When used judiciously, cesarean sections can save lives; but in the United States, prior research indicates that cesarean birth rates have risen beyond the threshold to help women and infants and become a contributor to increased maternal mortality and rising healthcare costs. Healthy People 2020 has set the goal for nulliparous, term, singleton, vertex (NTSV) cesarean birth rate at no more than 23.9% of births. Currently, cesarean rates vary from 6% to 69% in US hospitals, unexplained by clinical or demographic factors. This wide variation in cesarean use is also seen among individual providers of intrapartum care. Previous research of birth attitudes found providers of intrapartum care hold widely differing views, which may be a key underlying factor influencing practice variation; however, further study is needed to determine if differences in attitudes are associated with differences in clinical outcomes. The purpose of this study was to estimate the association between individual provider attitudes towards birth and their low-risk primary cesarean rate.Four hundred providers were drawn from a stratified random sample of all California providers of intrapartum care in 2013 and surveyed for their attitudes towards various aspects of labor and birth. Providers’ NTSV cesarean birth rates were obtained for 2013 and 2014. Covariates included gender, years of experience, practice location, and primary hospital’s NTSV cesarean rate. We used adjusted multivariate Poisson regression to compare cesarean rates and linear regression to compare attitude scores of providers meeting versus not meeting the Healthy People 2020 (HP2020) goal.p < 0.01.Two hundred nine total participants  completed surveys, of which 109 perform cesareans. Providers’ NTSV cesarean rate was significantly associated with their composite attitudes score [IRR for each one-point increase 1.21 (95% CI 1.002–1.45)]. Physicians meeting the HP2020 goal held attitudes which were significantly more favorable towards vaginal birth: mean 2.70 (95% CI 2.58–2.83) versus 2.91 (95% CI 2.82–3.00), Provider attitudinal differences are associated with NTSV cesarean rates. Those meeting the HP2020 goal hold attitudes more favorable towards vaginal birth. These findings may present a modifiable target for quality improvement initiatives to decrease low risk primary cesareans. Cesarean sections, when used judiciously, save lives; however, research indicates that the use of cesareans in the United States has risen well above the level of necessity and has become a contributor to maternal morbidity and mortality –5. WomenWide variation is also seen between providers, even those practicing within the same hospital and utilizing a laborist model . Over 90modifiable personal attribute that may contribute to cesarean overuse.Previous research found differences in birth attitudes between providers of different disciplines  , 16; yetOur primary aim was to assess the association between providers’ birth attitudes and their NTSV cesarean rates.A stratified random sample of providers listed as delivering clinicians on California birth certificates in 2013 were surveyed with a previously validated survey instrument of provider birth attitudes . We linkCalifornia has approximately 3500 providers that are listed as delivering clinicians in a given year; these include obstetricians (OB), Maternal-Fetal Medicine specialists, family medicine physicians, Certified Nurse Midwives (CNM), and California Licensed Midwives (LM). Practice settings are diverse, and include rural, urban, and frontier geographic locations, teaching hospitals, community hospitals, and tertiary care centers. Provider mix at each hospital may include only obstetricians or family physicians, or may include Maternal-Fetal Medicine specialists or midwives. The CMDC combines existing datasets, including monthly discharge and clinical data, birth certificate data, and semi-annual patient discharge data from the California Office of Statewide Health Planning and Development, to create hospital and physician-level quality improvement metrics. We obtained a stratified random sample from 2013 - the latest available year of complete data. At the time of final data collection, 2014 complete data became available, thus we included provider and hospital metrics from both 2013 and 2014 combined. Our primary sample size consideration was based on the six domain scores’ ability to account for variation in NTSV cesarean delivery rate. Using pilot data, we determined that a sample size of 116 patients would provide 80% power to detect an 11.1% partial R-square. To account for intermittent missing variables, we targeted a sample of 130 survey participants.Inclusion criteria for providers were: having been listed on a birth certificate as the delivering clinician in the year 2013, belonging to one of the key study disciplines , and having performed at least 10 deliveries per year. Providers with a license address outside the state of California and those without an identifiable discipline based either on license number prefix or NPI taxonomy code were excluded from sampling.Based on prior research pointing to important influences on provider practice –19, straThe birth attitudes survey, previously validated by Klein and colleagues, included 9 different domains assessing provider attitudes towards different aspects of labor and birth . Six of We used best practices for achieving maximal survey response as detailed by Dillman et al.  Survey dThe CMDC database uses the standard NQF-endorsed algorithm  to calcuNot all providers included in the survey sample had privileges to perform cesareans. In order to provide a complete picture of the spectrum of attitudes held by all providers practicing as independent clinicians in California, we chose to include all those listed as delivering clinicians on at least 10 deliveries per study year. As it would not be appropriate to assess provider-level cesarean rates in providers who cannot perform cesareans, we excluded these providers from the NTSV predictive model. Both CNMs and LMs do not perform cesareans under any circumstances. A majority of family medicine physicians who do prenatal and intrapartum care do not perform cesareans; however, there are family medicine physicians who have done additional training to qualify to perform cesareans. There is no central database that tracks these privileges, thus this was ascertained by anonymously calling individual family medicine physicians’ offices.Provider demographic data, including discipline , gender, years since graduation from medical school , practice geographic location , and hospital-level demographics including hospital-level NTSV rate, neonatal intensive care unit level, and percent Medicaid were obtained from the CMDC database. Some providers practiced at multiple hospitals; however, the hospital where they had their greatest number of NTSV births was assigned as their primary hospital of delivery.exclusive of provider’s contribution to this rate.Due to the large range of providers’ total NTSV birth volume, we used multivariate Poisson regression to maximize precision of our estimation model, using counts of NTSV cesarean deliveries per provider as the dependent variable and total deliveries per provider as an “exposure” variable whose log-transformed value is included as an offset term, to account for between-provider variation. We used robust standard error estimators to protect against model misspecification. Simple regression with predicted margins was used to compare attitudinal scores between discipline groups. Attitude domain and composite means were calculated from all non-missing items. For the dichotomous Healthy People 2020 provider comparison, we compared providers’ attitudes based on whether or not their NTSV cesarean rates met the Healthy People 2020 (HP2020) goal of less than 23.9% . We usedp < 0.001). Hospital NTSV rates were slightly lower for respondents compared with non-respondents . . For the most part, family medicine-CS physicians expressed attitudes on the individual domains that were slightly less favorable towards vaginal delivery than family medicine physicians; however, family medicine-CS tended to endorse less fear of vaginal delivery than their family medicine counterparts, though this was not statistically significant . The database attributed cesarean deliveries to 14 family medicine physicians without any cesarean privileges and 10 family medicine physicians with first assist privileges.Of the 53 responding family medicine physicians, only 8 had privileges to perform cesareans, though 22 had “first assist” privileges. Family medicine physicians with cesarean privileges (family medicine-CS) were identical to family medicine physicians without privileges on the composite scale , indicating that, for every 1 point increase in a provider’s score on the composite scale, their NTSV cesarean rate decreased relatively by 21%. For individual domains, attitudes towards the perceived benefits of cesarean and fear of vaginal birth approached significance . . Adjusted incidence rate ratios for domain and composite scale from the Poisson multiple regression are shown in Table p < 0.01].When dichotomizing providers by the Healthy People 2020 NTSV cesarean goal cut-off of 23.9%, our sample reflected the overall California distribution, with 35% of providers meeting this goal and 65% of providers whose rate exceeded the goal. Providers meeting the HP2020 goal held attitudes more favorable toward vaginal birth compared with those over the HP2020 goal [adjusted mean 2.70 (95% CI 2.58–2.83) versus 2.91 (95% CI 2.82–3.00), We found a significant association between providers’ attitudes and beliefs about birth to their own NTSV cesarean rate. This study was consistent with the findings of the original Canadian survey validation study , findingWe took prior work an important step further by connecting provider attitudes to their own measured clinical outcomes. For providers with cesarean privileges, those meeting the Healthy People 2020 goal held attitudes that were more favorable toward vaginal birth than those not meeting this goal. In our regression analysis, as attitudes became more favorable toward cesarean and less favorable toward vaginal birth, a provider’s NTSV cesarean rate increased proportionately. Additionally, we used a novel adjustor to account for local hospital culture – the primary hospital’s NTSV cesarean rate less the provider’s contribution to that rate. This adjustment highlights the impact of individual provider attitudes on top of that of local hospital norms and practices. These findings suggest that a provider’s underlying attitudes, values, and beliefs play an important role in intrapartum decisions that ultimately affect birth outcomes.Our results are consistent with, and provide a possible underlying mechanism for, other studies that have found provider differences such as demographics, litigation history, and practice environment are associated with provider self-report personal thresholds to perform cesareans , 23. By Of note, the actual magnitude of the point estimate of the association between composite attitudinal score and NTSV cesarean rate is quite large. For every 1 point increase in agreement with attitudes favoring cesarean, NTSV cesarean rates increased by a relative 21%. For a provider with a baseline rate of 25%, this would translate into an absolute change of 5.25%. In comparison, the QUARISMA trial found changes to the absolute cesarean rate of 0.7–2.3% resulting from an audit and feedback mechanism in conjunction with hospital-based best practices implementation . TargetiTraining may offer a promising target for influencing attitudes that favor vaginal birth. One study found that providers who trained in hospitals with lower obstetric complication rates continued to have lower complication rates once in practice . Most reThis cross-sectional study cannot draw conclusions regarding causality or time course between independent and dependent variables. Additionally, our a priori sample size justification assumed a linear regression analysis, but we found that a Poisson regression analysis was better suited to the outcome distribution. Thus, in order to assess the adequacy of the realized sample size for the reported effect sizes, one should consider the range of values included within the 95% CI . Where tFinally, the administrative data used to assess provider-level cesarean rate was restricted to the provider listed on the birth certificate and subject to errors of attribution. For example, the provider listed as “Delivering Clinician” on the Birth Certificate is usually but not always the person that managed the majority of a patient’s labor or made the decision to go to cesarean. Unfortunately, this method discounts information about providers who do not carry cesarean privileges but may play a major role in intrapartum care, and may be key decision-makers along the route that ends in either cesarean or vaginal delivery. For example, a provider may decide to admit a patient prior to the onset of active labor or use continuous electronic fetal monitoring despite a patient’s low-risk status, both of which increase the likelihood of that patient requiring a cesarean, yet the cesarean birth would be attributed to the clinician who performed the surgery itself and not the provider managing the labor. Further studies are needed to assess the interplay of personnel on labor and delivery wards, how key decisions are made and birth outcomes attributed.This is the first study we are aware of linking provider attitudinal differences to differences in measured birth outcomes. We found that the more providers’ birth attitudes favored cesarean, the higher their NTSV cesarean rate. In contrast to earlier studies that have focused on physician demographics or litigation history, which are rarely modifiable without major policy changes, we sought to evaluate the influence of a modifiable attribute – a provider’s personal attitudes and beliefs. These findings suggest further scrutiny is needed of how future providers are acculturated during training and while in practice, and how practice groups, hospital units, and inter-professional interactions may modify these attitudes to ultimately improve quality of care and health outcomes.


Preterm birth is commonly defined as any birth before 37 weeks completed weeks of gestation. An estimated 15 million infants are born preterm globally, disproportionately affecting low and middle income countries (LMIC). It contributes directly to estimated one million neonatal deaths annually and is a significant contributor to childhood morbidity. However, in many clinical settings, the information available to calculate completed weeks of gestation varies widely. Accurate dating of the last menstrual period (LMP), as well as access to clinical and ultrasonographic evaluation are important components of gestational age assessment antenatally. This case definition assign levels of confidence to categorisation of births as preterm, utilising assessment modalities which may be available across different settings. These are designed to enable systematic safety evaluation of vaccine clinical trials and post-implementation programmes of immunisations in pregnancy. An estimated 15 million infants are born preterm, with resulting complications. It is the principal cause of an estimated one million neonatal deaths annually and a significant contributor to childhood morbidities. Low and middle income countries (LMIC) carry a higher burden of disease attributed to preterm birth.•extremely preterm (<28 weeks);•very preterm (28–<32 weeks);•moderate or late preterm (32–<37 completed weeks of gestation).The World Health Organisation (WHO) defines preterm birth as any birth before 37 completed weeks of gestation, or fewer than 259 days since the first day of the woman's last menstrual period (LMP). This is further subdivided on the basis of gestational age (GA):This is the most extensively used and accepted definition of preterm birth The ability to accurately determine the completed weeks of gestation varies widely between pregnancies, with the most precise assessment methods not uniformly available across different settings. Vaccination in pregnancy has been widely implemented to protect women and their babies from tetanus and pertussis in recent years, with an increasing number of vaccines being developed and trialled for use in pregnancy against a variety of bacterial and viral infections. As preterm birth is such an important pregnancy outcome that may represent an adverse event, it is important to establish a case definition for use across vaccine studies and post-licensure surveillance that is able to make use of all methodologies used to calculate gestational age, and that incorporates a hierarchy based upon the precision of the various methods used.Pre-term: less than 259 days (37 weeks), term: 259–293 days (37–41 weeks). Post-term: 294 days (42 weeks) or more.The nomenclature of GA is typically discussed in terms of the number of completed weeks . Defining GA has been considered useful in terms of neonatal outcome. In the past, three groups have been classified and utilised according to delivery following the onset of the last menstrual period. early term (37 0/7 weeks of gestation through 38 6/7 weeks gestation); full term (39 0/7 weeks of gestation through 40 6/7 weeks of gestation); late term (41 0/7 weeks of gestation through 41 6/7 weeks of gestation); and, post term (42 0/7 weeks of gestation and beyond). The American College of Obstetricians and Gynaecologists (ACOG) and the Society for Maternal–Foetal Medicine (SMFM) has endorsed this recommendation and encourages its use for categorising GA A term birth has been defined as between 37 and 42 weeks and used to describe the optimal timing for a good outcome for the mother and baby. The International Classification of Diseases defines term pregnancy as a delivery from 37 completed weeks to less than 42 completed weeks (259–293 days) of gestation. However, neonatal outcomes vary within this wide gestational age range, with a 2012 international stakeholder working group recommending sub-categorisation of term birth to more accurately describe deliveries and their outcomes. These sub-categories are: 1.1.1Causes of preterm birth are complex and the pathophysiology that triggers preterm birth is largely unknown, however, contributing maternal, foetal and placental predisposing factors have been identified. The most common of these include: antepartum haemorrhage or abruption; mechanical factors such as uterine over-distention and cervical incompetence; hormonal changes; and, bacterial infection and inflammation Over the past 20 years the access to assisted reproduction technology (ART) in many high income countries has contributed to the rise in the number of multiple births and an overall increase in the rates of preterm delivery. Infants born from multiple pregnancies are more likely to be born preterm due to spontaneous labour or premature rupture of membranes (PROM), or as a result of maternal conditions such as pre-eclampsia or foetal disorders Epidemiologic studies have identified preterm birth risk factors as maternal age of less than 17 years or more than 35 years, being underweight, having an overweight pre-pregnancy body mass index, and short stature. Preterm birth rates vary geographically and within ethnic origins, with LMIC consistently having higher rates The assessment and diagnosis of preterm birth has remained problematic since it is not a defined disease and the WHO definition does not contain universally recognised reference standards. Different methodologies are used for assessing GA and because reporting rates vary widely between and within countries, accurate comparison of reporting rates of preterm birth and trending data is difficult to analyse 1.1.2Preterm birth defined as less than 37 completed weeks encompasses a wide gestational age range with rates varying across countries. The WHO subcategories of ‘extremely preterm’, ‘very preterm’ and ‘moderate or late preterm’ are recommended to improve comparability of preterm birth data in relation to immunisation.A limitation of the WHO definition is that there is no boundary between spontaneous abortion and a viable birth, complicating the assessment of preterm birth in the extremely preterm group of babies. A comparison between and within countries becomes complex with varying gestational lower limits of viability over time and across different settings. Determining a lower limit is complex as it is variably defined and arbitrary. It is often described in terms of risk factors and its causes, and is predominately developed according to postnatal viability and data quality in different settings Preterm births are reported only for live born infants. The pregnancy outcomes differ across countries where the upper limit for national or regional criteria for registration of a foetal death range from 16 weeks to 28 weeks, this impacting on the proportion of preterm births The registrations of births in LMIC often do not routinely record GA and the data on birthweight (BW) is often not recorded or compiled. It has been reported that 58% of babies in these countries are not weighed at birth and home based births are not represented 1.1.3Pregnant women are at increased risk of morbidity and mortality and adverse pregnancy outcomes, including preterm birth, due to vaccine preventable diseases. Vaccination in pregnancy is a recognised preventive measure for protecting the mother, foetus and infant Until the 1960s vaccines, including polio, influenza, diphtheria and tetanus toxoid vaccines, were routinely administered to pregnant women in maternal immunisation programmes. Studies in a variety of developed settings detected no increase in adverse consequences for the mother or foetus in vaccinated women Over recent decades, with further development of safe and immunogenic vaccines, as well as improved ability to explore pregnancy outcome datasets, ongoing studies have provided important information on vaccine safety. Immunisation with inactivated vaccines and toxoids during pregnancy has not been associated with any increased risk to the mother or baby. The extensive use of Tetanus Toxoid (TT) and Tetanus diphtheria (Td) in pregnant women, to prevent neonatal tetanus, has shown no clinically significant adverse events and no adverse pregnancy outcomes for women who have received the Tetanus, diphtheria, acellular pertussis (Tdap) vaccine during pregnancy The US Advisory Committee on Immunisation Practices (ACIP) in 2012 updated their recommendations to providers of prenatal care to implement a Tdap immunisation programme for all pregnant women to reduce the burden of pertussis in infants. Its recommendation is for its use with every pregnancy. Similarly, in October 2012, the United Kingdom Department of Health recommended a temporary Tdap programme in pregnancy in response to an outbreak Live viral vaccines, such as measles, mumps, rubella (MMR); varicella; intranasal live-attenuated influenza; Yellow Fever, and BCG however are contraindicated and not recommended during pregnancies, with a theoretical risk that the vaccine virus could be transmitted to the foetus 1.1.4Historically, preterm birth was determined using neonatal physical examination, reviewing clinical history and socio-demographics Early epidemiological studies of prematurity tended to include all low birth weight babies irrespective of gestation. BW was used as a criterion alone as it was objective, easily measured, and the survival of the very low birth weight (VLBW) neonates was described in birth weight specific categories However, BW based standards for preterms are complicated by physiological variables that occur more commonly pregnancies complicated by preterm birth The World Health Organisation (WHO) definition of preterm birth remains the most widely utilised and accepted definition. The International Classification of Diseases  defines preterm birth as less than 37 completed weeks (less than 259 days) of gestation. The duration of gestation is measured from the first day of the LMP. GA is expressed in completed days or completed weeks . Where the date of the LMP is not available, GA is based on the best clinical estimate A search of terminology databases, including the Global Alliance on Prevention of Prematurity (GAPPS), the National Institutes of Health, and the Common Terminology Criteria for Adverse Events (CTCAE) show a consistency with the WHO definition as the onset of labour before 37 completed weeks of pregnancy (full term is 40 completed weeks) Essential in any definition or the sub classification of preterm birth is the need for accurate dating. GA has evolved as the stand alone parameter for determining preterm birth. In 1949 the U.S. National Centre for Health Statistics of the Centres for Disease Control and Prevention revised the World Health Assembly definition and deleted the reference to BW, to include only the reporting of the length of pregnancy in weeks. In 1956 this was further revised to specify the reporting of completed weeks of gestation A 1961 report by the Expert Committee on Maternal and Child Health of the World Health Organisation highlighted the difference between premature and those infants of low birth weight (LBW) and in 1970 a working party of obstetricians and paediatricians at the Second European Congress of Perinatal Medicine set the boundary between preterm and term birth at 37 weeks of gestation This is the basis of the most recent widely utilised and accepted definition of prematurity Before the development of more accurate methods of estimating gestation, the LMP remains as the most widely available measure. This method is used where ultrasound (US) is not available or accessible and is recommended by the WHO for determining preterm birth When available, in the clinical context, it is a valid and applicable measure, especially when estimating gestation of less than or equal to 33 weeks. Its limitations are discussed as being a determinant based on self-reporting and therefore felt to be imprecise. Studies have shown, however, that women who were certain of their LMP were accurate in their assessment of pregnancy duration compared with their ultrasound dating When information about the LMP is absent or uncertain, estimates of gestational age can be determined from a clinical assessment including the description of pregnancy symptoms such as nausea, fatigue, tender swollen breasts, frequent urination, a pelvic examination when performed in the first trimester and fundal height (FH) ascertainment. One study demonstrated a high correlation between early pregnancy dating up to 9 completed weeks by a clinician based on an examination and history and that determined by ultrasound but this is influenced by the skill and experience of the clinician Fundal height (FH) is often used in conjunction with LMP and/or the BW of the neonate, especially in low resource settings. Women from many traditional societies however often do not record their LMP date and can present late in the first trimester. Variability in FH measurements relate to previous caesarean section (C/S), multiple pregnancy, race, maternal height, intrauterine growth retardation, maternal obesity, polyhydramnios, and a difference in examination techniques. FH measurement is not standardised and use beyond 16 weeks reduces in accuracy, affecting its reliability and the precision of dating Through the advent of US, the use of early home pregnancy tests, ART such as intrauterine insemination (IUI), and home ovulation test kits, the actual timing of conception can be determined and therefore accurate dating of gestational age performed. Pregnancies achieved through ART represent the most accurate method.Outside the use of ART, an ultrasound performed in the first trimester (≤13 6/7 weeks), is viewed as the most accurate and reliable measure. There has been a shift from using LMP to using US for predicting an actual date of delivery. Estimation of the foetal crown rump length ± biparietal diameter/femur length between the gestational age of 6–18 weeks shows an accuracy within 5–7 days. In women with uncertain dates an early US is recommended for optimal dating The methodology for US gestational age assessment, however, is not standardised and tends to give a transitory increase in preterm births when compared to the use of LMP alone. In addition, US accessibility in LMIC is limited for the majority of women and therefore cannot be considered a universal measure for determining preterm birth Where measurements such as LMP, US or antenatal clinical assessment are absent or likely to be inaccurate, a recommended criterion for determining preterm birth is a clinical estimation of gestational age based on the physical and neurological examination of the neonate The physical examination based systems have been refined and modified to improve their applicability and accuracy. Some methods now use external characteristics alone, enabling gestational age to be determined and estimated in all settings. Using the available methods ranging in the current criteria, there is a correlation with LMP based estimation of over 90%, but an acknowledged range of error for predicting clinical maturity of ±2.4 weeks. Physical examination tools alone, when used for neonates under 28–33 weeks are recognised to be inaccurate and are therefore are not recommended to be used as a measure to accurately estimate the gestation of neonates within the lower limit of viability The Ballard Maturational Score, known as the New Ballard Score, uses both physical and neurological assessment and has been refined and expanded to include extremely premature neonates and is described as a valid and accurate gestational assessment tool 1.2http://www.brightoncollaboration.org/internet/en/index/process.html, the Brighton Collaboration Preterm Birth Working Group was formed in 2015 and included members of clinical, academic, public health, and industry background. The composition of the working and reference group as well as results of the web-based survey completed by the reference group with subsequent discussions in the working group can be viewed at: http://www.brightoncollaboration.org/internet/en/index/workinggroups.html.Following the process described in the overview paper as well as on the Brighton Collaboration Website To guide the decision-making for the case definition and guidelines, a literature search was performed using Medline, Embase, PubMed, Cinahl, Clincal Key and the Cochrane Libraries for published work relevant to this review with the search terms including: ‘prematurity’; ‘preterm birth’; ‘neonatal outcomes’; ‘birth outcomes’; ‘gestational age’; ‘premature labour’; ‘preterm delivery’; ‘spontaneous labour’; ‘antenatal’; ‘low birth weight’; ‘neonatal mortality’; ‘stillbirth’; ‘extremely preterm’; ‘moderate preterm’; ‘late preterm’; ‘neurological assessment’; ‘vaccination’; ‘immunisation’; ‘viability’; ‘spontaneous labour’; ‘epidemiology’; ‘perinatal outcomes’; ‘last menstrual period’; ‘LMP’; ‘ultrasound’; ‘US’; ‘pregnancy duration’; ‘obstetrics’; ‘morbidity’; ‘small for gestational age’; ‘birthweight’; ‘foetal growth’; ‘multiple pregnancy’; ‘pregnancy’; ‘risk factors’; ‘fundal height’; and terms in combination. We focused on work published published in English language, but also included commonly referenced older publications. The search resulted in the identification of references, 262 articles with potentially relevant material were reviewed in more detail in order to identify studies using case definitions or, in their absence, providing clinical descriptions of the case material. This review resulted in a detailed summary of 99 articles, including information on the diagnostic criteria or case definition put forth.1.3An ideal standardised case definition aims to improve reliability and comparability of data collected from immunised mothers that deliver full term or preterm infants across all health care settings. A functional case definition is important for the evaluation and assessment of data to help determine whether a vaccine administered during pregnancy may or may not be implicated in a subsequent preterm birth. The definition must be applicable in regions that are geographically and administratively diverse, regardless of available health care personnel, training and resources.There has never been a single unanimously accepted definition of prematurity. The literature identified was notable for inconsistent definitions and numerous descriptions for preterm birth. Existing definitions categorise preterm birth by clinical presentation, BW and GA.There is hence no uniformly accepted definition of preterm birth that is able to be employed to evaluate its occurrence following antenatal immunisations. This poses a missed opportunity and a potential risk to immunisation trials and programmes as well as pregnant women, as potential associations may be falsely raised or dismissed without an agreed case definition. Data comparability across a myriad of trials and varying surveillance systems would facilitate data interpretation and improve the ability of investigators and regulators to confidently detect or rule out any association between immunisations in pregnancy and preterm birth. The work group recommended the use of the current definition for Preterm Birth with the subcategories of extremely preterm, very preterm and moderate or late preterm. Accurate dating is essential for the definition, with GA assessment criteria implicit for categorising.1.3.1It needs to be re-emphasised that the grading of definition levels is entirely about diagnostic certainty of the event, not its clinical severity. Thus, a very severe clinical event, like Preterm Birth, may appropriately be classified as Level Two or Three rather than Level One, based upon the level confidence the birth truly occurred preterm.The amount of information, including number of symptoms and/or signs, that will be documented for each case may vary considerably. The case definition has been formulated such that the Level 1 definition is highly specific (or confident) for preterm birth. As maximum specificity normally implies a loss of sensitivity, two additional diagnostic levels have been included in the definition, offering a stepwise increase of sensitivity from Level One down to Level Three, while retaining an acceptable level of specificity at all levels. In this way it is hoped that all possible cases of Preterm Birth can be captured.1.3.2The most accurate methods for GA assessment are included in Level 1 of diagnostic certainty. The GAIA Preterm Birth working group considered that pregnant women with a certain menstrual date or those who have undergone IUI or Embryo Transfer (ET) with a confirmatory 1st trimester scan (≤13 6/7 weeks) or a 1st trimester ultrasound established date (≤13 6/7 weeks) alone, as representing the “gold standard” of diagnostic certainty of GA assessment, and therefore the likelihood of preterm birth. If the LMP date and U/S date do not correlate, defaulting to U/S for GA assessment is required.Where there is no 1st trimester scan or ART performed, Level 2A of diagnostic certainty is used to describe gestational dating. With a certain menstrual date, either a confirmatory 2nd trimester ultrasound established date (14 0/7 weeks to 27 6/7 weeks) or a 1st trimester pelvic bimanual examination are considered the next most precise measurement methodologies. Where there is no menstrual date, it is recommended that the 2nd trimester ultrasound established date be used and categorised as Level 2B.Level 3A of diagnostic certainty represents a certain menstrual date with a 3rd trimester scan of 28 0/7 weeks+, or a confirmatory 2nd trimester fundal height, or birth weight. Where there is no menstrual date, a 1st trimester pelvic bimanual examination would meet the requirement. A separate level 3B category was recommended where there is an uncertain or no menstrual date. In this case a fundal height, or newborn physical assessment or birth weight would meet the requirement for this level.1.3.2.1In the absence of existing data supporting an association between any vaccine and preterm birth, the working group did not feel it was appropriate to define a ‘risk window’ following vaccination during pregnancy and subsequent possible Preterm Birth. The case definition of the outcome (Preterm Birth) is independent from the exposure . Therefore, to avoid selection bias, a restrictive time interval from immunisation to onset of Preterm Birth has not been included. Instead, where feasible, details of this interval should be assessed and reported as described in the data collection guidelines.Further, Preterm Birth often occurs outside the controlled settings of clinical trials or hospitals. In some settings it may be impossible to obtain a clear timeline of the event, particularly in less developed or rural settings. In order to avoid excluding such cases, the Brighton Collaboration case definition avoids setting arbitrary time frames.1.4As mentioned in the overview paper, the case definition is accompanied by guidelines which are structured according to the steps of conducting a clinical trial, i.e. data collection, analysis and presentation. Neither case definition nor guidelines are intended to guide or establish criteria for management of ill infants, children, or adults. Both were developed to improve data comparability.1.5Similar to all Brighton Collaboration case definitions and guidelines, review of the definition with its guidelines is planned on a regular basis (i.e. every three to five years) or more often if needed.22.1Definitions of terms used:•Intrauterine insemination (IUI) – A procedure in which a fine catheter is inserted through the cervix into the uterus to deposit a sperm sample directly into the uterus, to achieve fertilisation and pregnancy.•Embryo transfer – The procedure in which one or more embryos are placed in the uterus or fallopian tube.•Ultrasound (U/S)[62]:-1st trimester (≤13 6/7 weeks).-2nd trimester scan (14 0/7–27 6/7 weeks).-3rd trimester (28 0/7 + weeks).•LMP  – Gestational age is calculated from the first day of the mother's last menstrual period.If LMP and U/S do not correlate, default to U/S GA assessment*Certain LMP: (LMP date + 280 days): Use LMP if within 7 days at ≤14 weeks; within 14 days at ≤26 weeks; within 21 days beyond 26 weeks.*Uncertain LMP – first trimester (≤13 6/7 weeks by LMP): Use the approximate date of the last menstrual period (LMP) if corroborated by physical exam, or a first trimester ultrasound. If there is a discrepancy of >7 days between the LMP and the first trimester ultrasound, the ultrasound-established dates will take preference over LMP for gestational age dating.*Uncertain LMP – second trimester (14 0/7–27 6/7 weeks by LMP): Use the approximate date of the LMP if corroborated by physical exam including fundal height, or a second trimester ultrasound. If there is a discrepancy of >10 days between the LMP and the second trimester ultrasound, the ultrasound-established dates will take preference over LMP for gestational age dating.*Uncertain LMP – third trimester >28 weeks – third trimester ultrasound.*No LMP date: If menstrual dates are unknown, the ultrasound-established dates will be used for gestational age dating or 2nd trimester fundal height and/or newborn physical examination.•Pregnancy symptoms– nausea, fatigue, tender swollen breasts, frequent urination.•Antenatal Physical Examination– pelvic bimanual examination confirming enlarged uterus •Newborn Physical Examination– New Ballard Score – physical and neurological assessment – Appendix 1.•Fundal Height(FH) in cms – Appendix 2.•Birth Weight (BW) in grams – Appendix 2.2.2Level 1: (highest level of certainty)1.Certain LMP* or intrauterine insemination (IUI) date or embryo transfer (ET) datewithconfirmatory 1st trimester scan (≤13 6/7 weeks).OR2.1st trimester scan (≤13 6/7 weeks).Level 2A1.Certain LMP* with 2nd trimester scan (14 0/7 weeks to 27 6/7 weeks). If LMP and U/S do not correlate, default to U/S GA assessment.OR2.Certain LMP*with1st trimester physical examination.Level 2BUncertain LMP with 2nd trimester scan (14 0/7 weeks to 27 6/7 weeks).Level 3A1.Certain LMPwith3rd trimester scan – 28 0/7 weeks +.OR2.Certain LMPwithconfirmatory 2nd trimester FH.OR3.Certain LMP with birth weight.OR4.Uncertain LMP with 1st trimester physical examination.Level 3B1.Uncertain LMPwithFH.OR2.Uncertain LMPwithnewborn physical assessment.OR3.Uncertain LMPwithBirth weight.3It was the consensus of the Brighton Collaboration Working Group for Preterm Birth to recommend the following guidelines to enable meaningful and standardised collection, analysis, and presentation of information about Preterm Birth. However, implementation of all guidelines might not be possible in all settings. The availability of information may vary depending upon resources, geographical region, and whether the source of information is a prospective clinical trial, a post-marketing surveillance or epidemiological study, or an individual report of Preterm Birth. Also, as explained in more detail in the overview paper in this volume, these guidelines have been developed by this working group for guidance only, and are not to be considered a mandatory requirement for data collection, analysis, or presentation.3.1These guidelines represent a desirable standard for the collection of data on availability following immunisation to allow for comparability of data, and are recommended as an addition to data collected for the specific study question and setting. The guidelines are not intended to guide the primary reporting of Preterm Birth to a surveillance system or study monitor. Investigators developing a data collection tool based on these data collection guidelines also need to refer to the criteria in the case definition, which are not repeated in these guidelines.Guidelines numbers below have been developed to address data elements for the collection of adverse event information as specified in general drug safety guidelines by the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use 3.1.1(1)Date of report.(2)Name and contact information of person reporting(3)Name and contact information of the investigator or clinician responsible for the subject, as applicable.(4)Relation to the patient .For all cases and/or all study participants, as appropriate, the following information should be recorded:3.1.23.1.2.1(5)Case/study participant identifiers  or code (or in accordance with country-specific data protection laws).(6)Date of birth, age, and sex.(7)For infants: gestational age and birth weight.For all cases and/or all study participants, as appropriate, the following information should be recorded:3.1.2.2(8)Past medical history, including hospitalisations, underlying diseases/disorders, pre-immunisation signs and symptoms including identification of indicators for, or the absence of, a history of allergy to vaccines, vaccine components or medications; food allergy; allergic rhinitis; eczema; asthma.(9)Any medication history (other than treatment for the event described) prior to, during, and after immunisation including prescription and non-prescription medication as well as medication or treatment with long half-life or long term effect .(10)Immunisation history (i.e. previous immunisations and any adverse event following immunisation (AEFI)), in particular occurrence of Preterm Birth after a previous immunisation.For all cases and/or all study participants, as appropriate, the following information regarding the immunised woman should be recorded:3.1.3(11)Date and time of maternal immunisation(s).(12)Description of vaccine(s)  and number of dose if part of a series of immunisations against the same disease).(13)The anatomical sites (including left or right side) of all immunisations .(14)Route and method of administration , other injection devices).(15)Needle length and gauge.For all cases and/or all study participants, as appropriate, the following information should be recorded:3.1.4(16)For all cases at any level of diagnostic certainty and for reported events with insufficient evidence, the criteria fulfilled to meet the case definition should be recorded.Specifically document(17)Clinical description of signs and sympt oms of Preterm Birth in immunised woman and newborn and if there was medical confirmation of the event (i.e. patient seen by physician).(18)Date/time of onset,7(19)Concurrent signs, symptoms, and diseases in immunised woman and newborn.(20)Measurement/testing•Values and units of routinely measured parameters  – in particular those indicating the severity of the event.•Method of measurement .•Results of laboratory examinations, surgical and/or pathological findings and diagnoses if present.(21)Treatment given for Preterm Birth to mother and/or newborn, especially specify what medication and dosing, or specific interventions.(22)Outcome(23)Objective clinical evidence supporting classification of the event as “serious”.9(24)Exposures other than the immunisation 24 h before and after immunisation  considered potentially relevant to the reported event.3.1.5(25)The duration of surveillance for Preterm Birth should be until the pregnancy has been completed, but specific surveillance may be further predefined based on•Biologic characteristics of the vaccine e.g., live attenuated versus inactivated component vaccines.•Biologic characteristics of the vaccine-targeted disease.•Biologic characteristics of Preterm Birth including patterns identified in previous trials .•Biologic characteristics of the vaccinee .(26)The duration of follow-up reported during the surveillance period should be predefined likewise. It should aim to continue to resolution of the event.(27)Methods of data collection should be consistent within and between study groups, if applicable.(28)Follow-up of cases should attempt to verify and complete the information collected as outlined in data collection guidelines 1–24.(29)Investigators of patients with Preterm Birth should provide guidance to reporters to optimise the quality and completeness of information provided.(30)Reports of Preterm Birth should be collected throughout the study period regardless of the time elapsed between immunisation and the adverse event. If this is not feasible due to the study design, the study periods during which safety data are being collected should be clearly defined.3.2(31)Reported events should be classified in one of the following five categories including the three levels of diagnostic certainty. Events that meet the case definition should be classified according to the levels of diagnostic certainty as specified in the case definition. Events that do not meet the case definition should be classified in the additional categories for analysis.The following guidelines represent a desirable standard for analysis of data on Preterm Birth to allow for comparability of data, and are recommended as an addition to data analysed for the specific study question and setting.Event classification in 5 categories10Event meets case definition(1)Level 1: Criteria as specified in the Preterm Birth case definition.(2)Level 2: Criteria as specified in the Preterm Birth case definition.(3)Level 3: Criteria as specified in the Preterm Birth case definition.Event does not meet case definitionAdditional categories for analysis(4)Reported Preterm Birth with insufficient evidence to meet the case definition.11(5)Not a case of Preterm Birth.(32)The interval between immunisation and reported Preterm Birth could be defined as the date/time of immunisation to the date/time of onsetSubjects with preterm birth by interval to presentation(33)The duration of a possible Preterm Birth could be analysed as the interval between the date/time of onset(34)If more than one measurement of a particular criterion is taken and recorded, the value corresponding to the greatest magnitude of the adverse experience could be used as the basis for analysis. Analysis may also include other characteristics like qualitative patterns of criteria defining the event.(35)The distribution of data (as numerator and denominator data) could be analysed in predefined increments , where applicable. Increments specified above should be used. When only a small number of cases is presented, the respective values or time course can be presented individually.(36)Data on Preterm Birth obtained from subjects receiving a vaccine should be compared with those obtained from an appropriately selected and documented control group(s) to assess background rates of Preterm Birth in non-exposed populations, and should be analysed by study arm and dose where possible, e.g., in prospective clinical trials.3.3(37)All reported events of Preterm Birth should be presented according to the categories listed in guideline 31.(38)Data on possible Preterm Birth events should be presented in accordance with data collection guidelines 1–24 and data analysis guidelines 31–36.(39)Terms to describe Preterm Birth such as “low-grade”, “mild”, “moderate”, “high”, “severe” or “significant” are highly subjective, prone to wide interpretation, and should be avoided, unless clearly defined.(40)n/N) (and not only in percentages), if available.Data should be presented with numerator and denominator , of Improving the quality of reports of meta-analyses of randomised controlled trials (QUORUM), and of Meta-analysis Of Observational Studies in Epidemiology (MOOSE), respectively (41)The incidence of cases in the study population should be presented and clearly identified as such in the text.(42)If the distribution of data is skewed, median and range are usually the more appropriate statistical descriptors than a mean. However, the mean and standard deviation should also be provided.(43)Any publication of data on Preterm Birth should include a detailed description of the methods used for data collection and analysis as possible. It is essential to specify:•The study design.•The method, frequency and duration of monitoring for Preterm Birth.•The trial profile, indicating participant flow during a study including drop-outs and withdrawals to indicate the size and nature of the respective groups under investigation.•The type of surveillance .•The characteristics of the surveillance system .•The search strategy in surveillance databases.•Comparison group(s), if used for analysis.•The instrument of data collection .•Whether the day of immunisation was considered “day one” or “day zero” in the analysis.•Whether the date of onset•Use of this case definition for Preterm Birth, in the abstract or methods section of a publication.11Although immunisation safety surveillance system denominator data are usually not readily available, attempts should be made to identify approximate denominators. The source of the denominator data should be reported and calculations of estimates be described .


IGF1 and INS genes are major determinants of intrauterine growth, we examined the methylation of selected CpGs located in the regulatory region of these two genes.There are many reasons to think that epigenetics is a key determinant of fetal growth variability across the normal population. Since IGF1 and INS genes was measured in cord blood mononuclear cells collected at birth using bisulfite-PCR-pyrosequencing.Cord blood was sampled in 159 newborns born to mothers prospectively followed during their pregnancy. A 142-item questionnaire was filled by mothers at inclusion, during the last trimester of the pregnancy and at the delivery. The methylation of selected CpGs located in the promoters of the IGF1 CpG-137 correlated negatively with birth length . The same effect size was found after adjustment for maternal age, parity, and smoking: a 10% increase in CpG-137 methylation was associated with a decrease of length by 0.23 SDS.Methylation at IGF1 CpG-137 contributes to the individual variation of fetal growth by regulating IGF1 expression in fetal tissues.The current results suggest that the methylation of The online version of this article (10.1186/s13148-018-0489-9) contains supplementary material, which is available to authorized users. IGF1 and IGF2 genes in fetal tissues and placenta is essential for fetal growth, as indicated by studies of knock-out mice and genetic defects in human. Although IGF2 is more abundantly expressed in fetal serum and tissues than IGF1, IGF1 is more closely associated with fetal growth in a majority of species. Insulin secreted by the fetal ß cells is also a major growth factor during intrauterine life  + 0.43 . The correlation between CpG-218 (%) and birth length (SDS) is described by the equation: Birth length = − 0.016*[CpG-218 methylation] + 0.58 . (PPTX 752 kb)Additional file 6:Figure S5. Relationship between insulin rs689 genotype and birth weight (sds) and birth length (sds). Birth weight (sds) and birth length (sds) are independent from the rs 689 genotypes. (PPTX 161 kb)


Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons  born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors  significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size . Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. In developing countries, small size at birth is common, reflecting combined effects of inadequate intrauterine growth and preterm birth. Both birth conditions are associated with increased risks of poor postnatal growth  and infaIn exploring associations between health outcomes and multiple risk factors, observational epidemiologic studies often deal with data that include both a set of exposure variables and a set of outcome variables. Frequently, the variables are interrelated to some extent and consequently, multicollinearity often exists. Routine statistical approaches such as multiple linear regression or principal component regression (PCR) are usually challenged with multiple testing. Specifically, using separate statistical significance tests for each regression equation when there are multiple outcomes substantially increases the risk of Type 1 error . AdditioThe overall Jivita study protocol was reviewed and approved by both the Bangladesh Medical Research Council (BMRC) and the Institutional Review Board (IRB) of Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. Documented consent was given by all participating women.In this study, PLS regression was used to assess the effect of maternal and infant factors on newborn size at birth. The data reported in this analysis were collected during a field based study assessing the efficacy of weekly antenatal vitamin A or β-carotene versus placebo supplementation on maternal and infant mortality through 6 months of age from July 2001 to January 2007. Details of this study conducted in a contiguous ~435 sq km area in rural northwestern Gaibandha and Rangpur Districts of Bangladesh with a population of ~650,000 are available elsewhere .st trimester about household socioeconomic condition, education, demographic characteristics, previous pregnancy history, recent morbidity history and 7-day food intake and mid-upper arm circumference. A Living Standard Index (LSI) was constructed using principal component analysis from household socio-economic variables and was used to describe socio-economic status [nd half of the maternal trial [On enrollment into the intervention trial, consenting mothers were interviewed in the 1c status . At 3 moal trial . Shortlyal trial . Small ial trial . SingletThe maternal characteristics included in this study were age at enrollment, parity, mid upper arm circumference (cm), education (yrs), living standard index, number of antenatal care visits, and maternal supplementation group. Additional infant characteristics included preterm (<37 week of gestation) delivery and infant sex .The classical regression methods usually meet four main challenges with: (i) a large number of variables, (ii) correlated predictors, (iii) smaller sample size with a large number of variables and (iv) having more than one response variables simultaneously . To overAlthough PLS regression is comparatively new, its use in research is gradually increasing. The great strength of PLS regression is parsimony . InitialSimilar to principal component regression (PCR), PLS regression analysis is a data-dimension reduction method that extracts a set of orthogonal factors called latent variables which are used as predictors in the regression model . The majThe PLS regression identifies the latent variables stored in matrix T and they model X and predict Y simultaneously. Then the following expression can be written as,PT+ is the Moore-Penrose pseudo-inverse of PT. The matrix BPLS has J rows and K columns and is equivalent to the regression weights of multiple regression.The latent variables are computed iteratively using Singular Value Decomposition (SVD). In each iteration, SVD constructs orthogonal latent variables for X and Y and corresponding regression weights . The alg0 and Y0Step 1: Transform X and Y into Z-scores and store in matrices X0 and Y0, R1 = X0TY0Step 2: Compute the correlation matrix between X1 and produce two sets of orthogonal singular vectors w1 and c1 corresponding to the largest singular value, λ1.Step 3: Perform singular value decomposition (SVD) on R1 = X0Tw1.Step 4: The first latent variable for X is given by T1 such that T1TT1 = 1Step 5: Normalize T0 on T1 is computed asStep 6: The loadings of X1 = Y0c1andStep 7: Compute Ub1 is the slope of the regression of Ŷ on T1. 0. Matrices 1 are then subtracted from the original X0 and Y0 respectively to give deflated X1 and Y1.The scalar Step 8: Compute the input matrices for the next iteration,2 = X1T Y1 we get w2, c2, T2 and b2 and the new deflated matrices X2 and Y2.Step 9: The first set of latent variables has now been extracted. Now perform SVD on Rl).Step 10: The iterative process continues until X is completely decomposed in to L components (where L is the rank of X). When this is done, the weights  for x are stored in the J by L matrix W  for different number of components. The component at which the cross validation RMSEP has a meaningful change is used in the final model. To choose the optimum number of components for both PLS and principal component regression, root mean squared error of prediction (RMSEP) were calculated using different number of components. We performed approximate t-tests of regression coefficients based on jackknife variance estimates .1 vs t2, which helped us to assess if there is any natural grouping or interactions among variables.We constructed a correlation plot of the variables to observe how variables are correlated with each other and also between the birth size variables and maternal variables. The closer a variable appears to the perimeter of the circle, the better it is represented, and if two variables are highly correlated they appear near each other. If two variables are negatively correlated they will tend to appear in opposite extremes. If two variables are uncorrelated, they will be orthogonal to each other. We plotted the scores of first two components, tTo examine the advantage of PLS regression over principal component regression, we calculated Pearson’s correlation coefficients between the predicated values  and the observed values of infant’s size variables. This correlation coefficient indicate the predicative power of the model: if the model has perfect predictive ability then the correlation coefficient will be 1. So, the more the correlation coefficient, the higher the predictive power of a given model is. For this analysis, we used the R packages: “plsdepot”, “pls” and “mixOmics”.More than half of the infants were small at birth. More than 50% mother had parity of at least 1, mean (SD) maternal age was 21.96 (5.88) yrs and maternal early pregnancy MUAC was 22.99 (1.97) cm. About 75% mothers reported not having had any ANC visits throughout the pregnancy. Preterm delivery was 27% and 51% infants were male .The RMSEP was plotted against number of components used in the PLS regression model and principal component regression models in Figs Some of the coefficients estimated using the ordinary linear regression (LR) were different compared to the coefficients estimated using PLS regression . Coefficth quadrant. Among them, age, MUAC and parity were close to each other and they were also closer to the birth size variables in the 1st quadrant. Education, ANC visit, and LSI were very close to each other but were further from to the birth size variables compared to the prior cluster of maternal variables. On the other hand, preterm delivery alone fell in the 3rd quadrant just opposite to the birth size variables and infant’s sex alone fell in the 1st quadrant with the birth size variables. Maternal Vitamin A and β-carotene supplementation fell very close to the center indicating no effect on birth size. All the 5 birth size variables in the 1st quadrant were clustered close to each other. Therefore, this figure demonstrated that preterm delivery was the most important predictor in the opposite direction, followed by infant’s gender, parity, age and MUAC.The correlation plot of the variables for the first two components  depictedst and 2nd PLS components of the predictors displayed four distinct groupings among the study participants due to the interaction effect of preterm delivery and infant’s sex  (The score plot of the 1preterm) .We observed that the magnitude of correlation between the predicted values using PCR and observed values of birth size was substantially increasing with increasing the number of components, however, the correlation was very poor with the first few components . On the We conducted this study to assess the effect of maternal socio-demographic factors on newborn size at birth using PLS regression. Our study revealed that all the maternal variables examined, except vitamin A and β- carotene supplement receipt during pregnancy, were significantly associated with birth size. Preterm delivery had the greatest effect on birth size followed by infant sex, maternal parity, education, and age. In addition, PLS regression facilitated the finding of an interaction effect of preterm delivery and newborn sex on birth size, as revealed by a scatter plot of the first two components . PLS regIn addition to birth weight, the combination of all birth size measurements captures more information than a single measurement in isolation; however, they are highly correlated. Head circumference, for instance, indicates the brain volume  and it mThe PLS regression analysis suggested that maternal and infant factors age, parity, early pregnancy MUAC, LSI, maternal education, number of ANC visits and infant sex were significantly and positively associated with infant’s size at birth; however, preterm delivery had the greatest negative effect on birth size as anticipated. We did not find any effect of maternal vitamin A and β-carotene supplementation on infant’s size at birth and which is consistent with the result found by Christian et al.  and Kabi2 increases with an increasing number of predictors in the model, the variance of regression coefficients also increases. Thus, it is statistically advantageous to have a reduced number of explanatory variables in a given model. Compared to PCR, PLS regression requires a smaller number of components resulting in more stable estimates of regression coefficients. In this analysis, PLS regression required only two components but PCR required five. Despite the fact that PLS components explained a much lower variance (~24%) of the maternal and infant factors, it had more predictive power over the PCR where components explained ~73% variance. This is because, in PLS regression, a pair of components is chosen from the dependent and independent variables so that they are closest to each other; however, in PCR, a component is chosen from the independent variables which capture most of the variability without accounting for how close it is to the dependent variables. Criteria that give penalties on the number of variables, like Akaike Information Criterion (AIC), or those where model performance is evaluated, like the Mallows Cp criteria, also encourage the need to include more variables than the PLS method [PLS regression has the ability of handling multicollinearity and multiple dependent variables simultaneously. It has a proven ability to perform better than other regression methods, which are usually used to handle highly collinear data, such as stepwise multiple regression, PCR or model fitting techniques that apply Maximum Likelihood or Bayesian theory and in addition it equally performs in ideal situations ,33,40. TS method . This asPLS regression was a better choice to analyze this data. It needed only two components, while PCR, the most commonly used method, required 5 components to have similar predictive power. The coefficients obtained from PLS regression were more stable and accurate than that of the general linear model. Using PLS regression, the maternal factors identified as the significant predictors of newborn size at birth were commensurate with other studies –49. So, S1 Dataset(XLSX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Parental characteristics were obtained via self-reported questionnaires during pregnancy. Medical records helped identify very-low-birth-weight , term-small-for-gestational-age (term-SGA), and preterm-birth  infants. We calculated relative risks (RRs) for PTB, VLBW, and term-SGA birth based on parental characteristics. The prevalence of PTB, VLBW, and term-SGA was 4.5%, 0.4%, and 6.5%, respectively. Aged parents and ART were risk factors for PTB and VLBW. Maternal alcohol drinking during pregnancy increased the risk; a parental educational level of ≥16 years reduced risk of term-SGA. Maternal pre-pregnancy BMI of <18.5 kg/m2 increased the risk of PTB and term-SGA. The RR for low BMI was highest among mothers who have low educational level. Among various factors, appropriate nutritional education to maintain normal BMI is important to prevent PTB and term-SGA in Japan.From 1985 to 2013, the mean birth weight of infants in Japan decreased from 3120 g to 3000 g, and the low-birth-weight rate among live births increased from 6.3% to 9.6%. No prospective study has elucidated the risk factors for poor fetal growth and preterm birth in recent Japanese parents, such as increased parental age, maternal body figure, assisted reproductive technology (ART), and socioeconomic status. Participants were mother–infant pairs ( Poor fetal growth, such as low birth weight (LBW), small-for-gestational age (SGA), and preterm birth (PTB) have serious health effects not only during the neonatal period and infancy, but also later in life . LBW, SGReported maternal risk factors for PTB and SGA include maternal young and advanced age, low maternal body mass index (BMI), short stature, low weight, mother born as SGA, and cigarette smoking ,5,6,7. FIn this study, we aimed to determine parental characteristics, such as increased parental age, maternal body figure, family socioeconomic status, their life style, and medical treatment as risk factors for VLBW, term-SGA, and PTB in Japan.n = 1176). Thus, we eliminated the pathological causes of VLBW, term-SGA, and PTB, which could have masked and underestimated the risk factors of parental characteristics. Participants lacking information on the three outcomes of interest were also excluded (n = 344). Thus, a total of 18,059 participants were included in the statistical analysis that assessed the associations between parental factors and VLBW, term-SGA, and PTB.The Hokkaido Study on Environment and Children’s Health is an ongoing cohort study that began in 2002. The study’s aims and methods were described in three previous profile papers and are only briefly discussed in this study ,19,20. F2), maternal and paternal previous medical history, regular use of any supplement(s), maternal active smoking during the 1st trimester, maternal passive smoking during the 1st trimester, paternal smoking habit until the 1st trimester, parental drinking habit until the 1st trimester, and use of any assisted reproductive technologies (ART). We used paternal and maternal education level , and family household income , as socioeconomic indicators because these are important in young adulthood .,26.25,26p < 0.05 were considered statistically significant. All statistical estimates were calculated using JMP Clinical 5 statistical software .Since there were many missing values for parental factors, we imputed missing values using partial least squares regression. Two-sided values of All participating mothers provided written informed consent before participation in the Hokkaido Study. The study protocol was approved by the ethics review board for epidemiological studies at Hokkaido University Graduate School of Medicine  and the Hokkaido University Center for Environmental and Health Sciences , in accordance of with principles of the Declaration of Helsinki.n = 9096) of the total sample. There were 6621 (36.7%) primipara mothers. Maternal and paternal characteristics are shown in 2 vs. 18.5–25) and used ART were significantly associated in the crude model and adjusted models, but they were not statistically significant in the adjusted model.2) group, the RR was significantly higher for infants whose mothers were in the lowest BMI (<18.5 kg/m2) category  and was significantly lower for infants whose mothers were in the BMI (25.0–29.9 kg/m2) category . The RR of being born term-SGA was significantly higher in infants whose mothers continued to drink during the first trimester  compared with those whose mothers never drank alcohol, and whose mothers regularly used any supplement . The RR of being born term-SGA was significantly lower in infants whose mothers had >16 years (vs. 10–12 years) of education . The RR of being born term-SGA was significantly lower in infants whose fathers had >16 years (vs. 10–12 years) of education . Maternal passive smoking was not significantly associated after adjustment. 2) BMI compared with a standard (18.5–25 kg/m2) BMI ; infants whose mothers had any previous medical history ; and for those whose mothers used ART . The RR of PTB was also significantly higher for infants whose father’s highest level of education was 13–15 years compared with 10–12 years . On the other hand, the RR of PTB was significantly lower for infants whose parent’s household income was less than 3 million yen compared with 3–5 million yen . Paternal age, paternal smoking habit, paternal previous medical history, and maternal educational level were not significantly associated after adjustment.interactionP = 0.01) using ART and maternal age at entry (interactionP = 0.02) for PTB, and using maternal educational level and maternal age at entry (interactionP = 0.03) for VLBW. 2 vs. 18.5–25 kg/m2) to PTB was significantly associated among mothers’ educational level ≤9 years, 9–12, and >16 years, and among them, RR was the highest in the group of ≤9 years . After stratification by maternal age at entry, the RR of PTB was significantly associated only among infants whose mothers used ART  in the 25–35 years old mothers group. We found significant interaction effects between pre-pregnancy BMI and maternal educational level  has been previously reported as a significant risk factor for VLBW . In this2 vs. 18.5–25 kg/m2) in the crude and adjusted models, although the negative effect was insignificant in the DAG model. Studies conducted in the US and European countries have reported that a high pre-pregnancy BMI has a disadvantageous effect on fetal growth [2 as the standard criterion for high BMI. Previous studies reported that Asians have a lower BMI, but a higher percentage of body fat than Caucasians [2 in the present study, and the proportion of VLBW was only 0.4%. Hence, we were unable to detect the negative effect of >30 kg/m2 BMI. Notably, 2 vs. 18.5–25 kg/m2) was highest in the group with educational level of ≤9 years . Han et al. reported in a meta-analysis that a low BMI in pregnant women significantly increased the risk of VLBW, PTB, and intrauterine growth restriction [In this study, the higher BMI, the higher the RR of VLBW . Education could be an important factor to avoid the risk factors associated with VLBW, term-SGA, and PTB. In this study, maternal and paternal education (≥16 years vs. 10–12 years) significantly reduced the RR of term-SGA. As mentioned, not only maternal but also paternal educational level could be an important factor in avoiding the risks associated with term-SGA. A previous study in Japan reported that parental educational level was significantly associated with SGA , and indWe excluded women who had stillbirths, multiple births, pregnancy-induced hypertension, and gestational diabetes. Most stillbirth and multiple-birth infants show VLBW, term-SGA, or preterm characteristics. Pregnancy-induced hypertension and gestational diabetes have already been reported to have a decreased or increased effect on gestational age and birth weight . FurtherThe strengths of this study are as follows: first, it was a prospective birth cohort study design. Participants were recruited in a general hospital setting, such as local obstetric clinics. Second, the loss-to-follow-up rate was only 5.9%. Third, we initiated a DAG model to identify a minimum set of confounding adjustment, to avoid over-adjustment of our multiple analysis model. Limitations of this study included the following: first, the amount of missing data was relatively large. For example, 17.9% of household income data were missing. However, to estimate the effects of missing values, we imputed values using partial least square regression. The distributions of parental characteristics were comparable, and RRs were not different between raw and imputed data. Second, the participants of this cohort study were pregnant women who had visited hospitals or clinics within the Hokkaido Prefecture only. However, the participating hospitals and clinics were local medical institutions and distributed throughout the prefecture, accounting for approximately 40% of the institutes with delivery units in this prefecture . Moreove2 was a risk factor for both term-SGA and PTB. Moreover, the results of interaction effects between pre-pregnancy BMI and maternal educational level for PTB presents that the RR of low BMI was highest in the group with educational level of ≤9 years.This study showed that different parental factors were associated with three proxy indicators of poor fetal growth and preterm birth: VLBW, term-SGA, and PTB in Japan. These results suggest that both maternal and paternal advanced age and using ART are predictors of VLBW and PTB. Maternal alcohol drinking habit increased the risk of term-SGA, whereas both maternal and paternal high educational levels were protective to term-SGA infants. In addition, maternal pre-pregnancy BMI of <18.5 kg/m


More than 10% of babies are born too early resulting in over 15 million preterm births and more than one million new-born deaths globally. Although women with a previous spontaneous preterm birth (SPTB) are considered at high risk for recurrence, the majority occur in women without prior history. Prediction of SPTB risk allows for improved care and potential for targeting novel and existing therapeutics to prevent SPTB, which may result in improved outcomes for infant and mother. In this pilot study, a miRNA array was used to analyse plasma from healthy women in their first pregnancy at 20 weeks of gestation who then went on to deliver either at term or experience SPTB at 28–32 weeks. We identified specific miRNA expression profiles that differentiated between those mothers who delivered at term or delivered following SPTB. miR302b, miR1253 and a clustering of miR548 miRNAs were underexpressed in SPTB cases compared to term controls. Conversely, miR223 was elevated in mothers that later experienced a SPTB. The circulating miRNAs identified in the present study may therefore be attractive candidates as non-invasive biomarkers for the early prediction of SPTB. Further larger studies are now warranted to investigate the potential clinical utility of these markers. Preterm birth also increases the risk of death due to other causes, especially from neonatal infections3, and in almost all high-income and middle-income countries, preterm birth is the leading cause of child deaths1. Further, a number of those who survive will have a permanent disability. In New Zealand, approximately 8 out of 100 babies are born preterm and globally preterm birth has increased annually by around 2% over the last 20 years4. Approximately 60% of all preterm births occur after spontaneous onset of labour and in 40% babies are delivered early because of pregnancy complications. In some settings medically indicated preterm births are responsible for the increase in overall preterm birth over time5. Although women with a previous spontaneous preterm birth (SPTB) are considered to be at high risk for recurrence, the majority occur in women without prior history, particularly in women in their first pregnancy6. Therefore, accurate prediction of SPTB risk, before the clinical event, would allow for improved care, use of resources, and the potential for targeting novel and existing therapies to prevent preterm birth, which may result in improved outcomes for the infants and their mothers.Preterm birth and its complications are estimated to be responsible for 35% of the world’s 3.1 million annual neonatal deaths, and is the second most common cause of death globally after pneumonia in children under 59. Owing to the wide-spread involvement of miRNAs in the development of and protection from many diseases, there has been increasing interest surrounding their potential as novel biomarkers and therapeutic targets to treat and prevent a number of disease states. There is a rapidly emerging research area investigating the potential of circulating miRNA biomarkers of pregnancy complication and disease with recent work highlighting potential for miRNAs as biomarker of osteoporosis10, cancers11 and pre-eclampsia13. Recent examples have linked alterations in placental and decidua-derived mesenchymal stem cells miRNA expression profiles with pre-eclampsia and suggest that subsets of specific miRNAs may be potential biomarkers for this disease17. While pre-eclampsia has been a recent focus of miRNA profiling in the area of pregnancy complications, there remains a paucity of data with respect to SPTB. It has recently been shown that distinct cervical miRNA profiles are present in women destined to have a preterm birth18. It has also been reported that the levels of specific miRNAs in the human cervix during pregnancy are predictive of gestational age at delivery, and offer potential biomarkers of preterm birth risk19. Further work in mice by Mendelson and colleagues has also highlighted specific miRNA clusters that may play a role in parturition via modulation of progesterone receptor activity leading to initiation of labour22. However, there are few studies that have investigated maternal plasma miRNAs as potential biomarkers of later SPTB risk. Recent work by Elovitz et al. examined miRNA blood profiles in women at risk for preterm birth but concluded that profiles were not significantly different in women with preterm birth versus term controls23. However, in contrast to the present study, this previous work had examined maternal samples at the time of admission for preterm delivery and as such sampling could not be deemed “predictive”.Primarily involved in initiating post-transcriptional gene silencing, miRNAs, small noncoding RNAs (19–22 nucleotide sequences long), are known to play many roles in the regulation of gene expression24. As such, a methodological approach using plasma markers coupled with NanoString high-throughput digital quantification method has yet to be applied in the setting of prediction for those mothers at risk of SPTB. Use of NanoString has several benefits relative to microarray- and PCR-based technologies. NanoString utilises a hybridization method that directly interrogates target sequences via an amplification-free process and thus is highly sensitive, even for low-abundance transcripts such as with plasma miRNA. Thus, this method avoids potential bias that may be introduced via PCR amplification steps. Additionally, measurement is achieved using digital detection of uniquely bar-coded probes, providing absolute quantification. The present study therefore utilised NanoString technology to explore the potential of circulating miRNAs as biomarkers during early pregnancy to predict those individuals that go on to experience a later SPTB.In addition, the majority of previous analyses have utilised microarrays and quantitative real-time PCR approaches which lack the sensitivity of digital count technologies such as NanoStringThere were no significant differences in maternal age, ethnicity, body mass index and primigravida between groups. As expected, gestational age at delivery was significantly different P < 0.001) between groups . There were no differences in customized birth weight centiles, socioeconomic index score or gestational age at the time of maternal blood sampling  . Following analysis of 800 human miRNAs, 8 were differentially expressed between term and SPTB groups Fig.  includinOne miRNA (miR-223), was observed to be differentially expressed between term and preterm birth groups, with miR223 being significantly increased in plasma from the SPTB group . The re29. Whether changes in maternal miRNA profiles possess utility as a potential biomarker and “fingerprint” for later pregnancy/offspring outcomes is yet to be fully examined. In the current study we have identified distinct miRNAs measured in maternal plasma samples at 20 weeks’ of gestation that are associated with a later SPTB. Using the nCounter NanoString to identify >800 miRNAs in maternal plasma, our results demonstrate that differential expression of plasma miRNA may provide novel non-invasive biomarkers during early pregnancy to identify women at risk of SPTB long before any overt manifestation of preterm complications may occur.Given their stability in blood and, owing to the wide-spread involvement of miRNAs in the development of and protection from many diseases, there has been increasing interest surrounding their potential as novel biomarkers and therapeutic targets to treat and prevent a number of disease states and pregnancy-associated complicationset al. and Sanders et al. in that miR223 had a higher expression in patients that experienced SPTB30. However, these studies investigated uterine cervix miRNA expression profiles following term and preterm birth in association to gestational length and not a predictive circulating biomarker miRNA, as in the current study. A further study has examined miRNA blood profiles in women at risk for preterm birth but they concluded that profiles were not significantly different in women with preterm birth versus term controls23. Unlike the present study which investigates the potential predictive markers of SPTB in early pregnancy, the work by Elovitz et al. examined maternal samples at the time of admission for preterm delivery and as such sampling could not be considered “predictive”.The key miRNAs highlighted in this study include miR223, miR302b miR1253 and a very specific grouping of miR548a, miR548aa, miR548ai, miR548ak and miR548n Figs  and 2. I31 and therefore, all have a potential biological role in the setting of preterm birth. Furthermore, miR548 associated target genes have previously been implicated in having a role in breast cancer and immune response32. Studies have shown that down-regulation mir548 target genes that have been previously observed to be related to colorectal cancer aetiology. Similar to the current study, these studies showed that down regulation, rather than up-regulation of miR548 was important in the aetiology of disease. Furthermore, a recent paper by Li et al. reported a suppression of endogenous miR548 levels in the setting of viral infection32. Previous associations with the immune system and colorectal cancer re down regulation of miR548 is in agreement with results presented here, in that a similar down regulation of this miR548 cluster is associated with the incidence of SPTB in the current study. We speculate that the down regulation and their predicted gene associations may indicate that they act through translational repression and may not be repressing pathological mRNA expression changes during the onset and initiation of parturition therefore, allowing a pathological up-regulation of genes aiding the progression of a disease state or, in the case of the current study, initiation of SPTB.While miR548a, miR548aa, miR548ai, miR548ak and miR548n have not previously been associated with preterm birth, there remains an overall paucity of information about the function of the miR548 family in disease processes. Downstream targets of the miRNA 548 family have been shown to be involved in breast cancer, inflammatory responses and potential oestrogen receptor sensitivity33. In the first instance this would require identification of specific miRNAs directly in placental tissue from different disease pathologies and then analysis of maternal blood to assess repeatability and potential utility as biomarkers. Placental tissue was not available for the present study so direct correlations were not possible. To date, limited expression studies have shown that miRNA expression may not be mirrored between the placenta and maternal circulation for certain pathologies while others have shown that miRNAs in blood or in the placenta can increase or decrease in a similar pattern34.In conclusion, we have identified 7 miRNAs that at 19–21 weeks gestation were elevated in maternal plasma in women with term pregnancies but minimally expressed in those who went on to experience SPTB. We also confirm previous findings that elevated maternal plasma miR-223 expression is associated with SPTB and show the potential that miR-223 may be a viable candidate miRNA biomarker for the early prediction of SPTB risk. Furthermore, a cluster of related miR548s were highly differentially expressed between term and SPTB groups being markedly elevated in 20 week maternal plasma from those mothers that later delivered at term versus those that later experienced a SPTB. It needs to be considered that the change in regulation of the identified miRNAs may reflect a change in maternal immunological status and therefore acting as a sensitive marker of an altered inflammatory phenotype that precedes presentation of more overt symptoms later in pregnancy. Further the assumption cannot be made that the miRNAs identified in the present study are placentally derived. However, it is known that specific placental miRNAs are present in maternal plasma in different ways dependent upon whether the pregnancy is normal or pathologicalAlthough the low sample size is a significant limitation in the present study, the uniformity of the results obtained does serve to highlight the potential of circulating miRNAs as circulating biomarkers and suggests that specific miRNA profiles may be of clinical importance in the early identification of SPTB. However, due to miRNA levels being extremely difficult to detect in human plasma samples and the increased QC failure of a number of array plates in this preliminary study, larger studies are warranted to confirm the predictive value of these miRNAs in the current study. These novel data suggest that unique circulating miRNA profiles may provide attractive candidates as putative biomarkers for prediction of SPTB risk during early pregnancy. The current pilot study is limited by small sample size and relatively homogeneous subject population and further work in a larger heterogeneous population is now warranted to examine the applicability of the approach in predicting SPTB across a wider gestational age range. This will require an unselected obstetric population comprising of women of different ethnic backgrounds, parity, age, smoking status, BMI and gestational age at sampling and examining the effect of obstetric complications on the utility of the markers identified.Informed written consent was obtained from the participants in the SCOPE study, and ethical approval was granted by the Auckland Ethics Committee (AKX/02/00/364). All methods were performed in accordance with the relevant guidelines and regulations of the Auckland Ethics Committee.Normally, a pregnancy usually lasts about 40 weeks. Premature birth occurs before the start of the 37th week of pregnancy. Late preterm is defined as birth, between 34 and 36 weeks of pregnancy, moderately preterm, between 32 and 34 weeks of pregnancy and very preterm, at less than 32 weeks of pregnancy. Extremely preterm is defined as birth at or before 25 weeks of pregnancy. For the purposes of this study our case group involved participants whom went on to experience a very preterm birth between 28 and 32 weeks.2  study. Cases n = 12) were participants who had a spontaneous preterm birth resulting in delivery between 28 and 32 weeks gestation. Controls (n = 12) were healthy uncomplicated pregnancies with delivery >=37weeks. They were all non-smokers when they enrolled in the study at 14–16 wks and continued to be non-smokers at the 19–21 week visit, and were matched by ethnicity, age ± 3y, and BMI ± 3 kg/m2 were pa36. Customized birthweight centiles were adjusted for maternal height, booking weight and ethnicity, as well as gestational age at birth and sex of infant. Blood was collected in 6 ml sterile plastic EDTA (spray coated) plasma vacutainer BD tubes (Cat. #367873), placed on ice, centrifuged at 2400 g for 10 minutes at 4 °C, transferred into sterile tubes as 0.25 ml aliquots and stored at −80 °C within four hours of collection.The multicentre SCOPE study was a prospective cohort study of healthy nulliparous women  with the aim of developing predictive screening tests for preeclampsia, small for gestational age babies and SPTB. The participants were seen at 14–16 and 19–21 weeks of gestation and a detailed interview and clinical measurements were done and blood samples collected. Detailed methods concerning the SCOPE cohort have been previously reportedhttp://www.nanostring.com/products/nSolver) .RNA was extracted from control term versus preterm EDTA-preserved maternal plasma samples using the miRNeasy® Serum/Plasma kit (QIAGEN). RNA concentration and purity was estimated spectrophometrically on NanoDrop ND-100 spectrophotometer . Digital multiplexed NanoString nCounter analysis system was performed on total miRNA concentrations from each sample. Plasma miRNA samples were then analysed by nCounter Human miRNA panels (800 human miRNA targets) with 6 six internal housekeeping controls. nCounter miRNA Expression Assay kits were used to analyse the digital detection of the plasma miRNAs in a single reaction. Digital detection was performed in two parts; (a) Transcripts detected by probes bound to complimentary segments of DNA which are attached to a unique string of coloured fluorophores. (b) Number of total transcripts in the sample was counted by the number of times is the fluorophore was detected, scanning was performed in 600 fields of view. Nanostring counts represent molecules/100 ng of plasma extract. Following hybridization, counts were analysed by the nCounter Digital Analyzer. The resulting raw data were imported into nSolver Maternal clinical baseline data was analysed via Students t-test. MicroRNA data was analysed using online miRNA expression software analysis package, NanoString nSolver® platform . Data are shown as means ± SEM unless otherwise stated. An expression difference of p < 0.001 was considered statistically significant. All other maternal statistical analysis was undertaken using Sigmaplot 12.0 (Systat Software Inc.).