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HCUP DATA USE AGREEMENT REQUIREMENTS

All Healthcare Cost and Utilization Project (HCUP) data users, including data purchasers and collaborators, must complete the online HCUP Data Use Agreement (DUA) Training Course and read and sign the HCUP DUA. Proof of training completion and signed DUAs must be submitted to the HCUP Central Distributor.

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

The online DUA Training course is available at www.hcup-us.ahrq.gov/tech_assist/dua.jsp.

The HCUP Nationwide DUA is available on the HCUP User Support (HCUP-US) website at www.hcup-us.ahrq.gov

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HCUP CONTACT INFORMATION

HCUP Central Distributor and HCUP User Support

Information about the content of the HCUP databases is available on the HCUP User Support (HCUP-US) website (www.hcup-us.ahrq.gov).

If you have questions, please review the HCUP Frequently Asked Questions located at www.hcup-us.ahrq.gov/tech_assist/faq.jsp.

If you need further technical assistance, please contact the HCUP Central Distributor and User Support team at:

Phone: 866-290-HCUP (4287) (toll free in the United States)
Email: hcup@ahrq.gov
Fax: (866) 792-5313 (toll free in the United States)

Mailing address:
HCUP Central Distributor
IBM Watson Health
5425 Hollister Ave, Suite 140
Santa Barbara, CA 93111

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The 2019 NASS sample comprises data from 35 HCUP Partner organizations (34 States and the District of Columbia). Appendix A, Figure A.1 represents the geographic distribution of the HCUP Partner organizations that contributed to the 2019 NASS. The HCUP NASS States with the District of Columbia account for 83 percent of the U.S. population in 2019, an estimated 68 percent of hospital-owned facilities performing ambulatory surgeries, and an estimated 76 percent of ambulatory surgery encounters. Details on the percentage of population, encounters, and facilities by region are provided in Appendix A, Table A.4 and Appendix A, Table A.5.

The NASS is limited to encounters with at least one in-scope major ambulatory surgery on the record, performed at hospital-owned facilities. In-scope major ambulatory surgeries are defined as selected therapeutic Current Procedural Terminology (CPT)-coded procedures that require the use of an operating room, penetrate or break the skin, and involve regional anesthesia, general anesthesia, or sedation to control pain. These surgeries are flagged as narrow in the HCUP Surgery Flag Software.³ They also belong to a subset of Clinical Classifications Software for Services and Procedures (CCS-Services and Procedures)⁴ categories with a relatively high major ambulatory surgery volume or aggregate charge total, and evidence of reliable reporting from SASD/SEDD hospitals. Detailed major ambulatory surgery selection criteria are outlined in Section 3.2. A complete list of 2019 in-scope CCS-Services and Procedure categories is included in Appendix B. The 2019 sample includes 2,958 hospitals, 8,994,101 in-scope major ambulatory surgery encounters (unweighted), and 11,880,487 in-scope major ambulatory surgery encounters (weighted for national estimates).

Although encounters are limited to those with at least one in-scope major ambulatory surgery on the record, the NASS Supplemental File provides information on other surgical and nonsurgical procedures performed during these encounters (see Section 1.3).

1.2 Data Restrictions

Some HCUP Partner organizations that contributed data to the NASS imposed restrictions on the release of certain data elements. In addition, because of confidentiality laws, some data sources were prohibited from providing HCUP with encounter records that indicated specific medical conditions, such as HIV/AIDS or behavioral health conditions. Detailed information on these restrictions is available in Appendix C.

1.3 File Structure of the NASS

The NASS is delivered as a set of related files. A hospital file lists hospitals in the NASS along with hospital attributes (e.g., teaching status, bed size category) as well as the encounter weight and sample stratum information. An encounter file links to the hospital table and contains information on the major ambulatory surgery encounter (e.g., patient age, expected source of payment, diagnoses), including information about in-scope major ambulatory surgeries. A related supplemental file contains entries for out-of-scope procedures with a key linking to the encounter file. Finally, a diagnosis and procedure group file contains information about diagnosis groupings with a key linking to the encounter file. (Note that this file is not available in the 2016 or 2017 NASS.)

Hospital File: This hospital-level file contains one observation for each hospital included in the NASS, along with encounter weight and stratum data elements. For 2019, the NASS Hospital File has 2,958 hospital-specific records. A list of data elements in the Hospital File is provided in Appendix D, Table D.1.

Encounter File: This encounter-level file contains 100 percent of ambulatory surgery encounters containing a major ambulatory surgery from hospital-owned facilities in participating States and the District of Columbia that meet facility inclusion criteria. For 2019, the NASS Encounter File has about 9.0 million ambulatory surgery encounter records (unweighted). Refer to Appendix D, Table D.2 for a list of data elements in the NASS Encounter File.

Supplemental File: This encounter-level file contains information on procedures that were performed during encounters recorded in the Encounter File but not considered to be in-scope major ambulatory surgeries in the NASS. The Supplemental File contains about 6.4 million records for 2019. Procedures included on the Supplemental File are limited to Healthcare Common Procedure Coding System (HCPCS) Level I (CPT) procedure codes. HCPCS Level II codes were excluded from the Supplemental File. Refer to Appendix D, Table D.3 for a list of data elements in the NASS Supplemental File.

Diagnosis and Procedure Groups File: Available beginning with the 2019 NASS, this encounter-level file contains information about International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis groups and comorbidity indicators for all diagnoses associated with encounters recorded in the Encounter File, derived from the Clinical Classifications Software Refined (CCSR) for ICD-10-CM Tool⁵ and Elixhauser Comorbidity Software Refined for ICD-10-CM.⁶ Refer to Appendix D, Table D.4 for a list of data elements in the NASS Diagnosis and Procedure Groups File.

1.4 NASS Data Elements

The coding of data elements in the NASS is consistent with the coding in other HCUP databases. The following three objectives guided the definition of data elements in all HCUP databases:

• Ensure usability without extensive editing by analysts
• Retain the largest amount of information available from the original sources, while still maintaining consistency among sources
• Structure the information for efficient storage, manipulation, and analysis

More information on the coding of HCUP data elements is available on the HCUP User Support (HCUP-US) website (www.hcup-us.ahrq.gov/db/coding.jsp).

After analyzing the availability of information from the HCUP Partner organizations, a set of common fields to be available in the NASS was created. The NASS contains more than 100 clinical and nonclinical variables, such as the following:

• Patient demographics (e.g., sex, age, race and ethnicity, urban-rural designation of residence, national quartile of the median household annual income for the patient's ZIP Code)
• HCPCS Level I, also known as CPT procedure codes
• ICD-10-CM diagnosis codes
• Total charges and expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay)
• Hospital characteristics (e.g., ownership, teaching status, region of the United States)

For comprehensive information about the NASS data elements, please refer to the NASS documentation on the HCUP-US website (www.hcup-us.ahrq.gov/db/nation/nass/nassdbdocumentation.jsp).

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2 GETTING STARTED

The HCUP NASS is distributed as comma-separated value (CSV) files delivered via secure digital download from the Online HCUP Central Distributor. The files are compressed and encrypted with SecureZIP® from PKWARE®.

The NASS product is downloaded in a single zipped file, which contains several data-related files and accompanying documentation. The three data-related files include the following compressed files:

1. Hospital File (NASS_2019_Hospital.zip)
2. Encounter File (NASS_2019 Encounter.zip)
3. Supplemental File (NASS_2019_Supplemental.zip)
4. Diagnosis and Procedure Groups File (NASS_2019_DX_PR_GRPS.zip)

To load and analyze the NASS data on a computer, users will need the following:

• The password provided by the HCUP Central Distributor
• A hard drive with 50 to 100 gigabytes (GB) of space available
• A third-party zip utility such as ZIP Reader, SecureZIP®, WinZip®, or Stuffit Expander®
• SAS®, SPSS®, Stata®, or similar analysis software

The total size of the CSV version of the NASS is 14.2 GB. The NASS files loaded into SAS are about 6.3 GB. Most SAS data steps will require twice the storage space of the file so that the input and output files can coexist.

With a file of this size, space easily could become a problem in a multistep program. It is not unusual to have several versions of a file marking different steps while preparing it for analysis, and there may be more versions for the actual analyses. Therefore, users should plan carefully because the amount of space required could escalate rapidly.

2.1 Decompressing the NASS Files

To extract the data files from the compressed download file, follow these steps:

1. Create a directory for the NASS on your hard drive.
2. Unzip the compressed NASS product file into the new directory using a third-party zip utility. This will place three compressed, encrypted data-related files in the new directory. You will be prompted to enter the encryption password (sent separately by email) to decrypt the file.
   
   Please note that attempts to unzip encrypted files using the built-in zip utility in Windows® (Windows Explorer) or Macintosh® (Archive Utility) will produce an error message warning of an incorrect password and/or file or folder errors. The solution is to use a third-party zip utility.
   
   Third-party zip utilities are available from the following reputable vendors on their official websites.
   • ZIP Reader (Windows) (free download offered by the PKWARE corporation)
   • SecureZIP for Mac or Windows (free evaluation and licensed/fee software offered by the PKWARE corporation)
   • WinZip (Windows) (evaluation and fee versions offered by the WinZip corporation)
   • Stuffit Expander (Mac) (free evaluation and licensed/fee software offered by Smith Micro corporation)
3. Unzip each of the compressed, encrypted data-related files using the same password and third-party zip utility method. This will place the data-related CSV files in the same directory by default.

2.2 Downloading and Running the Load Programs

Programs to load the data into SAS, SPSS, or Stata are available on the HCUP-US website. To download and run the load programs, follow these steps:

1. Go to the NASS Database Documentation page on HCUP-US at www.hcup-us.ahrq.gov/db/nation/nass/nassdbdocumentation.jsp.
2. Go to the "File Specifications and Load Programs" section on this page.
3. Click on "Nationwide SAS Load Programs", "Nationwide SPSS Load Programs", or "Nationwide Stata Load Programs" to go to the corresponding Load Programs page.
4. Select the data year and the database ("NASS") from the drop-down lists on this page.
5. Select and save the load programs you need. The load programs are specific to the data year and data-related file. Save the load programs into the same directory as the NASS CSV files on your computer.
6. Edit and run the load programs as appropriate for your computing environment to create the analysis files. For example, modify the directory paths to point to the location of your input and output files.

NOTE: The Encounter File and Supplemental File load programs use the same variable names for the array of CPT codes (e.g., CPT1) and their associated CCS-Services and Procedures categories (e.g., CCSCPT1). When merging the Encounter and Supplemental Files, this can result in CPT and CCS-Services and Procedures codes being overwritten unintentionally in one file or the other. To avoid this problem, edit the Supplemental File load program to change the names of the CPT and CCS variable names (e.g., change CPT1 to SUPPCPT1 and CCSCPT1 to SUPCCSCPT1).

2.3 NASS Documentation

Comprehensive documentation for the NASS files is available on the HCUP-US website (www.hcup-us.ahrq.gov/db/nation/nass/nassdbdocumentation.jsp). Users of the NASS can access complete file documentation, including variable notes, file layouts, summary statistics, and related technical reports. Similarly, data users can download SAS, SPSS, and Stata load programs. These important resources help the user understand the structure and content of the NASS and aid in using the database. Appendix A, Table A.2 details the comprehensive NASS documentation available on HCUP-US.

2.4 HCUP Online Tutorials

For additional assistance, the Agency for Healthcare Research and Quality (AHRQ) has created the HCUP Online Tutorial Series, a series of free, interactive courses that provide information on using HCUP data and tools and 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 Sample Design tutorial is designed to help users learn how to account for sample design in their work with the HCUP nationwide databases. The tutorial will be updated in the future to directly address the NASS sampling design.
• The Producing National HCUP Estimates tutorial is designed to help users understand how three of the nationwide databases—the National (Nationwide) (NIS), the Nationwide Emergency Department Sample (NEDS), and the Kids' Inpatient Database (KID)—can be used to produce national and regional estimates. A tutorial specific to the NASS database will be added in the future.
• 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.
• The HCUP Software Tools tutorial introduces users to the HCUP software tools, which can be applied to HCUP and other administrative databases to create new data elements from existing data, thereby enhancing a researcher's ability to conduct analyses. There are four modules within this course grouping the HCUP tools by the following coding systems: ICD-10-CM diagnoses, ICD-10-PCS procedures, CPT and HCPCS Level II codes, and ICD-9-CM diagnoses and procedures. Users will learn about the purpose of each tool and receive technical guidance for applying the tools to their data.

Other tutorials about the design or use of the HCUP databases are also available, and new tutorials are added periodically. The Online Tutorial Series is located on the HCUP-US website at www.hcup-us.ahrq.gov/tech_assist/tutorials.jsp.

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3 METHODS

3.1 Creation of the NASS

Creation of the NASS requires the following steps:

• Identify in-scope major ambulatory surgeries. The HCUP Surgery Flag Software (see Section 3.2) is used to identify major ambulatory surgeries as those with a taxonomy category of narrow. These are major therapuetic procedures that require the use of an operating room, penetrate or break the skin, and involve regional anesthesia, general anesthesia, or sedation to control pain. Empirical selection criteria then are used to define qualifying CCS Services and Procedures⁷ categories or in-scope major ambulatory surgeries. Selection criteria for a given CCS-Services and Procedures category include meeting volume or charge thresholds (i.e., the surgery accounts for at least .05% of total major ambulatory surgery volume in the SASD/SEDD or at least .05% of total charges associated with major ambulatory surgery encounters in the SASD/SEDD) and evidence that SASD/SEDD hospitals are reliably reporting major ambulatory surgeries in the CCS-Services and Procedures category.
• Build the NASS hospital sampling frame. The NASS sampling frame is limited to facilities owned by community hospitals (excluding rehabilitaiton and long-term acute care hospitals) in the SASD and SEDD⁸ that perform in-scope major ambulatory surgeries. Additional restrictions imposed for the NASS sampling frame were that the hospital have no gross irregularities in quarterly reporting volume, submit data to the SASD/SEDD in all four quarters of 2019, and not have an unusually low volume of encounters containing an in-scope major ambulatory surgery.
• Build encounter predictive models. NASS sampling frame hospitals are used to create models for volumes of encounters containing in-scope major ambulatory surgeries. The predictive model can be applied to hospitals outside the NASS sampling frame.
• Construct the universe of hospitals and ambulatory surgery encounters. A national list of all hospitals performing ambulatory surgeries is created using SASD/SEDD and the American Hospital Association (AHA), consistent with the approach used for other HCUP nationwide databases. (Prior to data year 2019, the Centers for Medicare & Medicaid Services (CMS) Provider of Services (POS) file was also used.) The encounter predictive model is applied to hospitals outside the NASS sampling frame and then combined with observed data from sampling frame hospitals to create national encounter volume estimates.
• Develop NASS sample strata. Strata are created using hospital characteristics. When needed, strata are collapsed to achieve reasonable hospital sample/universe ratios.
• Compute sample weights. Hospitals are sampled from the NASS sampling frame. For 2019, 100 percent of hospitals were sampled from the frame. On the basis of the set of NASS sample hospitals and universe totals, sample weights are computed that project the NASS encounters and major ambulatory surgeries to the universe.
• Create the NASS database. All of the previous steps culminate in a NASS database, which is a set of four related files: Hospital, Encounter, Supplemental and Diagnosis and Procedure Groups.

The relationship between the NASS universe, the SASD/SEDD sample, and the NASS sampling frame is portrayed in Figure 1. The predictive model for hospital major ambulatory surgery encounter volume is developed using the NASS sampling frame hospitals and then is applied to all other hospitals not in the sampling frame to generate the encounter universe. In 2019, the NASS sample and sampling frame were identical because 100 percent of hospitals were sampled.

Figure 1. NASS Hospital Universe, SASD/SEDD Sample, and NASS Sampling Frame

Abbreviations: NASS, Nationwide Ambulatory Surgery Sample; SASD, State Ambulatory Surgery and Services Databases; SEDD, State Emergency Department Databases.

Four concentric circles illustrating the relationship between the NASS universe, the SASD sample, and the NASS sampling frame. The innermost, smallest circle represents the NASS hospital sample. The next largest circle represents the NASS hospital sampling frame. The next largest circle represents the SASD/SEDD hospital sample. The outermost, largest circle represents the NASS universe (i.e., the Ambulatory Surgery National Hospital List).

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The following sections describe several of the NASS development steps in greater detail.

3.2 Selection of Major Ambulatory Surgeries

3.2.1 Definition of Major Ambulatory Surgery

HCUP Surgery Flag Software⁹ was used to identify surgical procedures of interest for the NASS. The Surgery Flag Software processes CPT procedure codes and classifies them as narrow, broad, or neither. The NASS will focus on surgeries in the narrow class, or major ambulatory surgeries. The narrowly defined flag (narrow flag) is the most targeted and restrictive surgical identifier, consisting of major therapeutic procedures. A narrow procedure (1) requires the use of an operating room, (2) penetrates or breaks the skin, and (3) involves regional anesthesia, general anesthesia, or sedation to control pain. Some common narrow procedures are cataract surgery and cholecystectomy. Other examples include appendectomy, gastric bypass, hysterectomy, hernia repair, and spinal fusion. The following three procedures, which are primarily performed for a diagnostic purpose, are assigned a narrow surgery flag based on the degree of their invasiveness: biopsies if the procedure is within an internal organ (e.g., brain, deep cervical node, stomach), thoracotomy with or without biopsy, and exploratory laparotomy with or without biopsy.

3.2.2 Selection of In-Scope Major Ambulatory Surgeries

Several selection criteria were used to define in-scope major ambulatory surgeries for the NASS. Prior to application of selection criteria, all major ambulatory surgeries identified by HCUP Surgery Flag Software were grouped in categories defined by CCS for Services and Procedures.¹⁰

Beginning with data year 2019, the following criteria were applied at the CCS-Services and Procedures category level:

1. Volume and charges. The ambulatory surgery accounts for at least .05% of total major ambulatory surgery volume in the SASD/SEDD or at least .05% of total charges associated with major ambulatory surgery encounters in the SASD/SEDD.
2. Reporting quality. Hospitals are reliably submitting major ambulatory surgery data. Four CCS-Services and Procedures categories are excluded because SASD/SEDD hospital data showed evidence of unreliable reporting or underreporting of dental services, skin grafts, wound debridement, and percutaneous transluminal coronary angioplasty (PTCA).¹¹

The final set of included, or in-scope, CCS-Services and Procedures categories for the 2019 NASS is provided in Appendix B. Appendix B also includes a running list of changes to the in-scope procedure groups over time.

Prior to data year 2019, the following criteria were applied at the CCS-Services and Procedures category level:

1. Hospital share. A substantial share of major ambulatory surgeries occurs in hospital-owned facilities (at least 25 percent of major ambulatory surgeries for the CCS-Services and Procedures category).
2. Volume. A relatively high major ambulatory surgery volume is observed in the SASD (4,000 surgeries annually).
3. Reporting quality. Hospitals are reliably submitting major ambulatory surgery data.

Figure 2 illustrates the relationship between ambulatory surgeries, major ambulatory surgeries, and in-scope major ambulatory surgeries.

Note that although encounters are limited to those with at least one in-scope major ambulatory surgery on the record, the NASS Supplemental File provides information on other (or out-of-scope) procedures performed during these encounters.

In the remainder of this document, we use the term major ambulatory surgery as synonymous with in-scope major ambulatory surgery for brevity.

Figure 2. Ambulatory Surgeries, Major Ambulatory Surgeries, and In-Scope Major Ambulatory Surgeries

Abbreviation: CPT, Current Procedural Terminology.

Three overlapping circles illustrating the relationship between ambulatory surgeries, major ambulatory surgeries, and in-scope major ambulatory surgeries. The smallest circle represents in-scope major ambulatory surgeries (i.e., those meeting volume or charge thresholds and passing reporting quality screens). The next largest circle, which encapsulates the smallest circle, represents major ambulatory surgeries (i.e., CPT codes tagged as "narrow"). The largest circle, which encapsulates the smaller two circles, represents ambulatory surgeries (i.e., CPT codes tagged as "narrow" or "broad" surgeries).

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3.3 Sampling Design of the NASS

The NASS is a stratified cluster sample of major ambulatory surgery encounters (see Section 3.2) occurring in hospital-owned facilities. The main objective of a stratified sample is to ensure that it is representative of the target universe with respect to factors in the stratification scheme. In this section, we summarize the NASS setting and universe definition, the process for constructing the sampling frame, the sample strata, the sampling plan, and the calculation of sample weights.

3.3.1 Ambulatory Surgery Setting and Universe Definition

Ambulatory surgeries are performed in hospital-owned facilities, nonhospital-owned ambulatory surgery centers (ASCs), or office settings. In this context, office means a place of service that is neither a hospital-owned facility nor an ASC. The office setting may include professional facilities with procedure rooms or surgical suites.

HCUP Partners provide information on ambulatory surgeries in hospital-owned facilities. About half of the Partners also provide ambulatory surgery data from facilities that are not hospital owned. The designation of a facility as hospital owned is specific to its financial relationship with a hospital that provides inpatient care and is not related to its physical location. Hospital-owned ambulatory surgery and other outpatient care facilities may be contained within the hospital, physically attached to the hospital, or located in a different geographic area. The designation as hospital owned means that HCUP can verify that the hospital is billing for this service.

The NASS is restricted to major ambulatory surgeries performed in the hospital-owned facilities, either in the hospital itself or in physically separate hospital-owned facilities. There are two reasons for this restriction. First, the SASD have more than twice as many hospital-owned facilities as facilities that are not hospital owned. Second, although the HCUP hospital sampling frame is well understood, much less is known about the HCUP sample of surgery facilities that are not hospital owned compared with all freestanding ASCs.

In addition to restricting attention to the hospital-owned facilities, facilities are limitied to U.S. community hospitals, defined as "all non-Federal, short-term, general, and other specialty hospitals, excluding hospital units of institutions."¹² Noncommunity hospitals are excluded because of inconsistent capture of data across HCUP States. Additionally, community hospitals that are either rehabilitation or long-term acute care (LTAC) facilities are excluded because these hospitals treat a unique patient population that has longer stays and higher costs.

A key challenge for the NASS design is the creation of national major ambulatory surgery encounter volume estimates (encounter universe), tabulated in strata used in the sampling design. National estimates do not exist for several reasons, but the most important is the definition of ambulatory surgery itself. Organizations collecting survey information from hospitals, such as the AHA, rely on verbal descriptions of ambulatory surgery.¹³ These descriptions leave room for interpretation and result in significant variation in which encounters hospitals report as ambulatory surgeries. In contrast, hospitals generally report total inpatient admissions, births, and emergency department visits with reasonable accuracy.

The NASS uses a CPT code- and data-based definition of major ambulatory surgeries (see Section 3.2). Self-reported hospital ambulatory surgery volumes from the AHA may or may not be consistent with the HCUP definition of major ambulatory surgeries, and it is challenging to ascertain that consistency. Consequently, rather than using an external reference source for major ambulatory surgery volumes, the NASS universe of major ambulatory surgery encounters was constructed by combining observed encounter volumes for hospitals in the NASS sampling frame and estimated encounter volumes for all other hospitals performing major ambulatory surgeries. Estimated encounter volumes were generated using a predictive model, described in Section 3.4.1.

3.3.2 Generating the Ambulatory Surgery National Hospital List

A crucial step in developing the NASS was generating a list of hospitals performing major ambulatory surgery outside the NASS sampling frame and hospital-specific predictor variables to compute estimated encounters using a predictive model (see Section 3.4.1).

Hospitals were included in the national list if they were a community hospital. Rehabilitation and LTAC hospitals were excluded. Hospitals reporting no outpatient surgeries in the AHA Annual Survey were then excluded from the national list.

Model predictor variables were obtained from the AHA Annual Survey (for HCUP SASD/SEDD hospitals and hospitals reporting outpatient surgeries in the AHA Annual Survey). See Table 2 for a description of the predictor variables obtained from AHA.

3.4 NASS Sampling Frame

Selection of SASD/SEDD hospitals for the NASS sampling frame was limited to facilities owned by community hospitals, excluding rehabilitation and LTAC hospitals.¹⁴

Additional restrictions imposed for the NASS sampling frame were that the hospital (1) have no gross irregularities in quarterly reporting volume, (2) submit data to the SASD/SEDD in all four quarters of 2019, and (3) not have an unusually low volume of encounters containing an in-scope major ambulatory surgery.

A comparison between the NASS hospital universe and the final NASS sample is provided in Appendix A, Table A.3.

3.4.1 NASS Encounter Predictive Model

Creation of the major ambulatory surgery encounter universe requires a method for estimating the volume of encounters containing major ambulatory surgeries for hospitals outside the NASS sampling frame. This estimation was accomplished by building a predictive model for encounters using data for the 2,958 hospitals in the NASS sampling frame.

The hospital-specific number of encounters containing at least one in-scope major ambulatory surgery was the outcome variable in the model. A model predicting the number of major ambulatory surgery encounters per hospital was developed using the NASS sampling frame hospitals. Predictor variables used in the model are reported in Table 2.

Table 2. Independent Variables Included in Encounter Predictive Model

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3.4.2 NASS Sampling Strata

Sampling strata were selected using results from the encounter predictive model (which quantify the importance of stratification factors in encounter volume variation) and a study of the current NIS and NEDS stratification schemes. Table 3 contains values for the NASS stratification variables: census region, bed size category,¹⁵ location and teaching status, and ownership.¹⁶ There are 108 possible strata (i.e., unique combinations of region, bed size, location/teaching status, and ownership categories).

Table 3. NASS Stratification Variables

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A goal was established to have at least 10 hospitals assigned to each stratum with as many strata as possible having a sampling fraction greater than .20. ¹⁷

Assignment of hospitals to the initial stratification scheme of 108 levels results in a number of strata with fewer than 10 sampling frame hospitals small sampling fractions. In those cases, ownership category was collapsed, first by combining local and State government with voluntary hospitals.¹⁸ If the goal still was not achieved, all the ownership types were combined. Other stratum combinations were constructed manually, using the following conventions:

• Require that the region dimension persist.
• Examine the strata with deficient ratios or sample sizes. On the basis of the data, elect to collapse the location and teaching or bed size dimensions using the following rules:
  • Collapse location and teaching into two categories (urban and rural nonteaching vs. teaching) or collapse the entire location and teaching dimension if required.
  • Collapse the entire bed size dimension if required.

We used judgment when manually collapsing the strata to ensure that no single stratum had a large percentage of total encounter or hospital volume. This led to relaxing the number of sampling frame hospitals or sampling fraction criteria for some strata. After manual adjustments, the NASS had 62 strata for 2019. In the end, all strata had at least 10 hospitals and sampling fractions greater than .20.

3.5 Sample Weights

To obtain nationwide estimates, encounter weights were developed combining the NASS universe of hospitals and encounters with the NASS sample hospitals and encounters.

Computation of sample weights is straightforward. Given a universe of encounter volumes in stratum s, the sample weight is computed as the ratio of NASS universe to sample encounter volumes so that the sample volume is inflated to agree with the universe volume within the stratum.

3.5.1 Frame Sampling Rate

For the 2019 NASS, all hospitals in the sampling frame were selected for inclusion in the NASS, resulting in an approximate 68 percent sample of universe hospitals.

3.5.2 Encounter Weights

Encounter weights were calculated by stratum. Within stratum s for hospital i, the universe weight for each encounter in the NASS sample was calculated as follows:

W_is(universe) = [N_s(universe) ÷ N_s(sample)] * (4 ÷ Q_i),

where W_is(universe) is the encounter weight, N_s(universe) represents the number of ambulatory surgery encounters in the universe within stratum s, N_s(sample) is the number of ambulatory surgery encounters from sample hospitals selected for the NASS, and Q_i represents the number of quarters of ambulatory surgery encounters contributed by hospital i to the NASS (for the 2018 NASS, Q_i = 4 for all hospitals). Thus, each encounter's weight is equal to the number of universe ambulatory surgery encounters it represents in stratum s during that year. W_is(universe) is named DISCWT in the NASS encounter table (see Appendix D, Table D.2).

The 2019 NASS sampling frame required that all hospitals qualifying for the frame submit data in all four quarters of 2019.

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4 HOW TO USE THE NASS FOR DATA ANALYSIS

This section provides a synopsis of special considerations for using the NASS.

4.1 Calculating National Estimates

To produce national estimates, weights MUST be used.

The major ambulatory surgery encounter weight (DISCWT) in the NASS Encounter Table should be used for producing nationwide, encounter-level statistics where the ambulatory surgery encounter is the unit of analysis.

Because the NASS is a stratified cluster sample, proper statistical techniques must be used to calculate standard errors and confidence intervals. For detailed instructions, refer to the HCUP Methods Series report #2003-02, Calculating Nationwide Inpatient Sample (NIS) Variances for Data Years 2011 and Earlier, on the HCUP-US website (www.hcup-us.ahrq.gov/). The NASS used a stratified sampling design similar to the HCUP NIS prior to 2012, so techniques appropriate for the NIS prior to 2012 are also appropriate for the NASS

4.2 Choosing Data Elements for Analysis

For all data elements to be used in the analysis, the analyst first should perform descriptive statistics and examine the range of values, including the number of missing cases. When anomalies (such as large numbers of missing cases) are detected, descriptive statistics can be computed by region for that variable to determine whether there are region-specific differences. Sometimes, computing descriptive statistics by hospital can be helpful in detecting hospital-specific data anomalies.

4.3 ICD-10-CM Diagnosis Codes and CPT Procedure Codes

Each unique analysis should consider limitations related to ICD-10-CM and CPT procedure codes.

• CPT procedure codes, which are copyrighted by the American Medical Association, can change each year in January. It is essential to check all procedure codes used for analysis to ensure that the codes are in effect during the time period(s) studied.
• ICD-10-CM diagnosis 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-10-CM codes change every October as new codes are introduced and some codes are retired. It is critical to check all ICD-10-CM codes used for analysis to ensure that the codes are in effect during the time period studied.
• The NASS contains fields for up to 15 diagnoses, up to 30 in-scope HCPCS Level I or CPT-coded procedures, and up to 30 out-of-scope HCPCS Level I or CPT-coded procedures, although the number of code fields populated varies by State because of reporting differences. Some States provide more than the maximum code fields retained on the NASS. To reduce the file size of the NASS, the number of codes retained was limited. For 2019, approximately 7.5 percent of all ambulatory surgery records report more fields than the maximum allowed on the NASS.

4.4 Missing Values

Missing data values can compromise the quality of estimates. For example, if the outcome for ambulatory surgery encounters with missing values is different from the outcome for ambulatory surgery encounters with valid values, then sample estimates for that outcome will be biased and inaccurately represent the ambulatory surgery utilization patterns. Several techniques are 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. 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.

Alternatively, 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 because the nonmissing cases would be representative of the missing cases. However, some adjustment still may be necessary for the estimates of totals. Sums of data elements (such as aggregate ambulatory surgery charges) containing missing values would be incomplete because cases with missing values would be omitted from the calculations. Estimates of the sum of charges should use the product of the number of cases times the average charge to account for records with missing information.

4.5 Variance Calculations

It may be important for researchers to calculate a measure of precision for some estimates based on the NASS sample data. Variance estimates must account for both the sampling design and the form of the statistic. The NASS sampling design consists of a stratified, single-stage cluster sample. A stratified random sample of hospitals (clusters) providing major ambulatory surgeries was drawn, and then all encounters with in-scope major ambulatory surgeries were included from each selected hospital. To accurately calculate variances from the NASS, appropriate statistical software and techniques must be used. For detailed instructions, refer to the HCUP Methods Series report #2003-02, Calculating Nationwide Inpatient Sample (NIS) Variances for Data Years 2011 and Earlier, on the HCUP-US website (www.hcup-us.ahrq.gov/). The NASS used a stratified sampling design similar to the HCUP NIS prior to 2012, so techniques appropriate for the NIS prior to 2012 are also appropriate for the NASS.

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

A multitude of statistics can be estimated from the NASS data. Several computer programs that calculate statistics and their variances from sample survey data are listed in Section 4.6. Some of these programs use general methods of variance calculations (e.g., the jackknife and balanced half-sample replications) that account for the sampling design. However, it may be desirable to calculate variances using formulas specifically developed for certain 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 NASS, any estimates that attempt to accurately describe characteristics and interrelationships among hospitals and ambulatory surgery encounters during a specific year should be governed by finite-sample theory. Examples include estimates of expenditure and utilization patterns.

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 of 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, discharge, encounter, or visit 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.

4.6 Computer Software for Weighted and Variance Calculations

Computer programs are readily available to perform weighted variance calculations. Several statistical programming packages allow weighted analyses.¹⁹ 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 8 or later of SAS contains procedures (PROC SURVEYMEANS and PROC SURVEYREG) for calculating statistics on the basis of specific sampling designs. Stata and SUDAAN® are two other common statistical software packages that perform calculations for numerous statistics arising from the stratified, single-stage cluster sampling design. Examples of the use of SAS, SUDAAN, and Stata to calculate NIS variances are presented in the special report Calculating Nationwide Inpatient Sample (NIS) Variances for Data Years 2011 and Earlier, on the HCUP-US website (www.hcup-us.ahrq.gov/). For a helpful review of programs to calculate statistics from survey data, visit the following website: www.hcp.med.harvard.edu/statistics/survey-soft/.

The NASS includes a Hospital File with variables required by these programs to calculate finite-population statistics. The file includes synthetic hospital identifiers (Primary Sampling Units, or PSUs), stratification variables, and stratum-specific totals for the numbers of ambulatory surgery encounters 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. Depending on the analysis problem, a large number of observations may be available, and it may be feasible to set aside a part of the data for validation purposes. Standard errors and confidence intervals then can be calculated from the validation data.

If the analytic file is too small to set aside a large validation sample, cross-validation techniques may be used. For example, tenfold cross-validation would split the data into 10 subsets of equal size. The estimation would take place in 10 iterations. In each iteration, the outcome of interest is predicted for one-tenth of the observations by an estimate based on a model that is fit to the other nine-tenths of the observations. Unbiased estimates of error variance then are obtained by comparing the actual values to the predicted values obtained in this manner.

4.7 Limitations of the NASS

The NASS contains about 9.0 million ambulatory surgery encounter records and many clinical and nonclinical data elements. Many research studies can be conducted with the data, but some limitations should be considered:

The NASS contains encounter-level records, not patient-level records. This means that individual patients who visit a hospital facility for ambulatory surgery multiple times in 1 year may be present in the NASS multiple times. No uniform patient identifier is available that would allow a patient-level analysis to identify individuals with more than one ambulatory surgery encounter or to track outcomes or additional follow-up care received after an encounter. In contrast, some HCUP State databases may be used for this type of analysis.

The database includes only HCPCS Level I or CPT codes. HCPCS Level II codes are excluded.

4.8 Considerations for Trending Over Time

When conducting longitudinal analyses, users should exercise caution and consider several aspects of the NASS design and changes to the design over time.

• Procedures considered in scope for the NASS sample can change from year to year (see Appendix B). These changes may result from an increase or decrease in the volume of procedures performed in the outpatient setting, as this determines whether a CCS-Services and Procedures category meets the threshold for inclusion in the NASS sample. Additionally, the 2018 NASS applied updated versions of the HCUP Surgery Flag Software for Services and Procedures (that expanded the range of possible CPT codes) and the CCS Services and Procedures Tool. Combined, these updates contributed to changes in the CPT procedures and CCS-Services and Procedures categories defined as in-scope for the NASS sample. The NASS in-scope procedure criteria also changed between data years 2018 and 2019, which resulted in some changes to the CCS-Services and Procedures categories considered in scope for the NASS.


• Earlier years of the NASS (2016-2018) undercount certain emergent surgeries. Prior to data year 2019, the NASS sample was limited to SASD encounters that involved surgeries defined as "narrow" by the HCUP Surgery Flag Software for Services and Procedures. Subsequent analyses revealed additional encounters involving "narrow" or major surgeries that were started in the emergency department and appeared in the State Emergency Department Databases (SEDD) but not in the SASD. As a result, these surgeries are undercounted in the 2016-2018 NASS. The procedures most impacted by this issue include appendectomy and removal of ectopic pregnancy (each undercounted by more than 50%) and cholecystectomy (undercounted by approximately 10%). In subsequent data years, these emergent in-scope surgeries are captured in the NASS.


• The hospital-owned facility universe for the NASS changed between data years 2018 and 2019. First, the universe was expanded to include specialty hospitals such as surgical, cancer, heart, and orthopedic facilities owned by community hospitals that performed in-scope major ambulatory surgeries. This resulted in volume increases in certain surgeries commonly performed in these types of facilities. Second, the universe was limited to hospitals included in the AHA Annual Survey that reported performing outpatient surgeries. In prior years, the CMS POS data were used to augment the information.

Refer to Appendix E for a summary of CCS-Services and Procedures procedure category totals in the 2016-2019 NASS and contributing reasons for large changes over time. For the subset of CCS categories affected by NASS design changes, trend analyses based on CCS-Services and Procedure category are not recommended.

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5 USER FEEDBACK AND QUESTIONS

The NASS is a new HCUP database, with 2016-2019 as the first publicly available data years. To optimize the usefulness of the data and related documentation, HCUP would like to hear from data users regarding any suggestions, comments, or issues. Please contact HCUP User Support at hcup@ahrq.gov or (866) 290-HCUP (4287).

Appendix A: NASS Introductory Information

Table A.1. HCUP Partner Organizations Participating in the 2019 NASS

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Figure A.1. HCUP States and the District of Columbia Included in the 2018 NASS

Abbreviation: HCUP, Healthcare Cost and Utilization Project; NASS, Nationwide Ambulatory Surgery Sample.

(A map of the United States showing which HCUP States are included in the 2019 NASS. Shaded States represent the 35 Partners that provide Ambulatory Surgery and Services data included in the NASS (Alaska, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Hawaii, Illinois, Indiana, Iowa, Kansas, Kentucky, Maine, Maryland, Michigan, Minnesota, Missouri, Nebraska, Nevada, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, and Wisconsin.) Non participating states are Alabama, Arkansas, Arizona, Delaware, Idaho, Louisiana, Massachusetts, Mississippi, Montana, New Hampshire, New Mexico, Rhode Island, Virginia, West Virginia, Washington, Wyoming,.

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Table A.2. NASS-Related Reports and Database Documentation Available on the HCUP-US Website

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Table A.3. NASS Target Universe, Sampling Frame, and Final Sample Characteristics, 2019

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Table A.4. Percentage of Population in NASS Sample, by Census Region, 2019

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Table A.5. Percentage of Encounters and Facilities in NASS Sample, by Census Region, 2019

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Appendix B: 2019 NASS In-Scope Major Ambulatory Surgeries

Table B.1. NASS In-Scope Clinical Classifications Software (CCS) for Services and Procedure Categories

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Appendix C: Data Restrictions

Table C.1 enumerates the types of restrictions applied to the 2019 Nationwide Ambulatory Surgery Sample. Restrictions include the following types:

• Confidentiality of hospitals
• Confidentiality of records
• Limited reporting of diagnosis codes for medical misadventures and adverse effects
• Missing encounters for specific populations of patients

Table C.1. Data Restrictions

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Appendix D: NASS Files and Data Elements

Table D.1. NASS Hospital File Data Elements, 2019

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Table D.2. NASS Encounter File Data Elements, 2019

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Table D.3. NASS Supplemental File Data Elements, 2019

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Table D.4. NASS Diagnosis and Procedure Groups File Data Elements, 2019

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Appendix E: Encounter Totals by In-Scope Clinical Classifications Software (CCS) for Services and Procedure Category, 2016-2019

Table E.1. Encounter Totals by In-Scope Clinical Classifications Software (CCS) for Services and Procedure Category, 2016-2019

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