, education, diabetes status, chronic hypertension, early- or pre-pregnancy BMI, baby gender, and any chromosomal or congenital abnormalities.

b adjusted for the risk factors associated with LGA that were significant in the univariate analysis, with p-values <0.05: Gestational age, maternal age, parity, nationality, diabetes status, early- or pre-pregnancy BMI, baby gender, and employment status.

*The analysis presented here was conducted using the gestational age variable categorized into two groups. The full analysis was reconducted with gestational age categorized into five groups, and presented in S3 Table.

Bold values denote statistical significance at the p<0.05 level.

Univariate analysis for LGA as an outcome revealed that preterm birth (cRR, 1.63; 95% CI, 1.47–1.81), and male baby gender (cRR, 1.59; 95% CI, 1.47–1.72) were significantly related to LGA. In addition, LGA was more likely to occur amongst babies of mothers with advanced age (cRR, 1.29; 95% CI, 1.18–1.4), with parity ≥1 (cRR, 1.96; 95% CI, 1.76–2.19), from other Arabs origin (cRR, 1.31; 95% CI, 1.19–1.43), with GDM (cRR, 1.39; 95% CI, 1.28–1.5) and PGDM (cRR, 2.35; 95% CI, 2.02–2.73), who are overweight (cRR, 1.47; 95% CI, 1.24–1.74), obese (cRR, 1.89; 95% CI, 1.61–2.22), and unemployed (cRR, 1.97; 95% CI, 1.37–2.85). While LGA was less likely to happen with babies of adolescent mothers (cRR, 0.65 (0.46–0.93), and underweight (cRR, 0.32; 95% CI, 0.14–0.77). In the multivariate analysis, the mediators; preterm birth (aRR, 1.5; 95% CI, 1.07–2.02) and male baby gender (aRR, 1.82; 95% CI, 1.49–2.19), in addition to the confounding variables; parity (aRR, 2.16; 95% CI, 1.63–2.82), other Arabs (aRR, 1.53; 95% CI, 1.2–1.91), GDM (aRR, 1.36; 95% CI, 1.11–1.66), PGDM (aRR, 2.58; 95% CI, 1.8–3.47) were found to be significantly associated with LGA. The rest of the confounders became non-significant in the adjusted model (Table 2).

The highest aPAF among SGA births was observed for preterm birth, with an aPAF of 11.6%, indicating that almost 12% of SGA cases could have been prevented if mothers had not delivered preterm (Table 2 and S2 Table), whereas LGA preterm infants showed 4.5% for aPAF, indicating that only 5% of LGA cases could have been prevented if mothers had not delivered preterm (Table 2 and S2 Table). Among LGAs, the highest aPAF was 45.7% for Parity ≥1, indicating that almost half of the LGA cases could have been prevented if mothers were not parous, whereas SGA infants showed a negative aPAF of -29.9%, indicating that Parity ≥1 is a protective factor for SGA birth.

Univariate analysis with the gestational age categorized into five categories revealed that extreme to very preterm (cRR, 3.9; 95% CI, 2.95–5.15), moderate preterm (cRR, 3.46; 95% CI, 2.47–4.85), and late preterm (cRR, 2.16; 95% CI, 1.78–2.62) were significantly associated to a higher risk of SGA (S3 Table). Following adjustment for the confounding factors in the multivariate analysis, only extreme to very preterm (aRR, 3.07; 95% CI, 1.01–7.22), and late preterm birth (aRR, 1.92; 95% CI, 1.1–3.22) remained significant mediators for SGA (S3 Table). For LGA, we found that all five groups were significantly associated with LGA in the univariate model (S3 Table). However, following adjustment for the confounding factors, only late preterm (aRR, 1.68; 95% CI, 1.14–2.39) and early term (aRR, 1.4; 95% CI, 1.13–1.71) were found to be significant mediators for LGA birth (S3 Table).

3.3. Adverse outcomes associated with inappropriate weight for gestational age

Univariate logistic regression analysis revealed that SGA in comparison to AGA, was significantly associated with low Apgar <7 at 1 min (cRR, 4.28; 95% CI, 3.28–5.58), low Apgar <7 at 5 mins (cRR, 4.53; 95% CI, 2.13–9.63), NICU/death in LR/OT (cRR, 2.94; 95% CI, 2.61–3.3), and in-hospital mortality (cRR, 7.95; 95% CI, 4.7–13.46) (Table 3 and S4 Table). After adjustment, SGA was significantly associated with NICU/death in LR/OT (aRR, 2.14; 95% CI, 1.36–3.22) and in-hospital mortality (aRR, 226.56; 95% CI, 3.47–318.22) (Table 3). However, the relationship of SGA with low Apgar <7 at 1 min and 5 minutes became non-significant after adjustment (Table 3 and S4 Table).

Table 3. Outcomes associated with SGA and LGA.

Apgar <7 at 1 min NICU/death in LR/OT In–hospital mortality
  cRR (95% CI) p value aRR (95% CI) p value cRR (95% CI) p value aRR (95% CI) p value cRR (95% CI) p value aRR (95% CI) p value
Table 3–A: SGAa vs. AGA
SGA 4.28 (3.28–5.58) 0.000 1.59 (0.56–4.33) 0.372 2.94 (2.61–3.3) 0.000 2.14 (1.36–3.22) 0.002 7.95 (4.7–13.46) 0.000 226.56 (3.47–318.22) 0.016
AGA Ref Ref Ref Ref Ref Ref
Gestational Age*
Preterm 9.79 (8.03–11.95) 0.000 8.41 (4.23–15.81) 0.000 7.05 (6.54–7.6) 0.000 7.07 (5.7–8.4) 0.000 19.94 (12.08–32.91) 0.000 536.79 (5.66–629.22) 0.013
Term Ref Ref Ref Ref Ref Ref
Maternal Age
Young adults (20–34 years) Ref Ref Ref Ref Ref Ref
Adolescents (<20 years) 1.44 (0.79–2.6) 0.230 0.94 (0.11–7.17) 0.954 1.01 (0.75–1.36) 0.956 0.61 (0.17–2.01) 0.442 0.78 (0.11–5.67) 0.809 N/A
Advanced maternal age (≥35 years) 1.17 (0.92–1.5) 0.210 1.04 (0.4–2.68) 0.936 1.23 (1.11–1.36) 0.000 1.74 (1.2–2.43) 0.004 1.82 (1.09–3.05) 0.021 18.22 (0.58–193.76) 0.098
Parity
Nulliparous Ref Ref Ref Ref Ref Ref
Parity ≥1 0.46 (0.38–0.57) 0.000 0.55 (0.25–1.19) 0.132 0.64 (0.59–0.7) 0.000 0.61 (0.42–0.88) 0.007 0.88 (0.52–1.48) 0.621 3.75 (0.09–94.59) 0.490
Nationality
Qatari 1.06 (0.82–1.37) 0.636 1.11 (0.44–2.75) 0.835 1.06 (0.96–1.18) 0.249 1.11 (0.73–1.66) 0.618 0.62 (0.35–1.1) 0.097 1.23 (0.02–43.54) 0.919
Other Arabs 0.89 (0.69–1.14) 0.350 1.07 (0.44–2.59) 0.878 0.82 (0.74–0.92) 0.000 1.1 (0.75–1.6) 0.620 0.41 (0.23–0.75) 0.003 0.01 (0–2.81) 0.106
Other Nationalities Ref Ref Ref Ref Ref Ref
Education
Elementary and below 0.85 (0.41–1.77) 0.669 0.71 (0.16–3.11) 0.661 0.85 (0.64–1.13) 0.261 0.65 (0.32–1.27) 0.213 4.41 (1.25–15.58) 0.012 0.48 (0–63.5) 0.773
Secondary/Highschool 1.06 (0.7–1.61) 0.780 1.05 (0.47–2.29) 0.912 0.92 (0.78–1.09) 0.341 0.94 (0.64–1.35) 0.742 2.26 (0.76–6.71) 0.132 0.08 (0–3.2) 0.182
University or above Ref Ref Ref Ref Ref Ref
Diabetes Status
No DM Ref Ref Ref Ref Ref Ref
GDM 0.84 (0.66–1.06) 0.145 0.53 (0.21–1.29) 0.163 1.19 (1.08–1.31) 0.000 0.78 (0.53–1.12) 0.183 0.96 (0.56–1.64) 0.868 9.61 (0.37–125.31) 0.172
PGDM 1.56 (0.92–2.64) 0.098 2.4 (0.63–8.2) 0.199 2.65 (2.24–3.14) 0.000 2.48 (1.28–4.23) 0.009 0.61 (0.08–4.4) 0.618 N/A
Chronic Hypertension
No Ref Ref Ref Ref Ref Ref
Yes 2.8 (1.64–4.78) 0.000 N/A 2.02 (1.57–2.6) 0.000 1.27 (0.35–3.61) 0.702 N/A N/A
Pre-or early-pregnancy BMI
Normal Ref Ref Ref Ref Ref Ref
Underweight 0.7 (0.22–2.2) 0.533 0.84 (0.16–4.08) 0.840 1.54 (1.05–2.25) 0.031 1.63 (0.76–3.13) 0.203 1.56 (0.2–12.39) 0.672 N/A
Overweight 0.71 (0.47–1.07) 0.099 0.53 (0.2–1.35) 0.182 1.11 (0.93–1.33) 0.246 1.17 (0.78–1.71) 0.452 0.88 (0.32–2.43) 0.807 0.08 (0–8.02) 0.281
Obese 0.83 (0.56–1.22) 0.339 1 (0.4–2.46) 1.000 1.3 (1.09–1.55) 0.003 1.13 (0.72–1.72) 0.586 1.52 (0.62–3.7) 0.356 7.08 (0.11–162.88) 0.352
Baby gender
Male 1.31 (1.07–1.62) 0.009 0.96 (0.47–1.94) 0.919 1.29 (1.18–1.41) 0.000 1.3 (0.95–1.77) 0.100 0.9 (0.56–1.46) 0.672 0.2 (0.01–4.19) 0.304
Female Ref Ref Ref Ref Ref Ref
Chromosomal/Congenital abnormalities
No Ref Ref Ref Ref Ref Ref
Yes 9.73 (7.37–12.86) 0.000 7.68 (2.63–18.64) 0.000 8.25 (7.71–8.84) 0.000 8.58 (7.06–9.28) 0.000 94.1 (58.72–150.81) 0.000 447.22 (7.45–599.54) 0.007
Table 3–B: LGAb vs. AGA
LGA 1.5 (1.13–1.98) 0.004 0.54 (0.18–1.65) 0.287 1.52 (1.36–1.7) 0.000 1.15 (0.78–1.66) 0.483 0.84 (0.35–1.99) 0.688 N/A
AGA Ref Ref Ref Ref Ref Ref
Gestational Age*
Preterm 9.79 (8.03–11.95) 0.000 15.14 (7.85–26.59) 0.000 7.05 (6.54–7.6) 0.000 5.75 (4.52–7.04) 0.000 19.94 (12.08–32.91) 0.000 629.38 (0–0) 0.982
Term Ref Ref Ref Ref Ref Ref
Maternal Age
Young adults (20–34 years) Ref Ref Ref Ref Ref Ref
Adolescents (<20 years) 1.44 (0.79–2.6) 0.230 N/A 1.01 (0.75–1.36) 0.956 0.3 (0.04–1.94) 0.225 0.78 (0.11–5.67) 0.809 N/A
Advanced maternal age (≥35 years) 1.17 (0.92–1.5) 0.210 1.08 (0.41–2.78) 0.878 1.23 (1.11–1.36) 0.000 1.66 (1.2–2.25) 0.003 1.82 (1.09–3.05) 0.021 7.55 (0.43–93.11) 0.167
Parity
Nulliparous Ref Ref Ref Ref Ref Ref
Parity ≥1 0.46 (0.38–0.57) 0.000 0.83 (0.35–1.88) 0.651 0.64 (0.59–0.7) 0.000 0.59 (0.41–0.83) 0.002 0.88 (0.52–1.48) 0.621 0.48 (0.02–10.86) 0.647
Nationality
Qatari 1.06 (0.82–1.37) 0.636 1.14 (0.37–3.35) 0.826 1.06 (0.96–1.18) 0.249 1.14 (0.75–1.65) 0.534 0.62 (0.35–1.1) 0.097 N/A
Other Arabs 0.89 (0.69–1.14) 0.350 1.7 (0.66–4.28) 0.274 0.82 (0.74–0.92) 0.000 1.04 (0.73–1.48) 0.804 0.41 (0.23–0.75) 0.003 0.19 (0.01–2.64) 0.216
Other Nationalities Ref Ref Ref Ref Ref Ref
Diabetes Status
No DM Ref Ref Ref Ref Ref Ref
GDM 0.84 (0.66–1.06) 0.145 0.52 (0.19–1.35) 0.179 1.19 (1.08–1.31) 0.000 0.92 (0.66–1.27) 0.601 0.96 (0.56–1.64) 0.868 1.3 (0.06–24.49) 0.868
PGDM 1.56 (0.92–2.64) 0.098 2.9 (0.84–8.92) 0.091 2.65 (2.24–3.14) 0.000 1.85 (1.03–3.1) 0.041 0.61 (0.08–4.4) 0.618 N/A
Pre-or early-pregnancy BMI
Normal Ref Ref Ref Ref Ref Ref
Underweight 0.7 (0.22–2.2) 0.533 2.37 (0.47–9.92) 0.291 1.54 (1.05–2.25) 0.031 1.46 (0.61–3.09) 0.385 1.56 (0.2–12.39) 0.672 N/A
Overweight 0.71 (0.47–1.07) 0.099 0.95 (0.37–2.37) 0.912 1.11 (0.93–1.33) 0.246 1.11 (0.75–1.6) 0.606 0.88 (0.32–2.43) 0.807 1.38 (0.06–28.91) 0.842
Obese 0.83 (0.56–1.22) 0.339 0.88 (0.31–2.44) 0.808 1.3 (1.09–1.55) 0.003 1.21 (0.81–1.78) 0.355 1.52 (0.62–3.7) 0.356 2.41 (0.11–45.09) 0.579
Baby Gender
Male 1.31 (1.07–1.62) 0.009 0.87 (0.4–1.85) 0.726 1.29 (1.18–1.41) 0.000 1.24 (0.92–1.65) 0.164 0.9 (0.56–1.46) 0.672 1.36 (0.12–14) 0.802
Female Ref Ref Ref Ref Ref Ref
Employment Status
Employed Ref Ref Ref Ref Ref Ref
Unemployed N/A   N/A   0.68 (0.26–1.76) 0.413 1.38 (0.39–4.02) 0.607 N/A   N/A  
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Abbreviations: cRR, crude risk ratio; aRR, adjusted risk ratio; CI, confidence interval; Ref, referent; NA, not applicable; DM, diabetes mellitus; GDM, gestational diabetes mellitus; PGDM, pre-gestational diabetes mellitus; AGA, appropriate for gestational age; SGA, small for gestational age; LGA, large for gestational age; Apgar, Appearance, Pulse, Grimace, Activity, and Respiration; NICU, neonatal intensive care unit; LR, labor room; OT, operation theatre.

a adjusted for the risk factors associated with SGA that were significant in the univariate analysis, with p-values <0.05 (Table 2): Gestational age, maternal age, parity, nationality, education, diabetes status, chronic hypertension, early- or pre-pregnancy BMI, baby gender, and any chromosomal or congenital abnormalities (Table 3A).

b adjusted for the risk factors associated with LGA that were significant in the univariate analysis, with p-values <0.05 (Table 2): Gestational age, maternal age, parity, nationality, diabetes status, early- or pre-pregnancy BMI, baby gender, and employment status (Table 3B).

The variable Apgar<7 at 5 mins is not shown in this table due to missing data but shown in S4 Table. The full results are provided in S4 Table.

*The analysis presented here was conducted using the gestational age variable categorized into two groups. The full analysis was reconducted with gestational age categorized into five groups and is presented in S5 Table.

Bold values denote statistical significance at the p<0.05 level.

Univariate logistic regression analysis revealed that LGA, compared to AGA, was significantly associated with low Apgar <7 at 1 min (cRR, 1.50; 95% CI, 1.13–1.98) and NICU/death in LR/OT (cRR, 1.52; 95% CI, 1.36–1.7) (S4 Table). However, after adjustment the association of LGA with low Apgar <7 at 1 min, and NICU/death in LR/OT became non-significant (Table 3).

Univariate analyses with the gestational age categorized into five categories revealed that extreme to very preterm (cRR, 32.61; 95% CI, 26.11–40.74), moderate preterm (cRR, 8.85; 95% CI, 5.68–13.79), late preterm (cRR, 3.85; 95% CI, 2.83–5.22) were significantly associated with a higher risk of low Apgar score <7 at 1 minute, while early term was significantly associated with lower risk of low Apgar score <7 at 1 minute (cRR, 0.73; 95% CI, 0.53–1.01). Following adjustment in the multivariate analysis, only extreme to very preterm (aRR, 54.67; 95% CI, 30.85–65.65) remained significantly associated with low Apgar score <7 at 1 minute (S5 Table). For in hospital mortality, extreme to very preterm (cRR, 102.37; 95% CI, 49.57–211.4), moderate preterm (cRR, 39.94; 95% CI, 15.4–103.59), and late preterm (cRR, 11.69; 95% CI, 5.14–26.59) were significantly associated with in hospital mortality but these were not applicable when adjusting for all confounders due to missing data. Finally, for NICU admission or death at LR or OT, extreme to very preterm (cRR, 15.14; 95% CI, 14.01–16.37), moderate preterm (cRR, 14.45; 95% CI, 13.27–15.74), late preterm (cRR, 5.62; 95% CI, 5.03–6.28), and early term (cRR, 1.41; 95% CI, 1.25–1.58) were significantly associated with higher risk of NICU admission or death at LR or OT. When adjusting for the confounding factors, moderate preterm (aRR, 14.39; 95% CI, 10.22–15.11), late preterm (aRR, 7.49; 95% CI, 5.55–9.45) and early term (aRR, 2.16; 95% CI, 1.48–3.06) remained significantly associated with high risk of NICU admission/death in LR/OT. Data for extremely to very preterm was not applicable due to missing data when adjusting for all confounders.

SGA was significant in all univariate analyses but became non-significant (or not applicable) when adjusting for the confounders except for NICU admission/death in LR/OT where it remained significant (S5 Table). The same analysis was performed for LGA where similar results were found except that in the multivariate analysis all preterm groups became non-significant for Apgar score <7 at 1 minute and that LGA became non-significant in all multivariate analysis (S5 Table).

Kaplan-Meier analyses was also performed to investigate the risk stratified algorithms. The analysis showed significant differences among the three groups (SGA, LGA and AGA) in incidence of low Apgar score at 1 minute, NICU/death in LR/OT and in-hospital mortality during the course of 24–40 weeks of gestation. Low Apgar score at 1 minute was observed in 0.8% of the AGA, 0.7% of the LGA, and 7.8% for SGA (χ2 (2, 14,601) = 142.92; P < 0.001) (Fig 1A). Admission to the NICU/death in LR/OT was observed in 9.8% of the AGA, 14.9% of the LGA, and 28.9% of the SGA (χ2 (2, 14,640) = 351.01; P < 0.001) (Fig 1B). In-hospital mortality was observed in 0.3% of the AGA and LGA and 2.5% for SGA (χ2 (2, 14,641) = 98.08; P < 0.001) (Fig 1C).

Fig 1. Kaplan-Meier curves assessing differences in medians, among the three groups (AGA, SGA and LGA) for the outcomes during the course of 24–40 weeks of gestation.

Fig 1

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(A) Apgar score, (B) NICU/death in LR/OT, and (C) in-hospital mortality.

4. Discussion

This large population-based study is the first of its kind to assess the incidence, maternal risk factors and neonatal outcomes associated with SGA and LGA in Qatar. A total of 14,641 singleton births registered in the PEARL database from April 2017 to March 2018 were examined. Our population-based study showed an SGA incidence of 60 per 1000 total singleton births (6.0%), which was relatively lower than previously reported in other countries [52–58]. Recently, a prospective cohort data between 1983 and 2006 conducted among 75,296 infants from 12 European countries revealed an SGA prevalence ranging from 4.6% in Finland up to 15.3% in Portugal [59]. On the other hand, the LGA incidence was estimated to be 156 per 1000 total singleton births (15.6%), which was comparable to previous reports in Vietnam [56] and Thailand [60].

The main reason behind such disparities could be mainly due to differences in the characteristics of the study populations, especially ethnic origins, race, and dietary habits. Furthermore advanced antenatal care at our institution [61], high quality counselling and support could have minimized SGA incidence in this population. In addition, it is noteworthy to mention that the present study comprised a total of 64.79% overweight/obese women (32.45% overweight and 32.34% obese), which is remarkably higher than the overweight/obesity average incidence rates around the globe [WHO Global Health Observatory: share of adults that are overweight or obese in 2016: Americas (62.5%), Europe (58.7%), Eastern Mediterranean (49%), Western Pacific (31.7%), Africa (31.1%), South-East Asia (21.9%)] [62]. This could have contributed to the high LGA and comparably low SGA incidence rates in Qatar compared to global estimates. According to a meta-analysis by Gaudet et al. [63], maternal obesity is significantly associated with the development of fetal overgrowth, with an 142% increase in the odds of delivering LGA among obese women compared with their normal weight counterparts. Furthermore, it has been reported that the percentage of LGA infants was significantly higher among overweight women even in the absence of GDM [64]. These findings indicate that being overweight and obesity are both determinants of fetal growth regardless of the presence of other risk factors. Moreover, diabetes, which is also believed to be strongly associated with fetal growth [65], was found to be very high in our sample population (Total DM: 31.68%; GDM: 29.09% and PGDM: 2.60%), which is relatively high compared to the rest of the world. For instance, according to 2019 estimates from the International Diabetes Federation, the average diabetes prevalence was estimated to be 15.33% in the Pacific island small states, 11.37% in Middle East & North Africa, 11.24% in South Asia, 10.46% in North America, and lower than 10% in Latin America and Caribbean, East Asia and Pacific, countries of the European Union, and Sub-Saharan Africa [66].

Furthermore, it is worth noting that most studies have advocated the use of local or customized charts to estimate SGA and LGA in particular populations [35, 36]; however, these local charts are only relevant to the population and time from which they were derived and making comparison between populations and studies impossible, and thus limits generalisability to other populations. However, in the present study, SGA and LGA estimates were calculated based on the multinational recently released INTERGROWTH-21st standard, which offers a reliable multinational tool for estimating fetal weight percentiles [34].

In our population-based study, preterm birth was significantly associated with SGA and LGA, with male infants significantly less likely to be SGA but high likely to be LGA (Table 2). In addition, parity≥1 was significantly associated with a low risk of SGA but a high risk of LGA (Table 2). Moreover, infants born to overweight mothers were significantly less likely to be born SGA. Further, GDM and PGDM were significantly associated with LGA births (Table 2). Several sociodemographic factors were found to be significantly associated with inappropriate birth weight for gestational age, including nationality which was significantly associated with LGA births in the adjusted model. Further, unemployment was found to be independently associated with LGA births. These are all well-established risk factors for SGA and LGA among different racial and ethnic groups [67–70]. Nevertheless, in contrast with other studies, consanguinity, smoking, and preterm history had no effect on SGA or LGA in the univariate and the multivariate analyses.

Over the past four decades, there has been a tremendous improvement in perinatal care, which has significantly improved the survival of infants born with low birth weight [71]. SGA infants were found to be at higher mortality risk than non-SGA infants or infants born within the normal weight span [72, 73]. Despite the main focus of research being on low birth weight, a growing evidence suggests that there are existing U-shaped associations, with high birth weight linked to increased mortality risks [31–33]. To date, most studies investigating this area of research have primarily focused on investigating the link between birth weight and gestational age as separate components. A Swedish medical birth registry-based study has shown high mortality in individuals born early term [74]. In our study we found that NICU/death was 9.8% for AGA, 14.9% for LGA, and 28.9% for SGA (Table 1 and Fig 1). In-hospital mortality and admission to NICU/or death in LR/OT were significantly more likely to occur among SGA infants in comparison to AGA infants (Table 3). Furthermore, both SGA and LGA were significantly related to caesarean deliveries (Table 3). It is important to mention that while caesarean sections can be protective, they can lead to significant morbidities among both the mothers and their babies, and thus, the ideal delivery mode for SGA and LGA singletons remains controversial, particularly in preterm delivery cases [75].

Our study has several strengths. First of all, previous studies on this topic have investigated the risk factors and outcomes associated with birth weight and gestational age separately, but only few studies have looked at the risk factors and outcomes of birth weight in the context of gestational age, particularly LGA. The LGA group is a relatively new area of investigation, since most studies to date have focused on low birth weight, and only few reports have shown associations between high birth weight and increased mortality [31–33]. Moreover, in our study we also looked at the various categorisation of GA, there are very few studies that looked at extreme to very preterm, moderate preterm, late preterm, and early term in comparison to full term as we did in the present study. Furthermore, we were able to adjust for several demographic and medical confounding factors, known to affect fetal growth. It is generally recognized that inappropriate birth weight for gestational age is confounded by many factors, and published studies are very limited, particularly for LGA. So far, only few studies have determined PAFs for SGA and LGA, particularly, in the presence of confounding factors. Since unadjusted PAFs may be falsely high or low if confounding is present, the ability to adjust for relevant confounders and calculate multivariable-adjusted PAFs is another strength of the current analysis. We provided information on the population burden of SGA and LGA due to the underlying risk factors by determining the adjusted PAFs. The adjusted PAFs in the current paper can help our understanding of the extent to which SGA and LGA can be reduced if the assessed risk factors were eliminated. It gives a percentage of reducing the risk and improving the protective factors. Finally, this study used data from the PEARL-Peristat Study. The PEARL-Peristat Study is an ongoing cohort study based on the predesigned hospital data pertaining to mothers and their newborns. In its initial phase, the PEARL study was conducted from 2011 to 2013, while this phase covered the 2017–2019 period [76]. This registry reports data on maternal, neonatal and perinatal mortality, morbidities, and their correlates, including data on live births and neonatal mortality from all public and private maternity facilities in Qatar [76, 77]. This database is large enough with a sample size that is generally representative of births in Qatar. In addition, HMC is the main national hospital, the main provider of secondary and tertiary healthcare in Qatar, consisting of multiple regional hospitals that are widely distributed in different geographical areas of Qatar, and account for the majority of births in the country. Furthermore, selection bias was minimized via examining all available live births for the study period.

Despite being the largest study of its kind in the State of Qatar, this study has some limitations. Although we carefully adjusted for several potential confounders, we were unlikely to fully rule out the possibility of residual confounding. Thus, it is noteworthy to mention that the observed associations might be attributable to unmeasured confounders such as parents’ history of SGA or LGA births. In addition, there were missing data on some variables, which were excluded from the analysis. However, the missing data in each of these variables were comparable across the subgroups, therefore these missing data are unlikely to have affected our reported estimates. However, the sample size for some factors were very small (e.g., mortality amongst SGA = 22/882 (2.5%), which could have caused an overestimated RR, particularly after adjusting for confounding factors. In addition, empirical evidence indicates that the validity of regression models is only slightly affected after selective dropout. Thus, the relation between risk factors and outcome is unlikely to be considerably changed by selective dropout [78]. Our results therefore support the evidence on the association between different risk factors and fetal growth.

5. Conclusion

This is the first population-based study to assess the incidence, risk factors and feto-maternal outcomes associated with inappropriate fetal growth in Qatar. In summary, the present study identified several risk factors that are associated with SGA and LGA births, including maternal medical and social conditions. In addition, prematurity was found to be significantly associated with SGA and LGA births, with male infants being less likely to be born SGA but high likely to be born LGA, in comparison to female infants. Moreover, SGA increased the risk of neonatal mortality and admission to NICU, as well as death in labor room and operation theatre. It is noteworthy to mention that many of the identified risks are potentially modifiable (e.g., maternal medical conditions, or lifestyle habits), suggesting avenues for possible prevention of SGA and LGA in future pregnancies. Modifiable risk factors should be identified as early as possible and managed accordingly. Thus, perinatal monitoring and antenatal care are essential to reduce the burden of inappropriate fetal growth and increase the chance of survival.

Supporting information

S1 Table. Crosstabs of the associated risk factors and outcomes.

(XLSX)

Click here for additional data file. (35.2KB, xlsx)
S2 Table. Univariate and multivariate regression of the risk factors associated with SGA and LGA births, with gestational age categorized into two groups (preterm vs. term).

(XLSX)

Click here for additional data file. (35.4KB, xlsx)
S3 Table. Univariate and multivariate regression of the risk factors associated with SGA and LGA births, with gestational age categorized into five groups (extreme to very preterm, moderate preterm, late preterm, early term, and full term).

(XLSX)

Click here for additional data file. (36.6KB, xlsx)
S4 Table. Univariate and multivariate regression of the pregnancy and neonatal outcomes associated with SGA and LGA births, with gestational age categorized into two groups (preterm vs. term).

(XLSX)

Click here for additional data file. (36.4KB, xlsx)
S5 Table. Univariate and multivariate regression of the pregnancy and neonatal outcomes associated with SGA and LGA births, with gestational age categorized into five groups (extreme to very preterm, moderate preterm, late preterm, early term, and full term).

(XLSX)

Click here for additional data file. (34.1KB, xlsx)

Acknowledgments

The authors want to thank their respective institutions for their continued support.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The PEARL-Peristat study was funded by Qatar National Research Fund (Grant no NPRP 6-238-3-059) and was sponsored by the Medical Research Centre, Hamad Medical Corporation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PONE-D-21-07926

Incidence, Risk Factors, and Feto-Maternal Outcomes of Inappropriate Birth Weight for Gestational Age: A Population-Based Study

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: General comments

- The study would be of greater contribution if provides the performance of risk assessment models based on the significant risk factor. Risk-stratified algorithms are of great interest in clinical practice. Additionally, consider providing population prevalence and population attributable fractions for the associated factors.

- Detailed description of the method for estimating gestational age is crucial. It is strongly recommended to report (Suppl Info) the proportion of women whose gestational age was estimated by each method – for instance, by 1) LMP only; 2) early US only; 3) LMP and early US; 4) LMP and late US; or 5) late US only.

- Provide absolutes risks for the outcomes.

Title

- Make clear what is the setting of the study; which population is this study was related to?

Abstract

- (Background) Preferably, use inadequate than aberrant.

- What do you mean by “long-term complication during pregnancy”?

- Describe the reference chart applied for classifying adequacy of birth weight.

- The sentence “Preterm birth significantly predicted SGA and LGA” did not seem accurate. Prediction implies diagnostic performance assessment, which was not the case in this analysis. Please, revise. Same for GDM and PGDM significantly predicted LGA.

- Avoid using abbreviations in the abstract, especially when they were not previously detailed (e.g., PGDM and LR/OT).

- How could SGA and LGA lead to c-section if they are defined by a postnatal condition (birth weight)?

- Conclusion: The fact the this was the first study in Qatar does not necessarily novel contributions to the research field. In addition, it is not a reasonable conclusion foe the abstract.

Methods

- Consider using SGA- and LGA-related outcomes. Precious studies have shown that SGA and LGA may impact differently on perinatal outcomes (e.g. Neonatal Morbidity of Small- and Large-for-Gestational-Age Neonates Born at Term in Uncomplicated Pregnancies. Obstet Gynecol. 2017;130(3):511-9).

- Describe in more detail the tool used to estimate birth weight centiles. Several studies conducted in different populations concluded that chart-specific thresholds for a specific population should be considered in clinical practice, once different charts have different performance on identifying SGA and LGA babies. Why choosing this specific tool (Ref 34)?

- Definition of some variables are not clear enough. Please, make clear the definition for smoking (Have cessation during pregnancy been considered?), preterm history (any PTB or only spontaneous?), GDM (IADPSG criteria? ADA criteria? Local criteria?), maternal BMI (Self-reported weight? Have you also considered early pregnancy weight?).

- The risks for adverse outcomes according to gestational age may be different for the early, full and late terms. When calculating the risk ratios, consider using the three categories instead of only newborns delivered between 39 and 40 weeks.

- Birth weight categorized as low and normal seem useless.

Results

- Why using Odds ration instead of relative risk. Preferably, use aRR.

- Provide absolutes risks for the outcomes.

Reviewer #2: Thanks for the opportunity to review this manuscript where the aim was to assess the incidence, risk factors, and feto-maternal outcomes associated with small-forgestational age (SGA) and large-for-gestational age (LGA) infants.

Overall: It is well known both that SGA, LGA and gestational week will be associated to neonatal outcome. Even if this is the largest study made in Qatar, there are other studies with the same aim. Please clarify the novelty of this study and the reason it adds new knowledge.

Abstract:

1. For example, in this sentence and throughout the manuscript please use the word associations instead of predicted/predictors. Because what you have tested is the potential association, you havn’t performed any predictive models. A variable will not necessarily be a good predictor just because there is a significant association “GDM (aOR 1.45, 95% CI:1.13–1.86) and PGDM (aOR 3.51, 95% CI:2.08–5.92) significantly predicted LGA.”

2. “Both SGA (aOR, 1.56; 95% CI:1.06–2.31) and LGA (aOR, 1.34, 95% CI:1.04–1.73) significantly lead to caesarean deliveries”, According to the previous reasoning, SGA and LGA was associated to cesarean deliveries, it is hard from observational studies to be able to demonstrate causal relationships.

Background:

3. Gestational age is a strong indicator (??) of birthweight and fetal growth, both of which are influenced by a combination of environmental and genetic influences

Material and methods:

4. The study is based on 14000 deliveries from one large hospital, please give us some more information about the population to make it easier to evaluate the external validity of the study to other populations.

5. What was gestational age based on? Ultrasound in gw 10-12? Ultrasound in gw 18-20? Period data?

6. Throughout the world we use different scales fetal growth, hence SGA is not always the same in different countries. What is you definition of AGA/SGA/LGA based on? Which algorithm/model?

7. What was the definition of GDM? Was OGTT done? Which glucose values were defined as GDM?

8. You write that you adjust the models for all significant predictors. There are potential confounders and potential mediators, however you have not done predicting models and therefor it is not possible to call the variables predictors. Some of the variables you adjust for, as gestational age and fetal sex for example will not be potential confounder, they will be potential mediators, meaning that a part of the effect on SGA/LGA will pass through that mediator. The mediator could potentially explain a part of the association.

9. Please make the exposures in the regression clear, it is a bit hard to follow the reasoning.

Results:

10. Please change in the results so that confounders/mediators and so on is coherent.

Discussion

11. You write “we took this a step further and combined gestational age with weight”. Please explain in what way you did this.

Conclusion

12. In the conclusion you write: “The findings of this study should be applied in antenatal care; in particular nutritional surveillance, support, and monitoring should be controlled to reduce the burden of inappropriate fetal growth.” The results of your study are associations to SGA/LGA neonatals. That there are associations does not mean that we know the cause behind the associations or that we know what to do to decrease risks. Please keep the conclusion to what you have studied.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Oct 28;16(10):e0258967. doi: 10.1371/journal.pone.0258967.r002

Author response to Decision Letter 0

3 Jul 2021

Point by point response

PONE-D-21-07926

Incidence, Risk Factors, and Feto-Maternal Outcomes of Inappropriate Birth Weight for Gestational Age: A Population-Based Study

PLOS ONE

We would like to take this opportunity to thank the editor and the reviewers for their comments and for taking the time to evaluate our manuscript. We have revised the article accordingly and point-by-point responses to the comments are reported below. All the changes made to the manuscript are highlighted in the Main Document using “Track Changes” tool.

Please note that other reviewers have requested other changes, and thus these will also appear in the manuscript.

Please follow the clean version of the manuscript with track changes turned “Off” for the indicated line numbers.

Reviewer #1: General comments

- The study would be of greater contribution if provides the performance of risk assessment models based on the significant risk factor. Risk-stratified algorithms are of great interest in clinical practice. Additionally, consider providing population prevalence and population attributable fractions for the associated factors.

Response: We have done all of these extra analyses, including risk stratified algorithm using Kaplan Meier analysis (please see statistical analysis subsection Page 11, lines 176-180, results section Page 29, lines 329-340). Population prevalence and population attributable fractions were also calculated and added to Table 2, Table S2, and Table S3 (please see statistical analysis subsection Page 11, lines 167-175, and results section Pages 16-19, and page 20, lines 254-261).

- Detailed description of the method for estimating gestational age is crucial. It is strongly recommended to report (Suppl Info) the proportion of women whose gestational age was estimated by each method – for instance, by 1) LMP only; 2) early US only; 3) LMP and early US; 4) LMP and late US; or 5) late US only.

Response: We agree about the need for a detailed description of the method for estimating gestational age. Unfortunately, the Pearl-Peristat Study is an observational study primarily designed for broader maternal and neonatal outcomes in the perinatal period using routinely collected hospital data. Thus, the data was not exhaustive and we do not have access to such granular data which could have added value to our findings. We have added this issue also as a study limitation.

- Provide absolutes risks for the outcomes.

Response: we thank the reviewer for this suggestion, we calculated the absolute risks for SGA and LGA (presented in Table S2 and Table S3), and for Apgar score, NICU/death in LR/OT, and in-hospital mortality (Table S4-A and Table S4-B).

Title

- Make clear what is the setting of the study; which population is this study was related to?

Response: We changed the title to clarify the study setting and the study population.

Abstract

- (Background) Preferably, use inadequate than aberrant.

Response: We changed the word aberrant to abnormal. As the study is on both small-for-gestational age (SGA) and large-for-gestational age born infants, and thus we thought that the word abnormal might reflect the main variables (Page 3, line 2).

- What do you mean by “long-term complication during pregnancy”?

Response: We rephrased the sentence and removed long term complications during pregnancy (Page 3, lines 2,3).

- Describe the reference chart applied for classifying adequacy of birth weight.

Response: We added an explanation about the tool and methodology used to estimate the centiles and the adequacy of birth weight (Pages 7,8 lines 90-l03).

- The sentence “Preterm birth significantly predicted SGA and LGA” did not seem accurate. Prediction implies diagnostic performance assessment, which was not the case in this analysis. Please, revise. Same for GDM and PGDM significantly predicted LGA.

Response: We changed the words “predicted” / “predict” to “associated with” across the manuscript.

- Avoid using abbreviations in the abstract, especially when they were not previously detailed (e.g., PGDM and LR/OT).

Response: We replaced the indicated abbreviations with the full terms (Page 3, line 15-19).

- How could SGA and LGA lead to c-section if they are defined by a postnatal condition (birth weight)?

Response: We have removed the regression analysis of delivery mode (vaginal vs. cesarean), and removed it from the table of outcomes (Table 3). We have only left the descriptive statistics in Table 1.

- Conclusion: The fact the this was the first study in Qatar does not necessarily novel contributions to the research field. In addition, it is not a reasonable conclusion foe the abstract.

Response: The indicated sentence has been removed from the abstract (Page 3).

Methods

- Consider using SGA- and LGA-related outcomes. Precious studies have shown that SGA and LGA may impact differently on perinatal outcomes (e.g. Neonatal Morbidity of Small- and Large-for-Gestational-Age Neonates Born at Term in Uncomplicated Pregnancies. Obstet Gynecol. 2017;130(3):511-9).

Response: We considered Apgar score, in-hospital mortality, admission to neonatal intensive care unit or death in labor room or operation theatre as outcomes, unfortunately we don’t have any other perinatal outcomes in the current paper. In future investigations, we will build up on the current analysis, and include more feto-maternal outcomes.

- Describe in more detail the tool used to estimate birth weight centiles. Several studies conducted in different populations concluded that chart-specific thresholds for a specific population should be considered in clinical practice, once different charts have different performance on identifying SGA and LGA babies. Why choosing this specific tool (Ref 34)?

Response: This tool provides a simple spreadsheet-based estimated fetal weight percentile calculator and corresponding R software package to encompass 6 fetal growth standards: the INTERGROWTH-21st, World Health Organization (WHO), National Institute of Child Health and Human Development (NICHD), Perinatology Research Branch (PRB/NICHD), and the Hadlock et al3 and Fetal Medicine Foundation (FMF) standards. These calculations take into account mother characteristics including ethnic/race group as well as height and weight. Our study included different ethnicities and nationalities. Thus, the tool we used takes into account the population (ethnic/race) that is investigated in the study.

We have added this information on how we estimate birth weight centiles in the methodology (please see Pages 7,8, lines 90-103.

- Definition of some variables are not clear enough. Please, make clear the definition for smoking (Have cessation during pregnancy been considered?), preterm history (any PTB or only spontaneous?), GDM (IADPSG criteria? ADA criteria? Local criteria?), maternal BMI (Self-reported weight? Have you also considered early pregnancy weight?).

Response: We have put more details about some of the indicated variables including GDM Page 9, lines 124-134), maternal BMI (Page 9, lines 135-140). and preterm history (Page 10, lines 141,142). Unfortunately, we don’t have data for smoking cessation during pregnancy.

- The risks for adverse outcomes according to gestational age may be different for the early, full and late terms. When calculating the risk ratios, consider using the three categories instead of only newborns delivered between 39 and 40 weeks.

Response: We calculated the relative risk ratios for “preterm” using “term” as a reference group, which included all deliveries at at 37 weeks’ and above (Page 8, lines 105-107).

We have done extra analysis and further categorized gestational age into: extreme-to-very preterm, moderate preterm, late preterm, and early term, vs. full term (Page 8, lines 105-109). Please see Tables S3 and S5, for the full univariate and multivariate analyses repeated with the gestational age groups categorized as indicated. The findings were incorporated into the results section, page 21, lines 262-272, and page 28, lines 304-322.

The percentiles calculated using the FETAL-GPSXL calculator, are up to 40 weeks only (280 days) (Page 7, line 92), it was not applicable to calculate the percentiles for “late term” from 41 weeks, 0 days' to 41 weeks, 6 days' gestation, and “post-term” at 42 weeks'.

- Birth weight categorized as low and normal seem useless.

Response: we agree with the reviewer, we deleted it from the neonatal factors.

Results

- Why using Odds ration instead of relative risk. Preferably, use aRR.

Response: We converted all ORs to RRs and presented them in Table 2, Table 3, and the supplementary materials (Table S2, Table S3, Table S4, and Table S5). We rephrased the abstract and results accordingly.

- Provide absolutes risks for the outcomes.

Response: we thank the reviewer for this suggestion, we calculated the absolute risks for SGA and LGA (presented in Table S2 and Table S3), and for Apgar score, NICU/death in LR/OT, and in-hospital mortality (Table S4-A and Table S4-B).

Reviewer #2: Thanks for the opportunity to review this manuscript where the aim was to assess the incidence, risk factors, and feto-maternal outcomes associated with small-forgestational age (SGA) and large-for-gestational age (LGA) infants.

Response: we would like to take this opportunity to thank the editor and the reviewers for their comments and for taking the time to evaluate our manuscript. We have revised the article accordingly and point-by-point responses to the comments are reported below. All the changes made to the manuscript are highlighted in the Main Document using “Track Changes” tool.

Please note that other reviewers have requested other changes and thus these will also appear in the manuscript.

Please follow the clean version of the manuscript with track changes turned “off” for the indicated line numbers.

Overall: It is well known both that SGA, LGA and gestational week will be associated to neonatal outcome. Even if this is the largest study made in Qatar, there are other studies with the same aim. Please clarify the novelty of this study and the reason it adds new knowledge.

Response: We have indicated in detail the novelty of our study in the introduction section (please see Page 6, lines 57-72) and discussion (please see Pages 32,33, lines 386-416).

Abstract:

1. For example, in this sentence and throughout the manuscript please use the word associations instead of predicted/predictors. Because what you have tested is the potential association, you havn’t performed any predictive models. A variable will not necessarily be a good predictor just because there is a significant association “GDM (aOR 1.45, 95% CI:1.13–1.86) and PGDM (aOR 3.51, 95% CI:2.08–5.92) significantly predicted LGA.”

Response: We changed the words “predicted” / “predict” to “associated with” across all the manuscript.

2. “Both SGA (aOR, 1.56; 95% CI:1.06–2.31) and LGA (aOR, 1.34, 95% CI:1.04–1.73) significantly lead to caesarean deliveries”, According to the previous reasoning, SGA and LGA was associated to cesarean deliveries, it is hard from observational studies to be able to demonstrate causal relationships.

Response: We agree with the reviewer, we rephrased this section (Page 3, lines 11-20). The abstract was rephrased due to the changes made in the statistical analysis according to the recommendations indicated by the reviewer.

Background:

3. Gestational age is a strong indicator (??) of birthweight and fetal growth, both of which are influenced by a combination of environmental and genetic influences.

Response: We rephrased this section following the reviewer’s comment, and following the description in the Canadian Perinatal Health Report, 2000, 2003 (1, 2) (Page 5, lines 44-46).

Material and methods:

4. The study is based on 14000 deliveries from one large hospital, please give us some more information about the population to make it easier to evaluate the external validity of the study to other populations.

Response: We have added more information about the population and the hospitals involved in the study (Page 7, lines 80-88).

5. What was gestational age based on? Ultrasound in gw 10-12? Ultrasound in gw 18-20? Period data?

Response: We have added the information about gestational age in the methodology section (please see Page 8, lines 104,105).

6. Throughout the world we use different scales fetal growth, hence SGA is not always the same in different countries. What is you definition of AGA/SGA/LGA based on? Which algorithm/model?

Response: We used the FETALGPSXL tool. This tool provides a simple spreadsheet-based estimated fetal weight percentile calculator and corresponding R software package to encompass 6 fetal growth standards: the INTERGROWTH-21st, World Health Organization (WHO), National Institute of Child Health and Human Development (NICHD), Perinatology Research Branch (PRB/NICHD), and the Hadlock et al3 and Fetal Medicine Foundation (FMF) standards. These calculations take into account mother characteristics including ethnic/race group as well as height and weight. Our study included different ethnicities and nationalities. Thus, the tool we used takes into account the population (ethnic/race) that is investigated in the study.

We have added this information on how we estimate birth weight centiles in the methodology (please see Pages 7,8, lines, 90-103).

7. What was the definition of GDM? Was OGTT done? Which glucose values were defined as GDM?

Response: We have added this information in the methodology section (please see Page 9, lines 124-134)

8. You write that you adjust the models for all significant predictors. There are potential confounders and potential mediators, however you have not done predicting models and therefor it is not possible to call the variables predictors. Some of the variables you adjust for, as gestational age and fetal sex for example will not be potential confounder, they will be potential mediators, meaning that a part of the effect on SGA/LGA will pass through that mediator. The mediator could potentially explain a part of the association.

Response: We thank the reviewer for his/her pertinent comment and completely agree with his/her point. We changed the words “predicted” / “predict” to “associated with” across the manuscript.

We have also rephrased the results section to indicate that GA and gender are mediators while the other variables are confounders associated to SGA/LGA (please see results section).

9. Please make the exposures in the regression clear, it is a bit hard to follow the reasoning.

Response: We have explained the regression analysis in the statistical analysis subsection in detail (please see Pages 10,11, lines 151-166). We have also rephrased the results section to further clarify the exposures (please see the results section). We added information to indicate in detail the adjusted factors (please see the footnotes of Table 2, Table 3, and also in the supplementary materials: Table S2, Table S3, Table S4, and Table S5).

Results:

10. Please change in the results so that confounders/mediators and so on is coherent.

Response: We have made the explanation in the results section clearer and coherent with regards to confounders and mediators (Page 15, 20, 21, and 28). We have also indicated this in the statistical analysis subsection (Page 10).

Discussion

11. You write “we took this a step further and combined gestational age with weight”. Please explain in what way you did this.

Response: We agree that this sentence is not clear, and thus we have removed it. We have now explained in detail the tool and methodology on how we calculated the fetal growth according to gestational age (please see (Pages 7,8 lines 90-l03). We have also highlighted this in the introduction section (page 6, lines 57-60).

Conclusion

12. In the conclusion you write: “The findings of this study should be applied in antenatal care; in particular nutritional surveillance, support, and monitoring should be controlled to reduce the burden of inappropriate fetal growth.” The results of your study are associations to SGA/LGA neonatals. That there are associations does not mean that we know the cause behind the associations or that we know what to do to decrease risks. Please keep the conclusion to what you have studied.

Response: The conclusion section was rephrased as indicated (Pages 33,34; lines 428-434).

 

References

1. Health Canada. Canadian Perinatal Health Report, 2000. Ottawa, ON: Minister of Public Works and Government Services Canada, 2000.

2. Health Canada. Canadian Perinatal Health Report, 2003. Ottawa, ON: Minister of Public Works and Government Services Canada, 2003.

Attachment

Submitted filename: Response.docx

Click here for additional data file. (32.6KB, docx)

Decision Letter 1

Dayana Farias

4 Aug 2021

PONE-D-21-07926R1

Incidence, Risk Factors, and Feto-Maternal Outcomes of Inappropriate Birth Weight for Gestational Age Among Singleton Live Births in Qatar: A Population-Based Study

PLOS ONE

Dear Dr. Al-Dewik,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 18 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Dayana Farias, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

The authors have addressed almost all comments, but some clarification is still needed.

Does this method consider the sex of the newborn? If so, please include it on page 7 lines 91-92.

Page 8 line 108 the word preterm is missing “extreme to very... preterm”

Regarding mother age classification, the term “normal age” is not quite accurate. So, I suggest the use of young adults, adolescents, and advanced maternal age.

Page 10, Lines 148 and 149 have the same meaning, please keep only one version of it.

148 “All categorical and binary variables were expressed as numbers and percentages”

149 “Variables were summarized using numbers and percentages”

The definition of mediator factor is a variable that is in the middle of the causal path of exposure and outcome. SGA birth is not a cause of sex, for example. So, please check the classification of preterm birth and sex as mediators in the models.

Please check table 3 adjusted value of SGA for In-hospital mortality, the RR change from 7.95 (4.7– 13.46) to 226.56 (3.47– 318.22). Is it right? Which variables are causing this change?

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: - It is not clear which standardized birth weight chart was used to classify neonates into SGA, AGA and LGA. Why using six methods (charts) based on fetal growth/weight standards for classifying appropriateness of birth weight?

- Surprisingly, the method used for classifying adequacy of birth weight resulted in 6% of SGA and 16% of LGA neonates. Considering that the expected rate would be 10% for SGA and LGA, why did you find such discrepancy? What are the implications for 1) the interpretation of your findings and 2) the generalizability of your study?

- What contribution to clinical practice does your study give?

Reviewer #2: Thanks for the opportunity to re-review this manuscript where the aim was to assess the incidence, risk factors, and feto-maternal outcomes associated with small-forgestational age (SGA) and large-for-gestational age (LGA) infants.

The comments have been adressed, however it is still confusing and un-clear with the exposure and outcome in the different analyses. You write:“Firstly, logistic regression analysis was performed for risk factors/confounders (demographic and medical factors) and mediators (prematurity and gender) of appropriateness of fetal growth for the GA groups (SGA/LGA vs. AGA).”

As I understand it you have used SGA/LGA as outcome in these analysis and different risk factors as exposure. Which variables are confounders/mediators will depend on which variable is exposure and which one is the outcome. If SGA/LGA is the outcome preterm birth will be a mediator. However, in the analyses of outcome:

“Secondly, logistic regression was performed to investigate the outcomes of SGA and LGA including Apgar score, NICU/death in LR/OT, and in-hospital mortality. Multiple logistic regression was performed (including all significant confounders and mediators from the univariate analysis) to investigate the association of SGA/LGA with Apgar score, NICU/death in LR/OT, and in-hospital mortality as outcomes.”

I guess that in these analyses SGA/LGA were the exposure and then preterm birth has another role, in this setting preterm birth will be a confounder (or in some cases a mediator if we believe that the SGA/LGA caused the premature birth) Gender will be a mediator in the first analyses and a confounder in the second analyses. Please, make it clear what the exposure and the outcome is in the different analyses.

I note that attributable fractions are added. For example you write “not delivered preterm 11.6%, indicating that almost 12% of SGA cases could have been prevented if mothers had not delivered preterm”, it would be good to add, in the discussion part, something about possible unmeasured confounding.

The numbers in table 1, “pregnancy mode” look strange. The percentages do not add up to 100%.

In the conculsion you write: "SGA and LGA births are related multi-factor interactions of demographic and medical confounders that can be mediated by prematurity and gender of the baby". This sentence is un-clear. What do you mean? What do you mean with interactions?

**********

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Reviewer #2: No

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PLoS One. 2021 Oct 28;16(10):e0258967. doi: 10.1371/journal.pone.0258967.r004

Author response to Decision Letter 1

9 Sep 2021

Point by point response

PONE-D-21-07926R1

Incidence, Risk Factors, and Feto-Maternal Outcomes of Inappropriate Birth Weight for Gestational Age Among Singleton Live Births in Qatar: A Population-Based Study

PLOS ONE

We would like to take this opportunity to thank the editor and the reviewers for their comments and for taking the time to evaluate our manuscript. We have revised the article accordingly and point-by-point responses to the comments are reported below. All the changes made to the manuscript are highlighted in the Main Document using “Track Changes” tool.

Please note that other reviewers have requested other changes, and thus these will also appear in the manuscript.

Please follow the clean version of the manuscript with track changes turned “Off” for the indicated line numbers.

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response: All references have been checked, all are complete and correct.

Additional Editor Comments (if provided):

The authors have addressed almost all comments, but some clarification is still needed.

Does this method consider the sex of the newborn? If so, please include it on page 7 lines 91-92.

Response: Yes, the method considers newborn sex, the suggested change has been made (page 8, line 105).

Page 8 line 108 the word preterm is missing “extreme to very... preterm”

Response: The suggested change has been made, the word “preterm has been added (page 8, line 118).

Regarding mother age classification, the term “normal age” is not quite accurate. So, I suggest the use of young adults, adolescents, and advanced maternal age.

Response: The suggested changes have been made, the terms have been changed in the text (page 9, line 126; page 15, line 211, page 15, line 220, page 21, line 258, main tables (Tables 1-3), and supplementary materials (Tables S1-S5).

Page 10, Lines 148 and 149 have the same meaning, please keep only one version of it.

148 “All categorical and binary variables were expressed as numbers and percentages”

149 “Variables were summarized using numbers and percentages”

Response: The suggested changes have been made (page 10, line 158).

The definition of mediator factor is a variable that is in the middle of the causal path of exposure and outcome. SGA birth is not a cause of sex, for example. So, please check the classification of preterm birth and sex as mediators in the models.

Response: We rephrased the section to clarify the exact analyses performed, indicating the exposure and outcomes clearly, with the mediators and confounders in each analysis (page 10; lines 161,162, page 11, lines 166, 167, 171). Also, we fully reviewed the results section and made the necessary changes regarding the use of the word “mediator” or “confounder”.

Please check table 3 adjusted value of SGA for In-hospital mortality, the RR change from 7.95 (4.7– 13.46) to 226.56 (3.47– 318.22). Is it right? Which variables are causing this change?

Response: The indicated adjusted RR (226.56 (3.47– 318.22), is after adjusting for the risk variables associated with SGA that were significant in the univariate analysis, with p-values <0.05, presented in Table 2, which are as follows: gestational age, maternal age, parity, nationality, education, diabetes status, chronic hypertension, early- or pre-pregnancy BMI, baby gender, and any chromosomal or congenital abnormalities (Table 3–A). The number of SGA who died in the hospital is very small (n: 22/882 (2.5%), and thus after adjusting for other risk variables, the RR became overestimated. We have indicated this as one of the study limitations in the discussion (Page 36, Line 465-467)

Reviewers' comments:

Reviewer #1:

- It is not clear which standardized birth weight chart was used to classify neonates into SGA, AGA and LGA. Why using six methods (charts) based on fetal growth/weight standards for classifying appropriateness of birth weight?

Response: We have used a tool encompassing 6 fetal growth standards, among which we chose the INTERGROWTH-21st birth weight standard to calculate the percentiles. We have added a section in the introduction to explain in detail why we based our calculations on this particular reference (Pages 6,7; Lines 66-77). Also, we clarified it in the methods (Page 8, Lines 108-113).

- Surprisingly, the method used for classifying adequacy of birth weight resulted in 6% of SGA and 16% of LGA neonates. Considering that the expected rate would be 10% for SGA and LGA, why did you find such discrepancy? What are the implications for 1) the interpretation of your findings and 2) the generalizability of your study?

Response: We have added a section to discuss the comments indicated regarding the observed disparities, implications, interpretation, and generalizability (Pages 31,32, Lines 363-395).

- What contribution to clinical practice does your study give?

Response: We have added a section to further highlight the contribution of the study findings to clinical practice (page 36, lines 473-485).

Reviewer #2: Thanks for the opportunity to re-review this manuscript where the aim was to assess the incidence, risk factors, and feto-maternal outcomes associated with small-forgestational age (SGA) and large-for-gestational age (LGA) infants.

The comments have been adressed, however it is still confusing and un-clear with the exposure and outcome in the different analyses. You write:“Firstly, logistic regression analysis was performed for risk factors/confounders (demographic and medical factors) and mediators (prematurity and gender) of appropriateness of fetal growth for the GA groups (SGA/LGA vs. AGA).”

As I understand it you have used SGA/LGA as outcome in these analysis and different risk factors as exposure. Which variables are confounders/mediators will depend on which variable is exposure and which one is the outcome. If SGA/LGA is the outcome preterm birth will be a mediator. However, in the analyses of outcome: “Secondly, logistic regression was performed to investigate the outcomes of SGA and LGA including Apgar score, NICU/death in LR/OT, and in-hospital mortality. Multiple logistic regression was performed (including all significant confounders and mediators from the univariate analysis) to investigate the association of SGA/LGA with Apgar score, NICU/death in LR/OT, and in-hospital mortality as outcomes.” I guess that in these analyses SGA/LGA were the exposure and then preterm birth has another role, in this setting preterm birth will be a confounder (or in some cases a mediator if we believe that the SGA/LGA caused the premature birth) Gender will be a mediator in the first analyses and a confounder in the second analyses. Please, make it clear what the exposure and the outcome is in the different analyses.

Response: We agree and thus we rephrased the section to clarify the exact analyses performed, indicating the exposure and outcomes clearly, with the mediators and confounders in each analysis. We agree that both gender and preterm birth are confounders in the second analysis and not mediators, thus we deleted the word “mediators” from the second analysis (pages 10, 11; lines 161, 173), also, we fully reviewed the results section and made the necessary changes regarding the use of the word “mediator” or “confounder”.

I note that attributable fractions are added. For example you write “not delivered preterm 11.6%, indicating that almost 12% of SGA cases could have been prevented if mothers had not delivered preterm”, it would be good to add, in the discussion part, something about possible unmeasured confounding.

Response: We have added possible unmeasured confounding in the discussion (page 35, lines 459-462).

- The numbers in table 1, “pregnancy mode” look strange. The percentages do not add up to 100%.

Response: We agree with the reviewer, there was an error in the table, the table has been fully reviewed, and the error has been fixed (Table 1).

- In the conculsion you write: "SGA and LGA births are related multi-factor interactions of demographic and medical confounders that can be mediated by prematurity and gender of the baby". This sentence is un-clear. What do you mean? What do you mean with interactions?

Response: We do agree with the reviewer that the sentence is unclear. The indicated sentence has been rephrased (pages 36; lines 475-485).

Attachment

Submitted filename: Point by Point Response.docx

Click here for additional data file. (20KB, docx)

Decision Letter 2

Dayana Farias

11 Oct 2021

Incidence, Risk Factors, and Feto-Maternal Outcomes of Inappropriate Birth Weight for Gestational Age Among Singleton Live Births in Qatar: A Population-Based Study

PONE-D-21-07926R2

Dear Dr. Al-Dewik,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Dayana Farias, Ph.D

Academic Editor

PLOS ONE

Acceptance letter

Dayana Farias

15 Oct 2021

PONE-D-21-07926R2

Incidence, Risk Factors, and Feto-Maternal Outcomes of Inappropriate Birth Weight for Gestational Age Among Singleton Live Births in Qatar: A Population-Based Study

Dear Dr. Al-Dewik:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Dayana Farias

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Crosstabs of the associated risk factors and outcomes.

    (XLSX)

    Click here for additional data file. (35.2KB, xlsx)
    S2 Table. Univariate and multivariate regression of the risk factors associated with SGA and LGA births, with gestational age categorized into two groups (preterm vs. term).

    (XLSX)

    Click here for additional data file. (35.4KB, xlsx)
    S3 Table. Univariate and multivariate regression of the risk factors associated with SGA and LGA births, with gestational age categorized into five groups (extreme to very preterm, moderate preterm, late preterm, early term, and full term).

    (XLSX)

    Click here for additional data file. (36.6KB, xlsx)
    S4 Table. Univariate and multivariate regression of the pregnancy and neonatal outcomes associated with SGA and LGA births, with gestational age categorized into two groups (preterm vs. term).

    (XLSX)

    Click here for additional data file. (36.4KB, xlsx)
    S5 Table. Univariate and multivariate regression of the pregnancy and neonatal outcomes associated with SGA and LGA births, with gestational age categorized into five groups (extreme to very preterm, moderate preterm, late preterm, early term, and full term).

    (XLSX)

    Click here for additional data file. (34.1KB, xlsx)
    Attachment

    Submitted filename: Response.docx

    Click here for additional data file. (32.6KB, docx)
    Attachment

    Submitted filename: Point by Point Response.docx

    Click here for additional data file. (20KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting Information files.

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