AndreaBozzo/ceres-open-data-index huggingface dataset:
Daily Census Tract-Level PM2.5 Concentrations, 2001-2005

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2001-2005. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
Daily Census Tract-Level PM2.5 Concentrations, 2006-2010

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2006-2010. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
Daily Census Tract-Level PM2.5 Concentrations, 2011-2015

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2011-2015. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
Daily Census Tract-Level PM2.5 Concentrations, 2016 - 2020

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2016-2020. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
Daily Census Tract-Level PM2.5 Concentrations, 2021- 2022

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2021-2022. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Census tract-level concentration data are available in 5-year periods due to size constraints. This dataset and file will be updated until it is complete to cover 2021-2025. Earlier periods of census-tract data can be found on data.cdc.gov through the search function. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
Daily County-Level Ozone Concentrations 2001-2022

This dataset provides modeled predictions of ozone levels from the EPA's Downscaler model. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Data are at the county level for 2001-2022. The dataset includes the maximum, median, mean, and population-weighted mean concentration. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
Daily County-Level PM2.5 Concentrations 2001-2022

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the county level for 2001-2022. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
DASH - Global School-based Student Health Survey (GSHS)

2003-2015. Global School dataset. The Global School-based Student Health Survey (GSHS) was developed by the World Health Organization (WHO) in collaboration with the United Nations' UNICEF, UNESCO, and UNAIDS; and with technical assistance from CDC. The GSHS is a school-based survey conducted primarily among students aged 13-17 years in countries around the world. It uses core questionnaire modules that address the leading causes of morbidity and mortality among children and adults worldwide: 1) Alcohol use, 2) dietary behaviors, 3) drug use, 4) hygiene, 5) mental health, 6) physical activity, 7) protective factors, 8) sexual behaviors that contribute to HIV infection, other sexually-transmitted infections, and unintended pregnancy, 9) tobacco use, and 10) violence and unintentional injury. This dataset contains global data from 2003 – 2015. Additional information about the GSHS can be found at https://www.cdc.gov/gshs/index.htm.
DASH – School Health Profiles (Profiles) 2022

2022 School Health Profiles (Profiles) Dataset. Profiles is a system of surveys assessing school health policies and practices in states, school districts, territories, and tribes. Profiles surveys are conducted biennially by education and health agencies among middle and high school principals and lead health education teachers. Profiles monitors the current status of school health education requirements and content, physical education and physical activity, practices related to bullying and sexual harassment, school health policies related to tobacco-use prevention and nutrition, school-based health and mental health services, family engagement and community involvement, and school health coordination.
DASH - Youth Risk Behavior Surveillance System (YRBSS): High School - Excluding Sexual Identity

1991-2017. High School Dataset. The Youth Risk Behavior Surveillance System (YRBSS) monitors six categories of priority health behaviors among youth and young adults: 1) behaviors that contribute to unintentional injuries and violence; 2) tobacco use; 3) alcohol and other drug use; 4) sexual behaviors related to unintended pregnancy and sexually transmitted infections (STIs), including human immunodeficiency virus (HIV) infection; 5) unhealthy dietary behaviors; and 6) physical inactivity. In addition, YRBSS monitors the prevalence of obesity and asthma and other priority health behaviors.
DASH - Youth Risk Behavior Surveillance System (YRBSS): High School –  Including Sexual Orientation

2015-2017. High School Dataset – Including Sexual Orientation. The Youth Risk Behavior Surveillance System (YRBSS) monitors six categories of priority health behaviors among youth and young adults: 1) behaviors that contribute to unintentional injuries and violence; 2) tobacco use; 3) alcohol and other drug use; 4) sexual behaviors related to unintended pregnancy and sexually transmitted infections (STIs), including human immunodeficiency virus (HIV) infection; 5) unhealthy dietary behaviors; and 6) physical inactivity. In addition, YRBSS monitors the prevalence of obesity and asthma and other priority health behaviors. This dataset contains national, state, and local data from 2015 that includes two aspects of sexual orientation – sexual identity and sex of sexual contacts. Additional information about the YRBSS can be found at www.cdc.gov/yrbss.
DASH - Youth Risk Behavior Surveillance System (YRBSS): Middle School

1991-2017. Middle School Dataset. The Youth Risk Behavior Surveillance System (YRBSS) monitors six categories of priority health behaviors among youth and young adults: 1) behaviors that contribute to unintentional injuries and violence; 2) tobacco use; 3) alcohol and other drug use; 4) sexual behaviors related to unintended pregnancy and sexually transmitted infections (STIs), including human immunodeficiency virus (HIV) infection; 5) unhealthy dietary behaviors; and 6) physical inactivity. In addition, YRBSS monitors the prevalence of obesity and asthma and other priority health behaviors.
Death rates for suicide, by sex, race, Hispanic origin, and age: United States

Data on death rates for suicide, by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Vital Statistics System (NVSS); Grove RD, Hetzel AM. Vital statistics rates in the United States, 1940–1960. National Center for Health Statistics. 1968; numerator data from NVSS annual public-use Mortality Files; denominator data from U.S. Census Bureau national population estimates; and Murphy SL, Xu JQ, Kochanek KD, Arias E, Tejada-Vera B. Deaths: Final data for 2018. National Vital Statistics Reports; vol 69 no 13. Hyattsville, MD: National Center for Health Statistics. 2021. Available from: https://www.cdc.gov/nchs/products/nvsr.htm. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.
Deaths in 122 U.S. cities - 1962-2016. 122 Cities Mortality Reporting System

This file contains the complete set of data reported to 122 Cities Mortality Reposting System. The system was retired as of 10/6/2016. While the system was running each week, the vital statistics offices of 122 cities across the United States reported the total number of death certificates processed and the number of those for which pneumonia or influenza was listed as the underlying or contributing cause of death by age group (Under 28 days, 28 days - 1 year, 1-14 years, 15-24 years, 25-44 years, 45-64 years, 65-74 years, 75-84 years, and - 85 years). U:Unavailable. - : No reported cases.* Mortality data in this table were voluntarily reported from 122 cities in the United States, most of which have populations of >100,000. A death is reported by the place of its occurrence and by the week that the death certificate was filed. Fetal deaths are not included. Total includes unknown ages. More information on Flu Activity & Surveillance is available at http://www.cdc.gov/flu/weekly/fluactivitysurv.htm.
Delay or nonreceipt of needed medical care, prescription drugs, or dental care during the past 12 months due to cost: United States

Data on delay or nonreceipt of needed medical care, nonreceipt of needed prescription drugs, or nonreceipt of needed dental care during the past 12 months due to cost by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health Interview Survey, Family Core, Sample Child, and Sample Adult questionnaires. Data for level of difficulty are from the 2010 Quality of Life, 2011-2017 Functioning and Disability, and 2018 Sample Adult questionnaires. For more information on the National Health Interview Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.
Development of an Empirically Derived Measure of Food Safety Culture in Restaurants

A poor food safety culture has been described as an emerging risk factor for foodborne illness outbreaks, yet there has been little research on this topic in the retail food industry. The purpose of this study was to identify and validate conceptual domains around food safety culture and develop an assessment tool that can be used to assess food workers’ perceptions of their restaurant’s food safety culture. The study, conducted from March 2018 through March 2019, surveyed restaurant food workers for their level of agreement with 28 statements. We received 579 responses from 331 restaurants spread across eight different health department jurisdictions. Factor analysis and structural equation modeling supported a model composed of four primary constructs. The highest rated construct was Resource Availability ( =4.69, sd=0.57), which assessed the availability of resources to maintain good hand hygiene. The second highest rated construct was Employee Commitment (=4.49, sd=0.62), which assessed workers’ perceptions of their coworkers’ commitment to food safety. The last two constructs were related to management. Leadership (=4.28, sd=0.69) assessed the existence of food safety policies, training, and information sharing. Management Commitment (=3.94, sd=1.05) assessed whether food safety was a priority in practice. Finally, the model revealed one higher-order construct, Worker Beliefs about Food Safety Culture (=4.35, sd=0.53). The findings from this study can support efforts by the restaurant industry, food safety researchers, and health departments to examine the influence and effects of food safety culture within restaurants.
Diabetes State Burden Toolkit - Diabetes Mortality and Health-related Quality of Life

This section of the Burden Toolkit reports estimated diabetes mortality statistics in your state for people aged 15 years or older. This data consists of the number of deaths, rate of deaths per 100,000 people, years of potential life lost, and quality-adjusted life years. Data sources: Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). Multiple Cause of Death 2021 from CDC WONDER Online Database. Multiple Cause of Death 1999-2013 from CDC WONDER Online Database. 2013 United States Renal Data System (USRDS). 2013-2017 National Health Interview Survey (NHIS), 2021 Behavioral Risk Factor Surveillance System (BRFSS). For detail, see the Technical Report: https://www.cdc.gov/diabetes/burden-toolkit/docs/technical_documentation.pdf
Diabetes State Burden Toolkit - Economic Burden

This section of the Burden Toolkit reports the economic burden of diabetes in your state and consists of medical (direct) costs, indirect costs, and costs from the perspective of State Medicaid Programs, private insurers, and employers. Data Sources: 2014 and 2021 National Health Expenditure Accounts (NHEA) from CMS. 2014 State Health Expenditure Accounts (SHEA) from CMS. 2022 Current Population Survey. 2021 Behavioral Risk Factor Surveillance Survey (BRFSS). 2015-2019 Medical Expenditure Panel Survey (MEPS). 2013-2021 National Health Interview Survey (NHIS), 2022. 2021 CDC Wonder. For detail, see the Technical Report: https://www.cdc.gov/diabetes/burden-toolkit/docs/technical_documentation.pdf
Diabetes State Burden Toolkit - Health Burden

This section of the Burden Toolkit reports the health burden of diabetes in your state and consists of diabetes prevalence, diabetes incidence, and diabetes-associated conditions. The statistics for diabetes-associated conditions are based on self-reported data, hospitalization data, and Medicare data. For each condition, the toolkit reports the rate of the condition among people with diabetes and the number of cases attributable to diabetes. Data sources: 2020-2021 Behavioral Risk Factor Surveillance System (BRFSS). Agency for Healthcare Research and Quality (AHRQ), Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases (SID). 2021 Medicare Master Beneficiary Summary (MBS) File. For detail, see the Technical Report: https://www.cdc.gov/diabetes/burden-toolkit/docs/technical_documentation.pdf