Timeline / Data.gov — Health Datasets

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A new raw object was archived. Both versions are preserved. 3083 line(s) added, 3034 line(s) removed.

Evidence

What changed derived

This diff is not evidence. It was produced by civic-memory.diff_engine 1.1.0 at 2026-09-13T00:27:18+00:00 by normalizing the two archived objects above. The objects are authoritative; this reading of them can be regenerated or deleted without loss. 3083 line(s) added, 3034 line(s) removed.

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+ "distribution_titles": [],
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+ "fn": "Bacterial Diseases Branch Epidemiology & Surveillance Team",
+ "hasEmail": "mailto:bdbepigroup@cdc.gov"
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- "downloadURL": "https://datadiscovery.nlm.nih.gov/api/v3/views/khy6-95gu/export.csv?accessType=DOWNLOAD",
+ "downloadURL": "https://data.cdc.gov/api/v3/views/e2a5-s9pr/export.csv?accessType=DOWNLOAD",
  "mediaType": "text/csv"
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  "@type": "dcat:Distribution",
- "downloadURL": "https://datadiscovery.nlm.nih.gov/api/v3/views/khy6-95gu/query.json?accessType=DOWNLOAD",
+ "downloadURL": "https://data.cdc.gov/api/v3/views/e2a5-s9pr/query.json?accessType=DOWNLOAD",
  "mediaType": "application/json"
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+ "downloadURL": "https://data.cdc.gov/api/v3/views/e2a5-s9pr/query.xml?accessType=DOWNLOAD",
  "mediaType": "application/xml"
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- "issued": "2022-06-29",
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- "education",
- "training and instruction",
- "videos"
- ],
- "landingPage": "https://learn.nlm.nih.gov/",
- "license": "http://opendefinition.org/licenses/odc-odbl/",
+ "identifier": "https://data.cdc.gov/api/views/e2a5-s9pr",
+ "isPartOf": "Lyme Disease Public Use Datasets",
+ "issued": "2025-08-19",
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+ "lyme disease",
+ "surveillance"
+ ],
+ "landingPage": "https://www.cdc.gov/lyme/data-research/facts-stats/surveillance-data-1.html",
+ "language": [
+ "English"
+ ],
+ "license": "https://www.usa.gov/government-works",
  "modified": "2026-09-10",
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+ "009:028"
  ],
  "publisher": {
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- "name": "National Library of Medicine"
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- "theme": [
- "Health Education"
- ],
- "title": "Learning Resources Database"
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- "description": "The Learning Resources Database is a catalog of interactive tutorials, videos, online classes, finding aids, and other instructional resources on National Library of Medicine (NLM) products and services. Resources may be available for immediate use via a browser or downloadable for use in course management systems.",
+ "name": "Centers for Disease Control and Prevention"
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+ "references": [
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+ ],
+ "spatial": "United States of America",
+ "theme": [
+ "National Center for Emerging and Zoonotic Infectious Diseases"
+ ],
+ "title": "Lyme disease public use line-listed data without geography, 1992-2007"
+ },
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- "harvest_record_raw": "https://catalog.data.gov/harvest_record/eb7b52bd-050e-4f71-b0a5-d57b23bf0037/raw",
+ "harvest_record": "https://catalog.data.gov/harvest_record/864ae377-5249-40be-9850-50e7cbce2c25",
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+ "surveillance"
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@@ -214,96 +3045,98 @@
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  "slug": "hhs"
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- "slug": "learning-resources-database",
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  "spatial_shape": null,
  "theme": [
- "Health Education"
- ],
- "title": "Learning Resources Database",
+ "National Center for Emerging and Zoonotic Infectious Diseases"
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  "type": "dataset"
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+ "accrualPeriodicity": "irregular",
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- "description": "Overview:\nPublic health surveillance data are collected and reported voluntarily to CDC by U.S. states and territories through the National Notifiable Diseases Surveillance System (NNDSS) (https://www.cdc.gov/nndss/index.html). Data include demographic, clinical, and geographic information; data do not include direct identifiers. Two types of datasets of human Lyme disease case data collected through public health surveillance are available: one includes annual case count aggregated by county of residence according to specific demographic variables and one is line-listed with patient demographic factors, month of illness onset, and clinical presentation information but without corresponding geographic information. These privacy-protected datasets were implemented in accordance with methodology described in Lee et al. Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use. Public Health Rep. 2021 Sep-Oct;136(5):554-561. doi: 10.1177/00333549211026817.\n\nLyme disease became nationally notifiable in 1991. Different surveillance case definitions have been in effect over time; details are available here: https://ndc.services.cdc.gov/conditions/lyme-disease/. In 2008, a probable case definition was included in public health surveillance for the first time. In 2022, states with a high incidence of Lyme disease started reporting cases based on laboratory evidence alone without requirement for a clinical investigation, precluding comparison with historical data (for more information: https://www.cdc.gov/mmwr/volumes/73/wr/mm7306a1.htm?s_cid=mm7306a1_w). As such, Lyme disease surveillance data are grouped into separate datasets based on when these major changes occurred; data are provided for download separately for 1992–2007, 2008–2021, and 2022 to current. Data will be updated annually upon final verification of Lyme disease surveillance data by health departments.\n\nData Limitations:\nSurveillance data have significant limitations that must be considered in the analysis, interpretation, and reporting of results.\n1. Under-reporting and misclassification are features common to all surveillance systems. Not every case of Lyme disease is reported to CDC, and some cases that are reported may be reflect illness due to another cause.\n2. Please note that before the 2022 surveillance case definition went into effect, several states with high Lyme disease incidence had initiated alternative methods of surveillance and those data were not reportable to CDC.\n3. Final case data are subject to each state’s abilities to capture and classify cases, which is dependent upon budget and personnel. This can vary not only between states, but also from year to year within a given state. Consequently, a sudden or marked change in reported cases does not necessarily represent a true change in disease incidence. Every effort should be made to construct analyses to limit overinterpretation of this variation (see the following reference for more context: Kugeler KJ, Eisen RJ. Challenges in Predicting Lyme Disease Risk. JAMA Netw Open. 2020 Mar 2;3(3):e200328. doi: 10.1001/jamanetworkopen.2020.0328.)",
+ "fn": "National Center for Health Statistics",
+ "hasEmail": "mailto:cdcinfo@cdc.gov"
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+ "description": "This dataset contains information on the number of deaths and age-adjusted death rates for the five leading causes of death in 1900, 1950, and 2000.\n\nAge-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).\n\nSOURCES\n\nCDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).\n\nREFERENCES\n\n1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.\n\n2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.\n\n3. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf.\n\n4. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf.\n\n5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.",
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+ "en-US"
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  "license": "https://www.usa.gov/government-works",
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  "name": "Centers for Disease Control and Prevention"
  },
- "references": [
- "https://ndc.services.cdc.gov/conditions/lyme-disease/"
- ],
- "spatial": "United States of America",
- "theme": [
- "National Center for Emerging and Zoonotic Infectious Diseases"
- ],
- "title": "Lyme disease public use line-listed data without geography, 1992-2007"
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- "description": "Overview:\nPublic health surveillance data are collected and reported voluntarily to CDC by U.S. states and territories through the National Notifiable Diseases Surveillance System (NNDSS) (https://www.cdc.gov/nndss/index.html). Data include demographic, clinical, and geographic information; data do not include direct identifiers. Two types of datasets of human Lyme disease case data collected through public health surveillance are available: one includes annual case count aggregated by county of residence according to specific demographic variables and one is line-listed with patient demographic factors, month of illness onset, and clinical presentation information but without corresponding geographic information. These privacy-protected datasets were implemented in accordance with methodology described in Lee et al. Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use. Public Health Rep. 2021 Sep-Oct;136(5):554-561. doi: 10.1177/00333549211026817.\n\nLyme disease became nationally notifiable in 1991. Different surveillance case definitions have been in effect over time; details are available here: https://ndc.services.cdc.gov/conditions/lyme-disease/. In 2008, a probable case definition was included in public health surveillance for the first time. In 2022, states with a high incidence of Lyme disease started reporting cases based on laboratory evidence alone without requirement for a clinical investigation, precluding comparison with historical data (for more information: https://www.cdc.gov/mmwr/volumes/73/wr/mm7306a1.htm?s_cid=mm7306a1_w). As such, Lyme disease surveillance data are grouped into separate datasets based on when these major changes occurred; data are provided for download separately for 1992–2007, 2008–2021, and 2022 to current. Data will be updated annually upon final verification of Lyme disease surveillance data by health departments.\n\nData Limitations:\nSurveillance data have significant limitations that must be considered in the analysis, interpretation, and reporting of results.\n1. Under-reporting and misclassification are features common to all surveillance systems. Not every case of Lyme disease is reported to CDC, and some cases that are reported may be reflect illness due to another cause.\n2. Please note that before the 2022 surveillance case definition went into effect, several states with high Lyme disease incidence had initiated alternative methods of surveillance and those data were not reportable to CDC.\n3. Final case data are subject to each state’s abilities to capture and classify cases, which is dependent upon budget and personnel. This can vary not only between states, but also from year to year within a given state. Consequently, a sudden or marked change in reported cases does not necessarily represent a true change in disease incidence. Every effort should be made to construct analyses to limit overinterpretation of this variation (see the following reference for more context: Kugeler KJ, Eisen RJ. Challenges in Predicting Lyme Disease Risk. JAMA Netw Open. 2020 Mar 2;3(3):e200328. doi: 10.1001/jamanetworkopen.2020.0328.)",
+ "spatial": "US",
+ "temporal": "1950-01-01/2000-12-31",
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+ "National Center for Health Statistics"
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+ "title": "NCHS - Top Five Leading Causes of Death: United States, 1990, 1950, 2000"
+ },
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+ "National Center for Health Statistics"
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+ "fn": "Research Branch (RB) National Personal Protective Technology Laboratory (NPPTL)",
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+ "description": "Proper respirator fit is essential for ensuring that NIOSH Approved® N95® filtering facepiece respirators (FFRs) provide their expected level of respiratory protection. Because respirator fit varies according to both respirator design and an individual's facial dimensions, selecting a well-fitting respirator can be difficult, particularly in settings where formal respirator fit testing is unavailable or resources are limited. To better understand these relationships, the National Institute for Occupational Safety and Health (NIOSH) conducted a laboratory study evaluating the fit performance of 12 NIOSH Approved N95 FFR models distributed through the U.S. Strategic National Stockpile (SNS). Quantitative fit evaluations were performed using five ISO Static Advanced Headforms, which represent approximately 95% of the U.S. worker population based on NIOSH’s 2003 Anthropometric Survey, resulting in 540 individual headform fit tests. These experimental data were subsequently combined with anthropometric data from the 2003 NIOSH survey to develop and evaluate predictive models that estimate an individual's representative headform size from a limited set of facial measurements.\nThe datasets were collected to improve understanding of how respirator fit varies across representative facial sizes and to support development of tools that may assist workers, employers, emergency response organizations, researchers, and the public in identifying respirator models that are more likely to provide an adequate fit. The quantitative fit evaluation data were used to characterize fit performance across multiple respirator models and headform sizes, while the anthropometric modeling data were used to develop and evaluate multinomial logistic regression models capable of predicting headform size with high accuracy under laboratory conditions. Because the fit evaluation was conducted using laboratory manikin headforms rather than human subjects, the findings should not be interpreted as a replacement for OSHA-accepted respirator fit testing. Additionally, only selected NIOSH Approved N95 FFR models available through the SNS at the time of the study were evaluated, and further validation with human subjects is needed before predictive modeling approaches are applied in real-world respirator selection. Nevertheless, these datasets provide a valuable resource for research involving respirator fit, facial anthropometry, respirator design, and development of future respirator selection and fit assessment tools.",
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+ "National Institute for Occupational Safety and Health"
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- "fn": "Research Branch (RB) National Personal Protective Technology Laboratory (NPPTL)",
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- "description": "Proper respirator fit is essential for ensuring that NIOSH Approved® N95® filtering facepiece respirators (FFRs) provide their expected level of respiratory protection. Because respirator fit varies according to both respirator design and an individual's facial dimensions, selecting a well-fitting respirator can be difficult, particularly in settings where formal respirator fit testing is unavailable or resources are limited. To better understand these relationships, the National Institute for Occupational Safety and Health (NIOSH) conducted a laboratory study evaluating the fit performance of 12 NIOSH Approved N95 FFR models distributed through the U.S. Strategic National Stockpile (SNS). Quantitative fit evaluations were performed using five ISO Static Advanced Headforms, which represent approximately 95% of the U.S. worker population based on NIOSH’s 2003 Anthropometric Survey, resulting in 540 individual headform fit tests. These experimental data were subsequently combined with anthropometric data from the 2003 NIOSH survey to develop and evaluate predictive models that estimate an individual's representative headform size from a limited set of facial measurements.\nThe datasets were collected to improve understanding of how respirator fit varies across representative facial sizes and to support development of tools that may assist workers, employers, emergency response organizations, researchers, and the public in identifying respirator models that are more likely to provide an adequate fit. The quantitative fit evaluation data were used to characterize fit performance across multiple respirator models and headform sizes, while the anthropometric modeling data were used to develop and evaluate multinomial logistic regression models capable of predicting headform size with high accuracy under laboratory conditions. Because the fit evaluation was conducted using laboratory manikin headforms rather than human subjects, the findings should not be interpreted as a replacement for OSHA-accepted respirator fit testing. Additionally, only selected NIOSH Approved N95 FFR models available through the SNS at the time of the study were evaluated, and further validation with human subjects is needed before predictive modeling approaches are applied in real-world respirator selection. Nevertheless, these datasets provide a valuable resource for research involving respirator fit, facial anthropometry, respirator design, and development of future respirator selection and fit assessment tools.",
+ "fn": "National Center for Health Statistics",
+ "hasEmail": "mailto:cdcinfo@cdc.gov"
+ },
+ "description": "The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of loss of work due to illness with coronavirus for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included a question about the inability to work due to being sick or having a family member sick with COVID-19. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor work-loss days and work limitations in the United States. For example, in 2018, 42.7% of adults aged 18 and over missed at least 1 day of work in the previous year due to illness or injury and 9.3% of adults aged 18 to 69 were limited in their ability to work or unable to work due to physical, mental, or emotional problems. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who did not work for pay at a job or business, at any point, in the previous week because either they or someone in their family was sick with COVID-19. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/work.htm#limitations",
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- },
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+ "spatial": "US",
+ "temporal": "2020-06-09/2021-06-30",
+ "theme": [
+ "National Center for Health Statistics"
+ ],
+ "title": "Loss of Work Due to Illness from COVID-19"
+ },
+ "description": "The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of loss of work due to illness with coronavirus for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included a question about the inability to work due to being sick or having a family member sick with COVID-19. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor work-loss days and work limitations in the United States. For example, in 2018, 42.7% of adults aged 18 and over missed at least 1 day of work in the previous year due to illness or injury and 9.3% of adults aged 18 to 69 were limited in their ability to work or unable to work due to physical, mental, or emotional problems. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who did not work for pay at a job or business, at any point, in the previous week because either they or someone in their family was sick with COVID-19. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/work.htm#limitations",
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- "harvest_record_raw": "https://catalog.data.gov/harvest_record/03318f5d-3c05-4ea4-8192-31b19434db37/raw",
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+ "has_spatial": true,
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+ "rands"
+ ],
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+ "popularity": 3,
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+ "slug": "loss-of-work-due-to-illness-from-covid-19",
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  "theme": [
- "National Institute for Occupational Safety and Health"
- ],
- "title": "Quantitative headform fit evaluation and predictive modeling to assist with selecting N95 filtering facepiece respirators to mitigate respiratory hazards",
+ "National Center for Health Statistics"
+ ],
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- "description": "The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of loss of work due to illness with coronavirus for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included a question about the inability to work due to being sick or having a family member sick with COVID-19. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor work-loss days and work limitations in the United States. For example, in 2018, 42.7% of adults aged 18 and over missed at least 1 day of work in the previous year due to illness or injury and 9.3% of adults aged 18 to 69 were limited in their ability to work or unable to work due to physical, mental, or emotional problems. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who did not work for pay at a job or business, at any point, in the previous week because either they or someone in their family was sick with COVID-19. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/work.htm#limitations",
+ "description": "The NCHS National Post-acute and Long-term Care Study (NPALS) collects data on long-term care every two years for all 50 states and the District of Columbia to monitor the diverse post-acute and long-term care fields. The 2020 survey provided an opportunity to collect COVID-19-related data for residential care communities and adult day services centers, important long-term care settings. These data are not available from other data systems. These data are related to experiences of COVID-19 from January 2020 through mid-July 2021, including the number of COVID-19 cases, hospitalizations, and deaths among users and staff, practices taken to reduce COVID-19 exposure and transmission, and personal protective equipment (PPE) shortages.",
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- "downloadURL": "https://data.cdc.gov/api/v3/views/qgkx-mswu/export.csv?accessType=DOWNLOAD",
+ "downloadURL": "https://data.cdc.gov/api/v3/views/3j26-kg6d/export.csv?accessType=DOWNLOAD",
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- "downloadURL": "https://data.cdc.gov/api/v3/views/qgkx-mswu/query.json?accessType=DOWNLOAD",
+ "downloadURL": "https://data.cdc.gov/api/v3/views/3j26-kg6d/query.json?accessType=DOWNLOAD",
  "mediaType": "application/json"
  },
  {
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- "downloadURL": "https://data.cdc.gov/api/v3/views/qgkx-mswu/query.xml?accessType=DOWNLOAD",
+ "downloadURL": "https://data.cdc.gov/api/v3/views/3j26-kg6d/query.xml?accessType=DOWNLOAD",
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  }
  ],
- "identifier": "https://data.cdc.gov/api/views/qgkx-mswu",
- "issued": "2020-09-14",
- "keyword": [
+ "identifier": "https://data.cdc.gov/api/views/3j26-kg6d",
+ "issued": "2021-07-12",
+ "keyword": [
+ "adult day services centers",
  "covid-19",
- "rands"
- ],
- "landingPage": "https://www.cdc.gov/nchs/covid19/rands/work.htm",
+ "long-term care",
+ "residential care communities"
+ ],
+ "landingPage": "https://www.cdc.gov/nchs/covid19/npals.htm",
  "language": [
  "en-US"
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@@ -578,24 +3409,26 @@
  "name": "Centers for Disease Control and Prevention"
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- "temporal": "2020-06-09/2021-06-30",
+ "temporal": "2020-01/2021-07",
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  ],
- "title": "Loss of Work Due to Illness from COVID-19"
- },
- "description": "The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of loss of work due to illness with coronavirus for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included a question about the inability to work due to being sick or having a family member sick with COVID-19. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor work-loss days and work limitations in the United States. For example, in 2018, 42.7% of adults aged 18 and over missed at least 1 day of work in the previous year due to illness or injury and 9.3% of adults aged 18 to 69 were limited in their ability to work or unable to work due to physical, mental, or emotional problems. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who did not work for pay at a job or business, at any point, in the previous week because either they or someone in their family was sick with COVID-19. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/work.htm#limitations",
+ "title": "Long-term Care and COVID-19"
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+ "description": "The NCHS National Post-acute and Long-term Care Study (NPALS) collects data on long-term care every two years for all 50 states and the District of Columbia to monitor the diverse post-acute and long-term care fields. The 2020 survey provided an opportunity to collect COVID-19-related data for residential care communities and adult day services centers, important long-term care settings. These data are not available from other data systems. These data are related to experiences of COVID-19 from January 2020 through mid-July 2021, including the number of COVID-19 cases, hospitalizations, and deaths among users and staff, practices taken to reduce COVID-19 exposure and transmission, and personal protective equipment (PPE) shortages.",
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- "description": "This data release supports the following publication: \nHittle, Elizabeth, 2017, Longshore Water-Current Velocity and the Potential for Transport of Contaminants: A Pilot Study in Lake Erie from Walnut Creek to Presque Isle State Park beaches, Erie, Pennsylvania, June and August 2015: U.S. Geological Survey Open-File Report 2016–1206 126 p., https://doi.org/10.3133/ofr20161206 \nData were collected in Lake Erie between Walnut Creek and Presque Isle State Park (PSIP) Beach 1 in June and August 2015 to support a pilot study looking at water-current velocity and the potential for contaminant transport within that area. Water-current velocity transects were collected on June 24, 25, August 18 and 19 with a Teledyne Rio Grande 1200 kHz acoustic Doppler current profiler (ADCP). The data were processed within the Velocity Mapping Toolbox (Parsons and others., 2013) and visualized within ArcMap. \nWater quality was measured on select transects by sampling water temperature, specific conductance, and turbidity from collection points at approximately 10 verticals along the transect on June 24 and June 25. Measurements were collected with a YSI EXO water quality meter. \nNear-shore water quality was measured by collecting grab samples from shore on June 24, August 11, and August 19. Temperature was measured on site, and from the grab samples, turbidity and Escherichia coli (E. coli) bacteria concentration was measured. Water-quality grab samples were collected about a meter from shore and coincide with the 25 longshore water-current velocity transects as closely as conditions would allow. Samples were collected by Erie County Department of Health (ECDH) employees and Regional Science Consortium (RSC) interns. The nearshore water-quality samples were collected using grab-sample techniques described in Myers and others (2007). To maintain sterile conditions, grab samples were collected in at least 1 meter of water at approximately 0.3 meters below the water surface, being careful not to stir up bottom sediments. Water samples for bacteria analysis were collected in pre-sterilized 500-mL polypropylene bottles, allowing about 2 inches of head space for proper mixing, and were kept on ice prior to processing. Bacteria samples were analyzed for Escherichia coli (E. coli) using modified mTEC membrane-filtration techniques (U.S. Environmental Protection Agency, 2002) and were processed by RSC staff in the RSC laboratory within 6 hours of sample collection. \nOn June 24 and August 11 an additional sample was collected near-shore for suspended sediment analysis. Samples were collected in pre-tared 1000-mL polypropylene bottles by tilting the bottle at about a 45 degree angle away from the sampler and quickly moving it from just under the surface (where the bottle was uncapped) to just above the streambed and back in a smooth vertical motion to get as close to a depth-integrated, single-vertical grab sample as possible (Edwards and Glysson, 1999). There is no need to chill bottles for sediment analysis. Sediment samples were prepared for shipping and sent to the USGS sediment laboratory at the USGS Kentucky Water Science Center where they were analyzed for total suspended sediment concentration, sand/fine break (percent of sediment less than 4 mm), and fine components including percent fines less than 2 mm, 1mm, 0.5 mm, 0.25 mm, 0.125 mm, and &lt;0.0625mm. \nEdwards, T.K., and Glysson, G.D., 1999, Field methods for measurement of fluvial sediment: Techniques of Water-Resources Investigations of the U.S. Geological Survey, book 3, chap. C2, 89 p \nMyers, D.N., Stoeckel, D.M., Bushon, R.N., Francy, D.S., and Brady, A.M.G., 2007, Fecal indicator bacteria: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chap. A7, section 7.1 (version 2.0), available from http://pubs.water.usgs.gov/twri9A/. \nParsons, D.R., Jackson, P.R., Czuba, J.A., Oberg, K.A., Mueller, D.S., Rhoads, B., Best, J.L., Johnson, K.K., Engel, F., and Riley, J. (2013) Velocity Mapping Toolbox (VMT): a processing and visualization suite for moving-vessel ADCP measurements, Earth Surface Processes and Landforms. doi: 10.1002/esp.3367. \nUS Environmental Protection Agency (USEPA). 2009, Method 1603: Escherichia coli (E. coli) in Water by Membrane Filtration Using Modified membrane-Thermotolerant Escherichia coli Agar (Modified mTEC), EPA-821-R-09-007, December 2009",
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