Graph and download economic data for All Employees, Hospitals (CES6562200001) from Jan 1990 to Jun 2025 about hospitals, health, establishment survey, education, services, employment, and USA.

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

JMP was used to transpose, join, and split data.

ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform.

VARIABLE AND RECODING NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

The CarePrecise U.S. HCP/HCO Collection Dataset includes deep data on all 6.7 million U.S. HIPAA-covered healthcare practitioners and organizations. Monthly full updates. Includes linkages between the individual practitioners and their practice groups, hospitals, and hospital systems. Licensing plans are available for basic (internal use), derivative products, and redistribution. Data updates are delivered quarterly or monthly to suit customer need; FTP push is available, standard delivery is via CDN. Single download for evaluation is available. CarePrecise is a leader in the fields of HCP/HCO data, supplying provider data to the industry since 2008. Note regarding pricing: The Collection price shown in Pricing is separate from email addresses. Email addresses are priced as low as $0.075 per, based on volume. Pricing shown is without derivative product (DP) licensing for use in web applications; DP license ranges in price from $1,900/year to $9,000/year on top of data purchase, based on application and overall exposure estimate. DP license is sold in two-year term and requires a license agreement.

Attrition of nurses in the US Healthcare system is at an all-time high. It is a major area of focus, especially for hospitals.

This dataset contains employee and company data useful for supervised ML, unsupervised ML, and analytics. Attrition - whether an employee left or not - is included and can be used as the target variable.

The data is synthetic and based on the IBM Watson dataset for attrition. Employee roles and departments were changed to reflect the healthcare domain. Also, known outcomes for some employees were changed to help increase the performance of ML models.

Here's an app I use as a demo based on this dataset and an ML classification model.

https://i.imgur.com/Aft3t1E.png"> https://i.imgur.com/QNRX2LA.png">

US Healthcare NPI Data is a comprehensive resource offering detailed information on health providers registered in the United States.

Dataset Highlights:

• NPI Numbers: Unique identification numbers for health providers.
• Contact Details: Includes addresses and phone numbers.
• State License Numbers: State-specific licensing information.
• Additional Identifiers: Other identifiers related to the providers.
• Business Names: Names of the provider’s business entities.
• Taxonomies: Classification of provider types and specialties.

Taxonomy Data:

• Includes codes, groupings, and classifications.
• Facilitates detailed analysis and categorization of providers.

Data Updates:

• Weekly Delta Changes: Ensures the dataset is current with the latest changes.
• Monthly Full Refresh: Comprehensive update to maintain accuracy.

Use Cases:

• Market Analysis: Understand the distribution and types of healthcare providers across the US. Analyze market trends and identify potential gaps in healthcare services.
• Outreach: Create targeted marketing campaigns to reach specific types of healthcare providers. Use contact details for direct outreach and engagement with providers.
• Research: Conduct in-depth research on healthcare providers and their specialties. Analyze provider attributes to support academic or commercial research projects.
• Compliance and Verification: Verify provider credentials and compliance with state licensing requirements. Ensure accurate provider information for regulatory and compliance purposes.

Data Quality and Reliability:

• The dataset is meticulously curated to ensure high quality and reliability. Regular updates, both weekly and monthly, ensure that users have access to the most current information. The comprehensive nature of the data, combined with its regular updates, makes it a valuable tool for a wide range of applications in the healthcare sector.

Access and Integration: - CSV Format: The dataset is provided in CSV format, making it easy to integrate with various data analysis tools and platforms. - Ease of Use: The structured format of the data ensures that it can be easily imported, analyzed, and utilized for various applications without extensive preprocessing.

Ideal for:

• Healthcare Professionals: Physicians, nurses, and other healthcare providers who need to verify information about their peers.
• Analysts: Data analysts and business analysts who require detailed and accurate healthcare provider data for their projects.
• Businesses: Companies in the healthcare sector looking to understand market dynamics and reach out to providers.
• Researchers: Academic and commercial researchers conducting studies on healthcare providers and services.

Why Choose This Dataset?

• Comprehensive Coverage: Detailed information on millions of healthcare providers across the US.
• Regular Updates: Weekly and monthly updates ensure that the data remains current and reliable.
• Ease of Integration: Provided in a user-friendly CSV format for easy integration with your existing systems.
• Versatility: Suitable for a wide range of applications, from market analysis to compliance and research.

By leveraging the US Healthcare NPI & Taxonomy Data, users can gain valuable insights into the healthcare landscape, enhance their outreach efforts, and conduct detailed research with confidence in the accuracy and comprehensiveness of the data.

Summary:

• This dataset is an invaluable resource for anyone needing detailed and up-to-date information on US healthcare providers. Whether for market analysis, research, outreach, or compliance, the US Healthcare NPI & Taxonomy Data offers the detailed, reliable information needed to achieve your goals.

Success.ai’s Healthcare Industry Leads Data and B2B Contact Data for US Healthcare Professionals offers an extensive and verified database tailored to connect businesses with key executives and administrators in the healthcare industry across the United States. With over 170M verified profiles, including work emails and direct phone numbers, this dataset enables precise targeting of decision-makers in hospitals, clinics, and healthcare organizations.

Backed by AI-driven validation technology for unmatched accuracy and reliability, this contact data empowers your marketing, sales, and recruitment strategies. Designed for industry professionals, our continuously updated profiles provide the actionable insights you need to grow your business in the competitive healthcare sector.

Key Features of Success.ai’s US Healthcare Contact Data:

• Comprehensive Healthcare Sector Coverage Access detailed contact information for professionals across the healthcare spectrum:

Hospital Executives: CEOs, CFOs, and COOs managing top-tier facilities. Healthcare Administrators: Decision-makers driving operational excellence. Medical Professionals: Physicians, specialists, and nurse practitioners. Clinic Managers: Leaders in small and mid-sized healthcare organizations.

• AI-Validated Accuracy and Updates

  99% Verified Accuracy: Our advanced AI technology ensures data reliability for optimal engagement. Real-Time Updates: Profiles are continuously refreshed to maintain relevance and accuracy. Minimized Bounce Rates: Save time and resources by reaching verified contacts.

• Customizable Delivery Options Choose how you access the data to match your business requirements:

API Integration: Connect our data directly to your CRM or sales platform. Flat File Delivery: Receive customized datasets in formats suited to your needs.

Why Choose Success.ai for Healthcare Data?

• Best Price Guarantee We ensure competitive pricing for our verified contact data, offering the most comprehensive and cost-effective solution in the market.

• Compliance-Driven and Ethical Data Our data collection adheres to strict global standards, including HIPAA, GDPR, and CCPA compliance, ensuring secure and ethical usage.

• Strategic Benefits for Your Business Success.ai’s US healthcare professional data unlocks numerous business opportunities:

Targeted Marketing: Develop tailored campaigns aimed at healthcare executives and decision-makers. Efficient Sales Outreach: Engage with key contacts to accelerate your sales process. Recruitment Optimization: Access verified profiles to identify and recruit top talent in the healthcare industry. Market Intelligence: Use detailed firmographic and demographic insights to guide strategic decisions. Partnership Development: Build valuable relationships within the healthcare ecosystem.

• Data Highlights 170M+ Verified Profiles 50M Direct Phone Numbers 700M Global Professional Profiles 70M Verified Company Profiles

Key APIs for Advanced Functionality

• Enrichment API Enhance your existing contact data with real-time updates, ensuring accuracy and relevance for your outreach initiatives.

• Lead Generation API Drive high-quality lead generation efforts by utilizing verified contact information, including work emails and direct phone numbers, for up to 860,000 API calls per day.

• Use Cases

1. Healthcare Marketing Campaigns Target verified executives and administrators to deliver personalized and impactful marketing campaigns.

2. Sales Enablement Connect with key decision-makers in healthcare organizations, ensuring higher conversion rates and shorter sales cycles.

3. Talent Acquisition Source and engage healthcare professionals and administrators with accurate, up-to-date contact information.

4. Strategic Partnerships Foster collaborations with healthcare institutions and professionals to expand your business network.

5. Industry Analysis Leverage enriched contact data to gain insights into the US healthcare market, helping you refine your strategies.

• What Sets Success.ai Apart?

Verified Accuracy: AI-driven technology ensures 99% reliability for all contact details. Comprehensive Reach: Covering healthcare professionals from large hospital systems to smaller clinics nationwide. Flexible Access: Customizable data delivery methods tailored to your business needs. Ethical Standards: Fully compliant with healthcare and data protection regulations.

Success.ai’s B2B Contact Data for US Healthcare Professionals is the ultimate solution for connecting with industry leaders, driving impactful marketing campaigns, and optimizing your recruitment strategies. Our commitment to quality, accuracy, and affordability ensures you achieve exceptional results while adhering to ethical and legal standards.

No one beats us on price. Period.

Graph and download economic data for All Employees: Health Care: Hospitals in Boston-Cambridge-Newton, MA (NECTA Division) (SMU25716546562200001A) from 1990 to 2023 about hospitals, Boston, health, MA, employment, and USA.

This map service shows the locations of healthcare facilities (hospitals, medical centers, federally qualified health centers, home health services, and nursing homes) in the United States. The data was provided by the U.S. Department of Health Human Services and is current as of 2012.The data is symbolized by facility type:Hospital: an institution providing medical and surgical treatment and nursing care for sick or injured people.Medical Center: a health care facility staffed and equipped to care for many patients and for a large number of various kinds of diseases and dysfunctions, using sophisticated technology.Federally Qualified Health Center: a community-based organization that provides comprehensive primary care and preventative care, including health, oral, and mental health/substance abuse services to persons of all ages, regardless of their ability to pay or health insurance status.Home Health Service: health care or supportive care provided in the patient's home by health care professionals (often referred to as home health care or formal care).Nursing Home: provides a type of residential care. They are a place of residence for people who require constant nursing care and have significant deficiencies with activities of daily living.Other data sources include: Data.gov_Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

The All CMS Data Feeds dataset is an expansive resource offering access to 119 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system including nursing facility owners and accountable care organization participants contact data. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis too...

During the COVID-19 pandemic, certain occupations and industries were deemed "essential", and typically included individuals who worked in healthcare, food service, public transportation, etc. However, early on in the pandemic, while these workers faced disproportionately higher risks, they often did not receive adequate personal protective equipment (PPE), were unable to work from home, and were limited in their ability to take other precautions to safeguard their health (Chen et al., 2021). As a result, previous studies have documented higher rates of infection, hospitalization, and death among essential workers compared to their non-essential worker counterparts (Selden & Berdahl, 2021; Wei et al., 2022). This dataset provides users with information on the number and proportion of essential workers in census tracts or ZIP Code tabulation areas (ZCTAs) in the United States over the 2016-2020 period.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

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. (33 data elements)

• 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 current 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 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://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.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 (Interim-20-ID-02). 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 voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

The size of the US Health Information Exchange Industry market was valued at USD 0.66 Million in 2023 and is projected to reach USD 1.47 Million by 2032, with an expected CAGR of 12.12% during the forecast period. The U.S. HIE market has been enjoying a robust growth trajectory for years now and has received substantial impetus due to the requirements to improve care and outcome, occasioned by rising demand for healthcare providers to have their requirements of liquid sharing of data. HIE enables the electronic exchange of health information across various organizations and systems. This enables them to have broad access to patient information by healthcare professionals and reduces redundancies while enhancing care coordination. Key drivers in the market are driven by governments pushing interoperability and the use of EHRs seen within the 21st Century Cures Act, underlining the improvement of shared data. More attention is paid to value-based care models and population health management for health providers involved in better decision-making and improving patient care through HIE solutions. The geographic regions further illustrate an extensive array of public and private HIEs throughout the US; the fact that significant investment is occurring within both the public and private sectors speaks to the rapidly evolving market. Increased emphasis on advanced technologies such as cloud computing, artificial intelligence, and blockchain is being given to enable security and interoperability improvements for data systems as more healthcare organizations become conscious of the need for interconnected systems. Actually, the U.S. health information exchange industry is better poised to continue its growth in and around the future of healthcare delivery, one that is changing and further becoming efficient by its integration of collaboration among healthcare stakeholders. Recent developments include: In October 2022, Mpowered Health launched its xChange, the United States consumer-mediated healthcare data exchange. The exchange enables health plans, health systems, and other healthcare organizations to request and obtain medical records from consumers with their consent., In March 2022, mpro5 Inc announced its launch into the United States market with a strategy of enabling the collection and leverage of real-time data to simplify the most complex operational challenges in healthcare and hospitals.. Key drivers for this market are: Increasing Demand for Electronic Health Records Resulting in the Expansion of the Market, Government Support via Various Programs and Incentives; Reduction in Healthcare Cost and Improved Efficacy. Potential restraints include: Huge Initial Infrastructural Investment and Slow Return on Investment, Data Privacy and Security Concerns. Notable trends are: The Decentralized/Federated Model is Expected to Hold a Notable Market Share Over the Forecast Period.

United States - All Employees, Health Care was 18124.60000 Thous. of Persons in May of 2025, according to the United States Federal Reserve. Historically, United States - All Employees, Health Care reached a record high of 18124.60000 in May of 2025 and a record low of 8014.00000 in January of 1990. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - All Employees, Health Care - last updated from the United States Federal Reserve on July of 2025.

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Graph and download economic data for Employed full time: Wage and salary workers: Registered nurses occupations: 16 years and over (LEU0254487900A) from 2000 to 2024 about registered nurses, nursing, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

In 2023, there were over 7.4 million people employed in hospitals across the United States. This is the highest number in the recorded time period and hospital employment numbers have returned to and surpassed pre-pandemic levels.

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United States Employment: NF: PW: EH: Health Care & Social Assistance data was reported at 17,535.800 Person th in May 2018. This records an increase from the previous number of 17,490.600 Person th for Apr 2018. United States Employment: NF: PW: EH: Health Care & Social Assistance data is updated monthly, averaging 12,566.500 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 17,535.800 Person th in May 2018 and a record low of 8,083.600 Person th in Jan 1990. United States Employment: NF: PW: EH: Health Care & Social Assistance data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G030: Current Employment Statistics Survey: Employment: Production Worker: Non Farm.

Project Overview This project focused on identification and enhanced understanding of the stressors, challenges, and supports experienced by Ghanaian frontline healthcare workers (HCWs) during the COVID-19 pandemic through a qualitative study design. This study fills a gap in the literature by exploring the daily pandemic-related experiences of HCWs in an under-resourced country and includes recommendations for how they can be better supported during health crises. Data and Data Collection Overview Using a qualitative descriptive methodology, we conducted individual, semi-structured interviews to explore the experiences of Ghanaian healthcare workers during the pandemic. People with direct patient care experience or people who held leadership roles in their healthcare organizations for at least two years were recruited to take part in the study. A qualitative study design with semi-structured interviews lasting 60-90 minutes each was used to gain insights into the lived experiences of frontline nurses and community healthcare workers from Ghana, a leader in healthcare reform among African nations during the COVID-19 pandemic. Open-ended questions were used which prompted participants to reflect on the stressors they faced at home and work while navigating the changes brought about by the pandemic. The interviews were conducted between April and May 2021 by a US-based team via Zoom. All interview transcripts were produced by Landmark Associates, Inc. (https://thelai.com). Thematic analysis revealed the following themes: challenges and stressors, government support, overcoming challenges, and recommendations for leadership in healthcare organizations. Selection and Organization of Shared Data The data files shared here encompass the 20 de-identified interview transcripts with Ghanaian healthcare workers. The documentation files shared consist of the original informed consent used, the interview questionnaire, a Data Narrative and an administrative README file.