Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.Here, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages  and outperform comparable models trained using known risk factors . Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.The online version contains supplementary material available at 10.1186/s12916-022-02522-x. Preterm birth, occurring before 37 weeks of completed gestation, affects approximately 10% of pregnancies globally , 5. The Despite our understanding of numerous risk factors, there are no accurate methods to predict preterm birth. Some biomarkers associate with preterm birth, but their best performance is limited to a subset of all cases , 16. RecEHRs are scalable, readily available, and cost-efficient for disease-risk modeling . EHRs caMachine learning models have shown promise for accurate risk stratification across a variety of clinical domains –34. Howen = 35,282) with documented deliveries. We trained models (gradient-boosted decision trees) that combine demographic factors, clinical history, laboratory tests, and genetic risk with billing codes to predict preterm birth. We find models trained on only billing codes show potential for predicting preterm birth and outperform a similar model using only known clinical risk factors. By investigating the patterns learned by our models, we identify clusters with distinct preterm birth risk and comorbidity profiles. Finally, we demonstrate the generalizability of billing code-based models trained at Vanderbilt on an external, independent cohort from the University of California, San Francisco . Our findings provide a proof of concept that machine learning on rich phenotypes in EHRs shows promise for portable, accurate, and non-invasive prediction of preterm birth. The strong predictive performance across clinical context and preterm birth subtypes argues that machine learning models have the potential to add value during the management of pregnancy; however, further work is needed before these models can be applied in clinical settings.In this study, we combine multiple sources of data from EHRs to predict preterm birth using machine learning. From Vanderbilt’s EHR database (> 3.2 million records) and linked genetic biobank , we identified a large cohort of women , we quantified the pairwise overlap between billing codes and each other data type. The largest subset included women with billing codes paired with demographic data . The smallest subset was women with billing codes paired with genetic data , Black (n = 6178), or Hispanic . Since multiple gestation pregnancies are more likely to deliver preterm, we developed prediction models using singleton pregnancies unless otherwise stated.From the Vanderbilt EHR database (> 3.2 million patients), we identified a “delivery cohort” of 35,282 women with at least one delivery in the Vanderbilt hospital system Fig. A. In addn = 15,041, Fig. To determine the delivery date and type (preterm vs. not preterm) at scale across our large cohort, we developed a phenotyping algorithm using delivery-specific billing codes and estimated gestational age at delivery. For women with multiple pregnancies, we only considered the earliest delivery. We find that labeling preterm births using delivery-specific billing codes has high concordance  with EGA-based delivery labels Fig. E. Our fiUsing this richly phenotyped delivery cohort, we evaluated how well the entire clinical phenome, defined as billing codes (ICD-9 and CPT) before and after delivery, could identify preterm births. With counts of each billing code (excluding those used to ascertain delivery type), we trained gradient-boosted decision trees  to classpredicting preterm birth.In all evaluations, we held out 20% of the cohort as a test set and used the remaining 80% for training and validation  to mitigate sample size as a potential confounder while comparing the performance. We only considered active pregnancies at each time point; for example, delivery at 27 weeks would not be included in the 28-week model, since the outcome would already be known. The ROC-AUC increased from conception  to the highest performance at 28 weeks  due to the differing availability of each feature type.In addition to billing codes, EHRs capture aspects of an individual’s health through different types of structured and unstructured data. We tested whether incorporating additional features from EHRs can improve preterm birth prediction. Models were evaluated using data available at 28 weeks of gestation; we selected this time point as a tradeoff between being sufficiently early for some potential interventions and late enough for sufficient data to be present to enable accurate predictions using billing codes. From the EHRs, we extracted sets of features including demographic variables , clinical keywords from obstetric notes, clinical lab tests ran during the pregnancy, and predicted genetic risk (polygenic risk score for preterm birth). To measure the performance gain for each feature set, we compared the models trained using the feature set only, billing codes only, and billing codes combined with the feature set Fig. A. WithinModels using only demographic factors, clinical keywords, and genetic risk had ROC-AUC and PR-AUC similar to chance Fig. B. ClinicAlthough there are well-known risk factors for preterm birth, few validated risk calculators exist and even fewer are routinely implemented in clinical practice . We evalThe billing code-based model significantly outperformed a model trained with clinical risk factors at predicting preterm birth at 28 weeks of gestation  as preterm; this is significantly higher than the risk factor only model  scores , 53 for p-value = 0.02, Fisher’s exact test, Fig. p-value = 0.0002) and cluster 6  and were depleted (negative enrichment) in cluster 5 . Fetal abnormalities demonstrated a similar pattern with strong enrichment in cluster 1 . Extreme age at delivery (> 34 or < 18 years old) was enriched, though weakly (OR = 1.2 to 2.2) for all clusters except clusters 5 and 6. Pre-pregnancy BMI, pre-pregnancy hypertension, and gestational hypertension had similar patterns with the strongest enrichment in cluster 3. The remaining clinical risk factors show similar patterns and are provided in Additional file We also tested for enrichment of clinical risk factors of preterm birth in the clusters. We observed distinct patterns of enrichment and depletion for each clinical risk factor Fig. : Fig. S7By analyzing the feature importance values through UMAP embeddings, we identify interpretable clusters of individuals discovered by the machine learning model that reflect the complex and multi-faceted paths to preterm birth. Overall, the learned rules highlight the relationships between clinical factors and preterm birth prevalence. For example, some risk factors, such as age at delivery, are enriched in all clusters with high preterm birth prevalence. Other factors, such as pre-pregnancy BMI and hypertension, are strongly enriched only in specific clusters with high preterm birth prevalence. Thus, this approach enables us to interpret phenotypic patterns of risk and identify subgroups among cases learned from complex EHR features by the prediction model.n = 5475) singleton deliveries and one on vaginal deliveries . Preterm birth prediction accuracy was higher in the cesarean section cohort  compared to the vaginal delivery cohort  and a previous preterm birth influence preterm birth prediction. We trained two classifiers using billing codes (ICD-9 and CPT) occurring before 28 weeks of gestation: one on a cohort of cesarean section  and a held-out test set (20%) to evaluate the model performance the “” sectionn = 4215) and an independent cohort from UCSF (n = 5978). The UCSF cohort was ascertained using similar rules as the Vanderbilt cohort , but the performance was slightly lower than using both ICD and CPT codes Fig. A and PR-R = 0.93, p-value < 2.2E−308) between the held-out Vanderbilt set and the UCSF cohort  with the largest influence on the model predictions. We used SHAP , 55 scorWe evaluated how a simpler model that may be easier to implement in routine clinical practice would compare to the full gradient-boosted decision tree model. Using the only top 15 features identified in the Vanderbilt-28wk model Fig. C, we evaGiven systemic biases in healthcare, it is critical to evaluate the accuracy of prediction algorithms, especially those based on EHR or genetic data, on individuals of different races and ancestries. Furthermore, preterm birth prevalence varies by race with Black women at twice the risk compared to White women . We evalPreterm birth is a major health challenge that affects 5–20% of pregnancies , 2, 12 aDecision tree-based models are robust to correlated features can identify complex non-linear combinations and remain transparent for interpretation after training. In addition to these advantages, decision tree-based models have demonstrated strong performance in various clinical prediction tasks –60. PregOur approach has several distinct advantages compared to published preterm birth prediction models. First, our models have robust performance. Previous models using risk factors  to predict preterm birth, despite having cohorts up to two million women , have reSecond, our models use readily available data throughout pregnancy that do not require invasive sampling. While some studies have also obtained high ROC-AUCs , they used serum biomarkers across small cohorts  or acuteThird, the gradient-boosted decision trees we implement are easier to interpret than “black box” deep learning models that cannot easily identify features driving predictions. Transparency is an important, if not necessary, characteristic of machine/artificial learning models deployed in clinical practice , 64, andFinally, our approach generalizes across hospital systems. We demonstrate that billing code-based models trained at Vanderbilt achieve similar accuracy in an independent cohort from UCSF. The generalizability of machine learning models can be constrained by the sampling of the training data. Thus, the accurate prediction in an independent dataset from an external institution points to several inherent strengths of the approach. First, successful replication indicates the models’ ability to learn predictive signals despite regional variation in assigning billing codes to an EHR. Even with different demographic distribution between the two cohorts  at Vanderbilt Hospital based on the presence of delivery-specific billing codes, which included the International Classification of Diseases ninth and tenth editions  and Current Procedural Terminology (CPT) or estimated gestational age (EGA) documented in the EHR. Combining delivery-specific ICD-9/10 (“delivery-ICDs”), CPT (“delivery-CPTs”), and EGA data, we developed an algorithm to label each delivery as preterm or not preterm. Women with multiple gestations  were identified using ICD and CPT codes and excluded for singleton-based analyses. See Additional file We identified women with at least one delivery  ascertained using the delivery-ICDs. EGA values, extracted from structured fields across clinical notes, were mapped to multiple pregnancies using the same procedure. For women with multiple EGA recorded in their EHR, the most recent EGA value determined the time interval to group preceding EGA values. Based on the most recent EGA value for each pregnancy, we assigned labels to each delivery . After pooling the delivery labels based on delivery-ICDs and EGA, we assigned a consensus delivery label by selecting the oldest gestational age-based classification . By incorporating both billing code- and EGA-based delivery labels and selecting the oldest gestational classification, we expect this to increase the accuracy of this algorithm, which we evaluate by chart review (described in detail below).Since CPT codes do not encode delivery type, we combined the delivery-CPTs with timestamps of delivery-ICDs and EGAs to approximate the date of delivery. Delivery-CPTs were grouped into multiple pregnancies as described above. The most recent timestamp from delivery-CPTs, delivery-ICDs, and EGA values was used as the approximate delivery date for a given pregnancy.To validate the delivery type ascertained from billing codes and EGA, we used chart-reviewed labels as the gold standard. For 104 randomly selected EHRs from the delivery cohort, we extracted the date and gestational age at delivery from clinical notes. For the earliest delivery recorded in the EHR, we assigned a chart review-based label according to the gestational age at delivery . The precision/positive predictive value (PPV) for the ascertained delivery type as a binary variable (“preterm” or “not preterm”) was calculated using the chart reviewed label as the gold standard. To compare the ascertainment strategy to a simpler phenotyping algorithm, we compared the concordance of the label derived from delivery-ICDs to one based on the gestational age within 3 days of delivery. This simpler phenotyping approach resulted in a lower positive predictive value (85%) and recall  with a 22% preterm prevalence. As expected, this model did not do better than chance . All trained models with their optimized hyperparameters are provided at https://github.com/abraham-abin13/ptb_predict_ml.All models for predicting preterm birth used boosted decision trees as implemented in XGBoost v0.82 . Unless As the first step, we evaluated whether billing codes could discriminate between delivery types. Models were trained to predict preterm birth using the total counts of each ICD-9, CPT, or ICD-9 and CPT code across a woman’s EHR. We excluded any codes used to ascertain the delivery type or date. All three models were trained and evaluated on the same cohort of women who had at least one ICD-9 and CPT code  since women who already delivered were excluded at each time point.Next, we evaluated the machine learning models at 0, 13, 28, and 35 weeks of gestation by training using only features present before each time point. For the subset of women in our delivery cohort with EGA, we calculated the date of conception by subtracting EGA (recorded within 3 days of delivery) from the date of delivery. Next, we trained the models using ICD-9 and CPT codes time-stamped before different gestational time points with only singleton  compared to a prospective conception-based design  and yielded similar results.In addition to evaluating the models based on the date of conception, we trained the models at different time points before the date of delivery  was genotyped on the Illumina MEGAEX platform. We applied standard genome-wide association study (GWAS) quality control steps [p-value thresholds. We included the date of birth and five genetic principal components to control for ancestry. Our final polygenic risk score used 356 preterm birth-associated SNPs .A subset of women  using only the EHR feature being evaluated, (2) using ICD-9 and CPT codes, and 3) using the EHR feature with ICD-9 and CPT codes. Thus, all three models for a given EHR feature were trained and evaluated on the same cohort of deliveries . We selected well-established obstetric risk factors that included maternal and fetal factors across organ systems, occurred before and during pregnancy, and had moderate to high risk for preterm birth [We compared the performance of a model trained using billing codes (ICD-9 and CPT) present before 28 weeks of gestation with a model trained using clinical risk factors to predict preterm delivery Fig. . Both morm birth , 23, 44.rm birth , gestatirm birth , gestatirm birth , fetal arm birth , cervicarm birth , and sicrm birth  status wrm birth , age at rm birth –47, pre-rm birth , were exn = 16) were not assigned to a cluster (“outliers”). To visualize the cluster assignments, we performed UMAP on the feature importance matrix with default settings and two UMAP components and colored each individual by their cluster membership. Finally, we calculated the preterm birth prevalence and accuracy within each cluster.To better understand the decision-making process of our machine learning models, we calculated the feature importance value for the model predicting preterm birth at 28 weeks of gestation. We used SHapley Additive exPlanation values (SHAP) , 53, 55 10 scale of the odds ratio. For sickle cell disease, one cluster did not have any cases of sickle cell disease.We tested for enrichment of clinical risk factors within each cluster by using Fisher’s exact test as implemented in Scipy . For eacWe compared how models trained used billing codes (ICD-9 and CPT) performed in different clinical contexts. First, we evaluated the accuracy of predicting spontaneous preterm birth using models trained to predict all types of preterm births. From all preterm cases in the held-out set, we excluded women who met any of the following criteria to create a cohort of spontaneous preterm births: medically induced labor, delivery by cesarean section, or preterm premature rupture of membranes. The ICD-9 and CPT codes used to identify the exclusion criteria are provided in Additional file We trained the models to predict preterm birth among cesarean sections and vaginal deliveries separately using billing codes (ICD-9 and CPT) as features. Deliveries were labeled as cesarean sections or vaginal deliveries if they had at least one relevant billing code (ICD-9 or CPT) occurring within 10 days of the date of first delivery in the EHR. Billing codes used to determine the delivery type are provided in Additional file We evaluated how well models using billing codes could predict recurrent preterm birth. From our delivery cohort, we retained women whose first delivery in the EHR was preterm and a second delivery for which we ascertained the type (preterm vs. not preterm) as described above for the first delivery. We trained models using billing codes (ICD-9 and CPT) at time points before the date of delivery because the majority of this cohort did not have reliable EGA at the second delivery. As described earlier, separate models were trained using billing codes timestamped before the time point being evaluated Fig. : Fig. S9We evaluated how well models trained at Vanderbilt using billing codes perform in an external cohort assembled at UCSF. Only the first delivery in the EHR was used for prediction. Women with twins or multiple gestations, identified using billing codes  from this analysis if their race was annotated as “other” or had multiple categories because their subset counts were low (n < 143), therefore more likely to have sampling variability. Stratifying the held-out set by race resulted in four categories . Next, we evaluated the model performance on each subset and report the ROC-AUC and PR-AUC with the preterm birth prevalence within each subset.For the Vanderbilt-28wk model, we evaluated model performance on the Vanderbilt held-out set stratified by race. We excluded individuals (Additional file 1. Supplementary figures, tables, and methods.


Left ventricle (LV) pseudoaneurysm is a rare disorder post-acute myocardial infarction (AMI). Resection or closure of the pseudoaneurysm by surgery is recommended due to the high propensity of pseudoaneurysm rupture while surgery has also high risks. Conservative therapy could be acceptable in small pseudoaneurysms or patients with high surgical risks. Nevertheless, the risk evaluation and grasp of indication are not clear. This case reported an acute cyst-like LV pseudoaneurysm formation post-AMI-induced myocardial free wall rupture (MFWR), and the patient recovered with spontaneous closure of the fissure and shrinkage of the LV pseudoaneurysm through non-surgical therapy. Based on the observations in the echocardiogram, we proposed that intermittent closing of the fissure and interruption of the blood flow between the LV and the pseudoaneurysm due to LV contraction alleviated stress change on the pseudoaneurysm. The narrow fissure, small pseudoaneurysm, and intermittently interrupted blood flow that benefit fissure healing and pseudoaneurysm stabilization could indicate the prognosis of this patient. Drugs like β-blocker that decreased the stress on the pseudoaneurysm also led to the risk reduction of pseudoaneurysm rupture. To our knowledge, this is the first case that reports a spontaneous closure of LV pseudoaneurysm. The size of the fissure and the pseudoaneurysm, as well as the corresponding hemodynamic state, could be valuable to evaluate the risk and prognosis of the pseudoaneurysm. Optimized medical management was also helpful to pseudoaneurysm stabilization. Left ventricle (LV) pseudoaneurysm formation is a rare complication of acute myocardial infarction (AMI) with a poor prognosis . It is a*1.1 cm pseudoaneurysm with a width of 0.3 cm fissure in the lateral wall that connected the LV and the pseudoaneurysm  was about 160/80 mmHg, and his blood glucose was not monitored. He also had a smoking history of 20 years. No family history or psychosocial history was declared. The electrocardiogram (ECG) of the patient showed ST segment elevation in leads II, III, aVF, V5-V6, and V7-V9, which suggested inferior-, posterior-, and lateral-wall AMI . Primaryaneurysm , while caneurysm . Interesaneurysm ; while daneurysm . Comprehaneurysm . MeanwhiPseudoaneurysm is fatal because of the risk of rupture. According to previous literature , most paPseudoaneurysm keeps blood flow with LV, bearing stress from the ventricular chamber and pericardial cavity. With the movement of LV, altering stress could result in pseudoaneurysm expansion or even rupture. Through TTE and CMR, we observed a specific cardiac movement pattern in our patient with a small pseudoaneurysm, which is dissimilar from a big pseudoaneurysm that continuously keeps a connection with LV , 18. We Concerning to medical therapy, long-term medication of ACEI/ARB or β-blocker was observed in some conservative cases –15. SomeIn some cases, or small retrospective analysis, mechanical circulatory support (MCS) could be beneficial in stabilizing the hemodynamic conditions in patients with mechanical complications post-AMI and building a bridge to a safer timing of surgery. For example, La Torre et al.  describeThis is the first case report of spontaneous closure of a pseudoaneurysm post AMI with non-surgical therapy. The shape and movement pattern of the pseudoaneurysm could promote rupture healing. Careful conservative management based on stress control and wound healing synergistically facilitated the healing process. This case highlights the importance of shape characteristics and hemodynamic state of a pseudoaneurysm in the risk and prognosis evaluation. With the limitation of the number of cases, whether the experience can be expanded or even provide information for clinical decision need to be further investigated.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.XS designed the case report, analyzed the patient data, and revised the manuscript. XH analyzed the patient data and drafted the manuscript. YW designed the case report and collected the patient data. All authors read and approved the final manuscript.This work was supported by the National Natural Science Foundation of China (Grant Nos. 31901004 and 82070376).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Few of these studies have examined maternal racial/ethnic groups other than Non-Hispanic Black, non-Hispanic White and Hispanic, nor have they included paternal race. We aimed to examine the independent effects of PM, term low birthweight rate (TLBW) and gestational PM2.5 pollutant using multivariate regression models. Effect modification of air pollution exposures on birth outcomes by maternal and paternal race was evaluated using stratified models. All analyses were conducted with sample weights to provide national-scale estimates.We used data from the Early Childhood Longitudinal Study, Birth Cohort (ECLS–B), a longitudinal nationally representative cohort of 10,700 U.S. children born in 2001, which we linked to U.S.EPA’s Community Multi-scale Air Quality (CMAQ)-derived predicted daily PM2.5 concentrations at the centroid of each Census Bureau Zip Code Tabulation Area (ZCTA) for maternal residences. We examined relationships between term birthweight (TBW)2.5 was associated with reduced birthweights during second and third trimester and over the entire gestational period in adjusted regression models, although results did not reach statistical significance. In models stratified by maternal race and paternal race, one unit increase in PM2.5 was statistically significantly associated with lower birthweights among AAPI mothers, -5.6 g  and AAPI fathers, -7.6 g  during 3rd trimester and among births where father’s race was not reported, -14.2 g .The majority of mothers were White (61%). Fourteen percent of mothers identified as Black, 21% as Hispanic, 3% Asian American and Pacific Islander (AAPI) and 1% American Indian and Alaskan Native (AIAN). Fathers were also racially/ethnically diverse with 55% identified as White Non-Hispanic, 10% as Black Non-Hispanic, 19% as Hispanic, 3% as AAPI and 1% as AIAN. Results from the chi-square and ANOVA tests of significance for racial/ethnic differences indicate disparities in prenatal exposures and birth outcomes by both maternal and paternal race/ethnicity. Prenatal PMThese data suggest that paternal characteristics should be used, in addition to maternal characteristics, to describe the risks of adverse birth outcomes. Additionally, our study suggests that serious consideration should be given to investigating environmental and social mechanisms, such as air pollution exposures, as potential contributors to disparities in birth outcomes among AAPI populations.The online version contains supplementary material available at 10.1186/s12940-022-00856-w. These etituents . However.061783)]. Similarpositive , meaning2.5- related reductions in birth weight or risk of TLBW [2.5 effects reported for AAPIs has been mixed [2.5 exposures were higher among AAPIs compared to other population groups in our sample  among Asian mothers and couples compared to White mothers and couples , 74, 80.2.5 – 10) was enhanced for fathers without a college degree [2.5 exposures. Relative to White parents, racial and ethnic minority identity was associated with lower birth weights and increased odds of TLBW. We found that paternal race remained independently associated with birth weight, above and beyond maternal race (Models 4 and 8) suggesting paternal factors may be important to consider in investigations of birth outcomes. In stratified models by paternal race, Black, White, AIAN race and Hispanic ethnicity had favorable effects on PM2.5-birthweight relationship. Infants whose father’s race was “not stated” or API had significant BW reductions. The finding about father’s race “not stated” is in line with previous studies showing infants whose paternal race and ethnicity were unreported on birth certificates have higher rates of low birthweight, preterm birth and small for gestational age, regardless of maternal race/ethnicity [Existing evidence supports the finding of paternal race/ethnicity to be associated with pregnancy outcomes, independent of maternal race and ethnicity , 30, 33.e degree . Howevere degree . Babies thnicity , 95–97. Paternal factors may influence birth outcomes through a number of pathways that act directly  and indiAnother pathway could be through the effect of fathers on maternal health behaviors that include use of prenatal care, maternal smoking and other behaviors linked to adverse birth outcomes , 113. Fo2.5 – birthweight associations, controlling for smoking and adequacy of prenatal care. This result confirms findings by Gould and colleagues who argued that incomplete birth certificates provide an important maker for identifying high-risk women and vulnerable infants [Studies that have examined indicators of paternal involvement, such as marital status or completeness of paternal information on the birth certificate, have noted higher rates of preterm birth and low birth weights among father-absent births , 96, 97. infants . From ou infants .The results of this study point to several avenues for future research. Our findings, coupled with those of others, suggest a need to consider socio-ecological contexts of both moms and dads individually and together in their relationship with each other in studies of environmental pollutant impact on birth outcomes. Collecting individual level information directly from fathers on their involvement during pregnancy and afterwards and their relationships to the children’s mothers is challenging . The ECLOur study is not without limitations. Similar to previous air pollution and birth outcomes studies, non-differential exposure misclassification is one potential problem which could bias our results towards null findings, although we are uncertain if this is the case. We assu2.5 and birthweight. Previous studies assessing effect modification of race on the relationship between PM2.5 and birth outcomes have mostly been conducted in California, northeastern states, individual states and/or single urban areas with varying levels of population diversity. Studies of air pollution effects on birth outcomes have rarely considered the impact of paternal factors or on the racialized differences therein. Instead of treating paternal race as missing we included “race not stated” as its own category. Previous studies have shown that paternal race/ethnicity influences birth outcomes and that “missing” or “not stated” paternal race/ethnicity may be an important risk factor. Using the ECLS- data set, we were able to leverage demographic information from the 9-month records . Additionally, we adjusted for maternal smoking, which has been identified as a serious limitation of previous studies where this risk factor was not taken into consideration. The PM2.5 measures were derived from U.S. EPA’s CMAQ model, which is a widely used and validated model providing data for the entire country. Use of CMAQ allowed us to assign air pollution exposures to urban and rural ECLS-B participants (where air monitors are sparse).Our study has several strengths. This is the first national assessment of effect modification by both maternal and paternal race in the relationship between PM2.5 exposures on birth weight by race, though ours is the first using a nationally representative sample of births which included parental race/ethnicity information. We found disparities in prenatal PM2.5 exposures by both maternal and paternal race. Both maternal and paternal race/ethnicity were independently associated with birth outcomes in non-pollutant models. We found prenatal PM2.5 was associated with reduced birthweights during second and third trimester and over the entire gestational period in adjusted models, although results did not reach statistical significance. However, in models stratified by maternal race and paternal race, PM2.5 was statistically significantly associated with lower birthweights among AAPI mothers and fathers and among births where father’s race was not reported. These data suggest that paternal characteristics should be used, in addition to maternal characteristics, to describe the risks of adverse birth outcomes and that missing paternal information on birth certificate is important indicator of harmful environmental pollutant exposures and higher/enhanced risk.In summary, several prior studies have examined differential effect of prenatal PMAdditional file 1: Supplementary Table 1. Comparison of results from U.S. based studies that examined effect modification by parental race. Supplementary Table 2. Characteristics of U.S. singleton term births born in 2001 by birth weight status, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table3. Cross-tabulation of maternal and paternal race/ethnicity for U.S. singleton term births born in 2001, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table 4. Maternal marital status by maternal and paternal race/ethnicity for U.S. singleton term births born in 2001, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table 5. Household poverty status by maternal and paternal race/ethnicity and type of father at 9-months for U.S. singleton term births born in 2001, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table 6. Type of father at 9-months by maternal race/ethnicity for U.S. singleton term births born in 2001, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table 7. Type of father at 9-months by paternal race/ethnicity for U.S. singleton term births born in 2001, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table 8. Maternal educational attainment by paternal race/ethnicity for U.S. singleton term births born in 2001, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table 9. Maternal educational attainment by maternal race/ethnicity for U.S. singleton term births born in 2001, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table 10. Maternal educational attainment by type of father at 9-months for U.S. singleton term births born in 2001, Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) study sample. Supplementary Table 11. Summary of average daily PM2.5 levels during each trimester and whole pregnancy. Supplementary Table 12. Correlation of PM2.5 across trimesters. Supplementary Table 13. Crude and adjusted odds ratios of term low birth weight (LBW) associated with selected non-pollutant variables among the ECLS-B study population. Supplementary Table 14. Crude and adjusted odds ratios of term low birth weight (LBW) associated with average PM2.5 exposures by trimester and whole pregnancy among the ECLS-B study population. Supplementary Table 15. Adjusted odds ratios of term low birth weight (LBW) associated with average PM2.5 exposures by trimester and whole pregnancy among the ECLS-B study population stratified by maternal and paternal race.


Birth weight is a significant determinant of the likelihood of survival of an infant. Babies born at low birth weight are 25 times more likely to die than at normal birth weight. Low birth weight (LBW) affects one out of every seven newborns, accounting for about 14.6 percent of the babies born worldwide. Moreover, the prevalence of LBW varies substantially by region, with 7.2 per cent in the developed regions and 13.7 per cent in Africa, respectively. Ethiopia has a large burden of LBW, around half of Africa. These newborns were more likely to die within the first month of birth or to have long-term implications. These are stunted growth, low IQ, overweight or obesity, developing heart disease, diabetes, and early death. Therefore, the ability to predict the LBW is the better preventive measure and indicator of infant health risks.This study implemented predictive LBW models based on the data obtained from the Ethiopia Demographic and Health Survey 2016. This study was employed to compare and identify the best-suited classifier for predictive classification among Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, Random Forest (RF), Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting.Data preprocessing is conducted, including data cleaning. The Normal and LBW are the binary target category in this study. The study reveals that RF was the best classifier and predicts LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 percent ROC-AUC, 91.60 percent F1 Score, 1.05 percent Hamming loss, and 81.86 percent Jaccard score.The RF predicted the occurrence of LBW more accurately and effectively than other classifiers in Ethiopia Demographic Health Survey. Gender of the child, marriage to birth interval, mother’s occupation and mother’s age were Ethiopia’s top four critical predictors of low birth weight in Ethiopia.The online version contains supplementary material available at 10.1186/s12911-022-01981-9. Birth weight has a significant influence on its chances of survival. Low birth weight (LBW) is becoming more an issue, particularly in emerging countries. A major cause of neonatal death is low birth weight, less than 2500 g . Babies Evidence shows that the global prevalence of LBW dropped by 1.2 percent each year between 2000 and 2015, meaning that progress is insufficient to fulfil the World Health Assembly's low birth weight target of 30 percent by 2025 [Low birth weight can be caused by a variety of circumstances, depending on the region , 2. Low Numerous studies have found that the most prevalent causes of low birth weight are prematurity and intrauterine growth restriction. In developed countries, the most common reason is preterm birth, but in underdeveloped countries, the most common cause is intrauterine growth restriction . In addiEven though LBW is a worldwide concern, the condition is most prevalent in the world's poorest areas, Sub-Saharan Africa and Southern Asia , suggestAccording to , EthiopiEthiopia has a birth weight burden of around half of the SSA, at around 11% . As a reThis study ML flows first displays the data source, pre-processing stage, feature selection for the classifier, hyper-parameter tuning, and classifier's functioning technique for the classification.The data of this study was obtained from the fourth wave of the Ethiopia Demographic and Health Survey, which took place in 2016 (EDHS-2016). The EDHS-2016 data collection period ran from January 18 to June 27, 2016. The survey was conducted under Ethiopia's Central Statistical Agency (CSA), with technical assistance supplied by ICF and financial support provided by other international organizations. The goal of the EDHS is to produce up-to-date estimates of the population's significant demographic and health variables . The surSeparate enumeration areas (EAs) were chosen in two phases in each stratum. The EDHS-2016 selected 645 EAs at random from a total of 84 915 EAs in Ethiopia, with a probability proportional to EA size and independent selection in each stratum. The second stage of the sampling procedure involved picking a predetermined number of 28 households from each EA using an equal probability systematic approach . There wLow birth weight (LBW) is the study's target variable. Birth weight data were obtained in the survey for children born five years before the interview. There were 10,641 births in the five years before the survey. However, only 2110 newborns have birth weight data, accounting for around 14% (weighted) of the total number of births in the study.This study employed data pre-processing to eliminate missing values, noisy data, and incompatible data since it is done in its raw form. Pre-processing, cleaning, integrating, transforming, reducing, and discretizing data are helpful . In thisSecond, each sample in the dataset includes over a thousand features; therefore, feature selection is essential. A closer look reveals that the bulk of the features is unsuitable for the implementations. As a result, identifying significant features associated with LBW is an essential initial step. An assessment of the literature , 22 and Relevant features determine the performance of any machine learning model implementation. Feature selection refers to picking critical characteristics for the research work at hand. In addition to lowering data dimensionality, it improves data visualization and understanding. If further study is done, the effects of all 25 qualities must be considered. The topic of categorical data discretization is widely covered in . An imbaSome researchers have used machine learning approaches to anticipate medical problems and other disorders , 23. RanBecause of its ability to predict and ease usage, Extreme Gradient Boosting (XGB) has become one of the most popular machine learning approaches. The regression and classification algorithms are being monitored. The two principles that make up XGB are decision trees and gradient boosting. Decision trees are likely the most easily interpretable machine learning algorithms known, and they may be pretty powerful when used in conjunction with the correct techniques. The nodes and leaves of a decision tree resemble the nodes and leaves of a tree with a base and numerous branches .XGB is a boosting method that is part of an ensemble learning algorithm. The goal of XGB is to keep adding trees and performing feature splitting to expand a tree. When a tree is added, it learns a new function to suit the residuals of the previous prediction. The score of a sample is predicted when k trees are created after training. In reality, each tree will have a matching leaf node based on the features of this sample, and each leaf node correlates to a score. The sum of the scores for each tree equals the sample's predicted value. XGB adds a regular term to the loss function's second-order Taylor expansion to balance the model's complexity and the loss function's decline. It looks for the optimal solution globally and prevents model overfitting to a large extent.K-Nearest Neighbor (K-NN) method selects the K samples with the most significant sample similarity to be graded, the voting approach determines which group the samples belong to, and the instance is divided into this class. The K-NN classification algorithm performs poorly in an adversarial environment. It is simple to use and maybe put to good use in some circumstances .The Random Forest (RF) classifier has addressed classification and regression problems. Each split decision employs the notion of multiple random tree creation, with training bootstrap, sample bagging, voting system, and randomly picked characteristics. It takes substantially less input preparation, uses many types of features without normalization, and is quick to train and optimize according to hyperparameters .The LBW outcomes prediction model can be created and Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (K-NN), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) , 23, 24aHyperparameters describe the depiction architecture, and hyperparameter tuning is the process of optimizing model design. These approaches demonstrate how to use the space of potential hyperparameter values to describe likely model structures. This study employed Randomized Search cross-validation to improve the parameters of Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (K-NN), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) , 23, 24.Evaluation metrics are used to assess the performance and effectiveness of the implemented predictive model . Their sAccuracy measures how many of the cases are correctly identified/predicted by the model , i.e. coThe total count of the sample is 2110. As shown in Table Through data processing, the number of samples in the data set is 2110 instance and 25 features. The LBW and Normal weight samples ratio is 1:9, which belongs to the category imbalanced data set. The study dataset resampled to improve the imbalance ratio. This study employed the SMOTE method [Once the preprocessing is done, the implementation of predictive models can commence. Numerous combinations of parametric values and settings are carried out. The model is fine-tuned to get optimal performance. These settings are divided into four categories general, booster, learning task, and command line. Tuning can be done in a grid or by searching randomly. The grid search is used in this study. With many factors, grid search for the best solution might be challenging. It can be readily addressed by focusing on a smaller number of parameters with suitable parameter ranges. K-fold cross-validation is used during the model selection step to assess model performance. Packages and libraries of Python utilized. The grid search is done in the following manner.First, to check for over-fitting, the “n estimators” that define the model's epoch are set to 100 and early stopping rounds to 5. Second, find the best learning rate and gamma at the same time, as they have a direct impact on the model's performance. 0.01, 0.02, 0.03, 0.06, 0.1, 0.2, and 0.3 are the grid values for the learning rate, whereas 0, 0.1, 0.2, 0.5, and 1 are the grid values for the gamma. For model tuning, all potential combinations of these two parameter values are tested, and the ones that perform best are kept as the optimal values. Third, make a grid search across the max depth and min child weight in the ranges of 1–10 using the optimal learning rate values and gamma. Fourth, make a grid search across the regularization parameter reg lambda and subsample in the 0.1–1 range simultaneously. Fifth, re-examine the model using a simultaneous grid search over gamma, reg lambda, and subsample to check for discrepancies between the optimal values. This study divided the data into a training set and test set account 80% and 20%, respectively. The model performance, such as prediction evaluation metrics, can be compared against different ML algorithms and parameter settings. This study used the popular fivefold cross-validation to avoid overfitting.This study compared the eight different classification methods to verify the superiority of one of the proposed methods. The LBW outcome prediction of Logistic Regression (LR) Fig. , DecisoiIn this study, the RF model is the best classifier with all performance metrics and predicts LBW. 96.01% of average Precision implies the tradeoff between Precision and recall and computes the Area Under the Precision-Recall Curve. 89.72% of accuracy indicates the amount of correctly predicted cases in the XGB of LBW. 96.13% of Sensitivity or recall implies actual LBW cases predicted as LBW by the classifier. F1-Score implied result of the Precision and recall of the model. Area Under Curve (AUC) was 90.01%, which provides a comprehensive assessment of the accuracy of a model by screening the range of threshold values for decision making.As Table Furthermore, the Jaccard score is the ratio of the size of the intersection to the size of the union of predicted and ground truth label classes.When combining predicted and actual classes, it is called a similarity coefficient. The best classification is one, and the poorest rating is 0. This study’s Jaccard score is 0.819, implying that the actual labels are best predicted.Overall, the RF classifier outperforms the others. It is not proof that RF is always better than other algorithms, as the nature of the data might significantly impact which algorithms are employed. It could be argued that, among the most common tools, RF is the best tool for implementing a predictive model employing the EDHS. The confusion matrix is shown in Fig. Finally, RF classification of LBW, the results of an attribute in all the promotion trees are weighted and summed and then averaged to obtain the importance score. The  RF model provides an order of important feature for enhancing the accuracy of the prediction model while performing low birth weight prediction using the tree model method. As It is shown in Fig. According to previous research , 15, 16,As the feature importance rank identified, Gender of the child, marriage to birth interval, mother’s occupation and mother's age were the top four critical predictors of low birth weight in Ethiopia, according to the XGB model. This study is roughly in line with the findings research , 16, 22.According to a 2011 EDHS study, there is a link between maternal education and proxy LBW . MultiplThe mother's age was one of the most critical indicators of low birth weight in Ethiopia in this study. In Ethiopia, a mother's age increases the likelihood of a low birth weight baby. The similarities between these studies might be due to mothers' physiological, psychological, and behavioral immaturity in this age range, which affects their children's ability to provide enough nourishment and care. Maternal education was linked to proxy LBW . This stNeonates born in rural areas have a higher risk of proxy LBW than those born in urban areas . HoweverInfant mortality and its implications have been linked to low birth weight. Predicting the LBW is thus a valuable preventative tool and predictor of newborn health hazards. This study used the Ethiopia Demographic and Health Survey 2016 to develop predictive LBW models. This study used Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (K-NN), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) to compare and find the best classifier for predictive classification. Before applying the predictive models, data preprocessing is carried out, including data cleaning. In this study, the classifier categories are normal and LBW. RF was the best classifier, predicting LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 percent ROC-AUC, 91.60 percent F1 Score, 1.05 percent Hamming loss, and 81.86 percent Jaccard score, according to the research. As a result, the RF predicts the occurrence of LBW correctly and more effectively than other classifiers. Gender of the child, marriage to birth interval, mother’s occupation and mother’s age were Ethiopia's top four critical predictors of low birth weight. This study has a direct importance on Ethiopian health and preventative policymaking related with low birth weight.Additional file 1. Explanatory Data Analysis of Low birth Weight in Ethiopia based on EDHS 2016.


Despite the great concern triggered by the environmental crisis worldwide, the loss of temporal key functions and processes involved in biodiversity maintenance has received little attention. Species are restricted in their life cycles by environmental variables because of their physiological and behavioral properties; thus, the timing and duration of species’ presence and their activities vary greatly between species within a community. Despite the ecological relevance of such variation, there is currently no measure that summarizes the key temporal aspects of biological diversity and allows comparisons of community phenological patterns. Here, we propose a measure that synthesizes variability of phenological patterns using the Hill numbers-based attribute diversity framework.We constructed a new phenological diversity measure based on the aforementioned framework through pairwise overlapping distances, which was supplemented with wavelet analysis. The Hill numbers approach was chosen as an adequate way to define a set of diversity values of different order q, a parameter that determines the sensitivity of the diversity measure to abundance. Wavelet transform analysis was used to model continuous variables from incomplete data sets for different phenophases. The new measure, which we call Phenological Hill numbers (PD), considers the decouplings of phenophases through an overlapping area value between pairs of species within the community. PD was first tested through simulations with varying overlap in phenophase magnitude and intensity and varying number of species, and then by using one real data set.PD maintains the diversity patterns of order q as in any other diversity measure encompassed by the Hill numbers framework. Minimum PD values in the simulated data sets reflect a lack of differentiation in the phenological curves of the community over time; by contrast, the maximum PD values reflected the most diverse simulations in which phenological curves were equally distributed over time. PD values were consistent with the homogeneous distribution of the intensity and concurrence of phenophases over time, both in the simulated and the real data set.PD provides an efficient, readily interpretable and comparable measure that summarizes the variety of phenological patterns observed in ecological communities. PD retains the diversity patterns of order q characteristic of all diversity measures encompassed by the distance-based Hill numbers framework. In addition, wavelet transform analysis proved useful for constructing a continuous phenological curve. This methodological approach to quantify phenological diversity produces simple and intuitive values for the examination of phenological diversity and can be widely applied to any taxon or community’s phenological traits. Under tommunity . Despitei.e., depending on their life cycle and whether they are unitary or modular; e.g., growing, feeding, courtship, mating, breeding). Non-sessile organisms may respond rapidly to environmental variation by altering their behavior and activity patterns over time; in addition, capture and handling may be required to distinguish the phenophases of these organisms . We define PD as the variety of phenological patterns observed in ecological communities over a defined time period. Accordingly, PD reflects the distribution of temporal niches of the species occurring in the community and capture its relationship with the environment .We constructed our phenological diversity measure using the Hill numbers-based attribute diversity framework  for phenWavelet transform detects the frequency spectrum of discrete time series data and fits a smoothed curve to them . This an, and these differ mainly from each other in the signal resolution calculation (τ), which determines the phenological curve steepness. Values close to 0 translate into a highly smoothed curve; conversely, values approaching infinity translate into a wiggly curve, similar to the raw time series data. A value of 2 is used as a standard for wavelet analysis in mathematics software . Wavelet analysis must be performed on the discrete phenological curve or time series of each studied species in the community; thus, the number of total smoothed phenological curves that must be calculated equals the number of species in the community (S).Wavelet transform analyses require specifying two parameters. The first parameter is the scaling function. The most common scaling functions are Morlet, Paul, DOG, Biorthogonal and Mexican hatculation . For ourculation . The secq). This q parameter directly determines the sensitivity of the diversity measure (qD) to the relative abundances of species occurring in the community. Although q can take any non-negative real number, ecologists typically consider three values: 0, 1 and 2 . When q = 1, S is weighted by the proportions of the species abundances and can be interpreted as the effective number of species equally abundant within an assemblage, which is equivalent to exp  can thus be interpreted as the pairwise phenological distance between species  occurring in an assemblage within the time interval over which it was measured.Considering that both deterministic and stochastic variables contribute to the phenomena regulating phenology, similar phenological curves between pairs of species might suggest that they have similar environmental requirements, a common response to the same environmental cues, or similar evolutionary constraints. By contrast, dissimilar phenological curves suggest that the species have different environmental requirements or might simply reflect historical competitive displacement between species . As the Q. This factor is calculated as follows \docOij is calculated as in pi is the relative intensity or abundance of the phenological event measured on species i, defined as q, qPD, is calculated as q = 1 the exponent where qPD, we generated simulated data sets reflecting different community scenarios and applied our measure to these and one additional real data sets. Although these simulations are unlikely to occur in nature, they provide a glimpse into a real community phenological pattern   .qPD values represent the “phenological Hill numbers” and thus quantify the diversity of different phenological curves in a given assemblage. The contribution of the phenological curve of each species is considered unique and equally distinct from each other; thus, qPD values always range from >0 . Therefore, phenological Hill numbers values have lower values when q increases.As previously explained,  from >0  to the tqPD based on field data for the breeding phenological activity of an amphibian community from Madagascar . There was a decreasing pattern as q increased, with 2PD values being reduced to 8.4.e.g., empirical orthogonal function, wavelets or Hilbert-Huang method; τ). The scaling function relates to the nature of the time series data and the approach of the wavelet transform  as long as they do not overlap. If they do, this number is reduced to 0 when they are all identical because the distance between phenological curves is zero. When q >0, the phenological curves shared by increasingly larger numbers of species are assigned higher weight in determining the phenological Hill numbers values. In practice, this means that phenological curves that are highly similar to each other in terms of time and intensity are grouped together, which ultimately translates into the mean effective number of phenological curves. Thus, regardless of the value of q, higher values of phenological Hill numbers represent a more heterogeneous arrangement and lower temporal overlap in the phenological curves within a community.The proposed measure of phenological diversity is an extended application of the principle of Hill numbers used to measure phylogenetic  and funcq values  or 8 (q = 2) distinct ways in which species partition their activity temporally throughout the studied year. This can be explained by the fact that amphibian activity is highly tied to rainfall patterns, and several species respond similarly to this factor  are slightly larger than taxonomic diversity (40) because Q < 1. Thus, the distance measure used in this framework can alter diversity values but not the overall patterns.ctor see . Some amThe proposed method correctly incorporates the proportion of overlapping area and the intensity of phenological curves, making our phenological diversity measure consistent with the Hill numbers unified framework . MoreoveqPD is suitable for the analysis of massive datasets associated with the collection of phenological time series data and with any phenological process within a community.There is a need for more studies to examine phenological patterns, including long-term studies based on records of community phenological diversity patterns, to enhance our understanding of the environmental cues that underlie the structure of communities in different environments and how species share the temporal dimension in infra and supra annual scales, especially regarding the impacts of climate change and the problems associated with the increasing mismatch between phenophases of interacting species . Specifiad hoc management.The phenological Hill numbers framework presented here produces simple and intuitive values for phenological diversity evaluation and thus can be widely applied to any taxon or community phenological traits using long-term data. Therefore, our measure has the properties of other diversity frameworks, and comparisons among studies using this same measure are possible. Phenological Hill numbers has important implications for the design of conservation and restoration programs that consider species and community patterns for the long-term persistence of biodiversity and 10.7717/peerj.13412/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.13412/supp-2Supplemental Information 2Click here for additional data file.


A body of evidences showed that birth asphyxia is a serious public health problem in low income countries including Ethiopia. There are sparse data on the prevalence of birth asphyxia and its associated factors among neonates in low income countries like Ethiopia, as well as the research area.Therefore, this study determined the prevalence and associated factors of birth asphyxia among newborns administered in public hospitals in Northern Ethiopia, 2019.A cross-sectional study of health institution was carried out in December 2019. Systematic sampling technique was used. Data was collected through interviews and chart review. Multivariate logistical regression analysis was done to control confounders and identify significantly associated variable. AOR with 95% confidence intervals were computed to identify the factors independently assoiated with birth asphyxia.The finding showed that the prevalence of birth asphyxia was 20.0%. Induction of labor , Prolonged labor , meconium-stained amniotic fluid (AOR=3.49), referred mothers , instrumental delivery and primiparous mothers (AOR=2.048 95% CI: 1.10–3.80). were significantly associated with birth asphyxia.The Prevalence of birth asphyxia notable high. Therefore; intra-partum care services should be strengthened to prevent birth asphyxia. Birth asphyxia is a serious clinical problem worldwideThe most common criteria for diagnosing asphyxiation at birth in developing countries is the Apgar score. In addition, clinicians use the history, physical examination, and lab tests for accurate diagnosis5Although it is a global health issue, the extent of asphyxia at birth varies between low income and high income countries because of the difference in care and availability of resourcesAsphyxiation at birth results in a wide range of outcomes, such as neurological disorders and emotional impairmentsIn the last two decades, the Government of Ethiopia has implemented the health sector development program to improve access and quality of health services to all segments of the populationA body of evidence has shown that birth asphyxia is a serious public health problem in low income countries, including EthiopiaThere are sparse data on the prevalence of birth asphyxia and its associated factors among neonates in low income countries like Ethiopia as well as research areaThe study was done in Mekelle's public hospitals from 1 to 30 December 2019. Mekelle is the capital city of Tigray regional state. It is located 730 km north of Addis Ababa. According to central statistical agency the total population of Mekelle is 483,217 . It has one comprehensive referral Hospital, 3 general Hospitals and 9 health centres.A cross-sectional study of health institutions was carried out.All newborn babies with or without birth asphyxia delivered in the public hospitals in Mekelle city, 2019.Selected newborn babies with or without birth asphyxia delivered in the public hospitals in Mekelle city, 2019The sample size was calculated using a single population proportion formula based on the proportion of asphyxia at the birth of a study in the Jimma of Oromia, Ethiopia18. Assuming a 5% margin of error and a 95% confidence level and adding 10% of the non-response rate, the total sample size was 371.Systematic random sampling was used to select mothers who are born alive. The sampling was started by selecting the first mother and continued every k. K was calculated by taking average month delivery of public hospitals in Mekelle city. The average month delivery was calculated from the total annually delivery of public hospitals of Mekelle.Incomplete ANC visits- Mother with 1–3 ANC visitors. Birth asphyxia- Apgar scores below 7 in the first minute.A structured questionnaire was prepared and used following a review of relevant documents. The questionnaires focused on socio-demographic, prenatal, intrapartum and neonatal characteristics. Questionnaires prepared by the English, then translated into the local language Tigrigna to check its coherence and again translated back the English. Data were collected through interviews with mothers and file reviews. Data were collected by six BSc Midwives and two MSc Midwives supervised them.In order to guarantee data quality, training has been provided to data collectors. The Principal Investigator and the Supervisor verified the completeness, clarity and consistency of the data collected immediately at the end of each data collection day. The pre-test verified 5% (18 samples) at the Wukoro General Hospital and helped reorganize the order of the tools.The data was checked, coded and entered in epi-data version 4.2 and exported to SPSS version 20 for analysis. Descriptive data was presented by table and text. A bivariate logistic regression was carried out to examine the association of the dependent variable with each independent variable. A P-value 0.2 was used to consider candidate variables for multivariate analysis. Multivariate logistical regression analysis was done to control confounders and identify significantly associated variables. Adjusted Odds ratio with 95% confidence intervals was computed to identify the presence and strength of association A p-value <0.05 was used to identify variables significantly associated with asphyxiation at birth.A total of 371 mothers and their neonates were included in the study with a response rate of 100%. The median age of the mothers was 26 years and about one-third (34.2%) of the mothers were in the age group of 21–25 year. Approximately one-third (76.3 per cent) of mothers were urban residents. Over one quarter (27.2%) of mothers attained to higher education. Concerning the religion and ethnicity of mothers, 86% of were Orthodox by religion and 88.9% were by ethnicity. Most of the mother's income was greater than the median .Among 371 newborns, (97.5%) were single, (96.7%) had normal birth weight and (97.5%) had no gross congenital abnormalities. . Over haMore than half of them were multiparas (52.7%). Among multiparous mothers; 7% have a history of abortion and 5.8 % neonatal deaths, and among 371 mothers, 85.4% of mothers give birth within 24 hours of the onset of childbirth. Midwives accounted for 85.4% of births .Parity, mode of delivery, onset of labor, duration of labor, amniotic fluid level and reserved status were significant factors associated with asphyxia at birth . NewbornNeonates who were born by induced labour were 3.6 times more likely to be asphyxiated as compared to neonates born by spontaneous labour .The chances of asphyxiation at birth were 2.78 times higher among newborns born in prolonged labour than those born less than 24 hours. .Neonates who were delivered by instrument (forceps and vacuum) had more likely to have birth asphyxia as compare to those neonates who were delivered spontaneously .The odds of the asphyxia were 3.68 more for neonates of referred mothers compared to the neonates of non-referred mothers  The odds of asphyxia were 3.08 times higher for neonates whose mothers had meconium stained amniotic fluid compared to neonates of the mothers with clear amniotic fluid .The prevalence of birth asphyxia is nearly similar to with the study in Ethiopia Regarding parity, neonates from primiparous mothers had higher odds of birth asphyxia than multiparous mothers. A similar finding was revealed in BangladeshiNeonates delivered by instrument had more odds of having birth asphyxia as compare to those neonates delivered spontaneously. This finding agreed with the study done in Pakistan Neonates who were born by induction were more likely to have asphyxia as compared to neonates of mothers who started labor spontaneously. This finding agreed with studies Kenya Prolonged labor had a positive association with birth asphyxia. This finding is similar to the study done in Ethiopia The odds of birth asphyxia were higher for neonates whose mothers had meconium-stained amniotic fluid as compared to neonates of the mothers with clear amniotic fluid. The finding is supported by the study in BangladeshThe odds of the birth asphyxia were more for neonates of referred mothers as compared to the neonates of the non-referred mothers. This could be due to the possibility of delay in receiving care, which might have resulted from the delay in seeking care, transportation, and health facility. However, the referral status of mothers had no association with birth asphyxia in the study in Kenya The prevalence of birth asphyxia is high compared to the expected frequency of birth asphyxia in low income countries. Parity, onset of labour, duration of labour, booked status, mode of delivery, and amniotic fluid status were factors significantly associated with birth asphyxia. All these factors are preventable. Therefore, there is needed for Zonal health department of Mekelle strengthens intra-partum care services for prevention of birth asphyxia through prevention of delays. Hospitals and health professionals should strengthen close monitoring of labour with the partograph; Timely communication and are critical.


It is now 50 years since the first report from New Zealand of a randomised controlled trial (RCT) of antenatal corticosteroids (ACS) to accelerate fetal lung maturation in humans.Although the short‐term outcomes are favourable, there are far less data about outcomes into school‐age, and even less into adulthood. Long‐term health for the fetus could be affected through programming in utero, so short‐term benefits might be outweighed by adverse longer term effects. The limited data from RCTs that do exist suggest no beneficial or adverse effects, with few exceptions. For example, in the Cochrane review by McGoldrick et al.,Doubts about possible adverse long‐term outcomes will remain without conclusive evidence to the contrary, which is currently not available from the reported RCTs. Since many of the RCTS have been completed a long time ago, participants involved in the original RCTs are unlikely to be tracked into adulthood in the future, to try to fill the gaps in knowledge.In the absence of data from RCTs, the field becomes more reliant on observational data. In the current issue of the journal, Darlow et al. have reported the outcomes of adults born with a birth weight <1500 g in New Zealand in 1986.There are several problems in relying on observational data to weigh the balance between the three possible mutually exclusive outcomes for a fetus following exposure to ACS: (1) death, (2) survival with adverse long‐term outcomes or (3) survival free of adverse long‐term outcomes. Firstly, stillbirths are excluded in studies that include only live births, which apply to most extant observational cohorts. Secondly, there could be a bias towards better survival for those who received ACS, particularly for those born extremely preterm . ACS improve survival, but their use may also reflect an attitude of offering more active care, including transferring the pregnant mother to birth in a tertiary medical centre with immediate access to neonatal intensive care for the infant after birth. This is relevant as EP live births in tertiary medical centres have considerably higher survival rates than do those born EP outside such centres,A possible solution would be to obtain data from complete geographical cohorts with antenatal care and pregnancy outcomes, including stillbirth, linked to long‐term health outcomes for the offspring. For example, some Scandinavian countries are able to link data through the life course, and could potentially examine the association between adult outcomes and ACS exposure in huge whole‐population data sets. Even if adverse long‐term health issues are suspected through such linkage studies, this would not mean that ACS should not be used in pregnancies with threatened birth <35 weeks' gestation.Due to the established benefits of ACS in relation to survival and major neonatal morbidities, future placebo‐controlled RCTs of ACS in pregnancies <35 weeks' gestation would be considered unethical, though RCTs comparing different doses of corticosteroids and different types of corticosteroids would not be unethical, and are needed. In contrast, in more mature pregnancies there is plenty of scope for placebo‐controlled RCTs at term or late term, and in pregnancies complicated by diabetes, which has been an exclusion for most of the extant RCTs of ACS. Given that almost all infants born late preterm or at term will survive, a benefit of ACS on mortality is unrealistic and hence it will be vital to follow survivors from RCTs in more mature groups well into childhood, and ideally into adulthood, to establish if there are any adverse sequelae that are not known about today.Individuals who have been exposed to ACS in utero cannot do anything about their exposure many years later. However, they may want to know if such exposure increases their risks for any adverse outcomes, some of which may be modifiable  or extremely low birth weight (<1000 g).Lex W Doyle conceived, wrote and approved the submitted version of this article.The author has no conflicts to declare.


To compare efficacy and safety of the levonorgestrel-releasing intrauterine system (LNG-IUS) with medical treatments for women with heavy menstrual bleeding.We searched PubMed, Embase, the Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure (CNKI), and Wanfang databases for relevant randomized controlled trials (RCTs) in November 2021. All meta-analyses were performed using the random-effects model. PROSPERO registration number: CRD42021295379.n = 490; risk ratio [RR]: 1.72 ; I2 = 92%; nonsteroidal: one RCT; n = 42; RR: 2.34 ) and 12-month  endpoints, with no clear differences on number of dropouts, and the incidence of adverse events.A total of trials (with 14 references) reporting on 1,677 women were included in this systematic review. The majority of the included RCTs were rated with low-to-unclear risk of bias in selection, detection, attrition, reporting, and other bias. All RCTs were rated as high risk in performance bias because blinding was difficult to ensure in the compared groups. Results of meta-analyses revealed that the number of clinical responders was greater in the LNG-IUS group than that in the medical treatments group at both 6-month (steroidal: five RCTs; Evidence indicates that LNG-IUS is superior to the medical treatments in short-term and medium-term clinical responses, blood loss control, compliance, and satisfaction. Meanwhile, frequency of adverse events related to LNG-IUS is acceptable.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021295379.PROSPERO, identifier CRD42021259335,  Heavy menstrual bleeding is a common condition in women of childbearing age, and ~30% of women are negatively affected during their reproductive years; it can cause serious impacts on physical health, emotional life, and quality of social life among women –4. HowevThe levonorgestrel-releasing intrauterine system (LNG-IUS) is a highly effective sustained release system of intrauterine progesterone. It comprises 52 mg of levonorgestrel, which is released at a rate of approximately 20 μg/day during the first year. In addition to contraception, LNG-IUS is also approved for the treatment of disorders such as heavy menstrual bleeding . The mecExisting systematic reviews on the treatment of heavy menstrual bleeding using LNG-IUS were either published several years ago –13 or foCochrane Handbook for Systematic Reviews of Interventions and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard . The systematic review was reported in accordance with the standards described in the standard .We searched PubMed, Embase, the Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure (CNKI), and Wanfang databases from the establishment of the databases to November 2021, with no limitations on languages, regions, or publication years. The following terms were used and adapted for the searches in each database: (levonorgestrel releasing intrauterine system OR LNG-IUS OR mirena) AND . Detailed search strategies are presented in the We included relevant RCTs (in either English or Chinese) comparing LNG-IUS with medical treatments  in women (aged ≥ 18 years) diagnosed with primary or idiopathic heavy menstrual bleeding. Primary outcome was clinical response to treatment . Secondary outcomes included menstrual blood loss , and quality of life , adverse events , participant satisfaction, withdrawal of treatment, number of dropouts for any reason, amenorrhea, methemoglobin, and hemoglobin level. When possible, we divided all outcomes into short-term (≤6 months), medium-term (6–12 months), and long-term (>12 months) outcomes.Two reviewers  independently inspected the search results to identify all potentially relevant references based on titles and abstracts, or full texts when necessary. Any disagreement during screening was resolved by discussion and, when necessary, with assistance from a third party. Finally, a PRISMA flow diagram is presented to illustrate the study selection process .Two reviewers  independently extracted data of study designs, characteristics of participants, description of interventions, and results of outcomes from all included RCTs using a standardized data extraction form .We assessed the risk of bias for included RCTs using the Cochrane Risk of Bias Tool, including sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective outcome reporting . We geneI2 ≥ 50% accompanied by a statistically significant χ2 test (p < 0.1) as evidence of substantial levels of statistical heterogeneity. Except for different time points of outcome, we also performed meta-analysis separately based on whether steroids were used or not. Publication bias was also not investigated due to insufficient data well .For dichotomous outcomes, we estimated risk ratios (RRs) and their 95% confidence intervals (CIs). For continuous outcomes, we estimated mean differences (MDs) and their 95% CIs. We adopted the random-effects model for all meta-analyses using Review Manager 5.4.1 . We fullThe database search resulted in the identification of 3,388 references, and no additional references were identified through other sources. A total of 2,666 unique records remained after de-duplication. Among these, we excluded 2,600 unique records upon inspection of titles and abstracts. We read the remaining 66 unique records completely and subsequently excluded 52 unique records with reasons  involving 1,677 women were included , which reported that the VBS scores at endpoint were lower in the LNG-IUS group than that in the medical treatment group at 6 and 12 months. MBL was measured using the alkaline hematin method in four trials  measured MBL using MMAS at 2 and 5 years and reported that the average MMAS score was greater in the LNG-IUS group than in the medical treatments group  at 2 years; however, no clear difference was identified between the compared groups at 5 years . Data of other measurement methods and time points of measurement are presented in Meta-analysis was not performed because the included RCTs used different measurement tools. MBL was measured using PBAC in four trials , 27, 28 t et al.   with heavy menstrual bleeding and investigated the efficacy and safety of LNG-IUS. The findings of this review showed that the number of clinical responders was greater in the LNG-IUS group than that in the medical treatment group at both the 6- and 12-month endpoints. Data showed that LNG-IUS reduces participants' MBL at both the 6- and 12-month endpoints. LNG-IUS has a positive effect on HRQoL , although high-quality evidence was needed to draw a firm conclusion. In terms of treatment withdrawal, the data showed that fewer participants discontinued treatment in the LNG-IUS group than that in the steroidal medical treatment group at the short-term and medium-term follow-ups. This suggests that compliance with treatment was better in the LNG-IUS group than that in the steroidal medical treatment group and that missed doses of medical treatment is likely to occur in the process of treatment. Current evidence also showed that the percentage of satisfaction was higher in the LNG-IUS group than that in medical treatment group, although no significant statistical difference was identified between two compared groups. Amenorrhea only occurred in the LUG-IUS group and no significant statistical difference was identified as well due to very small sample size. There were no clear differences between the groups for the number of dropouts and most adverse events.Compared to the previous reviews , this syCochrane Handbook for Systematic Reviews of Interventions. The protocol was registered, and the search was comprehensive, thereby minimizing the bias in the production process. The overall risk of bias for the included studies in this systematic review was moderate. All the included trials had a high risk of performance bias due to the blinding of participants and personnel, which is likely to be difficult to ensure when comparing LNG-IUS with medical treatment. Objective outcomes should be considered in further research . In addition, the meta-analyses of some outcomes were not applicable because the included studies used varying definitions or measurement tools for the same outcome. For example, there is some confusion about the definition and scope of “abnormal” uterine bleeding in the field of gynecology (This systematic review strictly followed the standards of the necology . SimilarEvidence indicates that LNG-IUS is superior to medical treatment for women with heavy menstrual bleeding. The LNG-IUS is a more effective therapy, with low incidence of adverse events and high patient compliance, which increases the ease of use in clinical practice. The available studies have some limitations in terms of study design and the manner in which information on patient outcomes was reported, indicating that the evidence continues to be limited to a certain extent.The original contributions presented in the study are included in the article/SC and YZ contributed to review—writing and revising. All authors reviewed and approved the final manuscript and contributed to protocol development, study selection, and data collection.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The rate of low birth weight or preterm birth is known to vary according to the birth place of mothers. However, in Japan, studies that investigated the association between maternal nationalities and adverse birth outcomes are few. In this study, we investigated the association between maternal nationalities and adverse birth outcomes.We obtained live birth data from the Vital Statistics 2016–2020 of the Ministry of Health, Labour, and Welfare. We used data on maternal age, sex, parity, gestational age, birth weight, number of fetuses, household occupation, paternal nationality, and maternal nationality for each infant. We compared the rates of preterm birth and low birth weight at term among mothers whose nationalities were Japan, Korea, China, Philippines, Brazil, and other countries. Log binomial regression model was used to investigate the association between maternal nationality and the two birth outcomes using the other infants’ characteristics as covariates.In the analysis, data on 4,290,917 singleton births were used. Mothers from Japan, Korea, China, the Philippines, Brazil, and other nations had preterm birth rates of 4.61%, 4.16%, 3.97%, 7.43%, 7.69%, and 5.61%, respectively. The low birth weight rate among Japanese mothers was 5.36% and was the highest among the maternal nationalities. Regression analysis showed that the relative risk for preterm birth among Filipino, Brazilian, and mothers from other countries  was statistically significantly higher compared with Japanese mothers. In contrast, the relative risk for Korean and Chinese mothers  was statistically significantly lower compared with Japanese mothers. Mothers from Korea, China, the Philippines, Brazil, and other nations had a relative risk for low birth weight that was statistically significantly lower than that of Japanese mothers .Support for mothers from the Philippines, Brazil, and other countries are necessary to prevent preterm birth. A future study is necessary to investigate the differences in characteristics among mothers of different nationalities in order to uncover the reason for the high risk for low birth weight among Japanese mothers.The online version contains supplementary material available at 10.1186/s40748-023-00149-1. The rates of low birth weight or preterm birth are representative indicators of adverse birth outcomes and had been positively related with neonatal or infant mortality , 2. ThesOver the recent decades, the number of non-Japanese people in Japan had been increasing , along wIn this study, we investigated the association between maternal nationalities and adverse birth outcomes using the Vital Statistics in Japan.We used live birth data from the Vital Statistics from 2016 to 2020. The individual-level data were obtained from the Ministry of Health, Labour, and Welfare in Japan. We used data on maternal age, infant’s sex, parity, gestational age, birth weight, number of fetuses, household occupation, paternal nationality, and maternal nationality for each infant. Household occupations were classified as farmer, self-employed, full-time worker 1, full-time worker 2, other occupations, and unemployed. Full-time worker 1 meant workers of a company with < 100 employees, and full-time worker 2 meant public servants and workers or board members of a company with ≥ 100 employees. Japan, Korea, China, Philippines, Brazil, Thailand, the United States, the United Kingdom, Peru, and other countries were available as nationalities of fathers and others in the data. Any combinations of maternal and paternal nationalities between couples were included in the analysis, and non-Japanese couples were also included in the analysis.We used only singleton births, and illegitimate infants were not included, because paternal nationalities were included in the analysis. Maternal age was grouped into < 20 years, 20–24 years, 25–29 years, 30–34 years (reference), 35–39 years, and ≥ 40 years . Parity For each maternal nationality, we counted the number of births and the rates of preterm birth and low birth weight by the infants’ characteristics. Log binomial regression model was used to investigate the association between the two birth outcomes and maternal nationality using the other infants’ characteristics  as covariates. The traits of those other infants were included since they may have an impact on results and their distribution may vary based on the nationality of the mother. Log binomial regression model is a regression model used for binary outcome . Relativhttps://www.r-project.org/). The statistics shown in this study were processed by the authors using the Vital Statistics data obtained from the Ministry of Health, Labour, and Welfare, and these are different from the statistics published by the Ministry.In this study, complete-case analysis was carried out. An imputation utilizing the hot-deck imputation approach was also utilized as a sensitivity analysis . With thFigure Table Table Table Table The results of the regression analysis utilizing an imputation method on the relationship between mother nationality and preterm delivery and low birth weight at term are shown in the This study using the Vital Statistics in Japan revealed an association between maternal nationality and adverse birth outcomes. Compared with Japanese women, Filipino, Brazilian, and mothers from other countries had higher risk of preterm birth. On the other hand, the risk for low birth weight at term was higher in Japanese women than in mothers from the other countries. There were possible reasons and implications of the results.In a previous study, Filipinos in Japan were reported to have the highest infant and fetal mortality rates . A similIn contrast, Korean and Chinese mothers had lower risk, compared with Japanese women. Although the reason for the lower risk is uncertain, the reported preterm birth rates in Korea and China were not higher, compared with that in Japan , 31. In Compared with mothers from other nationalities, Japanese women had higher risk of delivering infants of low birth weight. This result was contrary to previous reports that foreign-born or immigrant women tended have adverse birth outcomes in other countries , 6, 35. This study found that women whose nationality was from the Philippines, Brazil, and other countries had relatively high risk of preterm birth rate in Japan. As an implication, unawareness of the available health services in Japan could make consults for prenatal care difficult among non-Japanese women. In that case, more community intervention by public health nurses to connect medical institutions with local non-Japanese residents might be needed. In addition, an epidemiological study on the differences in health behaviors and statuses among maternal nationalities is necessary to uncover the reason for the high risk of low birth weight among Japanese women. Additionally, governments should be aware of the findings about low birth weight since they can make it known to the general population. This could help Japanese women who are expecting to ameliorate their lifestyles. Local governments can also take measures in order for non-Japanese mothers to participate prenatal care or consult a physician. Obstetricians are also advised to be aware of the results because they can speak with both Japanese and non-Japanese moms and warn them of the risks.There were some limitations in this study. Some major characteristics of pregnant women, such as household income, education level, utilization of prenatal care, and BMI, could not be obtained, because the Vital Statistics data were used in this study. Investigating these factors in a future epidemiological study may help specify the reason for disparities. In addition, period of stay in Japan or Japanese language skill might need to be scrutinized.This study based on the Vital Statistics in Japan revealed an association between maternal nationality and adverse birth outcomes. The risk for preterm birth was significantly higher in Filipino, Brazilian, and mothers from other countries than in Japanese mothers but was significantly lower in Chinese and Korean mothers than in Japanese mothers. On the other hand, the risk for low birth weight in Japanese mothers was higher, compared with that in mothers from all the other countries.Additional file 1:Supplementary Table. The results of the regression analysis using an imputation method on the association of maternal nationality with preterm birth and low birth weight at term.