AimIn developed countries including Japan, gestational age (GA) is predicted by the last menstrual period (LMP) and/or fetal ultrasound. In some developing countries, GA is predicted by infant's foot length (FL). Pregnant women who did not have pregnancy check-up is not infrequent in Japan, therefore there are sometimes opportunities to estimate the GA from infants after the delivery. The aim of this study is to determine the estimated GA formula from infant's FL in Japanese.Methods2). The infant's FL of males and females, infant's FL of preterm and term, and infant's FL of low birth weight and appropriate weight infants were performed by the t-test as independent samples. A statistically significant difference was p < 0.05. Statistical analysis was performed using JMP Pro 16 .This study was a prospective cohort study. Infants between May 2021 and August 2021 at Iizuka Hospital and Tagawa Hospital or transferred from other hospitals within 24 hours of birth were collected. GA was determined using LMP and/or fetal ultrasound. The infant's FL was measured with a digital caliper within 24 hours of birth. The relationship between FL and GA was analyzed by simple regression analysis to determine the coefficient of determination (RResultsGA = 18.49 + 0.27 x infant's FL (R2 = 0.39), was determined.Ninety of the 135 infants were enrolled. The average GA was 38.2 ± 1.8 weeks, the average infant's FL was 7.230 ± 0.411 centimeter (cm), and the range of the infant's FL was 5.385 to 8.089 cm. The estimated GA formula, ConclusionsWe determined the estimated GA formula from the infant's FL. There are some limitations and care should be taken in the use. Gestational age (GA) predictions are based on 1) the last menstrual period (LMP), (2) fetal ultrasound with measurements of crown-rump length (CRL), biparietal diameter (BPD), and femur length, and (3) the Dubowitz method and the New Ballard method. In Japan, LMP and fetal ultrasound are generally used because GA and fetal ultrasound are simple and highly accurate. But LMP assumes a menstrual cycle of 28 days, does not consider delays in ovulation, and may cause inaccuracies of one to four weeks [ the lastIn Japan, many pregnant women have pregnancy check-ups during pregnancy, and many infants are decided at GA by LMP or fetal ultrasound. However, there are 0.23-8.06 infants who did not have pregnancy check-ups per 1000 pregnant women . There aWe conducted a prospective cohort study, which was performed from May 22, 2021, to August 31, 2021, at Iizuka Hospital and Tagawa Hospital, or transferred from other hospitals within 24 hours after birth to be a part of the study. The definition of appropriate birth weight infants was those with a weight between the 10th and 90th percentile of the standard Japanese weight, and the definition of low birth weight infants was those with a weight less than the 10th percentile of the standard Japanese weight . PretermThe infant's FL was measured by doctors or nurses within 24 hours after birth. Five doctors  and five nurses received explanations and were fully trained. The infant's FL was defined as the length from the center of the right heel to the longest right toe Figure . Measure2). The receiver operating characteristic (ROC) curve was performed to analyze the sensitivity and specificity between the infant's FL and GA. The comparison of infant's FL of preterm and term infants and infant's FL of appropriate birth weight and low birth weight infants were analyzed by the unpaired t-test to determine the p-values. A significant difference between the groups was defined when p<0.05. Statistical analysis was performed using JMP Pro 16 .The relationship between the infant's FL and GA, between the infant's FL of males and GA, and between the infant's FL of females and GA was analyzed by single regression analysis to determine the coefficient of determination .We collected 135 infants during the study period. Infants with no infant's FL measurements in four, infants with no pregnancy check-up in two, infants with twins in eight, and infants without appropriate birth weight or low birth weight in 31 were excluded. Finally, 90 of 135 infants were enrolled in this study, of which 35 were males (38.9%) and 55 were females (61.1%). GA ranged from 31 to 41 weeks, with 10 preterm infants  and 80 term infants  Tables . The 90 The average infant's FL of 90 infants was 7.230 ± 0.411 cm, and the range of FL was 5.385 to 8.089 cm. The average infant's FL of 35 male infants was 7.281 ± 0.418 cm : 7.138-7.307 cm), and the average infant's FL of 55 female infants was 7.197 ± 0.407 cm (95% CI: 7.087-7.307 cm). There was no statistically significant difference in infant's FL between male infants and female infants (p = 0.35)  and appropriate birth weight infants in 78 . The difference in infant's FL was not statistically significant in low birth weight infants and appropriate birth weight infants between male and female infants . Infant's FL had a statistically significant difference between total low birth weight infants and total appropriate infants (p < 0.0001) .The receiver operating characteristic curve (ROC) for term infants showed a cutoff of 7.094 cm, an area under the curve (AUC) of 0.8, a sensitivity of 77.5%, and a specificity of 80% .The aim of this study was to determine the estimated GA formula from the infant's FL of Japanese. The results of the estimated GA formula were shown in GA = 18.49 + 2.7 x FL (R2 = 0.39)". On the other hand, GA formulas were reported, such as "GA = 6.278 + 4.15 x FL (R2 = 0.86)" in India [2 = 0.65)" in India [2 = 0.75)" in Ethiopia [This study was induced in the estimated GA formula "GA = 18.49 + 2.7 x FL R2 = 0.9". On thEthiopia . An infaEthiopia . In thisEthiopia . A studyIn developing countries, GA is sometimes predicted using the infant's FL, the Dubowitz method, and/or the New Ballard method for infants born to pregnant women who did not have a pregnancy check-up . On the The limits of this study are: (1) the number of infants was small, (2) the proportion of preterm infants was only 10%, (3) the study was limited to regional perinatal facilities and does not include cases from higher medical institutions such as university hospitals, and (4) preterm infants, perinatal infants, very low birth weight infants, etc. were not included.We determined the estimated GA formula from infant's FL. There are some limitations and care should be taken in the use. It is a simple test and can contribute to improving the treatment of infants.


Low birth weight has been associated with a greater risk of cardiovascular disease (CVD). However, the interaction between low birth weight and adult lifestyle factors on the risk of CVD remains unclear.We included 20,169 men from the Health Professionals Follow-up Study , 52,380 women from the Nurses’ Health Study , and 85,350 women from the Nurses’ Health Study II  in the USA who reported birth weight and updated data on adult body weight, smoking status, physical activity, and diet every 2–4 years. Incident cases of CVD, defined as a combined endpoint of fatal and nonfatal coronary heart disease (CHD) and stroke, were self-reported and confirmed by physicians through reviewing medical records.P for linear trend <0.001). Additionally, we found an additive interaction between decreasing birth weight and unhealthy lifestyles on the risk of CHD among women, with a pooled relative excess risk due to interaction of 0.06 (95% CI: 0.04–0.08). The attributable proportions of the joint effect were 23.0% (95% CI: 11.0–36.0%) for decreasing birth weight alone, 67.0% (95% CI: 58.0–75.0%) for unhealthy lifestyle alone, and 11.0% (95% CI: 5.0–17.0%) for their additive interaction. Lower birth weight was associated with a greater stroke risk only among women, which was independent of later-life lifestyle factors.During 4,370,051 person-years of follow-up, 16,244 incident CVD cases were documented, including 12,126 CHD and 4118 stroke cases. Cox proportional hazards regression models revealed an increased risk of CHD during adulthood across categories of decreasing birth weight in all cohorts (all Lower birth weight may interact synergistically with unhealthy lifestyle factors in adulthood to further increase the risk of CHD among women.The National Institutes of Health grants. Low birth weight, a proxy measure of fetal intrauterine growth restriction, has been associated with a greater risk of cardiovascular disease (CVD). We performed a systematic search in PubMed and Web of Science from inception until March 22, 2022, using search terms (“birth weight”) AND . However, very few studies have explored the potential interaction between birth weight and adult lifestyle factors on the subsequent risk of CVD, particularly on an additive scale.Results from three large prospective cohorts consistently revealed that birth weight was inversely associated with the risk of developing coronary heart disease in later life, particularly among women who adopted an overall unhealthy lifestyle during adulthood. The association of birth weight with stroke risk was less consistent, which persisted only among women and was independent of later-life lifestyle factors.Our findings support the Developmental Origins of Health and Disease hypothesis suggesting the potential long-term health consequences of an adverse intrauterine environment and point to potential lifestyle interventions to reduce the risk of developing CVD among individuals with low birth weight.Alt-text: Unlabelled boxCardiovascular disease (CVD), including coronary heart disease (CHD) and stroke, killed an estimated 18.6 million people in 2019.17,Extensive evidence from randomized clinical trials and high-quality cohort studies shows that lifestyle factors including physical activity, tobacco use, nutrition, and overweight or obesity are important determinants of CVD.n = 51,529), NHS , and NHSII  are ongoing prospective cohort studies established in 1986, 1976, and 1989 in the USA, respectively, by recruiting male and female health professionals. Participants are followed biennially via postal or electronic questionnaires that collect data on demographic characteristics, lifestyle factors, and incident diseases. The follow-up response rate of each cycle exceeds 90% in all three cohorts. The initial food frequency questionnaire was completed by participants in HPFS, NHS, and NHS II in 1986, 1980, and 1991, respectively, which, served as the analysis baseline. The study protocol was approved by the institutional review boards of the Brigham and Women's Hospital and the Harvard TH Chan School of Public Health (Protocol number: 2009-P-002375). Returning a completed questionnaire indicates informed consent.The HPFS  and 137,730 women (87.23%) for the current analysis . BaselinP < 0.001).n = 528) and state birth records (n = 220) were 0.75 and 0.74, respectively.Participants in NHS reported their birth weight as “less than 5.0 pounds (lbs)”, “5.0 to 5.5 lbs”, “5.6 to 7.0 lbs”, “7.1 to 8.5 lbs”, “8.6 to 10.0 lbs”, “more than10.0 lbs”, and “not sure” on the 1992 questionnaire. Similarly, in the NHS II and HPFS cohorts, birth weight was reported as “less than 5.5 lbs”, “5.6 to 7.0 lbs”, “7.1 to 8.5 lbs”, “8.6 to 10.0 lbs”, “more than 10.0 lbs”, and “unknown” in the 1991 and 1994 questionnaires, respectively. Thus, the birth weight categories in our current analysis were (in kg): <2.5, 2.5-3.15, 3.16-3.82, 3.83-4.5, and >4.5. Women in NHS and NHS II also reported whether they were multiple births  or born two or more weeks premature . Among 3803 participants from HPFS, 68.6% reported the same birthweight category as reported by their mothers.CVD was defined as a combined endpoint of fatal and nonfatal CHD, including nonfatal myocardial infarction and coronary revascularization (coronary bypass or angioplasty), and stroke.2), smoking status (current smokers), diet quality (in the bottom 60% of DASH diet score), physical activity (<30 minutes/day of moderate-to-vigorous intensity activity), and alcohol consumption (non-moderate).,,Information on height and race/ethnicity were self-reported at baseline. Body weight, marital status, and health conditions  were self-reported at recruitment and updated every 2 or 4 years thereafter. We calculated BMI for each follow-up cycle. Maternal and paternal smoking status while living together with them during childhood and maternal history of diabetes and hypertension were self-reported via the biennial questionnaires. Women in NHS and NHSII also reported their maternal and paternal occupation and homeownership at the time of the nurse's birth. Physical activity was reported approximately every 2 years in men and 4 years in women. We calculated the total hours per week spent in moderate to vigorous activities  that require the expenditure of ≥3 metabolic equivalents of task per hour.Since there was no violation of the proportional hazard assumption based on the likelihood ratio test by adding an interaction term between birth weight and follow-up time, we used time-dependent Cox proportional hazards regression models to estimate the hazard ratios (HRs) and 95% confidential intervals (CI) for the associations of birth weight categories  with the risk of CVD . Participants contributed follow-up periods from the date of returning the analysis baseline questionnaire until the date of diagnosis of CVD, death, or end of follow-up , whichever occurred first. To control for age, calendar time, and any possible interactions between these two timescales, all analyses were stratified jointly by age in months at the start of follow-up and the calendar year for the current questionnaire cycle.2 statistic to test the heterogeneity of estimations between cohorts.To evaluate whether the joint effect of lower birth weight and unhealthy lifestyle together was larger than the product of individual effects, we assessed the multiplicative interaction by adding a cross-product term between any tested factors and birth weight in the multivariable Cox models. We also assess whether the joint effect of two factors together was larger than the sum of the individual effects of lower birth weight and unhealthy lifestyles, we estimated additive interaction by calculating the relative excess risk due to interaction (RERI) using the SAS codes developed by Li and Chambless.Several sensitivity analyses were conducted. First, we reanalysed the association between birth weight and CVD risk by classifying the participants who were multiple births  or born two or more weeks premature into a separate exposure category to assess if the associations were independent of multiple pregnancies or preterm birth. Second, we additionally included maternal and paternal occupation and homeownership at the time of the nurse's birth in multivariable Cox models to assess the potential influence of early-life socioeconomic status. We restricted these above-mentioned analyses to women in NHS and NHS II because data on multiple births, preterm birth, and parental occupation and homeownership was not collected among men from HPFS. Third, we included participants from HPFS, NHS, and NHS II who had missing data on lifestyle factors by creating a missing indicator in the multivariable Cox models to examine whether the exclusion could have biased the results. Fourth, we additionally included adult height, maternal and paternal smoking status, and maternal history of diabetes and hypertension in multivariable Cox models. Fifth, we excluded BMI from the calculation of overall unhealthy score to assess the combined influence of all other lifestyles. Sixth, we recalculated the overall unhealthy score by classifying participants who consumed alcohol less than 5 g/day into low-risk groups. All data were analysed using SAS 9.4 for UNIX (SAS Institute Inc).The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. Y-XW, LP, and JEC have access to the dataset and all authors have final responsibility for the decision to submit for publication.Our study included 20,169 men in HPFS, 52,380 women in NHS, and 85,350 women in NHS II, whose mean (SD) age, at analysis baseline, was 51.69 (9.02), 45.40 (7.12), and 36.00 (4.66) years, respectively. In total, 999 (5.0%) participants in HPFS, 5577 (10.7%) in NHS, and 6707 (7.9%) in NHS II reported a birth weight below 2.5 kg . Within P for linear trend <0.001; During 4,370,051 person-years of follow-up, 16,244 incident CVD cases were documented in all three cohorts, including 12,126 CHD and 4,118 stroke cases. The crude cumulative incidence of CVD was the highest among participants whose birth weight was below 2.5 kg in all cohorts . SimilarMultiplicative interaction was only observed between birth weight and BMI on the risk of CVD among men in HPFS (Table S3). Nevertheless, we observed a positive additive interaction between decreasing birth weight and the overall unhealthy score on the risk of CHD among women, but not in men (Table S4 and The association between birth weight and CVD risk persisted when we included participants who had missing data on lifestyle factors (Table S7), when we additionally included adult height, maternal and paternal smoking status, maternal history of diabetes and hypertension, participants’ socioeconomic status during infancy as covariates in the multivariable models (Tables S8–S10), and when we classified participants who were multiple births  or born two or more weeks premature into a separate exposure category (Table S11 and S12). The additive interaction of decreasing birth weight and the overall unhealthy score with the risk of CHD among women persisted when we excluded BMI from the calculation of the overall unhealthy lifestyle score and when we classified participants who consumed alcohol less than 5 g/day into low-risk groups (Table S13 and S14).Results from three large prospective cohorts all revealed that birth weight was inversely associated with subsequent risk of CHD during adulthood, which supports the Developmental Origins of Health and Disease (DOHaD) hypothesis showing that the earliest stages of human development — even during intrauterine life— could be critical periods for the development of chronic diseases later in life.,,,,,,,,While some studies have not reported an association between birth weight and CHD risk,2. Overweight and obesity have been strongly associated with a greater risk of CVD,2; 2.31%  for BMI<25 kg/m2], chance findings cannot be fully ruled out.While extensive evidence has revealed that lifestyle factors, including tobacco use, nutrition, physical activity, and overweight or obesity, are important for preserving good cardiovascular health over the life course,,The observed inverse associations of birth weight with subsequent CVD risk in adulthood may reflect shared mechanistic pathways in utero where metabolic stress leads to insulin resistance,Strengths of this study include the replication of the results across three cohorts, prospective design with long-term follow-up periods, a large number of participants and incident medical record-validated CVD, and the collection of various potential confounders  and lifestyle factors. The association between birth weight and CVD risk persisted when we excluded participants who were multiple births or born two or more weeks premature, indicating that low birth weight is an independent risk factor for CVD. Our study also has some limitations. First, our validation analysis showed that there is some misclassification of self-reported birth weight. In this case, however, such misclassification is likely to be non-differential with respect to incident CVD because of the prospective design of all cohorts, resulting in risk estimations biased towards the null. Second, a large proportion of participants were excluded due to missing data on birth weight, which may have induced selection bias. However, similar baseline characteristics and crude incidence of CVD were observed between included participants and those excluded due to missing data on birth weight. Compared to participants included in our current analyses, the crude and adjusted pooled HR of CVD among excluded participants was 1.02  and 1.02 , respectively. Third, despite the adjustment for multiple potential confounders, residual and unmeasured confounding cannot be fully ruled out. Fourth, our cohort participants were all health professionals and had a relatively homogeneous racial/ethnic and educational attainment, which may limit the generalizability of our findings. Fifth, we did not consider the presence of competing risks,In summary, results from three large prospective cohorts consistently showed an inverse association between birth weight and the risk of developing CHD in later life, particularly among women who adopted an overall unhealthy lifestyle. We also find evidence that lower birth weight may interact synergistically with unhealthy lifestyles in adulthood to further increase the risk of CHD. The inverse association between birth weight and stroke persisted only among women and was independent of lifestyles. Our results emphasize the potential long-term health consequences of an adverse intrauterine environment and point to potential lifestyle interventions to reduce the risk of developing CVD among women with low birth weight.Y.-X.W. analyzed and drafted the manuscript. Y.-X.W. and J.E.C. were involved in the study conception and design. Y.L. verified the underlying data and conducted a technical review. JEC obtained funding for the study. Y.-X.W., Y.L., J.W.R.-E., A.A.F., Z.S., S.W., J.E.M., K.J.M., E.B.R., and J.E.C. participated in the interpretation of the results and critical revision of the manuscript. Y.-X.W., Y.L., and J.E.C. had full access to all the data in the study. All authors accept responsibility to submit for publication.https://www.nurseshealthstudy.org/researchers (email: nhsaccess@channing.harvard.edu).Our data, including analytic code, can be available upon request with permission from our staff. Further information including the procedures for obtaining and accessing data from the Nurses’ Health Studies II is described at E.B.R. reports financial support from USDA/United States Highbush Blueberry Council, outside the submitted work. J.E.C. reports financial support from the National Institutes of Health, US Food and Drug Administration, US Centers for Disease Control and Prevention, Harvard Health Publications, Caixa Foundation, American Society for Reproductive Medicine, Northwestern University, Pacific Coast Reproductive Society, Medical University of Vienna, outside the submitted work. All other authors declare no competing interests.


Alpaca breeding is becoming increasingly popular in regions other than South America (the place of origin of the species), e.g., in North America and Europe. It is highly important to monitor reproductive disorders and to identify the causative factors of the problem as the profitability of alpaca breeding depends on satisfactory reproductive results. The analysis of the frequency and causes of reproductive problems in alpacas was carried out with the use of questionnaires completed voluntarily by alpaca owners in three regions . Cases of miscarriage and stillbirths were reported from about 1/3 of the farms, and in half of them, the cases of crias (young up to 6 months of life) and young alpacas fell within 12 months after birth occurred. Preterm births were reported from almost half of the farms. An important element increasing the cria survival rate of is the high birth weight and dams’ mineral supplementation. This study has indicated a relationship between fetal death and the occurrence of infectious diseases and scabies infestation in the herd. They may be potential causes of reproduction-related problems that are not discerned in time.The profitability of alpaca breeding depends on satisfactory reproductive results. The study aimed to analyze the frequency and causes of reproduction-related problems in alpacas, in particular miscarriages, stillbirths, preterm births, twin pregnancies, and falls of crias within 12 months after birth. This analysis was carried out with the use of questionnaires completed voluntarily by 109 alpaca owners in three regions . Cases of miscarriage and stillbirths were reported from 44% and 36% of the farms, respectively. In half of the farms, the cases of falls of crias and young alpacas within 12 months after birth were reported. Preterm births were reported from almost half of the farms. A large number of alpaca owners did not identify the cause of fetal death. An important element increasing the cria survival rate of is the birth weight . Crias from dams receiving mineral supplements had higher birth weight. This study indicated a relationship between fetal death and the occurrence of infectious diseases and scabies infestation in the herd. They may be potential causes of reproduction-related problems that are not discerned in time.  Vicugna pacos) breeding is becoming increasingly popular in regions other than South America (the place of origin of the species), e.g., in North America and Europe [Alpaca   and enviAlpacas usually produce one cria, whereas twins are extremely rare . Live-boThe aim of the study was to analyze the frequency and causes of reproduction-related problems in alpacas, in particular miscarriages, stillbirths, preterm births, and twin pregnancies as well as falls of crias within 12 months after birth.The analysis of the frequency and causes of reproductive problems in alpacas was carried out with the use of questionnaires completed voluntarily by owners of alpaca farms. The questionnaire was created in Google Forms and made available online for alpaca breeders in countries of continental Europe , the British Isles  and North America . To make the questionnaire available and invite respondents, it was sent directly to the farms’ e-mail addresses, or it was shared on social media on forums dedicated to alpaca rearing. The questionnaire was fully anonymous, and it was not possible to associate any of the responses with a specific keeper/farm. It was produced in two versions: Polish (for farms in Poland) and English (for farms in other countries). It was available for completion from October 2021 to January 2022.The questionnaire was completed by 109 alpaca owners from the three different regions .The questionnaire consisted of three parts: (1) farm characteristics (2) animal health status and (3) reproductive indicators . The quet-test was used to test the differences between two groups and, when the conditions for the use of this test were not fulfilled, the Mann–Whitney U test was employed. Differences between three or more groups were evaluated using the Kruskal–Wallis test. The Pearson r correlation was also used to determine the relationships between some variables. Statistically significant differences or correlations were indicated at the significance level of p < 0.05 and correlations close to statistical significance were indicated at p < 0.1. The database and statistical analysis were developed based on Statistica 9.1 computer software .The values of quantitative variables were presented using the mean value and standard deviation. In the case of the qualitative traits, the Chi2 test was used to detect relationships between the analyzed variables. The normality of the distribution of the variables in the studied groups was checked with the Shapiro–Wilk normality test. Student’s According to the survey results, smaller farms with up to 20 females and a maximum of 5 stud males accounted for 50% of the total number. The greatest number of large farms  were located in North America ; small farms in this area accounted for 20% . There were 7% of large farms and 22% of small farms in Central Europe, whereas large and small farms in the British Isles constituted 8% and 12%, respectively. Most farms (58%) were established less than 10 years ago. Other farm animals  are reared in addition to alpacas in a large number of the farms (41%). The birth weight of most cria (56%) was higher than 7 kg. Diagnosis of pregnancy with the use of ultrasound scan was declared by 69% of alpaca owners. Cases of miscarriage and stillbirths in the last 3 years were reported from 44% and 36% of the farms, respectively. In half of the farms, the owners reported cases of falls of crias within 12 months after birth. Most pregnant females received mineral (74%) and/or vitamin supplementation (60%). In 76% of farms, crias had to be bottle fed. Furthermore, 38% of the respondents reported cases of scabies infestation and/or infectious diseases (22%) in the herd. Preterm births were reported from almost half of the farms (48%), with a vast majority of these crias reaching the age of 6 months (78%).In the questionnaire, twin pregnancies in their herd were reported by 21 alpaca owners (19%). Due to the small size of the group, there were no significant relationships between the independent variables and the occurrence of twin pregnancies. As many as 17 (81%) of the 21 cases ended in miscarriage, neither of the twin crias survived birth in one case, and only one cria was born alive and reached the age of 6 months in two cases. There was only one case of both crias  born alive and reaching the age of 6 months.Significant correlations were found between some characteristics of the farm and herd health status and the selected reproductive indicators.p = 0.0135). The lowest mean birth weight was exhibited by preterm crias from North America; it differed significantly from the birth weight of crias born in the British Isles (p = 0.0006) and continental Europe (p = 0.014). There were no significant differences between the frequency of miscarriages, stillbirths, or falls within 12 months after birth and the location of the farm.The analysis of the relationship between the location of the farm and the analyzed indicators was performed using the Kruskal–Wallis test . The Manp < 0.01) than the mean birth weight of crias born at term (p < 0.05) (p < 0.01) crias   and post1) crias . Howeverp = 0.013). In turn, in the group of the post-term crias, these values were p = 0.011). Interestingly, there were more cases of stillbirths on the longer-run (p = 0.007) in a three-year scale. The mean birth weights of crias born at term were similar on farms run for less than 10 years and those run for a longer time (The Mann–Whitney U test and Student’s t-test (data on the birth weight of preterm crias) revealed significant differences between the duration of running the farm and the birth weight of crias, taking into account the birth term. A significant difference was found between the mean birth weight of preterm crias on farms run for less than 10 years (t-test (data on the birth weight of preterm crias) demonstrated higher mean birth weight of preterm crias in herds with less than 20 females, i.e., 6.13 kg, than the value of this parameter recorded in the larger herds, i.e., 5.16 kg (p = 0.029). There were more cases of miscarriages, stillbirths, and deaths within 12 months after birth in the larger  herds.The Mann–Whitney U test and Student’s The survey results highlighted both positive and negative aspects of herd health-related factors that may affect reproduction performance in alpacas.p = 0.0198). Similarly, the mineral supplementation had an effect on the birth weight of post-term born crias (p = 0.057). There were no such differences in the occurrence of miscarriages, stillbirths, or deaths within 12 months after birth. Similarly, no significant differences were found between these variables and the vitamin supplementation . Moreover, as shown by the Mann–Whitney U test (p = 0.0157). This group was also characterized by a greater frequency of stillbirths (bottle feeding: p = 0.045).The necessity to bottle feed a cria always indicates that the animal is at risk. The present study confirmed the relationship between preterm births and the necessity to bottle feed the crias . Moreover, a relationship was found between the occurrence of scabies or infectious diseases and the indication of an unknown cause of miscarriages by the questionnaire respondents  and between the occurrence of infectious diseases and an unknown cause of stillbirths . There was no such relationship with regard to the other causes of miscarriages or stillbirths  (0.918 > p > 0.123). Additionally, there was no correlation between the presence of other farm animals  and the prevalence of infectious diseases in the herd . The differences between the analyzed variables presented in p = 0.017), cases of miscarriages (disease in the herd: p = 0.0006), cases of stillbirths (disease in the herd: p = 0.0009), and cases of falls within 12 months after birth (disease in the herd: p = 0.019) . The present study has shown that the health status in the herd, not only the presence of scabies but also other infectious diseases , may have an impact on the reproduction performance of alpacas. There was a relationship between the frequency of preterm births and miscarriages in the herd and scabies infestations  .Rearing conditions undoubtedly exert a significant impact on the indicators of animal reproduction. Alpacas have atypical reproductive physiology with an essential role of the male in triggering ovulation. In these animals, the pregnancy is long, only one cria is born, and the fertility rate is low. Hence, in the case of these extremely valuable animals, any measures should be taken to prevent miscarriages and offer crias the best possible start in life.The cria born before term (before 335 days of gestation) had significantly lower birth weight than those born at term. As shown by Tibary et al. , the weihitehead , maternahitehead ,27,28,29Additionally, preterm birth was usually associated with the necessity to bottle feed the crias. Low birth-weight, preterm, or immature crias may be at risk of disorders related to passive transfer of immunoglobulins from the gastrointestinal tract ,16 and pAn important finding in the present study was the positive correlation between the birth weight of full-term, preterm, and post-term crias in the surveyed alpaca herds. It was found that post-term crias (born after 370 days of gestation) had higher weight than those born at term. Similar results were reported by Davis et al. , where tIn the present study, only 21 out of the 109 owners reported a twin pregnancy in their herd. As reported by Bravo et al.  and CampCampylobacter fetus) [Anaplasma phagocytophilum) [Coccidioides spp.) [Neospora spp.) [As demonstrated by Pearson et al. , 7% to 1r fetus) , anaplasophilum) , coccidies spp.) , toxoplaes spp.) ,39, and ra spp.) ,40. Mixera spp.) . It was ra spp.) ,28, infera spp.) . This phra spp.)  and impaA positive finding of the survey is the relationship between the supplementation provided to female alpacas and the positive effect on reproductive results. Adequate nutrition is one of the most important factors in animal reproductive success . As repoIt is evident that reproduction performance in alpaca herds is influenced by many factors related to animal rearing conditions, health, and supplementation. The present study has shown factors that may have a negative or positive impact on cria survival and on the occurrence or limitation of reproduction-related problems. An important element increasing the cria survival rate is the birth weight, as its higher value contributes to a lower risk of perinatal complications, eliminates the necessity of bottle feeding, and ensures a greater likelihood of proper growth of these animals. Equally important are the determinants of the occurrence of miscarriages and stillbirths. It is disturbing that a large number of alpaca owners do not identify the cause of fetal death, e.g., through an autopsy, while this study has indicated a relationship between such cases and the occurrence of infectious diseases and scabies infestation in the herd. It can therefore be presumed that they may be potential causes of reproduction-related problems that are not discerned in time. A positive aspect is the confirmation of the relationship between supplementation  in pregnant females and cria birth weight, which increases the chances of cria survival in the first months after birth.


P-value 0.12). Standardized CI in newborns with hypoglycaemia was higher compared to newborns without hypoglycaemia (median 1.27 (1.21–1.35) versus 1.24 (1.20–1.29); (P 0.002)). Multivariate logistic regression analyses showed both CI and standardized CI to be associated with the occurrence of hypoglycaemia (OR 1.48 (1.24–1.77) and OR 1.44 (1.13–1.83), respectively). The majority of hypoglycaemic events (96.1%) occurred in the first 6 h after birth.Small for gestational age (SGA) newborns are at risk of developing neonatal hypoglycaemia. SGA newborns comprise a heterogeneous group including both constitutionally small and pathologically growth restricted newborns. The process of fetal growth restriction may result in brain sparing at the expense of the rest of the body, resulting in disproportionally small newborns. The aim of this study was to discover whether body proportionality influences the risk of developing neonatal hypoglycaemia in SGA newborns. A retrospective cohort study was performed in 402 newborns who were SGA without additional risk factors for hypoglycaemia. Body proportionality was classified in two ways: (1) using symmetric (sSGA) or asymmetric (aSGA), defined as head circumference (HC) below or above the 10th percentile, respectively; (2) using cephalization index (HC/birth weight), standardized for gestational age. Hypoglycaemia was observed in 50% of aSGA and 40.9% of sSGA newborns (   Conclusion: Body proportionality might be of influence, depending on the classification used. Larger prospective studies with a clear consensus definition of body proportionality are needed.The online version contains supplementary material available at 10.1007/s00431-022-04592-8. Neonatal hypoglycaemia is an important complication in newborns , especiaSGA newborns may be proportionately  or disproportionately  small. In symmetric SGA (sSGA) newborns, anthropometric measurements are equally affected, resulting in a normal brain-to-body ratio. They may be either constitutionally small or may have suffered early intrauterine infection  or a genetic disorder  . AsymmetP-value < 0.01) [3) to classify body proportionality, where a low ponderal index (PI) indicates disproportionate or asymmetric growth restriction. However, the PI assesses whether length is spared at the expense of weight and not whether head growth is spared at the expense of the remaining body.In a retrospective study from India involving 127 SGA newborns, Bhat et al. observed a higher incidence of neonatal hypoglycaemia in asymmetric compared with symmetric SGA newborns  . In a pr < 0.01) . Both auIn our hospital, all SGA newborns are routinely screened for hypoglycaemia . If the We performed a retrospective cohort study, using data from newborns who were considered at risk of neonatal hypoglycaemia according to local protocol .The data was collected from a 4-year period (01-01-2010 until 01-01-2014) at the Radboudumc Amalia Children’s Hospital, Nijmegen, the Netherlands . At thatOur study population was limited to term SGA newborns of whom plasma glucose levels during the first 24 h were available. In order to study predominantly the effect of SGA, newborns who met the following criteria were excluded: death within the first 24 h after birth, severe asphyxia , gestational age (GA) < 37 weeks, maternal use of Labetalol during pregnancy, and the presence of any type of diabetes , 14.Neonatal characteristics included HC, BW, and gestational age at delivery. Additionally, maternal characteristics such as age, presence of FGR, and hypertension were recorded.Body proportionality was classified in two ways: (1) using symmetric (sSGA) or asymmetric (aSGA), defined as head circumference (HC) below or above the 10th percentile, respectively; (2) using cephalization index (HC/birth weight), standardized for gestational age.There is no internationally agreed definition of body proportionality in SGA newborns. Besides the PI which does not directly indicate brain sparing, several methods to determine brain-to-body ratio have been previously described to classify body proportionality, although not in relation to the incidence of hypoglycaemia , 15–19. The cephalization index (CI) is a ratio of head circumference to birthweight . We calcOur primary outcome was the occurrence of neonatal hypoglycaemia defined in our local protocol as a plasma glucose concentration lower than 2.2 mM in the first 24 h after birth. Our secondary outcome was the timing of the first hypoglycaemia in hours after birth.The variables were all extracted from the patient files. Plasma glucose concentrations were measured routinely 1, 3, 6, 12, and 24 h after birth, using a point-of-care (POC) glucose meters such as HemoCue Glu201DM, Nova StatStrip GluCard memory PC, and Roche OMNI-56 blood gas. In case of hypoglycaemia, capillary blood was sent to the laboratory for confirmation.Important risk factors for hypoglycaemia are gestational age (prematurity and postmaturity), maternal diabetes, low birth weight (< 2500 g), and twin delivery , 24. MatT or Mann–Whitney U tests were used for continuous variables (dependent on variable distributions). The potential association between hypoglycaemia and relevant variables was evaluated using univariate logistic regression analyses. Variables with P-value < 0.20 were considered for multivariate analysis. Stepwise backward multivariate analyses were performed and odds ratios and their 95% CI and P-values were calculated. For multiple regression analysis, two models were used: model 1 with body proportionality expressed as aSGA or sSGA; model 2 with body proportionality expressed as standardized CI. Collinearity was checked using Pearson correlation coefficients, tolerance, and variance inflation factor (VIF). Goodness of fit was assessed by the Hosmer and Lemeshow test. A receiver operating characteristic (ROC) curve was constructed to evaluate the performance of the standardized CI as an instrument to predict hypoglycaemia. The timing of the first hypoglycaemia was analyzed using the log rank test and shown in the Kaplan–Meier curve. Results were considered significant if the P-value was < 0.05. Data was analyzed using IBM SPSS Statistics for Windows, version 25 .Descriptive statistics were used to study demographic characteristics, including proportions and mean (SD). The Chi-square or Fisher exact tests were used for categorical variables and the Students This study was exempt from Regional Ethics Review Board approval, under the legal requirements for clinical research in the Netherlands (case number 2020-6531).In this study, 402 SGA newborns were eligible; 100 newborns (24.9%) could not be categorized due to missing HC and were excluded from further analysis. Of the 302 included SGA newborns, 126 were aSGA (41.7%) and 176 were sSGA (58.3%)  . Although very small due its scale, the difference in CI between newborns with versus without hypoglycaemia was also significant (median CI 0.12 (0.12–0.13) versus median CI 0.13 (0.12–0.14); P value < 0.001).The overall incidence of hypoglycaemia was 44.7%. Of the aSGA newborns, 50.0% experienced neonatal hypoglycaemia, compared to 40.9% of the sSGA newborns (Z-score and (standardized) CI and between GA and CI (Supplement P < 0.20) but not in the multivariate analyses. Body proportionality  was not associated with hypoglycaemia, taking into account GA and gender .ROC analysis , although 4 out of 5 newborns who developed hypoglycaemia at 24 h were sSGA (80%).The majority of the SGA newborns (96.1%) developed hypoglycaemia within the first 6 h; the remaining 5 SGA newborns developed their first hypoglycaemia at 24 h after birth (3.9%)  compared to sSGA (40.9%) newborns. However, both CI and standardized CI were significantly associated with increased odds of hypoglycaemia. These results suggest that body proportionality may affect the risk of hypoglycaemia in SGA newborns, depending on the definitions.Our study has several limitations. First of all, the lack of a Dutch newborn HC compelled us to use an alternative birthweight standard . AlthougTwo others studies  found a higher incidence of hypoglycaemia in aSGA compared to sSGA newborns , 9. HoweP-value 0.024). Although this is a statistically significant difference, the relative risk in aSGA newborns is still only 1.5 compared to sSGA newborns and the overall incidence remains high . Information on symptomatology would have been helpful to determine the clinical relevance of our findings.In our study, we found a much higher overall incidence of hypoglycaemia (44.7%) in comparison to the studies of Bhat et al. (25.2%) and Nieto et al. 10.8%). This variability in incidences may be partly due to different definitions used for the classification of SGA, different in- and exclusion criteria for the study population, and different approaches to the detection and prevention of hypoglycaemia. For example, Bhat et al. defined SGA as a birthweight below 1 SD , whereas in our study, newborns were classified as SGA when birthweight was < p10 %. This v. InteresZ-scores and percentiles). Our results showed a slightly higher (median) CI and standardized CI in newborns with hypoglycaemia, but the AUC was low indicating poor discriminative ability. Multivariate logistic regression analyses showed that higher (standardized) CI was associated with a significantly higher risk of hypoglycaemia. Newborns who experienced FGR but were not SGA at birth are not routinely subjected to glucose controls, making it challenging to determine whether an increased brain-to-body ratio irrespective of birthweight percentile could be associated with an increased risk of hypoglycaemia.We defined aSGA as the HC being “preserved” or above the statistical limit for gestational age, using, similar to our SGA cut-off for birthweight, the 10th percentile as a cut-off . AlthougThe aim of our study was to assess the association between body proportionality and risk of hypoglycaemia in SGA newborns. A clinically relevant difference in the incidence of hypoglycaemia could justify a more targeted approach in glucose controls. As stated previously, not all SGA newborns have experienced FGR and vice versa. Others have suggested alternative strategies to distinguish between newborns who are constitutionally small versus those pathologically growth-restricted and at risk of adverse outcomes. A recent study by Beune et al. suggested a consensus definition on “growth restriction in the newborn,” aiming to detect pathologically small newborns . Among oThe association between body proportionality and neonatal hypoglycaemia in SGA newborns needs further evaluation using consensus-based definitions of growth restriction and body proportionality. The (standardized) CI may be a promising tool. HC and length at birth should be routinely measured and registered in the Dutch perinatal database to enable the future development of Dutch HC and length at birth reference values. Ideally, our hypothesis should be evaluated in a prospective study where besides anthropometric measurements also clinical symptoms are collected.Supplementary file1: Cephalization index  in relation to gestational age (GA) (EPS 1430 KB)Supplementary file2: Standardized cephalization index  in relation to gestational age (GA) (EPS 1427 KB)Supplementary file3: Area under the curve of standardized CI and risk of hypoglycaemia (EPS 1326 KB)Supplementary file4: Pearson correlationcoefficients and collinearity statistics (DOCX 19 KB)Supplementary file5: Kaplan Meier curve fortiming of first hypoglycaemia for sSGA and aSGA newborns (EPS 1411 KB)cohort studies (DOCX 23 KB)Supplementary file6: STROBE Statement. Checklist of items that should be included in reports of Below is the link to the electronic supplementary material.


Epidermoid cysts are benign development alterations without the presence of adjacent structures such as sebaceous glands, hair follicles or sweat glands. It may start at any part of the body and they are more commonly found in the testicles and ovariesFemale, Caucasian, 60 years of age, complained of a bulging in the belly of her tongue, which she had noticed for about 4 months now. In her mouth we noticed a white-yellowish, smooth, soft, asymptomatic, exophytic, sessile lesion, without trauma history, measuring about 0.5x0.5cm, and the clinical diagnosis was of fibroma or lipoma.Her routine blood tests were all normal, then we took her to surgery in order to remove the lesion under local anesthesia and the specimen was sent to pathology.Macroscopically it was a fragment of soft, white-brownish tissue, with rough shape and surface, fibrous, measuring 0.8x0.4x0.5cm. When the specimen was cut, there was oozing of a caseous material from inside. Histopathology we observed a fragment of a cystic cavity, coated by stratified squamous epithelium, with a lumen fully filled by orthokeratin and a capsule made up of dense fibrous connective tissue, with moderate mononuclear inflammatory infiltrate and engorged vessels, and not other skin adjacent structures, thus leading us to the diagnosis of epidermoid cyst .Figure 1Epidermoid cysts’ etiology is highly challenged in the literatureAlthough the dermoid cyst represents a distinct entity, the word dermoid is normally used to identify three different types of cysts: epidermoid (without derm adjacent structures in its coating epithelium), dermoid  and teratoid (coating with structures from the three germinative layers) There are few reports of epidermoid cysts on the tongue, especially on its belly, because when it occurs in the oral cavity, it usually happens in the submental region. Therefore, we believe it is important to present this case, adding it to the few reports we have in the literature.Depending on its location, the differential diagnosis of epidermoid cysts can be made based on infectious processes, ranula, thyroglossal duct cyst, cystic hygroma and fat tissue build up1,Cyst location is a determining factor for surgery, suggesting an intraoral approach for the sublingual cyst and a submental approach for the submental and submandibular cysts6, in our case here, it was a small cyst located on the tongue and the patient did not have any abnormality in her lab work up, that is why we chose an intraoral approach.The epidermoid cyst, although rare and benign should not be underestimated. It is important to make the clinical and pathological differential diagnosis. Therefore, it is very important that dentists be aware of this disorder.


Birth weight is a consistent predictor of morbidity and survivability in infancy and later life. This study aims to assess the accuracy of the mother’s perception of size at birth to predict low birth weight(LBW). This study used data from Nepal Demographic and Health Survey (NDHS 2016). Information about 5060 mother pairs was obtained from the NDHS dataset. However, birth weight data were available for 3095 children, and therefore they were only included in the further analysis. The predictive accuracy of the mother’s perception of size at birth to predict LBW was measured by sensitivity, specificity, positive predictive value, and negative predictive value. Factors associated with the discordance among the mother’s perception of birth size and birth weight were calculated using multinomial logistic regression analysis. The mother’s perception of birth size had low sensitivity (62%) and positive predictive value (46.7%) but high specificity (90.1%) and negative predictive value (94.4%) to predict the LBW. The overall agreement between birth weight(<2500gram vs > = 2500 grams) and the mother’s perceived size at birth  was 86% , which is composed of a higher share of the agreement to identify non-LBW babies(79%) and a low share to identify LBW babies (7%). Among the five categories of mothers’ perception of size at birth and birth weight, the agreement was 67.2% . Education status, ethnicity, multiple births, and sex of the newborn child were significantly associated with the discordance between the mother’s perceived size at birth and birth weight. A moderate agreement was found among the mother’s perception of birth size and birth weight. Mothers were more likely to correctly identify non-LBW babies compared to LBW babies based on their perception of size at birth. Efforts should be intensified to promote the practice of weighing the baby at birth. Birth weight is a consistent predictor of morbidity and survivability in infancy, early childhood, and later life , 2. The Identification of LBW is key to ensuring the special care needed by these infants. A major challenge in dealing with the LBW burden among developing countries is not having birth weight data. Globally, about 48% of infants are not weighed at birth, with estimates ranging from as low as 10% in Latin America and the Caribbean to as high as 66% in South Asia , 5.The mother’s perception of the baby’s size at birth is associated with the sex of the newborn, the child’s vital status at the time of the survey, and contextual factors like the neighborhood . MothersNationally representative surveys, like the Demographic and Health Surveys (DHS) and Multiple Cluster Indicator Surveys (MCIS), regularly collect information about the mother’s perceived size at birth, but a significant proportion of babies in these surveys have missing birth weight data. Since the information regarding birth weight is not available for a considerable proportion of children, the mother’s perception of the baby’s size at birth is used as the proxy of birth weight.Studies have suggested using the perceived size at birth as a proxy for birth weight with caution as it doesn’t correctly predict birth weight , 11, 12.This study aims to harness the publicly available data from the Nepal Demographic and Health Survey (NDHS) to assess the accuracy of a mother’s perception of the size at birth to predict the birth weight among newborn babies, with a particular focus on the predictive accuracy to detect LBW.This study represents the data from the NDHS 2016. NDHS is a nationally representative sample survey conducted in every five years interval. NDHS employs a two-stage cluster sample survey methodology. Details about the methodology and survey deployment can be found in the survey report .The sampling frame of NDHS is the women of reproductive age 15–49 years in the selected households. The survey’s child datasets were used for this analysis. In the survey database information was available for 5060 (unweighted number: 5038) children born five years before the surveys. However, only about 61% of children had recorded birth weight in grams. A total of 3095 (unweighted number: 3122) children who had both measurements for the mother’s perception at birth and birth weight were included in the final analysis.“When (NAME) was born, was (NAME) very large, larger than average, average, smaller than average, or very small?” The response was recorded as very large, larger than average, average, smaller than average, very small, and don’t know. To analyze the predictive accuracy of this variable to detect the low birth weight, a separate variable was also created and labeled as “Perceived low size at birth”, with two categories- Small  and Average or larger .During the survey, mothers were asked “How much did (NAME) weigh?” The response was recorded based on the mother’s recall or the actual record from the record card. The information was recorded in the kilogram and up to three characters after the decimal. Later, the values were converted to grams. An indicator variable was created for LBW following the WHO criteria, LBW = 1 if the birth weight was <2500grams [During the survey, mothers were asked if the baby was weighed at birth. If the baby was weighed, they were further asked 500grams .The birth weight was normalized and categorized into five categories based on the standard deviation . This normalized variable was used to estimate the agreement between the mother’s perception of the birth size and measured birth weight.Agreement between the mother’s perceived size and measured birth weight was measured into three categories- concordant , underestimated , and overestimated .Covariates were selected based on the literature review and their likely clinical significance in the multivariable analysis. Place of residence, Ethnicity, Wealth quantile, Mother’s age at delivery, Mother’s education, Marital status, Antenatal care (ANC) visits, Place of delivery, and Delivery by cesarean section (CS) were included in the analysis. Also, child-specific factors like Multiple births, Sex of the child, Birth order, Preceding birth interval, and Pregnancy intentness were included in the analysis. Ethnicity was further categorized into three categories: Advantaged (including Brahmin/Chhetri cast), relatively disadvantaged , and disadvantaged . The wealth quintile was derived from the wealth index. The wealth index was calculated by principal component analysis using household assets and was categorized into five categories comprising 20 percent of the population in each category .The sample distribution of the total under-five children included in the NDHS survey and also the proportion of babies weighed at birth were tabulated according to the background characteristics. The likelihood of being weighed at birth in terms of odds ratio (OR) with a respective confidence interval (CI) was calculated using logistic regression analysis. Furthermore, the sampling distribution of children who were weighed at birth was calculated separately. One-way ANOVA was used to test if there was a significant difference in the mean birth weights of different perceived categories. The predictive accuracy of the mother’s perception of birth size to detect the LBW babies was estimated by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respective 95% CI.Similarly, the proportion of the different categories of agreement between mothers’ perception of size at birth and actual birth weight was calculated according to the background characteristics. The association between the characteristics and agreement was assessed in the descriptive analysis using the chi-square test .The reliability of the mother’s perception to predict birth weight was measured by kappa statistics separately–to predict the LBW (two categories), and also to detect birth weight . Kappa statistics with a 95% confidence interval were calculated together with the percentage of the expected and actual agreement , 16.A multivariable multinomial logistic regression analysis  was usedsvy” command in Stata. A p-value <0.05 was considered statistically significant throughout the analysis.Data analysis was performed using the statistical software STATA 16 using the survey analysis technique. Cluster survey design and individual sample weight were accompanied by the “Among the total 5060 under-five year children in the dataset, only 3095 (61.2%) children had information about birth weight.Among 3095 mother-child pairs who provided information on the birth weight (unweighted sample size = 3122), about 36.9% were from the rural area, about 22.3% of mothers had no formal education, and about 99% were married. Similarly, about 14.5% belonged to the poorest wealth quantile, while about 22% belonged to the richest wealth quantile. Similarly, about 27.4% of mothers were below 20 years of age at their recent delivery. About 14.2% had a cesarean delivery and about 1.2% of mothers gave birth to twin children .Only about 17.8% of the actual birth weight data were obtained from the written card, and the overwhelming majority (82.2%) of birth weight data was based on the mother’s recall. About 3.7% of mothers reported that their baby was very large size at birth and about 4.4% were very small at birth. A majority, 65.9%, reported that their baby was average size at birth .Detailed information on the distribution of overall 5060 mother-child pairs with the odds of having weighed at birth is included in the Digit preference for the recording and recall of birth weight data was assessed. About 68.3% of respondents responded to birth weight as a multiple of 500 grams. Particularly, 16.1% of children were reported as having a birth weight of 2500 grams. Likely, about 21.5% and 17.7% reported having birth weights of 3000 grams and 3500 grams .Although a majority of data were heaped around the multiples of 500, there was no apparent difference in digit preference in the source of the birth weight data, i.e., collected from a written care or mother’s recall .The mean birth weight was 3002.9 grams (SD = 651.30). About 12.3% of babies weighed below <2500 grams, while about 16.3% of mothers perceived their babies as small at birth. Results further revealed that about 62% of the babies who had birth weight <2500 grams were perceived as small size at birth  by their mothers (sensitivity = 61.99%). Similarly, among the babies with birth weight > = 2500 grams, about 90% were perceived as average or above-average size at birth  by their mothers(specificity = 90.09%). Similarly, about 46.7% of the babies perceived smaller by their mothers at birth had birth weight <2500 grams (PPV = 46.68%). While 94.4% of the babies perceived average or above-average size at birth by their mothers had birth weight > = 2500 grams (NPV = 94.42%) .The agreement between the mother’s perception of baby size  and actual birth weight measurement (LBW and not LBW) was 86.3%, which was composed of a higher share of the agreement to identify non-LBW babies (79%) and a low share to identify LBW babies (7%). Also, there was a moderate agreement between the mother’s perception of size at birth and measured birth weight  .There was only a fair agreement between five categories of the mother’s perceived birth size and normalized birth weight. The weighted kappa statistic was 0.39; , and simple (unweighted) kappa was even lower  .The mother’s perception of the size of the baby at birth was concordant with the normalized birth weight among 67.1% of babies, while the mother’s perceived size at birth underestimated the normalized size at birth among 16.9% of babies and overestimated among 16.1% of babies .The percentage distribution of babies according to the mother’s perception of size at birth and normalized birth weight is shown in the The mean birth weight of babies perceived as very small at birth was about 2017 grams. As the perceived size moves higher, the mean birth weight was also significantly higher (p<0.001) . The babMultivariable multinomial logistic regression analysis with concordant as the baseline category revealed that the mother’s education level, ethnicity, multiple births, and sex of the child were significantly associated with the underestimation or overestimation of weight at birth by the mother’s perception of the size at birth. On the other hand, place of residence, marital status, wealth quintile, age at delivery, four ANC visits, place of delivery, delivery by CS, birth order of the child, preceding birth interval, and unintended pregnancy variables were not significantly associated.Compared to mothers without school education, mothers with primary school education were less likely to underestimate than to report concordant size. Likely, mothers belonging to the relatively disadvantaged ethnicity were less likely to underestimate  and also less likely to overestimate  the birth weight than to report concordant size as compared to mothers from advantaged ethnicities .Similarly, mothers who had multiple (twin) births tend to underestimate their baby’s birth weight. Mothers were more likely to underestimate the size of multiple births for the first of multiple  and second of multiple births  than the reporting of concordant compared to single births. Similarly, mothers of female children were less likely to overestimate  the birth weight than the reporting of concordant compared to the mothers of male children .This study assessed the predictive accuracy of the mother’s perception of size at birth to the birth weight using the NDHS 2016 dataset. Among the low birth weight babies, only about 62% were correctly identified by the mother’s perception, while the agreement was moderate between the mother’s perception and birth weight regarding the detection of LBW. This study also found various socio-demographic and biological factors associated with the concordance of the mother’s perception of the baby’s size at birth to birth weight.The sensitivity and positive predictive value of mothers’ perception to detect LBW were considerably low. This implies that the mother’s perception is not a strong indicator of detecting low birth weight in Nepal. It generates considerably high false-negative LBWs (about 38%). The specificity and negative predictive value of the mother’s perception to detect LBW were higher than the sensitivity and PPV. This implies that mothers correctly perceive large(no LBW) babies as large more frequently than the low birth weight babies as small babies. Higher specificity compared to sensitivity might contribute to the higher overall observed agreement (86.3%) between birth weight and the mother’s perceived size at birth. These findings suggest that mothers can predict more accurately for non-LBW babies , but they are weak in identifying LBW (7.6%).Another study utilizing the data from Ethiopian DHS found similar results regarding the predictive accuracy of the mother’s perception of size at birth and birth weight. That study reported a sensitivity of 57.0%, specificity of 88.6%, PPV = 41.3% and NPV = 93.6% . AnotherA hospital-based study in Nepal reported higher sensitivity of the mother’s perceived size at birth to predict LBW (sensitivity = 75%) compared to the findings of this study. However, the discrepancy in the value of sensitivity across these two studies could be due to the variation in the study methods . Women uA multi-country study analyzing 62 nationally representative data from several countries between year 1990–2000 concluded that, on average, mothers’ perception generated 25% false-positive cases to detect LBW. The study recommended that instead of using the mother’s perceived size as the proxy of LBW, countries should promote the weighing of newborns .et al. from Ethiopia reported similar findings [The overall agreement between the mother’s perception and birth weight five categories , represents only a fair agreement. Nigatu D, findings . These fThis study identified that the concordance of the mother’s perception of size at birth and birth weight was associated with the mother’s education, ethnicity, multiple births, and the sex of a newborn child in Nepal.Mothers with primary school education, compared to mothers with no school education, were less likely to underestimate birth weight compared to reporting concordance. This finding revealed that mothers with primary education are more accurate in relating the birth size based on birth weight compared to mothers without education. Another study from Nepal found that illiterate mothers were less likely to identify LBW than educated mothers . SimilarMothers from relatively disadvantaged ethnicity were less likely to underestimate the birth weight of newborns compared to reporting concordance. This requires further study to explore why mothers from these ethnic groups tend to underestimate their baby’s birth weight.Similarly, mothers who gave twin births were more likely to underestimate the birth weight than reporting concordant in comparison to mothers who gave birth to a single child. Multiple births are identified as a risk factor for LBW and childhood illness –26. TherInterestingly, mothers of female children, compared to mothers of male children, were found significantly less likely to overestimate birth weight than reporting concordant. It could be partially attributed to the widespread perception that having a female infant is associated with LBW and having a male infant is associated with high birth weight. A qualitative study in Bangladesh, including mothers, and recently delivered women reported that the participants say that having a female child is a cause of LBW . This peThe percentage of newborn babies weighed at birth has improved in Nepal from 36% in 2011 to 61% in 2016 . HoweverThis study includes a large number of mother-children pairs from nationally representative surveys. DHS employs standard tools for data collection across the countries; therefore, the findings of this study are fairly generalizable and comparable locally, and globally.This study has some limitations. The sample size included in the analysis is a fraction of the sample frame (3095/5060 = 61.2%) due to the missing value on birth weight. The characteristics of those who reported their birth weight significantly differed in various background characteristics. Therefore, this study could not rule out the possibility of selection bias. Also, data heaping and digit preference in birth weight data suggests the possibility of some measurement error in the birth weights. Notably, the spike in the frequency of women reporting birth weights 2500 grams, 3000 grams, and 3500 grams could be due to the rounding by the mothers or health workers when they didn’t know or recorded the actual birth weight might question the accuracy of the birth weight. This study could not eliminate the possibility of recall bias in the birth weight as only 17.8% of the birth weights were retrieved from actual record cards, while an overwhelming 82.2% of birth weight data were obtained from the mother’s recall.Also, we compared the mother’s perception of the baby’s size with the birth weight. However, we aimed to estimate the predictive accuracy of baby size to birth weight, size could represent weight, length, or fat contents or a combination of these factors, which influenced the mother’s perception . InformaThis study found that the mother’s perception of birth size has low sensitivity and PPV to predict LBW in Nepal. Similarly, there is only a fair agreement between the mother’s perceived size and actual birth weight. The mother’s assessment of birth size somehow identifies babies with higher birth weights, but it lacks concordance in identifying LBW babies. These findings revealed that the mother’s perceived size of the baby at birth is a weak predictor of LBW. Furthermore, characteristics such as the mother’s education, ethnicity, multiple births, and sex of the newborn baby were significantly associated with the discordant birth size assessment. Therefore, when using a mother’s perception of size at birth to estimate LBW or birth weight, to reduce the chances of underestimation or overestimation, these background characteristics should be taken into consideration. To address the burden of LBW, efforts should be made to promote measuring and recording birth weight.This study used publicly available secondary data from NDHS 2016. NDHS obtained ethical approval from Nepal Health Research Council Kathmandu, Nepal, and Macro Institutional Review Board, Maryland, USA . NDHS obS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.


To identify neonatal risk for severe adverse perinatal outcomes across birth weight centiles in two Dutch and one international birth weight chart.th percentile are generally used as proxy for growth restriction. The choice of birth weight chart influences the specific cut-off by which birth weight is defined as abnormal, thereby triggering clinical management. Ideally, this cut-off should discriminate appropriately between newborns at low and at high risk of severe adverse perinatal outcomes and consequently correctly inform clinical management.Growth restricted newborns have not reached their intrinsic growth potential in utero and are at risk of perinatal morbidity and mortality. There is no golden standard for the confirmation of the diagnosis of fetal growth restriction after birth. Estimated fetal weight and birth weight below the 10n = 12 953) of women with a low-risk status at the start of pregnancy and that received primary antenatal care in the Netherlands were included.This is a secondary analysis of the IUGR Risk Selection (IRIS) study. Newborns  by categorizing birth weight centile groups and comparing the prognostic performance for severe adverse perinatal outcomes.Severe adverse perinatal outcomes were defined as a composite of one or more of the following: perinatal death, Apgar score < 4 at 5 min, impaired consciousness, asphyxia, seizures, assisted ventilation, septicemia, meningitis, bronchopulmonary dysplasia, intraventricular hemorrhage, periventricular leukomalacia, or necrotizing enterocolitis.rd percentile) . Discriminative abilities of the three birth weight charts across the entire range of birth weight centiles were poor with areas under the curve ranging from 0.57 to 0.61. Sensitivity rates of the various cut-offs were also low.We found the highest rates of severe adverse perinatal outcomes among the smallest newborns (< 3The clinical utility of all three charts in identifying high risk of severe adverse perinatal outcomes is poor. There is no single cut-off that discriminates clearly between newborns at low or high risk.NTR4367.Netherlands Trial Register th, 2014.Registration date March 20 Fetal growth restriction (FGR) refers to a condition in which a fetus does not reach its intrinsic growth potential, usually due to placental insufficiency . Unrecogth percentile [In the absence of a gold standard for the diagnosis of growth restriction in the newborn, small-for-gestational-age (SGA) is used as a screening tool. However, SGA represents a statistical deviation of birth weight from a population-based reference, predefined as a cut-off that is generally the 10rcentile . No intercentile , 16.th and 90th percentile) or as SGA has inherent flaws. First, FGR is a functional placental problem, only partly related to small size. However, since measures of placental function are not often available in daily practice and consequently not feasible in large studies SGA is an important parameter used to detect FGR, albeit an imperfect one. Therefore, it is important to identify which growth chart is most accurate in predicting severe adverse perinatal outcomes [th percentile are constitutionally small but healthy, while a significant proportion of fetuses that have experienced placental insufficiency have a birth weight above the 10th percentile [The dichotomous SGA approach, which defines a newborn as appropriate for gestational age  study , 34. TheIn the Netherlands, hospitals provide secondary and tertiary antenatal care whereas primary care midwives are independent medical practitioners who are qualified to provide full maternity care for women with low-risk pregnancies. Women receiving care from 60 participating midwifery practices and a normal 20 weeks anomaly scan and who, at that point, had a low-risk singleton pregnancy were eligible for inclusion in the IRIS study . MedicalAt 22.8 weeks’ gestation (SD = 2.4) on average, 13,520 low risk pregnant women were included in the IRIS study between February 2015 and February 2016. For 13,001 (96.1%) infants of these 13,520 eligible women, data on birth weight and/or gestational age were available that could also be linked to data of the Netherlands’ Perinatal Registry . In 48 oAt inclusion midwives collected and reported pregnant women’s information on age and ethnic background, parity, maternal length, and height. Information on birth weight, gestational age, and infant sex were retrieved from the Netherlands Perinatal Registry (Perined) and/or hospital records in a subsample of 2,339 participants [The Visser birth weight reference curve was used in the IRIS study as this was common practice in the Netherlands at the time . This isThe Hoftiezer chart is a prescriptive chart that was developed using the data from the Netherlands Perinatal Registry (Perined) of more than 1.5 million children born in the Netherlands between 2000 and 2014. Live-born singleton infants, born to healthy mothers after uncomplicated pregnancies and a spontaneous onset of labor were included in the cohort. Exclusion criteria for the Hoftiezer chart were risk factors for FGR including maternal conditions like pre-existent hypertension and respiratory disorders and obstetric complications such as gestational hypertension and pre-eclampsia. Newborns that were born before 23 week’s gestation or after 42 weeks’ gestation were also excluded . All ethThe INTERGROWTH chart is also a prescriptive chart. It was developed to establish international charts for birth weight to enable global use. Pregnant women from eight different countries with a low risk for FGR were selected for the cohort . 20,486 Perinatal death occurring from 28 weeks’ gestation until one week after birthApgar score < 4 at 5 minutesImpaired consciousness Asphyxia, defined as cord blood arterial base excess of less than minus 12 mmol/LSeizures on two or more occasions within 72 h after birthAssisted ventilation for more than 24 h via endotracheal tube initiated within 72 h of birthSepticemia ascertained by blood cultureMeningitis ascertained by cerebrospinal fluid cultureBronchopulmonary dysplasia (BPD), requiring oxygen at a postnatal gestational age from 36 completed weeks and ascertained by radiographyIntraventricular hemorrhage grade three or four and diagnosed by ultrasound or autopsyCystic periventricular leukomalacia (PVL) ascertained by ultrasonographyNecrotizing enterocolitis diagnosed by radiography, surgery, or autopsyThe main outcome of the IRIS study were severe adverse perinatal outcomes. These outcomes were selected using previous research and expert opinion . InformaFor each birth weight chart gestational age specific standard deviation scores (SDSs) were calculated using formulas of the Visser, Hoftiezer and INTERGROWTH charts. For each chart, the derived birth weight centiles were divided into the following centile groups: < p3, p3- < p5, p5- < p10, p10- < p25, p25- < p75, p75- < p90, p90- < p95, p95- < p97 and > p97. The distribution and rates of severe adverse perinatal outcomes in the birth weight centile groups were examined to investigate the diagnostic performance of each birth weight chart per centile group. Sensitivity and specificity rates and likelihood ratios were calculated for the various birth weight centile cut-offs. To explore the degree of accuracy by which the birth weight charts predict severe adverse perinatal outcomes, ROC curves were calculated. The area under the curve (AUC) of a ROC curve illustrates the diagnostic ability of the birth weight chart. If the AUC is 0.5 this means that the birth weight chart does not discriminate between low and high risk of severe adverse perinatal outcomes. An AUC between 0.7 and 0.8 is considered an acceptable diagnostic performance and an AUC above 0.8 is considered a good diagnostic performance.+0- and 42 + 6-weeks’ gestation” (https://intergrowth21.tghn.org/newborn-size-birth/).Analyses were carried out in Statistical Package for the Social Sciences (SPSS) 26. The Hoftiezer SDSs were calculated in R, version 3.02. The birth weight percentiles from the INTERGROWTH chart were calculated using the “Neonatal Size Calculator for newborn infants between 24Of the 12,953 women, 215 (1.7%) gave birth to a newborn that experienced at least one severe adverse perinatal outcome. The most common adverse outcome was asphyxia which occurred 144 times (1.11%). Table th percentile). Newborns with a birth weight below the 3rd percentile had particularly high rates of severe adverse perinatal outcomes.In Fig. th percentile had a slightly increased rate of severe adverse perinatal outcomes for the Visser and Hoftiezer charts but to a lesser degree than SGA newborns.Figure The distributions of severe adverse perinatal outcomes in the centile groups of each birth weight chart and the sensitivity and specificity rates for each cut-off are shown in Table th percentile (SGA) was used. Similar curvilinear patterns of the link between birth weight and severe adverse perinatal outcomes have been reported for perinatal mortality curves that were presented by Vasak et al [th percentile also had slightly higher rates of severe adverse perinatal outcomes.For each of the three charts used, high rates of severe adverse perinatal outcomes were observed at the extremes of the distribution of the birth weight centiles. However, the discriminative ability was poor when the cut-off at the 10ak et al . With deth percentile.The incidence of severe adverse perinatal outcomes in the < p3 percentile group was highest. This is in line with the ideas of the international consensus definitions for FGR and growth restriction in the newborn (GRN) . The sevFor all three charts the area under the curve was smaller than 0.7. The Hoftiezer birth weight chart had somewhat higher sensitivity rates for the < p3, p3- < p5 and p5- < p10 percentile groups but lower specificity rates. Rates of sensitivity and specificity vary with the prevalence of a certain disease . The lowOur study is one of the few studies that analyzed the occurrence of severe adverse perinatal outcomes across the full range of birth weight percentiles and that suggests that birth weight is curvilinearly related to severe adverse perinatal outcomes , 47–49.There is no specific birth weight percentile that reflects a safe weight for gestational age with regard to placental function and severe adverse perinatal outcomes, although we observed a gradient in which newborns with the lowest birth weight have the highest risk of severe adverse perinatal outcomes. This is in line with the idea that p10 should not be the cut-off for healthy size . The samBased on a Delphi consensus study that was performed among neonatologists to determine a definition of growth restriction in the newborn it was concluded that a birth weight < p3, on any birth weight chart, should be used to determine the risk of severe adverse perinatal outcomes. However, next to birth weight < p3 other factors should also be considered . CandidaBesides biometric and doppler measurements there are also other markers that can be used to detect impaired fetal condition or placental insufficiency such as mother-reported reduced fetal movements, electronic fetal monitoring, the amount of amniotic fluid, metabolites in maternal serum and placental biomarkers , 52. HowInterestingly, in previous trials including the IRIS study, routine third trimester ultrasonography generally increased the detection of SGA, but it did not reduce the rate of severe adverse perinatal outcomes compared to clinically indicated ultrasonography , 34, 53.Some suggest developing birth weight charts that better reflect growth potential, the so called ‘customized growth references’, for example by adjusting for maternal physiological characteristics like maternal height –57. In tA strength of this study is its rich dataset that is based on questionnaire data, perinatal registry data, and hospital records data all collected within in a prospective large-scale nationwide study.Our study also has some limitations. The population used for analyses is a low-risk Dutch population that received primary antenatal care at the time of inclusion. Two of the three analyzed birth weight charts were also Dutch charts. In this low-risk sample only 1.7% of the neonates had severe adverse perinatal outcomes. It is conceivable that rates of severe adverse perinatal outcomes are higher in the general population. Therefore, the test performance  of the birth weight charts might have been underestimated based on the current sample, as sensitivity rates vary with the prevalence of a certain disease . HoweverIn addition, only severe, and immediate adverse perinatal outcomes occurring during the first seven days after birth were included in the analyses. There is an increasing body of evidence about the subtle long term consequences for the growth restricted babies , 11. TheFinally, interventions were done if FGR was suspected. These interventions will probably have had an influence on the outcomes of the growth restricted newborns.While we observed a gradient in which newborns with the lowest birth weight, on either chart, have the highest risk of severe adverse perinatal outcomes, no single cut-off discriminated clearly between newborns at low- or high-risk. All examined birth weight charts demonstrated a poor clinical utility as a single predictor in identifying (the risk of) severe adverse perinatal outcomes. More research should be performed to find sensitive markers identifying suboptimal conditions in utero and impaired fetal growth and development, also applicable when the growth restriction is suspected as late as after birth. Currently, techniques for Doppler ultrasound  and biomarkers measurements  are being studied and show promising results in predicting and reducing severe adverse perinatal outcomes , 18, 52.