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HCUP User Support:

Information about the content of the HCUP databases and Requirements for Publishing with HCUP Data is available on the HCUP-US website (www.hcup-us.ahrq.gov). For questions about using the HCUP databases, software tools, supplemental files, and other HCUP products, or about data use retrictions and publishing with the data, please review the HCUP Frequently Asked Questions or contact HCUP User Support:

HCUP FAQs:
www.hcup-us.ahrq.gov/tech_assist/faq.jsp

Phone: (866)290-HCUP (4287) (toll free)
Email: hcup@ahrq.gov

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HEALTHCARE COST AND UTILIZATION PROJECT — HCUP
A FEDERAL-STATE-INDUSTRY PARTNERSHIP IN HEALTH DATA
Sponsored by the Agency for Healthcare Research and Quality

HCUP NATIONWIDE EMERGENCY DEPARTMENT SAMPLE (NEDS)

ABSTRACT

The Nationwide Emergency Department Sample (NEDS) is part of the Healthcare Cost and Utilization Project (HCUP) that is sponsored by the Agency for Healthcare Research and Quality (AHRQ).

The NEDS was created to enable analyses of emergency department (ED) utilization patterns and to support research, public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The ED serves a dual role in the U.S. healthcare system infrastructure, as a point of entry for approximately 50 percent of inpatient hospital admissions and as a setting for treat-and-release outpatient visits.¹ The NEDS has many research applications, because it contains information about geographic, hospital, and patient characteristics as well as descriptions of the nature of the visits (i.e., common reasons for ED visits, including injuries).

The NEDS is the largest all-payer ED database that is publicly available in the United States, containing information from 33.5 million ED visits at 984 hospitals that approximate a 20-percent stratified sample of U.S. hospital-owned EDs. Weights are provided to calculate national and encounter-level estimates representing about 145 million ED visits in the United States in 2017.

The NEDS is made possible by the voluntary participation of statewide data organizations that provide HCUP with data from ED visits that may or may not have resulted in hospital admission. Thirty-seven HCUP Partner organizations participated in the 2017 NEDS. See Appendix A, Table A.1 for a list of HCUP Partner organizations participating in the NEDS.

By stratifying on important hospital characteristics, the NEDS is designed to be representative of U.S. hospital-owned EDs. Stratified sampling is based on the following five hospital characteristics:

1. Geographic region (Northeast, Midwest, South, and West)
2. Trauma center designation (trauma level I, II, III, and nontrauma)
3. Urban-rural location of the hospital (large metropolitan, small metropolitan, micropolitan, and non-urban residual)
4. Teaching hospitals
5. Hospital ownership or control (public, for-profit, and not-for-profit).

Because ICD-10-CM/PCS was introduced October 1, 2015, trends that rely on diagnosis and procedures may be interrupted. Analyses that do not rely on diagnosis and procedure coding should not be affected.

Access to the NEDS is open to users who sign Data Use Agreements. Uses are limited to research and aggregate statistical reporting.

For more information on the NEDS, visit the AHRQ-sponsored HCUP-US website at www.hcup-us.ahrq.gov/db/nation/neds/nedsdbdocumentation.jsp.

Return to Introduction

 

INTRODUCTION TO THE NATIONWIDE EMERGENCY DEPARTMENT SAMPLE (NEDS)

 

Overview of NEDS Data

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and to support research, public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The ED serves a dual role in the U.S. healthcare system infrastructure, as a point of entry for approximately 50 percent of inpatient hospital admissions and as a setting for treat-and-release outpatient visits.² The NEDS has many research applications, because it contains information about geographic, hospital, and patient characteristics as well as the nature of visits (e.g., common reasons for ED visits, acute and chronic conditions, and injuries).

NEDS Data Sources, Hospitals, and ED Visits

The number of States, hospital-owned EDs, and ED visits included in the NEDS varies by year (Table 1). The specific HCUP Partner organizations that contribute to the NEDS are identified in Appendix A, Table A.1.

Table 1. Number of States, Hospital-Owned Emergency Departments, and Records in the NEDS by Year

Appendix A, Figure A.1 represents the geographic distribution of the 37 HCUP Partner organizations participating in the 2017 NEDS. Based on U.S. Census Bureau data, the HCUP NEDS States with the District of Columbia account for 80.9 percent of the U.S. population in 2017. The 37 Partner organizations account for 79.2 percent of the ED visits reported in the 2017 American Hospital Association (AHA) Annual Survey Database. Details on the percentage of population and ED visits by region are provided in Appendix A, Table A.2.

Identification of HCUP Records with Emergency Department Services

Records for ED events are contained in two existing HCUP databases:

• State Emergency Department Databases (SEDD) capture discharge information on all ED visits that do not result in an admission to that hospital (e.g., treat-and-release visits, transfers to another hospital, deaths).
• State Inpatient Databases (SID) contain information on patients initially seen in the emergency room and then admitted to the same hospital.

Both of these HCUP databases contain a core set of clinical and non-clinical data elements that are defined in a uniform scheme for all patients, regardless of payer. This scheme makes it possible to combine records across databases.

Selection of ED records from the SEDD and SID for use in the NEDS was based on evidence of ED services reported on the record. Differing methods are used by HCUP Partner organizations for identifying ED records. The HCUP criteria for identifying an ED record (i.e., a discharge record for a patient with an ED event) look for at least one of the following conditions to be true:

• Revenue center code of 450-459 reported on discharge record, indicating ED services.
• ED charge greater than zero dollars, when revenue center codes were not available.
• Physicians' Current Procedural Terminology (CPT) code of 99281-99285 reported on discharge record, indicating ED physician services.
• ED identified by admission source (National Uniform Billing Committee (NUBC) preferred coding prior to October 1, 2007), point of origin (NUBC preferred coding from October 1, 2007 to June 30, 2010), or condition code of P7 (NUBC preferred coding for public reporting as of July 1, 2010). These criteria are used primarily for ED admissions.

Five of the 37 HCUP Partner organizations (AR, AZ, CA, MA, and MS) provided a source file that contained only ED treat-and-release records. Because the data source provided a dedicated outpatient ED file, all of the SEDD records were considered to be ED records, even though information may not have been available to determine if HCUP criteria were met.

Partner-Specific Restrictions

Some HCUP Partner organizations that contributed data to the NEDS imposed restrictions on the release of certain data elements or on the number and types of hospitals that could be included in the database. In addition, because of confidentiality laws, some data sources were prohibited from providing HCUP with discharge records that indicated specific medical conditions, such as HIV/AIDS or behavioral health. Detailed information on these Partner-specific restrictions is available in Appendix B.

Return to Introduction

 

ICD-10-CM/PCS Started October 1, 2015 at the Beginning of Fiscal Year 2016

On October 1, 2015, the United States transitioned from using ICD-9-CM to ICD-10-CM/PCS code sets for reporting medical diagnoses and inpatient procedures.³ ICD-10-CM/PCS consists of two parts:

• ICD-10-CM: diagnosis coding on inpatient and outpatient data
• ICD-10-PCS: procedure coding on inpatient data.

The HCUP-US website has a section on ICD-10-CM/PCS Resources that summarizes key issues for researchers using HCUP and other administrative databases that include ICD-9-CM and ICD-10-CM/PCS coding. The web page provides general guidance and forewarning to users analyzing outcomes that may be affected by the transition to the ICD-10-CM/PCS coding system and lists other related web resources.

Return to Introduction

 

File Structure of the NEDS

Because of the size of the NEDS and the difference in information collected on records for patients admitted into the hospital directly from the ED (SID records) and for ED patients that are not admitted (SEDD records), the NEDS is divided into four types of files:

• Core File: This file contains records for 100 percent of the ED visits — whether resulting in admission or not — from the sample of hospitals in participating States and the District of Columbia.
  • This file is available in all years of the NEDS.


• Supplemental ED File: This file contains additional information for patients who were treated in the ED and not admitted directly to the hospital (e.g., released home, transferred, etc.). This information came from the SEDD.
  • This file is available in all years of the NEDS.
  • The unique NEDS record identifier (KEY_ED) provides the linkage between the NEDS Core File and the Supplemental ED File. For patients seen in the ED and admitted to the same hospital (SID records), information about the stay is contained in the Supplemental Inpatient File.


• Supplemental Inpatient File: This file contains data elements that are specific to the inpatient stay, such as total charges, length of inpatient stay, and procedure codes from the SID record. Procedures reported on the SID records may have been performed in the ED, but currently there is no way to verify this information.
  • This file is available in all years of the NEDS.
  • The unique NEDS record identifier (KEY_ED) provides the linkage between the NEDS Core File and the Supplemental Inpatient File.


• Hospital Weights File: This file contains one observation for each hospital-owned ED included in the NEDS and contains weights and variance estimation data elements. The unit of observation is the ED.
  • This file is available in all years of the NEDS.
  • The HCUP ED hospital identifier (HOSP_ED) provides the linkage between the NEDS Core File and the Hospital Weights File.

File Structure of the NEDS Beginning Data Year 2016

Beginning with data year 2016, the NEDS is an annual, calendar year file that includes data with diagnosis and inpatient procedure codes reported using the ICD-10-CM/PCS coding system. The file structure of the NEDS is similar to the file structure of the NEDS prior to 2015.

Data elements based on the AHRQ software tools that are derived from ICD-10-CM/PCS codes are not available. For users interested in applying the AHRQ software tools to the ICD-10-CM/PCS data, the AHRQ software tools are available for download on the HCUP Tools & Software section of the HCUP-US website. The Tools Loading tutorial is available to users interested in applying the AHRQ software tools at www.hcup-us.ahrq.gov/tech_assist/tutorials.jsp.

File Structure of the 2015 NEDS

The NEDS data files are annual, calendar-year files based on discharge date for all years except 2015. The introduction of ICD-10-CM/PCS in the United States on October 1, 2015 means that the 2015 NEDS includes a combination of codes:

• Nine months of the data with ICD-9-CM codes (January 1, 2015 to September 30, 2015)
• Three months of data with ICD-10-CM/PCS codes (October 1, 2015 to December 31, 2015).

To alert users to this change in the ICD coding scheme, the file structure of the 2015 NEDS differs from the annual files for other data years in three primary ways:

• The names of diagnosis- and procedure-related data elements under ICD-10-CM/PCS have been changed to identify the coding scheme with a prefix of "I10_".
• Diagnoses and procedures, and related data elements, have been moved out of the Core File and into Supplemental ED and Inpatient Files where the first three quarters of data (with ICD-9-CM codes) are stored separately from the fourth quarter of data (with ICD-10-CM/PCS codes).
• Data elements based on the AHRQ software tools that are derived from ICD-10-CM/PCS codes are not included in the fourth quarter data.

More information about the file structure of the 2015 NEDS is available in the Introduction to the NEDS, 2015, and on HCUP-US website at www.hcup-us.ahrq.gov/db/nation/neds/nedsdbdocumentation.jsp.

NEDS Data Elements

The coding of data elements in the NEDS is consistent with other HCUP databases. The following three objectives guided the definition of data elements in all HCUP databases:

• Ensure usability without extensive editing by analysts.
• Retain the largest amount of information available from the original sources, while still maintaining consistency among sources.
• Structure the information for efficient storage, manipulation, and analysis.

More information on the coding of HCUP data elements is available on HCUP-US website at: (www.hcup-us.ahrq.gov/db/coding.jsp).

After analyzing the availability of information from the HCUP Partner organizations, a set of common fields to be available in the NEDS was created. The NEDS contains more than 100 clinical and non-clinical variables provided in a hospital discharge abstract, such as:

• Patient demographics (e.g., sex, age, urban-rural designation of residence, national quartile of the median household annual income for the patient’s ZIP Code)
• Expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay)
• Hospital characteristics (e.g., indicator of trauma center level, including pediatric trauma centers, urban-rural designation of county, ownership, teaching status, region of the U.S.)
• ICD-9-CM diagnoses and external cause of injury codes (prior to October 1, 2015) and ICD-10-CM diagnoses and external cause of morbidity codes (starting October 1, 2015 at the beginning of fiscal year 2016).
• Identification of injury-related ED visits and, for ICD-9-CM data only, the mechanism, intent, and severity of the injury
• ICD-9-CM, ICD-10-PCS, and CPT procedure codes
• ED charges and total hospital charges for patients admitted as an inpatient through the ED.

Appendix C identifies the data elements in each NEDS file:

• Table C.1 for the NEDS Core File (record = ED visit)
• Table C.2 for the NEDS Supplemental ED File (record = ED visit that did not result in direct inpatient admission to the same hospital)
• Table C.3 for the NEDS Supplemental Inpatient File (record = ED visit that resulted in direct inpatient admission to the same hospital)
• Table C.4 for the Hospital Weights File (record = hospital-owned ED).

The tables in Appendix C provide summary documentation for the data. Please refer to the NEDS documentation on the HCUP-US website (www.hcup-us.ahrq.gov/db/nation/neds/nedsdde.jsp) for comprehensive information about data elements.

Getting Started

The HCUP NEDS is distributed as comma-separated value (CSV) files delivered via secure digital download from the Online HCUP Central Distributor. The files are compressed and encrypted with SecureZIP® from PKWARE.

The NEDS product is downloaded in a single zipped file for each year which contains several data-related files and accompanying documentation. The four data-related files include the following compressed files:

1. Core File (NEDS_2017_Core.zip)
2. Hospital Weights File (NEDS_2017_Hospital.zip)
3. Supplemental ED File (NEDS_2017_ED.zip)
4. Supplemental Inpatient File (NEDS_2017_IP.zip)

To load and analyze the NEDS data on a computer, users will need the following:

• The password provided by the HCUP Central Distributor
• A hard drive with 50 to 100 gigabytes (GB) of space available
• A third-party zip utility such as ZIP Reader, SecureZIP®, WinZip®, or Stuffit Expander®
• SAS®, SPSS®,Stata® or similar analysis software

The total size of the CSV version of the NEDS is 12 GB. The NEDS files loaded into SAS are about 10 GB. In SAS, the largest use of space typically occurs during PROC SORT, which requires work space about three times the size of the file. Thus, the NEDS files would require at least 30 GB of available workspace to perform a sort procedure. Most SAS data steps will require twice the storage of the file, so that both the input and output files can coexist. The NEDS files loaded into SPSS are about 27 GB. Because Stata loads the entire file into memory, it may not be possible to load every data element in the NEDS Core file into Stata. Stata users will need to maximize memory and use the "_skip" option to select a subset of data elements. More details are provided in the Stata load programs.

With a file of this size and without careful planning, space could easily become a problem in a multi-step program. It is not unusual to have several versions of a file marking different steps while preparing it for analysis, and there may be more versions for the actual analyses. Therefore, the amount of space required could escalate rapidly.

Decompressing the NEDS Files

To extract the data files from the compressed download file, follow these steps:

1. Create a directory for the NEDS on your hard drive.
2. Unzip the compressed NEDS product file into the new directory using a third-party zip utility. This will place four compressed, encrypted data-related files in the new directory. You will be prompted to enter the encryption password (sent separately by email) to decrypt the file.
   
   Please note that attempts to unzip encrypted files using the built-in zip utility in Windows® (Windows Explorer) or Macintosh® (Archive Utility) will produce an error message warning of incorrect password and/or file or folder errors. The solution is to use a third-party zip utility.
   
   Third-party zip utilities are available from the following reputable vendors on their official websites.
   
   
   
   • ZIP Reader (Windows) (free download offered by the PKWARE corporation)
   • SecureZIP® for Mac or Windows (free evaluation and licensed/fee software offered by the PKWARE corporation)
   • WinZip (Windows) (evaluation and fee versions offered by the WinZip corporation)
   • Stuffit Expander® (Mac) (free evaluation and licensed/fee software offered by Smith Micro corporation)


3. Unzip each of the compressed, encrypted data-related files using the same password and third-party zip utility method. This will place the data-related CSV files in this same directory by default.

Downloading and Running the Load Programs

Programs to load the data into SAS, SPSS, or Stata, are available on the HCUP-US website. To download and run the load programs, follow these steps:

1. Go to the NEDS Database Documentation page on HCUP-US at www.hcup-us.ahrq.gov/db/nation/neds/nedsdbdocumentation.jsp.
2. Go to the "File Specifications and Load Programs" section on this page.
3. Click on "Nationwide SAS Load Programs", "Nationwide SPSS Load Programs", or "Nationwide Stata Load Programs" to go to the corresponding Load Programs page.
4. Select the data year and the database ("NEDS") from the drop down lists on this page. Or you may select "NEDS Load All Years" to obtain a zipped file with all load programs for multiple years at once.
5. Select and save the load programs you need. The load programs are specific to the data year and data-related file. For example, the load program for the 2017 NEDS Core file is found under the link "SAS NEDS 2017 Core File" in the list generated by selecting "2017" and "NEDS." Save the load programs into the same directory as the NEDS CSV files on your computer.
6. Edit and run the load programs as appropriate for your computing environment to create the analysis files. For example, modify the directory paths to point to the location of your input and output files.

NEDS Documentation

Comprehensive documentation for the NEDS files is available on the HCUP-US website (www.hcup-us.ahrq.gov/db/nation/neds/nedsdbdocumentation.jsp). Users of the NEDS can access complete file documentation, including variable notes, file layouts, summary statistics, and related technical reports. Similarly, data users can download SAS, SPSS, and Stata load programs. These important resources help the user understand the structure and content of the NEDS and aid in using the database. Appendix A, Table A.3 details the comprehensive NEDS documentation available on HCUP-US.

Return to Introduction

 

HCUP Online Tutorials

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

• The Load and Check HCUP Data tutorial provides instructions on how to unzip (decompress) HCUP data, save it on your computer, and load the data into a standard statistical software package. This tutorial also describes how to verify that the data have loaded correctly.
• The HCUP Tools Loading Tutorial provides instructions on how to unzip (decompress) the HCUP Software Tools for ICD-10-CM/PCS, save it on the computer, and load the Tool into a standard statistical software package for application to HCUP or other administrative databases. Users will also learn how to verify that the Tool has loaded correctly. Information about the transition to ICD-10-CM/PCS is also included in this tutorial. While the instructions are specific to the Clinical Classifications Software (CCS) for ICD-10-CM/PCS, the steps are broadly applicable to other HCUP Tools.
• The HCUP Sampling Design tutorial is designed to help users learn how to account for sample design in their work with HCUP nationwide databases.
• The Producing National HCUP Estimates tutorial is designed to help users understand how three of the nationwide databases — the NIS, NEDS, and KID — can be used to produce national and regional estimates.
• 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 nationwide databases.
• The Multi-year Analysis tutorial presents solutions that may be necessary when conducting analyses that span multiple years of HCUP data.

New tutorials are added periodically. The Online Tutorial Series is located on the HCUP-US website at www.hcup-us.ahrq.gov/tech_assist/tutorials.jsp.

SAMPLING DESIGN OF THE NEDS

The NEDS is built using a 20 percent stratified sample of hospital-owned EDs in the United States. The main objective of a stratified sample is to ensure that it is representative of the target universe. By stratifying on important hospital characteristics, the NEDS represents a "microcosm" of EDs in the U.S. For example, by including trauma center designation in the sampling strategy, the NEDS has the same percentage of trauma hospitals as the entire U.S. The NEDS contains all of the ED visits for the sample of hospital-owned EDs selected.

Universe of Hospital-Owned Emergency Departments

A feasibility study performed in 2008 assessed several possible data sources for the universe of hospital-owned EDs in the United States: the American Hospital Association (AHA) Annual Survey Database (Health Forum, LLC © 2007); Verispan, LLC databases (now called IMS Health, Inc.); and the Centers for Medicare and Medicaid (CMS) Hospital Cost Reports. The AHA Annual Survey Database has the best data to apply for a couple of reasons. First, the AHA data provide the necessary hospital characteristics, such as ownership type and teaching status, and also report total ED visits for hospitals. Second, the crosswalk linkage from the HCUP databases to the AHA data is already established. The universe of hospital-owned EDs is therefore defined as the AHA community, nonrehabilitation hospitals that reported total ED visits. The AHA defines community hospitals as "all non-Federal, short-term, general, and other specialty hospitals open to the public."⁴ Included among community hospitals are pediatric institutions, public hospitals, and academic medical centers.

Sampling Frame of the NEDS

The sampling frame of the NEDS is limited to a subset of the universe: hospital-owned EDs in the States and District of Columbia for which HCUP ED data (SID and SEDD) are available. The list of hospital-owned EDs in the frame consists of all AHA community, nonrehabilitation hospitals that report total ED visits in each of the frame States and District of Columbia that could be matched to the ED data provided to HCUP. If an ED in the AHA survey could not be matched to the ED data provided by the HCUP data source, it was eliminated from the sampling frame (but not from the target universe).

Stratification Variables

The following hospital characteristics were used for sample stratification: U.S. Census region, trauma center designation, urban-rural location of the hospital, ownership, and teaching status. ED bed size was not used because no data source for this information could be identified. A number of data sources report the bed size of the hospital, but no source distinguishes between inpatient and ED beds.

The NEDS stratification variables are described below and detailed in Appendix A, Table A.4.

U.S. Census Region

The four Census regions – Northeast, Midwest, South, and West – were used to stratify EDs by geographic location because practice patterns may vary substantially by region. Appendix A, Figure A.1 shows the NEDS States by region.

Trauma Centers

A trauma center is a hospital that is equipped to provide comprehensive emergency medical services 24 hours a day, 365 days per year to patients with traumatic injuries. In 1976, the American College of Surgeons Committee on Trauma (ACS/COT) defined five levels of trauma centers:⁵

• Level I centers have comprehensive resources, are able to care for the most severely injured, and provide leadership in education and research.
• Level II centers have comprehensive resources and are able to care for the most severely injured, but do not provide leadership in education and research.
• Level III centers provide prompt assessment and resuscitation, emergency surgery and, if needed, transfer to a level I or II center.
• Level IV/V centers provide trauma support in remote areas in which no higher level of care is available. These centers resuscitate and stabilize patients and arrange transfer to an appropriate trauma facility.

The ACS/COT has a program that verifies hospitals as trauma level I, II, or III.⁶ It is important to note that although all level I, II, and III trauma centers offer a high level of trauma care, there may be differences in the specific services and resources offered by hospitals of different levels. Trauma levels IV and V are designated at the State level (and not by ACS/COT) with varying criteria applied across States.

The level of the trauma centers in the NEDS was identified using the Trauma Information Exchange Program (TIEP) database, a national inventory of trauma centers in the U.S collected by the American Trauma Society.⁷ The TIEP database identifies all U.S. trauma centers that are level I, II, and III that treat both adults and children. TIEP includes some information on trauma centers within children's hospitals, but this is not their focus. To ensure that all of trauma centers are identified for the NEDS, the ACS/COT list of trauma centers and all State-specific websites on emergency services are reviewed to identify any additional trauma centers within children's hospitals and their associated trauma levels.

The stratum for trauma center in the NEDS was limited to trauma levels I, II, and III. Level IV and V centers were not included because the criteria for designation varied across States. For hospital confidentiality purposes, a collapsed stratification was necessary if the strata size in the universe or frame was less than two hospitals. The grouping of trauma centers into collapsed categories varied by data year:

• Trauma centers levels I and II could be grouped together in all years of the NEDS
• Trauma levels I through level III could be grouped together in the 2006-2010 NEDS
• Trauma level III could be grouped with nontrauma hospitals beginning in the 2011 NEDS.

The change between the 2010 and 2011 NEDS was prompted by differences between injury-related services provided by trauma level I and II centers versus injury-related services provided by trauma level III centers. Services at trauma level III centers were more similar to nontrauma hospitals.

Urban-Rural Location of the ED

The urban-rural location of hospital-owned EDs was determined based on the county in which the hospital was located. The categorization is based on Urban Influence Codes (UIC).⁸ Starting in the 2014 NEDS, the categorization is a simplified adaptation of the 2013 version of the UIC. Prior to 2014, the categorization is a simplified adaptation of the 2003 version of the UIC. The twelve detailed UIC categories are combined into four broader categories:

• Large metropolitan area — areas with at least one million residents
• Small metropolitan area — areas with less than one million residents
• Micropolitan area — non-metropolitan area with at least 10,000 people or more
• Non-urban residual.

If the strata size in the universe or frame was less than two hospitals, a collapsed stratification of metropolitan (large and small), non-metropolitan (micropolitan and non-urban residual), small metropolitan and micropolitan,⁹ or all areas¹⁰ was necessary.

Teaching Status

A hospital-owned ED is considered a teaching hospital if it has one or more Accreditation Council for Graduate Medical Education (ACGME) approved residency program, is a member of the Council of Teaching Hospitals (COTH) or has a ratio of full-time equivalent interns and residents to beds of .25 or higher. Beginning with the 2014 NEDS, there is an increase in the number of hospitals identified as teaching facilities because the AHA Annual Survey showed an increase in facilities with approved residency programs. About this time, the ACGME became the primary organization for residency training approval. Because there are very few teaching hospitals in micropolitan and rural areas, teaching status was only used to stratify EDs in metropolitan areas.

Hospital Ownership

Hospital ownership or control was categorized according to information reported in the AHA Annual Survey Database. Ownership categories include:

• Public — government, non-Federal
• Voluntary — private, not-for-profit
• Proprietary — private, investor-owned/for-profit.

When there were enough hospitals of each type, EDs were stratified into public, voluntary, and proprietary categories. If necessary, because of small strata size in the universe, a collapsed stratification of public versus private was used; the voluntary, non-profit and proprietary/for-profit hospitals were combined to form a single "private" category. Stratification based on ownership or control was not advisable in some regions because of the dominance of one type of hospital (e.g., Northeast).

Return to Introduction

 

Sample Weights

To obtain nationwide estimates, weights were developed using the AHA universe as the standard. These were developed separately for analyses of hospital-owned EDs and ED visits. Hospital-level weights were developed to extrapolate NEDS sample EDs to the universe of hospital-owned EDs. Similarly, discharge-level discharge weights were developed to extrapolate NEDS sample ED visits to the universe of ED visits.

Hospital Weights

Hospital weights to the universe were calculated after sampling and by strata. Hospital-owned EDs were stratified on the same variables that were used for sampling: geographic region, trauma center designation, urban-rural location, teaching status, and ownership or control. The strata that were collapsed for sampling were also collapsed for sample weight calculations. Within each stratum, s, each ED in the NEDS sample received a weight:

HOSPWT = Ws(universe) = Ns(universe) ÷ Ns(sample)

where Ws(universe) was the ED universe weight, and Ns(universe) and Ns(sample) were the number of hospital-owned EDs within stratum s in the universe and sample, respectively. Thus, each hospital's universe weight (HOSPWT) is equal to the number of universe hospitals it represents during that year. Because 20 percent of the hospitals in each stratum were sampled when possible, the ED weights were usually near five.

Discharge Weights

Discharge weights to the universe were calculated after sampling and by strata. Hospital-owned EDs were stratified in a manner similar to that for universe hospital-weight calculations. Within stratum, s, for hospital, i, the universe weight for each visit in the NEDS sample, was calculated as:

DISCWT = DWis(universe) = [DNs(universe) ÷ ADNs(sample)] * (4 ÷ Qi)

where DWis(universe) was the discharge weight; DNs(universe) represented the number of ED visits from community, nonrehabilitation hospitals in the universe within stratum s; ADNs(sample) was the number of adjusted ED visits from sample hospitals selected for the NEDS; and Qi represented the number of quarters of ED visits contributed by hospital i to the NEDS (usually Qi = 4). Thus, each discharge's weight (DISCWT) is equal to the number of universe ED visits it represents in stratum s during that year.

Final NEDS Sample

The target universe for the NEDS was: (1) community, nonrehabilitation hospital-owned EDs in the United States that were included in the 2017 AHA Annual Survey Database, and (2) reported total ED visits. Excluded were a handful of non-rural hospitals that reported less than ten ED visits in a year.

The NEDS sampling frame included hospital-owned ED events from community, nonrehabilitation hospitals in the 37 HCUP Partner organizations that provided discharge abstracts on patients admitted to the hospital through the ED and on patients treated and released or transferred to another hospital from the ED. The HCUP hospitals were required to be represented in the AHA data and have no more than 90 percent of their ED visits resulting in admission. Appendix A, Table A.5 lists the final target universe and sampling frame for the NEDS.

The NEDS is a stratified probability sample of hospital-owned EDs in the frame. Sampling probabilities were calculated to select 20 percent of the universe contained in each stratum, which was defined by region, trauma designation, urban-rural location, teaching status, and hospital ownership or control. A sample size of 20 percent was based on previous experience with similar research databases. A larger sample would be cumbersome for data users, given that a 20 percent sample contains about 30 million records. A 20 percent sample also enables the user to split the NEDS into two 10 percent subsamples for estimation and validation of models.

Using the universe of U.S. hospital-owned EDs, strata were defined by region, trauma designation, urban-rural location, teaching status, and hospital ownership or control. Strata with less than two hospitals in the universe and frame were collapsed with adjacent stratum based on urban-rural location, trauma designation, or ownership or control. Prior to sampling, the list of frame hospitals within each stratum is sorted as follows to ensure geographic representation within strata: (1) sorted by the first three digits of the hospital's ZIP Code and (2) sorted by a random number within the three-digit ZIP Code.¹¹ After stratifying and sorting the frame hospitals, a random sample of up to 20 percent of the total number of hospital-owned EDs in the U.S. was selected within each stratum. A stratum with a shortfall was defined as having an insufficient number of EDs in the frame to meet the threshold of 20 percent of the universe for that stratum. In strata with shortfalls, the sampling rate from the universe was less than 20 percent and all possible EDs in the frame were selected for the NEDS. In contrast, the sampling rate is larger than 20 percent in some strata because protecting hospital confidentiality required a minimum of two sampled EDs in each stratum. Appendix A, Table A.6 lists the sampling rates by stratum for the NEDS.

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HOW TO USE THE NEDS FOR DATA ANALYSIS

This section provides a brief synopsis of special considerations for using the NEDS. For more details, refer to the comprehensive documentation on the HCUP-US website (www.hcup-us.ahrq.gov/).

All persons using the NEDS (whether or not they are the original recipient of the data) must complete the on-line Data Use Agreement Training Course available on the HCUP-US website (www.hcup-us.ahrq.gov/tech_assist/dua.jsp) and then read and sign a Data Use Agreement. A copy of the signed Data Use Agreements must be sent to the HCUP Central Distributor.

Limitations of the NEDS

The NEDS contains over 30 million ED records and over 100 clinical and non-clinical data elements. A multitude of research studies can be conducted with the data, but there are some limitations.

• The NEDS is an extremely large database that requires sophisticated, statistical software for analysis and 50 to 100 GB of available computer space. The CSV version of the 2017 NEDS is 12 GB. When loaded into statistical software such as SAS or SPSS, the file size generally will increase.


• In 2017, about 13 percent of all ED visits (weighted) are missing information about ED charges. For ED visits that result in admission, 24 percent of records are missing ED charges. For ED visits that do not result in admissions, 11 percent of records are missing ED charges. The missing information is concentrated in the West. Estimates of the sum of charges should use the product of the number of cases times the average charge to account for records with missing information.


• The NEDS contains encounter-level records, not patient-level records. This means that individual patients who visit the ED multiple times in one year may be present in the NEDS multiple times. There is no uniform patient identifier available that would allow a patient-level analysis to identify individuals with more than one ED visit. In contrast, some HCUP State databases may be used for this type of analysis.


• If a patient is directly admitted from the ED to the same hospital, one discharge record is included in the NEDS. If a patient is transferred from an ED to another ED, there would be two discharge records—one from the "transfer out" hospital and one from the receiving hospital. However, both of these records will be included in the NEDS only if both hospitals were selected for inclusion in the NEDS sample. It is possible that only one of these two records will be included in the NEDS, if only one of the hospitals was sampled. This type of transfer (from an ED to another ED or acute care hospital) only occurs in about 1.5 percent of the NEDS records.


• For a patient who was directly admitted to the same hospital through the ED, clearly identifying whether a procedure was performed in the ED or as part of the inpatient stay is not currently possible. Information on procedures for ED admissions is stored in the NEDS Supplemental Inpatient File.


• The reporting of outpatient surgery records that originate care in the ED (e.g., fracture and dislocation procedures, appendectomies, etc.) can vary by State. These types of events are captured in the NEDS if they are included in the SEDD.


• For hospital confidentiality purposes, trauma hospitals were grouped together in the HCUP data element HOSP_TRAUMA: trauma levels I and II (in all years of the NEDS), trauma levels I through III (in the 2006-2010 NEDS), and trauma level III and nontrauma (beginning in the 2011 NEDS). This protects hospital identification and limits the analyses that can be performed by individual levels of trauma centers, but does allow the general distinction of trauma versus nontrauma facilities.


• The NEDS is not linkable to other HCUP databases, does not intentionally contain the same hospitals as the HCUP NIS, and cannot be used for State-level analyses. In fact, States and the District of Columbia are not identified in the NEDS.

Identifying Different Types of ED Events

The HCUP data element ED event distinguishes among the different types of ED events: Appendix A, Table A.7 provides the number and percentage of records in the 2017 NEDS for each of the five types of ED event types.

Calculating National Estimates

To produce national estimates, weights MUST be used.

• The hospital weight (HOSPWT) should be used for producing nationwide hospital-level statistics for analyses that use the hospital-owned ED as the unit of analysis.


• The discharge weight (DISCWT) should be used for producing nationwide visits-level statistics for analyses that use the ED visit as the unit of analysis.

Because the NEDS is a stratified sample, proper statistical techniques must be used to calculate standard errors and confidence intervals. For detailed instructions, refer to the HCUP Methods Series report #2003-02 Calculating Nationwide Inpatient Sample Variances on the HCUP-US website (www.hcup-us.ahrq.gov). The HCUP Nationwide Inpatient Sample (NIS) prior to 2012 used stratified sample design similar to the NEDS, so techniques appropriate for the NIS prior to 2012 are also appropriate for the NEDS.

When creating national estimates, it is a good idea to check results against other data sources, if available. Summary benchmarks for national estimates from the NEDS are provided in Appendix D. Also included in Appendix D are comparable estimates from other ED data sources. For example, the National Hospital Ambulatory Medical Care Survey (NHAMCS) has an ED component and publishes national health statistics annually.

To ensure that weights are used appropriately and estimates and variances are calculated accurately, researchers can also use HCUPnet, the free online query system (https://datatools.ahrq.gov/hcupnet). HCUPnet is a web-based query tool for identifying, tracking, analyzing, and comparing statistics on hospitals at the national, regional, and State levels. HCUPnet offers easy access to national statistics and trends as well as selected State statistics about hospital stays, ED visits and ambulatory surgeries. This tool provides step-by-step guidance, helping researchers to quickly obtain the statistics they need. HCUPnet generates statistics using the HCUP databases.

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Choosing Data Elements for Analysis

For all data elements to be used in the analysis, the user should first perform descriptive statistics and examine the range of values, including number of missing cases. Summary statistics are available on the HCUP-US website under Database Documentation for the NEDS (www.hcup-us.ahrq.gov/db/nation/neds/nedssummstats.jsp). When anomalies (such as large numbers of missing cases) are detected, descriptive statistics can be performed by region for that variable to determine whether or not there are region-specific differences. Sometimes, performing descriptive statistics by hospital (HOSP_ED) can be helpful in detecting hospital-specific data anomalies.

ICD-9-CM and ICD-10-CM/PCS Diagnosis and Procedure Codes and CPT Procedure Codes

• The HCUP-US website has a section on ICD-10-CM/PCS Resources that summarizes key issues for researchers using HCUP and other administrative databases that include ICD-9-CM and ICD-10-CM/PCS coding. The web page provides general guidance and forewarning to users analyzing outcomes that may be affected by the transition to the ICD-10-CM/PCS coding system and lists other related web resources.
• The meaning of the first listed diagnosis (DX1) differs based on the type of ED visit. Please refer to the HCUP Methods Series Report on the Meaning of the First-Listed Diagnosis on Emergency Department and Ambulatory Surgery Records.¹² Diagnoses reported on an inpatient admission from the ED may be from both the ED and inpatient hospital settings. It may be useful to compare diagnostic-specific ED visits that do not result in hospitalization to those resulting in hospitalization.
• The NEDS include ICD-9-CM diagnosis and procedure codes on inpatient discharges prior to October 1, 2015. Starting on October 1, 2015, diagnosis and procedure codes are reported using ICD-10-CM/PCS. HCUP has developed recommendations for reporting statistics (e.g., counts, rates, averages) that are based on HCUP data with a mixture of ICD-9-CM and ICD-10-CM/PCS codes. These recommendations apply to calendar year 2015 data (which includes both ICD-9-CM and ICD-10-CM/PCS codes), as well as reporting trends that span the October 1, 2015 transition date (before and after the introduction of ICD10-CM/PCS).
• ICD-9-CM and ICD-10-CM diagnosis and ICD-10-PCS procedure codes provide valuable insights into the reasons for hospitalization and what procedures patients receive, but these codes need to be carefully used and interpreted. ICD-9-CM and ICD-10-CM/PCS codes change every October as new codes are introduced and some codes are retired. It is critical to check all ICD-9-CM and/or ICD-10-CM/PCS codes used for analysis to ensure the codes are in effect during the time period studied.
• The NEDS contains fields for up to 35 diagnoses starting in data year 2017 (30 diagnoses for data years 2014 through 2016; and 15 diagnoses prior to 2014), and E codes are now included at the end of the ICD-10-CM diagnosis array (prior to data year 2017, four E codes per ED record were included as a separate array). Some States provide more than the maximum code fields retained on the NEDS. To reduce the file size of the NEDS, the number of codes retained was limited. Less than one percent of all ED records report more fields than the maximum allowed on the NEDS.
• The NEDS contains fields for up to 15 ICD-9-CM or ICD-10-PCS procedures (nine prior to data year 2017) and 35 CPT procedures per ED record (15 prior to data year 2017), although the number of code fields populated varies by State due to reporting differences. Some States provide more than the maximum code fields retained on the NEDS. To reduce the file size of the NEDS, the number of diagnosis and procedure codes retained was limited. Less than one percent of all ED records report more fields than the maximum allowed on the NEDS.
• The collection and reporting of external cause of injury (E codes under ICD-9-CM) and external cause of morbidity (V, W, X, and Y codes under ICD-10-CM) also varies across hospitals depending on the presence of State laws or mandates for the collection of these codes. Some States do not require hospitals to report codes for "misadventures to patients during surgical and medical care" (codes E870-E879 under ICD-9-CM) which means that these occurrences will be underreported. Beginning with the 2017 NEDS, separate data elements for external cause diagnosis codes are discontinued (formerly HCUP data elements I10_ECAUSEn). External cause codes are now included at the end of the ICD-10-CM diagnosis array. The length of the diagnosis array has increased from 30 to 35 codes to accommodate this change.

Missing Values

Missing data values can compromise the quality of estimates. For example, if the outcome for ED visits with missing values is different from the outcome for ED visits with valid values, then sample estimates for that outcome will be biased and inaccurately represent the ED utilization patterns. There are several techniques available to help overcome this bias. One strategy is to use imputation to replace missing values with acceptable values. Another strategy is to use sample weight adjustments to compensate for missing values. Descriptions of such data preparation and adjustment are outside the scope of this report; however, it is recommended that researchers evaluate and adjust for missing data, if necessary.

Alternatively, if the cases with and without missing values are assumed to be similar with respect to their outcomes, no adjustment may be necessary for estimates of means and rates because the non-missing cases would be representative of the missing cases. However, some adjustment may still be necessary for the estimates of totals. Sums of data elements (such as aggregate ED charges) containing missing values would be incomplete because cases with missing values would be omitted from the calculations. Estimates of the sum of charges should use the product of the number of cases times the average charge to account for records with missing information.

Variance Calculations

It may be important for researchers to calculate a measure of precision for some estimates based on the NEDS sample data. Variance estimates must take into account both the sampling design and the form of the statistic. The sampling design consisted of a stratified, single-stage cluster sample. A stratified random sample of hospital-owned EDs (clusters) was drawn and then all ED visits were included from each selected hospital. To accurately calculate variances from the NEDS, appropriate statistical software and techniques must be used. For detailed instructions, refer to the HCUP Methods Series report #2003-02 Calculating Nationwide Inpatient Sample Variances on the HCUP-US website (www.hcup-us.ahrq.gov/). The HCUP Nationwide Inpatient Sample (NIS) prior to 2012 used stratified sample design similar to the NEDS, so techniques appropriate for the NIS prior to 2012 are also appropriate for the NEDS.

A multitude of statistics can be estimated from the NEDS data. Several computer programs that calculate statistics and their variances from sample survey data are listed in the next section. Some of these programs use general methods of variance calculations (e.g., the jackknife and balanced half-sample replications) that take into account the sampling design. However, it may be desirable to calculate variances using formulas specifically developed for certain statistics.

These variance calculations are based on finite-sample theory, which is an appropriate method for obtaining cross-sectional, nationwide estimates of outcomes. According to finite-sample theory, the intent of the estimation process is to obtain estimates that are precise representations of the nationwide population at a specific point in time. In the context of the NEDS, any estimates that attempt to accurately describe characteristics and interrelationships among hospitals and ED visits during a specific year should be governed by finite-sample theory. Examples would be estimates of expenditure and utilization patterns.

Alternatively, in the study of hypothetical population outcomes not limited to a specific point in time, the concept of a "superpopulation" may be useful. Analysts may be less interested in specific characteristics of the finite population (and time period) from which the sample was drawn than they are in hypothetical characteristics of a conceptual superpopulation from which any particular finite population in a given year might have been drawn. According to this superpopulation model, the nationwide population in a given year is only a snapshot in time of the possible interrelationships among hospital, market, and discharge characteristics. In a given year, all possible interactions between such characteristics may not have been observed, but analysts may wish to predict or simulate interrelationships that may occur in the future.

Under the finite-population model, the variances of estimates approach zero as the sampling fraction approaches one. This is the case because the population is defined at that point in time and because the estimate is for a characteristic as it existed when sampled. This is in contrast to the superpopulation model, which adopts a stochastic viewpoint rather than a deterministic viewpoint. That is, the nationwide population in a particular year is viewed as a random sample of some underlying superpopulation over time. Different methods are used for calculating variances under the two sample theories. The choice of an appropriate method for calculating variances for nationwide estimates depends on the type of measure and the intent of the estimation process.

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Computer Software for Weighted and Variance Calculations

The hospital weights are useful for producing hospital-level statistics for analyses that use the hospital-owned ED as the unit of analysis. In contrast, the discharge weights are useful for producing visit-level statistics for analyses that use the ED visit as the unit of analysis.

In most cases, computer programs are readily available to perform these calculations. Several statistical programming packages allow weighted analyses.¹³ For example, nearly all SAS procedures incorporate weights. In addition, several statistical analysis programs have been developed to specifically calculate statistics and their standard errors from survey data. Version 8 or later of SAS contains procedures (PROC SURVEYMEANS and PROC SURVEYREG) for calculating statistics based on specific sampling designs. Stata and SUDAAN are two other common statistical software packages that perform calculations for numerous statistics arising from the stratified, single-stage cluster sampling design. Examples of the use of SAS, SUDAAN, and Stata to calculate NIS variances are presented in the special report Calculating Nationwide Inpatient Sample Variances on the HCUP-US website (www.hcup-us.ahrq.gov). Although the examples using the NIS also apply to the NEDS, it should be noted that the NEDS is a much larger data set. Please consult the documentation for the different software packages concerning the use of large databases. For an excellent review of programs to calculate statistics from survey data, visit the following website: www.hcp.med.harvard.edu/statistics/survey-soft/.

The NEDS includes a Hospital Weights File with variables required by these programs to calculate finite-population statistics. The file includes synthetic hospital identifiers (Primary Sampling Units or PSUs), stratification variables, and stratum-specific totals for the numbers of ED visits and hospitals so that finite-population corrections can be applied to variance estimates.

In addition to these subroutines, standard errors can be estimated by validation and cross-validation techniques. Given that a very large number of observations will be available for most NEDS analyses, it may be feasible to set aside a part of the data for validation purposes. Standard errors and confidence intervals then can be calculated from the validation data.

If the analytic file is too small to set aside a large validation sample, cross-validation techniques may be used. For example, ten-fold cross-validation would split the data into 10 subsets of equal size. The estimation would take place in 10 iterations. In each iteration, the outcome of interest is predicted for one-tenth of the observations by an estimate based on a model that is fit to the other nine-tenths of the observations. Unbiased estimates of error variance are then obtained by comparing the actual values to the predicted values obtained in this manner.

COMPARABLE ED DATA SOURCES

To aid in understanding of NEDS, national estimates from the NEDS are compared to available sources of similar data (Table 2). Each of the following ED data sources has potential for use in research addressing ED utilization and policy.

Table 2. Sources of Emergency Department (ED) Data by Type

Information on total ED visits in 2017 for the U.S. was available from four data sources (AHA, NEDS, NEDI, and NHIS)¹⁴. Appendix D, Figure D.1, displays the range of total ED visits; Appendix D, Table D.1 lists the total ED visits in the U.S and the totals by census region. The total U.S. ED visit counts are relatively consistent across the data sources. The South consistently had the highest number of ED visits.

Information on the total number of ED visits by region and the percentage of all ED visits resulting in inpatient admissions are available from one data source (NEDS) and are displayed in Appendix D, Table D.2.

Estimates of the number of hospital-owned EDs by ED visit volume are available from two data sources (NEDS and AHA) and are displayed in Appendix D, Table D.3.

Estimates of the number of ED visits related to nonfatal ED visits are available from two data sources (NEDS and NEISS-AIP) and are displayed in Appendix D, Table D.4.

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Appendix A: NEDS Introductory Information

 

Table A.1. States Participating in the 2017 NEDS

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Figure A.1. HCUP States and the District of Columbia Included in the 2017 NEDS

The above graphic outlines states in the NEDS by Region. In the Western region, AZ, CA, CO, MT, NV, OR, UT, WY are in the HCUP NEDS. The following states are not in the NEDS in this region - AK, HI, ID, NM, WA. In the Midwestern region, IA, IN, IL, KS, MN, MO, ND, NE, OH, SD, WI are in the HCUP NEDS. The following state is not in the NEDS in this region - MI. In the Northeastern region, CT, MA, ME, NJ, NY, RI, VT are in the HCUP NEDS. The following states are not in the NEDS in this region - NH, PA. In the Southern region, AR, DC, FL, GA, KY, MD, MS, NC, SC, TN, TX are in the HCUP NEDS. The following states are not in the NEDS in this region - AL, DE, LA, OK, VA, WV.

Table A.2. Percentage of U.S Population and ED Visits Accounted for by the 37 HCUP Organizations Participating in the NEDS, 2017

Source: Population count from the U.S. Census Bureau, Annual Estimates of the Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2018 (NST-EST2018-01, released December 2018). ED visits in the U.S. from the American Hospital Association Annual Survey of Hospitals, 2017.

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Table A.3. NEDS-Related Reports and Database Documentation Available on the HCUP-US Website

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Table A.4. NEDS Sampling Stratifiers

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Table A.5. NEDS Target Universe, Sampling Frame, and Final Sample Characteristics, 2017

Source: HCUP Nationwide Emergency Department Sample, 2017

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Table A.6. NEDS Sampling Rates by Region, 2017

NEDS stratum is defined by 5 digits:

1st digit - Region: (1) Northeast, (2) Midwest, (3) South, (4) West
2nd digit - Trauma: (0) Not a trauma center, (1) Trauma center level I, (2) Trauma center level II, (3) Trauma center level III, Collapsed categories used for strata with small sample sizes: (8) Trauma center level I or II, (4) Trauma center level III and Non-Trauma.
3rd digit - Urban-rural location: (1) Large metropolitan, (2) Small metropolitan, (3) Micropolitan, (4) Non-urban residual, Collapsed categories used for strata with small sample sizes: (6) Any urban-rural location, (7) Small Metro and Micro, (8) Metropolitan (large and small), (9) Non-metropolitan (micropolitan and non-urban location)
4th digit - Teaching: (0) Metropolitan non-teaching, (1) Metropolitan teaching, (2) Non-metropolitan teaching and non-teaching
5th digit - Control: (0) All (used for combining public, voluntary, and private), (1) Public - government, non-Federal, (2) Voluntary - private, non-profit, (3) Proprietary - private, investor-owned/for-profit, (4) Private (used for combining private voluntary and proprietary)

Source: HCUP Nationwide Emergency Department Sample, 2017

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Table A.7. Different Types of ED Events in the NEDS, 2017

Source: HCUP Nationwide Emergency Department Sample, 2017.

Appendix B: Partner-Specific Restrictions

 

The table below enumerates the types of restrictions applied to the 2017 Nationwide Emergency Department Sample. Restrictions include the following types:

• Confidentiality of hospitals
• Confidentiality of records
• Limited reporting of diagnosis codes for medical misadventures and adverse effects
• Missing discharges for specific populations of patients.

Table B.1. Partner-Specific Restrictions

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Appendix C: NEDS Data Elements and Codes

Table C.1. Data Elements in the 2017 NEDS Core File

For data years prior to 2017, refer to the NEDS Description of Data Elements page on the HCUP-US website or to previous versions of the NEDS Introduction.

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Table C.2. Data Elements in the 2017 NEDS Supplemental ED File

For data years prior to 2017, refer to the NEDS Description of Data Elements page on the HCUP-US website or to previous versions of the NEDS Introduction.

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Table C.3.Data Elements in the 2017 NEDS Supplemental Inpatient File

For data years prior to 2017, refer to the NEDS Description of Data Elements page on the HCUP-US website or to previous versions of the NEDS Introduction.

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Table C.4. Data Elements in the 2017 NEDS Hospital Weights File

For data years prior to 2017, refer to the NEDS Description of Data Elements page on the HCUP-US website or to previous versions of the NEDS Introduction.

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Appendix D: Comparisons of the NEDS with Existing Sources of ED Data

 

Figure D.1. Emergency Department Visit Counts in the United States, 2017

Abbreviations: AHA, American Hospital Association Annual Survey Database; ED, emergency department; NEDI, National Emergency Department Inventory; NEDS, Nationwide Emergency Department Sample; NHIS, National Health Interview Survey

The above graphic outlines the number of emergency department visits in the United States in 2017. For 2017 it is estimated to be 144,818,803 according to the HCUP Nationwide Emergency Department Sample (NEDS); 144,818,803 according to the American Hospital Association Annual Survey Database (AHA); 107,132,334 according to the National Health Interview Survey (NHIS); and 158,719,684 according to the National Emergency Department Inventory (NEDI).

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Table D.1. Estimates of ED Visits by U.S. Geographic Region from Four ED Data Sources, 2017

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Table D.2. Estimates of the ED Visits Resulting in Inpatient Admissions (Admission Rate) by U.S. Geographic Region, 2017

Abbreviations: ED, emergency department; NEDS, Nationwide Emergency Department Sample.

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Table D.3. Estimates of the Number of Hospital-Owned EDs by ED Visit Volume from Two ED Data Sources, 2017

Abbreviations: AHA, American Hospital Association Annual Survey Database; ED, emergency department; NEDS, Nationwide Emergency Department Sample.
¹Column percent indicates the percentage of the total records in the ED data source that are in each group of ED visits.

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Table D.4. Estimates of the Number of ED Visits Related to Nonfatal Injures from Two ED Data Sources, 2017

Abbreviations: ED, emergency department; NA, not available; NEDS, Nationwide Emergency Department Sample; NEISS-AIP = National Electronic Injury Surveillance System All-Injury Program.
¹ Any diagnosis in the following range:

• Codes starting with S
• Codes starting with T07-T34
• Codes starting with T36-T50 with a 6th character of 1, 2, 3, or 4
• Codes starting with T51-T65
• Codes starting with T66-T76
• Codes starting with T79
• Codes starting with M97
• Codes O9A.2-O9A.5.

Counts for all injuries allowed any 7th character for the injury diagnosis code; counts for the initial encounter limited injury diagnosis codes to those with a 7th character of A, B, C, or missing.
Codes starting with T36.9, T37.9, T39.9, T41.4, T42.7, T43.9, T45.9, T47.9, and T49.9 with a 5th character of 1, 2, 3, or 4 and a 6th character of "X" should have been included in the injury criteria in the 2017 NEDS, but were not. The exclusion of these ICD-10-CM diagnosis codes resulted in 3,775 records (0.01 percent of the 2017 NEDS) not being identified as an injury.
² Data from WISQARS Query System (https://webappa.cdc.gov/sasweb/ncipc/nfirates.html). Includes non-fatal, all-cause injuries for all ages and sexes. Patients who died on arrival to the ED or during treatment in the ED are excluded. Queried November 25, 2019.
³ For the NEISS-AIP, other includes left against medical advice, sent for observations, and unknown destination. For the NEDS, other include left against medical advice. Patients who are treated in the ED and then observed cannot be identified. If they were discharged home from observation, they are counted under "treated and released from the ED"; if they were admitted to the hospital from observation, they are counted under "admitted to the same hospital". The estimate for other in 2017 was not shown in the WISQARS Query System because it was unstable due to small sample size and/or a coefficient of variation > 30%.

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¹ Merrill, C. T. and Owens, P. L. (2007). Hospital Admissions That Began in the Emergency Department for Children and Adolescents, 2004. HCUP Statistical Brief #32. June 2007. Agency for Healthcare Research and Quality, Rockville, MD. Retrieved June 9, 2008 from www.hcup-us.ahrq.gov/reports/statbriefs/sb32.pdf
² Merrill, C. T. and Owens, P. L. (2007). Hospital Admissions That Began in the Emergency Department for Children and Adolescents, 2004. HCUP Statistical Brief #32. June 2007. Agency for Healthcare Research and Quality, Rockville, MD. Retrieved June 9, 2008 from www.hcup-us.ahrq.gov/reports/statbriefs/sb32.pdf.
³ ICD-10-CM/PCS: International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System
⁴ More of the AHA "community hospital designation" is available at www.ahadataviewer.com/glossary.
⁵ MacKenzie EJ, Hoyt DB, Sacra JC, et al. National inventory of hospital trauma centers. JAMA. 2003;289:1515-1522.
⁶ American College of Surgeons Committee on Trauma, Verification, Review, and Consultation Program for Hospitals. Additional details are available at www.facs.org/quality-programs/trauma/vrc. Accessed September 2018.
⁷ American Trauma Society. Trauma Information Exchange Program. Available at: www.amtrauma.org/?page=TIEP. Accessed December 2019.
⁸ United States Department of Agriculture Economic Research Service (www.ers.usda.gov/data-products/urban-influence-codes.aspx)
⁹ The collapsing of small metropolitan and micropolitan areas was required in the South in 2011-2015.
¹⁰ The collapsing of all areas was required in the South in 2014.
¹¹The ZIP Code of the hospital is not included in the NEDS data files.
¹² This HCUP Methods Series report is available at www.hcup-us.ahrq.gov/reports/methods/2011_03.pdf.
¹³ Carlson BL, Johnson AE, Cohen SB. An evaluation of the use of personal computers for variance estimation with complex survey data. J Off Statistics. 1993;9(4):795-814.
¹⁴ At the time this document was created, the 2017 NHAMCS public use file was not available for developing comparative estimates.

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