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.

The Child Care and Development Fund (CCDF) provides federal money to states and territories to provide assistance to low-income families, to obtain quality child care so they can work, attend training, or receive education. Within the broad federal parameters, states and territories set the detailed policies. Those details determine whether a particular family will or will not be eligible for subsidies, how much the family will have to pay for the care, how families apply for and retain subsidies, the maximum amounts that child care providers will be reimbursed, and the administrative procedures that providers must follow. Thus, while CCDF is a single program from the perspective of federal law, it is in practice a different program in every state and territory. The CCDF Policies Database project is a comprehensive, up-to-date database of CCDF policy information that supports the needs of a variety of audiences through (1) analytic data files, (2) a project website and search tool, and (3) an annual report (Book of Tables). These resources are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of child care subsidy policies and practices on the children and families served. A description of the data files, project website and search tool, and Book of Tables is provided below: 1. Detailed, longitudinal analytic data files provide CCDF policy information for all 50 States, the District of Columbia, and the United States Territories and outlying areas that capture the policies actually in effect at a point in time, rather than proposals or legislation. They capture changes throughout each year, allowing users to access the policies in place at any point in time between October 2009 and the most recent data release. The data are organized into 32 categories with each category of variables separated into its own dataset. The categories span five general areas of policy including: Eligibility Requirements for Families and Children (Datasets 1-5) Family Application, Terms of Authorization, and Redetermination (Datasets 6-13) Family Payments (Datasets 14-18) Policies for Providers, Including Maximum Reimbursement Rates (Datasets 19-27) Overall Administrative and Quality Information Plans (Datasets 28-32) The information in the data files is based primarily on the documents that caseworkers use as they work with families and providers (often termed "caseworker manuals"). The caseworker manuals generally provide much more detailed information on eligibility, family payments, and provider-related policies than the CCDF Plans submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between CCDF Plan dates. Each dataset contains a series of variables designed to capture the intricacies of the rules covered in the category. The variables include a mix of categorical, numeric, and text variables. Most variables have a corresponding notes field to capture additional details related to that particular variable. In addition, each category has an additional notes field to capture any information regarding the rules that is not already outlined in the category's variables. 2. The project website and search tool provide access to a point-and-click user interface. Users can select from the full set of public data to create custom tables. The website also provides access to the full range of reports and products released under the CCDF Policies Database project. The project website and search tool and the data files provide a more detailed set of information than what the Book of Tables provides, including a wider selection of variables and policies over time. 3. The annual Book of Tables provides key policy information for October 1 of each year. The report presents policy variations across the states and territories and is available on the project website. The Book of Tables summarizes a subset of the information available in the full database and data files, and includes information about eligibility requirements for families; application, redetermination, priority, and waiting list policies; family co-payments; and provider policies and reimbursement rates. In many cases, a variable in the Book of Tables will correspond to a single variable in the data files. Usuall

Since 1987, every year all primary and secondary schools in Slovenia participate in SLOfit, national surveillance system for physical and motor development of children and youth.

With the help of the SLOfit data children, youth and their parents can monitor their physical and motor development, while teachers and physicians acquire important information necessary for the planning and implementation of intervention in the cases when children experience difficulties in their physical and motor development or professional guidance when children show extraordinary abilities.

Increase the number of eligible children receiving mental health treatment from 87,500 in 2014 to 91,000 by 2018.

This administrative dataset provides descriptive information about the families and children served through the federal Child Care and Development Fund (CCDF). CCDF dollars are provided to states, territories, and tribes to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, or depending on their state's policy, to attend training or receive education. The Personal Responsibility and Work Opportunity Act of 1996 requires states and territories to collect information on all family units receiving assistance through the CCDF and to submit monthly case-level data to the Office of Child Care. States are permitted to report case-level data for the entire population, or a sample of the population, under approved sampling guidelines.

The Summary Records file contains monthly state-level summary information including the number of families served. The Family Records file contains family-level data including single parent status of the head of household, monthly co-payment amount, date on which child care assistance began, reasons for care (e.g., employment, training/education, protective services, etc.), income used to determine eligibility, source of income, and the family size on which eligibility is based. The Child Records file contains child-level data including ethnicity, race, and date of birth. The Setting Records file contains information about the type of child care setting, the total amount paid to the provider, and the total number of hours of care received by the child. The Pooling Factor file provides state-level data on the percentage of child care funds that is provided through the CCDF, the federal Head Start region the grantee (state) is in and is monitored by, and the state FIPS code for the grantee.

Units of Response: United States and Territories, CCDF Family Recipients, CCDF Children Recipients

Type of Data: Administrative

Tribal Data: No

Periodicity: Annual

Demographic Indicators: Ethnicity;Household Income;Household Size;Race

SORN: Not Applicable

Data Use Agreement: Not Applicable

Data Use Agreement Location: https://www.icpsr.umich.edu/rpxlogin

Granularity: Family;Individual

Spatial: United States

Geocoding: Tribe

Users can view maps and tables related to child welfare policies in the United States. Background The State Child Welfare Policy Database contains a variety of information related to child welfare policies in each state. Data topics are grouped under three categories: child welfare financing; kinship care policies; older youth in foster care. Child welfare financing provides data on topics such as total expenditures, TANF, Title IV, and medicaid. Kinship care policies includes information on locating kin, guardianship policies, foster care and private kin arrangement s. Older youth in foster care includes information on foster care age limits, placements for older youth, and state-funded independent living transition services. User FunctionalityUsers can search by topic or by state. Data is presented in either a table (for state specific information) or by map (for data topic information). Data is available on a state level. Data tables are available for download in Excel format. Data Notes The data source is clearly labeled, and a link to the data source or to the state's welfare website is provided.

The Monthly Child Care Services Data Report - Families Served by ZIP Code data set includes demographic data of parents and families of children receiving Child Care and Development Fund (CCDF) assistance. The Administration for Children and Families (ACF) Office of Child Care (OCC) collects data regarding the children and families served through the Child Care and Development Fund (CCDF) as well as the types of child care settings and facilities providing services. Each quarterly data set contains data aggregated by ZIP code for each month of the quarter. Counts less than 5 are masked with an asterisk (*) to protect the confidentiality of individuals in this report.

The CCDF Policies Database is a source of information on the detailed policies used to operate child care subsidy programs under the Child Care and Development Fund (CCDF). Since 2008, the CCDF Policies Database has collected, coded, and disseminated the CCDF policies in effect across the 50 states, the District of Columbia, and U.S. territories and outlying areas, using consistent methods across places and over time. The information in the CCDF Policies Database is based primarily on the documents that caseworkers use as they work with families and providers, as well as the CCDF Plans and amendments submitted by States/Territories to ACF, state law, and regulations used by the staff operating the program. The Database captures detailed information on eligibility, family payments, application procedures, and provider-related policies, including dates of enactment and some of the policy variations that exist within states/territories. The database is updated and released annually and can be accessed via the Child and Family Data Archive and the project website. Users are encouraged to use the most recent release for the most accurate policy records.

Units of Response: State, Territory

Type of Data: Administrative

Tribal Data: No

Periodicity: Annual

Demographic Indicators: Disability;Geographic Areas;Household Income;Household Size;Housing Status

SORN: Not Applicable

Data Use Agreement: No

Data Use Agreement Location: Not Applicable

Granularity: County;State;US Territory; Region

Spatial: States

Geocoding: County;State;FIPS Code;Region;US Territory

Data for School year-end 1994 through year-end 2020. State oral health surveys are the data sources for these indicators. States periodically conduct independent screening surveys of a probability sample designed to be representative of all third-grade students in the state. Some states also conduct surveys of students in other grades in school, or of Head Start program enrollees. This surveillance activity is voluntary. States submit their data to the Association of State and Territorial Dental Directors (ASTDD), where the survey design and data collected are reviewed for quality and against the criteria for inclusion in NOHSS, before being sent to CDC for inclusion in Oral Health Data. For more information, see: http://www.cdc.gov/oralhealthdata/overview/childIndicators/

Comprehensive dataset of 164 Children's furniture stores in Washington, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Child Health System in Wales; includes birth registration and monitoring of child health examinations and immunisations.

The Performance Dashboard (formerly Performance Outcomes System) datasets are developed in accordance with legislative mandates to improve outcomes and inform decision-making for beneficiaries receiving Medi-Cal Specialty Mental Health Services (SMHS). The intent of the Dashboard is to gather information relevant to particular mental health outcomes to provide useful summary reports for ongoing quality improvement and to support decision-making. Please note: the Excel file Performance Dashboard has been discontinued and replaced with the SMHS Performance Dashboards found on Behavioral Health Reporting (ca.gov).

The study aimed to evaluate quality of diet and associated factors in schoolchildren from first to fifth grade in a public school in Itajaí, Santa Catarina. The quality of the diet was analyzed using an index that gave to each food item a score as their frequency of consumption. Socioeconomic, demographic, and health-related behaviors were identified by questionnaire. Crude, adjusted prevalence ratios (PR), and theirs 95% confidence intervals were calculated (95%CI) through Poisson regression. Were evaluated 523 children (88.9% of the calculated sample) and 52.6% (95%CI: 48.3-56.9%) had their diet rated as poor, with a mean score of 6.97 ranging from -9 to +17 points. Female gender (PR=0.81, 95%CI: 0.66-0.98) and lower education (PR=1.35, 95%CI: 1.04-1.75) were associated with the outcome. Children with abdominal obesity and who took meals in front of the TV had poorer diet quality, it is known that food consumption in front of television is associated with larger volumes of intake. Children and caregivers variables were related with worse diet quality. Thus, activities to improve the quality of the diet should involve children and their caregivers.

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.

This map symbolizes the relative counts of the youth population (total individuals age 0 - 17) for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2018 five-year samples. Please refer to the map's legend for context to the color shading -- darker hues indicate more youth population.If you click on each Data Division, you can view other Census demographic information about that Data Division in addition to the population count.About the Census Data:The data comes from the U.S. Census Bureau's American Community Survey's 2014-2018 five-year samples. The American Community Survey (ACS) is an ongoing survey conducted by the federal government that provides vital information annually about America and its population. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.For more information about the Census Bureau's ACS data and process of constructing the survey, visit the ACS's About page.About the City's Data Divisions:As a planning analytic tool, an interdepartmental working group divided Rochester into 12 “data divisions.” These divisions are well-defined and static so they are positioned to be used by the City of Rochester for statistical and planning purposes. Census data is tied to these divisions and serves as the basis for analyses over time. As such, the data divisions are designed to follow census boundaries, while also recognizing natural and human-made boundaries, such as the River, rail lines, and highways. Historical neighborhood boundaries, while informative in the division process, did not drive the boundaries. Data divisions are distinct from the numerous neighborhoods in Rochester. Neighborhood boundaries, like quadrant boundaries, police precincts, and legislative districts often change, which makes statistical analysis challenging when looking at data over time. The data division boundaries, however, are intended to remain unchanged. It is hoped that over time, all City data analysts will adopt the data divisions for the purpose of measuring change over time throughout the city.

This layer shows children by age group by parents' labor force participation. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent of children with no available (residential) parent in the labor force. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B23008 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 1.900 % in 2015. This records an increase from the previous number of 1.000 % for 2010. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 2.100 % from Dec 1987 (Median) to 2015, with 6 observations. The data reached an all-time high of 4.700 % in 2006 and a record low of 0.500 % in 1987. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new 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; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

United States US: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 0.500 % in 2012. This records a decrease from the previous number of 0.800 % for 2009. United States US: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 0.900 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 1.100 % in 2005 and a record low of 0.500 % in 2012. United States US: 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 USA – Table US.World Bank: 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.

This dataset is part of the local revitalization database project data.