Symptomatic Mullerian duct cyst is a rare entity in children. A 9-month-old male infant presented with bowel and urinary obstructive symptoms. Imaging investigations revealed a cystic mass in the rectovesical pouch compressing bladder neck and rectum. At laparotomy, a Mullerian duct cyst was found. Most of the cyst was excised and the remaining cyst mucosa was cauterized. The child improved thereafter. A 9-month-old male infant presented with urinary retention. The parents also gave history of constipation and passing of small amount of urine per void since the last three months. Abdominal examination revealed distended bladder. Child was catheterized and enema was given for rectal evacuation. Following bladder decompression a smooth, firm lump of approximately 7 cm X 5 cm was palpable in the hypogastrium. Rectal examination revealed a firm lump which was palpable anteriorly. Ultrasound showed an anechoic cystic lesion posterior to the urinary bladder with septations within it. CECT abdomen revealed a 7 cm X 3 cm X 2.5 cm well defined elongated fluid attenuating hypodense cyst in the recto-vesical pouch. There were no calcifications within the mass. The mass was causing compression on the bladder anteriorly and the rectum and sigmoid colon posteriorly. The urinary bladder showed diffuse wall thickening.The child was taken up for cystoscopy and laparoscopic cyst excision. Cystoscopy did not show any communication of the cyst with the posterior urethra. The bladder was found to be trabeculated. Laparoscopy showed hypertrophied bladder and thick walled cystic swelling posterior to bladder and anterior to rectum compressing both.[Fig. 1 and 2] The cyst was densely adhered to rectum and bladder. Adhesiolysis was difficult laparoscopically and hence open deroofing of the cyst was done. Both vas deferens were identified and saved. Majority of the cyst was excised and the remaining cyst cavity mucosa was cauterized. A drain was placed in rectovesical space. Postoperatively, the child improved and was discharged. The fluid cytology revealed few inflammatory cells in a proteinous background. Histopathological analysis of the excised wall showed largely ulcerated areas with focal areas showing intact mucosa formed by cuboidal epithelium consistent with the diagnosis of Mullerian duct cyst. The child presented for follow up in outpatient department after three months and was doing well without any symptoms.Anti Mullerian Hormone in males leads to regression of the paramesonephric duct. Failure of regression and focal dilatation leads to the development of Mullerian duct cysts. The differential diagnosis of midline pelvic swellings in the male include prostatic utricle cyst, prostatic retention cysts, prostatic abscesses, bladder diverticulum, rectal duplication cyst or any pelvic neoplasm. Trans-rectal ultrasound, CT and magnetic resonance imaging are useful in diagnosis. Prostatic utricle cysts generally present within the first two decades and may communicate with the posterior urethra resulting in post void dribbling. They also do not extend beyond the base of the prostate and are associated with hypospadias and disorders of sexual differentiation.[3] Such a large pelvic cyst extending beyond the prostate with no communication with the urethra led us to make a diagnosis of Mullerian duct cyst.Mullerian duct cyst may present with urinary retention, urinary tract symptoms, renal failure or constipation as was the scenario in the index case. [4-7] All these cases were mostly reported from adult population except the one from which was in 5 year old child.[7] There are also reports of association of such cysts with renal agenesis and malignant transformation. Rarely it may present in infancy with mass effect leading to urinary and bowel obstructive symptoms as found in the index case.Treatment of small asymptomatic cysts remains conservative. Transurethral deroofing of cyst and laparoscopic excision are the options for small cyst but for large pelvic cyst, as in our case, surgical excision is a better option. Robot- assisted laparoscopic excision has also been reported in boys and young adults.[10]To summarize, Mullerian duct cysts though rare anomaly, should be kept in the differential diagnosis of midline swellings in rectovesical cystic location. Transrectal ultrasound and CT scans are helpful in diagnosis. Surgical excision of the cyst results in good recovery.Source of Support: NilConflict of Interest: None declared


Dandy-Walker malformation (DWM) is a posterior fossa anomaly characterized by hypoplasia and upward rotation of the cerebellar vermis and cystic dilation of the fourth ventricle. The cyst of DWM rarely extends posteriorly to almost completely fill the entire posterior fossa, which mimics primary cerebellar agenesis, a cerebellar porencephalic cyst, and an arachnoid cyst due to the lack of clarity of the thin cystic wall. A 10-month-old female born at 23 weeks' gestation with cerebellar hemorrhage in the neonatal period was admitted to our hospital with dysphagia and side-to-side head bobbing. The detection of hemosiderin deposits enveloping the cyst wall by T2 star-weighted angiography (SWAN) was useful for the differential diagnosis of an acquired form of DWM from primary cerebellar agenesis. Cyst fenestration successfully improved dysphagia and head bobbing. A pathological specimen of the perforated cyst consisted of collagen fibers with hemosiderin deposits but lacked congenital cyst components. In infants with posterior fossa cysts, SWAN will be useful for a differential diagnosis between DWM and primary cerebellar agenesis. Dandy-Walker malformation (DWM) is a posterior fossa anomaly characterized by hypoplasia and upward rotation of the cerebellar vermis and cystic dilation of the fourth ventricle. DWM was previously recognized as a congenital brain malformation but is rarely caused by intrauterine and neonatal brain insults including cerebellar hemorrhage and intraventricular hemorrhage –3 and, tWe herein describe an infantile case of DWM caused by cerebellar hemorrhage in the neonatal period. Although a nonrecognizable cerebellar hemisphere in the posterior fossa mimicked primary cerebellar agenesis , T2 starA 10-month-old female was admitted to our hospital with intermittent side-to-side head bobbing that had persisted for one month. She was delivered via cesarean section at 23 weeks' gestation with a birth weight of 575 g due to chorioamnionitis and placental abruption. Apgar scores were 6 and 8 at 1- and 5-minute postpartum. A brain echogram on day 3 revealed high echoic lesions within the cerebellar hemispheres, suggesting the presence of acute cerebellar hemorrhage. Neither a cerebral abnormal lesion nor intraventricular hemorrhage was detected . She wasAt 10 months old (5 months' corrected age), she controlled her head and was rolling over, but intermittently presented side-to-side head bobbing regardless of her posture, particularly in the morning. A physical examination showed left intermittent exotropia and mild truncal instability, but not nystagmus, involuntary movements, or an apparent pyramidal sign. Her weight was 6.2 kg (−1.0 standard deviation (SD)), height 58.8 cm (−1.7 SD), and head circumference 41.3 cm (0 SD) of the Japanese corrected age-matched references. The clinical symptoms of increased intracranial pressure such as seizures, colic, and vomiting were absent. Electroencephalography showed no epileptic discharge including hypsarrhythmia.Brain MRI at 10 months old revealed an almost nonrecognizable cerebellum and a giant CSF cyst filling the posterior fossa with enveloping hemosiderin deposits and brainstem compression Figures . Fast imCyst fenestration with a posterior cervical approach was performed to improve brainstem compression at 12 months old. Neuroendoscopic observations from the perforated hole reconfirmed that the posterior fossa cyst was an individual cyst without a septum. After surgery, dysphagia immediately improved, and head bobbing disappeared within one month. Postsurgical FIESTA images detected the cyst wall segment .A histological examination of a perforated cyst segment showed collagen fibers with hemosiderin deposits . An immuWe herein presented an infantile case of an acquired form of DWM associated with cerebellar hemorrhage in the neonatal period, which ultimately compressed the brainstem in the late infantile period. In this clinical course, side-to-side head bobbing was noted as an initial symptom before dysphagia and the detection of brainstem compression and hydrocephalus on neuroimaging. This nonvoluntary movement was previously reported as the “no-no” type bobble-head doll sign, which has been recognized as voluntary rhythmic head waving in an attempt to release the cerebrospinal fluid obstruction caused by the posterior fossa cyst .The brain morphology of this case was characterized as a subtotal absence of the cerebellar vermis and hemispheres, a hypoplastic brainstem, and a giant cyst filling the posterior fossa and fourth ventricle. Regarding cyst distribution, a neuroendoscopic view at cyst fenestration showed a single cyst extending from the posterior fossa to the fourth ventricle. Furthermore, brain MRI at 2 days post surgery showed air distribution in the lateral ventricles. These results indicated communication between the lateral ventricles and posterior fossa cyst. Collectively, neuroimaging and neuroendoscopic findings resulted in a diagnosis of an acquired form of DWM. The posterior fossa cyst of “acquired” DWM grows after birth ; therefoIn terms of a histological diagnosis, DWM generally contains a glial layer covered on the lateral surface of the cyst by an ependymal layer . The cysIn the pathogenesis in an acquired form of DWM, cerebellar hemorrhage in the neonatal period may have played a role by obstructing the outlet foramina of the fourth ventricle. Cerebellar hemorrhage rarely occurs in premature infants born before 25 weeks' gestation , 9 becauThe cyst wall of the posterior fossa cyst in this case was not detected by SPGR or FIESTA, whereas SWAN revealed hemosiderin deposits enveloping the posterior fossa cyst. MR cisternography including FIESTA is useful in evaluations of the cystic wall because of the higher contrast-to-noise ratio and brain tissue/CSF contrast  but lackIn conclusion, we herein present a case of an acquired form of DWM associated with cerebral hemorrhage in the neonatal period. The detection of hemosiderin deposits enveloping the cyst wall was useful for a differential diagnosis of “acquired” DWM from primary cerebellar agenesis.


A recent study has shown that it is possible to accurately estimate gestational age (GA) at birth from the DNA methylation (DNAm) of fetal umbilical cord blood/newborn blood spots. This DNAm GA predictor may provide additional information relevant to developmental stage. In 814 mother-neonate pairs, we evaluated the associations between DNAm GA and a number of maternal and offspring characteristics. These characteristics reflect prenatal environmental adversity and are expected to influence newborn developmental stage.DNAm GA acceleration  of the offspring at birth was associated with maternal age of over 40 years at delivery, pre-eclampsia and fetal demise in a previous pregnancy, maternal pre-eclampsia and treatment with antenatal betamethasone in the index pregnancy, lower neonatal birth size, lower 1-min Apgar score, and female sex. DNAm GA deceleration  of the offspring at birth was associated with insulin-treated gestational diabetes mellitus (GDM) in a previous pregnancy and Sjögren’s syndrome. These findings were more accentuated when the DNAm GA calculation was based on the raw difference between DNAm GA and GA than on the residual from the linear regression of DNAm GA on GA.Our findings show that variations in the DNAm GA of the offspring at birth are associated with a number of maternal and offspring characteristics known to reflect exposure to prenatal environmental adversity. Future studies should be aimed at determining if this biological variation is predictive of developmental adversity.The online version of this article (doi:10.1186/s13148-017-0349-z) contains supplementary material, which is available to authorized users. The median absolute difference between these methylation age biomarkers and chronological age has been shown to vary from between 2.9 and 4.9 years [Biomarkers of cellular aging have attracted increasing attention over the past few years. Such biomarkers hold the potential to identify individuals who are at risk of aging-related diseases so that they may be offered timely, targeted preventive interventions, hopefully decades before the onset of disease. DNA methylation (DNAm) is an epigenetic mechanism characterized by the addition of one methyl group primarily to cytosine-phosphate-guanine (CpG) sites on DNA. The epigenome is known to undergo age-related changes –6, and s.9 years . Epigene.9 years , and a r.9 years .Epidemiological studies have shown that exposure to adverse environmental events in the prenatal period predicts increased risk of aging-related diseases –18. Thesr = 0.91) with ultrasound-based GA [r = 0.81) and last menstrual period-based GA (r = 0.71) [The Hannum and Horvath epigenetic age predictors are not, however, suitable for epigenetic age estimation using fetal cord blood. The Hannum age predictor was based on whole blood taken from 19–101-year-old individuals  and the based GA . The avebased GA . Using f = 0.71) ; Only 23 = 0.71) .In the present study, we studied the DNAm GA predictor at birth based on fetal umbilical cord blood methylation data as developed by Knight et al. . We testr = 0.51; p < 0.0001; Fig. r = −0.30; p < 0.0001). DNAm GA residual did not correlate with GA .The associations of maternal and neonatal characteristics with the raw DNAm GA difference (arithmetic difference between DNAm GA and GA) and with the DNAm GA residual  were tested in 814 women and their singleton neonates participating in the Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) study , DNAm GA 0.11 (p = 0.002), raw DNAm GA difference 0.09 (p = 0.01), and DNAm GA residual 0.11 (p = 0.01).Pearson correlations between the Horvath DNAm age and our values were as follows: GA 0.03  . Our samSeveral strengths of this study are also limitations. The ethnic homogeneity of our sample may preclude generalizations for other ethnic groups. Our inclusion criteria, which resulted in enrichment of pregnancy disorders in the study population and increased statistical power to detect their effects, also limit generalizability of these findings to women without such risk factors. Finally, our findings are limited to one tissue type; therefore, we could not test cross-tissue correlations. It is also important to note that for some prenatal characteristics, e.g., for insulin-treated diabetes in a previous pregnancy, maternal Sjögren’s syndrome, less than ten pairs of women and neonates were present in the risk group. Therefore, although we observed significant associations, they should be interpreted with caution and need to be replicated. Finally, while maternal pre-eclampsia in the index pregnancy, maternal treatment with betamethasone, lower birth weight and length, and lower placental weight remained associated with GAA after Bonferroni correction for multiple testing, this correction may be too conservative as the tested associations were not independent.Our findings show that a number of maternal and offspring characteristics known to reflect the offspring’s prenatal environment are associated with variations in the offspring’s DNAm GA at birth based on data from cord blood DNA methylation. Whether this variation is predictive of developmental outcomes in later life is the subject of ongoing studies.Data were taken from the PREDO study, which is a longitudinal multicenter pregnancy cohort study of Finnish women and their singleton children born alive between 2006 and 2010 . We recrhttp://bioconductor.org/packages/release/bioc/html/minfi.html). The samples were excluded if they were duplicates, outliers in the median intensities, and because of sex discrepancy. Furthermore, any probes on chromosome X or Y, cross-hybridizing probes as well as probes containing SNPs, and CpGs with a detection P value > 0.01 in at least 50% of the samples, or maternal blood contamination were excluded. Maternal blood contamination was tested using DNAm data at 10 CpGs independently identified as differentially methylated between cord and adult blood and indicative of maternal blood contamination (paper under review). The samples with DNAm values above the previously identified thresholds at five or more of these CpGs were considered contaminated and removed from all future analyses. The final dataset contained 428,619 CpGs. Additional file Fetal cord blood samples were collected according to standard procedures. DNA was extracted at the National Institute for Health and Welfare, Helsinki, Finland, and the Department of Medical and Clinical Genetics, University of Helsinki, Finland. Methylation analyses were performed at the Max Planck Institute of Psychiatry in Munich, Germany. DNA was bisulphite-converted using the EZ-96 DNA Methylation kit . Genome-wide methylation status of over 485,000 CpG sites was measured using the Infinium Human Methylation 450 BeadChip  according to the manufacturer’s protocol. The arrays were scanned using the iScan System . The quality control (QC) pipeline was set up using the R-package minfi T cells, and CD8(+)T cells) were estimated from cord blood methylation using the method of Bakulski et al.  which waP value for deviation from Hardy-Weinberg-Equilibrium (P > 1.0 e-06) were kept in the analysis. After QC, 587,290 SNPs were available. Any sample pair with IBD estimates >0.125 was checked for relatedness. For most pairs, high IBD estimates could be explained due to partly African origin. As we corrected for admixture in our analyses, these samples were kept except for one pair. For this pair, the high IBD estimate could not be resolved and the other one of this pair was excluded from further analysis. The samples showing discrepancies between phenotypic and genotypic sex were removed. We also checked for heterozygosity outliers, but found none. In total, we genotyped 996 samples of which 13 were excluded. Of the samples with DNA methylation data, eight were excluded based on the above criteria  analysis on the IBS matrix of quality-controlled genotypes. Apart of those samples with African admixture, no outliers were detected. The first two components depict this admixture and were included as covariates in the regression analysis.Pre-pregnancy risk factors of pre-eclampsia and intrauterine growth restriction were derived from medical records screened for by trained study nurses or clinic personnel at maternity clinics of study hospitals at enrolment into the study. These pre-pregnancy risk factors are listed in Table Pregnancy disorders, derived from hospital records and further verified by a clinical jury , 41, incInformation on the antenatal corticosteroid (betamethasone) treatment was derived from hospital records , and timing of exposure was further defined as the number of days before birth (over 30 vs 30 days or less before birth).Maternal smoking during pregnancy , parity (primiparous vs multiparous) and mode of delivery  were derived from the MBR, and alcohol use (yes vs no) and education level  were reported at 12 + 0–13 + 6 gestational weeks + days.3) was calculated. We further divided birth weight and length into normal and small (≤ − 2SD) for GA using Finnish national growth references [Weight (kg), length (cm), head circumference (cm), fetal cord blood venous and arterial pH, and 1-min Apgar score were measured at birth, and the birth ponderal index .We tested the associations between maternal and offspring characteristics with the raw DNAm GA difference, DNAm GA residual, and Horvath epigenetic age with linear regressions. All models were adjusted for cell-type composition and population stratification estimated with two multi-dimensional scaling components based on genome-wide data. Maternal characteristic data were further adjusted for birth weight SD score and neonatal anthropometric data for child’s sex. Unstandardized regression coefficients and 95% confidence intervals (CI) represent effect sizes in weeks (raw DNAm GA difference and Horvath epigenetic age) and SD units with a mean of 0 and SD 1 . Nominal 2-tailed


Although research on the fertility response to childhood mortality is widespread in demographic literature, very few studies focused on the two-way causal relationships between infant mortality and fertility. Understanding the nature of such relationships is important in order to design effective policies to reduce child mortality and improve family planning. In this study, we use dynamic panel data techniques to analyse the causal effects of infant mortality on birth intervals and fertility, as well as the causal effects of birth intervals on mortality in rural Bangladesh, accounting for unobserved heterogeneity and reverse causality. Simulations based upon the estimated model show whether (and to what extent) mortality and fertility can be reduced by breaking the causal links between short birth intervals and infant mortality. We find a replacement effect of infant mortality on total fertility of about 0.54 children for each infant death in the comparison area with standard health services. Eliminating the replacement effect would lengthen birth intervals and reduce the total number of births, resulting in a fall in mortality by 2.45 children per 1000 live births. These effects are much smaller in the treatment area with extensive health services and information on family planning, where infant mortality is smaller, birth intervals are longer, and total fertility is lower. In both areas, we find evidence of boy preference in family planning. According to the demographic transition theory, there is a strong correlation between childhood mortality and fertility, with an important role for birth spacing in shaping this relationship. Understanding the empirical nature of such relationships is important to design effective policies for reducing child mortality and fertility. The importance of this is illustrated by, for example, United Nations Sustainable Development Goal 3.2: “By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births” , while oThe observed associations between child mortality, birth spacing, and fertility may not only be due to causal mechanisms but can also be explained by common unobserved factors (omitted variables or confounding factors) that drive the various processes. To evaluate policies aimed at optimal birth spacing, reducing mortality, and reducing fertility, identifying the importance of the various causal mechanisms and alternative explanations is crucial. If associations reflect spurious correlation or reverse causation instead of the presumed causal effects, then the policy implications can be dramatically altered –10.One of the most important policy parameters in this context is the replacement effect: how does mortality affect total fertility? In the seminal work of Olsen, the issue was raised that straightforward estimates of this effect can be biased because mortality will also be driven by fertility decisions, for example through birth intervals . Olsen dAn alternative to the Olsen approach is the multivariate panel data framework developed by Bhalotra and van Soest that models all mortality-fertility causal relationships within each family . This tyIn this study, we use a panel data model similar to the one introduced in  to analyThe main novelty compared to  is that Another specific feature of our data is that the study area is randomly split into villages with standard government provided health services (the “comparison area”) and villages with additional extensive health and family planning programmes, such as more health clinics and regular visits of health officers  –17. Due There is an extensive literature of reduced form studies on the associations between mortality, birth intervals, and fertility, and we do not have the ambition to review this literature. Studies typically found a strong negative association between child mortality and subsequent birth intervals and, accordingly, a positive relationship between child mortality and the number of subsequent births, especially in developing countries. For Bangladesh, for example, it was found that infant death is associated with a reduction of the median of the subsequent birth interval from 37.2 to 24.1 months ,12, concAccording to the classical demographic transition theory, child mortality affects fertility mainly in two ways: physiological/biological changes and volitional /replacement effects. The physiological effect can be explained by the fact that breastfeeding is interrupted with a child death, shortening the postpartum infecundable period . As a reexpected mortality of the offspring, while replacement is the response to an actual child death. Hoarding is not expected to be very important in the current setting, since women can usually respond to the realized infant death by having another birth.An alternative way in which mortality may affect fertility is hoarding , 20. Hoanext child, particularly when the preceding sibling survived [Many studies also found an association between a short birth interval and infant death of the survived –23. An esurvived –25. Hencsurvived . An altesurvived . This masurvived —the mothCouples continue childbearing until they reach their desired family size and composition. It was found that in Bangladesh, the median birth interval after the death of a child is shorter when the deceased child was a boy or when the family has at most one boy . In GhanObserved clustering in infant or child mortality of successive children may also be due to unobserved confounding factors instead of causal mechanisms. Older studies of birth spacing and childhood mortality usually do not control for both. More recent studies for India and Kenya reveal that the causal effect on child mortality of mortality of the previous sibling is overestimated when unobserved heterogeneity is not accounted for , 31. TheSince 1966 icddr,b maintained a Health and Demographic Surveillance System (HDSS) in Matlab, aiming to support the Bangladesh Health and Family Planning programme. In Matlab, an area located about 60 km southeast of Dhaka, all births, deaths, causes of deaths, pregnancy histories, migrations in and out of the area, marriages, divorces, and several indicators of socioeconomic status are recorded for the complete population of about 220,000 people. The HDSS data on the timing of pregnancy outcomes and deaths are considered to be of very high quality because they are collected during regular visits (every two weeks until the late 1990s and every month since then) by well-trained female community health workers , 17. VilThe complete data set has records on about 63,000 mothers, with more than 165,000 childbirths, including multiple births and stillbirths. We eliminated mothers with multiple births (3401 births in both areas together) as children of a multiple birth face much higher odds of dying and require a separate analysis (as documented in the demographic literature). We also eliminate mothers with incomplete birth history information. For example, if a mother has had four live births, she should appear four times, with four recorded birth dates. In all other cases e.g., if a child was born outside Matlab or before the study period, we do not have the required information on this child, and had to delete the mother from the sample. Similarly, we deleted mothers who migrated out of Matlab during the period under study. The sample therefore only contains mothers who continuously lived in the Matlab area since the birth of their first child until the end of the study period.Moreover, we have excluded the children born in three villages that shifted from the icddr,b area to the comparison area in 2000. These sample selection steps lead to a sample in the comparison area of 32,366 children out of the original raw sample of 74,214 and 11,856 mothers out of the original 30,264. In the icddr,b area the working sample has 31,968 children  and 13,232 mothers . We acknowledge that these sample selection criteria imply that our results are not necessarily representative for the complete population of the Matlab area. Studying the relation between child mortality and, for example, migration into or out of Matlab, is a topic for future research.Furthermore, we discarded stillbirths . One reason for this is that gender, an important covariate in our analysis, is missing for stillbirths. Accordingly, we define birth intervals as intervals between reported dates of live births, ignoring stillbirths in between live births. This may imply that some long birth intervals are due to stillbirths. Distinguishing stillbirths from other mechanisms leading to long birth intervals is another topic left for future research. Since stillbirths then have to be modelled, it will be an additional layer of complexity in an already rather complex model. This is also that our study fails to address local community (village) specific effects. On the other hand, we do not have any reason to expect that by excluding stillbirths this induces a systematic selection bias in a specific direction lengthen birth intervals. For example, stillbirths are more common among mothers in the comparison area compared to the icddr,b area; however, the length of birth intervals is longer in the icddr,b area. Future study may address more rigorous analysis by handling the potential bias.The non-parametric regressions of infant mortality on the preceding birth interval in Figs The model is similar to the one used in . A diffeConceptually, the model is based upon the notion that a family’s mortality outcomes, birth intervals, and fertility are determined partly by decisions of the family and partly by external factors that can vary over time  or are persistent over the complete observation period . We do not explicitly distinguish between external factors and factors controlled by the family and do not aim to work with a fully structural model in which the family solves a dynamic optimization problem that determines its family planning and health behaviour. Instead, we formulate separate equations for the various outcomes, accounting for observed and unobserved time persistent and time varying variables that may affect each of the outcomes, and allowing each of the outcomes to depend upon previous outcomes in the same family. For example, the interval to the next birth may depend upon gender and mortality outcomes of the previous births, the mortality outcome may depend upon the preceding birth interval, etc. This exploits the timing of events  to identify of the econometric model. Confounding factors, such us unobserved family attitudes towards modern health behaviour and family planning, are allowed for as long as they are persistent over the mother’s reproductive period, and are captured in unobserved heterogeneity terms that can be correlated across equations.The benchmark model taken from  has equai denoting a mother and t = 1,..,Ti denoting her consecutive live births:To be more precise, the endogenous variables in the model are the following, with Mit: Infant mortality dummy: 1 if child t dies; 0 if it survives the first twelve months after birth.Fit: Having a next birth (1) or not (0) after birth t.Bit: Log birth interval preceding birth of child t (t>1 only)The sequence of events is illustrated using the time line in Mi1. The second is the event whether there will be a next birth (Fit = 1) or not (Fit = 0). This is not always observed—if a second birth is observed in the data we know that Fit = 1. If not, this can be because Fit = 0 or because the next birth interval is too long in the sense that it exceeds the observation window or the woman’s reproductive span (set to 45 years of age).The timing of the first birth is taken as given. Our data do not provide date of marriage so we do not observe the interval from marriage to first birth. The first event we explain is infant survival of the first born child Fit = 1 and the birth interval is short enough to have the next birth within the observation window, we observe the second birth and the birth interval Bit. The second born child can die during infancy or survive; depending on this outcome, there will be a next birth or not (Fi3), etc.—the sequence of events continues until the mother has no more children (FiT = 1), at the end of her fertile period (age 45), or at the end of the observation window (December 2005).If We use probit equations for the binary outcomes  and a regression equation for the continuous outcomes . Below we discuss the equations for the various outcomes in more detail.t  of mother i, the equation isFor higher birth orders, a dynamic probit equation with (random) mother specific effects is used. The explanatory variables include the preceding birth interval and variables related to the mother’s age at birth, which is a function of previous birth intervals: For child Xit contains (functions of) the strictly exogenous variables, such as gender of the child, socio-economic status indicators of the household  and characteristics of the village where the household resides. Zit is the vector of explanatory variables that are functions of previous outcomes (and are therefore not strictly exogenous), including the preceding log birth interval Bit, (functions of) age of the mother at birth t and, following the literature on scarring and in particular important work by Arulampalam and Bhalotra, survival status of the previous child Mit-1 [αmt captures unobservable time invariant characteristics influencing the infant mortality risk of all children in the family. The error term umit captures idiosyncratic health shocks specific to child t. We assume that the umit follow a standard normal distribution, independent of each other and of all covariates, and that αmt is normally distributed with mean 0 and variance σ2(αm) independent of all umit and Xit (but not of Zit). Note that Eq  parameters to be estimated and the error term umi1 is assumed to satisfy the same assumptions as the other umit.Here T(i) children, we observe the exact log durations in between two consecutive births bi2, …., biT(i), preceding births 2, …, T(i). We model these intervals using the following equation:For a mother who has given births to Xit denotes the vector of strictly explanatory variables, as before. Another determinant of birth spacing would be the use of contraceptives. We do not include this since it is not observed in the comparison area and may be correlated with unobservables in the model. Z(b)it includes survival status of the preceding sibling and family composition variables (functions of the numbers of surviving girls and boys). We assume gender of each birth is exogenous and thus do not incorporate the possibility of selective abortion. Descriptive statistics confirm that this is not an issue in these data, revealing no relation at all between gender of a newborn child and gender composition of the older children in the family.Here αbi captures unobserved time invariant characteristics of the mother (or her household or village) influencing the birth interval. The error term ubit captures idiosyncratic errors. We assume that the ubit follow a normal distribution, independent of each other and of all covariates, and that αbi is normally distributed independent of all ubit and Xit (but not of Z(b)it).The unobserved heterogeneity term There is right-censoring in the data since some mothers will not have completed their reproductive span at the time of the survey. After the end of the observation window (ultimo 2005), some mothers will still have another birth, and others will not. In principle, this could be captured with a birth interval after the last observed birth that lasts longer than until end 2005. Following , howevert, the woman gives at least one other birth (Fit= 1) or not (Fit= 0) is specified as follows:The equation determining whether, after birth Xit denotes the vector of strictly exogenous explanatory variables. The vector Z(f)it includes survival status of the preceding sibling and family composition variables (based upon the number of surviving girls and boys). The mother specific unobserved heterogeneity term +αfi captures unobservable time invariant characteristics influencing the probability to have a next birth and the term ufit captures idiosyncratic errors. We assume that the errors ufit are standard normally distributed, independent of each other and the Xit. The mother specific unobserved heterogeneity terms αfi are normally distributed with mean 0 and variance σ2(αf), independent of all ufit and Xit.As before, Fit is observed only partially. If birth t is not the last birth (t<Ti) then we know that the mother will have at least on more birth, so that Fit = 1. But if t = Ti, she may have decided to stop having children (Fit = 0), but it may also be the case that the next birth interval extends beyond reproductive age or the end of the observation window (Fit = 1 and right censoring). The data on actual births and birth intervals are not informative about this distinction so that Fit is observed only partially.The outcome αfi,αmi, and αbi. We will assume they are drawn from a three-dimensional normal distribution with zero mean and an arbitrary covariance matrix, independent of the Xit and the error terms ufit,umit, and ubit.Confounding unobserved factors are controlled for by allowing arbitrary correlations amongst The equations of this model Eqs –4 are esThe Akaike Information Criterion  and the Bayes Information Criterion  x log (number of observations) both indicate that the benchmark model in panel 1 has the best fit . This is the complete model of the previous section, which simultaneously estimates equations for birth interval, the probability to have another child (fertility equation), and for mortality of the first born and later born children, and includes unobserved heterogeneity terms in each of the equations.Replacing normally distributed errors by logistic errors (panel 4) gives slightly higher AIC and BIC, and very similar results in terms of parameter estimates and simulation outcomes (which are available upon request). Panel 2 gives substantially larger  AIC or BIC values, emphasizing the importance of incorporating unobserved heterogeneity and accounting for the simultaneous nature of the various mechanisms, rendering separate estimation of each equation inconsistent due to endogeneity. Finally, the model in which fertility and birth spacing are captured by only one equation (panel 3), seems intuitively more appealing (see section 3), but is clearly outperformed by the benchmark model in terms of goodness of fit. Still, most estimates of the simpler models (see panel 2 and panel 3) are qualitatively similar to those of the benchmark model.We focus on the results for the comparison area without health and family planning interventions. Estimation results for the complete model Eqs , 3 and 4log birth interval x previous sibling died) is not significant. Keeping the birth interval and other covariates constant, the effects of mortality of the previous sibling are insignificant .The parameter estimates of Eq  are presThe estimated coefficients on the covariates are in line with those in . We focuParameter estimates for mortality of the first-born child (Eq ) are preThe third panel of Birth intervals shorten with birth order as in, for example, . They arThe right hand panel of The estimates of the covariance matrix of the three unobserved heterogeneity terms are given in For the birth spacing equation the idiosyncratic noise terms have an estimated standard deviation 0.4356  for the actual sample of mothers. For each mother, we generated unobserved heterogeneity terms, error terms, and new outcomes (the dependent variables in our model) using the estimated parameters of each equation. The outcomes were generated recursively, using the timing of the events as sketched in Section 4. For example, for a given mother, we take the date of first birth as given and first generate the mortality outcome of the first child (using Eq ). Given The other columns present percent deviations from the benchmark for scenarios in which some behavioural or non-behavioural mechanisms are “switched off.” Column 2 switches off the replacement effects of infant mortality on both birth intervals and the probability of having another child. The estimates imply that families respond to infant mortality by shortening the next birth interval and increasing the number of births, and this is incorporated in the benchmark simulation in column 1. The simulation in column 2 produces the counterfactual outcomes that would arise if families would space their births and plan the number of births as if every child survived its infancy. The results show that this increases median birth interval length by 5.89% and 6.35% in the two areas, in line with the replacement effects on birth intervals found in other studies.In the comparison area, the total replacement effect resulting from the infant mortality rate of 68.5 per 1000 live births is an increase in the number of births by 3.72%, that is, 0.54 births for every infant that died (37.2/68.5). In the icddr,b area, the replacement effect is an increase of the total number of births by 2.20%, or 0.42 births for every infant that died. The larger effect in the comparison area is mainly due to the larger response of the number of births to the family composition variables in that area. Because of the longer birth intervals and the reduction in births, eliminating the replacement effects also has an indirect effect on infant mortality in the comparison area: it falls by 3.57% (2.45 per 1000 live births). In the icddr,b area, this indirect effect is much smaller (-0.27%).Column 3 shows what happens if the direct effect of mortality of the previous child on survival chances is eliminated. (It does not eliminate replacement effects.) Since this direct effect was negative in both areas, eliminating it increases infant mortality: by 4.85% (2.51 infant deaths per 1000 live births) in the icddr,b area and by 1.56% (1.07 per 1000 live births) in the comparison area. The difference between the two areas is due to the larger negative state dependence effect in the icddr,b area for short birth intervals, which can be explained by learning. Eliminating such a learning effect would increase infant mortality among children whose previous sibling died. Because of replacement behaviour, the larger infant mortality rates indirectly also shorten birth intervals and increase the probability to have a next birth, leading to higher fertility, but The final simulation (column 4) illustrates the importance of gender composition in the number of births and birth spacing. We suppress gender preference by simulating counterfactual birth and birth spacing processes, assuming that families behave as if all their children were boys. This would lengthen the median birth interval by 3.86% in the icddr,b area and by 3.18% in the comparison area, and it would reduce total fertility by 3.32% in the icddr,b area and by 5.68% in the comparison area. The interpretation is that for many families, not only the target family size matters for family planning, but also the target number of sons (“son preference”). Although these behavioural changes would reduce the infant mortality rates for higher order births, the ultimate effect on the infant mortality rate is positive. This is due to a composition effect: since the number of higher order births is reduced, the weight of relatively risky first births in the total infant mortality rate has increased.We analysed infant mortality and fertility behaviour with an important role of short birth intervals, distinguishing causal mechanisms from unobserved heterogeneity and reverse causality by using dynamic panel data techniques, building on the model of Bhalotra and van Soest. We used prospective data covering two rural areas in Matlab, Bangladesh: a treatment area with extensive health and family planning services and a comparison area with the standard health services provided by the government.The main goal was to explore the causal mechanisms between infant deaths and total fertility, and how birth spacing shapes this relationship. Comparing the findings in the two areas reveals several significant differences resulting from the interventions . We alsoControlling for birth spacing, unobserved heterogeneity, and a large set of socio-demographic covariates, we found insignificant negative state dependence in the comparison area but significant negative state dependence in the treatment area in case of short birth intervals. In other words, a child born after a short birth interval has higher chances to survive its infancy if the previous sibling died than if it survived. This can be due to learning, stimulated by the extensive family health services in the treatment area. This finding is unique among studies of infant mortality. For example,  found hiWe find evidence of causal effects in two directions: a short preceding birth interval reduces survival chances of infants, and an infant death increases the probability of a next birth and shortens the time until the next birth (replacement behaviour). For the comparison area we estimate that, as a result of replacement, 0.54 children are born for each infant death, compared to 0.42 children in the icddr,b area with additional health and family planning services. This finding is in line with , where aThe negative effect of infant mortality on the birth interval is large in comparison area. The literature provides evidence of biological effects through truncating the lactation period providing protection against fertility after a child death . It is wWe find that in the comparison area, mortality risk falls with birth interval length over almost the complete range. In the icddr,b area, however, higher mortality risk is found after a long birth interval, particularly after an infant death, suggesting that after an infant death and a long interval the mother may behave as a mother who gives birth to her first child .Estimates of reproductive behaviour are consistent with gender preference: having more surviving boys significantly reduces the probability of having a next child and this effect is strongest in the comparison area, in line with, e.g., . In bothConcerning policies targeted at achieving the sustainable development goals to improve reproductive health and reduce child mortality, the difference between the findings for the two areas highlight the important role of extensive maternal and child health interventions. Comprehensive health infrastructure, providing extensive health services and health and family planning information in the treatment area, strengthens learning effects that can reduce mortality risk. Moreover, it changes family planning and reduces the size of replacement effects after an infant death as well as the additional number of children due to boy preference.Our study has several limitations that were already briefly discussed in the modelling and data sections. We have used a selected sample, excluding families who migrated into or out of the area during the relevant time period. Lack of available data prevented us from performing a sensitivity analysis to investigate the effect of the selection procedure. On the other hand, results in S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.S5 Table(DOC)Click here for additional data file.S6 Table(DOC)Click here for additional data file.S7 Table(DOC)Click here for additional data file.


Maternal smoking during pregnancy negatively impacts fetal growth, but the effect is not homogenous across the population. We sought to determine how the relationship between cigarette use and fetal growth is modified by the social and physical environment.N = 232,291). Maternal smoking status was self-reported as the number of cigarettes smoked per day usually at the first prenatal care visit. Census dissemination areas (DAs) were used as neighbourhood-level units and linked to individual births using residential postal codes to assign exposure to particulate air pollution (PM2.5) and neighbourhood-level attributes such as socioeconomic status (SES), proportion of post-secondary education, immigrant density and living in a rural place. Random coefficient models were used with cigarettes/day modeled with a random slope to estimate its between-DA variability and test cross-level interactions with the neighbourhood-level variables on continuous birth weight.Birth records with covariates were obtained from the BC Perinatal Database Registry (2.5 and immigrant density were negatively associated with birth weight, but showed positive interactions with increased levels of smoking. Older maternal age and suspected drug or alcohol use both had negative interactions with increased levels of maternal smoking.A significant negative and non-linear association was found between maternal smoking and birth weight. There was significant between-DA intercept variability in birth weight as well as between-DA slope variability of maternal smoking on birth weight of which 68 and 30 % respectively was explained with the inclusion of DA-level variables and their cross-level interactions. High DA-level SES had a strong positive association with birth weight but the effect was moderated with increased cigarettes/day. Conversely, heavy smokers showed the largest increases in birth weight with rising neighbourhood education levels. Increased levels of PMMaternal smoking had a negative and non-linear dose-response association with birth weight which was highly variable between neighbourhoods and evidence of effect modification with neighbourhood-level factors. These results suggest that focusing exclusively on individual behaviours may have limited success in improving outcomes without addressing the contextual influences at the neighbourhood-level. Further studies are needed to corroborate our findings and to understand how neighbourhood-level attributes interact with smoking to affect birth outcomes. Smoking during pregnancy is a modifiable risk factor associated with adverse birth outcomes and may impart long-term health consequences –3. This 2.5, particles with aerodynamic diameter ≤ 2.5 μm) has shown to be a consistent risk factor associated with reduced birth weight [2.5 includes elemental and organic carbon compounds, metals and gases that stem predominantly from vehicle exhaust, residential heating and industrial emissions [2.5 and its constituents adversely affect the reproductive system are not fully understood; however, evidence supports the potential for a shared mode of developmental toxicity with tobacco smoke exposure [2.5 and tobacco smoke penetrate deep into pulmonary alveolar tissues and translocate to extrapulmonary tissues causing systemic cardiovascular and immunological alterations, including platelet activation, coagulation, endothelial dysfunction, DNA damage and mutagenesis [Exposure to the fine fraction of particulate matter  to investigate neighbourhood-level differences in the effect of cigarette smoking during pregnancy on birth weight and to quantify the degree to which individual and neighbourhood-level variables explain any observed differences. Specifically, we sought to determine whether exposure to PMorbidity . UnderstN = 237,470). Data from the BC Perinatal Database Registry were provided by Perinatal Services BC (PSBC), and included information on individual-level maternal-infant health status and outcomes, reproductive history, socio-demographics, risk factors, and residential postal codes. The Registry accounts for nearly 100 % of births and stillbirths in BC of at least 20 weeks gestation or at least 500 g birth weight. Research data access is provided by a Partnership Accord /Memorandum of Agreement between all BC Health Authorities and PSBC through the Freedom of Information and Privacy Protection Act [This was a population-based cross-sectional study of singleton births in British Columbia from 2001 to 2006 (tion Act . Researcn = 1096), non-viable births prior to 20 weeks gestation or less than 500 g (n = 15), and the list-wise deletion of births missing important data including: cigarettes smoked per day , PM2.5 (n = 1512), birth weight (n = 46). Table n = 245, min.21 max.80). Two variables indicating the use of alcohol or drugs  to be a risk factor in pregnancy as identified by a physician were combined into a single dichotomous variable.The outcome variable was continuous birth weight of singleton births. Included were all births (stillbirth and live) for gestational ages of 20 to 42 weeks. Excluded birth records included: out-of-province and invalid postal codes n = 096, non-GeoRef by DMTI [ArcGIS 10.2 [Birth records were geocoded based on the latitude-longitude coordinate of the mother’s residential postal code at the time of delivery using  by DMTI . Birth rGIS 10.2 . DAs repGIS 10.2 , they caGIS 10.2 , 30. BirGIS 10.2 .2.5 exposure was estimated using a national land-use regression (LUR) model developed to estimate PM2.5 at the census street block-face level [2.5 concentrations. Individual birth records were related to the block-face point estimates using a nearest-point procedure in ArcGIS10.2. Street block-face point estimates were related to individual birth records using a nearest-point procedure in ArcGIS10.2 and then aggregated to their DA-level mean to represent an area-level air pollution variable on individual births.PMce level . The mod2.5 are also high and could therefore confound any PM2.5 effect. Furthermore, concentrated ethnic communities may impart buffering mechanisms through enhanced social interactions and support networks [2.5 with the saved residuals representing the uncorrelated and independent contribution of immigrant density on birth weight freed from its collinearity with SESi and PM2.5 (r = −0.62 and 0.53 respectively). This method was repeated for SESi and education (r = 0.26) creating a residual immigrant density and residual education variable. The education and immigrant data were obtained by access to ABACUS via the Data Liberation Initiative [Three related but independent datasets all based on the 2006 Statistics Canada national census were used to represent the DA-level SES and demographic data. The first was a Canadian SES index (SESi) developed by Chan et al. which provides a measure of overall socioeconomic neighbourhood well-being . The secnetworks , 24. A snetworks . Here, iitiative .Imputation for missing SES, education and immigrant density values was performed in order to avoid data loss of rural DAs with low population counts. Taking advantage of the nested hierarchical structure of the administrative census and health boundaries, the mean SESi value for a larger encompassing census subdivision (CSD) or local health area (LHA) was imputed for a nested DA with a missing value. There were 1441 values imputed in 52 DAs for SESi , and 3170 values imputed in 108 DAs for both education and immigrant density . Sensitivity analyses were performed using only the non-imputed data.R2 [Hierarchical (multilevel) linear regression models were used to test our research questions, thereby accounting for the clustering, or non-independence, of individuals (level-1) belonging to a given DA neighbourhood (level-2). The multilevel model allows the intercept and slope to act as random parameters having between-area (DA) variability from an overall (BC-wide) mean intercept and slope. Therefore each DA has its own intercept and slope in which their variability from the overall mean intercept and slope can be investigated with the addition of individual (level-1) and DA-level (level-2) variables and their interactions . We follR2 . We starR2 .Stata 13IC [The Null model was followed by Model that included the individual-level covariates as well as the addition of a random slope for the continuous variable of maternal smoking . By allowing cigarettes/day to be random, the mean within-DA effect of maternal smoking is allowed to differ between DAs. The presence of a significant random slope indicates that its effect on birth weight is not constant  for all DAs; that is, there are important unexplained differences between the within-DA group effects of maternal smoking on birth weight. Subsequent models included the DA-level variables along with cross-level interactions to assess their fixed effects on birth weight but to also determine if their inclusion addresses any unexplained slope variance. Several models were tested using the Akaike Information Criterion (AIC) to evaluate model performance. We report the results of three models to compare the degree of change between the level-1 and level-2 homogeneous (non-interaction) models and a model with effect-measure variation. All statistical analyses were conducted in ata 13IC .n = 23,836) with an average of 7.5 cigarettes/day among smokers. Table 2.5, higher Asian immigrant density and rural DAs were all significantly associated with lower birth weights. Season of birth  was also significantly association with reduced birth weight. The results in Table After exclusions, the final dataset included 232,291 singleton (live and stillborn) births located in 6338 neighbourhood DAs . Table 2.5, Asian immigrant density and rural residence on birth weight and their interactions with specified levels of maternal smoking  and level-1 by level-1 interactions to explain the between-DA random intercept and random slope variability. The model results are presented in Table ing Fig. . Recall 2.5 levels had a significant non-linear association with reduced birth weight; however, it showed a positive interaction with maternal smoking such that the effect of increased smoking on birth weight was attenuated in DAs with higher levels of PM2.5 . The addition of DA-level variables in Model 2 removed a lot of the DA-level variance reducing the ICCadj to 0.6 %.The random effects, the explained proportional change in variance (PVC), and model diagnostics are presented in Table The level-2 random intercept variance term (reported as standard deviations in Table The level-1 and level-2 explained PCV (L1-PCV & L2-PCV) summarizes the relative degree of explained variance at the different levels between the different models Table . Using t1 = 229,067 in 6230 DAs) showed very minor differences in magnitude of significant variables. Most of the DAs that were missing data were in rural areas with small population numbers, the likely reason why their data were suppressed from the census tables. While some parameters were slightly attenuated, many of the interaction terms increased in magnitude. The random-intercept standard deviation was also slightly smaller than that of the same model using the full dataset while random-slope standard deviation showed no difference. In a second sensitivity analysis, we restricted the sample to only term births excluding stillbirths and congenital anomalies. As expected, there was a large reduction in the random slope variability (L2 slope (sd) = 5.7) and a small decrease in the random intercept variability (L2 intercept (sd) = 43.0) due to using only term births. Changes in the coefficients for the DA-level variables as well as their cross-level interactions with cigarettes/day were minor, within their 95 % confidence intervals reported in Table p = 0.151), but its interaction with cigarettes/day did remain significant (p = 0.025). The maternal-level variables were attenuated but remained significant with the exception of the interaction between drug/alcohol flag and cigarettes/day which was no longer significant (p = 0.106).Sensitivity analyses using only the non-imputed DAs . Furthermore, the addition of the level-2 variables explained 68.5 % of the random-intercept variance compared to the Null (empty) model. However in light of these findings, significant inter-DA variance remained for both the random intercept and slope.While the application of multilevel models in perinatal epidemiology have become more common , most han = 2501, 1.1 % of sample).This study used self-reports of smoking (cigarettes/day) recorded at the first prenatal visit; however, there were no data on exposure to environmental tobacco smoke or whether smoking reduction or cessation occurred during the pregnancy. The self-reporting bias of cigarette consumption can lead to the attenuation of the true effect of smoking on birth weight , and may2.5 exposure assessment which could affect its estimates. First, the LUR PM2.5 concentrations may be underestimated with less variability compared to compiled monitoring data which could potentially underestimate its association with birth weight in certain areas [2.5 LUR model is cross-sectional based on 2006 air quality monitoring data, and we therefore assume that the study population was exposed to the same levels of PM2.5 across 6 year study period based on their residential DA. Finally, our analysis was based on maternal place of residence at delivery, and therefore intra-urban commuting and potential inter-urban relocation within the pregnancy period was not accounted for. Time-activity patterns show that pregnant women spend more time at home in the later stages of pregnancy, but mobility patterns may differ by age, parity and SES [Another limitation includes potential measurement error and misclassification bias in the PMin areas . Also th and SES , 64.A main strength of this study is the quality of the perinatal registry data . The nea2.5 variables explained 68.5 % of the random intercept variability in DA-mean birth weight. However, the random slope variability was only partially explained by the cross-level interactions suggesting other contextual factors are involved in determining the magnitude of maternal smoking on birth weight. Further studies are needed to corroborate our findings and to understand how neighbourhood-level attributes interact with smoking to affect birth outcomes.The effect of maternal smoking on birth weight is not constant across geography, but rather is context specific given the social and physical environment. The use of random coefficient models revealed neighbourhood-level differences in how maternal smoking negatively impacted birth weight demonstrating effect modification by neighbourhood and maternal-level factors. The inclusion of the DA-level SES, demographic and PM


Paraovarian cysts are a rare pathology, constituting 10-20% of the adnexal masses. The origin can be represented by paramesonephric ducts (Hydatid cysts of Morgagni), vestiges of mesonephric ducts also represented by mesothelium, or neoplastic (cystadenomas or cystadenofibromas) that are mostly benign. Borderline or malignant paraovarian tumors are encountered less often. This article presents a case of paraovarian cyst in a 37-year-old patient, with a history of 2 pregnancies, completed by cesarean. The patient sought medical attention for an asymptomatic voluminous ovarian cyst, detected in a routine ultrasound scan. Laboratory tests and tumor markers were within normal limits. Transvaginal ultrasound and color Doppler revealed a cystic adnexal mass with 10 cm transonic, smooth, homogeneous content, avascular walls with no internal papillary projections, with a “hyperechoic line” sign of delimitation from the ovarian capsule, mostly visible when the adnexa was mobilized. The diagnostic and curative laparoscopic surgery was successful, followed by a quick recovery. The histopathological exam confirmed the benignity and the origin of the paraovarian cyst. The case was discussed in the context of the literature review concerning this pathology, drawing attention to the real possibility of differentiating ovarian from paraovarian cysts by ultrasound. The cases have been reported in all female age groups and seemed to be most common in the third to fifth decades of life. The origin of these pelvic masses may be non-neoplastic, simple cyst or neoplastic [2]. Paraovarian simple cyst originates from the embryologic remnants of the urogenital system (mesonephric or Wolffian and paramesonephric or Mullerian ducts) [1]. In embryonic life, mesonephric and paramesonephric ducts are characteristic for both sexes. In female embryos, paramesonephric ducts rise to the main genital organs. It is possible that some portions from the cranial and caudal segments of the excretory tubules to persist in the female fetus in the mesovarium, forming the epoophoron and paroophoron. The mesonephric duct disappears, except for one small cranial segment located at the epoophoron level, and sometimes except for one small caudal segment that may remain in the uterus or the vaginal wall. A paraovarian cyst may be formed from these structures during the lifetime [3]. Another origin can be represented by the mesothelium, resulted from the invagination of the tube’s serosa. A simple cyst can suffer multiple transformations, resulting in a neoplastic paraovarian cyst, that is usually benign, or in a serous cyst similar to benign ovarian tumor (cystadenomas or cystadenofibromas). Borderline or malignant paraovarian tumors are encountered less often [5]. Paraovarian cysts are found in the broad ligament between the ovary and the fallopian tube [6]. In a review of 79 female patients with paratubal cysts, the paramesonephric variant was the most commonly encountered [7]. Paraovarian cysts constitute about 10-20% of the adnexal masses, and previously had a lower reported rate. A study from Italy [2] estimated their incidence to be of about 3%, while an autopsy study of postmenopausal women detected them in about 4% of the cases [4]. They are not uncommon, but due to their frequent asymptomatic presence, the actual incidence is not known. The reported incidence of malignancy is of about 2–3% , the risk of malignancy is higher when the size of the cyst is bigger than 5 cm, but other clinical or surgical differentiating criteria were not investigated. The preoperative ultrasound scan is mandatory intending to obtain defining information for a differential diagnosis between benign and malignant cysts, and on the other hand, between ovarian and paraovarian cysts. Unfortunately, no other different criteria than IOTA rules are available, despite the different origin of the paraovarian cysts. In order to establish the distinguishing criteria and for a proper diagnosis, computer tomography or magnetic resonance imaging may be performed in the preoperative evaluation, but none of these imaging techniques succeeded to provide a concrete result so far, and to precisely eliminate the misdiagnosis as an ovarian mass that remains to be a problem.A simple, asymptomatic paratubal or paraovarian cyst can be managed expectantly without further follow-up. Surgical exploration and removal is indicated for these lesions, if they undergo torsion, cause persistent pain, pressure symptoms, or appear neoplastic with suspicious findings by ultrasonography . Laparoscopy is currently the most common surgical approach in the management of paraovarian cysts. Surgeons commonly use two techniques: the first includes the aspiration of the cystic fluid via the laparoscope, and the second includes the performance of a fenestration of the cyst before removing it. The risk of both described procedures is the spillage of a neoplastic cyst, with intraperitoneal dissemination of any existing malignant cells. Therefore, it is very important to differentiate the simple paraovarian cyst from the neoplastic paraovarian one, before deciding the surgical approach. The current data on the discrete characteristics of simple, versus neoplastic paraovarian cyst are limited. According to some studies .The optimal treatment of women with pelvic masses clinically suspected of being of adnexal origin requires knowledge of the exact nature of the mass. Ultrasound is considered the first-line diagnostic imaging tool for this task [At the ultrasound exam, the paraovarian cyst appears as a round or oval well defined cystic mass, located close to the ipsilateral ovary, but clearly separated. The mobility and dissociation of the cyst from the ovary by the hyperechoic line is characteristic and defining for the diagnostic. The mobility of a paraovarian cyst depends on its size, position in the pelvis and anatomical relationship with the uterus and ovaries.In the case presented in this article, the paraovarian cyst was not misdiagnosed during the ultrasound exam and was not confused with other pelvic tumoral masses.


The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti‐ and pro‐inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi‐cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others.Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2‐dimensional Kolmogorov‐Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications.Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy‐induced hypertension), possibly indicating multiple causes for the complication.This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. During pregnancy, the mother's immune system recognizes her fetus as “foreign,” yet instead of treating it as a target for rejection, the fetus is tolerated and nurtured through gestation. Indeed, pregnancy is a tight balancing act between different immune cells, hormones, nutrition, and infection under very strict immune regulation.This paradoxical success of the “fetal allograft” in the face of a potentially hostile maternal immune system has been suggested to be due to immunomodulation at the maternal‐fetal interfaceIt has been proposed that successful pregnancy in mice is a Th2 phenomenon and that abnormally elevated concentrations of Th1‐type cytokines are associated with spontaneous miscarriage in mice and humans.Given this scenario, we and others have undertaken studies with the objective of elucidating the possible roles of cytokines in human pregnancy and to ascertain whether there are differences in cytokine profiles in normal human pregnancy as compared to unexplained pregnancy complications. We analysed supernatants of mitogen‐stimulated peripheral blood lymphocytes cultures, for a selected panel of Th1 and Th2 cytokines. Our data, as well as of others, support the hypothesis that normal successful pregnancy is a Th2‐phenomenon, while several other unexplained pregnancy complications such as recurrent spontaneous miscarriage (RSM), premature rupture of membranes (PROM), pregnancy‐induced hypertension (PIH), and preterm delivery (PTD) are associated with an elevated Th1‐type cytokine profile.However, the majority of the studies have compared individual levels or ratios of a small number of cytokines. Given that cytokines form a network of interacting entities and that a single cytokine or a ratio of two may not provide sufficient information about the overall immune reactivity, it is of interest to study the combined levels of several cytokines as this may provide a better picture of immune reactivity. Moreover, multivariate cytokine profile analysis may also suggest cytokine importance or a mathematical measure of the contribution of individual cytokines in separating the test group from its comparable/matching healthy control. This will shed light on the extent to which cytokine profiles are related (or can predict) different pregnancy conditions: If the cytokine levels can predict with high accuracy the pregnancy conditions, this would suggest that the cytokines are an important element of the disease process. If instead the cytokine levels only poorly predict pregnancy conditions, this would suggest that factors other than cytokines are contributing to a greater extent than just the cytokines being studied. This may also have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach.how different these groups are. Rather, many previous studies have focused on whether statistically significant differences between groups of normal vs complication groups could be found by, for example, comparing the mean cytokine concentration values of each group. However, merely statistical significance via finding a small P‐value is not the whole story; it is also very interesting to know if the values are really very different, or only slightly different, despite having small P‐values. Indeed, we should consider the practical significance, not just the statistical significance, of the difference in cytokine values between groups. Practical, or clinical, significance can be expected to be related to the actual size of the difference; if the difference is small , this suggests little practical or clinical significance or benefit. On the other hand, if the difference is large, then it suggests practical significance, such as the ability of a drug to alleviate symptoms.Looking to pregnancy complication data from another angle, it does not appear that earlier work has focused strongly on Another question, which has received little if any attention in the analysis of cytokine data for pregnancy complications, is the presence of subgroups in the data. It may be helpful to investigate whether there are subgroups within the same pregnancy complication. Because the cause of the pregnancy complications studied was unknown (cases with known cause were excluded), patient groups may be made up of different subgroups within a certain complication. For example, cytokine levels may display a large difference from the normal pattern in 1 subgroup, while there may also be another subgroup of women in the same group presenting the same complication, but due to other unknown reasons where cytokines are contributors but not necessarily the major players.Keeping the above points in mind, we aimed to use the statistical approaches to study and quantify the connection between cytokine profiles and different categories of pregnancy complications as compared to normal controls. More specifically, using the cytokine data we have previously gathered, here we investigate whether groups are different; how different they are; and in what ways they are different. Lastly, we also investigate whether there are subgroups within each complicated pregnancy condition.22.1The groups of subjects studied are as detailed elsewherest Tri vs RSM; 2nd Tri vs PTD; ND vs PIH; and ND vs PROM)  and who had been investigated clinically. The PTD group comprised women who were admitted to the hospital with spontaneous preterm labor, with intact membranes. These patients were in active labor, with the cervix dilated more than 3 cm and they delivered prematurely (before 34 weeks of gestation). Intrauterine infection was ruled out in these patients by high vaginal swab culture, urine culture, complete blood count, total and differential WBC count, and estimation of levels of C‐reactive protein. However, amniocentesis to exclude intrauterine infection was not feasible. The PIH group comprised women who: (i) were normotensive before pregnancy and during the first 20 weeks of gestation, (ii) developed hypertension (blood pressure > 140/90 mm Hg on two or more occasions 6 hour apart) associated with proteinuria >300 mg per 24 hour, and (iii) established labor either by induction or spontaneous onset. The PROM groups comprised women who were admitted with rupture of the fetal membranes at term prior to the onset of regular uterine contractions. Each group was compared to the gestationally age‐matched healthy normal pregnancy control group , spontaneous miscarriage (RSM), or anomalous end of gestation with PTD, PIH, or PROM. Therefore cytokine profiles reflect the situation existing in the periphery at that point in time. All subjects were enrolled at 2 high‐risk pregnancy clinics at Kuwait Maternity Hospital; informed consent was obtained from all subjects. All studies had the approval of the Ethics Committees of the Faculty of Medicine at Kuwait University and of the Maternity Hospital, Kuwait.2.25 cells per well and then challenged with phytohaemagglutinin (PHA)  at a concentration of 5 μg/mL for a period of 96 hours. Supernatants were collected for cytokine estimation on day 4 and stored at −80°C.Peripheral blood from subjects in the groups of Normal Delivery, RSM, PTD, PIH, and PROM were stimulated with mitogen. Peripheral blood was obtained by venipuncture from the subjects and PBMC were separated by Ficoll‐Hypaque  density gradient centrifugation, suspended in RPMI medium  containing 10% fetal calf serum, aliquoted into 96‐well tissue culture plates at a density of 102.3Cytokine levels were determined by sandwich ELISA using kits obtained from Immunotech SA (France). The manufacturer's protocols were followed up for the assays. Samples were tested in duplicate and absorbance values measured in an ELISA Reader. Accurate sample concentrations of cytokines were determined by comparing their respective absorbancies with those obtained for the reference standards plotted on a standard curve using reference recombinant cytokines. The profile consisted of the Th1  and Th2  cytokines. All standards, controls, and samples were measured in duplicates. The sensitivity of each of the assays were as follows: 5 pg/mL of IL‐2, 10 pg/mL of TNF‐α, 3 pg/mL of IFN‐γ, 5 pg/mL of IL‐4, 1 pg/mL of IL‐5, 3 pg/mL of IL‐6, and 5 pg/mL of IL‐10.2.4All zero/undetectable cytokine concentration values were replaced with the minimum detectable  value, as is standard practice. The small fraction of missing cytokine values were replaced with the median values of the samples . All concentrations were log‐transformed because a log scale is a more natural scale to study cytokine concentrations on. Log‐transforming is also standard in the literature. After this, the data were centered (by subtracting the mean) and scaled (divided by the standard deviation).P‐value of <.05 was considered statistically significant in this study. Data and statistical analysis were performed with Scikit‐learniPython interface.Some of the cytokine values  deviated strongly from normal distributions; hence, we employed non‐parametric statistical tests to measure the differences in groups (see below). A We compared the multivariate cytokine data between groups. To do so, the main tools used were as follows:1Cramer TestP‐value from comparing 2 multivariate distributions, testing whether the 2 groups are different or not with respect to the pattern of all measured cytokines. Such multivariate tests have the advantage of being able to handle multiple variables, and their potentially complex interactions and correlations. On the other hand, as with all such multivariate tools, their ability to detect small univariate differences is slightly weakened, due to the “noise” introduced by potentially irrelevant variables.Multivariate comparison of the cytokine pattern between each of the complication groups with its gestational age‐matched control using the 2Cytokine importance, ie, which cytokine variables are most responsible for separating 2 groups, we used the classification algorithm CART (classification and regression trees) where “importance” give the average weighting of each cytokine indicating how strongly each cytokine drives the separation between 2 groups. The method gives a list of the cytokines and a value from 0 to 1, indicating how important the cytokine is in separating the groups, where higher values indicate greater importance. The default gini method was used for creating trees. Hyper‐parameters were optimized by cross‐validation while searching over these values: Max tree depth 1, 2, 3, and 4; minimum samples per leaf 5, 6, 7; the number of features to consider when looking for the best split was 2, 3, 4,…, 7. Cross‐validation was undertaken by 500 random stratified samples, splitting the data into train/test sets. The stratified test sets were chosen to be 10% of the number of samples of data, as is standard practice. As a comment, it is true that studies of individual or ratios of cytokines can also give cytokine importance. For example, if a set of cytokines is compared between 2 groups, and a few are found to be statistically different, then these cytokines would, of course, be important in distinguishing the groups. However, the individual/ratio approach has a weakness in that it is not straightforward to say which cytokines are more important or to rank them in terms of importance. Such a ranking has obvious clinical relevance, for example, in drug design and treatments.To investigate 3how different the groups are, using various methods: Evaluating Partial least squares regression (PLSR) figures—This method will plot a 2D projection (or “shadow”) of the multivariate data of all cytokines tested. The algorithm searches for the best way to project the data into a lower dimension so as to identify the best linear separation between the groups. If the projections of each group overlap a lot, this suggests that the cytokines value distributions are quite similar; if they do not overlap a lot, it means that the characteristics of the cytokines are quite different between the groups. Such visualizations aid in highlighting in what ways the 2 groups differ, for example, whether 2 groups are completely separate, or 1 group is a subset within another group, etc. As a final cautionary comment, with a smaller number of samples and a larger number of variables, PLSR projections will tend to overemphasize the difference between 2 multivariate groups, because in the algorithm searches many projections for the best way to separate the 2 groups (Cf. “p‐hacking”). Hence, because some of our group samples are not large, the projection separations and K‐S values (below) may be a little larger than the true values (which might be obtained with very large samples).2‐dimensional Kolmogorov‐Smirmov test—While PLSR visualizations are helpful, it can sometimes be hard to visually judge how much or how little overlap there is between groups. With this in mind, a more quantitative way to compare the projections of 2 groups is to use the PLSR 2 dimensions (2D) plots and compare the 2 groups using the 2D analog of the Kolmogorov‐Smirmov (K‐S) test.Classification ability—Another way to measure how strongly 2 multivariate groups differ in distribution is to compare how accurately a classification algorithm can classify samples from each group. Intuitively, if 2 groups have very different cytokine values, then samples from each group will be easily classified, or associated, to the correct group. Conversely, if the cytokine profiles of 2 groups are very similar , then a classification algorithm will only be able to correctly classify a small fraction of the samples . Here, we used the CART classification trees to perform such classification of train/test samples for each pair of groups (as described above). However, instead of measuring the classification accuracy, we report the ROC AUC: the mean receiver operator characteristic (ROC) area under the curve (AUC) for the predicted class of the test samples . The ROC AUC is a standard method for measuring the accuracy of binary classification predictions. The AUC score will be 0.0 if all the predictions are incorrect, and a score of ~0.5 corresponds to just random guessing  classifications, and a score of 1.0 corresponds to perfect classification accuracy. Hence, for example, a score 0.6 indicates a fairly poor classification ability, while 0.9 is very good. The reason for using the AUC instead of simply the classification accuracy (fraction of correct classifications) is that the relative proportion of each class largely determines the accuracy. So, for example, if 90% of the samples are 1 class and the other 10%, then a 90% accuracy could be achieved even by an algorithm that assigns all sample to large class. Hence quoting the accuracy can be misleading indicator of classification ability, while the ROC AUC accounts for different class proportions.4principal component analysis (PCA) to visualize the multivariate data in 2 dimensions. Doing so helps to reveal any obvious clustering or grouping. We complemented this method using the gap statistic (using k‐means clustering),Finally, we investigated if there were subgroups within each complication group. To do this, we first used 33.1P = .0001, .023, .0097, and .009 respectively)  and IFN‐γ (0.32). As compared to ND, cytokine importance for PIH were those of IL‐10 (0.33) and IL‐5 (0.17), while those for PROM group were IL‐5 (0.68) and IL‐4 (0.25) , they show considerable overlap in cytokine levels in PLSR figure Figure C. FurtheFinally, comparing ND to PROM, Figure P‐values in multivariate Cramer test, they vary considerably in both how strongly, and in which ways they differ.Thus we see that while all pairs of groups are statistically significantly different with small 3.4We further investigated whether there exist subgroups within each of the pregnancy complications . The reason for this investigation is that subgroups may imply different causes for the complication.PIH‐out, and the other group PIH‐in. Using the Gap Statistic, we also found that the PIH group has 2 subgroups/clusters. Further, employing the K‐means clustering method (with K = 2) to assign each sample to 1 of 2 subgroups, we find an exact agreement between the subgroups visually apparent in the PCA plot, and the subgroups found by the gap statistic with K‐means.We were not able to find clear evidence of subgroups within RSM, PTD, or PROM. However, the clearest result of clusters among a pregnancy complication was in the PIH group. To search for subgroups, we first examined PCA plots of all the groups. Figure Having found 2 subgroups, it is interesting to analyze them further. Figure P‐value, the K‐S distance as well as the classification accuracy are far more distinct between ND vs PIH‐out as opposed to ND vs PIH‐in combination. In other words, the subgroup PIH‐in is very similar to ND, but not exactly identical in distribution; while the subgroup PIH‐out is very different from ND.Table In summary, we found that the PIH group is actually made up of 2 subgroups/clusters, one having a multivariate cytokine profile very similar to ND, while the other subgroup is quite different to ND. This suggests that among the patients in our PIH group, a subgroup (PIH‐out) had PIH consequences which were reflected/shown by the cytokine imbalances as compared to ND, while this was not the case for the PIH‐in subgroup of patients suggesting their presentation is not mediated/reflected by cytokine imbalances.P = .087), while, as expected, the P‐value between ND and PIH‐out was extremely significant (P = .0000001). The maximum cytokine importance/contribution in PIH‐out group were for IL‐5 (0.89) and IFN‐γ (0.10), , Table .4Cytokines are known to work in a complex hierarchical network and most of them show pleiotropic, redundant, and synergetic actions, making the full understanding of the balance very challenging. While several reports have suggested the use of cytokines as potential biomarkers, a single biomarker may be insufficient; thus it was suggested that it would be more appropriate to use ratios of 2 cytokines or to develop a multivariate “cytokine signature” based on the pattern of several cytokines produced by peripheral blood mononuclear cells.We compared multivariate cytokine profiles of several pregnancy complications to gestationally age‐matched control groups, using several statistical techniques. It was previously established that the groups are different in terms of individual cytokine levels and simple ratios, but we sought to investigate these differences further. Studying all tested cytokines, our main aim was to see how different are the panels and in which ways they are different in pathological pregnancies as compared to normal controls.We, therefore, analyzed our data with a variety of multivariate statistical techniques that can detect more complex differences in patterns than simple differences in individual levels or ratios. It is useful to employ such multivariate methods because it is in principle possible that none of the individual variables show statistically significant differences in median levels when comparing samples of 2 groups, while a multivariate method may nonetheless uncover a difference in the multivariate pattern. On the other hand, the ability to detect multivariate differences comes at the expense of a reduced ability to detect individual cytokine differences.The multivariate analysis showed significant differences between normal pregnancy and pathological complications. This may reflect the complexity and importance of cytokine balance in pregnancy and encourage further studies to select and include more parameters, study other complications and examine other disease conditions. Building on this, and considering the potential of the clinical approach, it would be very useful to know which of the tested cytokines most contribute to the deviation from the normal physiological cytokine balance. Using the data available, we used a classification algorithm (CART) and found the mean contribution of individual cytokines and their importance in differentiating the pathological presentation from the normal cohort.It is interesting that the multivariate cytokine profiles of RSM and healthy controls were quite distinct and the main cytokine separating the 2 groups was IL‐10. IL‐10 has been shown to play a pro‐pregnancy role in early gestation,Similarly, both the PIH and PROM groups showed lowered Th2 cytokines to be the most differentiating cytokines as compared to normal delivery. IL‐4 and IL‐5 are classical Th2 cytokines and several researchers have reported the association of their lower levels with pregnancy complications.However, the main cytokine importance for the PTD group were of the Th1 cytokines IL‐2 and IFN‐γ. These findings are in concordance with what has been reported by several investigators on the role of pro‐inflammatory cytokines in PTD. For example, it was demonstrated that the presence of IFN‐γ in cervicovaginal fluid in late second and early third trimesters is an important risk factor for PTD in asymptomatic women.It is also interesting and novel that the analysis of our PIH group showed that it is made up of 2 clusters, one with multivariate cytokine profile, that is, similar to healthy controls, while the other is quite distinct. This method of analysis and the findings may help in explaining the long and wide controversy in literature about the association of different cytokines in different pregnancy complications. Taking PIH as an example, while there is substantial evidence supporting a role of cytokines in the pathogenesis of PIH, the underlying pathophysiologic mechanisms are still unclear with several proposed pathways.To the best of our knowledge, this is the first study to report simultaneous measurement of multiple cytokines from several different pregnancy complications followed by comparisons of cytokines in multivariate statistical pattern techniques as compared to healthy controls. However, there are limitations. From a biological point of view, measuring the production of certain cytokines by PBMC in an in vitro setup only partly reflects the much more complicated in vivo scenario at the feto‐maternal interface, where T‐cells are in close contact with other regulatory lymphocytes, natural killer cells and a multitude of interacting cytokines and factors. From a statistical point of view, the statistical power of multivariate analysis is dependent on sample numbers, and there is an increased risk of “over‐fitting” or observing false positive results when analyzing high‐dimensional data with just few samples . Given our data set of a small number samples, and especially because the data has several variables ie, several cytokines, both the accuracies and cytokine importance values should be taken as tentative results, subject to future corroboration, or adjustment. Having said that, our analysis and findings do point to several interesting avenues to be explored further.Several authors have modeled cytokine‐mediated inflammatory processes; examples include rheumatoid arthritisThe authors declare no conflict of interest.


Mechanical birth-related injuries to the neonate are declining in incidence with advances in prenatal diagnosis and care. These injuries, however, continue to represent an important source of morbidity and mortality in the affected patient population. In the United States, these injuries are estimated to occur among 2.6% of births. Although more usual in context of existing feto-maternal risk factors, their occurrence can be unpredictable. While often superficial and temporary, functional and cosmetic sequelae, disability or even death can result as a consequence of birth-related injuries. The Agency for Healthcare research and quality (AHRQ) in the USA has developed, through expert consensus, patient safety indicators which include seven types of birth-related injuries including subdural and intracerebral hemorrhage, epicranial subaponeurotic hemorrhage, skeletal injuries, injuries to spine and spinal cord, peripheral and cranial nerve injuries and other types of specified and non-specified birth trauma. Understandably, birth-related injuries are a source of great concern for the parents and clinician. Many of these injuries have imaging manifestations. This article seeks to familiarize the reader with the clinical spectrum, significance and multimodality imaging appearances of neonatal multi-organ birth-related trauma and its sequelae, where applicable.Teaching pointsMechanical trauma related to birth usually occurs with pre-existing feto-maternal risk factors.• Several organ systems can be affected; neurologic, musculoskeletal or visceral injuries can occur.• Injuries can be mild and transient or disabling, even life-threatening.• Imaging plays an important role in injury identification and triage of affected neonates.• The online version of this article (10.1007/s13244-017-0586-x) contains supplementary material, which is available to authorized users. The process of birth, whether spontaneous or assisted, is inherently traumatic for the newborn. Birth-related injuries encompass both mechanical and hypoxic-ischemic events. This review focuses mostly on mechanical trauma sustained by the neonate owing to the forces of labor and delivery. For conciseness of this review, birth-related hypoxic-ischemic injuries to the neonate will not be separately addressed.Trauma related to birth may affect several organ systems of the neonate ESM_. The exaBirth-related trauma can occur without identifiable risk factors; however, it is more common in context of predisposing feto-maternal risk factors. Risk factors can be fetal  . This reUltrasound has a role in preoperative evaluation of postganglionic brachial plexus in children with neonatal brachial plexus palsy; it demonstrated 68% sensitivity and 40% specificity for lower trunk involvement in a recent retrospective cohort study . A smallOver the long term, progressive glenohumeral deformity may result. A Swedish population-based study found persistent anomalies in approximately 25% patients with neonatal brachial plexus palsy . These aPhrenic nerve palsy can occur as an accompaniment of traumatic brachial plexopathy. A retrospective review by Bowerson et al.  describeRadial nerve palsy can occur in context of a humeral shaft fracture .Traumatic facial nerve injury can occur as a consequence of difficult extraction, particularly in context of forceps use. Prognosis for recovery is excellent; 90% recover completely .Application of excessive traction to the head during breech delivery can result in unilateral recurrent laryngeal nerve injury and abductor paralysis. Left recurrent laryngeal nerve tends to be involved more often due to its longer course . PrognosMusculoskeletal injuries encompass both bony fractures and soft tissue injuries. Birth-related fractures in the newborn period, though overall rare, are important to recognize due to non-specific signs or symptoms, increased likelihood of missing them due to unossified cartilage and necessity of differentiating these from abusive trauma. These include fractures of both flat and long bones .Clavicular fractures Fig.  can occuRib fractures have been described to be associated with dystocic birth. Based on a recent case series, birth-related rib fractures tend to be mid-posterior in location .Humeral fractures can involve the humeral shaft Fig. a or the Femur fractures Fig. a though Sternocleidomastoid hematomas can be seen with a dystocic birth . AlternaOperative intervention or botulinum toxin injection are considered in the rare circumstance where physical therapy fails .Of the visceral organs affected by trauma, injuries to the liver , 49, splTracheal rupture can be anterior subglottic or distal tracheal in location. This rare and potentially fatal entity can occur in context of dystocic birth, and should be promptly suspected in neonates who develop subcutaneous emphysema or pneumomediastinum shortly after birth , 52. BroMechanical trauma related to birth can affect different organ systems of the neonate. While often of little clinical consequence, traumatic events can lead to cosmetic deformity, functional impairment and in extreme circumstances, even death. Imaging is important for detection, assignment of prognostic significance and follow-up, making it important for radiologists to be familiar with the imaging manifestations of these entities and their sequelae.ESM 1(PDF 446 kb)ESM 2(PDF 64 kb)ESM 3(PDF 60 kb)ESM 4(PDF 71 kb)