![](media/image1.png){width="6.499305555555556in"
height="4.780555555555556in"}

####### HCUP Nationwide Inpatient Sample 

####### Design of the HCUP Nationwide Inpatient Sample, 2000

May 24, 2002

###### Table of Contents

[EXECUTIVE SUMMARY 1](#executive-summary)

[Hospital Sample Design 1](#hospital-sample-design)

[NIS Sample 2](#nis-sample)

[Ten Percent Subsamples 2](#ten-percent-subsamples)

[Sampling Weights 2](#sampling-weights)

[Comparability with Prior NIS Releases
3](#comparability-with-prior-nis-releases)

[Data Analysis 3](#data-analysis)

[Variance Calculations 3](#variance-calculations)

[Longitudinal Analyses 3](#longitudinal-analyses)

[INTRODUCTION 4](#introduction)

[The NIS Hospital Universe 5](#the-nis-hospital-universe)

[Hospital Merges, Splits, and Closures
6](#hospital-merges-splits-and-closures)

[Stratification Variables 6](#stratification-variables)

[HOSPITAL SAMPLING FRAME 8](#hospital-sampling-frame)

[HOSPITAL SAMPLE DESIGN 9](#hospital-sample-design-1)

[Design Requirements 9](#design-requirements)

[Overview of the Sampling Procedure
10](#overview-of-the-sampling-procedure)

[Ten Percent Subsamples 10](#ten-percent-subsamples-1)

[Change to Hospital Sampling Procedure beginning with the 1998 NIS
10](#change-to-hospital-sampling-procedure-beginning-with-the-1998-nis)

[Zero-Weight Hospitals 11](#zero-weight-hospitals)

[FINAL HOSPITAL SAMPLE 11](#final-hospital-sample)

[SAMPLING WEIGHTS 17](#sampling-weights-1)

[Hospital Weights 17](#hospital-weights)

[Discharge Weights 18](#discharge-weights)

[Discharge Weights for 10 Percent Subsamples
18](#discharge-weights-for-10-percent-subsamples)

[DATA ANALYSIS 19](#data-analysis-1)

[Variance Calculations 19](#variance-calculations-1)

[Computer Software for Variance Calculations
19](#computer-software-for-variance-calculations)

[Longitudinal Analyses 20](#longitudinal-analyses-1)

[Discharge Subsamples 21](#discharge-subsamples)

[ENDNOTES 21](#endnotes)

###### Index of Tables

[**Table 1. Number of NIS States, Hospitals and Discharges, by Year**
4](#__RefHeading___Toc8727914)

[**Table 2. Hospital Universe** 5](#__RefHeading___Toc8727915)

[**Table 3. Bed Size Categories, by Region**
7](#__RefHeading___Toc8727916)

[**Table 4. Hospital Frame** 9](#__RefHeading___Toc8727917)

[**Table 5. Number of Sample Hospitals and Discharges, by Year**
11](#__RefHeading___Toc8727918)

[**Table 6. Number of Hospitals in the 2000 Universe, Frame, Target,
Sample and Surplus by Region** 12](#__RefHeading___Toc8727919)

[**Table 7. Percentage of U.S. Population in 2000 NIS States, by
Region** 12](#__RefHeading___Toc8727920)

[**Table 8. Number of Hospitals and Discharges in the 2000 Universe,
Frame, and Sample for States in the Sampling Frame**
14](#__RefHeading___Toc8727921)

[**Table 9. Texas Hospitals Included and Excluded from Sampling Frame by
Bed Size Category** 15](#__RefHeading___Toc8727922)

[Table 10. Texas Hospitals and Discharges Included and Excluded from
Sampling Frame By Control 16](#__RefHeading___Toc8727923)

# EXECUTIVE SUMMARY

The Nationwide Inpatient Sample (NIS) of the Healthcare Cost and
Utilization Project (HCUP) was developed to provide analyses of hospital
utilization, charges and quality of care across the United States. The
target universe includes all discharges from non-rehabilitation,
community hospitals in the United States that were open during any part
of the calendar year 2000. There were 4,839 hospitals in the hospital
universe in 2000. The 2000 NIS comprises all discharges from a sample of
hospitals in this target universe.

This report provides a detailed description of the 2000 NIS sample
design, as well as a summary of the resultant hospital sample. Sample
weights were developed to obtain national estimates of hospital and
inpatient parameters. These weights and other special-use weights are
described in detail. Previous NIS releases covered 1988 through 1999.
Cumulative information is provided for all previous years to provide a
longitudinal view of the database.

## Hospital Sample Design

The sampling frame of hospitals was composed of all AHA community
hospitals in each of the frame states that could be matched to the
discharge data provided to HCUP. There were 3,034 hospitals in the 2000
sampling frame, a 20% increase from the 1999 NIS. The NIS is a
stratified probability sample of hospitals in the frame, with sampling
probabilities calculated to select 20 percent of the universe contained
in each stratum. The overall objective was to select a sample of
hospitals generalizable to the target universe. With this objective in
mind, NIS sampling strata were defined based on five hospital
characteristics contained in the AHA hospital files.

1.  Geographic Region - Northeast, Midwest, West, and South;

2.  Control -- public, private not-for-profit and proprietary;

3.  Location -- urban or rural;

4.  Teaching Status -- teaching or non-teaching;

5.  Bed size -- small, medium, and large.

Once the universe of hospitals was stratified, up to 20 percent of the
total number of U.S. hospitals was randomly selected within each
stratum. If too few frame hospitals were in the stratum, then all frame
hospitals were selected for the NIS, subject to sampling restrictions
specified by states. The resulting sample included 994 hospitals, 20.5%
of the total hospital universe.

At the time the 2000 sample was drawn, the Agency for Healthcare
Research and Quality (AHRQ) had agreements with data sources from
twenty-eight states. Over 90% of the hospital universe is included in
the sampling frame for all but five of these states. For four states,
Hawaii, Illinois, Missouri and South Carolina, sampling restrictions
were dictated by the data source with the result that 61% to 82% of
state hospitals were included in the sampling frame. (Restrictions from
other states did not have an appreciable effect on the percentage of
hospitals in the sampling frame.)

For one state, Texas, only 70% of the hospitals supplied data to HCUP.
Certain Texas state-licensed hospitals, primarily the smaller hospitals,
are exempt from statutory reporting requirements. As a result, small
hospitals are substantially less likely to be included in the sampling
frame, while larger hospitals are more likely to be included. Although
the number of hospitals omitted appears sizable, these missing hospitals
contain only 6% of Texas discharges.

While 20% of the hospitals from each region are selected for the NIS,
the comprehensiveness of the sampling frame varies by region. In the
Northeast, 90% of hospitals are included in the sampling frame, while
77% of those hospitals in the West are included, with 63% in the South,
and 40% in the Midwest.

Because the NIS sampling frame has a disproportionate representation of
the more populous states, and hospitals with more annual discharges, its
comprehensiveness in terms of discharges is higher. The proportion of
the regional population in the NIS states ranges from 95% in the
Northeast to 45% in the Midwest.

## NIS Sample {#nis-sample .unnumbered}

The final NIS sample included 7,450,992 discharges from 994 hospitals
selected from all 28 frame states. Hospitals were sampled throughout
each region of the United States. In the Northeast and West, where a
higher proportion of states are represented, relatively fewer hospitals
are sampled from each state than in the South and Midwest, where the
proportion of states in the NIS is lower.

Ideally, relationships among outcomes and their correlates estimated
from the NIS should generally hold across all U.S. hospitals. However,
since only 28 states contributed data to this 2000 release, some
estimates may differ from estimates derived from comparative data
sources. See the *1999 HCUP Nationwide Inpatient Sample (NIS) Comparison
Report* for a comparison of statistics calculated from the 1999 NIS with
estimates from databases with similar populations to assess the
comparability of estimates. This special report is available on the 2000
NIS Documentation CD-ROM and on the HCUP Web site at
<http://www.ahrq.gov/data/hcup>/.

## Ten Percent Subsamples

Two non-overlapping 10 percent subsamples of discharges are drawn from
the NIS file for several reasons pertaining to data analysis. One reason
for creating the subsamples was to reduce processing costs for selected
studies that will not require the entire NIS. Another reason is that the
two subsamples may be used to validate models and obtain unbiased
estimates of standard errors. The subsamples were selected by drawing
every tenth discharge starting with two different randomly selected
starting points. Having a different starting point for each of the two
subsamples guaranteed that the resulting subsamples would not overlap.

## Sampling Weights {#sampling-weights .unnumbered}

It is necessary to incorporate sample weights to obtain national
estimates. Therefore, sample weights were developed separately for
hospital- and discharge-level analyses. Within a stratum, each NIS
sample hospital\'s universe weight is equal to the number of universe
hospitals it represents during the year. Since 20% of the AHA universe
hospitals in each stratum are sampled when possible, the hospital
weights are usually around five. The calculations for discharge-level
sampling weights are similar to the calculations of hospital-level
sampling weights. In the 10 percent subsamples, each discharge has a 10
percent chance of being drawn. Therefore, the discharge weights are
multiplied by 10 for each of the subsamples.

In the 2000 NIS files, the discharge weight data elements are named
DISCWT and DISCWTCHARGE.

-   Prior to the 2000 NIS, DISCWTCHARGE is not available, and DISCWT
    should be used to create all national estimates.

-   For the 2000 NIS, DISCWT should be used to create national estimates
    for all analyses except those that involve total charges, and
    DISCWTCHARGE should be used to create national estimates of total
    charges. Texas discharges were not included in the calculation of
    DISCWTCHARGE, and DISCWTCHARGE was set to zero for all Texas
    discharges because total charges were not available for the first
    half of the year from that state. Consequently, DISCWTCHARGE differs
    from DISCWT for NIS hospitals in the South region.

## Comparability with Prior NIS Releases {#comparability-with-prior-nis-releases .unnumbered}

There has been an upward trend since the first NIS release across
several dimensions: there are more states, more hospitals, more
discharges and greater representativeness of the national population.
The latter is a natural result of the major increase in states included
in the sampling frame. The twenty-eight 2000 NIS states include four
more states than the 1999 NIS and 20 more states than the original 1988
NIS. The four Southern states added to the 2000 NIS have substantially
increased the percentage of the regional population included, from 46%
in the 1999 NIS to 81% in the 2000 NIS. The annual number of hospitals
in the NIS has grown from 758 for 1988 to the present 994, while the
number of annual discharges has increased from 5.2 million to the
current 7.5 million.

## Data Analysis

### Variance Calculations 

It may be important for researchers to calculate a measure of precision
for some estimates based on the NIS sample data. Variance estimates must
take into account both the sampling design and the form of the
statistic. Standard formulas for a stratified, single-stage cluster
sampling without replacement may be used to calculate statistics and
their variances in most applications.

The NIS database includes a Hospital Weights file with variables
required by statistical software to calculate finite population
statistics. In addition to the sample weights described earlier,
hospital identifiers (Primary Sampling Units or PSUs), stratification
variables, and stratum-specific totals for the numbers of discharges and
hospitals are included so that finite-population corrections (FPCs) can
be applied to variance estimates.

### Longitudinal Analyses 

All frame hospitals within a stratum have an equal probability of
selection for the sample, regardless of whether they had been in prior
NIS samples. This deviates from the procedure used for earlier samples,
prior to data year 1998, which maximized the longitudinal component of
the NIS series. Hospitals that continue in the NIS for multiple
consecutive years are a subset of the hospitals in the NIS for any one
of those years. Consequently, longitudinal analyses of hospital-level
outcomes may be biased if they are based on any subset of NIS hospitals
limited to continuous NIS membership. The analyses may be more efficient
(e.g., produce more precise estimates) if they account for the potential
correlation between repeated measures on the same hospital over time.

# INTRODUCTION 

The Nationwide Inpatient Sample (NIS) of the Healthcare Cost and
Utilization Project (HCUP) was developed to provide analyses of hospital
utilization, charges and quality of care across the United States. The
target universe includes all acute-care discharges from all community
hospitals in the United States. The NIS comprises all discharges from a
sample of hospitals in this target universe. The 2000 NIS includes data
for calendar year 2000. Previous releases covered 1988 through 1999.
Table 1 shows the number of states, hospitals and discharges in each
year, and shows that prior years of HCUP included fewer states.

+--------+----------------------------+----------+---------+----------+
| []{#_  |                            |          |         |          |
| _RefHe |                            |          |         |          |
| ading_ |                            |          |         |          |
| __Toc8 |                            |          |         |          |
| 727914 |                            |          |         |          |
| .anc   |                            |          |         |          |
| hor}Ta |                            |          |         |          |
| ble 1. |                            |          |         |          |
| Number |                            |          |         |          |
| of NIS |                            |          |         |          |
| S      |                            |          |         |          |
| tates, |                            |          |         |          |
| Hos    |                            |          |         |          |
| pitals |                            |          |         |          |
| and    |                            |          |         |          |
| Disch  |                            |          |         |          |
| arges, |                            |          |         |          |
| by     |                            |          |         |          |
| Year   |                            |          |         |          |
+========+============================+==========+=========+==========+
| #### C | **States in the Frame**    | **Number | *       | **Sample |
| alenda |                            | of       | *Sample | Disc     |
| r Year |                            | States** | Hosp    | harges** |
|        |                            |          | itals** |          |
|        |                            |          |         | **(Mil   |
|        |                            |          |         | lions)** |
+--------+----------------------------+----------+---------+----------+
| 1988   | California, Colorado,      | 8--11    | 7       | 5.2--6.2 |
| --1992 | Florida, Iowa, Illinois,   |          | 58--875 |          |
|        | Massachusetts, New Jersey, |          |         |          |
|        | Washington, Arizona,       |          |         |          |
|        | Pennsylvania, and          |          |         |          |
|        | Wisconsin                  |          |         |          |
+--------+----------------------------+----------+---------+----------+
| 1993   | Add Connecticut, Kansas,   | 17       | 913     | 6.5      |
|        | Maryland, New York,        |          |         |          |
|        | Oregon, South Carolina     |          |         |          |
+--------+----------------------------+----------+---------+----------+
| 1994   | No new additions           | 17       | 904     | 6.4      |
+--------+----------------------------+----------+---------+----------+
| 1995   | Add Missouri, Tennessee    | 19       | 938     | 6.7      |
+--------+----------------------------+----------+---------+----------+
| 1996   | No new additions           | 19       | 906     | 6.5      |
+--------+----------------------------+----------+---------+----------+
| 1997   | Add Georgia, Hawaii, and   | 22       | 1012    | 7.1      |
|        | Utah                       |          |         |          |
+--------+----------------------------+----------+---------+----------+
| 1998   | No new additions           | 22       | 984     | 6.8      |
+--------+----------------------------+----------+---------+----------+
| 1999   | Add Maine, Virginia        | 24       | 984     | 7.2      |
+--------+----------------------------+----------+---------+----------+
| 2000   | Add Kentucky, North        | 28       | 994     | 7.5      |
|        | Carolina, Texas, and West  |          |         |          |
|        | Virginia                   |          |         |          |
+--------+----------------------------+----------+---------+----------+

Potential research issues focus on both discharge- and hospital-level
outcomes. Discharge outcomes of interest include trends in inpatient
treatments with respect to:

-   frequency,

-   charges,

-   lengths of stay,

-   effectiveness,

-   quality of care,

-   appropriateness, and

-   access to hospital care.

    Hospital outcomes of interest include:

```{=html}
<!-- -->
```
-   mortality rates,

-   complication rates,

-   patterns of care,

-   diffusion of technology, and

-   trends toward specialization.

These and other outcomes are of interest for the nation as a whole and
for policy-relevant inpatient subgroups defined by geographic regions,
patient demographics, hospital characteristics, physician
characteristics, and pay sources.

This report provides a detailed description of the NIS 2000 sample
design, as well as a summary of the resultant hospital sample. Sample
weights were developed to obtain national estimates of hospital and
inpatient parameters. These weights are described in detail. Tables
include cumulative information for all previous NIS releases to provide
a longitudinal view of the database.

# The NIS 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 American Hospital Association
(AHA) Annual Survey of Hospitals. For purposes of the NIS, the
definition of a community hospital is that used by the AHA: \"all
nonfederal short-term general and other specialty hospitals, excluding
hospital units of institutions.\" Consequently, Veterans Hospitals and
other federal hospitals (Department of Defense and Indian Health
Service) are excluded. Beginning with the 1998 NIS, rehabilitation
hospitals were excluded from the NIS hospital universe because the type
of care provided and the characteristics of the discharges from these
hospitals were markedly different from other short-term hospitals. Table
2 shows the number of universe hospitals for each year based on the AHA
Annual Survey.

  -------------------------------------------- --------------------------
  []{#__RefHeading___Toc8727915 .anchor}Table  
  2. Hospital Universe[^1]                     

  **Year**                                     **Number of Hospitals**

  1988                                         5,607

  1989                                         5,548

  1990                                         5,468

  1991                                         5,412

  1992                                         5,334

  1993                                         5,313

  1994                                         5,290

  1995                                         5,260

  1996                                         5,182

  1997                                         5,113

  1998                                         4,915

  1999                                         4,859

  2000                                         4,839
  -------------------------------------------- --------------------------

## Hospital Merges, Splits, and Closures 

All U.S. hospital entities that were designated community hospitals in
the AHA hospital file, except rehabilitation hospitals, were included in
the hospital universe. Therefore, if 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 for the year of the merge. Likewise, if a community
hospital split, the original hospital and all newly created community
hospitals were separate entities in the universe for the year of the
split. Finally, community hospitals that closed during a year were
included as long as they were in operation during some part of the
calendar year.

## Stratification Variables 

The NIS sampling strata were defined based on five hospital
characteristics contained in the AHA hospital files. In order to improve
the representativeness of the NIS, given the expansion of the number of
contributing states, the sampling and weighting strategy was evaluated
and revised for 1998 and subsequent data years. This included changes to
the definitions of the strata variables. A full description of this
process can be found in the special report on *Changes in NIS Sampling
and Weighting Strategy for 1998.* This report is available on the 2000
NIS Documentation CD-ROM and on the HCUP Web site at
<http://www.ahrq.gov/data/hcup>/. A description of the sampling
procedures and definitions of strata variables used from 1988 through
1997 can be found in the special report: *Design of the HCUP Nationwide
Inpatient Sample, Release 6.* This report is available on the 1997 NIS
Documentation CD-ROM and on the HCUP Web site. Beginning with the 1998
NIS, the stratification variables were defined as follows:

1.  *Geographic Region -- Northeast, Midwest, West, and South*. This is
    an important stratification variable 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.

2.  *Control -- government nonfederal (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 nonteaching, and Western urban
    nonteaching 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.

4.  *Teaching Status -- teaching or nonteaching*. The missions of
    teaching hospitals differ from nonteaching hospitals. In addition,
    financial considerations differ between these two hospital groups.
    Currently, the Medicare DRG payments are uniformly higher to
    teaching hospitals than to nonteaching hospitals. A hospital is
    considered to be a teaching hospital if it has an AMA-approved
    residency program, is a member of the Council of Teaching Hospitals
    (COTH) or has a ratio of full-time equivalent interns and residents
    to beds of .25 or higher.

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 shown in Table 3.

+-----------------------------------------------------------------------+
|   ------                                                              |
| ------------------------- ------------- -------------- -------------- |
|   []{#__RefHeading___Toc8727916                                       |
|   .anchor}Table 3. Bed Size                                           |
|   Categories, by Region                                               |
|                                                                       |
|   **Location and Teaching         **Hospital                          |
|   Status**                        Bed size**                          |
|                                                                       |
|                                                                       |
|                                **Small**     **Medium**     **Large** |
|                                                                       |
|   **NORTHEAST**                                                       |
|                                                                       |
|   Rural                           1-49          50-99          100+   |
|                                                                       |
|   Urban, nonteaching              1-124         125-199        200+   |
|                                                                       |
|   Urban, teaching                 1-249         250-424        425+   |
|                                                                       |
|   **MIDWEST**                                                         |
|                                                                       |
|   Rural                           1-29          30-49          50+    |
|                                                                       |
|   Urban, nonteaching              1-74          75-174         175+   |
|                                                                       |
|   Urban, teaching                 1-249         250-374        375+   |
|                                                                       |
|   **SOUTH**                                                           |
|                                                                       |
|   Rural                           1-39          40-74          75+    |
|                                                                       |
|   Urban, nonteaching              1-99          100-199        200+   |
|                                                                       |
|   Urban, teaching                 1-249         250-449        450+   |
|                                                                       |
|   **WEST**                                                            |
|                                                                       |
|   Rural                           1-24          25-44          45+    |
|                                                                       |
|   Urban, nonteaching              1-99          100-174        175+   |
|                                                                       |
|   Urban, teaching                 1-199         200-324        325+   |
|   ------                                                              |
| ------------------------- ------------- -------------- -------------- |
+-----------------------------------------------------------------------+

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 be in each bed size category (small, medium or large).
Different cutoff points for rural, urban nonteaching, 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 with small numbers of
hospitals in them.

Rural hospitals were not split according to teaching status, because
rural teaching hospitals were rare. For example, in 2000 rural teaching
hospitals were less than 1% of 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.

To further ensure geographic representativeness, implicit stratification
variables included state and three-digit zip code (the first three
digits of the hospital\'s five-digit zip code). The hospitals were
sorted according to these variables prior to systematic random sampling.

# HOSPITAL SAMPLING FRAME 

The *universe* of hospitals was established as all community hospitals
located in the U.S. with the exception, beginning in 1998, of
rehabilitation hospitals. However, it was not feasible to obtain and
process all-payer discharge data from a random sample of the entire
universe of hospitals because it would have been too costly to obtain
data from individual hospitals, and it would have been too burdensome to
process each hospital\'s unique data structure.

Therefore, the NIS *sampling frame* was constructed from the subset of
universe hospitals that released their discharge data for research use.
Two sources for all-payer discharge data were state agencies and private
data organizations, primarily state hospital associations. At the time
the 2000 sample was drawn, the Agency for Healthcare Research and
Quality (AHRQ) had agreements with 28 data sources that maintain
statewide, all-payer discharge data files to include their data in the
HCUP databases. Prior years of HCUP included fewer states, as shown in
Table 1.

The list of the entire frame of hospitals was composed of all AHA
community hospitals in each of the frame states *that could be matched
to the discharge data provided to HCUP*. If an AHA community hospital
could not be matched to the discharge data provided by the data source,
it was eliminated from the sampling frame (but not from the target
universe). Further restrictions were put on the sampling frames for
Georgia, Hawaii, Illinois, South Carolina, Missouri, and Tennessee, as
described below.

The Illinois Health Care Cost Containment Council stipulated that no
more than 40 percent of the discharges provided by Illinois could be
included in the database for any calendar quarter. Consequently, it was
necessary to reduce the number of Illinois hospitals in the NIS sampling
frame by drawing a systematic random sample of Illinois frame hospitals
prior to drawing the NIS sample. By trial and error, it was determined
that a sample of 67 percent of Illinois frame hospitals ultimately
yielded just under 40% of the discharges supplied by Illinois in each
calendar quarter of the NIS.

Georgia, Hawaii, South Carolina and Tennessee stipulated that only
hospitals that appear in sampling strata with two or more hospitals from
the state were to be included in the NIS. Due to this restriction, two
Georgia hospitals, five Hawaii hospitals, seven South Carolina hospitals
and one Tennessee hospital were excluded from the 2000 NIS sampling
frame. Two additional South Carolina hospitals, although in sampling
strata with other hospitals, were removed from the sampling frame due to
unique characteristics that would make them identifiable.

Missouri stipulated that only hospitals that had signed releases for
public use of the data should be included in the NIS. For 2000, 32
Missouri hospitals did not sign releases for public use of the data.
These hospitals were excluded from the sampling frame, leaving 73
hospitals in the 2000 frame.

The number of frame hospitals for each year is shown in Table 4.

  ----------------------------------------- -----------------------------
  []{#__RefHeading___Toc8727917             
  .anchor}Table 4. Hospital Frame           

  **Year**                                  **Number of Hospitals**

  1988                                      1,247

  1989                                      1,658

  1990                                      1,620

  1991                                      1,604

  1992                                      1,591

  1993                                      2,168

  1994                                      2,135

  1995                                      2,284

  1996                                      2,268

  1997                                      2,452

  1998                                      2,438

  1999                                      2,520

  2000                                      3,034
  ----------------------------------------- -----------------------------

# HOSPITAL SAMPLE DESIGN 

## Design Requirements 

The NIS is a stratified probability sample of hospitals in the frame,
with sampling probabilities calculated to select 20 percent of the
universe contained in each stratum. The overall objective was to select
a sample of hospitals generalizable to the target universe, which
includes hospitals outside the frame (i.e., having zero probability of
selection). Moreover, this sample was to be geographically dispersed,
yet drawn from the subset of states with inpatient discharge data that
agreed to provide such data to the project.

It should be possible, for example, to estimate DRG-specific average
lengths of stay over all U.S. hospitals using weighted average lengths
of stay, based on averages or regression estimates from the NIS.
Ideally, relationships among outcomes and their correlates estimated
from the NIS should generally hold across all U.S. hospitals. However,
since only 28 states contributed data to this 2000 release, some
estimates may differ from estimates from comparative data sources. When
possible, estimates based on the NIS should be checked against national
benchmarks, such as Medicare data or data from the National Hospital
Discharge Survey to determine the appropriateness of the NIS for
specific analyses.

The target sample size was 20 percent of the total number of community
hospitals in the U.S. for 2000. This sample size was determined by AHRQ
based on their experience with similar research databases.

Alternative stratified sampling allocation schemes were considered.
However, allocation proportional to the number of hospitals is preferred
for several reasons:

-   AHRQ researchers wanted a simple, easily understood sampling
    methodology. It was an appealing idea that the NIS sample could be a
    \"miniaturization\" of the universe of hospitals (with the obvious
    geographical limitations imposed by data availability).

-   AHRQ statisticians considered other optimal allocation schemes,
    including sampling hospitals with probabilities proportional to size
    (number of discharges), and they concluded that sampling with
    probability proportional to the number of hospitals was preferable.
    Even though it was recognized that the approach chosen would not be
    as efficient, the extremely large sample sizes yield good estimates.
    Furthermore, because the data are to be used for purposes other than
    producing national estimates, (e.g., regression modeling), it is
    critical that all hospital types, including small hospitals, are
    adequately represented.

## Overview of the Sampling Procedure 

Once the universe of hospitals was stratified, up to 20 percent of the
total number of U.S. hospitals was randomly selected within each
stratum. If too few frame hospitals were in the stratum, then all frame
hospitals were selected for the NIS, subject to sampling restrictions
specified by states. To simplify variance calculations, at least two
hospitals were drawn from each stratum. If fewer than two frame
hospitals were contained in a stratum, then that stratum was merged with
an \"adjacent\" stratum containing hospitals with similar
characteristics.

A systematic random sample was drawn from each stratum, after sorting
hospitals by state within each stratum, then by the three-digit zip code
(the first three digits of the hospital\'s five-digit zip code) within
each state, and then by a random number within each three-digit zip
code. These sorts ensured further geographic generalizability of
hospitals within the frame states, and random ordering of hospitals
within three-digit zip codes.

Generally, three-digit zip codes that are near in value are
geographically near within a state. Furthermore, the U.S. Postal Service
locates regional mail distribution centers at the three-digit level.
Thus, the boundaries tend to be a compromise between geographic size and
population size.

## Ten Percent Subsamples 

Two nonoverlapping 10 percent subsamples of discharges were drawn from
the NIS file for each year. The subsamples were selected by drawing
every tenth discharge starting with two different starting points
(randomly selected between 1 and 10). Having a different starting point
for each of the two subsamples guaranteed that they would not overlap.
Discharges were sampled so that 10 percent of each hospital\'s
discharges in each quarter were selected for each of the subsamples. The
two samples can be combined to form a single, generalizable 20 percent
subsample of discharges.

## Change to Hospital Sampling Procedure beginning with the 1998 NIS

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

Further description of the sampling procedures for earlier releases of
the NIS can be found in the special report: *Design of the HCUP
Nationwide Inpatient Sample, Release 6*. This report is available on the
1997 NIS Documentation CD-ROM and on the HCUP Web site. For a
description of the development of the new sample design for 1998 and
subsequent data years, see the special report: *Changes in NIS Sampling
and Weighting Strategy for 1998.* This report is available on the 2000
NIS Documentation CD-ROM and on the HCUP Web site.

## Zero-Weight Hospitals

Beginning with the 1993 NIS, the NIS samples no longer contain
zero-weight hospitals. For a description of zero-weight hospitals in the
1988-1992 samples, see the special report: *Design of the HCUP
Nationwide Inpatient Sample, Release 1.* This report is available on the
1988-1992 NIS Documentation CD-ROM.

# FINAL HOSPITAL SAMPLE 

Table 5 shows the annual numbers of hospitals and discharges in each
year of the NIS. For the 1988-1992 NIS, zero-weight hospitals were
maintained to provide a longitudinal sample, so figures are presented
for both the regular NIS sample and the total sample.

  ---------------------------------------------------------------------------------------------
  []{#__RefHeading___Toc8727918                                                
  .anchor}Table 5. Number of                                                   
  Sample Hospitals and                                                         
  Discharges, by Year                                                          
  ------------------------------- ------------- ---------------- ------------- ----------------
                                  **Regular                      **Total       
                                  Sample**                       Sample**      

  **Year**                        **Number of   **Number of      **Number of   **Number of
                                  Hospitals**   Discharges**     Hospitals**   Discharges**

  1988                            758           5,242,904        759           5,265,756

  1989                            875           6,067,667        882           6,110,064

  1990                            861           6,156,638        871           6,268,515

  1991                            847           5,984,270        859           6,156,188

  1992                            838           6,008,001        856           6,195,744

  1993                            913           6,538,976        \-            \-

  1994                            904           6,385,011        \-            \-

  1995                            938           6,714,935        \-            \-

  1996                            906           6,542,069        \-            \-

  1997                            1,012         7,148,420        \-            \-

  1998                            984           6,827,350        \-            \-

  1999                            984           7,198,929        \-            \-

  2000                            994           7,450,992        \-            \-
  ---------------------------------------------------------------------------------------------

Table 6 shows a summary of the 2000 NIS hospital sample by geographic
region and the number of:

-   universe hospitals (Universe),

-   frame hospitals (Frame),

-   target hospitals (Target = 20 percent of the universe),

-   sampled hospitals (Sample), and

-   surplus hospitals (Surplus = Target - Sample).

+---------+---------+---------+---------+---------+---------+---------+
| [       |         |         |         |         |         |         |
| ]{#__Re |         |         |         |         |         |         |
| fHeadin |         |         |         |         |         |         |
| g___Toc |         |         |         |         |         |         |
| 8727919 |         |         |         |         |         |         |
| .ancho  |         |         |         |         |         |         |
| r}Table |         |         |         |         |         |         |
| 6.      |         |         |         |         |         |         |
| Number  |         |         |         |         |         |         |
| of      |         |         |         |         |         |         |
| Ho      |         |         |         |         |         |         |
| spitals |         |         |         |         |         |         |
| in the  |         |         |         |         |         |         |
| 2000    |         |         |         |         |         |         |
| Uni     |         |         |         |         |         |         |
| verse,\ |         |         |         |         |         |         |
| Frame,  |         |         |         |         |         |         |
| Target, |         |         |         |         |         |         |
| Sample  |         |         |         |         |         |         |
| and     |         |         |         |         |         |         |
| Surplus |         |         |         |         |         |         |
| by      |         |         |         |         |         |         |
| Region  |         |         |         |         |         |         |
+=========+=========+=========+=========+=========+=========+=========+
| **Hos   | **Uni   | **      | **% of  | **T     | **S     | **Su    |
| pital** | verse** | Frame** | U       | arget** | ample** | rplus** |
|         |         |         | niverse |         |         |         |
| **R     |         |         | in      |         |         |         |
| egion** |         |         | Frame** |         |         |         |
+---------+---------+---------+---------+---------+---------+---------+
| No      | 675     | 610     | 90.4%   | 135     | 139     | 4       |
| rtheast |         |         |         |         |         |         |
+---------+---------+---------+---------+---------+---------+---------+
| Midwest | 1,398   | 558     | 39.9%   | 280     | 286     | 6       |
+---------+---------+---------+---------+---------+---------+---------+
| South   | 1,860   | 1,173   | 63.1%   | 372     | 381     | 9       |
+---------+---------+---------+---------+---------+---------+---------+
| West    | 9,06    | 693     | 76.5%   | 181     | 188     | 7       |
+---------+---------+---------+---------+---------+---------+---------+
| Total   | 4,839   | 3,034   | 62.7%   | 968     | 994     | 26      |
+---------+---------+---------+---------+---------+---------+---------+

For example, in 2000 the Northeast region contained 675 hospitals in the
universe. It also contained 610 hospitals in the frame, of which 139
hospitals were drawn for the sample. This was four more than the target
sample size of 139 hospitals, resulting in a surplus of four hospitals
over the target. The total sample exceeded the target by 26 hospitals,
with a resulting sample of 20.5% of the total hospital universe. There
was a sample surplus in each region because the number of hospitals
sampled in each stratum was rounded up to the next highest integer
whenever the sample target of 20 percent of the universe was not an
integer number of hospitals.

Table 7 shows a summary of the estimated population of the U.S. on July
1, 2000, and of states in the 2000 NIS, by geographic region, based on
U.S. Census Bureau population estimates released on the Internet on
December 29, 2000. For each geographic region Table 7 shows:

-   the estimated U.S. population on July 1, 2000,

-   the estimated population on July 1, 2000, of states in the 2000 NIS,
    and

-   the percentage of estimated U.S. population included in states in
    the 2000 NIS.

+-----------------+-----------------+-----------------+-----------------+
| []{#__RefHeadi  |                 |                 |                 |
| ng___Toc8727920 |                 |                 |                 |
| .anchor}Table   |                 |                 |                 |
| 7. Percentage   |                 |                 |                 |
| of U.S.         |                 |                 |                 |
| Population in   |                 |                 |                 |
| 2000 NIS        |                 |                 |                 |
| States, by      |                 |                 |                 |
| Region          |                 |                 |                 |
+-----------------+-----------------+-----------------+-----------------+
| **Region**      | **Estimated\    | **Population    | **Percent of    |
|                 | U.S.            | of\             | U.S.            |
|                 | Population**    | 2000 NIS        | Population**    |
|                 |                 | States**        |                 |
|                 |                 |                 | **in NIS        |
|                 |                 |                 | States**        |
+-----------------+-----------------+-----------------+-----------------+
| Northeast       | 53,644,868      | 50,745,042      | 94.6            |
+-----------------+-----------------+-----------------+-----------------+
| Midwest         | 64,473,034      | 29,031,025      | 45.0            |
+-----------------+-----------------+-----------------+-----------------+
| South           | 99,991,010      | 81,302,933      | 81.3            |
+-----------------+-----------------+-----------------+-----------------+
| West            | 63,444,653      | 56,280,631      | 88.7            |
+-----------------+-----------------+-----------------+-----------------+

For example, the estimated population of the Northeast region on July 1,
2000 was 53,644,868. The estimated population on July 1, 2000, of states
in the Northeast region that were included in the 2000 NIS was
50,745,042. This represents 94.6% of the total Northeast region
population. The percentage of estimated U.S population included in
states in the 2000 NIS was almost as high in the West (88.7%), but was
much lower in the Midwest (45.0%). The four Southern states added to the
2000 NIS have substantially increased the percentage of the regional
population included, from 45.9% in the 1999 NIS to 81.3% in the 2000
NIS.

Table 8 shows the number of hospitals and discharges in the universe,
frame, and sample for each state in the sampling frame for 2000. The
difference between the universe and the frame represents the difference
in the number of community hospitals in the 2000 AHA Annual Survey of
Hospitals and the number of community hospitals for which data were
supplied to HCUP in all states except Georgia, Hawaii, Illinois,
Missouri and South Carolina.

-   The number of hospitals in the Georgia frame is two less than the
    Georgia universe. Two hospitals were excluded because of sampling
    restrictions stipulated by Georgia.

-   The number of hospitals in the Hawaii frame is eight less than the
    Hawaii universe. Five hospitals were excluded because of sampling
    restrictions stipulated by Hawaii, and three hospitals identified in
    AHA data were not included in the data supplied to HCUP.

```{=html}
<!-- -->
```
-   The number of hospitals in the Illinois frame was randomly reduced
    to approximately 67 percent of the hospitals in the Illinois
    universe in order to comply with the agreement with the data source
    concerning the restriction on the number of Illinois discharges.

-   The number of hospitals in the Missouri frame is forty-six less than
    the Missouri universe. Thirty-two hospitals were excluded because
    they signed release for confidential use only, and fourteen
    hospitals identified in AHA data were not included in the data
    supplied to HCUP.

```{=html}
<!-- -->
```
-   The number of hospitals in the South Carolina frame is eleven less
    than the South Carolina universe. Nine hospitals were excluded
    because of sampling restrictions stipulated by South Carolina, and
    two hospitals identified in AHA data were not included in the data
    supplied to HCUP.

-   The number of hospitals in the Tennessee frame is eight less than
    the Tennessee universe. One hospital was excluded because of
    sampling restrictions stipulated by Tennessee, and seven hospitals
    identified in AHA data were not included in the data supplied to
    HCUP.

+-------------+-------------+-------------+-------------+-------------+
| []{#__R     |             |             |             |             |
| efHeading__ |             |             |             |             |
| _Toc8727921 |             |             |             |             |
| .anc        |             |             |             |             |
| hor}**Table |             |             |             |             |
| 8. Number   |             |             |             |             |
| of          |             |             |             |             |
| Hospitals   |             |             |             |             |
| and         |             |             |             |             |
| Discharges  |             |             |             |             |
| in the 2000 |             |             |             |             |
| Universe,   |             |             |             |             |
| Frame, and  |             |             |             |             |
| Sample for  |             |             |             |             |
| States in   |             |             |             |             |
| the         |             |             |             |             |
| Sampling    |             |             |             |             |
| Frame**     |             |             |             |             |
+=============+=============+=============+=============+=============+
| **State**   | **Number of | **Number of | **Number of | **Number of |
|             | Hospitals   | Hospitals   | Hospitals\  | Discharges\ |
|             | in          | in Frame**  | in Sample** | in Sample** |
|             | Universe**  |             |             |             |
+-------------+-------------+-------------+-------------+-------------+
| **AZ**      | 59          | 55          | 14          | 186,060     |
+-------------+-------------+-------------+-------------+-------------+
| **CA**      | 384         | 379         | 93          | 815,757     |
+-------------+-------------+-------------+-------------+-------------+
| **CO**      | 67          | 66          | 22          | 131,639     |
+-------------+-------------+-------------+-------------+-------------+
| **CT**      | 34          | 31          | 6           | 67,516      |
+-------------+-------------+-------------+-------------+-------------+
| **FL**      | 193         | 190         | 55          | 578,074     |
+-------------+-------------+-------------+-------------+-------------+
| ##### GA    | 150         | 148         | 60          | 314,302     |
+-------------+-------------+-------------+-------------+-------------+
| **HI**      | 21          | 13          | 3           | 19,619      |
+-------------+-------------+-------------+-------------+-------------+
| **IA**      | 115         | 115         | 54          | 208,064     |
+-------------+-------------+-------------+-------------+-------------+
| **IL**      | 194         | 130         | 69          | 634,799     |
+-------------+-------------+-------------+-------------+-------------+
| **KS**      | 133         | 121         | 57          | 185,410     |
+-------------+-------------+-------------+-------------+-------------+
| **KY**      | 100         | 95          | 31          | 152,427     |
+-------------+-------------+-------------+-------------+-------------+
| **MA**      | 73          | 68          | 10          | 35,261      |
+-------------+-------------+-------------+-------------+-------------+
| **MD**      | 48          | 47          | 13          | 182,428     |
+-------------+-------------+-------------+-------------+-------------+
| **ME**      | 36          | 36          | 16          | 205,671     |
+-------------+-------------+-------------+-------------+-------------+
| **MO**      | 119         | 73          | 40          | 322,345     |
+-------------+-------------+-------------+-------------+-------------+
| **NC**      | 112         | 109         | 36          | 343,893     |
+-------------+-------------+-------------+-------------+-------------+
| **NJ**      | 76          | 75          | 16          | 221,254     |
+-------------+-------------+-------------+-------------+-------------+
| **NY**      | 213         | 213         | 49          | 545,429     |
+-------------+-------------+-------------+-------------+-------------+
| **OR**      | 59          | 58          | 18          | 94,703      |
+-------------+-------------+-------------+-------------+-------------+
| **PA**      | 192         | 187         | 42          | 371,138     |
+-------------+-------------+-------------+-------------+-------------+
| **SC**      | 62          | 51          | 20          | 118,436     |
+-------------+-------------+-------------+-------------+-------------+
| **TN**      | 116         | 108         | 31          | 162,266     |
+-------------+-------------+-------------+-------------+-------------+
| **TX**      | 408         | 287         | 93          | 723,081     |
+-------------+-------------+-------------+-------------+-------------+
| **UT**      | 41          | 40          | 14          | 22,629      |
+-------------+-------------+-------------+-------------+-------------+
| **VA**      | 86          | 84          | 21          | 227,599     |
+-------------+-------------+-------------+-------------+-------------+
| **WA**      | 83          | 82          | 24          | 169,811     |
+-------------+-------------+-------------+-------------+-------------+
| **WI**      | 120         | 119         | 66          | 356,523     |
+-------------+-------------+-------------+-------------+-------------+
| **WV**      | 54          | 54          | 21          | 54,858      |
+-------------+-------------+-------------+-------------+-------------+
| **TOTAL**   | **3,348**   | **3,034**   | **994**     | **          |
|             |             |             |             | 7,450,992** |
+-------------+-------------+-------------+-------------+-------------+

It can be seen in Table 9 that only 70% of Texas hospitals were supplied
to HCUP. Certain Texas state-licensed hospitals are exempt from
statutory reporting requirements. Exempt hospitals include:

1.  Hospitals that do not seek insurance payment or government
    reimbursement, and

2.  Rural providers.

> The Texas statute that exempts rural providers from being required 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 that 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 the smaller hospitals, and as can be
seen from Table 9 below, small hospitals are substantially less likely
to be included in the sampling frame, while larger hospitals are more
likely to be included.

+--------+----------+---------+----------+---------+---------+---------+
| []{#_  |          |         |          |         |         |         |
| _RefHe |          |         |          |         |         |         |
| ading_ |          |         |          |         |         |         |
| __Toc8 |          |         |          |         |         |         |
| 727922 |          |         |          |         |         |         |
| .an    |          |         |          |         |         |         |
| chor}* |          |         |          |         |         |         |
| *Table |          |         |          |         |         |         |
| 9.     |          |         |          |         |         |         |
| Texas  |          |         |          |         |         |         |
| Hos    |          |         |          |         |         |         |
| pitals |          |         |          |         |         |         |
| In     |          |         |          |         |         |         |
| cluded |          |         |          |         |         |         |
| and    |          |         |          |         |         |         |
| Ex     |          |         |          |         |         |         |
| cluded |          |         |          |         |         |         |
| from   |          |         |          |         |         |         |
| Sa     |          |         |          |         |         |         |
| mpling |          |         |          |         |         |         |
| Frame  |          |         |          |         |         |         |
| by Bed |          |         |          |         |         |         |
| Size   |          |         |          |         |         |         |
| Cate   |          |         |          |         |         |         |
| gory** |          |         |          |         |         |         |
+--------+----------+---------+----------+---------+---------+---------+
| **Bed  | **Number | *       | **Number | *       | **      | **      |
| Size   | of       | *Number | of       | *Number | Percent | Percent |
| Cate   | n        | of AHA  | Ho       | of      | of      | of      |
| gory** | on-rehab | Disch   | spitals\ | Hos     | Ho      | Dis     |
|        | AHA      | arges** | In       | pitals\ | spitaIs | charges |
|        | C        |         | Sampling | Not     | I       | I       |
|        | ommunity |         | Frame**  | I       | ncluded | ncluded |
|        | Hos      |         |          | ncluded | in      | in      |
|        | pitals** |         |          | in      | S       | S       |
|        |          |         |          | S       | ampling | ampling |
|        |          |         |          | ampling | Frame** | Frame** |
|        |          |         |          | Frame** |         |         |
+--------+----------+---------+----------+---------+---------+---------+
| Small  | 199      | 350,660 | 109      | 90      | 55%     | 80%     |
+--------+----------+---------+----------+---------+---------+---------+
| Medium | 127      | 935,381 | 99       | 28      | 78%     | 92%     |
+--------+----------+---------+----------+---------+---------+---------+
| Large  | 82       | 1,      | 79       | 3       | 96%     | 99%     |
|        |          | 388,588 |          |         |         |         |
+--------+----------+---------+----------+---------+---------+---------+
| ####   | **408**  | **2,67  | **121**  | **287** | **70%** | **94%** |
|  Total |          | 4,629** |          |         |         |         |
+--------+----------+---------+----------+---------+---------+---------+

While the number of hospitals omitted appears sizable, it can be seen in
Table 10 below that the hospitals available to the NIS includes 94% of
AHA inpatient discharges from Texas hospitals. At the hospital level,
over 85% of non-profit hospitals and 95% of proprietary hospitals are
included in the NIS sampling frame.

  ------------------------------- -------------- ------------- --------------- --------- ------------- --------------
  []{#__RefHeading___Toc8727923                                                                        
  .anchor}Table 10. Texas                                                                              
  Hospitals and Discharges                                                                             
  Included and\                                                                                        
  Excluded from Sampling Frame By                                                                      
  Control                                                                                              

  **Included in Sampling Frame**  **Control**    **Number of   **Number of AHA **Mean    **Percent of  **Percent of
                                                 non-rehab AHA Discharges**    Bed       Hospitals**   Discharges**
                                                 Community                     Size**                  
                                                 Hospitals**                                           

  NO                              Public         95            103,987         31        73%           22%

                                  Non-Profit     19            39,193          54        13%           3%

                                  Proprietary    7             14,485          41        5%            2%

  **TOTAL HOSPITALS NOT IN                       **121**       **157,665**     **35**    **30%**       **6%**
  FRAME**                                                                                              

                                                                                                       

  YES                             Public         36            360,358         183       27%           78%

                                  Non-Profit     123           1,294,022       194       87%           97%

                                  Proprietary    128           862,584         141       95%           98%

  **TOTAL FRAME HOSPITALS**                      **287**       **2,516,964**   **169**   **70%**       **94%**

  **ALL**                                        **408**       **2,674,629**   **130**   **100%**      **100%**
  ------------------------------- -------------- ------------- --------------- --------- ------------- --------------

![](media/image2.jpeg){width="6.492361111111111in" height="4.875in"}

The above map shows that hospitals were sampled throughout each region
of the United States. It also shows the effect of stratification on the
sample. In the Northeast and West, where a higher proportion of states
are represented, relatively fewer hospitals are sampled from each state
than in the South and Midwest, where the proportion of states in the NIS
is lower.

# SAMPLING WEIGHTS 

Although the sampling design was simple and straightforward, it is
necessary to incorporate sample weights to obtain state and national
estimates. Therefore, sample weights were developed separately for
hospital- and discharge-level analyses. Hospital-level weights were
developed to weight NIS sample hospitals to the hospital universe.
Similarly, discharge-level weights were developed to weight NIS sample
discharges to the hospital universe.

## Hospital Weights 

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

W~s~(universe) = N~s~(universe) ÷ N~s~(sample),

where W~s~(universe) was the hospital universe weight, N~s~(universe)
and N~s~(sample) were the number of community hospitals within stratum s
in the universe and sample, respectively. Thus, each hospital\'s
universe weight (HOSPWT) is equal to the number of universe hospitals it
represented during that year. Since 20% of the hospitals in each stratum
were sampled when possible, the hospital weights are usually around
five.

## Discharge Weights 

The calculations for discharge-level sampling weights were similar to
the calculations of hospital-level sampling weights. The discharge
weights usually are constant for all discharges within a stratum.

The only exceptions were 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 NIS. For those
hospitals, we *adjusted* the number of observed discharges by a factor
of 4 ÷ Q, where Q was the number of calendar quarters for which the
hospital contributed discharges to the NIS. For example, when a sample
hospital contributed only two quarters of discharge data to the NIS, the
*adjusted* number of discharges was double the observed number. This
adjustment was done only for weighting purposes. The NIS dataset only
includes the actual (unadjusted) number of observed discharges.

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

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

DW~is~(universe) = \[DN~s~(universe) ÷ ADN~s~(sample)\] \* (4 ÷ Q~i~),

where DW~is~(universe) was the discharge weight, DN~s~(universe) was the
number of discharges from community hospitals in the universe within
stratum s; ADN~s~(sample) was the number of *adjusted* discharges from
sample hospitals selected for the NIS; and Q~i~ was the number of
quarters of discharge data contributed by hospital i to the NIS (usually
Q~i~ = 4). Thus, each discharge\'s weight (DISCWT) is equal to the
number of universe discharges it represented in stratum s during that
year. Since all discharges from 20% of the hospitals in each stratum
were sampled when possible, the discharge weights are usually around
five.

In the 2000 NIS files, the discharge weight data elements are named
DISCWT and DISCWTCHARGE. To produce national estimates, use DISCWT or
DISCWTCHARGE to weight sampled discharges in the Core file to the
discharges from all non-rehabilitation community hospitals located in
the U.S.

-   Prior to the 2000 NIS, DISCWTCHARGE is not available, and DISCWT
    should be used to create all national estimates.

-   For the 2000 NIS, DISCWT should be used to create national estimates
    for all analyses except those that involve total charges, and
    DISCWTCHARGE should be used to create national estimates of total
    charges. Texas discharges were not included in the calculation of
    DISCWTCHARGE, and DISCWTCHARGE was set to zero for all Texas
    discharges because total charges were not available for the first
    half of the year from that state. Consequently, DISCWTCHARGE differs
    from DISCWT for NIS hospitals in the South region.

## Discharge Weights for 10 Percent Subsamples 

In the 10 percent subsamples, each discharge had a 10 percent chance of
being drawn. Therefore, the discharge weights contained in the Hospital
Weights file were multiplied by 10 for each of the subsamples, and
DISCWT or DISCWTCHARGE should be multiplied by 5 for the two subsamples
combined.

# DATA ANALYSIS 

## Variance Calculations 

It may be important for researchers to calculate a measure of precision
for some estimates based on the NIS sample data. Variance estimates must
take into account both the sampling design and the form of the
statistic. The sampling design was a stratified, single-stage cluster
sample. A stratified random sample of hospitals (clusters) was drawn and
then *all* discharges were included from each selected hospital.

If hospitals inside the frame were similar to hospitals outside the
frame, the sample hospitals can be treated as if they were randomly
selected from the entire universe of hospitals within each stratum.
Standard formulas for a stratified, single-stage cluster sampling
without replacement could be used to calculate statistics and their
variances in most applications.

A multitude of statistics can be estimated from the NIS data. Several
computer programs are listed below that calculate statistics and their
variances from sample survey data. Some of these programs use general
methods of variance calculations (e.g., the jackknife and balanced
half-sample replications) that take into account the sampling design.
However, it may be desirable to calculate variances using formulas
specifically developed for some statistics.

These variance calculations are based on finite-sample theory, which is
an appropriate method for obtaining cross-sectional, nationwide
estimates of outcomes. According to finite-sample theory, the intent of
the estimation process is to obtain estimates that are precise
representations of the nationwide population at a specific point in
time. In the context of the NIS, any estimates that attempt to
accurately describe characteristics (such as expenditure and utilization
patterns or hospital market factors) and interrelationships among
characteristics of hospitals and discharges during a specific year from
1988 to 2000 should be governed by finite-sample theory.

Alternatively, in the study of hypothetical population outcomes not
limited to a specific point in time, 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, since the population is
defined at that point in time, and because the estimate is for a
characteristic as it existed at the time of sampling. This is in
contrast to the superpopulation model, which adopts a stochastic
viewpoint rather than a deterministic viewpoint. That is, the nationwide
population in a particular year is viewed as a random sample of some
underlying superpopulation over time. Different methods are used for
calculating variances under the two sample theories. The choice of an
appropriate method for calculating variances for nationwide estimates
depends on the type of measure and the intent of the estimation process.

## Computer Software for Variance Calculations 

The hospital weights will be useful for producing hospital-level
statistics for analyses that use the *hospital* as the unit of analysis,
and the discharge weights will be useful for producing discharge-level
statistics for analyses that use the *discharge* as the unit of
analysis. 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 (Statistical Analysis System)
procedures incorporate weights. In addition, several statistical
analysis programs have been developed that specifically calculate
statistics and their standard errors from survey data. Version 8 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 do
calculations for numerous statistics arising from the stratified,
single-stage cluster sampling design. Examples of the use of SAS, SUDAAN
and STATA to calculate variances in the NIS are presented in the special
report: *Calculating Nationwide Inpatient Sample Variances, 2000*. This
report is available on the 2000 NIS Documentation CD-ROM and on the HCUP
Web site. For an excellent review of programs to calculate statistics
from survey data, visit the following web site:
http://www.fas.harvard.edu/\~stats/survey-soft/.

The NIS database includes a Hospital Weights file with variables
required by these programs to calculate finite population statistics. In
addition to the sample weights described earlier, hospital identifiers
(Primary Sampling Units or PSUs), stratification variables, and
stratum-specific totals for the numbers of discharges and hospitals are
included so that finite-population corrections (FPCs) 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
variables are available for the entire universe of hospitals, including
those outside the sampling frame. For instance, the variables from the
AHA surveys and from the Medicare Cost Reports are available for nearly
all hospitals. To the extent that hospital-level outcomes correlate with
these variables, they may be used to sharpen regional and nationwide
estimates.

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

## Longitudinal Analyses 

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

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

## Discharge Subsamples 

The two nonoverlapping 10 percent subsamples of discharges were drawn
from the NIS file for each year for several reasons pertaining to data
analysis. One reason for creating the subsamples was to reduce
processing costs for selected studies that will not require the entire
NIS. Another reason is that the two subsamples may be used to validate
models and obtain unbiased estimates of standard errors. That is, one
subsample may be used to estimate statistical models, and the other
subsample may be used to test the fit of those models on new data. This
is a very important analytical step, particularly in exploratory
studies, where one runs the risk of fitting noise.

For example, it is well known that the percentage of variance explained
by a regression, R^2^, is generally overestimated by the data used to
fit a model. The regression model could be estimated from the first
subsample and then applied to the second subsample. The squared
correlation between the actual and predicted value in the second
subsample is an unbiased estimate of the model\'s true explanatory power
when applied to new data.

# ENDNOTES

[^1]: Most AHA surveys do not cover a January-to-December calendar year
    for every hospital. The numbers of hospitals for 1988-1991 are based
    on the HCUP calendar-year version of the AHA Annual Survey files. To
    create a calendar-year reporting period, data from the AHA surveys
    must be apportioned in some manner across calendar years. Survey
    responses were converted to calendar-year periods for 1988-1991 by
    merging data from adjacent survey years. The numbers of hospitals
    for 1992-1999 are based on the AHA Annual Survey files.

[^2]: Carlson, B.L., A.E. Johnson, and S.B. Cohen (1993). An Evaluation
    of the Use of Personal Computers for Variance Estimation with
    Complex Survey Data. *Journal of Official Statistics*, Vol. 9, No.
    4, 795-814.