Between 2015 and 2021, regardless of their age, the share of children owning a smartphone in the United States grew. During the 2021 survey, it was found that 31 percent of responding 8-year-olds owned a smartphone, up from only 11 percent in 2015.

The goal of this study was to test specific hypotheses illustrating the relationships among serious victimization experiences, the mental health effects of victimization, substance abuse/use, and delinquent behavior in adolescents. The study assessed familial and nonfamilial types of violence. It was designed as a telephone survey of American youth aged 12-17 living in United States households and residing with a parent or guardian. One parent or guardian in each household was interviewed briefly to establish rapport, secure permission to interview the targeted adolescent, and to ensure the collection of comparative data to examine potential nonresponse bias from households without adolescent participation. All interviews with both parents and adolescents were conducted using Computer-Assisted Telephone Interviewing (CATI) technology. From the surveys of parents and adolescents, the principal investigators created one data file by attaching the data from the parents to the records of their respective adolescents. Adolescents were asked whether violence and drug abuse were problems in their schools and communities and what types of violence they had personally witnessed. They were also asked about other stressful events in their lives, such as the loss of a family member, divorce, unemployment, moving to a new home or school, serious illness or injury, and natural disaster. Questions regarding history of sexual assault, physical assault, and harsh physical discipline elicited a description of the event and perpetrator, extent of injuries, age at abuse, whether alcohol or drugs were involved, and who was informed of the incident. Information was also gathered on the delinquent behavior of respondents and their friends, including destruction of property, assault, theft, sexual assault, and gang activity. Other questions covered history of personal and family substance use and mental health indicators, such as major depression, post-traumatic stress disorders, weight changes, sleeping disorders, and problems concentrating. Demographic information was gathered from the adolescents on age, race, gender, number of people living in household, and grade in school. Parents were asked whether they were concerned about violent crime, affordable child care, drug abuse, educational quality, gangs, and the safety of their children at school. In addition, they were questioned about their own victimization experiences and whether they discussed personal safety issues with their children. Parents also supplied demographic information on gender, marital status, number of children, employment status, education, race, and income.

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.

The dataset contains Academic-year-, Programme- and Course-wise historically compiled data on the Number of International Students who have enrolled in the United States of America (U.S.A) for pursuing various types of Graduate, Under-graduate and OPT courses, along with percentage number of students pursing on each specific type of education

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

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

USER NOTE: This database no longer contains the most up-to-date information. Some errors and missing data from the previous years have been fixed in the most recent data release in the CCDF Policies Database Series. The most recent release is a cumulative file which includes the most accurate version of this and all past years' data. Please do not use this study's data unless you are attempting to replicate the analysis of someone who specifically used this version of the CCDF Policies Database. For any other type of analysis, please use the most recent release in the CCDF Policies Database Series.

The Child Care and Development Fund (CCDF) provides federal money 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, 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 inter-related sources of CCDF policy information that support the needs of a variety of audiences through (1) Analytic Data Files and (2) a Book of Tables. These are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of alternative child care subsidy policies and practices on the children and families served, specifically parental employment and self-sufficiency, the availability and quality of care, and children's development. A description of the Data Files and Book of Tables is provided below:

1. Detailed, longitudinal Analytic Data Files of CCDF policy information for all 50 States, the District of Columbia, and United States Territories that capture the policies actually in effect at a point in time, rather than proposals or legislation. They focus on the policies in place at the start of each fiscal year, but also capture changes during that fiscal year. 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 documents submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between submission 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. Every variable has 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 Book of Tables is available as four datasets (Datasets 33-37) and they present key aspects of the differences in CCDF funded programs across all states, territories, and tribes as of October 1, 2013. The Book of Tables includes variables that are calculated us

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black). Later vintages of this layer have a different age group for children that includes age 18. This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National 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 has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

Background: In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis.FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2020 Participant and Program Characteristics (PC2020) is the 17th to be completed using the prototype PC reporting system. In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Processing methods and equipment used: Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2020. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency. Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2020.Study spatial scale (size of replicates and spatial scale of study area): In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Level of true replication: UnknownSampling precision (within-replicate sampling or pseudoreplication):State Agency Data Submissions. PC2020 is a participant dataset consisting of 7,036,867 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data.PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 7 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public.National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The national sample consists of 1 percent of the total number of participants, or 70,368 records. The distribution by category is 5,469 pregnant women, 6,131 breastfeeding women, 4,373 postpartum women, 16,817 infants, and 37,578 children.Level of subsampling (number and repeat or within-replicate sampling): The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample.Study design (before–after, control–impacts, time series, before–after-control–impacts): None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertaken: Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers.Description of any gaps in the data or other limiting factors: All State agencies provided data on a census of their WIC participants.Resources in this dataset:Resource Title: WIC PC 2020 National Sample File Public Use Codebook.; File Name: PC2020 National Sample File Public Use Codebook.docx; Resource Description: WIC PC 2020 National Sample File Public Use CodebookResource Title: WIC PC 2020 Public Use CSV Data.; File Name: wicpc2020_public_use.csv; Resource Description: WIC PC 2020 Public Use CSV DataResource Title: WIC PC 2020 Data Set SAS, R, SPSS, Stata.; File Name: PC2020 Ag Data Commons.zipResource; Description: WIC PC 2020 Data Set SAS, R, SPSS, Stata One dataset in multiple formats

This web map provides and in-depth look at school districts within the United States. Clicking on a school district in the map will reveal different statistics about each district in the pop-up. The statistics presented in this map are approximations based on summarizing American Community Survey(ACS) data using tract centroids. They may differ from published statistics by school districts found on data.census.gov. A few things you will learn from this map:How many public and private schools fall within a district?Socioeconomic factors about the Census Tracts which fall within the district:School enrollment for grades Kindergarten through 12thDisconnected children in the districtChildren living below the poverty level Children with no internet at home Children without a working parentRace/ethnicity breakdown of population under the age of 19 in the districtFor more information about the data sources:This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases estimates, so values in the map always reflect the newest data available.Current School Districts Layer:The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are single-purpose administrative units designed by state and local officials to organize and provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to support educational research and program administration, and the boundaries are essential for constructing district-level estimates of the number of children in poverty.The Census Bureau’s School District Boundary Review program (SDRP) (https://www.census.gov/programs-surveys/sdrp.html) obtains the boundaries, names, and grade ranges from state officials, and integrates these updates into Census TIGER. Census TIGER boundaries include legal maritime buffers for coastal areas by default, but the NCES composite file removes these buffers to facilitate broader use and cleaner cartographic representation. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop the composite school district files. The inputs for this data layer were developed from Census TIGER/Line and represent the most current boundaries available. For more information about NCES school district boundary data, see https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries.Public Schools Layer:This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.Private Schools Layer:This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.Web Map originally owned by Summers Cleary

United States US: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data was reported at 3.000 % in 2012. This records a decrease from the previous number of 3.200 % for 2009. United States US: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data is updated yearly, averaging 3.600 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 4.500 % in 2002 and a record low of 3.000 % in 2012. United States US: Prevalence of Stunting: Height for Age: Male: % 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 stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; 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.

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.

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.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

The Child Care and Development Fund (CCDF) provides federal money to States and Territories to provide assistance to low-income families receiving or in transition from temporary public assistance, 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 inter-related sources of CCDF policy information that support the needs of a variety of audiences through (1) Analytic Data Files and (2) a Book of Tables. These are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of alternative child care subsidy policies and practices on the children and families served, specifically parental employment and self-sufficiency, the availability and quality of care, and children's development. A description of the Data Files and Book of Tables is provided below: 1. Detailed, longitudinal Analytic Data Files of CCDF policy information for all 50 States, the District of Columbia, and United States Territories that capture the policies actually in effect at a point in time, rather than proposals or legislation. They focus on the policies in place at the start of each fiscal year, but also capture changes during that fiscal year. 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 documents submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between submission 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. Every variable has 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 Book of Tables is available as eight datasets (Datasets 33-40) and they present key aspects of the differences in CCDF funded programs across all states and territories as of October 1, 2016. The Book of Tables includes variables that are calculated using several variables from the Data Files (Datasets 1-32). The Book of Tables summarizes a subset of the information available in the Data Files, and includes information about eligibility requirements for families; application, redetermination, priority, and waiting list policies; family co-payments; provider policies and reimbursement rates; and select administration and quality development information. In many cases, a variable in the Book of Tables will correspond to a single variable in the Data File. Usually, the variable options used in the Book of Tables will match the variable options in the Data File. In some cases, the wording of the variable options may have been slightly modified for the tables. The Data Files provide a more detailed set of information than what the Book of Tables provide, including a wider s

This asset includes environmental justice-related analyses of population located within a mile of Superfund and RCRA Corrective Action sites. It characterizes demographics and socio-economic characteristics of near-site communities as compared to the average U.S. population. It also examined children of up to 17 years of age living within 1 mile of SF and RCRA CA sites where human health protective measures may not have been in place. It compared data on the health status of these children to the status of all children in the U.S. Information from this study contributed to the America's Children and the Environment (ACE) report for 2013.

Abstract copyright UK Data Service and data collection copyright owner.The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP. The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage. The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage. Safe Room Access FRS data In addition to the standard End User Licence (EUL) version, Safe Room access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 7196, where the extra contents are listed. The Safe Room version also includes secure access versions of the Households Below Average Income (HBAI) and Pensioners' Incomes (PI) datasets. The Safe Room access data are currently only available to UK HE/FE applicants and for access at the UK Data Archive's Safe Room at the University of Essex, Colchester. Prospective users of the Safe Room access version of the FRS/HBAI/PI will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.FRS, HBAI and PIThe FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503 respectively. The secure access versions are held within the Safe Room FRS study under SN 7196 (see above). The FRS aims to: support the monitoring of the social security programme; support the costing and modelling of changes to national insurance contributions and social security benefits; provide better information for the forecasting of benefit expenditure. From April 2002, the FRS was extended to include Northern Ireland. Detailed information regarding anonymisation within the FRS can be found in the anonymised variables volume of the dataset documentation. For the second edition (October 2014) the data have been re-grossed following revision of the FRS grossing methodology to take account of the 2011 Census mid-year population estimates. New variable GROSS4 has been added to the dataset. Main Topics: Household characteristics (composition, tenure type); tenure and housing costs including Council Tax, mortgages, insurance, water and sewage rates; school milk and meals; educational grants and loans; children in education; informal care (given and received); childcare; occupation and employment; health restrictions on work; children's health; wage details; self-employed earnings; personal and occupational pension schemes; income and benefit receipt; income from pensions and trusts, royalties and allowances, maintenance and other sources; income tax payments and refunds; National Insurance contributions; earnings from odd jobs; children's earnings; interest and dividends; investments; National Savings products; assets. Standard Measures Standard Occupational Classification Multi-stage stratified random sample Face-to-face interview Computer Assisted Personal Interviewing 2007 2008 ABSENTEEISM ACADEMIC ACHIEVEMENT ADMINISTRATIVE AREAS AGE APARTMENTS APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BANK ACCOUNTS BEDROOMS BONDS BUILDING SOCIETY AC... BUSES BUSINESS RECORDS CARE OF DEPENDANTS CARE OF THE DISABLED CARE OF THE ELDERLY CHARITABLE ORGANIZA... CHILD BENEFITS CHILD CARE CHILD DAY CARE CHILD MINDERS CHILD MINDING CHILD SUPPORT PAYMENTS CHILD WORKERS CHILDREN CHRONIC ILLNESS CIVIL PARTNERSHIPS COHABITATION COLOUR TELEVISION R... COMMERCIAL BUILDINGS COMMUTING CONCESSIONARY TELEV... CONSUMPTION COST OF LIVING COSTS COUNCIL TAX CREDIT UNIONS Consumption and con... DAY NURSERIES DEBILITATIVE ILLNESS DEBTS DISABILITIES DISABILITY DISCRIMI... DISABLED CHILDREN DISABLED PERSONS DOMESTIC RESPONSIBI... ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATION EDUCATIONAL BACKGROUND EDUCATIONAL FEES EDUCATIONAL GRANTS EDUCATIONAL INSTITU... EDUCATIONAL VOUCHERS ELDERLY EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ETHNIC GROUPS EXPENDITURE EXTRACURRICULAR ACT... FAMILIES FAMILY MEMBERS FINANCIAL DIFFICULTIES FINANCIAL INSTITUTIONS FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD FREE SCHOOL MEALS FRIENDS FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION Family life and mar... GENDER GIFTS GRANDPARENTS GRANTS HEADS OF HOUSEHOLD HEALTH HEALTH SERVICES HEARING IMPAIRED PE... HEARING IMPAIRMENTS HIGHER EDUCATION HOLIDAY LEAVE HOME BASED WORK HOME OWNERSHIP HOME SHARING HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLD HEAD S OC... HOUSEHOLD INCOME HOUSEHOLDS HOUSING HOUSING FACILITIES HOUSING FINANCE HOUSING TENURE INCOME INCOME TAX INDUSTRIES INSURANCE INSURANCE PREMIUMS INTEREST FINANCE INVESTMENT INVESTMENT RETURN Income JOB DESCRIPTION JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LEAVE LOANS LODGERS LOW PAY MANAGERS MARITAL STATUS MARRIED WOMEN MARRIED WOMEN WORKERS MATERNITY LEAVE MATERNITY PAY MEDICAL PRESCRIPTIONS MORTGAGE PROTECTION... MORTGAGES MOTORCYCLES NEIGHBOURS Northern Ireland OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS ONE PARENT FAMILIES ONLINE BANKING OVERTIME PARENTS PART TIME COURSES PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PASSENGERS PATERNITY LEAVE PENSION CONTRIBUTIONS PENSIONS PHYSICALLY DISABLED... PHYSICIANS POVERTY PRIVATE EDUCATION PRIVATE PERSONAL PE... PRIVATE SCHOOLS PROFITS QUALIFICATIONS RATES REBATES REDUNDANCY REDUNDANCY PAY REMOTE BANKING RENTED ACCOMMODATION RENTS RESIDENTIAL MOBILITY RETIREMENT ROOM SHARING ROOMS ROYALTIES SAVINGS SAVINGS ACCOUNTS AN... SCHOLARSHIPS SCHOOL MILK PROVISION SCHOOLCHILDREN SCHOOLS SEASONAL EMPLOYMENT SECONDARY EDUCATION SECONDARY SCHOOLS SELF EMPLOYED SEWAGE DISPOSAL AND... SHARES SHIFT WORK SICK LEAVE SICK PAY SICK PERSONS SOCIAL CLASS SOCIAL HOUSING SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIAL SECURITY CON... SOCIAL SERVICES SOCIAL SUPPORT SOCIO ECONOMIC INDI... SOCIO ECONOMIC STATUS SPECIAL EDUCATION SPOUSES STATE EDUCATION STATE HEALTH SERVICES STATE RETIREMENT PE... STUDENT HOUSING STUDENT LOANS STUDENTS STUDY SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS Social stratificati... TAXATION TELEPHONES TELEVISION LICENCES TELEVISION RECEIVERS TEMPORARY EMPLOYMENT TENANCY AGREEMENTS TENANTS HOME PURCHA... TERMINATION OF SERVICE TIED HOUSING TIME TOP MANAGEMENT TRAINING UNEARNED INCOME UNEMPLOYED UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS VISION IMPAIRMENTS VISUALLY IMPAIRED P... VOCATIONAL EDUCATIO... VOLUNTARY WORK WAGES WATER RATES WIDOWED WORKING MOTHERS WORKING WOMEN property and invest...

The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.

United States US: Prevalence of Wasting: Weight for Height: % of Children Under 5 data was reported at 0.500 % in 2012. This stayed constant from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: % of Children Under 5 data is updated yearly, averaging 0.500 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.400 % in 2001. United States US: Prevalence of Wasting: 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 USA – Table US.World Bank: Health Statistics. Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; 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.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken out by age group. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update.

Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.