28 datasets found
  1. National Inpatient Sample (NIS) - Restricted Access Files

    • catalog.data.gov
    • data.virginia.gov
    • +2more
    Updated Feb 22, 2025
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    Agency for Healthcare Research and Quality, Department of Health & Human Services (2025). National Inpatient Sample (NIS) - Restricted Access Files [Dataset]. https://catalog.data.gov/dataset/hcup-national-nationwide-inpatient-sample-nis-restricted-access-file
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    Dataset updated
    Feb 22, 2025
    Description

    The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time. The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

  2. NIS_2016

    • redivis.com
    application/jsonl +7
    Updated Jan 27, 2025
    + more versions
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    Center for Surgery and Public Health (2025). NIS_2016 [Dataset]. https://redivis.com/datasets/e4ms-4dp8mape8
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    avro, sas, parquet, csv, arrow, stata, spss, application/jsonlAvailable download formats
    Dataset updated
    Jan 27, 2025
    Dataset provided by
    Redivis Inc.
    Authors
    Center for Surgery and Public Health
    Description

    Usage

    The National (Nationwide) Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient healthcare database designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from around 7 million hospital stays each year. Weighted, it estimates around 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

  3. NIS_2021

    • redivis.com
    application/jsonl +7
    Updated Jan 28, 2025
    + more versions
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    Center for Surgery and Public Health (2025). NIS_2021 [Dataset]. https://redivis.com/datasets/0k8c-e251d58hc
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    stata, sas, parquet, csv, spss, application/jsonl, arrow, avroAvailable download formats
    Dataset updated
    Jan 28, 2025
    Dataset provided by
    Redivis Inc.
    Authors
    Center for Surgery and Public Health
    Description

    Usage

    The National (Nationwide) Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient healthcare database designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from around 7 million hospital stays each year. Weighted, it estimates around 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

  4. a

    ‘Healthcare Cost and Utilization Project (HCUP) - National Inpatient Sample’...

    • analyst-2.ai
    Updated Jan 27, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Healthcare Cost and Utilization Project (HCUP) - National Inpatient Sample’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-healthcare-cost-and-utilization-project-hcup-national-inpatient-sample-6aba/aee06443/
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    Dataset updated
    Jan 27, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Healthcare Cost and Utilization Project (HCUP) - National Inpatient Sample’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5fd2d275-4019-407f-af21-58e453bc8caa on 27 January 2022.

    --- Dataset description provided by original source is as follows ---

    2001 forward. The National (Nationwide) Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient health care database in the United States, yielding national estimates of hospital inpatient stays. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data are organized by indicator, and they include CVDs (e.g., heart failure). The data can be plotted as trends and stratified by age group, sex, and race/ethnicity.

    --- Original source retains full ownership of the source dataset ---

  5. n

    Hospital Admission Data from the Agency for HealthCare Research and Quality...

    • cmr.earthdata.nasa.gov
    Updated Apr 20, 2017
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    (2017). Hospital Admission Data from the Agency for HealthCare Research and Quality (AHRQ) [Dataset]. https://cmr.earthdata.nasa.gov/search/concepts/C1214136020-SCIOPS
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    Dataset updated
    Apr 20, 2017
    Time period covered
    Jan 1, 1970 - Present
    Description

    The Agency for Healthcare Research and Quality (AHRQ, formerly the Agency for Health Care Policy and Research) maintains the Healthcare Cost and Utilization Project (HCUP). HCUP is a Federal-State-industry partnership to build a standardized, multi-State health data system. AHRQ has taken the lead in developing HCUP databases, Web-based products, and software tools and making them available for restricted access public release.

    HCUP comprises a family of administrative longitudinal databases-including State-specific hospital-discharge databases and a national sample of discharges from community hospitals.

    HCUP databases contain patient-level information compiled in a uniform format with privacy protections in place. * The Nationwide Inpatient Sample (NIS) includes inpatient data from a national sample (about 20% of U.S. community hospitals) including roughly 7 million discharges from about 1,000 hospitals. It is the largest all-payer inpatient database in the U.S.; data are now available from 1988-1998. The NIS is ideal for developing national estimates, for analyzing national trends, and for research that requires a large sample size. * The State Inpatient Databases (SID) cover individual data sets in community hospitals from 22 participating States that represent more than half of all U.S. hospital discharges. The data have been translated into a uniform format to facilitate cross-State comparisons. The SID are particularly well-suited for policy inquiries unique to a specific State, studies comparing two or more States, market area research, and small area variation analyses.

    • The State Ambulatory Surgery Databases (SASD) contain data from ambulatory care encounters in 9 participating States. The SASD capture surgeries performed on the same day in which patients are admitted and released form hospital- affiliated ambulatory surgery sites. The SASD are well suited for research that requires complete enumeration of hospital-based ambulatory surgeries within market areas and States.
    • The project's newest restricted access public release is the Kids' Inpatient Database (KID), containing hospital inpatient stays for children 18 years of age and younger. Researchers and policymakers can use the KID to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The KID is the only all-payer inpatient care database for children in the U.S. It contains data from approximately 1.9 million hospital discharges for children. The data are drawn from 22 HCUP 1997 State Inpatient Databases and include a sample of pediatric general discharges from over 2,500 U.S. community hospitals (defined as short-term, non-Federal, general and specialty hospitals, excluding hospital units of other institutions). A key strength of the KID is that the large sample size enables analyses of both common and rare conditions; uncommon treatments, and organ transplantation. The KID also includes charge information on all patients, regardless of payer, including children covered by Medicaid, private insurance, and the uninsured.

      HCUP also contains powerful, user-friendly software that can be used with both HCUP data and with other administrative databases. The AHRQ has developed three powerful software tools Quality Indicators (QIs), Clinical Classification Software (CCS) and HCUPnet. See more on the agency's webpages.

  6. AHRQ Healthcare Cost and Utilization Project

    • openicpsr.org
    Updated Feb 21, 2025
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    AHRQ (2025). AHRQ Healthcare Cost and Utilization Project [Dataset]. http://doi.org/10.3886/E220328V2
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    Dataset updated
    Feb 21, 2025
    Dataset provided by
    Agency for Healthcare Research and Qualityhttp://www.ahrq.gov/
    Authors
    AHRQ
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Summary Trend TablesThe HCUP Summary Trend Tables include information on hospital utilization derived from the HCUP State Inpatient Databases (SID), State Emergency Department Databases (SEDD), National Inpatient Sample (NIS), and Nationwide Emergency Department Sample (NEDS). State statistics are displayed by discharge month and national and regional statistics are displayed by discharge quarter. Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD.The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics:Overview of trends in inpatient and emergency department utilizationAll inpatient encounter typesInpatient encounter typeNormal newbornsDeliveriesNon-elective inpatient stays, admitted through the EDNon-elective inpatient stays, not admitted through the EDElective inpatient staysInpatient service lineMaternal and neonatal conditionsMental health and substance use disordersInjuriesSurgeriesOther medical conditionsED treat-and-release visitsDescription of the data source, methodology, and clinical criteria (Excel file, 43 KB)Change log (Excel file, 65 KB)For each type of inpatient stay, there is an Excel file for the number of discharges, the percent of discharges, the average length of stay, the in-hospital mortality rate per 100 discharges,1 and the population-based rate per 100,000 population.2 Each Excel file contains State-specific, region-specific, and national statistics. For most files, trends begin in January 2017. Also included in each Excel file is a description of the HCUP databases and methodology.

  7. AHRQ Healthcare Cost and Utilization Project Summary Tables

    • datalumos.org
    Updated Feb 21, 2025
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    AHRQ (2025). AHRQ Healthcare Cost and Utilization Project Summary Tables [Dataset]. http://doi.org/10.3886/E220328V1
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    Dataset updated
    Feb 21, 2025
    Dataset provided by
    Agency for Healthcare Research and Qualityhttp://www.ahrq.gov/
    Authors
    AHRQ
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Summary Trend TablesThe HCUP Summary Trend Tables include information on hospital utilization derived from the HCUP State Inpatient Databases (SID), State Emergency Department Databases (SEDD), National Inpatient Sample (NIS), and Nationwide Emergency Department Sample (NEDS). State statistics are displayed by discharge month and national and regional statistics are displayed by discharge quarter. Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD.The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics:Overview of trends in inpatient and emergency department utilizationAll inpatient encounter typesInpatient encounter typeNormal newbornsDeliveriesNon-elective inpatient stays, admitted through the EDNon-elective inpatient stays, not admitted through the EDElective inpatient staysInpatient service lineMaternal and neonatal conditionsMental health and substance use disordersInjuriesSurgeriesOther medical conditionsED treat-and-release visitsDescription of the data source, methodology, and clinical criteria (Excel file, 43 KB)Change log (Excel file, 65 KB)For each type of inpatient stay, there is an Excel file for the number of discharges, the percent of discharges, the average length of stay, the in-hospital mortality rate per 100 discharges,1 and the population-based rate per 100,000 population.2 Each Excel file contains State-specific, region-specific, and national statistics. For most files, trends begin in January 2017. Also included in each Excel file is a description of the HCUP databases and methodology.

  8. Data from: Perioperative outcomes.

    • plos.figshare.com
    xls
    Updated Feb 29, 2024
    + more versions
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    Yang-Fan Liu; Te-Li Chen; Ching-Hsueh Tseng; Jen-Yu Wang; Wen-Ching Wang (2024). Perioperative outcomes. [Dataset]. http://doi.org/10.1371/journal.pone.0299256.t002
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    xlsAvailable download formats
    Dataset updated
    Feb 29, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Yang-Fan Liu; Te-Li Chen; Ching-Hsueh Tseng; Jen-Yu Wang; Wen-Ching Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundObesity is a global health issue with increasing prevalence. Surgical procedures, such as surgical stabilization of rib fractures (SSRF), may be affected by obesity-related complications. The objective of the study is to investigate the effects of obesity on SSRF outcomes in multiple rib fractures.MethodsThis retrospective study analyzed data from adults aged ≥ 20 years in the Nationwide Inpatient Sample (NIS) database diagnosed with multiple rib fractures who underwent SSRF between 2005 and 2018. It investigated the relationship between obesity and in-patient outcomes, such as discharge status, length of stay (LOS), in-hospital mortality, hospital costs, and adverse events using logistic and linear regression analyses.ResultsAnalysis of data from 1,754 patients (morbidly obese: 87; obese: 106; normal weight: 1,561) revealed that morbid obesity was associated with longer LOS (aBeta = 0.07, 95% CI: 0.06, 0.07), higher hospital costs (aBeta = 47.35, 95% CI: 38.55, 56.14), increased risks of adverse events (aOR = 1.63, 95% CI: 1.02, 2.61), hemorrhage/need for transfusion (aOR = 1.77, 95% CI: 1.12, 2.79) and mechanical ventilation ≥ 96 hours (aOR = 2.14, 95% CI: 1.28, 3.58) compared to normal weight patients. Among patients with flail chest, morbid obesity was significantly associated with tracheostomy (aOR = 2.13, 95% CI: 1.05, 4.32), ARDS/respiratory failure (aOR = 2.01, 95% CI: 1.09, 3.70), and mechanical ventilation ≥ 96 hours (aOR = 2.80, 95% CI: 1.47, 5.32). In contrast, morbid obesity had no significant associations with these adverse respiratory outcomes among patients without a flail chest (p > 0.05).ConclusionsMorbid obesity is associated with adverse outcomes following SSRF for multiple rib fractures, especially for flail chest patients.

  9. u

    Data from: US Regional and Demographic Differences in Prescription Opioid...

    • datacatalog.hshsl.umaryland.edu
    Updated Aug 9, 2018
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    (2018). US Regional and Demographic Differences in Prescription Opioid and Heroin-Related Overdose Hospitalizations [Dataset]. https://datacatalog.hshsl.umaryland.edu/search?keyword=subject_keywords:Fentanyl%20Overdose
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    Dataset updated
    Aug 9, 2018
    Description

    Dataset is comprised of heroin overdose-related and prescription opioid overdose-related hospitalization rates for the years 2000 through 2014. Data is derived from the Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS). Included are rates by census region and division with separate rates for age and race.

  10. u

    Heroin in Transition Study (HIT)

    • datacatalog.hshsl.umaryland.edu
    Updated Apr 28, 2018
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    (2018). Heroin in Transition Study (HIT) [Dataset]. https://datacatalog.hshsl.umaryland.edu/search?keyword=subject_keywords:Heroin%20Purity
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    Dataset updated
    Apr 28, 2018
    Description

    Dataset comprises hospitalization rates for opioid injection-related skin and soft-tissue infections (O-SSTI) correlated with changes in the price and purity of heroin for the years 1993 through 2010, inclusive. Data is derived from the Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) and the Drug Enforcement Administration (DEA) System to Retrieve Information from Drug Evidence (STRIDE) databases for 27 Metropolitan Statistical Areas (MSAs).

  11. Inpatient Arthroplasty Procedures by Type and Sex USBJI 2010-2011

    • johnsnowlabs.com
    csv
    Updated Jan 20, 2021
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    John Snow Labs (2021). Inpatient Arthroplasty Procedures by Type and Sex USBJI 2010-2011 [Dataset]. https://www.johnsnowlabs.com/marketplace/inpatient-arthroplasty-procedures-by-type-and-sex-usbji-2010-2011/
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    csvAvailable download formats
    Dataset updated
    Jan 20, 2021
    Dataset authored and provided by
    John Snow Labs
    Time period covered
    2010 - 2011
    Area covered
    United States
    Description

    This dataset is provided for both of the two national hospital discharge databases for comparison purposes. Although they vary slightly in the number of cases, overall they provide relatively consistent estimates of inpatient joint replacement procedures. Data comes from the National Hospital Discharge Survey (NHDS) and the Nationwide Inpatient Sample (NIS).

  12. f

    Demographic data for patients included in the study from 2001–2007.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
    + more versions
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    Adam C. Stein; John Nick Gaetano; Jeffrey Jacobs; Rangesh Kunnavakkam; Marc Bissonnette; Joel Pekow (2023). Demographic data for patients included in the study from 2001–2007. [Dataset]. http://doi.org/10.1371/journal.pone.0161523.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Adam C. Stein; John Nick Gaetano; Jeffrey Jacobs; Rangesh Kunnavakkam; Marc Bissonnette; Joel Pekow
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Data is for weighted samples.

  13. f

    Dataset for Obesity and the Risk of Sepsis, a nation-wide inpatient study

    • figshare.com
    bin
    Updated Jun 5, 2023
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    Anh Nguyen (2023). Dataset for Obesity and the Risk of Sepsis, a nation-wide inpatient study [Dataset]. http://doi.org/10.6084/m9.figshare.2008266.v1
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    binAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    figshare
    Authors
    Anh Nguyen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Nationwide Inpatient Sample (NIS) is part of the Healthcare Cost and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality (AHRQ), formerly the Agency for Health Care Policy and Research (“HCUP-US NIS Overview”). Data are collected annually and made publicly available 2 years afterward. The NIS 2011 dataset was released in June 2013.Since modelling with multi-level survey data was complex, we could only use 10% random subsample which accounted for 673,727 hospital discharges

  14. U

    Intertwined Epidemics: National Demographic Trends in Hospitalizations for...

    • datacatalog.hshsl.umaryland.edu
    Updated Aug 28, 2019
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    George J. Unick; Daniel Rosenblum; Sarah G. Mars; Daniel Ciccarone (2019). Intertwined Epidemics: National Demographic Trends in Hospitalizations for Heroin- and Opioid-Related Overdoses, 1993-2009 [Dataset]. https://datacatalog.hshsl.umaryland.edu/dataset/78
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    Dataset updated
    Aug 28, 2019
    Dataset provided by
    HS/HSL
    Authors
    George J. Unick; Daniel Rosenblum; Sarah G. Mars; Daniel Ciccarone
    Time period covered
    Jan 1, 1993 - Dec 31, 2009
    Area covered
    United States
    Description

    This study investigated demographic trends over time in the use of prescription opioids versus heroin among addicted individuals. ICD9 codes associated with hospitalizations for overdoses from either prescription opioids (POD) or heroin (HOD) were harvested from the Nationwide Inpatient Sample (NIS) for the years 1993 through 2009, inclusive. Population data were taken from U.S. Census statistics. Demographic specific rates of POD and HOD hospital admissions were analyzed to determine if fluctuations in the dynamics of one form of opiate, such as supply-based reduction, are correlated with changes in the rates of overdoses of the other. Dataset includes statistical and demographic data.

  15. f

    Weighted BRFSS and NIS Demographic Characteristics and National Prevalence...

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Elie S. Al Kazzi; Brandyn Lau; Tianjing Li; Eric B. Schneider; Martin A. Makary; Susan Hutfless (2023). Weighted BRFSS and NIS Demographic Characteristics and National Prevalence of Risk Factors, 2011. [Dataset]. http://doi.org/10.1371/journal.pone.0140165.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Elie S. Al Kazzi; Brandyn Lau; Tianjing Li; Eric B. Schneider; Martin A. Makary; Susan Hutfless
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Estimates based on weights provided from the BRFSS and NIS [26–29]BRFSS, Behavioral Risk Factor Surveillance SystemNIS, Nationwide Inpatient SampleWeighted BRFSS and NIS Demographic Characteristics and National Prevalence of Risk Factors, 2011.

  16. f

    Association between breast cancer diagnosis and clinically diagnosed anxiety...

    • figshare.com
    xls
    Updated May 30, 2023
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    Neomi Vin-Raviv; Tomi F. Akinyemiju; Sandro Galea; Dana H. Bovbjerg (2023). Association between breast cancer diagnosis and clinically diagnosed anxiety and depression among matched cases and controls, Nationwide inpatient sample, 2006–2009+. [Dataset]. http://doi.org/10.1371/journal.pone.0129169.t002
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    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Neomi Vin-Raviv; Tomi F. Akinyemiju; Sandro Galea; Dana H. Bovbjerg
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    1Adjusted for race/ethnicity2Adjusted for race/ethnicity, residential income, insurance and residential region*** p-value

  17. f

    Multivariate analysis comparing south versus north.

    • plos.figshare.com
    xls
    Updated Jun 5, 2023
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    Adam C. Stein; John Nick Gaetano; Jeffrey Jacobs; Rangesh Kunnavakkam; Marc Bissonnette; Joel Pekow (2023). Multivariate analysis comparing south versus north. [Dataset]. http://doi.org/10.1371/journal.pone.0161523.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Adam C. Stein; John Nick Gaetano; Jeffrey Jacobs; Rangesh Kunnavakkam; Marc Bissonnette; Joel Pekow
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Variables included in the multivariate analysis included race, age, and payer status.

  18. Association between in-hospital mortality and clinically diagnosed anxiety...

    • figshare.com
    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Neomi Vin-Raviv; Tomi F. Akinyemiju; Sandro Galea; Dana H. Bovbjerg (2023). Association between in-hospital mortality and clinically diagnosed anxiety and depression among breast cancer cases, Nationwide inpatient sample, 2006–2009+. [Dataset]. http://doi.org/10.1371/journal.pone.0129169.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Neomi Vin-Raviv; Tomi F. Akinyemiju; Sandro Galea; Dana H. Bovbjerg
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    1Adjusted for age at admission, race/ethnicity and stage2Adjusted for age at admission, race/ethnicity, stage, residential income, region, insurance3Adjusted for age at admission, race/ethnicity, stage, residential income, region, insurance, LOS, COM*** p-value

  19. f

    Adjusted proportions of lower extremity amputations for Black and White...

    • figshare.com
    xls
    Updated Jun 3, 2023
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    Ché Matthew Harris; Aiham Albaeni; Roland J. Thorpe; Keith C. Norris; Marwan S. Abougergi (2023). Adjusted proportions of lower extremity amputations for Black and White patients. [Dataset]. http://doi.org/10.1371/journal.pone.0216832.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ché Matthew Harris; Aiham Albaeni; Roland J. Thorpe; Keith C. Norris; Marwan S. Abougergi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Adjusted proportions of lower extremity amputations for Black and White patients.

  20. f

    Regression models for primary outcomes and variables of interest in the 3...

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Kimon Bekelis; Symeon Missios; Kendrew Wong; Todd A. MacKenzie (2023). Regression models for primary outcomes and variables of interest in the 3 most common procedure subgroups. [Dataset]. http://doi.org/10.1371/journal.pone.0121191.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Kimon Bekelis; Symeon Missios; Kendrew Wong; Todd A. MacKenzie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    OR: Odds Ratio; 95% CI: 95% Confidence Interval¶The payment amount was ln transformed to provide a better fit for the data1Based on a logistic regression model including age, sex, income, payer, CCI, race, hospital region, hospital location, hospital bedsize, density of neurosurgeons as covariates2Based on a generalized linear model including age, sex, income, payer, CCI, race, hospital region, hospital location, hospital bedsize, density of neurosurgeons as covariates3Based on a generalized linear model including age, sex, income, payer, CCI, race, hospital region, hospital location, hospital bedsize, density of neurosurgeons as covariates. Hospitalization charges underwent an ln transformation because this provided the best fit for our data§The point estimates from these linear regressions represent beta coefficients and not odds ratiosRegression models for primary outcomes and variables of interest in the 3 most common procedure subgroups.

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Agency for Healthcare Research and Quality, Department of Health & Human Services (2025). National Inpatient Sample (NIS) - Restricted Access Files [Dataset]. https://catalog.data.gov/dataset/hcup-national-nationwide-inpatient-sample-nis-restricted-access-file
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National Inpatient Sample (NIS) - Restricted Access Files

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6 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Feb 22, 2025
Description

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time. The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

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