Skip Navigation

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

2009

 

 

These pages provide only an introduction to the KID package.

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

 

Issued June 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

 

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 LIMITATIONS

***** REMINDER *****


All users of the KID 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 Kids’ Inpatient Database.


Return to Introduction

HCUP CONTACT INFORMATION

The KID 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 KIDS' INPATIENT DATABASE (KID)?

  • The 2009 KID contains six additional states: Maine and Pennsylvania returned, and Louisiana, Montana, New Mexico and Wyoming are new.


  • The following data elements describing hospital structural characteristics and provision of outpatient services were added to the Hospital File beginning with the 2009 KID:
    • Multi-hospital system membership (HOSP_MHSMEMBER)
    • Multi-hospital system cluster code (HOSP_MHSCLUSTER)
    • Percentage of RNs among nurses, RNs and LPNs (HOSP_RNPCT)
    • RN FTEs per 1000 adjusted patient days (HOSP_RNFTEAPD)
    • LPN FTEs per 1000 adjusted inpatient days (HOSP_LPNFTEAPD)
    • Nurse aides per 1000 adjusted inpatient days (HOSP_NAFTEAPD)
    • Percentage of all surgeries performed in the outpatient setting (HOSP_OPSURGPCT).


  • To facilitate analyses by hospital ownership, the data element containing hospital ownership categories without any collapsing (H_CONTRL) was restored to the Hospital File beginning with the 2009 KID.


  • The following data elements were added to the Core File beginning with the 2009 KID:
    • DRG in use on discharge date, calculated without Present On Admission (POA) indicators (DRG_NoPOA) and MDC in use on discharge date, calculated without POA indicators (MDC_NoPOA). These two data elements 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).
    • Ten additional secondary diagnoses (DX16-DX25)
    • Ten additional secondary Clinical Classifications Software (CCS) diagnosis categories (DXCCS16-DXCCS25)
    • HCUP Emergency Department service indicator (HCUP_ED)
    • Number of chronic conditions (NCHRONIC)
    • Major operating room procedure indicator (ORPROC)
    • Point of origin for admission or visit, as received from source (PointOfOrigin_X)
    • Point of origin for admission or visit, UB-04 standard coding (PointOfOriginUB04)
    • Transfer In Indicator (TRAN_IN)


  • The data element for the disposition of patient was renamed to indicate a switch from UB-92 standard coding to UB-04 standard coding (DISPUB04 replaces DISPUB92).


  • PL_NHCS2006 was renamed as PL_NCHS2006.


  • Previously, separate data elements retained the Mental Health and Substance Abuse Clinical Classification Software (CCS-MHSA) categories for diagnoses. Beginning with the 2009 KID, the CCS-MHSA scheme was incorporated into the CCS system and is included in the data elements DXCCS1-DXCCS15.


  • The following data elements were added to the DX_PR_GRPS File beginning with the 2009 KID:
    • Multi-level CCS codes for the principal diagnosis (DXMCCS1)
    • Multi-level CCS codes for the first listed E-Code (E_MCCS1)
    • Multi-level CCS codes for the principal procedure (PRMCCS1)
    • Ten additional Chronic Condition Indicators (CHRON16-CHRON25)
    • Ten additional Chronic Condition Indicators - body system (CHRONB16-CHRONB25)


  • Because they are no longer supported by the software vendor, the following data elements were dropped from the Severity File beginning with the 2009 KID:
    • Disease Staging: Length of Stay Level (DS_LOS_Level)
    • Disease Staging: Length of Stay Scale (DS_LOS_Scale)
    • Disease Staging: Length of Stay Scale (DS_Mrt_Level)
    • Disease Staging: Mortality Scale (DS_Mrt_Scale)
    • Disease Staging: Resource Demand Level (DS_RD_Level)
    • Disease Staging: Resource Demand Scale (DS_RD_Scale)


  • The 2009 KID is distributed on a single DVD-ROM instead of two CD-ROMs.

Return to Introduction


UNDERSTANDING THE KID

This document, Introduction to the KID, 2009, summarizes the content of the KID and describes the development of the KID 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 KID is available on the HCUP User Support (HCUP-US) Website (www.hcup-us.ahrq.gov).

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.

The KID is the only dataset on hospital use, outcomes, and charges designed to study children’s use of hospital services in the United States. The KID is a sample of 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.

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 organ transplantation. 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.

Inpatient stay records in the KID include clinical and resource use information typically available from discharge abstracts. Discharge weights are provided for calculating national estimates. The KID can be linked to hospital-level data from the American Hospital Association's 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.

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) Website at http://www.hcup-us.ahrq.gov.

Return to Introduction

INTRODUCTION TO THE HCUP KIDS’ INPATIENT DATABASE (KID)

Overview of KID Data

The Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID) was developed to enable analyses of hospital utilization by children across the United States. The target universe includes pediatric discharges from community, non-rehabilitation hospitals in the United States.1

The sampling frame is limited to pediatric discharges from community, non-rehabilitation hospitals in the participating HCUP Partner States shown in Figure 1 of Appendix I.

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).

Unlike the HCUP Nationwide Inpatient Sample (NIS), the KID does not involve a two-stage sampling procedure. Instead, the KID includes a sample of pediatric discharges from all 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 State, hospital, DRG, and a random number within each DRG. Systematic random sampling is used to select 10% of "normal newborns" born in the hospital and 80% of 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. These were the same characteristics used to define the NIS sampling strata (ownership/control, bedsize, 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 Website (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. 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:

Return to Introduction

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 data sources 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 DVD

The KID 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 KID, it is distributed on a single DVD. It includes the following files:

On the HCUP-US Website (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:

Not all data elements in the KID are uniformly coded or available across all States. The tables in Appendix III are not complete documentation for the data. Please refer to the KID documentation located on the HCUP-US Website (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 WinZip® or a similar utility. (Evaluation versions of WinZip may be downloaded from the WinZip Website at http://www.winzip.com/win/en/index.htm.)

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 KID Core file is linked to "Core File" under "2009 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 Website (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 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.

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 Website (http://www.hcup-us.ahrq.gov).

Calculating National Estimates

KID Data Year Name of Discharge Weight on the Core File to Use for Creating Nationwide Estimates
2003 forward • DISCWT 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.
1997 • DISCWT_U for all analyses

Studying Trends

Choosing Data Elements for Analysis

Hospital-Level Data Elements

A detailed description of the data elements is available on HCUP-US. Note that some HCUP states do not allow the release of this information.

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 15% of discharges in the 2009 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. 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 41% 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.

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) Variances. This report is available on the HCUP-US Website 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.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 KID variances are presented in the special report: Calculating Kids’ Inpatient Database (KID) Variances. This report is available on the HCUP-US Website 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 Website: http://www.hcp.med.harvard.edu/statistics/survey-soft/.

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), the KID does not involve sampling hospitals. Instead, the KID includes a sample of pediatric discharges from all hospitals in the sampling frame. 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. These were the same characteristics used to define the NIS sampling strata (ownership/control, bedsize, 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 number of AHA newborns for newborn discharges and in proportion to the total number of (non-newborn) AHA discharges for non-newborn 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.) Table 2 (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 Website. 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. These were the same characteristics used to define the HCUP Nationwide Inpatient Sample (NIS) sampling strata, with the addition of a stratum for stand-alone children’s hospitals. The definitions of some of the NIS strata were revised for 1998 and subsequent data years, and we used the revised strata 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 Website 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:

  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 1 highlights the KID States by region, and Table 5 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). These types of 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, hospitals were stratified as public, voluntary, and proprietary. This stratification was used for Southern rural, Southern urban non-teaching, and Western urban non-teaching hospitals. For smaller strata — the Midwestern rural and Western rural hospitals — a collapsed stratification of public versus private was used, 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 2006 KID, 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 an MSA as urban hospitals, while we classified hospitals outside a MSA as rural hospitals. Beginning with the 2006 KID, 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.


  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 DRG payments are uniformly higher to teaching hospitals. Prior to 2006, the teaching status of hospitals identified as children's hospitals by the National Association of Children's Hospitals and Related Institutions (NACHRI) was based on an indicator provided by NACHRI. For 2006, the NACHRI teaching status indicator was not available, so teaching status was determined using only information from the AHA Annual Survey Database for all hospitals. For 2009, both NACHRI and AHA information were used to define the teaching status of children's hospitals.

    In the 1997 KID, we considered other hospitals to be teaching hospitals if they 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).
    Beginning with the 2000 KID, we considered other hospitals to be teaching hospitals if they met any one of the following three criteria:
    • Residency training approval by the Accreditation Council for Graduate Medical Education (ACGME)
    • Membership in the Council of Teaching Hospitals (COTH)
    • A ratio of full–time equivalent interns and residents to beds of .25 or higher.3

  5. Bed Size – small, medium, and large. Bed size categories are based on hospital beds and are specific to the hospital's region, location, and teaching status, as illustrated in Table 6 of Appendix I. The bed size cutoff points were chosen 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). Different cutoff points for rural, urban non-teaching, and urban teaching hospitals were used 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.

    Rural hospitals were not split according to teaching status, because rural teaching hospitals were rare. For example, rural teaching hospitals generally comprise about 2% or less than the total hospital universe. The bed size categories were defined 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.


  6. Hospital Type – freestanding children’s or other hospital. Children’s hospitals restrict admissions to children, while other hospitals admit both adults and children. There may be significant differences in practice patterns, severity of illness, and available services between children’s hospitals and other hospitals. Data from NACHRI were used to help verify and correct the AHA list of children’s hospitals. Children’s units in general hospitals were not stratified as children’s hospitals.

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 7 of Appendix I shows the number of AHA, HCUP SID, and KID hospitals by State. In most cases, the difference between the universe and the frame represents the difference between the number of community, non-rehabilitation hospitals in the 2009 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.

The largest discrepancy between HCUP data and AHA data is in Texas, as is evident in Table 7 of 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 KID include over 96% of inpatient discharges from Texas universe hospitals because excluded hospitals tended 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 KID include over 91% of inpatient discharges from Louisiana universe hospitals.

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 and agree to contribute to the KID.

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, if available, because not all States contribute data to the KID. For example, the National Hospital Discharge Survey (http://www.cdc.gov/nchs/about/major/hdasd/nhds.htm) can provide benchmarks against which to check your national estimates for hospitalizations with more than 5,000 cases.

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

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).4 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 use the NIS community hospital universe and strata definitions for the KID. We revised some of the NIS hospital universe and strata definitions for 1998 and subsequent data years, and we used these revised definitions beginning with the 2000 KID. These changes included:

A full description of the evaluation and revision of the NIS sampling strategy for 1998 and subsequent data years can be found in the special report, Changes in NIS Sampling and Weighting Strategy for 1998. This document is available on the HCUP-US Website at http://www.hcup-us.ahrq.gov/db/nation/kid/kidrelatedreports.jsp.

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 NIS includes 100% of the discharges from hospitals in the NIS sample. Consequently, in the NIS outcomes can be estimated without sampling error for individual hospitals that are identified in the sample. However, 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, we post-stratified hospitals on six characteristics contained in the AHA hospital files. These were the same characteristics used to define the NIS sampling strata, 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.

The discharge weights were created by stratum, in proportion to the number of AHA discharges for newborns and non-newborns. Refer to the report Design of the HCUP Kids’ Inpatient Database (KID), 1997 for a discussion of the analysis and development of the KID weighting scheme. This report is available on the HCUP-US Website at http://www.hcup-us.ahrq.gov/db/nation/kid/kidrelatedreports.jsp.

We used NACHRI data to help verify and correct the AHA list of children's hospitals in the target universe. Many of these children's hospitals are units of larger institutions (AHA hospital type 10). Consequently, we do not have separate reporting for them either in the AHA survey or in the HCUP SID. However, data analysts may find it useful to identify hospitals that contain children's units, which can be accomplished using the NACHTYPE data element in the KID.

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:

Qi is the number of quarters of discharge data contributed by hospital i to the KID (usually Qi = 4).

Tk / Ak estimates the number of discharges in the population that is represented by each discharge in the sampling frame. Rk adjusts for the fact that we are taking a sample of the frame in each stratum.

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 discharge counts include all non-birth discharges, not just non-birth pediatric discharges. 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 discharges 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 extrapolate 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.

Return to Introduction

THE FINAL KID SAMPLE

In Appendix I, we present tables and figures that summarize the final KID sample. Table 8 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 9 displays the unweighted and weighted number of uncomplicated births, complicated births, and pediatric non-births by hospital type in the KID.

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

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.

Because the KID sampling frame has a disproportionate representation of the more populous States and contains hospitals with more annual discharges, its comprehensiveness in terms of discharges is higher. Figure 3 summarizes the estimated U.S. population by geographic region on July 1, 2009. For each region, the figure reveals:

And, Figure 4 presents the number of discharges in the KID for each State in the sampling frame for 2009.

Special consideration was needed to handle the Massachusetts data in the 2006 KID. Fourth quarter data from sampled hospitals in Massachusetts were unavailable for inclusion in the 2006 KID. To account for the missing quarter of data, we sampled one fourth of the Massachusetts KID discharges from the first three quarters and modified the records to represent the fourth quarter. To ensure a representative sample, we sorted the Massachusetts KID 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 KID 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 KID States (excluding Massachusetts) divided by the mean total charges in the first, second, or third quarter for all Northeastern KID 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. This adjustment only applies to the 2006 KID.

Appendix I: Tables and Figures

Table 1. Data Sources
State Data Organization
AR Arkansas Department of Health & Human Services
AZ Arizona Department of Health Services
CA Office of Statewide Health Planning & Development
CO Colorado Hospital Association
CT Chime, Inc.
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
NM New Mexico Health Policy Commission
NJ New Jersey Department of Health & Senior Services
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 & Family Services
WV West Virginia Health Care Authority
WY Wyoming Hospital Association

Return to Introduction

Table 2. Summary of KID Data Sources, Hospitals, and Inpatient Stays, 1997-2009
  2009 2006 2003 2000 1997
Number of States 44 38 36 27 22
Data Sources 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, non-rehabilitation hospitals Community, non-rehabilitation hospitals Community, non-rehabilitation hospitals Community hospitals, including rehabilitation hospitals
Hospital Universe5 5,128 5,124 4,836 4,839 5,113
Number of KID Hospitals 4,121 3,739 3,438 2,784 2,521
Hospital identifiers Available for 26 out of 44 States Available for 24 out of 38 States Available for 23 out of 36 States Available for 19 out of 27 States None – only general descriptors of hospital types
Definition of pediatric discharges Age at admission of 20 years or less Age at admission of 20 years or less Age at admission of 20 years or less Age at admission of 20 years or less Age at admission of 18 years or less
Number of pediatric discharges (unweighted) 3,407,146 3,131,324 2,984,129 2,516,833 1,905,797
Number of pediatric discharges (weighted) 7,370,203 7,558,812 7,409,162 7,291,032 6,657,326

Return to Introduction

Figure 1. KID States, by Region, 2009

text version

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

Table 3: 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 4. 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 Beggining in 2009, 1 year of data in ASCII format on a single DVD-ROM

Return to Introduction

Table 5. 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, 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 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 1997-2009


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
Corrections to the KID
  • 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
  • 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 6. 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 7. Number of AHA, HCUP SID, and KID Hospitals, by State, 20096
State AHA Universe Hospitals SID Community, Non-Rehabilitation Hospitals SID Community, Non-Rehabilitation Hospitals with Pediatric Discharges KID Sampling- Frame Hospitals KID Sample Hospitals
Non-frame 334 0 0 0 0
Arizona 76 74 73 73 73
Arkansas 88 86 82 82 81
California 350 347 341 341 340
Colorado 81 74 73 73 73
Connecticut 34 29 29 28 28
Florida 201 199 191 191 191
Georgia 151 148 143 97 97
Hawaii 24 23 19 15 14
Illinois 185 184 183 183 182
Indiana 127 116 114 113 111
Iowa 118 117 117 117 117
Kansas 142 125 123 122 122
Kentucky 103 101 100 100 100
Louisiana 168 111 104 101 101
Maine 36 36 36 32 32
Maryland 47 47 47 47 47
Massachusetts 73 64 64 64 64
Michigan 156 146 134 107 107
Minnesota 133 127 123 123 122
Missouri 130 121 120 119 119
Montana 52 42 39 39 39
Nebraska 89 86 84 78 77
Nevada 37 36 35 35 35
New Hampshire 26 26 26 26 26
New Jersey 71 70 66 66 66
New Mexico 39 39 37 33 33
New York 187 187 181 181 180
North Carolina 115 113 107 107 107
Ohio 191 160 160 160 160
Oklahoma 133 126 116 113 112
Oregon 59 58 58 58 58
Pennsylvania 181 178 165 165 165
Rhode Island 11 11 11 11 11
South Carolina 66 59 59 53 53
South Dakota 58 49 47 45 44
Tennessee 128 110 107 106 106
Texas 486 394 347 346 342
Utah 46 43 43 43 43
Vermont 14 14 14 14 14
Virginia 83 80 79 46 45
Washington 89 88 85 85 85
West Virginia 53 53 51 51 51
Wisconsin 131 130 125 125 125
Wyoming 26 25 25 23 23
Total 5,128 4,452 4,283 4,137 4,121

Return to Introduction

Table 8. Number of Hospitals and Discharges in the AHA Universe, SID, and KID, by Hospital Type, 2009
  AHA Universe SID KID
Hospital Type Hospitals Discharges (Including Births) Hospitals with Pediatric Discharges Pediatric Discharges Hospitals Pediatric Discharges
Not a Children's Hospital 5,046 38,818,501 4,222 6,274,757 4,067 3,049,790
Children's Hospital 82 616,455 61 511,411 54 357,356
Total 5,128 39,434,956 4,283 6,786,168 4,121 3,407,146


Table 9. 2009 KID Discharges, by Hospital Type
Hospital Type Uncomplicated Births Complicated Births Pediatric Non-Births Total Pediatric Discharges
Unweighted:
Not a Children's Hospital 254,379 854,621 1,940,790 3,049,790
Children's Hospital 624 3,499 353,233 357,356
Total 255,003 858,120 2,294,023 3,407,146
Weighted:
Not a Children's Hospital 2,803,447 1,174,807 2,810,323 6,788,577
Children's Hospital 6,637 4,636 570,353 581,626
Total 2,810,083 1,179,444 3,380,636 7,370,203


Figure 2. Number of Hospitals in the 2009 AHA Universe, SID, and KID, by Region

text version

Figure 2. Number of Hospitals in the 2009 AHA Universe, SID, and KID, by Region


Return to Introduction

Figure 3. Percentage of U.S. Population in 2009 KID States, by Region Calculated Using the Estimated U.S. population on July 1, 2009.7

text version

Figure 3. Percentage of U.S. Population in 2009 KID States, by Region Calculated using the estimated U.S. Population on July 1, 2009



Figure 4. Number of Discharges in the 2009 KID, by State

text version

Figure 4. Number of Discharges in the 2009 KID, by State


Return to Introduction

Appendix II: State-Specific Restrictions

The table below enumerates the types of restrictions applied to the KIDS’ Inpatient Database. 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 KID:
  • AR: Arkansas Department of Health & Human Services
  • CT: Chime, Inc.
  • 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
  • M0: Hospital Industry Data Institute
  • NE: Nebraska Hospital Association
  • NM: New Mexico Department of Health
  • OH: Ohio Hospital Association
  • OK: Oklahoma State Department of Health
  • SC: South Carolina State Budget & Control Board
  • SD: South Dakota Association of Healthcare Organizations
  • TN: Tennessee Hospital Association
  • TX: Texas Department of State Health Services
  • WY: Wyoming Hospital Association
In these States the following data elements are set to missing for all hospitals:
  • DSHOSPID*, data source hospital identifier
  • HOSPSTCO*, unmodified hospital State, county FIPS code
  • HFIPSSTCO*, modified hospital State, county FIPS code
  • 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
The following additional data elements are set to missing for all Georgia hospitals:
  • PEDS_PCT, percent of hospital discharges, 20 years old or younger.
  • PEDS_DISC, number of hospital discharges; 20 years or younger.
  • TOTAL_DISC, total number of discharges.
*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: Chime, Inc.
  • 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.
Confidentiality of Hospitals - Limitation on Sampling
Limitations on sampling were needed for the following data sources:
  • CT: CHIME, Inc.
    • CHIME requested that one stand-alone children's hospital be excluded from the sampling frame.
  • GA: GHA: An Association of Hospitals & Health Systems
    • GHA requested that no more than 60% of Georgia hospitals be included in the KID.
    • One stand-alone children's hospital was excluded from the sampling frame.
    • Ninety-seven out of 162 Georgia hospitals (60%) were included in the 2009 KID.
  • IL: Illinois Department of Public Health
    • Illinois Department of Public Health requested that no more than 40% of Illinois discharges appear in any discharge quarter of KID data.
    • 2009 KID – About 9% of the discharges in Illinois were sampled. No hospitals were dropped from the sampling frame.
  • MI: Michigan Health & Hospital Association
    • Reporting of total charge is limited in the Michigan data. Twenty seven out of 134 hospitals were dropped from the sampling frame because they did not report 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.
  • NE: Nebraska Hospital Association
    • Nebraska Hospital Association requested that the two stand-alone children’s hospitals be excluded from the sampling frame.
  • SC: South Carolina State Budget & Control Board
    • South Carolina requested that two hospitals be excluded from the sampling frame.
  • VA: Virginia Health Information
    • The KID may not include more than 50% of the hospitals in Virginia.
    • Forty-six of 93 hospitals (49%) of the hospitals in Virginia were included in the 2009 KID.
Some States limit the hospitals that can be included in the KID. 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 KID for each KID State, refer to Table 7 in Appendix I.

  • CT: Chime, Inc.
  • 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 KID:
  • CT: Chime, Inc.
  • 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
  • NACHTYPE, National Association of Children’s Hospitals and Related Institutions (NACHRI) hospital type
  • 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 Months, or 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), age in months (AGEMONTH) and age in years (AGE) are suppressed for some records. In some cases, AGE is set to the midpoint of the age category.
  • CT: Chime, Inc.
    • Age in days at admission (AGEDAY) is set to missing on all records.
    • Age in months at admission (AGEMONTH) is set to missing on all records.
  • FL: Florida Agency for Health Care Administration
    • Age in days (AGEDAY) is set to missing on all records.
    • Age in months at admission (AGEMONTH) 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 months at admission (AGEMONTH) 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
15-20 17
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.
    • Age in months at admission (AGEMONTH) 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 months at admission (AGEMONTH) 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 6 age groups for the general patient population.
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
18-20 19
  • Texas also requested that age in years (AGE) be set missing for HIV or alcohol/drug use patients. The HIV or drug/alcohol use patients are 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’, ‘042’, ‘V08’.
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), age in days, (AGEDAY), age in years (AGE), age in months (AGEMONTH), gender (FEMALE), and race (RACE), are suppressed for some records. In some cases, AGE is set to the midpoint of the age category.
  • CT: Chime, Inc.
    • Admission month (AMONTH) is set to missing on all records.
  • FL: Florida Agency for Health Care Administration
    • Admission month (AMONTH) is set to missing on all records.
  • 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)
  • NY: New York State Department of Health
    • Birth Weight (BWT) is set to missing on all records.
  • OK: Oklahoma State Department of Health
    • Days from admission to procedure (PRDAYn) is set to missing on all records.
    • Birth Weight (BWT) is set to missing on all records.
Confidentiality of Physicians
The following data sources restrict the release of physician identifiers:
  • CT: CHIME, Inc.
  • 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
Missing Discharges
The following data sources may be missing discharge records for specific populations of patients:
  • IA: Iowa Hospital Association
    • The Iowa Hospital Association prohibits the release of two types of discharges: HIV Infections (defined by MDC of 25) and behavioral health including chemical dependency care or psychiatric care (defined by a service code of BHV). These discharges were not included in the source file provided to HCUP and are therefore not included in the KID.
  • 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 are therefore not included in the KID.
  • NY: New York State Department of Health
    • Beginning with data year 2009, the New York State Department of Health masks the hospital identifiers on abortion records. As a result, these records are not included in the KID.

Return to Introduction

Appendix III: Data Elements

Table 1. Data Elements in the KID Inpatient Core File

Note: Not all data elements in the KID are uniformly coded or available across all States. Each KID release differs in that some data elements were dropped, some were added, and the values of some data elements were changed.

Data elements that are italicized are not included in the 2009 KID, 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 2000, 2003, 2006, 2009 Admission on weekend: (0) admission on Monday-Friday, (1) admission on Saturday-Sunday  
ADAYWK 1997 Admission day of week: (1) Sunday, (2) Monday, (3) Tuesday, (4) Wednesday, etc.  
Admission month AMONTH 1997, 2000, 2003, 2006, 2009 Admission month coded from (1) January to (12) December CT, FL
Admission source ASOURCE 1997, 2000, 2003, 2006, 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, FL, GA, HI, IA, KS, KY, ME, MI, MN, MO, MT, NC, NE, OK, OR, PA, SC, SD, TN, TX, UT, VT, WA, WI, WY
ASOURCE_X 2000, 2003, 2006, 2009 Admission source, as received from data source using State-specific coding AZ, FL, GA, HI, IA, KS, KY, ME, MI, MN, MO, MT, NC, NE, OK, OR, PA, SC, SD, TN, TX, UT, VT, WA, WI, WY
ASOURCEUB92 2003, 2006, 2009 Admission source (UB-92 standard coding). For newborn admissions (ATYPE = 4): (1) normal delivery, (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 AZ, CA, FL, GA, HI, IA, KS, KY, MD, ME, MI, MN, MO, MT, NC, NE, OK, OR, PA, SC, SD, TN, TX, UT, VT, WA, WI, WY
POINTOFORIGIN_X 2009 Point of origin for admission or visit, as received from source CA, MD, ME
PPOINTOFORIGIN_UB04 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, MD, ME
TRAN_IN 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 1997, 2000, 2003, 2006, 2009 Admission type, uniform coding: (1) emergency, (2) urgent, (3) elective, (4) newborn, (5) trauma center beginning in 2003 data, (6) other CA
ELECTIVE 2003, 2006, 2009 Indicates elective admission: (1) elective, (0) non-elective admission  
Age at admission AGE 1997, 2000, 2003, 2006, 2009 Age in years coded 0-124 years  
AGEDAY 1997, 2000, 2003, 2006, 2009 Age in days coded 0-365 only when the age in years is less than 1 CT, FL, MA, ME, NH, SC, TX
AGEMONTH 1997, 2000, 2003, 2006, 2009 Age in months (when age < 11 years) CT, FL, ME, SC, TX, VA
Birth weight BWT 2000, 2003, 2006, 2009 Birth weight in grams CA, FL, IA, KS, LA, ME, MI, MN, MO, NE, NH, NV, NY, OH, OK, PA, SC, SD, TN, TX, UT, WA, WI, WV, WY
Chronic Conditions NCHRONIC 2009 Number of chronic conditions  
Clinical Classifications Software (CCS) category DXCCS1 - DXCCS25 2000, 2003, 2006, 2009 CCS category for all diagnoses. Beginning in 2009, the diagnosis array was increased from 15 to 25.  
DCCHPR1 1997 CCS category for principal diagnosis in 1997. CCS was formerly called the Clinical Classifications for Health Policy Research (CCHPR)  
PRCCS1 - PRCCS15 2000, 2003, 2006, 2009 CCS category for all procedures  
PCCHPR1 1997 CCS category for principal procedure in 1997. CCS was formerly called the Clinical Classifications for Health Policy Research (CCHPR)  
Diagnosis information DX1 - DX25 1997, 2000, 2003, 2006, 2009 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.  
DXV1 - DXV15 1997 Diagnosis validity flags  
HOSPBRTH 1997, 2000, 2003, 2006, 2009 Birth diagnosis, in this hospital  
NDX 1997, 2000, 2003, 2006, 2009 Number of diagnoses coded on the original record  
UNCBRTH 1997, 2000, 2003, 2006, 2009 Normal, uncomplicated birth in hospital  
Diagnosis Related Group (DRG) DRG 1997, 2000, 2003, 2006, 2009 DRG in use on discharge date  
DRG_NoPOA 2009 DRG in use on discharge date, calculated without Present On Admission (POA) indicators  
DRGVER 2000, 2003, 2006, 2009 Grouper version in use on discharge date  
DRG10 1997 DRG Version 10 (effective October 1992 - September 1993)  
DRG18 2000, 2003 DRG Version 18 (effective October 2000 - September 2001)  
DRG24 2006, 2009 DRG Version 24 (effective October 2006 - September 2007)  
Discharge quarter DQTR 1997, 2000, 2003, 2006, 2009 Coded: (1) Jan - Mar, (2) Apr - Jun, (3) Jul - Sep, (4) Oct - Dec  
DQTR_X 2006, 2009 Discharge quarter, as received from data source  
Discharge weights DISCWT 2000, 2003, 2006, 2009 Weight to discharges in AHA universe for national estimates. In 2000, the discharge weight DISCWTcharge should be used for estimates of total charges.  
DISCWT_U 1997 Weight to discharges in AHA universe for national estimates.  
DISCWTcharge 2000 Weight to discharges in AHA universe for total charge estimates.  
Discharge year YEAR 1997, 2000, 2003, 2006, 2009 Calendar year  
Disposition of patient (discharge status) DIED 1997, 2000, 2003, 2006, 2009 Indicates in-hospital death: (0) did not die during hospitalization, (1) died during hospitalization  
DISP 1997 Disposition of patient, uniform coding in 1997: (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  
DISPUB92 2000, 2003, 2006 Disposition of patient (UB-92 standard coding)  
DISPUB04 2009 Disposition of patient (UB-04 standard coding) CA, MD, ME
DISPUNIFORM 2000, 2003, 2006, 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, 2006, 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, 2006, 2009 CCS category for the external cause of injury and poisoning codes  
NECODE 2003, 2006, 2009 Number of external cause of injury codes on the original record.  
Gender of patient FEMALE 2000, 2003, 2006, 2009 Indicates gender for KID beginning in 1998: (0) male, (1) female  
SEX 1997 Indicates gender in 1997 KID: (1) male, (2) female  
Hospital information DSHOSPID 2000, 2003, 2006, 2009 Hospital number as received from the data source CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPID 2000, 2003, 2006, 2009 HCUP hospital number (links to Hospital file)  
HOSPNUM 1997 HCUP hospital number in 1997 (links to Hospital file)  
HOSPST 2000, 2003, 2006, 2009 State postal code for the hospital (e.g., AZ for Arizona)  
HOSPSTCO 2000 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 file.  
KID_STRATUM 2000, 2003, 2006, 2009 Hospital stratum used for weights.  
Indicates Emergency Department service HCUP_ED 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  
Length of Stay LOS 1997, 2000, 2003, 2006, 2009 Length of stay, edited  
LOS_X 1997, 2000, 2003, 2006, 2009 Length of stay, as received from data source ME
Location of the patient PL_UR_CAT4 2003 Urban–rural designation for patient’s county of residence: (1) large metropolitan, (2) small metropolitan, (3) micropolitan, (4) non-core  
PL_NCHS2006 2006, 2009 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 MA
Major Diagnosis Category (MDC) MDC 1997, 2000, 2003, 2006, 2009 MDC in use on discharge date  
MDC_NoPOA 2009 MDC in use on discharge date, calculated without Present on Admission (POA) indicators  
MDC10 1997 MDC Version 10 (effective October 1992 - September 1993)  
MDC18 2000, 2003 MDC Version 18 (effective October 2000 - September 2001)  
MDC24 2006, 2009 MDC Version 24 (effective October 2006 - September 2007)  
Median household income for patient's ZIP Code ZIPINC_QRTL 2003, 2006, 2009 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.  
ZIPINC 2000 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 1997 Median household income category in 1997: (1) $1-$25,000, (2) $25,001-$30,000, (3) $30,001-$35,000, (4) $35,001 and above  
Neonatal/ maternal flag NEOMAT 1997, 2000, 2003, 2006, 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 1997, 2000, 2003, 2006, 2009 Expected primary payer, uniform: (1) Medicare, (2) Medicaid, (3) private including HMO, (4) self-pay, (5) no charge, (6) other  
PAY1_N 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 2000, 2003, 2006, 2009 Expected primary payer, as received from the data source ME
PAY2 1997, 2000, 2003, 2006, 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 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 2000, 2003, 2006, 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 MDNUM1_R 2003, 2006, 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
MDID_S 1997, 2000 Synthetic attending physician number in 1997 and 2000 KID  
MDNUM2_R 2003, 2006, 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
SURGID_S 1997, 2000 Synthetic second physician number in 1997 and 2000 KID  
Procedure information PR1 - PR15 1997, 2000, 2003, 2006, 2009 Procedures, principal and secondary (ICD-9-CM)  
PRV1 -PRV15 1997 Procedure validity flag  
NPR 1997, 2000, 2003, 2006, 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  
PRDAY1 1997, 2000, 2003, 2006, 2009 Number of days from admission to principal procedure OH, OK, UT, WV
PRDAY2 - PRDAY15 2000, 2003, 2006, 2009 Number of days from admission to secondary procedures CO, IN, OH, OK, UT, VA, WI, WV
Race of Patient RACE 1997, 2000, 2003, 2006, 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 RECNUM 1997, 2003, 2006, 2009 HCUP unique record number  
KEY 2000 Unique record number for 2000 KID file  
Total Charges TOTCHG 1997, 2000, 2003, 2006, 2009 Total charges, edited  
TOTCHG_X 1997, 2000, 2003, 2006, 2009 Total charges, as received from data source ME

Return to Introduction

Table 2. Data Elements in the KID Hospital File

Note: Not all data elements in the KID are uniformly coded or available across all States. Each 2000 KID release differs in that some data elements were dropped, some were added, and the values of some data elements were changed.

Data elements that are italicized are not included in the 2009 KID, but are only available in previous years' files.
Type of Data Element HCUP Data Element Name Years Available Coding Notes Unavailable in 2009 for:
Universe Counts N_DISC_U 1997, 2000, 2003, 2006, 2009 Number of universe discharges in the KID_STRATUM  
N_BRTH_U 1997, 2000, 2003, 2006, 2009 Number of universe births in KID_STRATUM  
N_HOSP_U 1997, 2000, 2003, 2006, 2009 Number of universe hospitals in KID_STRATUM  
Sample Counts S_DISC_U 1997, 2000, 2003, 2006, 2009 Number of sampled discharges in the sampling stratum (KID_STRATUM or STRATUM)  
S_BRTH_U 1997, 2000, 2003, 2006, 2009 Number of sample births in KID_STRATUM  
S_CHLD_U 1997, 2000, 2003, 2006, 2009 Number of sample pediatric non-births in KID_STRATUM  
S_CMPB_U 1997, 2000, 2003, 2006, 2009 Number of sample complicated births in KID_STRATUM  
S_UNCB_U 1997, 2000, 2003, 2006, 2009 Number of sample uncomplicated births in KID_STRATUM  
S_HOSP_U 1997, 2000, 2003, 2006, 2009 Number of sample hospitals in KID_STRATUM  
SID (Frame) Counts PEDS_DISC 2000, 2003, 2006, 2009 Number of discharges, 20 years old or younger, from this hospital in the SID GA
PEDS_PCT 2000, 2003, 2006, 2009 Percentage of hospital discharges, 20 years old or younger, from this hospital in the SID GA
TOTAL_DISC 2000, 2003, 2006, 2009 Total number of discharges from this hospital in the SID GA
TOTDSCHG 1997 Total number of discharges from this hospital in the SID  
Hospital Identifiers HOSPID 2000, 2003, 2006, 2009 HCUP hospital identification number (links to inpatient Core files)  
HOSPNUM 1997 HCUP hospital identification number (links to inpatient Core files)  
AHAID 2000, 2003, 2006, 2009 AHA hospital identifier that matches AHA Annual Survey Database CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
IDNUMBER 2000, 2003, 2006, 2009 AHA hospital identifier without the leading 6 CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPNAME 2000, 2003, 2006, 2009 Hospital name from AHA Annual Survey Database AR, CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
NACHTYPE 1997, 2000, 2003, 2006, 2009 National Association of Children’s Hospitals and Related Institutions (NACHRI) hospital type: (0) not identified as a children’s hospital by NACHRI, (1) children’s general hospital, (2) children’s specialty hospital, (3) children’s unit in a general hospital GA, NE, OK
Hospital Location HOSPADDR 2000, 2003, 2006, 2009 Hospital address from AHA Annual Survey Database AR, CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPCITY 2000, 2003, 2006, 2009 Hospital city from AHA Annual Survey Database AR, CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPST 2000, 2003, 2006, 2009 Hospital State postal code for hospital (e.g., AZ for Arizona)  
HOSPSTCO 2003, 2006, 2009 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) CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HOSPZIP 2000, 2003, 2006, 2009 Hospital ZIP Code from AHA Annual Survey Database AR, CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
HFIPSSTCO 2006, 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) CT, GA, HI, IN, KS, LA, ME, MI, MO, NE, NM, OH, OK, SC, SD, TN, TX, WY
Hospital Characteristics KID_STRATUM 2000, 2003, 2006, 2009 Hospital stratum used for weights  
STRATUM 1997 Hospital stratum used for weights in 1997  
HOSP_BEDSIZE 2000, 2003, 2006, 2009 Bed size of hospital: (1) small, (2) medium, (3) large  
H_BEDSZ 1997 Bed size of hospital: (1) small, (2) medium, (3) large  
HOSP_CONTROL 2000, 2003, 2006, 2009 Control/ownership of hospital: (0) government or private, collapsed category, (1) government, nonfederal, public, (2) private, non-profit, voluntary, (3) private, invest-own, (4) private, collapsed category  
H_CONTRL 1997, 2009 Control/ownership of hospital: (1) government, nonfederal (2) private, non-profit (3) private, invest-own  
HOSP_LOCATION 2000, 2003, 2006, 2009 Location: (0) rural, (1) urban  
H_LOC 1997 Location: (0) rural, (1) urban  
HOSP_LOCTEACH 2000, 2003, 2006, 2009 Location/teaching status of hospital: (1) rural, (2) urban non-teaching, (3) urban teaching  
H_LOCTCH 1997 Location/teaching status of hospital: (1) rural, (2) urban non-teaching, (3) urban teaching  
HOSP_REGION 2000, 2003, 2006, 2009 Region of hospital: (1) Northeast, (2) Midwest, (3) South, (4) West  
H_REGION 1997 Region of hospital: (1) Northeast, (2) Midwest, (3) South, (4) West  
HOSP_TEACH 2000, 2003, 2006, 2009 Teaching status of hospital: (0) non-teaching, (1) teaching  
H_TCH 1997 Teaching status of hospital: (0) non-teaching, (1) teaching  
Discharge Years YEAR 1997, 2000, 2003, 2006, 2009 Calendar year  
Note: Because the following variables are not needed for calculating national estimates, they are no longer included in the KID Hospital file.
Discharge Weights CHLDWT 2000 Weight to pediatric non-births in universe for national estimates. In 2000, the discharge weight CHLDWTcharge should be used for estimates of total charges.  
CHLDWT_U 1997 Weight to pediatric cases in universe for national estimates. In the 1997 data, one weight CHLDWT_U is used to create all estimates.  
CHLDWTCHARGE 2000 Weight to pediatric non-births in universe for total charge estimates  
CMPBWT 2000 Weight to complicated births in universe for national estimates. In 2000, the discharge weight CMPBWTcharge should be used for estimates of total charges.  
CMPBWTCHARGE 2000 Weight to complicated births in universe for total charge estimates  
UNCBWT 2000 Weight to uncomplicated births in universe for national estimates. In 2000, the discharge weight UNCBWTcharge should be used for estimates of total charges.  
UNCBWTCHARGE 2000 Weight to uncomplicated births in universe for total charge estimates  
Frame Counts H_BRTH_F 1997, 2000 Number of frame HCUP births in KID_STRATUM  
H_CHLD_F 1997, 2000 Number of frame HCUP pediatric non-births in KID_STRATUM  
H_CMPB_F 1997, 2000 Number of frame HCUP complicated births in KID_STRATUM  
H_UNCB_F 1997, 2000 Number of frame HCUP uncomplicated births in KID_STRATUM  
H_DISC_F 1997, 2000 Number of frame HCUP discharges in KID_STRATUM  
H_HOSP_F 1997, 2000 Number of frame HCUP hospitals in KID_STRATUM  
Sample Counts S_CHLD 1997, 2000 Pediatric non-births sampled  
S_CMPB 1997, 2000 Complicated births sampled  
S_UNCB 1997, 2000 Uncomplicated births sampled  

Return to Introduction

Table 3. Data Elements in the KID Disease Severity Measures File

All data elements listed below are available for all States in the 2009 KID Disease Severity Measures Files.
Type of Data Element HCUP Data Element Name Years Available Coding Notes
AHRQ Comorbidity Software (AHRQ) CM_AIDS 2003, 2006, 2009 AHRQ comorbidity measure: Acquired immune deficiency syndrome
CM_ALCOHOL 2003, 2006, 2009 AHRQ comorbidity measure: Alcohol abuse
CM_ANEMDEF 2003, 2006, 2009 AHRQ comorbidity measure: Deficiency anemias
CM_ARTH 2003, 2006, 2009 AHRQ comorbidity measure: Rheumatoid arthritis/collagen vascular diseases
CM_BLDLOSS 2003, 2006, 2009 AHRQ comorbidity measure: Chronic blood loss anemia
CM_CHF 2003, 2006, 2009 AHRQ comorbidity measure: Congestive heart failure
CM_CHRNLUNG 2003, 2006, 2009 AHRQ comorbidity measure: Chronic pulmonary disease
CM_COAG 2003, 2006, 2009 AHRQ comorbidity measure: Coagulopathy
CM_DEPRESS 2003, 2006, 2009 AHRQ comorbidity measure: Depression
CM_DM 2003, 2006, 2009 AHRQ comorbidity measure: Diabetes, uncomplicated
CM_DMCX 2003, 2006, 2009 AHRQ comorbidity measure: Diabetes with chronic complications
CM_DRUG 2003, 2006, 2009 AHRQ comorbidity measure: Drug abuse
CM_HTN_C 2003, 2006, 2009 AHRQ comorbidity measure: Hypertension, uncomplicated and complicated
CM_HYPOTHY 2003, 2006, 2009 AHRQ comorbidity measure: Hypothyroidism
CM_LIVER 2003, 2006, 2009 AHRQ comorbidity measure: Liver disease
CM_LYMPH 2003, 2006, 2009 AHRQ comorbidity measure: Lymphoma
CM_LYTES 2003, 2006, 2009 AHRQ comorbidity measure: Fluid and electrolyte disorders
CM_METS 2003, 2006, 2009 AHRQ comorbidity measure: Metastatic cancer
CM_NEURO 2003, 2006, 2009 AHRQ comorbidity measure: Other neurological disorders
CM_OBESE 2003, 2006, 2009 AHRQ comorbidity measure: Obesity
CM_PARA 2003, 2006, 2009 AHRQ comorbidity measure: Paralysis
CM_PERIVASC 2003, 2006, 2009 AHRQ comorbidity measure: Peripheral vascular disorders
CM_PSYCH 2003, 2006, 2009 AHRQ comorbidity measure: Psychoses
CM_PULMCIRC 2003, 2006, 2009 AHRQ comorbidity measure: Pulmonary circulation disorders
CM_RENLFAIL 2003, 2006, 2009 AHRQ comorbidity measure: Renal failure
CM_TUMOR 2003, 2006, 2009 AHRQ comorbidity measure: Solid tumor without metastasis
CM_ULCER 2003, 2006, 2009 AHRQ comorbidity measure: Peptic ulcer disease excluding bleeding
CM_VALVE 2003, 2006, 2009 AHRQ comorbidity measure: Valvular disease
CM_WGHTLOSS 2003, 2006, 2009 AHRQ comorbidity measure: Weight loss
All Patient Refined DRG (3M) APRDRG 2003, 2006, 2009 All Patient Refined DRG
APRDRG_Risk_Mortality 2003, 2006, 2009 All Patient Refined DRG: Risk of Mortality Subclass
APRDRG_Severity 2003, 2006, 2009 All Patient Refined DRG: Severity of Illness Subclass
All-Payer Severity-adjusted DRG (HSS, Inc.) APSDRG 2003, 2006, 2009 All-Payer Severity-adjusted DRG
APSDRG_Mortality_Weight 2003, 2006, 2009 All-Payer Severity-adjusted DRG: Mortality Weight
APSDRG_LOS_Weight 2003, 2006, 2009 All-Payer Severity-adjusted DRG: Length of Stay Weight
APSDRG_Charge_Weight 2003, 2006, 2009 All-Payer Severity-adjusted DRG: Charge Weight
Disease Staging (Medstat) DS_DX_Category1 2003, 2006, 2009 Disease Staging: Principal Disease Category
DS_Stage1 2003, 2006, 2009 Disease Staging: Stage of Principal Disease Category
DS_LOS_Level 2003, 2006 Disease Staging: Length of Stay Level
DS_LOS_Scale 2003, 2006 Disease Staging: Length of Stay Scale
DS_Mrt_Level 2003, 2006 Disease Staging: Mortality Level
DS_Mrt_Scale 2003, 2006 Disease Staging: Mortality Scale
DS_RD_Level 2003, 2006 Disease Staging: Resource Demand Level
DS_RD_Scale 2003, 2006 Disease Staging: Resource Demand Scale
Linkage Variables HOSPID 2003, 2006, 2009 HCUP hospital identification number
RECNUM 2003, 2006, 2009 HCUP record identifier

Return to Introduction

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

All data elements listed below are available for all States in the 2009 KID Diagnosis and Procedure Groups files.
Type of Data Element HCUP Variable Name Years Available Coding Notes
Clinical Classifications Software category for Mental Health and Substance Abuse (CCS-MHSA) CCSMGN1 – CCSMGN15 2006 CCS-MHSA general category for all diagnoses
CCSMSP1 – CCSMSP15 2006 CCS-MHSA specific category for all diagnoses
ECCSMGN1–ECCSMGN4 2006 CCS-MHSA general category for all external cause of injury codes
Chronic Condition Indicator CHRON1 – CHRON25 2006, 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 2006, 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 CCS: Principal Diagnosis DXMCCS1 2009 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 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
Multi-Level CCS: Principal Procedure 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 2006, 2009 Procedure Class for all procedures: (1) Minor Diagnostic, (2) Minor Therapeutic, (3) Major Diagnostic, (4) Major Therapeutic
Linkage Variables HOSPID 2006, 2009 HCUP hospital identification number
RECNUM 2006, 2009 HCUP record identifier

Return to Introduction

ENDNOTES

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

2 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.

3 We used the following American Hospital Association Annual Survey Database (Health Forum, LLC © 2012) 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 VARIABLE */
/*******************************************************/
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 We performed this analysis during the development of the original 1997 KID.

5 Most AHA surveys do not cover a January-to-December calendar year for every hospital. The numbers of hospitals for the KID are based on the AHA Annual Survey files.

6 The columns in Table 7 are defined as follows:

7 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 KID. Healthcare Cost and Utilization Project (HCUP). January 2013. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov//db/nation/kid/kid_2009_introduction.jsp.
Are you having problems viewing or printing pages on this Website?
If you have comments, suggestions, and/or questions, please contact hcup@ahrq.gov.
Privacy Notice, Viewers & Players
Last modified 1/15/13