Study population and statistical analysis

The study population was restricted to pregnancies of at least 28 gestational weeks for comparability of viable gestational age between countries, and singleton births with no congenital malformation. We excluded deliveries with missing data on birthweight, gestational age, and infant gender, as well as pregnancies that lasted less than 22 weeks or more than 42 weeks with congenital malformation.

To overcome the existing deficiency in birthweight references in LMICs, and taking into account birthweight variations across countries, we adopted methodology to generate local (country-specific) fetal weight and birthweight references developed by Mikolajczyk et al. [8].

To generate a country weight-reference standard, first we used the mean birthweight for infants born to married mothers aged 20–34 years with schooling years ≥12, who had no pregnancy complications, and who vaginally delivered singleton infants with no complications at 40 completed weeks of gestation (40 weeks+0 days to 40 weeks+6 days). Next, we based the birthweight (mean and SD) reference on a gestational age of 40 weeks, and we obtained the mean fetal-weight and percentiles across each gestational week for all countries participating in this study. We defined SGA as a birthweight below the 10^th percentile, AGA as between the 10^th and 90^th percentiles and large-for-gestational age (LGA) as above the 90^th percentile at the gestational ages of 28 to 41 weeks by infant gender. The study population was restricted to deliveries with a birthweight below the 90^th percentile, excluding LGA due to the condition’s high risk of adverse birth outcomes.

We considered the following variables as exposures at the individual level and further categorized them as shown in tables: maternal age defined as completed years at the time of delivery; marital status; years of education, parity; presence of chronic hypertension, preeclampsia or eclampsia, severe anaemia with haemoglobin <7 mg/dl, malaria or dengue, HIV or AIDS and other conditions defined as the presence of disease or injury affecting the heart, lungs, liver and kidneys. Additionally, we adjusted our analysis for facility capacity and the human development index (HDI). Facility capacity was used in previous studies and is defined as the total score of essential and additional services provided by health facilities with further categorization into high, medium and low capacity [31]. The human development index (HDI) for each country was adopted from 2012 UN development program estimates [32].

Perinatal outcomes considered in the study were fresh stillbirths (excluding macerated stillbirths), early neonatal death, perinatal death (both fresh stillbirth and early neonatal death) and neonatal near miss [33]. Neonatal near miss is defined as a neonate who survived a life-threatening condition and presented with any of the following conditions: any intubation at birth or anytime within the first week of life, nasal continuous positive airway pressure, surfactant administration, cardiopulmonary resuscitation (cardiac massage), any surgery, or use of any vasoactive drug, anticonvulsants, phototherapy in the first 24 hours, steroids to treat refractory hypoglycaemia, or therapeutic intravenous antibiotics. Early neonatal deaths were defined as intra-hospital deaths that occurred on or before the seventh day after delivery.

Statistical analysis

We divided our sample into two groups by gestational age: preterm (<37 weeks gestational age) and term (37–41 weeks gestational age) deliveries. The characteristics and outcomes of SGA compared to AGA infants in these groups were analysed separately. We compared preterm SGA vs preterm AGA, term SGA vs term AGA.

We performed the Chi-square test by taking into account the clustering and probability-sampling effects of the survey design. Also, after considering the study sampling design and clustering effects (health facility and country) on individual outcomes, we constructed multilevel logistic regression models with random effects for three levels: individual, facility and country. In our analyses of the association between SGA and fresh stillbirths and early neonatal death, we adjusted for maternal age, marital status, education, parity, medical conditions during pregnancy such as chronic hypertension, preeclampsia/eclampsia, severe anaemia, malaria/dengue and HIV/AIDs at the individual level, and capacity of health facilities at the facility level by four categorised HDI groups. The categories comprised as follows: very high HDI countries included Japan, Qatar and Argentina; high HDI countries included Mexico, Lebanon, Peru, Brazil, Ecuador and Sri Lanka; medium HDI countries included Jordan, China, Thailand, Mongolia, the Occupied Palestinian Territory, Paraguay, Philippines, Vietnam, Nicaragua, India and Cambodia, and low HDI countries included Kenya, Pakistan, Angola, Nigeria, Nepal, Uganda, Afghanistan, the Democratic Republic of Congo and Niger. In the ‘overall category’, we adjusted three-level structure random effects regression models to obtain odds ratios (ORs): individual (level 1), facility (level 2) and country (level 3).

Statistical analysis was conducted using Stata/MP version 12.0 (Stata Corp LP, College Station, Texas) and a P-value<0.05 was considered statistically significant.

Ethics committee approval

The HRP Specialist Panel on Epidemiological Research reviewed and approved the study protocol for technical content. This study was approved by the WHO Ethical Review Committee and the relevant ethical clearance mechanisms in all countries. Written consent from individual participants was not required, although patient records was anonymized and de-identified prior to analysis.

Main findings

We determined the maternal risk factors and adverse perinatal outcomes in preterm- and term-SGA infants in 29 countries globally using a large multi-country dataset. After adjusting for country-, facility- and individual-level effects, we found no association between increased risks of preterm SGA and sociodemographic status, such as age or education, compared with preterm AGA; however, we did observe that nulliparity and medical conditions, such as chronic hypertension and preeclampsia/eclampsia, were significantly associated with increased risks of preterm SGA compared with preterm AGA.

Strengths and limitations

To the best of our knowledge, this is the most current and extensive multi-country study to compare and examine risk factors and their adverse outcomes in preterm SGA and term SGA deliveries compared with preterm and term AGA deliveries using country-specific generic references. We used SGA criteria that incorporates country-specific reference standards developed by Mikolajczyk et al. [8]. This generic, global reference for fetal-weight and birthweight percentiles is more effective in predicting adverse perinatal outcomes compared with non-customised fetal-weight references, and is easier to use than the customised fetal-weight reference. A large sample size and the use of standardized questionnaires across countries allowed us to examine outcomes and stratify countries by five HDI groups.

Our study has several limitations. First, the quality of the data, especially birthweight and gestational age, is questionable in some countries. Errors might occur in dating the pregnancy, especially in countries where gestational age is based on the last menstrual period or where the birthweight is rounded up or down by a full 100 g. Due to this limitation, we focused on identifying the risk factors of SGA rather than focusing on SGA prevalence in each country.

Another limitation is a lack of data on maternal characteristics that have been noted in previous studies to be associated with the delivery of SGA infants, including smoking, alcohol and caffeine intake, maternal BMI, malnutrition, gestational weight gain, maternal stature, psychosocial stress, interpregnancy interval, and previous history of miscarriage [10]–[16]. Lack of adjustment for these variables may have led to an overestimation of the risk of SGA delivery, especially for women of a younger or older age, with less education or in low HDI-scoring countries.

Lastly, by using multilevel multiple regression analysis we were able to generalize our findings among facility-based settings; however, adverse perinatal outcomes and maternal medical conditions may have been overestimated because only the most severe cases are presented in higher-level facilities. Furthermore, the risk of neonatal mortality and morbidity could be underestimated due to the 7-day period in this study for neonatal follow-up. It should be noted that mortality due to infections, necrotising enterocolitis and other complications may occur after this period. Thus, the outcomes and conditions cannot be considered representative of the general population.

Interpretation

Our results suggest that nulliparity, chronic hypertension and preeclampsia/eclampsia are associated with a higher risk of preterm SGA. This result is consistent with other studies [18], [34]. In a national birth cohort study in Denmark, Catov et al. found that risk of preterm SGA increased 5.5 (95% confidence interval [CI] 3.2–9.4) times and term SGA increased 1.5 (95% CI 1.0–2.2) times among women with chronic hypertension [34]. The result is also consistent with the findings of Villar et al. who analysed data from WHO antenatal care trials and observed that nulliparity, chronic hypertension and obesity are also risk factors for preeclampsia in developing countries, but not low socioeconomic status [18]. Preeclampsia may cause an inadequate vascular response to abnormal placentation in pregnancy and may represent a distinct pathogenesis, which might affect fetal growth [6], [35]. Increased risk screening in antenatal care visits and referral to higher facilities for high-risk cases at an earlier stage in the pregnancy may help to reduce the incidence of severe preeclampsia or eclampsia.

We found that sociodemographic factors such as age, marital status and education were not significantly associated with the risk of preterm SGA, but sociodemographic status factors were related to term SGA. The results indicated that preterm SGA deliveries are more likely to be related to a maternal medical condition, especially preeclampsia, which tends to terminate the pregnancy earlier. On the other hand, term SGA may be more significantly relevant to lifestyle factors, such as sociodemographic status, malnutrition or other factors, and various medical conditions such as anaemia, HIV/AIDS and others. Our results are consistent with other studies that have observed a significant increased risk of term SGA associated with maternal age [10], [11] and nulliparity [11], [12]. Previous studies confirm that sociodemographic status is associated with a greater risk of SGA, although these studies did not divide SGA by preterm and term delivery [15], [36]. Berg et al. conducted path analysis to examine the relationship between maternal education and SGA using population-based cohort study data and showed that a significantly increased risk of SGA delivery among women with less education was related foremost to maternal smoking and, to some degree, to maternal height [15]. A population-based case-control study using Finnish birth register data also confirmed that between high and low socioeconomic status groups, 50% of the difference in risk of SGA was due to smoking [36].

Very high HDI countries showed no significant increase in the mortality risk for preterm and term SGA deliveries. This might be explained by the high quality of intrapartum care including access to care, human resources and drugs or medical equipment in very high HDI countries, which could reduce the mortality risk for preterm and term SGA deliveries. However, low to high HDI countries had risks two to four times higher compared to preterm AGA. These results are consistent with the population-based secondary analysis conducted in 20 cohorts in LMICs by the Child Health Epidemiology Reference Group (CHERG), which showed that the risk of early neonatal mortality increased about 16 times for preterm SGA delivery compared with preterm non-SGA delivery [37]. The reason for these different degrees of mortality risk might be due to the definition of SGA used by the authors, which they adapted from the US population birthweight reference standard and applied to LMICs. Another population-based cohort study in France showed that the risk of stillbirth was 2.6 times higher in preterm SGA deliveries, which is a similar result to our overall mortality risks [38]. Simchen et al. found that singleton preterm SGA infants had a significantly higher mortality rate with more culture-proven sepsis episodes [23].

In our findings, the risk of mortality in both preterm and term SGA deliveries was higher compared to preterm and term AGA, respectively, in low to high HDI countries. However, very high HDI countries had no significant mortality difference between preterm SGA and AGA, but had higher risks of mortality for term SGA, especially in fresh stillbirths.

Our findings indicate that if LMICs give appropriate care comparable with very high HDI countries, such as including regular risk screening in antenatal care visits and providing adequate treatment and care to those who need treatment at an earlier stage, it might be possible to decrease perinatal mortality among preterm SGA infants. Term SGA infants were three to four times significantly more likely to experience perinatal mortality than term AGA infants, irrespective of HDI groups. This finding supports Lubchenco’s report from 1976, which found that the risk of neonatal mortality was six times more likely in term SGA infants compared with term AGA infants [39]. Risk of perinatal mortality is significantly higher among term SGA deliveries compared with preterm AGA deliveries, irrespective of quality of care.

Neonatal near miss is higher risk, irrespective of HDI, although it has a high prevalence in neonates born at less than 32 weeks’ gestation. In very high HDI countries, 80% of neonates born at less than 32 gestational weeks experienced neonatal near miss, although perinatal mortality was around 11%. In low HDI countries, 49% of neonates born at less than 32 gestational weeks experienced neonatal near miss, and 70% of them died. The quality of neonatal intensive care is vital to prevent mortality.

Neonatal clinical management should be considered in the development of health policies for reducing neonatal mortality, such as screening high-risk neonates for early complications and the referral of pregnant women with hypertensive diseases for delivery in health facilities with special care units. Careful follow-up is necessary for SGA neonates who are at a higher risk of acquiring non-communicable diseases in the future.

Further research could define SGA using the customized rather than standard intrauterine growth curves, especially for countries that adopt curves based on populations from diverse ethnic groups. Ideally the standard questionnaire should include variables such as weight gain during pregnancy and pre-pregnancy BMI.

Contributors

Members of the WHO MCS Research Group include: João Paulo Souza (WHO – Global Study Coordinator), Ahmet Metin Gülmezoglu (WHO – Global Study Coordinator), Guillermo Carroli (Centro Rosarino de Estudios Perinatales [CREP], Argentina – Coordinator for Latin America), Pisake Lumbiganon (Khon Kaen University, Thailand – Coordinator for Asia), Zahida Qureshi (University of Nairobi, Kenya – Coordinator for Africa), Maria José Costa (WHO Angola), Bukola Fawole (University of Ibadan, Nigeria), Yvonne Mugerwa (Makerere University, Uganda), Idi Nafiou (Université Abdou Moumouni de Niamey, Niger), Isilda Neves (Delegação Provincial de Saúde de Luanda, Angola), Jean-José Wolomby-Molondo (Cliniques Universitaires de Kinshasa, Democratic Republic of Congo), Hoang Thi Bang (WHO Office in Vietnam), Nomita Chandhiok (Indian Council of Medical Research – ICMR, India), Kannitha Cheang (WHO Office in Cambodia), Kang Chuyun (Peking University, China), Kapila Jayaratne (Ministry of Health, Government of Sri Lanka), Chandani Anoma Jayathilaka (WHO office in Sri Lanka), Syeda Batool Mazhar (Pakistan Institute of Medical Sciences, Pakistan), Rintaro Mori (National Center for Child Health and Development, Japan), Mir Lais Mustafa (Afghan Public Health Institute, Afghanistan), Laxmi Raj Pathak (Ministry of Health and Population, Government of Nepal), Deepthi Perera (Ministry of Health, Government of Sri Lanka), Tung Rathavy (National Maternal and Child Health Center, Cambodia), Zenaida Recidoro (National Center for Disease Prevention and Control, The Philippines), Malabika Roy (Indian Council of Medical Research – ICMR, India), Pang Ruyan (Peking University, China), Naveen Shrestha (CIST College, Nepal), Surasak Taneepanichsku (Chulalongkorn University, Thailand), Nguyen Viet Tien (National Obstetrics and Gynaecology Hospital, Vietnam), Ganchimeg Togoobaatar (National Center for Child Health and Development, Japan), Mira Wehbe (American University of Beirut, Lebanon), Buyanjargal Yadamsuren (Ministry of Health, Government of Mongolia), Wang Yan (Peking University, China), Khalid Yunis (American University of Beirut, Lebanon), Vicente Bataglia (Hospital Nacional de Itauguá, Paraguay), José Guilherme Cecatti (University of Campinas – UNICAMP, Brazil), Bernardo Hernandez (Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA), Juan Manuel Nardin (Centro Rosarino de Estudios Perinatales, Argentina), Alberto Narváez (Colegio Medico de Pichincha and Fundación Salud, Ambiente y Desarollo, Ecuador), Eduardo Ortiz-Panozo (Instituto Nacional de Salud Pública, Mexico), Ricardo Pérez-Cuevas (Social Protection and Health Division, Inter American Development Bank, Mexico City, Mexico), Eliette Valladares (Universidad Nacional Autónoma de Nicaragua, Nicaragua), Nelly Zavaleta (Instituto de Investigación Nutricional, Peru), Anthony Armson (Dalhousie University, Canada), Caroline Crowther (University of Adelaide, Australia), Carol Hogue (Emory University, USA), Gunilla Lindmark (Uppsala University, Sweden), Suneeta Mittal (All India Institute of Medical Sciences, India), Robert Pattinson (University of Pretoria, South Africa), Mary Ellen Stanton (United States Agency for International Development – USAID, USA), Liana Campodonico (Centro Rosarino de Estudios Perinatales [CREP], Argentina), Cristina Cuesta (Centro Rosarino de Estudios Perinatales [CREP], Argentina), Daniel Giordano (Centro Rosarino de Estudios Perinatales – CREP Argentina), Nirun Intarut (Chulalongkorn University, Thailand), Malinee Laopaiboon (Khon Kaen University, Thailand), Armando Seuc (WHO), Joshua Vogel (WHO), Rajiv Bahl (WHO), Nguyen Thi My Huong (WHO), Jose Martines (WHO), Matthews Mathai (WHO), Mario Merialdi (WHO), Lale Say (WHO). We thank Ms. Emma Barber for her editorial support.

Further information on the Multi-country Survey on Maternal and Newborn Health and derivatives can be found at: http://www.who.int/reproductivehealth/topics/maternal_perinatal/nearmiss