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

HCUP Nationwide Ambulatory Surgery Sample (NASS)

ABSTRACT

The Nationwide Ambulatory Surgery Sample (NASS) is part of the Healthcare Cost and Utilization Project (HCUP), which is sponsored by the Agency for Healthcare Research and Quality (AHRQ).

The NASS was created to enable analyses of selected ambulatory surgery utilization patterns and to support public health professionals, administrators, policymakers, and clinicians in their decision making regarding this critical source of care. The NASS contains clinical and resource-use information that is included in a typical hospital-owned facility record abstract, including patient characteristics, clinical diagnostic and surgical procedure codes, disposition of patients, total charges, expected source of payment, and facility characteristics. Therefore, it enables government entities, industry professionals, and researchers to develop research concepts with data-driven applications.

The NASS is the largest all-payer ambulatory surgery database that has been constructed in the United States, yielding national estimates of major ambulatory surgery encounters performed in hospital-owned facilities. It contains information from 7.6 million ambulatory surgery encounters at 2,751 hospital-owned facilities that approximate an estimated 63 percent stratified sample of U.S. hospital-owned facilities performing ambulatory surgeries. Weights are provided to calculate national estimates totaling 10.6 million ambulatory surgery encounters in 2016.

The NASS is drawn from statewide data organizations that provide HCUP with data from ambulatory surgery encounters. Thirty-four HCUP Partner organizations participated in the 2016 NASS. See Appendix A, Table A.1 for a list of HCUP Partner organizations that contributed to the 2016 NASS.

By stratifying on important facility characteristics, the NASS is designed to be representative of U.S. hospital-owned facilities that perform ambulatory surgeries. Stratification is based on the following characteristics:

• Geographic region (Northeast, Midwest, South, and West)
• Hospital bed size (small, medium, and large dependent on region, location, and teaching status)
• Urban-rural location of the hospital (metropolitan and npnmetropolitan)
• Hospital teaching status
• Hospital ownership or control (public, for profit, and not for profit)

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

For more information on the NASS, visit the AHRQ-sponsored HCUP User Support (HCUP-US) website at www.hcup-us.ahrq.gov/db/nation/nass/nassdbdocumentation.jsp.

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INTRODUCTION TO THE NATIONWIDE AMBULATORY SURGERY SAMPLE (NASS)

 

1. Overview of NASS Data

The Healthcare Cost and Utilization Project (HCUP) Nationwide Ambulatory Surgery Sample (NASS) was created to enable analysis of selected ambulatory surgery utilization patterns and to support public health professionals, administrators, policymakers, and clinicians in their decision making regarding this critical source of care. The NASS has many research, policy, and other data-driven applications because it contains clinical and nonclinical information about major ambulatory surgeries and diagnoses as well as geographic, facility, and patient characteristics.

1.1 NASS Data Sources, Hospitals, and Encounters

The NASS is sampled from the HCUP State Ambulatory Surgery and Services Databases (SASD), which include various types of outpatient services, such as observation stays, lithotripsy, radiation therapy, imaging, chemotherapy, and labor and delivery. The specific types of ambulatory surgeries and outpatient services included in each SASD vary by State and data year. All SASD include data on ambulatory surgery encounters from hospital-owned facilities. Some States include data from nonhospital-owned facilities, although these are not included in the NASS.¹.

The 2016 NASS sample comprises data from 34 HCUP Partner organizations (33 States and the District of Columbia) States and the District of Columbia. Appendix A, Figure A.1 represents the geographic distribution of the HCUP Partner organizations that contributed to the 2016 NASS. The HCUP NASS States with the District of Columbia account for 83 percent of the U.S. population in 2016, an estimated 63 percent of hospital-owned facilities performing ambulatory surgeries, and an estimated 72 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 invasive, therapeutic surgical Current Procedural Terminology (CPT)-coded procedures that typically require the use of an operating room and regional anesthesia, general anesthesia, or sedation. These surgeries are flagged as narrow in the HCUP Surgery Flag Software.² They also belong to a subset of Clinical Classifications Software (CCS)³ procedure groups with a relatively high major ambulatory surgery volume, a substantial share of major ambulatory surgeries performed in hospital-owned facilities, and evidence of reliable reporting from SASD hospitals. Detailed major ambulatory surgery selection criteria are outlined in Section 3.2. A complete list of in-scope CCS procedure groups is included in Appendix B. The 2016 sample includes 2,751 hospitals, 7,608,879 in-scope major ambulatory surgery encounters (unweighted), and 10,623,113 in-scope major ambulatory surgery encounters (weighted for national estimates).

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.

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 tables. A hospital table 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 table 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 table contains entries for out-of-scope procedures with a key linking to the encounter table.

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 2016, the NASS Hospital File has 2,751 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 the sample of hospitals in participating States and the District of Columbia. For 2016, the NASS Encounter File has about 7.6 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 5 million records for 2016. 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.

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, 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 Current Procedure Terminology (CPT) procedure codes
• International Classification of Diseases, Tenth Revision, Clinical Modification (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_2016_Hospital.zip)
2. Encounter File (NASS_2016_Encounter.zip)
3. Supplemental File (NASS_2016_Supplemental.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 3.2 GB. The NASS files loaded into SAS are about 5.5 GB. In SAS, the largest use of space typically occurs during the sort procedure (PROC SORT), which requires workspace that is about three times the size of the file. Thus, the NASS files would require approximately 17 GB of available workspace to perform a sort procedure. 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.

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 Tools Loading Tutorial provides instructions on how to unzip (decompress) the HCUP Software Tools for ICD-10-CM/Procedure Coding System (PCS), save it on the computer, and load the tool into a standard statistical software package for application to HCUP or other administrative databases. Users also will learn how to verify that the tool has loaded correctly. Information about the transition to ICD-10-CM/PCS also is included in this tutorial. Although the instructions are specific to the Clinical Classifications Software (CCS) for ICD-10-CM/PCS, the steps are broadly applicable to other HCUP Tools.
• 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) Inpatient Sample (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.

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 invasive therapeutic surgical procedures that typically require the use of an operating room and regional anesthesia, general anesthesia, or sedation. Empirical selection criteria then are used to define qualifying CCS for Services and Procedures⁴ for procedure groups or in-scope major ambulatory surgeries. Selection criteria for a given CCS procedure group include the share of major ambulatory surgeries performed in hospital-owned facilities, the absolute volume of ambulatory surgeries in the SASD, and evidence that SASD hospitals are reliably reporting major ambulatory surgeries in the CCS group.
• Build the NASS hospital sampling frame. The NASS sampling frame is limited to SASD community nonrehabilitation hospitals with general acute care or children's service types also performing 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 in all four quarters of 2016, 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, American Hospital Association (AHA), and Centers for Medicare & Medicaid Services (CMS) data sources. 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 2016, 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 three related tables: Hospitals, Encounters, and Supplemental.

The relationship between the NASS universe, the SASD 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 2016, the NASS sample and sampling frame were identical because 100 percent of hospitals were sampled.

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

Abbreviations: NASS, Nationwide Ambulatory Surgery Sample; SASD, State Ambulatory Surgery and Services 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 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 Software⁵ 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 invasive therapeutic surgical procedures. A narrow procedure (1) involves incision, excision, manipulation, or suturing of tissue that penetrates or breaks the skin; (2) typically requires use of an operating room; and (3) requires 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.

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.⁶ Then, the following criteria were applied at the CCS procedure group level.

1. Hospital share. A substantial share of major ambulatory surgeries occurs in hospital-owned facilities.
2. Volume. A relatively high major ambulatory surgery volume is observed in the SASD.
3. Reporting quality. Hospitals are reliably submitting major ambulatory surgery data.

The selection criteria were developed in multiple stages and through analysis of SASD data from 2013 through 2016. We first set hospital-owned facility market share criteria: at least 25 percent of major ambulatory surgeries for the CCS procedure category. Then, we created a threshold for average annual multistate SASD major ambulatory surgery volumes based on observed data for 2013 through 2016 (we chose a threshold of 4,000 surgeries annually). Finally, we excluded three CCS procedure groups because SASD hospital data showed evidence of unreliable reporting of dental services, skin grafts, and wound debridement. The CCS category for Percutaneous Transluminal Coronary Angioplasty (PTCA) is in-scope for the 2016 NASS. However, the HCUP subsequently learned that Level II HCPCS codes, which are required for complete capture of PCTA procedures, were systematically missing from some sample hospitals. Beginning with the 2018 NASS, it was classified as out-of-scope based on this inability to obtain Level II HCPCS codes from all hospital-owned facilities in the sample. The final set of included, or in-scope, CCS procedure groups for the NASS is provided in Appendix B.

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 share, volume, and 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, facility selection criteria for the NASS are (1) community nonrehabilitation hospital type and (2) a service type of either general acute care or children's. This restriction is imposed because of a lack of uniformity across States regarding inclusion of other types of hospitals, such as psychiatric, long-term acute care, and specialty hospitals. This means that specialty hospitals (e.g., heart, orthopedic, women's) are not included in the current NASS design.

A key challenge for the NASS design is 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.⁷ 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 nonrehabilitation facility, with a service type of either general acute care or children's. 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 hospitals and hospitals reporting outpatient surgeries in the AHA Annual Survey) or the CMS POS file (for all other hospitals). See Table 1 for a description of the predictor variables obtained from AHA and CMS sources.

3.4 NASS Sampling Frame

Selection of SASD hospitals for the NASS sampling frame was limited to community nonrehabilitation hospitals with general acute care or children's service types also performing some in-scope major ambulatory surgeries.⁸

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 in all four quarters of 2016, 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 2,751 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 1.

Table 1. 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 2 contains values for the NASS stratification variables: census region, bed size category,⁹ location and teaching status, and ownership.¹⁰ There are 108 possible strata (i.e., unique combinations of region, bed size, location/teaching status, and ownership categories).

Table 2. 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. ¹⁰

Assignment of hospitals to the initial stratification scheme of 108 levels results in a number of strata with fewer than 10 sampling frame hospitals or small sampling fractions. In those cases, ownership category was collapsed, first by combining local and State government with voluntary hospitals.¹¹ 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 55 strata for 2016. 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 2016 NASS, all hospitals in the sampling frame were selected for inclusion in the NASS, resulting in an approximate 63 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 2016 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 2016 NASS sampling frame required that all hospitals qualifying for the frame submit data in all four quarters of 2016.

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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 HCUP NIS prior to 2012 used a stratified sampling design similar to the NASS, so techniques appropriate for the NIS prior to 2012 also are 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 2016, less than 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 HCUP NIS prior to 2012 used a stratified sample design similar to the NASS, so techniques appropriate for the NIS prior to 2012 also are 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.¹² 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 7.6 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:

• For 2016, about 12 percent of all ambulatory surgery encounters (unweighted) are missing information about total charges. Estimates of the sum of charges should use the product of the number of cases multiplied by the average charge to account for records with missing information.
• 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.
• 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.

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5 USER FEEDBACK AND QUESTIONS

The NASS is a new HCUP database, with 2016 as the first publicly available data year. 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 2016 NASS

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Figure A.1. HCUP States and the District of Columbia Included in the 2016 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 2016 NASS. Shaded States represent the 34 Partners that provide Ambulatory Surgery and Services data included in the NASS (California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Hawaii, Iowa, Illinois, Indiana, Kansas, Kentucky, Maryland, Maine, Michigan, Minnesota, Missouri, North Carolina, North Dakota, Nebraska, New Jersey, Nevada, New York, Ohio, Oklahoma, Oregon, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Wisconsin).)

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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, 2016

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Table A.4. Percentage of Population in NASS Sample, by Census Region, 2016

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Table A.5. Percentage of Encounters and Facilities in NASS Sample, by Census Region, 2016

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Appendix B: NASS In-Scope Major Ambulatory Surgeriess

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 2016 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, 2016

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Table D.2. NASS Encounter File Data Elements, 2016

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Table D.3. NASS Supplemental File Data Elements, 2016

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¹ The following States have at least one freestanding facility in the HCUP SASD: California, Florida, Illinois, Kentucky, Michigan, Missouri, North Carolina, Nevada, New York, Oklahoma, Oregon, Pennsylvania, South Carolina, Utah, and Wisconsin.
² Agency for Healthcare Research and Quality. Surgery Flag Software for Services and Procedures. Healthcare Cost and Utilization Project (HCUP). Last modified August 7, 2019. www.hcup-us.ahrq.gov/toolssoftware/surgeryflags_svcproc/surgeryflagssvc_proc.jsp. Accessed June 30, 2020. The terms narrow and broad are specific to the Surgery Flag Software. The 2016 NASS applied a preliminary version of v2019.2 that included narrow surgeries identified in the following ranges of CPT codes: surgical (10004-69990), emerging technology (0100T-0588T), and cardiac-related medical (92920-93986).
³The Clinical Classifications Software for Services and Procedures (CCS-Services and Procedures) is HCUP software that provides a method for classifying CPT codes and Healthcare Common Procedure Coding System (HCPCS) codes into clinically meaningful procedure categories. More than 9,000 CPT/HCPCS codes and 6,000 HCPCS codes are collapsed into 244 clinically meaningful categories that may be more useful for presenting descriptive statistics than are individual CPT or HCPCS codes. For more information, visit www.hcup-us.ahrq.gov/toolssoftware/ccs_svcsproc/ccssvcproc.jsp.
⁴Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for Services and Procedures. Healthcare Cost and Utilization Project (HCUP). Last modified May 2, 2018. www.hcup-us.ahrq.gov/toolssoftware/ccs_svcsproc/ccssvcproc.jsp. Accessed June 30, 2020.
⁵ Agency for Healthcare Research and Quality. Surgery Flag Software for Services and Procedures. Healthcare Cost and Utilization Project (HCUP). Last modified August 7, 2019. www.hcup-us.ahrq.gov/toolssoftware/surgeryflags_svcproc/surgeryflagssvc_proc.jsp. Accessed June 30, 2020. The terms narrow and broad are specific to the Surgery Flag Software. The 2016 NASS applied a preliminary version of v2019.2 that included narrow surgeries identified in the following ranges of CPT codes: surgical (10004-69990), emerging technology (0100T-0588T), and cardiac-related medical (92920-93986).
⁶ Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for Services and Procedures. Healthcare Cost and Utilization Project (HCUP). Last modified May 2, 2018. www.hcup-us.ahrq.gov/toolssoftware/ccs_svcsproc/ccssvcproc.jsp. Accessed June 30, 2020.
⁷ The AHA Annual Survey definition for outpatient surgery is as follows: Scheduled surgical services provided to patients who do not remain in the hospital overnight. The surgery may be performed in operating suites also used for inpatient surgery, specially designated surgical suites for outpatient surgery, or procedure rooms within an outpatient care facility. (American Hospital Association. TrendWatch Chartbook 2018 - Glossary. https://www.aha.org/system/files/2018-07/2018-aha-chartbook.pdf. Accessed June 30, 2020.)
⁸ The HCUP SASD contain a number of hospital-owned facilities performing major ambulatory surgeries that are not inpatient hospitals. In the NASS, these facilities are assigned the identifier of the hospital owner. Stratification, sampling, weighting, and reporting are performed using the hospital owner identifier and hospital characteristics.
⁹Bed size categories were originally established for the development of the Nationwide (National) Inpatient Sample (NIS). Cutoff points were chosen so that approximately one-third of the hospitals in each region, location, and teaching status combination would fall within each bed size category (small, medium, or large). For more information, reference the Introduction to the NIS, available at www.hcup-us.ahrq.gov/db/nation/nis/nisdbdocumentation.jsp.
¹⁰Hospital service type (general acute care and children's) was not used as a stratum because of its weaker predictive power in the predictive models and the small number of children's hospitals in the sample.
¹¹ The sampling fraction is defined as (number units in sample)/(number units in universe). The overall sampling fraction for the 2016 universe of hospitals and sampling frame is (2,751/4,398) = 0.63.
¹² The ownership category was chosen for collapsing because it had lower explanatory power in the predictive models than did bed size or location and teaching status. Census region was considered as an essential stratum to include in the design.
¹³ Carlson BL, Johnson AE, Cohen SB. An evaluation of the use of personal computers for variance estimation with complex survey data. J Off Statistics. 1993;9(4):795-814.

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