ANC

This dataset includes data on weight status for children aged 3 months to 4 years old from Women, Infant, and Children Participant and Program Characteristics (WIC-PC). This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about WIC-PC visit https://www.fns.usda.gov/wic/national-survey-wic-participants.

This dataset supports a publication on the determinants of infant growth in a birth cohort in the Nepal plains.This study aimed to identify the determinants of infant growth in terms of length-for-age z-score (LAZ) in a birth cohort (n=602) in the plains of Nepal. Children were enrolled within 72 hours of birth and followed-up every 28 days until they were 2 years. We fitted mixed-effects linear regression models controlling for multiple measurements within individuals to examine the impact of household and maternal factors, feeding practices and infection on infant LAZ. We conducted separate analyses for the age periods 0-6 months (exclusive breastfeeding period) and 7-24 months (complementary feeding period) to check whether the importance of determinants differed by child age.The data are useful to those seeking to understand the factors associated with longitudinal changes in nutritional status in children from birth to 2 years in the plains of Nepal.

The WIC Infant and Toddler Feeding Practices Study–2 (WIC ITFPS-2) (also known as the “Feeding My Baby Study”) is a national, longitudinal study that captures data on caregivers and their children who participated in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) around the time of the child’s birth. The study addresses a series of research questions regarding feeding practices, the effect of WIC services on those practices, and the health and nutrition outcomes of children on WIC. Additionally, the study assesses changes in behaviors and trends that may have occurred over the past 20 years by comparing findings to the WIC Infant Feeding Practices Study–1 (WIC IFPS-1), the last major study of the diets of infants on WIC. This longitudinal cohort study has generated a series of reports. These datasets include data from caregivers and their children during the prenatal period and during the children’s first five years of life (child ages 1 to 60 months). A full description of the study design and data collection methods can be found in Chapter 1 of the Second Year Report (https://www.fns.usda.gov/wic/wic-infant-and-toddler-feeding-practices-st...). A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Processing methods and equipment used Data in this dataset were primarily collected via telephone interview with caregivers. Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. Study date(s) and duration Data collection occurred between 2013 and 2019. Study spatial scale (size of replicates and spatial scale of study area) Respondents were primarily the caregivers of children who received WIC services around the time of the child’s birth. Data were collected from 80 WIC sites across 27 State agencies. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) This dataset includes sampling weights that can be applied to produce national estimates. A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Level of subsampling (number and repeat or within-replicate sampling) A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Study design (before–after, control–impacts, time series, before–after-control–impacts) Longitudinal cohort study. Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains caregiver-level responses to telephone interviews. Also available in the dataset are children’s length/height and weight data, which were objectively collected while at the WIC clinic or during visits with healthcare providers. In addition, the file contains derived variables used for analytic purposes. The file also includes weights created to produce national estimates. The dataset does not include any personally-identifiable information for the study children and/or for individuals who completed the telephone interviews. Description of any gaps in the data or other limiting factors Please refer to the series of annual WIC ITFPS-2 reports (https://www.fns.usda.gov/wic/infant-and-toddler-feeding-practices-study-2-fourth-year-report) for detailed explanations of the study’s limitations. Outcome measurement methods and equipment used The majority of outcomes were measured via telephone interviews with children’s caregivers. Dietary intake was assessed using the USDA Automated Multiple Pass Method (https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-h...). Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. Resources in this dataset:Resource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data CSV. File Name: itfps2_enrollto60m_publicuse.csvResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data Codebook. File Name: ITFPS2_EnrollTo60m_PUF_Codebook.pdfResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data SAS SPSS STATA R Data. File Name: ITFP@_Year5_Enroll60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data CSV. File Name: ampm_1to60_ana_publicuse.csvResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Tot to 60 Months Public Use Data Codebook. File Name: AMPM_1to60_Tot Codebook.pdfResource Description: ITFP2 Year 5 Tot to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data Codebook. File Name: AMPM_1to60_Ana Codebook.pdfResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data SAS SPSS STATA R Data. File Name: ITFP@_Year5_Ana_60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Tot to 60 Months Public Use Data CSV. File Name: ampm_1to60_tot_publicuse.csvResource Description: ITFP2 Year 5 Tot to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Tot to 60 Months Public Use SAS SPSS STATA R Data. File Name: ITFP@_Year5_Tot_60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Tot to 60 Months Public Use SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use Data CSV. File Name: ampm_foodgroup_1to60m_publicuse.csvResource Description: ITFP2 Year 5 Food Group to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use Data Codebook. File Name: AMPM_FoodGroup_1to60m_Codebook.pdfResource Description: ITFP2 Year 5 Food Group to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use SAS SPSS STATA R Data. File Name: ITFP@_Year5_Foodgroup_60_SAS_SPSS_STATA_R.zipResource Title: WIC Infant and Toddler Feeding Practices Study-2 Data File Training Manual. File Name: WIC_ITFPS-2_DataFileTrainingManual.pdf

Notes: NW- normal weight (BMI85), OW-overweight (P85≤BMI95), OB- obese (BMI≥P95),.

BackgroundPremature delivery interrupts the natural growth of the fetus. The postnatal healthy management of preterm infants still follows term standards after a postmenstrual age (PMA) of 40 weeks and there is a lack of research on the longitudinal dynamic postnatal growth tracks of preterm infants.MethodsBased on the database established by the Wuhan University Internet+ Early Childhood Development Alliance in China, information on preterm infants, including birth registration and health follow-ups from 2016 to 2022, was incorporated into the health management system. Standardized anthropometric measurements of preterm infants were recorded from birth to a corrected age (CA) of 36 months. A generalized additive model based on location, scale, and shape was used to establish the percentile values and growth curves.ResultsIn total, 79,514 preterm infants were included in this study, and the birth weights at each gestational age (GA) were similar to Chinese standards. When evaluated by term birth weight, we found that the proportions of extrauterine growth retardation at a PMA of 40 weeks were all above 10% in the GA ≤34-week groups and reached between 17.19% and 55.56% in very preterm infants (VPIs). There was a high incidence of preterm infants with a weight below the third percentile in VPIs when referring to term standards at CAs of 0, 6, 12, 24, and 36 months (p 

The research presents a hospital-based age-matched case-control study conducted at Paropakar Maternity and Women’s Hospital, Kathmandu, Nepal, to identify key determinants of low birth weight (LBW) among newborns. A total of 171 mothers (57 cases and 114 controls) were included. Data were collected through structured interviews and medical record reviews.

Percentage of Births with Low Birthweight.

1) Live births with a recorded birth weight under 2500g and a gestational age of at least 37 complete weeks as a percentage of all live births with recorded birth weight and a gestational age of at least 37 complete weeks since 2005. Births without a recorded birth weight are excluded.

2) Percentage of live and still births occurring in the respective calendar year with birthweights under 2500 grams since 2004. This indicator includes all births.

3) Percentage of live births with birthweights under 2500 grams since 1999.The percentage of low birthweight live births is calculated as the number of low birthweight live births divided by the total number of live births where birthweight is stated, and expressed as a percentage. This indicator is no longer being updated.

Allocation to geographical area is based on mother’s place of usual residence.

An association has also been shown between low birth weight and adverse health in later childhood and adulthood.

Low birthweight is an enduring aspect of childhood morbidity, a major factor in infant mortality and has serious consequences for health in later life (NICE). There are social inequalities in low birthweight in England and Wales and these inequalities are likely to affect childhood and adult health inequalities in the future, hence strategies will need to address differences in low birthweight and further monitoring of trends is therefore desirable (Moser K, Li L, and Power C, Social inequalities in low birthweight in England and Wales: trends and implications for future population health, Journal of Epidemiology and Community Health 2003).

Births for the City of London have been included with those for Hackney.

Data for term babies from the PHOF, data for all babies from NHS IC.

Map image here.

Egypt EG: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 7.000 % in 2014. This records an increase from the previous number of 6.800 % for 2008. Egypt EG: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 9.050 % from Dec 1988 (Median) to 2014, with 10 observations. The data reached an all-time high of 10.800 % in 1995 and a record low of 5.400 % in 2005. Egypt EG: Prevalence of Underweight: Weight for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Health Statistics. Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

The Mortality - Infant Deaths (from Linked Birth / Infant Death Records) online databases on CDC WONDER provide counts and rates for deaths of children under 1 year of age, occuring within the United States to U.S. residents. Information from death certificates has been linked to corresponding birth certificates. Data are available by county of mother's residence, child's age, underlying cause of death, sex, birth weight, birth plurality, birth order, gestational age at birth, period of prenatal care, maternal race and ethnicity, maternal age, maternal education and marital status. Data are available since 1995. The data are produced by the National Center for Health Statistics.

Background: The femoral vein is an important site for central venous access in newborns and infants. The objectives of this study are to determine whether age or weight can be used clinically to predict the size of the femoral vein in newborns and infants, and to compare the size of the vein in each individual in both the supine and reverse Trendelenburg positions. Results: Analysis was done in 24 euvolemic individuals, each studied in both the supine and reverse Trendelenburg positions. Twelve of these individuals were newborns and 12 were infants. We used two-factor analysis of variance to explore differences between groups and multiple linear regression analysis to estimate the strength of the relationship between variables. In the infant group, there was a correlation between femoral vein diameter and weight. There was no correlation between weight and vessel size in newborns. In both the newborn and infant groups, vessel diameter increased with subjects in the reverse Trendelenburg position (P < 0.01). Conclusion: Weight is predictive of femoral vein diameter in infants, but not in newborns. In infants, weight might serve as a more sensitive index for estimating size of the femoral vein in order to determine accurately the size of intravascular catheter appropriate for cannulation. The diameter of the femoral vein increases in the reverse Trendelenburg position compared with that in the supine position in both newborns and infants. A large prospective study is required to validate these findings.

The GI Baby 3 database contains numerical data and supplementary information relating to a randomised controlled trial study that measures the influence of maternal diet on the the birthweight of the infant. The study aims to identify risk factors associated with gestational diabetes, high birth weight and child obesity.

Data was collected at the Royal Prince Alfred (RPA) Hospital in Sydney, NSW, from a randomised sample of 120 women, recruited between 12 and 18 weeks pregnancy. The women were put on two diets, and data was collected at four-weekly intervals until the baby was born. At birth, data relating to the delivery method, gestational age, birth weight and body fat percentage of the infant was collected.

The resulting dataset consists of tables of information in spreadsheet (MS Excel) format, including clinical and demographic data about each participant, including height, weight, age, ethnicity, dietary factors, and numerical data from survey responses. The dataset also contains clinical data about each infant, including the results of glucose tolerance tests, cord blood glucose tests, delivery methods, gestational age, birth weight and body fat percentage of the infant. The dataset is delivered via a custom Filemaker Database, which is used to manage the spreadsheets and associated metadata, and track appointments and tests undertaken by each of the participants throughout the project.

The project is expected to be completed in 2016. Once the project is complete, the dataset will be de-identified, and may be able to be shared, subject to the appropriate level of human ethics clearance.

Project contributors include Professor Jennie Brand-Miller, Associate Professor Gareth Denyer, Dawn Tan, Dr Tania Markovik, Dr Glynis Ross, Dr Jonathan Hyett, Dr Ros Muirhead, Research Dietitian Shannon Overs, and PhD candiate Nathalie Kizirian.

This dataset presents the percentage of full-term babies (born at 37 weeks gestation or later) who had a low birth weight, defined as less than 2,500 grams. It provides a key indicator of neonatal health and maternal wellbeing, based on data collected by the Office for National Statistics (ONS) through birth registrations in England and Wales. The measure helps identify trends and disparities in birth outcomes across different populations and regions.

Rationale Low birth weight in full-term babies is associated with increased risks of infant morbidity and mortality, as well as long-term developmental challenges. Reducing the proportion of low birth weight births is a public health priority, as it reflects improvements in maternal health, antenatal care, and broader social determinants of health. This indicator supports efforts to monitor and address inequalities in early life outcomes.

Numerator The numerator is the number of live births at term (37 weeks gestation or more) with a recorded birth weight of less than 2,500 grams. This data is sourced from the Office for National Statistics (ONS) through the Births Characteristics in England and Wales dataset.

Denominator The denominator is the total number of live births at term (37 weeks gestation or more) with a recorded birth weight. This is also derived from the ONS Births Characteristics dataset.

Caveats While data linkage is highly successful nationally (99.4%), not all birth records include valid information on both birth weight and gestational age. This may affect the completeness and accuracy of the indicator in some areas.

External References Public Health England - Fingertips Tool

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Results listed for maternal age of 26, gestational age 38 weeks, gestational weight gain 11.25 kg, averaged over effects for dichotomous factors.

ObjectiveTo test whether the assessment of growth in very low birth weight infants during the hospital stay using z-score differences (Zdiff) is confounded by gestational age (GA), birth weight percentiles (BW%ile), and length of the observation period (LOP). We hypothesize that Zdiff calculated from growth charts based on birth weight data introduces a systematic statistical error leading to falsely classified growth as restricted in infants growing similarly to the 50th percentile.MethodsThis observational study included 6,926 VLBW infants from the German Neonatal Network (2009 to 2015). Inclusion criterion was discharge between 37 and 41 weeks postmenstrual age. For each infant, Zdiff, weight gain velocity, and reference growth rate (50th percentile Fenton) from birth to discharge were calculated. To account for gestational age dependent growth rates, assessment of growth was standardized calculating the weight gain ratio (WGR) = weight gain velocity/reference growth rate. The primary outcome is the variation of the Zdiff-to-WGR relationship.ResultsZdiff and WGR showed a weak agreement with a Zdiff of -0.74 (-1.03, -0.37) at the reference growth rate of the 50th percentile (WGR = 1). A significant proportion (n = 1,585; 23%) of infants with negative Zdiff had weight gain velocity above the 50th percentile’s growth rate. Zdiff to WGR relation was significantly affected by the interaction of GA x BW%ile x LOP.ConclusionThis study supports the hypothesis that Zdiff, which are calculated using birth weights, are confounded by skewed reference data and can lead to misinterpretation of growth rates. New concepts like individualized growth trajectories may have the potential to overcome this limitation.

Replication data and codes of "Maternal Education and Infants’ Health". This paper explores the effect of mother's education on birth outcomes using the Minimum Dropout Age policies as instrument.

Results listed for maternal age of 26, gestational age 38 weeks, prepregnancy BMI 30, averaged over effects for dichotomous factors.

ABSTRACT Objective To calculate the frequency and evaluate the factors associated with low birth weight. Methods A retrospective study, with data from pregnant women who participated in the Programa de Atenção às Gestantes do Programa Einstein na Comunidade de Paraisópolis, between 2011 and 2014, and who returned for the postpartum evaluation of their newborns. Variables related to the pregnant woman, pregnancy, and newborn were evaluated. The outcome variable was low birth weight, defined as <2.5kg. The associations between the independent variables and low birth weight were assessed by χ2 and Mann-Whitney tests. Logistic regression models analyzed the combined effects of the independent variables on low birth weight. Results Data of 794 pregnant women and their newborns (52.1% males) were analyzed. The age of pregnant women varied from 13 to 44 years (median of 24 years), and the majority reported being married or living in cohabitation (74.7%), and having between 9 to 11 years of schooling (53.4%). The proportion of low birth weight was 7.6% (newborn mean weight of 3.2kg) and, in multivariate analysis, presence of twinning, age group of the pregnant women (showing protection for low birth weight between ages ≥18 years and <35 years), and cesarean section were associated with low birth weight. Conclusion The proportion of low birth weight was 7.6% and twining, age of the pregnant woman, and cesarean delivery were associated with the occurrence of low birth weight.

The All Our Babies/Families (AOB/F) study is a prospective community-based pregnancy cohort that follows maternal-infant pairs across the early life course to describes the mental health and psychosocial characteristics of mothers living in Calgary and area, as well as estimating rates of low birth weight, small for gestational age and preterm births. This study is intended to help understand women's prenatal care experiences in Calgary and identify barriers and facilitators to accessing care. AOB/F data covers subject areas including: a) Maternal Prenatal Health (e.g., prenatal care, maternal gestation); b) Pregnancy History (e.g., previous pregnancies, previous preterm birth); c) Pre-pregnancy (e.g., pregnancy planning advice received, contraception use, pregnancy intention); d) Health Care Services (e.g., utilization of services for mother and baby); e) Food, Exercise and Housing (e.g., nutrition, housing, neighbourhood safety and cohesion); f) Lifestyle (e.g., T-ACE Screen for alcohol consumption risk, smoking, drugs); g) Social Support (Medical Outcomes Study Social Support Scale, partner/relationship satisfaction); h) Mental Health (e.g., Edinburgh Postnatal Depression Scale, Spielberger State Anxiety Scale, Perceived Stress Scale, optimism [Life Orientation Te st-Revised]); i) Life Events (e.g., previous mental health, abuse); j) Sociodemographic Outcomes (e.g., age, marital status, education, ethnicity, work status); k) Birth Outcomes - maternal and delivery (e.g. site of birth, delivery type, medication, delivery support); l) Birth Outcomes -infant (e.g., sex, birth weight, gestational age); m) Baby Health/Development (e.g., doctor, check-up, vaccinations); n) Parenting/ Postpartum Experiences (e.g. Parenting Morale Index, breastfeeding [Montreal Children's Hospital Feeding Scale], community service utilization) Searchable metadata can also be found on the SAGE metadata website: http://sagemetadata.policywise.com/nada/index.php/catalog/1

Bangladesh BD: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 21.700 % in 2022. This records a decrease from the previous number of 22.600 % for 2019. Bangladesh BD: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 42.400 % from Dec 1986 (Median) to 2022, with 27 observations. The data reached an all-time high of 66.800 % in 1986 and a record low of 21.700 % in 2022. Bangladesh BD: Prevalence of Underweight: Weight for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;Linear mixed-effect model estimates;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.