In 2022, the mean age of Hispanic mothers at first birth amounted to 25.7 years in the United States. In comparison, the mean age of mothers of all races and origins in the U.S. was 27.4 years.

This statistic shows the distribution of births in the United States from 2011 to 2019, by age of the mother. In 2019, women aged 20 to 29 years birthed the most children in the United States at ***** percent of all births in that year.

Over the past 70 years in the United States, women have gradually started having children at a later point in their lives. Before the 1980s, women in their early twenties had the highest birth rates, however women in their late twenties had the highest rates between 1980 and 2015, but were recently overtaken by women in their early thirties. Another major trend is the decline of teenage pregnancies, which was less than a quarter of it's 1950-1955 rate in the years between 2015 and 2020. In fact, birth rates among 15-19 years olds often doubled birth rates of women aged 35-39 throughout the late twentieth century, but in 2020, the opposite is true.

For women in their forties, birth rates have remained comparatively lower than rates among the other age groups. The high figures in the 1950s and 1960s, can be attributed to the baby boom that followed the Second World War. In more recent decades, rising birth rates among older age groups is not only due to societal trends, but has also been aided by improvements in assisted reproductive technology (ART), such as in vitro fertilization (IVF). Such technologies have granted thousands of women the ability to conceive in circumstances where this would not have been possible in years past.

The figures show a gradual increase in the average age of mothers at childbirth in Italy between 2002 and 2024. As of 2023, the average age of mothers at childbirth in Italy was estimated to be **** years, an increase of two years compared to 2002. Sicily was the Italian region with the lowest average age, with mothers aged **** years on average when giving birth. Fertility rate Most developed countries today are characterized by a decline in the number of births. Italy hit a new low in 2022, with less than ******* births. In 2024, it was among the countries with the lowest fertility rate in the world, with every woman having **** babies on average. Assisted reproductive treatment (ART) In 2023, roughly four percent of pregnancies in Italy were obtained through assisted reproductive treatments (ART) and women above ** had the highest rate of pregnancies resulting from ART, with **** percent. The number of ART undertaken in Italy increased constantly in the past two decades, except for a slight decline in 2020 when a general decrease in the number of births was experienced with the outbreak of COVID-19.

The mean childbearing age in Europe was just over 30 years in 2022, compared with 26.4 years in 1979, which was the lowest fertility rate in the provided period.

Between 1991 and 2021 there has been a clear trend of mothers having children later in life in the United Kingdom, with the average age of mothers in the increasing from 27.7 in 1991 to 30.9 by 2021.

This data set contains estimated teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) by county and year. DEFINITIONS Estimated teen birth rate: Model-based estimates of teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates “borrow strength” across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information—including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables—to provide a stable estimate of the county teen birth rate. Median teen birth rate: The middle value of the estimated teen birth rates for the age group 15–19 for counties in a state. Bayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model. NOTES Data on the number of live births for women aged 15–19 years were extracted from the National Center for Health Statistics’ (NCHS) National Vital Statistics System birth data files for 2003–2015 (5). Population estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used. Hierarchical Bayesian space–time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003–2015 (1,2,3,4). The Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-α)% Bayesian credible interval for an unknown parameter vector θ and observed data vector y is a subset C of parameter space Ф such that 1-α≤P({C│y})=∫p{θ │y}dθ, where integration is performed over the set and is replaced by summation for discrete components of θ. The probability that θ lies in C given the observed data y is at least (1- α) (6). County borders in Alaska changed, and new counties were formed and others were merged, during 2003–2015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003–2015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005–2007 bridged-race population file geographies (7). SOURCES National Center for Health Statistics. Vital statistics data available online, Natality all-county files. Hyattsville, MD. Published annually. For details about file release and access policy, see NCHS data release and access policy for micro-data and compressed vital statistics files, available from: http://www.cdc.gov/nchs/nvss/dvs_data_release.htm. For natality public-use files, see vital statistics data available online, available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. National Center for Health Statistics. U.S. Census populations with bridged race categories. Estimated population data available. Postcensal and intercensal files. Hyattsville, MD

United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births data was reported at 14.000 Ratio in 2015. This stayed constant from the previous number of 14.000 Ratio for 2014. United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births data is updated yearly, averaging 13.000 Ratio from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 15.000 Ratio in 2009 and a record low of 11.000 Ratio in 1998. United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Weighted average; This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator for monitoring maternal health.

In 2022 the average age of mothers giving birth to their first child in England and Wales was 29.2 years of age, followed by 31.5 years for the second child, 32.6 for the third child, and 33.6 for the fourth child.

In 2021, the birth rate in the United States was highest in families that had under 10,000 U.S. dollars in income per year, at 62.75 births per 1,000 women. As the income scale increases, the birth rate decreases, with families making 200,000 U.S. dollars or more per year having the second-lowest birth rate, at 47.57 births per 1,000 women. Income and the birth rate Income and high birth rates are strongly linked, not just in the United States, but around the world. Women in lower income brackets tend to have higher birth rates across the board. There are many factors at play in birth rates, such as the education level of the mother, ethnicity of the mother, and even where someone lives. The fertility rate in the United States The fertility rate in the United States has declined in recent years, and it seems that more and more women are waiting longer to begin having children. Studies have shown that the average age of the mother at the birth of their first child in the United States was 27.4 years old, although this figure varies for different ethnic origins.

To determine the impact of prenatal and infant/toddler nurse home visiting on maternal and child mortality over 20 years following program enrollment. Three randomized controlled trials (Elmira, NY; Memphis, TN; Denver, CO) designed originally to assess program impacts on pregnancy outcomes and maternal and child health through child age 2. Each trial included a control group, a group that received the full intervention (nurse visitation through child age 2), and an alternative treatment group (nurse home visitation through pregnancy only or visitation by paraprofessionals through child age 2). Due to sample size considerations, the Elmira and Denver samples were combined for all mortality analyses. For determining mortality, background information used for determining mortality status was available for all 1138 mothers randomized to a Memphis treatment condition and all but 13 of the live-born children (n=1076). For the combined Elmira and Denver group, background information was available for all 1135 mothers randomized to any one of the treatment conditions and all but 10 of the live born children (n=1087). Pregnant women and their first-born children who were enrolled in one of three trials of the Nurse-Family Partnership (Elmira, Memphis, Denver) were included in the current study. The Elmira sample (N = 400) was enrolled between April 1978 and September 1980 with an 80% recruitment rate. At enrollment, 47% of the participating women were younger than 19 years of age, 62% were unmarried, and 61% came from families in Hollingshead’s social classes IV and V (semi-skilled and unskilled laborers). In the Memphis trial, a total of 1138 out of 1289 eligible women (88.3%) completed informed consent and were randomized from June 1, 1990, through August 31, 1991. We enrolled primarily African American women at less than 29 weeks of gestation, with no previous live births, and with at least 2 of the following socio-demographic risk characteristics: unmarried, less than 12 years of education, and unemployed. Of the women enrolled, 92.1% were African American, 98.1% were unmarried, 64.1% were 18 years or younger at registration, and 85.1% came from households with annual incomes below the US federal poverty guidelines. Denver trial enrollment took place between March 1994 and June 1995 with a total of 735 out of 1178 consecutive pregnant women with no previous live births who were eligible for Medicaid or who had no private health insurance enrolled in the trial and were randomized to control, paraprofessional, or nurse-visited conditions. 86% of participating women were unmarried, 42% were under 19 years of age, 45% were Latino, and on average participants lived in census tracts where 20% of the population was below the poverty line. The current study was approved by the University of Rochester Institutional Review Board and the Combined Institutional Review Board of the University of Colorado.

The adolescent birth rate in Latin America and the Caribbean stood at an average of 60.7 births per 1,000 women aged 15 to 19, according to 2020 estimates. The highest rate in the region was registered in Nicaragua and Honduras, where it surpassed 79 births per 1,000 women in that same age group as of 2022. In South America, Chile and Uruguay recorded some of the lowest teenage birth rates.

🔗 Check out my notebook here: Link

This dataset includes malnutrition indicators and some of the features that might impact malnutrition. The detailed description of the dataset is given below:

• Percentage-of-underweight-children-data: Percentage of children aged 5 years or below who are underweight by country.

• Prevalence of Underweight among Female Adults (Age Standardized Estimate): Percentage of female adults whos BMI is less than 18.

• GDP per capita (constant 2015 US$): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.

• Domestic general government health expenditure (% of GDP): Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

• Maternal mortality ratio (modeled estimate, per 100,000 live births): Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

• Mean-age-at-first-birth-of-women-aged-20-50-data: Average age at which women of age 20-50 years have their first child.

• School enrollment, secondary, female (% gross): Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

Differences between observed and simulated-pregnancy PANDiet scores (mean ± SEM) by observed PANDiet score quartiles among French and US women of childbearing age.

IntroductionEstimating a reliable gestational age (GA) is essential in providing appropriate care during pregnancy. With advancements in data science, there are several publications on the use of artificial intelligence (AI) models to estimate GA using ultrasound (US) images. The aim of this meta-analysis is to assess the accuracy of AI models in assessing GA against US as the gold standard.MethodsA literature search was performed in PubMed, CINAHL, Wiley Cochrane Library, Scopus, and Web of Science databases. Studies that reported use of AI models for GA estimation with US as the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Mean error in GA was estimated using STATA version-17 and subgroup analysis on trimester of GA assessment, AI models, study design, and external validation was performed.ResultsOut of the 1,039 studies screened, 17 were included in the review, and of these 10 studies were included in the meta-analysis. Five (29%) studies were from high-income countries (HICs), four (24%) from upper-middle-income countries (UMICs), one (6%) from low-and middle-income countries (LMIC), and the remaining seven studies (41%) used data across different income regions. The pooled mean error in GA estimation based on 2D images (n = 6) and blind sweep videos (n = 4) was 4.32 days (95% CI: 2.82, 5.83; l2: 97.95%) and 2.55 days (95% CI: −0.13, 5.23; l2: 100%), respectively. On subgroup analysis based on 2D images, the mean error in GA estimation in the first trimester was 7.00 days (95% CI: 6.08, 7.92), 2.35 days (95% CI: 1.03, 3.67) in the second, and 4.30 days (95% CI: 4.10, 4.50) in the third trimester. In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). Most studies exhibited an unclear or low risk of bias in various domains, including patient selection, index test, reference standard, flow and timings and applicability domain.ConclusionPreliminary experience with AI models shows good accuracy in estimating GA. This holds tremendous potential for pregnancy dating, especially in resource-poor settings where trained interpreters may be limited.Systematic Review RegistrationPROSPERO, identifier (CRD42022319966).

IntroductionLatinas in the US are underrepresented in miscarriage research, yet face several risk factors for having a miscarriage, including intimate partner violence, and increasing maternal age. Increased acculturation is associated to increased risk of intimate partner violence and adverse pregnancy outcomes among Latinas yet is also understudied in the realm of miscarriage. Thus, this study aimed to analyze and compare sociodemographic characteristics, health-related factors, intimate partner violence, and acculturation among Latinas with and without a history of miscarriage.MethodsThis study utilizes a cross-sectional design to analyze baseline data from a randomized clinical trial on the effectiveness of “Salud/Health, Educación/Education, Promoción/Promotion, y/and Autocuidado/Self-care” (SEPA), a human immunodeficiency virus risk reduction intervention for Latinas. Survey interviews were conducted in a private room at the University of Miami Hospital. Survey data analyzed include demographics, a bi-dimensional acculturation scale, a health and sexual health survey, and the hurt, insult, threaten, and scream tool. This study's sample was 296 Latinas, 18 to 50 years old, with and without a history of miscarriage. Data analyses included descriptive statistics, t-tests for continuous variables, negative binomial for counts, and chi-square for dichotomous or categorical variables.ResultsMost Latinas were Cuban (53%), lived in the U.S. an average of 8.4 years, had 13.7 years of education, and a monthly family income of $1,683.56. Latinas with history of miscarriage were significantly older, had more children, more pregnancies, and poorer self-rated health than Latinas without history of miscarriage. Although not significant, a high percentage of intimate partner violence (40%) and low levels of acculturation were reported.DiscussionThis study contributes new data about different characteristics of Latinas who have and have not experienced a miscarriage. Results can help identify Latinas at risk for miscarriage or its adverse-related outcomes and help develop public health policies focusing on preventing and managing miscarriage among Latinas. Further research is warranted to determine the role of intimate partner violence, acculturation, and self-rated health perceptions among Latinas who experience miscarriage. Certified nurse midwives are encouraged to provide Latinas with culturally tailored education on the importance of early prenatal care for optimal pregnancy outcomes.

BackgroundPregnancy to postpartum (PtP) applications (apps) are becoming more common tools to document everything from pregnancy and delivery to nutrient allocation, life taboos, and infant medical examinations. However, the dependability, quality, and efficacy of these apps remain unclear. This study examined the features and functions of mobile PtP care apps accessible in China and the United States and to identify the major gaps that need to be addressed.MethodsApps were selected by searching the Apple App Store and Android Markets (in the US and China) for the terms “pregnancy” and “postpartum” in Chinese and English. The apps' security, quality, and effectiveness were investigated, and chi-square tests and analysis of variance were performed to examine the differences in characteristics between apps available in the US and China.ResultsA total of 84 mobile PtP care apps (45 from the US and 39 from China) were included. A total of 89.7% (35/39) of Chinese mobile apps did not provide safety statements or supporting evidence. The objective app quality ratings for Chinese and US apps were 3.20 ± 0.48 (mean ± standard deviation) and 3.56 ± 0.45, respectively (p > 0.05). A greater number of Chinese apps provided app-based monitoring functions, namely recording fetal size (n = 18, 46.2% in China vs. n = 3, 6.7% in the US), contractions (n = 11, 28.2% in China vs. n = 0, 0% in the US), pregnancy weight (n = 11, 28.2% in China vs. 0, 0% in the US), and pregnancy check-up reminders (n = 10, 25.6% in China vs. n = 0, 0% in the US). Meanwhile, a greater number of US apps provided exercise modules, namely pregnancy yoga (n = 2, 5.1% in China vs. n = 21, 46.7% in the US), pregnancy workouts (n = 2, 5.1% in China vs. n = 13, 28.9% in the US), and pregnancy meditation (n = 0, 0% in China vs. 10, 22.2% in the US) (p < 0.01). A medium security risk was identified for 40% (18/45) of apps in the US and 82.1% (32/39) of apps in China (p < 0.01).ConclusionsThe functionality and characteristics of in-store mobile apps for PtP care varied between China and the US. Both countries' apps, particularly Chinese apps, encountered issues related to a lack of evidence-based information, acceptable content risk, and program evaluations. Both countries' apps lacked proper mental health care functions. The findings suggest that the design of app features should be enhanced in both countries, and increased interaction between app creators and users is recommended.

Maternal obesity has been associated with a higher risk of pregnancy-related complications in mothers and offspring; however, effective interventions have not yet been developed. We tested two common interventions, calorie restriction and pravastatin administration, during pregnancy in a rhesus macaque model with the hypothesis that these interventions would normalize metabolic dysregulation in pregnant mothers leading to an improvement in infant metabolic and cognitive/social development. A total of 19 obese mothers were assigned to either one of the two intervention groups (n=5 for calorie restriction; n=7 for pravastatin) or an obese control group (n=7) with no intervention, and maternal gestational samples and postnatal infant samples were compared with lean control mothers (n=6). Gestational calorie restriction normalized one-carbon metabolism dysregulation in obese mothers but altered energy metabolism in their offspring. Although administration of pravastatin during pregnancy tended to normalize blood cholesterol in the mothers, it potentially impacted the gut microbiome and kidney function of their offspring. In the offspring, both calorie restriction and pravastatin administration during pregnancy tended to normalize the activity of AMPK in the brain at 6 months, and while results of the Visual Paired-Comparison test, which measures infant recognition memory, were not significantly impacted by either of the interventions, gestational pravastatin administration, but not calorie restriction, tended to normalize anxiety assessed by the Human Intruder test. Although the two interventions tested in a non-human primate model led to some improvements in metabolism and/or infant brain development, negative impacts were also found in both mothers and infants. Our study emphasizes the importance of assessing gestational interventions for maternal obesity on both maternal and offspring long-term outcomes. Methods Study population Pregnant female rhesus macaques (Macaca mulatta) from an indoor breeding colony at the California National Primate Research Center with appropriate social behavior and previous successful pregnancies were enrolled. Animal handling was approved by the UC Davis Institutional Animal Care and Use Committee (IACUC) (#19299). A qualitative real-time PCR assay (Jimenez & Tarantal, 2003) was used to identify mothers with male fetuses to include in this study. Since obesity is defined as subjects with body fat above 30% for women, according to guidelines from the American Society of Bariatric Physicians, American Medical Association, and in some publications (Okorodudu et al., 2010; Shah & Braverman, 2012), a Body Condition Score (BCS) of 3.5 (32.8 % body fat on average (Summers et al., 2012)) was used as the cutoff. Therefore, mothers with BCS of 3.5 and above were categorized as obese. Obese mothers were randomly assigned to the Obese Control (OC) group, OR group (received calorie Restriction), or OP group (received Pravastatin). Mothers with BCS of 2.5 and below were assigned to the Lean Control (LC) group. The unbalanced sample size was because some mothers were removed from the analyses due to fetal deaths for unknown reasons, misidentification of a female fetus, different timing for study enrollment, or technical issues upon collecting samples. The number of animals was six for the LC, seven for the OC, five for the OR, and seven for the OP groups. Feeding, rearing, and interventions Adult female animals were provided monkey diet (High Protein Primate Diet Jumbo #5047; LabDiet, St. Louis, MO, USA) twice a day between 6–9 am and 1–3 pm. The calories were provided as 56% from carbohydrates, 30% from protein, and 13% from. Mothers in the LC, OC, and OP groups were fed nine biscuits twice a day once pregnancy was confirmed. Mothers in the OR group received a restricted supply of food once the pregnancy was detected and was maintained throughout pregnancy. The food restriction was set such that the average total weight increase would be 8% body weight from the last day before conception because the recommended total weight gain in the 2nd and 3rd trimesters is 5-9 kg for the average US woman with obesity who weighs 80 kg and is 1.6 m in height (Body Mass Index of 30), according to the Institute of Medicine 2009 guidelines (Institute of Medicine and National Research Council, 2009). During nursing of infants older than 4 months, all mothers were provided twelve biscuits. Fresh produce was provided biweekly, and water was provided ad libitum for all mothers. Mothers in the OP group were given pravastatin sodium (ApexBio Technology, Houston, TX, USA) at 20 mg/kg body weight prepared in a neutralized syrup (20 mg/mL sodium bicarbonate dissolved in a fruit-flavored syrup (Torani, San Leandro, CA, USA)) once a day from the time pregnancy was confirmed until delivery. The caloric value of the administration was made so as not to influence body weight or skew nutritional value of the diet among all treatment groups. Both interventions were applied only during gestation. Although most mothers were allowed to deliver naturally, cesarean delivery was performed for fetal indications when recommended by veterinarians (2 for each of the LC and OC groups, and 1 for the OP group). These mothers did not accept their infant following birth, so foster mothers were provided. Sample Collection and pre-processing prior to sample storage The animal caretakers and researchers who collected samples were blinded for group assignment by coding all animals by IDs. The collected biological samples were randomized by using random numbers and the group assignment was blinded during the data collection. Both mothers (during pregnancy) and infants were weighed every week. One day before sample collection, food was removed 30 min after the afternoon feeding, and biological samples were collected prior to the morning feeding. To collect biological samples, animals were anesthetized using 5–30 mg/kg ketamine or 5–8 mg/kg telazol. Both maternal and infant blood was collected using 5 mL lavender top (EDTA) tubes (Monoject, Cardinal Health, Dublin, OH, USA) and urine was collected from the bladder by ultrasound-guided transabdominal cystotomy using a 22-gauge needle and stored in a 15 mL Falcon tube. A placental sample was collected at GD150 transabdominally under ultrasound guidance using an 18-gauge needle attached to a sterile syringe. Sample processing was as previously described in (Hasegawa et al., 2022). Necropsy was conducted between 9:30 am–1:30 pm. First, infants at the age of PD180 were fasted and anesthetized with ketamine, and plasma and urine were collected. Then, euthanasia was performed with 120 mg/kg pentobarbital, followed by heparin injection, clamping of the descending aorta, and flushing with saline until clear. The kidney and brain (amygdala, hippocampus, hypothalamus, and prefrontal cortex) were collected, weighed, and immediately frozen on dry ice or liquid nitrogen to store at -80 °C until further analyses. Metabolite extraction and analysis by 1H NMR, and measurement of insulin, cholesterol, cytokine, and cortisol Detailed procedures were previously described (Hasegawa et al., 2022). Briefly, plasma and urine samples were filtered using Amicon Ultra Centrifugal Filter (3k molecular weight cutoff; Millipore, Billerica, MA, USA), and the supernatant was used for analysis. For both the placental and brain tissue samples, polar metabolites were extracted using our previously reported method (Hasegawa et al., 2020). A total of 180 μL of sample (tissue extract or filtered urine or serum) was transferred to 3 mm Bruker NMR tubes (Bruker, Billerica, MA, USA). Within 24 h of sample preparation, all 1H NMR spectra were acquired using the noesypr1d pulse sequence on a Bruker Avance 600 MHz NMR spectrometer (Bruker, Billerica, MA, USA) (O’Sullivan et al., 2013). Chenomx NMRSuite (version 8.1, Chenomx Inc., Edmonton, Canada) (Weljie et al., 2006) was used to identify and quantify metabolites. Heparin-treated plasma samples were used to measure insulin and 17 cytokines and chemokines (hs-CRP, Granulocyte-macrophage colony-stimulating factor, IFN-γ, TNF-α, transforming growth factor-α, monocyte chemoattractant protein-1, macrophage inflammatory protein-1β (MIP-1β), and interleukin (IL)-1β, IL-1 receptor antagonist (IL-1ra), IL-2, IL-6, IL-8, IL-10, IL-12/23 p40, IL-13, IL-15, and IL-17A) using a multiplex Bead-Based Kit (Millipore) on a Bio-Plex 100 (Bio-rad, Hercules, CA) following the manufacturer’s protocol. For each sample, a minimum of fifty beads per region were collected and analyzed with Bio-Plex Manager software using a 5-point standard curve with immune marker quantities extrapolated based on the standard curve. Two samples were removed for analysis of TNF-α and IL-1ra as technical errors (both from Animal ID 1132103: 895.2 and 1115.1 pg/mL at gestational days (GD) 90; 510.8 and 617.2 pg/mL at GD120, respectively). Plasma cholesterol level was measured by Clinical Laboratory Diagnostic Product (OSR6116) on Beckman Coulter AU480 (Beckman Coulter, Brea, CA). Infant plasma cortisol level at PD110 was assessed as previously described (Vandeleest et al., 2019; Walker et al., 2018). In short, infants were transferred to a test room at 9 am and blood was drawn at 11 am (Sample 1), followed by another blood collection at 4 pm (Sample 2) and intramuscular injection of 500 μg/kg dexamethasone (Dex) (American Regent Laboratories, Inc., Shirley, NY). On the next day, a blood sample was collected at 8:30 am (Sample 3), and then 2.5 IU of adrenocorticotropic hormone (Amphastar Pharmaceuticals, Inc., Rancho Cucamonga, CA) was injected intramuscularly. The last blood was collected (Sample 4) 30 min after adrenocorticotropic hormone injection. The collected blood samples were processed and stored, and cortisol concentration was assessed by a chemiluminescent assay on the ADVIA Centaur CP platform

Background: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. Methods: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) and then in a large, outpatient, pediatric sample (N=14,695). Results: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best-performing alternative models. Conclusions: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0–36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve. Methods Sources of data: Information on infants was ascertained from two sources: the STORK birth cohort and the STARR research registry. (1) Detailed methods for the STORK birth cohort have been described previously. In brief, a multiethnic cohort of mothers and babies was followed from the second trimester of pregnancy to the babies’ third birthday. Healthy women aged 18–42 years with a single-fetus pregnancy were enrolled. Households were visited every four months until the baby’s third birthday (nine baby visits), with the weight of the baby at each visit recorded in pounds. Medical charts were abstracted for birth weight and length. (2) STARR (starr.stanford.edu) contains electronic medical record information from all pediatric and adult patients seen at Stanford Health Care (Stanford, CA). STARR staff provided anonymized information (weight, height and age in days for each visit through age three years; sex; race/ethnicity) for all babies during the period 03/2013–01/2022 followed from birth to at least 36 months of age with at least five well-baby care visits over the first year of life.
Inclusion of data for modeling: All observed weight and height values were evaluated in kilograms (kg) and centimeters (cm), respectively. Any values assessed beyond 1,125 days (roughly 36 months) and values for height and weight deemed implausible by at least two reviewers (e.g., significant losses in height, or marked outliers for weight and height) were excluded from the analysis. Additionally, weights assessed between birth and 19 days were excluded. At least five observations across the 36-month period were required: babies with fewer than five weight or height values after the previous criteria were excluded from analyses. Model: We developed our weight model using values from STORK babies and then replicated it with values from the STARR babies. Height models were evaluated in STARR babies only because STORK data on height were scant. The Michaelis-Menten equation is described as follows: v = Vmax ([S]/(Km + [S]) , where v is the rate of product formation, Vmax is the maximum rate of the system, [S] is the substrate concentration, and Km is a constant based upon the enzyme’s affinity for the particular substrate. For this study the equation became: P = a1 (Age/(b1+ Age)) + c1, where P was the predicted value of weight (kg) or height (cm), Age was the age of the infant in days, and c1 was an additional constant over the original Michaelis-Menten equation that accounted for the infant’s non-zero weight or length at birth. Each of the parameters a1, b1 and c1 was unique to each child and was calculated using the nonlinear least squares (nls) method. In our case, weight data were fitted to a model using the statistical language R, by calling the formula nls() with the following parameters: fitted_model <-nls(weights~(c1+(a1*ages)/(b1+ages)), start = list(a1 = 5, b1 = 20, c1=2.5)), where weights and ages were vectors of each subject’s weight in kg and age in days. The default Gauss-Newton algorithm was used. The optimization objective is not convex in the parameters and can suffer from local optima and boundary conditions. In such cases good starting values are essential: the starting parameter values (a1=5, b1=20, c1=2.5) were adjusted manually using the STORK dataset to minimize model failures; these tended to occur when the parameter values, particularly a1 and b1, increased without bound during the iterative steps required to optimize the model. These same parameter values were used for the larger STARR dataset. The starting height parameter values for height modeling were higher than those for weight modeling, due to the different units involved (cm vs. kg) (a1=60, b1=530, c1=50). Because this was a non-linear model, goodness of fit was assessed primarily via root mean squared error (RMSE) for both weight and height. Imputation tests: To test for the influence of specific time points on the models, we limited our analysis to STARR babies with all recommended well-baby visits (12 over three years). Each scheduled visit except day 1 occurred in a time window around the expected well-baby visit (Visit1: Day 1, Visit2: days 20–44, Visit3: 46–90, Visit4: 95–148, Visit5: 158–225, Visit6: 250–298, Visit7: 310–399, Visit8: 410–490, Visit9: 500–600, Visit10: 640–800, Visit11: 842–982, Visit12: 1024–1125). We considered two different sets: infants with all scheduled visits in the first year of life (seven total visits) and those with all scheduled visits over the full three-year timeframe (12 total visits). We fit these two sets to the model, identifying baseline RMSE. Then, every visit, and every combination of two to five visits were dropped, so that the RMSE or model failures for a combination of visits could be compared to baseline. Prediction: We sought to predict weight or height at 36 months (Y3) from growth measures assessed only up to 12 months (Y1) or to 24 months (Y1+Y2), utilizing the “last value” approach. In brief, the last observation for each child (here, growth measures at 36 months) is used to assess overall model fit, by focusing on how accurately the model can extrapolate the measure at this time point. We identified all STARR infants with at least five time points in Y1 and at least two time points in both Y2 and Y3, with the selection of these time points based on maximizing the number of later time points within the constraints of the well-baby visit schedule for Y2 and Y3. The per-subject set of time points (Y1-Y3) was fitted using the modified Michaelis-Menten equation and the mean squared error was calculated, acting as the “baseline” error. The model was then run on the subset of Y1 only and of Y1+Y2 only. To test predictive accuracy of these subsets, the RMSE was calculated using the actual weights or heights versus the predicted weights or heights of the three time series. Comparison with other models: We examined how well the modified Michaelis-Menten equation performed interpolation in STARR babies compared to ten other commonly used interpolation methods and pediatric growth models including: (1) the ‘last observation carried forward’ model; (2) the linear model; (3) the robust linear model (RLM method, base R MASS package); (4) the Laird and Ware linear model (LWMOD method); (5) the generalized additive model (GAM method); (6) locally estimated scatterplot smoothing (LOESS method, base R stats package); (7) the smooth spline model (smooth.spline method, base R stats package); (8) the multilevel spline model (Wand method); (9) the SITAR (superimposition by translation and rotation) model and (10) fast covariance estimation (FACE method). Model fit used the holdout approach: a single datapoint (other than birth weight or birth length) was randomly removed from each subject, and the RMSE of the removed datapoint was calculated as the model fitted to the remaining data. The hbgd package was used to fit all models except the ‘last observation carried forward’ model, the linear model and the SITAR model. For the ‘last observation carried forward’ model, the holdout data point was interpolated by the last observation by converting the random holdout value to NA and then using the function na.locf() from the zoo R package. For the simple linear model, the holdout-filtered data were used to determine the slope and intercept via R’s lm() function, which were then used to calculate the holdout value. For the SITAR model, each subject was fitted by calling the sitar() function with df=2 to minimize failures, and the RMSE of the random holdout point was subsequently calculated with the predict() function. For this analysis, set.seed(1234) was used to initialize the pseudorandom generator.

Number and percentage of live births, by month of birth, 1991 to most recent year.