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Introduction to the HCUP Nationwide Emergency Department Sample (NEDS), 2009

HEALTHCARE COST AND UTLIZATION PROJECT – HCUP
A FEDERAL-STATE-INDUSTRY PARTNERSHIP IN HEALTH DATA

Sponsored by the Agency for Healthcare Research and Quality

 

 

INTRODUCTION TO

THE HCUP NATIONWIDE EMERGENCY DEPARTMENT SAMPLE (NEDS)

2009

 

 

These pages provide introductory-level information about the NEDS.

  For full documentation and notification of changes,
visit the HCUP User Support (HCUP-US) Website at http://www.hcup-us.ahrq.gov.

 

Issued September 2011

Updated November 2015

 

Agency for Healthcare Research and Quality
Healthcare Cost and Utilization Project (HCUP)

Phone: (866) 290-HCUP (4287)
E-mail: hcup@ahrq.gov
Website: http://www.hcup-us.ahrq.gov

 

NEDS Data and Documentation Distributed by:
HCUP Central Distributor

Phone: (866) 556-4287 (toll-free)
Fax: (866) 792-5313
E-mail: HCUPDistributor@ahrq.gov



Table of Contents



HCUP NATIONWIDE EMERGENCY DEPARTMENT SAMPLE (NEDS)
SUMMARY OF DATA USE LIMITATIONS

***** REMINDER *****


All users of the NEDS must take the on-line HCUP Data Use Agreement (DUA) training course, and read and sign a Data Use Agreement.

Authorized users of HCUP data agree to the following restrictions: ‡

  • Will not use the data for any purpose other than research or aggregate statistical reporting.

  • Will not re-release any data to unauthorized users.

  • Will not redistribute HCUP data by posting on any Website or other publically-accessible online repository

  • Will not identify or attempt to identify any individual, including by the use of vulnerability analysis or penetration testing. Methods that could be used to identify individuals directly or indirectly shall not be disclosed or published.

  • Will not publish information that could identify individual establishments (e.g., hospitals) and will not contact establishments.

  • Will not use the data concerning individual establishments for commercial or competitive purposes involving those establishments, and will not use the data to determine rights, benefits, or privileges of individual establishments.

  • Will not use data elements from the proprietary severity adjustment software packages (3M APR-DRGs, HSS APS-DRGs, and Thomson Reuters Disease Staging) for any commercial purpose or to disassemble, decompile, or otherwise reverse engineer the proprietary software.

  • Will acknowledge in reports that data from the "Healthcare Cost and Utilization Project (HCUP)" were used, including names of the specific databases used for analysis.

  • Will acknowledge that risk of individual identification of persons is increased when observations (i.e., individual discharge records) in any given cell of tabulated data is less than or equal to 10.

Any violation of the limitations in the Data Use Agreement is punishable under Federal law by a fine of up to $10,000 and up to 5 years in prison. Violations may also be subject to penalties under State statutes.

† The on-line Data Use Agreement training session and the Data Use Agreement are available on the HCUP User Support (HCUP-US) Web site at http://www.hcup-us.ahrq.gov.
‡ Specific provisions are detailed in the Data Use Agreement for Nationwide Databases.



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HCUP CONTACT INFORMATION

All HCUP data users, including data purchasers and collaborators, must complete the online HCUP Data Use Agreement (DUA) Training Tool, and read and sign the HCUP Data Use Agreement. Proof of training completion and signed Data Use Agreements must be submitted to the HCUP Central Distributor as described below.

The on-line DUA training course is available at: http://www.hcup-us.ahrq.gov/tech_assist/dua.jsp.

The HCUP Nationwide Data Use Agreement is available on the AHRQ-sponsored HCUP User Support (HCUP-US) Web site at: http://www.hcup-us.ahrq.gov

HCUP Central Distributor

Data purchasers will be required to provide their DUA training completion code and will execute their DUAs electronically as a part of the online ordering process. The DUAs and training certificates for collaborators and others with access to HCUP data should be submitted directly to the HCUP Central Distributor using the contact information below.

The HCUP Central Distributor can also help with questions concerning HCUP database purchases, your current order, training certificate codes, or invoices, if your questions are not covered in the Purchasing FAQs on the HCUP Central Distributor Web site.

HCUP User Support:

Information about the content of the HCUP databases is available on the HCUP User Support (HCUP-US) Web site (http://www.hcup-us.ahrq.gov). If you have questions about using the HCUP databases, software tools, supplemental files, and other HCUP products, please review the HCUP Frequently Asked Questions or contact HCUP User Support:

 

WHAT IS THE NATIONWIDE EMERGENCY DEPARTMENT SAMPLE (NEDS)?

 

  • The Nationwide Emergency Department Sample (NEDS) tracks information about emergency department (ED) visits across the country. Information includes geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, acute and chronic conditions, and injuries).


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


  • The 2009 NEDS is a publicly available database that can be purchased through the HCUP Central Distributor.


  • There are 29 HCUP Partner States that contributed 2009 ED data to the NEDS: AZ, CA, CT, FL, GA, HI, IA, IL, IN, KS, KY, MA, MD, ME, MN, MO, NC, NE, NH, NJ, NY, OH, RI, SC, SD, TN, UT, VT, and WI.


  • The NEDS describes over 130 million ED visits for 2009, an exceptional resource for conducting research on high-profile emergent health delivery issues. One of the most distinctive features of the NEDS is its large sample size, which allows for analysis across hospital types and the study of relatively uncommon disorders and procedures.


  • New to the NEDS in 2009 is a series of data elements that identify injuries by severity, mechanism, and intent


  • Users must complete an on-line Data Use Agreement training prior to receiving the data.

 

UNDERSTANDING THE NEDS

 

  • This document, Introduction to the NEDS, 2009, summarizes the content of the NEDS and describes the development of the2009 NEDS sample and weights.


  • Important considerations for data analysis are highlighted and references to further resources are provided.


  • In-depth documentation for the NEDS is available on the HCUP User Support (HCUP-US) Website (www.hcup-us.ahrq.gov). Please refer to detailed documentation before using the data.

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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 2009 NEDS is a publicly available database that can be purchased through the HCUP Central Distributor.

The NEDS was created to enable analyses of emergency department (ED) utilization patterns and to support public health professionals, administrators, policy makers, 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% 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 almost 29 million ED visits at 964 hospitals that approximate a 20-percent stratified sample of U.S. hospital-based EDs. Weights are provided to calculate national estimates pertaining to almost 130 million ED visits in 2009.

The NEDS is drawn from States that provide HCUP with data from ED visits that may or may not have resulted in hospital admission. Twenty-nine HCUP States participated in the 2009 NEDS. See Appendix I, Table 1 for a list of data organizations participating in the NEDS.

By stratifying on important hospital characteristics, the NEDS represents a microcosm of U.S. hospital-based EDs. Stratification is based on the following five characteristics:

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 User Support (HCUP-US) Website at .

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INTRODUCTION TO THE HCUP 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 public health professionals, administrators, policy makers, 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% 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).

The number of States, hospital-based EDs, and ED visits included in the NEDS varies by year (listed below). Appendix I, Table 1 identifies the specific statewide data organizations that contribute to the NEDS.

Data Year HCUP States in the NEDS Number of Hospital-Based EDs Number of ED Events Unweighted Number of ED Events, Weighted for National Estimates
2009 AZ, CA, CT, FL, GA, HI, IA, IN, KS, KY, IL, MA, MD, ME, MN, MO, NC, NE, NH, NJ, NY, OH, RI, SC, SD, TN, UT, VT, and WI (Added IL) 964 28,861,047 128,885,040
2008 AZ, CA, CT, FL, GA, HI, IA, IN, KS, KY, MA, MD, ME, MN, MO, NC, NE, NH, NJ, NY, OH, RI, SC, SD, TN, UT, VT, and WI (Added KY) 980 28,447,148 124,945,264
2007 AZ, CA, CT, FL, GA, HI, IA, IN, KS, MA, MD, ME, MN, MO, NC, NE, NH, NJ, NY, OH, RI, SC, SD, TN, UT, VT, and WI (Added NC, NY, RI) 966 26,627,923 122,331,739
2006 AZ, CA, CT, FL, GA, HI, IA, IN, KS, MA, MD, ME, MN, MO, NE, NH, NJ, OH, SC, SD, TN, UT, VT, and WI 955 25,702,597 120,033,570

Appendix 1, Figure 1 represents the geographic distribution of the 29 participating HCUP Partner States in 2009. Based on U.S. Census Bureau data, the HCUP NEDS States account for 66.8% of the U.S. population in 2009. The 29 States account for 65.5% of the ED visits reported in the 2009 American Hospital Association (AHA) Annual Survey Database. Details on the percentage of population and ED visits by region are provided in Appendix I, Table 2.

Identification of HCUP Records with Emergency Department Services

Information on patients with ED events are contained in two existing HCUP databases:

Both of these HCUP databases contain a core set of clinical and non-clinical information 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. The HCUP criteria for identifying an ED record (i.e., a discharge record for a patient with an ED event) require that at least one of the following conditions is true:

Because five of the 29 Partners (CA, HI, MA, NC, and OH) did not provide ED charge information (either in revenue codes or a separate charge field) on records in the SEDD, this limited the ability to clearly identify ED visits using the HCUP criteria. Therefore, the identification of ED records in these States was evaluated on a State-by-State basis.

State-Specific Restrictions

Some sources 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 State-specific restrictions is available in Appendix II.

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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 different files:

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:

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

After analyzing the availability of information from the HCUP Partner States, 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:

Appendix III identifies the data elements in each NEDS file:

Not all data elements in the NEDS are uniformly coded or available across all States. The tables in Appendix III provide summary documentation for the data. Please refer to the NEDS documentation located on the HCUP-US Website (http://www.hcup-us.ahrq.gov) for comprehensive information about data elements and the files.

Getting Started

The NEDS is an extremely large database that requires sophisticated statistical software for analysis. The following computer properties are needed in order to load and analyze the NEDS data:

The total size of the comma-delimited version (CSV) of the NEDS is almost 14 GB. The NEDS files loaded into SAS are about 11 GB. In SAS, the largest use of space typically occurs during a sort, which requires work space about three times the size of the file. Thus, the NEDS files would require about 33 GB of available workspace to perform a sort. 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 under 30 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 variables. 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.

Copying and Decompressing the Comma-Delimited Files

To copy and decompress the comma-delimited (CSV) NEDS files from the DVD, the following steps are outlined:

  1. Create a directory for the NEDS on the hard drive.

  2. Unzip each CSV file from the DVD, saving it into a new directory. [Please note that attempts to unzip files larger than 4 GB using versions of Windows prior to Vista will produce an error message similar to the following: "The Compressed (zipped) Folder is invalid or corrupted." The solution is to use a third-party zip utility such as WinZip or 7-Zip rather than the built-in Windows Explorer function to open the archive. Evaluation versions of WinZip may be downloaded from the WinZip Website at www.winzip.com. Exit Disclaimer 7-Zip can be downloaded free of charge at http://www.7-zip.org/.] Exit Disclaimer

NEDS Documentation

Comprehensive documentation for the NEDS files is available on the HCUP-US Website (http://www.hcup-us.ahrq.gov). 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 client understand the structure and content of the NEDS and aid in using the database.

To locate the NEDS documentation on HCUP-US, the user is instructed to:

Appendix 1, Table 3 details the comprehensive NEDS documentation available on HCUP-US.

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Downloading and Running the Load Programs

Programs to load the data into SAS, SPSS, or Stata, are available on the HCUP User Support Website (HCUP-US). These steps are used to download and run the load programs:

  1. Go to the NEDS Database Documentation page on HCUP-US at http://www.hcup-us.ahrq.gov/db/nation/neds/nedsdbdocumentation.jsp.
  2. Go to the "Load Programs" section on this page.
  3. Click on "SAS Load Programs", "SPSS Load Programs", or "Stata Load Programs" to go to the corresponding Load Programs page.
  4. Select and download the load programs. They are specific to the data year. For example, the load program for the 2009 NEDS Core file is linked to "Core File" under "2009 NEDS." Save the load programs into the same directory as the NEDS CSV files on the computer.
  5. Edit and run the load programs as appropriate for the environment, to load and save the analysis files. For example, add directory paths for the input and output files as needed.

HCUP On-Line Tutorials

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

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

SAMPLING DESIGN OF THE NEDS

Similar to the design of the Nationwide Inpatient Sample (NIS), the NEDS is built using a 20% stratified sample of institutions. The NIS is a sample of U.S. hospitals; the NEDS is a sample of U.S. hospital-based EDs. 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-based EDs selected.

Universe of Hospital-Based Emergency Departments

A feasibility study performed in 2008 assessed several possible data sources for the universe of hospital-based EDs in the United States: the American Hospital Association (AHA) Annual Survey Database (Health Forum, LLC © 2012); Verispan, LLC databases (now called SDI Health LLC); and the Centers for Medicare and Medicaid (CMS) Hospital Cost Reports. The AHA Annual Survey Database has the best data to apply for a number 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. Third, the AHA Annual Survey Database is used as the target universe for the NIS. The universe of hospital-based EDs is therefore defined as the AHA community, non-rehabilitation hospitals that reported total ED visits. The AHA defines community hospitals as "all non-Federal, short-term, general, and other specialty hospitals."

Sampling Frame of the NEDS

The sampling frame of the NEDS is limited to a subset of the universe: hospital-based EDs in the States for which HCUP ED data (SID and SEDD) are available. The list of hospital-based EDs in the frame consists of all AHA community, non-rehabilitation hospitals that report total ED visits in each of the frame States 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 I, Table 5.

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 I, Figure 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. For the NEDS, trauma centers treating adults and children were identified through the Trauma Information Exchange Program database (TIEP), a national inventory of trauma centers in the U.S. Information is collected by the American Trauma Society and the Johns Hopkins Center for Injury Research and Policy and funded by the Centers for Disease Control and Prevention. 3,4

The TIEP database identifies all U.S. hospitals that are designated as trauma centers by a State or regional authority or verified by the American College of Surgeons' Committee on Trauma (ACS/COT). These trauma centers treat both adults and children. Designation of trauma center levels I, II, and III is based on criteria developed by the ACS/COT. Level I and II centers have comprehensive resources and are able to care for the most severely injured. Level I centers also 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 and V centers are State-defined and often located in remote areas. These centers resuscitate and stabilize patients and arrange transfer to an appropriate trauma facility. For the NEDS, levels I, II, and III were used to identify a trauma center. Level IV and V centers were set aside within the context of these data because many states choose not to designate hospitals at these levels of trauma care and their institutional characteristics have many similarities to community (non-trauma) hospitals in other areas. It is also 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 services and resources offered by hospitals of different levels. Further, hospitals of different levels may be utilized in diverse ways within the context of individual state trauma systems or the geographic areas in which they operate. Hospital information from TIEP was matched to the AHA via the corresponding AHA hospital identifier and then added to the HCUP ED data.

For trauma centers within children's hospitals, the following process was employed:

In the NEDS, trauma centers that are level I, II, and III were distinguished unless the strata size in the universe or frame was less than two hospitals. In that case, a collapsed stratification of levels I and II or levels I, II, and III was necessary.

Urban-Rural Location of the ED

The urban-rural location of hospital-based EDs was determined based on the county in which the hospital was located. The categorization is a simplified adaptation of the 2003 version of the Urban Influence Codes (UIC).5 The 12 categories of the UIC are combined into four broader categories:

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

Teaching Status

A hospital-based ED is considered to be a teaching facility if the associated hospital has an American Medical Association (AMA) 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 0.25 or higher according to the AHA Annual Survey Database. 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:

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).

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Sample Weights

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

where Ws(universe) was the ED universe weight, and Ns(universe) and Ns(sample) were the number of hospital-based 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% 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 by poststratification. Hospital-based 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:

where DWis(universe) was the discharge weight; DNs(universe) represented the number of ED visits from community, non-rehabilitation 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, non-rehabilitation hospitals in the United States that were included in the 2009 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-based ED events from community, non-rehabilitation hospitals in the 29 HCUP Partner States 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% of their ED visits resulting in admission. Appendix I, Table 6 lists the final target universe and sampling frame for the NEDS.

The NEDS is a stratified probability sample of hospital-based EDs in the frame. Sampling probabilities were calculated to select 20% 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% sample contains over 29 million records. A 20% sample also enables the user to split the NEDS into two 10% subsamples for estimation and validation of models.

To further ensure accurate geographic representation, hospitals were implicitly stratified by State and three-digit ZIP Code (i.e., the first three digits of the hospital's five-digit ZIP Code). This was accomplished through sorting by three-digit ZIP Code within each stratum prior to drawing a systematic random sample of hospitals. Within the three-digit ZIP Code, hospitals were sorted by a random number to ensure further geographic generalizability of hospitals within the frame States; otherwise, generally, three-digit ZIP Codes that are proximal in value are geographically near one another within a State. Furthermore, the U.S. Postal Service locates regional mail distribution centers at the three-digit level. Thus, the boundaries tend to be a compromise between geographic size and population size.

Using the universe of U.S. hospital-based 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.

After stratifying and sorting the universe of hospitals, a random sample of up to 20% of the total number of hospital-based EDs in the U.S. was selected within each stratum. A shortfall was defined as an insufficient number of EDs in the frame to meet the threshold of 20% of the universe. In strata with shortfalls, the sampling rate from the universe was less than 20% and all possible EDs in the frame were selected for the NEDS. In contrast, the sampling rate is larger than 20% in some strata because protecting hospital confidentiality required a minimum of two sampled EDs in each stratum. Appendix I, Table 7 lists the sampling rates for the NEDS.

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HOW TO USE THE NEDS FOR DATA ANALYSIS

This section provides a brief synopsis of special considerations when using the NEDS. For more details, refer to the comprehensive documentation on the HCUP-US Website (http://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 available on the HCUP-US Website (http://www.hcup-us.ahrq.gov) and then read and sign a Data Use Agreement. A copy of the signed Data Use Agreements must be sent to AHRQ. See page 2 of this document for the mailing address.

Limitations of the NEDS

The NEDS contains almost 29 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.

Identifying Different Types of ED Events

The HCUP data element ED event distinguishes among the different types of ED events. Appendix 1, Table 4 provides the number and percentage of records in the 2009 NEDS for each of the five ED event types.

There may be a bias to the records in which the type of ED event is unknown. Some States have a large percentage of missing information.

Calculating National Estimates

To produce national estimates, the weighting data elements provided to weight ED events in the NEDS to hospital-based ED visits from all U.S. community, non-rehabilitation hospitals should be used. In order to produce national estimates, weights MUST be used.

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 special report Calculating Nationwide Inpatient Sample Variances on the HCUP-US Website (www.hcup-us.ahrq.gov). The HCUP Nationwide Inpatient Sample (NIS) uses the same stratified sample design, so techniques appropriate for the NIS 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 IV. Also included in Appendix IV 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://hcupnet.ahrq.gov/#setup). 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 and ED visits. 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. 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 Diagnosis and Procedure Codes

ICD-9-CM diagnosis and procedure codes provide valuable insights into the reasons for ED visits and hospitalizations as well as what procedures patients receive, but these codes need to be carefully used and interpreted. ICD-9-CM codes change every October as new codes are introduced and some codes are retired. See the Conversion Table at http://www.cdc.gov/nchs/datawh/ftpserv/ftpicd9/ftpicd9.htm which shows ICD-9-CM code changes over time. It is essential to check all ICD-9-CM codes used for analysis to ensure the codes are in effect during the time period(s) studied.

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.6

Diagnoses reported on an ED admission may be from both the ED and hospital settings. It may be useful to compare diagnostic-specific ED visits that do not result in hospitalization to those resulting in hospitalization.

CPT procedure codes, which are copyrighted by the American Medical Association, also provide valuable insight into the procedures performed. CPT codes can change dramatically each year. It is essential to check all CPT procedure codes used for analysis to ensure that the codes are in effect during the time period(s) studied.

Up to four external cause-of-injury codes (E codes) are retained in separate data elements (ECODE1-ECODE4). The first listed E code (ECODE1) is not necessarily the underlying or principal cause of the injury.

The collection and reporting of E codes vary greatly across States. Some States have laws or mandates for the collection of E codes; others do not. In addition, some States do not require hospitals to report E codes in the range E870-E879 ("misadventures to patients during surgical and medical care") which means that these occurrences will be underreported.

Although the NEDS contains fields for up to 15 diagnoses, four E codes, 15 CPT procedures, and 9 ICD-9-CM procedures per ED event, 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 2% of all ED records report more fields than the maximum allowed on the NEDS. Four data elements are provided. These data elements tell users exactly how many diagnoses and procedures were on the original records (NDX for diagnoses, NECODE for E codes, NCPT for CPT procedures, and NPR for ICD-9-CM procedures).

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-based 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 details, see the special report Calculating Nationwide Inpatient Sample Variances on the HCUP-US Website (www.hcup-us.ahrq.gov). The NIS uses the same stratified sample design, so techniques appropriate for the NIS are also appropriate for the NEDS.

If hospitals inside the sampling frame are similar to hospitals outside the frame, the sample hospitals can be treated as if they were randomly selected from the entire universe of hospitals within each stratum. Standard formulas for a stratified, single-stage cluster sample without replacement could be used to calculate statistics and their variances in most applications.

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.

Return to Introduction

 

Computer Software for Weighted and Variance Calculations

The hospital weights are useful for producing hospital-level statistics for analyses that use the hospital-based 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.6 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 (http://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: http://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 A). Each of the following ED data sources has potential for use in research addressing ED utilization and policy and has data available for 2009.

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

Description
Type of ED Data ED Data Source
National inventories of hospital-based EDs American Hospital Association (AHA) Annual Survey Database Database containing characteristics and descriptions of U.S. hospitals reported by hospitals via survey. Owned by Health Forum.
National Emergency Department Inventory (NEDI) - USA Inventory of U.S. ED locations and annual ED visit volume that integrates information from the AHA Annual Database, the Hospital Market Profiling Solution©, Internet searches, and direct communication with hospital staff. Created by the Emergency Medicine Network (EMNet).
ED visit information from a sample of hospital-based EDs HCUP Nationwide Emergency Department Sample (NEDS) Nationwide sample drawn from the HCUP SID and SEDD, stratified and weighted to be nationally representative of ED visits and facilities. Sponsored by the Agency for Healthcare Research and Quality (AHRQ) of the U.S. Department of Health and Human Services (DHHS).
National Hospital Ambulatory Medical Care Survey (NHAMCS) National probability sample survey of utilization and provision of ambulatory services in hospital emergency and outpatient departments. Sponsored by the National Center for Health Statistics (NCHS) of the DHHS' Centers for Disease Control and Prevention (CDC).
National Electronic Injury Surveillance System - All Injury Program (NEISS-AIP) National probability sample providing counts of injuries seen in the ED. Sponsored by the National Center for Injury Prevention and Control (NCIPC) of the DHHS' CDC and the U.S. Consumer Product Safety Commission (CPSC).
ED visit information from a sample of patients National Health Interview Survey (NHIS) A comprehensive survey of the civilian non-institutionalized population residing in the United States at the time of the interview. Sponsored by the National Center for Health Statistics (NCHS) of the DHHS CDC.

Information on total ED visits in 2009 for the U.S. was available from five data sources (AHA, NEDS, NEDI, NHAMCS, and NHIS). Appendix IV, Figure 1 displays the range of total ED visits; Appendix IV, Table 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 and the West had the lowest 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 two data sources (NHAMCS and NEDS) and are displayed in Appendix IV, Table 2.

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

Estimates of the number of injury-related ED visits are available from three data sources (NEDS, NHAMCS, and NEISS-AIP) and are displayed in Appendix IV, Table 4.

Return to Introduction



Appendix I: NEDS Introductory Information

 

Table 1. HCUP Partners Participating in the 2009 NEDS

State HCUP Data Source
Arizona Arizona Department of Health Services
California Office of Statewide Health Planning and Development
Connecticut Connecticut Hospital Association
Florida Florida Agency for Health Care Administration
Georgia Georgia Hospital Association
Hawaii Hawaii Health Information Corporation
Illinois Illinois Department of Public Health
Indiana Indiana Hospital Association
Iowa Iowa Hospital Association
Kansas Kansas Hospital Association
Kentucky Kentucky Cabinet for Health and Family Services
Maine Maine Health Data Organization
Maryland Health Services Cost Review Commission
Massachusetts Massachusetts Division of Health Care Finance and Policy
Minnesota Minnesota Hospital Association
Missouri Hospital Industry Data Institute
Nebraska Nebraska Hospital Association
New Hampshire New Hampshire Department of Health & Human Services
New Jersey New Jersey Department of Health and Senior Services
New York New York State Department of Health
North Carolina North Carolina Department of Health and Human Services
Ohio Ohio Hospital Association
Rhode Island Rhode Island Department of Health
South Carolina South Carolina State Budget & Control Board
South Dakota South Dakota Association of Healthcare Organizations
Tennessee Tennessee Hospital Association
Utah Utah Department of Health and Utah Department of Health, Bureau of Emergency Medical Services
Vermont Vermont Association of Hospitals and Health Systems
Wisconsin Wisconsin Department of Health Services

Return to Introduction



Figure 1. HCUP States Participating in the 2009 NEDS

Region States in HCUP NEDS States not in HCUP
West AZ, CA, HI, UT AK, CO, ID, MT, NM, NV, OR, WA, WY
Midwest IA, IN, IL, KS, MN, MO, NE, OH, SD, WI MI, ND
Northeast CT, MA, ME, NH, NJ, NY, RI, VT PA
South FL, GA, KY, MD, NC, SC, TN AL, AR, DE, DC, LA, MS, OK, TX, VA, WV


Table 2. Percentage of U.S. Population and AHA ED Visits Accounted for by the 28 HCUP States Participating in the NEDS, 2009

Region U.S. Population in HCUP ED States Percentage of U.S. Population in HCUP ED States (%) AHA ED Visits in HCUP ED States Percentage of AHA ED Visits in HCUP ED States (%)
Northeast 42,640,323 77.2 19,066,004 76.0
Midwest 56,169,650 84.1 25,025,066 83.8
South 58,526,776 51.7 26,160,129 51.4
West 47,544,424 66.5 14,144,224 61.5
Nation 204,881,173 66.8 84,395,423 66.5

Return to Introduction



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

Restrictions on the Use of the NEDS
  • Data Use Agreement for the NEDS

  • Requirements for publishing with HCUP data
  Corrections to the NEDS

  • 2006 AND 2007
Description of the NEDS Files
  • Introduction to the NEDS, 2009 – this document

  • Introduction to the NEDS for prior years

  • HCUP Quality Control Procedures – describes procedures used to assess data quality

  • File Specifications – details data file names, number of records, record length, and record layout
  Load Programs

Programs to load the ASCII data files into statistical software:
  • SAS Load Programs
  • SPSS Load Programs
  • Stata Load Programs
Description of Data Elements in the NEDS
  • Description of Data Elements – details uniform coding and State-specific idiosyncrasies

  • Summary Statistics – lists means and frequencies on nearly all data elements

  • HCUP Coding Practices – describes how HCUP data elements are coded
  • HCUP Hospital Identifiers – explains data elements that characterize individual hospitals
  HCUP Tools: Labels and Formats

  • Overview of Clinical Classifications Software (CCS)
  • Format library programs to create value labels
    • DRG formats
    • HCUP formats
    • HCUP diagnoses and procedure groups, including CCS categories
    • ICD-9-CM formats

NEDS-Related Reports

  • Calculating Nationwide Inpatient Sample Variances (methods also apply to the NEDS)

Return to Introduction



Table 4. Different Types of ED Events in the NEDS

ED Event Number of ED Visits Percent of ED Visits
ED visit in which the patient is treated and released 106,643,719 82.7
ED visit in which the patient is admitted to this same hospital 19,592,545 15.2
ED visit in which the patient is transferred to another short-term hospital 1,877,016 1.5
ED visit in which the patient died in the ED 196,737 0.2
ED visit in which patient is not admitted to this same hospital, destination unknown 573,692 0.5
ED visit in which the patient is discharged alive, destination unknown (but not admitted) 1,330 0.0


Table 5. NEDS Stratifiers

Stratifier Values
Region 1: Northeast
2: Midwest
3: South
4: West
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
9: Trauma center level I, II or III
Urban-Rural 1: Large metropolitan
2: Small metropolitan
3: Micropolitan
4: Non-urban residual

Collapsed categories used for strata with small sample sizes
8: Metropolitan (large and small)
9: Non-metropolitan (micropolitan and non-urban location)
Teaching 0: Metropolitan non-teaching
1: Metropolitan teaching
2: Non-metropolitan teaching and non-teaching
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)

Return to Introduction



Table 6. 2009 NEDS Target Universe, Sampling Frame, and Final Sample Characteristics

  Description Number of Hospital-Based EDs Number of ED Events
Target Universe EDs in community, non-rehabilitation U.S. hospitals that reported total ED visits in the AHA Annual Survey Database 4,820 128,885,040
Sampling Frame EDs in the 29 HCUP States that provide information on ED visits that result and do not result in admission 2,630 78,238,162
2009 NEDS 20% sample of target universe drawn from the sampling frame 964 28,861,047

Return to Introduction



Table 7. NEDS Sampling Rates, 2009

NEDS Stratum Number of Hospital-Based EDs Sampling Rate
NEDS Stratum AHA Universe 20% of Universe Frame Frame Shortfall NEDS NEDS to Universe NEDS to Frame
Total 4,820 998 2,630 34 964 20.0% 36.7%
Northeast
10100 145 29 95 0 29 20.0% 30.5%
10110 86 18 67 0 18 20.9% 26.9%
10200 97 20 59 0 20 20.6% 33.9%
10210 24 5 14 0 5 20.8% 35.7%
10320 77 16 44 0 16 20.8% 36.4%
10420 53 11 42 0 11 20.8% 26.2%
11110 46 10 34 0 10 21.7% 29.4%
11210 12 3 4 0 3 25.0% 75.0%
12100 6 2 4 0 2 33.3% 50.0%
12110 15 3 11 0 3 20.0% 27.3%
12200 9 2 4 0 2 22.2% 50.0%
12210 16 4 9 0 4 25.0% 44.4%
13110 2 2 2 0 2 100.0% 100.0%
13800 6 2 4 0 2 33.3% 50.0%
13920 10 2 6 0 2 20.0% 33.3%
18320 6 2 2 0 2 33.3% 100.0%
Midwest
20100 187 38 144 0 38 20.3% 26.4%
20110 50 10 38 0 10 20.0% 26.3%
20200 163 33 104 0 33 20.2% 31.7%
20210 31 7 23 0 7 22.6% 30.4%
20320 230 46 187 0 46 20.0% 24.6%
20421 198 40 175 0 40 20.2% 22.9%
20424 248 50 173 0 50 20.2% 28.9%
21100 2 2 2 0 2 100.0% 100.0%
21110 39 8 34 0 8 20.5% 23.5%
21200 3 2 2 0 2 66.7% 100.0%
21210 25 5 16 0 5 20.0% 31.3%
22100 28 6 27 0 6 21.4% 22.2%
22110 15 3 12 0 3 20.0% 25.0%
22200 17 4 16 0 4 23.5% 25.0%
22210 34 7 23 0 7 20.6% 30.4%
22324 10 2 5 0 2 20.0% 40.0%
23100 19 4 18 0 4 21.1% 22.2%
23110 8 2 7 0 2 25.0% 28.6%
23200 31 7 30 0 7 22.6% 23.3%
23210 14 3 14 0 3 21.4% 21.4%
23321 5 2 2 0 2 40.0% 100.0%
23324 36 8 34 0 8 22.2% 23.5%
23420 24 5 22 0 5 20.8% 22.7%
South
30101 36 8 12 0 8 22.2% 66.7%
30102 151 31 81 0 31 20.5% 38.3%
30103 208 42 66 0 42 20.2% 63.6%
30110 79 16 40 0 16 20.3% 40.0%
30201 75 15 32 0 15 20.0% 46.9%
30202 140 28 74 0 28 20.0% 37.8%
30203 154 31 41 0 31 20.1% 75.6%
30210 37 8 10 0 8 21.6% 80.0%
30321 82 17 27 0 17 20.7% 63.0%
30322 107 22 64 0 22 20.6% 34.4%
30323 82 17 32 0 17 20.7% 53.1%
30421 203 41 58 0 41 20.2% 70.7%
30422 179 36 73 0 36 20.1% 49.3%
30423 90 18 29 0 18 20.0% 62.1%
31110 34 7 12 0 7 20.6% 58.3%
31210 32 7 15 0 7 21.9% 46.7%
32110 11 3 6 0 3 27.3% 50.0%
32210 15 3 6 0 3 20.0% 50.0%
33800 55 11 10 1 10 18.2% 100.0%
33810 33 7 3 4 3 9.1% 100.0%
38800 30 6 4 2 4 13.3% 100.0%
39920 59 12 2 10 2 3.4% 100.0%
West
40101 21 5 16 0 5 23.8% 31.3%
40102 110 22 88 0 22 20.0% 25.0%
40103 79 16 52 0 16 20.3% 30.8%
40110 48 10 32 0 10 20.8% 31.3%
40201 30 6 18 0 6 20.0% 33.3%
40202 70 14 51 0 14 20.0% 27.5%
40203 49 10 16 0 10 20.4% 62.5%
40210 22 5 15 0 5 22.7% 33.3%
40321 39 8 14 0 8 20.5% 57.1%
40323 3 2 3 0 2 66.7% 66.7%
40324 66 14 26 0 14 21.2% 53.8%
40421 98 20 13 7 13 13.3% 100.0%
40424 84 17 18 0 17 20.2% 94.4%
43800 42 9 4 5 4 9.5% 100.0%
43920 35 7 2 5 2 5.7% 100.0%
48800 36 8 18 0 8 22.2% 44.4%
49810 69 14 38 0 14 20.3% 36.8%
Stratum:

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, (9) Trauma center level I, II, or III. Note: NEDS children’s hospitals with trauma centers are included with adult/pediatric trauma centers in the appropriate stratum.

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: (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)

Return to Introduction



Appendix II: State-Specific Restrictions

 

The table below enumerates the types of restrictions applied to the 2009 Nationwide Emergency Department Sample. Restrictions include the following types:

For each restriction type the data sources are listed alphabetically by State. Only data sources that have restrictions are included. Data sources that do not have restrictions are not included.

Table 1. State-Specific Restrictions

Confidentiality of Hospitals
Limitations on sampling are required to ensure hospital confidentiality:

  • All States:
    • Prior to collapsing stratum: if there is a "unique" hospital in the State, it is excluded from sampling. "Unique" is defined as the only hospital in the state universe for a stratum. For example, if there is only one rural, non-teaching, trauma level III hospital in a State, then it is excluded from the sampling frame.
    • After sampling: stratifier data elements are set to missing if the stratum had fewer than two hospitals in the universe of the State's hospitals.


  • CT: Connecticut Hospital Association (CHA)
    • CHA is to be notified if more than 50% of their hospitals appear in the NEDS. The 2009 NEDS includes less than 30 percent of CT hospitals.

 

Confidentiality of Records
Limitations on selected data elements are required by the following data sources to ensure patient confidentiality:

  • CT: Connecticut Hospital Association (CHA)
    • Admission month (AMONTH) is set to missing on all records.


  • FL: Florida Agency for Health Care Administration
    • Admission month (AMONTH) is set to missing on all records.

 

Limited Reporting of External Cause of Injury Codes
The following data sources have limitations on the reporting of external cause of injury codes (E codes):

  • CA: Office of Statewide Health Planning and Development
    • California does not require the reporting of E codes in the range E870-E879 (medical misadventures and abnormal reactions).


  • GA: Georgia Hospital Association (GHA)
    • GHA removes E codes in the range E870-E879 (medical misadventures) and E930-E949 (adverse effects) from the data files supplied to HCUP.


  • SC: South Carolina State Budget & Control Board
    • South Carolina removes E codes in the range E870-E879 (medical misadventures and abnormal reactions) from the data files supplied to HCUP.

 

Missing Discharges for Specific Populations of Patients
The following data sources may be missing discharge records for specific populations of patients:

  • IA: Iowa Hospital Association
    • The Iowa Hospital Association prohibits the release of two types of discharges: HIV infections (defined by MDC of 25) and behavioral health including chemical dependency care or psychiatric care (defined by a service code of BHV). These discharges were not included in the source file provided to HCUP and were therefore not included in the NEDS.


  • NE: Nebraska Hospital Association
    • The Nebraska Hospital Association prohibits the release of discharge records for patients with HIV diagnoses. These discharges were not included in the source file provided to HCUP and were therefore not included in the NEDS.

 

Return to Introduction



Appendix III: NEDS Data Elements and Codes

Table 1. Data Elements in the NEDS Core File

Type of
Data Element
HCUP
Data Element
Coding Notes
Admission timing AWEEKEND Admission on weekend: (0) admission on Monday-Friday, (1) admission on Saturday-Sunday
AMONTH Admission month coded from (1) January to (12) December
Age at admission AGE Age in years coded 0-124 years
Diagnosis information DX1 – DX15 ICD-9-CM diagnoses
DXCCS1 – DXCCS15 Clinical Classifications Software (CCS) category for all diagnoses
CHRON1 – CHRON15 Chronic condition indicator for all diagnoses: (0) non-chronic condition, (1) chronic condition
NDX Number of diagnoses coded on the original record. A maximum of 15 codes are retained on the NEDS.
INTENT_SELF_HARM Diagnosis reported on records indicates intended self harm: (0) not intended self harm, (1) intended self harm
Discharge timing DQTR Coded: (1) Jan - Mar, (2) Apr - Jun, (3) Jul - Sep, (4) Oct - Dec
YEAR Calendar year of ED visits
Disposition of patient from the ED DISP_ED Disposition from ED: (1) routine, (2) transfer to short-term hospital, (5) other transfers, including skilled nursing facility, intermediate care, and another type of facility, (6) home health care, (7) against medical advice, (9) admitted as an inpatient to this hospital, (20) died in ED, (98) not admitted, destination unknown, (99) discharged alive, destination unknown (but not admitted)
DIED_VISIT Died in ED: (0) did not die (1) died in the ED, (2) died in the hospital
ED event EDevent Type of ED event: (1) ED visit in which the patient is treated and released, (2) ED visit in which the patient is admitted to this same hospital, (3) ED visit in which the patient is transferred to another short-term hospital, (9) ED visit in which the patient died in the ED, (98) ED visits in which patient was not admitted, destination unknown, (99) ED visit in which patient was discharged alive, destination unknown (but not admitted)
Injury-related variables INJURY Injury diagnosis reported: (0) no injury diagnoses reported, (1) injury is reported in first-listed diagnosis, (2) injury is reported in a diagnosis other than the first-listed diagnosis
MULTINJURY Multiple injuries reported: (0) one or no injury diagnosis reported, (1) more than one injury diagnosis reported, regardless of position
INJURY_SEVERITY Injury severity score assigned by ICDPIC Stata program. Range of 1 to 75 with 75 being the most severe. Value of 99 means severity of injury could not be determined.
ECODE1 - ECODE4 External cause of injury and poisoning codes (ICD-9-CM).
E_CCS1 - E_CCS4 CCS category for the external cause of injury and poisoning codes
NECODE Number of external cause of injury codes on the original record. A maximum of 4 codes are retained on the NEDS
INTENT_SELF_HARM E Codes and/or diagnoses indicate intended self harm: (0) not intended self harm, (1) intended self harm
INTENT_UNINTENTIONAL E Codes indicate injury was unintentional: (0) no unintentional injury, (1) unintentional injury
INTENT_ASSAULT E Codes indicate injury by assault: (0) no injury by assault, (1) injury by assault
INJURY_CUT E Codes indicate injury by cutting or piercing: (0) no injury by cutting or piercing, (1) injury by cutting or piercing
INJURY_DROWN E Codes indicate injury by drowning or submersion: (0) no injury by drowning by submersion, (1) injury by drowning by submersion
INJURY_FALL E Codes indicate injury by falling: (0) no injury by falling, (1) injury by falling
INJURY_FIRE E Codes indicate injury by fire, flame, or hot object: (0) no injury by fire, flame, or hot object, (1) injury by fire, flame, or hot object
INJURY_FIREARM E Codes indicate injury by firearm: (0) no injury by firearm, (1) injury by firearm
INJURY_MACHINERY E Codes indicate injury by machinery: (0) no injury by machinery, (1) injury by machinery
INJURY_MVT E Codes indicate injury involving motor vehicle traffic, including the occupant of a car, motorcyclist, pedal cyclist, pedestrian, or unspecified person: (0) no injury involving motor vehicle traffic, (1) injury involving motor vehicle traffic
INJURY_NATURE E Codes indicate injury involving natural or environmental causes, including bites and stings: (0) no injury involving natural or environmental causes, (1) injury involving natural or environmental causes
INJURY_POISON E Codes indicate injury by poisoning: (0) no injury by poisoning, (1) injury by poisoning
INJURY_STRUCK E Codes indicate injury involving being struck by or against something: (0) no injury involving being struck by or against, (1) injury involving being struck by or against
INJURY_SUFFOCATION E Codes indicate injury by suffocation: (0) no injury by suffocation, (1) injury by suffocation
Gender of patient FEMALE Indicates gender: (0) male, (1) female
Urban-rural location of the patient’s residence PL_NHCS2006 Urban–rural designation for patient’s county of residence: (1) large central metropolitan, (2) large fringe metropolitan, (3) medium metropolitan, (4) small metropolitan, (5) micropolitan, (6) not metropolitan or micropolitan
National quartile for median household income of patient's ZIP Code ZIPINC_QRTL Median household income quartiles for patient's ZIP Code. For 2009, the median income quartiles are defined as: (1) $1 - $39,999; (2) $40,000 - $49,999; (3) $50,000 - $65,999; and (4) $66,000 or more.
Payer information PAY1 Expected primary payer, uniform: (1) Medicare, (2) Medicaid, (3) private including HMO, (4) self-pay, (5) no charge, (6) other
PAY2 Expected secondary payer, uniform: (1) Medicare, (2) Medicaid, (3) private including HMO, (4) self-pay, (5) no charge, (6) other
Total ED charges TOTCHG_ED Total charges for ED services, edited
HCUP source file HCUPFILE Source of HCUP record: (SEDD) from SEDD file, (SID) from SID file
Discharge weight DISCWT Discharge weight used to calculate national estimates. Weights ED visits to AHA universe.
Hospital identifier, synthetic HOSP_ED Unique HCUP NEDS hospital number – links to NEDS Hospital Weights file, but not to other HCUP databases
Hospital information HOSP_REGION Region of hospital: (1) Northeast, (2) Midwest, (3) South, (4) West
NEDS_STRATUM Stratum used to sample hospitals, based on geographic region, trauma, location/teaching status, and control. Stratum information is also contained in the Hospital Weights file.
Record identifier, synthetic KEY_ED Unique HCUP NEDS record number – links to NEDS Supplemental files, but not to other HCUP databases

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Table 2. Data Elements in the NEDS Supplemental ED File

Type of
Data Element
HCUP
Data Element
Coding Notes
CPT procedure information CPT1 – CPT15 CPT/HCPCS procedures performed in the ED
CPTCCS1-CPTCCS15 Clinical Classifications Software (CCS) category for all CPT/HCPCS procedures
NCPT Number of procedures coded on the original record. A maximum of 15 CPT codes are retained on the NEDS.
ICD-9-CM procedure information PR_ED1 – PR_ED9 ICD-9-CM procedures performed in ED
PRCCS_ED1 – PRCCS_ED9 Clinical Classifications Software (CCS) category for all ICD-9-CM procedures
PCLASS_ED1 – PCLASS_ED9 Procedure class for all ICD-9-CM procedures: (1) Minor Diagnostic, (2) Minor Therapeutic, (3) Major Diagnostic, (4) Major Therapeutic
NPR_ED Number of procedures coded on the original record. A maximum of 9 ICD-9-CM procedure codes are retained on the NEDS.
HCUP source file HCUPFILE Source of HCUP record: (SEDD) from SEDD file, (SID) from SID file
Discharge weight DISCWT Discharge weight used to calculate national estimates. Weights ED visits to AHA universe.
Hospital identifier, synthetic HOSP_ED Unique HCUP NEDS hospital number – links to NEDS Hospital Weights file, but not to other HCUP databases
Record identifier, synthetic KEY_ED Unique HCUP NEDS record number – links to NEDS Supplemental files, but not to other HCUP databases

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Table 3. Data Elements in the NEDS Supplemental Inpatient File

Type of
Data Element
HCUP
Data Element
Coding Notes
Disposition of patient from the hospital DISP_IP Disposition from hospital admission: (1) routine, (2) transfer to short-term hospital, (5) other transfers, including skilled nursing facility, intermediate care, and another type of facility, (6) home health care, (7) against medical advice, (20) died in hospital, (99) discharged alive, destination unknown
Diagnosis Related Group (DRG) DRG DRG in use on discharge date
DRG_NoPOA DRG assignment made without the use of the present on admission flags for the diagnoses
DRGVER Grouper version in use on discharge date
MDC Major Diagnosis Category (MDC) in use on discharge date
MDC_NoPOA MDC in use on discharge date, calculated without the use of the present on admission flags for the diagnoses
Length of hospital inpatient stay LOS_IP Length of stay, edited
Total charges for inpatient stay TOTCHG_IP Total charges for ED and inpatient services, edited
ICD-9-CM procedure information PR_IP1 – PR_IP9 ICD-9-CM procedures coded on ED admissions. Procedure may have been performed in the ED or during the hospital stay.
PRCCS_IP1 – PRCCS_IP9 Clinical Classifications Software (CCS) category for all ICD-9-CM procedures
PCLASS_IP1 – PCLASS_IP9 Procedure class for all ICD-9-CM procedures: (1) Minor Diagnostic, (2) Minor Therapeutic, (3) Major Diagnostic, (4) Major Therapeutic
NPR_IP Number of procedures coded on the original record. A maximum of 9 ICD-9-CM procedure codes are retained on the NEDS.
HCUP source file HCUPFILE Source of HCUP record: (SEDD) from SEDD file, (SID) from SID file
Discharge weight DISCWT Discharge weight used to calculate national estimates. Weights ED visits to AHA universe.
Hospital identifier, synthetic HOSP_ED Unique HCUP NEDS hospital number – links to NEDS Hospital Weights file, but not to other HCUP databases
Record identifier, synthetic KEY_ED Unique HCUP NEDS record number – links to NEDS Supplemental files, but not to other HCUP databases

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Table 4. Data Elements in the NEDS Hospital Weights File

Type of
Data Element
HCUP
Data Element
Coding Notes
Discharge counts N_DISC_U Number of AHA universe ED visits in the stratum
S_DISC_U Number of sampled ED visits in the sampling stratum
TOTAL_EDvisits Total number of ED visits for this hospital in the NEDS
Discharge weights DISCWT Discharge weight used to calculate national estimates. Weights ED visits to AHA universe.
Discharge Year YEAR Discharge year
Hospital counts N_HOSP_U Number of AHA universe hospital-based EDs in the stratum
S_HOSP_U Number of sampled hospital-based EDs in the stratum
Hospital identifier, synthetic HOSP_ED Unique HCUP NEDS hospital number – links to NEDS Hospital Weights file, but not to other HCUP databases
Hospital characteristics HOSP_URCAT4 Hospital urban-rural location: (1) large metropolitan areas with at least 1 million residents, (2) small metropolitan areas with less than 1 million residents, (3) micropolitan areas, (4) not metropolitan or micropolitan, (8) metropolitan, collapsed category of large and small metropolitan, (9) non-metropolitan, collapsed category of micropolitan and rural
HOSP_CONTROL Control/ownership of hospital: (0) government or private, collapsed category, (1) government, nonfederal, public, (2) private, non-profit, voluntary, (3) private, invest-own, (4) private, collapsed category
HOSP_REGION Region of hospital: (1) Northeast, (2) Midwest, (3) South, (4) West
HOSP_TRAUMA Trauma center level: (0) non-trauma center, (1) trauma level I, (2) trauma level II (3) trauma level III, (8) trauma level I or II, collapsed category (9) trauma level I, II, or III, collapsed category. Note: In 2009 NEDS, children’s hospitals with trauma centers are classified with adult/pediatric trauma centers.
HOSP_UR_TEACH Teaching status of hospital: (0) metropolitan non-teaching, (1) metropolitan teaching, (2) non-metropolitan
NEDS_STRATUM Stratum used to sample EDs, includes geographic region, trauma, location/teaching status, and control
Hospital weight HOSPWT Weight to hospital-based EDs in AHA universe (i.e., total U.S.)

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

 

Figure 1. Emergency Department Visit Counts (in thousands) in the United States, 2009

Data Source ED Visits (in millions)
NEDS 128,885,040
AHA 128,885,040
NEDI 127,120,372
NHAMCS 136,072,130
NHIS 121,835,465

Notes: ED = emergency department; NEDS = HCUP Nationwide Emergency Department Sample; AHA = American Hospital Association Annual Survey Database; NEDI=National Emergency Department Inventory – USA; NHAMCS = National Hospital Ambulatory Medical Care Survey; NHIS = National Health Interview Survey.

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Table 1. Estimates of ED Visits by U.S. Geographic Region from Five ED Data Sources, 2009

ED Visits ED Data Source
NEDS1 AHA NEDI2 NHAMCS NHIS 3
N (weighted) % N % N % N (weighted) % N (weighted) %
By Census Region
Northeast 25,083,087 19% 25,083,087 19% 23,355,718 18% 24,831,800 18% 20,651,583 17%
Midwest 29,866,844 23% 29,866,844 23% 28,250,463 22% 31,935,241 23% 31,375,850 26%
South 50,944,041 40% 50,944,041 40% 52,100,046 41% 53,732,832 39% 48,036,401 39%
West 22,991,068 18% 22,991,068 18% 23,414,145 18% 25,572,257 19% 21,771,632 18%
 
Total U.S. 128,885,040 100% 128,885,040 100% 127,120,372 100% 136,072,130 100% 121,835,465 100%
Notes: ED = emergency department; NEDS = HCUP Nationwide Emergency Department Sample; AHA = American Hospital Association Annual Survey Database; NEDI = National Emergency Department Inventory - USA; NHAMCS = National Hospital Ambulatory Medical Care Survey; NHIS = National Health Interview Survey.

1 NEDS weighted counts by geographic region exactly match the AHA counts because the AHA data were used as control totals for the NEDS discharge weights.

2 Data from the Emergency Medicine Network, 2009 National Emergency Department Inventory (NEDI) - USA. Available at: http://www.emnet-usa.org/nedi/nedi2009statedata.xls. Exit Disclaimer Accessed 09/10/2011.

3 NHIS estimates were calculated using the midpoint of the ranges provided in the survey (0, 1, 2-3, 4-5, 6-7, 8-9, 10-12, and 13-15). For the upper range of visits in the survey (16 or more ED visits), 16 ED visits were used for the estimate.

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Table 2. Estimates of the ED Visits Resulting in Inpatient Admissions (Admission Rate) by U.S. Geographic Region from Two ED Data Sources, 2009

ED Visits Resulting in Inpatient Admissions ED Data Sources
NEDS NHAMCS
N (weighted) % of all ED Visits N (weighted) % of all ED Visits
By Census Region
Northeast 4,242,090 16.9 3,617,941 14.6
Midwest 4,233,474 14.2 4,527,462 14.2
South 7,680,222 15.1 5,398,969 10.0
West 3,436,759 14.9 2,775,258 10.9
 
Total U.S. 19,592,545 15.2 16,319,630 12.0
Notes: ED = emergency department; NEDS = HCUP Nationwide Emergency Department Sample; NHAMCS = National Hospital Ambulatory Medical Care Survey.

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Table 3. Estimates of the Number of Hospital-Based EDs by ED Visit Volume from Three ED Data Sources, 2009

Volume of ED Visits in 2009 Data Sources
NEDS AHA NEDI
N (weighted) % N % N %
Less than 10,000 visits 1,293 26.8 1,691 35.1 1,497 30.1
10,000 - 19,999 visits 879 18.2 869 18.0 1,006 20.3
20,000 - 29,999 visits 723 15.0 636 13.2 747 15.0
30,000 - 39,999 visits 555 11.5 460 9.5 622 12.5
40,000 - 49,999 visits 478 9.9 362 7.5 419 8.4
50,000 or more visits 892 18.5 802 16.6 676 13.6
 
All Hospital-based EDs 4,820 100.0 4,820 100.0 4,967 100.0
Notes: ED = emergency department; NEDS = Nationwide Emergency Department Sample from the Healthcare Cost and Utilization Project; AHA = American Hospital Association Annual Survey Database; NEDI = National Emergency Department Inventory - USA.

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Table 4. Estimates of the Number of Injury-Related ED Visits from Three ED Data Sources, 2009

  Data Sources
NEDS1 NHAMCS2 NEISS-AIP3
Total number of ED visits for injuries (weighted) 29,583,705 31,382,877 29,636,366
Injury Intent
Unintentional 28,941,107 30,010,027 27,632,781
Assault 1,314,097 2,065,431 1,545,534
Self-harm4 1,110,426 1,191,560 374,486
Injury Mechanism
Cutting/piercing 2,296,414 2,730,103 2,193,754
Drowning/submersion 17,961 36,858 6,519
Falling 8,986,480 10,437,925 8,782,664
Fire, flame or hot object 420,162 584,277 381,012
Firearm 76,006 146,393 66,769
Machinery 129,974 257,430 207,125
Motor vehicle traffic 3,250,752 3,953,892 3,202,067
Natural/environmental (incl. bites and stings) 1,300,710 2,187,717 1,481,095
Poisoning 924,720 1,383,974 919,582
Struck by or against 4,303,267 4,669,471 5,812,931
Suffocation 53,080 140,501 44,081
Notes: ED = emergency department; NEDS = Nationwide Emergency Department Sample from the Healthcare Cost and Utilization Project; NHAMCS = National Hospital Ambulatory Medical Care Survey; NEISS-AIP = National Electronic Injury Surveillance System All-Injury Program.

1 Injury diagnosis of 800-909.2, 909.4, 909.9, 910-994.9, 995.5-995.59, 995.80-995.85 (HCUP variable INJURY > 0).

2 ED visit with an injury diagnosis of 800-909.2, 909.4, 909.9, 910-994.9, 995.5-995.59, 995.80-995.85.

3 Data from WISQARS Query System (http://webappa.cdc.gov/sasweb/ncipc/nfirates.html). Includes non-fatal, all-cause injuries. Patients who died on arrival to the ED or during treatment in the ED are excluded. Queried September 9, 2011.

4 For NEDS and NHAMCS counts, self-harm include diagnosis code V6284 (suicidal ideation) in addition to E Codes.

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 http://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 http://www.hcup-us.ahrq.gov/reports/statbriefs/sb32.pdf.
3 MacKenzie EJ, Hoyt DB, Sacra JC, et al. National inventory of hospital trauma centers. JAMA. 2003;289:1515-1522.
4 American Trauma Society. Trauma Information Exchange Program. Available at: http://www.amtrauma.org/?page=TIEP. Exit Disclaimer Accessed September 2011.
5 United States Department of Agriculture Economic Research Service, 2007.
6 This HCUP Methods Series report is available at http://www.hcup-us.ahrq.gov/reports/methods/2011-03.jsp.

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Internet Citation: 2009 Introduction to the NEDS. Healthcare Cost and Utilization Project (HCUP). July 2016. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/db/nation/neds/NEDS_Introduction_2009.jsp.
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