Context

This official dataset from the Medicare.gov Nursing Home Compare website allows for comparison of over 15,000 Medicare and Medicaid-certified nursing homes in the country.

Content

Separate data collections include:

• Deficiencies, including fire safety, health, and inspection cycle types

• Ownership details, including ownership percentage

• Penalties, including filing date, fee, and payment date

• Provider details, including non or for profit status, staff ratings, and survey scores

• Quality MSR (Minimum Savings Rate) claims, including adjusted and observed scores

• MDS (Minimum Data Set) quality measures, scored on a quarterly basis

• State averages, including total number of quarterly deficiencies, and nurse staffing hours

• Survey summaries for each nursing home

Inspiration

• How would you determine what the top ten best nursing homes in the country are? The least?

• Which states have the best level of nursing home care? The least?

• In general, what are the most common types of complaints and deficiencies?

Acknowledgements

This dataset was collected by Medicare.gov, and the original files can be accessed here.

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Certified by the Executive Office of Aging & Independence, Assisted Living Residences (ALRs) are private residences that offer, for a monthly fee, housing, meals, and personal care services to aging adults who live independently.

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HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 06/2025

Health Facility General Information

Art 28, 36 & 40 Accredited Facilities & Programs

By Health Data New York [source]

About this dataset

This dataset contains the locations of Article 28, Article 36 and Article 40 health care facilities and programs from the Health Facilities Information System (HFIS), including hospitals, nursing homes, certified home health care agencies, hospices and diagnostic treatment centers. These facilities are fundamental to providing necessary medical services throughout the state and it is up to local governments to properly track them.

In this dataset you will find information such as facility name, short description, address information for both the operator and cooperator (as applicable), zip codes for each location, a unique certificate number issued by HFIS for each facility or program, latitude/longitude coordinates for each location as well as the type of ownership of that facility. All of this data helps ensure that people can access essential medical services in their area quickly and easily.

So no matter what type of medical service you are looking for or which locality within New York State you're located in, you can use this insight-backed dataset to understand where these important services are located near you! Before using the data provided be sure to download and read through our Terms of Service for an understanding of proper usage requirements

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How to use the dataset

This dataset can be used to provide information about the location of Article 28, Article 36 and Article 40 health care facilities and programs. This data provides information about each health facility, such as its name, address, phone number, website, type of ownership and more. With this data you can easily explore which health care facilities are available in your area or around the world.

To get started with this dataset there are a few simple steps you need to take: - Read through the Terms of Service: Be sure to read through all terms before using this dataset as it sets out what is allowed and not allowed when using it. - View the Column Labels: This will help you understand which columns best correspond with any searches or analysis that you want to do on this data set - Conduct Your Search/Analysis: Once you've determined which columns best match your query begin running your own searches/analysis on the Health Facilities Information System (HFIS). - Filter for Relevant Datapoints: Utilize filters if needed to narrow down specific datapoints that are relevant for your search/analysis 5 Enjoy Your Results!: Enjoy exploring all of the results of your analysis

Research Ideas

• Create an interactive map to show the distribution of health facilities and programs across different states, cities and regions.
• Analyze the data to identify areas with increased or decreased need for certain types of health care services and develop appropriate strategies for service improvisation in these areas.
• Use customer feedback from patient surveys to compare facilities based on their performance in providing quality healthcare services including cost efficiency and patient satisfaction rates, in order to improve the overall standards of care within these establishments

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-facility-general-information-1.csv | Column name | Description | |:---------------------------------|:-----------------------------------------------------------------| | Facility Name | Name of the health care facility or program. (String) | | Short Description | A brief description of the facility or program. (String) | | Facility Open Date | The date the facility or program opened. (Date) | | Facility Address 1 | The first line of the facility's address. (String) | | Facility Address 2 | The second line of the facility's address. (String) | | Facility City | The city the facility is located in. (String) | | Facility State | The state the facility is located in. (String) | | Facility Zip Code | The zip code of the facility. (String) ...

This National Master Facility Inventory data file is intended to provide a comprehensive list of nursing homes and related care homes in the United States in 1982. The criteria for inclusion were that a home maintained three or more inpatient beds and provided one or more personal care services (such as help with eating, walking, correspondence, dressing, bathing, or using the toilet) or supervisory care. The file contains the names and addresses of 26,817 such facilities along with characteristics such as total beds set up and staffed for use, number of beds certified by Medicare or Medicaid as skilled and intermediate beds, total residents, and type of ownership. Facilities that only provided services to the mentally ill or the mentally retarded are not included in this file. Each facility has a unique identification number that incorporates the Federal Information Processing Standards state and county codes for sorting and selection by state, county, city, or zip code.

On an annual basis (based on individual Long-Term Care (LTC) facility fiscal year end), California licensed LTC facilities report detailed financial data on facility information, ownership information, patient days & discharges, Balance Sheet, Equity Statement, Cash Flows, Income Statement, Revenue by type and payer, Expense Detail, and Labor Detail. Based on the selected data set, the pivot tables display summarized data on a Profile page and also provides charts on various data items such as Patient Days, Revenue & Expense, and Revenue.

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Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company Priya-Nursing-Home-Health-Care.

This dataset represents the location of assisted living facilities within the City of Baltimore. The list of facilities is from the Maryland Office of Health Care and Quality (OHCQ). To leave feedback or ask a question about this dataset, please fill out the following form: Assisted Living Facilities feedback form.

USA Census Block Groups (CBG) for Urban Search and Rescue. This layer can be used for search segment planning. Block groups generally contain between 600 and 5,000 people and the boundaries generally follow existing roads and waterways. The field segment_designation is the last 6 digits of the unique identifier and matches the field in the SARCOP Segment layer.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.* *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the block group itself.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

This is one of over 400 major media market consumer surveys which have been gifted to Washington State University (WSU) by Leigh Stowell & Company, Inc. of Seattle, Washington, USA. This is a market research firm which specializes in providing newspapers, television affiliates and cable operators with market segmentation research pertinent to consumer purchasing patterns and the effective marketing of goods and services to program audiences. The data in the Stowell Archive were collected via random digit dialing and computer-aided telephone interviews (CATI). Most of the surveys focus on the marketing needs of mass media clients and contain demographics, psychographics, media exposure information, and purchasing behavior data about consumers in major metropolitan areas of the United States and Canada starting in 1989. The sample sizes of the surveys range from 500 to 3,000 respondents, averaging 1,000 observations per study. Data are available at the respondent level, and all observations are keyed to zip code or other geographic identifiers. Additional surveys are anticipated, with over twenty new media marke t studies being donated annually. The University's relationship with Leigh Stowell & Company, Inc. was cultivated by Dr. Nicholas Lovrich, Director of WSU's Division of Governmental Studies and Services (DGSS) and by Dr. John Pierce, former Dean of the WSU College of Liberal Arts over the course of a decade. DGSS collaborated with WSU Libraries Digital Services to process the gifted data files into this digital archive which features powerful search and download capabilities. Further refinement of the archive in accordance with the Data Documentation Initiative is progressing with support from the Office of the Provost, the College of Liberal Arts and the WSU Libraries. It is important to note that the year indicated by the study's title is the year that the original survey was published, and is not necessarily the year in which the interviews were conducted. Refer to the metadata field "Dates of Collection" to di scern the interview dates of each specific survey. Refer also to date fields within the data file itself.

This dataset denotes HUD subsidized Multifamily Housing properties excluding insured hospitals with active loans. HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here.

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This material contains the data, do-files, and codebook for replicating the results in the article.

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Cost comparison table showing 2023 and 2024 median costs by location

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