This cross-sectional study uses data from the 2019 Global Burden of Disease study to determine the trends in incidence and mortality of neonatal preterm birth at the global, regional, and national levels to quantify its burden from 1990 to 2019. What is the burden of neonatal preterm birth at the global, regional, and national levels?In this cross-sectional study using data from the Global Burden of Disease study, incident cases and deaths of neonatal preterm birth, overall age-standardized incidence rates (ASIRs), and age-standardized mortality rates (ASMRs) of neonatal preterm birth decreased from 1990 to 2019; however, ASIRs and ASMRs increased in some regions with high sociodemographic index regions and in Southern Sub-Saharan Africa, respectively. There was a positive correlation between estimated annual percentage change of ASIR and sociodemographic index or universal health coverage index in 2019, while there was a negative correlation between estimated annual percentage change in ASMR and sociodemographic index or universal health coverage index in 2019 at the national level.Preterm birth remains a crucial issue in children worldwide and discovery research into the underlying mechanisms of neonatal preterm birth and the development of innovative interventions is urgent. Preterm birth complications are the leading cause of death in children younger than 5 years worldwide. Despite advancing knowledge of risk factors and mechanisms related to preterm labor, the preterm birth rate has risen in most industrialized countries. Moreover, the burden of neonatal preterm birth remains unclear across the world.To determine the trends in incidence and mortality of neonatal preterm birth at the global, regional, and national levels to quantify its burden from 1990 to 2019 using data from the 2019 Global Burden of Disease study.Annual incident cases, deaths, age-standardized incidence rates (ASIRs), and age-standardized mortality rates (ASMRs) of neonatal preterm birth between 1990 and 2019 were collected from the 2019 Global Burden of Disease study. The percentage of relative changes in incident cases and deaths as well as the estimated annual percentage changes (EAPCs) of ASIRs and ASMRs were calculated to quantify their temporal trends. Correlations of EAPC of ASIRs and ASMRs with sociodemographic index (SDI) and universal health coverage index were evaluated by Pearson correlation analyses.Infants born alive before 37 completed weeks of gestation between 1990 and 2019.Incident cases, deaths, ASIRs, and ASMRs of neonatal preterm birth.Globally, the incident cases of neonatal preterm birth decreased by 5.26% from 16.06 million in 1990 to 15.22 million in 2019, and the deaths decreased by 47.71% from 1.27 million in 1990 to 0.66 million in 2019. The overall ASIR  and ASMR  decreased in this period. The ASIR of neonatal preterm birth increased by a mean of 0.25%  in high-SDI regions from 1990 to 2019. The ASMR of neonatal preterm birth increased by a mean of 2.09%  in Southern Sub-Saharan Africa in this period. A positive correlation was observed between EAPC of ASIR and SDI or universal health coverage index in 2019, while a negative correlation was observed between EAPC in ASMR and SDI or universal health coverage index in 2019 at national levels.Preterm birth remains a crucial issue in children worldwide, with an increasing trend in ASIR in high-SDI regions and in ASMR in Southern Sub-Saharan Africa between 1990 to 2019. Efforts to reduce both the incidence and mortality of preterm births are essential. To our knowledge, there is currently no study to systematically clarify both the incidence and mortality of neonatal preterm birth at the global, regional, and national levels, as well as their association with socioeconomic status. Therefore, we retrieved detailed data on the incidence and mortality of neonatal preterm birth and socioeconomic status from the 2019 Global Burden of Disease (GBD) study to determine the global incidence and mortality of neonatal preterm birth and their associations with socioeconomic status at the national level to provide a more comprehensive perspective to make global and regional targeted interventions and health care policies for the prevention and control of neonatal preterm birth.Preterm infants are particularly vulnerable to complications due to impaired respiration, difficulty in feeding, poor body temperature regulation, and high risk of infection.5 In addition, the GBD study used standardized tools to generate estimates for the incidence and mortality of most diseases by age, sex, location, and year.7 In the GBD study, preterm birth is defined as infants born alive before 37 weeks of pregnancy according to the WHO.9 The detailed methods of the modeling strategy and alternative approaches for estimating neonatal preterm birth have been reported in previous studies.7 The study did not involve human participants and/or animals; therefore, no ethics approval or informed consent was needed.The 2019 GBD study modeled nonfatal disease burden using DisMod-MR version 2.1, a meta-analysis tool that uses a compartmental model structure with a series of differential equations that synthesize sparse and heterogeneous epidemiologic data for nonfatal diseases, including neonatal disorders.10 Data were available from a total of 204 countries and territories, and these were categorized into 5 regions in terms of sociodemographic index (SDI) and 21 GBD regions according to geographical contiguity. Data on sociodemographic states, including SDI and universal health coverage index (UHCI), in 204 countries and territories used in this study were also collected from the Global Health Data Exchange query tool.9This study used data of annual incident cases, deaths, age-standardized incidence rates (ASIRs), and age-standardized mortality rates (ASMRs) of neonatal preterm birth from 1990 to 2019 by sex, age, and location, collected from the Global Health Data Exchange query tool.9 It is the geometric mean of 0 to 1 indices of lag distributed income per capita, mean years of schooling for individuals 15 years and older, and total fertility rate for individuals younger than 25 years. A location with an SDI of 0 indicates a theoretical minimum level of development status relevant to health outcomes, while a location with an SDI of 1 indicates a theoretical maximum level.9 The SDIs of 204 countries and territories in 2019 are shown in eTable 1 in the The SDI is a composite indicator of development status strongly correlated with health outcomes.11 The indicators of UHCI involved either direct measures of intervention coverage  or outcome-based indicators, such as mortality-to-incidence ratios, to approximate access to quality care.12 The UHCI indicators are reported on a scale of 0 to 100.12 The UHCIs of 204 countries and territories in 2019 are shown in eTable 1 in the The UHCI developed following GBD 2019 comprises 23 indicators drawn across a range of health care service areas and is meant to represent health care needs over the life course.13 The percentage of relative changes in incident cases of neonatal preterm birth from 1990 to 2019 was calculated by the equation: percentage of relative changes = (incident cases in 2019 − incident cases in 1990 / incident cases in 1990) × 100%. The percentage of relative changes in deaths of neonatal preterm birth were calculated using a similar equation. The EAPC is a summary and widely used measure of the age-standardized rate trend over a specified time interval. A regression line was fitted to the natural logarithm of the age-standardized rate, ie, y = α + βx + ε, where y = ln (age-standardized rate) and x = calendar year. The EAPC was calculated as 100 × (eβ − 1) and its 95% CI was calculated to reflect the temporal trend in age-standardized rate. The trend in age-standardized rate is reflected in EAPC value and its 95% CI age-standardized rate is in an upward trend when the EAPC and the lower boundary of the 95% CI are positive; conversely, age-standardized rate is in a downward trend when EAPC and the upper boundary of the 95% CI are negative. We calculated the EAPCs of ASIR and ASMR of neonatal preterm birth to reflect their temporal trends.We calculated the percentage of relative changes in incident cases and deaths of neonatal preterm birth and the estimated annual percentage changes (EAPCs) of ASIRs and ASMRs to quantify the trends in incidence of neonatal preterm birth. To compare the incidence and mortality rates of neonatal preterm birth across different populations, the ASIRs and ASMRs were carried out by applying the age-specific rates for each location, sex, and year to a GBD world standard population to adjust for potential confounding of age structure.P value less than .05 was considered statistically significant.Moreover, the correlations of EAPC of ASIR and ASMR with SDI values (2019) and UHCI (2019) in 204 countries and territories were evaluated by Pearson correlation analyses to define the potential factors affecting EAPC. The polynomial curves were also modeled. All analyses were conducted with SAS version 9.4 (SAS Institute) and Origin 2019b (OriginLab). A 2-tailed Globally, the number of incident cases of neonatal preterm birth decreased by 5.26% from 16.06 million in 1990 to 15.22 million in 2019, and the number of deaths of neonatal preterm birth decreased by 47.71% from 1.27 million in 1990 to 0.66 million in 2019 . The oveIn high-SDI regions, the incident cases of neonatal preterm birth decreased by 5.00% from 1990 to 2019 , whereasAcross the 21 GBD regions, the incident cases of neonatal preterm birth decreased in 66.7% of the regions (14 GBD regions) and the deaths of neonatal preterm birth decreased in 81.0% of the regions (17 regions) from 1990 to 2019, with the largest decrease both in incident cases (−48.10%) and deaths (−85.94%) in East Asia . The neaFor 204 countries and territories, the absolute number of incident cases of neonatal preterm birth in India (3.10 million) and Pakistan (1.04 million) accounted for approximately one-third of the global incident cases (15.22 million) in 2019 , followed by Papua New Guinea (90.45%), while the most pronounced decrease was detected in Cook Islands (97.15%) (eTable 3 in the P < .001) and UHCI in 2019  (P < .001) and UHCI in 2019   A and B.  < .001) C and D.To our knowledge, this is the first comprehensive assessment of the global landscape, long-term trends, and regional differences in the incidence and mortality of neonatal preterm birth, as well as the association with socioeconomic status at the national level, using data from the 2019 GBD study. In this study, we found that the global ASIR and ASMR of neonatal preterm birth decreased by a mean of 0.19% and 1.52% per year from 1990 to 2019, respectively. Meanwhile, the global absolute number of incident cases and deaths of neonatal preterm birth also decreased by 5.26% and 47.71% worldwide in this period, respectively. For SDI regions, the ASIRs of neonatal preterm birth increased in high-SDI regions by a mean of 0.25% per year from 1990 to 2019. The ASMR of neonatal preterm birth increased by a mean of 2.09% per year in Southern Sub-Saharan Africa in this period. Oceania experienced the largest increase both in the number of incident cases (82.72%) and deaths (60.39%) of neonatal preterm birth. Notably, we found a positive association of EAPC of ASIR with SDI or UHCI in 2019 and a negative association of EAPC of ASMR with SDI or UHCI in 2019 at the national level.15 Thus, reducing the incidence and mortality of preterm birth is significant. A better understanding of the incidence and mortality of preterm birth at the regional and national levels is needed to improve access to effective obstetric and neonatal care. In line with the decreasing trend in the global preterm birth rate,3 this current study found a decreasing trend in the ASIR and ASMR of neonatal preterm birth worldwide. This is largely attributed to improvements in maternal and newborn health care.17 Infant mortality from preterm birth can be reduced through interventions delivered to the birthing parent before or during pregnancy and to the preterm infant after birth.4 The progress in the frequency of the presence of a skilled birth attendant at birth in many countries presents a major opportunity to reduce intrapartum stillbirth and neonatal mortality.16 In addition, high-quality health care for female individuals is also essential in the prevention of neonatal preterm birth.2 Finally, improvements in nutritional status and medical interventions for reducing preterm birth are also beneficial for reducing the incidence and mortality of preterm birth.16Preterm birth is an important perinatal health problem, contributing to increased mortality risk of children younger than 5 years directly and maternal mortality indirectly.19 Several possible factors contributing to but not completely explaining this increasing trend in the ASIR of neonatal preterm birth include increasing rates of multiple births, increases in the proportion of births among individuals older than 34 years, greater use of assisted reproduction techniques, and changes in clinical practices, such as greater use of elective cesarean delivery.21 For example, the increasing age of individuals giving birth in North America causes more maternal complications and cesarean deliveries and ultimately leads to an increased risk of preterm birth.9 In addition, the increasing use of ultrasonography rather than the date of the last menstrual period to estimate gestational age may have resulted in larger numbers of births being classified as preterm.9 In high-income countries, the reduction in mortality rates in infants who were born preterm has been driven largely by improved maternal and newborn health care.4 One previous study reported that preterm survival rates have increased in high-income countries, while preterm newborns still die because of a lack of adequate newborn care in many low-income and middle-income countries.2 Almost all births are attended by skilled staff and 50% of the neonates of less than 24 weeks’ gestation survive in high-income countries, whereas even the neonates older than 32 weeks’ gestation have only a 50% chance of survival owing to lack of available resources and/or low quality of specialized care in low-income countries.22 In high-income countries, administration of antenatal steroids is standard care for birthing parents with anticipated preterm labor, which has been verified to be very effective in preventing neonatal mortality; however, the coverage of antenatal steroid therapy remains low in low- and middle-income countries.23 In addition, intrauterine infection or lack of availability of drugs, such as tocolytic agents, might contribute to an increased risk of preterm birth and deaths of neonatal preterm birth in low-income countries.9 Thus, the development of interventions to reduce neonatal preterm birth is urgently needed for all countries, especially low-income countries where the incidence of neonatal preterm birth is high and the trend in mortality of neonatal preterm birth is increasing. These findings highlight the urgent need for discovery research into the underlying mechanisms of neonatal preterm birth and the development of innovative interventions.This study found that the ASIRs and ASMRs of neonatal preterm birth varied significantly across regions and nations. We found an increasing trend in the ASIR and a decreasing trend in the ASMR in high-SDI regions between 1990 and 2019, which was especially common in high-income countries, such as Greece, Bahrain, Japan, the UK, and the US. However, this study observed opposite trends in the ASIR and ASMR in some low-income countries: the ASMR of neonatal preterm birth increased but the ASIR of neonatal preterm birth decreased in Southern Sub-Saharan Africa. Similar to our findings, several previous studies reported that the incidence of preterm birth increased in high-income countries in the past 2 decades.This current study comprehensively assessed the global landscape, long-term trends, and regional differences in the incidence and mortality of neonatal preterm birth as well as the association with socioeconomic status using data from GBD estimates, which fill a gap where actual data on disease burden are sparse or unavailable. However, several limitations should be noted. First, the availability of data and the quality of available data limited the accuracy and robustness of the estimates of the incidence of mortality of neonatal preterm birth in the modeling, which might lead to bias when national surveillance and population-based studies were lacking. Second, data of gestational ages were not available in the GBD study; thus, we cannot further analyze the trends in incidence and mortality of neonatal preterm birth according to different gestational ages. Third, EAPC in ASIRs and ASMRs as well as the relative change in the number of incident cases and deaths were used to assess its long-term trends from 1990 to 2019, which might mask the recent short-term trends that reflected the effectiveness of the recent prevention interventions of preterm birth. Finally, the contribution of the causes of preterm birth is unknown globally.In summary, the global ASIR and ASMR of preterm birth gradually decreased from 1990 to 2019, while the ASIR increased in high SDI region and the ASMR increased in Southern Sub-Saharan Africa in this period. Preterm birth remains a crucial issue in children, both in high- and low-resource countries. Thus, efforts to reduce both the incidence and mortality of preterm births are essential worldwide.


Small for gestational age (SGA) is defined as a newborn with a birth weight for gestational age < 10th percentile. Routine third-trimester ultrasound screening for fetal growth assessment has detection rates (DR) from 50 to 80%. For this reason, the addition of other markers is being studied, such as maternal characteristics, biochemical values, and biophysical models, in order to create personalized combinations that can increase the predictive capacity of the ultrasound. With this purpose, this retrospective cohort study of 12,912 cases aims to compare the potential value of third-trimester screening, based on estimated weight percentile (EPW), by universal ultrasound at 35–37 weeks of gestation, with a combined model integrating maternal characteristics and biochemical markers (PAPP-A and β-HCG) for the prediction of SGA newborns. We observed that DR improved from 58.9% with the EW alone to 63.5% with the predictive model. Moreover, the AUC for the multivariate model was 0.882 (0.873–0.891 95% C.I.), showing a statistically significant difference with EPW alone (AUC 0.864 (95% C.I.: 0.854–0.873)). Although the improvements were modest, contingent detection models appear to be more sensitive than third-trimester ultrasound alone at predicting SGA at delivery. Fetal growth restriction (FGR) is defined as a failure to achieve the endorsed growth potential. This definition includes the so-called true FGR, which associates alterations in the Doppler study, suggesting a hemodynamic redistribution that reflects fetal adaptation to malnutrition/hypoxia, as well as histological and biochemical signs of placental disease with an increased risk of preeclampsia . These fWhile these definitions seem conceptually simple, the distinction in clinical practice can be challenging. Most small for gestational age (SGA) babies go unnoticed until birth, even when a routine third-trimester ultrasound is performed ,10. On tNowadays, the diagnostic strategy for the detection of these fetuses prenatally is routine third-trimester ultrasound, performed around 35–37 weeks of gestation, which evaluates fetal growth. However, this has quite low detection rates (DR), ranging from 50% to 80% , and theFor this reason, the addition of other markers, such as maternal characteristics and biochemical and biophysical parameters, is being studied. Hence, combined models are being designed that either increase the predictive capacity of basic ultrasound in the third trimester of pregnancy to predict SGA or select patients at risk of giving birth to late-onset SGA fetuses ,16,17,18Recent evidence suggests that the pathologies underlying FGR and SGA take place in the first trimester. An earlier assessment, before the establishment of placental dysfunction, may have the potential to improve treatment and prognosis in clinical practice . The cosSome studies have already evaluated the individual capacity of PAPP-A and β-hCG to predict SGA. They found that these markers have an independent influence on the final birth weight and correlated a lower PAPP-A with a higher risk of the fetus developing SGA. However, their predictive powers are insufficient for them to be used alone for SGA detection ,28,29,30The objective of our study was to compare the predictive capacity for SGA neonates of fetal biometry, performed in the third-trimester ultrasound on all pregnant women in a Spanish hospital between 35 and 37 weeks of gestation, with a multivariate model composed of the aforementioned ultrasound, plus maternal characteristics and biochemical markers used for the screening of chromosomal abnormalities in the first trimester of gestation (PAPP-A and β-HCG), tests which are performed in all low-risk pregnant women.This was a retrospective cohort study of births assisted at the Miguel Servet University Hospital (MSUH), Zaragoza, Spain, between March 2012 and December 2018. The inclusion criteria were as follows: live singleton pregnancies, controlled at the MSUH from the first trimester of gestation; fetal ultrasound assessment at a gestational age of 35 weeks (range 34–36); and deliveries between 37 and 42 weeks of gestational age, with fetuses without stillbirth associated with malformations or chromosomal abnormalities. Of the 16,361 deliveries assisted in our hospital in the study period, only the 12,912 cases that fulfilled the inclusion criteria, such as data availability to estimate percentile weights by standards, were considered. The selected sample of study participants is described, in detail, in The last menstrual period was adjusted by the first trimester ultrasound . UniversEPWs were calculated according to the local MSUH standard, customized to fetal gender, built using a modified version of Hadlock et al. growth charts , and adjThe AC percentile was estimated according to the Smulian et al. methodology . These aData were descriptively analyzed using the medians and interquartile ranges for continuous variables and absolute and relative frequencies for categorical variables. Differences between SGA and non-SGA groups were tested using Mann–Whitney and chi-square tests, as appropriate.The predictive ability of EPW, provided by the MSUH standard, to predict SGAs was analyzed using the area under the receiver operating characteristic curve (AUC) . This arTo improve the prediction of SGA, we explored the added predictive ability of maternal–fetal characteristics and pathologies. These corresponded to: maternal age and body mass index at the start of pregnancy, maternal height, parity, previous cesarean, in vitro fertilization, infant gender, PAPP-A, β-HCG, smoking habits, hypertension, and diabetes. In addition, the AC percentile, estimated at the 35th week of gestational age, was added as a complementary predictor of the EPW to identify SGA fetuses at birth.AUCs were compared using a bootstrap test , and theCalibration graphically analyzes the concordance between the predictions and real occurrence of the outcome, usually through calibration curves and two informative parameters: ‘intercept’ , which measures the difference between average predictions and average outcome; and ‘slope’, which reflects the average effect of predictions on the outcome.The CUC reflects the consequences of choosing a cutoff point, in terms of patients with a wrongly classified outcome of interest versus processes avoided. In this curve, the X axis corresponds to the possible threshold probability points, and the Y axis represents the percentage of two measures; the first corresponds to the percentage of missing positive cases below the selected cut-off (FN), and the second to the number of individuals below the cut-off.Analyses were performed using the R version 4.0.3 language programming package  . We explored EPW as a predictor of SGA using a logistic regression model, with EPW adjusted for restricted cubic splines with four knots. The AUC was 0.864 (0.854–0.873 95% C.I.), showing a good discriminative ability.p-values for significant variables.Moreover, we constructed a multivariate model by adding maternal–fetal characteristics and AC percentiles. p-value < 0.001), although the increase in AUC was modest. The ROC curves for both models are presented in p-value = 0.067). The AUC for the multivariate model was 0.882 (0.873–0.891 95% C.I.), showing a statistically significant difference with the EPW at week 35  using EPW, or from 52.3% with the AC percentile alone (threshold percentile AC 24.9%) to 63.5% using the predictive model, for a 10% FPR. However, this improvement is limited and comparable to the findings of other studies predicting neonates with a birth weight < 10th centile at, or after, term using combined models. These report DRs between 51% and 74%, at a 10% FPR ,17,42,43The biochemical markers used in our study, β-hCG and PAPP-A, are routinely tested in the first trimester of pregnancy to screen for chromosomal disorders, and their correlations with chromosomal disorders are already known. Hence, PAPP-A is an independent factor influencing final birth weight, and the lower the PAPP-A, the higher the risk of a fetus developing SGA. However, their predictive powers are not sufficient for them to be used alone for SGA detection ,28,29,30With regards to combined SGA prediction models, a few studies have examined the performance of screening for SGA at 35–37 weeks’ gestation by combining EFW and different markers. One study of 5121 pregnancies reported that, in screening by maternal factors and EFW, the DR of SGA < 10th percentile delivering at >37 weeks was 66%, at a 10% screen-positive rate, and this did not improve with addition of the artery pulsatility index (UtA-PI) and mean arterial pressure . Similarp < 0.001 and p = 0.002, respectively) [On the other hand, Miranda et al. 2017 used a combined screening model, including a priori risk , third trimester (32 + 0 to 36 + 6) EPW, UtA-PI, PlGF, and estriol , and achieved a DR of 61% ) for SGA cases and 77% ) for FGR. The combined model performed significantly better than using EPW alone (ctively) . DespiteIn their combined model, Souka et al. 2012 used AC, EFW, UA Doppler, smoking status, and first-trimester indices (free β-hCG and PAPP-A multiples of the median) and obtained an AUC = 0.88 for the prediction of SGA, a marginal improvement on EPW or AC alone, but without statistically significant differences . The resCiobanu et al. reported a positive DR of 32%  in the detection by maternal factors, 66%  by maternal factors and EFW at 35–36 weeks of gestation, and 69%  with the addition of biomarkers  [The strengths of our screening model are its simplicity and affordability, as it includes the standard tests used in screening for chromosomal abnormalities in the first trimester. It is based on variables easily obtained in the routine control of normal pregnancy, without requiring additional tests or parameters to elaborate the predictive model, such as Doppler studies or angiogenic biomarkers.Several studies have shown that the performance of screening for SGA using a combined model of maternal characteristics and medical history , EFW, and biophysical and biochemical markers is acceptably high for a preterm birth, but disappointingly low for delivery at term ,42. BothWe analyzed the clinical utility of EPW at the 35th week, as well as the predictive model using maternal–fetal characteristics, by means of the CUC. In this curve we showed the percentage of SGA incorrectly classified using a threshold point, as well as the fetuses at low risk of being SGA at birth. For the EPW at week 35, assuming a loss of 10% of fetuses that would be SGA at birth, 59% of fetuses can be considered as low risk. Alternatively, assuming a loss of 20% SGA cases, 71% of fetuses would be at low risk. Using the predictive model, assuming a loss in SGA cases of 10%, 62% would be considered as low risk; with a loss of 20%, a total of a 74% would be at low risk. From these findings, it can be deduced that, with the addition of maternal fetal characteristics, 3% fewer fetuses would require more controlled follow-up.Detection of SGA at delivery by third-trimester ultrasound, either by EPW or CA, even with models combined with other maternal variables and first-trimester biochemical markers, is limited, and new tools are required to improve this.Contingent screening models appear to be more sensitive than third-trimester ultrasound screening as the sole technique for predicting SGA at delivery. However, these improvements are modest . AC at 35-week ultrasound does not appear to be superior to EPW or significantly improve on the full model.


There is consensus that caesarean delivery (CD) is a lifesaving procedure for both mother and child in emergency situations and that CD without medical indication should be avoided. However, the rate that optimally balances the risks and benefits of CD is unresolved. In 1985, the World Health Organization concluded that the CD rate should be no more than 10-15%; subsequent reviews relating CD rates to infant mortality show no benefits at the country-level for rates higher than 15-19%. However, stillbirth has not been investigated because comparable international stillbirth data are not readily available.We conducted an ecological study in 25 European countries from 2015 to 2019 utilizing data from routine birth data sources aggregated using the Euro-Peristat PHIRI federated data analysis protocol. We assessed country-level associations between CD rates and perinatal outcomes  for all years using Pearson correlations, adjusted for clustering of years within country. Correlations were also estimated between linear trends over time in the indicators.The median [range] of CD rates was 23.1% [16.2 to 56.9] in 21 participating countries, while these were 6.9% [5.3 to 11.9] for preterm birth, 3.3 per 1000 total births [1.8 to 7.6] for stillbirth and 1.9 per 1000 live births [0.7 to 6.1] for neonatal mortality. The CD rate was not associated with the stillbirth rate  or with the neonatal mortality rate . However, there was a strong positive correlation with the preterm birth rate . Results were similar in time trend analyses.Higher CD rates were not associated with lower stillbirth or neonatal mortality rates, but were strongly correlated with higher preterm birth rates. This study suggests no benefits and indicates potential harms for higher CD rates in Europe.


A shift in focus towards healthy reproductive outcomes may reveal opportunities for novel interventions and strategies to promote optimal health. Using variables from the National Center for Health Statistics restricted use natality files, we calculated Empirical Bayes smoothed (EBS) rates of optimal birth for the all live births—both overall and by maternal race/ethnicity—by applying the smoothing tool in GeoDa version 1.18.0.10 We defined counties achieving greater racial birth equity as those where the overall EBS optimal birth rate was greater than the national 75th percentile and the absolute difference between maternal racial/ethnic categories was smaller than the national 25th percentile difference. During the study period, 49.80% of overall births could be classified as an optimal birth according to the study definition. Of the 3140 US counties, only 282 (8.98%) appeared to advance White-Black equity in optimal births, and 205 (6.53%) appeared to advance White-Hispanic equity in optimal births. In the effort improve maternal health, we should focus not only on the absence of negative outcomes, but also the occurrence of positive outcomes. Our analytic results suggest that optimal births can be measured and that geographic inequities by race occur. Likewise, maternal mortality has been recognized as a key indicator of population health and of overall social and economic development2. However, adverse outcomes represent only one facet of many in perinatal experiences and outcomes. An alternate approach based on “asset-framing”—as opposed to “deficit framing”3—may reveal opportunities for novel interventions and strategies to promote optimal maternal health.Similar to most public health studies, maternal and infant health research tends to use deficit models or to center on adverse outcomes. Work of this nature is invaluable given that adverse outcomes may represent life-course health experiences and can also serve as early indicators for other phenomena, such as chronic health conditions and even socioeconomic disparities. For instance, preterm birth and low birthweight have the potential to increase an infant’s risk of developing neurological disorders4. An Iranian study defined a positive birth experience as an uncomplicated vaginal delivery with cephalic presentation and an overall healthy infant and found that survey respondents frequently mentioned themes of control, empowerment, coping, support, self-esteem, and self-efficacy to describe their birth experiences5. Similarly, a study of new mothers in Sweden used self-administered surveys and found that positive birth experiences depended on perceived self-efficacy and self-confidence, as well as the presence of personal advocates, such as close family members or midwives6.Studies on optimal birth outcomes remain sparse. While there is not yet a standardized definition of optimal birth, some studies have designed methods for measuring optimal births within specific populations. Although research on this topic varies in study design and population, themes center on the need for delivery of competent and respectful maternity care. For instance a measure of “uncomplicated pregnancies” , a New Zealand study found that the majority of their study sample (61.6%) had an uncomplicated pregnancy7. Another CDC report estimated that in 2018, non-Hispanic Black infants had the highest mortality rate , followed by non-Hispanic Native Hawaiian and other Pacific Islanders (NHOPI) (9.39), non-Hispanic AIAN (8.15), Hispanic (4.86), non-Hispanic White (4.63), and non-Hispanic Asian (3.63) individuals8. Conceptually, racial equity involves theoretical differences in race, not just quantitative differences9. Maternal health researchers thusly argue for the need to achieve birth equity, defined as “the assurance of the conditions of optimal births for all people with a willingness to address racial and social inequities in a sustained effort”10.In the experiences preceding, during, and following pregnancy, racism can lead to inequities in outcomes and experiences. The Centers for Disease Control and Prevention (CDC) estimated that during the 2007–2016 period, non-Hispanic Black and American Indian/Alaska Native (AIAN) individuals had significantly more pregnancy-related deaths per 100,000 births when compared to non-Hispanic White and Asian/Pacific Islander individuals11. We further sought to estimate optimal birth prevalence by racial and ethnic identity and operationalize a county-level measure of birth equity.To date, no population-level studies have focused on optimal reproductive health outcomes in the US, and none address the degree to which optimal reproductive health is (or is not) equitably experienced according to race and/or ethnicity. The purpose of this exploratory analysis was to measure the prevalence of optimal births in US counties from 2018 to 2019. We also sought to explore the spatial distribution of optimal births, in concordance with other public health studies who use area specific analyses of aggregated data to identify shared contextual factors among neighboring regionsThis is a retrospective, ecologic and spatial analysis of county-level optimal birth rates. Data used in this report are from the National Center for Health Statistics (NCHS) restricted use natality files, 2018–2019. Raw data files were accessed following the granting of administrative permissions from the NCHS. These data include birth records for every birth occurring in the US, with Federal Information Processing System (FIPS) codes for identification of maternal county of residence in the 2019 US Census Bureau county-level shapefile. As this dataset contains only deidentified, secondary data, the study was deemed exempt by the Tulane University Institutional Review Board. All analyses were performed in accordance with the Declaration of Helsinki.12. The counts of birth records meeting these criteria in 2018 and 2019 were aggregated by county and divided by the sum of births occurring in the county over the same time period in order to estimate a raw rate of optimal births among live births. Additionally, a study by Jeon et. al found that it was best to racially stratify optimal birth weights and terms to account for differences in race13 and by subgroups based on maternal race/ethnicity . For this reason, we calculated both overall and race-specific rates of optimal births by county.Optimal births were those that met the following criteria: live births without complications , vaginal delivery at a gestational age of 37 weeks or later, and infant birth weight of > 2500 grams with no congenital anomalies , emergency medical intervention beyond standard care , and 5-min APGAR score ≥ 7https://spatial.uchicago.edu/geoda). The empirical Bayes smoothing approach computes a weighted average between the raw estimate for each county and the national average, with weights proportional to the underlying population at risk15. As a result, spurious outliers  are adjusted to a greater degree than counties with larger numbers of births and more stable rates. This method improves the precision of calculated rates by accounting for the high amount of population variance among counties in the United States by allowing geographic areas with smaller populations to “borrow strength” from those with larger populations in order to improve estimate precision16. In addition to the calculation of the smoothed rates, GeoDa was also used to generate corresponding maps of county level smoothed rates  rates of optimal birth for all live births and by maternal race/ethnicity by applying the smoothing tool in GeoDa version 1.18.0.10  was smaller than the national 25th percentile difference. While using White as a reference group is not always appropriate—due to the group’s increasing heterogeneity and the possibility of further entrenching existing racial inequities18.Frequencies were calculated for categorical maternal characteristics—both overall and by optimal birth status. For maternal characteristics that were continuous variables, the mean, median, standard deviation, 25th percentile, and 75th percentile values were calculated. Spatial analyses were conducted to map all EBS rates by county, as well as the indicator for birth equity. We ran Global Moran’s I tests to examine clustering of birth equity across counties and Local Indicators of Spatial Autocorrelation (LISA) for local clustering of the raw rates of optimal births to identify clusters of counties with similar characteristics , normal birthweight (89.82%), no gestational diabetes (93.21%), no gestational hypertension (92.52%), no abnormal conditions (88.90%), no need for emergency interventions (88.75%), a vaginal delivery (65.18%), and 97.58% had a five-minute APGAR score greater than or equal to 7. Race-stratified univariate results demonstrated some differences in the prevalence of optimal births among non-Hispanic Black (45.21%), compared to non-Hispanic White (50.44%) or Hispanic (50.99%) births. The White and Hispanic maternal race groups show a higher proportion of optimal births within their respective racial categories when compared to the lower proportion shown among the Black and Other categories.LISA estimates identified statistically significant spatial clusters including counties with high rates of optimal birth that were surrounded by neighboring counties with high rates of optimal birth (high-high clusters), counties with low rates of optimal birth surrounded by neighboring counties with low rates of optimal birth (low-low clusters) and outliers on both ends .Figure Of the 3140 census-designated counties within the US, only 282 (8.98%) appeared to be advancing equity in optimal births between White and Black populations  in which the birth occurs. Additionally, some of the birth records were missing data on the variables used in the optimal births measure. Because this is a county-level analysis, small counts of births prohibited our stratifying by maternal race/ethnicity beyond the three groups we included, or further stratifying within these non-monolithic categorizations. This statistical problem does not undermine the need for research and interventions to improve optimal birth and advance birth equity among other racial and ethnic identities. The concentration of counties achieving equity in the Midwest could be due to low Black and Hispanic population counts compared to White which may have led to overestimations. Lastly, because this is an ecologic and spatial analysis of the distribution of optimal birth rates, we are careful not to draw any conclusions about individual-level risk.23. In order to guarantee optimal outcomes, all aspects of the birth process should be addressed. Future analyses should continue to explore and monitor optimal birth and its correlates in order to broaden our understanding of reproductive health in the US. While this analysis is descriptive in nature, multivariate models may reveal associations between optimal births and sociocontextual, environmental, and/or policy factors that may be manipulated through intervention for the purpose of advancing racial health equity. Additionally, longitudinal analyses could show how optimal births change over time, especially as the healthcare landscape continues to evolve with new economic and political policies motivated by a growing acknowledgement of the role that medical and structural racism play in maintaining racial health inequities.Increased focus on optimal birth outcomes could reveal opportunities for improvement of care procedures in the perinatal period. A systematic review identified factors related to empowerment during the perinatal period, namely perceived control of decision-making and resources, as well as belief in one’s ability24. Our analytic results suggest that optimal births can be measured and that spatial patterns exist at the county level for this outcome.In the effort improve maternal health, we should focus not only on the absence of negative outcomes, but also the occurrence of positive outcomes, a paradigm shift that may prove insightful and effectiveSupplementary Information.


Low birth weight (LBW) is one of the major predictors of perinatal survival, infant morbidity, and mortality, as well as the risk of developmental disabilities and illnesses in future lives. The effect of the nutritional status of pregnant women on birth outcomes is becoming a common research agenda, but evidence on the level of low birth weight (LBW) and its association with prenatal iron status in Ethiopia, particularly among rural residents, is limited. Thus, this study aimed to assess the prevalence, predictors of LBW, and its association with maternal iron status using serum ferritin concentration in Haramaya district, eastern Ethiopia, 2021.p-value < 0.05.A community-based prospective cohort study design was conducted. Of a total of 427 eligible pregnant women followed until birth, 412 (96.48%) were included in the final analysis. Iron status was determined using serum ferritin (SF) concentration from venous blood collected aseptically from the ante-cubital veins analyzed on a fully automated Cobas e411  immunoassay analyzer. Iron deficiency(ID) and iron deficiency anemia (IDA) were classified as having SF less than 15 μg/L and SF less than 15 μg/L and Hb level of < 11.0 g/dl during the first or third trimester or < 10.5 g/dl during the second trimester as well, respectively. Birthweight was measured within 72 h of birth and < 2500 g was considered LBW. Birthweight was measured within 72 h of birth and < 2500 g was considered as LBW. A Poisson regression model with robust variance estimation was used to investigate the factors associated with LBW and the association between maternal iron status and LBW. An adjusted prevalence ratio with a 95% confidence interval was reported to show an association using a About 20.2% (95% CI: 16%-24%) of neonates were born with LBW. The prevalence of LBW was 5.04 (95% CI = 2.78–9.14) times higher among women who were iron deficient during pregnancy compared to those who were normal. The neonates of women who were iron deficient during pregnancy had lower birth weight  than the neonates of women who were normal. Prevalence of LBW was higher among mothers who were undernourished (MUAC < 23cm) , stunted (height <145cm)  and among female neonates . However, women who were supplemented with iron and folic acid (IFAS) during pregnancy had a 45% decreased chance of delivering low birth weight .We found that LBW is of public health significance in this predominantly rural setting. ID during pregnancy is found to have a negative effect on birth weight. IFA supplementation, the maternal under-nutrition, height, and sex of neonates were identified as predictors of low weight at birth. To improve maternal nutritional status, health interventions must address targeted strategies promoting desirable food behavior and nutritional practices. These include; promoting the consumption of diversified and rich iron food to improve the maternal nutritional status. A continued effort is needed in enhancing universal access and compliance with IFA supplementation to improve maternal health. Intervention strategies that are complementary and comprehensive across the vulnerable periods for women during pregnancy and their neonates that are based on a life-cycle approach are suggested. Pregnancy is associated with considerable changes in the physiological, anatomical, and biochemical attributes in women that affect the fetus’s developmental stages and, subsequently, impact the neonates’ birth weight and other related birth outcomes, such as gestation age at birth, stillbirth, and neonatal mortality . MoreoveThe prevalence of LBW differs across regions in Ethiopia. Several studies tried to assess the prevalence of LBW have witnessed the prevalence of LBW ranging from 8.6 to 28.3% . HoweverWomen in low-income countries often enter pregnancy with micronutrient deficiencies. They are exacerbated in pregnancy due to the increased demands of the developing fetus, leading to potentially adverse effects on the mother and babies . Iron deStudies endeavored to determine the effect of prenatal iron deficiency on birthweight concluded ambiguously –24. EvidThe study was embedded into the Haramaya Health Demographic Surveillance and Health Research Centre (HDS-HRC), which was established in 2018. The HDS-HRC covers 12 rural kebeles (the lowest administrative unit in Ethiopia) out of 33 kebeles found in the district located approximately 500 KM away from the capital city, Addis Ababa. From 5252 pregnant women in the district during the study period, 2306 were under follow-up of the HDS-HRC , 28.A community-based prospective cohort study was conducted. All pregnant women whose serum ferritin and hemoglobin concentrations were determined at their second or third trimester and who later gave singleton live births were eligible for the study. Neonates reached later on 72 h of birth were excluded. We also excluded pregnant women whose exposure was determined in the first trimester, as the fetal weight gain in the first trimester is known to be minimal. This study is a longitudinal study that obtained birth outcome information of pregnant women, the exposure of LBW was determined from Charan and Biswas's study , with thAs described in the previous papers , 32, froUpon recruitment and signing of informed consent, we assessed socio-economic characteristics, dietary practices, anthropometric measurements, and collected blood samples for hematology and biochemical analysis. We carried out the face-to-face interviews to collect knowledge, attitudes, and obstetric and other pregnancy-related data on house-to-house visits. The two-day training was given by research experts to train data collectors, laboratory professionals, and supervisors before the pre-test on each data collection tool. A day field pre-testing session followed the training in a nearby rural district. These trained data collectors carried out all data collection for this study at the randomly selected kebeles in the HDS-HRC.After the baseline study, participants' telephone numbers and addresses were collected and entered into a book, and we entered the expected date of delivery for each participant. The occurrence of births in the cohort was promptly found and informed by pre-established local community health promoters which the country labeled as ''Women Developmental Army (WDA)" and health extension workers (HEW). We used a structured questionnaire to gather data on the birth experiences of each participant. Birth outcomes such as gestational age at birth, birth weight, birth delivery method, neonatal mortality, stillbirth, abortions, and postpartum morbidity of mothers were obtained. Among all the birth outcomes data, birth weight was used as the outcome variable in this study. Birth weight was gauged within 72 h of birth by trained and well-experienced data collectors who were employed by HDS-HRC. Among all the birth outcomes data, birth weight was used as the outcome variable in this study.As described in the previous papers , 32, in A 5 ml venous blood was aseptically drawn from the antecubital veins and aliquoted into plain test tubes without anticoagulants. The blood samples were centrifuged, followed by separation of serum, stored frozen at -80 °C, and analyzed at the national chemistry laboratory in Ethiopian Public Health Institute (EPHI). We measured SF and serum high-sensitive C-reactive protein (hsCRP). SF was analyzed on a fully automated Cobas e411  immunoassay analyzer by the electro-chemiluminescence (ECL) method using commercial kits supplied by Roche Company, Germany at National Clinical Chemistry Reference Laboratory, EPHI. Whereas, highly sensitive C-protein reactive (hsCRP) was analyzed by Roche/Hitachi Cobas 6000 (c501):  fully automated clinical chemistry analyzer . The tesTwo levels of quality control (QC) samples were performed at least once every 24 h when the test is in use, once per reagent kit, and following each calibration to evaluate the functionality of the instrument and reagent, and the results of QC were evaluated using the Levey–Jennings chart (Wesgard rules). The calibration method has been standardized against the WHO International Standard NIBSC code: 03/178, 1st International Standard (IS) NIBSC  "Reagent for Ferritin (human liver)" 80/602, and Reference preparation of the IRMM  BCR470/CRM470 (RPPHS-Reference Preparation for Proteins in Human Serum) for serum ferritin and serum hsCRP respectively. Calibration was performed as per the standard operating procedures (SOPs).The high ferritin cut-off point (SF < 15 μg/L) recommended by WHO for developing countries was used to define ID to compensate for the effect of infection, which can lead to elevation of the level of ferritin. Serum ID, moderate iron depletion, and ID were termed as SF less than 15 μg/L, 15–30 μg/L, and > 30 μg/L . Serum CTo estimate the economic level of families, a wealth index was employed. The wealth dispersion was generated by applying principal component analysis (PCA). The index was calculated based on the ownership of latrine, agricultural land and size, selected household asset, a quantity of livestock, and source of water for drinking containing 41 household variables. As described in the previous paper  nutritioThe detailed methods of data quality assurances used are described in previous papers , in the baseline survey. Quality assurance during laboratory analysis was monitored in the National Reference Laboratory for Clinical Chemistry at the Ethiopian Public Health Institute (EPHI). The EPHI laboratory is accredited by the Ethiopian National Accreditation Office (ENAO) to conduct tests under ISO 15189:2012, Quality and Competence Medical Laboratory Requirements (accreditation no. M 0025) by well-trained and experienced laboratory professionals and standard operating procedures were strictly followed for respective parameters.p < 0.25 in the binary analyses were included in the multivariable analysis after checking for multi-collinearity using variance inflation factors. Adjustments were done for independent variables  for assessing iron status and birth weight. The direction and strength of statistical association were measured using adjusted prevalence ratio (aPR) along with their corresponding 95% confidence interval (CI). An adjusted prevalence ratio  statistical software. The outcome variable was dichotomized as LBW (= 1) if newborns were born with a birth weight of < 2500 g or normal (= 0), otherwise. Thus, the Poisson regression analysis model with a robust variance estimate was fitted to identify predictors of LBW. Bivariate analysis and multivariable analyses were done to identify the association between independent variables and LBW. The backward regression was fitted with selected socio-economic and fertility-related variables. The goodness of fit was checked by Hosmer–Lemeshow statistic and omnibus tests. Possible interactions between covariates were tested. Akaike's information criterion (AIC) and Bayesian information criterion (BIC) were used to test for model fitness. All variables with To estimate the economic level of families, a wealth index was employed. The wealth dispersion was generated by applying principal component analysis (PCA). The index was calculated based on the ownership of the latrine, agricultural land and size, selected household assets, the quantity of livestock, and source of water for drinking containing 41 household variables. As described in the previous paper , nutritiAll methods of this study were carried out under the Declaration of Helsinki's ethical principle for medical research involving human subjects . Ethicalp > 0.05). Of women included in the study 107(25.97%), 187(47.82%), and 193(46.84%) were stunted, undernourished, and iron deficient respectively. The majority of the respondents could not read or write (73.79%). Only 20.63% of the respondents were in the richest quintiles, Table 1Of a total of 427 followed eligible pregnant women, 412 (96.48%) were included in the final analysis. The retained and excluded participants did not significantly vary by wealth index, maternal age, educational status, nutritional status, iron status, anemia status, CRP and status, and dietary diversity . In the bi-variable analysis supplementations of IFA, occupation of the woman, sex of neonates, fertility desire, time to health facility, maternal MUAC, and maternal height were found to be a candidate for multivariable analysis at p < 0.25. The prevalence of LBW was higher among mothers who were undernourished (MUAC < 23 cm) , stunted (height <145 cm)  and among female neonates . However, women who were supplied with iron and folic acid during pregnancy had a 45% decreased chance of delivering LBW neonates , Table 2The mean neonates' birth weight was 2916.75 (± 546.55) grams. The mean male and female neonates’ birth weight was 2996.244 (± 486.60) and 2831.658 ((± 593.70) grams respectively. About one-fifth (20.2%) of neonates (95% CI: 16%-24%) were born with LBW. An independent t-test was used to identify if there is a significant difference in the birth weight of the neonates among the sex group of the newborn. Hence, we conclude that there is a significant difference in birth weight among the sex groups , which were statistically associated with low birth weight in the bivariate analysis. The adjusted modes revealed significant associations between prenatal iron status and birth weight. The prevalence of low birth weight was 5.04 (95% CI = 2.78–9.14) times higher among women who were iron deficient during pregnancy compared to those who were normal, Table This study aimed to assess the prevalence, predictors of low birth weight, and its association with maternal iron status using serum ferritin concentration in Haramaya district, eastern Ethiopia. The prevalence of LBW was 20.2% (95% CI: 16–24%). Factors associated with LBW were supplementations of IFA, maternal under-nutrition, stunting, and sex of neonates. We found that there is an increased risk of low birth weight in neonates born to iron-deficient mothers.Low birth weight is considered a public health concern when it exceeds 15% . There iVarious investigations have shown a positive association between IFA supplementation and fetus development , 47. In In this study, maternal height was a significant predictor of LBW. Our finding is in agreement with the reports of studies carried out in Cameroon , India , and EthThe current study has witnessed a significant association between prenatal iron status and infants' birth weight. From our result, babies born to iron-deficient mothers were lighter than their counterparts. Our finding is in line with previous studies reporting the positive correlation between maternal serum ferritin and birth weight , 61, 63.The important strength of this study was that data were collected prospectively with multiple variables. Additionally, serum ferritin concentrations biomarkers have been used to measure iron status. Especially to make a decision on the co-occurrence of inflammation in the interpretation of the finding of iron status, an analysis of conceptually important confounder (CRP) was carried out and was used for the adjustments of serum ferritin concentration in the present study. The study limitations should be considered when interpreting this result. The reality that prenatal serum ferritin concentration was evaluated only at one time in the whole pregnancy, can over or underestimate its association with birth weight as the exposure status to the deficiency might not be fixed. As birth weight is known to decline by 5–7% in the first three days of life , the stuWe found that low birth weight is of public health significance in this predominantly rural setting and demands attention in intervention in the community. Iron deficiency during pregnancy is found to harm birthweight. There is a risk of low birth weight in babies born to iron deficient mothers. Iron and folic acid supplementation, maternal under-nutrition, stunting, and sex of neonates were identified as predictors of low weight at birth. The study witnessed that there is an increased risk of low birth weight in neonates born to iron-deficient mothers. Health interventions must address targeted strategies promoting desirable food behavior and nutritional practices. These include; promoting the consumption of diversified and rich iron food to improve the maternal nutritional status. A continued effort is needed in enhancing universal access and compliance with IFA supplementation to improve maternal health. The reduction of low birth weight requires a comprehensive strategy that is based on a life-cycle approach to the problem. Thus, planners should consider the whole reproductive cycle and create a combination of strategies that are complementary and comprehensive across the vulnerable periods for women during pregnancy and their infants.


The relationship between high sFlt-1/PlGF scores and LBW (low birth weight) was described. In this study, we attempted to assess whether uric acid could be used as a secondary marker in estimating the pregnancy risk associated with LBW. (2) Material and methods: 665 pregnant women with a suspected or confirmed form of placental insufficiency were enrolled. In each of the patients, sFlt-1 and PlGF and uric acid levels were determined. Patients were divided into two groups according to birth weight below and above the third percentile for the given gestational age with the criteria of the neonatal definition of FGR . (3) Results: A significant negative correlation between neonatal birth weight and the uric acid level across the entire study group was observed. We found a significant negative correlation between neonatal birth weight and the uric acid level with birth weights < 3rd percentile. (4) Conclusions: There is a significant link between the uric acid concentration and LBW in the group with placental insufficiency. Uric acid can improve the prediction of LBW. An algorithm for LBW prognosis that makes use of biophysical (ultrasound) and biochemical  parameters yields better results than using these parameters separately from each other. Low birth weight (LBW) is a serious health problem present not only in developing countries. In those regions, maternal malnutrition and underweight, as well as inadequate care during pregnancy, are the principle causes of this gestational complication . In highIntrauterine growth restriction entails a multitude of complications in both early and adult life. It is the second major cause of perinatal mortality . It may Ultrasound is the gold standard for assessing fetal growth disorders . GordijnPresently, an array of compounds is being investigated for a potential correlation with low birth weight further on in the pregnancy. An abnormally functioning placenta produces angiogenic and anti-angiogenic factors in a disturbed manner. Among these factors, the most important are soluble fms-like tyrosine kinase-1 (sFlt-1), also referred to as soluble VEGF receptor-1 (VEGFR-1), and placental growth factor (PlGF). These are the only placental insufficiency markers identified and used in clinical practice so far. German and UK recommendations treat these parameters as standard in diagnosing preeclampsia . SpecifiDuring all our studies of placental insufficiency, we carefully evaluate the clinical and biochemical parameters of pregnancies, requiring sFlt-1/PlGF determinations, i.e., those suspected of having any of the forms of placental insufficiency. In addition to the markers that we discussed in our previous publications, we have noticed important correlations between birth weight and uric acid levels. When analyzing the available literature, we have come across reports of a relationship between different uric acid levels and low birth weight ,17.In the present study, we attempted to assess whether uric acid could be used as a secondary marker in estimating the pregnancy risk associated with low birth weight in pregnancies requiring an sFlt-1/PlGF determination, i.e., those with a suspected or confirmed form of placental insufficiency.Gestational hypertension, i.e., hypertension diagnosed beyond 20 weeks of gestation (wkGA), with values exceeding 140/90.Preeclampsia, i.e., hypertension with proteinuria or renal or hepatic impairment, and hematological disorders, as well as the clinical signs of uteroplacental dysfunction in the form of intrauterine death or FGR.HELLP syndrome, i.e., the occurrence of such signs as hemolysis, increased aminotransferase levels, and thrombocytopenia.Eclampsia, i.e., the occurrence of tonic-clonic seizures, is secondary to preeclampsia or not.FGR according to specific ultrasound criteria  after ruPlacental abruption was diagnosed on the basis of clinical signs, bleeding, and uterine tetanus, and a final diagnosis was made postpartum.The study included 665 pregnant women hospitalized in the Clinical Department of Obstetrics and Gynecology of the Pomeranian Medical University of Szczecin between May 2017 and June 2020 with a suspected or confirmed form of placental insufficiency. The qualification was based on criteria for identifying individual disease entities in accordance with the applicable guidelines:Patients diagnosed with placental insufficiency were monitored according to generally accepted standards using biochemical, biophysical, and general condition parameters. Additionally, the angiogenesis markers sFlt-1 and PlGF, as well as uric acid levels, were determined in all patients.Standardized reagent kits using the “ECLIA” electrochemiluminescent immunoassay and a colorimetric enzymatic assay were used for biochemical determinations.Blood for the determinations was sampled upon each patient’s informed consent, and then centrifuged for 30 min. and stored at −80 °C awaiting analysis.Upon delivery, birth weight and duration of gestation at birth were assessed, and based on these data, the Fenton growth charts were used to determine the corresponding birth weight percentiles . The newThe research was conducted with the consent of the local ethics committee of the Pomeranian Medical University (no. n.KB-0012/122/12) and supplemented by a consent to the expansion of the research team (no. n.KB-0012/46/18).p < 0.05 for all statistical differences was adopted. p-values are not adjusted for multiple tests and should be interpreted exploratorily only. A comparative analysis was performed between the different patient groups using the following statistical methods:The Shapiro–Wilk test was used to study data normality. Non-normal distributions of the evaluated parameters were observed.Due to the non-normal distributions, non-parametric tests were applied. The Mann–Whitney U test was used to identify the correlations between the parameters analyzed.The correlations between the compounds studied and the individual clinical, biochemical, and ultrasound parameters and the perinatal outcomes were compared using a Pearson correlation test and a significance test for regression coefficients.The data obtained in the study were statistically analyzed using Statistica 13.1 by StatSoft. For the individual variables, the basic descriptive statistics—the mean, the standard deviation, and the median—were calculated. The minimum and maximum values were determined. Percentages were calculated for the descriptive, non-numerical variables. The significance level of Our patients were evaluated according to the birth weight percentile criterion, i.e., by distinguishing between those with a neonatal birth weight < 3rd PC (n = 97) and >3rd PC (n = 568). In these groups, a multitude of significant differences were found, primarily between ultrasound parameters, the most significant ones being between the uterine artery and umbilical artery flow Doppler ultrasound measurements—these parameters are part of the definition of growth disorders proposed in 2016 by a team of experts according to the Delphi method . Signifip < 0.05). The individual correlations across the entire study group are shown in Our analysis of the correlations between individual parameters and birth weight across the entire study group showed numerous significant relationships, especially with ultrasound and angiogenesis parameters. What drew our attention was the significant negative correlation between neonatal birth weight and the uric acid level , and significant correlations with the disturbed angiogenesis markers. As for the group of patients with neonatal birth weights < 3rd PC, uric acid levels significantly correlated with birth weight . The individual correlations with uric acid levels, both across the entire study group and within the group of patients with neonatal birth weights < 3rd PC, are shown in Across the entire study group, significant positive correlations were shown between uric acid levels and abnormal Doppler ultrasound results for the umbilical artery flows  and biochemical  parameters yields better results than using these parameters separately from each other.


We used quasi-experimental difference-in-difference (DD) models to compare outcomes among SF births before and after PPLO to outcomes among births in control counties. Births from January 2017 through December 2018 among working San Francisco (SF) people were considered “exposed” to PPLO; births during this time among working people outside of SF, as well as all births before 2017, served as controls. We conducted subgroup analyses by race/ethnicity, education and Medicaid coverage at delivery. Overall analyses adjusting for covariates and indicators for time and seasonality indicated no association between PPLO and birth outcomes. Our results indicate that PPLO may not have affected the birth outcomes we examined among marginalized groups who, due to structural racism, are at heightened risk of poor outcomes. We speculate that this result is due to the PPLO’s design and focus on postnatal leave. Future work should examine the policy’s effects on other outcomes.Since 2017, San Francisco’s Paid Parental Leave Ordinance (PPLO) has allowed parents who work for private-sector employers to take 6 weeks of fully paid postnatal parental leave. Previous studies have linked paid parental leave with health improvements for birthing people and babies, although evidence for birth outcomes is limited. We hypothesized that the PPLO may have improved birth outcomes via reduced stress during pregnancy due to anticipation of increased financial security and postnatal leave. We used linked California birth certificate and hospital discharge records from January 2013 to December 2018 ( Only 23% of workers in the US have access to paid family leave (PFL) through their employers and, without adequate pay, many return to work within weeks after birth or adoption of a baby ,2. ReseaWhile beneficial to economic security and health, access to paid leave is not equitably distributed : 40% of While there is no national paid leave policy in the United States, the Family and Medical Leave Act (FMLA) allows for up to 12 weeks of unpaid job protected leave; also, several states, including California, have PFL laws that provide some wage replacement. In an effort to extend the benefits of parental leave to lower-income workers who may not be able to afford unpaid or partially paid leave, in 2017, San Francisco implemented the first fully-paid leave law in the US. San Francisco’s Paid Parental Leave Ordinance (PPLO) builds on the California PFL program which provides six weeks of partially paid leave  to care for a new baby. The PPLO requires that most private-sector employers in San Francisco pay their workers “supplementary compensation” so that the beneficiary receives 100% wage replacement for at least six weeks after the birth of a child. The PPLO is the first law in the country to ensure fully-paid parental leave to private employees, but the evidence for its intended impact on health—and birth outcomes in particular—is limited.In this study, we investigated whether the 2017 PPLO, which allows new parents working in San Francisco to take six weeks fully paid parental leave, was associated with improved perinatal outcomes. We employed a quasi-experimental design and leveraged population-level data from birth certificates and hospital discharge records. We also examined potential equity impacts of PPLO on subgroups more at-risk for adverse birth outcomes.We used a database of linked birth certificates and hospital discharge records from the California Office of State Health Planning and Development (OSHPD) for birthing people and infants from one year before birth to one year after birth. We included live singleton California births from January 2013 to December 2018 with a gestational age between 20 and 44 weeks at delivery. We excluded multiple births, births missing complete information on gestational age at delivery, and births with an implausible birthweight at delivery . We also excluded births occurring in the last quarter (October–December) of 2016 before the policy went into effect in January 2017 because these parents would have been eligible for the PPLO in the months following their baby’s birth. We then excluded the last quarter of all years to make the estimates comparable. The Committee for the Protection of Human Subjects within the Health and Human Services Agency of the State of California approved study protocols (2019-024).San Francisco’s PPLO was passed in April 2016 and went into effect on 1 January 2017 for employers with 50 or more employees, on 1 July 2017 for employers with 35 or more employees and on 1 January 2018 for employers with 20 or more employees. To be eligible for the benefit, individuals must have worked for their San Francisco-based employer for at least 180 days, have performed at least 8 h of work per week in San Francisco and spent at least 40% of their weekly hours in San Francisco.Birth certificates included data on the self-reported “date last worked” before birth, industry and occupation; we used this information and the date of birth to estimate whether the woman had worked during the pregnancy. We excluded births among people who likely did not work during the pregnancy to restrict the sample to those most likely to be eligible for the PPLO benefits see .Exposure to PPLO was determined by date of birth and maternal residence as listed on birth certificates: exposed births occurred from January 2017 through December 2018 among likely employed people living in San Francisco; unexposed births occurred during this time among similar people who lived outside of San Francisco and before January 2017 for all likely employed people regardless of location of residence. PPLO eligibility is based on employment location, rather than residence, but our data did not include this level of detail. Instead, we imputed employment in San Francisco using the county in which they gave birth. This may have resulted in some measurement error.We focused on infant outcomes that may be affected by the pregnancy related leave-taking via reduced financial, physical and psychological stress in pregnancy. Infant outcomes included binary variables representing whether the infant was preterm ); early term ; low birthweight ; or small for gestational age (SGA), as well as continuous measures of birthweight  and gestational age (in weeks).We included covariates that were likely to be associated with infant outcomes and leave-taking: maternal age , race/ethnicity , education , Medicaid insurance at delivery (vs. private insurance), parity , nativity (US- vs. foreign-born) and WIC receipt. We also included infant’s sex.We first examined sample characteristics among San Francisco births and California births outside of San Francisco before and after implementation of the PPLO using Chi-square tests and t-tests. Next, we estimated the average treatment effect of PPLO on birth outcomes using a quasi-experimental difference-in-difference (DD) design. DD designs are often used to estimate the effects of policies in the absence of random assignment and have been used in previous analyses of the effects of family leave policies ,11,12.In DD analyses, we compared the average change over time in the outcome variables for births among SF resientscompared to the average change over time among birthing people who lived in other California counties. Independent variables included county (SF vs control counties), an indicator for PPLO (whether the birth occurred before or after January 2017 when the PPLO was implemented), an interaction term between county and PPLO, indicator variables for year and quarter to account for annual and seasonal effects and the covariates listed above. We used complete case analysis, as missingness was minimal across covariates. We used multivariable linear regressions, including for binary outcomes, as is standard in DD analyses to support the correct interpretation of the interaction term.We conducted our analysis in three ways, with three different control groups that might represent the best counterfactual group for San Francisco. The DD models compared outcomes among San Francisco births before and after PPLO to outcomes from births in three control groups: (1) other Bay Area counties ; (2) other urban California counties ; and (3) all other California counties. While Bay Area counties are likely to be most similar in terms of demographic and occupational characteristics, there may have been spillover of program impacts for residents of other counties who were employed in San Francisco (and therefore eligible for PPLO benefits) that might bias estimates towards the null. We conducted stratified subgroup analyses to test whether there was heterogeneity in estimates by race/ethnicity, education and use of Medicaid at delivery. This is because prior research suggests that policy effects may differ for those in socially marginalized groups, either because they stand the most to gain or because they are least able to take advantage of a policy’s rollout.To explore sensitivity to possible anticipatory effects, we also re-estimate the models changing the PPLO start date from the actual implementation date (1 January 2017) to instead use the legislative enactment date (1 April 2016).In another sensitivity analysis, we removed all births that occurred in 2018 due to changes in the statewide California paid leave policies that may have affected birth outcomes.DD relies on the assumption that pre–post differences in outcomes would have been similar between births among SF residents and births among non-SF residents in the absence of the PPLO; this counterfactual is not empirically testable. Instead, we assessed whether pre-policy trends in outcomes were parallel across study groups, through visual inspection of the data for outcomes trends and quantitative inspection of coefficients from regression of each outcome on a time indicator in the pre-policy period. Differences in trends during the pre-policy period would indicate that birthing people outside of San Francisco are not a suitable control group. We also assessed whether there were significantly different compositional changes in covariates across treated and control groups before and after the policy went into effect by modeling each covariate as the dependent variable and including a place by time (pre/post policy implementation) interaction term.We also conducted analyses using a synthetic control for each outcome of interest . The strThe final sample included 1,812,189 births . OverallVisual inspection of pre-policy trends indicated that the parallel trends assumption held for gestational age and birthweight ; trends Overall DD analyses comparing SF county to other Bay Area counties indicated no evidence of statistically significant associations between the PPLO and any of the birth outcomes . ResultsSubgroup analyses were performed among 10 subgroups for each of the four outcomes. They show marginally statistically significant relationships in four of the 40 subgroup models . PTB decSensitivity analyses largely supported the main effects . We obseThe synthetic control approach also confirmed the null hypothesis for all outcomes of interest. Our results indicate that San Francisco’s Paid Parental Leave Ordinance had little effect on birthweight, gestational age at birth and related outcomes. Despite rigorous modeling of multiple control groups consisting of other Bay Area, urban California and all other California counties, as well as a synthetic control method, null results persisted across outcomes of interest. The confidence intervals rule out even relatively small effect sizes for the continuous outcomes: for birthweight the upper bound of the 95 percent confidence interval is 13.41 g (0.4 percent of the mean) and for gestational age the upper bound is 0.06 weeks (0.2 percent of the mean). However, for the binary outcomes the confidence intervals are not able to rule out meaningful effect sizes: the lower confidence interval bounds are a 1.0 percentage point reduction in preterm birth, a 0.5 percentage point reduction in early term birth, a 0.7 percentage point reduction in low birthweight and a 0.6 percentage point reduction in SGA. Our results suggest that the implementation of PPLO may not have affected the specified perinatal outcomes among pregnant people in the immediate two years after the policy went into effect and in particular among those at highest risk of adverse birth outcomes. While the PPLO was intended to benefit low-income families who may not have been able to take advantage of existing partially-paid leave policies, research has shown that it may not have reached low-income pregnant people . By limiOn the other hand, California’s PFL program, which provides partial wage replacement, doubled maternity leave utilization from approximately 3 to 6 weeks, with the strongest effects among Black, non-college educated, unmarried and Hispanic birthing people . The larFurthermore, the financial benefits of PPLO are only received in the post-partum period during parental bonding leave. Health benefits during the perinatal period would require that parents understand the future financial benefit from PPLO and respond to it in advance, such as via earlier prenatal leave or a reduction in psychological stress. Evidence from other contexts suggests, though, that many people do not exhibit this type of forward-looking response in anticipation of future benefits, perhaps because of lack of information, inattention, or uncertainty ,26. Unlike previous evidence that CA PFL narrows gaps in leave-taking between economically advantaged and disadvantaged populations , our resOur results indicate that future antenatal and postnatal leave programs should be designed with an equity approach to reach birthing people most in need of economic and employment supports. Proposed ideas for designing policies to advance population health equity include: requiring that wage replacement rates are high enough that low-income workers can afford to take the time off; upholding job-protection; increasing coverage for part-time and variable workers; and increasing campaigns to improve knowledge and uptake among marginalized populations. Previous research on PPLO has concluded that reducing complexity and increasing awareness will be necessary to improve equity in utilization and outcomes . With prOur results show that even a fully paid postpartum leave law may not be enough to overcome structural barriers to accessing leave during pregnancy, which may be most critical for birth outcomes. Policy impacts therefore may be limited to more advantaged workers. Future work should examine the effects of policy amendments to enhance the generosity of existing PFL policies or the more recently implemented state policies that provide job protection and higher wage replacement for low-income parents to determine whether these resulted in greater leave taking and improved health. Taking an explicit equity approach to improving population health means acknowledging differential access to time off in pregnancy and postpartum due to structural factors that influence work environments. Paid leave policies could take a Targeted Universalism approach to improving population health , as workThis study has several limitations. Birth certificate data lack detailed employment information and do not measure actual leave-taking. We utilized all available employment information  on the birth certificate to more accurately identify the population of pregnant people who would likely have been eligible for PPLO. However, our attempts to focus on only birthing people who were eligible for the PPLO may have been inadequate given insufficient details available about individual employment history and characteristics of employers , resulting in misclassification of the exposure to PPLO. Additionally, one limitation of all DD analyses is that there may have been other policies enacted at the same time in San Francisco and not in other counties that may have affected the outcomes in question, although we were not able to identify any such confounding policies. Finally, the results may not readily generalize to future policies implemented in other regions, e.g., where existing state policies are more or less generous than California’s PFL policy.Our findings suggest that the PPLO had limited beneficial impact on birth outcomes among birthing people in SF. We suggest that this was likely due to the policy’s design, including focus on only the postnatal period. In order to ensure that future paid family leave expansions reach their intended beneficiaries and do not perpetuate inequities, policymakers need to focus on policy design and implementation issues that could limit realized access, like coverage and eligibility restrictions and limited public awareness. Future work could examine other outcomes of interest such as parental mental health and stress.