This publication provides descriptive statistics for U.S. hospital inpatient stays in 1995 using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample. National estimates are provided for all discharges by principal diagnosis and by principal procedure. Statistics are presented on the number of discharges, mean length of stay, mean charges, charges in quartiles (25th, 50th, and 75th percentiles), percent who died in the hospital, percent male, and mean age. Diagnoses and procedures are categorized using the Clinical Classifications for Health Policy Research (CCHPR), a system for collapsing diagnosis and procedure codes into clinically meaningful categories. This update of the CCHPR incorporates International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) coding changes through September 1998.
CCHPR categories can be employed in many types of projects analyzing data on diagnoses and procedures, such as identifying populations for disease- or procedure-specific studies; providing statistical information (such as charges and length of stay) about relatively specific conditions; defining comorbidities; and cross-classifying procedures by diagnoses to provide insight into the variety of procedures performed for particular diagnoses.
By Anne Elixhauser, Ph.D., MEDTAP International; Claudia A. Steiner, M.D., M.P.H., Agency for Health Care Policy and Research; Charlotte A. Whittington, B.A., Social and Scientific Systems; and Eileen McCarthy, B.A., National Center for Health Statistics.
Today's unprecedented volume and pace of change in the health care system requires a new information paradigm in which evidence-based, standardized databases and the tools for using them are available to State and local users in a timely, meaningful format. Health care managers and other decisionmakers need user-friendly data and tools that will help them assess the effects of health care program and policy choices; accurately measure outcomes, community access to care, utilization, and costs; and guide future health care policymaking that will maintain both high quality of care and good value for the health care dollar.
The Agency for Health Care Policy and Research (AHCPR) has developed an array of health care decisionmaking and research tools that may be used by program managers, researchers, and others at the State and local levels. The Healthcare Cost and Utilization Project (HCUP) Clinical Classifications for Health Policy Research (CCHPR) tool, which is the subject of this Research Note, is the latest one to be released. This version of the CCHPR includes all ICD-9-CM coding changes through September 1998. To download electronic versions of CCHPR classification schemes, select to access Clinical Classifications for Health Policy Research, Version 2: Software and User's Guide.
The CCHPR software program aggregates ICD-9-CM codes, about 12,000 diagnosis codes and 3,500 procedure codes in total, into a smaller number of clinically meaningful, relatively homogenous clusters. ICD-9-CM codes are the standard codes used in all institutionally based records (e.g., hospitals, outpatient surgery centers) and in insurance claims data. CCHPR can be applied to all ICD-9-CM data from 1980 to date, with simple adjustments needed for data prior to 1993.
Without the CCHPR tool, clinical researchers have two options, neither of which is satisfactory. Researchers may work directly with the ICD-9-CM coded data; but 12,000 diagnosis codes and 3,500 procedure codes are cumbersome and often too detailed to be useful. Or researchers may use software that was designed to bundle or aggregate procedures for purposes of payment, such as diagnosis-related groups (DRGs). Unfortunately, however, these "reimbursement groupers" mask clinically important details about procedures that were performed. CCHPR, a "clinical grouper," makes ICD-9-CM data more amenable to clinically focused statistical analyses.
In studying hysterectomies, for example, if clinically focused researchers were to work only with ICD-9-CM coded data, they would need to search for six ICD-9-CM codes to reign in all hysterectomies. If these same researchers were to use ICD-9-CM coded data grouped into DRGs, the data would mask clinically relevant data, such as the number of oophorectomies that were performed as part of the hysterectomy. By using CCHPR, researchers can easily identify hysterectomies as well as oophorectomies.
Other types of questions that CCHPR may be used to address include the following:
CCHPR users include insurers and managed care organizations that want to examine claims experience in a clinically meaningful way, State data organizations or hospitals that want to develop clinically based utilization profiles, and pharmaceutical and device manufacturers that want to assess unmet clinical needs.
More information on CCHPR and other AHCPR tools and data for policymakers and researchers is available online. We invite you to write to the address below to tell us how you are using CCHPR and to share your comments on how CCHPR might be improved to better meet your research and decisionmaking needs.
Irene Fraser, Ph.D.
Director
Center for Organization and Delivery
Studies
Agency for Health Care Policy and Research
2101 East Jefferson
Street, Suite 605
Rockville, MD 20852
E-mail: ifraser@ahrq.gov
HCUP comprises both national and statewide databases, which support a variety of studies across providers (hospitals and physicians), payers (public and private), and time. These databases permit comprehensive assessment of factors affecting the use and costs of health services. State data can be used to study small-area variations, hospital markets, and State health care reforms.
The HCUP databases can be used to study a variety of topics, including:
AHCPR is currently in the third phase of the HCUP project, the collection of annual data for 1988-96, from State government and private health data organizations. By integrating core data elements comparable to those in a typical discharge abstract, HCUP has created a multi-State database in a uniform format that promotes comparative studies of health care services by researchers both inside and outside AHCPR. Each of the two HCUP databases is built around data on inpatient hospital stays and ambulatory surgery visits. HCUP contains safeguards to protect the privacy of individual patients and physicians.
HCUP also includes statewide encounter data on services other than inpatient hospital care, such as hospital-based ambulatory surgery, from States that maintain such data.
AHCPR supplements the inpatient databases with data on hospitals and local communities from a variety of sources. The American Hospital Association has provided data from its Annual Survey of Hospitals and various special surveys since 1970. County-level statistics are obtained from the Area Resource File, compiled by the Bureau of Health Professions of the Health Resources and Services Administration. Statistics from the Bureau of the Census at the ZIP-Code level, provided by CACI Marketing Systems, are also used.
The statistics presented in this report are based on data pertaining to hospital inpatient stays from the Healthcare Cost and Utilization Project, a Federal-State-industry partnership in health care data. HCUP's objectives are to obtain data from statewide information sources, primarily State governments and hospital associations; design and develop a multi-State health care database to be used for health services research and health policy analysis; and release data to public and private users.
The HCUP Nationwide Inpatient Sample (NIS) contains resource use information included in a typical discharge abstract. NIS Release 4 covers 19 States (Arizona, California, Colorado, Connecticut, Florida, Illinois, Iowa, Kansas, Maryland, Massachusetts, Missouri, New Jersey, New York, Oregon, Pennsylvania, South Carolina, Tennessee, Washington, and Wisconsin) and includes 938 hospitals and over 6.7 million discharges for 1995. The NIS is designed to be a 20-percent sample of U.S. "community" hospitals, as defined by the American Hospital Association (AHA). The AHA defines community hospitals as "all nonfederal, short-term, general and other specialty hospitals, excluding hospital units of institutions" (American Hospital Association, 1993). The HCUP sample is a stratified probability sample of hospitals in the frame, with sampling probabilities proportional to the number of U.S. community hospitals in each stratum.
The hospital universe is defined using the AHA Annual Survey of Hospitals. This universe of hospitals is divided into strata using five hospital characteristics: ownership/control, bedsize, teaching status, rural/urban location, and geographic region. Hospitals from HCUP-3 participating States (the sampling frame) are selected to represent these strata, and all discharges from sampled hospitals are included in the database. Weights indicate the number of discharges that the sample discharge represents in the universe of discharges from U.S. hospitals for that year in that stratum. The total number of discharges in the universe from that stratum is taken from the AHA Annual Survey of Hospitals.
Because administrative data on inpatient stays were not created for research purposes, there may be problems with the reliability and validity of certain data elements. Green and Wintfield (1993) summarized the literature on coding errors for hospital administrative data and described a decline in error rates during the 1970s and 1980s. Fisher, Whaley, Krushat et al. (1992) reported that the accuracy of principal diagnosis and procedure has improved since 1983, when such information became important for determining reimbursement by Medicare and other payers. Green and Wintfield (1993) reported the results of a reabstraction study using records from the California Office of Statewide Health Planning and Development. Information on age and sex was most reliable (error rates less than 1 percent), and principal diagnosis was inaccurate in 9 percent of records. Whittle, Steinberg, Anderson et al. (1991) reported that estimates of cancer incidence rates based on Medicare claims data were within 6 percent of estimates using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) data.
Other problems inherent in hospital inpatient data include missing data, underreporting of socially stigmatized conditions such as alcoholism and drug abuse, and underreporting of minor procedures.
Analyses limited to principal diagnoses and procedures will produce an underestimate of diagnoses that tend to appear in secondary positions such as hypertension, osteoporosis, and Alzheimer's disease (May, Kelly, Mendlein et al., 1991). Diagnostic and minor therapeutic procedures, which usually appear as secondary procedures, will likewise be underrepresented when the focus is on principal procedures. Because the unit of analysis for this study is the inpatient stay rather than the patient, principal diagnoses and procedures are employed to reduce double-counting of stays. Furthermore, the principal diagnosis is of greater interest because it represents the diagnosis which, after evaluation, was the primary reason for admission to the hospital. Similarly, the principal procedure is of interest because it should be the primary therapeutic procedure received by the patient during the stay. Despite these definitions, other diagnoses and procedures are coded into the principal position. For example, diagnostic procedures are often coded into the principal procedure field.
The entire sample of NIS discharges for 1995 was used for this study (N = 6,714,935). These discharges were weighted to obtain estimates that are representative of hospital inpatient discharges in the United States. The estimated total number of discharges represented in these analyses is 34,791,981. This estimate compares favorably with the estimate of 34.6 million discharges (including newborns) based on the National Hospital Discharge Survey (Graves and Gillum, 1997) and the monthly vital statistics report (Ventura, Martin, Curtin et al., 1997).
The unit of analysis is the discharge, or hospital stay, rather than the patient. Because the NIS is limited to inpatient hospital data, conditions treated and procedures performed on an ambulatory basis are not represented here.
Diagnoses and procedures for hospital inpatient stays are coded using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), Fifth Edition (Public Health Service and Health Care Financing Administration, 1994). The ICD-9-CM consists of over 12,000 diagnosis codes and 3,500 procedure codes. Although it is possible to present descriptive statistics for individual ICD-9-CM codes, it is often helpful to aggregate codes into clinically meaningful categories that group similar conditions or procedures.
The CCHPR consists of two related classification systems. The first is a system of mutually exclusive categories for both diagnoses and procedures ("CCHPR"). These categories were developed to provide a convenient way to report hospital statistics by diagnosis or procedure. The second system is an expansion of the mutually exclusive categories into a multi-level, hierarchical system ("expanded CCHPR"). These expanded categories were developed to support the production of aggregate statistics for larger groupings of CCHPR categories as well as more detailed statistics about subgroups of diagnoses and procedures within these categories. The development of the CCHPR categories was described in Elixhauser and McCarthy (1996) and Elixhauser (1996).
The diagnosis CCHPR aggregates illnesses and conditions into 259 mutually exclusive categories, most of which are clinically homogeneous. Some heterogeneous categories combine several less common individual conditions. The procedure CCHPR contains 231 mutually exclusive categories. Many of the categories represent single procedures; however, some procedures that occur infrequently are grouped according to the body system on which they are performed, whether they are used for diagnostic or therapeutic purposes, and whether they are considered operating room or non-operating room procedures (DRGs: Diagnosis related groups definitions manual, 1994).
All ICD-9-CM coding changes in effect from January 1980 through September 1998 have been incorporated into the coding scheme. "External causes of injury and poisoning" (E codes) are grouped into a single category because they are not used consistently in inpatient data. Appendix A provides details and coding for the CCHPR diagnosis classification and Appendix B provides the same information for procedures. Electronic versions of the classification systems are available online.
After CCHPR Version 2 was completed, expanded, hierarchical systems for both diagnoses and procedures were constructed by aggregating CCHPR codes into larger groupings and disaggregating them into smaller groupings of one or more individual ICD-9-CM codes. A four-level system was developed for diagnoses and a three-level system was developed for procedures.
The first level of both hierarchical systems is most general and conforms, for the most part, to the body systems and conditions that define ICD-9-CM chapters. Examples for diagnoses include "2. Neoplasms," "5. Mental disorders," and "7. Diseases of the circulatory system"; and for procedures, "9. Operations on the digestive system" and "13. Obstetrical procedures."
The second level consists of more precisely defined categories within the first level, the third level contains even more specific categories, and so on. Eighteen major diagnosis groupings were divided into more than 600 diagnosis categories and 16 major procedure groupings were divided into about 300 procedure categories.
Within a level, the categories are mutually exclusive; for example, the number of cases in categories 1.1.1, 1.1.2, 1.1.3, and 1.1.4 will sum to the number of cases in "1.1 Bacterial infection." Minor differences can be attributed to rounding error (because the numbers of discharges are weighted to national estimates).
Appendix C provides details and coding for the expanded diagnosis classification and Appendix D provides the same information for procedures. Electronic versions of the expanded CCHPR can also be obtained online.
For all analyses, only the principal diagnosis and principal procedure were used. The statistics include:
Both mean and median charges are presented because the mean may be strongly influenced by extreme values. Charge information for procedures is not the charge for the procedure itself. Instead, it refers to the total charge for the hospitalization in which this procedure was listed as the principal procedure. All charge data are charges for the hospitalization. Charges do not necessarily reflect costs nor are they synonymous with reimbursements. In the past, missing charge data often presented a problem. In this study, charge data were present for 98 percent of all discharges, although the percentage of missing data for diagnosis and procedure categories varied. For example, charge data were available for more than 90 percent of cases in all but two diagnosis categories. The exceptions were "82. Paralysis" (present for 83.7 percent of cases) and "255. Administrative/social admission" (present for 89 percent of cases).
Results are not presented for any diagnosis or procedure category for which the unweighted number of discharges is less than 70. Using a generalized variance technique for proportions, it was determined that a sample of at least 70 discharges is required to assure, with 95-percent confidence, that the reported proportions had a relative error of less than 30 percent (i.e., if the reported value is p, the error is <.3p).
In general, the diagnosis and procedure categories listed in the tables and appendixes follow the order determined by the ICD-9-CM system. For the most part, chapter headings have been maintained and the individual categories are arranged by numeric ICD-9-CM code.
The approach to finding a specific condition or procedure will vary depending on the information desired. To locate a general condition, body system, or procedure, one approach is to scan the major headings provided in the Table of Contents in front of each table. Then go to that section of the table and browse through the specific categories until the appropriate condition or procedure is found. To locate a particular ICD-9-CM code, go directly to the appendixes. The categories are arranged by the first listed ICD-9-CM code in the grouping. Scan the column of codes in the appendixes to the appropriate location. Identify the corresponding category number and go to that category number in the table.
This Research Note provides descriptive statistics on inpatient stays in U.S. community hospitals for 1995 using the HCUP-3 Nationwide Inpatient Sample. These statistics should prove useful to health policy analysts, health services researchers, and health care administrators in need of information on hospital discharges by principal diagnosis and principal procedure.
All discharges were categorized using the Clinical Classifications for Health Policy Research. These classification systems can be employed in many types of projects analyzing data on diagnoses and procedures. The mutually exclusive classification scheme (Appendixes A and B) should be useful for those who want to aggregate their own data by a relatively small number of diagnosis and procedure categories for reporting purposes. This scheme can also help to identify populations for disease- or procedure-specific studies. The expanded categories (Appendixes C and D) can be used to provide statistical information about relatively specific conditions. Because they are hierarchical, they can also be used to examine the contribution of specific conditions to larger diagnosis categories.
AHCPR thanks the following organizations for their contributions of data to the 1988-96 HCUP NIS and SID: Arizona Department of Health Services, California Office of Statewide Health Planning and Development, Colorado Hospital Association, Connecticut Hospital Research and Education Foundation, Inc., Florida Agency for Health Care Administration, Illinois Health Care Cost Containment Council, Iowa Hospital Association, Kansas Hospital Association, Maryland Health Services Cost Review Commission, Massachusetts Rate Setting Commission, Hospital Industry Data Institute (Missouri), New Jersey Department of Health, New York State Department of Health, Oregon Department of Human Resources, Pennsylvania Health Care Cost Containment Council, South Carolina Budget and Control Board, Tennessee Hospital Association, Washington State Department of Health, and Wisconsin Office of the Commissioner of Insurance.
Data files for HCUP were constructed under the technical direction of AHCPR by The MEDSTAT Group (formerly SysteMetrics, Inc.), Santa Barbara, CA, and its subcontractors Abt Associates and the National Association of Health Data Organizations. Social and Scientific Systems, Inc., Bethesda, MD, provides programming support for AHCPR researchers. We thank Elizabeth Jacinto, Andrea Dziuba, and Satyapal Khera at Social and Scientific Systems for work in computer programming.
HCUP-3 Research Notes are derived from analyses conducted by staff in AHCPR's Center for Organization and Delivery Studies.
American Hospital Asociation hospital statistics. 1993-94 edition. Chicago, IL: American Hospital Association; 1993.
DRGs: Diagnosis related groups definitions manual, version 12.0. Wallingford, CT: 3M Health Information Systems; 1994.
Elixhauser A, McCarthy E. Clinical classifications for health policy research, version 2: Hospital inpatient statistics. Healthcare Cost and Utilization Project (HCUP-3) Research Note 1. Rockville, MD: Agency for Health Care Policy and Research; 1996. AHCPR Publication No. 96-0017.
Elixhauser A. Clinical classifications for health policy research, version 2: Software and user's guide. Healthcare Cost and Utilization Project (HCUP-3) Research Note 2. Rockville, MD: Agency for Health Care Policy and Research; 1996. AHCPR Publication No. 96-0046.
Elixhauser A, Andrews RM, Fox S. Clinical classifications for health policy research: Discharge statistics by principal diagnosis and procedure. Provider Studies Research Note 17. Rockville, MD: Agency for Health Care Policy and Research; 1993. AHCPR Publication No. 93-0043.
Fisher ES, Whaley FS, Krushat WM, et al. The accuracy of Medicare's hospital claims data: Progress has been made, but problems remain. American Journal of Public Health 1992; 82: 243-248.
Graves EJ, Gillum BS. Detailed diagnoses and procedures, National Hospital Discharge Survey, 1995. Vital Health Stat 13(130). National Center for Health Statistics; 1997.
Green J, Wintfield N. How accurate are hospital discharge data for evaluating effectiveness of care? Medical Care 1993; 31(8): 719-731.
May DS, Kelly JJ, Mendlein JM, Garbe PL. Surveillance of major causes of hospitalization among the elderly, 1988. Morbidity and Mortality Weekly Reports 1991 40(SS-1): 7-17.
Public Health Service and Health Care Financing Administration. International classification of diseases, 9th revision, clinical modification. Vols. 1, 2, and 3; fifth edition. Washington, DC: Public Health Service; 1994. HHS Publication No. (PHS) 94-1260.
Ventura SJ, Martin JA, Curtin SC, Mathews TJ. Report of final natality statistics, 1995. Monthly vital statistics report; vol. 45, No. 11, supp. 2. Hyattsville, MD: National Center for Health Statistics; 1997.
Whittle J, Steinberg E, Anderson G, Herbert R. Accuracy of Medicare claims data for estimation of cancer incidence and resection rates among elderly Americans. Medical Care 1991; 29(12): 1226-1236.
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Clinical Classifications for Health Policy Research: Hospital Inpatient
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