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Introduction to the HCUP Nationwide Inpatient Sample (NIS), 2011

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 INPATIENT SAMPLE (NIS)

2011

 

 

These pages provide only an introduction to the NIS package.

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

 

Issued June 2013

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

 

NIS 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 INPATIENT SAMPLE (NIS)
SUMMARY OF DATA USE LIMITATIONS

***** REMINDER *****



All users of the NIS 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 Truven Health Analytics 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) Website at http://www.hcup-us.ahrq.gov.
‡ Specific provisions are detailed in the Data Use Agreement for Nationwide Databases.


Return to Introduction



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 are is available on the AHRQ-sponsored HCUP User Support (HCUP-US) website 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 website.

HCUP User Support:

Information about the content of the HCUP databases is available on the HCUP User Support (HCUP-US) website (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'S NEW IN THE 2011
NATIONWIDE INPATIENT SAMPLE (NIS)?

  • The 2011 NIS contains one new State: North Dakota. However New Hampshire data was not available in time to be included in the 2011 NIS.
  • The following data elements were added to the Core File beginning with the 2011 NIS:
    • Disease Staging: Principal Disease Category (DS_DX_Category1)
    • Disease Staging: Stage of Principal Disease Category (DS_Stage1)

UNDERSTANDING THE NIS

This document, Introduction to the NIS, 2011, summarizes the content of the NIS and describes the development of the NIS sample and weights. Cumulative information for all previous years is included to provide a longitudinal view of the database. Important considerations for data analysis are provided along with references to detailed reports. In-depth documentation for the NIS is available on the HCUP User Support (HCUP-US) Website (www.hcup-us.ahrq.gov).


Return to Introduction



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

Sponsored by the Agency for Healthcare Research and Quality

The Agency for Healthcare Research and Quality and
the staff of the Healthcare Cost and Utilization Project (HCUP) thank users for
purchasing the HCUP National Inpatient Sample (NIS).



HCUP National Inpatient Sample (NIS)

ABSTRACT

The Nationwide Inpatient Sample (NIS) is part of the Healthcare Cost and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality (AHRQ), formerly the Agency for Health Care Policy and Research.

The NIS is a database of hospital inpatient stays. Researchers and policy makers use the NIS to identify, track, and analyze national trends in healthcare utilization, access, charges, quality, and outcomes.

The NIS is the largest all-payer inpatient care database that is publicly available in the United States, containing data from approximately 8 million hospital stays from about 1,000 hospitals sampled to approximate a 20-percent stratified sample of U.S. community hospitals. The NIS is drawn from those States participating in HCUP. For 2011, these States comprise over 97 percent of the U.S. population. Weights are provided to calculate national estimates. See Table 1 in Appendix I for a list of the statewide data organizations participating in the NIS. The number of sample hospitals and discharges by State and year are available in Table 2 in Appendix I.

The NIS is available yearly, beginning with 1988, allowing analysis of trends over time. (Analyses of time trends are recommended from 1993 forward. For NIS data 1997 and earlier, revised weights should be used to make estimates comparable to later data. Refer to NIS Trends Weights Files and the report Using the HCUP Nationwide Inpatient Sample to Estimate Trends, available on the HCUP User Support (HCUP-US) Website, for details.)

The NIS includes charge information for all patients, regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. For Medicare, the NIS includes Medicare Advantage patients, a population that is missing from Medicare claims data but that comprises as much as 20 percent of Medicare beneficiaries. The NIS' large sample size enables analyses of rare conditions, such as specific types of cancer; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured.

Inpatient stay records in the NIS include clinical and resource use information typically available from discharge abstracts. Hospital and discharge weights are provided for producing national estimates. The NIS can be linked to hospital-level data from the American Hospital Association (AHA) Annual Survey Database (Health Forum, LLC © 2012) and county-level data from the Bureau of Health Professions' Area Resource File, except in those States that do not allow the release of hospital identifiers. In 2011, 19 of the 46 States do not include the hospital name and 18 of these do not include the AHA hospital identifier. Thus 40% of hospitals in the NIS do not include AHA hospital identifiers and cannot be linked to other data sources at the hospital level.

Beginning in 1998, the NIS differs from previous NIS releases: some data elements were dropped; some were added; for some data elements, the coding was changed; and the sampling and weighting strategy was revised to improve the representativeness of the data. (See the report, Changes in the NIS Sampling and Weighting Strategy for 1998, available on the HCUP-US Website, which describes these changes.) Periodically, new data elements are added to the NIS and some are dropped; see Appendix III for a summary of data elements and when they are effective.

Access to the NIS is open to users who sign data use agreements. Uses are limited to research and aggregate statistical reporting.

For more information on the NIS, please visit the AHRQ–sponsored HCUP–US Website at http://www.hcup-us.ahrq.gov.

Return to Introduction



INTRODUCTION TO THE HCUP NATIONWIDE INPATIENT SAMPLE (NIS)

Overview of NIS Data

The Nationwide Inpatient Sample (NIS) contains all-payer data on hospital inpatient stays from States participating in the Healthcare Cost and Utilization Project (HCUP). Each year of the NIS provides information on approximately 8 million inpatient stays from about 1,000 hospitals. All discharges from sampled hospitals are included in the NIS database.

The NIS contains clinical and resource use information included in a typical discharge abstract. The NIS can be linked directly to hospital-level data from the American Hospital Association (AHA) Annual Survey Database (Health Forum, LLC © 2012) and to county-level data from the Health Resources and Services Administration Bureau of Health Professions' Area Resource File (ARF), except in those States that do not allow the release of hospital identifiers.

The NIS is designed to approximate a 20-percent sample of U.S. community hospitals, defined by the AHA to be "all non-Federal, short-term, general, and other specialty hospitals, excluding hospital units of institutions." Included among community hospitals are specialty hospitals such as obstetrics-gynecology, ear-nose-throat, orthopedic, and pediatric institutions. Also included are public hospitals and academic medical centers. Starting in 2005, the AHA included long term acute care facilities in the definition of community hospitals, therefore such facilities are included in the NIS sampling frame. These facilities provide acute care services to patients who need long term hospitalization (stays of more than 25 days). Excluded from the NIS are short-term rehabilitation hospitals (beginning with 1998 data), long-term non-acute care hospitals, psychiatric hospitals, and alcoholism/chemical dependency treatment facilities.

This universe of U.S. community hospitals is divided into strata using five hospital characteristics: ownership/control, bed size, teaching status, urban/rural location, and U.S. region.

The NIS is a stratified probability sample of hospitals in the frame, with sampling probabilities proportional to the number of U.S. community hospitals in each stratum. The frame is limited by the availability of inpatient data from the data sources currently participating in HCUP.

In order to improve the representativeness of the NIS, the sampling and weighting strategy was modified beginning with the 1998 data. The full description of this process can be found in the special report on Changes in NIS Sampling and Weighting Strategy for 1998. This report is available on the AHRQ–sponsored HCUP-US Website at http://www.hcup-us.ahrq.gov. To facilitate the production of national estimates, both hospital and discharge weights are provided, along with information necessary to calculate the variance of estimates. Detailed information on the design of the NIS prior to 2006 is available in the year–specific special reports on Design of the Nationwide Inpatient Sample found on the HCUP-;US Website (http://hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp). Starting with the 2006 NIS, the information on the design of the NIS was incorporated into this report.

The NIS is available yearly, beginning with 1988, allowing analysis of trends over time. (Analyses of time trends are recommended from 1993 forward. For NIS data 1997 and earlier, revised weights should be used to make estimates comparable to later data. Refer to NIS Trends Weights Files and the report, Using the HCUP Nationwide Inpatient Sample to Estimate Trends, available on the HCUP User Support (HCUP-US) Website, for details.)

See Table 3 in Appendix I for a summary of NIS releases. Each release of the NIS includes:

NIS Data Sources, Hospitals, and Inpatient Stays

Table 4 in Appendix I contains a summary of the data sources, number of hospitals, and number of unweighted and weighted inpatient stays in NIS data.

State-Specific Restrictions

Some data sources that contributed data to the NIS imposed restrictions on the release of certain data elements or on the number and types of hospitals that could be included in the database. Because of confidentiality laws, some data sources were prohibited from providing HCUP with discharge records that indicated specific medical conditions and procedures, specifically HIV/AIDS, behavioral health, and abortion. Detailed information on these State-specific restrictions is available in Appendix II.

Contents of DVD

The NIS is distributed as fixed-width ASCII formatted data files compressed with SecureZIP® from PKWARE. Beginning with the 2010 NIS, the files are encrypted. Previously it was distributed on two CD-ROMs, but beginning with the 2009 NIS, it is distributed on a single DVD. The 2011 NIS includes the following compressed files:

On the HCUP-US Website (http://www.HCUP-US.ahrq.gov), NIS purchasers can access complete file documentation, including data element notes, file layouts, summary statistics, and related technical reports. Similarly, purchasers can also download SAS, SPSS, and Stata load programs. Available online documentation and supporting files are detailed in Appendix I, Table 5.

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NIS Data Elements

All releases of the NIS contain two types of data: inpatient stay records and hospital information with weights to calculate national estimates. Appendix III identifies the data elements in each NIS file:

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

Getting Started

In order to load and analyze the NIS data on a computer, you will need the following:

Copying and Decompressing the ASCII Files

To copy and decompress the data from the DVD, follow these steps:

  1. Create a directory for the NIS on your hard drive.
  2. Unzip each ASCII file from the DVD, saving it into the new directory using a third-party zip utility such as ZIP Reader, WinZip, or 7-Zip. Beginning with the 2010 NIS, you will be prompted to enter the encryption password (sent separately by e-mail) to decrypt each file.

    NOTE:Attempts to unzip encrypted files using Windows will produce an error message similar to the following: "Compressed (Zipped) Folders Error. The password you typed is not correct. Try typing it again." The solution is to use a third-party zip utility such as ZIP Reader, 7-Zip, or WinZip rather than the built-in Windows Explorer function to open the archive. ZIP Reader may be downloaded for free at https://www.pkware.com/zip-reader. 7-Zip can be downloaded for free at http://www.7-zip.org/. Evaluation versions of WinZip may be downloaded from the WinZip Website at http://www.winzip.com/.

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). To download and run the load programs, follow these steps:

  1. Go to the NIS Database Documentation page on HCUP-US at http://www.hcup-us.ahrq.gov/db/nation/nis/nisdbdocumentation.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 you need. The load programs are specific to the data year. For example, the load program for the 2011 NIS Core file is linked to "Core File" under "2011 NIS". Save the load programs into the same directory as the NIS ASCII files on your computer.
  5. Edit and run the load programs as appropriate for your environment to load and save the analysis files. For example, add directory paths for the input and output files if needed.

NIS Documentation

Year-specific NIS documentation files on the HCUP-US Website (http://www.hcup-us.ahrq.gov) provide important resources for the user. Refer to these resources to understand the structure and content of the NIS and to aid in using the database.

Table 5 in Appendix I details both the NIS related reports and the comprehensive NIS database documentation available on HCUP-US.

HCUP On-Line Tutorials

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

The 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 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 the three 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.

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

Return to Introduction



HOW TO USE THE NIS FOR DATA ANALYSIS

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

Calculating National Estimates

NIS Year Name of Discharge Weight on the Core File to Use for Creating Nationwide Estimates Name of Discharge Weight on the 10% Subsample Core File to Use for Creating Nationwide Estimates
2005 forward
  • DISCWT for all analyses
  • The 10% Subsample Core File was discontinued with the 2005 NIS.
2001 - 2004
  • DISCWT for all analyses
  • DISCWT10 for all analyses
2000
  • DISCWT to create nationwide estimates for all analyses except those that involve total charges.


  • DISCWTCHARGE to create nationwide estimates of total charges.
  • DISCWT10 to create nationwide estimates for all analyses, except those that involve total charges.


  • DISCWTCHARGE10 to create nationwide estimates of total charges.
1998 - 1999
  • DISCWT for all analyses
  • DISCWT10 for all analyses
1988 - 1997
  • DISCWT_U for all analyses
  • D10CWT_U for all analyses

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Why the NIS Should not be Used to Make State-Level Estimates

AHRQ strongly advises researchers against using the NIS to estimate State-specific statistics. Prior to 2012, State is available as a NIS data element. However, these NIS samples were not designed to yield a representative sample of hospitals at the State level. AHRQ recommends that researchers employ the SID for State-level estimates.

Each NIS sample is drawn from the sampling frame consisting of discharge data submitted by HCUP Partners-statewide data organizations that agree to participate in the NIS. Data from non-Partner States are missing completely from the sampling frame, and data from Partner States are sometimes incomplete because of different State reporting requirements, different State restrictions, or other data omissions. The NIS is designed to represent hospitals and discharges nationally, including those outside the sampling frame.

To accomplish this, within each hospital sampling stratum the NIS draws a number of hospitals from the sampling frame required to net a total of 20 percent of hospitals nationally. The sampling strata are defined by census region (4 regions), hospital ownership (3 categories), urban-rural location, teaching status, and bed size (3 categories). As a result, the proportion of NIS hospitals in a stratum that are from a given State is unlikely to equal the State's actual proportion of hospitals in that stratum. Consequently, the sample of NIS hospitals is unlikely to be representative of hospitals in the State, and the NIS sample weights will not be appropriate at the State level.

The level of this "misrepresentation" varies across the States in any given year of the NIS, which further confounds State-to-State comparisons on the basis of State-specific estimates from the NIS. Moreover, for a given State the level of misrepresentation changes from year to year as States (and hospitals) enter and exit the sampling frame over time. This further confounds State-specific trends on the basis of State-specific estimates from the NIS.

Finally, because the NIS was not designed to be representative at the State level, design-based estimates of standard errors are not possible, which severely hampers State-level inferences. Moreover, the NIS is composed of all discharges from a sample of hospitals (a cluster sample). The hospital-to-hospital variation and the small number of hospitals available in the NIS for many States make Statelevel estimates very imprecise at best and biased at worst.

Studying Trends

Choosing Data Elements for Analysis

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Hospital-Level Data Elements

Constructing Patient Population Characteristics

ICD-9-CM Diagnosis and Procedure Codes

Missing Values

Missing data values can compromise the quality of estimates. If the outcome for discharges with missing values is different from the outcome for discharges with valid values, then sample estimates for that outcome will be biased and inaccurately represent the discharge population. For example, race is missing on over 10% of discharges in the 2011 NIS because some hospitals and HCUP State Partners do not supply it. (The percentage of missing race values was higher in previous years.) Therefore race-specific estimates may be biased. This is especially true for estimates of discharge totals by race. Another set of data elements that are missing are hospital identifiers, which allow you to link to other datasets with the AHA hospital identifier. In 2011, over 40% of hospitals were missing specific identifiers.

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.1 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.

On the other hand, 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. This is 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 charges) containing missing values would be incomplete because cases with missing values would be omitted from the calculations.

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Variance Calculations

It may be important for researchers to calculate a measure of precision for some estimates based on the NIS 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 hospitals (clusters) was drawn and then all discharges were included from each selected hospital. To accurately calculate variances from the NIS, you must use appropriate statistical software and techniques. For details, see the special report, Calculating Nationwide Inpatient Sample Variances. This report is available on the HCUP-US Website at http://www.hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp.

If hospitals inside the 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 NIS data. Several computer programs are listed below that calculate statistics and their variances from sample survey data. 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 some 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 NIS, any estimates that attempt to accurately describe characteristics and interrelationships among hospitals and discharges during a specific year should be governed by finite-sample theory. Examples of this would be estimates of expenditure and utilization patterns or hospital market factors.

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 from 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.

Computer Software for Variance Calculations

The hospital weights are useful for producing hospital-level statistics for analyses that use the hospital as the unit of analysis, while the discharge weights are useful for producing discharge-level statistics for analyses that use the discharge as the unit of analysis. The discharge weights may be used to estimate nationwide population statistics.

In most cases, computer programs are readily available to perform these calculations. Several statistical programming packages allow weighted analyses.2 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 eight 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. This report is available on the HCUP-US Website at http://www.hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp. 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 NIS database includes a Hospital Weights file with data elements required by these programs to calculate finite population statistics. The file includes hospital identifiers (Primary Sampling Units or PSUs), stratification data elements, and stratum-specific totals for the numbers of discharges 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 analyses, it may be feasible to set aside a part of the data for validation purposes. Standard errors and confidence intervals can then 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 ten subsets of equal size. The estimation would take place in ten iterations. In each iteration, the outcome of interest is predicted for one-tenth of the observations by an estimate based on a model 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.

Finally, it should be noted that a large array of hospital-level data elements are available for the entire universe of hospitals, including those outside the sampling frame. For instance, the data elements from the AHA surveys and from the Medicare Cost Reports are available for nearly all hospitals in the U.S., although hospital identifiers are suppressed in the NIS for a number of States. For these States it will not be possible to link to outside hospital-level data sources. To the extent that hospital-level outcomes correlate with these data elements, they may be used to sharpen regional and nationwide estimates.

As a simple example, the number of Cesarean sections performed in each hospital would be correlated with their total number of deliveries. The figure for Cesarean sections must be obtained from discharge data, but the number of deliveries is available from AHA data. Thus, if a regression model can be fit predicting this procedure from deliveries based on the NIS data, that regression model can then be used to obtain hospital-specific estimates of the number of Cesarean sections for all hospitals in the AHA universe.

Longitudinal Analyses

Hospitals that continue in the NIS for multiple consecutive years are a subset of the hospitals in the NIS for any one of those years. Consequently, longitudinal analyses of hospital-level outcomes may be biased, if they are based on any subset of NIS hospitals limited to continuous NIS membership. In particular, such subsets would tend to contain fewer hospitals that opened, closed, split, merged, or changed strata. Further, the sample weights were developed as annual, cross-sectional weights, rather than longitudinal weights. Therefore, different weights might be required, depending on the statistical methods employed by the analyst.

One approach to consider in hospital-level longitudinal analyses is to use repeated-measure models that allow hospitals to have missing values for some years. However, the data are not actually missing for some hospitals, such as those that closed during the study period. In any case, the analyses may be more efficient (e.g., produce more precise estimates) if they account for the potential correlation between repeated measures on the same hospital over time, yet incorporate data from all hospitals in the sample during the study period.

Return to Introduction



Discharge Subsamples

Prior to the 2005 NIS, two non-overlapping 10% subsamples of NIS discharges were provided each year for analytic purposes. Beginning with the 2005 NIS, 10% subsamples are no longer provided. However, users may still draw their own subsamples, if desired. One use of 10% subsamples would be to validate models and obtain unbiased estimates of standard errors. That is, one subsample may be used to estimate statistical models, while the other subsample may be used to test the fit of those models on new data. This is a very important analytical step, particularly in exploratory studies, where one runs the risk of fitting noise in the data.

It is well known that the percentage of variance explained by a regression, R2, is generally overestimated by the data used to fit a model. The regression model could be estimated from the first subsample and then applied to the second subsample. The squared correlation between the actual and predicted value in the second subsample is an unbiased estimate of the model's true explanatory power when applied to new data.

SAMPLING OF HOSPITALS

Sampling of Hospitals Included in the NIS

The NIS Hospital Universe

The hospital universe is defined as all hospitals located in the U.S. that are open during any part of the calendar year and designated as community hospitals in the AHA Annual Survey Database (Health Forum, LLC © 2012). The AHA defines community hospitals as follows: "All non-Federal, short-term, general, and other specialty hospitals, excluding hospital units of institutions." Starting in 2005, the AHA included long term acute care facilities in the definition of community hospitals. These facilities provide acute care services to patients who need long term hospitalization (stays of more than 25 days). Consequently, Veterans Hospitals and other Federal facilities (Department of Defense and Indian Health Service) are excluded. Beginning with the 1998 NIS, we excluded short-term rehabilitation hospitals from the universe because the type of care provided and the characteristics of the discharges from these facilities were markedly different from other short-term hospitals. Figure 1 in Appendix I displays the number of universe hospitals for each year based on the AHA Annual Survey Database (Health Forum, LLC © 2012).

For more information on how hospitals in the data set were mapped to hospitals as defined by the AHA, refer to the special report, HCUP Hospital Identifiers. For a list of all data sources, refer to Table 1 in Appendix I. Detailed information on the design of the NIS prior to 2006 is available in the year-specific special reports on Design of the Nationwide Inpatient Sample found on the HCUP-US Website. Starting with the 2006 NIS, the design information was incorporated into this report.

Hospital Merges, Splits, and Closures

All U.S. hospital entities designated as community hospitals in the AHA hospital file, except short-term rehabilitation hospitals, were included in the hospital universe. Therefore, when two or more community hospitals merged to create a new community hospital, the original hospitals and the newly-formed hospital were all considered separate hospital entities in the universe during the year they merged. Similarly, if a community hospital split, the original hospital and all newly-created community hospitals were treated as separate entities in the universe during the year this occurred. Finally, community hospitals that closed during a given year were included in the hospital universe, as long as they were in operation during some part of the calendar year.

Return to Introduction



Stratification Data Elements

Given the increase in the number of contributing States, the NIS team evaluated and revised the sampling and weighting strategy for 1998 and subsequent data years, in order to best represent the U.S. This included changes to the definitions of the strata data elements, the exclusion of rehabilitation hospitals from the NIS hospital universe, and a change to the calculation of hospital universe discharges for the weights. A full description of this process can be found in the special report on Changes in NIS Sampling and Weighting Strategy for 1998. This report is available on the HCUP-US Website at http://www.hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp. (A description of the sampling procedures and definitions of strata data elements used from 1988 through 1997 can be found in the special report: Design of the HCUP Nationwide Inpatient Sample, 1997. This report is also available on the HCUP-US Website.)

The NIS sampling strata were defined based on five hospital characteristics contained in the AHA hospital files. Beginning with the 1998 NIS, the stratification data elements were defined as follows:

  1. Geographic Region ̵ Northeast, Midwest, West, and South. This is an important stratification data element because practice patterns have been shown to vary substantially by region. For example, lengths of stay tend to be longer in East Coast hospitals than in West Coast hospitals. Figure 2 highlights the NIS States by region, and Table 6 lists the States that comprise each region. Both can be found in Appendix I.


  2. Control — government non-Federal (public), private not-for-profit (voluntary), and private investor-owned (proprietary). Depending on their control, hospitals tend to have different missions and different responses to government regulations and policies. When there were enough hospitals of each type to allow it, we stratified hospitals as public, voluntary, and proprietary. We used this stratification for Southern rural, Southern urban non-teaching, and Western urban non-teaching hospitals. For smaller strata - the Midwestern rural and Western rural hospitals - we used a collapsed stratification of public versus private, with the voluntary and proprietary hospitals combined to form a single "private" category. For all other combinations of region, location, and teaching status, no stratification based on control was advisable, given the number of hospitals in these cells.


  3. Location — urban or rural.Government payment policies often differ according to this designation. Also, rural hospitals are generally smaller and offer fewer services than urban hospitals. Beginning with the 2004 NIS, we changed the classification of urban or rural hospital location for the sampling strata to use the newer Core Based Statistical Area (CBSA) codes, rather than the older Metropolitan Statistical Area (MSA) codes. The CBSA groups are based on 2000 Census data, whereas the MSA groups were based on 1990 Census data. Also, the criteria for classifying the counties differ. For more information on the difference between CBSAs and MSAs, refer to the U.S. Census Bureau Website (https://www.census.gov/topics/housing/housing-patterns/about/core-based-statistical-areas.html).

    Previously, we classified hospitals in a MSA as urban hospitals, while we classified hospitals outside a MSA as rural hospitals. Beginning with the 2004 NIS, we categorized hospitals with a CBSA type of Metropolitan or Division as urban, while we designated hospitals with a CBSA type of Micropolitan or Rural as rural. This change contributed to a slight decline in the number of hospitals that were classified as rural and a corresponding increase in the number of hospitals categorized as urban. For the 2003 NIS, 44.9% of hospitals in the AHA universe were classified as rural hospitals; for 2004, only 41.3% of AHA universe hospitals were classified as rural.


  4. Teaching Status — teaching or non-teaching. The missions of teaching hospitals differ from non-teaching hospitals. In addition, financial considerations differ between these two hospital groups. Currently, the Medicare Diagnosis Related Group (DRG) payments are uniformly higher to teaching hospitals. Prior to the 1998 NIS, we considered a hospital to be a teaching hospital if it had any residents or interns and met one of the following two criteria:

    • Residency training approval by the Accreditation Council for Graduate Medical Education (ACGME)
    • Membership in the Council of Teaching Hospitals (COTH).

  5. Beginning with the 1998 NIS, we considered a hospital to be a teaching hospital if it met any one of the following three criteria:

    • Residency training approval by the Accreditation Council for Graduate Medical Education (ACGME)
    • Membership in the Council of Teaching Hospitals (COTH)
    • A ratio of full-time equivalent interns and residents to beds of .25 or higher. 3

  6. Bed Size — small, medium, and large. Bed size categories were based on the number of hospital beds and were specific to the hospital's region, location, and teaching status, as shown in Table 7 in Appendix I. We chose the bed size cutoff points so that approximately one-third of the hospitals in a given region, location, and teaching status combination would fall within each bed size category (small, medium, or large). We used different cutoff points for rural, urban non-teaching, and urban teaching hospitals because hospitals in those categories tend to be small, medium, and large, respectively. For example, a medium-sized teaching hospital would be considered a rather large rural hospital. Further, the size distribution is different among regions for each of the urban/teaching categories. For example, teaching hospitals tend to be smaller in the West than they are in the South. Using differing cutoff points in this manner avoids strata containing small numbers of hospitals.

    We did not split rural hospitals according to teaching status, because rural teaching hospitals were rare. For example, in 2011, rural teaching hospitals comprised less than 2% of the total hospital universe. We defined the bed size categories within location and teaching status because they would otherwise have been redundant. Rural hospitals tend to be small; urban non-teaching hospitals tend to be medium-sized; and urban teaching hospitals tend to be large. Yet it was important to recognize gradations of size within these types of hospitals. For example, in serving rural discharges, the role of "large" rural hospitals (particularly rural referral centers) often differs from the role of "small" rural hospitals.

    To further ensure geographic representativeness, implicit stratification data elements included State and three-digit ZIP Code (the first three digits of the hospital's five-digit ZIP Code). The hospitals were sorted according to these data elements prior to systematic random sampling. Detailed information on the design of the NIS prior to 2006 is available in the year-specific special reports on Design of the Nationwide Inpatient Sample found on the HCUP-US Website. Starting with the 2006 NIS, the design information was incorporated into this report.

Hospital Sampling Frame

The universe of hospitals was established as all community hospitals located in the U.S. with the exception, beginning in 1998, of short-term rehabilitation hospitals. However, some hospitals do not supply data to HCUP. Therefore, we constructed the NIS sampling frame from the subset of universe hospitals that released their discharge data to AHRQ for research use. The number of State Partners contributing data to the NIS has expanded over the years, as shown in Table 2 of Appendix I. As a result, the number of hospitals included in the NIS sampling frame has also increased over the years, as depicted in Figure 3, also in Appendix I.

The list of the entire frame of hospitals was composed of all AHA community hospitals in each of the frame States that could be matched to the discharge data provided to HCUP. If an AHA community hospital could not be matched to the discharge data provided by the data source, it was eliminated from the sampling frame (but not from the target universe).

Figure 4 in Appendix I illustrates the number of hospitals in the universe, frame, and sample and the percentage of universe hospitals in the frame for each State in the sampling frame for 2011. In most cases, the difference between the universe and the frame represents the difference in the number of community, non-rehabilitation hospitals in the 2011 AHA Annual Survey Database (Health Forum, LLC © 2012) and the hospitals for which data were supplied to HCUP that could be matched to the AHA data.

The largest discrepancy between HCUP data and AHA data is in Texas. As is evident in Figure 4 (Appendix I). Certain Texas State-licensed hospitals are exempt from statutory reporting requirements. Exempt hospitals include:

The Texas statute that exempts rural providers from the requirement to submit data defines a hospital as a rural provider if it:

(I.) Is located in a county that:

(A.) Has a population estimated by the United States Bureau of the Census to be not more than 35,000 as of July 1 of the most recent year for which county population estimates have been published; or

(B.) Has a population of more than 35,000, but does not have more than 100 licensed hospital beds and is not located in an area that is delineated as an urbanized area by the United States Bureau of the Census; and

(II.) Is not a State-owned hospital or a hospital that is managed or directly or indirectly owned by an individual, association, partnership, corporation, or other legal entity that owns or manages one or more other hospitals.

These exemptions apply primarily to smaller rural public hospitals and, as a result, these facilities are less likely to be included in the sampling frame than other Texas hospitals. While the number of hospitals omitted appears sizable, those available for the NIS include over 97% of inpatient discharges from Texas universe hospitals because excluded hospitals tend to have relatively few discharges.

Similar to Texas, because smaller Louisiana hospitals are not required to submit data to the Louisiana Department of Health and Hospitals, a significant portion of Louisiana hospitals are omitted from the sampling frame. However, because excluded hospitals tend to have relatively few discharges, those available for the NIS include about 92% of inpatient discharges from Louisiana universe hospitals.

Refer to Table 8 of Appendix I for a full list of the number of hospitals, and discharges included in the 2011 AHA universe, frame, and NIS by State. Fewer hospitals may be in a State's frame than in the universe because data is not always received from every hospital and hospitals are sometimes excluded because of State requirements.

Return to Introduction



Hospital Sample Design

Design Considerations

The NIS is a stratified probability sample of hospitals in the frame, with sampling probabilities calculated to select 20% of the universe of U.S. community, non-rehabilitation hospitals contained in each stratum. This sample size was determined by AHRQ based on their experience with similar research databases. The overall design objective was to select a sample of hospitals that accurately represents the target universe, which includes hospitals outside the frame (i.e., having zero probability of selection). Moreover, this sample was to be geographically dispersed, yet drawn only from data supplied by HCUP Partners.

It should be possible, for example, to estimate DRG-specific average lengths of stay across all U.S. hospitals using weighted average lengths of stay, based on averages or regression coefficients calculated from the NIS. Ideally, relationships among outcomes and their correlates estimated from the NIS should accurately represent all U.S. hospitals. It is advisable to verify your estimates against other data sources, if available, because not all States contribute data to the NIS. Table 2 in Appendix I lists the number of NIS States, hospitals, and discharges by year. For example, the National Hospital Discharge Survey (http://www.cdc.gov/nchs/nhds.htm) can provide benchmarks against which to check your national estimates for hospitalizations with more than 5,000 cases.

The NIS Comparison Report assesses the accuracy of NIS estimates by providing a comparison of the NIS with other data sources. The most recent report is available on the HCUP-US Website (http://www.hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp).

The NIS team considered alternative stratified sampling allocation schemes. However, allocation proportional to the number of hospitals was preferred for several reasons:

Overview of the Sampling Procedure

To further ensure accurate geographic representation, we implicitly stratified the hospitals by State and three-digit ZIP Code (the first three digits of the hospital's five-digit ZIP Code). This was accomplished by sorting by three-digit ZIP Code within each stratum prior to drawing a systematic random sample of hospitals.

After stratifying the universe of hospitals, we sorted hospitals by stratum, the three-digit ZIP Code within each stratum, and by a random number within each three-digit ZIP Code. These sorts ensured further geographic generalizability of hospitals within the frame States, as well as random ordering of hospitals within three-digit ZIP Codes. 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.

We then drew a systematic random sample of up to 20% of the total number of U.S. hospitals within each stratum. If too few frame hospitals appeared in a cell, we selected all frame hospitals for the NIS, subject to sampling restrictions specified by States. To simplify variance calculations, we drew at least two hospitals from each stratum. If fewer than two frame hospitals were available in a stratum, we merged it with an "adjacent" cell containing hospitals with similar characteristics.

Subsamples

Prior to the 2005 NIS, we drew two non-overlapping 10% subsamples of discharges from the NIS file for each year. The subsamples were selected by drawing every tenth discharge, starting with two different starting points (randomly selected between 1 and 10). Having a different starting point for each of the two subsamples guaranteed that they would not overlap. Discharges were sampled so that 10% of each hospital's discharges in each quarter were selected for each of the subsamples. The two samples could be combined to form a single, generalizable 20% subsample of discharges. Beginning with the 2005 NIS, 10% subsamples are no longer provided. However, users may still draw their own subsamples, if desired.

Change to Hospital Sampling Procedure Beginning with the 1998 NIS

Beginning with the 1998 NIS sampling procedures, all frame hospitals within a stratum have an equal probability of selection for the sample, regardless of whether they appeared in prior NIS samples. This deviates from the procedure used for earlier samples, which maximized the longitudinal component of the NIS series.

Further description of the sampling procedures for earlier releases of the NIS can be found in the special report: Design of the HCUP Nationwide Inpatient Sample, 1997. This report is available on the HCUP-US Website at http://www.hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp. For a description of the development of the new sample design for 1998 and subsequent data years, see the special report: Changes in NIS Sampling and Weighting Strategy for 1998. This report is available on the HCUP-US Website.

Zero-Weight Hospitals

Beginning with the 1993 NIS, the NIS samples no longer contain zero-weight hospitals. For a description of zero-weight hospitals in the 1988-1992 samples, refer to the special report: Design of the HCUP Nationwide Inpatient Sample, Release 1. This report is available on the HCUP-US Website at http://www.hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp.

Final Hospital Sample

In Appendix I, we present three figures describing the final hospital sample. Figure 5 depicts the numbers of hospitals sampled each year, while Figure 6 presents the numbers of discharges in each year of the NIS. The number of discharges in the sample may vary slightly depending on the hospitals selected for the NIS in any given year. For the 1988-1992 NIS, zero-weight hospitals were maintained to provide a longitudinal sample. Therefore, two figures exist for each of these years: one number for the regular NIS sample and another number for the total sample.

Figure 7 displays the weighted number of discharges sampled each year. Note that this number decreased from 35,408,207 in 1997 to 34,874,001 in 1998, a difference of 534,206 (1.5%). This slight decline is associated with two changes to the 1998 NIS design: the exclusion of community, rehabilitation hospitals from the hospital universe, and a change to the calculation of hospital universe discharges for the weights. Prior to 1998, we calculated discharges as the sum of total facility admissions (AHA data element ADMTOT), which includes long-term care admissions, plus births (AHA data element BIRTHS) reported for each U.S. community hospital in the AHA Annual Survey Database (Health Forum, LLC © 2012).

Beginning in 1998, we calculate discharges as the sum of hospital admissions (AHA data element ADMH) plus births for each U.S. community, non-rehabilitation hospital. This number is more consistent with the number of discharges we receive from the State data sources. We also substitute total facility admissions, if the number of hospital admissions is missing. Without these changes, the weighted number of discharges for 1998 would have been 35,622,743. The exclusion of community, rehabilitation hospitals reduced the number of universe hospitals by 177 and the number of weighted discharges by 214,490. The change in the calculation of discharges reduced the weighted number of discharges by 534,252.

The small declines in the weighted number of discharges for 2009-2011 are not related to any change in the sampling or weighting strategy. The reduction in the number of discharges is consistent with the information from the AHA Annual Survey of Hospitals.

Figure 8 presents a summary of the 2011 NIS hospital sample by geographic region and the number of:

Figure 9 summarizes the estimated U.S. population by geographic region. For each region, the figure reveals:

Figure 10 depicts the number of discharges in the 2011 sample for each State.

Special consideration was needed to handle the Massachusetts data in the 2006 and the 2007 NIS. Fourth quarter data from sampled hospitals in Massachusetts were unavailable for inclusion in the 2006 and the 2007 NIS. To account for the missing quarter of data, we sampled one fourth of the Massachusetts NIS discharges from the first three quarters and modified the records to represent the fourth quarter. To ensure a representative sample, we sorted the Massachusetts NIS discharges by hospital, discharge quarter, Clinical Classifications Software (CCS) diagnosis group for the principal diagnosis, gender, age, and a random number before selecting every fourth record. The following describes the adjustments made to the selected Massachusetts NIS records:

  1. We relabeled the discharge quarter (DQTR) to four and saved the original discharge quarter in a new data element (DQTR_X).
  2. We adjusted the admission month (AMONTH) by the number of months corresponding to the change in the discharge quarter.
  3. We adjusted the total charges (TOTCHG and TOTCHG_X) using quarter-specific adjustment factors calculated as the mean total charges in the fourth quarter for all Northeastern NIS States (excluding Massachusetts) divided by the mean total charges in the first, second, or third quarter for all Northeastern NIS States (excluding Massachusetts).

We then adjusted the discharge weights for the Massachusetts records to appropriately account for the shifting of quarter one through three discharges to quarter four.

Return to Introduction



SAMPLE WEIGHTS

To obtain nationwide estimates, we developed discharge weights using the AHA universe as the standard. These were developed separately for hospital-and discharge-level analyses. Hospital-level weights were developed to extrapolate NIS sample hospitals to the hospital universe. Similarly, discharge-level weights were developed to extrapolate NIS sample discharges to the discharge universe.

Hospital Weights

Hospital weights to the universe were calculated by post-stratification. For each year, hospitals were stratified on the same data elements that were used for sampling: geographic region, urban/rural location, teaching status, bed size, and control. The strata that were collapsed for sampling were also collapsed for sample weight calculations. Within each stratum s, each NIS sample hospital's universe weight was calculated as:

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

where Ws(universe) was the hospital universe weight, and Ns(universe) and Ns(sample) were the number of community hospitals 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 hospital weights are usually near five.

Discharge Weights

The calculations for discharge-level sampling weights were similar to the calculations for hospital-level sampling weights. The discharge weights are usually constant for all discharges within a stratum. The only exceptions are for strata with sample hospitals that, according to the AHA files, were open for the entire year but contributed less than a full year of data to the NIS. For those hospitals, we adjusted the number of observed discharges by a factor of 4 ÷ Q, where Q was the number of calendar quarters for which the hospital contributed discharges to the NIS. For example, when a sample hospital contributed only two quarters of discharge data to the NIS, the adjusted number of discharges was double the observed number. This adjustment was performed only for weighting purposes. The NIS data set includes only the actual (unadjusted) number of observed discharges.

With that minor adjustment, each discharge weight is essentially equal to the number of AHA universe discharges that each sampled discharge represents in its stratum. This calculation was possible because the number of total discharges was available for every hospital in the universe from the AHA files. Each universe hospital's AHA discharge total was calculated as the sum of newborns and hospital discharges.

Discharge weights to the universe were calculated by post-stratification. Hospitals were stratified just as they were for universe hospital weight calculations. Within stratum s, for hospital i, each NIS sample discharge’s universe weight was calculated as:

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

where DWis(universe) was the discharge weight; DNs(universe) represented the number of discharges from community hospitals in the universe within stratum s; ADNs(sample) was the number of adjusted discharges from sample hospitals selected for the NIS; and Qi represented the number of quarters of discharge data contributed by hospital i to the NIS (usually Qi = 4). Thus, each discharge’s weight (DISCWT) is equal to the number of universe discharges it represents in stratum s during that year. Because all discharges from 20% of the hospitals in each stratum were sampled when possible, the discharge weights are usually near five.

Return to Introduction



Appendix I: Tables and Figures

Table 1: 2011 Data Sources for the NIS*
State Data Organization
AK Alaska State Hospital and Nursing Home Association
AR Arkansas Department of Health
AZ Arizona Department of Health Services
CA Office of Statewide Health Planning & Development
CO Colorado Hospital Association
CT Connecticut Hospital Association
FL Florida Agency for Health Care Administration
GA Georgia Hospital Association
HI Hawaii Health Information Corporation
IA Iowa Hospital Association
IL Illinois Department of Public Health
IN Indiana Hospital Association
KS Kansas Hospital Association
KY Kentucky Cabinet for Health and Family Services
LA Louisiana Department of Health and Hospitals
MA Division of Health Care Finance and Policy
MD Health Services Cost Review Commission
ME Maine Health Data Organization
MI Michigan Health & Hospital Association
MN Minnesota Hospital Association
MO Hospital Industry Data Institute
MS Mississippi Department of Health
MT MHA - An Association of Montana Health Care Providers
NC North Carolina Department of Health and Human Services
ND North Dakota (data provided by the Minnesota Hospital Association)
NE Nebraska Hospital Association
NJ New Jersey Department of Health
NM New Mexico Department of Health
NV Nevada Department of Health and Human Services
NY New York State Department of Health
OH Ohio Hospital Association
OK Oklahoma State Department of Health
OR Oregon Association of Hospitals and Health Systems
PA Pennsylvania Health Care Cost Containment Council
RI Rhode Island Department of Health
SC South Carolina State Budget & Control Board
SD South Dakota Association of Healthcare Organizations
TN Tennessee Hospital Association
TX Texas Department of State Health Services
UT Utah Department of Health
VT Vermont Association of Hospitals and Health Systems
VA Virginia Health Information
WA Washington State Department of Health
WI Wisconsin Department of Health Services
WV West Virginia Health Care Authority
WY Wyoming Hospital Association
*New Hampshire data was not available in time to be included in the 2011 NIS.

Return to Introduction



Table 2: Number of NIS States, Hospitals, and Discharges, by Year
Calendar Year States in the Frame Number of States Sample Hospitals Sample Discharges
1988 California, Colorado, Florida, Iowa, Illinois, Massachusetts, New Jersey, and Washington 8 758 5,265,756
1989 Added Arizona, Pennsylvania, and Wisconsin 11 875 6,110,064
1990 No new additions 11 861 6,268,515
1991 No new additions 11 847 6,156,188
1992 No new additions 11 838 6,195,744
1993 Added Connecticut, Kansas, Maryland, New York, Oregon, and South Carolina 17 913 6,538,976
1994 No new additions 17 904 6,385,011
1995 Added Missouri and Tennessee 19 938 6,714,935
1996 No new additions 19 906 6,542,069
1997 Added Georgia, Hawaii, and Utah 22 1,012 7,148,420
1998 No new additions 22 984 6,827,350
1999 Added Maine and Virginia 24 984 7,198,929
2000 Added Kentucky, North Carolina, Texas, and West Virginia 28 994 7,450,992
2001 Added Michigan, Minnesota, Nebraska, Rhode Island, and Vermont 33 986 7,452,727
2002 Added Nevada, Ohio, and South Dakota; Dropped Arizona 35 995 7,853,982
2003 Added Arizona, Indiana, and New Hampshire; Dropped Maine 37 994 7,977,728
2004 Added Arkansas; Dropped Pennsylvania 37 1,004 8,004,571
2005 Added Oklahoma; Dropped Virginia 37 1,054 7,995,048
2006 Added Virginia 38 1,045 8,074,825
2007 Added Maine and Wyoming 40 1,044 8,043,415
2008 Added Louisiana and Pennsylvania 42 1,056 8,158,381
2009 Added Montana and New Mexico 44 1,050 7,810,762
2010 Added Alaska and Mississippi; Dropped New Hampshire* 45 1,051 7,800,441
2011 Added North Dakota 46 1,049 8,023,590
* New Hampshire data was not available in time to be included in the 2010 or 2011 NIS.

Return to Introduction



Table 3. Summary of NIS Releases
Data from Media/Format Options Structure of Releases
1988-1992
  • 8 States in 1988
  • 11 States in 1989-1992
On CD-ROM,
In ASCII format
5 years of data in a 6-CD set, compressed files
Two 10% subsamples of discharges for each year
1993
  • 17 States
1994
  • 17 States
1995
  • 19 States
1996
  • 19 States
1997
  • 22 States
1998
  • 22 States
1999
  • 24 States
2000
  • 28 States
2001
  • 33 States


On CD-ROM,
In ASCII format
1 year of data in a 2-CD set, compressed files

Two 10% subsamples of discharges for each year
2002
  • 35 States
2003
  • 37 States
2004
  • 37 States
On CD-ROM,
In ASCII format
1 year of data in a 2-CD set,
compressed files

Two 10% subsamples of discharges for
each year

A companion file with four different sets
of severity measures
2005
  • 37 States
2006
  • 38 States
2007
  • 40 States
2008
  • 42 States
On CD-ROM,
In ASCII format
1 year of data in a 2-CD set,
compressed files

A companion file with four different sets of severity measures, and also
diagnosis and procedure groups
2009
  • 44 States
2010
  • 45 States
2011
  • 46 States
On DVD-ROM,
In ASCII format
1 year of data on a DVD-ROM,
compressed files

A companion file with of severity measures,
and also diagnosis and procedure groups


Table 4. Summary of NIS Data Sources, Hospitals, and Inpatient Stays, 1988-2010
Year Data Sources Number of Hospitals Number of Discharges in the NIS, Unweighted Number of Discharges in the NIS, Weighted for National Estimates
1988 CA CO FL IL IA MA NJ WA 759 5,265,756 35,171,448
1989 AZ CA CO FL IL IA MA NJ PA WA WI
(Added AZ, PA, WI)
882 6,110,064 35,104,645
1990 AZ CA CO FL IL IA MA NJ PA WA WI
(No change)
871 6,268,515 35,215,397
1991 AZ CA CO FL IL IA MA NJ PA WA WI
(No change)
859 6,156,188 35,036,492
1992 AZ CA CO FL IL IA MA NJ PA WA WI
(No change)
856 6,195,744 35,011,385
1993 AZ CA CO CT FL IL IA KS MD MA NJ NY OR PA SC WA WI
(Added CT, KS, MD, NY, OR, SC)
913 6,538,976 34,714,530
1994 AZ CA CO CT FL IL IA KS MD MA NJ NY OR PA SC WA WI
(No change)
904 6,385,011 34,622,203
1995 AZ CA CO CT FL IL IA KS MD MA MO NJ NY OR PA SC TN WA WI
(Added MO, TN)
938 6,714,935 34,791,998
1996 AZ CA CO CT FL IL IA KS MD MA MO NJ NY OR PA SC TN WA WI
(No change)
906 6,542,069 34,874,386
1997 AZ CA CO CT FL GA HI IL IA KS MD MA MO NJ NY OR PA SC TN UT WA WI
(Added GA, HI, UT)
1,012 7,148,420 35,408,207
1998 AZ CA CO CT FL GA HI IL IA KS MD MA MO NJ NY OR PA SC TN UT WA WI
(No change)
984 6,827,350 34,874,001
1999 AZ CA CO CT FL GA HI IL IA KS MD MA ME MO NJ NY OR PA SC TN UT VA WA WI
(Added ME, VA)
984 7,198,929 35,467,673
2000 AZ CA CO CT FL GA HI IL IA KS KY MD MA ME MO NC NJ NY OR PA SC TN TX UT VA WA WI WV
(Added KY, NC, TX, WV)
994 7,450,992 36,417,565
2001 AZ CA CO CT FL GA HI IL IA KS KY MD MA ME MI MN MO NC NE NJ NY OR PA RI SC TN TX UT VA VT WA WI WV
(Added MI, MN, NE, RI, VT)
986 7,452,727 37,187,641
2002 CA CO CT FL GA HI IL IA KS KY MD MA ME MI MN MO NC NE NJ NY NV OH OR PA RI SC SD TN TX UT VA VT WA WI WV
(Added NV, OH, SD; AZ data were not available)
995 7,853,982 37,804,021
2003 AZ CA CO CT FL GA HI IL IN IA KS KY MD MA MI MN MO NC NE NH NJ NY NV OH OR PA RI SC SD TN TX UT VA VT WA WI WV
(Added AZ, IN, NH; ME data were not available)
994 7,977,728 38,220,659
2004 AR AZ CA CO CT FL GA HI IL IN IA KS KY MD MA MI MN MO NC NE NH NJ NY NV OH OR RI SC SD TN TX UT VA VT WA WI WV
(Added AR; PA data were not available)
1,004 8,004,571 38,661,786
2005 AR AZ CA CO CT FL GA HI IL IN IA KS KY MD MA MI MN MO NC NE NH NJ NY NV OH OK OR RI SC SD TN TX UT VT WA WI WV
(Added OK; VA data were not available)
1,054 7,995,048 39,163,834
2006 AR AZ CA CO CT FL GA HI IL IN IA KS KY MD MA MI MN MO NC NE NH NJ NY NV OH OK OR RI SC SD TN TX UT VA VT WA WI WV
(Added VA)
1,045 8,074,825 39,450,216
2007 AR AZ CA CO CT FL GA HI IL IN IA KS KY MD MA ME MI MN MO NC NE NH NJ NY NV OH OK OR RI SC SD TN TX UT VA VT WA WI WV WY
(Added ME and WY)
1,044 8,043,415 39,541,948
2008 AR AZ CA CO CT FL GA HI IL IN IA KS LA KY MD MA ME MI MN MO NC NE NH NJ NY NV OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY
(Added LA and PA)
1,056 8,158,381 39,885,120
2009 AR AZ CA CO CT FL GA HI IL IN IA KS LA KY MD MA ME MI MN MO NT NC NE NH NJ NM NY NV OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY
(Added NM and MT)
1,050 7,810,762 39,434,956
2010 AK AR AZ CA CO CT FL GA HI IL IN IA KS LA KY MD MA ME MI MN MO MS MT NC NE NJ NM NY NV OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY
(Added AK and MS; NH data were not available)
1,051 7,800,441 39,008,298
2011 AK AR AZ CA CO CT FL GA HI IL IN IA KS LA KY MD MA ME MI MN MO MS MT NC ND NE NJ NM NY NV OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY
(Added ND; NH data were not available)
1,049 8,023,590 38,590,733

Return to Introduction



Table 5. NIS Related Reports and Database Documentation Available on HCUP-US
Restrictions on the Use of the NIS
  • Data Use Agreement for the NIS


Description of the NIS Files
  • Introduction to the NIS, 2011 - this document
  • 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
  • Sources of NIS Data, NIS Data Elements, and State-Specific Restrictions (included in this document beginning in 2006) - identifies the NIS data sources and restrictions on sampling and the release of data elements


Availability of Data Elements
  • Availability of NIS data elements from 1988-2011


Description of Data Elements in the NIS
  • Description of Data Elements - details uniform coding and State-specific idiosyncrasies
  • Summary Statistics - lists means and frequencies on nearly all data elements
  • NIS Severity Measures - provides detailed documentation on the different types of measures
  • HCUP Coding Practices - describes how HCUP data elements are coded
  • HCUP Hospital Identifiers - explains data elements that characterize individual hospitals


Known Data Issues
  • Information on corrections to the NIS data sets
  • Link to NIS Trends Weights Files
Load Programs
Programs to load the ASCII data files into statistical software:
  • SAS
  • SPSS
  • Stata


HCUP Tools: Labels and Formats
  • Overview of Clinical Classifications Software (CCS), a categorization scheme that groups ICD-9-CM diagnosis and procedure codes into mutually exclusive categories
  • Labels file for CCS categories
  • Labels file for multiple versions of Diagnosis Related Groups (DRGs) and Major Diagnostic Categories (MDCs)
  • NIS SAS format library program to create value labels
  • NIS ICD-9-CM formats to label ICD-9-CM diagnoses and procedures
  • NIS Severity formats to label severity data elements


NIS Related Reports
Links to HCUP-US page with various NIS related reports such as the following:
  • Design of the Nationwide Inpatient Sample for 1988 to 2005 (included in this document beginning in 2006)
  • Changes in NIS Sampling and Weighting Strategy for 1998
  • Calculating Nationwide Inpatient Sample Variances
  • Using the HCUP Nationwide Inpatient Sample to Estimate Trends
  • NIS Comparison Reports (available for years in which the NIS sample changed)
  • HCUP Data Quality Reports for 1988-2011
  • HCUP E-Code Evaluation Report


HCUP Supplemental Files
  • Cost-to-Charge Ratio files
  • Hospital Market Structure (HMS) files
  • NIS Trends Supplemental files


SAS File Information
  • File Information for all States and years


Figure 1: Hospital Universe, by Year4

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Figure 1: Bar chart of number of hospitals listed vertically and years listed horizontally

Return to Introduction



Figure 2: NIS States, by Region

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Figure 2: Map of United States of America broken into different regions

Return to Introduction



Table 6: All States, by Region*
Region States
1: Northeast Connecticut, Maine, Massachusetts, New Hampshire†, New Jersey, New York, Pennsylvania, Rhode Island, Vermont.
2: Midwest Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin.
3: South Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia.
4: West Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming.

*States/areas in italics do not participate in HCUP.
†New Hampshire participates in HCUP, but did not provide data in time for the 2010-2011 NIS.



Table 7: Bed Size Categories, by Region
Location and Teaching Status Hospital Bed Size
Small Medium Large
NORTHEAST
Rural 1 - 49 50 - 99 100+
Urban, non-teaching 1 - 124 125 - 199 200+
Urban, teaching 1 - 249 250 - 424 425+
MIDWEST
Rural 1 - 29 30 - 49 50+
Urban, non-teaching 1 - 74 75 - 174 175+
Urban, teaching 1 - 249 250 - 374 375+
SOUTH
Rural 1 - 39 40 - 74 75+
Urban, non-teaching 1 - 99 100 - 199 200+
Urban, teaching 1 - 249 250 - 449 450+
WEST
Rural 1 - 24 25 - 44 45+
Urban, non-teaching 1 - 99 100 - 174 175+
Urban, teaching 1 - 199 200 - 324 325+

Return to Introduction



Figure 3: NIS Hospital Sampling Frame, by Year

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Figure 3: Bar chart of number of hospitals listed vertically and years listed horizontally

Return to Introduction



Figure 4: Number of Hospitals in the 2011 Universe, Frame, and Sample for Frame States - Part A: Alasks – Montana

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Figure 4: Bar chart of number of hospitals listed horizontally and states Alaska through Montana listed vertically



Figure 4: Number of Hospitals in the 2011 Universe, Frame, and Sample for Frame States - Part B: North Carolina – Wyoming

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Figure 4: Bar chart of number of hospitals listed horizontally and states North Carolina through Wyoming listed vertically



Table 8: Number of Hospitals and Discharges in 2011 AHA Universe, Frame, and NIS, by State
State Number of Hospitals and Discharges in 2011 AHA Universe, Frame, and NIS, by State
AHA Frame NIS
Hospitals Discharges Hospitals Discharges Hospitals Discharges Weighted Discharges
Non-NIS States 191 1,214,109 0 0 0 0 0
Alaska 23 63,694 6 6,807 2 3,796 18,840
Arizona 76 791,413 73 760,981 18 168,667 850,583
Arkansas 84 398,281 74 329,145 19 87,095 414,366
California 353 3,888,230 347 3,764,190 80 834,410 4,085,849
Colorado 82 491,448 75 474,044 19 136,934 608,249
Connecticut 34 439,293 30 422,226 8 99,594 497,642
Florida 208 2,684,475 207 2,588,908 49 584,887 2,761,395
Georgia 153 1,075,373 149 1,039,271 38 205,583 992,142
Hawaii 22 112,500 18 87,370 3 235 1,229
Illinois 185 1,642,572 184 1,572,328 42 349,835 1,650,861
Indiana 134 844,665 120 794,486 30 230,634 1,085,394
Iowa 119 373,791 117 337,570 26 61,618 306,250
Kansas 140 347,068 122 285,132 26 61,618 306,250
Kentucky 106 637,945 105 610,218 22 128,410 615,885
Louisiana 167 689,142 108 575,097 26 137,103 661,499
Maine 36 154,017 32 75,548 7 16,660 89,958
Maryland 45 739,207 44 699,195 11 220,059 1,026,589
Massachusetts 73 897,842 64 842,846 11 153,881 752,966
Michigan 150 1,292,785 115 969,105 25 200,895 928,800
Minnesota 133 656,719 127 534,442 30 142,629 673,746
Mississippi 103 430,919 101 382,283 27 105,108 506,072
Missouri 125 891,025 120 865,476 28 249,518 1,171,063
Montana 52 104,535 41 94,272 8 9,145 46,774
Nebraska 90 232,498 81 183,685 17 24,522 126,528
Nevada 39 291,721 38 282,606 9 58,015 268,490
New Jersey 69 1,132,241 69 1,058,017 14 207,319 1,006,990
New Mexico 37 197,381 32 181,546 8 46,313 255,873
New York 180 2,709,044 179 2,546,178 40 598,902 2,895,074
North Carolina 119 1,146,806 116 1,101,586 28 250,166 1,186,156
North Dakota 41 105,633 15 87,690 4 13,916 72,751
Ohio 194 1,625,766 156 1,555,936 35 326,764 1,528,985
Oklahoma 134 505,818 126 427,123 35 95,997 454,514
Oregon 60 387,507 59 372,303 15 81,472 423,435
Pennsylvania 187 1,870,888 185 1,794,428 41 400,938 1,977,819
Rhode Island 11 131,656 11 130,652 4 35,921 169,327
South Carolina 64 555,725 54 454,196 13 118,814 605,854
South Dakota 57 114,401 47 76,694 10 28,714 137,520
Tennessee 125 848,949 106 782,280 26 205,619 998,712
Texas 486 2,984,604 401 2,772,207 90 638,165 3,090,507
Utah 49 289,946 47 269,677 9 69,054 329,612
Vermont 14 61,278 14 52,214 4 25,278 144,483
Virginia 84 881,610 82 847,948 24 251,779 1,163,049
Washington 88 668,614 88 641,182 19 109,487 547,486
West Virginia 52 294,220 52 285,135 9 70,698 326,188
Wisconsin 131 641,877 130 604,256 30 153,115 726,853
Wyoming 27 51,502 24 29,166 7 10,356 54,328
Total 5,132 38,590,733 4,491 34,647,645 1,049 8,023,590 38,590,733

Return to Introduction



Figure 5: Number of Hospitals Sampled, by Year

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Figure 5: Bar chart of number of hospitals listed horizontally and years listed vertically

Return to Introduction



Figure 6: Number of NIS Discharges, Unweighted, by Year

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Figure 6: Bar chart of number of discharges in millions, unweighted listed horizontally and years listed vertically



Figure 7: Number of NIS Discharges, Weighted, by Year

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Figure 7: Bar chart of number of discharges in millions, weighted listed horizontally and years listed vertically

Return to Introduction



Figure 8: Number of Hospitals in the 2011 Universe, Frame, Sample, Target, and Surplus*, by Region

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Figure 8: Bar chart of region listed horizontally and number of hospitals listed vertically

*The surplus is the difference between the actual number of hospitals sampled and the sample target.



Figure 9: Percentage of U.S.Population in 2010 NIS States, by Region
Calculated using the estimated U.S. population on July 1, 2011.5

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Figure 9: Bar chart of population listed horizontally and region listed vertically

Return to Introduction



Figure 10: Number of Discharges in the 2011 NIS, by State.

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Figure 10: Bar chart of discharges listed horizontally and state listed vertically

Return to Introduction



Appendix II: State-Specific Restrictions

The table below enumerates the types of restrictions applied to the Nationwide Inpatient 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.



Confidentiality of Hospitals — Restricted Identification of Hospitals
The following data sources required that hospitals not be identified in the NIS:
  • AK: Alaska State Hospital and Nursing Home Association
  • AR: Arkansas Department of Health
  • GA: Georgia Hospital Association
  • HI: Hawaii Health Information Corporation
  • IN: Indiana Hospital Association
  • KS: Kansas Hospital Association
  • LA: Louisiana Department of Health and Hospitals
  • ME: Maine Health Data Organization
  • MI: Michigan Health & Hospital Association
  • MO: Missouri Hospital Industry Data Institute
  • NE: Nebraska Hospital Association
  • NM: New Mexico Department of Health
  • OH: Ohio Hospital Association
  • OK: Oklahoma State Department of Health
  • SC: South Carolina State Budget & Control Board
  • SD: South Dakota Association of Healthcare Organizations
  • TN: Tennessee Hospital Association
  • TX: Texas Department of State Health Services
  • WY: Wyoming Hospital Association
In these States the following data elements are set to missing for all hospitals:
  • IDNUMBER, AHA hospital identifier without leading 6*
  • AHAID, AHA hospital identifier with leading 6*
  • HOSPNAME, hospital name
  • HOSPCITY, hospital city
  • HOSPADDR, hospital address
  • HOSPZIP, hospital ZIP Code
  • DSHOSPID, data source hospital identifier*
  • HOSPSTCO, hospital State, modified county FIPS code*
  • HFIPSSTCO, hospital State, unmodified county FIPS code*
*Available in AR.
Confidentiality of Hospitals — Restricted Hospital Structural Characteristics
The following data sources restricted the identification of hospital structural characteristics.
  • CO: Colorado Hospital Association
  • CT: Connecticut Hospital Association
  • GA: Georgia Hospital Association
  • SC: South Carolina State Budget & Control Board
In these States the following data elements are set to missing for all hospitals:
  • HOSP_MHSMEMBER, Multi-hospital system membership*
  • HOSP_MHSCLUSTER, System cluster code*
  • HOSP_RNPCT, Percentage of RNs among nurses (RNs and LPNs)
  • HOSP_RNFTEAPD, RN FTEs per 1000 adjusted patient days
  • HOSP_LPNFTEAPD, LPN FTEs per 1000 adjusted inpatient days
  • HOSP_NAFTEAPD, Nurse aides per 1000 adjusted inpatient days
  • HOSP_OPSURGPCT, Percentage of all surgeries performed in the outpatient setting.**
*Available in GA.
** Available in GA and SC.

Return to Introduction



Confidentiality of Hospitals — Limitation on Sampling
Limitations on sampling were required for the following data sources:
  • AR: Arkansas Department of Health
    • Nine hospitals were dropped from the sampling frame because they had incomplete payer information.

  • MI: Michigan Health & Hospital Association
    • Reporting of total charge is limited in the Michigan data. Twenty-eight of 143 hospitals were dropped from the sampling frame because they did not report any total charges. These hospitals were fairly evenly distributed by hospital type. There were no sampling strata in the State containing only hospitals without total charges.

  • SC: South Carolina State Budget & Control Board
    • Two hospitals were dropped from the sampling frame to meet additional South Carolina confidentiality requirements.
Some States limit the hospitals that can be included in the NIS. The following data sources requested that hospitals be dropped from the sampling frame whenever there were fewer than two hospitals in a sampling stratum. For more details about the number of hospitals included in the AHA Universe, Frame, and NIS for each NIS State, refer to Table 8 in Appendix I.

  • AK: Alaska State Hospital and Nursing Home Association
  • GA: Georgia Hospital Association
  • HI: Hawaii Health Information Corporation
  • IN: Indiana Hospital Association
  • KS: Kansas Hospital Association
  • LA: Louisiana Department of Health and Hospitals
  • ME: Maine Health Data Organization
  • MI: Michigan Health & Hospital Association
  • MO: Missouri Hospital Industry Data Institute
  • NE: Nebraska Hospital Association
  • NM: New Mexico Department of Health
  • OH: Ohio Hospital Association
  • OK: Oklahoma State Department of Health
  • SC: South Carolina State Budget & Control Board
  • SD: South Dakota Association of Healthcare Organizations
  • TN: Tennessee Hospital Association
  • TX: Texas Department of State Health Services
  • WY: Wyoming Hospital Association
Confidentiality of Hospitals — Restricted Release of Stratifiers
Stratifier data elements were restricted for the following data sources to further ensure hospital confidentiality in the NIS:
  • AK: Alaska State Hospital and Nursing Home Association
  • GA: Georgia Hospital Association
  • HI: Hawaii Health Information Corporation
  • IN: Indiana Hospital Association
  • KS: Kansas Hospital Association
  • LA: Louisiana Department of Health and Hospitals
  • ME: Maine Health Data Organization
  • MI: Michigan Health & Hospital Association
  • MO: Missouri Hospital Industry Data Institute
  • NE: Nebraska Hospital Association
  • NM: New Mexico Department of Health
  • OH: Ohio Hospital Association
  • OK: Oklahoma State Department of Health
  • SC: South Carolina State Budget & Control Board
  • SD: South Dakota Association of Healthcare Organizations
  • TN: Tennessee Hospital Association
  • TX: Texas Department of State Health Services
  • WY: Wyoming Hospital Association
For the above States, stratifier data elements were set to missing if the cell, as defined by the data elements below, had fewer than two hospitals in the universe of the State's hospitals:
  • H_CONTRL, control/ownership of hospital, without collapsing
  • HOSP_CONTROL, control/ownership of hospital
  • HOSP_LOCATION, location (urban/rural) of hospital
  • HOSP_TEACH, teaching status of hospital
  • HOSP_BEDSIZE, bed size of hospital
  • HOSP_LOCTEACH, location/teaching status of hospital
  • HOSP_MHSMEMBER, hospital is part of multiple hospital system
  • HOSP_MHSCLUSTER, AHA multiple hospital system cluster code
Confidentiality of Records — Restricted Release of Age in Years, Age in Days
The following data sources restrict or limit the release of age:

  • CA: Office of Statewide Health Planning and Development
    • Age in days (AGEDAY) and age in years (AGE) are suppressed for some records. In some cases, AGE is set to the midpoint of the age category.

  • FL: Florida Agency for Health Care Administration
    • Age in days (AGEDAY) is set to missing on all records

  • GA: Georgia Hospital Association
    • Set age in years values greater than 99 to 99.

  • MA: Division of Health Care Finance and Policy
    • Age in days (AGEDAY) is set to missing on all records

  • ME: Maine Health Data Organization
    • Age in days, (AGEDAY) is set to missing on all records
    • Age in years (AGE) is set to midpoints of five-year ranges as follows:

    Maine Restriction on AGE for General Patient Population
    Age Range New value of AGE
    under 1 year 0
    1-4 2
    5-9 7
    10-14 12
    15-19 17
    20-24 22
    25-29 27
    30-34 32
    35-39 37
    40-44 42
    45-49 47
    50-54 52
    55-59 57
    60-64 62
    65-69 67
    70-74 72
    75-79 77
    80-84 82
    85 years & over 87
    unknown Missing (.)


  • SC: South Carolina State Budget & Control Board
    • Age in days (AGEDAY) is set to missing on all records

  • TX: Texas Department of State Health Services
    • Age in days (AGEDAY) is set to missing on all records
    • Age in years (AGE) is set to the midpoints of age ranges defined by the data source. There were 22 age groups for the general patient population and 5 age groups for the HIV or alcohol/drug use patients. The age groups are shown below:


    • Texas Restriction on AGE for General Patient Population other than HIV or Drug/Alcohol Use Patients
Age Range New value of AGE
0 0
1-4 2
5-9 7
10-14 12
15-17 16
18-19 19
20-24 22
25-29 27
30-34 32
35-39 37
40-44 42
45-49 47
50-54 52
55-59 57
60-64 62
65-69 67
70-74 72
75-79 77
80-84 82
85-89 87
90 and above 90
Texas Restriction on AGE for HIV or Drug/Alcohol Use Patients
Age Range New value of AGE
0 0
1-17 8
18-44 31
45-64 54
65-74 69
75 and above 75
The HIV or drug/alcohol use patients were identified by any principal or secondary diagnosis code on the record having the first four characters equal to one of the values in the following list: ‘2910’, ‘2911’, ‘2912’, ‘2913’, ‘2914’, ‘2915’, ‘2918’, ‘2919’, ‘2920’, ‘2921’, ‘2922’, ‘2928’, ‘2929’, ‘3030’, ‘3039’, ‘3040’, ‘3041’, ‘3042’, ‘3043’, ‘3044’, ‘3045’, ‘3046’, ‘3047’, ‘3048’,‘3049’, ‘3050’, ‘3052’, ‘3053’, ‘3054’, ‘3055’, ‘3056’, ‘3057’, ‘3058’, ‘3059’, ‘7903’, ‘V08’, and ‘042’.

Return to Introduction



Confidentiality of Records — Other Restrictions
The following data sources restrict or limit the release of data elements for patient confidentiality:
  • CA: Office of Statewide Health Planning & Development
    • Admission month (AMONTH), gender (FEMALE), and race (RACE) are suppressed for some records.
  • FL: Florida Agency for Health Care Administration
    • Admission month (AMONTH) is set to missing on all records
  • MA: Division of Health Care Finance and Policy
    • NCHS-defined Patient Urban-Rural Codes (PL_NCHS2006) is set to missing on all records
  • ME: Maine Health Data Organization
    • The following data elements are suppressed:
      • Admission Source, UB-92 standard coding (ASOURCEUB92)
      • Admission Source, as received from source (ASOURCE_X)
      • Disposition of patient, UB04 standard coding (DISPUB04)
      • Length of stay, as received from source (LOS_X)
      • Primary expected payer, as received from source (PAY1_X)
      • Secondary expected payer, as received from source (PAY2_X)
      • Point of origin for admission or visit, UB-04 standard coding (PointOfOriginUB04)
      • Point of origin for admission or visit, as received from source (PointOfOrigin_X)
      • Total Charges, as received from source (TOTCHG_X)
  • WV: West Virginia Health Care Authority
    • Admission month (AMONTH) is set to missing on all records
Missing Discharges
The following data sources may be missing discharge records for specific populations of patients:
  • IA: Iowa Hospital Association
    • Beginning in data year 2001, 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 NIS.
  • 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 NIS.
  • NY: New York State Department of Health
    • Beginning with data year 2008, the New York State Department of Health masks the hospital identifiers on abortion records. As a result, these records are not included in the NIS.

Return to Introduction



Appendix III: Data Elements

Table 1. Data Elements in the NIS Inpatient Core Files

Data elements that are italicized are not included in the 2011 NIS Inpatient Core files, but are only available in previous years’ files.

Type of
Data Element
HCUP
Name
Years
Available
Coding Notes Unavailable in 2011 for:
Admission information
Admission day AWEEKEND 1998 - 2011 Admission on weekend: (0) admission on Monday-Friday, (1) admission on Saturday-Sunday  
ADAYWK 1988 - 1997 Admission day of week: (1) Sunday, (2) Monday, (3) Tuesday, (4) Wednesday, etc.  
Admission month AMONTH 1988 - 2011 Admission month coded from (1) January to (12) December FL, WV
Admission source ASOURCE 1988 - 2011 Admission source, uniform coding: (1) ER, (2) another hospital, (3) another facility including long-term care, (4) court/law enforcement, (5) routine/birth/other AK, AZ, CO, CT, FL, GA, HI, IA, KS, KY, ME, MI, MN, MO, MT, NC, ND, NE, NM, OK, OR, PA, RI, SC, SD, TN, TX, UT, VT, WA, WI, WY
ASOURCE_X 1998 - 2011 Admission source, as received from data source using State-specific coding AK, AZ, CO, CT, FL, GA, HI, IA, KS, KY, ME, MI, MN, MO, MT, NC, ND, NE, NM, OK, OR, PA, RI, SC, SD, TN, TX, UT, VT, WA, WI, WY
ASOURCEUB92 2003 - 2011 Admission source (UB-92 standard coding). For newborn admissions (ATYPE = 4): (1) normal newborn, (2) premature delivery, (3) sick baby, (4) extramural birth; For non-newborn admissions (ATYPE NE 4): (1) physician referral, (2) clinic referral, (3) HMO referral, (4) transfer from a hospital, (5) transfer from a skilled nursing facility, (6) transfer from a another healthcare facility, (7) emergency room, (8) court/law enforcement, (A) transfer from a critical access hospital, (B) transfer from another home health agency, (C) readmission to same home health agency, (D) transfer from one distinct unit of the hospital to another distinct unit of the same hospital resulting in a separate claim to the payer, (E) transfer from ambulatory surgery center, (F) transfer from hospice and under hospice plan AK, AZ, CA, CO, CT, FL, GA, HI, IA, KS, KY, MD, ME, MI, MN, MO, MT, NC, ND, NE, NM, OK, OR, PA, RI, SC, SD, TN, TX, UT, VT, WA, WI, WY
POINTOFORIGIN_X 2009 - 2011 Point of origin for admission or visit, as received from source CA, MA, MD, ME
POINTOFORIGIN_UB04 2007 - 2011 Point of origin for admission or visit, UB-04 standard coding. For newborn admission (ATYPE = 4): (5) Born inside this hospital, (6) Born outside of this hospital; For non-newborn admissions (ATYPE NE 4): (1) Non-healthcare facility point of origin, (2) Clinic, (4) Transfer from a hospital (different facility), (5) Transfer from a skilled Nursing Facility (SNF) or Intermediate Care Facility (ICF), (6) Transfer from another healthcare facility, (7) Emergency room, (8) Court/law enforcement, (B) Transfer from another Home Health Agency, (C) Readmission to Same Home Health Agency, (D) Transfer from one distinct unit of the hospital to another distinct unit of the same hospital resulting in a separate claim to the payer, (E) Transfer from ambulatory surgery center, (F) Transfer from hospice and is under a hospice plan of care or enrolled in a hospice program CA, MA, MD, ME
Transferred into hospital TRAN_IN 2008 - 2011 Transfer In Indicator: (0) not a transfer, (1) transferred in from a different acute care hospital [ATYPE NE 4 & (ASOURCE=2 or POO=4)], (2) transferred in from another type of health facility [ATYPE NE 4 & (ASOURCE=3 or POO=5,6)]  
Indicator of emergency department service HCUP_ED 2007-2011 Indicator that discharge record includes evidence of emergency department (ED) services: (0) Record does not meet any HCUP Emergency Department criteria, (1) Emergency Department revenue code on record, (2) Positive Emergency Department charge (when revenue center codes are not available), (3) Emergency Department CPT procedure code on record, (4) Admission source of ED, (5) State-defined ED record; no ED charges available  
Admission type ATYPE 1988 - 2011 Admission type, uniform coding: (1) emergency, (2) urgent, (3) elective, (4) newborn, (5) Delivery (coded in 1988-1997 data only), (5) trauma center beginning in 2003 data, (6) other CA
ELECTIVE 2002 - 2011 Indicates elective admission: (1) elective, (0) non-elective admission  
Patient demographic location information
Age at admission AGE 1988 - 2011 Age in years coded 0-124 years  
AGEDAY 1988 - 2011 Age in days coded 0-365 only when the age in years is less than 1 FL, HI, MA, ME, SC, TX
Sex of patient FEMALE 1998 - 2011 Indicates gender for NIS beginning in 1998: (0) male, (1) female  
SEX 1988 - 1997 Indicates gender for NIS prior to 1998: (1) male, (2) female  
Race of patient RACE 1988 - 2011 Race, uniform coding: (1) white, (2) black, (3) Hispanic, (4) Asian or Pacific Islander, (5) Native American, (6) other MN, ND, OH, WV
Location of patient's residence PL_NCHS2006 2007 - 2011 Patient Location: NCHS Urban-Rural Code (V2006). This is a six-category urban-rural classification scheme for U.S. counties: (1) "Central" counties of metro areas of >=1 million population,(2) "Fringe" counties of metro areas of >=1 million population,(3) Counties in metro areas of 250,000-999,999 population,(4) Counties in metro areas of 50,000-249,999 population,(5) Micropolitan counties,(6) Not metropolitan or micropolitan counties MA
PL_UR_CAT4 2003-2006 Urban–rural designation for patient's county of residence: (1) large metropolitan, (2) small metropolitan, (3) micropolitan, (4) non-metropolitan or micropolitan  
Median household income for patient's ZIP Code ZIPINC_QRTL 2003 - 2011 Median household income quartiles for patient's ZIP Code. For 2008, the median income quartiles are defined as: (1) $1 - $38,999; (2) $39,000 - $47,999; (3) $48,000 - 62,999; and (4) $63,000 or more.  
ZIPINC 1998 - 2002 Median household income category in files beginning in 1998: (1) $1-$24,999, (2) $25,000-$34,999, (3) $35,000-$44,999, (4) $45,000 and above  
ZIPINC4 1988-1997 Median household income category in files prior to 1998: (1) $1-$25,000, (2) $25,001-$30,000, (3) $30,001-$35,000, (4) $35,001 and above  
ZIPINC8 1988 - 1997 Median household income category in files prior to 1998: (1) $1-$15,000, (2) $15,001-$20,000, (3) $20,001-$25,000, (4) $25,001-$30,000, (5) $30,001-$35,000, (6) $35,001-$40,000, (7) $40,001-$45,000, (8) $45,001 or more  
Payer information
Primary expected payer PAY1 1988 - 2011 Expected primary payer, uniform: (1) Medicare, (2) Medicaid, (3) private including HMO, (4) self-pay, (5) no charge, (6) other  
PAY1_N 1988-1997 Expected primary payer, nonuniform: (1) Medicare, (2) Medicaid, (3) Blue Cross, Blue Cross PPO, (4) commercial, PPO, (5) HMO, PHP, etc., (6) self-pay, (7) no charge, (8) Title V, (9) Worker's Compensation, (10) CHAMPUS, CHAMPVA, (11) other government, (12) other  
PAY1_X 1998 - 2011 Expected primary payer, as received from the data source ME
Secondary expected payer PAY2 1988 - 2011 Expected secondary payer, uniform: (1) Medicare, (2) Medicaid, (3) private including HMO, (4) self-pay, (5) no charge, (6) other AZ, CA, FL, HI, IA, OH, OK, RI, SD, VA
PAY2_N 1988 - 1997 Expected secondary payer, nonuniform: (1) Medicare, (2) Medicaid, (3) Blue Cross, Blue Cross PPO, (4) commercial, PPO, (5) HMO, PHP, etc., (6) self-pay, (7) no charge, (8) Title V, (9) Worker's Compensation, (10) CHAMPUS, CHAMPVA, (11) other government, (12) other  
PAY2_X 1998 - 2011 Expected secondary payer, as received from the data source AZ, CA, FL, HI, IA, ME, OH, OK, RI, SD, VA
Diagnosis and procedure information
ICD-9-CM diagnoses DX1 - DX25 1988-2010 Diagnoses, principal and secondary (ICD-9-CM). Beginning in 2003, the diagnosis array does not include any external cause of injury codes. These codes have been stored in a separate array ECODEn. Beginning in 2009, the diagnosis array was increased from 15 to 25.  
NDX 1988 - 2011 Number of diagnoses coded on the original record  
DSNDX 1988 - 1997 Number of diagnosis fields provided by the data source  
DXSYS 1988 - 1997 Diagnosis coding system (ICD-9-CM)  
DXV1 - DXV15 1988 - 1997 Diagnosis validity flags  
External causes of injury and poisoning ECODE1 - ECODE4 2003 - 2011 External cause of injury and poisoning code, primary and secondary (ICD-9-CM). Beginning in 2003, external cause of injury codes are stored in a separate array ECODEn from the diagnosis codes in the array DXn. Prior to 2003, these codes are contained in the diagnosis array (DXn).  
NECODE 2003 - 2011 Number of external cause of injury codes on the original record. A maximum of 4 codes are retained on the NIS.  
ICD-9-CM procedures PR1 - PR15 1988 - 2011 Procedures, principal and secondary (ICD-9-CM)  
NPR 1988 - 2011 Number of procedures coded on the original record  
DSNPR 1988 - 1997 Number of procedure fields in this data source  
PRSYS 1988 - 1997 Procedure system (1) ICD-9-CM, (2) CPT-4, (3) HCPCS/CPT-4  
PRV1 - PRV15 1988 - 1997 Procedure validity flag: (0) Indicates a valid and consistent procedure code, (1) Indicates an invalid code for the discharge date  
PRDAY1 1988 - 2011 Number of days from admission to principal procedure. OH, OK, UT, WV
PRDAY2 - PRDAY15 1988 - 2011 Number of days from ad mission to secondary procedures HI, OH, OK, UT, WI, WV
DRG information
Diagnosis Related Group (DRG) DRG 1988 - 2011 DRG in use on discharge date  
DRG_NoPOA 2008 - 2011 DRG in use on discharge date, calculated without Present On Admission (POA) indicators  
DRGVER 1988 - 2011 Grouper version in use on discharge date  
DRG10 1988 - 1999 DRG Version 10 (effective October 1992 - September 1993)  
DRG18 1998 - 2005 DRG Version 18 (effective October 2000 - September 2001)  
DRG24 2006 - 2011 DRG Version 24 (effective October 2006 - September 2007)  
Major Diagnosis Category (MDC) MDC 1988 - 2011 MDC in use on discharge date  
MDC_noPOA 2009 - 2011 MDC in use on discharge date, calculated without Present on Admission (POA) indicators  
MDC10 1988 - 1999 MDC Version 10 (effective October 1992 - September 1993)  
MDC18 1998 - 2005 MDC Version 18 (effective October 2000 - September 2001)  
MDC24 2006 - 2010 MDC Version 24 (effective October 2006 - September 2007)  
Other data elements derived from ICD-9-CM codes see also:
Table 3, Data Elements in the NIS Disease Severity Measures File and Table 4, Data Elements in the NIS Diagnosis and Procedures Groups File
Clinical Classifications Software (CCS) category DXCCS1 - DXCCS25 1998 - 2011 Clinical Classifications Software (CCS) category for all diagnoses for NIS beginning in 1998. Beginning in 2009, the diagnosis array was increased from 15 to 25.  
DCCHPR1 1988 - 1997 CCS category for principal diagnosis for NIS prior to 1998. CCS was formerly called the Clinical Classifications for Health Policy Research (CCHPR).  
E_CCS1 - E_CCS4 2003 - 2011 CCS category for the external cause of injury and poisoning codes  
PRCCS1 - PRCCS15 1998 - 2011 CCS category for all procedures for NIS beginning in 1998  
PCCHPR1 1988 - 1997 CCS category for principal procedure for NIS prior to 1998. CCS was formerly called the Clinical Classifications for Health Policy Research (CCHPR)  
Number of chronic conditions NCHRONIC 2008 - 2011 Count of chronic conditions in the diagnosis vector  
Operating room procedure indicator ORPROC 2009 - 2011 Major operating room procedure indicator for the record: (0) no major operating room procedure, (1) major operating room procedure  
Neonatal/ maternal flag NEOMAT 1988 - 2011 Assigned from diagnoses and procedure codes: (0) not maternal or neonatal, (1) maternal diagnosis or procedure, (2) neonatal diagnosis, (3) maternal and neonatal on same record  
Indicates in-hospital birth HOSPBRTH 2006 - 2011 Indicator that discharge record includes diagnosis of birth that occurred in the hospital: (0) Not an in-hospital birth, (1) In-hospital birth  
Resource use information
Total charges TOTCHG 1988-2011 Total charges, edited  
TOTCHG_X 1988-2011 Total charges, as received from data source ME
Length of stay LOS 1988-2011 Length of stay, edited  
LOS_X 1988-2011 Length of stay, as received from data source ME
Discharge information
Discharge quarter DQTR 1988-2011 Coded: (1) First quarter, Jan - Mar, (2) Second quarter, Apr - Jun, (3) Third quarter, Jul - Sep, (4) Fourth quarter, Oct - Dec  
DQTR_X 2006-2011 Discharge quarter, as received from data source  
Discharge year YEAR 1988-2011    
Disposition of patient (discharge status) DISP 1988-1997 Disposition of patient, uniform coding used prior to 1998: (1) routine, (2) short-term hospital, (3) skilled nursing facility, (4) intermediate care facility, (5) another type of facility, (6) home healthcare, (7) against medical advice, (20) died  
DIED 1988-2011 Indicates in-hospital death: (0) did not die during hospitalization, (1) died during hospitalization  
DISPUB92 1998-2006 Disposition of patient, UB-92 coding: (1) routine, (2) short-term hospital, (3) skilled nursing facility, (4) intermediate care, (5) another type of facility, (6) home healthcare, (7) against medical advice, (8) home IV provider, (20) died in hospital, (40) died at home, (41) died in a medical facility, (42) died, place unknown, (43) alive, Federal health facility, (50) Hospice, home, (51) Hospice, medical facility, (61) hospital-based Medicare approved swing bed, (62) another rehabilitation facility, (63) long-term care hospital, (64) certified nursing facility, (65) psychiatric hospital, (66) critical access hospital, (71) another institution for outpatient services, (72) this institution for outpatient services, (99) discharged alive, destination unknown  
DISPUB04 2006-2011 Disposition of patient, UB04 standard coding: (1 )Discharged to Home or Self Care (Routine Discharge), (2) Discharged/transferred to a Short-Term Hospital for Inpatient Care, (3) Discharged/transferred to a Skilled Nursing Facility (SNF), (4) Discharged/transferred to an Intermediate Care Facility (ICF), (5) Discharged/transferred to a Designated Cancer Center or Children's Hospital (Effective 10/1/07), (5) Discharged/transferred to another type of institution not defined elsewhere (Effective prior to 10/1/07), (6) Discharged/transferred to Home under care of Organized Home Health Service Organization, (7) Left Against Medical Advice or Discontinued Care, (8) home IV provider, (9) Admitted as an inpatient to this hospital - valid only on outpatient data, (20) Expired, (40) Expired at home, (41) Expired in a Medical Facility, (42) Expired - place unknown, (43) Discharged/transferred to a Federal Health Care Facility, (50) Hospice - Home, (51) Hospice - Medical Facility, (61) Discharged/transferred to a Hospital-Based Medicare approved Swing Bed, (62) Discharged/transferred to an Inpatient Rehabilitation Facility (IRF) including Rehabilitation Distinct part unit of a hospital, (63) Discharged/transferred to a Medicare certified Long Term Care Hospital (LTCH), (64) Discharged/transferred to a Nursing Facility certified by Medicaid, but not certified by Medicare, (65) Discharged/transferred to a Psychiatric Hospital or Psychiatric distinct part unit of a hospital, (66) Discharged/transferred to a Critical Access Hospital (CAH), (70) Discharged/transferred to another type of institution not defined elsewhere (Effective 10/1/07), (71) Another institution for outpatient services, (72) This institution for outpatient services, (99) Discharged alive, destination unknown CA, MD, ME
DISPUNIFORM 1998-2011 Disposition of patient, uniform coding used beginning in 1998: (1) routine, (2) transfer to short-term hospital, (5) other transfers, including skilled nursing facility, intermediate care, and another type of facility, (6) home healthcare, (7) against medical advice, (20) died in hospital, (99) discharged alive, destination unknown  
TRAN_OUT 2010 - 2011 Transfer Out Indicator: (0) not a transfer, (1) transferred out to a different acute care hospital, (2) transferred out to another type of health facility  
Weights (to calculate national estimates)
Discharge weights
(Weights for 1988-1993 are on Hospital Weights file)
DISCWT 1998-2011 Discharge weight on Core file and Hospital Weights file for NIS beginning in 1998. In all data years except 2000, this weight is used to create national estimates for all analyses. In 2000 only, this weight is used to create national estimates for all analyses, excluding those that involve total charges.  
DISCWT_U 1993-1997 Discharge weight on Core file and Hospital Weights file for NIS prior to 1998  
DISCWTcharge 2000 Discharge weight for national estimates of total charges. In 2000 only, this weight is used to create national estimates for analyses that involve total charges.  
DISCWT10 1998-2004 Discharge weight on 10% subsample Core file for NIS from 1998 to 2004. In all data years except 2000, this weight is used to create national estimates for all analyses. In 2000 only, this weight is used to create national estimates for all analyses, excluding those that involve total charges.  
D10CWT_U 1993-1997 Discharge weight on 10% subsample Core file for NIS prior to 1998  
DISCWTcharge10 2000 Discharge weight for national estimates of total charges on 10% subsample file. In 2000 only, this weight is used to create national estimates for analyses that involve total charges.  
Hospital information
Hospital identifiers (encrypted) DSHOSPID 1988-2011 Hospital number as received from the data source AK, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPID 1988-2011 HCUP hospital number (links to Hospital Weights file)  
Hospital location HOSPST 1988-2011 State postal code for the hospital (e.g., AZ for Arizona)  
HOSPSTCO 1988-2002 Modified Federal Information Processing Standards (FIPS) State/county code for the hospital links to Area Resource File (available from the Bureau of Health Professions, Health Resources and Services Administration). Beginning in 2003, this data element is available only on the Hospital Weights file.  
Hospital stratifier NIS_STRATUM 1998-2011 Stratum used to sample hospitals, based on geographic region, control, location/teaching status, and bed size. Stratum information is also contained in the Hospital Weights file.  
Other identifiers
Physician identifiers,
synthetic
MDID_S 1988-2000 Synthetic attending physician number in files prior to 2001  
MDNUM1_R 2003-2009 Re-identified attending physician number in files starting in 2003  
MDNUM1_S 2001-2002 Synthetic attending physician number in files beginning in 2001 and discontinued in 2003  
SURGID_S 1988-2000 Synthetic primary surgeon number in files prior to 2001  
MDNUM2_R 2003-2009 Re-identified secondary physician number in files starting in 2003  
MDNUM2_S 2001-2002 Synthetic secondary physician number in files beginning in 2001 and discontinued in 2003  
Data source information DSNUM 1988-1997 Data source number  
DSTYPE 1988-1997 Data source type: (1) State data organization, (2) Hospital association, (3) Consortia  
Record identifier, synthetic KEY 1998-2011 Unique record number for file beginning in 1998  
SEQ 1988-1997 Unique record number for NIS prior to 1998  
SEQ_SID 1994-1997 Unique record number for NIS and SID prior to 1998  
PROCESS 1988-1997 Processing number for NIS prior to 1998  


Return to Introduction

Table 2. Data Elements in the NIS Hospital Weights Files

Data elements that are italicized are not included in the 2011 NIS Hospital Weights File, but are only available in previous years' files.

Type of Data Element HCUP Name Years Available Coding Notes Unavailable in 2011 for:
Discharge counts N_DISC_U 1988-2011 Number of AHA universe discharges in the stratum  
S_DISC_U 1988-2011 Number of sampled discharges in the sampling stratum (NIS_STRATUM or STRATUM)  
S_DISC_S 1988-1997 Number of sampled discharges in the stratum STRAT_ST  
N_DISC_F 1988-1997 Number of frame discharges in the stratum  
N_DISC_S 1988-1997 Number of State’s discharges in the stratum  
TOTAL_DISC 1998-2011 Total number of discharges from this hospital in the NIS  
TOTDSCHG 1988-1997 Total number of discharges from this hospital in the NIS  
Discharge weights DISCWT 1998-2011 Discharge weight used in the NIS beginning in 1998. In all data years except 2000, this weight is used to create national estimates for all analyses. In 2000 only, this weight is used to create national estimates for all analyses, excluding those that involve total charges.  
DISCWT_U 1988-1997 Discharge weights used in the NIS prior to 1998.  
DISCWT_F 1988-1997 Discharge weights to the sample frame are available only in 1988-1997  
DISCWT_S 1988-1997 Discharge weights to the State are available only in 1988-1997  
DISCWTcharge 2000 Discharge weight for national estimates of total charges for 2000 only.  
Discharge Year YEAR 1988-2011 Discharge year  
Hospital counts N_HOSP_F 1988-1997 Number of frame hospitals in the stratum  
N_HOSP_S 1988-1997 Number of State’s hospitals in the stratum  
N_HOSP_U 1988-2011 Number of AHA universe hospitals in the stratum  
S_HOSP_S 1988-1997 Number of sampled hospitals in STRAT_ST  
S_HOSP_U 1988-2011 Number of sampled hospitals in the stratum (NIS_STRATUM or STRATUM)  
Hospital identifiers HOSPID 1988-2011 HCUP hospital number (links to Inpatient Core files)  
AHAID 1988-2011 AHA hospital identifier that matches AHA Annual Survey Database (not available for all States) AK, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
IDNUMBER 1988-2011 AHA hospital identifier without the leading 6 (not available for all States) AK, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPNAME 1993-2011 Hospital name from AHA Annual Survey Database (not available for all States) AK, AR, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
Hospital location HOSPADDR 1993-2011 Hospital address from AHA Annual Survey Database (not available for all States) AK, AR, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPCITY 1993-2011 Hospital city from AHA Annual Survey Database (not available for all States) AK, AR, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPST 1988-2011 Hospital State postal code for hospital (e.g., AZ for Arizona)  
HOSPSTCO 2002-2011 Modified Federal Information Processing Standards (FIPS) State/county code AK, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HFIPSSTCO 2005-2011 Unmodified Federal Information Processing Standards (FIPS) State/county code for the hospital. Links to the Area Resource File (available from the Bureau of Health Professions, Health Resources and Services Administration) AK, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPZIP 1993-2011 Hospital ZIP Code from AHA Annual Survey Database (not available for all States) AK, AR, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
Hospital characteristics HOSP_BEDSIZE 1998-2011 Bed size of hospital (STRATA): (1) small, (2) medium, (3) large  
H_BEDSZ 1993-1997 Bed size of hospital: (1) small, (2) medium, (3) large  
ST_BEDSZ 1988-1992 Bed size of hospital: (1) small, (2) medium, (3) large  
HOSP_CONTROL 1998-2011 Control/ownership of hospital collapsed (STRATA): (0) government or private, collapsed category, (1) government, nonfederal, public, (2) private, non-profit, voluntary, (3) private, invest-own, (4) private, collapsed category  
H_CONTRL 1993-1997, 2008-2011 Control/ownership of hospital: (1) government, nonfederal (2) private, non-profit (3) private, investor-owned  
ST_OWNER 1988-1992 Control/ownership of hospital: (1) public (2) private, non-profit (3) private for profit  
HOSP_LOCATION 1998-2011 Location: (0) rural, (1) urban  
H_LOC 1993-1997 Location: (0) rural, (1) urban  
HOSP_LOCTEACH 1998-2011 Location/teaching status of hospital (STRATA): (1) rural, (2) urban non-teaching, (3) urban teaching  
HOSP_MHSMEMBER 2007-2011 Multi-hospital system membership: (0) non-member, (1) member CO, CT, SC
HOSP_MHSCLUSTER 2007-2011 Multi-hospital system cluster code: (1) centralized health system, (2) centralized physician/insurance health system, (3) moderately centralized health system, (4) decentralized health system, (5) independent hospital system, (6) unassigned AK, CO, CT, SC, VT
HOSP_RNPCT 2007-2011 Percentage of RNs among all nurses (RNs and LPNs) CO, CT, GA, SC
HOSP_RNFTEAPD 2007-2011 RN FTEs per 1000 adjusted inpatient days CO, CT, GA, SC
HOSP_LPNFTEAPD 2007-2011 LPN FTEs per 1000 adjusted inpatient days CO, CT, GA, SC
HOSP_NAFTEAPD 2007-2011 Nurse aides per 1000 adjusted inpatient days CO, CT, GA, SC
HOSP_OPSURGPCT 2007-2011 Percentage of all surgeries performed in outpatient setting CO, CT
H_LOCTCH 1993-1997 Location/teaching status of hospital: (1) rural, (2) urban non-teaching, (3) urban teaching  
LOCTEACH 1988-1992 Location/teaching status of hospital: (1) rural, (2) urban non-teaching, (3) urban teaching  
HOSP_REGION 1998-2011 Region of hospital (STRATA): (1) Northeast, (2) Midwest, (3) South, (4) West  
H_REGION 1993-1997 Region of hospital: (1) Northeast, (2) Midwest, (3) South, (4) West  
ST_REG 1988-1992 Region of hospital: (1) Northeast, (2) Midwest, (3) South, (4) West  
HOSP_TEACH 1998-2011 Teaching status of hospital: (0) non-teaching, (1) teaching  
H_TCH 1993-1997 Teaching status of hospital: (0) non-teaching, (1) teaching  
NIS_STRATUM 1998-2011 Stratum used to sample hospitals beginning in 1998; includes geographic region, control, location/teaching status, and bed size  
STRATUM 1988-1997 Stratum used to sample hospitals prior to 1998; includes geographic region, control, location/teaching status, and bed size  
STRAT_ST 1988-1997 Stratum for State-specific weights  
Hospital weights HOSPWT 1998-2011 Weight to hospitals in AHA universe (i.e., total U.S.) beginning in 1998  
HOSPWT_U 1988-1997 Weight to hospitals in AHA universe (i.e., total U.S.) prior to 1998  
HOSPWT_F 1988-1997 Weight to hospitals in the sample frame  
HOSPWT_S 1988-1997 Weight to hospitals in the State  


Return to Introduction

Table 3. Data Elements in the NIS Disease Severity Measures Files

Data elements that are italicized are not included in the 2011 NIS Inpatient Core files, but are only available in previous years' files. All other data elements listed below are available for all States in the 2011 NIS Disease Severity Measures files.

Type of Data Element HCUP Name Years Available Coding Notes
AHRQ Comorbidity Software (AHRQ) CM_AIDS 2002-2011 AHRQ comorbidity measure: Acquired immune deficiency syndrome: (0) Comorbidity is not present, (1) Comorbidity is present
CM_ALCOHOL 2002-2011 AHRQ comorbidity measure: Alcohol abuse: (0) Comorbidity is not present, (1) Comorbidity is present
CM_ANEMDEF 2002-2011 AHRQ comorbidity measure: Deficiency anemias: (0) Comorbidity is not present, (1) Comorbidity is present
CM_ARTH 2002-2011 AHRQ comorbidity measure: Rheumatoid arthritis/collagen vascular diseases: (0) Comorbidity is not present, (1) Comorbidity is present
CM_BLDLOSS 2002-2011 AHRQ comorbidity measure: Chronic blood loss anemia: (0) Comorbidity is not present, (1) Comorbidity is present
CM_CHF 2002-2011 AHRQ comorbidity measure: Congestive heart failure: (0) Comorbidity is not present, (1) Comorbidity is present
CM_CHRNLUNG 2002-2011 AHRQ comorbidity measure: Chronic pulmonary disease: (0) Comorbidity is not present, (1) Comorbidity is present
CM_COAG 2002-2011 AHRQ comorbidity measure: Coagulopathy: (0) Comorbidity is not present, (1) Comorbidity is present
CM_DEPRESS 2002-2011 AHRQ comorbidity measure: Depression: (0) Comorbidity is not present, (1) Comorbidity is present
CM_DM 2002-2011 AHRQ comorbidity measure: Diabetes, uncomplicated: (0) Comorbidity is not present, (1) Comorbidity is present
CM_DMCX 2002-2011 AHRQ comorbidity measure: Diabetes with chronic complications: (0) Comorbidity is not present, (1) Comorbidity is present
CM_DRUG 2002-2011 AHRQ comorbidity measure: Drug abuse: (0) Comorbidity is not present, (1) Comorbidity is present
CM_HTN_C 2002-2011 AHRQ comorbidity measure: Hypertension, (combine uncomplicated and complicated): (0) Comorbidity is not present, (1) Comorbidity is present
CM_HYPOTHY 2002-2011 AHRQ comorbidity measure: Hypothyroidism: (0) Comorbidity is not present, (1) Comorbidity is present
CM_LIVER 2002-2011 AHRQ comorbidity measure: Liver disease: (0) Comorbidity is not present, (1) Comorbidity is present
CM_LYMPH 2002-2011 AHRQ comorbidity measure: Lymphoma: (0) Comorbidity is not present, (1) Comorbidity is present
CM_LYTES 2002-2011 AHRQ comorbidity measure: Fluid and electrolyte disorders: (0) Comorbidity is not present, (1) Comorbidity is present
CM_METS 2002-2011 AHRQ comorbidity measure: Metastatic cancer: (0) Comorbidity is not present, (1) Comorbidity is present
CM_NEURO 2002-2011 AHRQ comorbidity measure: Other neurological disorders: (0) Comorbidity is not present, (1) Comorbidity is present
CM_OBESE 2002-2011 AHRQ comorbidity measure: Obesity: (0) Comorbidity is not present, (1) Comorbidity is present
CM_PARA 2002-2011 AHRQ comorbidity measure: Paralysis: (0) Comorbidity is not present, (1) Comorbidity is present
CM_PERIVASC 2002-2011 AHRQ comorbidity measure: Peripheral vascular disorders: (0) Comorbidity is not present, (1) Comorbidity is present
CM_PSYCH 2002-2011 AHRQ comorbidity measure: Psychoses: (0) Comorbidity is not present, (1) Comorbidity is present
CM_PULMCIRC 2002-2011 AHRQ comorbidity measure: Pulmonary circulation disorders: (0) Comorbidity is not present, (1) Comorbidity is present
CM_RENLFAIL 2002-2011 AHRQ comorbidity measure: Renal failure: (0) Comorbidity is not present, (1) Comorbidity is present
CM_TUMOR 2002-2011 AHRQ comorbidity measure: Solid tumor without metastasis: (0) Comorbidity is not present, (1) Comorbidity is present
CM_ULCER 2002-2011 AHRQ comorbidity measure: Peptic ulcer disease excluding bleeding: (0) Comorbidity is not present, (1) Comorbidity is present
CM_VALVE 2002-2011 AHRQ comorbidity measure: Valvular disease: (0) Comorbidity is not present, (1) Comorbidity is present
CM_WGHTLOSS 2002-2011 AHRQ comorbidity measure: Weight loss: (0) Comorbidity is not present, (1) Comorbidity is present
All Patient Refined DRG (3M) APRDRG 2002-2011 All Patient Refined DRG
APRDRG_Risk_Mortality 2002-2011 All Patient Refined DRG: Risk of Mortality Subclass: (0) No class specified, (1) Minor likelihood of dying, (2) Moderate likelihood of dying, (3) Major likelihood of dying, (4) Extreme likelihood of dying
APRDRG_Severity 2002-2011 All Patient Refined DRG: Severity of Illness Subclass: (0) No class specified, (1) Minor loss of function (includes cases with no comorbidity or complications), (2) Moderate loss of function, (3) Major loss of function, (4) Extreme loss of function
All-Payer Severity-adjusted DRG (Optum Insight) APSDRG 2002-2009 All-Payer Severity-adjusted DRG
APSDRG_Mortality_Weight 2002-2009 All-Payer Severity-adjusted DRG: Mortality Weight
APSDRG_LOS_Weight 2002-2009 All-Payer Severity-adjusted DRG: Length of Stay Weight
APSDRG_Charge_Weight 2002-2009 All-Payer Severity-adjusted DRG: Charge Weight
Disease Staging (Thomson Reuters) DS_DX_Category1 2002-2010 Disease Staging: Principal Disease Category
DS_Stage1 2002-2010 Disease Staging: Stage of Principal Disease Category
DS_LOS_Level 2002-2007 Disease Staging: Length of Stay Level: (1) Very low (less than 5% of patients), (2) Low (5 - 25% of patients), (3) Medium (25 - 75% of patients), (4) High (75 - 95% of patients), (5) Very high (greater than 95% of patients)
DS_LOS_Scale 2002-2007 Disease Staging: Length of Stay Scale
DS_Mrt_Level 2002-2007 Disease Staging: Mortality Level: (0) Extremely low - excluded from percentile calculation (mortality probability less than .0001), (1) Very low (less than 5% of patients), (2) Low (5 - 25% of patients), (3) Medium (25 - 75% of patients), (4) High (75 - 95% of patients), (5) Very high (greater than 95% of patients)
DS_Mrt_Scale 2002-2007 Disease Staging: Mortality Scale
DS_RD_Level 2002-2007 Disease Staging: Resource Demand Level: (1) Very low (less than 5% of patients), (2) Low (5 - 25% of patients), (3) Medium (25 - 75% of patients), (4) High (75 - 95% of patients), (5) Very high (greater than 95% of patients)
DS_RD_Scale 2002-2007 Disease Staging: Resource Demand Scale
Linkage Data Elements HOSPID 2002-2011 HCUP hospital identification number
KEY 2002-2011 HCUP record identifier


Return to Introduction

Table 4. Data Elements in the NIS Diagnosis and Procedure Groups Files

Data elements that are italicized are not included in the 2011 NIS Inpatient Core files, but are only available in previous years' files. All other data elements listed below are available for all States in the 2011 NIS Diagnosis and Procedure Groups files.
Type of Data Element HCUP Name Years Available Coding Notes
Clinical Classifications Software category for Mental Health and Substance Abuse (CCS-MHSA) CCSMGN1 – CCSMGN15 2005-2006 CCS-MHSA general category for all diagnoses
CCSMSP1 – CCSMSP15 2005-2006 CCS-MHSA specific category for all diagnoses
ECCSMGN1 – ECCSMGN4 2005-2006 CCS-MHSA general category for all external cause of injury codes
Chronic Condition Indicator CHRON1 – CHRON25 2005-2011 Chronic condition indicator for all diagnoses: (0) non-chronic condition, (1) chronic condition. Beginning in 2010, the diagnosis array was increased from 15 to 25.
CHRONB1 – CHRONB25 2005-2011 Chronic condition indicator body system for all diagnoses: (1) Infectious and parasitic disease, (2) Neoplasms, (3) Endocrine, nutritional, and metabolic diseases and immunity disorders, (4) Diseases of blood and blood-forming organs, (5) Mental disorders, (6) Diseases of the nervous system and sense organs, (7) Diseases of the circulatory system, (8) Diseases of the respiratory system, (9) Diseases of the digestive system, (10) Diseases of the genitourinary system, (11) Complications of pregnancy, childbirth, and the puerperium, (12) Diseases of the skin and subcutaneous tissue, (13) Diseases of the musculoskeletal system, (14) Congenital anomalies, (15) Certain conditions originating in the perinatal period, (16) Symptoms, signs, and ill-defined conditions, (17) Injury and poisoning, (18) Factors influencing health status and contact with health services. Beginning in 2010, the diagnosis array was increased from 15 to 25.
Multi-Level Clinical Classifications Software (CCS) Category DXMCCS1 2009-2011 Multi-level clinical classification software (CCS) for principal diagnosis. Four levels for diagnoses presenting both the general groupings and very specific conditions
E_MCCS1 2009-2011 Multi-level clinical classification software (CCS) for first listed E Code. Four levels for E codes presenting both the general groupings and very specific conditions
PRMCCS1 2009-2011 Multi-level clinical classification software (CCS) for principal procedure. Three levels for procedures presenting both the general groupings and very specific conditions
Procedure Class PCLASS1 – PCLASS15 2005-2011 Procedure Class for all procedures: (1) Minor Diagnostic, (2) Minor Therapeutic, (3) Major Diagnostic, (4) Major Therapeutic
Linkage Data Elements HOSPID 2002-2011 HCUP hospital identification number
KEY 2002-2011 HCUP record identifier

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ENDNOTES

1Refer to Chapter 10 in Foreman, EK, Survey Sampling Principles. New York: Dekker, 1991.

2Carlson BL, Johnson AE, Cohen SB. "An Evaluation of the Use of Personal Computers for Variance Estimation with Complex Survey Data." Journal of Official Statistics, vol. 9, no. 4, 1993: 795-814.

3We used the following American Hospital Association Annual Survey Database (Health Forum, LLC © 2012) data elements to assign the NIS Teaching Hospital Indicator:

AHA Data Element Name = Description [HCUP Data Element Name].
BDH = Number of short–term hospital beds [B001H].
BDTOT = Number of total facility beds [B001].
FTRES = Number of full time employees: interns & residents (medical & dental) [E125].
PTRES = Number of part-time employees: interns & residents (medical & dental) [E225].
MAPP8 = Council of Teaching Hospitals (COTH) indicator [A101].
MAPP3 = Residency training approval by the Accreditation Council for Graduate Medical Education (ACGME) [A102].

Prior to the 1998 NIS, we used the following SAS code to assign the NIS teaching hospital status indicator, H_TCH:
/* FIRST ESTABLISH SHORT-TERM BEDS DEFINITION */
IF BDH NE . THEN BEDTEMP = BDH ;      /* SHORT TERM BEDS */
ELSE IF BDH =. THEN BEDTEMP=BDTOT ;   /* TOTAL BEDS PROXY */

/*******************************************************/
/* NEXT ESTABLISH TEACHING STATUS BASED ON F-T & P-T */
/* RESIDENT/INTERN STATUS FOR HOSPITALS. */
/*******************************************************/
RESINT = (FTRES + .5*PTRES)/BEDTEMP ;
IF RESINT > 0 & (MAPP3=1 OR MAPP8=1) THEN H_TCH=1; /*1=TEACHING */
ELSE H_TCH=0 ;                                 /* 0=NONTEACHING */
Beginning with the 1998 NIS, we used the following SAS code to assign the teaching hospital status indicator, HOSP_TEACH:
/*******************************************************/
/* FIRST ESTABLISH SHORT-TERM BEDS DEFINITION */
/*******************************************************/
IF BDH NE . THEN BEDTEMP = BDH ;      /* SHORT TERM BEDS */
ELSE IF BDH =. THEN BEDTEMP = BDTOT ; /* TOTAL BEDS PROXY */
/*******************************************************/
/* ESTABLISH IRB NEEDED FOR TEACHING STATUS */
/* BASED ON F-T P-T RESIDENT INTERN STATUS */
/*******************************************************/
IRB = (FTRES + .5*PTRES) / BEDTEMP ;
/*******************************************************/
/* CREATE TEACHING STATUS DATA ELEMENT */
/*******************************************************/
IF (MAPP8 EQ 1) OR (MAPP3 EQ 1) THEN HOSP_TEACH = 1 ; 
ELSE IF (IRB GE 0.25) THEN HOSP_TEACH = 1 ;
ELSE HOSP_TEACH = 0 ;
   
4 Most AHA Annual Survey Database files do not cover a January-to-December period for every hospital. The numbers of hospitals for 1988-1991 are based on adjusted versions of the files which we created by apportioning the data from adjacent survey files across calendar years. The numbers of hospitals for later years are based on the unadjusted AHA Annual Survey Database files.

5 Table 1. Annual Estimates of the Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2012 (NST-EST2012-01). Source: U.S. Census Bureau, Population Division. Release Date: December 2012.

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Internet Citation: 2011 Introduction to the NIS. Healthcare Cost and Utilization Project (HCUP). July 2016. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2011.jsp.
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