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Introduction to the HCUP KIDS' Inpatient Database (KID), 2012

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 KIDS' INPATIENT DATABASE (KID)

2012

 

 

 

Issued July 2014

Updated November 2015

 

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

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

 

KID 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 KIDS' INPATIENT DATABASE (KID) SUMMARY OF DATA USE RESTRICTIONS

***** REMINDER *****


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

Authorized users of HCUP data agree to the following 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 redistribute HCUP data by posting on any Website or other publically-accessible online repository.

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

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

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

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

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

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

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

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


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HCUP CONTACT INFORMATION

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

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

The HCUP Nationwide Data Use Agreement are is available on the AHRQ-sponsored HCUP User Support (HCUP-US) Web site at:

http://www.hcup-us.ahrq.gov


HCUP Central Distributor

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

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

HCUP User Support:

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



Return to Introduction


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

Sponsored by the Agency for Healthcare Research and Quality

The Agency for Healthcare Research and Quality and
the staff of the Healthcare Cost and Utilization Project (HCUP) thank you for
purchasing the HCUP Kids' Inpatient Database (KID).



HCUP Kids' Inpatient Database (KID)

ABSTRACT

The Kids’ Inpatient Database (KID) 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 (AHRQ).

The KID is the largest publicly-available all-payer pediatric inpatient care database in the United States, yielding national estimates of hospital inpatient stays by children. The KID is a sample of pediatric discharges from all community, non-rehabilitation hospitals in States participating in HCUP. The target universe includes pediatric discharges from community, non-rehabilitation hospitals in the United States. Pediatric discharges are defined as all discharges where the patient was age 20 or less at admission. See Table 1 in Appendix I for a list of the statewide data organizations participating in the KID. The number of sample hospitals and discharges by State and year are available in Table 2 in Appendix I.

Inpatient stay records in the KID include clinical and resource use information typically available from discharge abstracts created by hospitals for billing. The KID contains charge information on all patients, regardless of payer, including persons covered by private insurance, Medicaid, Medicare, and the uninsured. The KID's large sample size enables analyses of rare conditions, such as congenital anomalies and uncommon treatments, such as cardiac surgery. It can be used to study a wide range of topics including the economic burden of pediatric conditions, access to services, quality of care and patient safety, and the impact of health policy changes. Discharge weights are provided for calculating national estimates.

The KID is available every three years beginning with 1997. Periodically, new data elements are added to the KID and some are dropped; see Appendix III for a summary of data elements and when they are effective.

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

For more information on the KID, visit the AHRQ-sponsored HCUP User Support (HCUP-US) Web site at http://www.hcup-us.ahrq.gov.

Return to Introduction

INTRODUCTION TO THE HCUP KIDS’ INPATIENT DATABASE (KID)

Overview of KID Data

The Kids' Inpatient Database (KID) is part of the Healthcare Cost and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality (AHRQ).

The KID is the largest publicly-available all-payer pediatric inpatient care database in the United States, yielding national estimates of hospital inpatient stays by children. The KID is a sample of pediatric discharges from all community, non-rehabilitation hospitals in States participating in HCUP. See Table 1 of Appendix I for a list of the statewide data organizations participating in the KID.

Inpatient stay records in the KID include clinical and resource use information typically available from discharge abstracts created by hospitals for billing. The KID contains charge information on all patients, regardless of payer, including persons covered by private insurance, Medicaid, Medicare, and the uninsured. The KID's large sample size enables analyses of rare conditions, such as congenital anomalies and uncommon treatments, such as cardiac surgery. It can be used to study a wide range of topics including the economic burden of pediatric conditions, access to services, quality of care and patient safety, and the impact of health policy changes. Discharge weights are provided for calculating national estimates.

The KID target universe includes pediatric discharges from community, non-rehabilitation hospitals in the United States.1 Pediatric discharges are defined as all discharges where a patient was 20 years or less at admission. Discharges with missing, invalid, or inconsistent ages are excluded. Pediatric discharges are identified as one of three types of records:

In-hospital births (HOSPBRTH = 1) are identified by any principal or secondary diagnosis code in the range of V3000 to V3901 with the last two digits of "00" or "01" and the patient is not transferred from another acute care hospital or health care facility. Uncomplicated births (UNCBRTH = 1) have a Diagnosis Related Group (DRG) indicating "Normal Newborn" (391 prior to 2009, or 796 beginning in 2009).

The KID includes a sample of pediatric discharges from all HCUP hospitals in the sampling frame — the State Inpatient Databases (SID) that agreed to participate in the KID. For sampling, pediatric discharges are stratified by uncomplicated in-hospital birth, complicated in-hospital birth, and all other pediatric cases. To further ensure an accurate representation of each hospital's pediatric case-mix, the discharges are sorted by hospital, DRG, and a random number within each DRG. Systematic random sampling is used to select 10% of uncomplicated in-hospital births and 80% of other pediatric cases from each frame hospital.

To obtain national estimates, discharge weights are developed using the American Hospital Association (AHA) universe of community, non-rehabilitation hospitals as the standard. For the weights, hospitals are post-stratified on six characteristics contained in the AHA hospital files—ownership/control, bed size, teaching status, rural/urban location, and U.S. region, with the addition of a stratum for freestanding children's hospitals. To create weights, if there were fewer than two frame hospitals, 30 uncomplicated births, 30 complicated births, and 30 non-birth pediatric discharges sampled in a stratum, that stratum is combined with an "adjacent" stratum containing hospitals with similar characteristics. Discharge weights are created by stratum in proportion to the number of AHA newborns for newborn discharges and in proportion to the total number of (non-newborn) AHA discharges for non-newborn discharges.

Detailed information on the design of the KID prior to 2006 is available in the year-specific special reports on Design of the Kids’ Inpatient Database found on the HCUP-US Web site (http://hcup-us.ahrq.gov/db/nation/kid/kidrelatedreports.jsp). Starting with the 2006 KID, the information on the design of the KID was incorporated into this report, which describes the KID sample and weights, summarizes the contents of the KID, and discusses data analysis issues. This document highlights cumulative information for all previous KID releases to provide a longitudinal view of the database. Over time, we have enhanced the nationwide representation of the sample by incorporating data from additional HCUP State Partners.

KID data sets are currently available for multiple years. See Table 3 of Appendix I for a summary of KID releases. Each release of the KID includes:

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KID Data Sources, Hospitals, and Inpatient Stays

Table 2 in Appendix I contains a summary of the data sources, number of hospitals, and number of inpatient stays in each KID database. It also lists the differences in types of hospitals and age inclusion for pediatric cases.

State-Specific Restrictions

Some HCUP Partners that contributed data to the KID 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, such as HIV/AIDS or behavioral health. Detailed information on these State-specific restrictions is available in Appendix II.

Contents of KID

The KID is distributed as fixed-width ASCII formatted data files compressed with SecureZip® from PKWARE. Beginning with the 2012 KID, the files are encrypted. Previously it was distributed on two CD-ROMs, but beginning with the 2009 KID, it is distributed on a single DVD. It includes the following files:

On the HCUP-US Web site (http://www.hcup-us.ahrq.gov), KID 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 4.

KID Data Elements

The KID contains two types of data: inpatient stay core records and hospital information. Appendix III identifies the data elements in each KID file:

The tables in Appendix III are not complete documentation for the data. Please refer to the KID documentation located on the HCUP-US Web site (http://hcup-us.ahrq.gov) for comprehensive information about data elements and the files.

Getting Started

In order to load and analyze the KID 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 KID on your hard drive.
  2. Unzip each ASCII file from the DVD, saving it into the new directory using a third-party zip utility such as ZIP Reader, WinZip, or 7-Zip. Beginning with the 2012 KID, you will be prompted to enter the encryption password (sent separately by email) to decrypt each file.

    NOTE: Attempts to unzip encrypted files using Windows will produce an error message similar to the following: "Compressed (Zipped) Folders Error. The password you typed is not correct. Try typing it again." The solution is to use a third-party zip utility such as ZIP Reader, 7-Zip, or WinZip rather than the built-in Windows Explorer function to open the archive.

    ZIP Reader may be downloaded for free at https://www.pkware.com/zip-reader. Exit Disclaimer

    7-Zip can be downloaded for free at http://www.7-zip.org/. Exit Disclaimer

    Evaluation versions of WinZip may be downloaded from the WinZip Web site at www.winzip.com.

Downloading and Running the Load Programs

Programs to load the data into SAS, SPSS, or Stata, are available on the HCUP User Support Web site (HCUP-US). To download and run the load programs, follow these steps:

  1. Go to the KID Database Documentation page on HCUP-US at http://www.hcup-us.ahrq.gov/db/nation/kid/kiddbdocumentation.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 2012 KID Core file is linked to "Core File" under "2012 KID." Save the load programs into the same directory as the KID 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.

KID Documentation

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

Table 4 in Appendix I details both the KID related reports and the comprehensive KID documentation available on HCUP-US.

HCUP On-Line Tutorials2

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 national (nationwide) databases.

The Producing National HCUP Estimates tutorial is designed to help users understand how the three national (nationwide) databases — the National Inpatient Sample (NIS), Nationwide Emergency Department Sample (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 national (nationwide) databases.

The HCUP Multi-year Analysis tutorial presents solutions that may be necessary when conducting analyses that span multiple years of HCUP data.

New tutorials are added periodically and existing tutorials are updated when necessary. The Online Tutorial Series is located on the HCUP-US Web site at http://hcup-us.ahrq.gov/tech_assist/tutorials.jsp.

Return to Introduction

HOW TO USE THE KID FOR DATA ANALYSIS

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

Calculating National Estimates

The KID Comparison Reports (available on www.hcup-us.ahrq.gov) assess the accuracy of KID estimates. KID Comparison reports are available for 1997 and 2003. These reports show that the KID's large sample size enables reliable national estimates.

Studying Trends

Choosing Data Elements for Analysis

Hospital-Level Data Elements

ICD-9-CM Diagnosis and Procedure Codes

Return to Introduction

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 about 8% of discharges in the 2012 KID 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.

There are several techniques available to help overcome this bias.5 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.

Variance Calculations

It may be important for researchers to calculate a measure of precision for some estimates based on the KID sample data. Variance estimates must take into account both the sampling design and the form of the statistic. 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. Discharges were randomly selected from within each hospital. Standard formulas for stratified, two-stage cluster samples without replacement may be used to calculate statistics and their variances in most applications. To accurately calculate variances from the KID, you must use appropriate statistical software and techniques. For details, see the special report, Calculating Kids' Inpatient Database (KID) Variances6 This report is available on the HCUP-US Web site at http://www.hcup-us.ahrq.gov/db/nation/kid/kidrelatedreports.jsp.

A multitude of statistics can be estimated from the KID data. Several computer programs that calculate statistics and their variances from sample survey data are listed in the section below. 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 KID, any estimates that attempt to accurately describe characteristics (such as expenditure and utilization patterns or hospital market factors) and interrelationships among characteristics of hospitals and discharges during a specific year should be governed by finite-sample theory.

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 discharge weights would be used to weight the sample data in estimating population statistics. In most cases, computer programs are readily available to perform these calculations. Several statistical programming packages allow weighted analyses.7 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 KID variances are presented in the special report: Calculating Kids' Inpatient Database (KID) Variances. This report is available on the HCUP-US Web site at http://www.hcup-us.ahrq.gov/db/nation/kid/kidrelatedreports.jsp. For an excellent review of programs to calculate statistics from survey data, visit the following Web site: http://www.hcp.med.harvard.edu/statistics/survey-soft/. Exit Disclaimer

The KID database includes a Hospital file with data elements required 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 analytical file is too small to set aside a large validation sample, cross-validation techniques may be used. For example, tenfold cross-validation would split the data into ten equal-sized subsets. 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 KID 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.

Return to Introduction

SAMPLING OF DISCHARGES

Sampling of Discharges Included in the KID

Unlike the HCUP Nationwide Inpatient Sample (NIS) prior to 2012, the KID has never involved sampling hospitals. Instead, the KID includes a sample of pediatric discharges from all hospitals in the sampling frame.8 For the sampling, pediatric discharges in all participating States are stratified by uncomplicated in-hospital birth, complicated in-hospital birth, and all other pediatric cases. To further ensure an accurate representation of each hospital's pediatric case-mix, the discharges are sorted by State, hospital, DRG, and a random number within each DRG. Systematic random sampling is used to select 10% of uncomplicated in-hospital births and 80% of complicated in-hospital births and other pediatric cases from each frame hospital.

To obtain national estimates, discharge weights are developed using the AHA universe as the standard. For the weights, hospitals are post-stratified on six characteristics contained in the AHA hospital files—ownership/control, bed size, teaching status, rural/urban location, and U.S. region, with the addition of a stratum for freestanding children's hospitals. If there were fewer than two frame hospitals, 30 uncomplicated births, 30 complicated births, and 30 non-birth pediatric discharges sampled in a stratum, that stratum is combined with an "adjacent" stratum containing hospitals with similar characteristics. Discharge weights are created by stratum in proportion to the total number of AHA newborns for in-hospital births and in proportion to the total number of AHA non-newborn admissions for non-birth pediatric discharges.

The KID Hospital Universe

The hospital universe is defined as all hospitals located in the U.S. that were open during any part of the calendar year and that were designated as community hospitals in the AHA Annual Survey Database. 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 (more than 25 days stays). Consequently, Veterans Hospitals and other Federal facilities (Department of Defense and Indian Health Service) are excluded. Beginning with the 2000 KID, short-term rehabilitation hospitals were excluded 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. (The 1997 KID includes short-term rehabilitation hospitals. The KID Trend Weights, described earlier in this report, remove these hospitals and adjust for other design changes in the 2000 KID.) Table 2 in Appendix I displays the number of hospitals in the universe for each year, based on the corresponding AHA Annual Survey Database.

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 KID prior to 2006 is available in the year-specific special reports on Design of the Kids’ Inpatient Database found on the HCUP-US Web site at http://www.hcup us.ahrq.gov/db/nation/kid/kidrelatedreports.jsp. Starting with the 2006 KID, the design information was incorporated into this report.

Hospital Merges, Splits, and Closures

All U.S. hospital entities that were designated 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.

Stratification data elements

For the purpose of calculating discharge weights, we post-stratified hospitals on six characteristics contained in the AHA hospital files—ownership/control, bed size, teaching status, rural/urban location, and U.S. region, with the addition of a stratum for freestanding children's hospitals. The definitions of some of the strata were revised beginning with the 2000 KID. (A description of the strata used for the 1997 KID can be found in the Kids’ Inpatient Database (KID) Design Report, 1997. This report is available on the HCUP-US Web site at http://www.hcup-us.ahrq.gov/db/nation/kid/kidrelatedreports.jsp.)

Beginning with the 2000 KID, the stratification data elements were defined as follows:

Return to Introduction

Hospital Sampling Frame

The universe of hospitals was established as all community hospitals located in the U.S. with the exception, beginning in 2000, of short-term rehabilitation hospitals. However, some hospitals do not supply data to HCUP. Therefore, we constructed the KID sampling frame from the subset of universe hospitals that released their discharge data to AHRQ for research use. The number of State Partners and hospitals contributing data to the KID has expanded over the years, as shown in Table 2 of Appendix I.

The list of the entire frame of hospitals was composed of all AHA community, non-rehabilitation hospitals in each of the frame States that could be matched to the discharge data provided to HCUP. If an AHA 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).

Table 6 of Appendix I shows the number of AHA, HCUP SID, and KID hospitals by Region and by Census Division. In most cases, the difference between the universe and the frame represents the difference between the number of community, non-rehabilitation hospitals in the 2012 AHA Annual Survey Database and the number of hospitals with children's discharges that were supplied to HCUP that could be matched to the AHA data.

Beginning with the 2000 KID, pediatric discharges were defined as having an age at admission of 20 or less. This differs from the 1997 KID, which included discharges with an admission age of 18 or less. Discharges with missing, invalid, or inconsistent ages were excluded.

Hospital Sample Design

Design Considerations

The overall design objective was to select a sample of pediatric discharges that accurately represents the target universe of U.S. community, non-rehabilitation hospitals. Moreover, this sample was to be geographically dispersed, yet drawn exclusively from hospitals in States that participate in HCUP.

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 KID. Ideally, relationships among outcomes and their correlates estimated from the KID should accurately represent all U.S. hospitals. It is advisable to verify your estimates against other data sources, especially for specific patient populations (e.g. organ transplant recipients).

In order to sample and project births up to the number of births reported by the AHA, which reports in-hospital births, the KID development team identified all in-hospital births in the KID data. We further separated the in-hospital births in HCUP data into uncomplicated births and complicated births. We sampled uncomplicated births at a lower rate because they have little variation in their outcomes.

To determine the best way to identify in-hospital births, we ran cross-tabulations of different combinations of data elements on all cases that had any of the following possible birth indicators: age of zero days (AGEDAY=0), neonatal diagnosis (NEOMAT>=2), neonatal Major Diagnostic Category (MDC 15), or admission type of birth (ATYPE=4).9 D Based on reviews of the cross-tabulations, the MDC 15 DRG definitions, and ICD-9-CM birth diagnosis codes, the following screen was devised for births: an in-hospital birth diagnosis code (any diagnosis code in the range V3000 - V3901 with a fourth digit of zero, indicating born in the hospital, and a fifth digit of zero or one, indicating delivered without mention of cesarean delivery, or delivered by cesarean delivery), without an admission source of another hospital or health facility (ASOURCE not equal to 2 or 3).

We classified neonates transferred from other facilities as pediatric non-births because they are not included in births reported by the AHA. An age of zero days was not a reliable in-hospital birth indicator because neonates transferred from another hospital or born before admission to the hospital could also have an age of zero days. There were also some cases with birth diagnoses, but with ages of a few days. Because the HCUP data are already edited for neonatal diagnoses inconsistent with age, we did not include any age criteria in the in-hospital birth screen.

Uncomplicated, in-hospital births are identified as cases that meet the above screen and have a Diagnosis Related Group (DRG) indicating "Normal Newborn" (391 prior to 2009, or 796 beginning in 2009). In the KID, a small percentage of the cases with a DRG of "Normal Newborn" do not meet the in-hospital birth screen. These cases have diagnoses that imply a newborn, but do not specifically indicate an in-hospital birth. It is possible that some of these may have actually been born in the hospital but lacked the proper diagnosis code. Others may be readmissions or may have been born before admission to the hospital. Some of these cases have an admission type of newborn (ATYPE = 4).

Changes to Sampling and Weighting Strategy Beginning with the 2000 KID

We revised some of the hospital universe and strata definitions beginning with the 2000 KID. These changes included:

Sampling Procedure

The KID includes a sample of pediatric discharges from all hospitals in the sampling frame. For the sampling, we stratified the pediatric discharges by uncomplicated in-hospital birth, complicated in-hospital birth, and pediatric non-birth. To further ensure an accurate representation of each hospital's pediatric case-mix, we also sorted the discharges by State, hospital, DRG, and a random number within each DRG. We then used systematic random sampling to select 10% of "normal newborns" born in the hospital and 80% of other pediatric cases from each frame hospital.

It should be observed that the KID includes fewer than 100% of the pediatric discharges for each hospital in the database. Therefore, researchers will not be able to calculate hospital-specific outcomes with certainty.

Return to Introduction

SAMPLE WEIGHTS

To obtain national estimates, we developed discharge weights using the AHA universe as the standard. For the weights, hospitals are post-stratified on six characteristics contained in the AHA hospital files—ownership/control, bed size, teaching status, rural/urban location, and U.S. region, with the addition of a stratum for freestanding children's hospitals. We also stratified the KID discharges according to whether the discharge was an uncomplicated in-hospital birth, a complicated in-hospital birth, or a non-newborn pediatric discharge. If there were fewer than two frame hospitals, 30 uncomplicated births, 30 complicated births, and 30 non-birth pediatric discharges sampled in a stratum, we merged that stratum with an "adjacent" stratum containing hospitals with similar characteristics.

We used Children's Hospital Association (CHA) data to help verify the AHA list of children's hospitals in the target universe. Data analysts may find it useful to identify discharges from children's hospitals. Prior to 2012, children's hospitals within general hospitals were not stratified as children's hospitals, but they could be selected using the NACHTYPE data element in the KID. Beginning with 2012 data, NACHTYPE is no longer available, but discharges from freestanding children's hospitals are stratified as children's hospitals (KID_STRATUM=9999 or 9998).

Discharge Weights

The discharge weights usually are constant for all discharges of the same type (uncomplicated in-hospital birth, complicated in-hospital birth, and other pediatric discharge) 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 their full year of data to the KID. For those hospitals, we adjusted the number of observed discharges by a factor of 4 ÷ Q, where Q was the number of calendar quarters that the hospital contributed discharges to the KID. For example, when a sample hospital contributed only two quarters of discharge data to the KID, the adjusted number of discharges was double the observed number.

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 numbers of total discharges and births were available for every hospital in the universe from the AHA files.

Discharge weights to the universe were calculated by post-stratification. Hospitals were stratified on geographic region, urban/rural location, teaching status, bed size, control, and hospital type. In some instances, strata were collapsed for sample weight calculations. Within stratum k, for hospital i, each KID sample discharge's universe weight was calculated as:

Wik = [Tk / (Rk * Ak)] * (4 ÷ Qi)

In the birth strata (both complicated and uncomplicated):

In the non-newborn strata:

Uncomplicated in-hospital births were sampled at a lower rate than other discharges because the variation in hospital outcomes for uncomplicated births is considerably less than that for other pediatric cases and because we expect research to focus much more on other pediatric patients. We sampled uncomplicated births at the nominal rate of 10% and sampled other pediatric discharges (complicated newborns and other pediatric cases) at the nominal rate of 80% from the discharges available in the (restricted) frame. To avoid rounding errors in the weights calculation, the actual sampling rate for a discharge type (uncomplicated in-hospital birth, complicated in-hospital birth, or non-birth pediatric discharge) in stratum k, Rk, was calculated as follows:

Rk = Sk / Hk

The AHA birth counts include both uncomplicated and complicated births. Therefore, the weights in the uncomplicated birth strata implicitly assume that the proportion of births that are uncomplicated in the frame is representative of the proportion of births that are uncomplicated in the population for each stratum. A similar assumption is made for complicated newborns.

Similarly, the non-birth AHA counts include all non-birth admissions, not just non-birth pediatric counts. Consequently, the weights in the non-birth strata implicitly assume that the proportion of non-birth discharges that are pediatric across the HCUP SID hospitals is the same as the proportion of non-birth admissions that are pediatric across the universe of AHA hospitals, in the aggregate within each hospital stratum.

Weight Data Elements

To produce nationwide estimates, use the discharge weights to project sampled discharges in the Core file to the discharges from all U.S. community, non-rehabilitation hospitals. Beginning with the 2003 KID, use DISCWT to calculate nationwide estimates for all analyses. For the 2000 KID, use DISCWT to create nationwide estimates for all analyses except those that involve total charges, and use DISCWTCHARGE to create nationwide estimates of total charges. For the 1997 KID, use DISCWT_U for all analyses. (For trends analysis using 1997 KID data, see the previous section of this report regarding "Studying Trends.")

Return to Introduction

THE FINAL KID SAMPLE

In Appendix I, we present tables and figures that summarize the final KID sample.

Table 2 summarizes information across all years of the KID, including the KID States, data sources, sample hospitals, and sample discharges.

Table 7 shows the number of hospitals and discharges for children's hospitals and other hospitals. For each hospital type, the table shows the number of:

Table 8 displays the unweighted and weighted number of uncomplicated births, complicated births, and pediatric non-births by hospital type in the KID.

Figure 2 displays the KID hospitals by geographic region. For each region, the chart presents:

Although pediatric discharges from hospitals in each region are selected for the KID, the comprehensiveness of the sampling frame varies by region, as shown in Figure 2.

Figure 3 summarizes the estimated U.S. population by geographic region on July 1, 2012. For each region, the figure reveals:

This figure shows that the sampling frame for the KID includes states that comprise over 95 percent of the U.S. population.

APPENDIX I: TABLES AND FIGURES

Table 1. Data Sources for the 2012 KID
State Data Organization
AK Alaska State Hospital and Nursing Home Association
AR Arkansas Department of Health
AZ Arizona Department of Health Services
CA Office of Statewide Health Planning & Development
CO Colorado Hospital Association
CT Connecticut Hospital Association
FL Florida Agency for Health Care Administration
GA Georgia Hospital Association
HI Hawaii Health Information Corporation
IA Iowa Hospital Association
IL Illinois Department of Public Health
IN Indiana Hospital Association
KS Kansas Hospital Association
KY Kentucky Cabinet for Health and Family Services
LA Louisiana Department of Health and Hospitals
MA Division of Health Care Finance and Policy
MD Health Services Cost Review Commission
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
ND North Dakota (data provided by the Minnesota Hospital Association)
NE Nebraska Hospital Association
NJ New Jersey Department of Health
NM New Mexico Department of Health
NV Nevada Department of Health and Human Services
NY New York State Department of Health
OH Ohio Hospital Association
OK Oklahoma State Department of Health
OR Oregon Association of Hospitals and Health Systems
PA Pennsylvania Health Care Cost Containment Council
RI Rhode Island Department of Health
SC South Carolina State Budget & Control Board
SD South Dakota Association of Healthcare Organizations
TN Tennessee Hospital Association
TX Texas Department of State Health Services
UT Utah Department of Health
VA Virginia Health Information
VT Vermont Association of Hospitals and Health Systems
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. Summary of KID States, Hospitals, and Inpatient Stays, 1997-2012
  2012 2009 2006 2003 2000 1997
Number of States 44 44 38 36 27 22
Data Sources AK AR AZ CA CO CT FL GA HI IA IL IN KS KY LA MA MD MI MN MO MT NC ND NE NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY (Added AK, ND. ME and NH are not included) AR AZ CA CO CT FL GA HI IA IL IN KS KY LA MA ME MD MI MN MO MT NC NE NH NM NJ NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY (Added LA,ME, MT, NM, PA and WY) AR AZ CA CO CT FL GA HI IA IL IN KS KY MA MD MI MN MO NC NE NH NJ NV NY OH OK OR RI SC SD TN TX UT VA VT WA WI WV (Added AR and OK. ME and PA are not included) AZ CA CO CT FL GA HI IA IL IN KS KY MD MA MI MN MO NC NE NH NJ NV NY OH OR RI SC SD TN TX UT VA VT WA WI WV (Added IL, IN, MI, MN, NE, NH, NV, OH, RI, SD, VT. ME and PA are not included) AZ CA CO CT FL GA HI IA KS KY MD MA ME MO NC NJ NY OR PA SC TN TX UT VA WA WI WV (Added KY, ME, NC, TX, VA, WV. IL is not included) AZ CA CO CT FL GA HI IL IA KS MD MA MO NJ NY OR PA SC TN UT WA WI
Hospitals Community, non-rehabilitation hospitals Community hospitals, including rehabilitation hospitals
Hospital Universe 10 5,118 5,128 5,124 4,836 4,839 5,113
Number of hospitals with pediatric discharges 4,179 4,121 3,739 3,438 2,784 2,521
Definition of pediatric discharges Age at admission of 20 years or less for all years after 1997 Age at admission of 18 years or less
Number of pediatric discharges (unweighted) 3,195,782 3,407,146 3,131,324 2,984,129 2,516,833 1,905,797
Number of pediatric discharges (weighted) 6,675,222 7,370,203 7,558,812 7,409,162 7,291,032 6,657,326

Return to Introduction

Figure 1. KID States, by Region, 201211

text version

Figure 1: Map of the United States outlining KID States by Region

All States, by Region12
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 3. Summary of KID Releases
Data from   Media/format options Structure of Releases
• 1997
• 22 States
bracket spanning the years 1997-2006 for the media format: DVD-ROM, In ASCII format On CD–ROM in ASCII format

1 year of data on one CD, compressed files

Beginning in 2003, a companion file with four different sets of severity measures

Beginning in 2006, a companion file with diagnosis and procedure groups
• 2000
• 27 States
• 2003
• 36 States
• 2006
• 38 States
• 2009
• 44 States
bracket for 2009 the media format: DVD-ROM,
In ASCII format On DVD-ROM, in ASCII format Beginning in 2009, 1 year of data in ASCII format on a single DVD-ROM
• 2012
• 44 States

Return to Introduction

Table 4. KID Related Reports and Database Documentation Available on HCUP-US
Restrictions on the Use of the KID
  • Data Use Agreement for the KID


Description of the KID Files
  • Introduction to the KID, 2012 – 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 KID Data and State-Specific Restrictions (included in this document beginning 2006) – identifies the KID data sources and restrictions on sampling and the release of data elements


Availability of Data Elements
  • Availability of KID data elements from 1997-2012


Description of Data Elements in the KID
  • Description of Data Elements – details uniform coding and State-specific idiosyncrasies
  • Summary Statistics – lists means and frequencies on nearly all data elements
  • KID Severity Measures – provides detailed documentation on the different types of measures
  • HCUP Coding Practices – describes how HCUP data elements are coded
  • HCUP Hospital Identifiers – explains data elements that characterize individual hospitals
Known Data Issues
  • Information on corrections to the KID data sets
  • Link to KID 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
  • Label file for multiple versions of Diagnosis Related Groups (DRGs) and Major Diagnostic Categories (MDC)
  • KID SAS format library program to create value labels


KID Related Reports
Links to HCUP-US page with various KID related reports such as the following:
  • Design of the Kids' Inpatient Databases for 1997, 2000 and 2003 (included in this document beginning 2006)
  • Changes in NIS Sampling and Weighting Strategy for 1998 (for information on the 1998 NIS universe and strata which the KID uses)
  • Calculating KID Variances
  • File Composition by State
  • KID Trends Report
  • KID Comparison Reports
  • HCUP E-Code Evaluation Report


HCUP Supplemental Files
  • Cost-to-Charge Ratio files
  • Hospital Market Structure files
  • KID Trends Supplemental File

Return to Introduction

Table 5. 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+


Table 6. Number of AHA, HCUP SID, and KID Hospitals, by Region (the KID Stratifier) and by Census Division, 2012
Census Region / Division AHA Universe Hospitals* SID Community, Non-Rehab Hospitals SID Community, Non-Rehab Hospitals with Peds Discharges KID Sampling Frame Hospitals KID Sample Hospitals
Total 5,118 4,540 4,213 4,213 4,179
Census Region
1: Northeast 629 544 522 522 518
2: Midwest 1,500 1,381 1,322 1,322 1,310
3: South 2,031 1,742 1,534 1,534 1,520
4: West 958 873 835 835 831
Census Division
1: New England 197 118 117 117 115
2: Middle Atlantic 432 426 405 405 403
3: East North Central 790 737 714 714 710
4: West North Central 710 644 608 608 600
5: South Atlantic 741 715 686 686 683
6: East South Central 432 311 204 204 202
7: West South Central 858 716 644 644 635
8: Mountain 408 339 321 321 321
9: Pacific 550 534 514 514 510

*The columns in the table are defined as follows:


Return to Introduction

Table 7. Number of Hospitals and Discharges in the AHA Universe, SID, and KID, by Hospital Type, 2012
  AHA Universe SID KID
Hospital Type Hospitals Admissions Plus Births Hospitals with Pediatric Discharges Pediatric Discharges Hospitals Pediatric Discharges
Children's Hospital 80 621,259 70 505,999 70 400,835
Not a Children's Hospital 5,038 37,432,053 4,143 5,681,148 4,109 2,794,947
Total 5,118 38,053,312 4,213 6,187,147 4,179 3,195,782


Table 8. 2012 KID Discharges, by Hospital Type
Hospital Type Uncomplicated Births Complicated Births Pediatric Non-Births Total Pediatric Discharges
Unweighted:
Children's Hospital 566 3,921 396,348 400,835
Not a Children's Hospital 249,994 853,092 1,691,861 2,794,947
Total 250,560 857,013 2,088,209 3,195,782
Weighted:
Children's Hospital 6,996 6,055 586,499 599,550
Not a Children's Hospital 2,610,848 1,109,861 2,354,963 6,075,672
Total 2,617,844 1,115,916 2,941,462 6,675,222


Figure 2. Number of Hospitals in the 2012 AHA Universe, SID, and KID, by Region

text version

Figure 2. Number of Hospitals in the 2012 AHA Universe, SID, and KID, by Region


Return to Introduction

Figure 3. Percentage of U.S. Population in 2012 KID States, by Region Calculated using the estimated U.S. population on July 1, 2012.13

text version

Figure 3. Percentage of U.S. Population in 2012 KID States, by Region Calculated using the estimated U.S. population on July 1, 2012


Return to Introduction

APPENDIX II: STATE-SPECIFIC RESTRICTIONS

The table below enumerates the types of restrictions applied to the KID. Restrictions include the following types:


Return to Introduction

APPENDIX III: DATA ELEMENTS

Table 1. Data Elements in the 2012 KID Inpatient Core File

For prior years, refer to documentation on HCUP-US (e.g. the table of data element availability by years http://hcup-us.ahrq.gov/db/nation/kid/Availability_of_KID_Data_Elements_2012.pdf or previous versions of the KID Introduction).

 
Type of
Data Element
HCUP Name Coding Notes
Admission day of week or weekend AWEEKEND Admission on weekend: (0) admission on Monday-Friday, (1) admission on Saturday-Sunday
Admission month AMONTH Admission month coded from (1) January to (12) December
Transferred into hospital TRAN_IN 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 ELECTIVE Indicates elective admission: (1) elective, (0) non-elective admission
Age at admission AGE Age in years coded 0-124 years
Chronic Conditions NCHRONIC Number of chronic conditions
Clinical Classifications Software (CCS) category DXCCS1 - DXCCS25 CCS category for all diagnoses. Beginning in 2009, the diagnosis array was increased from 15 to 25.
PRCCS1 - PRCCS15 CCS category for all procedures
Diagnosis information DX1 - DX25 Diagnoses, principal and secondary (ICD-9-CM). Beginning in 2003, the diagnosis array does not include any of 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.
HOSPBRTH Birth diagnosis, in this hospital
NDX Number of diagnoses coded on the original record
UNCBRTH Normal, uncomplicated birth in hospital
Diagnosis Related Group (DRG) DRG DRG in use on discharge date
DRG_NoPOA DRG in use on discharge date, calculated without Present On Admission (POA) indicators
DRGVER Grouper version in use on discharge date
DRG24 DRG Version 24 (effective October 2006 - September 2007)
Discharge quarter DQTR Coded: (1) Jan - Mar, (2) Apr - Jun, (3) Jul - Sep, (4) Oct - Dec
Discharge weights DISCWT Weight to discharges in AHA universe for national estimates. In 2000, the discharge weight DISCWTCHARGE should be used for estimates of total charges.
Discharge year YEAR Calendar year
Disposition of patient (discharge status) DIED Indicates in-hospital death: (0) did not die during hospitalization, (1) died during hospitalization
DISPUNIFORM 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
TRAN_OUT Transfer Out Indicator: (0) not a transfer, (1) transferred out to a different acute care hospital, (2) transferred out to another type of health facility
External causes of injury and poisoning ECODE1 - ECODE4 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 CCS category for the external cause of injury and poisoning codes
NECODE Number of external cause of injury codes on the original record.
Gender of patient FEMALE Indicates gender for KID beginning in 1998: (0) male, (1) female
Hospital information HOSP_REGION Region of hospital: (1) Northeast, (2) Midwest, (3) South, (4) West Prior to 2012, region of hospital is only available in the KID Hospital File.
KID_STRATUM Hospital stratum used for weights.
Indicates Emergency Department service HCUP_ED 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
Length of Stay LOS Length of stay, edited
Location of the patient PL_NCHS2006 Urban-rural designation for patient's county of residence: (1) "Central" counties of metro areas >= 1 million population, (2) "Fringe" counties of metro areas >= 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) non-core counties
Major Diagnosis Category (MDC) MDC MDC in use on discharge date
MDC_NoPOA MDC in use on discharge date, calculated without Present on Admission (POA) indicators
MDC24 MDC Version 24 (effective October 2006 - September 2007)
Median household income for patient's ZIP Code ZIPINC_QRTL Median household income quartiles for patient's ZIP Code. Because these estimates are updated annually, the value ranges for the ZIPINC_QRTL categories vary by year. Check the HCUP-US Website for details.
Neonatal/ maternal flag NEOMAT 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 Expected primary payer, uniform: (1) Medicare, (2) Medicaid, (3) private including HMO, (4) self-pay, (5) no charge, (6) other
Procedure information PR1 - PR15 Procedures, principal and secondary (ICD-9-CM)
NPR Number of procedures coded on the original record
ORPROC Major operating room procedure indicator: (0) no major operating room procedure, (1) major operating room procedure
PRDAY1 Number of days from admission to principal procedure.
PRDAY2 - PRDAY15 Number of days from admission to secondary procedures.
Race of Patient RACE 14 Race, uniform coding: (1) white, (2) black, (3) Hispanic, (4) Asian or Pacific Islander, (5) Native American, (6) other
Record identifier, synthetic RECNUM HCUP unique record number
Total Charges TOTCHG Total charges, edited

Return to Introduction

Table 2. Data Elements in the 2012 KID Hospital File

For prior years, refer to documentation on HCUP-US (e.g. the table of data element availability by years http://hcup-us.ahrq.gov/db/nation/kid/Availability_of_KID_Data_Elements_2012.pdf or previous versions of the KID Introduction).

 
Type of Data Element HCUP Name Coding Notes
Universe Counts N_DISC_U Number of universe discharges in the KID_STRATUM
N_BRTH_U Number of universe births in KID_STRATUM
N_HOSP_U Number of universe hospitals in KID_STRATUM
Sample Counts S_DISC_U Number of sampled discharges in the sampling stratum (KID_STRATUM or STRATUM)
S_BRTH_U Number of sample births in KID_STRATUM
S_CHLD_U Number of sample pediatric non-births in KID_STRATUM
S_CMPB_U Number of sample complicated births in KID_STRATUM
S_UNCB_U Number of sample uncomplicated births in KID_STRATUM
S_HOSP_U Number of sample hospitals in KID_STRATUM
Hospital Characteristics KID_STRATUM Hospital stratum used for weights
HOSP_BEDSIZE Bed size of hospital (STRATA): (1) small, (2) medium, (3) large
H_CONTRL Control/ownership of hospital (STRATA): (1) government, nonfederal, (2) private, non-profit, (3) private, invest-own
HOSP_LOCTEACH Location/teaching status of hospital (STRATA): (1) rural, (2) urban non-teaching, (3) urban teaching
HOSP_REGION Region of hospital (STRATA): (1) Northeast, (2) Midwest, (3) South, (4) West
HOSP_DIVISION Census Division of hospital: (1) New England, (2) Middle Atlantic, (3) East North Central, (4) West North Central, (5) South Atlantic, (6) East South Central, (7) West South Central, (8) Mountain, (9) Pacific
Discharge Year YEAR Calendar year

Return to Introduction

Table 3. Data Elements in the 2012 KID Disease Severity Measures Files

For prior years, refer to documentation on HCUP-US (e.g. the table of data element availability by years http://hcup-us.ahrq.gov/db/nation/kid/Availability_of_KID_Data_Elements_2012.pdf or previous versions of the KID Introduction).

 
Type of Data Element HCUP Name Coding Notes
AHRQ Comorbidity Software (AHRQ) CM_AIDS AHRQ comorbidity measure: Acquired immune deficiency syndrome
CM_ALCOHOL AHRQ comorbidity measure: Alcohol abuse
CM_ANEMDEF AHRQ comorbidity measure: Deficiency anemias
CM_ARTH AHRQ comorbidity measure: Rheumatoid arthritis/collagen vascular diseases
CM_BLDLOSS AHRQ comorbidity measure: Chronic blood loss anemia
CM_CHF AHRQ comorbidity measure: Congestive heart failure
CM_CHRNLUNG AHRQ comorbidity measure: Chronic pulmonary disease
CM_COAG AHRQ comorbidity measure: Coagulopathy
CM_DEPRESS AHRQ comorbidity measure: Depression
CM_DM AHRQ comorbidity measure: Diabetes, uncomplicated
CM_DMCX AHRQ comorbidity measure: Diabetes with chronic complications
CM_DRUG AHRQ comorbidity measure: Drug abuse
CM_HTN_C AHRQ comorbidity measure: Hypertension, uncomplicated and complicated
CM_HYPOTHY AHRQ comorbidity measure: Hypothyroidism
CM_LIVER AHRQ comorbidity measure: Liver disease
CM_LYMPH AHRQ comorbidity measure: Lymphoma
CM_LYTES AHRQ comorbidity measure: Fluid and electrolyte disorders
CM_METS AHRQ comorbidity measure: Metastatic cancer
CM_NEURO AHRQ comorbidity measure: Other neurological disorders
CM_OBESE AHRQ comorbidity measure: Obesity
CM_PARA AHRQ comorbidity measure: Paralysis
CM_PERIVASC AHRQ comorbidity measure: Peripheral vascular disorders
CM_PSYCH AHRQ comorbidity measure: Psychoses
CM_PULMCIRC AHRQ comorbidity measure: Pulmonary circulation disorders
CM_RENLFAIL AHRQ comorbidity measure: Renal failure
CM_TUMOR AHRQ comorbidity measure: Solid tumor without metastasis
CM_ULCER AHRQ comorbidity measure: Peptic ulcer disease excluding bleeding
CM_VALVE AHRQ comorbidity measure: Valvular disease
CM_WGHTLOSS AHRQ comorbidity measure: Weight loss
All Patient Refined DRG (3M) APRDRG All Patient Refined DRG
APRDRG_Risk_Mortality All Patient Refined DRG: Risk of Mortality Subclass
APRDRG_Severity All Patient Refined DRG: Severity of Illness Subclass
Linkage Variables HOSP_KID KID hospital number (links to Hospital Weights file; does not link to previous years)
RECNUM HCUP record identifier (links to KID discharge level files; does not link to previous years)

Return to Introduction

Table 4. Data Elements in the 2012 KID Diagnosis and Procedure Groups Files

For prior years, refer to documentation on HCUP-US (e.g. the table of data element availability by years http://hcup-us.ahrq.gov/db/nation/kid/Availability_of_KID_Data_Elements_2012.pdf or previous versions of the KID Introduction).

 
Type of Data Element HCUP Name Coding Notes
Chronic Condition Indicator CHRON1 - CHRON25 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 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 CCS: Principal Diagnosis DXMCCS1 Multi-level clinical classification software (CCS) for principal diagnosis. Four levels for diagnoses presenting both the general groupings and very specific conditions
Multi-Level CCS: E Code 1 E_MCCS1 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
Multi-Level CCS: Principal Procedure PRMCCS1 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 Procedure Class for all procedures: (1) Minor Diagnostic, (2) Minor Therapeutic, (3) Major Diagnostic, (4) Major Therapeutic
Linkage Variables HOSP_KID KID hospital number (links to Hospital Weights file; does not link to previous years)
RECNUM HCUP record identifier

Return to Introduction

APPENDIX IV: TEACHING HOSPITAL INDICATOR ASSIGNMENT

We used the following American Hospital Association Annual Survey Database (Health Forum, LLC © 2013) data elements to assign the KID 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 KID, we used the following SAS code to assign the KID 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 KID, 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 ;


Return to Introduction

1 Community hospitals, as defined by the American Hospital Association (AHA), include "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. These facilities provide acute care services to patients who need long term hospitalization (stays of more than 25 days). Excluded from the KID are short-term rehabilitation hospitals (beginning with 2000 data), long-term non-acute care hospitals, psychiatric hospitals, and alcoholism/chemical dependency treatment facilities.

2 As of July, 2014, the HCUP Online Tutorials had not yet been updated for the 2012 KID data element changes. However, the same statistical techniques should be used to calculate standard errors and confidence intervals. There is one change in example programs: HOSPID (the encrypted hospital identifier) should be replaced by HOSP_KID.

3 Prior to 2000, the discharge weight was named DISCWT_U. For 2000 only, use DISCWT to create national estimates for all analyses except those that involve total charges; and use DISCWTCHARGE to create national estimates of total charges.

4 As of July, 2014, this report had not yet been updated for the 2012 KID data element changes. However, the same statistical techniques should be used to calculate standard errors and confidence intervals. There is one change in example programs: HOSPID (the encrypted hospital identifier) should be replaced by HOSP_KID.

5 See, for example, van Buuren, S. (2012). Flexible Imputation of Missing Data. CRC Press, Boca Raton, FL.

6 As of July, 2014, this report had not yet been updated for the 2012 KID data element changes. However, the same statistical techniques should be used to calculate standard errors and confidence intervals. There is one change in example programs: HOSPID (the encrypted hospital identifier) should be replaced by HOSP_KID.

7 Carlson 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.

8 As of 2012, the sampling strategy for National Inpatient Sample (NIS) was redesigned as a sample of discharges from all HCUP-participating hospitals. For more information on the new design for the NIS, see the NIS Overview, available on HCUP-US Web site at http://www.hcup-us.ahrq.gov/nisoverview.jsp.

9 We performed this analysis during the development of the original 1997 KID.

10 Most AHA survey responses from hospitals cover a fiscal year other than a January-to-December calendar year. The numbers of hospitals for the KID are based on the AHA Annual Survey files.

11 New Hampshire, Maine, and Mississippi participate in HCUP, but did not provide data in time for the 2012 KID.

12 States and areas in italics do not participate in HCUP.

13 Table 1. Annual Estimates of the Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2013 (NST-EST2013-01). Source: U.S. Census Bureau, Population Division. Release Date: December 2013.

14 Race contains missing values on more than 8% of the records.


Return to Introduction


Internet Citation: 2012 Introduction to the KID. Healthcare Cost and Utilization Project (HCUP). June 2016. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/db/nation/kid/kid_2012_introduction.jsp.
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