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HEALTHCARE COST AND UTILIZATION PROJECT — HCUP
A FEDERAL-STATE-INDUSTRY PARTNERSHIP IN HEALTH DATA
Sponsored by the Agency for Healthcare Research and Quality

The Agency for Healthcare Research and Quality and
the staff of the Healthcare Cost and Utilization Project (HCUP) thank you for
purchasing the HCUP 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:

• Uncomplicated in-hospital births (HOSPBRTH = 1 and UNCBRTH = 1)
• Complicated in-hospital births (HOSPBRTH = 1 and UNCBRTH = 0)
• All other pediatric cases (HOSPBRTH = 0).

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 healthcare 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:

• Data in fixed-width ASCII format on DVD or CD-ROM (prior to 2009).
• 2 million to 3 million pediatric inpatient records per year.
• 2,500 to 4,100 hospitals per year (all SID hospitals with pediatric discharges).
• Discharge-level weights to calculate national estimates for discharges.
• Hospital File to link the KID to data from the AHA Annual Survey Database.
• KID Documentation and tools – including file specifications, programming source code for loading ASCII data into SAS and SPSS, and value labels. Beginning in 2006, code is also provided for loading the KID ASCII file into STATA.

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:

Inpatient Core File: The Core file contains pediatric discharges sampled from community, non-rehabilitation hospitals in participating HCUP States. The unit of observation is an inpatient stay record. The Core file contains data elements for linkage, patient demographics, clinical information, and payment information. Sample weights for the three types of records, uncomplicated in-hospital births, complicated in-hospital births, and all other pediatric cases, are calculated separately by stratum and are added to each discharge in the Core File, as appropriate, so that only one discharge weight data element (DISCWT) is needed. See Table 1 of Appendix III for a list of data elements in the Inpatient Core File. This file is available in all years of the KID.

Hospital File: The hospital-level file contains one observation for each hospital included in the KID and contains variance estimation data elements, linkage data elements, and data elements that describe basic characteristics about hospitals. The unit of observation is the hospital. The HCUP hospital identifier (HOSPID) provides the linkage between the KID Inpatient Core file and the Hospital file. See Table 2 of Appendix III for a list of data elements in the Hospital File. This file is available in all years of the KID.

Disease Severity Measures File: This discharge-level file contains information from four different sets of disease severity measures. Information from the severity file is to be used in conjunction with the inpatient Core file. The unit of observation is an inpatient stay record. The HCUP unique record identifier (RECNUM) provides the linkage between the Core file and the Disease Severity Measures files. See Appendix III, Table 3 for a list of data elements in the Severity Measures Files. This file is available beginning with the 2003 KID.

Diagnosis and Procedure Groups Files: This discharge-level file contains data elements from AHRQ software tools designed to facilitate the use of the ICD-9-CM diagnostic and procedure information in the HCUP databases. The unit of observation is an inpatient stay record. The HCUP unique record identifier (RECNUM) provides the linkage between the Core file and the Diagnosis and Procedure Groups file. Table 4 in Appendix III contains a list of data elements in the Diagnosis and Procedure Groups files. These files are available beginning with the 2006 KID.

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:

• Table 1 for the Inpatient Core files (record = inpatient stay)
• Table 2 for the Hospital Weights files (record = hospital)
• Table 3 for the Disease Severity Measures files (record = inpatient stay). This file was added beginning with the 2003 KID.
• Table 4 for the Diagnosis and Procedure Groups files (record = inpatient stay). This file was added beginning with the 2006 KID.

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:

• A DVD drive
• A hard drive with 10 gigabytes of space available
• SAS, SPSS, STATA or similiar analysis software.

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.

• To locate the KID documentation on HCUP-US, choose "Databases" from the home page (http://www.hcup-us.ahrq.gov). The section labeled KIDS’ Inpatient Database (KID) is specific to the KID.

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).

• If anyone other than the original purchaser uses the KID data, be sure to have them read and sign a Data Use Agreement, after viewing the on-line Data Use Agreement Training Tool available on the HCUP-US Website (http://www.hcup-us.ahrq.gov). A copy of the signed Data Use Agreements must be sent to AHRQ. See page 2 for the mailing address.



• The KID contains discharge-level records, not patient-level records. This means that individual patients who are hospitalized multiple times in one year may be present in the KID multiple times. There is no uniform patient identifier available that allows a patient-level analysis with the KID. This will be especially important to remember for certain conditions for which patients may be hospitalized multiple times in a single year.

Calculating National Estimates

• To produce national estimates, use one of the following discharge weights to weight discharges in the KID Core files to pediatric discharges from all U.S. community, non-rehabilitation hospitals. The name of the discharge weight data element depends on the year of data and the type of analysis. In order to produce national estimates, you MUST use discharge weights.

• Similar to the NIS, proper statistical techniques must be used to calculate standard errors and confidence intervals when using the KID. For detailed instructions, refer to the special report Calculating Nationwide Inpatient Sample Variances on the HCUP-US Website (www.hcup-us.ahrq.gov). A report specific to the KID, Calculating Kids’ Inpatient Database (KID) Variances, is also available on www.hcup-us.ahrq.gov.



• The KID Comparison Reports (available on http://www.hcup-us.ahrq.gov) assess the accuracy of KID estimates. KID Comparison reports are available for 1997 and 2003.



• When creating national estimates, it is a good idea to check your estimates against other data sources, if available. For example, the National Hospital Discharge Survey (http://www.cdc.gov/nchs/products/series.htm#sr13) can provide benchmarks against which to check your national estimates for hospitalizations with more than 5,000 discharges.



• To ensure that you are using the weights appropriately and calculating estimates and variances accurately, you can also use HCUPnet, the free online query system (https://datatools.ahrq.gov/hcupnet). HCUPnet is a Web-based query tool for identifying, tracking, analyzing, and comparing statistics on hospitals at the national, regional, and State level. HCUPnet offers easy access to national statistics and trends and selected State statistics about hospital stays. This tool provides step–by–step guidance, helping researchers to quickly obtain the statistics they need. HCUPnet generates statistics using the NIS, KID, and SID for those States that have agreed to participate. In addition, HCUPnet provides Quick Statistics — ready-to-use tables on commonly requested information — as well as national statistics based on the AHRQ Quality Indicators.

Studying Trends

• When studying trends over time using the KID, be aware that the sampling frame for the KID changes over time (i.e., more States have been added). Estimates from earlier years of the KID may be subject to more sampling bias than later years of the KID. In order to facilitate analysis of trends using multiple years of KID data, an alternate set of KID discharge and hospital weights for the 1997 HCUP KID were developed. These alternative weights were calculated in the same way as the weights for the 2000 and later years of the KID. The report, Using the Kids’ Inpatient Database (KID) to Estimate Trends, includes details regarding the alternate weights and other recommendations for trends analysis. Both the KID trends report and the alternate weights are available on the HCUP-US Website under Methods Series (http://www.hcup-us.ahrq.gov/reports/methods/methods.jsp).



• Short-term rehabilitation hospitals are included in the 1997 KID, but are excluded from later years of the KID. Patients treated in short-term rehabilitation hospitals tend to have lower mortality rates and longer lengths of stay than patients in other community hospitals. The elimination of rehabilitation hospitals may affect trends but the effect is likely small since only about 3% of community hospitals are short-term rehabilitation hospitals and not all State data sources included short term rehabilitation hospitals. The KID-Trends weights account for this change in KID sampling.

Choosing Data Elements for Analysis

• For all data elements you plan to use in your analysis, first perform descriptive statistics and examine the range of values, including number of missing cases. Summary statistics for the entire KID are provided on the Summary Statistics page of the HCUP-US Website (http://hcup-us.ahrq.gov/db/nation/kid/kidsummarystats.jsp). When you detect anomalies (such as large numbers of missing cases), perform descriptive statistics by State for that data element to detect if there are State-specific differences. Sometimes performing descriptive statistics by hospital can be helpful in detecting hospital-specific data anomalies.


• Not all data elements in the KID are provided by each State data source. These data elements are provided on the KID because they can be valuable for research purposes but they should be used cautiously. For example, RACE is missing for a number of States; thus, national estimates using RACE should be interpreted and reported with caveats. Check the documentation and run frequencies by State to identify if a data element is not available in one or more States.


• Differences exist across the State data sources in the collection of information that could not be accounted for during HCUP processing to make the data uniform. Be sure to read State-specific notes for each data element that you use in your analysis – this information can be found on the Description of Data Elements page on the HCUP-US Website (http://hcup-us.ahrq.gov/db/nation/kid/kiddde.jsp).


• Data elements with "_X" suffixes contain State-specific coding (i.e., these data elements are provided by the data sources and have not been altered in any way). For some data elements (e.g., LOS_X and TOTCHG_X) this means that no edit checks have been applied. For other data elements (e.g., PAY1_X), the coding is specific to each State and may not be comparable to any other State.

Hospital-Level Data Elements

• Note that specific hospital identifiers, including the AHA identifier, are not available for 41% of hospitals in the KID because certain states do not release hospital identifiers. This means that for those hospitals, you will not be able to link the KID to outside data sources that require hospital-specific identifiers. However, there are hospital-level data elements for all hospitals in the KID that allow you to study certain hospital characteristics including ownership/control, teaching status, rural/urban location, bedsize, and Census region of the country.


• New hospital-level data elements were added to the KID hospital file beginning in 2009. These data elements, which are listed in Table 2 of Appendix III, are derived from the AHA Annual Survey Database (Health Forum, LLC © 2012). While these data elements enable a greater breadth of analysis, certain limitations apply.


1. Some of the new data elements pertain to nurse staffing at hospitals, which is reported as a total for the hospital/facility. Therefore, it is possible that some hospitals may also have included counts from nursing homes.
2. The adjusted patient days are based on inpatient days with an adjustment made for outpatient activities, calculated as: Inpatient days * (1 + Outpatient Revenue / Inpatient Revenue).
3. Data for hospitals that do not complete the AHA Annual Survey are missing from the new 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

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


• Although the KID contains up to 25 diagnoses (15 prior to the 2009 KID) and 15 procedures, the number of diagnoses and procedures varies by State. Some States provide as many as 31 diagnoses and procedures or more, while other States provide as few as 9 diagnoses and 6 procedures. Because very few cases have more than 25 diagnoses or 15 procedures, the diagnosis and procedure vectors were truncated to save space in the KID data files. Two data elements are provided which tell you exactly how many diagnoses and procedures were on the original records (NDX and NPR). See the notes on diagnoses (http://www.hcup-us.ahrq.gov/db/vars/dxn/nisnote.jsp) and procedures (http://www.hcup-us.ahrq.gov/db/vars/_prn/nisnote.jsp) on the HCUP-US website to view the number of diagnosis and procedure fields provided by each state.


• The collection and reporting of external cause of injury (E codes) varies greatly across States. Some States have laws or mandates for the collection of E codes; others do not. Some States do not require hospitals to report E codes in the range E870-E879 - "misadventures to patients during surgical and medical care" - which means that these occurrences will be underreported. Beginning with the 2003 KID, E codes have been separated from the other diagnoses stored in DX1-DX25 and placed in ECODE1-ECODE4. Be sure to read the State-specific notes on diagnoses for more details; this information can be found on the Description of Data Elements page on the HCUP-US Website (http://hcup-us.ahrq.gov/db/nation/kid/kiddde.jsp).

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 (https://www.census.gov/topics/housing/housing-patterns/about/core-based-statistical-areas.html).
   
   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.

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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:

• Hospitals that do not seek insurance payment or government reimbursement
• Rural providers.

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

(I) Is located in a county that:


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

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

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

These exemptions apply primarily to smaller rural public hospitals and, as a result, these facilities are less likely to be included in the sampling frame than other Texas hospitals. While the number of hospitals omitted appears sizable, those available for the 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:

• Revising definitions of the strata data elements
• Excluding rehabilitation hospitals from the hospital universe
• Changing the calculation of hospital universe discharges for the weights.

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.

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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:

W_ik = [T_k / (R_k * A_k)] * (4 ÷ Q_i)

In the birth strata (both complicated and uncomplicated):

• T_k is the total number of births reported in the AHA survey.
• A_k is the total number of adjusted births in the restricted sampling frame.
• In the uncomplicated birth strata, R_k is the frame sampling rate for uncomplicated in-hospital births calculated as the sum of the adjusted number of uncomplicated births sampled divided by the sum of the adjusted number of uncomplicated births in the restricted frame.
• In the complicated birth strata, R_k is the frame sampling rate for complicated in-hospital births.

In the non-newborn strata:

• T_k is the total number of non-newborns reported in the AHA survey.
• A_k is the total number of adjusted non-newborn discharges in the sampling frame.
• R_k is the frame sampling rate for non-newborns from all non-newborn discharges in the sampling frame.

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

T_k / A_k estimates the number of discharges in the population that is represented by each discharge in the sampling frame. R_k 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, R_k, was calculated as follows:

R_k = S_k / H_k

• S_k is the number of adjusted discharges sampled for the discharge type in stratum k.
• H_k is the number of adjusted discharges in the sampling frame for the discharge type in stratum k.

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.

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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:

• AHA universe hospitals and total discharges, including births
• Non-rehabilitation community hospitals in the SID and associated pediatric discharges
• Hospitals and pediatric discharges included in the KID.

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:

• The number of hospitals in the AHA universe
• The number of SID community hospitals with pediatric discharges
• The number of hospitals in the KID (and the percentage of AHA universe hospitals).

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:

• The estimated U.S. population
• The estimated population of States in the KID
• The percentage of estimated U.S. population included in KID States.

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

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Figure 1. KID States, by Region, 2009

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

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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

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

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Appendix II: State-Specific Restrictions

The table below enumerates the types of restrictions applied to the KIDS’ Inpatient Database. Restrictions include the following types:

• Confidentiality of hospitals
• Restricted identification of hospitals
• Limitation on sampling
• Restricted release of stratifiers
• Confidentiality of records
• Restricted release of age in years, age in months, or age in days
• Other restrictions
• Confidentiality of physicians
• Missing discharges.

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.

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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.

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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:

• "AHA Universe Hospitals" lists all community, non-rehabilitation hospitals in the AHA Survey data.
• "SID Community, Non-Rehabilitation Hospitals" lists potential KID sampling-frame hospitals before applying restrictions to the frame and before excluding hospitals without any pediatric discharges.
• "SID Community, Non-Rehabilitation Hospitals with Pediatric Discharges" lists potential KID sampling-frame hospitals with pediatric discharges before applying restrictions to the frame.
• "KID Sampling-Frame Hospitals" lists hospitals with pediatric discharges in the sampling frame after applying state-specific restrictions to the frame.
• "KID Sample Hospitals" lists the hospitals selected for the KID. Some hospitals may not be included in the KID because they had so few pediatric discharges that none were randomly sampled.

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.

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