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Introduction to the HCUP Nationwide Inpatient Sample (NIS), 2009
 

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

Sponsored by the Agency for Healthcare Research and Quality

 

 

INTRODUCTION TO

THE HCUP NATIONWIDE INPATIENT SAMPLE (NIS)

2009

 

 

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 May 2011

 

Agency for Healthcare Research and Quality
Healthcare Cost and Utilization Project (HCUP)
540 Gaither Road, 5th Floor
Rockville, Maryland 20850

Phone: (866) 290-HCUP (4287)
Fax: (301) 427-1430
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 Data Use Agreement (DUA) training session, sign a Data Use Agreement, and send a copy to AHRQ.

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

  • 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 identify or attempt to identify any individual. Will not report any statistics where the number of observations (i.e., individual discharge records) in any given cell of tabulated data is less than or equal to 10.

  • Will not link HCUP data to data from another source that identifies individuals.

  • Will not report information that could identify individual establishments (e.g., hospitals).

  • Will not use the data concerning individual establishments for commercial or competitive purposes involving those establishments.

  • Will not use the data to determine rights, benefits, or privileges of individual establishments.

  • Will not identify or attempt to identify any establishment when its identity has been concealed on the database.

  • Will not contact establishments included in the data.

  • Will not attribute to data contributors any conclusions drawn from the data.

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

  • Must acknowledge the "Healthcare Cost and Utilization Project (HCUP)", as described in the Data Use Agreement, in reports.

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 Inpatient Sample.


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HCUP CONTACT INFORMATION

The NIS Data Use Agreement Training Tool and the Data Use Agreement are available on the AHRQ-sponsored HCUP User Support (HCUP-US) Website:

After completing the on-line training tool, please submit signed Data Use Agreements to HCUP at:

For technical assistance:


WHAT’S NEW IN THE 2009
NATIONWIDE INPATIENT SAMPLE (NIS)?

  • The 2009 NIS contains two additional states: New Mexico and Montana.
  • The following data elements were added to the Core File beginning with the 2009 NIS:
    • Ten additional secondary diagnoses for a total of 25 diagnoses
    • Ten additional secondary Clinical Classifications Software (CCS) diagnosis categories
    • Major Diagnosic Category (MDC) in use on discharge date, calculated without Present on Admission (POA) indicators (MDC_NoPOA). MDC_NoPOA and the Diagnosis Related Group (DRG) calculated without POA indicators (DRG_NoPOA) are useful because the lack of POA flags from many states prevents the assignment of the standard MDC and DRG for a few DRGs involving Hospital Acquired Conditions (HAC).
  • The following data elements were added to the Diagnosis and Procedure Groups (DX_PR_GRPS) File beginning with the 2009 NIS:
    • Multi-level CCS categories for the principal diagnosis and the first listed E-code and procedure
    • Ten additional Chronic Condition Indicators
    • Ten additional Chronic Condition Body System Indicators
  • Georgia and Illinois provide race/ethnicity data for the first time for data year 2009. In addition, Iowa improved their race/ethnicity data in 2009 by capturing information about the Hispanic population.
  • The 2009 NIS is distributed on a single DVD-ROM instead of two CD-ROMs.

UNDERSTANDING THE NIS

This document, Introduction to the NIS, 2009, 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).


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

Sponsored by the Agency for Healthcare Research and Quality

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



HCUP Nationwide 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 health care utilization, access, charges, quality, and outcomes.

States, containing data from 5 to 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 2009, these states comprise 96 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 is the only national hospital database with charge information on 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 2009, 18 of the 44 states do not include the hospital name and 17 of these do not include the AHA hospital identifier. Thus 43% 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.

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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 5 million to 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, short-term rehabilitation, 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 WinZip®. Previously it was distributed on two CD ROMs, but beginning with the 2009 NIS, it is distributed on a single DVD. It includes the following 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 either Microsoft Windows Vista or later or a third-party zip utility such as WinZip or 7-Zip. [Attempts to unzip files larger than 4 GB using versions of Windows prior to Vista will produce an error message similar to the following: "The Compressed (zipped) Folder is invalid or corrupted." The solution is to use a third-party zip utility such as WinZip or 7-Zip rather than the built-in Windows Explorer function to open the archive. Evaluation versions of WinZip may be downloaded from the WinZip Website at www.winzip.com. 7-Zip can be downloaded for free at http://www.7-zip.org/.]

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 2009 NIS Core file is linked to "Core File" under "2009 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

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

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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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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 15% of discharges in the 2009 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 2009, about 43% 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 on the NIS CD-ROMs. 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.

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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 (http://www.census.gov/population/metro/).

    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:


  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 2009, 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 2009. In most cases, the difference between the universe and the frame represents the difference in the number of community, non-rehabilitation hospitals in the 2009 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:

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 96% 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 over 91% 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 2009 AHA universe, frame, and NIS by State. Fewer hospitals may be in a State’s frame than in the universe because data are 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 on the NIS CD-ROMs. 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. 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 decline in both the number of discharges in the sample and the weighted number of discharges for 2009 is 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 2009 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 2009 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: 2009 Data Sources
State Data Organization
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
MT MHA - An Association of Montana Health Care Providers
NC North Carolina Department of Health and Human Services
NE Nebraska Hospital Association
NH New Hampshire Department of Health & Human Services
NJ New Jersey Department of Health & Senior Services
NM New Mexico Health Policy Commission
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

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

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
On DVD-ROM,
In ASCII format
1 year of data on a DVD-ROM,
compressed files

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


Table 4. Summary of NIS Data Sources, Hospitals, and Inpatient Stays, 1988-2009
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 MT 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

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, 2009 — 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 1988-2009


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


Corrections to the NIS
  • 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 1988-2009
  • 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

text version

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

Return to Introduction



Figure 2: NIS States, by Region

text version

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.

 

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

text version

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

Return to Introduction



Figure 4: Number of Hospitals in the 2009 Universe, Frame, and Sample for Frame States - Part A: Arkansas – Indiana

text version

Figure 4: Bar chart of number of hospitals listed horizontally and states Arkansas through Indiana listed vertically



Figure 4: Number of Hospitals in the 2009 Universe, Frame, and Sample for Frame States - Part B: Kansas – North Carolina

text version

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



Figure 4: Number of Hospitals in the 2009 Universe, Frame, and Sample for Frame States - Part C: Nebraska – Rhode Island

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Figure 4: Bar chart of number of hospitals listed horizontally and states Nebraska through South Carolina listed vertically



Figure 4: Number of Hospitals in the 2009 Universe, Frame, and Sample for Frame States - Part D: South Carolina – Wyoming

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Figure 4: Bar chart of number of hospitals listed horizontally and states South Caroina through Wyoming listed vertically



Table 8: Number of Hospitals and Discharges in 2009 AHA Universe, Frame, and NIS, by State
State Number of Hospitals and Discharges in 2009 AHA Universe, Frame, and NIS, by State
AHA Frame NIS
  Hospitals Discharges Hospitals Discharges Hospitals Discharges Weighted Discharges
Non-NIS States 334 1,770,421 0 0 0 0 0
Arizona 76 807,765 74 778,259 17 140,894 685,556
Arkansas 88 418,197 86 397,989 20 82,524 428,744
California 350 3,951,394 347 3,858,856 82 901,279 4,325,433
Colorado 81 508,064 74 484,707 19 156,401 728,694
Connecticut 34 445,506 29 428,021 7 98,266 526,881
Florida 201 2,638,379 199 2,549,132 50 670,080 3,390,281
Georgia 151 1,086,555 148 1,058,371 43 269,146 1,344,582
Hawaii 24 119,206 20 94,532 3 6,712 35,134
Illinois 185 1,699,263 184 1,630,925 41 364,160 1,856,905
Indiana 127 825,609 115 786,089 27 188,850 954,454
Iowa 118 388,287 117 348,142 25 64,231 318,532
Kansas 142 364,391 125 331,520 27 64,830 319,477
Kentucky 103 636,294 101 623,309 23 146,772 747,019
Louisiana 168 737,376 109 580,777 29 107,695 546,237
Maine 36 160,419 32 78,601 6 12,472 65,883
Maryland 47 776,403 47 767,921 8 148,423 749,834
Massachusetts 73 879,945 64 844,948 14 133,087 704,454
Michigan 156 1,327,020 117 933,986 27 178,485 888,910
Minnesota 133 687,926 127 565,150 30 121,238 601,893
Missouri 130 902,346 121 881,195 29 198,586 1,007,699
Montana 52 111,473 42 102,050 10 23,754 128,417
Nebraska 89 235,584 82 164,802 19 45,187 224,389
Nevada 37 296,058 36 293,531 8 69,276 361,382
New Hampshire 26 133,136 26 127,211 5 41,564 187,847
New Jersey 71 1,175,760 70 1,110,425 14 226,033 1,203,081
New Mexico 39 211,386 35 163,082 7 20,469 93,764
New York 187 2,749,912 187 2,598,322 41 540,878 2,924,354
North Carolina 115 1,147,716 113 1,114,952 30 300,636 1,514,419
Ohio 191 1,663,083 160 1,611,394 40 425,216 2,116,207
Oklahoma 133 514,346 123 463,488 39 153,430 753,767
Oregon 59 391,238 58 374,261 15 103,204 506,581
Pennsylvania 181 1,923,806 178 1,851,202 37 359,292 1,883,556
Rhode Island 11 138,576 11 136,323 3 28,303 148,482
South Carolina 66 577,150 53 460,132 13 88,263 449,046
South Dakota 58 114,916 47 76,900 10 10,238 48,682
Tennessee 128 915,433 109 812,682 29 163,242 816,271
Texas 486 3,078,618 393 2,784,103 99 592,091 2,983,644
Utah 46 279,129 43 265,093 8 30,613 137,045
Vermont 14 56,378 14 53,535 2 3,516 18,900
Virginia 83 878,520 80 845,142 21 230,905 1,143,213
Washington 89 682,327 88 646,178 20 107,166 536,700
West Virginia 53 291,280 53 283,023 14 53,488 279,243
Wisconsin 131 679,421 130 626,284 31 127,838 652,114
Wyoming 26 58,944 23 32,884 6 12,029 97,252
Total 5,128 39,434,956 4,390 35,019,429 1,050 7,810,762 39,434,956

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 2009 Universe, Frame, Sample, Target, and Surplus1, by Region

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

1The surplus is the difference between the actual number of hospitals samples and the sample target.



Figure 9: Percentage of U.S.Population in 2009 NIS States, by Region
Calculated using the estimated U.S. population on July 1, 2009.5

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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 2009 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:
  • AR: Arkansas Department of Health & Human Services
  • GA: GHA: An Association of Hospitals & Health Systems
  • HI: Hawaii Health Information Corporation
  • IN: Indiana Hospital & Health 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 Health Policy Commission
  • 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: GHA: An Association of Hospitals & Health Systems
  • 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:
  • MI: Michigan Health & Hospital Association
    • Reporting of total charge is limited in the Michigan data. Twenty-nine of 146 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.

  • GA: GHA: An Association of Hospitals & Health Systems
  • HI: Hawaii Health Information Corporation
  • IN: Indiana Hospital & Health 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 Health Policy Commission
  • 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:
  • GA: GHA: An Association of Hospitals & Health Systems
  • HI: Hawaii Health Information Corporation
  • IN: Indiana Hospital & Health 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 Health Policy Commission
  • 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 & 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

  • 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 (.)


  • NH: New Hampshire Department of Health & Human Services
    • Age in days (AGEDAY) is set to missing on all records

  • 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)
Confidentiality of Physicians
The following data sources restrict the release of physician identifiers:
  • CT: Connecticut Hospital Association
  • MA: Division of Health Care Finance and Policy
  • NC: North Carolina Department of Health and Human Services
  • UT: Utah Department of Health
  • VT: Vermont Association of Hospitals and Health Systems
  • WV: West Virginia Health Care Authority
In these states the following data elements are set to missing for all records:
  • MDNUM1_R/MDNUM2_R (beginning in 2003)
  • MDNUM1_S/MDNUM2_S (2001 to 2002)
  • MDID_S/SURGID_S (prior to 2001)
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 2009 NIS Inpatient Core files; but are only available in previous years’ files.

Type of
Data Element
HCUP
Data Element Name
Years
Available
Coding Notes Unavailable in 2009 for:
Admission day of week or weekend AWEEKEND 1998-2009 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 1988-2009 Admission month coded from (1) January to (12) December FL
Admission source ASOURCE 1988-2009 Admission source, uniform coding: (1) ER, (2) another hospital, (3) another facility including long-term care, (4) court/law enforcement, (5) routine/birth/other AZ, CT, FL, GA, HI, IA, KS, KY, ME, MI, MN, MO, MT, NC, NE, NM, OK, OR, PA, RI, SC, SD, TN, TX, UT, VT, WA, WI, WY
ASOURCE_X 1998-2009 Admission source, as received from data source using State-specific coding AZ, CT, FL, GA, HI, IA, KS, KY, ME, MI, MN, MO, MT, NC, NE, NM, OK, OR, PA, RI, SC, SD, TN, TX, UT, VT, WA, WI, WY
ASOURCEUB92 2003-2009 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 another health care 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 AZ, CA, CT, FL, GA, HI, IA, KS, KY, MD, ME, MI, MN, MO, MT, NC, NE, NM, OK, OR, PA, RI, SC, SD, TN, TX, UT, VT, WA, WI, WY
POINTOFORIGIN_X 2009 Point of origin for admission or visit, as received from source CA, MA, MD, ME, NH
POINTOFORIGIN_UB04 2007-2009 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-health care 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 health care 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, NH
TRAN_IN 2008-2009 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)]  
Admission type ATYPE 1988-2009 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 2002-2009 Indicates elective admission: (1) elective, (0) non-elective admission  
Age at admission AGE 1988-2009 Age in years coded 0-124 years  
AGEDAY 1988-2009 Age in days coded 0-365 only when the age in years is less than 1 FL, MA, ME, NH, SC, TX
Chronic Conditions NCHRONIC 2008-2008 Number of chronic conditions  
Clinical Classifications Software (CCS) category DXCCS1 - DXCCS25 1998-2009 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).  
PRCCS1 - PRCCS15 1998-2009 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).  
Data source information DSNUM 1988-1997 Data source number  
DSTYPE 1988-1997 Data source type: (1) State data organization, (2) Hospital association, (3) Consortia  
Diagnosis information DX1 - DX25 1988-2009 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 1988-2009 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  
Diagnosis Related Group (DRG) DRG 1988-2009 DRG in use on discharge date  
DRG_NoPOA 2008-2009 DRG in use on discharge date, calculated without Present On Admission (POA) indicators  
DRGVER 1988-2009 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 2006-2009 DRG Version 24 (effective October 2006 - September 2007)  
Discharge quarter DQTR 1988-2009 Coded: (1) First quarter, Jan - Mar, (2) Second quarter, Apr - Jun, (3) Third quarter, Jul - Sep, (4) Fourth quarter, Oct - Dec  
DQTR_X 2006-2009 Discharge quarter, as received from data source  
Discharge weights
(Weights for 1988-1993 are on Hospital Weights file)
DISCWT 1998-2009 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.  
Discharge year YEAR 1988-2009    
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 health care, (7) against medical advice, (20) died  
DIED 1988-2009 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 health care, (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 2006-2009 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 1998-2009 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 health care, (7) against medical advice, (20) died in hospital, (99) discharged alive, destination unknown  
External causes of injury and poisoning ECODE1 - ECODE4 2003-2009 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).  
E_CCS1 - E_CCS4 2003-2009 CCS category for the external cause of injury and poisoning codes  
NECODE 2003-2009 Number of external cause of injury codes on the original record. A maximum of 4 codes are retained on the NIS.  
Gender of patient FEMALE 1998-2009 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  
Hospital information DSHOSPID 1988-2009 Hospital number as received from the data source GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPID 1988-2009 HCUP hospital number (links to Hospital Weights file)  
HOSPST 1988-2009 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.  
NIS_STRATUM 1998-2009 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.  
Indicates Emergency Department service HCUP_ED 2007-2009 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  
Indicates in-hospital birth HOSPBRTH 2006-2009 Indicator that discharge record includes diagnosis of birth that occurred in the hospital: (0) Not an in-hospital birth, (1) In-hospital birth  
Length of Stay LOS 1988-2009 Length of stay, edited  
LOS_X 1988-2009 Length of stay, as received from data source ME
Location of the patient 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  
PL_NCHS2006 2007-2009 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
Major Diagnosis Category (MDC) MDC 1988-2009 MDC in use on discharge date  
MDC_noPOA 2009 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 2006-2008 MDC Version 24 (effective October 2006 - September 2007)  
Median household income for patient’s ZIP Code ZIPINC_QRTL 2003-2009 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  
Neonatal/maternal flag NEOMAT 1988-2009 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  
Payer information PAY1 1988-2009 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 1998-2009 Expected primary payer, as received from the data source ME
PAY2 1988-2009 Expected secondary payer, uniform: (1) Medicare, (2) Medicaid, (3) private including HMO, (4) self-pay, (5) no charge, (6) other AZ, CA, CO, FL, HI, IA, NH, 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 1998-2009 Expected secondary payer, as received from the data source AZ, CA, CO, FL, HI, IA, ME, NH, OH, OK, RI, SD, VA
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 CA, CT, HI, IL, IN, LA, MA, NC, OH, OK, UT, VT, WI, WV
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 CA, CT, HI, IL, IN, LA, MA, NC, OH, OK, UT, VT, WI, WV
MDNUM2_S 2001-2002 Synthetic secondary physician number in files beginning in 2001 and discontinued in 2003  
Procedure information PR1 - PR15 1988-2009 Procedures, principal and secondary (ICD-9-CM)  
NPR 1988-2009 Number of procedures coded on the original record  
ORPROC 2009 Major operating room procedure indicator: (0) no major operating room procedure, (1) major operating room procedure  
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 1988-2009 Number of days from admission to principal procedure OH, OK, UT, WV
PRDAY2 - PRDAY15 1998-2009 Number of days from admission to secondary procedures CO, IN, OH, OK, UT, VA, WI, WV
Race of Patient RACE 1988-2009 Race, uniform coding: (1) white, (2) black, (3) Hispanic, (4) Asian or Pacific Islander, (5) Native American, (6) other MN, NC, OH, WV
Record identifier, synthetic KEY 1998-2009 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  
Total Charges TOTCHG 1988-2009 Total charges, edited  
TOTCHG_X 1988-2009 Total charges, as received from data source ME


Return to Introduction

Table 2. Data Elements in the NIS Hospital Weights Files

Data elements that are italicized are not included in the 2008 NIS Hospital Weights File, but are only available in previous years’ files.

Type of Data Element HCUP Data Element Name Years Available Coding Notes Unavailable in 2008 for:
Discharge counts N_DISC_U 1988-2009 Number of AHA universe discharges in the stratum  
S_DISC_U 1988-2009 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 1998-2009 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 1998-2009 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 1988-2009 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 1988-2009 Number of AHA universe hospitals in the stratum  
S_HOSP_S 1988-1997 Number of sampled hospitals in STRAT_ST  
S_HOSP_U 1988-2009 Number of sampled hospitals in the stratum (NIS_STRATUM or STRATUM)  
Hospital identifiers HOSPID 1988-2009 HCUP hospital number (links to Inpatient Core files)  
AHAID 1988-2009 AHA hospital identifier that matches AHA Annual Survey Database (not available for all States) GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
IDNUMBER 1988-2009 AHA hospital identifier without the leading 6 (not available for all States) GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPNAME 1993-2009 Hospital name from AHA Annual Survey Database (not available for all States) AR, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
Hospital location HOSPADDR 1993-2009 Hospital address from AHA Annual Survey Database (not available for all States) AR, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPCITY 1993-2009 Hospital city from AHA Annual Survey Database (not available for all States) AR, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPST 1988-2009 Hospital State postal code for hospital (e.g., AZ for Arizona)  
HOSPSTCO 2002-2009 Modified Federal Information Processing Standards (FIPS) State/county code GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HFIPSSTCO 2005-2009 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) GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPZIP 1993-2009 Hospital ZIP Code from AHA Annual Survey Database (not available for all States) GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
Hospital characteristics HOSP_BEDSIZE 1998-2009 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 1998-2009 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, 2008-2009 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 1998-2009 Location: (0) rural, (1) urban  
H_LOC 1993-1997 Location: (0) rural, (1) urban  
HOSP_LOCTEACH 1998-2009 Location/teaching status of hospital (STRATA): (1) rural, (2) urban non-teaching, (3) urban teaching  
HOSP_MHSMEMBER 2007-2009 Multi-hospital system membership: (0) non-member, (1) member CO, CT, SC
HOSP_MHSCLUSTER 2007-2009 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 CO, CT, SC, VT
HOSP_RNPCT 2007-2009 Percentage of RNs among all nurses (RNs and LPNs) CO, CT, GA, SC
HOSP_RNFTEAPD 2007-2009 RN FTEs per 1000 adjusted inpatient days CO, CT, GA, SC
HOSP_LPNFTEAPD 2007-2009 LPN FTEs per 1000 adjusted inpatient days CO, CT, GA, SC
HOSP_NAFTEAPD 2007-2009 Nurse aides per 1000 adjusted inpatient days CO, CT, GA, SC
HOSP_OPSURGPCT 2007-2009 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 1998-2009 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 1998-2009 Teaching status of hospital: (0) non-teaching, (1) teaching  
H_TCH 1993-1997 Teaching status of hospital: (0) non-teaching, (1) teaching  
NIS_STRATUM 1998-2009 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 1998-2009 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

All data elements listed below are available for all States in the 2009 NIS Disease Severity Measures files.

Type of Data Element HCUP Data Element Name Years Available Coding Notes
AHRQ Comorbidity Software (AHRQ) CM_AIDS 2002-2009 AHRQ comorbidity measure: Acquired immune deficiency syndrome : (0) Comorbidity is not present, (1) Comorbidity is present
CM_ALCOHOL 2002-2009 AHRQ comorbidity measure: Alcohol abuse: (0) Comorbidity is not present, (1) Comorbidity is present
CM_ANEMDEF 2002-2009 AHRQ comorbidity measure: Deficiency anemias : (0) Comorbidity is not present, (1) Comorbidity is present
CM_ARTH 2002-2009 AHRQ comorbidity measure: Rheumatoid arthritis/collagen vascular diseases : (0) Comorbidity is not present, (1) Comorbidity is present
CM_BLDLOSS 2002-2009 AHRQ comorbidity measure: Chronic blood loss anemia: (0) Comorbidity is not present, (1) Comorbidity is present
CM_CHF 2002-2009 AHRQ comorbidity measure: Congestive heart failure: (0) Comorbidity is not present, (1) Comorbidity is present
CM_CHRNLUNG 2002-2009 AHRQ comorbidity measure: Chronic pulmonary disease: (0) Comorbidity is not present, (1) Comorbidity is present
CM_COAG 2002-2009 AHRQ comorbidity measure: Coagulopathy: (0) Comorbidity is not present, (1) Comorbidity is present
CM_DEPRESS 2002-2009 AHRQ comorbidity measure: Depression: (0) Comorbidity is not present, (1) Comorbidity is present
CM_DM 2002-2009 AHRQ comorbidity measure: Diabetes, uncomplicated: (0) Comorbidity is not present, (1) Comorbidity is present
CM_DMCX 2002-2009 AHRQ comorbidity measure: Diabetes with chronic complications: (0) Comorbidity is not present, (1) Comorbidity is present
CM_DRUG 2002-2009 AHRQ comorbidity measure: Drug abuse: (0) Comorbidity is not present, (1) Comorbidity is present
CM_HTN_C 2002-2009 AHRQ comorbidity measure: Hypertension, (combine uncomplicated and complicated): (0) Comorbidity is not present, (1) Comorbidity is present
CM_HYPOTHY 2002-2009 AHRQ comorbidity measure: Hypothyroidism: (0) Comorbidity is not present, (1) Comorbidity is present
CM_LIVER 2002-2009 AHRQ comorbidity measure: Liver disease: (0) Comorbidity is not present, (1) Comorbidity is present
CM_LYMPH 2002-2009 AHRQ comorbidity measure: Lymphoma: (0) Comorbidity is not present, (1) Comorbidity is present
CM_LYTES 2002-2009 AHRQ comorbidity measure: Fluid and electrolyte disorders: (0) Comorbidity is not present, (1) Comorbidity is present
CM_METS 2002-2009 AHRQ comorbidity measure: Metastatic cancer: (0) Comorbidity is not present, (1) Comorbidity is present
CM_NEURO 2002-2009 AHRQ comorbidity measure: Other neurological disorders: (0) Comorbidity is not present, (1) Comorbidity is present
CM_OBESE 2002-2009 AHRQ comorbidity measure: Obesity: (0) Comorbidity is not present, (1) Comorbidity is present
CM_PARA 2002-2009 AHRQ comorbidity measure: Paralysis: (0) Comorbidity is not present, (1) Comorbidity is present
CM_PERIVASC 2002-2009 AHRQ comorbidity measure: Peripheral vascular disorders: (0) Comorbidity is not present, (1) Comorbidity is present
CM_PSYCH 2002-2009 AHRQ comorbidity measure: Psychoses: (0) Comorbidity is not present, (1) Comorbidity is present
CM_PULMCIRC 2002-2009 AHRQ comorbidity measure: Pulmonary circulation disorders: (0) Comorbidity is not present, (1) Comorbidity is present
CM_RENLFAIL 2002-2009 AHRQ comorbidity measure: Renal failure: (0) Comorbidity is not present, (1) Comorbidity is present
CM_TUMOR 2002-2009 AHRQ comorbidity measure: Solid tumor without metastasis: (0) Comorbidity is not present, (1) Comorbidity is present
CM_ULCER 2002-2009 AHRQ comorbidity measure: Peptic ulcer disease excluding bleeding: (0) Comorbidity is not present, (1) Comorbidity is present
CM_VALVE 2002-2009 AHRQ comorbidity measure: Valvular disease: (0) Comorbidity is not present, (1) Comorbidity is present
CM_WGHTLOSS 2002-2009 AHRQ comorbidity measure: Weight loss: (0) Comorbidity is not present, (1) Comorbidity is present
All Patient Refined DRG (3M) APRDRG 2002-2009 All Patient Refined DRG
APRDRG_Risk_Mortality 2002-2009 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 2002-2009 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 (HSS, Inc.) 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 (Medstat) DS_DX_Category1 2002-2009 Disease Staging: Principal Disease Category
DS_Stage1 2002-2009 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 2002-2009 HCUP hospital identification number
KEY 2002-2009 HCUP record identifier


Return to Introduction

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

All data elements listed below are available for all States in the 2009 NIS Diagnosis and Procedure Groups files.
Type of Data Element HCUP Data Element 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 2005-2009 Chronic condition indicator for all diagnoses: (0) non-chronic condition, (1) chronic condition. Beginning in 2009, the diagnosis array was increased from 15 to 25.
CHRONB1 – CHRONB25 2005-2009 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 2009, the diagnosis array was increased from 15 to 25.
Multi-Level Clinical Classifications Software (CCS) Category DXMCCS1 2009 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 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 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 2005-2009 Procedure Class for all procedures: (1) Minor Diagnostic, (2) Minor Therapeutic, (3) Major Diagnostic, (4) Major Therapeutic
Linkage Data Elements HOSPID 2002-2009 HCUP hospital identification number
KEY 2002-2009 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: Preliminary Annual Estimates of the Population for the United States, Regions, States, and Puerto Rico: April 1, 2000 to July 1, 2010 (NST-PEST2010-01). Source: Population Division, U.S. Census Bureau. Release Date: February 2011.

Return to Introduction


Internet Citation: 2009 Introduction to the NIS. Healthcare Cost and Utilization Project (HCUP). January 2013. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2009.jsp.
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