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.

The 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 all-payer inpatient health care database in the United States, yielding national estimates of hospital inpatient stays. The NIS can be used to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. Data may not be available for all states across all years.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. 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.

NIS 2002-2011 Within Year Merge

• Each year of the NIS has a Core, Hospital and Severity file: File Level ID Core discharge KEY, HOSPID Hospital hospital HOSPID Severity discharge KEY, HOSPID

1. The 2 dischrage-level files will trimmed down to desired variables and then merged by KEY and saved into a temporary SAS dataset.
2. The hospital file will be trimmed and then merged into the core-severity and saved into a permanent SAS dataset with following notation: NIS_YYYY
3. Working directory cleared after every year since very large datasets.

The Nationwide Inpatient Sample (NIS) is a database focused on hospital stay information. Users are able to use the NIS to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. Background The Nationwide Inpatient Sample (NIS) is maintained by the Healthcare Cost and Utilization Project. The NIS is the largest all-payer inpatient care database in the United States. It contains data from approximately 8 million hospital stays each year. The 2009 NIS contains all discharge data from 1,050 hospitals located in 44 States, approximating a 20-percent stratified sample of U.S. community hospitals. The sampling frame for the 2009 NIS is a sample of hospitals that comprises approximately 95 percent of all hospital discharges in the United States. The NIS is the only national hospital database containing charge information on all patients, regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. User functionality Users must pay to access the database. NIS releases for data years 1988-2009 are available from the HCUP Central Distributor. The 2009 NIS may be purchased for $50 for students and $350 for all others on a single DVD-ROM with accompanying documentation. . Data Notes NIS data are available from 1988 to 2009. The number of states in the NIS has grown from 8 in the first year to 44 at present. Beginning with the 2002 NIS, severity adjustment data elements including APR-DRGs, APS-DRGs, Disease Staging, and AHRQ Comorbidity Indicators, are available. Begi nning with the 2005 NIS, Diagnosis and Procedure Groups Files containing data elements from AHRQ software tools designed to facilitate the use of the ICD-9-CM diagnostic and procedure information are available. Beginning with the 2007 NIS, data elements describing hospital structural characteristics and provision of outpatient services are available in the Hospital Weights file. NIS Release 1 includes data from 8-11 States and spans the years 1988 to 1992. NIS Releases 2 and 3 contain data from 17 States for 1993 and 1994, respectively. NIS Releases 4 and 5 contain data from 19 States for 1995 and 1996. NIS Release 6 contains data from 22 States for 1997. NIS 1998 contains data from 22 States. NIS 1999 contains data from 24 States. NIS 2000 contains data from 28 States. NIS 2001 contains data from 33 States. NIS 2002 contains data from 35 States. NIS 2003 contains data from 37 States. NIS 2004 contains data from 37 States. NIS 2005 contains data from 37 States. NIS 2006 contains data from 38 States. NIS 2007 contains data from 40 States. NIS 2008 contains data from 42 States.

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

Abstract

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.

Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, uncommon treatments, and special populations.

Usage

The 2017 & 2018 Severity records that were missing from the previous version of this dataset have been recovered and are now included in the dataset, meaning no records are missing.

Also %3Cu%3EDO NOT%3C/u%3E

use this data without referring to the NIS Database Documentation, which includes:

• Description of NIS Database
• Restrictions on Use

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• Data Elements
• Additional Resources for Data Elements
• ICD-10-CM/PCS Data Included in the NIS Starting with 2015 (More details about this transition available here.)
• Known Data Issues
• NIS Supplemental Files
• HCUP Tools: Labels and Formats
• Obtaining HCUP Data

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Before Manuscript Submission

%3Cu%3E%3Cstrong%3EAll manuscripts%3C/strong%3E%3C/u%3E

(and other items you'd like to publish) %3Cu%3E%3Cstrong%3Emust be submitted to%3C/strong%3E%3C/u%3E

%3Cu%3E%3Cstrong%3Ephsdatacore@stanford.edu%3C/strong%3E%3C/u%3E

for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

You must also %3Cu%3E%3Cstrong%3Emake sure that your work meets all of the AHRQ (data owner) requirements for publishing%3C/strong%3E%3C/u%3E

with HCUP data--listed at https://hcup-us.ahrq.gov/db/nation/nis/nischecklist.jsp

HCUP Online Tutorials

For additional assistance, AHRQ has created the HCUP Online Tutorial Series, a series of free, interactive courses which provide training on technical methods for conducting research with HCUP data. Topics include an HCUP Overview Course and these tutorials:

• The HCUP Sampling Design tutorial is designed to help users learn how to account for sample design in their work with HCUP national (nationwide) databases. • The Producing National HCUP Estimates tutorial is designed to help users understand how the three national (nationwide) databases – the NIS, Nationwide Emergency Department Sample (NEDS), and Kids' Inpatient Database (KID) – can be used to produce national and regional estimates. HCUP 2020 NIS (8/22/22) 14 Introduction • The Calculating Standard Errors tutorial shows how to accurately determine the precision of the estimates produced from the HCUP nationwide databases. Users will learn two methods for calculating standard errors for estimates produced from the HCUP national (nationwide) databases. • The HCUP Multi-year Analysis tutorial presents solutions that may be necessary when conducting analyses that span multiple years of HCUP data. • The HCUP Software Tools Tutorial provides instructions on how to apply the AHRQ software tools to HCUP or other administrative databases.

New tutorials are added periodically, and existing tutorials are updated when necessary. The Online Tutorial Series is located on the HCUP-US website at www.hcupus.ahrq.gov/tech_assist/tutorials.jsp.

Important notes about the 2015 data

In 2015, AHRQ restructured the data as described here:

https://hcup-us.ahrq.gov/db/nation/nis/2015HCUPNationalInpatientSample.pdf

Some key points:

• For the 2015 data, all diagnosis and procedure data elements, including any data elements derived from diagnoses and procedures, were moved out of the Core File and into the Diagnosis and Procedure Groups Files.
• Prior to 2015, and for Q1-3 of 2015, the DX1-30 and PR1-15 variables (which use ICD-9 codes) variables were used, but starting in Q4 of 2015, the I10_DX1-30 and I10_PR1-I10-15 (which use ICD-10 codes) were used. The best way to identify discharges for quarter 1-3 or quarter 4 is based on the value of the diagnosis version (DXVER); For quarters 1-3, DXVER has a value of 9; while for quarter 4, DXVER has a value of 10.
• Some other variables al

Jordan A. Kempker, MD, MSc August 4, 2016

NIS 2002-2011 Hospital-Level Variables

This Code will create the below hospital-level variables for the entire dataset and then merge these statistics back into each observation.

hosp_vol Number of Annual Hospital Discharges

hospmort Annual Hospital In-Hospital Mortality Rate

        Average Annual Hospital Elixhauser Score

Of note, this code was first tested on a random 5% sample stratified by year before applying to entire dataset. Below, is the pared down functional version that resulted but does not contain any of the internal tests.

There are several Microsoft Word documents here detailing data creation methods and with various dictionaries describing the included and derived variables.The Database Creation Description is meant to walk a user through some of the steps detailed in the SAS code with this project.The alphabetical list of variables is intended for users as sometimes this makes some coding steps easier to copy and paste from this list instead of retyping.The NIS Data Dictionary contains some general dataset description as well as each variable's responses.

Vaccination Coverage among Adolescents (13-17 Years) • Data on adolescent vaccination coverage and selected sociodemographic characteristics by State, HHS Region, and the United States from the National Immunization Survey-Teen (NIS-Teen). • Additional information available at https://www.cdc.gov/vaccines/imz-managers/coverage/teenvaxview/index.html

Cumulative Influenza Vaccination Coverage Age Group, Race/Ethnicity, and Jurisdiction, Adults 18 Years and Older, United States, National Immunization Survey Adult COVID Module. The National Immunization Survey-Adult COVID Module (NIS-ACM) was launched in April 2021 among adults 18 years and older. The survey was used to monitor COVID-19 vaccination uptake and confidence in vaccination among adults and included questions about influenza vaccination.

This dataset tracks the updates made on the dataset "HCUP National (Nationwide) Inpatient Sample (NIS) - Restricted Access File" as a repository for previous versions of the data and metadata.

This data set contains the NEAR infrared spectrometer (NIS) data for the CRUISE3 phase. The data set begins on 1998-01-27T00:00:00.000 and ends 1998-12-22T23:59:59.999 . The data are raw telemetry data, provided in engineering units, that have been reformatted into FITS file format (NASA Office of Science and Technology (NOST), 100-1.0). In addition to the raw spectrometer data, a calibration file and algorithm are available. This data set is archived as a set of CDROM images as a part of the NEAR EDR volume set.

This data set contains the NEAR infrared spectrometer (NIS) data for the EROS/ORBIT phase. The data set begins on 2000-01-11T00:00:00.000 and ends 2001-02-12T23:59:59.999 . The data are raw telemetry data, provided in engineering units, that have been reformatted into FITS file format (NASA Office of Science and Technology (NOST), 100-1.0). In addition to the raw spectrometer data, a calibration file and algorithm are available. This data set is archived as a set of CDROM images as a part of the NEAR EDR volume set.

BackgroundWorldwide, asthma is a leading cause of morbidity, mortality and economic burden, with significant gender and racial disparities. However, little attention has been given to the independent role of age on lifetime asthma severity and hospitalization. We aimed to assess the effect of age, gender, race and ethnicity on indicators of asthma severity including asthma related hospitalization, mortality, hospital cost, and the rate of respiratory failure.MethodsWe analyzed the 2011 and 2012 Healthcare Cost and Utilization Project- National Inpatient Sample (NIS). We validated and extended those results using the National Heart, Lung, and Blood Institute-Severe Asthma Research Program (SARP; 2002–2011) database. Severe asthma was prospectively defined using the stringent American Thoracic Society (ATS) definition.ResultsHospitalization for asthma was reported in 372,685 encounters in 2012 and 368,528 in 2011. The yearly aggregate cost exceeded $2 billion. There were distinct bimodal distributions for hospitalization age, with an initial peak at 5 years and a second at 50 years. Likewise, this bimodal age distribution of patients with severe asthma was identified using SARP. Males comprised the majority of individuals in the first peak, but women in the second. Aggregate hospital cost mirrored the bimodal peak distribution. The probability of respiratory failure increased with age until the age of 60, after which it continued to increase in men, but not in women.ConclusionsSevere asthma is primarily a disease of young boys and middle age women. Greater understanding of the biology of lung aging and influence of sex hormones will allow us to plan for targeted interventions during these times in order to reduce the personal and societal burdens of asthma.

This dataset contains breast histology images from four classes: normal, benign, in situ carconima and invasive carcinoma. A trained Convolutional Neural Network for the classification of these images is also available. To access the dataset please request your password via the link http://bioimglab.inesctec.pt/?page_id=893 and fill the form. Users of this dataset should cite the following article: Teresa Araújo, Guilherme Aresta, Eduardo Castro, José Rouco, Paulo Aguiar, Catarina Eloy, António Polónia, and Aurélio Campilho, Classification of Breast Cancer Histology Images Using Convolutional Neural Networks, PLOS ONE, 2017. Available at: https://doi.org/10.1371/journal.pone.0177544 Please also refer the link of the dataset download page (this page): https://rdm.inesctec.pt/dataset/nis-2017-003 In addition, we appreciate to hear about any publications that use this dataset. The contact e-mail is tfaraujo@inesctec.pt.

This data set contains the NEAR infrared spectrometer (NIS) data for the CRUISE1 phase. The data set begins on 1996-02-20T00:00:00.000 and ends 1997-06-24T23:59:59.999 . The data are raw telemetry data, provided in engineering units, that have been reformatted into FITS file format (NASA Office of Science and Technology (NOST), 100-1.0). In addition to the raw spectrometer data, a calibration file and algorithm are available. This data set is archived as a set of CDROM images as a part of the NEAR EDR volume set.

This data set contains the NEAR infrared spectrometer (NIS) data for the CRUISE4 phase. The data set begins on 1998-12-24T00:00:00.000 and ends 2000-01-10T23:59:59.999 . The data are raw telemetry data, provided in engineering units, that have been reformatted into FITS file format (NASA Office of Science and Technology (NOST), 100-1.0). In addition to the raw spectrometer data, a calibration file and algorithm are available. This data set is archived as a set of CDROM images as a part of the NEAR EDR volume set.

The IMP-CRS 2024 dataset contains 5333 colorectal biopsy and polypectomy slides, retrieved from the data archive of IMP Diagnostics laboratory, Portugal, digitised at 40X by 2 Leica GT450 WSI scanners. All cases are classified within one of the three categories: Non-neoplastic (label 0) Low-grade lesions (label 1) - conventional adenomas with low-grade dysplasia High-grade lesions (label 2) - conventional adenomas with high-grade dysplasia, intra-mucosal carcinomas and invasive adenocarcinomas. Please read the Download and Usage information document below Access to the dataset: https://open-datasets.inesctec.pt/NQ3sxFMZ/

This dataset contains 2 subsets of anonymized video capsule endoscopy images with annotated red lesions. Folder "Set 1" has 3,295 non-sequential frames in sub-folder "A" and the corresponding annotated masks in sub-folder "B"; Similarly, folder "Set 2" has 600 sequential frames. All frames are 320x320 pixels wide. This dataset was used in the article "A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies" published at ICIAR 2018 to evaluate deep learning U-Net architecture, detect and segment red lesions in the small bowel. To access the dataset please use this password: 7PbuQHER