Skip Navigation U.S. Department of Health and Human Services www.hhs.gov
Agency for Healthcare Research Quality www.ahrq.gov
Archive print banner
National Healthcare Disparities Report, 2003

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

This information is for reference purposes only. It was current when produced and may now be outdated. Archive material is no longer maintained, and some links may not work. Persons with disabilities having difficulty accessing this information should contact us at: https://info.ahrq.gov. Let us know the nature of the problem, the Web address of what you want, and your contact information.

Please go to www.ahrq.gov for current information.

Prepared by Rosanna Coffey, Ph.D., Marguerite Barrett, M.S., Bob Houchens, Ph.D., Ernest Moy, M.D., M.P.H., Roxanne Andrews, Ph.D., Ed Kelley, Ph.D., Beth Kosiak, Ph.D., Denise Remus, Ph.D., R.N.

June 6, 2003


Introduction

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 (and termed the HCUP QIs), recently have been revised and improved by the University of California San Francisco and Stanford University (UCSF-Stanford) 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, 2001; Davies et al., 2001).
  • 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.2, 6
  • Patient Safety Indicators (PSIs) reflect quality of care inside hospitals, by focusing on surgical complications and other iatrogenic events.3, 9

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 2000 HCUP Statewide Inpatient Databases (SID), a census of hospitals (with all of their discharges), from 16 participating States were pooled together for use in this report. States included Arizona, California, Connecticut, Florida, Georgia, Kansas, Maryland, Missouri, Massachusetts, New Jersey, New York, South Carolina, Tennessee, Texas, Virginia, 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 4) identification of statistical methods. These steps, described briefly below, are presented in detail in the Technical Specifications for HCUP Measures in the National Healthcare Quality Report and the National Healthcare Disparities Report.9 available from AHRQ on request.

Return to Contents

QI Software Review and Modification.

For this report, we started with the following QI software versions: PQI Version 2.1, IQI Version 2.1, and PSI (beta test version, July 2002). 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, refer to 9) We also added two indicators: immunization-preventable pneumococcal pneumonia and immunization-preventable influenza.

Return to Contents

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 multi-State QI rates, we needed State 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. We chose Claritas, which uses intra-census methods to estimate ZIP-Code-level statistics 5 because the Census 2000 data by ZIP Code were not yet available. ZIP-Code-level counts were necessary for statistics by median income and location of the patient's ZIP Code.

Return to Contents

Preparation of HCUP Data.

Several HCUP data issues had to be resolved before applying the QI algorithms. First, we selected community hospitals only and eliminated rehabilitation hospitals in the 2000 SID because the completeness of reporting for rehabilitation hospitals was inconsistent across States. Second, because some statewide data organizations, do not report data for all community hospitals in the State, we weighted hospitals in the SID to the State's universe of hospitals in the American Hospital Association Annual Survey of Hospitals based on hospital characteristics. Third, discharges from hospitals operating for all quarters of the year but not contributing data for all quarters of a year were weighted up to annual estimates for that institution. Fourth, for missing age, gender, ZIP Code, race/ethnicity (go to item 4 below for more specifics on HCUP data preparation for these categories), 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. Fifth, we assigned median household income and patient location based on ZIP Code data obtained from Claritas linked to patient ZIP Code in the HCUP databases. Sixth, we assessed the problem of non-resident discharges from individuals who primarily cross State lines for hospital services, but did not adjust for this problem because of the infeasibility of addressing the issue consistently across the States.

Return to Contents

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 quality of the race/ethnicity data by State. We identified States in which race/ethnicity was missing for a high proportion of cases. We also checked the proportion of hospitals in each State that had 50 percent or more discharges with race/ethnicity as "missing." The results of that analysis are shown in Table 3 at the end of this methods section.

As a result, we limited the number of States for the race/ethnicity analysis to 16 of the 29 States in the database in 2000. All 16 States had fewer than 10% of discharges with race/ethnicity as "missing." For these States, we imputed the missing race values using a hot-decking imputation algorithm (refer to Reference 4 for details). For the small number of hospitals with more than 50 percent of the discharges missing race, we manually selected donor hospitals for the hot decking using location, hospital characteristics, and payer distribution (percent Medicare and Medicaid).

The table immediately below compares aggregated totals of various measures for the 16 States as a percent of the national measure. In 2000, the 16 States accounted for 54 percent of U.S. hospital discharges (based on the American Hospital Association's Annual Survey). They accounted for about half of various subgroups of the nation (based on 2000 Census data), with the exception of Asian/Pacific Islanders and Hispanics; the 16 States included 70 percent of the Asian/Pacific Islander population and 79 percent of the Hispanic population.

Measure Total of 16 HCUP States with race/ethnicity
as a percent of national total
Hospital discharges 54%
Total resident population 56%
Population by race/ethnicity:  
  White 50%
  African American 58%*
  Asian/Pacific Islander 70%*
  Hispanic 79%
Population by age:  
  Population under age 18 56%
  Population age 18-64 56%
  Population over age 64 55%
Population with income under the poverty level 56%

*Calculated using Claritas and 1990 Census race definitions; all other estimates are from Census 2000.

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.

The 16 States were not weighted to national estimates for the QIs reported by race/ethnicity and income by race/ethnicity. This is because the 16 States do not represent regions well enough on race/ethnicity to develop regional and thus a national estimate (refer to table below for details). For these reasons, the race/ethnicity QIs are only representative of the 16 States that comprise this analysis.

Region Number of States used for
the NHDR race/ethnicity analysis
Number of States
in the region
Percent of States in the region
included in the race/ethnicity analysis
Northeast 4 9 44%
Midwest 3 12 25%
South 7 16 44%
West 2 13 15%
Total 16 50 32%

Return to Contents

Statistical Methods.

Statistical issues involved age-gender adjustment for all QIs, and severity/comorbidity adjustment for the discharge-based PSIs, and derivation of standard errors and appropriate hypothesis tests. For all but the discharge-based 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 were calculated for estimates from the 16 State SID; there is no sampling error associated with Claritas population counts. HCUP-SID standard errors were based on the HCUP report entitled "Calculating Nationwide Inpatient Sample (NIS) Variances"8 without adjustments for the cluster sample effects of the NIS. The appropriate statistics were obtained through the Statistical Analysis System (SAS) procedure called PROC SURVEYMEANS. For the discharge-based PSIs, adjustments were made for age, gender, age-gender interaction, DRG cluster, and comorbidity, using a regression-based standardization developed by UCSF-Stanford. The threshold selected for reporting estimates in this report is at least 70 unweighted cases in the denominator. A sample of at least 70 discharges was required to assure a relative error routinely used in Federal sample surveys of less than 30 percent. Statistical calculations are explained in Appendix A to this report and in Reference 4).

Return to Contents

Caveats

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:

Return to Contents

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. These problems were overcome by 1) dropping "secondary procedure day" from two QIs for all States and 2) using additional data elements to work around the "admission type" problem in two States. For "admission source" for one State, admission source could not be identified, but at most only 7 percent of discharges in that State (and 0.1% of discharges for the 16 States combined) were involved for any QI.

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 4 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. Many of the Patient Safety Indicators require external cause of injury (E-code) data to identify complications of care. The PSIs and other QIs also use E-codes to exclude cases (e.g., poisonings, self-inflicted injury, trauma) from numerators and denominators. The proportion of records with at least one PSI-related E-code across the States is as low 4.6 percent and as high as 15.3 percent, as shown in Table 4 at the end of this methods section. Uneven capture of these data may affect the QI rates and should be kept in mind when judging the level of these events.

Race/ethnicity coding: Even excluding States 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.

Table B.1. AHRQ Quality Indicators Selected for the National Healthcare Disparities Report

QI No. Description
  Prevention Quality Indicators
PQI 1 Adult admissions for diabetes with short-term complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population age 18 years and older (PQI 1)

* Ketoacidosis, hyperosmolarity, or coma.
PQI 2 Perforated appendices per 1000 admissions with appendicitis* (excluding obstetric and neonatal admissions and transfers from other institutions) (PQI 2)
PQI 3 Adult admissions for diabetes with long-term complications (excluding obstetric admissions and transfers from other institutions) per 100,000 population age 18 years and older (PQI 3)

* Renal, eye, neurological, circulatory, or other unspecified complications.
PQI 4 Pediatric asthma admissions (excluding obstetric and neonatal admissions and transfers from other institutions) per 100,000 population age less than 18 years (PQI 4)
PQI 5 Adult admissions for chronic obstructive pulmonary disease (COPD) (excluding obstetric admissions and transfers from other institutions) per 100,000 population age 18 years and older (PQI 5)
PQI 6 Pediatric gastroenteritis admissions (excluding obstetric and neonatal admissions and transfers from other institutions) per 100,000 population age less than 18 years (PQI 6)
PQI 7 Adult admissions for hypertension (excluding patients with cardiac procedures, obstetric and neonatal conditions, and transfers from other institutions) per 100,000 population age 18 years and older (PQI 7)
PQI 8 Adult admissions for congestive heart failure (excluding patients with cardiac procedures, obstetric and neonatal conditions, and transfers from other institutions) per 100,000 population age 18 years and older (PQI 8)
PQI 11 Bacterial pneumonia admissions (excluding sickle cell or hemoglobin-S conditions, transfers from other institutions, and obstetric or neonatal admissions) per 100,000 population (PQI 11)
PQI 13 Adult admissions for angina (excluding surgical patients, transfers from other institutions, and obstetric and neonatal admissions) per 100,000 population age 18 years and older (PQI 13)
PQI 14 Adult admissions for uncontrolled diabetes without complication* (excluding obstetric and neonatal admissions and transfers from other institutions) per 100,000 population age 18 years and older (PQI 14)

* Without short-term (ketoacidosis, hyperosmolarity, coma) or long-term (renal, eye, neurological, circulatory, other unspecified) complications.
PQI 15 Adult asthma admissions (excluding obstetric admissions and transfers from other institutions) per 100,000 population age 18 years and older (PQI 15)
PQI 16 Lower extremity amputations for adults with diabetes (excluding trauma, obstetric admissions, and transfers from other institutions) per 100,000 population age 18 years and older (PQI 16)
PQI 17 Immunization-preventable pneumococcal pneumonia admissions for elderly (excluding transfers from other institutions) per 100,000 population age 65 years and older (added as PQI 17)
PQI 18 Immunization-preventable influenza admissions for elderly (excluding transfers from other institutions) per 100,000 patients age 65 years and older (added as PQI 18)
  Inpatient Quality Indicators
IQI 21 Cesarean deliveries per 1000 deliveries (IQI 21)
IQI 22 Vaginal births per 1000 women with previous Cesarean deliveries (IQI 22)
IQI 26 Coronary artery bypass grafts (CABG) for adults age 40 years and older (excluding obstetric admissions) per 100,000 population age 40 and older (IQI 26)
IQI 27 Percutaneous transluminal coronary angioplasties (PTCA) for adults age 40 years and older (excluding obstetric admissions) per 100,000 population age 40 and older (IQI 27)
IQI 28 Hysterectomies for adults (excluding obstetric conditions, genital cancer, and pelvic trauma) per 100,000 female population age 18 years and older (IQI 28)
IQI 29 Laminectomies or spinal fusions for adults (excluding obstetric conditions) per 100,000 population age 18 years and older (IQI 29)
  Patient Safety Indicators
PSI 1 Complications of anesthesia per 1000 surgical discharges (excluding patients with such complications who also have substance use disorders) (PSI 1)
PSI 2 Deaths per 1000 admissions in low mortality DRGs (DRGs with a NIS 1997 benchmark of less than 0.5% mortality, excluding trauma, immunocompromised, and cancer patients) (PSI 2)
PSI 3 Decubitus ulcers per 1000 discharges of length 4 or more days (excluding paralysis patients and patients admitted from long-term-care facilities and neonates) (PSI 3)
PSI 4 Failure to rescue (death) per 1000 discharges with complications potentially resulting from care (excluding transferred patients and those admitted from long-term-care facilities) (PSI 4)
PSI 5 Foreign body left in during procedure per 1000 medical and surgical discharges (excluding neonates; based on secondary diagnoses only*) (PSI 5)

* That is, excludes admissions specifically for treatment of foreign body left, such as cases from earlier admissions or from other hospitals.
PSI 6 Iatrogenic pneumothorax per 1000 discharges (excluding patients with trauma, thoracic surgery, lung or pleural biopsy, or cardiac surgery and neonates; based on secondary diagnoses only*) (PSI 6)

* That is, excludes admissions specifically for iatrogenic pneumothorax, such as cases from earlier admissions or from other hospitals. Includes barotrauma (including acute respiratory distress syndrome) and central line placement.
PSI 7 Infection due to intravenous lines or catheters per 1000 discharges (excluding immunocompromised or cancer patients and neonates; based on secondary diagnoses only*) (PSI 7)

* That is, excludes admissions specifically for such infections, such as cases from earlier admissions, from other hospitals, or from other settings.
PSI 8 Postoperative hip fracture for adults per 1000 surgical patients age 18 years and older who were not susceptible to falling* (PSI 8)

* That is, excluding patients with musculoskeletal disease; those admitted for seizures, syncope, stroke, coma, cardiac arrest, poisoning, trauma, delirium, psychoses, anoxic brain injury; patients with metastatic cancer, lymphoid malignancy, bone malignancy, and self-inflicted injury.
PSI 9 Postoperative hemorrhage or hematoma with surgical drainage or evacuation, not verifiable as following surgery*, per 1000 surgical discharges (excluding obstetrical and neonatal admissions; based on secondary diagnoses only*) (PSI 9)

* Because procedure day indicator not available for all States. Also, excludes admissions specifically for such problems, such as cases from earlier admissions, from other hospitals, or from other settings.
PSI 10 Postoperative physiologic and metabolic derangements per 1000 elective-surgery patients (excluding some serious disease* and obstetric and neonatal admissions) (PSI 10)

* That is, excluding patients with diabetic coma and patients with renal failure who also were diagnosed with AMI, cardiac arrhythmia, cardiac arrest, shock, hemorrhage, or gastrointestinal hemorrhage.
PSI 11 Postoperative respiratory failure per 1000 elective-surgery discharges (excluding patients with respiratory disease, circulatory disease, and obstetric or neonatal conditions) (PSI 11)
PSI 12 Postoperative pulmonary embolus or deep vein thrombosis (DVT) per 1000 surgical discharges (excluding patients admitted for DVT, obstetrics, neonatal, and plication of vena cava before or after surgery*; based on secondary diagnoses of DVT only*) (PSI 12)

* Because timing of plication unavailable for 11 States. Also, excludes admissions specifically for such thromboemboli, such as cases from earlier admissions, from other hospitals, or from other settings.
PSI 13 Postoperative septicemia per 1000 elective-surgery discharges of longer than 3 days (excluding patients admitted for infection; patients with cancer or immunocompromised states, and obstetric and neonatal conditions) (PSI 13)
PSI 14 Postoperative abdominal wound dehiscence per 1000 abdominopelvic-surgery discharges (excluding obstetric and neonatal conditions; based on secondary diagnoses only*) (PSI 14)

* That is, excludes admissions specifically for such wound dehiscence, such as cases from earlier admissions or from other hospitals.
PSI 15 Accidental puncture or laceration during procedures per 1000 discharges (excluding obstetric and neonatal admissions; based on secondary diagnoses only*) (PSI 15)

* That is, excludes admissions specifically for such problems, such as cases from earlier admissions or from other hospitals.
PSI 16 Transfusion reactions per 1000 discharges (excluding neonates; based on secondary diagnoses only*) (PSI 16)

* That is, excludes admissions specifically for transfusion reactions, such as cases from earlier admissions or from other hospitals.
PSI 17 Birth trauma injury per 1000 live births (excluding preterm and osteogenesis imperfecta births) (PSI 17)
PSI 18 Obstetric trauma per 1000 instrument-assisted vaginal deliveries (PSI 18)
PSI 19 Obstetric trauma per 1000 vaginal deliveries without instrument assistance (PSI 19)
PSI 20 Obstetric trauma per 1000 Cesarean deliveries (PSI 20)
PSI 21 Foreign body left in during procedure in hospital (excluding neonatal procedures; based on principal and secondary diagnoses*) per 100,000 population (PSI 21)

* That is, includes admissions specifically for treatment of foreign body left, such as cases from earlier admissions or from other hospitals.
PSI 22 Iatrogenic pneumothorax discharges (excluding patients with trauma, thoracic surgery, lung or pleural biopsy, or cardiac surgery and neonates; based on principal and secondary diagnoses*) per 100,000 population (PSI 22)

* That is, includes admissions specifically for iatrogenic pneumothorax, such as cases from earlier admissions or from other hospitals. Also, includes barotrauma (including acute respiratory distress syndrome) and central line placement.
PSI 23 Infection due to intravenous lines or catheters (excluding immunocompromised or cancer patients and neonates; based on principal and secondary diagnoses*) per 100,000 population (PSI 23)

* That is, includes admissions specifically for such infections, such as cases from earlier admissions, from other hospitals, or from other settings.
PSI 24 Postoperative abdominal wound dehiscence in hospital (excluding obstetric and neonatal conditions; based on principal and secondary diagnoses*) per 100,000 population (PSI 24)

* That is, includes admissions specifically for such wound dehiscence, such as cases from earlier admissions or from other hospitals.
PSI 25 Accidental puncture or laceration during procedures in hospital (excluding obstetric and neonatal admissions; based on principal and secondary diagnoses*) per 100,000 population (PSI 25)

* That is, includes admissions specifically for such problems, such as cases from earlier admissions or from other hospitals.
PSI 26 Transfusion reactions in hospital (excluding neonates; based on principal and secondary diagnoses*) per 100,000 population (PSI 26)

* That is, includes admissions specifically for transfusion reactions, such as cases from earlier admissions or from other hospitals.

Table B.2: Sources of HCUP Data

State Data Source
Arizona Arizona Department of Health Services
California Office of Statewide Health Planning & Development
Connecticut CHIME, Inc.
Florida Florida Agency for Health Care Administration
Georgia GHA: An Association of Hospitals & Health Systems
Kansas Kansas Hospital Association
Maryland Health Services Cost Review Commission
Massachusetts Division of Health Care Finance and Policy
Missouri Hospital Industry Data Institute
New Jersey New Jersey Department of Health & Senior Services
New York New York State Department of Health
South Carolina South Carolina State Budget & Control Board
Tennessee Tennessee Hospital Association
Texas Texas Health Care Information Council
Virginia Virginia Health Information
Wisconsin Wisconsin Dept of Health & Family Services

Table B.3. Analysis of HCUP State Inpatient Databases (SID), 2000, by coding of race/ethnicity, sorted by percent missing race

State Total Discharges Hospital*Count Percent Missing Race Number of hospitals*
with >50% missing race/ ethnicity
States included in the NHDR race analysis        
AZ 591,960 55 0.00 0
CT 385,117 31 0.00 0
SC 516,775 60 0.01 0
GA 958,282 150 0.06 0
MD 643,302 47 0.26 0
TX 2,443,897 287 0.46 4
MO 756,331 105 0.96 1
CA 3,656,040 379 0.96 0
FL 2,193,347 190 1.10 0
KS 300,589 121 1.87 1
NJ 1,111,234 75 2.53 0
MA 782,108 68 5.31 1
WI 614,090 119 5.42 3
TN 788,845 109 6.36 5
VA 804,933 84 6.78 2
NY 2,441,128 213 6.95 8

* Community hospitals, excluding rehabilitation hospitals, as well as long-term hospitals, psychiatric hospitals, and alcoholism/chemical dependency treatment hospitals

Table 4. Number of diagnosis and procedure fields and the percent of discharges that include PSI-related cause of injury codes (E-codes) by State

State Maximum number of diagnoses Maximum number of procedures Percent of HCUP discharges
with PSI-related E-codes
AZ 11 6 12.4
CA* 30 21 9.8
CT 30 30 11.9
FL 10 10 8.2
GA 10 6 11.4
KS* 30 25 8.2
MA 16 15 11.5
MD 16 15 14.1
MO 30 25 15.3
NJ 10 8 8.4
NY 17 15 12.2
SC*, ** 10 10 4.6
TN 10 6 11.1
TX* 10 6 8.1
VA 10 6 10.5
WI 10 6 13.7

* These are states that do not have laws or mandates for the collection of external cause of injury coding (E-codes) in statewide hospital discharge systems. State health departments or other regulating bodies (for example, state hospital associations) may have the authority to monitor compliance of reporting E-codes through the electronic transfer to a centralized database.10

* CA and SC percent of E-codes may be artificially low because these data sources do not require hospitals to report E-codes in the range 870-879 ("misadventures to patients during surgical and medical care").

** SC percent of E-codes may be artificially low because separate E-codes fields available in South Carolina source data were not obtained for HCUP; however, E-codes are present in other diagnosis fields in SC.

Return to Contents

References

1. 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. Rockville, MD: Agency for Healthcare Research and Quality, 2001.

2. 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. Rockville, MD: Agency for Healthcare Research and Quality, 2002.

3. Agency for Healthcare Research and Quality. AHRQ Quality Indicators' Guide to Patient Safety Indicators. Rockville, MD: Agency for Healthcare Research and Quality (expected publication date March 13, 2003).

4. Barrett ML, Houchen R, Coffey RM, Kelley E, Andrews R, Moy E, Kosiak B, Remus D. Technical Specifications for HCUP Measures in the National Healthcare Quality Report and the National Healthcare Disparities Report. HCUP Contract Task 290-00-004 Deliverable #185. Washington, DC: The Medstat Group, Inc., January 2003.

Claritas, Inc. The Claritas Demographic Update Methodology, May 2001.

6. Davies SM, Geppert J, McClellan M, et al. Refinement of the HCUP Quality Indicators. Technical Review Number 4 (prepared by UCSF-Stanford Evidence-based Practice Center under Contract No. 290-97-0013), AHRQ Pub. No. 01-0035. Rockville, MD: Agency for Healthcare Research and Quality, May 2001.

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

8. HCUP. HCUP Nationwide Inpatient Sample: Calculating Nationwide Inpatient Sample Variances, 2000. (http://www.hcup-us.ahrq.gov/reports/CalculatingNISVariances200106092005.pdf [PDF Help]), May 24, 2002.

9. McDonald K, Romano P, Geppert J, et al. Measures of Patient Safety Based on Hospital Administrative Data' The Patient Safety Indicators. Technical Review 5 (prepared by the University of California San Francisco-Stanford Evidence-based Practice Center under Contract No. 290-97-0013). AHRQ Pub. No. 02-0038. Rockville, MD: Agency for Healthcare Research and Quality, August 2002.

10. Trauma Foundation/San Francisco Injury Center. How States are Collecting and Using Cause of Injury Data. Report of the Data Committee Injury Control and Emergency Health Services Section, American Public Health Association, September 1998.

Return to Appendix B: Methods
Proceed to Next Section
2003 National Healthcare Disparities Report

 

The information on this page is archived and provided for reference purposes only.

 

AHRQ Advancing Excellence in Health Care