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National Healthcare Disparities Report, 2005

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Methods Applying AHRQ Quality Indicators to Healthcare Cost and Utilization Project (HCUP) Data for the Third (2005) National Healthcare Disparities Report

By Rosanna Coffey, Ph.D., Marguerite Barrett, M.S., Bob Houchens, Ph.D., Ernest Moy, M.D., M.P.H., Roxanne Andrews, Ph.D.

October 19, 2005

The Agency for Healthcare Research and Quality (AHRQ) Quality Indicators (QIs) were applied to the HCUP hospital discharge data for several measures in this report. The AHRQ QIs, originally developed by AHRQ staff, recently have been revised and improved under contract with AHRQ. The QIs are measures of quality associated with processes of care that occurred in an outpatient or an inpatient setting. The QIs rely solely on hospital inpatient administrative data and, for this reason, are screens for examining quality that may indicate the need for more in-depth studies. The AHRQ QIs include three sets of measures:

  • Prevention Quality Indicators (PQIs)—or ambulatory care sensitive conditions—identify hospital admissions that evidence suggests could have been avoided, at least in part, through high-quality outpatient care (AHRQ, 2004).
  • Inpatient Quality Indicators (IQIs) reflect quality of care inside hospitals and include measures of utilization of procedures for which there are questions of overuse, underuse, or misuse (AHRQ, 2004).
  • Patient Safety Indicators (PSIs) reflect quality of care inside hospitals, by focusing on surgical complications and other iatrogenic events (AHRQ, 2004).

The QI measures selected for this report are described in Table 1 at the end of this methods section.

The Healthcare Cost and Utilization Project (HCUP) is a family of healthcare databases and related software tools and products developed through a Federal-State-Industry partnership and sponsored by AHRQ. HCUP databases bring together the data collection efforts of State data organizations, hospital associations, private data organizations, and the Federal government to create a national information resource of discharge-level health care data. HCUP includes the largest collection of longitudinal hospital care data in the United States, with all-payer, encounter-level information beginning in 1988. These databases enable research on a broad range of health policy issues, including cost and quality of health services, medical practice patterns, access to health care programs, and outcomes of treatments at the national, State and local market levels.

The 2002 HCUP State Inpatient Databases (SID), a census of hospitals (with all of their discharges), from 22 participating States were used to create a disparities analysis file designed to provide national estimates on disparities for this report. A sample of hospitals from the following States were included Arizona, California, Colorado, Connecticut, Florida, Georgia, Hawaii, Kansas, Maryland, Massachusetts, Michigan, Missouri, New Jersey, New York, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Virginia, Vermont, and Wisconsin. For the list of the HCUP data sources, go to Table 2 at the end of this methods section.

To apply the AHRQ Quality Indicators to HCUP hospital discharge data, several steps were taken: 1) QI software review and modification, 2) acquisition of population-based data, 3) general preparation of HCUP data, 4) special methods for race/ethnicity reporting, and 5) identification of statistical methods. These steps, described briefly below, are presented in detail in the Technical Specifications for HCUP Measures in the Third (2005) National Healthcare Quality Report and the National Healthcare Disparities Report (Barrett, Houchens, Coffey, et al., 2005), available from AHRQ on request.

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1. QI Software Review and Modification.

For this report, we started with the following QI software versions: PQI Version 2.1 (revision 3, downloaded September 2004), IQI Version 2.1 (revision 3, downloaded September 2004), and PSI Version 2.1 (revision 2, downloaded November 2004). Because these software modules did not include all of the reporting categories needed for the NHDR, some changes to the QI calculations were necessary. (For details, see Barrett, Houchens, Coffey, et al., 2005). We also added two indicators: immunization-preventable influenza and adult asthma, age 65 years or older.

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2. Acquisition of Population-Based Data.

Generally, a QI as a measure of an event that occurs in a hospital requires a numerator count of the event of interest and a denominator count of the population (within the hospital or within the geographic area) to which the event relates. These denominator counts had to be located for all reporting categories and for all adjustment categories listed in the HCUP-based tables. Age-gender adjustments were made by 18 five-year increments of age by male-female gender. Thus, to develop the QI rates, we needed national-level data for the QI denominators by each reporting category by the 36 classes for age-gender adjustments. The HCUP data were used for discharge denominator counts for QIs that related to providers. Population ZIP-Code-level counts by age, gender, race, and ethnicity from Claritas were used for denominator counts for QIs that related to geographic areas. Claritas uses intra-census methods to estimate ZIP-Code-level statistics (Claritas, Inc., 2002). ZIP-Code-level counts were necessary for statistics by median income and urban-rural location of the patient's ZIP Code.

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3. Special Methods for Race/Ethnicity Reporting:

Race and ethnicity measures can be problematic in hospital discharge databases. Many hospitals do not code race and ethnicity completely. Because race/ethnicity is a pivotal measure for the NHDR, we explored the reporting of the race/ethnicity data in the 36 States that participate in 2002 HCUP SID. Ten States did not provide information on patient race to HCUP. Three States did not report Hispanic ethnicity, and one State only reports patient race as white, non-white, and Hispanic. The remaining 22 States were used for the creation of the disparities analysis file. The following table demonstrates the representation by region of the 22 States.

Region Number of States used for the disparities analysis file Number of States in the region Percent of States in the region included in the disparities analysis file
Northeast 7 9 78%
Midwest 4 12 33%
South 7 16 44%
West 4 13 31%
Total 22 50 44%

The table below compares aggregated totals of various measures for the 22 States as a percent of the national measure. In 2002, the 22 States accounted for 65 percent of U.S. hospital discharges (based on the American Hospital Association's Annual Survey). They accounted for about 60 percent of various subgroups of the nation (based on 2002 Claritas data), with the exception of Asian/Pacific Islanders; the 22 States included 76 percent of the Asian/Pacific Islander population.

Measure Total of 22 HCUP States with race/ethnicity
as a percent of national total
Hospital discharges 63%
Total resident population 66%*
Population by race/ethnicity:  
White 62%*
African American 67%*
Asian/Pacific Islander 82%*
Hispanic 84%*
Population by age:  
Population under age 18 66%*
Population age 18-64 66%*
Population over age 64 66%*
Population with income under the poverty level 68%**

* Calculated using 2002 Claritas and 1990 Census race definitions.
** Calculated using Urban Institute and Kaiser Commission on Medicaid and the Uninsured estimates based on pooled March 2002 and 2003 Current Population Surveys.

Data on Hispanics is collected differently among the States and also can differ from the Census methodology of collecting information on race (White, African American, Asian, American Native) separately from ethnicity (Hispanic, non-Hispanic). States often collect Hispanic ethnicity as one of several categories that include race. Clerks use these combined race/ethnicity categories to classify patients on admission to the hospital, often by observing rather than asking the patient. The HCUP databases maintain the combined categorization of race and ethnicity. When a State and its hospitals collect Hispanic ethnicity separately from race, HCUP processing for a uniform database, uses Hispanic ethnicity to override any other race category.

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4. Preparation of HCUP Data and Development of the Disparities Analysis File.

Several HCUP data issues had to be resolved before applying the QI algorithms. First, we selected community1 hospitals only from the 22 States and eliminated rehabilitation hospitals in the 2002 SID because the completeness of reporting for rehabilitation hospitals was inconsistent across States. Second, community hospitals from these 22 States were sampled to approximate a 20-percent stratified sample of U.S. community hospitals. The sampling strata were defined based on five hospital characteristics: geographic region, hospital control (i.e., public, private not-for-profit, and proprietary), urbanized location, teaching status, and bed size. Hospitals were excluded from the sampling frame if the coding of patient race was suspect (i.e., more than 30% of the discharges in the hospital had the race reported as "other", more than 50% of the discharges in the hospital had no information on the race of the patient, all of the discharges in the hospital had race coded as white, other, or missing, or 100% of the discharges in the hospital had race coded as white and the hospital had more than 50 beds). Once the 20-percent sample was drawn, discharge-level weights were developed to produce national-level estimates when applied to the disparities analysis file. The sampling and weighting strategy used for the disparities analysis file is similar to the method used to create the HCUP Nationwide Inpatient Sample (NIS), except that the disparities analysis file samples from 22 of the 35 States included in the 2002 NIS. The final disparities analysis file included almost 8 million hospital discharges from almost 1,000 hospitals. Third, for missing age, gender, ZIP Code, race/ethnicity, and payer data that occurred on a small proportion of discharge records, we used a "hot deck" imputation method (which draws donors from strata of similar hospitals and patients) to assign values while preserving the variance within the data. Fourth, we assigned median household income and patient location based on ZIP Code data obtained from Claritas linked to patient ZIP Code in the SID.

1 Community hospitals are defined by the AHA as "non-Federal, short-term, general, and other specialty hospitals, excluding hospital units of institutions." Specialty hospitals included among community hospitals are obstetrics-gynecology, ear-nose-throat, short-term rehabilitation, orthopedic, and pediatric institutions. Also included are public hospitals and academic medical centers. Excluded are short-term rehabilitation hospitals, long-term hospitals, psychiatric hospitals, and alcoholism/chemical dependency treatment facilities.

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5. Statistical Methods.

Statistical issues involved age-gender adjustment for all QIs, severity/comorbidity adjustment for the discharge-based IQIs and PSIs, and derivation of standard errors and appropriate hypothesis tests. For the PQIs and area-based IQIs and PSIs, age-gender adjustments were made for age and gender differences across other population subgroups and were based on methods of direct standardization (Fleiss, 1973). Standard errors calculations for the disparities analysis file were based on the HCUP report entitled "Calculating Nationwide Inpatient Sample (NIS) Variances" (Houchens, et al., 2005). There is no sampling error associated with Claritas population counts. The appropriate statistics were obtained through the Statistical Analysis System (SAS) procedure called PROC SURVEYMEANS so that the sampling effects of the disparities analysis file were taken into account. For the discharge-based PSIs, adjustments were made for age, gender, age-gender interaction, DRG cluster, and comorbidity, using the regression-based standardization that is part of the AHRQ PSI software. For the discharge-based IQIs, adjustments were made for age, gender, age-gender interaction, and 3Mā"¢ All Patient Refined Diagnosis Related Groups (APR-DRGs)risk of mortality or severity score using the regression-based standardization that is part of the AHRQ IQI software. The threshold selected for reporting estimates in this report is a relative standard error less than 30% and at least 10 unweighted cases in the denominator. Statistical calculations are explained in Appendix A to this report and in Barrett, Houchens, and Coffey et al. (2005).

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Some caution should be used in interpreting the AHRQ QI statistics presented in this report. The caveats relate to inter-State differences in data collection:

Data Collection Differences among States:

Organizations that collect statewide data, generally collect data using the Uniform Hospital Discharge Data Set (UHDDS) and the Uniform Bill (UB-92) formats. However, not every statewide data organization collects all data elements nor codes them the same way. For this report, uneven availability of a few data elements underlie some estimates, as noted next.

Data Elements Needed in Some QIs: Three data elements not available in every State that are required for certain QIs are: "secondary procedure day," admission type" (elective, urgent, and emergency), and "admission source" (e.g., transfer from another institution, emergency room, etc.). These data elements are used to exclude specific cases from some QI measures. Seven of the 22 States (i.e., AZ, CO, FL, KS, MI, VA, WI) in the NHDR analysis file were missing information on secondary procedure day. The two PSIs that use secondary procedure day were modified to not use this information for any State. Admission type of elective and newborn are used in four PSIs. For all but two States (i.e., CA and KS), we imputed the missing admission type using available information. In Kansas, about 4.5% of the discharges were missing admission type. An admission type of "elective" was assigned if the DRG did not indicate trauma, delivery, or newborn. About 500 discharges were assigned an admission type of "elective". Some of these records might have been assigned to "emergency" or "urgent" if admission source was available for Kansas data. For California, that did not provide any information on admission type, information on scheduled admissions was used to identify elective admissions and DRGs were used to identify newborn admissions. Because the Kansas data did not identify admission source, transfers into the hospital could not be excluded for many of the QIs.

Number of Clinical Fields: Another data collection issue relates to the number of fields that statewide data organizations permit for reporting patients' diagnoses and procedures during the hospitalization and whether they specifically require coding of external-cause of injury (E codes). The SID for different States contain as few as 6 or as many as 30 fields for reporting diagnoses and procedures, as shown in Table 3 at the end of this methods section. The more fields used the more quality-related events that can be captured in the statewide databases. However, even for States with 30 diagnosis fields available in the year 2000, 95 percent of their discharge records captured all of patients' diagnoses in 10 to 13 data elements. For States with 30 procedure fields available, 95 percent of records captured all of patients' procedures in 5 fields. Thus, limited numbers of fields available for reporting diagnoses and procedures are unlikely to have much effect on results, because all statewide data organizations participating in HCUP allow at least 9 diagnoses and 6 procedures. We decided not to truncate artificially the diagnosis and procedure fields reported, so that the full richness of the databases would be used. Another issue relates to external cause of injury reporting. Eight of the 29 Patient Safety Indicators use external cause of injury (E code) data to help identify complications of care or to exclude cases (e.g., poisonings, self-inflicted injury, trauma) from numerators and denominators, as shown in Table 4 at the end of this methods section. Although E codes in the AHRQ PSI software have been augmented wherever possible with the related non-E codes in the ICD-9-CM system, go to Table 4 for specific details. E codes are still included in some AHRQ PSI definitions, and uneven capture of these data has the potential of affecting some PSI rates and should be kept in mind when judging the level of these events.

Race/ethnicity coding: Even excluding hospitals with a large proportion of race/ethnicity coding that was missing, there may still remain differences in racial and ethnicity coding among States that affect estimates. For example, some States include Hispanic ethnicity as a category among racial categories, some ask about Hispanic ethnicity separately from race. At the hospital-level, policies vary on methods for collecting such data. Some hospitals ask the patient to identify their race and ethnicity, some determine it from observation. The effect of these and other unmeasured differences in coding of race and ethnicity across the States and hospitals cannot be assessed.

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Agency for Healthcare Research and Quality. AHRQ Quality Indicators—Guide to Prevention Quality Indicators: Hospital Admission for Ambulatory Care Sensitive Conditions, AHRQ Pub. No. 02-R0203, Revision 3. Rockville, MD: Agency for Healthcare Research and Quality, 2004.

Agency for Healthcare Research and Quality. AHRQ Quality Indicators—Guide to Inpatient Quality Indicators: Quality of Care in Hospitals—Volume, Mortality, and Utilization, AHRQ Pub. No. 02-R0204, Revision 3. Rockville, MD: Agency for Healthcare Research and Quality, 2004.

Agency for Healthcare Research and Quality. AHRQ Quality Indicators—Guide to Patient Safety Indicators, AHRQ Pub. No. 03-R203, Revision 2. Rockville, MD: Agency for Healthcare Research and Quality, 2004.

Barrett ML, Houchen R, Coffey RM, Moy E, Andrews R, Kelley E. Technical Specifications for HCUP Measures in the Third (2005) National Healthcare Quality Report and the National Healthcare Disparities Report. Washington, DC: The Medstat Group, Inc., 2005.

Claritas, Inc. The Claritas Demographic Update Methodology, July 2002.

Fleiss JL. Statistical Methods for Rates and Proportions. New York: Wiley, 1973.

Houchens R, Elixhauser A. Final Report on Calculating Nationwide Inpatient Sample (NIS) Variances, 2001. HCUP Methods Series Report #2003-2. ONLINE. June 2005 (revised June 6, 2005). U.S. Agency for Healthcare Research and Quality.




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