Healthcare Cost and Utilization Project (HCUP)
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.
October 30, 2006
This section discusses methods for applying the Agency for Healthcare Research and Quality (AHRQ) Quality Indicators (QIs) to the Healthcare Cost and Utilization Project (HCUP) hospital discharge data for several measures in the 2006 National Healthcare Disparities Report.
AHRQ Quality Indicators
The AHRQ 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 used for this report 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.1
- 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
- Patient Safety Indicators (PSIs) reflect quality of care inside hospitals, by focusing on surgical complications and other iatrogenic events.3
The QI measures selected for this report are described in Table B.1.
The Healthcare Cost and Utilization Project is a family of health care 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 2003 HCUP State Inpatient Databases (SID), a census of hospitals (with all of their discharges), from 23 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 Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Virginia, Vermont, and Wisconsin. For the list of the HCUP data sources, see Table B.2.
Steps for Applying the AHRQ Quality Indicators to HCUP Data
To apply the AHRQ Quality Indicators to HCUP hospital discharge data, several steps were taken; these steps, described briefly below, are presented in detail in the “Technical Specifications for HCUP Measures in the Third and Fourth National Healthcare Quality Report and the National Healthcare Disparities Report”4 (available from AHRQ on request).
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.4 We also added two indicators: immunization-preventable influenza and adult asthma, age 65 years or older.
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 5-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.5 ZIP Code-level counts were necessary for statistics by median income and urban-rural location of the patient's ZIP Code.
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 38 States that participated in 2003 HCUP SID. Ten States did not provide information on patient race to HCUP; 4 States did not report Hispanic ethnicity; and 1 State only reported patient race as “white,” “non-white,” and “Hispanic.” The remaining 23 States were used for the creation of the disparities analysis file. The following table demonstrates the representation by region of the 23 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 8 9 89% Midwest 4 12 33% South 7 16 44% West 4 13 31% Total 23 50 44% The table below compares aggregated totals of various measures for the 23 States as a percent of the national measure. In 2003, the 23 States accounted for 63 percent of U.S. hospital discharges (based on the American Hospital Association's Annual Survey). They accounted for about 60 percent of White and African Americans in the Nation (based on 2003 Claritas data) and about 80% of Asian/Pacific Islanders and Hispanics.
Measure Total of 23 HCUP States with race/ethnicity as a percent of national total Hospital discharges 63% Total resident population 67%* Population by race/ethnicity: White 63%* African American 67%* Asian/Pacific Islander 82%* Hispanic 84%* Population by age: Population under age 18 67%* Population age 18-64 67%* Population over age 64 67%* Population with income under the poverty level 68%** * Calculated using 2003 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, Native Hawaiian or Other Pacific Islander, American Indian or Alaska 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.
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 community* hospitals only from the 23 States and eliminated rehabilitation hospitals in the 2003 SID because the completeness of reporting for rehabilitation hospitals was inconsistent across States. Second, community hospitals from these 23 States were sampled to approximate a 40-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 40-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 23 of the 37 States included in the 2003 NIS and is a 40-percent sample of community hospitals rather than a 20-percent sample as in the NIS. The final disparities analysis file included about 14.5 million hospital discharges from more than 1,700 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.
Identification of 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.6 Standard errors calculations for the disparities analysis file were based on the HCUP report entitled “Calculating Nationwide Inpatient Sample (NIS) Variances”.7 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 3MTM 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 the “Statistical Methodology and Calculations” section below.
Caveats Relating to Data Collection Differences Among States
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 and are discussed below.
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 below:
Data Elements Needed in Some QIs – Two data elements not available in every State that are required for certain QIs are “secondary procedure day” and “admission type” (elective, urgent, and emergency). These data elements are used to exclude specific cases from some QI measures. Seven of the 23 States (i.e., Arizona, Colorado, Florida, Kansas, Michigan, Virginia, Wisconsin) 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. We imputed the missing admission type using available information. For all States except California, an admission type of “elective” was assigned if the DRG did not indicate trauma, delivery, or newborn. An admission type of newborn was assigned if the DRG indicated a newborn. 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.
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 B.3. 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 B.4. 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 (see Table B.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, and 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.
| QI No. | Description |
|---|---|
| Prevention Quality Indicators (PQIs) | |
| PQI 1 | Admissions for diabetes with short-term complications* (excluding obstetric admissions and transfers from other institutions) per 100,000 population, age 18 years and older * Ketoacidosis, hyperosmolarity, or coma. |
| PQI 2 | Admissions with perforated appendix per 1000 admissions with appendicitis (excluding obstetric and neonatal admissions and transfers from other institutions) |
| PQI 3 | Admissions for diabetes with long-term complications* (excluding obstetric admissions and transfers from other institutions) per 100,000 population, age 18 years and older * 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 5 | 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 6 | Admissions for pediatric gastroenteritis (excluding obstetric and neonatal admissions and transfers from other institutions) per 100,000 population, age less than 18 years |
| PQI 7 | 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 14 | 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 * Without short-term (ketoacidosis, hyperosmolarity, coma) or long-term (renal, eye, neurological, circulatory, other unspecified) complications. * 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 (modified) | Adult asthma admissions (excluding obstetric admissions and transfers from other institutions) per 100,000 population, age 65 years and older |
| (Added) | Immunization-preventable influenza admissions (excluding transfers from other institutions) per 100,000 population, age 65 years and older |
| Inpatient Quality Indicators (IQIs) | |
| IQI 11 | Deaths per 1000 admissions with abdominal aortic aneurysm (AAA) repair (excluding obstetric and neonatal admissions and transfers to another hospital) |
| IQI 12 | Deaths per 1000 admissions with coronary artery bypass graft (CABG), age 40 and older (excluding obstetric and neonatal admissions and transfers to another hospital) |
| IQI 15 | Deaths per 1000 admissions with acute myocardial infarction (AMI) as principal diagnosis, age 18 and older (excluding transfers to another hospital) |
| IQI 16 | Deaths per 1000 admissions with congestive heart failure (CHF) as principal diagnosis, age 18 and older (excluding obstetric and neonatal admissions and transfers to another hospital) |
| IQI 20 | Deaths per 1000 admissions with pneumonia as principal diagnosis, age 18 and older (excluding obstetric and neonatal admissions and transfers to another hospital) |
| IQI 21 | Cesarean deliveries per 1000 deliveries |
| IQI 22 | Vaginal birth after cesarean (VBAC) per 1000 women with previous cesarean deliveries |
| 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 27 | Percutaneous transluminal coronary angioplasties (PTCAs) for adults age 40 years and older (excluding obstetric admissions) per 100,000 population age 40 and older |
| IQI 30 | Deaths per 1000 adult admissions age 40 and older with percutaneous transluminal coronary angioplasties (PTCAs) (excluding obstetric and neonatal admissions and transfers to another hospital) |
| Patient Safety Indicators (PSIs) | |
| PSI 1 | Complications of anesthesia per 1000 surgical discharges (excluding patients with such complications who also have substance use disorders) |
| 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 3 | Decubitus ulcers per 1000 discharges of length 5 or more days (excluding paralysis patients, patients admitted from long-term-care facilities, patients with diseases of the skin, subcutaneous tissue, and breast, and obstetrical admissions) |
| PSI 4 | Failure to rescue or deaths per 1000 discharges having developed specified complications of care during hospitalization (excluding patients transferred in or out, patients admitted from long-term-care facilities, neonates, and patients over 74 years old) |
| PSI 5 | Foreign body accidentally left in during procedure per 1000 medical and surgical discharges (excluding neonates*) * Also 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 neonates, obstetrical admissions, and patients with trauma, thoracic surgery, lung or pleural biopsy, or cardiac surgery*) * Also 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 | Selected infections due to medical care per 1000 discharges (excluding immunocompromised and cancer patients and neonates)* * Also 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* (excluding obstetrical admissions) * 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 admissions) * Postoperative hemorrhage or hematoma is not verifiable as following surgery because information on day of procedure is not available for all discharges. 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 surgical discharges (excluding some serious disease* and obstetric admissions) * 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 surgical discharges (excluding patients with respiratory disease, circulatory disease, and obstetric conditions) |
| PSI 12 | Postoperative pulmonary embolus (PE) or deep vein thrombosis (DVT) per 1000 surgical discharges (excluding patients admitted for DVT, obstetrics, and plication of vena cava before or after surgery*) * Also excludes admissions specifically for such thromboembuli, such as cases from earlier admissions, from other hospitals, or from other settings. |
| PSI 13 | Postoperative sepsis per 1000 elective-surgery discharges of longer than 3 days (excluding patients admitted for infection; patients with cancer or immunocompromised states, and obstetric conditions) |
| PSI 14 | Reclosure of postoperative disruption of abdominal wall (postoperative abdominal wound dehiscence) per 1000 abdominopelvic-surgery discharges (excluding obstetric conditions*) * Also 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 admissions*) * Also 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*) * Also excludes admissions specifically for transfusion reactions, such as cases from earlier admissions or from other hospitals. |
| PSI 17 | Birth trauma - injury to neonate per 1000 live births (excluding preterm and osteogenesis imperfecta births) |
| PSI 21 | Foreign body accidentally left in during procedure* per 100,000 population (excluding neonatal procedures) * Includes admissions specifically for treatment of foreign body left, such as cases from earlier admissions or from other hospitals. |
| PSI 22 | Iatrogenic pneumothorax cases* per 100,000 population (excluding neonates, obstetrical admissions, and patients with trauma, thoracic surgery, lung or pleural biopsy, or cardiac surgery) * 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 | Selected infections due to medical care* per 100,000 population (excluding immunocompromised or cancer patients and neonates) * Includes admissions specifically for such infections, such as cases from earlier admissions, from other hospitals, or from other settings. |
| PSI 24 | Reclosure of postoperative disruption of abdominal wall (postoperative abdominal wound dehiscence*) per 100,000 population (excluding obstetric conditions) * 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* per 100,000 population (excluding obstetric admissions) * Includes admissions specifically for such problems, such as cases from earlier admissions or from other hospitals. |
| PSI 27 | Obstetric trauma with 3rd degree, 4th degree, or other obstetric lacerations per 1,000 instrument-assisted vaginal deliveries |
| PSI 28 | Obstetric trauma with 3rd degree, 4th degree, or other obstetric lacerations per 1,000 vaginal deliveries without instrument assistance |
| PSI 29 | Obstetric trauma with 3rd degree, 4th degree, or other obstetric lacerations per 1,000 Cesarean deliveries |
| State | Data Source |
|---|---|
| Arizona | Arizona Department of Health Services |
| California | Office of Statewide Health Planning & Development |
| Colorado | Colorado Health & Hospital Association |
| Connecticut | Chime, Inc. |
| Florida | Florida Center for Health Information & Policy Analysis |
| Georgia | Georgia Hospital Association (GHA) |
| Hawaii | Hawaii Health Information Corporation |
| Kansas | Kansas Hospital Association |
| Maryland | Health Services Cost Review Commission |
| Massachusetts | Division of Health Care Finance and Policy |
| Michigan | Michigan Health & Hospital Association |
| Missouri | Hospital Industry Data Institute |
| New Hampshire | New Hampshire Department of Health & Human Services |
| New Jersey | New Jersey Department of Health & Senior Services |
| New York | New York State Department of Health |
| Pennsylvania | Pennsylvania Health Care Cost Containment Council |
| Rhode Island | Rhode Island Department of Health |
| South Carolina | South Carolina State Budget & Control Board |
| Tennessee | Tennessee Hospital Association |
| Texas | Texas Department of State Health Services |
| Vermont | Vermont Association of Hospitals and Health Systems |
| Virginia | Virginia Health Information |
| Wisconsin | Wisconsin Department of Health & Family Services |
| State | Maximum number of diagnoses | Maximum number of procedures |
|---|---|---|
| Arizona | 11 | 6 |
| California | 30 | 21 |
| Colorado | 15 | 15 |
| Connecticut | 30 | 30 |
| Florida | 10 | 10 |
| Georgia | 10 | 6 |
| Hawaii | 15 | 15 |
| Kansas | 30 | 25 |
| Massachusetts | 16 | 15 |
| Maryland | 16 | 15 |
| Michigan | 30 | 30 |
| Missouri | 30 | 25 |
| New Hampshire | 10 | 6 |
| New Jersey | 10 | 8 |
| New York | 17 | 15 |
| Pennsylvania | 10 | 6 |
| Rhode Island | 12 | 11 |
| South Carolina | 12 | 10 |
| Tennessee | 10 | 6 |
| Texas | 10 | 6 |
| Virginia | 10 | 6 |
| Vermont | 21 | 20 |
| Wisconsin | 10 | 6 |
| PSI * | Codes used for defining the numerator | Codes used for defining exclusions | ||
|---|---|---|---|---|
| E codes | Similar ICD-9-CM codes | E codes | Similar ICD-9-CM codes | |
| 1 | E8763, E8551, E9381 – E9389 | 9681-9684, 9687 | Self-inflicted injury (E95nn) | None |
| 5 | E8710 – E8719 | 9984, 9987 | None | None |
| 8 | None | None | Poisoning (E85nn, E86nn, E95nn, E96nn, E98nn) | 9600-9799 |
| 15 | E8700 – E8709 | 9982 | None | None |
| 16 | E8760 | 9996-9997 | None | None |
| 21 | E8710 – E8719 | 9984, 9987 | None | None |
| 25 | E8700 – E8709 | 9982 | None | None |
| 26 | E8760 | 9996-9997 | None | None |
* All other Patient Safety Indicators do not use E codes.
Statistical Methodology and Calculations
This section explains the statistical methods and gives formulas for the calculations of standard errors and hypothesis tests. These statistics are derived from the disparities analysis file created from the HCUP SID and Claritas (a vendor that compiles and adds value to Bureau of Census data). For disparities analysis file estimates, the standard errors are calculated as described in the HCUP report entitled “Calculating Nationwide Inpatient Sample (NIS) Variances”.7 We will refer to this report simply as the NIS Variance Report throughout this section. This method takes into account the cluster and stratification aspects of the disparities analysis file sample design when calculating these statistics using the SAS procedure PROC SURVEYMEANS. For Claritas population counts, there is no sampling error.
Even though the disparities analysis file contains discharges from a finite sample of hospitals, we treat the sample as though it was drawn from an infinite population. We do not employ finite population correction factors in estimating standard errors. We take this approach because we view the outcomes as a result of myriad processes that go into treatment decisions rather than being the result of specific, fixed processes generating outcomes for a specific population and a specific year. We consider the disparities analysis file to be a sample from a “super-population” for purposes of variance estimation. Further, we assume the counts (of QI events) to be binomial.
Section 1. Area Population QIs Using Claritas Population Data
Standard error estimates for discharge rates per 100,000 population using the 2003 Claritas population data.
The observed rate was calculated as foyllows:
(A.1)wi and xi, respectively, are the discharge weight and variable of interest for patient i in the disparities analysis file. To obtain the estimate of S and its standard error, SES, we followed instructions in the NIS Variance Report.
The population count in the denominator is a constant. Consequently, the standard error of the rate R was calculated as:
(A.2)Standard error estimates for age/sex adjusted inpatient rates per 100,000 population using the 2003 Claritas data.
We adjusted rates for age and sex using the method of direct standardization.6 We estimated the observed rates for each of 36 age/sex categories. We then calculated a weighted average of those 36 rates using weights proportional to the percentage of a standard population in each cell. Therefore, the adjusted rate represents the rate that would be expected for the observed study population if it had the same age and sex distribution as the standard population.
For the standard population, we used the age and sex distribution of the United States as a whole according to the year 2000. In theory, differences among adjusted rates were not attributable to differences in the age and sex distributions among the comparison groups because the rates were all calculated with a common age and sex distribution.
The adjusted rate was calculated as follows (and subsequently multiplied by 100,000):
(A.3)g = Index for the 36 age/sex cells.
Ng,std = Standard population for cell g (year 2000 total U.S. population in cell g).
Ng,obs = Observed population for cell g (year 2001 subpopulation in cell g; e.g., Medicare insureds, age greater than 65, etc.).
n(g) = Number in the sample for cell g.
xg,i = Observed quality indicator for observation i in cell g (e.g., 0 or 1 indicator).
wg,i = Disparities analysis file discharge weight for observation i in cell g.
The estimates for the numerator, S*, and its standard error, SES*, were calculated in similar fashion to the unadjusted estimates for the numerator S in formula A.1. The only difference was that the weight for patient i in cell g was redefined to account for the weighting for direct standardization and the discharge weight as:
(A.4)Following instructions in the NIS Variance Report, we used PROC SURVEYMEANS to obtain the estimate of S* (A.3), the weighted sum in the numerator using the revised weights (A.4), and the estimate SES*, the standard error of the weighted sum S*. The denominator of the rate is a constant. Therefore, the standard error of the adjusted rate, A, was calculated as
(A.5)
Section 2. Provider-Based QIs Using Weighted Discharge Data (Disparities Analysis File)
Standard error estimates for inpatient rates per 1,000 discharges using discharge counts in both the numerator and the denominator.
We calculated the observed rate as follows:
(A.6)Following instructions in the HCUP NIS Variance Report, we used PROC SURVEYMEANS to obtain estimates of the discharge weighted mean, S/N, and the standard error of that weighted mean, SES/N. We multiplied this standard error by 1,000.
Standard error estimates for age/sex adjusted inpatient rates per 1,000 discharges using inpatient counts in both the numerator and the denominator.
We used the 2000 Nationwide Inpatient Sample estimates for the standard inpatient population age-sex distribution. For each of the 36 age-sex categories, we estimated the number of U.S. inpatient discharges,
, in category g. We calculated the directly adjusted rate:
(A.7)g = Index for the 36 age/sex cells.
= Standard inpatient population for cell g (estimate of year 2000 total U.S. inpatient population for cell g).n(g) = Number in the sample for cell g.
xg,i = Observed quality indicator for observation i in cell g.
wg,i = Disparities analysis file discharge weight for observation i in cell g.
Note that
is the proportion of the standard inpatient population in cell g. Consequently, the adjusted rate is a weighted average of the cell-specific rates with cell weights equal to 
. These cell weights are merely a convenient, reasonable standard inpatient population distribution for the direct standardization. Therefore, we treat these cell weights as constants in the variance calculations:
(A.8)The variance of the ratio enclosed in parentheses was estimated separately for each cell g by squaring the SE calculated using the method of Section 2.a:
(A.9)Following instructions in the HCUP NIS Variance Report, we used PROC SURVEYMEANS to obtain estimates of the discharge- and standardization-weighted means, Rg, and their standard errors.
Section 3. Significance Tests
Let R1 and R2 be either observed or adjusted rates calculated for comparison groups 1 and 2, respectively. Let SE1 and SE2 be the corresponding standard errors for the two rates. We calculated the test statistic and (two-sided) p-value:
(A.10)
where Z is a standard normal variate.
Note: the following functions calculate p in SAS and EXCEL:
SAS: p = 2 * (1 - PROBNORM(ABS(t)));
EXCEL: = 2*(1- NORMDIST(ABS(t),0,1,TRUE))
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, Revision 3. Rockville, MD: Agency for Healthcare Research and Quality, 2004.
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, Revision 3. Rockville, MD: Agency for Healthcare Research and Quality, 2004.
3. 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.
4. Barrett ML, Houchen R, Coffey RM, Moy E, Andrews R, Kelley E. Technical Specifications for HCUP Measures in the Third and Fourth National Healthcare Quality Report and the National Healthcare Disparities Report. Washington, DC: The Medstat Group, Inc., 2006.
5. Claritas, Inc. The Claritas Demographic Update Methodology, July 2003.
6. Fleiss JL. Statistical Methods for Rates and Proportions. New York: Wiley, 1973
7. 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. Available at: http://www.hcup-us.ahrq.gov/reports/methods.jsp
* 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.
Return to Appendix B: Detailed Methods
2006 National Healthcare Disparities Report