In electric field modified flames, the electric body force on fluid elements can play a role in modifying the flow field, affecting flame characteristics by this modified flow motion. Numerical studies have developed ion kinetic mechanisms and appropriate transport models for charged species, validating them with a voltage-current trend in 1D premixed flames. Recent experimental approaches have measured the electric field by adopting the Electric Field Induced Second Harmonic generation (EFISH) technique. However, the quantification has turned out very challenging due to the inherent distortion in the EFISH signal, as well as inhomogeneous temperature and concentration fields in the combustion field. Here, we propose measurement and calibration schemes to quantify the EFISH signal in a laminar counterflow nonpremixed flame and present comparison with numerical results using an in-house multi-physics CFD  code. Overall, the quantified electric fields agreed well with those from numerical simulation, specifically capturing null electric fields near the flame in the sub-saturated regime due to the electric field screening effect. In the saturated regime, notable discrepancy was found in a fuel stream when electrons moved through it: experiment indicated a significant number of negative ions in the fuel stream, whereas numerical results predicted negligible negative ions, due to the implemented ion-mechanism. This suggested that the experimentally obtained electric fields may serve as validation data for modeling studies to improve transport models and ion-mechanism. In-situ measurement of charged species in the presence of external electric fields should be a future work. The electrical aspects of flames and electrically modified flames have been investigated for almost a century. Lawton and Weinberg well developed and summarized early phenomenological findings and theoretical fundamentals behind1. Later on, ion-chemistry occurring in flames was extensively reviewed by Fialkov2. The first comprehensive ion reaction mechanism for hydrocarbon flames was configured by Prager et al.3, triggering following kinetic and numerical studies. Stimulated by advanced diagnostic and simulation capabilities, this topic has been revived since early 2000; those recent progresses were well reviewed in literature5.Flames—particularly those burning hydrocarbons—are known to show intrinsic electrical characteristics because charged particles such as electrons and ions are generated in a reaction zone via chemi-ionization7. As a result of the ion current, a bulk flow motion (ionic wind) is generated by an electric body force due to a local electric field and the space charges in a fluid element11. Since flow characteristics significantly affect the characteristics of combustion and a flame via transport, stretch, and disturbance, the ionic wind may influence some phenomena of interest. For this reason, many studies have been conducted to utilize the electric fields to control propagating flame speeds12, reduce soot emissions13, and increase flame stabilities14. Recently, numerical studies with CFD  modeling have advanced this research field14. Most previous studies only used one-dimensional current responses for a validation; thus, the numerical approaches can be further improved as other experimental data are available for comparison.An electric field, externally applied to a flame, induces a movement of charged particles by the Lorentz force acting on them, resulting primarily in an ionic current. As the applied electric field increases, the current increases quadratically (sub-saturated regime) and then becomes saturated being limited by the production rate of charged species (saturated regime)In a CFD simulation, to properly calculate the electric body force term to the momentum equation, the electric field and space charge distribution must be known. Therefore, the use of a proper ion-chemistry set and rigorous transport properties of charged species is central for the accurate estimation of the space charge distribution and, in turn, the electric field via a charged species transport equation and the Poisson equation (Gauss’s Law), respectively. A space charge profile is significantly affected by the mobilities and the production rate of the charged species. Thus, the electric field also includes the information of the ion-chemistry, the transport properties, as well as the externally applied electric field. This indicates that the electric field is not only important for the generation of the ionic wind but also useful for the comparison between experimental and numerical results, indirectly validating a used chemi-ionization mechanism and the transport modeling of the charged species in a numerical method.18 and plasma jets19. The EFISH technique is known to have a good temporal and spatial resolution, because a short-pulsed high power laser system is usually used with a focused beam at a measuring point. Although there have been a few attempts to apply the EFISH method to combustion systems23, it has been difficult to calibrate the EFISH signal with a flame; this was partly due to problems determining third order susceptibility and local number density of species, since the EFISH signal depends on them. In addition, a recent study24 reported some limits in the EFISH method, emphasizing that the EFISH signal could be distorted by the length of the laser beam path exposed to the electric field24; specifically, the null electric field could not be measured if an incident beam was exposed to an electric field other than a measuring point. Therefore, EFISH measurement requires specific measuring and calibrating schemes to measure the electric field in a flame properly.Electric Field Induced Second Harmonic generation (EFISH) is a laser-based electric field measurement technique that has been recently employed in many plasma systems such as gas discharges25. To improve the measurement and calibration of EFISH proposed in the previous study20, (i) a ns-high-voltage switch was newly introduced to effectively freeze a space charge profile, and (ii) a new measurement scheme was developed to consistently maintain the integration length of the incident laser beam. A multi-physics CFD simulation, using an OpenFOAM based in-house code, was conducted for numerical electric field data. The state-of-the-art numerical models—chemi-ionization mechanism and transport data—from literatures29 were implemented to obtain up-to-date data based on the best available knowledge in the field.Toward this end, the scope of this work was to compare electric field data from an experiment and those from a numerical simulation, aiming to provide experimental data of reference and to discuss a predictive capability of the simulation. A canonical nonpremixed flame in a counterflow burner system was selected, since this has been a flame of interest in many previous studies20. The central jet of the counterflow burner was supplied through a nozzle with an inner diameter of 10 mm; a coaxial nozzle (20-mm inner diameter) surrounded the central nozzle to supply N2 sheath flow. The central nozzle was machined in a tapered way, and thus the wall thickness between the central jet and the surrounding N2 sheath was negligible at the plane of exit. To apply a uniform external electric field in a gap between two opposed nozzles, a 1.5-mm thick perforated plate with a diameter of 80 mm was placed at the outlet of each nozzle. The diameter of a hole in the perforated plate was 0.8 mm, and the number density of the holes was 79/cm2. For electrical insulation, the upper and lower parts of the counterflow burner were placed on an acrylic frame, setting a separation distance between the opposed surfaces of the perforated plates at 10 mm. Cooling water controlled the temperature of the burner and consistently maintained the reactant temperature.The experimental apparatus consisted of a counterflow burner system, a power supply system, and a laser diagnostic system, as shown in Fig. 4/N2 and O2/N2 mixtures were controlled to satisfy the stoichiometric mixture fraction (Zst) to be 0.5, where 2 to CH4 (ν) and the mass fractions of CH4 ; the methane was also diluted with nitrogen (22.2% of CH4 by volume) and supplied from the lower nozzle. Central jet velocities from both nozzles and the corresponding N2 sheath flow were set at 20 cm/s.Methane, oxygen, and nitrogen established a non-premixed flame of interest. To locate the flame at a stagnation plane, the compositions of CHVa) and current were measured using an HV probe  and current preamplifier , respectively, with an oscilloscope .A power supply  was used to apply the electric field to the flame. The power supply amplified a signal from a function generator  and applied direct current (DC) high-voltage (HV) to either the upper or the lower part of the burner, grounding the counter part of the burner. In this way, the direction of the electric field could be changed, leading electrons to move through either the fuel stream , or the oxidizer stream . From this point on, the ETF and ETO indicated the direction of the electric field with respect to the flame configuration. The applied voltage  was employed to effectively freeze the motion of charged species in time and space. This switch  rapidly lowered the applied voltage to zero in the order of 100 ns. Using the ns-HV switch to turn off the applied voltage, a space charge distribution, caused by the applied voltage, can be reasonably preserved during the voltage decay. This was confirmed by comparing the charge species profiles and the electric fields in extended simulations  of the burner, using a convex lens . The LP filter (> 800 nm), placed in the downstream of the convex lens, removed any parasitic second harmonic (532 nm) generated from the optical elements in the upstream. As the focused incident beam passed through the electric field, a Second Harmonic Signal  was generated from the incident beam. A second convex lens  collected both incident and SHS beam and collimated them again. Finally, a prism separated the incident and SHS beam spatially, and the PD and the PMT measured each intensity, respectively, in conjunction with the oscilloscope. A delay generator  moderated the timing between the laser beam and the ns-HV switch to precisely measure the optical signal at a voltage condition of interest.The EFISH measurement system consisted of a picosecond Nd:YAG laser , lenses, dichroic mirrors, a half wave plate, a Long-Pass filter (LP), a prism, and detection devices, such as a PhotoDiode  and a PhotoMultiplier Tube , as shown in Fig. z-axis vertically. The interval between measuring points was fixed at 0.1 mm. Due to interference caused by the electrodes’ surfaces, the incident beam could not probe the proximity to both electrodes (< 0.7 mm).The burner setup was placed on a 2-D translational stage to align the incident beam with the center of the burner and move the measuring point along the 30:ρ, p, M is the total number of types of charged species; qk and nk are charge number and the number density of charged species of k, respectively; e is the elementary charge (e = 1.602 × 10−19 C). The electric field, E, is defined by the Gauss law as below:V is the electric potential, nc), and ε0 (= 8.85 −12 F/m) is the permittivity of free space. In the species equation ; hence, the cross-section data obtained were based on gas temperature. For the mobility of electrons, Eq. (27:γ = (2e/me)1/2, me = 9.1094 × 10−31 kg, and To obtain the transport coefficient of the electron and the ions, a numerical model, suggested by previous studies, was employedsing Eq. 26, and ts in Eq. 26:8\docuons, Eq.  was used29. The ion-mechanism could be considered as the most advanced one in the combustion community.For the combustion kinetic mechanism, 40 neutral species were considered, reduced from Aramco 2.0 by maintaining the accuracy of a flame propagation speed, ignition delay, and the production of CH radicals. Eleven charged species were adopted including electrons, and the chemi-ionization mechanism was employed (consisting of 63 reactions), suggested by Belhi et al. and validated to reasonably predict voltage-current (V-I) relations in flat premixed flamesr < 10 mm) was maintained at 25 µm and gradually increased to 100 µm for r > 10 mm. A vertical grid size was uniformly maintained at 25 µm throughout the domain. The computational cost was 0.5 M core hours to achieve a converged solution using 860 CPU cores and 5-ns time step. The detailed grid- and time-independence test results can be found in Figs. S3 and S4 (SI)32. Table We configured a 2D axisymmetric computational domain as shown in Fig. 7. In the case of ETO, the saturated current was lower than that of ETF, and the flame became unstable at around Va = 1.5 kV (ETO), similar to the previous observation with nonpremixed propane flame6. In the sub-saturated regime, the rate of current increase with respect to Va was higher in the case of ETF than in the ETO. This can be attributed to the lack of space charges in the fuel stream for ETF, since there are limited chances for electrons to form negative ions in collisions with CH4, pyrolyzed species, or N23. This type of different electrical response caused by space charges, depending on the field direction, has been theoretically well described previously7.To determine experimental conditions, which can describe the characteristic regimes of the flame under the electric fields well, a preliminary experiment was conducted, measuring a V-I trend  was tuned from a literature value3 by 150%. This tuning method was also adopted in previous studies29.The numerically obtained currents for these 8 cases are also coplotted in Fig. χ(3) is the third order non-linear susceptibility, N is the number density of species, E is the electric field in z direction, Iω is the intensity of the incident beam, L is the path length of the laser beam exposed to the electric field, ∆k is the wave vector mismatch between the incident and second harmonic wave, zR is the Rayleigh range of a Gaussian beam, and x is the propagating direction of the incident beam16. Particularly, χ(3) and N are strongly affected by gas composition and temperature, which vary significantly across a flame region.When the laser beam is exposed to the electric field, the electric field induces the second harmonic signal, χ(3) and N in our previous study20. In the suggested method, Eq.  compared to the flame (~ 20 mm in diameter).Using the ns-HV switch, EextVa, referred to as Stot, and the other signal (S*) was with an abruptly reduced applied voltage (V*), obtained at 8 ns after turning off the voltage using the ns-HV switch, as shown in Fig. Esc. Comparing Stot and S*, a calibration constant, csat(z) (explicitly including the information of χ(3) and N at a measuring point), can be obtained as follows: the subtraction of those two EFISH signals in Eq. (For the saturated regime (mentclasspt{minimaEtot and E* with Eext (at Va) + Esc and Eext (at V*) + Esc, respectively, as in Eq. (Eext = Eext (at Va) – Eext (at V*) and measured EFISH signals asBy substituting s in Eq. , the calEext = 0.41 and 0.48 kV/cm were experimentally obtained at Va = 2.0 and 2.5 kV, respectively.Note that ΔStot):Finally, the electric field can be deduced using the calibration constant and measured EFISH signal . Therefore, in this region , obtained for the saturated regime could be used. Stot was measured at the intended Va, and Etot was deduced from Eq.  at V(t) = 0 . Because Ssc was purely originated from the field applied by the space charges only (Esc), Etot by adding the uniform Eext to Esc  = 0) in a region surrounding the flame, due to the electric field screening effect33 in the sub-saturated regime. As shown schematically in Fig. Etot(z) for both Va = 0.5 and 0.9 kV. The range of z for Etot(z) = 0, was unknown, but it had to be within the central region. Esc was drawn schematically for both Va by subtracting uniform Eext = 0.5 and 0.9 kV/cm, respectively . Because the original temperature profile without Va was maintained in the sub-saturated regime for both ETF and ETO cases , for the flat region in the sub-saturated regime could be calculated by modifying Eq.  could be determined, as we plotted the resulted Esc for the entire z (0–10 mm). Figure Esc for both ETF (a) and ETO (b) cases at Va = 0.5 kV. For the near electrode region, csat(z) obtained from Eq.  was used (Eq. csub(z) was intrinsically valid only for the region of the flat electric field, the resulting Esc in Fig. z < 6.4 mm and 3.8 < z < 6.9 mm for the ETF and ETO case, respectively. The continuous Esc for the ETF case at z = 2 mm across the border between two regions  deviated significantly from the uniform electric fields. Because ΔEsc (z) was no longer ΔEext = 0.4 kV/cm for the outside of the overlapped null electric fields, it was not appropriate to use csat(z). Unfortunately, these shaded areas remain open for a future calibration.However, for the shaded areas in Fig. The measured electric field data for all tested conditions are illustrated in Fig. Va increased, the electric field near the electrodes increased, while the null field in the central region remained (comparing the cases at 0.5 and 0.9 kV).In the sub-saturated regime, the null electric fields due to the electric field screening effect by the ions and electrons in the flame zone were properly captured. As Va, the uniform electric field in the central region also increased (comparing the cases at 2.0 and 2.5 kV).For the saturated regime, the overall electric field profiles seem to be lifted from the sub-saturated one. Although the electric fields were uniform in the central region, the null field cannot be observed anymore because there were no ions and electrons left in the flame zone (by the nature of the saturated regime) to shield the field from the external one. For further increased 2− via the electron attachment reaction3 because these move through the oxygen stream, while positive ions are evacuated through the fuel stream. As the previous one-dimensional theoretical study showed7, this effective electron transfer to heavier molecules resulted in increased negative space charges in the oxygen stream. Those space charges near both electrodes resulted in the augmented local electric field. Thus, for both saturated and sub-saturated regimes, the profile of the electric field showed a relatively symmetrical shape, demonstrating minimum field intensity near the flame region as shown in Fig. In the ETO cases, electrons readily create an OVa = 0.5 kV , linear dashed lines were used to connect two regions for visibility.For the sub-saturated regime at  kV Fig. , the nul20 using the ns-switch. The previous method did not consider matching the beam integration length between Stot and Ssc and should also bring significant uncertainty with the null electric field surrounded by a finite one. Unlike the numerical and the present experimental results, it showed a non-zero electric field in the central region (flame zone). It is also interesting to compare other result in a previous study using a different EFISH calibration method22. The resulted electric field in a similar counterflow geometry did not exhibit a null region even at the sub-saturated regime, demonstrating uniform one across the entire gap22, which was far from the trend of the present numerical result.The newly proposed measurement and calibration method in this study could be an accurate way to conduct an EFISH experiment. An extra experiment was performed adopting the previous methodVa = 2.5 kV), the measured and simulated electric fields also showed a good agreement. The simulation slightly overpredicted the electric field in and near the flame  and underpredicted near both electrodes. An additional experiment with the previous method20 adopting the ns-switch did not show better agreement with both numerical and experimental results. This indicates that, although there was no null electric field in the flame region in the saturated regime, the influence of the integration length on the EFISH could not be insignificant, supporting the validity of the present method.For the saturated regime . In addition, the numerically obtained electric field near the ground electrode (oxygen stream) showed a good agreement with the experiment, but it significantly underpredicted the experiment near the HV electrode (fuel stream).In the ETF cases, we could also confirm that the present methods produced physically reasonable data as compared with the numerical result and the additional experimental data obtained using the previous method3– (93%) and electrons (6.1%) although overall number density was extremely low as compared to that of H3O+ (99.9%) near the ground electrode. The numerical result also showed that H3O+ and electrons were two major charged species in the flame region. This indicated that there were no significant proton transfer reactions from H3O+ to others, as it moved through the oxygen stream. Also, considering no negative ions, originated from the non-oxidative fragments of the fuel in the ion-chemistry3, and the very low charge density in the numerical result, electron transfer to CHO3– could be insignificant, resulting in fast evacuation of electrons from the system for the sub-saturated regime with ETF field direction.Both experimental and numerical results showed slightly increased electric fields near the anode (fuel stream). This implied that there must be negative space charges in the fuel stream. The numerically found composition of the space charge near the fuel outlet was CHOz > 7.5 mm, the profiles and values of both electric fields did not match well for z < 7.5 mm. Particularly, this discrepancy could be attributed to the negative space charges near the fuel outlet as mentioned previously, because the augmented electric field near the fuel stream could only be possible when there existed a significant pile of negative ions based on Gauss’s law. Considering the ion-chemistry adopted in the present study, electrons are expected to pass through the fuel stream, leaving a negligible footprint of ions due to the absence of O2. This resulted in a limited increase in the electric field toward the HV electrode in the simulation. Note that the additional experiment with the previous method20 adopting the ns-HV switch showed even worse profile.Unlike the previously compared cases, the biggest discrepancy between the experiment and the simulation was found in the ETF saturated regime Fig. . Althoug3. Thus, the experimental result showing a significant level of negative charges in the fuel stream seems to be somewhat counterintuitive. In the numerical result, the most populated negative ion was CHO3−, demonstrating negligible concentration as compared to positive ions in the oxygen stream. To explain this experimentally obtained augmented electric field near the fuel outlet, we may need to revisit the used modeling and ion-chemistry. Considering the significant discrepancy between the experiment and numerical results was only found in the ETF cases, specifically due to negative space charges in the fuel stream, we think that the ion-chemistry needs to include carbonaceous ionic species such as C2H−, C4H−, and C3−. Goodings et al. found these carbonaceous negative ions in a methane-oxygen flame experimentally34. Therefore, the in-situ measurement of the ionic species in the presence of the external electric field can help to confirm the validity of the experimental findings and, thus, improve the ion-chemistry. A mass spectrometry will be used in a modified burner system by collecting samples at the center of nozzle exit.As discussed above, in the ETF cases, electrons were expected to move through fragmented fuel species, with little chance to form negative ions, based on the well-known ion-chemistryElectric fields in a counterflow nonpremixed flame with externally applied uniform electric fields were obtained experimentally and numerically. To quantify the electric field, a picosecond-laser based EFISH (Electric Field Induced Second Harmonic generation) measurement was performed. To overcome some known issues with the conventional EFISH method, we proposed new measurement and calibration schemes that were specific to the regime of the voltage-current (V-I) response in the flame. A multi-physics CFD simulation was also conducted, using an in-house OpenFOAM-based code with the state-of-the-art modeling and kinetic mechanisms to compare with the experimental results.20, an ns-high-voltage switch was successfully employed to effectively preserve the electric field due to space charges, while the applied voltage (Va) dropped abruptly. In the saturated regime, to obtain local calibration constants for EFISH, the integration length of the incident beam exposed to the electric field was maintained. However, for the sub-saturated regime, to circumvent the issue of measuring null electric field near the flame surrounded by finite electric field, EFISH signals with the space charges only were measured.Motivated by our previous study2−.Overall, the measured electric fields agreed reasonably well with the numerical results for both field directions—Electrons move Through a Fuel stream (ETF) or Oxygen stream (ETO), respectively—in the sub-saturated regime. Specifically, the null electric field caused by the electric field screening (due to the flame’s ions and electrons for the sub-saturated regime) was successfully captured. For the saturated regime, the measured electric fields qualitatively agreed well with the numerical results in the ETO cases, whereas, in the ETF case, significantly different electric fields were found in the fuel stream. The EFISH measurement indicated that there must be a significant number of negative ions in the fuel stream for the ETF. The implemented ion-mechanism in simulation could not produce a noticeable number of negative ions for the ETF cases, since all negative ions are produced from OTo further confirm the validity of both experiment and simulation in ETF saturated cases, the in-situ measurement of ionic species in the fuel stream should be a future study. Applying the present experimental and numerical methods to a canonical premixed flame would also be very informative.Supplementary Information.


Abnormal birth weight – small for gestational age (SGA) and large for gestational age (LGA) – are important indicators for newborn health. Due to changes in lifestyle in recent decades, it is essential to keep up-to-date with the latest information on maternal factors linked to abnormal birth weight. The aim of this study is to investigate SGA and LGA in relation to maternal individual, lifestyle and socioeconomic characteristics. .This is a register-based cross-sectional study. Self-reported data from Sweden’s Salut Programme maternal questionnaires (2010–2014) were linked with records in the Swedish Medical Birth Register (MBR). The analytical sample comprised 5089 singleton live births. A Swedish standard method using ultrasound-based sex-specific reference curves defines the abnormality of birth weight in MBR. Univariable and multivariable logistic regressions were used to examine crude and adjusted associations between abnormal birth weights and maternal individual, lifestyle and socioeconomic characteristics. A sensitivity analysis, using alternative definitions of SGA and LGA under the percentile method, was undertaken.In multivariable logistic regression, maternal age and parity were associated with LGA  and . Maternal overweight and obesity were strongly associated with LGA  and , respectively. As parity increased, the odds of delivering SGA babies decreased  and preterm deliveries were associated with SGA . The well-known maternal determinants of abnormal birthweight, such as unhealthy lifestyles and poor socioeconomic factors, were not statistically significant in this Swedish setting.The main findings suggest that multiparity, maternal pre-pregnancy overweight and obesity are strong determinants for LGA babies. Public health interventions should address modifiable risk factors, especially maternal overweight and obesity. These findings suggest that overweight and obesity is an emerging public health threat for newborn health. This might also result in the intergenerational transfer of overweight and obesity. These are important messages for public health policy and decision making. Birth weight is a consequence of both foetal and maternal factors and an important indicator of intrauterine growth and newborn health. Abnormal birth weights, such as small for gestational age (SGA) and large for gestational age (LGA), are recognizable risk factors for neonatal morbidity , 2. SGA Epidemiological studies on birth weight have reported that the prevalence of large babies (LGA) has increased over the last few decades in some European countries, including Sweden , 7, 8. MSGA babies, on the other hand, have a considerably higher risk of morbidity and mortality in neonatal and post-neonatal periods compared to normal weight babies , 10, 11.Previous newborn studies have shown that birth weight is influenced by maternal health, nutrition, genetics, and environmental and lifestyle factors during pregnancy . Thus, tLifestyle behaviours have changed considerably in recent decades . Hence tFurther knowledge of association between maternal characteristics and abnormal birth weight will inform understanding of intergenerational patterns, and ways of preventing child morbidities and mortalities –19. The This is a registry based cross-sectional study. Data from the Salut Child Health Promotion Programme in Region Västerbotten, northern Sweden, are enriched with individual level data from the National Board of Health and Welfare , 20, 21.The study sample is derived from a merged longitudinal dataset constructed to follow families from pregnancy and through childhood. The Salut antenatal questionnaire which includes important information on lifestyle is enriched by a selection of variables from the MBR hosted by the National Board of Health and Welfare in Sweden. The maternal data from Salut Programme 2010 to 2014) was linked with Swedish Medical Birth Register . A final sample of 5089 singleton newborns and 4683 mothers was obtained for the analysis. Variables selected from the self-reported maternal questionnaire included age at pregnancy, height, pre-pregnancy weight, parity, physical activity, alcohol consumption, employment status and education level. The variables used from the MBR were birth weight, gestational week at birth, maternal smoking and snuffing tobacco at enrolment. See Fig. 0 to 2014The best estimates for dating gestational age at birth were obtained either by the first or second trimester, or the traditional last menstrual period (LMP) method. Gestational dating using ultrasound is offered to all pregnant women in Sweden; about 97% of all pregnant women agree to this. In the final merged dataset, the variables maternal BMI, smoking, and employment had 8.5%, 3.3% and 4.4% of missing data respectively. For the remaining variables missing data was ≤ 2%; maternal age and parity had complete data.Two methods were used to calculate SGA and LGA. They are the percentile method (< 10th & >90th percentile) and the standard deviation method (mean ± 2 Standard deviations) , 23. The2 = a person’s self-reported weight in kilograms divided by their self-reported height in squared metre. According to WHO’s definition, BMI is classified into underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obesity (≥ 30 kg/m2).Pregnant women’s age, height and weight were self-reported in the maternal questionnaires just prior to the first antenatal care visit. Maternal pre-pregnancy BMI was calculated by the formula: BMI = kg/mParity is the number of previous deliveries which is further grouped into primiparity (first pregnancy), multiparity (2nd to 4th pregnancy) and grand multiparity (5th or more). Gestational age at the time of delivery was defined as preterm delivery (< 37 week), term delivery (37–41 week) and post-term delivery (≥ 42week). The offspring’s sex characteristics  were used in the classification of SGA and LGA, and considered in the data analysis.Data about maternal tobacco smoking and snuffing were extracted from the MBR and coded as yes/no questions at the time of enrolment. Data for physical activity and alcohol consumption originated from the Salut maternal questionnaires. This information reflected women’s lifestyle up to 12 months before their pregnancy.To estimate individual alcohol risk, the Alcohol Use Disorders Identification Test  was used in the Salut maternal questionnaires to measure the level of alcohol consumption, drinking behaviours and alcohol-related problems within 12 months before the current pregnancy . The AUDThe level of physical activity was measured by total duration spent on physical activities during a week. As per WHO recommendation, at least 150 min of moderate-intensity physical activity throughout the week should be maintained for good health in adults, 18–64 years of age, including pregnant women . The levPost-secondary education or higher (> 12 years of education) was classified as high education and secondary school level or lower (≤ 12 years of education) as medium or low education. Employment status was categorised as unemployed or employed (either full-time or part-time). These socioeconomic variables were also encoded as binary variables for the analysis.Firstly, maternal characteristics and delivery outcomes were summarised in descriptive tables using numbers and proportions. Secondly, birth weights in relation to maternal pre-pregnancy BMI and parity were illustrated by stacked bar graphs. Thirdly, univariable logistic regressions estimated with Generalized Estimating Equations (to handle correlation between siblings) using an exchangeable correlation structure were performed to identify crude associations between SGA and LGA and each maternal characteristic. Lastly, multivariable logistic regression estimated with Generalized Estimating Equations was executed including those variables for which the p-value < 0.05 in the crude analysis. The statistical associations between variables were reported as odds ratios (OR) with 95% confidence intervals (CI). The statistical threshold for level of significance was set to 0.05. All the statistical analyses were performed using Stata16.0 Software.In this sample, the mean birth weights of newborns were 3626 g (Standard deviation (SD): 541 g) for males and 3515 g (SD: 506 g) for females. The median gestational age at delivery was 40 weeks (range 25–43) and male-female ratio was 51.2: 48.8. The median birth weight at 40-weeks were 3747 g and 3590 g for male and female newborns. Newborn with macrosomia (> 4000 g) accounted for 18.8% of the sample but low birth weight (< 2500 g) newborns were only 2.8%. The birth weight for gestational age calculations as per Swedish national standard result in 2.8%, 95.6% and 1.6% for LGA, AGA and SGA, respectively.Around 86.8% of pregnant women were within the age range of 20–35 years. Teenage pregnancies (< 20 years old) were only 0.9% while pregnancies > 35 years of age accounted for 12.4% of the sample. Primiparity contributed to 44.5% of the sample. Almost 30% of women in this local sample were either overweight or obese (19.7% overweight and 9.0% obese), 67.3% of the women had normal BMI and 4% were underweight. Preterm, term and post-term deliveries comprised 4.3%, 88.0% and 7.7%, of the sample respectively.Regarding maternal lifestyle characteristics, only 1.5% of the women smoked and 3.8% had snuff (tobacco) using behaviour at the enrolment to AN care. According to the AUDIT score, 6.9% of the pregnant mothers had a risk behaviour regarding alcohol consumption during 12 months before the enrolment to ANC. The recommended physical exercise level (150 min/week) was not achieved by 36.1% of the pregnant mothers; 86.2% of the study population were employed and 61.7% had more than 12 years of education. See Table The following bar graphs illustrate increasing LGA and decreasing SGA with increasing maternal BMI and parity, as shown in Figs. Older maternal age, parity, high BMI and physical activity showed statistically significant associations with LGA in univariable logistic regression. In the adjusted analysis, a one year increase in maternal age corresponded to an increased odds ratio of 5% for LGA (Adjusted odds ratio (aOR) = 1.05, CI = 1.00, 1.09). The odds for having LGA increased with each pregnancy, . Overweight and obesity compared with normal BMI group (reference) had significantly higher odds of having LGA,  and  respectively. The statistical association with physical activity was attenuated in the adjusted analysis. See Table The univariable analysis showed that SGA had significant statistical associations with parity and preterm delivery. In the adjusted analysis, those results remained. The odds decreased for each pregnancy . The association of preterm birth with SGA was found with wide confidence interval  compared to normal term deliveries. The interpretation of the high odds ratio should be cautious, given the small number of observations with this criterion. See Table The large sample  allowed us to use the percentile method to define SGA and LGA . In thisFor the sensitivity analysis, the logistic regressions were executed with the SGA and LGA determined by the percentile method. The statistical associations with LGA were significant with parity and high BMI, but not with maternal age and physical activity. On the other hand, SGA had statistical association with parity, low BMI, preterm and smoking. Thus, maternal high BMI and parity produced the similar results in the sensitivity analysis. See Tables The main finding was that the occurrence of LGA (2.8%) was more common than SGA (1.6%) in this sample of Swedish newborns. The high prevalence of macrosomia babies (18.8%) compared to the low birth weight (2.8%) further supports this fact. The international standard of newborn weight in the multinational newborn cross-sectional study of the INTERGROWTH21st Project reported the 50th percentile birth weight of newborns at 40-week gestational age as 3380 g for males and 3260 g for females . The corIn high income countries like Sweden, maternal over-nutrition is more common than under-nutrition. This claim is supported by the percentage of maternal BMI categories which are largely occupied by overweight (19.7%), and obesity (9%) compared to the underweight (4%). It is also reasonable that overweight and obese mothers deliver large babies . OverweiMaternal overweight and obesity are modifiable risk factors and largely preventable with diet, lifestyle, and weight control measures . Thus, tThe Salut program holds a continuous data collection over decades. This is an advantage for monitoring the lifestyle and socioeconomic conditions of parents-to-be in the Swedish population and the relationship with child health. One of the most important findings was that there was no significant statistical association between abnormal birth weight and the well-known maternal determinants of abnormal birthweight such as unhealthy lifestyle habits and poor socioeconomic factors. This negative finding indicates that these factors are less prominent in the Swedish context. This could be a sign of over nutrition which has become a new challenge re-directing some attention from health problems associated with low socioeconomic status.There is strong scientific evidence that smoking can cause preterm deliveries and decreased birth weight –36. In SIn terms of socioeconomic characteristics, abnormal birth weights had no significant statistical association with maternal education level and employment. In general, education is a strong determinant of low-birth-weight outcomes. Most pregnant women in the sample had post-secondary education level or higher (> 12 years of education), and the percentage of women who had less than 9 years of school education in the maternal questionnaire was < 1% (not mentioned in the tables). This might be a reason why education does not determine abnormal birth weight in this Swedish setting.Indeed, moderate physical exercise is necessary for healthy lifestyle whereas the sedentary lifestyle  is highly associated with overweight and obesity. In our analysis, the physical activity hours per week had a significant association with a decreased LGA in the univariable analysis. Yet the statistical association was diminished in adjusted analysis. This could be due to the strong mediation effect of overweight and obesity in the association pathway. From a public health perspective, unhealthy lifestyles are considered potentially modifiable by means of public health interventions. The health services should continue to provide a good family planning service, adequate preconception care, and proper antenatal care, giving women opportunities to make lifestyle changes, reduce modifiable risk factors and optimise medical conditions .This study further supports relationships between maternal factors and abnormal birth weights. Among the independent variables, the maternal age, parity, pre-pregnancy BMI and preterm deliveries were prominent factors associated with abnormal birth weight in newborns. But recognised health determinants, such as lifestyle and socioeconomic factors, did not have any significant associations with abnormal birth weight in this Swedish population.This study used a large sample collected over a 5 year period. A wide range of maternal variables were used for analysing abnormal birth weights on both ends (SGA and LGA). The exposure was measured at the early time of pregnancy which reflects the lifestyle of women in reproductive age in a general population, and the outcomes were measured at the time of delivery. However, only a small proportion of the study participants had an unhealthy lifestyle, i.e., 1.5% smoking, 3.8% snuffing tobacco and 6.9% risky alcohol consumption. Thus, the statistical power for analysis of these specific lifestyle characteristics was low. Further research with larger sample sizes, or focused study designs for specific lifestyles, is needed to increase knowledge about maternal lifestyle and the newborn birth weight deviances.Since the self-reported questionnaires were used in data collection, there was a slight chance of a recall bias from the respondents, for instance, under-reporting of unhealthy lifestyles and women’s body weight. Such conditions could result in an information bias and might lead to an under-estimation of the associations between maternal characteristics and abnormal birth weights. Moreover, pregnant women often make positive lifestyle changes during pregnancy period which might reduce the risk of abnormal birthweight outcomes.Maternal morbidity during pregnancy and genetic factors have direct relevance for birth weight in addition to maternal lifestyle, social and environmental factors. For instance, maternal health conditions such as gestational diabetes mellitus, and excessive gestational weight gain could lead to over-nutrition of the foetus resulting in a LGA baby at birth . Small-sThe main findings in this research suggest that multiparity, maternal pre-pregnancy overweight or obesity are strong determinants for LGA babies. Public health interventions should address modifiable risk factors especially maternal overweight and obesity. For public health policy level decision making, our study highlights that overweight and obesity is a new public health threat affecting newborn health, and that this might also convey an intergenerational transfer of overweight and obesity.


To describe the rates of and risk factors associated with iatrogenic and spontaneous preterm birth and the variation in rates between hospitals.Cohort study using electronic health records.English National Health Service.Singleton births between 1 April 2015 and 31 March 2017.Multivariable Poisson regression models were used to estimate adjusted risk ratios (adjRR) to measure association with maternal demographic and clinical risk factors.Preterm births (<37 weeks of gestation) were defined as iatrogenic or spontaneous according to mode of onset of labour.2 adjRR 1.59, 95% CI 1.50–1.69) and previous caesarean . Spontaneous preterm birth was less common in women with a higher BMI  and in women with a previous caesarean . More variation between NHS hospital trusts was observed in rates of iatrogenic, compared with spontaneous, preterm births.Of the births, 6.1% were preterm and of these, 52.8% were iatrogenic. The proportion of preterm births that were iatrogenic increased after 32 weeks. Both sub‐groups were associated with previous preterm birth, extremes of maternal age, socio‐economic deprivation and smoking. Iatrogenic preterm birth was associated with higher body mass index (BMI) (BMI >40 kg/mJust over half of all preterm births resulted from iatrogenic intervention. Iatrogenic births have overlapping but different patterns of maternal demographic and clinical risk factors to spontaneous preterm births. Iatrogenic and spontaneous sub‐groups should therefore be measured and monitored separately, as well as in aggregate, to facilitate different prevention strategies. This is feasible using routinely acquired hospital data.  This article includes Author Insights, a video abstract available at: https://vimeo.com/745289966. It is not known how the rates of iatrogenic and spontaneous preterm birth have changed within this aggregated total.2The data for this study were obtained from two population‐level electronic data sets, linked together for the purposes of a national audit of maternity care, including births that occurred from 1 April 2015 to 31 March 2017.The maternity information system record contained information about gestational age, the mode of onset of labour, the birth, and maternal and neonatal characteristics including parity, ethnic group and gestational age. Socio‐economic status was evaluated using the Index of Multiple Deprivation, an area‐level measure of deprivation identified by the woman's recorded postcode in maternity information systems.All 1 254 484 live births of at least 22 weeks of gestation in 133 NHS hospital trusts were eligible for inclusion. Births in 23 hospital trusts were excluded from the study because of poor quality data , or poor linkage (<70% of records with complete identifiers to enable linkage to HES). Stillbirths were excluded because it was not possible to identify whether a stillbirth occurred antepartum or intrapartum. Multiple births were excluded because the frequency of preterm birth in multiple pregnancies differs and therefore should be considered separately. In all, 9283 births without a record of labour onset were excluded, representing less than 1% of the cohort. In total, 963 800  live singleton births with complete data about gestational age, method of labour onset, and delivery method were included in the analyses Figure .Births were considered preterm if they had a recorded gestation of 37 completed weeks or fewer at birth. Births were defined as iatrogenic if there was a record of induction of labour or of caesarean section (CS) before the onset of labour and as spontaneous if the recorded onset was spontaneous. Details of variable definitions are available in Table To investigate associations between risk factors and outcomes, chi‐square tests were used. Multivariable Poisson regression models with robust standard errors were used to estimate the association between maternal risk factors for preterm birth overall and stratified into iatrogenic and spontaneous. The purpose of these models was to describe the prognostic factors for each of spontaneous and iatrogenic preterm birth. The maternal risk factors included were age, body mass index (BMI), ethnicity, socio‐economic status, smoking status at booking, parity, previous CS and previous preterm birth. Interaction terms were included for parity and each of previous CS and previous preterm birth. In estimating the strength of the relationship between each risk factor and preterm birth in regression analyses, clustering of preterm births in individual units was accounted for by using robust standard errors, which do not change the estimate of the association but widen the confidence intervals to allow for similarities between individuals within clusters.Funnel plots were used to visually explore the variation between hospital organisations in rates of preterm birth, both overall and disaggregated into iatrogenic and spontaneous. In these plots, results for each hospital organisation were adjusted using the multivariable Poisson regression models described above. These plots indicate whether the difference between rate of preterm birth in a hospital organisation and the national mean is statistically significant at the 5% level (if the preterm birth rate is outside the inner funnel limit) or 0.2% level (if the preterm birth rate is outside the outer funnel limit), given the different case‐mix of women treated in each individual hospital.Two clinicians (JJ and DP) mapped possible indications for iatrogenic preterm birth to International Classification of Diseases 10th revision codes. These were then identified in the maternal record. Further details of codes used are available in Table Three sensitivity analyses were conducted to assess whether the effects of maternal risk factors were sensitive to the inclusion criteria and methods used. In the first, births associated with preterm prelabour rupture of membranes (PPROM) were excluded. PPROM represents an area of relative uncertainty as it may represent the beginning of a mechanistic process leading to spontaneous preterm birth. It may also result in iatrogenic preterm birth due to clinical need such as infection. Excluding PPROM therefore allows a more robust evaluation of factors related to ‘pure’ spontaneous and iatrogenic preterm birth. In the second sensitivity analysis, maternal diabetes, hypertension and pre‐eclampsia/eclampsia were included in the regression models to explore whether the observed associations were explained by these maternal risk factors. Further details are available in Table 3The cohort included 963 800 women who had a singleton live birth in England between 1 April 2015 and 31 March 2017 Figure . A totalThe highest rates of preterm birth were seen in women with a previous preterm birth; 12.8% of these women had spontaneous and 10.9% had iatrogenic preterm births  or iatrogenic  preterm birth compared with women in the least deprived neighbourhoods. Similarly, smoking was associated with an increased risk in both sub‐groups ; iatrogenic  , but lower rates of spontaneous term birth   or iatrogenic  preterm birth when compared with white women. Black women had similar rates of spontaneous  but higher rates of iatrogenic  preterm birth.Our findings were not materially different in the sensitivity analyses excluding 3842 preterm births with PPROM  and restricted to complete cases Tables  and S6.2 or higher compared with BMI 18.5–24.9 kg/m2; adjRR 1.13, 95% CI 1.05–1.21) and the associations between preterm birth and maternal South Asian and black ethnicity were no longer present , diabetes (7.4%) or pre‐eclampsia/eclampsia (25.7%)  and growth restriction (22.9%); the most common maternal indications were hypertensive disease (18.2%) and diabetes (12.4%) Table 3.2Risk‐adjusted rates of iatrogenic preterm birth ranged from 0.95% to 4.72% (interquartile range 2.78%–3.73%) and of spontaneous preterm birth from 1.37% to 5.96% (interquartile range 2.55%–3.12%) in the 110 hospital trusts included in this study. The funnel plots demonstrate larger between‐hospital variation in the rates of iatrogenic preterm births than rates of spontaneous preterm births  were born as a result of iatrogenic intervention. This figure is larger than the 25–30% previously quoted for the UKIatrogenic and spontaneous preterm birth have areas of overlap in their risk factors. Both are strongly associated with previous preterm birth, socio‐economic deprivation and smoking. However, there are also important differences between the two sub‐groups. Older women, those with higher BMI, previous CS and medical comorbidities are more likely to experience iatrogenic preterm birth. Spontaneous preterm birth is more common among younger women, but less common in those with a higher BMI and who have had a previous CS; this may be partially accounted for by these women being more likely to have an iatrogenic intervention if they show signs of labour.The observed association between ethnic group and iatrogenic preterm birth disappeared after adjustment for maternal hypertension, pre‐eclampsia and diabetes, suggesting that differences in iatrogenic preterm birth between ethnic groups is largely accounted for by different prevalence of comorbidities in non‐white ethnic groups. Similarly, we show that the association between iatrogenic preterm birth and obesity is partly explained by the higher prevalence of maternal medical conditions among women with a raised BMI. In contrast, the association between socio‐economic deprivation and preterm birth remained consistent across all analyses.4.2This study uses a large contemporary data setThe adjusted results may be affected by residual confounding from information not available to us. For example, we were unable to include previous early pregnancy loss or cervical surgery, which are both recognised risk factors for preterm birth.The absence of some risk factors for preterm birth from our data set may also account for some of the observed variation in iatrogenic preterm birth. For example, we could not account for neonatal conditions requiring surgery, for which iatrogenic birth may be planned in specific units necessitating in utero transfer. Such births, however, make up a small proportion of all preterm births.Our categorisation of preterm birth does not separately consider PPROM. The approach to PPROM in the literature is heterogeneous, with some studies treating it as a distinct category4.3This study shows that the proportion of preterm births in England that are iatrogenic in onset is greater than has previously been recognisedBoth spontaneous and iatrogenic preterm birth are strongly associated with smoking and therefore antenatal interventions to encourage smoking cessationFor iatrogenic preterm birth, prevention may be targeted towards modifiable upstream factors to ensure that women enter pregnancy well, with a normal BMI, as well as appropriate and timely surveillance of comorbidities and pregnancy complications. This requires multiagency involvement extending beyond obstetric and midwifery care. Research into complications of pregnancy, such as pre‐eclampsia and diabetes must focus on identifying pregnancies for which it is possible to delay birth while ensuring optimal maternal outcomes.For spontaneous preterm birth, targeted monitoring and intervention for women identified at higher risk is effective.The study demonstrates the feasibility of using routinely acquired hospital administrative data to measure risk‐adjusted rates of iatrogenic and spontaneous preterm birth and to compare these between hospitals. This offers potential value as a tool for measuring progress towards reducing preterm birth. The primary goal of iatrogenic preterm birth is to improve outcomes for the mother and baby; avoiding or delaying iatrogenic preterm birth may be associated with poorer outcomes.5This study adds to the literature supporting the finding that iatrogenic and spontaneous preterm birth are associated with different patterns of maternal demographic and clinical risk factors.DP conceived the study. All authors contributed to the design of the study. JJ performed the analyses. HA reviewed the literature. HA and JJ wrote the manuscript. All authors critically revised the manuscript for important intellectual content and have given approval of the final version before submission.The National Maternity and Perinatal Audit is commissioned by the Healthcare Quality Improvement Partnership (HQIP) as part of the National Clinical Audit and Patient Outcomes Programme and funded by NHS England and the Scottish and Welsh Governments. Neither HQIP nor the funders had any involvement in designing the study; collecting, analysing and interpreting the data; writing the report; or in making the decision to submit the article for publication.The authors declare no conflict of interests. HA, JJ, HK, JH, TH, IGU, DP have all received support to deliver the National Maternity and Perinatal Audit. JvdM chairs the National Maternity and Completed disclosure of interests form available to view online as supporting information.This study used data collected to evaluate service provision and performance and therefore it was exempt from ethical review by the NHS Health Research Authority. The use of data without patient consent was approved by the Confidentiality Advisory Group of the NHS Health Research Authority for the purpose of national clinical audit and health service evaluation (16/CAG/0058).Appendix S1Click here for additional data file.ICMJEClick here for additional data file.ICMJEClick here for additional data file.ICMJEClick here for additional data file.ICMJEClick here for additional data file.ICMJEClick here for additional data file.ICMJEClick here for additional data file.ICMJEClick here for additional data file.ICMJEClick here for additional data file.ICMJEClick here for additional data file.


Clinical instability defined as any one of heart rate < 100 beats per minute for ≥ 20 s in the first 10 min after birth, maximum fraction of inspired oxygen of ≥ 0.70 in the first 10 min after birth, 5-min Apgar score of < 7, intubated in the delivery room or admitted to the neonatal unit for respiratory support. Four hundred and seventy-three infants were included. The median (IQR) gestational age at birth was 39+4 (38+4—40+4) weeks. Eighty (17%) infants met the criteria for clinical instability. Independent risk factors for clinical instability were labour without oxytocin administration, presence of a medical pregnancy complication, difficult extraction at birth and unplanned caesarean section in labour. Decision tree analysis determined that infants at highest risk were those whose mothers did not receive oxytocin during labour (25% risk). Infants at lowest risk were those whose mothers received oxytocin during labour and did not have a medical pregnancy complication (7% risk).To identify characteristics associated with delivery room clinical instability in at-risk infants. Prospective cohort study. Two perinatal centres in Melbourne, Australia. Infants born at ≥ 35Conclusions: We identified characteristics associated with clinical instability that may be useful in alerting less experienced clinicians to call for senior assistance early. The decision trees provide intuitive visual aids but require prospective validation.The online version contains supplementary material available at 10.1007/s00431-022-04684-5. Many infants require assistance to successfully complete the transition from fetal to neonatal life . PerinatPredicting which infants may benefit from a senior’s presence is challenging. Delays in recognising and/or effectively supporting a compromised infant increase the risk of neonatal morbidity and infant-mother separation , 3. Accuclinical instability to describe these instances based on physiological parameters and/or the level of resuscitation provided. Our aims were to:We hypothesised that among births attended by first-line paediatric clinicians, perinatal characteristics could help identify a subgroup of infants where an early call for senior assistance is appropriate. We use the term +0 weeks’ gestation.Identify characteristics associated with increased risk of clinical instability in the delivery room for infants born at ≥ 35Describe the probability of clinical instability in relation to these risk factors.We used data from infants concurrently recruited to a randomised controlled trial and prospective cohort study (ACTRN12618000621213)  at The R+0 weeks’ gestation at birth. ≥ 35Paediatric doctor requested by the obstetric or midwifery team to attend an at-risk birth, as determined by local hospital policy . HR and SpO2 were displayed on a portable Intellivue X2  or Infinity M540  monitor, visible to the clinician. A GoPro Hero Session  captured the monitor screen, T-piece manometer , oxygen blender dial and an audio recording of the events after birth. The videos were downloaded for offline data extraction. HR and SpO2 data were manually extracted at 10-s intervals for 10 min after birth. Data points containing poor QRS or SpO2 waveforms were excluded. Data were extracted unblinded to the study outcome by four assessors , trained by two senior researchers  who also verified datapoints where HR and SpO2 were uncertain.Immediately after birth, a researcher dried the infant and placed three ECG chest leads and a preductal pulse oximeter to monitor the infant’s heart rate (HR) and oxygen saturation (SpOHR < 100 beats per minute (bpm) for ≥ 20 s in the first 10 min after birth2) of ≥ 0.70 in the first 10 min after birthMaximum fraction of inspired oxygen . For categorical variables, comparison was performed using a χ2 test or Fisher’s Exact Test as appropriate. Continuous variables were compared using a two-sided t-test for normally distributed variables and a Mann–Whitney U test for skewed variables. We report risk ratios and their 95% CIs, and statistically significant differences were defined as those with p < 0.05. To simplify the number of risk factors and facilitate clinical applicability, we performed an adjusted analysis using backward stepwise log-binomial regression retaining variables with p < 0.1. We report risk ratios and their respective 95% CIs for variables in the final model with p < 0.05.Analyses were performed using IBM SPSS Statistics version 27 and p value, the algorithm chooses which variables to split, their discriminatory value and the order in which the splitting occurs. Outcome discrimination can thus be maximised at each step by accounting for the relationships between variables while limiting both overfitting and biased variable selection [partykit package in R with default settings of the ‘ctree’ function. For the subgroup of infants born by vaginal birth, there was no Bonferroni-adjusted p value < 0.05 to generate any splits. We therefore used the chi square value for variable selection to generate an interpretable tree.In keeping with our aim to describe the probability of delivery room clinical instability in relation to the identified risk factors, we created conditional inference decision trees . Decisioelection . We used+4 (38+4–40+4) weeks  from the randomised controlled trial and 358 (76%) from the prospective cohort study  infants met the criteria for clinical instability . This is likely to reflect local obstetric practice, where there was a low threshold to provide assistance at monitored births. Instrumental births were not significantly associated with clinical instability in the multivariable analysis, possibly due to overlap with oxytocin administration during labour that had a greater protective effect. Unplanned caesarean section in labour was, however, associated with increased clinical instability risk in the multivariable analysis.Difficult extraction of the fetus was also independently associated with clinical instability and featured in the decision tree for caesarean births. This variable was not included in previous studies. We included this variable based on clinical experience of instances where the first-line clinician calls for senior support, or is advised to by obstetric colleagues, in anticipation of poor neonatal condition at birth. Difficult extraction was determined to have occurred by the attending researcher if there was prolonged delay between uterine entry and delivery of the fetus at caesarean section, or prolonged requirement for instrumentation/manoeuvres to complete delivery of the head and shoulders at vaginal births. As this was a subjective assessment without pre-defined criteria, this variable may be unreliable. Clinical utility of the variable may also be undermined by the fact that it can only be known at the time of birth and is generally assessed in retrospect. However, we felt that inclusion of the variable was justified as it provided additional characterisation of the circumstances around each birth not otherwise represented by the other study variables. Working towards more objective measures of a difficult extraction at birth would be useful.Infants born through meconium-stained amniotic fluid have increased likelihood of experiencing respiratory distress . HoweverStrengths of this study include the inclusion of variables previously omitted by other studies that gave added granularity to the dataset, and the prospective recruitment of at-risk births from 2 sites with contemporary obstetric and neonatal practice. A greater number of infants were born at The RWH compared to MMC (414 vs 59 infants) due to differing researcher availability at each hospital. However, the proportion of infants with clinical instability at each site was similar (18% at The RWH vs 12% at MMC), reflecting the similar nature of births attended at both sites. These are tertiary perinatal centres. Further to the limitations highlighted above, most infants were recruited in-hours, so our population may not be representative of infants born overnight who may be delivered in more emergent situations. Time of birth is likely to be one of several other potential predictive factors that may further enrich the ability to accurately predict episodes of clinical instability. Our results, including the decision trees, reflect the local context and recruitment strategy. Therefore, prospective validation of our findings in a variety of settings is warranted prior to clinical application.This study identifies characteristics associated with increased clinical instability risk among the large number of at-risk births typically attended by first-line paediatric doctors. We use decision trees to provide an intuitive visual tool for clinical decision-making. Following prospective validation, these models may enable clinicians to quickly identify when to call for a senior colleague, ensuring appropriately skilled staff are present for infants at increased risk of clinical instability and to provide better support to less experienced colleagues.Supplementary file1 (JPG 503 KB)Below is the link to the electronic supplementary material.


P value <0.0001), second  and third  months of lockdown, but not in the fourth month of lockdown , although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second , third  and fourth  months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries  and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second , third  and fourth  months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways.Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from −90% to +30%, were reported in many countries following early COVID-19 pandemic response measures (‘lockdowns’). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95–0.98,  An analysis of 52 million births in 26 countries shows small reductions in preterm birth during the first to third months of lockdown. Further research is needed to examine causal pathways. Preterm birth and related complications are the leading cause of infant mortality, and those who survive face an increased risk of morbidity and mortality across the life course2. While most preterm births are spontaneous, some are planned to reduce the risk of adverse outcomes including stillbirth, which account for two million in utero deaths globally each year4. A decline in preterm birth rates can therefore be an indicator that high-risk women and their babies are not receiving timely, quality care, potentially leading to increases in stillbirths.Approximately 10% of babies are born preterm , corresponding to nearly 15 million preterm births annually6), Israel (40%) (ref. 7) and some European countries (16–91%) (refs. 13). Conversely, data from Nepal, Uruguay and California showed increases of 11–30% in preterm birth rates16, whereas national data from Canada, Spain, Sweden and the United States indicated small or no changes22. In parallel, studies from low- and middle-income countries  and HICs  reported increases in stillbirth rates of 2–22% (refs. 24), but few studies analysed preterm birth and stillbirth simultaneously.In the first few months following the introduction of pandemic-related restrictions (henceforth referred to as ‘lockdowns’) in response to the first wave of coronavirus disease 2019 (COVID-19), there were markedly varying reports of changes in preterm birth and stillbirth rates across countries. Substantial reductions in preterm birth were reported from a number of high-income countries (HICs), including Australia (29–36%)  0.91, 95% confidence interval (CI) 0.84–0.99; 795,105 pregnancies from 12 studies) and an increase in stillbirth rates 27. However, comparison across studies was hampered by methodological differences. Notably, only one study in the review accounted for pre-pandemic trends in preterm births in their analysis28. Additionally, most studies used facility-based data, which are difficult to interpret because changes in perinatal outcome rates at individual health facilities could reflect lockdown-induced changes in healthcare delivery  rather than true population-level changes in perinatal outcomes. Indeed, a living systematic review and meta-analysis demonstrated important differences in the estimated impact of pandemic restrictions on preterm birth, depending on whether the study used single-centre  or regional/national-level data 26.There have been several systematic reviews and meta-analyses examining the impact of pandemic restrictions on perinatal outcomes. These studies have generally found insufficient evidence to suggest an overall change in global preterm birth and stillbirth rates, but they have reported changes in certain subgroups29. The most well-established cause of spontaneous preterm birth is infection30, and it is plausible that an immediate and substantial reduction in circulating infections during lockdown, due to reductions in social interaction and increased hygiene measures32, could directly influence preterm birth rates. Additionally, observational studies have shown an increased risk of poor pregnancy outcomes at times of high air pollution, particularly in association with exposure in the third trimester34; thus, reductions in air pollution linked with lockdown could potentially have an immediate impact on preterm birth36. It is, however, also plausible that any reduction in preterm birth rates might signal that high-risk women were not receiving timely and quality maternity care37, and the reduction in preterm births may have been offset by an increase in stillbirths.While methodological challenges have hindered robust conclusions on whether lockdowns led to reductions in preterm births, there were undoubtedly unprecedented health, social and economic impacts that occurred as part of lockdowns that could potentially lead to reductions in preterm birth ratesIn this Article, given the uncertainties in the available evidence on the impact of COVID-19 pandemic lockdowns on perinatal outcomes, particularly where studies have not used population-based data, we aimed to conduct a rigorous, standardized analysis using high-quality data from across the world through the International Perinatal Outcomes in the Pandemic (iPOP) study. Specifically, we explored whether lockdowns in response to the first wave of the COVID-19 pandemic were associated with a change in preterm birth rates using interrupted time series (ITS) analysis, and whether any associations identified varied by country income level or by type or timing of preterm birth, or could be explained by changes in stillbirth rates. Detailed time-series data enabled us to use pre-lockdown trends in preterm birth and stillbirth rates to forecast the expected trend in these outcomes had lockdown not occurred, and compare these forecasted rates with the observed rates for each country individually and combined across countries in a meta-analysis.38). As described in Supplementary Table We included 52,067,596 births occurring between January 2015 and July 2020. Of these, 51,340,025 (98.6%) were from the 18 population-based datasets capturing whole countries or regions and 727,571 (1.4%) were from the 26 non-population-based datasets , second , and third month  of lockdown, but none in the fourth month   was 0%, 64%, 53% and 34% for the first to fourth month of lockdown, respectively. Stratifying by country income level indicated similar reductions in the odds of preterm birth for both high and upper-middle-income country settings, with higher between-country heterogeneity among upper-middle-income countries  in one facility in Nigeria, and up to 1.91 (95% CI 0.97–3.76) in another facility in Nigeria   Fig. –36.Fig. P = 0.10, I2 = 0%), but an increase was observed when restricting to HICs , driven by Canada  , third  and possibly fourth month  of lockdown. These ORs were driven largely by Brazil , third month  and fourth month  of lockdown  to 1.35 in New South Wales, Australia (95% CI 0.93–1.96) in the population-based data in the first month of lockdown  Fig. . There wIn the non-population-based data, the ORs for stillbirth in the first month of lockdown ranged from 0.24 in a facility in Nigeria (95% CI 0.08–0.69) to 3.20 in a facility in Poland (95% CI 0.61–16.74)  relative reductions in the overall rates of preterm birth following lockdown, although with some variation among countries. Reductions in spontaneous preterm birth rates were observed in HICs only, and no change in very preterm birth was observed. The observed decrease in preterm births did not appear to be driven by a reciprocal increase in stillbirth rates in HICs. We did, however, find evidence for increases in stillbirth in Brazil in the second, third and fourth months of lockdown. It remains plausible that some reduction in preterm birth rates was linked to increased stillbirth rates in HICs, but we had limited power to detect this due to the relatively small number of stillbirths. Our patient partners’ interpretation of these results are provided in 11, and several meta-analyses have been conducted27. However, there have been important differences in data quality across prior studies, many of which did not apply analytical approaches that could account for pre-pandemic trends in perinatal outcomes28. Notably, few studies have included both preterm birth and stillbirth rates, despite the importance of considering perinatal outcomes together40. Our findings provide evidence by applying an ITS design to high-quality population-based data from 18 countries, and evaluating potentially competing outcomes  in parallel. Even though we used the same analytical approach across data from different countries, between-country differences in the association between lockdown and both preterm birth and stillbirth rates were seen. These could be driven by contextual differences in the implementation of lockdown and differences in the impact of lockdown, which in turn may be driven by the resilience of health or social systems.Multiple studies have assessed the effects of pandemic lockdowns on perinatal outcomes following initial reports of dramatic reductions in preterm birth rates41. It is possible that a reduction in physician-initiated preterm births also contributed to the overall reduction in preterm birth in some settings42, although we could not investigate this directly, as data on medically indicated preterm births were not available for all countries and could not be reliably inferred. The increase in stillbirth with lockdown in some countries might reflect reduced access to timely quality antenatal and intrapartum care43. As our findings represent the average impact of lockdown across populations, we cannot differentiate the relative contribution of specific factors, nor whether the impact of lockdown differed between specific population subgroups. For example, an increased risk of preterm birth in some women  might have been offset at the population level by public health responses reducing other risk factors, such as air pollution and infectious diseases other than COVID-19.Lockdowns had important and diverse impacts on several exposures known to influence preterm birth, offering some possible explanations for the small reductions observed in our study. For spontaneous preterm birth, although the aetiology is poorly understood, putative mediating factors include reductions in air pollution and, in particular, non-COVID infections, both of which were shown to decline across a diverse range of countries, albeit to varying degrees19 experienced SARS-CoV-2 infection at this early stage of the pandemic. As SARS-CoV-2 infection increases the risk of both preterm birth and stillbirth46, it is possible that our results have underestimated the impact of lockdown on preterm birth and overestimated the impact on stillbirth, although any influence would be minimal given the relatively much smaller proportion of women experiencing infection compared with the broader effects of lockdown.Using aggregate data, it was not possible to distinguish the impacts of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from those of pandemic-related restrictions. Relative to the essentially universal exposure of all pregnant women to lockdowns, only a small fraction14, urging caution in the interpretation of results from studies of single facilities. In addition, the paucity of population-based data in LMICs—where the majority of preterm births and stillbirths occur48—was striking. We made extensive efforts to identify high-quality data from across different country income levels, including iterative development of the data collection tools with groups from a range of different countries and, in consideration of the more intensive data preparation required in some countries to harness data on perinatal outcomes, special funding allocations to support the preparation of data from LMIC settings. While there have been substantial efforts globally to improve perinatal data and outcomes through stillbirth and preterm birth prevention initiatives, such as Every Newborn Action Plan49 and parent-led global organizations such as the International Stillbirth Alliance50, we echo previous calls for the urgent need to develop systems that routinely capture high-quality data on perinatal outcomes with standardized definitions across countries52.Our results highlight the paucity of population-based data in many settings, and the challenges of interpreting non-population-based data to assess changes in perinatal outcomes over time. First, in some countries, we observed large variation in preterm birth and stillbirth rates between facilities. These might reflect differences in case mix as well as challenges in accurate reporting of key variables, particularly in estimating gestational age when routine antenatal ultrasound is unavailable. Second, some facilities within the same country documented markedly different impacts of lockdown on preterm birth and stillbirth rates. In some countries, there were dramatic shifts in how and where pregnant women accessed intrapartum care53, as well as differentiation between population and non-population-based data and country income settings.The strengths of our study include the broad global coverage, use of pre-defined and internationally recognized outcome measures, and analytical approaches to account for time trends and seasonal patterns in perinatal outcomes38. This is a crude measure to approximate the severity of pandemic-related restrictions in each country as a whole; it does not reflect within-country variations or individual experiences in lockdown measures. The Oxford Stringency Index also does not capture variations in access to maternity and healthcare nor provide information on the extent to which restrictions were enforced or followed. This is likely to be particularly problematic for large countries such as Brazil and the United States, but, unfortunately, subnational data on perinatal outcomes were not available for this study. Second, ITS analyses are vulnerable to confounding by unmeasured events occurring simultaneously to the intervention that might also impact the outcomes. Third, we used aggregate data and could not differentiate within-population differences on the impact of lockdown measures, which is likely to vary by socio-economic status, region and age. Fourth, as we focused on the association between lockdown and perinatal outcomes for the first four months following the lockdowns in response to the first wave of COVID-19, we mainly captured the potential impact of lockdown on pregnancies that were in their third trimester at the start of lockdown; further studies should be conducted to assess whether there was an association between lockdown and perinatal outcomes for pregnancies that were at earlier gestations in lockdown. Fifth, where we found no evidence for an association , we cannot rule out that there was no change as these were relatively rare with wide CIs. The use of equivalence tests to formally test whether there was no evidence for a clinically meaningful change in our outcomes was considered but ultimately not conducted as there is no minimum clinically meaningful difference for stillbirth or preterm births, with any change being of interest. Finally, the interpretation of our results is limited by difficulties with data capture, population coverage and data quality from some countries. We therefore conducted separate analyses for population-based data considered to be of high quality, yielding more robust estimates.We acknowledge several limitations. First, we defined onset of lockdown as the month during which a country first exceeded 50 on the Oxford Stringency Index1). Understanding the underlying pathways linking lockdown with the reduction in preterm births could have implications for clinical practice and policy. Our study also highlights the need to develop further capacity for high-quality and appropriate standardized data collection in LMICs54. Finally, the iPOP platform offers novel opportunities to rapidly conduct harmonized perinatal health research globally during the COVID-19 pandemic and beyond.In summary, this international study provides evidence on global changes in preterm birth and stillbirth across 26 countries during the initial COVID-19 pandemic lockdowns. Overall, we observed a 3–4% relative reduction in the preterm birth rate during the first three months of lockdown based on population-based data from HICs and upper-middle-income countries. This decrease in preterm births did not appear to be linked with an increase in stillbirths in most settings. Consistent evidence of an increase in stillbirths was only observed in Brazil following lockdown, the causes of which certainly warrant further exploration. Although relatively small, the observed changes in preterm birth are meaningful at the population level: assuming the observed decline during lockdown is real and consistent worldwide, our findings suggest that nearly 50,000 preterm births  were averted in the first month of lockdown alone, based on a global pre-pandemic preterm birth rate of 10.6%  guideline56.We engaged with national and subnational data custodians to standardize and analyse aggregate-level data on monthly numbers of births stratified by gestational age from population-based data sources, and to conduct exploratory analyses using non-population-based data sources. Detailed time-series data enabled us to use pre-lockdown trends in preterm birth and stillbirth rates to forecast the expected trend in these outcomes had lockdown not occurred, and compare these forecasted rates with the observed rates for each country individually and combined across countries in a meta-analysis. The study was conducted using a common protocol57.Contributors from countries where the data were not publicly available obtained ethics approval from their respective institutional review boards , pooled data from a group of health facilities (two datasets from two countries) and demographic surveillance sites (one dataset from one country)  Databank. We collected information on national income levels from the World Bank58  not being able to identify readily available reliable data for all our study settings (air pollution and adherence to lockdown) or (2) finding little or no variation between the included datasets beyond that captured by country-income level .To ensure data and measures from different settings were comparable, consistent and coherent, we developed a detailed protocol, including standardized outcome definitions and data collection templates38. In brief, this index collects information on different social, health and economic government policies instituted in response to the COVID-19 pandemic.For each country, we defined the start of lockdown using the Oxford Stringency IndexWe considered the onset of a country’s initial lockdown as the date at which the stringency score first exceeded 50 on the Oxford Stringency Index (range 0–100). This cut-off was pre-specified in the study protocol and based on expert advice. For dates of lockdown that occurred between the 1st and 15th of the month, the first month of lockdown was assigned to that month; for dates after the 15th, the first month of lockdown was assigned to the following month. As described below, we explored the impact on perinatal outcomes in the first four months from a country’s initial lockdown, regardless of whether the Oxford Stringency Index dropped below 50 during this time. We restricted the analysis to the first four months to facilitate comparison between different countries included in this study; this was when the strictest lockdowns were in place in response to the first wave of COVID-19, with increasing variability between countries beyond this timeframe.60 and were developed in consultation with our international collaborators to ensure that all data contributors captured these outcomes consistently.Data contributors recorded monthly numbers of births categorized into pre-specified gestational age groups, according to our data collection template. The outcome definitions aligned with global standard definitions for preterm birth and stillbirth61. We calculated the very preterm birth rate per 100 births as the number of births from 22 + 0 to 31 + 6 weeks gestation divided by the total number of births. We estimated the spontaneous preterm birth rate per 100 births as the number of births from 22 + 0 to 36 + 6 weeks gestation with spontaneous onset divided by the total number of births. The preterm, very preterm and spontaneous preterm birth rates were calculated, where available, using all births and live births only for settings where data on stillbirths were not available. We were not prescriptive in how data contributors should identify and define spontaneous births, beyond specifying that these should capture births preceded by spontaneous contractions or preterm prelabour rupture of membranes. Further details of the methods used to estimate gestational age across the different datasets are provided in Supplementary Table For each month, we calculated the preterm birth rate per 100 births, as the number of births from 22 + 0 to 36 + 6 weeks gestation divided by the total number of birthsA detailed description of the steps to clean and prepare the data before undertaking the analysis is provided in Supplementary 62 to model the effect of lockdown on perinatal outcomes. First, we fitted weighted ITS models on the entire time series of the monthly log(odds) of the outcomes. Weights were based on the total number of births per month; imputed values for missing data  to forecast the log(odds) of perinatal outcomes in the first to fourth month of lockdown assuming lockdown had not occurred. This was due to non-population based datasets varying in data availability with respect to the pre-lockdown study period, frequency of reporting of outcomes, and degree of missingness. To capture changes by season and annual trends pre-lockdown in our forecasted estimates, the model included month  and year (continuous), with year also included as a squared term to account for settings with non-linear changes in the perinatal outcome rates over time. We then calculated the OR quantifying the impact of lockdown on the perinatal outcomes by dividing the observed odds of each perinatal outcome by the forecasted odds for each of the first four months of lockdown.For each country-specific population-based dataset, we undertook an ITS analysis64, and this was done separately for the population-based data and the non-population-based data. For the population-based data, we stratified the meta-analysis by country income level (where sufficient datasets for each category permitted): high income versus upper-middle income. For non-population-based data, we used a three-level meta-analysis model to account for the dependency of observations of the impact of lockdown between facilities in the same country65. The I2 statistic, which captures the percentage of the variability in the ORs between countries that is due to heterogeneity rather than sampling error, was used to assess for evidence of between-country heterogeneity in the ORs66. We did not conduct equivalence tests to assess whether there was evidence that there was no association between the pandemic and our outcomes, as these tests require identifying a minimum clinically significant difference below which we would conclude that there was no change in our outcomes. There is no clear clinically significant difference that can be used for preterm birth or stillbirth, with any increase being of concern. Where relevant, we report P values for the probability of observing a relative difference in our outcomes at least as big as that in our data under the assumption that there was no association between the pandemic and our outcomes.The ORs from each dataset for each perinatal outcome at each month after lockdown were pooled using random-effects meta-analysisAll analyses were performed in R version 4.1.1.n = 15 datasets). Second, to evaluate the impact of including datasets with a different lower limit for gestational age in the main population-based analysis for preterm birth, we restricted the time-series analysis to births from 28 weeks gestational age onwards, the lower threshold recommended by the World Health Organization for international comparisons60. Third, we also conducted a sensitivity analysis for our meta-analysis of the association between lockdown and preterm birth among all population-based datasets, excluding Brazil and the United States, which together contributed slightly over 70% of the births included in the study.We conducted three sensitivity analyses. First, to assess the potential impact of including datasets that only provided data on preterm birth among live births  in the main analysis, we conducted ITS analysis restricted to live births among datasets which also provided data on all births (Parent representatives from four national patient partner organizations were included from the inception of the iPOP study to inform the common goal of timely implementation of quality research. We used mechanisms to ensure meaningful collaboration through inclusion on meeting agendas and facilitating meeting processes so that everyone had an equal voice to ensure patient partners were treated with mutual respect. Patient partners from Brazil, Canada, Hungary and Ireland co-developed the iPOP protocol, attended all iPOP meetings to ensure meaningful collaboration, edited and provided input to this manuscript, and are continuing to co-build meaningful and innovative knowledge translation strategies.Further information on research design is available in the Supplementary InformationSupplementary methods, discussion, Figs. 1–44, Tables 1–6 and references.Reporting Summary