NOTE: On October 19, 2021, estimates for 2016–2018 by health insurance status were revised to correct errors. Changes are highlighted and tagged at https://www.cdc.gov/nchs/data/hus/2019/012-508.pdf Data on health conditions among children under age 18, by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health Interview Survey, Family Core and Sample Child questionnaires. For more information on the National Health Interview Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 0.700 % in 2012. This records an increase from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 0.550 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.100 % in 2001. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; 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.

About this dataset

Context

The dataset tabulates the Olympia population by age. The dataset can be utilized to understand the age distribution and demographics of Olympia.

Content

The dataset constitues the following three datasets

• Olympia, WA Age Group Population Dataset: A complete breakdown of Olympia age demographics from 0 to 85 years, distributed across 18 age groups
• Olympia, WA Age Cohorts Dataset: Children, Working Adults, and Seniors in Olympia - Population and Percentage Analysis
• Olympia, WA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Child and Adult Care Food Participation plays a vital role in improving the quality of day care for children and elderly adults by making care more affordable for many low-income families. Through CACFP, nearly 3 million children and 90,000 adults receive nutritious meals and snacks each day as part of the day care they receive. The data set contains participation; meals served, and cash payments to states.

About this dataset

Context

The dataset tabulates the United States population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of United States. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 202.77 million (61% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the United States population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in United States is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the United States is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Age. You can refer the same here

This dataset of U.S. mortality trends since 1900 highlights childhood mortality rates by age group for age at death.

Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).

Age groups for childhood death rates are based on age at death.

SOURCES

CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.

2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

3. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf.

4. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Our Demographics package in the USA offers data pertaining to the households of residents of the United States of America at Census Block Level. Each data variable is available as a sum, or as a percentage of the total population within each selected area.

What is included?

At the Census Block level, this dataset includes some of the following key features:

Household Data

• Household Statistics: Number of Households and Age of Children in Households.
• Marriage Status: Married, Divorced, Never Married and Widowed.
• Household Types: Single-Person Households, Households with Children etc.
• Language Spoken in Households: English, Limited English etc.

• • Household Statistics: Marketers can use this data to understand the size and composition of households, allowing for targeted campaigns. For instance, companies offering family-oriented products can tailor promotions based on the presence and age of children in households, ensuring that their messaging resonates with the specific needs of different family structures.
  • Marriage Status: This data provides valuable insights into the marital status of individuals, enabling marketers to tailor their messaging based on relationship status. For example, businesses offering wedding-related services may focus on advertising to engaged or newly married individuals, while financial institutions may customize their promotions based on the unique financial considerations of divorced or widowed individuals.
  • Household Types: Understanding the various household types helps marketers create targeted campaigns. For example, real estate agencies may target single-person households with advertisements for apartments or studios, while companies offering family-friendly products can focus on households with children.
  • Language Spoken in Households: This data variable assists marketers in tailoring their communication strategies based on the predominant language spoken in households. For instance, businesses can create multilingual advertising content to effectively reach and engage diverse linguistic demographics, ensuring that their messages are understood and resonate with the target audience.

• This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.

  Still looking for demographic data at the postal code level? Contact sales.

• There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:

  • Commuting
  • Educational Attainment
  • Employment
  • Families
  • Finance
  • Housing

Health, United States is an annual report on trends in health statistics, find more information at http://www.cdc.gov/nchs/hus.htm.

Data on obesity among children and adolescents aged 2-19 years by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

About this dataset

Context

The dataset tabulates the House population by age. The dataset can be utilized to understand the age distribution and demographics of House.

Content

The dataset constitues the following three datasets

• House, NM Age Group Population Dataset: A complete breakdown of House age demographics from 0 to 85 years, distributed across 18 age groups
• House, NM Age Cohorts Dataset: Children, Working Adults, and Seniors in House - Population and Percentage Analysis
• House, NM Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.-9A '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.-8A '-8' means that the estimate is not applicable or not available.-6A '-6' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.-5A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate.-3A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.-2A '-2' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

The Early Head Start-Child Care Partnerships (EHS-CCP) datasets contains data from two studies: (1) the 2016 National Descriptive Study (the NDS), which collected information about the 2015 EHS-CC Partnerships grantees and their child care partners (including child care centers and family child care providers) and the activities they engaged in to develop and maintain partnerships and meet the Head Start Program Performance Standards, assess their quality improvement needs, and support high quality caregiving and learning environments for infants and toddlers; and (2) the 2022 EHS-CCP Sustainability Study (the Sustainability Study), a follow-up study of the 2016 NDS, which collected information about how partnerships from the first round of grants had fared as of 2022 and factors that supported or impeded partnership sustainability. Both studies were conducted by Mathematica. The NDS collected data through web-based surveys of grantee directors and a sample of child care directors and family child care providers. The Sustainability Study collected data using web-based surveys of EHS program directors and child care providers in sustained and dissolved partnerships. It also collected qualitative data using semi-structured interviews to provide more in-depth information from purposively selected providers in sustained and dissolved partnerships. (Both NDS and Sustainability Study analyses of EHS programs are conducted at the grant level, with information about partnerships between providers and any delegate agencies rolled up to the level of the EHS-CCP grant. The term "program" is used in the Sustainability Study. Previously, in the NDS, "grantee" was used to refer to the same level of analysis.) The responses to the survey of grantees and their delegate agencies produced three NDS datasets. The first dataset, Partnership Grantee and Delegate Agency Director Survey, contains data from a survey of Early Head Start grantees and their delegate agencies. This dataset contains questions answered by the grantee or delegate agencies about themselves and contains one observation per grantee. Datasets two and three are also associated with the grantee and delegate agency survey. The second dataset, Grantee and Delegate Agencies Partner Characteristics, contains responses to the initial survey from the grantee or delegate regarding characteristics of all of their child care partners. This dataset was used to create a random sampling of approximately 20 percent of the child care partners for additional questions as well as a separate Child Care Partner survey, which were used to create both datasets three and four. Dataset three, Grantee and Delegate Agencies Randomly Sampled Partner Characteristics, contains responses from grantee and delegate agencies regarding the partners identified by the random sampling created from dataset two. The second survey conducted by Mathematica was of these selected child care partners, and dataset four, Child Care Partner Survey, is comprised of responses to questions asked of the child care partners about themselves. Demographic information contained in these datasets includes education level, degree field, length of occupation, and occupation. The Sustainability Study examined how partnerships from the first round of grants had fared as of 2022 and factors that supported or impeded partnership sustainability. The Sustainability Study also looks at features of sustained partnerships (partnerships from the NDS that were still in place at the time of the Sustainability Study) as well as active partnerships (which include sustained partnerships as well as those that are new since the NDS, and regardless of whether they are funded through an EHS-CC Partnerships grant). There are four Sustainability Study data files. Two program director survey files (one at the program level, and one at the provider level, for information about individual child care providers reported by the program director), one child care provider survey file, and one file containing transcripts of semi-structured interviews with dissolved and sustained partnership providers. Citation Skidmore, S., Clochard, A., Carlson, B., Doran, E., Cannon, J., Bernstein, S., Albanese, S., Del Grosso, P., and Xue, Y. (2023). Early Head Start-Child Care Partnerships Sustainability Study Data Documentation. Washington, DC: Office of Planning, Res

This data represents unaccompanied children who are taken into custody by Customs and Border Protection brought to a facility and processed for transfer to the Department of Health and Human Services (HHS) as required by law. HHS holds the child for testing and quarantine, and shelters the child until the child is placed with a sponsor here in the United States.

Effective June 28, 2023, this dataset will no longer be updated. Similar data are accessible from CDC WONDER (https://wonder.cdc.gov/mcd-icd10-provisional.html).

Cumulative deaths involving COVID-19 reported to NCHS by sex and age in years, in the United States.

About this dataset

Context

The dataset tabulates the Boston population by age. The dataset can be utilized to understand the age distribution and demographics of Boston.

Content

The dataset constitues the following three datasets

• Boston, MA Age Group Population Dataset: A complete breakdown of Boston age demographics from 0 to 85 years, distributed across 18 age groups
• Boston, MA Age Cohorts Dataset: Children, Working Adults, and Seniors in Boston - Population and Percentage Analysis
• Boston, MA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The National Child Abuse and Neglect Data System (NCANDS) Child File data set consists of child-specific data of all reports of maltreatment to State child protective service agencies that received an investigation or assessment response. NCANDS is a Federally-sponsored national data collection effort created for the purpose of tracking the volume and nature of child maltreatment reporting each year within the United States. The Child File is the case-level component of the NCANDS. Child File data are collected annually through the voluntary participation of States. Participating States submit their data after going through a process in which the State's administrative system is mapped to the NCANDS data structure. Data elements include the demographics of children and their perpetrators, types of maltreatment, investigation or assessment dispositions, risk factors, and services provided as a result of the investigation or assessment.

CDC child growth charts consist of a series of percentile curves that illustrate the distribution of selected body measurements in U.S. children. Pediatric growth charts have been used by pediatricians, nurses, and parents to track the growth of infants, children, and adolescents in the United States since 1977. Growth charts are not intended to be used as a sole diagnostic instrument. Instead, growth charts are tools that contribute to forming an overall clinical impression for the child being measured.

This survey of prosecutors was undertaken to describe current practice and identify "promising practices" with respect to cases involving domestic violence and child victims or witnesses. It sought to answer the following questions: (1) What are the challenges facing prosecutors when children are exposed to domestic violence? (2) How are new laws regarding domestic violence committed in the presence of children, now operating in a small number of states, affecting practice? (3) What can prosecutors do to help battered women and their children? To gather data on these topics, the researchers conducted a national telephone survey of prosecutors. Questions asked include case assignment, jurisdiction of the prosecutor's office, caseload, protocol for coordinating cases, asking about domestic violence when investigating child abuse cases, asking about children when investigating domestic violence cases, and how the respondent found out when a child abuse case involved domestic violence or when a domestic violence case involved children. Other variables cover whether police routinely checked for prior Child Protective Services (CPS) reports, if these cases were heard by the same judge, in the same court, and were handled by the same prosecutor, if there were laws identifying exposure to domestic violence as child abuse, if there were laws applying or enhancing criminal penalties when children were exposed to domestic violence, if the state legislature was considering any such action, if prosecutors were using other avenues to enhance penalties, if there was pertinent caselaw, and if the respondent's office had a no-drop policy for domestic violence cases. Additional items focus on whether the presence of children influenced decisions to prosecute, if the office would report or prosecute a battered woman who abused her children, or failed to protect her children from abuse or from exposure to domestic violence, how often the office prosecuted such women, if there was a batterers' treatment program in the community, how often batterers were sentenced to attend the treatment program, if there were programs to which the respondent could refer battered mothers and children, what types of programs were operating, and if prosecutors had received training on domestic violence issues.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this study was to conduct content and process evaluations of current internet safety education (ISE) program materials and their use by law enforcement presenters and schools. The study was divided into four sub-projects. First, a systematic review or "meta-synthesis" was conducted to identify effective elements of prevention identified by the research across different youth problem areas such as drug abuse, sex education, smoking prevention, suicide, youth violence, and school failure. The process resulted in the development of a KEEP (Known Elements of Effective Prevention) Checklist. Second, a content analysis was conducted on four of the most well-developed and long-standing youth internet safety curricula: i-SAFE, iKeepSafe, Netsmartz, and Web Wise Kids. Third, a process evaluation was conducted to better understand how internet safety education programs are being implemented. The process evaluation was conducted via national surveys with three different groups of respondents: Internet Crimes Against Children (ICAC) Task Force commanders (N=43), ICAC Task Force presenters (N=91), and a sample of school professionals (N=139). Finally, researchers developed an internet safety education outcome survey focused on online harassment and digital citizenship. The intention for creating and piloting this survey was to provide the field with a research-based tool that can be used in future evaluation and program monitoring efforts.

About this dataset

Context

The dataset tabulates the data for the Many, LA population pyramid, which represents the Many population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Many, LA, is 26.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Many, LA, is 35.1.
• Total dependency ratio for Many, LA is 61.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Many, LA is 2.8.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Many population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Many for the selected age group is shown in the following column.
• Population (Female): The female population in the Many for the selected age group is shown in the following column.
• Total Population: The total population of the Many for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Many Population by Age. You can refer the same here