Browse the corpus

Although human growth, from cell to whole body, is recognized as a universal biologic process, some entrenched views persist regarding fetal growth, in particular that it should be compared with a site-specific rather than prescriptive population. This view is not held by the World Health Organization (WHO) or by the Centers for Disease Control & Prevention,1, 2 which recommend using international neonatal standards. Likewise, such standards have now been adopted to estimate the burden and consequences of babies being born small for gestational age in low- and middle-income countries.3 We have summarized the key statistical, physiologic, ethnic, and genetic evidence relating to this issue.4, 5 Practically, the debate focuses on whether it is correct to monitor fetal growth using 1 of the many site-specific charts available. Typically, such charts are based on different populations for each fetal body structure and have been developed at hospital level.4 These multiple, site-specific charts are references, not international standards that are used commonly in most other areas of biology and medicine.

he many site-specific charts available. Typically, such charts are based on different populations for each fetal body structure and have been developed at hospital level.4 These multiple, site-specific charts are references, not international standards that are used commonly in most other areas of biology and medicine. This neglected aspect of obstetric practice means that clinical decisions are made based on reference charts that were derived from a wide range of different study populations. For example, a woman may have an early gestational age assessment with the use of a fetal crown-rump length chart based on a study of 80 women from Glasgow, Scotland,6, 7 followed by a clinical assessment with the use of a fundal height chart based on 313 women from Cardiff, Wales.8 Fetal biometry values may then be compared with 1 of many local charts,9 and, during the same ultrasound scan, estimated fetal weight may be determined from an equation based on 109 fetuses studied in Texas during the 1980s,10, 11 complemented by a recent chart from other US populations.12 If the woman requires further assessment, the umbilical Doppler measures are judged with the use of yet another reference population.13 At birth, the anthropometric measures of the newborn infant could be evaluated with the use of a multiplicity of reference charts, all of which are totally unrelated to the fetal growth charts that were being used just a few weeks earlier.

Doppler measures are judged with the use of yet another reference population.13 At birth, the anthropometric measures of the newborn infant could be evaluated with the use of a multiplicity of reference charts, all of which are totally unrelated to the fetal growth charts that were being used just a few weeks earlier. The INTERGROWTH-21st Project aimed to resolve these issues by conducting studies of human growth and development that involved pregnant women who were enrolled at <14 weeks gestation specifically to monitor their fetuses, newborn infants, and children prospectively up to 2 years of age to generate a single set of international standards to make judgements on the growth of all humans.14 The studies were based conceptually on the WHO prescriptive approach to constructing human growth standards.15 The study populations across geographically delimited areas were selected because they had the recommended health, nutrition, and socioeconomic status that was required to construct international standards.15 Hence, the INTERGROWTH-21st Standards (from maternal weight gain, to pregnancy dating, fetal growth and estimated fetal weight, to brain structures, amniotic fluid volume, umbilical artery Doppler measures, and newborn body composition) are prescriptive because they are based on a cohort of “healthy” pregnancies and babies from the same geographically selected populations in which most of the health and nutritional needs of mothers were met and adequate antenatal care provided.

d volume, umbilical artery Doppler measures, and newborn body composition) are prescriptive because they are based on a cohort of “healthy” pregnancies and babies from the same geographically selected populations in which most of the health and nutritional needs of mothers were met and adequate antenatal care provided. Nevertheless, the question always remains with studies that are focused on fetal growth as to how “healthy” were these children after birth and during childhood (ie, are they truly healthy?). We took this question seriously very early in the planning of the project and added a clinical and developmental follow-up evaluation16, 17, 18 beyond the customary early neonatal period as a further criterion to support the assertion that INTERGROWTH-21st babies represent true standard populations.19 The key milestone of 2 years of age was identified as a realistic and biologically relevant time point.20 Hence, we first compared the INTERGROWTH-21st Standards4, 21, 22 with the WHO Child Growth Standards.23 We demonstrated that, during the early neonatal period, the participants who were selected were appropriate and met the WHO prescriptive criteria for optimal growth.15 We then extended, for the first time in this literature, the prescriptive evaluation by designing the Infant Follow-up Study of the INTERGROWTH-21st Project.

monstrated that, during the early neonatal period, the participants who were selected were appropriate and met the WHO prescriptive criteria for optimal growth.15 We then extended, for the first time in this literature, the prescriptive evaluation by designing the Infant Follow-up Study of the INTERGROWTH-21st Project. This study aimed to evaluate the growth, nutrition, morbidity, and motor development at 2 years of age of the infants who were included in the international fetal and preterm growth standards to reinforce their prescriptive nature against which fetuses and preterm infants worldwide can now be compared. Materials and Methods INTERGROWTH-21st was a multicenter, population-based project that was conducted between 2009 and 2016 in 8 locations: Pelotas, Brazil; Turin, Italy; Muscat, Oman; Oxford, UK; Seattle, WA; Shunyi County, Beijing, China; the central area of Nagpur, India, and the Parklands suburb of Nairobi, Kenya.14, 24 The primary aim of the project was to study growth, health, nutrition, and neurodevelopment from <14 weeks gestation to 2 years of age.14 In the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project,21 we recruited women from these 8 populations who initiated antenatal care at <14 weeks gestation and who met the entry criteria of optimal health, nutrition, education, and socioeconomic status.14

ment from <14 weeks gestation to 2 years of age.14 In the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project,21 we recruited women from these 8 populations who initiated antenatal care at <14 weeks gestation and who met the entry criteria of optimal health, nutrition, education, and socioeconomic status.14 Gestational age was estimated based on the date of the last menstrual period and corroborated by ultrasound measurement of crown-rump length at 9+0 to 13+6 weeks gestation with the use of a standard protocol. All fetuses in the Fetal Growth Longitudinal Study were eligible to contribute data to the construction of the international fetal growth standards; all infants who were born at <37 weeks gestation in the Fetal Growth Longitudinal Study were eligible to contribute data to the construction of the international Postnatal Growth Standards for Preterm Infants. At each postnatal visit, a record of any illnesses in the preceding months was noted in addition to anthropometric measurements and a developmental assessment. Weight, length, and head circumference were obtained within 12 hours (and no >24 hours) of birth on the postnatal wards and at follow-up visits that were scheduled at 1 and 2 years of age (±1 month). Measurements were taken exclusively by the same teams who were trained and standardized at regular intervals for the INTERGROWTH-21st Project.25

circumference were obtained within 12 hours (and no >24 hours) of birth on the postnatal wards and at follow-up visits that were scheduled at 1 and 2 years of age (±1 month). Measurements were taken exclusively by the same teams who were trained and standardized at regular intervals for the INTERGROWTH-21st Project.25 All study sites used the same methods and equipment: electronic scales (Seca, Hangzhou, China) for weight (sensitivity of 10 g to 20 Kg); a specially designed Harpenden infantometer (Chasmors Ltd, London, UK) for recumbent length, and a metallic nonextendable tape (Chasmors Ltd) for head circumference.26, 27 Measurement procedures were standardized according to WHO recommendations.28 During the central standardization sessions for anthropometrists, the intra- and interobserver error of measurement values for recumbent length ranged from 0.3–0.6 cm and for head circumference from 0.2–0.5 cm.25 Measurements were taken twice, independently, by 2 of the study anthropometrists. If the difference between the 2 measures exceeded for weight 50 g for newborn infants and ≤100 g at 1 and 2 years of age (length, 7 mm; head circumference, 5 mm), then both observers independently repeated that measurement a second time and, if necessary, a third time.25, 27

ndently, by 2 of the study anthropometrists. If the difference between the 2 measures exceeded for weight 50 g for newborn infants and ≤100 g at 1 and 2 years of age (length, 7 mm; head circumference, 5 mm), then both observers independently repeated that measurement a second time and, if necessary, a third time.25, 27 When the Infant Follow-up Study started, some enrolled children had passed their second birthday already. The families of these children were invited to a follow-up visit with the maximum age at assessment for the child being 27 months. Similarly, those children who already had passed their first birthday, but were <2 years old, were invited initially for the first visit up to the age of 18 months. In total, only 14% of 1- and 2-year visits occurred outside the protocol-designated age range for assessment. Detailed information was obtained from the mother about the infant’s health, severe morbidities, length of breastfeeding, timing of the introduction of food, feeding practices, and food intake with the use of standardized forms that were produced especially for the project (www.intergrowth21.org).

When the Infant Follow-up Study started, some enrolled children had passed their second birthday already. The families of these children were invited to a follow-up visit with the maximum age at assessment for the child being 27 months. Similarly, those children who already had passed their first birthday, but were <2 years old, were invited initially for the first visit up to the age of 18 months. In total, only 14% of 1- and 2-year visits occurred outside the protocol-designated age range for assessment. Detailed information was obtained from the mother about the infant’s health, severe morbidities, length of breastfeeding, timing of the introduction of food, feeding practices, and food intake with the use of standardized forms that were produced especially for the project (www.intergrowth21.org). WHO protocols were followed to assess motor development milestones.29 We focused on 4 WHO milestones that are less likely to be affected by recall bias: sitting without support, hands and knees crawling, standing alone, and walking alone. Data were collected by trained staff using a form with pictures of the relevant child positions and corresponding definitions. Parents were asked to report the age in months and weeks when they first observed or “never observed” the milestones (http://www.intergrowth21.org.uk).

wling, standing alone, and walking alone. Data were collected by trained staff using a form with pictures of the relevant child positions and corresponding definitions. Parents were asked to report the age in months and weeks when they first observed or “never observed” the milestones (http://www.intergrowth21.org.uk). We collected the same information from parents at 1 and 2 years of age to evaluate the consistency of the reported dates. There were 7965 pairs of values recorded at year 1 and the year 2 interviews, of which 92.6% were identical at both visits. Among the 588 discrepant values, the median difference ranged between –1 week (interquartile range, −4.3–4.3) for hands and knees crawling to –0.2 weeks (interquartile range, –6.3–2.3) for standing alone. In these cases, after investigation, the values that were obtained at the 1-year visit were used. Across all study sites, standardized clinical care and feeding practices were implemented based on protocols that were developed by the INTERGROWTH-21st Neonatal Group (http://www.intergrowth21.org.uk).30, 31, 32 Exclusive breastfeeding up to 6 months was promoted for all babies, with supplementation for preterm infants as recommended.30, 33, 34

ndardized clinical care and feeding practices were implemented based on protocols that were developed by the INTERGROWTH-21st Neonatal Group (http://www.intergrowth21.org.uk).30, 31, 32 Exclusive breastfeeding up to 6 months was promoted for all babies, with supplementation for preterm infants as recommended.30, 33, 34 Age- and sex-specific z-scores and percentiles were estimated for each child at 2 years of age comparing their weight, length, and head circumference to the WHO Child Growth Standards.35 Corrected age was used for the preterm subgroup.36 Four values (3 for weight and 1 for head circumference) were above or below 5 standard deviations (SD) of the mean of the study population and were excluded. Variance components analysis was performed to calculate the percentage of variance in infant length at birth, 1, and 2 years because of between- and within-site variance. A multilevel mixed effects model was fitted with random intercepts for the study site and individual levels (with individuals nested within sites). The model, which was fitted with unstructured covariance structure, was adjusted by age (after fractional polynomial transformation) and sex. Both age and sex were treated as fixed effects. We analyzed 2026 mother-father-infant trios to compare the “mean parental height” with a predicted adult height for each infant, defined as twice their length at 2 years of age.37

Variance components analysis was performed to calculate the percentage of variance in infant length at birth, 1, and 2 years because of between- and within-site variance. A multilevel mixed effects model was fitted with random intercepts for the study site and individual levels (with individuals nested within sites). The model, which was fitted with unstructured covariance structure, was adjusted by age (after fractional polynomial transformation) and sex. Both age and sex were treated as fixed effects. We analyzed 2026 mother-father-infant trios to compare the “mean parental height” with a predicted adult height for each infant, defined as twice their length at 2 years of age.37 For infants reported to have achieved the milestones, the proportions within the WHO motor development windows of achievement35 were estimated, and z-scores were calculated by subtraction of the median age of achievement reported in the WHO motor development study from the median age of achievement in our cohort, and division by the SD in the WHO motor development study. Corrected age was used for the preterm subgroup. The proportion of infants who received breast milk and vitamin and mineral supplements and those who followed a special diet were estimated at 1 and 2 years of age.38, 39 We used Stata software (version 12; StataCorp, College Station, TX). Data were entered locally into the specially developed online data management system (http://medscinet.com).40

The proportion of infants who received breast milk and vitamin and mineral supplements and those who followed a special diet were estimated at 1 and 2 years of age.38, 39 We used Stata software (version 12; StataCorp, College Station, TX). Data were entered locally into the specially developed online data management system (http://medscinet.com).40 The INTERGROWTH-21st Project was approved by the Oxfordshire Research Ethics Committee “C” (reference: 08/H0606/139), the research ethics committees of the individual institutions, and the regional health authorities where the project was implemented. Participants provided written consent to be involved in the study.

We used Stata software (version 12; StataCorp, College Station, TX). Data were entered locally into the specially developed online data management system (http://medscinet.com).40 The INTERGROWTH-21st Project was approved by the Oxfordshire Research Ethics Committee “C” (reference: 08/H0606/139), the research ethics committees of the individual institutions, and the regional health authorities where the project was implemented. Participants provided written consent to be involved in the study. Results Population characteristics There were 4321 singleton newborn infants who were alive at birth without congenital malformations whose mothers were recruited at <14 weeks gestation and included in the cohort of the international INTERGROWTH-21st fetal growth standards.21 Among these, 183 infants were lost to follow up or withdrew consent during pregnancy; 298 infants were ineligible for the Infant Follow-up Study because the study site in Seattle, WA, could not participate. There were 6 neonatal deaths before hospital discharge (neonatal mortality rate, 1.6/1000 live births), 1 congenital malformation that was detected after birth, and 5 infant deaths, which represented a total infant mortality rate of 3 per 1000 live births. In addition, 103 mothers withdrew consent early in the study. Finally, 14 infants were >27 months old at the time the follow-up started; they therefore were not invited to participate. Hence, 3711 newborn infants were eligible for the Infant Follow-up Study, of these, 669 infants were lost to follow up. Thus, the total cohort that was studied comprised 3042 infants (Figure 1) who represented 82% of those eligible (86% for the preterm subgroup, 143/166; Supplementary Figure).Figure 1 Study flow of the INTERGROWTH-21st Infant Follow-up Study

e for the Infant Follow-up Study, of these, 669 infants were lost to follow up. Thus, the total cohort that was studied comprised 3042 infants (Figure 1) who represented 82% of those eligible (86% for the preterm subgroup, 143/166; Supplementary Figure).Figure 1 Study flow of the INTERGROWTH-21st Infant Follow-up Study The chart indicates the cohort that contributed data to the construction of the INTERGROWTH-21st Fetal Growth Standards.21 CM, congenital malformation; USA, United States of America. Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. The means (±SD) of the age at which measures were obtained were 24.4±1.2 and 23.2±0.7 months for the total cohort and the preterm subgroup, respectively; 86% of the 2-year measures were obtained from 23–25 months for the total cohort and 93% were obtained for the preterm subgroup. The neonatal characteristics of the infants divided into those that completed the 2-year follow-up evaluations (n=3042) and those lost to follow up (n=669) are presented in Table 1. Both groups were similar in terms of anthropometric measures at birth and neonatal morbidity. A similar comparison within the preterm subgroup is presented in Table 2.Table 1 Neonatal characteristics of children who were included in the INTERGROWTH-21st Fetal Growth Standards21 who were evaluated at 2 years of age compared with children who were lost to follow-up

metric measures at birth and neonatal morbidity. A similar comparison within the preterm subgroup is presented in Table 2.Table 1 Neonatal characteristics of children who were included in the INTERGROWTH-21st Fetal Growth Standards21 who were evaluated at 2 years of age compared with children who were lost to follow-up Characteristic Evaluated at 2 years of age (n=3042) Not evaluated at 2 years of agea (n=669) Gestational age at delivery, wkb 39.4±1.4 39.4±1.4 Birthweight, kgb 3.2±0.5 3.2±0.5 Birth length, cmb 49.1±2.0 49.2±2.0 Head circumference, cmb 33.7±1.4 33.9±1.3 Apgar at 5 minb 9.6±0.6 9.7±0.6 Age at hospital discharge, dc 3 (2–4) 2 (1–4) Early preterm, <34 wk gestation, n (%) 18 (0.6) 3 (0.4) Boys, n (%) 1516 (49.8) 324 (48.4) Neonatal intensive care unit stay >1 d but <3 d, n (%) 160 (5.3) 35 (5.2) Hyperbilirubinemia, n (%) 137 (4.5) 37 (5.5) Respiratory distress syndrome, n (%) 51 (1.7) 15 (2.2) Transient tachypnea of the newborn infant, n (%) 65 (2.1) 15 (2.2) Exclusive breastfeeding at discharge, n (%) 2698 (88.8) 591 (88.5) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Children lost to follow-up before evaluation at 2 years of age b Data are means±standard deviation c Data are given as median (interquartile range). Table 2 Neonatal characteristics of children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 who were evaluated at 2 years of age compared with children lost to follow-up

a Children lost to follow-up before evaluation at 2 years of age b Data are means±standard deviation c Data are given as median (interquartile range). Table 2 Neonatal characteristics of children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 who were evaluated at 2 years of age compared with children lost to follow-up Characteristic Evaluated at 2 years of age (n=143) Not evaluated at 2 years of agea (n=24) Gestational age at delivery, wkb 35.5±1.6 35.7±1.4 Birthweight, kgb 2.5±0.5 2.4±0.5 Birth length, cmb 45.7±2.7 45.6±2.3 Head circumference, cmb 31.8±1.7 31.8±1.5 Apgar at 5 minb 9.2±0.9 9.2±1.2 Age at hospital discharge, dc 4 (2–9) 4 (2–7) Early preterm, <34 weeks gestation, n (%) 19 (13.3) 3 (12.5) Boys, n (%) 73 (51.0) 8 (33.3) Neonatal intensive care unit stay >1 but <3 d, n (%) 59 (41.3) 11 (45.8) Hyperbilirubinemia, n (%) 29 (20.3) 3 (12.5) Respiratory distress syndrome, n (%) 20 (14.0) 6 (25.0) Transient tachypnea of the newborn infant, n (%) 23 (16.1) 1 (4.2) Exclusive breastfeeding at discharge, n (%) 106 (74.1) 19 (79.2) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Children lost to follow-up before evaluation at 2 years of age b Data are given as mean±standard deviation c Data are given as median (interquartile range).

Characteristic Evaluated at 2 years of age (n=143) Not evaluated at 2 years of agea (n=24) Gestational age at delivery, wkb 35.5±1.6 35.7±1.4 Birthweight, kgb 2.5±0.5 2.4±0.5 Birth length, cmb 45.7±2.7 45.6±2.3 Head circumference, cmb 31.8±1.7 31.8±1.5 Apgar at 5 minb 9.2±0.9 9.2±1.2 Age at hospital discharge, dc 4 (2–9) 4 (2–7) Early preterm, <34 weeks gestation, n (%) 19 (13.3) 3 (12.5) Boys, n (%) 73 (51.0) 8 (33.3) Neonatal intensive care unit stay >1 but <3 d, n (%) 59 (41.3) 11 (45.8) Hyperbilirubinemia, n (%) 29 (20.3) 3 (12.5) Respiratory distress syndrome, n (%) 20 (14.0) 6 (25.0) Transient tachypnea of the newborn infant, n (%) 23 (16.1) 1 (4.2) Exclusive breastfeeding at discharge, n (%) 106 (74.1) 19 (79.2) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Children lost to follow-up before evaluation at 2 years of age b Data are given as mean±standard deviation c Data are given as median (interquartile range). Feeding practices At hospital discharge, 89% of the total cohort and 74% of the preterm subgroup were exclusively breast-milk fed. Similar patterns were seen among the children who were lost to follow-up at 2 years of age. Exclusive breastfeeding was stopped at a median of 5 months (interquartile range, 3–6 months); this was similar in the preterm subgroup. Breastfeeding stopped entirely at a median of 12 months (interquartile range, 6–18 months) for the total cohort and 11 months (interquartile range, 5–18 months) for the preterm subgroup.

lusive breastfeeding was stopped at a median of 5 months (interquartile range, 3–6 months); this was similar in the preterm subgroup. Breastfeeding stopped entirely at a median of 12 months (interquartile range, 6–18 months) for the total cohort and 11 months (interquartile range, 5–18 months) for the preterm subgroup. In the total cohort, the proportion of children who still were receiving breast milk fell from 59% at 1 year to 11% at 2 years, by which time 34% of the children were formula fed. All children received dairy products of some type (including human milk) at both ages. Food supplements had been given routinely to 33% of children by 1 year and 21% by 2 years. At 1 year of age, 51% of the infants in the preterm subgroup were still receiving breast milk; the figure fell to 8% at 2 years, by which time 34% of children were receiving formula. Food supplements that included vitamins and minerals were given to 36% of the infants in the first year and 28% of the infants by the second year in the preterm cohort. Complementary feeding practices were considered appropriate in terms of diversity, the timing of introduction, and the food variety across sites (Supplementary Tables 1 and 2).34

ded vitamins and minerals were given to 36% of the infants in the first year and 28% of the infants by the second year in the preterm cohort. Complementary feeding practices were considered appropriate in terms of diversity, the timing of introduction, and the food variety across sites (Supplementary Tables 1 and 2).34 Postnatal morbidity The overall morbidity rate in the total cohort was low (Table 3); only 9% of infants were hospitalized (median length of stay, 3 days) in the second year of life. The most frequently reported or diagnosed conditions were acute respiratory infections, diarrhea, and/or gastrointestinal problems with few repeated episodes, skin problems, and febrile episodes. Antibiotics were prescribed on >3 occasions in 10.9% and 15.8% of children in the first and second years, respectively, which corresponds closely to the rate of reported fever episodes (Table 3). Similar patterns were seen in the preterm subgroup (Table 4). Most of the infants were fully vaccinated in accordance with recommended policies.Table 3 Morbidity in the previous year of children who were included in the INTERGROWTH-21st Fetal Growth Standards21 at 1 and 2 years of age

reported fever episodes (Table 3). Similar patterns were seen in the preterm subgroup (Table 4). Most of the infants were fully vaccinated in accordance with recommended policies.Table 3 Morbidity in the previous year of children who were included in the INTERGROWTH-21st Fetal Growth Standards21 at 1 and 2 years of age Medical condition 1 Year of age (n=2834), n (%) 2 Years of age (n=3042), n (%) Hospitalized at least once 344 (12.1) 272 (8.9) Total no. of days hospitalized 3 (1–5)a 3 (1–5)a Any prescription made by a healthcare professional 1783 (62.9) 1911 (62.9) Antibiotics (≥3 regimens) 308 (10.9) 481 (15.8) Iron/folic acid/vitamin B12/other vitamins 815 (28.8) 430 (14.1) Up-to-date with local vaccination policies 2607 (92.0) 2903 (95.4) Otitis media/pneumonia/bronchiolitis 228 (8.0) 293 (9.6) Parasitosis/diarrhea/vomiting 148 (5.2) 139 (4.6) Seizures/cerebral palsy/neurologic disorders 9 (0.3) 9 (0.3) Exanthema/skin disease 456 (16.1) 399 (13.1) UTI/pyelonephritis 4 (0.1) 10 (0.3) Fever ≥3 d (≥3 episodes) 293 (10.3) 309 (10.2) Malaria 13 (0.5) 12 (0.4) Meningitis 5 (0.2) 0 (0.0) Other infections that required antibiotics 69 (2.4) 79 (2.6) Hearing problems 4 (0.1) 3 (0.1) Asthma 24 (0.8) 42 (1.4) Cardiovascular problems 9 (0.3) 7 (0.2) Blindness 6 (0.2) 4 (0.1) Gastroesophageal reflux 88 (3.1) 9 (0.3) Any hemolytic condition 14 (0.5) 22 (0.7) Any malignancy 3 (0.1) 6 (0.2) Cow’s milk protein allergy NA 21 (0.7) Food allergies NA 52 (1.7) Injury trauma 43 (1.5) 130 (4.3) Any condition that required surgery 31 (1.1) 34 (1.1) NA, not applicable (data were not collected at the 1-year follow-up visit); UTI, urinary tract infection.

ic condition 14 (0.5) 22 (0.7) Any malignancy 3 (0.1) 6 (0.2) Cow’s milk protein allergy NA 21 (0.7) Food allergies NA 52 (1.7) Injury trauma 43 (1.5) 130 (4.3) Any condition that required surgery 31 (1.1) 34 (1.1) NA, not applicable (data were not collected at the 1-year follow-up visit); UTI, urinary tract infection. Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Data are given as median (interquartile range). Table 4 Morbidity of children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 at 1 and 2 years of age

ic condition 14 (0.5) 22 (0.7) Any malignancy 3 (0.1) 6 (0.2) Cow’s milk protein allergy NA 21 (0.7) Food allergies NA 52 (1.7) Injury trauma 43 (1.5) 130 (4.3) Any condition that required surgery 31 (1.1) 34 (1.1) NA, not applicable (data were not collected at the 1-year follow-up visit); UTI, urinary tract infection. Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Data are given as median (interquartile range). Table 4 Morbidity of children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 at 1 and 2 years of age Medical condition 1 Year of age (n=154), n (%) 2 Years of age (n=143), n (%) Hospitalized at least once 34 (22.1) 7 (4.9) Total number of days hospitalized 5 (3–8)a 7 (3–9)a Any prescription made by a healthcare professional 98 (63.6) 72 (50.3) Antibiotics (≥3 regimens) 31 (20.1) 12 (8.4) Iron/folic acid/vitamin B12/other vitamins 56 (36.4) 23 (16.1) Up-to-date with local vaccination policies 139 (90.3) 136 (95.1) Otitis media/pneumonia/bronchiolitis 13 (8.4) 7 (4.9) Parasitosis/diarrhea/vomiting 11 (7.1) 10 (7.0) Seizures/cerebral palsy/neurologic disorders 1 (0.6) 0 Exanthema/skin disease 27 (17.5) 21 (14.7) UTI/pyelonephritis 0 0 Fever ≥3 d (≥3 episodes) 11 (7.1) 5 (3.5) Malaria 0 1 (0.7) Meningitis 0 0 Other infections that required antibiotics 2 (1.3) 4 (2.8) Hearing problems 0 (0.0) 0 Asthma 2 (1.3) 1 (0.7) Cardiovascular problems 0 1 (0.7) Blindness 0 0 Gastroesophageal reflux 6 (3.9) 0 Any hemolytic condition 2 (1.3) 2 (1.4) Any malignancy 1 (0.6) 0 Cow’s milk protein allergy NA 3 (2.1) Food allergies 1 (0.6) 3 (2.1) Injury trauma 1 (0.6) 4 (2.8) Any condition that required surgery 2 (1.3) 4 (2.8) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018.

.9) 0 Any hemolytic condition 2 (1.3) 2 (1.4) Any malignancy 1 (0.6) 0 Cow’s milk protein allergy NA 3 (2.1) Food allergies 1 (0.6) 3 (2.1) Injury trauma 1 (0.6) 4 (2.8) Any condition that required surgery 2 (1.3) 4 (2.8) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. NA, not applicable (data were not collected at the 1-year follow-up visit); UTI, urinary tract infection. a Data are given as median (interquartile range). Growth and development from birth to 2 years of age At 1 year of age, a comparison of the total cohort with the age- and sex-specific WHO Child Growth Standards showed that length and head circumference had a mean ± SD z-score of 0.0±1.1 for both measures and that the medians were at the 49th and 48th percentiles of the WHO Child Growth Standards, respectively; for weight, the mean z-score was 0.2±1.1 and median at the 58th percentile.

d sex-specific WHO Child Growth Standards showed that length and head circumference had a mean ± SD z-score of 0.0±1.1 for both measures and that the medians were at the 49th and 48th percentiles of the WHO Child Growth Standards, respectively; for weight, the mean z-score was 0.2±1.1 and median at the 58th percentile. At the 2-year visit, the growth of the children who were included in the INTERGROWTH-21st Fetal Growth Standards plotted almost perfectly onto the WHO Child Growth Standards (ie, 93% for length, 91% for weight [with the expected larger variability], and 92% for head circumference, respectively. Our cohort’s values were within the 3rd and 97th cut-off points of the WHO Child Growth Standards (Table 5; Figure 2). For length and head circumference, the mean ± SD z-score was 0.0±1.1 for both measures, and the medians were at the 49th and 50th percentiles of the WHO Child Growth Standards, respectively. For weight, the mean ± SD z-score was 0.2±1.1, and median was at the 58th percentile. Figure 2 also shows the 3rd, 50th and 97th percentiles of the distributions of our data (the same percentiles of the WHO Child Growth Standards are included in Figure 2 at years 1 and 2). As shown, the percentiles from our population are almost identical to those of the WHO standards.Figure 2 Anthropometric measures at 1 and 2 years of age of the children included in the INTERGROWTH-21st Fetal Growth Standards

same percentiles of the WHO Child Growth Standards are included in Figure 2 at years 1 and 2). As shown, the percentiles from our population are almost identical to those of the WHO standards.Figure 2 Anthropometric measures at 1 and 2 years of age of the children included in the INTERGROWTH-21st Fetal Growth Standards Data are for children who were included in the INTERGROWTH-21st Fetal Growth Standards21 (grey circles) and children who were included in the Preterm Postnatal Growth Standards22 (red circles). Values are superimposed onto the 3rd, 50th, and 97th percentiles of the World Health Organization Child Growth Standards23 (girls [pink lines] and boys [blue lines]). For children born preterm, corrected postnatal age was used. Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. Table 5 Anthropometric measures at 2 years of age of children who were included in the INTERGROWTH-21st Fetal Growth Standards21 compared with the World Health Organization Child Growth Standardsa Variable N INTERGROWTH-21st World Health Organization Child Growth Standards Mean±standard deviationb Median (interquartile range) Mean z-score±standard deviation Median percentile Weight, kg 3025 12.3±1.7 12.2 (11.1–13.3) 0.2±1.1 58 Length, cm 3010 87.4±3.6 87.3 (85.0–89.7) 0.0±1.1 49 Head circumference, cm 3003 47.8±1.6 47.8 (46.7–48.8) 0.0±1.1 50 Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Age and gender-specific z-scores and percentiles compared with the World Health Organization Child Growth Standards23

Variable N INTERGROWTH-21st World Health Organization Child Growth Standards Mean±standard deviationb Median (interquartile range) Mean z-score±standard deviation Median percentile Weight, kg 3025 12.3±1.7 12.2 (11.1–13.3) 0.2±1.1 58 Length, cm 3010 87.4±3.6 87.3 (85.0–89.7) 0.0±1.1 49 Head circumference, cm 3003 47.8±1.6 47.8 (46.7–48.8) 0.0±1.1 50 Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Age and gender-specific z-scores and percentiles compared with the World Health Organization Child Growth Standards23 b Mean values were estimated from raw data. At 1 year of age, a comparison of the preterm cohort only with the age- and sex-specific WHO Child Growth Standards at postnatal corrected age, length, and head circumference had a mean z-score of 0.1 for both measures; the medians were at the 52nd percentiles of the WHO Child Growth Standards; for weight, the mean ± SD z-score was 0.2±1.1, and the median was at the 57th percentile.

e age- and sex-specific WHO Child Growth Standards at postnatal corrected age, length, and head circumference had a mean z-score of 0.1 for both measures; the medians were at the 52nd percentiles of the WHO Child Growth Standards; for weight, the mean ± SD z-score was 0.2±1.1, and the median was at the 57th percentile. At 2 years of age, the growth of the children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards also plotted similarly onto the WHO Child Growth Standards (Table 6; Figure 2). For length and head circumference, the mean ± SD z-scores were -0.1±1.2 and 0.0±1.1, respectively, and the median was at the 47th percentile for head circumference of the WHO Child Growth Standards for both measures. For weight, the mean ± SD z-score was 0.2±1.1, and the median was at the 53rd percentile.Table 6 Anthropometric measures at 2 years of age of children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 compared with the World Health Organization Child Growth Standardsa Variable N INTERGROWTH-21st Comparison with World Health Organization Child Growth Standards Mean±standard deviationb Median (interquartile range) Mean z-score±standard deviation Median percentile Weight, kg 142 12.0±1.7 11.7 (10.8–13.2) 0.2±1.1 53 Length, cm 141 86.2±3.7 86.2 (83.8–88.3) –0.1±1.2 47 Head circumference, cm 140 47.7±1.6 47.6 (46.7–48.6) 0.0±1.1 47 Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018.

e) Mean z-score±standard deviation Median percentile Weight, kg 142 12.0±1.7 11.7 (10.8–13.2) 0.2±1.1 53 Length, cm 141 86.2±3.7 86.2 (83.8–88.3) –0.1±1.2 47 Head circumference, cm 140 47.7±1.6 47.6 (46.7–48.6) 0.0±1.1 47 Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Corrected age was used to obtain age and gender-specific z-scores and percentiles comparing to the World Health Organization Child Growth Standards23 b Mean values were estimated from raw data. The mean postnatal ages, at which the 4 main WHO milestones for gross motor development29 were achieved for the total cohort and preterm subgroup (chronologic and corrected age) are presented in Figure 3. Both groups overlapped well for these milestones at the 50th, 3rd, and 97th percentiles of the WHO range for normal term infants. By 2 years of age, >99% of the children had achieved the 4 motor development milestones with >97% within the range of the WHO milestones (data not shown). However, although the preterm subgroup overlapped very well when corrected age was used, they displayed a delay of approximately 1 month in achieving the “walking alone” and “standing alone” milestones, when estimated age after birth was used (Figure 3).Figure 3 Median age of achievement (3rd and 97th percentiles) of 4 gross motor development milestones

oup overlapped very well when corrected age was used, they displayed a delay of approximately 1 month in achieving the “walking alone” and “standing alone” milestones, when estimated age after birth was used (Figure 3).Figure 3 Median age of achievement (3rd and 97th percentiles) of 4 gross motor development milestones Data are for children who were included in the INTERGROWTH-21st Fetal Growth Standards21 (purple) and children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 (blue). The diamonds represent the use of corrected age for the children who were born preterm. For comparison, the 3rd and 97th percentiles of the World Health Organization windows of achievement35 for the same milestones are presented in grey (with the median shown as a vertical line). Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018.

Data are for children who were included in the INTERGROWTH-21st Fetal Growth Standards21 (purple) and children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 (blue). The diamonds represent the use of corrected age for the children who were born preterm. For comparison, the 3rd and 97th percentiles of the World Health Organization windows of achievement35 for the same milestones are presented in grey (with the median shown as a vertical line). Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. The variability in children’s length among study sites compared with that among individuals within a study site Maintaining the same analytic approach to the 2-year follow-up data that was adopted for the fetus and newborn infant,4, 16, 37 we summarized the variability in skeletal growth and size during pregnancy, at birth, and in infancy and childhood (ie, quantifying the variability among study sites, as opposed to that among individuals within a given site). We estimated that the variance among our study sites from birth through 1–2 years of age explains only 5.5% of the total variability in length between birth and 2 years of age; the variance among individuals within a study site explains 42.9% (ie, 8 times the amount after we controlled for age and sex). In Table 7, we compared the present results with the previously published INTERGROWTH-21st data from the first trimester of pregnancy to 2 years of age. In all these periods of rapid growth, the variance among sites explains <10% of the total variability in skeletal growth.Table 7 Variance components analysis for fetal, newborn infant, and childhood skeletal growth from the cohort of the INTERGROWTH-21st Project

from the first trimester of pregnancy to 2 years of age. In all these periods of rapid growth, the variance among sites explains <10% of the total variability in skeletal growth.Table 7 Variance components analysis for fetal, newborn infant, and childhood skeletal growth from the cohort of the INTERGROWTH-21st Project Variance Fetal ultrasound measures16, % Size at birth16 (newborn infant lengtha), % Infancy/childhood, % 1st-trimester fetal crown-rump lengtha 2nd- and 3rd-trimester fetal head circumference Preterm infant length22 Present study lengthb Among study sites 1.9 2.6 3.5 0.2 5.5 Among individuals within a site — 18.6 — 57.1 42.9 Residual 98.1 78.8 96.5 42.7 51.6 Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Variance between individuals for these measures could not be estimated, given the cross-sectional nature of the data b Includes length measurements at birth, 1 and 2 years, controlled for age and sex.

Variance Fetal ultrasound measures16, % Size at birth16 (newborn infant lengtha), % Infancy/childhood, % 1st-trimester fetal crown-rump lengtha 2nd- and 3rd-trimester fetal head circumference Preterm infant length22 Present study lengthb Among study sites 1.9 2.6 3.5 0.2 5.5 Among individuals within a site — 18.6 — 57.1 42.9 Residual 98.1 78.8 96.5 42.7 51.6 Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. a Variance between individuals for these measures could not be estimated, given the cross-sectional nature of the data b Includes length measurements at birth, 1 and 2 years, controlled for age and sex. Estimated adult height of the children included in the INTERGROWTH- 21st fetal growth standards We estimated the difference between the observed mean parental height and the expected mean adult height (equal to approximately double the mean length at 2 years of age).37 In the study sites in low-middle income countries (n=1611), an increase in mean expected adult height of 8.9 cm over mean parental height is predicted to occur in a single generation, provided that infants and children are exposed to adequate health, environmental and nutritional conditions from early pregnancy onwards (Figure 4). Conversely, in high-income country sites (N=415), this cohort will be on average 2.2 cm taller than their parents (Figure 4).Figure 4 Expected increase from parental height

rovided that infants and children are exposed to adequate health, environmental and nutritional conditions from early pregnancy onwards (Figure 4). Conversely, in high-income country sites (N=415), this cohort will be on average 2.2 cm taller than their parents (Figure 4).Figure 4 Expected increase from parental height Mean (95% confidence interval) difference between estimated adult height (calculated by doubling infant length at 2 years of age) and mean parental height (calculated as the average of maternal and paternal heights) for children who were included in the INTERGROWTH-21st Fetal Growth Standards21 for study sites located in low- and middle-income countries and high-income countries. Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. Comment Main findings The participants included in the construction of the Fetal Growth Standards, the Newborn Size at Birth and the Preterm Postnatal Growth Standards of the INTERGROWTH-21st Project were selected during early pregnancy specifically to generate international standards.15 The comprehensive data presented here, which describe for the first time the postnatal physical growth, infant mortality rate, morbidity and motor development of the INTERGROWTH-21st participants, corroborate that they conformed to the WHO prescriptive approach for the construction of human growth standards. They are a cohort with continuous very low rates of clinical conditions that could affect optimal growth and development.

mortality rate, morbidity and motor development of the INTERGROWTH-21st participants, corroborate that they conformed to the WHO prescriptive approach for the construction of human growth standards. They are a cohort with continuous very low rates of clinical conditions that could affect optimal growth and development. Our findings reinforce the a priori concept17 that it is possible to identify a subset of mostly moderate and late preterm infants, with no evidence of intrauterine growth restriction and limited neonatal morbidity,41 which constitutes an adequate approximation (in terms of growth, health, nutrition, and development) to a prescriptive population for the construction of preterm postnatal growth standards up to 64 weeks postmenstrual age, the time at which they match the WHO Child Growth Standards.22, 23

limited neonatal morbidity,41 which constitutes an adequate approximation (in terms of growth, health, nutrition, and development) to a prescriptive population for the construction of preterm postnatal growth standards up to 64 weeks postmenstrual age, the time at which they match the WHO Child Growth Standards.22, 23 Strengths and limitations of the study in the context of the existing literature As far as we are aware, this is the first time that a fetal cohort that has been included in longitudinal studies for the specific purpose of constructing prescriptive growth standards has been evaluated up to 2 years of age. Most ultrasound studies that aimed to create reference charts for fetal growth have not reported any postnatal assessment, nor is it likely that such an assessment has been carried out, given the time that has elapsed since these studies were conducted.9 We selected the 2-year milestone because nutrition indicators that are measured at this age are strongly predictive of adult measures of nutrition, human capital, attained height, and intelligence.42 Before the age of 2 years, it has been shown that children often cross growth percentiles, whereas after this age the phenomenon known as “growth channelization” has been demonstrated, because children tend to grow along the same percentile.20

t measures of nutrition, human capital, attained height, and intelligence.42 Before the age of 2 years, it has been shown that children often cross growth percentiles, whereas after this age the phenomenon known as “growth channelization” has been demonstrated, because children tend to grow along the same percentile.20 Our unique data are derived from a prospective follow-up evaluation of individuals from 7 different regions of the world from the first trimester of pregnancy to 2 years of age. These findings strengthen the case for the worldwide use of the international INTERGROWTH-21st standards that complement the WHO Child Growth Standards in postnatal life. The similarities between the INTERGROWTH-21st and WHO studies mean that the size of children, measured at 2 years of age, who were born to healthy mothers, with adequate nutrition, from healthy populations at low risk of adverse pregnancy outcomes, is consistent between the studies and across time. Other strong features of the study include careful standardization of the outcome measures and the comprehensiveness of the standardized clinical and developmental assessments at 2 years of age.43 Furthermore, we followed 82% of the children and up to 86% of the preterm subgroup, which are excellent rates for free-living urban subjects. Baseline similarities between the infants who were evaluated and those lost to follow-up demonstrate that selection bias is very unlikely to have influenced the observed results. We acknowledge some limitations that relate to practical difficulties of carrying out such a large, multicenter study.

Other strong features of the study include careful standardization of the outcome measures and the comprehensiveness of the standardized clinical and developmental assessments at 2 years of age.43 Furthermore, we followed 82% of the children and up to 86% of the preterm subgroup, which are excellent rates for free-living urban subjects. Baseline similarities between the infants who were evaluated and those lost to follow-up demonstrate that selection bias is very unlikely to have influenced the observed results. We acknowledge some limitations that relate to practical difficulties of carrying out such a large, multicenter study. First, the information on morbidity refers mostly to substantive clinical episodes, and data on gross motor development were obtained from parental report rather than direct observation; however, we informed parents about the Infant Follow-up Study protocol at enrolment and asked them to record severe conditions and infant developmental milestones. In addition, parents were encouraged to bring sick children for care to the participating centers; illness therefore was recorded at the time of the event.

however, we informed parents about the Infant Follow-up Study protocol at enrolment and asked them to record severe conditions and infant developmental milestones. In addition, parents were encouraged to bring sick children for care to the participating centers; illness therefore was recorded at the time of the event. Second, the study outcomes do not extend >2 years of age. This juncture was selected because it is a key time for the detection of postnatal growth faltering,44, 45 and an anthropometric and clinical evaluation at this age is a very good predictor of subsequent growth. Finally, the Seattle, WA, study site did not participate in the follow-up of children because of logistic issues that were associated with this inner city, highly mobile population. Although they represented only 298 newborn infants of more than 4000 in the total cohort and it is very unlikely that they would have affected the overall results presented here, it would have been better to have studied this subsample as well.

issues that were associated with this inner city, highly mobile population. Although they represented only 298 newborn infants of more than 4000 in the total cohort and it is very unlikely that they would have affected the overall results presented here, it would have been better to have studied this subsample as well. The WHO motor development assessment is a simple, pragmatic, and reliable tool to describe normal variation in the achievement of milestones that are reached progressively across infancy. It is especially recommended for studying a large number of infants at the cohort level, rather than individual level.35 We have observed that, using chronologic age, gross motor indicators for 2-year-old children who were born preterm (despite being always within WHO recommended windows) are below those of the total cohort by approximately 1 month. This pattern disappears with the use of corrected age (Figure 3). Thus, it is likely that the true range of development in uncomplicated preterm infants is between chronologic and corrected age. However, it is possible that levels of preterm postnatal development may be associated with etiologic phenotypes, as was shown with early neonatal morbidity.41 We presently are studying these issues in the INTERBIO-21st Study, which is the extension to the INTERGROWTH-21st Project.

etween chronologic and corrected age. However, it is possible that levels of preterm postnatal development may be associated with etiologic phenotypes, as was shown with early neonatal morbidity.41 We presently are studying these issues in the INTERBIO-21st Study, which is the extension to the INTERGROWTH-21st Project. In all the INTERGROWTH-21st publications, we have emphasized that the relevant question when comparing growth across populations is whether the variability in skeletal growth within a population (interindividual genetic difference) is larger than the variability among populations (interpopulation genetic difference) when nutritional and health needs are met. We have used variance components analysis in cohorts that were followed prospectively to identify the proportional contribution of the within and between sites variance components.4, 16, 23 We have repeated this analysis for the present article (Table 7). The variance from birth to 2 years of age within a geographic area is 8 times larger than that among geographic areas (Table 7). Hence, it is very unlikely that variability among geographic areas explains >10% of the total variability in infant and child length in healthy, well-nourished, low-risk populations who receive adequate healthcare. These results are in very close agreement with the data from the WHO Child Growth Standards for children <5 years of age, where the variability within study sites explained 70% of the total variance as opposed to a figure of 3.4% that is explained by the between-study sites variability.23

o receive adequate healthcare. These results are in very close agreement with the data from the WHO Child Growth Standards for children <5 years of age, where the variability within study sites explained 70% of the total variance as opposed to a figure of 3.4% that is explained by the between-study sites variability.23 This clinical/epidemiologic finding is of great biologic interest because it is consistent with a metaanalysis of 22 genome-wide association studies that showed that the polygenic scores, based on 180 single nucleotide polymorphisms that previously were associated with adult height, explained only a very small proportion of the total variance in birth and infant length (0.13% and 2.95%, respectively).46 Long-term implications Our 2-year follow-up evaluation of this large cohort of healthy children allowed their mean predicted adult height to be estimated based on the assumption that health, nutritional, and socioeconomic conditions would remain adequate (Figure 4). Thus, the participants in the low-middle income countries sites (and by implication those from other similar countries) are expected to be approximately 8 cm taller as adults than the mean height of their parents; these data are very close to the 6.2–7.8 cm results that were observed in a similar, secondary analysis of the WHO Multicentre Growth Reference Study database.37 However, because optimal growth largely has been achieved in the parents from high-income country sites, their children are expected to be, on average, only 2.2 cm taller (Figure 4).

e to the 6.2–7.8 cm results that were observed in a similar, secondary analysis of the WHO Multicentre Growth Reference Study database.37 However, because optimal growth largely has been achieved in the parents from high-income country sites, their children are expected to be, on average, only 2.2 cm taller (Figure 4). Our results confirm a pattern and magnitude of apparent transgenerational “washout”47 that has previously been described in the Multicentre Growth Reference Study populations.37 This effect on skeletal growth suggests that a highly sensitive response to environmental changes (eg, better intrauterine and infant nutrition and healthcare) can occur in 1 generation (ie, in a much shorter timeframe than evolution allows). The mechanisms, which may be mediated by modifications in gene expression that are not linked to DNA sequence changes, are being investigated currently at the molecular level in the INTERBIO-21st Study.

d infant nutrition and healthcare) can occur in 1 generation (ie, in a much shorter timeframe than evolution allows). The mechanisms, which may be mediated by modifications in gene expression that are not linked to DNA sequence changes, are being investigated currently at the molecular level in the INTERBIO-21st Study. The observation that this healthy cohort was at the 58th percentile of the sex-specific weight for age of the WHO Child Growth Standards at 2 years of age has potential implications in describing the natural history of becoming overweight among healthy infants. Because we did not implement any specific nutritional intervention, other than to promote breastfeeding, this weight distribution may represent the initial stages of the overweight epidemic facing many urban children who are exposed to westernized diets. Recent standardized, prospectively collected, fetal data have confirmed the complex effect of nutrition, the environment, migration, and social-cultural issues on fetal growth patterns.48, 49, 50 The short-term shift in weight distribution in an otherwise healthy population that we have described also reinforces the concept that comparisons among populations to evaluate growth potential should be based on length rather than weight because of its sensitivity to acute influences.

The observation that this healthy cohort was at the 58th percentile of the sex-specific weight for age of the WHO Child Growth Standards at 2 years of age has potential implications in describing the natural history of becoming overweight among healthy infants. Because we did not implement any specific nutritional intervention, other than to promote breastfeeding, this weight distribution may represent the initial stages of the overweight epidemic facing many urban children who are exposed to westernized diets. Recent standardized, prospectively collected, fetal data have confirmed the complex effect of nutrition, the environment, migration, and social-cultural issues on fetal growth patterns.48, 49, 50 The short-term shift in weight distribution in an otherwise healthy population that we have described also reinforces the concept that comparisons among populations to evaluate growth potential should be based on length rather than weight because of its sensitivity to acute influences. A larger question that goes beyond the scope of this article relates to the timing, velocity, and individual tracking of growth from conception to 2 years of age vis-à-vis feeding and morbidity in high-risk populations. The exploration of these questions in a longitudinal fashion, including interactions, has considerable statistical complexity, which we are presently investigating in the INTERBIO-21st Study.

y, and individual tracking of growth from conception to 2 years of age vis-à-vis feeding and morbidity in high-risk populations. The exploration of these questions in a longitudinal fashion, including interactions, has considerable statistical complexity, which we are presently investigating in the INTERBIO-21st Study. In summary, we have presented evidence that the participants who are enrolled in the international Fetal Growth Standards and the Preterm Postnatal Growth Standards of the INTERGROWTH-21st Project and who were selected based on the WHO prescriptive approach for growth standards remain healthy and have adequate growth and development patterns at the key milestone of 2 years of age. This is additional strong confirmation of the sample’s appropriateness for the construction of international growth standards. The INTERGROWTH-21st international standards are freely available (www.intergrowth21.tghn.org) for use worldwide.

dequate growth and development patterns at the key milestone of 2 years of age. This is additional strong confirmation of the sample’s appropriateness for the construction of international growth standards. The INTERGROWTH-21st international standards are freely available (www.intergrowth21.tghn.org) for use worldwide. Contributors J.V. and S.H.K. conceptualized and designed the INTERGROWTH-21st Project. J.V., S.H.K., D.G.A., and A.J.N. prepared the original protocol, with later input from A.T.P., L.C.I., F.C.B., and ZAB. J.V., A.T.P., L.C.I., A.L., and Z.A.B. supervised and coordinated the project’s overall undertaking. E.S.U., E.O.O., and D.G.A. carried out data management and analysis in collaboration with J.V. R.P., F.C.B., R.O., Y.A.J., E.B., and M.P. collaborated in the overall project and implemented it in their respective countries. F.G. assisted in the global coordination of the project; L.C.I. and C.C. led the quality control of the anthropometric component, and M.F. and A.S. led the neurodevelopment assessment component. J.V. and S.K. wrote the report with significant contributions by F.G., C.G., C.G.V., F.C.B., and Z.A.B. All coauthors read the report and made suggestions on its content. Members of the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st) and its Committees Scientific Advisory Committee: M. Katz (Chair from January 2011), M.K. Bhan, C. Garza, S. Zaidi, A. Langer, P.M. Rothwell (from February 2011), Sir D. Weatherall (Chair until December 2010).

Contributors J.V. and S.H.K. conceptualized and designed the INTERGROWTH-21st Project. J.V., S.H.K., D.G.A., and A.J.N. prepared the original protocol, with later input from A.T.P., L.C.I., F.C.B., and ZAB. J.V., A.T.P., L.C.I., A.L., and Z.A.B. supervised and coordinated the project’s overall undertaking. E.S.U., E.O.O., and D.G.A. carried out data management and analysis in collaboration with J.V. R.P., F.C.B., R.O., Y.A.J., E.B., and M.P. collaborated in the overall project and implemented it in their respective countries. F.G. assisted in the global coordination of the project; L.C.I. and C.C. led the quality control of the anthropometric component, and M.F. and A.S. led the neurodevelopment assessment component. J.V. and S.K. wrote the report with significant contributions by F.G., C.G., C.G.V., F.C.B., and Z.A.B. All coauthors read the report and made suggestions on its content. Members of the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st) and its Committees Scientific Advisory Committee: M. Katz (Chair from January 2011), M.K. Bhan, C. Garza, S. Zaidi, A. Langer, P.M. Rothwell (from February 2011), Sir D. Weatherall (Chair until December 2010). Steering Committee: Z.A. Bhutta (Chair), J. Villar (Principal Investigator), S. Kennedy (Project Director), D.G. Altman, F.C. Barros, E. Bertino, F. Burton, M. Carvalho, L. Cheikh Ismail, W.C. Chumlea, M.G. Gravett, Y.A. Jaffer, A. Lambert, P. Lumbiganon, J.A. Noble, R.Y. Pang, A.T. Papageorghiou, M. Purwar, J. Rivera, C. Victora.

mittee: Z.A. Bhutta (Chair), J. Villar (Principal Investigator), S. Kennedy (Project Director), D.G. Altman, F.C. Barros, E. Bertino, F. Burton, M. Carvalho, L. Cheikh Ismail, W.C. Chumlea, M.G. Gravett, Y.A. Jaffer, A. Lambert, P. Lumbiganon, J.A. Noble, R.Y. Pang, A.T. Papageorghiou, M. Purwar, J. Rivera, C. Victora. Executive Committee: J. Villar (Chair), D.G. Altman, Z.A. Bhutta, L. Cheikh Ismail, S. Kennedy, A. Lambert, J.A. Noble, A.T. Papageorghiou. Project Coordinating Unit: J. Villar (Head), S. Kennedy, L. Cheikh Ismail, A. Lambert, A.T. Papageorghiou, M. Shorten, L. Hoch (until May 2011), H.E. Knight (until August 2011), E.O. Ohuma (from September 2010), C. Cosgrove (from July 2011), I. Blakey (from March 2011). Data Analysis Group: D.G. Altman (Head), E.O. Ohuma, E. Staines Urias (from April 2016), J. Villar. Data Management Group: D.G. Altman (Head), F. Roseman, N Kunnawar, S.H. Gu, J.H. Wang, M.H. Wu, M. Domingues, P. Gilli, L. Juodvirsiene, L. Hoch (until May 2011), N. Musee (until June 2011), H. Al-Jabri (until October 2010), S. Waller (until June 2011), C. Cosgrove (from July 2011), D. Muninzwa (from October 2011), E.O. Ohuma (from September 2010), D. Yellappan (from November 2010), A. Carter (from July 2011), D. Reade (from June 2012), R. Miller (from June 2012), ESU (from April 2016).

(until June 2011), H. Al-Jabri (until October 2010), S. Waller (until June 2011), C. Cosgrove (from July 2011), D. Muninzwa (from October 2011), E.O. Ohuma (from September 2010), D. Yellappan (from November 2010), A. Carter (from July 2011), D. Reade (from June 2012), R. Miller (from June 2012), ESU (from April 2016). Ultrasound Group: A.T. Papageorghiou (Head), L. Salomon (Senior external advisor), A. Leston, A. Mitidieri, F. Al-Aamri, W. Paulsene, J. Sande, W.K.S. Al-Zadjali, C. Batiuk, S. Bornemeier, M. Carvalho, M. Dighe, P. Gaglioti, N. Jacinta, S. Jaiswal, J.A. Noble, K. Oas, M. Oberto, E. Olearo, M.G. Owende, J. Shah, S. Sohoni, T. Todros, M. Venkataraman, S. Vinayak, L. Wang, D. Wilson, Q.Q. Wu, S. Zaidi, Y. Zhang, P. Chamberlain (until September 2012), D. Danelon (until July 2010), I. Sarris (until June 2010), J. Dhami (until July 2011), C. Ioannou (until February 2012), C.L. Knight (from October 2010), R. Napolitano (from July 2011), S. Wanyonyi (from May 2012), C. Pace (from January 2011), V. Mkrtychyan (from June 2012). Anthropometry Group: L. Cheikh Ismail (Head), W.C. Chumlea (Senior external advisor), F. Al-Habsi, Z.A. Bhutta, A. Carter, M. Alija, J.M. Jimenez-Bustos, J. Kizidio, F. Puglia, N. Kunnawar, H. Liu, S. Lloyd, D. Mota, R. Ochieng, C. Rossi, M. Sanchez Luna, Y.J. Shen, H.E. Knight (until August 2011), D.A. Rocco (from June 2012), I.O. Frederick (from June 2012).

ad), W.C. Chumlea (Senior external advisor), F. Al-Habsi, Z.A. Bhutta, A. Carter, M. Alija, J.M. Jimenez-Bustos, J. Kizidio, F. Puglia, N. Kunnawar, H. Liu, S. Lloyd, D. Mota, R. Ochieng, C. Rossi, M. Sanchez Luna, Y.J. Shen, H.E. Knight (until August 2011), D.A. Rocco (from June 2012), I.O. Frederick (from June 2012). Neonatal Group: Z.A. Bhutta (Head), E. Albernaz, M. Batra, B.A. Bhat, E Bertino, P. Di Nicola, F. Giuliani, I. Rovelli, K. McCormick, R. Ochieng, R.Y. Pang, V. Paul, V. Rajan, A. Wilkinson, A. Varalda (from September 2012). Environmental Health Group: B. Eskenazi (Head), L.A. Corra, H. Dolk, J. Golding, A. Matijasevich, T. de Wet, J.J. Zhang, A. Bradman, D. Finkton, O. Burnham, F. Farhi. Participating countries and local investigators Brazil: F.C Barros (Principal Investigator), M. Domingues, S. Fonseca, A. Leston, A. Mitidieri, D. Mota, I.K. Sclowitz, M.F. da Silveira. China: R.Y. Pang (Principal Investigator), Y.P. He, Y. Pan, Y.J. Shen, M.H. Wu, Q.Q. Wu, J.H. Wang, Y. Yuan, Y. Zhang. India: M. Purwar (Principal Investigator), A. Choudhary, S. Choudhary, S. Deshmukh, D. Dongaonkar, M. Ketkar, V. Khedikar, N. Kunnawar, C. Mahorkar, I. Mulik, K. Saboo, C. Shembekar, A. Singh, V. Taori, K. Tayade, A. Somani. Italy: E. Bertino (Principal Investigator), P. Di Nicola, M. Frigerio, G. Gilli, P. Gilli, M. Giolito, F. Giuliani, M. Oberto, L. Occhi, C. Rossi, I. Rovelli, F. Signorile, T. Todros.

India: M. Purwar (Principal Investigator), A. Choudhary, S. Choudhary, S. Deshmukh, D. Dongaonkar, M. Ketkar, V. Khedikar, N. Kunnawar, C. Mahorkar, I. Mulik, K. Saboo, C. Shembekar, A. Singh, V. Taori, K. Tayade, A. Somani. Italy: E. Bertino (Principal Investigator), P. Di Nicola, M. Frigerio, G. Gilli, P. Gilli, M. Giolito, F. Giuliani, M. Oberto, L. Occhi, C. Rossi, I. Rovelli, F. Signorile, T. Todros. Kenya: W. Stones and M. Carvalho (Co- Principal Investigators), J. Kizidio, R. Ochieng, J. Shah, S. Vinayak, N. Musee (until June 2011), C. Kisiang’ani (until July 2011), D. Muninzwa (from August 2011). Oman: Y.A. Jaffer (Principal Investigator), J. Al-Abri, J. Al-Abduwani, F.M. Al-Habsi, H. Al-Lawatiya, B. Al-Rashidiya, W.K.S. Al-Zadjali, F.R. Juangco, M. Venkataraman, H. Al-Jabri (until October 2010), D. Yellappan (from November 2010). UK: S. Kennedy (Principal Investigator), L. Cheikh Ismail, A.T. Papageorghiou, F. Roseman, A. Lambert, E.O. Ohuma, S. Lloyd, R. Napolitano (from July 2011), C. Ioannou (until February 2012), I. Sarris (until June 2010). USA: M.G. Gravett (Principal Investigator), C. Batiuk, M. Batra, S. Bornemeier, M. Dighe, K. Oas, W. Paulsene, D. Wilson, I.O. Frederick, H.F. Andersen, S.E. Abbott, A.A. Carter, H. Algren, D.A. Rocco, T.K. Sorensen, D. Enquobahrie, S. Waller (until June 2011). Appendix Supplementary Figure Study flow of INTERGROWTH-21st Preterm Postnatal Follow-up at 2 years The chart shows the cohort that contributed data to the construction of the INTERGROWTH-21st Preterm Postnatal Growth Standards.22

USA: M.G. Gravett (Principal Investigator), C. Batiuk, M. Batra, S. Bornemeier, M. Dighe, K. Oas, W. Paulsene, D. Wilson, I.O. Frederick, H.F. Andersen, S.E. Abbott, A.A. Carter, H. Algren, D.A. Rocco, T.K. Sorensen, D. Enquobahrie, S. Waller (until June 2011). Appendix Supplementary Figure Study flow of INTERGROWTH-21st Preterm Postnatal Follow-up at 2 years The chart shows the cohort that contributed data to the construction of the INTERGROWTH-21st Preterm Postnatal Growth Standards.22 Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. Supplementary Table 1 Twenty-four–hour dietary intake of children who were included in the INTERGROWTH-21st Fetal Growth Standards21 at 1 and 2 years of age Food group given to the child at least once a day 1 Year of age (n=2832), n (%) 2 Years of age (n=3041), n (%) Grains, roots, and tubers 2811 (99.3) 3031 (99.7) Legumes and nuts 1124 (39.7) 1375 (45.2) Dairy products 2822 (99.6) 3040 (100.0) Flesh foods 1676 (59.2) 2083 (68.5) Eggs 575 (20.3) 889 (29.2) Vitamin-A-rich fruits 1907 (67.3) 1950 (64.1) Other fruits and vegetables 2606 (92.0) 2863 (94.1) Fats: spreads/oils 885 (31.3) 1342 (44.1) Sugars: sweets/sugar products/jelly/sweetened drinks 435 (15.4) 989 (32.5) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. Supplementary Table 2 Twenty-four–hour dietary intake for children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 evaluated at 1 and 2 years of age

Food group given to the child at least once a day 1 Year of age (n=2832), n (%) 2 Years of age (n=3041), n (%) Grains, roots, and tubers 2811 (99.3) 3031 (99.7) Legumes and nuts 1124 (39.7) 1375 (45.2) Dairy products 2822 (99.6) 3040 (100.0) Flesh foods 1676 (59.2) 2083 (68.5) Eggs 575 (20.3) 889 (29.2) Vitamin-A-rich fruits 1907 (67.3) 1950 (64.1) Other fruits and vegetables 2606 (92.0) 2863 (94.1) Fats: spreads/oils 885 (31.3) 1342 (44.1) Sugars: sweets/sugar products/jelly/sweetened drinks 435 (15.4) 989 (32.5) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. Supplementary Table 2 Twenty-four–hour dietary intake for children who were included in the INTERGROWTH-21st Preterm Postnatal Growth Standards22 evaluated at 1 and 2 years of age Food group given to the child at least once a day 1 Year of age (n=154) 2 Years of age (n=143) Grains, roots, and tubers 153 (99.4) 142 (99.3) Legumes and nuts 54 (35.1) 68 (47.6) Dairy products 154 (100.0) 143 (100.0) Flesh foods 95 (61.7) 101 (70.6) Eggs 35 (22.7) 35 (24.5) Vitamin-A-rich fruits 104 (67.5) 84 (58.7) Other fruits and vegetables 130 (84.4) 133 (93.0) Fats: spreads/oils 34 (22.1) 55 (38.5) Sugars: sweets/sugar products/jelly/sweetened drinks 14 (9.1) 45 (31.5) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018.

(22.7) 35 (24.5) Vitamin-A-rich fruits 104 (67.5) 84 (58.7) Other fruits and vegetables 130 (84.4) 133 (93.0) Fats: spreads/oils 34 (22.1) 55 (38.5) Sugars: sweets/sugar products/jelly/sweetened drinks 14 (9.1) 45 (31.5) Villar et al. Validation of the INTERGROWTH-21st fetal growth standards. Am J Obstet Gynecol 2018. Acknowledgments We would like to thank the Health Authorities in Pelotas, Brazil; Beijing, China; Nagpur, India; Turin, Italy; Nairobi, Kenya; Muscat, Oman; Oxford, UK and Seattle, USA, who facilitated the project by allowing participation of these study sites as collaborating centers. We are extremely grateful to Philips Medical Systems who provided the ultrasound equipment and technical assistance throughout the project. We also thank MedSciNet U.K. Ltd for setting up the INTERGROWTH-21st web-site and for the development, maintenance and support of the on-line data management system.

ating centers. We are extremely grateful to Philips Medical Systems who provided the ultrasound equipment and technical assistance throughout the project. We also thank MedSciNet U.K. Ltd for setting up the INTERGROWTH-21st web-site and for the development, maintenance and support of the on-line data management system. We thank the parents and infants who participated in the studies and the more than 200 members of the research teams who made the implementation of this project possible. The participating hospitals included: Brazil, Pelotas (Hospital Miguel Piltcher, Hospital São Francisco de Paula, Santa Casa de Misericórdia de Pelotas, and Hospital Escola da Universidade Federal de Pelotas); China, Beijing (Beijing Obstetrics & Gynecology Hospital, Shunyi Maternal & Child Health Centre, and Shunyi General Hospital); India, Nagpur (Ketkar Hospital, Avanti Institute of Cardiology Private Limited, Avantika Hospital, Gurukrupa Maternity Hospital, Mulik Hospital & Research Centre, Nandlok Hospital, Om Women’s Hospital, Renuka Hospital & Maternity Home, Saboo Hospital, Brajmonhan Taori Memorial Hospital, and Somani Nursing Home); Kenya, Nairobi (Aga Khan University Hospital, MP Shah Hospital and Avenue Hospital); Italy, Turin (Ospedale Infantile Regina Margherita Sant’ Anna and Azienda Ospedaliera Ordine Mauriziano); Oman, Muscat (Khoula Hospital, Royal Hospital, Wattayah Obstetrics & Gynaecology Poly Clinic, Wattayah Health Centre, Ruwi Health Centre, Al-Ghoubra Health Centre and Al-Khuwair Health Centre); UK, Oxford (John Radcliffe Hospital) and USA, Seattle (University of Washington Hospital, Swedish Hospital, and Providence Everett Hospital). Full acknowledgement of all those who contributed to the development of the INTERGROWTH-21st Project protocol appears at www.intergrowth21.org.uk.

and Al-Khuwair Health Centre); UK, Oxford (John Radcliffe Hospital) and USA, Seattle (University of Washington Hospital, Swedish Hospital, and Providence Everett Hospital). Full acknowledgement of all those who contributed to the development of the INTERGROWTH-21st Project protocol appears at www.intergrowth21.org.uk. Supported by the INTERGROWTH-21st grant 49038 from the Bill & Melinda Gates Foundation to the University of Oxford. The funder played no role in the study design; collection, analysis and interpretation of data; the writing of the report or the decision to submit the article for publication. The authors report no conflict of interest.

Pandemics are personal. The emergence of a new disease that spreads easily between people and jumps from region to region has an objective narrative, but each of us experiences our own private outbreak. Many people working in public health and clinical medicine know where they were when the 2009 H1N1 influenza pandemic became real to them. I was in the Emergency Operations Center of the Centers for Disease Control and Prevention (CDC).

on has an objective narrative, but each of us experiences our own private outbreak. Many people working in public health and clinical medicine know where they were when the 2009 H1N1 influenza pandemic became real to them. I was in the Emergency Operations Center of the Centers for Disease Control and Prevention (CDC). Emergence of the pandemic As Chief Health Officer in CDC's 2009 pandemic influenza response, I was a scientific spokesperson for the agency. By late April, I had already done press briefings and several individual interviews, where I shared what little information we had at the time, and tried to place that in context. One evening, after successfully finishing what was expected to be a very difficult live television interview, I was feeling much relief and delight. I returned to our crowded emergency operations center to reconnect with my colleagues. I found them huddled around a speaker phone, looking ashen. They had just conferred with a state's public health officials about a pregnant woman who was critically ill with what was likely to be the new virus. She was not expected to make it. The 2009 H1N1 virus was not a practice exercise. Real people were going to get very ill and people who were not supposed to die that year were going to–even if we all did everything we could, even if all the parts of the public health and health care delivery systems rose to the occasion, and even if we had much good fortune. That is what a new influenza virus does. At this point, we could not say how large the problem would get or how many lives were threatened. But we knew that for this woman, and her family and caregivers, the reality was overwhelming.

alth care delivery systems rose to the occasion, and even if we had much good fortune. That is what a new influenza virus does. At this point, we could not say how large the problem would get or how many lives were threatened. But we knew that for this woman, and her family and caregivers, the reality was overwhelming. Women and childbirth What is it about women dying in childbirth? Each of us was once a newborn completely dependent on our mother–that is, if she survived our delivery. The 2009 pandemic reminded me that mothers dying in childbirth used to be commonplace in the United States–and how lucky we are that times have changed. It also reminded me that a woman dying in childbirth is still far too common in many places around the world. I had personally been intrigued by childbirth since my grandmother first told me her family's story. She was born in 1893–that seemed exotic even in the 1960s and 1970s when I was growing up. I used to pepper her with questions about all the changes in the world she had witnessed. Greatest development of modern life? The ice cube. Memories about prohibition? The town tippler asleep on the doorstep of the family's general store, after overdoing it with a case of lemon extract. How did anesthesia work when you had your babies? That one required a long story.

changes in the world she had witnessed. Greatest development of modern life? The ice cube. Memories about prohibition? The town tippler asleep on the doorstep of the family's general store, after overdoing it with a case of lemon extract. How did anesthesia work when you had your babies? That one required a long story. I knew that my father was born at home, upstairs over the West Virginia general store my grandparents ran. A few years earlier, my grandparents had been living in Baltimore, MD, and things had been different for my grandmother's first delivery. She described being in the hospital when a nurse told her “Don't worry, we won't let what happened to your sister happen to you.” My grandmother had not been told yet, but her beloved sister Bessie, who lived in another state down south, had recently died during childbirth. There had been some sort of collusion to keep that information from her, but the nurse had not been in on the full plan. My grandmother said on hearing the nurse's words, she immediately realized what must have happened, and she fainted. When she came to, her own healthy baby girl was there. They named their daughter Betsy, in memory of the aunt she would never meet. And Grandma said for each of her next 3 deliveries, she just passed out–reliving the shock she had experienced at learning of her sister's death, and miraculously being spared the pains of childbirth.

e to, her own healthy baby girl was there. They named their daughter Betsy, in memory of the aunt she would never meet. And Grandma said for each of her next 3 deliveries, she just passed out–reliving the shock she had experienced at learning of her sister's death, and miraculously being spared the pains of childbirth. This family story was important to me. While I occasionally suspected someone had slipped my grandmother some ether, I generally took her words at face value. I was grateful to live in a time and place where women did not die in their twenties like my great aunt Bessie had. I was grateful times had changed from when medical problems were kept secret by family members or clinicians, from when pregnant women had to be protected from information their caregivers thought they could not handle. I was grateful I did not have to leave school at eighth grade to go to work, like my grandmother had, and instead had the chance to become a doctor or anything else I wanted to be.

family members or clinicians, from when pregnant women had to be protected from information their caregivers thought they could not handle. I was grateful I did not have to leave school at eighth grade to go to work, like my grandmother had, and instead had the chance to become a doctor or anything else I wanted to be. My first pandemic: AIDS The month I began medical school, my grandmother died. I could no longer interrogate her as I learned about modern medical practices. But I soon became close to her daughter–the same aunt Betsy who had been named for Grandma's sister Bessie. Betsy and my uncle Jerry lived down the street from the New York City hospitals where I did internal medicine training. They fed me regularly and provided a sounding board as I recounted the dramas of internship and residency–stories of the torrent of terribly ill people whose care was being entrusted to me. Many of them were dying from a newly recognized disease–acquired immune deficiency syndrome (AIDS). We did not call it a pandemic then–but it was definitely personal. I was a 25-year-old intern. How could I not be overwhelmed watching the deaths of so many young people? In those days, we struggled with the very private information behind each patient's diagnosis, and tried to figure out how to explain events to our patients' families when they were too sick to do so themselves.

My first pandemic: AIDS The month I began medical school, my grandmother died. I could no longer interrogate her as I learned about modern medical practices. But I soon became close to her daughter–the same aunt Betsy who had been named for Grandma's sister Bessie. Betsy and my uncle Jerry lived down the street from the New York City hospitals where I did internal medicine training. They fed me regularly and provided a sounding board as I recounted the dramas of internship and residency–stories of the torrent of terribly ill people whose care was being entrusted to me. Many of them were dying from a newly recognized disease–acquired immune deficiency syndrome (AIDS). We did not call it a pandemic then–but it was definitely personal. I was a 25-year-old intern. How could I not be overwhelmed watching the deaths of so many young people? In those days, we struggled with the very private information behind each patient's diagnosis, and tried to figure out how to explain events to our patients' families when they were too sick to do so themselves. Health care workers in a pandemic: SARS My aunt Betsy died in 2003, while I was in Beijing, China, investigating the epidemic of severe acute respiratory syndrome (SARS) there. I got news of her passing a few days later, and was unable to get to her funeral or grieve together with our family. Instead, I was immersed in the public health emergency in China, where the SARS pandemic was centered. My public health counterparts in Beijing had the difficult task of triaging the scarce intensive care unit beds spread across the city. They had convened each evening to sort out who was about to die, how many more beds would thus open up, and which patients would get them. There were doctors, nurses, and medical professors in some of those intensive care unit beds. My counterparts had to manage the epidemic while some of their longtime colleagues and friends were dying from it. The Chinese professionals rose to the occasion–the government built a new 1000-bed facility in record time–but you knew that this pandemic was personal for them.

in some of those intensive care unit beds. My counterparts had to manage the epidemic while some of their longtime colleagues and friends were dying from it. The Chinese professionals rose to the occasion–the government built a new 1000-bed facility in record time–but you knew that this pandemic was personal for them. After I got back home that summer, I went to visit my uncle Jerry. We reminisced about my aunt and the many great years they had together. She had fought to stay out of the hospital while her cancer worsened. She had always been suspicious about diseases being spread in hospital settings. SARS was the poster child for that problem during its emergence in cities from Hong Kong to Toronto. Good infection control, isolation, and quarantine had eventually interrupted the virus' spread. Betsy was born in a hospital, but died as she had wanted, at home.

icious about diseases being spread in hospital settings. SARS was the poster child for that problem during its emergence in cities from Hong Kong to Toronto. Good infection control, isolation, and quarantine had eventually interrupted the virus' spread. Betsy was born in a hospital, but died as she had wanted, at home. 2009 H1N1 pandemic influenza and pregnancy During the 2009 H1N1 pandemic, CDC's workforce rallied. People from across the agency united to support whatever was needed. Our obstetric and neonatal care experts from diverse programs converged to address the evolving issues of H1N1 influenza in pregnancy. They served in many roles: conducting clinical investigations; developing guidance for diagnosing and treating new cases; establishing systems to identify and report severe illness; and designing education materials and resources to assist pregnant women and obstetrician-gynecologists. They organized the gathering of new knowledge, such as the articles that appear in this special issue of the American Journal of Obstetrics and Gynecology. And they supported clinicians around the country, who were themselves on the real front lines–some of them caring for women who were very ill, and others busy clarifying the concerns of healthy pregnant patients who sought out advice from their most trusted authority–their own obstetrician-gynecologists.

nd Gynecology. And they supported clinicians around the country, who were themselves on the real front lines–some of them caring for women who were very ill, and others busy clarifying the concerns of healthy pregnant patients who sought out advice from their most trusted authority–their own obstetrician-gynecologists. Because the risk of serious influenza-related complications is higher among pregnant women compared with that of the general population, for years we have recommended flu vaccine for pregnant women. Unfortunately, too few pregnant women have been getting flu vaccinations. This was a time to change the dynamic. The prominence of pregnant women among H1N1 influenza–associated hospitalizations and deaths during the spring wave of the 2009 experience was striking. This kind of thing was not supposed to happen during pregnancy–at least not in this era. We realized that early antiviral treatment could make a difference in H1N1 influenza patients, but pregnant women and many of their clinicians were also reluctant to use these medications. We realized that rapid diagnostic test results were unreliable, and some women were not being treated because such tests were falsely negative. We realized that there were decades of medical practice, as well as cultural and behavioral norms, that were barriers to the efforts aimed at preventing influenza complications in pregnancy. Scientific investigations bore out the value of antiviral treatment, but science was not going to be enough. Effective communication was critically needed.

e decades of medical practice, as well as cultural and behavioral norms, that were barriers to the efforts aimed at preventing influenza complications in pregnancy. Scientific investigations bore out the value of antiviral treatment, but science was not going to be enough. Effective communication was critically needed. Risk communication The principles of risk communication underpinned our media and communication strategies.1, 2 Communicating in the face of much uncertainty requires different strategies. It also turns out that people do not generally panic when they hear bad news. They may not even process the news at all–but there are some ways that you can communicate risks that have a better chance of getting through when a listener is very concerned. Key components of risk communication are expressing empathy, acknowledging uncertainty, and being honest and transparent. How different it was in my grandmother's day, when withholding of information and promising perfect outcomes were the norm. For me as a spokesperson, it was easy to express empathy during interviews. Each of the severe illnesses in pregnant women was jarring. It was easy to imagine the families behind each woman. And the media explored some of these stories in depth, so you did not even have to use too much imagination. It was easy to acknowledge uncertainty, because influenza is so unpredictable that you would have to be uninformed to claim certainty about what was going to happen. And it was a privilege to be a spokesperson when honesty and transparency are the standards of public health communication.

e to use too much imagination. It was easy to acknowledge uncertainty, because influenza is so unpredictable that you would have to be uninformed to claim certainty about what was going to happen. And it was a privilege to be a spokesperson when honesty and transparency are the standards of public health communication. About 6 weeks into the 2009 H1N1 emergency response, my uncle Jerry collapsed in his apartment. He'd had a bad stroke. He was 90 years old. My nephew went to visit him in the hospital. The television was on in his room, and he apparently saw me up on the screen, presumably talking about flu. According to my nephew, he seemed to recognize me. I hope I was using the principles of risk communication–being open and honest, acknowledging the uncertainty ahead, and expressing my genuine empathy. I like to think that what Jerry saw on the screen was me saying goodbye.

on the screen, presumably talking about flu. According to my nephew, he seemed to recognize me. I hope I was using the principles of risk communication–being open and honest, acknowledging the uncertainty ahead, and expressing my genuine empathy. I like to think that what Jerry saw on the screen was me saying goodbye. Influenza and maternal mortality In the early 20th century, maternal mortality was a fact of life.3 But even in those difficult years, the toll that influenza took stood out. When it came to vital statistics, countries differed on whether they recorded the cause of death among women as pregnancy or influenza, but the major spike in the United States in 1918 is unmistakable (Figure). I first saw the data in this graph in February 2010, and again found myself feeling grateful to live in the modern era. A few months later, I read a book a colleague had sent me, William Maxwell's novel They Came Like Swallows. It is a story of a family's experience set during the 1918 great influenza pandemic. In the story, a pregnant mother is sent away from her young son, to protect her after her son had been exposed to the fever that was going around at the time. The pain of the separation on both the mother and child is palpable.FIGURE Maternal mortality (deaths/100,000 live births) in United States, 1915 through 2007 Data are for death registration area for 1915 through 1932 and for United States for 1933 through 2007. Data are coded according to relevant International Classification of Disease revision over time period. Schuchat. Reflections on pandemics. Am J Obstet Gynecol 2011.

Influenza and maternal mortality In the early 20th century, maternal mortality was a fact of life.3 But even in those difficult years, the toll that influenza took stood out. When it came to vital statistics, countries differed on whether they recorded the cause of death among women as pregnancy or influenza, but the major spike in the United States in 1918 is unmistakable (Figure). I first saw the data in this graph in February 2010, and again found myself feeling grateful to live in the modern era. A few months later, I read a book a colleague had sent me, William Maxwell's novel They Came Like Swallows. It is a story of a family's experience set during the 1918 great influenza pandemic. In the story, a pregnant mother is sent away from her young son, to protect her after her son had been exposed to the fever that was going around at the time. The pain of the separation on both the mother and child is palpable.FIGURE Maternal mortality (deaths/100,000 live births) in United States, 1915 through 2007 Data are for death registration area for 1915 through 1932 and for United States for 1933 through 2007. Data are coded according to relevant International Classification of Disease revision over time period. Schuchat. Reflections on pandemics. Am J Obstet Gynecol 2011. Source: Centers for Disease Control and Prevention/National Center for Health Statistics, National Vital Statistics System, Mortality.

Data are for death registration area for 1915 through 1932 and for United States for 1933 through 2007. Data are coded according to relevant International Classification of Disease revision over time period. Schuchat. Reflections on pandemics. Am J Obstet Gynecol 2011. Source: Centers for Disease Control and Prevention/National Center for Health Statistics, National Vital Statistics System, Mortality. While I was reading this, I finally put two and two together. If my aunt Betsy died during the SARS epidemic at nearly age 85 years, what year was she born? If Grandma's sister Bessie died in childbirth earlier that year, what had she actually died from? My parents supplied the part of the family history that my grandmother had not thought to tell me. Bessie was already ill when they took her off the trolley–that was why she had been rushed to the hospital, not because of a difficult labor. It was 1918. The article reflects personal views of the author and does not represent Centers for Disease Control and Prevention or US government policy. Conflict of Interest: none. Publication of this article was supported by the Centers for Disease Control and Prevention and the Association of Maternal and Child Health Programs.

Pneumonia is a common infection of the pulmonary parenchyma1 that is a significant cause of hospitalization for respiratory disorders during pregnancy,2 complicating 0.5-1.5 per 1000 pregnancies in the United States.3, 4, 5, 6 Pneumonia is the most frequent cause of fatal nonobstetric maternal death in the United States.7, 8, 9 It is widely held that several physiologic and immunologic changes experienced during pregnancy may predispose pregnant women toward a more severe course of pneumonia,10 which may result in greater maternal and fetal morbidity and mortality.6, 11, 12, 13, 14, 15 The relationship between pneumonia and pregnancy outcome has long been a topic of interest among researchers. A growing number of studies have found that women with pneumonia were more likely to have preterm deliveries3, 5, 10, 12, 15, 16, 17, 18, 19, 20, 21, 22 as well as lower average birthweight2, 14, 15, 16 and small for gestational age (SGA) infants4, 14 compared to women without pneumonia. Moreover, Romanyuk et al16 also found that pneumonia was significantly associated with placental abruption, intrauterine growth restriction, cesarean section (CS), low Apgar scores, and severe preeclampsia.

ge birthweight2, 14, 15, 16 and small for gestational age (SGA) infants4, 14 compared to women without pneumonia. Moreover, Romanyuk et al16 also found that pneumonia was significantly associated with placental abruption, intrauterine growth restriction, cesarean section (CS), low Apgar scores, and severe preeclampsia. Even though several studies have explored the risk of adverse pregnancy outcomes among women with pneumonia, their studies generated inconsistent findings that remain to be resolved. A number of studies failed to observe any increased risk of preterm4, 23, 24, 25 and low birthweight (LBW) infants4 among women with pneumonia. In addition, Shariatzadeh and Marrie6 and Jin et al4 suggested that pneumonia may not have any negative effects related to fetal outcome at all, and speculated that pneumonia may be very well tolerated during pregnancy. Therefore, the relationship between pneumonia and pregnancy outcomes remains unclear to date. Since prior studies dealing with the present topic have tended to be hospital-based studies often characterized by low case numbers or population subgroups, their inconsistent finding may have been due to the use of selective data, limited sample sizes, and inadequate control of confounding factors.

ns unclear to date. Since prior studies dealing with the present topic have tended to be hospital-based studies often characterized by low case numbers or population subgroups, their inconsistent finding may have been due to the use of selective data, limited sample sizes, and inadequate control of confounding factors. To fill this gap in the literature this study aimed to examine the risk of adverse pregnancy outcomes (LBW, preterm birth, SGA, CS, congenital anomalies, Apgar scores at 5 minutes, and preeclampsia/eclampsia) in pregnant women with pneumonia using a nationwide population-based dataset in Taiwan. To the best of our knowledge, this is the largest and most complete nationwide population-based study to investigate the relationship between pneumonia and adverse pregnancy outcomes.

scores at 5 minutes, and preeclampsia/eclampsia) in pregnant women with pneumonia using a nationwide population-based dataset in Taiwan. To the best of our knowledge, this is the largest and most complete nationwide population-based study to investigate the relationship between pneumonia and adverse pregnancy outcomes. Materials and Methods Database Two nationwide population-based datasets were used in this study: The Taiwan National Health Insurance (NHI) Research Dataset (NHIRD) and the Taiwan national birth certificate registry. The NHIRD includes all the registration files as well as original claims data for reimbursements covered by the Taiwan NHI program for about 25.68 million enrollees in Taiwan. Taiwan launched the NHI program in 1995 and has since maintained >95% enrollment rate, with the coverage >98.5% since 2007. The NHIRD thus allows researchers to follow up on all the medical service utilizations of every pregnant women in Taiwan. In addition, many studies have demonstrated the high validity of the Longitudinal Health Insurance Database 2000,26, 27 with hundreds of papers employing the NHIRD having been published in internationally peer-reviewed journals. The second dataset was the Taiwan national birth certificate registry, which included data on both infant and parental birth dates, gestational week at birth, birthweight, sex, parity, place of birth, parental educational level, and maternal marital status. Since it is mandatory that all births are registered in Taiwan, birth certificate data are considered to be very accurate and comprehensive.

ncluded data on both infant and parental birth dates, gestational week at birth, birthweight, sex, parity, place of birth, parental educational level, and maternal marital status. Since it is mandatory that all births are registered in Taiwan, birth certificate data are considered to be very accurate and comprehensive. With the assistance of the Bureau of Health Promotion, Department of Health, Taiwan, these 2 nationwide population-based datasets were linked. Since these 2 datasets consist of de-identified secondary data, this study was waived from full review by the Institutional Review Board of Taipei Medical University. Study sample This cross-sectional study features a study group and a comparison group. As for the selection of the study group, we first identified 218,776 women with live singleton births between Jan. 1, 2005, and Dec. 31, 2005. If a woman experienced >1 singleton birth during the study period, we only included the first birth in the study group. We also designated this delivery as the index delivery. Thereafter, we identified 1462 women who had been hospitalized with a diagnosis of pneumonia (International Classification of Diseases, Ninth Revision, Clinical Modification codes 480–483.8, 485–486, and 487.0) during their pregnancies from the total 218,776 women who we selected above. In Taiwan, it is standardized practice that all pregnant women with pneumonia are hospitalized.

with a diagnosis of pneumonia (International Classification of Diseases, Ninth Revision, Clinical Modification codes 480–483.8, 485–486, and 487.0) during their pregnancies from the total 218,776 women who we selected above. In Taiwan, it is standardized practice that all pregnant women with pneumonia are hospitalized. Data on the gestational age and delivery date of each infant were also available in this study, which allowed us to calculate the period of pregnancy for each woman. In addition, we randomly retrieved 7310 comparison women (5 for every woman with pneumonia) to match the distribution of the study group in terms of age (<20, 20-24, 25-29, 30-34, and ≥35 years). As a result, 8772 women were included in this study. Variables of interest The selected variables for adverse pregnancy outcomes were LBW (<2500 g), preterm gestation (<37 completed weeks of gestation), SGA (birthweight <10th percentile for gestational age–specific birthweight distribution), major congenital anomalies (hydrocephaly, anencephaly, microcephaly, meningomyelocele, encephalocele, and spina bifida), Apgar scores at 5 minutes (<7), preeclampsia/eclampsia, and CS. This study also took potential confounding factors into consideration in the regression models. These included factors consisting of maternal characteristics (highest educational level, gestational diabetes, gestational hypertension, coronary heart disease [CHD], anemia, hyperlipidemia, alcohol abuse/alcohol dependence syndrome, and obesity), infant sex and parity, and paternal age.

tion in the regression models. These included factors consisting of maternal characteristics (highest educational level, gestational diabetes, gestational hypertension, coronary heart disease [CHD], anemia, hyperlipidemia, alcohol abuse/alcohol dependence syndrome, and obesity), infant sex and parity, and paternal age. Statistical analysis We performed all the analyses conducted in this study using a software package (SAS System for Windows, version 8.2; SAS Institute Inc, Cary, NC). We used χ2 tests to explore the differences in maternal, paternal, and infant characteristics between women with and without pneumonia. We further used conditional logistic regression analyses (conditioned on maternal age) to calculate the odds of adverse pregnancy outcomes between women with and without pneumonia after adjusting for maternal, paternal, and infant characteristics. A 2-sided P value < .05 was considered statistically significant in this study.

her used conditional logistic regression analyses (conditioned on maternal age) to calculate the odds of adverse pregnancy outcomes between women with and without pneumonia after adjusting for maternal, paternal, and infant characteristics. A 2-sided P value < .05 was considered statistically significant in this study. Results Of the 1462 women with pneumonia, 1363 (about 93%) were bacterial pneumonia. Table 1 presents the distributions of maternal, paternal, and infant characteristics between women with and without pneumonia. After matching for maternal age, we found that there was no significant difference in the distribution of infant sex, parity, maternal highest educational level, and geographic region. In addition, there was no significant difference in the prevalence of comorbidities of gestational diabetes, gestational hypertension, anemia, hyperlipidemia, alcohol abuse/alcohol dependence syndrome, and obesity between women with and without pneumonia. However, women with pneumonia were more likely to have CHD than women without pneumonia (P = .002).TABLE 1 Sociodemographic characteristics of pregnant women with and without pneumonia in Taiwan, 2005 (n = 8772)

rlipidemia, alcohol abuse/alcohol dependence syndrome, and obesity between women with and without pneumonia. However, women with pneumonia were more likely to have CHD than women without pneumonia (P = .002).TABLE 1 Sociodemographic characteristics of pregnant women with and without pneumonia in Taiwan, 2005 (n = 8772) Variable Women with pneumonia (n = 1462) Comparison women (n = 7310) P value No. % No. % Infant characteristics Sex .068 Male 788 53.9 3749 51.3 Female 674 46.1 3561 48.7 Maternal characteristics Parity .616 1 744 50.9 3625 50.0 2 554 37.9 2784 38.1 ≥3 164 11.2 874 11.9 Age, y 1.000 <20 50 3.4 250 3.4 20-24 352 24.1 1760 24.1 25-29 454 31.1 2270 31.1 30-34 427 29.2 2135 29.2 >34 179 12.2 895 12.2 Education level .070 ≤Junior high school 160 10.9 713 9.7 Senior high school 1041 71.2 5121 70.1 ≥College 261 17.9 1476 20.2 Alcohol abuse/alcohol dependence syndrome 3 0.2 7 0.4 .258 Gestational diabetes 48 3.3 227 3.1 .722 Gestational hypertension 44 3.0 192 2.6 .409 Anemia 158 10.8 723 9.9 .287 Coronary heart disease 20 1.4 45 0.6 .002 Hyperlipidemia 31 2.1 139 1.9 .579 Obesity 9 0.6 36 0.5 .548 Geographic region .604 North 614 42.0 3105 42.5 Center 393 26.9 1988 27.2 South 412 28.2 2045 27.9 East 43 2.9 172 2.4 Paternal age, y .892 <30 518 35.4 2602 35.6 30-34 466 31.9 2363 32.3 >34 478 31.7 2345 32.1 Chen. Pneumonia and pregnancy. Am J Obstet Gynecol 2012.

idemia 31 2.1 139 1.9 .579 Obesity 9 0.6 36 0.5 .548 Geographic region .604 North 614 42.0 3105 42.5 Center 393 26.9 1988 27.2 South 412 28.2 2045 27.9 East 43 2.9 172 2.4 Paternal age, y .892 <30 518 35.4 2602 35.6 30-34 466 31.9 2363 32.3 >34 478 31.7 2345 32.1 Chen. Pneumonia and pregnancy. Am J Obstet Gynecol 2012. The prevalence of adverse pregnancy outcomes is presented in Table 2. It shows that women with pneumonia had a higher prevalence of LBW infants (9.8% vs 5.9%, P < .001), preterm births (12.3% vs 7.1%, P < .001), SGA infants (20.7% vs 16.2%, P < .001), CS (55.5% vs 40.6%, P < .001), preeclampsia/eclampsia (2.7% vs 0.8%, P < .001), and Apgar scores <7 at 5 minutes (0.7% vs 0.2%, P < .001) than women without pneumonia. There were no significant differences in the prevalence of major congenital anomalies (0.9% vs 0.7%, P = .396) between women with and without pneumonia. Moreover, the distributions of adverse pregnancy outcomes did not differ significantly for women with viral and with bacterial pneumonia (data not shown in table).TABLE 2 Distributions of adverse pregnancy outcomes associated with pneumonia Variable Women with pneumonia (n = 1462) Comparison women (n = 7310) P value No. % No. % Low birthweight 143 9.8 430 5.9 < .001 Preterm birth 180 12.3 520 7.1 < .001 Small for gestational age 303 20.7 1187 16.2 < .001 Cesarean section 812 55.5 2965 40.6 < .001 Congenital anomalies 13 0.9 50 0.7 .396 Low Apgar score at 5 min 10 0.7 12 0.2 < .001 Preeclampsia/eclampsia 39 2.7 60 0.8 < .001 Chen. Pneumonia and pregnancy. Am J Obstet Gynecol 2012.

Preterm birth 180 12.3 520 7.1 < .001 Small for gestational age 303 20.7 1187 16.2 < .001 Cesarean section 812 55.5 2965 40.6 < .001 Congenital anomalies 13 0.9 50 0.7 .396 Low Apgar score at 5 min 10 0.7 12 0.2 < .001 Preeclampsia/eclampsia 39 2.7 60 0.8 < .001 Chen. Pneumonia and pregnancy. Am J Obstet Gynecol 2012. Table 3 presents the crude and adjusted odds ratios (ORs) for adverse pregnancy outcomes between women with and without pneumonia. Conditional logistic regression analyses (conditioned on maternal age group) revealed that compared to women without pneumonia, the OR for LBW, preterm birth, SGA, CS, Apgar scores <7 at 5 minutes, and preeclampsia/eclampsia in women with pneumonia were 1.73 (95% confidence interval [CI], 1.41–2.12), 1.71 (95% CI, 1.42–2.05), 1.35 (95% CI, 1.17–1.56), 1.77 (95% CI, 1.58–1.98), 3.86 (95% CI, 1.64–9.06), and 3.05 (95% CI, 2.01–4.63) respectively, after adjusting for highest maternal educational level, marital status, geographic region, gestational diabetes, gestational hypertension, CHD, anemia, hyperlipidemia, obesity, and alcohol abuse/alcohol dependence syndrome, as well as infant sex and parity, and paternal age. There was no increased OR for congenital anomalies for women with pneumonia.TABLE 3 Risks of adverse pregnancy outcomes associated with pneumonia

l diabetes, gestational hypertension, CHD, anemia, hyperlipidemia, obesity, and alcohol abuse/alcohol dependence syndrome, as well as infant sex and parity, and paternal age. There was no increased OR for congenital anomalies for women with pneumonia.TABLE 3 Risks of adverse pregnancy outcomes associated with pneumonia Variable Women with pneumonia vs comparison women Low birthweight ORa (95% CI) 1.74c (1.43–2.12) Adjusted ORb (95% CI) 1.73c (1.41–2.12) Preterm birth ORa (95% CI) 1.84c (1.53–2.20) Adjusted ORb (95% CI) 1.71c (1.42–2.05) Small for gestational age ORa (95% CI) 1.35c (1.17–1.56) Adjusted ORb (95% CI) 1.35c (1.17–1.56) Cesarean section ORa (95% CI) 1.83c (1.63–2.06) Adjusted ORb (95% CI) 1.77c (1.58–1.98) Congenital anomalies ORa (95% CI) 1.30 (0.71–2.41) Adjusted ORb (95% CI) 1.15 (0.62–2.15) Low Apgar score at 5 min ORa (95% CI) 4.19c (1.81–9.72) Adjusted ORb (95% CI) 3.86d (1.64–9.06) Preeclampsia/eclampsia ORa (95% CI) 3.31c (2.20–4.98) Adjusted ORb (95% CI) 3.05c (2.01–4.63) CI, confidence interval; OR, odds ratio. Chen. Pneumonia and pregnancy. Am J Obstet Gynecol 2012. a Calculated by conditional logistic regression (conditioned on maternal age group); b Adjustment made for mother's education, gestational diabetes, gestational hypertension, anemia, coronary heart disease, hyperlipidemia, obesity, alcohol abuse/alcohol dependence syndrome, geographic region, paternal age, and infant's sex, and parity; c P < .001; d P < .01.

a Calculated by conditional logistic regression (conditioned on maternal age group); b Adjustment made for mother's education, gestational diabetes, gestational hypertension, anemia, coronary heart disease, hyperlipidemia, obesity, alcohol abuse/alcohol dependence syndrome, geographic region, paternal age, and infant's sex, and parity; c P < .001; d P < .01. Furthermore, we analyzed the OR for adverse pregnancy outcomes according to pregnancy trimester. We found that the onset of pneumonia in about 93.6% of the women analyzed in this study occurred during the first trimester. Table 4 shows that when compared to comparison women, the adjusted OR for LBW, preterm birth, SGA, CS, Apgar scores <7 at 5 minutes, and preeclampsia/eclampsia in women with pneumonia during the first trimester of pregnancy were 1.73, 1.70, 1.35, 1.79, 3.74, and 3.17, respectively.TABLE 4 Risks of adverse pregnancy outcomes according to pregnancy trimester

mparison women, the adjusted OR for LBW, preterm birth, SGA, CS, Apgar scores <7 at 5 minutes, and preeclampsia/eclampsia in women with pneumonia during the first trimester of pregnancy were 1.73, 1.70, 1.35, 1.79, 3.74, and 3.17, respectively.TABLE 4 Risks of adverse pregnancy outcomes according to pregnancy trimester Variable Comparison women n = 7310 Women with pneumonia First trimester n = 1368 Second trimester n = 45 Third trimester n = 49 Low birthweight Adjusted ORa (95% CI) 1.00 1.73b (1.40–2.13) 1.18 (0.36–3.88) 1.34c (1.01–1.80) Preterm birth Adjusted ORa (95% CI) 1.00 1.70b (1.41–2.05) 0.85 (0.25–2.84) 1.43c (1.12–1.84) Small for gestational age Adjusted ORa (95% CI) 1.00 1.35b (1.17–1.57) 1.34 (0.64–2.80) 1.12 (0.89–1.42) Cesarean section Adjusted ORa (95% CI) 1.00 1.79b (1.59–1.99) N/A N/A Congenital anomalies Adjusted ORa (95% CI) 1.00 1.04 (0.54–2.03) 2.75 (0.36–21.19) 1.41 (0.72–2.75) Low Apgar score at 5 min Adjusted ORa (95% CI) 1.00 3.74c (1.55–9.01) N/A 2.18d (1.09–4.38) Preeclampsia/eclampsia Adjusted ORa (95% CI) 1.00 3.17b (2.08–4.83) N/A N/A CI, confidence interval; N/A, case number <5; OR, odds ratio. Chen. Pneumonia and pregnancy. Am J Obstet Gynecol 2012. a Adjustments made for mother's education, gestational diabetes, gestational hypertension, anemia, coronary heart disease, hyperlipidemia, obesity, alcohol abuse/alcohol dependence syndrome, geographic region, paternal age, and infant's sex, and parity; b P < .001; c P < .01; d P < .05.

Chen. Pneumonia and pregnancy. Am J Obstet Gynecol 2012. a Adjustments made for mother's education, gestational diabetes, gestational hypertension, anemia, coronary heart disease, hyperlipidemia, obesity, alcohol abuse/alcohol dependence syndrome, geographic region, paternal age, and infant's sex, and parity; b P < .001; c P < .01; d P < .05. Comment Our nationwide population-based study demonstrated that after adjusting for comorbidities and potential confounders, mothers with pneumonia were 1.73, 1.71, 1.35, 1.77, 3.86, and 3.05 times more likely than unaffected mothers to have LBW, preterm birth, SGA, CS, low Apgar scores, and preeclampsia/eclampsia, respectively. Furthermore, we analyzed the risk for adverse pregnancy outcomes according to pregnancy trimester. We found that the onset of pneumonia of about 93.6% of the women with pneumonia analyzed in this study occurred during the first trimester. These women were also found to be more likely than comparison women to have adverse pregnancy outcomes. However, we further investigated the effect of etiology on the occurrence of adverse pregnancy outcomes among women with a case of pneumonia during their pregnancies, and failed to detect a statistically significant difference in the occurrence of LBW, preterm birth, SGA, CS, and preeclampsia/eclampsia between women with viral and bacterial pneumonia.

the effect of etiology on the occurrence of adverse pregnancy outcomes among women with a case of pneumonia during their pregnancies, and failed to detect a statistically significant difference in the occurrence of LBW, preterm birth, SGA, CS, and preeclampsia/eclampsia between women with viral and bacterial pneumonia. Our findings are consistent with prior studies that found women with pneumonia were more likely to have preterm deliveries,3, 5, 10, 12, 15, 16, 17, 18, 19, 20, 21, 22 babies with lower average birthweights,3, 14, 15, 16 and SGA infants3, 14 than women without pneumonia. In addition, our results are also in line with 1 study conducted by Romanyuk et al16 that found pneumonia to be significantly associated with CS, low Apgar scores, and preeclampsia. Although some previous studies failed to observe any increased risk of preterm4, 23, 24, 25 or LBW3 infants among women with pneumonia, these inverse conclusions were mostly based on studies utilizing patient self-reports and characterized by relatively small sample sizes and an inadequate control of confounding. Therefore, their recall bias and other potential limitations may have resulted in an under-ascertainment of pneumonia during the study pregnancy, which would have clearly undermined the strength of their findings.

self-reports and characterized by relatively small sample sizes and an inadequate control of confounding. Therefore, their recall bias and other potential limitations may have resulted in an under-ascertainment of pneumonia during the study pregnancy, which would have clearly undermined the strength of their findings. The mechanisms by which pneumonia produces adverse pregnancy outcomes are still unclear. Development of the fetus is largely determined by the morphology and functioning of the mother-placenta-fetus system.28, 29 It is possible that pneumonia during pregnancy may infect the placenta. Infection may then be transmitted to the fetus from the placenta through the umbilical vein, or via the aspiration or ingestion of amniotic fluid contaminated by placental or genital infections. Intrauterine infection has emerged as a frequent and important mechanism of disease in preterm birth.30, 31, 32, 33 The onset of preterm labor can be considered a mechanism of the host defense against intrauterine infection whereby the mother eliminates infected tissues (membranes, decidua, and/or fetus) to maintain reproductive fitness.34 Moreover, a higher risk of LBW and SGA was noted in the pneumonia group, probably due to the lower gestational age at delivery.

considered a mechanism of the host defense against intrauterine infection whereby the mother eliminates infected tissues (membranes, decidua, and/or fetus) to maintain reproductive fitness.34 Moreover, a higher risk of LBW and SGA was noted in the pneumonia group, probably due to the lower gestational age at delivery. On the other hand, there is a widespread general belief that pregnant women with severe acute respiratory syndrome have frequent episodes of oxygen desaturation. Their gravid uterus has been shown to elevate the diaphragm by up to 4 cm in the third trimester, while oxygen consumption is increased by 20% during pregnancy and functional residual capacity is decreased, rendering the woman intolerant to hypoxia.35 Therefore, severe maternal respiratory illness affecting the fetal oxygen supply may seriously endanger the fetus. Wong et al20 found that these patients had frequent episodes of oxygen desaturation, often falling <90%. The situation resembles that of those living at high altitudes, causing a low arterial partial pressure of oxygen36, 37 and consequent adverse pregnancy outcomes. In addition, since the stress of severe hypoxia usually necessitates delivery by CS, a higher risk of CS was also noted in our pneumonia mothers.

lling <90%. The situation resembles that of those living at high altitudes, causing a low arterial partial pressure of oxygen36, 37 and consequent adverse pregnancy outcomes. In addition, since the stress of severe hypoxia usually necessitates delivery by CS, a higher risk of CS was also noted in our pneumonia mothers. This study used a large, unselected national dataset to demonstrate that women with pneumonia were at an increased risk for having adverse pregnancy outcomes compared to unaffected mothers. Moreover, in this study the majority of pregnant women with a case of pneumonia experienced the onset of disease during their first trimester. This finding is supported by a study conducted by Lindsay et al38 that reported a decline in the rate of influenza-like illness episodes as the stage of pregnancy progressed. One possible reason underlying this finding may involve behavioral changes that may be associated with a woman's pregnancy status and knowledge of her pregnancy status. Women in the second and third trimesters of their pregnancies, who are more likely to be aware of their pregnancy status, may be more prudent in avoiding occasions where they may encounter people with colds, the flu, or other respiratory track infections, and may adopt a stricter practice of other preventative behaviors such as an increased frequency of hand washing. On the other hand, women who are not yet pregnant or have become pregnant but are unaware of their pregnancy status may be less likely to engage in such preventative behaviors. It is further possible that some of the women experiencing pneumonia in this study had already encountered or had been infected with pneumonia at the time they became pregnant but were only at a subclinical or incubation stage of the disease. Nevertheless, the underlying factors contributing to the higher incidence of pneumonia during the first trimester remain obscure and deserve further investigation.

already encountered or had been infected with pneumonia at the time they became pregnant but were only at a subclinical or incubation stage of the disease. Nevertheless, the underlying factors contributing to the higher incidence of pneumonia during the first trimester remain obscure and deserve further investigation. There are substantial implications of this study. We believe that the increased risk for adverse birth outcomes among women with pneumonia during their pregnancies warrants a higher level of surveillance among this population to ensure that medical intervention be exercised as soon as possible. This recommendation is supported by 1 recent review study which observed that concern for fetal outcome should not delay treatment, as improvement in maternal status and most particularly oxygenation is the best way to ensure that the fetus will be protected. In addition, the treatment in the gravid patient should generally follow standard guidelines for the treatment of pneumonia in adults.39 Therefore, early recognition of the disease process and prompt treatment are required to best ensure for an optimal outcome for both mother and fetus. Furthermore, primary prevention in the form of a pneumococcal vaccine is both available and recommended for pregnant women with underlying diseases (eg, immunocompromised states, diabetes, chronic cardiopulmonary diseases) to reduce their risks of pneumonia episodes.39 The results of this study further underscore the utility of this vaccine.

evention in the form of a pneumococcal vaccine is both available and recommended for pregnant women with underlying diseases (eg, immunocompromised states, diabetes, chronic cardiopulmonary diseases) to reduce their risks of pneumonia episodes.39 The results of this study further underscore the utility of this vaccine. The clinical course of pneumonia in pregnancy was well described 20-30 years ago. However, to the best of our knowledge all the previous studies investigating pregnancy outcomes among women with pneumonia have been conducted in Western countries with this investigation being the first study regarding pregnancy outcomes among women with pneumonia in Asia. Unlike prior studies that included participants from diverse ethnic groups, >98% of Taiwan's residents are of Chinese Han ethnicity, so the composition of the population is quite homogenous. While this may exempt our study from potential confounding by race, it also means that our results may not be generalizable to other ethnic groups. In addition, we used nationwide population-based datasets, linking the NHIRD with the national registry of births, which leaves little room for selection and nonresponse biases. Moreover, the very large sample size used in this study provides ample statistical power to detect differences between pregnant women with and without pneumonia in risk of adverse birth outcomes.

sets, linking the NHIRD with the national registry of births, which leaves little room for selection and nonresponse biases. Moreover, the very large sample size used in this study provides ample statistical power to detect differences between pregnant women with and without pneumonia in risk of adverse birth outcomes. Despite the strengths of our study mentioned above, our findings still need to be interpreted with caution due to several important limitations. First, the NHIRD lacks clinical information, and therefore did not allow us to differentiate study participants according to the severity of their pneumonia. Secondly, the NHIRD uses discharge diagnoses provided by treating physicians, and no standardized criteria were used to define cases. This may have left room for bias due to case misclassification. Lastly, although we have adjusted for the influence of some potential maternal and pregnancy-specific confounders, information such as maternal smoking history, substance abuse, alcohol consumption, and body mass index (particularly prepregnancy maternal body mass index) was not available through our datasets.

ication. Lastly, although we have adjusted for the influence of some potential maternal and pregnancy-specific confounders, information such as maternal smoking history, substance abuse, alcohol consumption, and body mass index (particularly prepregnancy maternal body mass index) was not available through our datasets. Our study demonstrated that after adjusting for potential confounders, women with pneumonia during pregnancy had significantly higher risks of LBW, preterm birth, SGA, low Apgar scores, CS, and preeclampsia/eclampsia, compared to unaffected mothers. Since the exact mechanisms underlying these associations are not yet known, future studies are recommended, both to replicate the results of this study and to clarify the mechanisms behind them, enabling more specific interpretation of these findings. The authors report no conflict of interest. Cite this article as: Chen Y-H, Keller J, Wang I-T, et al. Pneumonia and pregnancy outcomes: a nationwide population-based study. Am J Obstet Gynecol 2012;207:288.e1-7.