The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who do and do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population (CNP) at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Units (PSUs) and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the CNP at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort.At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the CNP at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the CNP at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort.Please refer to the Public-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Wave 4.5 was a special data collection for youth only who were aged 12 to 17 at the time of the Wave 4.5 interview. Wave 4.5 was the fourth annual follow-up wave for those who were members of the Wave 1 Cohort. For those who were sampled at Wave 4, Wave 4.5 was the first annual follow-up wave.Wave 5.5, conducted in 2020, was a special data collection for Wave 4 Cohort youth and young adults ages 13 to 19 at the time of the Wave 5.5 interview. Also in 2020, a subsample of Wave 4 Cohort adults ages 20 and older were interviewed via the PATH Study Adult Telephone Survey (PATH-ATS).Wave 7.5 was a special collection for Wave 4 and Wave 7 Cohort youth and young adults ages 12 to 22 at the time of the Wave 7.5 interview. For those who were sampled at Wave 7, Wave 7.5 was the first annual follow-up wave. Dataset 1002 (DS1002) contains the data from the Wave 4.5 Youth and Parent Questionnaire. This file contains 1,395 variables and 13,131 cases. Of these cases, 11,378 are continuing youth having completed a prior Youth Interview. The other 1,753 cases are "aged-up youth" having previously been sampled as "shadow youth." Datasets 1112, 1212, and 1222, (DS1112, DS1212, and DS1222) are data files comprising the weight variables for Wave 4.5. The "all-waves" weight file contains weights for participants in the Wave 1 Cohort who completed a Wave 4.5 Youth Interview and completed interviews (if old enough to do so) or verified their information with the study (if not old enough to be interviewed) in Waves 1, 2, 3, and 4. There are two separate files with "single wave" weights: one for the Wave 1 Cohort and one for the Wave 4 Cohort. The "single-wave" weight file for the Wave 1 Cohort contains weights for youth who completed an interview in Wave 1 an

There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.

The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic and beyond, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of the SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of the SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. Wave 4 of the SPDLC was collected in October 2022. Parents who participated in either of the first three waves were invited to participate again in Wave 4, and another new cohort of parents was also recruited to participate in the Wave 4 survey. Wave 5 of the SPDLC was collected in October 2023. Parents who participated in any of the first four waves were invited to participate again in Wave 5, and another new cohort of parents was also recruited to participate in the Wave 5 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. SPDLC data will be released to the public two years after data is collected; Waves 1-4 are currently publicly available. Wave 5 will be publicly available in October 2025, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available.

What We Eat in America (WWEIA) is the dietary intake interview component of the National Health and Nutrition Examination Survey (NHANES). WWEIA is conducted as a partnership between the U.S. Department of Agriculture (USDA) and the U.S. Department of Health and Human Services (DHHS). Two days of 24-hour dietary recall data are collected through an initial in-person interview, and a second interview conducted over the telephone within three to 10 days. Participants are given three-dimensional models (measuring cups and spoons, a ruler, and two household spoons) and/or USDA's Food Model Booklet (containing drawings of various sizes of glasses, mugs, bowls, mounds, circles, and other measures) to estimate food amounts. WWEIA data are collected using USDA's dietary data collection instrument, the Automated Multiple-Pass Method (AMPM). The AMPM is a fully computerized method for collecting 24-hour dietary recalls either in-person or by telephone. For each 2-year data release cycle, the following dietary intake data files are available: Individual Foods File - Contains one record per food for each survey participant. Foods are identified by USDA food codes. Each record contains information about when and where the food was consumed, whether the food was eaten in combination with other foods, amount eaten, and amounts of nutrients provided by the food. Total Nutrient Intakes File - Contains one record per day for each survey participant. Each record contains daily totals of food energy and nutrient intakes, daily intake of water, intake day of week, total number foods reported, and whether intake was usual, much more than usual or much less than usual. The Day 1 file also includes salt use in cooking and at the table; whether on a diet to lose weight or for other health-related reason and type of diet; and frequency of fish and shellfish consumption (examinees one year or older, Day 1 file only). DHHS is responsible for the sample design and data collection, and USDA is responsible for the survey’s dietary data collection methodology, maintenance of the databases used to code and process the data, and data review and processing. USDA also funds the collection and processing of Day 2 dietary intake data, which are used to develop variance estimates and calculate usual nutrient intakes. Resources in this dataset:Resource Title: What We Eat In America (WWEIA) main web page. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/wweianhanes-overview/ Contains data tables, research articles, documentation data sets and more information about the WWEIA program. (Link updated 05/13/2020)

The NEISS-AIP is designed to provide national incidence estimates of all types and external causes of nonfatal injuries and poisonings treated in U.S. hospital EDs. Data on injury-related visits are obtained from a national sample of U.S. NEISS hospitals, which were selected as a stratified probability sample of hospitals in the United States and its territories with a minimum of six beds and a 24- hour ED. The sample includes separate strata for very large, large, medium, and small hospitals, defined by the number of annual ED visits per hospital, and children's hospitals. The scope of reporting goes beyond routine reporting of injuries associated with consumer- related products in CPSC's jurisdiction to include all injuries and poisonings. The data can be used to (1) measure the magnitude and distribution of nonfatal injuries in the United States; (2) monitor unintentional and violence-related nonfatal injuries over time; (3) identify emerging injury problems; (4) identify specific cases for follow-up investigations of particular injury-related problems; and (5) set national priorities. A fundamental principle of this expansion effort is that preliminary surveillance data will be made available in a timely manner to a number of different federal agencies with unique and overlapping public health responsibilities and concerns. The final edited data will be released annually as public use data files for use by other public health professionals and researchers. These public use data files provide NEISS-AIP data on nonfatal injuries collected from January through December each year. NEISS-AIP is providing data on approximately over 500,000 cases annually. Data obtained on each case include age, race/ethnicity, sex, principal diagnosis, primary body part affected, consumer products involved, disposition at ED discharge (i.e., hospitalized, transferred, treated and released, observation, died), locale where the injury occurred, work-relatedness, and a narrative description of the injury circumstances. Also, major categories of external cause/mechanism of injury (e.g., motor vehicle, falls, cut/pierce, poisoning, fire/burn) and of intent of injury (e.g., unintentional, assault, intentional self-harm, legal intervention) are being coded for each case in a manner consistent with the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding rules and guidelines. NEISS has been managed and operated by the U.S. Consumer Product Safety Commission since 1972 and is used by the Commission for identifying and monitoring consumer product-related injuries and for assessing risk to all U.S. residents. These product- related injury data are used for educating consumers about hazardous products and for identifying injury-related cases used in detailed studies of specific products and associated hazard patterns. These studies set the stage for developing both voluntary and mandatory safety standards. Since the early 1980s, CPSC has assisted other federal agencies by using NEISS to collect injury- related data of special interest to them. In 1992, an interagency agreement was established between NCIPC and CPSC to (1) collect NEISS data on nonfatal firearm- related injuries for the CDC Firearm Injury Surveillance Study; (2) publish NEISS data on a variety of injury-related topics, such as in- line skating, firearms, BB and pellet guns, bicycles, boat propellers, personal water craft, and playground injuries; and (3) to address common concerns. CPSC also uses NEISS to collect data on work-related injuries for the National Institute of Occupational Safety and Health (NIOSH), CDC. In 1997, the interagency agreement was modified to conduct the three-month NEISS All Injury Pilot Study at 21 NEISS hospitals (see Quinlan KP, Thompson MP, Annest JL, et al. Expanding the National Electronic Injury Surveillance System to Monitor All Nonfatal Injuries Treated in US Hospital Emergency Departments. Annals Emerg. Med. 1999;34:637-643.) This study demonstrated the feasibility of expanding NEISS to collect data on all injuries. National estimates based on this study indicated product-related injuries that fall into CPSC's jurisdiction accounted for approximately 50% of injuries treated in U.S. hospital EDs. The study also indicated that NEISS is a cost-effective system for capturing data on all injuries treated in U

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample of 5,000 households across the US was purchased from Survey Sampling, Inc. located in Connecticut. This sample is based on Random Digit samples.

This sample was stratified by state to match the percentage of U.S. residents living in each of the fifty states.

The 5,000 sampled households were randomly assigned to one of three different experimental treatments (normal, personalized and personalised plus 2$ incentive)

The experiment was done for purposes of evaluating response rate effects of alternative means of contacting US residents.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

State and territorial executive orders, administrative orders, resolutions, proclamations, and other official publicly available government communications are collected from government websites and cataloged and coded using Microsoft Excel by one or more coders with one or more additional coders conducting quality assurance.

Data were collected to determine when individuals in states and territories were subject to executive orders, administrative orders, resolutions, proclamations, and other official publicly available government communications related to COVID-19 banning gatherings of various sizes either (1) generally, or specified that the gathering limit applied only when social distancing was not possible, or (2) even if participants practiced social distancing.

These data are derived from on the publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly ban gatherings found by the CDC, COVID-19 Community Intervention and Critical Populations Task Force, Monitoring and Evaluation Team & CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 11, 2020 through August 15, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded, as well as official government communications such as announcements that counties have progressed through new phases of reopening pursuant to an executive order, directive, or other executive branch action, and posted to government websites; news media reports on restrictions were excluded. Recommendations and guidance documents not included or adopted by reference in an order are not included in these data. These data do not include mandatory business closures, curfews, or requirements/recommendations for people to stay in their homes. Due to limitations of the National Environmental Public Health Tracking Network Data Explorer, these data do not include tribes or cities, nor was a distinction made between county orders that applied county-wide versus those that were limited to unincorporated areas of the county. Effective and expiration dates were coded using only the date provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

Beginning March 1, 2022, the "COVID-19 Case Surveillance Public Use Data" will be updated on a monthly basis. This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data. CDC has three COVID-19 case surveillance datasets: COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements) COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements) COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (32 data elements) The following apply to all three datasets: Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf. Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. Some data cells are suppressed to protect individual privacy. The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the previously updated datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured. Datasets are updated monthly. Datasets are created using CDC’s operational Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy. For more information about data collection and reporting, please see https://wwwn.cdc.gov/nndss/data-collection.html For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html Overview The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020 to clarify the interpretation of antigen detection tests and serologic test results within the case classification. The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported volun

The National Health and Nutrition Examination Surveys (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES combines personal interviews and physical examinations, which focus on different population groups or health topics. These surveys have been conducted by the National Center for Health Statistics (NCHS) on a periodic basis from 1971 to 1994. In 1999 the NHANES became a continuous program with a changing focus on a variety of health and nutrition measurements which were designed to meet current and emerging concerns. The surveys examine a nationally representative sample of approximately 5,000 persons each year. These persons are located in counties across the United States, 15 of which are visited each year. The 1999-2000 NHANES contains data for 9,965 individuals (and MEC examined sample size of 9,282) of all ages. Many questions that were asked in NHANES II, 1976-1980, Hispanic HANES 1982-1984, and NHANES III, 1988-1994, were combined with new questions in the NHANES 1999-2000. The 1999-2000 NHANES collected data on the prevalence of selected chronic conditions and diseases in the population and estimates for previously undiagnosed conditions, as well as those known to and reported by respondents. Risk factors, those aspects of a person's lifestyle, constitution, heredity, or environment that may increase the chances of developing a certain disease or condition, were examined. Data on smoking, alcohol consumption, sexual practices, drug use, physical fitness and activity, weight, and dietary intake were collected. Information on certain aspects of reproductive health, such as use of oral contraceptives and breastfeeding practices, were also collected. The interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests. Demographic data file variables are grouped into three broad categories: (1) Status Variables: Provide core information on the survey participant. Examples of the core variables include interview status, examination status, and sequence number. (Sequence number is a unique ID assigned to each sample person and is required to match the information on this demographic file to the rest of the NHANES 1999-2000 data). (2) Recoded Demographic Variables: The variables include age (age in months for persons through age 19 years, 11 months; age in years for 1-84 year olds, and a top-coded age group of 85+ years), gender, a race/ethnicity variable, an education variable (high school, and more than high school education), country of birth (United States, Mexico, or other foreign born), and pregnancy status variable. Some of the groupings were made due to limited sample sizes for the two-year dataset. (3) Interview and Examination Sample Weight Variables: Sample weights are available for analyzing NHANES 1999-2000 data. For a complete listing of survey contents for all years of the NHANES see the document -- Survey Content -- NHANES 1999-2010.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

This dataset is updated more frequently and can be visualized on NCWQR's data portal.

If you have any questions, please contact Dr. Laura Johnson or Dr. Nathan Manning.

The National Center for Water Quality Research (NCWQR) is a research laboratory at Heidelberg University in Tiffin, Ohio, USA. Our primary research program is the Heidelberg Tributary Loading Program (HTLP), where we currently monitor water quality at 22 river locations throughout Ohio and Michigan, effectively covering ~half of the land area of Ohio. The goal of the program is to accurately measure the total amounts (loads) of pollutants exported from watersheds by rivers and streams. Thus these data are used to assess different sources (nonpoint vs point), forms, and timing of pollutant export from watersheds. The HTLP officially began with high-frequency monitoring for sediment and nutrients from the Sandusky and Maumee rivers in 1974, and has continually expanded since then.

Each station where samples are collected for water quality is paired with a US Geological Survey gage for quantifying discharge (http://waterdata.usgs.gov/usa/nwis/rt). Our stations cover a wide range of watershed areas upstream of the sampling point from 11.0 km2 for the unnamed tributary to Lost Creek to 19,215 km2 for the Muskingum River. These rivers also drain a variety of land uses, though a majority of the stations drain over 50% row-crop agriculture.

At most sampling stations, submersible pumps located on the stream bottom continuously pump water into sampling wells inside heated buildings where automatic samplers collect discrete samples (4 unrefrigerated samples/d at 6-h intervals, 1974–1987; 3 refrigerated samples/d at 8-h intervals, 1988-current). At weekly intervals the samples are returned to the NCWQR laboratories for analysis. When samples either have high turbidity from suspended solids or are collected during high flow conditions, all samples for each day are analyzed. As stream flows and/or turbidity decreases, analysis frequency shifts to one sample per day. At the River Raisin and Muskingum River, a cooperator collects a grab sample from a bridge at or near the USGS station approximately daily and all samples are analyzed. Each sample bottle contains sufficient volume to support analyses of total phosphorus (TP), dissolved reactive phosphorus (DRP), suspended solids (SS), total Kjeldahl nitrogen (TKN), ammonium-N (NH4), nitrate-N and nitrite-N (NO2+3), chloride, fluoride, and sulfate. Nitrate and nitrite are commonly added together when presented; henceforth we refer to the sum as nitrate.

Upon return to the laboratory, all water samples are analyzed within 72h for the nutrients listed below using standard EPA methods. For dissolved nutrients, samples are filtered through a 0.45 um membrane filter prior to analysis. We currently use a Seal AutoAnalyzer 3 for DRP, silica, NH4, TP, and TKN colorimetry, and a DIONEX Ion Chromatograph with AG18 and AS18 columns for anions. Prior to 2014, we used a Seal TRAACs for all colorimetry.

2017 Ohio EPA Project Study Plan and Quality Assurance Plan

Project Study Plan

Quality Assurance Plan

Data quality control and data screening

The data provided in the River Data files have all been screened by NCWQR staff. The purpose of the screening is to remove outliers that staff deem likely to reflect sampling or analytical errors rather than outliers that reflect the real variability in stream chemistry. Often, in the screening process, the causes of the outlier values can be determined and appropriate corrective actions taken. These may involve correction of sample concentrations or deletion of those data points.

This micro-site contains data for approximately 126,000 water samples collected beginning in 1974. We cannot guarantee that each data point is free from sampling bias/error, analytical errors, or transcription errors. However, since its beginnings, the NCWQR has operated a substantial internal quality control program and has participated in numerous external quality control reviews and sample exchange programs. These programs have consistently demonstrated that data produced by the NCWQR is of high quality.

A note on detection limits and zero and negative concentrations

It is routine practice in analytical chemistry to determine method detection limits and/or limits of quantitation, below which analytical results are considered less reliable or unreliable. This is something that we also do as part of our standard procedures. Many laboratories, especially those associated with agencies such as the U.S. EPA, do not report individual values that are less than the detection limit, even if the analytical equipment returns such values. This is in part because as individual measurements they may not be considered valid under litigation.

The measured concentration consists of the true but unknown concentration plus random instrument error, which is usually small compared to the range of expected environmental values. In a sample for which the true concentration is very small, perhaps even essentially zero, it is possible to obtain an analytical result of 0 or even a small negative concentration. Results of this sort are often “censored” and replaced with the statement “

Censoring these low values creates a number of problems for data analysis. How do you take an average? If you leave out these numbers, you get a biased result because you did not toss out any other (higher) values. Even if you replace negative concentrations with 0, a bias ensues, because you’ve chopped off some portion of the lower end of the distribution of random instrument error.

For these reasons, we do not censor our data. Values of -9 and -1 are used as missing value codes, but all other negative and zero concentrations are actual, valid results. Negative concentrations make no physical sense, but they make analytical and statistical sense. Users should be aware of this, and if necessary make their own decisions about how to use these values. Particularly if log transformations are to be used, some decision on the part of the user will be required.

Analyte Detection Limits

https://ncwqr.files.wordpress.com/2021/12/mdl-june-2019-epa-methods.jpg?w=1024

For more information, please visit https://ncwqr.org/

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

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​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

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Documentation

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1920 census data was collected in January 1920. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.

• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.

• Coded variables derived from string variables are still in progress. These variables include: occupation and industry.

• Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, MORTGAGE, FARM, CLASSWKR, OCC1950, IND1950, MARST, RACE, SEX, RELATE, MTONGUE. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.

• Most inconsistent information was not edited for this release, thus there are observations outside of the universe for some variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next release.

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Section 2

This dataset was created on 2020-01-10 18:46:34.647 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1920 households: This dataset includes all households from the 1920 US census.

IPUMS 1920 persons: This dataset includes all individuals from the 1920 US census.

IPUMS 1920 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1920 datasets.

Beginning in July 2000, the National Center for Injury Prevention and Control (NCIPC), Centers for Disease Control and Prevention (CDC) in collaboration with the United States Consumer Product Safety Commission (CPSC) expanded the National Electronic Injury Surveillance System (NEISS) to collect data on all types and causes of injuries treated in a representative sample of United States hospitals with emergency departments (EDs). This system is called the NEISS-All Injury Program (NEISS-AIP). The NEISS-AIP is designed to provide national incidence estimates of all types and external causes of nonfatal injuries and poisonings treated in U.S. hospital EDs. Data on injury-related visits are being obtained from a national sample of U.S. NEISS hospitals, which were selected as a stratified probability sample of hospitals in the United States and its territories with a minimum of six beds and a 24-hour ED. The sample includes separate strata for very large, large, medium, and small hospitals, defined by the number of annual ED visits per hospital, and children's hospitals. The scope of reporting goes beyond routine reporting of injuries associated with consumer-related products in CPSC's jurisdiction to include all injuries and poisonings. The data can be used to (1) measure the magnitude and distribution of nonfatal injuries in the United States; (2) monitor unintentional and violence-related nonfatal injuries over time; (3) identify emerging injury problems; (4) identify specific cases for follow-up investigations of particular injury-related problems; and (5) set national priorities. A fundamental principle of this expansion effort is that preliminary surveillance data will be made available in a timely manner to a number of different federal agencies with unique and overlapping public health responsibilities and concerns. Also, annually, the final edited data will be released as public use data files for use by other public health professionals and researchers. NEISS-AIP data on nonfatal injuries were collected from January through December each year except the year 2000 when data were collected from July through December (ICPSR 3582). NEISS AIP is providing data on approximately over 500,000 cases annually. Data obtained on each case include age, race/ethnicity, gender, principal diagnosis, primary body part affected, consumer products involved, disposition at ED discharge (i.e., hospitalized, transferred, treated and released, observation, died), locale where the injury occurred, work-relatedness, and a narrative description of the injury circumstances. Also, major categories of external cause of injury (e.g., motor vehicle, falls, cut/pierce, poisoning, fire/burn) and of intent of injury (e.g., unintentional, assault, intentional self-harm, legal intervention) are being coded for each case in a manner consistent with the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding rules and guidelines. NEISS has been managed and operated by the United States Consumer Product Safety Commission since 1972 and is used by the Commission for identifying and monitoring consumer product-related injuries and for assessing risk to all United States residents. These product-related injury data are used for educating consumers about hazardous products and for identifying injury-related cases used in detailed studies of specific products and associated hazard patterns. These studies set the stage for developing both voluntary and mandatory safety standards. Since the early 1980s, CPSC has assisted other federal agencies by using NEISS to collect injury- related data of special interest to them. In 1990, an interagency agreement was established between NCIPC and CPSC to (1) collect NEISS data on nonfatal firearm-related injuries for the CDC Firearm Injury Surveillance Study; (2) publish NEISS data on a variety of injury-related topics, such as in-line skating, firearms, BB and pellet guns, bicycles, boat propellers, personal water craft, and playground injuries; and (3) to address common concerns. CPSC also uses NEISS to collect data on work-related injuries for the National Institute of Occupational Safety and Health (NIOSH), CDC. In 1997, the interagency agreement was modified to conduct the three-month NEISS All Injury Pilot Study at 21 NEISS hospitals (see Quinlan KP, Thompson MP, Annest JL, et al. Expanding the National Electronic Injury Surveillance System to Monitor All Nonfatal Injuries Treated in US Hospital Emergency Departments. Annals Emerg. Med. 1999;34:637-643.) This study demonstrated the feasibility of expanding NEISS to collect data on all injuries. National estimates based on this study indicated product-related injuries that fall into CPSC's jurisdiction accounted for approximately 50 percent of injuries treated in U.S. hospital EDs. The study also indicated that NEISS is a cost-effective system for capturing data on all injuries treated in U.S. hospital EDs. The NEISS-AIP provides an excellent data source for monitoring national estimates of nonfatal injuries over time. Analysis and dissemination of these surveillance data through the ICPSR, and Internet publications will help support NCIPC's mission of reducing all types and causes of injuries in the United States, as well as assist other federal agencies with responsibilities for injury prevention and control.

This dataset includes COVID-19 self-test result data voluntarily reported by users of tests through the MakeMyTestCount website (makemytestcount.org). All fields are self-reported by the user with the exception of fields derived from the self-reported zip code. This dataset will be updated monthly. If there are any questions, please direct them to the data steward, Jasmine Chaitram zoa6@cdc.gov.

This dataset includes the following self-reported data:
- Date (by week)– date of test shown by week starting date
- Age group (years) – age of individual taking the test, categorized into the following: 2-4, 5-11, 12-15, 16-17, 18-29, 30-39, 40-49, 50-64, 65-74, 75+
- Race – race of individual taking the test: American Indian or Alaska Native, Asian, Black, Native Hawaiian or Other Pacific Islander, White, Multiple or Other, missing - Ethnicity – ethnicity of individual taking the test: Hispanic, Non-Hispanic, missing - Sex – sex of individual taking the test: male, female, missing - Test result – positive, negative, inconclusive

The dataset also includes the following columns to support analyses. These columns are based on the self-reported zip code:
- State abbreviation
- State name
- State FIPS code - FEMA region

Please note that there are limitations with these data, including:

1. Data are not comprehensive of all self-tests performed. Data represent results voluntarily reported by an individual via the MakeMyTestCount website. These data do not include self-test results that were reported to state and local health departments if they were not also reported through the MakeMyTestCount website. The true denominator (known number of tests completed in the US) cannot be ascertained and reflects a small fraction of the number of self-tests used.

2. Data are not verified. The quality of specimen, appropriate execution of self-test, result produced, and person tested are unverified; therefore, reported interpretation of results cannot be confirmed. All results and accompanying demographic information are also self-reported and cannot be verified.

3. Data reports are not complete. Individual submissions vary widely in terms of the data elements collected. Not all data elements are required (only date, age, and zip code), and some results are missing demographic information.

4. Data are not representative. Based on the limited number of self-reported test results, this dataset is not representative of the use of self-testing by demographic, nor is the dataset inclusive of all self-testing completed within each jurisdiction. This dataset represents a small proportion of overall COVID-19 testing conducted and reported volumes are much lower than testing conducted in point of care and laboratory settings.

5. Data represent individual test results, not persons tested. Data in this dataset are not linkable and do not allow for analyses around serial testing. Data also cannot be disaggregated to identify multiple reports by the same individual.

All analyses should be completed with these limitations in mind.

For more information about the challenges and opportunities around self-test data, please refer to the following article: Ritchey MD, Rosenblum HG, Del Guercio K, et al. COVID-19 Self-Test Data: Challenges and Opportunities — United States, October 31, 2021–June 11, 2022. MMWR Morb Mortal Wkly Rep 2022;71:1005–1010. DOI: http://dx.doi.org/10.15585/mmwr.mm7132a1