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AHRQ Quality Indicators

Guide to Prevention Quality Indicators:

Hospital Admission for

Ambulatory Care Sensitive Conditions

Department of Health and Human Services

Agency for Healthcare Research and Quality

[http://www.qualityindicators.ahrq.gov](http://www.qualityindicators.ahrq.gov/)

October 2001

Version 3.1 (March 12, 2007)

# Preface {#preface .HeadOther}

In health care as in other arenas, that which cannot be measured is
difficult to improve. Providers, consumers, policy makers, and others
seeking to improve the quality of health care need accessible, reliable
indicators of quality that they can use to flag potential problems,
follow trends over time, and identify disparities across regions,
communities, and providers. As noted in a 2001 Institute of Medicine
study, *Envisioning the National Health Care Quality Report*, it is
important that such measures cover not just acute care but multiple
dimensions of care: staying healthy, getting better, living with illness
or disability, and coping with the end of life.

The Agency for Healthcare Research and Quality (AHRQ) Quality Indicators
(QIs) are one Agency response to this need for a multidimensional,
accessible family of quality indicators. They include a family of
measures that providers, policy makers, and researchers can use with
inpatient data to identify apparent variations in the quality of either
inpatient or outpatient care. AHRQ's Evidence-Based Practice Center
(EPC) at the University of California San Francisco (UCSF) and Stanford
University adapted, expanded, and refined these indicators based on the
original Healthcare Cost and Utilization Project (HCUP) Quality
Indicators developed in the early 1990s.

The new AHRQ QIs are organized into four modules: Prevention Quality
Indicators (PQIs), Inpatient Quality Indicators (IQIs), Patient Safety
Indicators (PSIs), and Pediatric Quality Indicators (PDIs). AHRQ has
published the modules as a series. Full technical information on the
first two modules can be found in *Refinement of the HCUP Quality
Indicators (Summary), May 2001* prepared by the UCSF-Stanford EPC. It
can be accessed at
[[http://www.qualityindicators.ahrq.gov/downloads.htm]{.underline}](http://www.qualityindicators.ahrq.gov/downloads.htm).

This first module focuses on preventive care services---outpatient
services geared to staying healthy and living with illness. Researchers
and policy makers have agreed for some time that inpatient data offer a
useful window on the quality of preventive care in the community.
Inpatient data provide information on admissions for "ambulatory care
sensitive conditions" that evidence suggests could have been avoided, at
least in part, through better outpatient care. Hospitals, community
leaders, and policy makers can then use such data to identify community
need levels, target resources, and track the impact of programmatic and
policy interventions.

One of the most important ways we can improve the quality of health care
in America is to reduce the need for some of that care by providing
appropriate, high-quality preventive services. For this to happen,
however, we need to be able to track not only the level of outpatient
services but also the outcome of the services people do or do not
receive. The PQIs are intended to facilitate such efforts. The PQIs are
already being applied at the national level in the National Healthcare
Quality Report (<http://qualitytools.ahrq.gov/qualityreport>) and
National Healthcare Disparities Report
(<http://qualitytools.ahrq.gov/disparitiesreport>.) As always, we would
appreciate hearing from those who use our measures and tools so that we
can identify how they are used, how they can be refined, and how we can
measure and improve the quality of the tools themselves.

Irene Fraser, Ph.D., Director

Center for Organization and Delivery Studies

# Acknowledgments {#acknowledgments .HeadOther}

  ------------------------ --------- -------------- ---------- ------------------------
  Support efforts,                                             
  including refinement and                                     
  enhancement of the AHRQ                                      
  Quality Indicators and                                       
  related products, are                                        
  provided by the Support                                      
  for Quality Indicators                                       
  contract team.                                               

                                                               

  The following                                                
  individuals from                                             
  **Battelle Memorial                                          
  Institute,** **Stanford                                      
  University,** and                                            
  **University of                                              
  California (UC)**                                            
  constitute the Support                                       
  for Quality                                                  
  Indicators-II core team:                                     

                                                               

  Sheryl M. Davies, M.A.   Mark                                Kathryn M. McDonald,
                           Gritz,                              M.M.
                           Ph.D.                               

  Bruce Ellis, M.S.        Theresa                             Patrick Romano, M.D.,
                           Schaaf,                             M.P.H
                           P.M.P.                              

  Jeffrey Geppert, J.D.    Elaine                              Jeff Schoenborn, B.S.
                           Keller,                             
                           M.Ed.                               

                                                               

  The **Agency for                                             
  Healthcare Research and                                      
  Quality** Support for                                        
  Quality Indicators-II                                        
  team includes:                                               

  Marybeth Farquhar,                                Mary B.    
  Project Officer                                   Haines,    
                                                    Contract   
                                                    Officer    

  Mamatha Pancholi,                                            
  Project Officer                                              
  ------------------------ --------- -------------- ---------- ------------------------

The following staff from the **Evidence-based Practice Center (EPC) at
UCSF-Stanford** performed the evidence review, completed the empirical
evaluation, and created the programming code and technical documentation
for the new Quality Indicators:

***Core Project Team***

+---------------------------------------+------------------------------+
| Mark McClellan, M.D., Ph.D.,          | Jeffrey Geppert, J.D.        |
| *principal investigator*              |                              |
|                                       | Patrick Romano, M.D., M.P.H. |
| Kathryn M. McDonald, M.M., *EPC       |                              |
| coordinator*                          | Kaveh G. Shojania, M.D.      |
|                                       |                              |
| Sheryl M. Davies, M.A.                |                              |
+---------------------------------------+------------------------------+

***Other Contributors***

+-------------------+-------------------------+------------------------+
| Amber Barnato,    | Paul Matz, M.D.         | Herb Szeto, M.D.       |
| M.D.              |                         |                        |
|                   | Courtney Maclean, B.A.  | Carol Vorhaus, M.B.A   |
| Paul Collins,     |                         |                        |
| B.A.              | Susana Martins, M.D.    | Peter Weiss, M.D.      |
|                   |                         |                        |
| Bradford Duncan   | Kristine McCoy, M.P.H.  | Meghan Wheat, B.A.     |
| M.D.              |                         |                        |
|                   | Suzanne Olson, M.A.     | ***Consultants***      |
| Michael Gould,    |                         |                        |
| M.D., M.S.        | L. LaShawndra Pace,     | Douglas Staiger, Ph.D. |
|                   | B.A.                    |                        |
| Paul Heidenreich, |                         |                        |
| M.D.              | Mark Schleinitz, M.D.   |                        |
|                   |                         |                        |
| Corinna           |                         |                        |
| Haberland, M.D.   |                         |                        |
+-------------------+-------------------------+------------------------+

The following staff from **Social & Scientific Systems**, **Inc.**
developed this software product, documentation, and guide:

+---------------------------------------+------------------------------+
| ***Programmers***                     | ***Technical Writer***       |
|                                       |                              |
| Leif Karell                           | Patricia Burgess             |
|                                       |                              |
| Kathy McMillan                        | ***Graphics Designer***      |
|                                       |                              |
| Fred Rohde                            | Laura Spofford               |
+---------------------------------------+------------------------------+

Contributors from the **Agency for Healthcare Research and Quality:**

+---------------------------------------+------------------------------+
| Anne Elixhauser, Ph.D.                | H. Joanna Jiang, Ph.D.       |
|                                       |                              |
| Denise Remus, Ph.D., R.N.             | Margaret Coopey, R.N.,       |
|                                       | M.G.A, M.P.S.                |
+---------------------------------------+------------------------------+

We also wish to acknowledge the contribution of the peer reviewers of
the evidence report and the beta-testers of the software products, whose
input was invaluable.

Table of Contents

[**Preface iii**](#preface)

[Acknowledgments iv](#acknowledgments)

[1.0 Introduction to the AHRQ Prevention Quality Indicators
1](#introduction-to-the-ahrq-prevention-quality-indicators)

[1.1 What Are the Prevention Quality Indicators?
1](#what-are-the-prevention-quality-indicators)

[1.2 How Can the PQIs Be Used in Quality Assessment?
2](#how-can-the-pqis-be-used-in-quality-assessment)

[1.3 What does this Guide Contain? 3](#what-does-this-guide-contain)

[2.0 Origins and Background of the Quality Indicators
4](#origins-and-background-of-the-quality-indicators)

[2.1 Development of the AHRQ Quality Indicators
4](#development-of-the-ahrq-quality-indicators)

[2.2 AHRQ Quality Indicator Modules 4](#ahrq-quality-indicator-modules)

[3.0 Methods of Identifying, Selecting, and Evaluating the Quality
Indicators
6](#methods-of-identifying-selecting-and-evaluating-the-quality-indicators)

[3.1 Step 1: Obtain Background Information on QI Use
6](#step-1-obtain-background-information-on-qi-use)

[3.2 Step 2: Search the Literature to Identify Potential QIs
6](#step-2-search-the-literature-to-identify-potential-qis)

[3.3 Step 3: Review the Literature to Evaluate the QIs According to
Predetermined Criteria
7](#step-3-review-the-literature-to-evaluate-the-qis-according-to-predetermined-criteria)

[3.4 Step 4: Perform a Comprehensive Evaluation of Risk Adjustment
8](#step-4-perform-a-comprehensive-evaluation-of-risk-adjustment)

[3.5 Step 5: Evaluate the Indicators Using Empirical Analyses
9](#step-5-evaluate-the-indicators-using-empirical-analyses)

[4.0 Summary Evidence on the Prevention Quality Indicators
11](#summary-evidence-on-the-prevention-quality-indicators)

[4.1 Version 3.1 PQIs 11](#version-3.1-pqis)

[4.2 Strengths and Limitations in Using the PQIs
14](#strengths-and-limitations-in-using-the-pqis)

[4.3 Questions for Future Work 15](#questions-for-future-work)

[5.0 Detailed Evidence for Prevention Quality Indicators
17](#detailed-evidence-for-prevention-quality-indicators)

[5.1 Diabetes Short-term Complications Admission Rate (PQI 1)
20](#diabetes-short-term-complications-admission-rate-pqi-1)

[5.2 Perforated Appendix Admission Rate (PQI 2)
22](#perforated-appendix-admission-rate-pqi-2)

[5.3 Diabetes Long-term Complications Admission Rate (PQI 3)
24](#diabetes-long-term-complications-admission-rate-pqi-3)

[5.4 Chronic Obstructive Pulmonary Disease Admission Rate (PQI 5)
26](#chronic-obstructive-pulmonary-disease-admission-rate-pqi-5)

[5.5 Hypertension Admission Rate (PQI 7)
28](#hypertension-admission-rate-pqi-7)

[5.6 Congestive Heart Failure Admission Rate (PQI 8)
30](#congestive-heart-failure-admission-rate-pqi-8)

[5.7 Low Birth Weight Rate (PQI 9) 32](#low-birth-weight-rate-pqi-9)

[5.8 Dehydration Admission Rate (PQI 10)
34](#dehydration-admission-rate-pqi-10)

[5.9 Bacterial Pneumonia Admission Rate (PQI 11)
36](#bacterial-pneumonia-admission-rate-pqi-11)

[5.10 Urinary Tract Infection Admission Rate (PQI 12)
38](#urinary-tract-infection-admission-rate-pqi-12)

[5.11 Angina without Procedure Admission Rate (PQI 13)
40](#angina-without-procedure-admission-rate-pqi-13)

[5.12 Uncontrolled Diabetes Admission Rate (PQI 14)
42](#uncontrolled-diabetes-admission-rate-pqi-14)

[5.13 Adult Asthma Admission Rate (PQI 15)
44](#adult-asthma-admission-rate-pqi-15)

[5.14 Rate of Lower-extremity Amputation among Patients with Diabetes
(PQI 16)
46](#rate-of-lower-extremity-amputation-among-patients-with-diabetes-pqi-16)

[6.0 Using Different Types of QI Rates
48](#using-different-types-of-qi-rates)

[7.0 References 50](#references)

[Appendix A: Links A-1](#appendix-a-links)

List of Tables

[Table 1. Prevention Quality Indicators
12](#__RefHeading___Toc160441066)

[Table 2. Diabetes-related Prevention Quality Indicators
14](#__RefHeading___Toc160441067)

# Introduction to the AHRQ Prevention Quality Indicators

Prevention is an important role for all health care providers. Providers
can help individuals stay healthy by preventing disease, and they can
prevent complications of existing disease by helping patients live with
their illnesses. To fulfill this role, however, providers need data on
the impact of their services and the opportunity to compare these data
over time or across communities. Local, State, and Federal policymakers
also need these tools and data to identify potential access or
quality-of-care problems related to prevention, to plan specific
interventions, and to evaluate how well these interventions meet the
goals of preventing illness and disability.

The Agency for Healthcare Research and Quality (AHRQ) Prevention Quality
Indicators (PQIs) represent one such tool. Local, State, or national
data collected using the PQIs can flag potential problems resulting from
a breakdown of health care services by tracking hospitalizations for
conditions that should be treatable on an outpatient basis, or that
could be less severe if treated early and appropriately. The PQIs
represent the current state of the art in measuring the outcomes of
preventive and outpatient care through analysis of inpatient discharge
data.

This update of the AHRQ PQIs (Version 3.1) reflects changes in
indicators associated with ICD-9-CM coding updates for FY 2007
(effective 10-1-2006).

The Risk Adjustment and Hierarchical Modeling (RAHM) Workgroup
recommended that the AHRQ adopt a hierarchical modeling approach with
the AHRQ QI. As a result, in the FY2007 release the parameter file of
risk adjustment covariates is computed using a hospital random-effect
instead of the existing simple logistic model. Because the covariates
are computed on such a large dataset with thousands of hospitals and
millions of patients, the adoption of the hierarchical model will be
relatively transparent to current users of the indicators. In other
words, the hierarchical model does not change the values of the
coefficients very much. The univariate shrinkage estimator is unchanged.
For more information on the work of the RAHM workgroup, see the draft
report at
(<http://www.qualityindicators.ahrq.gov/listserv_archive_2006.htm#Oct13>).

Population figures through 2007 for use with AHRQ Quality Indicator
software were derived from U. S. Census Bureau data using estimates for
2000 through 2005 and modified projections for 2006 and 2007. The 2007
file uses the same inter-censal estimates for the years 1995 through
1999 as the 2006 file, so counts for these years did not change.

## What Are the Prevention Quality Indicators?

The PQIs are a set of measures that can be used with hospital inpatient
discharge data to identify \"ambulatory care sensitive conditions\"
(ACSCs). ACSCs are conditions for which good outpatient care can
potentially prevent the need for hospitalization, or for which early
intervention can prevent complications or more severe disease.

Even though these indicators are based on hospital inpatient data, they
provide insight into the quality of the health care system *outside* the
hospital setting. Patients with diabetes may be hospitalized for
diabetic complications if their conditions are not adequately monitored
or if they do not receive the patient education needed for appropriate
self-management. Patients may be hospitalized for asthma if primary care
providers fail to adhere to practice guidelines or to prescribe
appropriate treatments. Patients with appendicitis who do not have ready
access to surgical evaluation may experience delays in receiving needed
care, which can result in a life-threatening condition---perforated
appendix. The PQIs consist of the following 14 ambulatory care sensitive
conditions, which are measured as rates of admission to the hospital:

  ---------- ------------------------------------------------------------
  **PQI      **Prevention Quality Indicators**
  Number**   

  1          Diabetes short-term complication admission rate

  2          Perforated appendix admission rate

  3          Diabetes long-term complication admission rate

  5          Chronic obstructive pulmonary disease admission rate

  7          Hypertension admission rate

  8          Congestive heart failure admission rate

  9          Low Birth Weight

  10         Dehydration admission rate

  11         Bacterial pneumonia admission rate

  12         Urinary tract infection admission rate

  13         Angina admission without procedure

  14         Uncontrolled diabetes admission rate

  15         Adult asthma admission rate

  16         Rate of lower-extremity amputation among patients with
             diabetes
  ---------- ------------------------------------------------------------

PQIs #4 and #6 have been moved to the Pediatric Quality Indicators
module. All PQIs now apply only to adult populations.

Although other factors outside the direct control of the health care
system, such as poor environmental conditions or lack of patient
adherence to treatment recommendations, can result in hospitalization,
the PQIs provide a good starting point for assessing quality of health
services in the community. Because the PQIs are calculated using readily
available hospital administrative data, they are an easy-to-use and
inexpensive screening tool. They can be used to provide a window into
the community---to identify unmet community heath care needs, to monitor
how well complications from a number of common conditions are being
avoided in the outpatient setting, and to compare performance of local
health care systems across communities.

## How Can the PQIs Be Used in Quality Assessment?

While these indicators use hospital inpatient data, their focus is on
outpatient health care. Except in the case of patients who are
readmitted soon after discharge from a hospital, the quality of
inpatient care is unlikely to be a significant determinant of admission
rates for ambulatory care sensitive conditions. Rather, the PQIs assess
the quality of the health care system as a whole, and especially the
quality of ambulatory care, in preventing medical complications. As a
result, these measures are likely to be of the greatest value when
calculated at the population level and when used by public health
groups, State data organizations, and other organizations concerned with
the health of populations.[^1]

These indicators serve as a screening tool rather than as definitive
measures of quality problems. They can provide initial information about
potential problems in the community that may require further, more
in-depth analysis. Policy makers and health care providers can use the
PQIs to answer questions such as:

-   Does the admission rate for diabetes complications in my community
    suggest a problem in the provision of appropriate outpatient care to
    this population?

-   How does the admission rate for congestive heart failure vary over
    time and from one region of the country to another?

State policy makers and local community organizations can use the PQIs
to assess and improve community health care. For example, an official at
a State health department wants to gain a better understanding of the
quality of care provided to people with diabetes in her State. She
selects the four PQIs related to diabetes and applies the statistical
programs downloaded from the AHRQ Web site to hospital discharge
abstract data collected by her State.

Based on output from the programs, she examines the age- and
sex-adjusted admission rates for these diabetes PQIs for her State as a
whole and for communities within her State. The programs provide output
that she uses to compare different population subgroups, defined by age,
ethnicity, or gender. She finds that admission rates for short-term
diabetes complications and uncontrolled diabetes are especially high in
a major city in her State and that there are differences by
race/ethnicity. She also applies the PQI programs to multiple years of
her State's data to track trends in hospital admissions over time. She
discovers that the trends for these two PQIs are increasing in this city
but are stable in the rest of the State. She then compares the figures
from her State to national and regional averages on these PQIs using
HCUPnet---an online query system providing access to statistics based on
HCUP data.[^2] The State average is slightly higher than the regional
and national averages, but the averages for this city are substantially
higher.

After she has identified disparities in admission rates in this
community and in specific patient groups, she further investigates the
underlying reasons for those disparities. She attempts to obtain
information on the prevalence of diabetes across the State to determine
if prevalence is higher in this city than in other communities. Finding
no differences, she consults with the State medical association to begin
work with local providers to discern if quality-of-care problems
underlie these disparities. She contacts hospitals and physicians in
this community to determine if community outreach programs can be
implemented to encourage patients with diabetes to seek care and to
educate them on lifestyle modifications and diabetes self-management.
She then helps to develop specific interventions to improve care for
people with diabetes and reduce preventable complications and resulting
hospitalizations.

## What does this Guide Contain?

This guide provides background information on the PQIs. First, it
describes the origin of the entire family of AHRQ Quality Indicators.
Second, it provides an overview of the methods used to identify, select,
and evaluate the AHRQ Quality Indicators. Third, the guide summarizes
the PQIs specifically, describes strengths and limitations of the
indicators, documents the evidence that links the PQIs to the quality of
outpatient health care services, and then provides in-depth two-page
descriptions of each PQI.

The section, \"Using Different Types of QI Rates,\" explains the various
types of rates calculated by the software and presents tips on selecting
the appropriate type of rate to use for given situations.

The document *Prevention Quality Indicators Technical Specifications*
outlines the specific definitions of each PQI, with complete ICD-9-CM
coding specifications. The document *Prevention Quality Indicators
Comparative Data,* provides the current area rates, area standard
deviation, population rates, and ratings for each indicator.

See [Appendix A](#appendix-a-links) for links to these and other
documents as well as Web sites that may be of interest to PQI users.

# Origins and Background of the Quality Indicators

In the early 1990s, in response to requests for assistance from
State-level data organizations and hospital associations with inpatient
data collection systems, AHRQ developed a set of quality measures that
required only the type of information found in routine hospital
administrative data---diagnoses and procedures, along with information
on patient's age, gender, source of admission, and discharge status.
These States were part of the Healthcare Cost and Utilization Project,
an ongoing Federal-State-private sector collaboration to build uniform
databases from administrative hospital-based data.

AHRQ developed these measures, called the HCUP Quality Indicators, to
take advantage of a readily available data source---administrative data
based on hospital claims---and quality measures that had been reported
elsewhere.[^3] The 33 HCUP QIs included measures for avoidable adverse
outcomes, such as in-hospital mortality and complications of procedures;
use of specific inpatient procedures thought to be overused, underused,
or misused; and ambulatory care sensitive conditions.

Although administrative data cannot provide definitive measures of
health care quality, they can be used to provide indicators of health
care quality that can serve as the starting point for further
investigation. The HCUP QIs have been used to assess potential
quality-of-care problems and to delineate approaches for dealing with
those problems. Hospitals with high rates of poor outcomes on the HCUP
QIs have reviewed medical records to verify the presence of those
outcomes and to investigate potential quality-of-care problems.[^4] For
example, one hospital that detected high rates of admissions for
diabetes complications investigated the underlying reasons for the rates
and established a center of excellence to strengthen outpatient services
for patients with diabetes.

## Development of the AHRQ Quality Indicators

Since the original development of the HCUP QIs, the knowledge base on
quality indicators has increased significantly. Risk-adjustment methods
have become more readily available, new measures have been developed,
and analytic capacity at the State level has expanded considerably.
Based on input from current users and advances to the scientific base
for specific indicators, AHRQ funded a project to refine and further
develop the original QIs. The project was conducted by the UCSF-Stanford
EPC.

The major constraint placed on the UCSF-Stanford EPC was that the
measures could require only the type of information found in hospital
discharge abstract data. Further, the data elements required by the
measures had to be available from most inpatient administrative data
systems. Some State data systems contain innovative data elements, often
based on additional information from the medical record. Despite the
value of these record-based data elements, the intent of this project
was to create measures that were based on a common denominator discharge
data set, without the need for additional data collection. This was
critical for two reasons. First, this constraint would result in a tool
that could be used with any inpatient administrative data, thus making
it useful to most data systems. Second, this would enable national and
regional benchmark rates to be provided using HCUP data, since these
benchmark rates would need to be calculated using the universe of data
available from the States.

## AHRQ Quality Indicator Modules

The work of the UCSF-Stanford EPC resulted in the AHRQ Quality
Indicators, which are available as four separate modules:

-   **Prevention Quality Indicators**. These indicators consist of
    "ambulatory care sensitive conditions," hospital admissions that
    evidence suggests could have been avoided through high-quality
    outpatient care or that reflect conditions that could be less
    severe, if treated early and appropriately.

-   **Inpatient Quality Indicators**. These indicators reflect quality
    of care inside hospitals and include inpatient mortality;
    utilization of procedures for which there are questions of overuse,
    underuse, or misuse; and volume of procedures for which there is
    evidence that a higher volume of procedures is associated with lower
    mortality.

-   **Patient Safety Indicators**. These indicators also reflect quality
    of care inside hospitals, but focus on surgical complications and
    other iatrogenic events.

-   **Pediatric Quality Indicators**. This module, made available in
    February, 2006, contains indicators that apply to the special
    characteristics of the pediatric population.

The core of the Pediatric Quality Indicators (PDIs) is formed by
indicators drawn from the original three modules. Some of these
indicators were already geared to the pediatric population (for example,
PQI 4 -- Pediatric Asthma Admission Rate). These indicators are being
removed from the original modules.

Others were adapted from indicators that apply to both adult and
pediatric populations. These indicators remain in the original module,
but will apply only to adult populations.

# Methods of Identifying, Selecting, and Evaluating the Quality Indicators

In developing the new quality indicators, the UCSF-Stanford EPC applied
the Institute of Medicine's widely cited definition of quality care:
"the degree to which health services for individuals and populations
increase the likelihood of desired health outcomes and are consistent
with current professional knowledge."[^5] They formulated six specific
key questions to guide the development process:

-   Which indicators are currently in use or described in the literature
    that could be *defined using hospital discharge data*?

-   What are the *quality relationships* reported in the literature that
    could be used to define new indicators using hospital discharge
    data?

-   What evidence exists for *indicators not well represented* in the
    original indicators---pediatric conditions, chronic disease, new
    technologies, and ambulatory care sensitive conditions?

-   Which indicators have *literature-based evidence* to support face
    validity, precision of measurement, minimum bias, and construct
    validity of the indicator?

-   What *risk-adjustment method* should be suggested for use with the
    recommended indicators, given the limits of administrative data and
    other practical concerns?

-   Which indicators perform well on *empirical tests* of precision of
    measurement, minimum bias, and construct validity?

As part of this project, the UCSF-Stanford EPC identified quality
indicators reported in the literature and used by health care
organizations, evaluated the original quality indicators and potential
indicators using literature review and empirical methods, incorporated
risk adjustment for comparative analysis, and developed new programs
that could be employed by users with their own hospital administrative
data. This section outlines the steps used to arrive at a final set of
quality measures.

## Step 1: Obtain Background Information on QI Use

The project team at the UCSF-Stanford EPC interviewed 33 individuals
affiliated with hospital associations, business coalitions, State data
groups, Federal agencies, and academia about various topics related to
quality measurement, including indicator use, suggested indicators, and
other potential contacts. Interviews were tailored to the specific
expertise of interviewees. The sample was not intended to be
representative of any population; rather, individuals were selected to
include QI users and potential users from a broad spectrum of
organizations in both the public and private sectors.

Three broad audiences were considered for the quality measures: health
care providers and managers, who could use the quality measures to
assist in initiatives to improve quality; public health policy makers,
who could use the information from indicators to target public health
interventions; and health care purchasers, who could use the measures to
guide decisions about health policies.

## Step 2: Search the Literature to Identify Potential QIs

The project team performed a structured review of the literature to
identify potential indicators. They used Medline to identify the search
strategy that returned a test set of known applicable articles in the
most concise manner. Using the Medical Subject Heading (MeSH) terms
"hospital, statistic, and methods" and "quality indicators" resulted in
approximately 2,600 articles published in 1994 or later. After screening
titles and abstracts for relevancy, the search yielded 181 articles that
provided information on potential quality indicators based on
administrative data.

Clinicians, health services researchers, and other team members
abstracted information from these articles in two stages. In the first
stage, preliminary abstraction, they evaluated each of the 181
identified articles for the presence of a defined quality indicator,
clinical rationale, and strengths and weaknesses. To qualify for full
abstraction, the articles must have explicitly defined a novel quality
indicator. Only 27 articles met this criterion. The team collected
information on the definition of the quality indicator, validation, and
rationale during full abstraction.

In addition, they identified additional potential indicators using the
CONQUEST database; the National Library of Healthcare Indicators
developed by the Joint Commission on Accreditation of Healthcare
Organizations (JCAHO); a list of ORYX-approved indicators provided by
JCAHO; and telephone interviews.

## Step 3: Review the Literature to Evaluate the QIs According to Predetermined Criteria

The project team evaluated each potential quality indicator against the
following six criteria, which were considered essential for determining
the reliability and validity of a quality indicator:

-   **Face validity.** An adequate quality indicator must have sound
    clinical or empirical rationale for its use. It should measure an
    important aspect of quality that is subject to provider or health
    care system control.

-   **Precision.** An adequate quality indicator should have relatively
    large variation among providers or areas that is not due to random
    variation or patient characteristics. This criterion measures the
    impact of chance on apparent provider or community health system
    performance.

-   **Minimum bias.** The indicator should not be affected by systematic
    differences in patient case-mix, including disease severity and
    comorbidity. In cases where such systematic differences exist, an
    adequate risk adjustment system should be possible using available
    data.

-   **Construct validity.** The indicator should be related to other
    indicators or measures intended to measure the same or related
    aspects of quality. In general, better outpatient care (including,
    in some cases, adherence to specific evidence-based treatment
    guidelines) can reduce patient complication rates.

-   **Fosters real quality improvement.** The indicator should be robust
    to possible provider manipulation of the system. In other words, the
    indicator should be insulated from perverse incentives for providers
    to improve their reported performance by avoiding difficult or
    complex cases, or by other responses that do not improve quality of
    care.

-   **Application.** The indicator should have been used in the past or
    have high potential for working well with other indicators.
    Sometimes looking at groups of indicators together is likely to
    provide a more complete picture of quality.

Based on the initial review, the team identified and evaluated over 200
potential indicators using these criteria. Of this initial set, 45
indicators passed this initial screen and received comprehensive
literature and empirical evaluation. In some cases, whether an indicator
complemented other promising indicators was a consideration in retaining
it, allowing the indicators to provide more depth in specific areas.

For this final set of 45 indicators, the team reviewed an additional
2,000 articles to provide evidence on indicators during the evaluation
phase. They searched Medline for articles relating to each of the six
areas of evaluation described above. Clinicians and health services
researchers reviewed the literature for evidence and prepared a
referenced summary description on each indicator.

As part of the review process, the team assessed the link between each
indicator and health care quality along the following dimensions:

-   **Proxy.** Some indicators do not specifically measure a patient
    outcome or a process measure of quality. Rather, they measure an
    aspect of care that is correlated with process measures of quality
    or patient outcomes. These indicators are best used in conjunction
    with other indicators measuring similar aspects of clinical care, or
    when followed with more direct and in-depth investigations of
    quality.

-   **Selection bias.** Selection bias results when a substantial
    percentage of care for a condition is provided in the outpatient
    setting, so the subset of inpatient cases may be unrepresentative.
    In these cases, examination of outpatient care or emergency room
    data may help reduce selection bias.

-   **Information bias.** Quality indicators are based on information
    available in hospital discharge data sets, but some missing
    information may actually be important to evaluating the outcomes of
    hospital care. In these cases, examination of missing information
    may help to improve indicator performance.

-   **Confounding bias.** Patient characteristics may substantially
    affect performance on a measure and may vary systematically across
    areas. In these cases, adequate risk adjustment may help to improve
    indicator performance.

-   **Unclear construct validity.** Problems with construct validity
    include uncertain or poor correlations with widely accepted process
    measures or with risk-adjusted outcome measures. These indicators
    would benefit from further research to establish their relationship
    with quality care.

-   **Easily manipulated.** Quality indicators may create perverse
    incentives to improve performance without actually improving
    quality. Although very few of these perverse responses have been
    proven, they are theoretically important and should be monitored to
    ensure true quality improvement.

-   **Unclear benchmark.** For some indicators, the "right rate" has not
    been established, so comparison with national, regional, or peer
    group means may be the best benchmark available. Very low PQI rates
    may flag an underuse problem; that is, providers may fail to
    hospitalize patients who would benefit from inpatient care. On the
    other hand, overuse of acute care resources may potentially occur
    when patients who do not clinically require inpatient care are
    hospitalized.

## Step 4: Perform a Comprehensive Evaluation of Risk Adjustment

The project team identified potential risk-adjustment systems by
reviewing the applicable literature and asking the interviewees in step
1 to identify their preferences. Generally, users preferred that the
system be (1) open, with published logic; (2) cost-effective, with data
collection costs minimized and additional data collection being well
justified; (3) designed using a multiple-use coding system, such as
those used for reimbursement; and (4) officially recognized by
government, hospital groups, or other organizations.

In general, diagnosis-related groups (DRGs) seemed to fit more of the
user preference-based criteria than other alternatives. A majority of
the users interviewed already used the 3M™ All-Patient Refined DRG[^6]
(APR-DRG) system, which has been reported to perform well in predicting
resource use and death when compared to other DRG-based systems.

APR-DRGs were used to conduct indicator evaluations to determine the
impact of measured differences in patient severity on the relative
performance of providers and to provide the basis for implementing
APR-DRGs as an optional risk-adjustment system for hospital-level QI
measures. The implementation of APR-DRGs is based on an ordinary least
squares regression model. Area indicators (including all PQIs) were
risk-adjusted only for age and sex differences.

## Step 5: Evaluate the Indicators Using Empirical Analyses

The project team conducted extensive empirical testing of all potential
indicators using the 1995-97 HCUP State Inpatient Databases (SID) and
Nationwide Inpatient Sample (NIS) to determine precision, bias, and
construct validity. The 1997 SID contains uniform data on inpatient
stays in community hospitals for 22 States covering approximately 60% of
all U.S. hospital discharges. The NIS is designed to approximate a 20%
of U.S. community hospitals and includes all stays in the sampled
hospitals. Each year of the NIS contains between 6 million and 7 million
records from about 1,000 hospitals. The NIS combines a subset of the SID
data, hospital-level variables, and hospital and discharge weights for
producing national estimates. The project team conducted tests to
examine three things: precision, bias, and construct validity.

**Precision.** The first step in the analysis involved precision tests
to determine the reliability of the indicator for distinguishing real
differences in provider performance. For indicators that may be used for
quality improvement, it is important to know with what precision, or
surety, a measure can be attributed to an actual construct rather than
random variation.

For each indicator, the variance can be broken down into three
components: variation within a provider (actual differences in
performance due to differing patient characteristics), variation among
providers (actual differences in performance among providers), and
random variation. An ideal indicator would have a substantial amount of
the variance explained by between-provider variance, possibly resulting
from differences in quality of care, and a minimum amount of random
variation. The project team performed four tests of precision to
estimate the magnitude of between-provider variance on each indicator:

-   Signal standard deviation was used to measure the extent to which
    performance of the QI varies systematically across hospitals or
    areas.

-   Provider/area variation share was used to calculate the percentage
    of signal (or true) variance relative to the total variance of the
    QI.

-   Signal-to-noise ratio was used to measure the percentage of the
    apparent variation in QIs across providers that is truly related to
    systematic differences across providers and not random variations
    (noise) from year to year.

-   In-sample R-squared was used to identify the incremental benefit of
    applying multivariate signal extraction methods for identifying
    additional signal on top of the signal-to-noise ratio.

In general, random variation is most problematic when there are
relatively few observations per provider, when adverse outcome rates are
relatively low, and when providers have little control over patient
outcomes or variation in important processes of care is minimal. If a
large number of patient factors that are difficult to observe influence
whether or not a patient has an adverse outcome, it may be difficult to
separate the "quality signal" from the surrounding noise. Two signal
extraction techniques were applied to improve the precision of an
indicator:

-   Univariate methods were used to estimate the "true" quality signal
    of an indicator based on information from the specific indicator and
    1 year of data.

-   Multivariate signal extraction (MSX) methods were used to estimate
    the "true" quality signal based on information from a set of
    indicators and multiple years of data. In most cases, MSX methods
    extracted additional signal, which provided much more precise
    estimates of true hospital or area quality.

**Bias.** To determine the sensitivity of potential QIs to bias from
differences in patient severity, unadjusted performance measures for
specific hospitals were compared with performance measures that had been
adjusted for age and gender. All of the PQIs and some of the Inpatient
Quality Indicators (IQIs) could only be risk-adjusted for age and sex.
The 3M™ APR-DRG System Version 12 with Severity of Illness and Risk of
Mortality subclasses was used for risk adjustment of the utilization
indicators and the in-hospital mortality indicators, respectively. Five
empirical tests were performed to investigate the degree of bias in an
indicator:

-   Rank correlation coefficient of the area or hospital with (and
    without) risk adjustment---gives the overall impact of risk
    adjustment on relative provider or area performance.

-   Average absolute value of change relative to mean---highlights the
    amount of absolute change in performance, without reference to other
    providers' performance.

-   Percentage of highly ranked hospitals that remain in high
    decile---reports the percentage of hospitals or areas that are in
    the highest deciles without risk adjustment that remain there after
    risk adjustment is performed.

-   Percentage of lowly ranked hospitals that remain in low
    decile---reports the percentage of hospitals or areas that are in
    the lowest deciles without risk adjustment that remain there after
    risk adjustment is performed.

-   Percentage that change more than two deciles---identifies the
    percentage of hospitals whose relative rank changes by a substantial
    percentage (more than 20%) with and without risk adjustment.

**Construct validity.** Construct validity analyses provided information
regarding the relatedness or independence of the indicators. If quality
indicators do indeed measure quality, then two measures of the same
construct would be expected to yield similar results. The team used
factor analysis to reveal underlying patterns among large numbers of
variables---in this case, to measure the degree of relatedness between
indicators. In addition, they analyzed correlation matrices for
indicators.

# Summary Evidence on the Prevention Quality Indicators

The rigorous evaluations performed by the UCSF-Stanford EPC, based on
literature review and empirical testing of indicators, resulted in 16
indicators that reflect ambulatory care sensitive conditions (ACSCs).
These ACSCs have been reported and tested in a number of published
studies involving consensus processes involving panels of expert
physicians, using a range of methodologies and decision criteria. Two
sets of ambulatory care sensitive conditions are widely used:

-   The set developed by John Billings in conjunction with the United
    Hospital Fund of New York includes 28 ambulatory care sensitive
    conditions, identified by a panel of six physicians.[^7]

-   The set developed by Joel Weissman includes 12 avoidable admissions
    identified through review of the literature and evaluation by a
    panel of physicians.[^8]

Many of the ACSCs have practice guidelines associated with them,
including almost all of the chronic conditions and about half of the
acute medical or pediatric conditions. Studies have shown that better
outpatient care (including, in some cases, adherence to specific
evidence-based treatment guidelines) can reduce patient complication
rates of existing disease, including complications leading to hospital
admissions. Empirically, most of the hospital admission rates for ACSCs
are correlated with each other, suggesting that common underlying
factors influence many of the rates.

Five of these 16 PQIs were included in the original HCUP
QIs---perforated appendix, low birth weight, pediatric asthma, diabetes
short-term complications, and diabetes long-term complications---where
they were measured at the hospital level. In contrast, the 16 new
indicators were constructed at the community level, defined as a
Metropolitan Statistical Area (MSA) or a rural county. For each
indicator, lower rates indicate potentially better quality.

## Version 3.1 PQIs

A modified version of the process described in Section 3.0 is repeated
on an annual basis when the PQIs are evaluated and new indicators are
considered. With this release two of the original 16 indicators dealing
with pediatric asthma and pediatric gastroenteritis have been moved to
the Pediatric Quality Indicators (PDI) module.

New micropolitan statistical areas and updated metropolitan statistical
areas were established by the federal Office of Management and Budget
(OMB) circular 03-04 (last revised December 4, 2005). To reflect these
changes, all PQI documentation now refers to Metro Area instead of MSA.
The SAS software allows users to specify stratification by county level
with U.S. Census FIPS or modified FIPS, or by Metro Area with OMB 1999
or OMB 2003 definition. The AHRQ QI Windows Application allows users to
generate reports stratified by all four of these, as well as by State.
See [Appendix A](#appendix-a-links) for links to additional information.

Table 1 summarizes the results of the literature review and empirical
evaluations on the PQIs. It lists each indicator, provides its
definition, recommends a risk adjustment strategy, and summarizes
important caveats identified from the literature review.

Rating of performance on empirical evaluations, as described in step 5
above, ranged from 0 to 26. (The average score for the 16 original PQIs
is 14.6.) The scores were intended as a guide for summarizing the
performance of each indicator on four empirical tests of precision
(signal variance, area-level share, signal ratio, and R-squared) and
five tests of minimum bias (rank correlation, top and bottom decile
movement, absolute change, and change over two deciles), as described in
the previous section.

The Literature Review Findings column summarizes evidence specific to
each potential concern on the link between the PQIs and quality of care,
as described in step 3 above. A question mark (**?**) indicates that the
concern is theoretical or suggested, but no specific evidence was found
in the literature. A check mark (![](media/image2.png){width="0.125in"
height="0.11458333333333333in"}) indicates that the concern has been
demonstrated in the literature.

Scores for Area Rate, Area Standard Deviation, Population Rate, and
Rating are provided in the document *Prevention Quality Indicators
Comparative Data.* A complete description of each PQI is included later
in the guide in Section 5.0, \"Detailed Evidence for Prevention Quality
Indicators\" that starts on page
[17](#detailed-evidence-for-prevention-quality-indicators), and in the
document *Prevention Quality Indicators Technical Specifications*. (See
[Appendix A](#appendix-a-links).)

[]{#__RefHeading___Toc160441066 .anchor}Table 1. Prevention Quality
Indicators

+----------------+-----------------+--------------+--------------------+
| **Indicator    | **Description** | **Risk       | **Literature       |
| Name**         |                 | Adjustment   | Review             |
|                |                 | In           | Findings^a^**      |
| **(Number)**   |                 | corporated** |                    |
+================+=================+==============+====================+
| Diabetes       | Number of       | Age and sex. | **?** Proxy        |
| Short-term     | admissions for  |              |                    |
| Complication   | diabetes        |              | **?** Confounding  |
| Admission Rate | short-term      |              | bias               |
|                | complications   |              |                    |
| (PQI 1)        | per 100,000     |              |                    |
|                | population.     |              |                    |
+----------------+-----------------+--------------+--------------------+
| Perforated     | Number of       | Age and sex. | **?** Proxy        |
| Appendix       | admissions for  |              |                    |
| Admission Rate | perforated      |              |                    |
|                | appendix as a   |              |                    |
| (PQI 2)        | share of all    |              |                    |
|                | admissions for  |              |                    |
|                | appendicitis    |              |                    |
|                | within an area. |              |                    |
+----------------+-----------------+--------------+--------------------+
| Diabetes       | Number of       | Age and sex. | **?** Proxy        |
| Long-term      | admissions for  |              |                    |
| Complication   | long-term       |              | **?** Confounding  |
| Admission Rate | diabetes per    |              | bias               |
|                | 100,000         |              |                    |
| (PQI 3)        | population.     |              | **?** Easily       |
|                |                 |              | manipulated        |
|                |                 |              |                    |
|                |                 |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear benchmark  |
+----------------+-----------------+--------------+--------------------+
| Chronic        | Number of       | Age and sex. | **?** Proxy        |
| Obstructive    | admissions for  |              |                    |
| Pulmonary      | COPD per        |              | **?** Confounding  |
| Disease        | 100,000         |              | bias               |
| Admission Rate | population.     |              |                    |
|                |                 |              | **?** Easily       |
| (PQI 5)        |                 |              | manipulated        |
|                |                 |              |                    |
|                |                 |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear benchmark  |
+----------------+-----------------+--------------+--------------------+
| Hypertension   | Number of       | Age and sex. | **?** Proxy        |
| Admission Rate | admissions for  |              |                    |
|                | hypertension    |              | **?** Easily       |
| (PQI 7)        | per 100,000     |              | manipulated        |
|                | population.     |              |                    |
|                |                 |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear benchmark  |
+----------------+-----------------+--------------+--------------------+
| Congestive     | Number of       | Age and sex. | **?** Proxy        |
| Heart Failure  | admissions for  |              |                    |
| Admission Rate | CHF per 100,000 |              | **?** Easily       |
|                | population.     |              | manipulated        |
| (PQI 8)        |                 |              |                    |
|                |                 |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear benchmark  |
+----------------+-----------------+--------------+--------------------+
| Low Birth      | Number of low   | Not risk     | **?** Proxy        |
| Weight Rate    | birth weight    | adjusted.    |                    |
|                | births as a     |              | **?** Confounding  |
| (PQI 9)        | share of all    |              | bias               |
|                | births in an    |              |                    |
|                | area.           |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear construct  |
+----------------+-----------------+--------------+--------------------+
| Dehydration    | Number of       | Age and sex. | **?** Proxy        |
| Admission Rate | admissions for  |              |                    |
|                | dehydration per |              | **?** Unclear      |
| (PQI 10)       | 100,000         |              | construct          |
|                | population.     |              |                    |
|                |                 |              | **?** Easily       |
|                |                 |              | manipulated        |
|                |                 |              |                    |
|                |                 |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear benchmark  |
+----------------+-----------------+--------------+--------------------+
| Bacterial      | Number of       | Age and sex. | **?** Proxy        |
| Pneumonia      | admissions for  |              |                    |
| Admission Rate | bacterial       |              | **?** Unclear      |
|                | pneumonia per   |              | construct          |
| (PQI 11)       | 100,000         |              |                    |
|                | population.     |              | **?** Easily       |
|                |                 |              | manipulated        |
|                |                 |              |                    |
|                |                 |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear benchmark  |
+----------------+-----------------+--------------+--------------------+
| Urinary Tract  | Number of       | Age and sex. | **?** Proxy        |
| Infection      | admissions for  |              |                    |
| Admission Rate | urinary         |              | **?** Unclear      |
|                | infection per   |              | construct          |
| (PQI 12)       | 100,000         |              |                    |
|                | population.     |              | **?** Easily       |
|                |                 |              | manipulated        |
|                |                 |              |                    |
|                |                 |              | -   Unclear        |
|                |                 |              |     benchmark      |
+----------------+-----------------+--------------+--------------------+
| Angina without | Number of       | Age and sex. | **?** Proxy        |
| Procedure      | admissions for  |              |                    |
| Admission Rate | angina without  |              | **?** Unclear      |
|                | procedure per   |              | construct          |
| (PQI 13)       | 100,000         |              |                    |
|                | population.     |              | **?** Easily       |
|                |                 |              | manipulated        |
|                |                 |              |                    |
|                |                 |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear benchmark  |
+----------------+-----------------+--------------+--------------------+
| Uncontrolled   | Number of       | Age and sex. | **?** Proxy        |
| Diabetes       | admissions for  |              |                    |
| Admission      | uncontrolled    |              | **?** Confounding  |
| Rate^b^        | diabetes per    |              | bias               |
|                | 100,000         |              |                    |
| (PQI 14)       | population.     |              | **?** Easily       |
|                |                 |              | manipulated        |
+----------------+-----------------+--------------+--------------------+
| Adult Asthma   | Number of       | Age and sex. | **?** Proxy        |
| Admission Rate | admissions for  |              |                    |
|                | asthma in       |              | **?** Easily       |
| (PQI 15)       | adults per      |              | manipulated        |
|                | 100,000         |              |                    |
|                | population.     |              | !                  |
|                |                 |              | [](media/image2.pn |
|                |                 |              | g){width="0.125in" |
|                |                 |              | height="0.114      |
|                |                 |              | 58333333333333in"} |
|                |                 |              | Unclear benchmark  |
+----------------+-----------------+--------------+--------------------+
| Rate of        | Number of       | Age and sex. | **?** Proxy        |
| L              | admissions for  |              |                    |
| ower-extremity | lower-extremity |              | **?** Unclear      |
| Amputation     | amputation      |              | construct          |
| Among Patients | among patients  |              |                    |
| with Diabetes  | with diabetes   |              |                    |
|                | per 100,000     |              |                    |
| (PQI 16)       | population.     |              |                    |
+----------------+-----------------+--------------+--------------------+

+---+--------------------------------------------------------------------+
| ^ | Notes under Literature Review Findings:                            |
| a |                                                                    |
| ^ | **Proxy** -- Indicator does not directly measure patient outcomes  |
|   | but an aspect of care that is associated with the outcome; thus,   |
|   | it is best used with other indicators that measure similar aspects |
|   | of care.                                                           |
|   |                                                                    |
|   | **Confounding bias** -- Patient characteristics may substantially  |
|   | affect the performance of the indicator; risk adjustment is        |
|   | recommended.                                                       |
|   |                                                                    |
|   | **Unclear construct** -- There is uncertainty or poor correlation  |
|   | with widely accepted process measures.                             |
|   |                                                                    |
|   | **Easily manipulated** -- Use of the indicator may create perverse |
|   | incentives to improve performance on the indicator without truly   |
|   | improving quality of care.                                         |
|   |                                                                    |
|   | **Unclear benchmark** -- The "correct rate" has not been           |
|   | established for the indicator; national, regional, or peer group   |
|   | averages may be the best benchmark available.                      |
|   |                                                                    |
|   | **?** -- The concern is theoretical or suggested, but no specific  |
|   | evidence was found in the literature.                              |
|   |                                                                    |
|   | ![](media/image2.png){width="0.125in"                              |
|   | height="0.11458333333333333in"}-- Indicates that the concern has   |
|   | been demonstrated in the literature.                               |
+---+--------------------------------------------------------------------+
| ^ | Uncontrolled diabetes is designed to be combined with diabetes     |
| b | short-term complications                                           |
| ^ |                                                                    |
+---+--------------------------------------------------------------------+

The software provides the option to generate condition-specific rates
(e.g., using the number of diabetics in the denominator) by state and
age. Table 2 describes the four diabetes-related PQIs, expressed per
1,000.

[]{#__RefHeading___Toc160441067 .anchor}Table 2. Diabetes-related
Prevention Quality Indicators

+-----------------+-------------------+-----------+-------------------+
| **Indicator     | **Description**   | **Risk    | **Literature      |
| Name**          |                   | A         | Review Findings** |
|                 |                   | djustment |                   |
| **(Number)**    |                   | Incor     |                   |
|                 |                   | porated** |                   |
+-----------------+-------------------+-----------+-------------------+
| Diabetes        | Number of         | N/A       | **?** Proxy       |
| Short-term      | admissions for    |           |                   |
| Complication    | diabetes          |           | **?** Confounding |
| Admission Rate  | short-term        |           | bias              |
|                 | complications per |           |                   |
| (PQI 1)         | 1,000 diabetic.   |           |                   |
+-----------------+-------------------+-----------+-------------------+
| Diabetes        | Number of         | N/A       | **?** Proxy       |
| Long-term       | admissions for    |           |                   |
| Complication    | long-term         |           | **?** Confounding |
| Admission Rate  | diabetes per      |           | bias              |
|                 | 1,000 diabetic.   |           |                   |
| (PQI 3)         |                   |           | **?** Easily      |
|                 |                   |           | manipulated       |
|                 |                   |           |                   |
|                 |                   |           | ![]               |
|                 |                   |           | (media/image2.png |
|                 |                   |           | ){width="0.125in" |
|                 |                   |           | height="0.1145    |
|                 |                   |           | 8333333333333in"} |
|                 |                   |           | Unclear benchmark |
+-----------------+-------------------+-----------+-------------------+
| Uncontrolled    | Number of         | N/A       | **?** Proxy       |
| Diabetes        | admissions for    |           |                   |
| Admission       | uncontrolled      |           | **?** Confounding |
| Rate^a^         | diabetes per      |           | bias              |
|                 | 1,000 diabetic.   |           |                   |
| (PQI 14)        |                   |           | **?** Easily      |
|                 |                   |           | manipulated       |
+-----------------+-------------------+-----------+-------------------+
| Rate of         | Number of         | N/A       | **?** Proxy       |
| Lower-extremity | admissions for    |           |                   |
| Amputation      | lower-extremity   |           | **?** Unclear     |
| Among Patients  | amputation among  |           | construct         |
| with Diabetes   | patients with     |           |                   |
|                 | diabetes per      |           |                   |
| (PQI 16)        | 1,000 diabetic.   |           |                   |
+-----------------+-------------------+-----------+-------------------+

  ----- --------------------------------------------------------------------
  ^a^   Uncontrolled diabetes is designed to be combined with diabetes
        short-term complications

  ----- --------------------------------------------------------------------

## Strengths and Limitations in Using the PQIs

The PQIs represent the current state of the art in assessing quality of
health services in local communities using inpatient discharge data.
These indicators measure the outcomes of preventive care for both acute
illness and chronic conditions, reflecting two important components of
the quality of preventive care---effectiveness and timeliness. For
example, with effective drug therapy in the outpatient setting, hospital
admissions for hypertension can be prevented. Likewise, accurate
diagnosis and timely access to surgical treatment will help reduce the
incidence of perforated appendix. The PQIs are thus valuable tools for
identifying potential quality problems in outpatient care that help to
set the direction for more in-depth investigation. Because the PQIs are
based on readily available data---hospital discharge
abstracts---resource requirements are minimal. With uniform definitions
and standardized programs, the PQIs will allow comparisons across
States, regions, and local communities over time.

Despite the unique strengths of the PQIs, there are several issues that
should be considered when using these indicators. First, for some PQIs,
differences in socioeconomic status have been shown to explain a
substantial part---perhaps most---of the variation in PQI rates across
areas. The complexity of the relationship between socioeconomic status
and PQI rates makes it difficult to delineate how much of the observed
relationships are due to true access to care difficulties in potentially
underserved populations, or due to other patient characteristics,
unrelated to quality of care, that vary systematically by socioeconomic
status. For some of the indicators, patient preferences and hospital
capabilities for inpatient or outpatient care might explain variations
in hospitalizations. In addition, environmental conditions that are not
under the direct control of the health care system can substantially
influence some of the PQIs. For example, the COPD and asthma admission
rates are likely to be higher in areas with poorer air quality.

Second, the evidence related to potentially avoidable hospital
admissions is limited for each indicator, because many of the indicators
have been developed as parts of sets. Only five studies have attempted
to validate individual indicators rather than whole measure sets.[^9]
[^10] [^11] [^12] [^13] A limitation of this literature is that
relatively little is known about which components represent the
strongest measures of access and quality. Most of the five papers that
did report on individual indicators also used a single variable, such as
median area-specific income or rural residence, for construct
validation. All but one of these papers^10^ included adjustment only for
demographic factors (e.g., age, sex, and race).

Third, despite the relationships demonstrated at the patient level
between higher quality ambulatory care and lower rates of hospital
admission, few studies have directly addressed the question of whether
effective treatments in outpatient settings would reduce the overall
incidence of hospitalizations. The extent to which the reporting of
admission rates for ambulatory care sensitive conditions may lead to
changes in ambulatory practices and admission rates also is unknown.
Providers may admit patients who do not clinically require inpatient
care or they may do the opposite---fail to hospitalize patients who
would benefit from inpatient care.

## Questions for Future Work

The limitations discussed above suggest some directions for future work
on development and use of the PQIs. Additional data and linkages could
provide insights into the underlying causes of hospitalization for these
conditions and could facilitate the exploration of potential
interventions to prevent such events.

-   Studies examining health and risk behaviors in a population could
    illuminate patient factors associated with the incidence of
    ambulatory care sensitive conditions.

-   Examining environmental data, such as air pollution levels, could
    provide insight into factors outside the direct control of the
    health care system that are associated with hospitalization for such
    conditions.

-   Exploring differences in disease prevalence in specific areas could
    help to discern whether variations in hospitalization rates can be
    attributed to differences in disease burden across communities that
    would exist even with optimum preventive care.

-   Studies could examine the relationship between rural-urban location
    and distance to health care resources and hospital admission for
    ambulatory care sensitive conditions. Such studies would require
    information on patients' residence such as patient ZIP codes.

-   Linkages with data on local medical resources could help to
    illuminate the relationship between hospitalization for ACSCs and
    the supply of medical services and resources, such as the number of
    primary care and specialty physicians in a community or the supply
    of hospital beds. For example, the Dartmouth Atlas provides analyses
    for the Medicare population that suggest that the supply of hospital
    beds in a community is linked to ambulatory care sensitive
    admissions, but reported no relationship with local physician
    supply.[^14]

-   Physician office data and outpatient clinic data may provide
    important information regarding care prior to hospital admission.
    Outpatient data would enable analyses that examine the processes of
    care that can prevent hospitalizations due to these conditions.

-   Combining inpatient data with emergency department data would
    support the construction of a more complete picture of quality of
    care related to ambulatory care sensitive conditions. Some of these
    conditions are seen in emergency departments without being admitted
    for inpatient care. This is particularly relevant for the uninsured
    or underinsured who are more likely to use emergency departments as
    a routine source of care.

# Detailed Evidence for Prevention Quality Indicators

This section provides an abbreviated presentation of the details of the
literature review and the empirical evaluation for each PQI, including:

-   The relationship between the indicator and quality of health care
    services

-   A suggested benchmark or comparison

-   The definition of each indicator

-   The outcome of interest (or numerator)

-   The population at risk (or denominator)

-   The results of the empirical testing

Empirical testing rated the statistical performance of each indicator,
as described in step 5 in the previous section. Scores ranged from 0 to
26 (mean for the 16 original PQIs = 14.6), except for low birth weight
for which bias was not tested because adequate risk adjustment was not
available. The scores are intended as a guide for summarizing the
performance of each indicator on four empirical tests of precision
(signal variance, area-level share, signal ratio, and R-squared) and
five tests of minimum bias (rank correlation, top and bottom decile
movement, absolute change, and change over two deciles), as described in
the previous section.

The magnitude of the scores, reported in the document *Prevention
Quality Indicators Comparative Data*, provides an indication of the
relative rankings of the indicators. These scores were based on
indicator performance after risk-adjustment and smoothing, that is, they
represent the "best estimate" of the indicator's true value after
accounting for case-mix and reliability. The score for each individual
test is an ordinal ranking (e.g., very high, high, moderate, and low).
The final summary score was derived by assigning a weight to each
ranking (e.g., 3, 2, 1, 0) and summing across these nine individual
tests. Higher scores indicate better performance on the empirical tests.
The two-page descriptions for each indicator also include a discussion
of the summary of evidence, the limitations on using each indicator, and
details on:

-   Face validity -- Does the indicator capture an aspect of quality
    that is widely regarded as important and subject to provider or
    public health system control?

-   Precision -- Is there a substantial amount of provider or community
    level variation that is not attributable to random variation?

-   Minimum bias -- Is there either little effect on the indicator of
    variations in patient disease severity and comorbidities, or is it
    possible to apply risk adjustment and statistical methods to remove
    most or all bias?

-   Construct validity -- Does the indicator perform well in identifying
    true (or actual) quality-of-care problems?

-   Fosters true quality improvement -- Is the indicator insulated from
    perverse incentives for providers to improve their reported
    performance by avoiding difficult or complex cases, or by other
    responses that do not improve quality of care?

-   Prior use -- Has the measure been used effectively in practice? Does
    it have potential for working well with other indicators?

**Summary of Evidence Reported for All or Multiple PQIs**

The literature review of the evidence related to potentially avoidable
hospital admissions is limited for each indicator because many of the
individual indicators have been developed as parts of sets. This section
provides a summary of the general evidence reviewed applicable to all
PQIs.

-   Precision. The precision of avoidable hospitalization rates is
    likely to depend on the size of the denominator.

-   Minimum bias. Previous studies have documented several
    characteristics that are associated with either the risk of an
    avoidable hospitalization (at the individual level) or the avoidable
    hospitalization rate (at the area level), including prevalence of
    the condition, race, socioeconomic status (SES), chronic disease and
    health of the population.[^15]^,^[^16]^,^[^17] These characteristics
    may be confounding factors, but also might be measuring subtle
    aspects of access to care.

-   Construct validity. Most previous studies have assessed the validity
    of an entire set of avoidable hospital conditions, rather than each
    condition alone, and have used SES as a marker of access to care.
    These studies have repeatedly shown strong correlations between
    household income and avoidable hospitalizations, both at the
    individual level and the area level. At the zip code level, income
    alone explains 51-84% of the variability in ACS admission rates
    across 15 metropolitan areas in the US.[^18] This association is
    substantially weaker among persons 65 or more years of
    age,[^19]^,^[^20] as one would expect if it is driven by access to
    care rather than underlying social factors. Avoidable
    hospitalization rates are higher among uninsured or
    Medicaid-enrolled persons than among privately insured persons, even
    after adjustment for race and income.[^21]

Fewer studies have tested true measures of access to care. In the best
of these studies, Bindman and colleagues^16^ showed that self-reported
"difficulty in receiving medical care when needed" explained 50% of the
variability in hospitalization rates for 5 chronic medical conditions
(asthma, CHF, COPD, diabetes, and hypertension). Adjustment for
condition prevalence, propensity to seek care, physician admitting
style, and ecological measures of income, education, insurance, race,
and gender, had little effect on the association. Having a regular
source of care, and primary care physician/population ratios, were also
independently associated with avoidable hospitalization rates, when
substituted for self-reported access.[^22] These relationships did
**not** hold in two separate studies of rural zip codes, suggesting that
avoidable hospitalization rates are invalid indicators of access in
rural areas.[^23]^,^[^24]

In other studies, the physician/population ratio for family and general
physicians has been more strongly associated with avoidable
hospitalization rates than measures that include internists,
pediatricians, or all physicians.[^25]^,^[^26] In studies of Medicaid
populations, provider continuity in ambulatory care[^27] and usual care
received from a community health center[^28] were associated with lower
avoidable hospitalization rates, and not having a primary care physician
was associated with higher rates of avoidable hospitalization.[^29]
However, having a regular source of care (for more than 50% of physician
office visits) was not associated with lower avoidable hospitalization
rates.[^30]

Several studies of Medicare beneficiaries have shown weak and
inconsistent associations between access indicators and avoidable
hospitalization rates. For example, persons in the Medicare Current
Beneficiary Survey who reported problems obtaining health care, or lived
in a health professional shortage area, were not at increased risk of
preventable hospitalization.^17^ Instead, their risk was heavily
influenced by clinical factors. However, beneficiaries in fair or poor
health reportedly were at increased risk if they lived in a **primary
care** shortage area.[^31] An area-level analysis based on Medicare
claims suggests that the association between admission rates and
physician/population ratios is limited to the 10% of health care service
areas with the most severe shortage of physicians.[^32]

A full report on the literature review and empirical evaluation can be
found in *Refinement of the HCUP Quality Indicators* by the
UCSF-Stanford EPC, Detailed coding information for each PQI is provided
in the document *Prevention Quality Indicators Technical
Specifications.* See [Appendix A](#appendix-a-links) for links to these
and other documents.

## Diabetes Short-term Complications Admission Rate (PQI 1)

Short-term complications of diabetes mellitus include diabetic
ketoacidosis, hyperosmolarity, and coma. These life-threatening
emergencies arise when a patient experiences an excess of glucose
(hyperglycemia) or insulin (hypoglycemia).

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment and adherence to   |
| Quality             | care may reduce the incidence of diabetic      |
|                     | short-term complications, and lower rates      |
|                     | represent better quality care.                 |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for diabetic short-term             |
|                     | complications per 100,000 population.          |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal/non-neonatal discharges of    |
|                     | age 18 years and older with ICD-9-CM principal |
|                     | diagnosis codes for diabetes short-term        |
|                     | complications (ketoacidosis, hyperosmolarity,  |
|                     | coma).                                         |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for diabetes short-term complications is a PQI that
would be of most interest to comprehensive health care delivery systems.
Short-term diabetic emergencies arise from the imbalance of glucose and
insulin, which can result from deviations in proper care,
misadministration of insulin, or failure to follow a proper diet.

Although risk adjustment with age and sex does not impact the relative
or absolute performance of areas, this indicator should be
risk-adjusted. Some areas may have higher rates of diabetes as a result
of racial composition and systematic differences in other risk factors.

Areas with high rates of diabetic emergencies may want to examine
education practices, access to care, and other potential causes of
non-compliance when interpreting this indicator. Also, areas may
consider examining the rates of hyperglycemic versus hypoglycemic events
when interpreting this indicator.

### Limitations on Use

As a PQI, short-term diabetes complication rate is not a measure of
hospital quality, but rather one measure of outpatient and other health
care. Rates of diabetes may vary systematically by area, creating bias
for this indicator. Examination of both inpatient and outpatient data
may provide a more complete picture of diabetes care.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

High-quality outpatient management of patients with diabetes has been
shown to lead to reductions in almost all types of serious avoidable
hospitalizations. However, tight control may be associated with more
episodes of hypoglycemia, which leads to more admissions.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Based on empirical evidence, this indicator is moderately precise, with
a raw area level rate of 36 per 100,000 population and a standard
deviation of 24.6.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is moderate, at 51.7%,
indicating that some of the observed differences in age-sex adjusted
rates do not represent true differences in area performance. Using
multivariate signal extraction techniques appears to have little
additional impact on estimating true differences across areas.

*Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?*

Minorities have higher rates of diabetes, and higher hospitalization
rates may result in areas with higher minority concentrations. Empirical
results show that area rankings and absolute performance are not
affected by age-sex risk adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Studies of precipitating events of admission for diabetic emergencies
often rely on self-report, which may be a biased measurement in and of
itself. The results of one study showed that over 60% of patients with
known and treated diabetes had made an error in insulin administration
or had omitted insulin.[^33] In a potentially under-served population of
urban African-Americans, two-thirds of admissions were due to cessation
of insulin therapy---over half of the time for financial or other
difficulties obtaining insulin.[^34]

Bindman reported that an area's self-rated access to care report
explained 46% of the variance in admissions for diabetes, although the
analysis was not restricted to diabetic emergencies.[^35] Weissman found
that uninsured patients had more than twice the risk of admission for
diabetic ketoacidosis and coma than privately insured patients.[^36]

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Because diabetic emergencies are potentially life-threatening, hospitals
are unlikely to fail to admit patients requiring hospitalization.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

Admission for diabetic emergencies was included in both Billings'[^37]
and Weissman's[^38] sets of avoidable hospitalization measures. This
indicator, defined as a provider-level indicator, was an original HCUP
QI.

## Perforated Appendix Admission Rate (PQI 2)

Perforated appendix may occur when appropriate treatment for acute
appendicitis is delayed for a number of reasons, including problems with
access to care, failure by the patient to interpret symptoms as
important, and misdiagnosis and other delays in obtaining surgery.

+---------------------+------------------------------------------------+
| Relationship to     | Timely diagnosis and treatment may reduce the  |
| Quality             | incidence of perforated appendix, and lower    |
|                     | rates represent better quality care.           |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for perforated appendix per 100     |
|                     | admissions for appendicitis within Metro Area  |
|                     | or county.                                     |
+---------------------+------------------------------------------------+
| Outcome of Interest | Discharges with ICD-9-CM diagnosis code for    |
|                     | perforation or abscess of appendix in any      |
|                     | field among cases meeting the inclusion        |
|                     | criteria for the denominator (population at    |
|                     | risk).                                         |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
+---------------------+------------------------------------------------+
| Population at Risk  | All non-maternal discharges of age 18 years    |
|                     | and older within Metro Area or county with     |
|                     | diagnosis code for appendicitis in any field.  |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for perforated appendix is a PQI that would be of
most interest to comprehensive health care delivery systems. With prompt
and appropriate care, acute appendicitis should not progress to
perforation or rupture. Rates for perforated appendix are higher in the
uninsured or underinsured in both adult and pediatric populations, which
may be caused by patients failing to seek appropriate care, difficulty
in accessing care, or misdiagnoses and poor quality care.

Perforated appendix rates vary systematically by race, although the
cause is unknown. Areas with high rates of perforated appendix may want
to target points of intervention by using chart reviews and other
supplemental data to investigate the reasons for delay in receiving
surgery. Hospital contributions to the overall area rate may be
particularly useful for this indicator, because misdiagnoses and other
delays in receiving surgery in an emergency room may contribute
substantially to the rate.

### Limitations on Use

As a PQI, admission for perforated appendix is not a measure of hospital
quality, but rather one measure of outpatient and other health care.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Perforated appendix results from delay in surgery, potentially
reflecting problems in access to ambulatory care, misdiagnosis, and
other delays in obtaining surgery.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Perforated appendix occurs in one-fourth to one-third of hospitalized
acute appendicitis patients.[^39] Based on empirical evidence, this
indicator is precise, with a raw area level rate of 33.3% and a
substantial standard deviation of 14.4%.

Relative to other indicators, a higher percentage of the variation
occurs at the area level rather than the discharge level. However, the
signal ratio (i.e., the proportion of the total variation across areas
that is truly related to systematic differences in area performance
rather than random variation) is low, at 26.5%, indicating that much of
the observed differences in age-sex adjusted rates likely do not
represent true differences across areas. Applying multivariate signal
extraction methods can improve estimation of true differences in area
performance.

*Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?*

Higher rates of perforated appendix have been noted in males, patients
with mental illness or substance abuse disorders, people with diabetes,
and blacks,[^40] as well as in children under the age of 4 (although
appendicitis is rare in this age group).[^41]

Some of the observed variation in performance is due to systematic
differences in patient characteristics. No evidence exists in the
literature that clinical characteristics that would vary systematically
increase the likelihood of perforated appendix. Therefore, this
indicator is unlikely to be clinically biased. Empirical results show
that area rankings and absolute performance are not affected by age-sex
risk adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Braveman et al. found that the rate of perforated appendix was 50%
higher for patients with no insurance or Medicaid than HMO-covered
patients, and 20% higher for patients with private fee-for-service
insurance. A follow-up study by Blumberg et al. concluded that the high
rate of perforated appendix in the black population at an HMO may be
explained by delay in seeking care, rather than differences in the
quality of health care.[^42] Weissman et al. found that uninsured (but
not Medicaid) patients are at increased risk for ruptured appendix after
adjusting for age and sex.[^43]

Based on empirical results, areas with high rates of perforated appendix
admissions tend to have lower rates of admissions for other ACSCs.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Use of this quality indicator might lead to more performance of
appendectomies in cases of questionable symptoms, in addition to
reducing the occurrence of rupture.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

Perforated appendix was included in the original HCUP QI indicator set,
as well as in Weissman's set of avoidable hospitalizations.

## Diabetes Long-term Complications Admission Rate (PQI 3)

Long-term complications of diabetes mellitus include renal, eye,
neurological, and circulatory disorders. Long-term complications occur
at some time in the majority of patients with diabetes to some degree.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment and adherence to   |
| Quality             | care may reduce the incidence of diabetic      |
|                     | long-term complications, and lower rates       |
|                     | represent better quality care.                 |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for diabetic long-term              |
|                     | complications per 100,000 population.          |
+---------------------+------------------------------------------------+
| Outcome of Interest | Discharges age 18 years and older with         |
|                     | ICD-9-CM principal diagnosis codes for         |
|                     | long-term complications of diabetes (renal,    |
|                     | eye, neurological, circulatory, or             |
|                     | complications not otherwise specified).        |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for diabetes long-term complications is a PQI that
would be of most interest to comprehensive health care delivery systems.
Long-term diabetes complications are thought to arise from sustained
long-term poor control of diabetes. Intensive treatment programs have
been shown to decrease the incidence of long-term complications in both
Type 1 and Type 2 diabetes.

Sociodemographic characteristics of the population, such as race, may
bias the indicator, since Native Americans and Hispanic Americans have
higher rates of diabetes and poorer glycemic control. The importance of
these factors as they relate to admission rates is unknown. Risk
adjustment for observable characteristics, such as racial composition of
the population, is recommended.

It is unclear whether poor glycemic control arises from poor quality
medical care, non-compliance of patients, lack of education, or access
to care problems. Areas with high rates may wish to examine these
factors when interpreting this indicator.

**Limitations on Use**

As a PQI, diabetes long-term complication rate is not a measure of
hospital quality, but rather one measure of outpatient and other health
care. Rates of diabetes may vary systematically by area, creating bias
for this indicator. Examination of both inpatient and outpatient data
may provide a more complete picture of diabetes care.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Several observational studies have linked improved glycemic control to
substantially lower risks of developing complications in both Type 1 and
Type 2 diabetes.[^44] Given that appropriate adherence to therapy and
consistent monitoring of glycemic control help to prevent complications,
high-quality outpatient care should lower long-term complication rates.
However, adherence to guidelines aimed at reducing complications
(including eye and foot examinations and diabetic education) has been
described as modest,[^45] with only one-third of patients receiving all
essential services.[^46]

*Precision: Is there a substantial amount of provider or community level
variation tha*t is not attributable to random variation?

Diabetes affects a large number of people, as do diabetic complications.
However, few studies have documented hospitalization rates for diabetic
complications and the extent to which they vary across areas. Based on
empirical evidence, this indicator is moderately precise, with a raw
area level rate of 80.8 per 100,000 population and a standard deviation
of 58.1.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is high, at 75.6%, indicating
that the observed differences in age-sex adjusted rates likely represent
true differences across areas.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

Rates of diabetes are higher in black, Hispanic, and especially Native
American populations than in other ethnic groups. Hyperglycemia appears
to be particularly frequent among Hispanic and Native American
populations.[^47] The duration of diabetes is positively associated with
the development of complications. Empirical results show that area
rankings and absolute performance are moderately affected by age-sex
risk adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Compliance of physicians and patients is essential to achieve good
outcomes, and it seems likely that problems with both access to and
quality of care, as well as patient compliance, may contribute to the
occurrence of complications.

Based on empirical results, areas with high rates of diabetes long-term
complications also tend to have high rates of admission for other ACSCs.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Providers may decrease admission rates by failing to hospitalize
patients who would truly benefit from inpatient care. No published
evidence indicates that worse health outcomes are associated with
reduced hospitalization rates for long-term complications of diabetes.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator, defined as a hospital-level indicator, is an original
HCUP QI.

## Chronic Obstructive Pulmonary Disease Admission Rate (PQI 5)

Chronic obstructive pulmonary disease (COPD) comprises three primary
diseases that cause respiratory dysfunction---asthma, emphysema, and
chronic bronchitis---each with distinct etiologies, treatments, and
outcomes. This indicator examines emphysema and bronchitis; asthma is
discussed separately for children and adults.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment may reduce         |
| Quality             | admissions for COPD, and lower rates represent |
|                     | better quality care.                           |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for COPD per 100,000 population.    |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM principal diagnosis    |
|                     | codes for COPD.                                |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for COPD is a PQI that would be of most interest to
comprehensive health care delivery systems. COPD can often be controlled
in an outpatient setting. Areas may wish to use chart reviews to
understand more clearly whether admissions are a result of poor quality
care or other problems.

This indicator is measured with high precision, and the observed
variance likely reflects true differences across areas. Risk adjustment
for age and sex appears to most affect the areas with the highest rates.
Several factors that are likely to vary by area may influence the
progression of the disease, including smoking and socioeconomic status.
Risk adjustment for observable characteristics is recommended.

Areas may wish to identify hospitals that contribute the most to the
overall area rate for this indicator. The patient populations served by
these hospitals may be a starting point for interventions.

**Limitations on Use**

As a PQI, COPD is not a measure of hospital quality, but rather one
measure of outpatient and other health care. This indicator has unclear
construct validity, because it has not been validated except as part of
a set of indicators. Providers may reduce admission rates without
actually improving quality by shifting care to an outpatient setting.
Some COPD care takes place in emergency rooms, so combining inpatient
and emergency room data may give a more accurate picture.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Admissions for COPD include exacerbations of COPD, respiratory failure,
and (rarely) lung volume reduction surgery or lung transplantation.
Practice guidelines for COPD have been developed and published over the
last decade.[^48] With appropriate outpatient treatment and compliance,
hospitalizations for the exacerbations of COPD and decline in lung
function should be minimized.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

COPD accounts for a substantial number of hospital admissions,
suggesting that the indicator is reasonably precise.[^49] Based on
empirical evidence, this indicator is very precise, with a raw area
level rate of 324.0 per 100,000 population and a standard deviation of
203.8.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is very high, at 93.4%,
indicating that the differences in age-sex adjusted rates likely
represent true differences across areas.

Minimal bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

Factors that have been associated with increased admissions for COPD
include disease severity, smoking status, age, and socioeconomic status,
which are candidates for risk adjustment. Empirical results show that
area rankings and absolute performance are somewhat affected by age-sex
risk adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Bindman et al. reported that self-reported access to care explained 27%
of the variation in COPD hospitalization rates at the ZIP code cluster
level.[^50] Millman et al. found that low-income ZIP codes had 5.8 times
more COPD hospitalizations per capita than high-income ZIP codes.[^51]
Physician adherence to practice guidelines and patient compliance also
influence the effectiveness of therapy.

Based on empirical results, areas with high rates of COPD admissions
also tend to have high rates of admissions for other ACSCs.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

One study found that higher rates of COPD admission may in part reflect
improvements in access to care, which results in more detection of
significant respiratory impairment in the community.[^52] A decline in
COPD admission rates may simply reflect a reverse change in coding
practices.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator was originally developed by Billings et al. in
conjunction with the United Hospital Fund of New York.[^53] It was
subsequently adopted by the Institute of Medicine and has been widely
used in studies of avoidable hospitalizations.

##  {#section .unnumbered}

## Hypertension Admission Rate (PQI 7)

Hypertension is a chronic condition that is often controllable in an
outpatient setting with appropriate use of drug therapy.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment may reduce         |
| Quality             | admissions for hypertension, and lower rates   |
|                     | represent better quality care.                 |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for hypertension per 100,000        |
|                     | population.                                    |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM principal diagnosis    |
|                     | codes for hypertension.                        |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
|                     |                                                |
|                     | -   with cardiac procedure codes in any field  |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for hypertension is a PQI that would be of most
interest to comprehensive health care delivery systems. Little evidence
exists regarding the validity of this indicator, although one study did
relate admission rates to access to care problems. This indicator is
measured with adequate precision, but some of the variance in age-sex
adjusted rates does not reflect true differences in area performance.
Adjustment for age-sex is recommended.

Areas may wish to identify hospitals that contribute the most to the
overall area rate for this indicator. The patient populations served by
these hospitals may be a starting point for interventions.

### Limitations on Use

As a PQI, hypertension is not a measure of hospital quality, but rather
one measure of outpatient and other health care. Providers may reduce
admission rates without actually improving quality by shifting care to
an outpatient setting.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Hypertension is often controllable in an outpatient setting with
appropriate use of drug therapy.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Although hypertension is a common condition, hospitalizations for
complications of hypertension are relatively uncommon. One study noted
that hypertension accounted for only 0.5% of total admissions for
ACSCs.[^54]

Based on empirical evidence, this indicator is moderately precise, with
a raw area level rate of 37.1 per 100,000 population and a substantial
standard deviation of 32.2. The signal ratio (i.e., the proportion of
the total variation across areas that is truly related to systematic
differences in area performance rather than random variation) is
moderate, at 69.9%, indicating that some of the observed differences in
age-sex adjusted rates likely do not represent true differences in area
performance.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

Little evidence exists on potential biases for this indicator. The age
structure of the population may possibly affect admission rates for this
condition. Weissman et al. reported a reduction of 100% in relative risk
for Medicaid patients when adjusting for age and sex.[^55] No evidence
was found on the effects of comorbidities such as obesity or other risk
factors that may vary systematically by area on admission rates for
hypertension complications in the area. Empirical results show that
age-sex adjustment affects the ranking of those areas in the highest
decile.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Bindman et al. found that an area's self-rated access to care explained
22% of admissions for hypertension.[^56] Weissman et al. found that
uninsured patients had a relative risk of admission for hypertension of
2.38 in Massachusetts after adjustment for age and sex, while Maryland
had a corresponding relative risk of 1.93.[^57] Millman et al. reported
that low-income ZIP codes had 7.6 times more hypertension
hospitalizations per capita than high-income ZIP codes.[^58]

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Little evidence exists on the impact of this quality improvement measure
on the delivery of outpatient care for hypertension. There is no
published evidence of worse health outcomes in association with reduced
hospitalization rates for hypertension. Such an effect seems
implausible, given that only the most serious episodes of accelerated or
malignant hypertension are treated on an inpatient basis.

Prior use: Has the indicator been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator was included originally developed by Billings et al. in
conjunction with the United Hospital Fund of New York.[^59] It was
subsequently adopted by the Institute of Medicine and has been widely
used in a variety of studies of avoidable or preventable
hospitalizations.[^60] This indicator was also included in Weissman's
set of avoidable hospitalizations.

##  {#section-1 .unnumbered}

## Congestive Heart Failure Admission Rate (PQI 8)

Congestive heart failure (CHF) can be controlled in an outpatient
setting for the most part; however, the disease is a chronic progressive
disorder for which some hospitalizations are appropriate.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment may reduce         |
| Quality             | admissions for CHF, and lower rates represent  |
|                     | better quality care.                           |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for CHF per 100,000 population.     |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal/non-neonatal discharges of    |
|                     | age 18 years and older with ICD-9-CM principal |
|                     | diagnosis codes for CHF.                       |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
|                     |                                                |
|                     | -   with cardiac procedure codes in any field  |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Congestive heart failure is a PQI that would be of most interest to
comprehensive health care delivery systems. This indicator is measured
with high precision, and most of the observed variance reflects true
differences across areas.

Risk adjustment for age and sex appears to affect the areas with the
highest and lowest raw rates. Areas with high rates may wish to examine
the clinical characteristics of their patients to check for a more
complex case mix. Patient age, clinical measures such as heart function,
and other management issues may affect admission rates.

As the causes for admissions may include poor quality care, lack of
patient compliance, or problems accessing care, areas may wish to review
CHF patient records to identify precipitating causes and potential
targets for intervention.

### Limitations on Use

As a PQI, CHF is not a measure of hospital quality, but rather one
measure of outpatient and other health care. Providers may reduce
admission rates without actually improving quality by shifting care to
an outpatient setting.

Some CHF care takes place in emergency rooms. As such, combining
inpatient and emergency room data may give a more accurate picture of
this indicator.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Physician management of patients with congestive heart failure differs
significantly by physician specialty.[^61] [^62] Such differences in
community practices may be reflected in differences in CHF admission
rates.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Relatively precise estimates of admission rates for CHF can be obtained,
although random variation may be important for small hospitals and rural
areas. Based on empirical evidence, this indicator is very precise, with
a raw area level rate of 521.0 per 100,000 population and a standard
deviation of 286.5.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is very high, at 93.0%,
indicating that the observed differences in age-sex adjusted rates very
likely represent true differences across areas.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

Important determinants of outcomes with CHF include certain demographic
variables, such as patient age; clinical measures; management issues;
and treatment strategies.[^63] Limited evidence exists on the extent to
which these factors can explain area differences in CHF admission rates.
Empirical results show that area rankings and absolute performance are
somewhat affected by age-sex risk adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Billings et al. found that low-income ZIP codes in New York City had 4.6
times more CHF hospitalizations per capita than high-income ZIP
codes.[^64] Millman et al. reported that low-income ZIP codes had 6.1
times more CHF hospitalizations per capita than high-income ZIP
codes.[^65]

Based on empirical results, areas with high rates of CHF also tend to
have high rates of admission for other ACSCs.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Outpatient interventions such as the use of protocols for ambulatory
management of low-severity patients and improvement of access to
outpatient care would most likely decrease inpatient admissions for
CHF.[^66]

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator was originally developed by Billings et al. in
conjunction with the United Hospital Fund of New York. It was
subsequently adopted by the Institute of Medicine and has been widely
used in a variety of studies of avoidable hospitalizations.

##  {#section-2 .unnumbered}

## Low Birth Weight Rate (PQI 9)

Infants may be low birth weight because of inadequate interuterine
growth or premature birth. Risk factors include sociodemographic and
behavioral characteristics, such as low income and tobacco use during
pregnancy.

+---------------------+------------------------------------------------+
| Relationship to     | Proper preventive care may reduce incidence of |
| Quality             | low birth weight, and lower rates represent    |
|                     | better quality care.                           |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Number of low birth weight infants per 100     |
|                     | births.                                        |
+---------------------+------------------------------------------------+
| Outcome of Interest | Number of births with ICD-9-CM diagnosis codes |
|                     | for birth weight less than 2500 grams in any   |
|                     | field among cases meeting the inclusion and    |
|                     | exclusion rules for the denominator            |
|                     | (population at risk).                          |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
+---------------------+------------------------------------------------+
| Population at Risk  | The definition of **newborn** is any neonate   |
|                     | with either 1) an ICD-9-CM diagnosis code for  |
|                     | an in-hospital live birth or 2) an admission   |
|                     | type of newborn (ATYPE=4), age in days at      |
|                     | admission equal to zero, and not an ICD-9-CM   |
|                     | diagnosis code for an out-of-hospital birth.   |
|                     | A **neonate** is defined as any discharge with |
|                     | age in days at admission between zero and 28   |
|                     | days (inclusive).  If age in days is missing,  |
|                     | then a neonate is defined as any DRG in MDC    |
|                     | 15, an admission type of newborn (ATYPE=4), an |
|                     | ICD-9-CM diagnosis code for neonate            |
|                     | observation and evaluation, or an ICD-9-CM     |
|                     | diagnosis code for an in-hospital live birth.. |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Low birth weight is a PQI that would be of most interest to
comprehensive health care delivery systems. Healthy People 2010 has set
a goal of reducing the percentage of low birth weight infants to
0.9%.[^67]

Mothers who give birth to low birth weight infants generally receive
less prenatal care than others, and prenatal care persists as a risk
factor for low birth weight when adjusting for potential confounds.
However, comprehensive care programs in high-risk women have failed to
reduce low birth weights. In some studies, specific counseling aimed at
reducing a specific risk factor in a specific population may have some
impact on reducing low birth weight.

Adequate risk adjustment may require linkage to birth records, which
record many of the sociodemographic and behavioral risk factors noted in
the literature review (race, age, drug use, stress). Birth records in
some States are a rich source of information that could help to identify
causes of low birth weight and help to delineate potential areas of
intervention.

Where risk adjustment is not possible, results may provide some guidance
to case mix in the area if considered in light of measures of
socioeconomic status (as determined by insurance status or ZIP code).

### Limitations on Use

As a PQI, low birth weight is not a measure of hospital quality, but
rather one measure of outpatient and other health care. This indicator
could have substantial bias that would require additional risk
adjustment from birth records or clinical data.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Risk factors for low birth weight may be addressed with adequate
prenatal care and education. Prenatal education and care programs have
been established to help reduce low birth weight and other complications
in high-risk populations.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Although low birth weight births account for only a small fraction of
total births, the large number of births suggest that this indicator
should be precisely measurable for most areas. Based on empirical
evidence, this indicator is precise, with a raw area level rate of 3.9%
and a standard deviation of 2.3%. The signal ratio (i.e., the proportion
of the total variation across areas that is truly related to systematic
differences in area performance rather than random variation) is
moderate, at 67.1%, indicating that some of the observed differences in
age-sex adjusted rates do not represent true differences in area
performance.

*Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical me*thods to remove
most or all bias?

Socioeconomic measures such as parental education and income have been
shown to be negatively associated with rates of low birth weight
infants.[^68] [^69] Demographic factors such as age and race also appear
important, and may be correlated with socioeconomic factors. Mothers
under 17 years and over 35 years are at a higher risk of having low
birth weight infants.[^70] [^71] One study of all California singleton
births in 1992 found that after risk adjustment, having a black mother
remained a significant risk factor.[^72] Little evidence exists on the
extent to which each of these factors contributes to differences in the
rate of low birth weight births across geographic areas.

*Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?*

While specific studies have demonstrated an impact of particular
interventions, especially in high-risk populations, evidence on the
impact of better prenatal care on low birth weight rates for area
populations is less well developed. In one study, the use of prenatal
care accounted for less than 15% of the differences between low birth
weight in black and white mothers enrolled in an HMO. However,
increasing the level of prenatal care was associated with lower rates of
low birth weight, particularly in the black patient population.[^73]

Low birth weight is inversely related to the other ACSCs and is
positively related to perforated appendix rate. Empirical evidence
suggests that this indicator at an area level could be potentially
biased.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Use of this indicator is unlikely to lead to apparent reductions in the
rate of low birth weight births that did not represent true reductions.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

Low birth weight is an indicator in the Health Plan Employer Data and
Information Set (HEDIS) measure set for insurance groups and is used by
United Health Care and the University Hospital Consortium. This
indicator, along with very low birth weight, was previously an HCUP QI.

##  {#section-3 .unnumbered}

## Dehydration Admission Rate (PQI 10)

Dehydration is a serious acute condition that occurs in frail patients
and patients with other underlying illnesses following insufficient
attention and support for fluid intake. Dehydration can for the most
part be treated in an outpatient setting, but it is potentially fatal
for elderly, very young children, frail patients, or patients with
serious comorbid conditions.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment may reduce         |
| Quality             | admissions for dehydration, and lower rates    |
|                     | represent better quality care.                 |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for dehydration per 100,000         |
|                     | population.                                    |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM principal diagnosis    |
|                     | code for hypovolemia (276.5).                  |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county.            |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for dehydration is a PQI that would be of most
interest to comprehensive health care delivery systems. Admission for
dehydration is somewhat common, suggesting that the indicator will be
measured with adequate precision, and most of the observed variation is
likely to reflect true differences in admission rates.

This indicator is subject to minimal bias. Risk adjustment appears to
affect modestly the areas with the highest and lowest rates. Age may be
a particularly important factor, and the indicator should be
risk-adjusted for age. Areas with high rates of dehydration admissions
also tend to have high rates of admission for other ACSCs.

The considerable variations across areas suggest opportunities for
quality improvement in care for patients at risk for dehydration. When
high rates of dehydration are identified for a particular hospital,
additional study may uncover problems in primary or emergency care in
the surrounding area. Appropriate interventions can be developed to
address those problems.

### Limitations on Use

As a PQI, dehydration is not a measure of hospital quality, but rather
one of the measures of outpatient and other health care.

This indicator has unclear construct validity, because it has not been
validated except as part of a set of indicators. Providers may reduce
admission rates without actually improving quality by shifting care to
an outpatient setting. Some dehydration care takes place in emergency
rooms. As such, combining inpatient and emergency room data may give a
more accurate picture of this indicator.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Dehydration is a potentially fatal condition, and appropriate attention
to fluid status can prevent the condition. If left untreated in older
adults, serious complications, including death (over 50%), can
result.[^74]

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Little evidence exists in the literature on the precision of this
indicator. Based on empirical evidence, this indicator is precise, with
a raw area level rate of 139.9 per 100,000 population and a standard
deviation of 103.2.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is high, at 88.5%, indicating
that the observed differences in age-sex adjusted rates likely represent
true differences across areas.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

The age structure of the population may affect admission rates for this
condition, as the elderly and very young are more susceptible to
dehydration. Socioeconomic factors may also affect admission rates.
Differences in thresholds for admission of patients with dehydration may
contribute to area rate differences. Empirical results show that area
rankings are not affected by age-sex risk adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Billings et al. found that low-income ZIP codes in New York City had 2
times more dehydration hospitalizations per capita than high-income ZIP
codes.[^75] Household income explained 42% of this variation. In
addition, Millman et al.[^76] reported that low-income ZIP codes had 2
times more dehydration hospitalizations per capita than high-income ZIP
codes.

Based on empirical results of this study, areas with high rates of
dehydration admissions also tend to have high rates of admission for
other ACSCs.

*Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?*

Use of this indicator might lead to higher thresholds of admission for
patients with dehydration, potentially denying needed care to some
patients. Because some dehydration can be managed on an outpatient
basis, a shift to outpatient care may occur.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator was originally developed by Billings et al. in
conjunction with the United Hospital Fund of New York.

##  {#section-4 .unnumbered}

## Bacterial Pneumonia Admission Rate (PQI 11)

Bacterial pneumonia is a relatively common acute condition, treatable
for the most part with antibiotics. If left untreated in susceptible
individuals---such as the elderly---pneumonia can lead to death.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment may reduce         |
| Quality             | admissions for bacterial pneumonia in          |
|                     | non-susceptible individuals, and lower rates   |
|                     | represent better quality care.                 |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for bacterial pneumonia per 100,000 |
|                     | population.                                    |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM principal diagnosis    |
|                     | code for bacterial pneumonia.                  |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
|                     |                                                |
|                     | -   With diagnosis code for sickle cell anemia |
|                     |     or HB-S disease                            |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county.            |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for bacterial pneumonia is a PQI that would be of
most interest to comprehensive health care delivery systems. High
admission rates may reflect a large number of inappropriate admissions
or low-quality treatment with antibiotics. Admission for pneumonia is
relatively common, suggesting that the indicator will be measured with
good precision, and most of the observed variation reflects true
differences in admission rates.

This indicator is subject to some moderate bias, and risk adjustment
appears to affect the areas with the highest rates the most. Age may be
a particularly important factor, and the indicator should be
risk-adjusted for this factor. Areas may wish to examine the outpatient
care for pneumonia and pneumococcal vaccination rates to identify
potential processes of care that may reduce admission rates. The patient
populations served by hospitals that contribute the most to the overall
area rate for pneumonia may be a starting point for interventions.

### Limitations on Use

As a PQI, admission for bacterial pneumonia is not a measure of hospital
quality, but rather one measure of outpatient and other health care.

This indicator has unclear construct validity, because it has not been
validated except as part of a set of indicators. Providers may reduce
admission rates without actually improving quality by shifting care to
an outpatient setting. Because some pneumonia care takes place in an
emergency room setting, combining inpatient and emergency room data may
give a more accurate picture of this indicator.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Vaccination for pneumococcal pneumonia in the elderly and early
management of bacterial respiratory infections on an ambulatory basis
may reduce admissions with pneumonia. A vaccine developed for the
elderly has been shown to be 45% effective in preventing
hospitalizations during peak seasons.[^77]

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Little evidence exists in the literature on the precision or variation
in pneumonia admission rates. Based on empirical evidence, this
indicator is precise, with a raw area level rate of 395.6 per 100,000
population and a standard deviation of 208.5.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is very high, at 92.9%,
indicating that the observed differences in age-sex adjusted rates
likely represent true differences across areas. Using multivariate
signal extraction techniques appears to have little additional impact on
estimating true differences across areas.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

A review of the literature suggests that comorbidities or other risk
factors that may vary systematically by area do not significantly affect
the incidence of hospitalization for pneumonia. Differences in
thresholds for admission of patients with bacterial pneumonia may
contribute to area rate differences. Empirical results show that area
rankings and absolute performance are somewhat affected by age-sex risk
adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Billings et al. found that low-income ZIP codes in New York City had 5.4
times more pneumonia admissions per capita than high-income ZIP
codes.[^78] Household income explained 53% of this variation. In
addition, Millman et al.[^79] reported that low-income ZIP codes had 5.4
times more pneumonia hospitalizations per capita than high-income ZIP
codes.

Based on empirical results, areas with high rates of bacterial pneumonia
admissions also tend to have high rates of admissions for other ACSCs.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Use of this indicator might lead to higher thresholds of admission for
pneumonia patients. Because pneumonia can be managed on an outpatient
basis, a shift to outpatient care may occur, which might be
inappropriate for more severely ill patients.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator was included in Weissman's set of avoidable
hospitalizations.[^80]

##  {#section-5 .unnumbered}

## Urinary Tract Infection Admission Rate (PQI 12)

Urinary tract infection is a common acute condition that can, for the
most part, be treated with antibiotics in an outpatient setting.
However, this condition can progress to more clinically significant
infections, such as pyelonephritis, in vulnerable individuals with
inadequate treatment.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment may reduce         |
| Quality             | admissions for urinary infection, and lower    |
|                     | rates represent better quality care.           |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for urinary tract infection per     |
|                     | 100,000 population.                            |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM principal diagnosis    |
|                     | code for urinary tract infection.              |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
|                     |                                                |
|                     | -   with diagnosis code of kidney/urinary      |
|                     |     tract disorder                             |
|                     |                                                |
|                     | -   with diagnosis code of immunocompromised   |
|                     |     state                                      |
|                     |                                                |
|                     | -   with immunocompromised state procedure     |
|                     |     code                                       |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county.            |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for urinary tract infection is a PQI that would be of
most interest to comprehensive health care delivery systems. Admission
for urinary tract infection is uncommon, but the observed variation is
likely to reflect true differences across areas.

Risk adjustment appears to affect the areas with the highest rates the
most, and using this indicator without risk adjustment may result in the
misidentification of some areas as outliers. This indicator is subject
to some moderate bias and should be adjusted for age and sex. The
patient populations served by hospitals that contribute the most to the
overall area rate for urinary tract infection may be a starting point
for interventions.

### Limitations on Use

As a PQI, admission for urinary tract infection is not a measure of
hospital quality, but rather one measure of outpatient and other health
care. This indicator has unclear construct validity, because it has not
been validated except as part of a set of indicators. Providers may
reduce admission rates without actually improving quality by shifting
care to an outpatient setting. Some urinary tract infection care takes
place in emergency rooms. As such, combining inpatient and emergency
room data may give a more accurate picture of this indicator.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Uncomplicated urinary tract infections can be treated with antibiotics
in the ambulatory setting; however, inappropriate treatment can lead to
more serious complications. Admission for urinary tract infection among
children, which is rare, is associated with physiological abnormalities.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Little evidence exists in the literature on the precision and variation
associated with this indicator. Based on empirical evidence, this
indicator is precise, with a raw area level rate of 145.1 per 100,000
population and a standard deviation of 89.5. The signal ratio (i.e., the
proportion of the total variation across areas that is truly related to
systematic differences in area performance rather than random variation)
is high, at 84.9%, indicating that the observed differences in age-sex
adjusted rates likely represent true differences across areas. Using
multivariate signal extraction techniques appears to have little
additional impact on estimating true differences across areas.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

Differences in thresholds for admission of patients with urinary tract
infection may contribute to area rate differences. Empirical results
show that area rankings and absolute performance are somewhat affected
by age-sex risk adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Billings et al. found that low-income ZIP codes in New York City had 2.2
times more urinary tract infection admissions than high-income ZIP
codes.[^81] Household income explained 28% of this variation. In
addition, Millman et al.[^82] reported that low-income ZIP codes had 2.8
times more urinary tract infection hospitalizations per capita than
high-income ZIP codes.

Based on empirical results, areas with high admission rates for urinary
tract infections also tend to have high admission rates for other ACSCs.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Use of this indicator might lead to higher thresholds of admission for
patients with urinary tract infections.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator was originally developed by Billings et al. in
conjunction with the United Hospital Fund of New York. It is included in
Weissman's set of avoidable hospitalizations.[^83]

##  {#section-6 .unnumbered}

## Angina without Procedure Admission Rate (PQI 13)

Both stable and unstable angina are symptoms of potential coronary
artery disease. Effective management of coronary disease reduces the
occurrence of major cardiac events such as heart attacks, and may also
reduce admission rates for angina.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment may reduce         |
| Quality             | admissions for angina (without procedures),    |
|                     | and lower rates represent better quality care. |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for angina (without procedures) per |
|                     | 100,000 population.                            |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM principal diagnosis    |
|                     | codes for angina.                              |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
|                     |                                                |
|                     | -   with a code for cardiac procedure in any   |
|                     |     field                                      |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for angina is a PQI that would be of most interest to
comprehensive health care delivery systems. Admission for angina is
relatively common, suggesting that the indicator will be measured with
good precision. The observed variation likely reflects true differences
in area performance.

Age-sex adjustment has a moderate impact. Other risk factors for
consideration include smoking, hyperlipidemia, hypertension, diabetes,
and socioeconomic status. The patient populations served by hospitals
that contribute the most to the overall area rate for angina may be a
starting point for interventions.

### Limitations on Use

As a PQI, angina without procedure is not a measure of hospital quality,
but rather one measure of outpatient and other health care. This
indicator has unclear construct validity, because it has not been
validated except as part of a set of indicators. Providers may reduce
admission rates without actually improving quality of care by shifting
care to an outpatient setting. Some angina care takes place in emergency
rooms. Combining inpatient and emergency room data may give a more
accurate picture.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

Stable angina can be managed in an outpatient setting using drugs such
as aspirin and beta blockers, as well as advice to change diet and
exercise habits.[^84] Effective treatments for coronary artery disease
reduce admissions for serious complications of ischemic heart disease,
including unstable angina.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Reasonably precise estimates of area angina rates should be feasible, as
one study shows that unstable angina accounts for 16.3% of total
admissions for ACSCs.[^85] Based on empirical evidence, this indicator
is adequately precise, with a raw area level rate of 166.0 per 100,000
population and a standard deviation of 135.7.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is very high, at 91.6%,
indicating that the observed differences in age-sex adjusted rates
likely represent true differences across areas. Using multivariate
signal extraction techniques appears to have little additional impact on
estimating true differences across areas.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

No evidence exists in the literature on the potential bias of this
indicator. The incidence of angina is related to age structure and risk
factors (smoking, hyperlipidemia, hypertension, diabetes) in a
population. Elderly age (over 70), diabetes, and hypertension have also
been associated with being at higher risk for angina.[^86]

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Billings et al. found that low-income ZIP codes in New York City had 2.3
times more angina hospitalizations than high-income ZIP codes.[^87]
Household income explained 13% of this variation. In addition, Millman
et al.[^88] reported that low-income ZIP codes had 2.7 times more angina
hospitalizations per capita than high-income ZIP codes.

Based on empirical study, areas with high rates of angina admissions
tend to have higher rates of other ACSC admissions.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Use of this quality indicator might raise the threshold for admission of
angina patients. Because some angina can be managed on an outpatient
basis, a shift to outpatient care may occur but is unlikely for severe
angina.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator was originally developed by Billings et al. in
conjunction with the United Hospital Fund of New York.

##  {#section-7 .unnumbered}

## Uncontrolled Diabetes Admission Rate (PQI 14)

Uncontrolled diabetes should be used in conjunction with short-term
complications of diabetes, which include diabetic ketoacidosis,
hyperosmolarity, and coma.\*

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment and adherence to   |
| Quality             | care may reduce the incidence of uncontrolled  |
|                     | diabetes, and lower rates represent better     |
|                     | quality care.                                  |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for uncontrolled diabetes per       |
|                     | 100,000 population.                            |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM principal diagnosis    |
|                     | codes for uncontrolled diabetes, without       |
|                     | mention of a short-term or long-term           |
|                     | complication.                                  |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

\* This indicator is designed to be combined with "Short Term Diabetes
Complication Admission Rate" to create the Healthy People 2010
indicator. To do so, users may simply add the rates of the two
indicators together.

**Summary of Evidence**

Hospital admission for uncontrolled diabetes is a PQI that would be of
most interest to comprehensive health care delivery systems. Healthy
People 2010 has established a goal to reduce the hospitalization rate
for uncontrolled diabetes in persons 18-64 years of age from 7.2 per
10,000 population to 5.4 per 10,000 population.[^89] Combining this
indicator with the short-term diabetes indicator will result in the
Healthy People 2010 measure, except that this QI excludes transfers from
another institution to reduce double counting of cases. As a result the
rate for the AHRQ QI may be minimally lower than the Healthy People 2010
indicator.

This indicator is moderately precise. The observed differences across
areas likely reflect true differences in area performance. Age-sex
adjustment slightly changes area rankings.

### Limitations on Use

As a PQI, uncontrolled diabetes is not a measure of hospital quality,
but rather one measure of outpatient and other health care. Rates of
diabetes may vary systematically by area, creating bias for this
indicator.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

High-quality outpatient management of diabetic patients has been shown
to lead to reductions in almost all types of serious avoidable
hospitalizations. However, tight control may be associated with more
episodes of hypoglycemia that lead to more admissions.

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Based on empirical evidence, this indicator is moderately precise, with
a raw area level rate of 34.7 per 100,000 population and a standard
deviation of 28.1.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is high, at 72.6%, indicating
that the observed differences in age-sex adjusted rates likely represent
true differences in area performance. Using multivariate signal
extraction techniques appears to have little additional impact on
estimating true differences across areas.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

Minorities have higher rates of diabetes, and higher hospitalization
rates may result in areas with higher minority concentrations. Empirical
results show that area rankings in the highest and lowest deciles are
slightly affected by age-sex adjustment.

*Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?*

Based on empirical results, areas with high rates of uncontrolled
diabetes also tend to have high rates of admission for other ACSCs.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

Because diabetic emergencies are potentially life-threatening, hospitals
are unlikely to fail to admit patients requiring hospitalization.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This measure corresponds closely with the measure of short-term diabetes
that was developed by Billings et al. and described in this
document.[^90] The key exception is the ICD-9-CM codes 25002 and 25003,
which are the only codes included for uncontrolled diabetes.

##  {#section-8 .unnumbered}

## Adult Asthma Admission Rate (PQI 15)

Asthma is one of the most common reasons for hospital admission and
emergency room care. Most cases of asthma can be managed with proper
ongoing therapy on an outpatient basis. Most published studies combine
admission rates for children and adults; therefore, areas may wish to
examine this indicator together with pediatric asthma.

+---------------------+------------------------------------------------+
| Relationship to     | Proper outpatient treatment may reduce the     |
| Quality             | incidence or exacerbation of asthma requiring  |
|                     | hospitalization, and lower rates represent     |
|                     | better quality care.                           |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for adult asthma per 100,000        |
|                     | population.                                    |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM principal diagnosis    |
|                     | codes for asthma.                              |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
|                     |                                                |
|                     | -   with any diagnosis code of cystic fibrosis |
|                     |     and anomalies of the respiratory system    |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admission for asthma is a PQI that would be of most interest to
comprehensive health care delivery systems.

Environmental factors such as air pollution, occupational exposure to
irritants, or other exposure to allergens have been shown to increase
hospitalization rates or exacerbate asthma symptoms. While race has been
shown to be associated with differences in admission rates, it is
unclear whether this is due to differences in severity of disease or
inadequate access to care. Adjustment for race is recommended.

Admission rates have been associated with lower socioeconomic status.
Areas may wish to identify hospitals that contribute the most to the
overall area rate for this indicator. The patient populations served by
these hospitals may be a starting point for interventions.

### Limitations on Use

As a PQI, adult asthma is not a measure of hospital quality, but rather
one measure of outpatient and other health care. Providers may reduce
admission rates without actually improving quality by shifting care to
an outpatient setting.

Admission rates that are drastically below or above the average or
recommended rates should be further examined.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

According to the National Asthma Education Program, asthma is a readily
treatable chronic disease that can be managed effectively in the
outpatient setting.[^91] Observational studies offer some evidence that
inhaled steroids may decrease risk of admission by up to 50%.[^92] [^93]

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Asthma is a common cause of admission for adults, and as such this
measure is likely to have adequate precision. Based on empirical
evidence, this indicator is adequately precise, with a raw area level
rate of 107.9 per 100,000 population and a standard deviation of 81.7.
The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is high, at 83.6%, indicating
that the observed differences in age-sex adjusted rates likely represent
true differences across areas.

Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statistical methods to remove most
or all bias?

Numerous environmental risk factors for asthma have been identified,
including allergens, tobacco smoke, and outdoor air pollution. Race
represents one of the most complex potentially biasing factors for this
indicator. Black patients have consistently been shown to have higher
asthma admission rates, even when stratifying for income and age.[^94]
Adjustment for race is recommended. Empirical results show that area
rankings are not affected by age-sex risk adjustment.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Billings et al. found that low-income ZIP codes in New York City had 6.4
times more asthma hospitalizations than high-income ZIP codes.[^95]
Household income explained 70% of this variation. In addition, Millman
et al.[^96] reported that low-income ZIP codes had 5.8 times more asthma
hospitalizations per capita than high-income ZIP codes.

Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other responses that do
not improve quality of care?

There is little evidence to suggest that asthmatics are being
inappropriately denied admission to the hospital. However, because some
asthma can be managed on an outpatient basis, a shift to outpatient care
may occur.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator was originally developed by Billings et al. in
conjunction with the United Hospital Fund of New York, and is included
in Weissman's set of avoidable hospitalizations.[^97]

##  {#section-9 .unnumbered}

## Rate of Lower-extremity Amputation among Patients with Diabetes (PQI 16)

Diabetes is a major risk factor for lower-extremity amputation, which
can be caused by infection, neuropathy, and microvascular disease.

+---------------------+------------------------------------------------+
| Relationship to     | Proper and continued treatment and glucose     |
| Quality             | control may reduce the incidence of            |
|                     | lower-extremity amputation, and lower rates    |
|                     | represent better quality care.                 |
+---------------------+------------------------------------------------+
| Benchmark           | State, regional, or peer group average.        |
+---------------------+------------------------------------------------+
| Definition          | Admissions for lower-extremity amputation in   |
|                     | patients with diabetes per 100,000 population. |
+---------------------+------------------------------------------------+
| Outcome of Interest | All non-maternal discharges of age 18 years    |
|                     | and older with ICD-9-CM procedure codes for    |
|                     | lower-extremity amputation in any field and    |
|                     | diagnosis code for diabetes in any field.      |
|                     |                                                |
|                     | Exclude cases:                                 |
|                     |                                                |
|                     | -   transferring from another institution (SID |
|                     |     ASOURCE=2)                                 |
|                     |                                                |
|                     | -   MDC 14 (pregnancy, childbirth, and         |
|                     |     puerperium)                                |
|                     |                                                |
|                     | -   MDC 15 (newborn and other neonates)        |
|                     |                                                |
|                     | -   with trauma diagnosis code in any field    |
+---------------------+------------------------------------------------+
| Population at Risk  | Population in Metro Area or county, age 18     |
|                     | years and older.                               |
+---------------------+------------------------------------------------+

**Summary of Evidence**

Hospital admissions for lower-extremity amputation among patients with
diabetes is a PQI that would be of most interest to comprehensive health
care delivery systems.

Lower-extremity amputation (LEA) affects up to 15% of all patients with
diabetes in their lifetimes.[^98] A combination of factors may lead to
this high rate of amputation, including minor trauma to the feet, which
is caused by loss of sensation and may lead to gangrene.[^99] Proper
long-term glucose control, diabetes education, and foot care are some of
the interventions that can reduce the incidence of infection,
neuropathy, and microvascular diseases. Healthy People 2010 has set a
goal of reducing the number of LEAs to 1.8 per 1,000 persons with
diabetes.[^100]

Studies have shown that LEA varies by age and sex, and age-sex risk
adjustment affects moderately the relative performance of areas. Race
may bias the indicator, since the rates of diabetes and poor glycemic
control are higher among Native Americans and Hispanic Americans.
However, results must be interpreted with care when adjusting for race,
because poor quality care may also vary systematically with racial
composition.

### Limitations on Use

As a PQI, lower-extremity amputations among patients with diabetes is
not a measure of hospital quality, but rather one measure of outpatient
and other health care. PQIs are correlated with each other and may be
used in conjunction as an overall examination of outpatient care.

### Details

Face validity: Does the indicator capture an aspect of quality that is
widely regarded as important and subject to provider or public health
system control?

In the United States, diabetes is the leading cause of nontraumatic
amputations (approximately 57,000 per year).[^101] Possible
interventions include foot clinics, wearing proper footwear, and proper
care of feet and foot ulcers.[^102]

Precision: Is there a substantial amount of provider or community level
variation that is not attributable to random variation?

Based on empirical evidence, this indicator is moderately precise, with
a raw area level rate of 30.5 per 100,000 population and a substantial
standard deviation of 42.7.

The signal ratio (i.e., the proportion of the total variation across
areas that is truly related to systematic differences in area
performance rather than random variation) is moderate, at 68.5%,
indicating that some of the observed differences in age-sex adjusted
rates likely do not represent true differences in area performance.
Using multivariate signal extraction techniques appears to have little
additional impact on estimating true differences across areas.

*Minimum bias: Is there either little effect on the indicator of
variations in patient disease severity and comorbidities, or is it
possible to apply risk adjustment and statisti*cal methods to remove
most or all bias?

Several sociodemographic variables are associated with the risk of
lower-extremity amputation, including age, duration of diabetes, and
sex.[^103] [^104] Empirical results show that age-sex adjustment affects
the relative performance of areas.

Construct validity: Does the indicator perform well in identifying true
(or actual) quality-of-care problems?

Several studies of intervention programs have noted a decrease in
amputation risk. One recent study noted a 1-year post-intervention
decrease of 79% in amputations in a low-income African American
population. Interventions included foot care education, assistance in
finding properly fitting footwear, and prescription footwear.[^105] One
observational study found that patients who receive no outpatient
diabetes education have a three-fold higher risk of amputation than
those receiving care.[^106]

*Fosters true quality improvement: Is the indicator insulated from
perverse incentives for providers to improve their reported performance
by avoiding difficult or complex cases, or by other respon*ses *that do
not improve quality of care?*

Given the severity of conditions requiring lower-extremity amputation,
hospitals are unlikely to fail to admit patients requiring
hospitalization.

Prior use: Has the measure been used effectively in practice? Does it
have potential for working well with other indicators?

This indicator is not widely used; however, it is included in the DEMPAQ
measure set for outpatient care.

#  {#section-10 .unnumbered}

# Using Different Types of QI Rates

When should you use the observed, expected, risk adjusted, and/or
smoothed rates generated by the AHRQ QI software? Here are some
guidelines.

If the user's primary interest is to identify cases for further
follow-up and quality improvement, then the observed rate would help to
identify them. The observed rate is the raw rate generated by the QI
software from the data the user provided. Areas for improvement can be
identified by the magnitude of the observed rate compared to available
benchmarks and/or by the number of patients impacted.

Additional breakdowns by the default patient characteristics used in
stratified rates (e.g., age, gender, or payer) can further identify the
target population. Target populations can also be identified by
user-defined patient characteristics supplemented to the case/discharge
level flags. Trend data can be used to measure change in the rate over
time.

Another approach to identify areas to focus on is to compare the
observed and expected rates. The expected rate is the rate the provider
would have if it performed the same as the reference population given
the provider's actual case-mix (e.g., age, gender, DRG, and comorbidity
categories).

If the observed rate is higher than the expected rate (i.e., the ratio
of observed/expected is greater than 1.0, or observed minus expected is
positive), then the implication is that the provider performed worse
than the reference population for that particular indicator. Users may
want to focus on these indicators for quality improvement.

If the observed rate is lower than the expected rate (i.e., the ratio of
observed/expected is less than 1.0, or observed minus expected is
negative), then the implication is that the provider performed better
than the reference population. Users may want to focus on these
indicators for identifying best practices.

Users can also compare the expected rate to the population rate reported
in the *Comparative Data* document to determine how their case-mix
compares to the reference population. The population rate refers to the
overall rate for the reference population. The reference population is
defined in the *Comparative Data* document.

If the population rate is higher than the expected rate, then the
provider's case-mix is less severe than the reference population. If the
population rate is lower than the expected rate, then the provider's
case-mix is more severe than the reference population.

We use this difference between the population rate and the expected rate
to "adjust" the observed rate to account for the difference between the
case-mix of the reference population and the provider's case-mix. This
is the provider's risk-adjusted rate.

If the provider has a less severe case-mix, then the adjustment is
positive (population rate \> expected rate) and the risk-adjusted rate
is higher than the observed rate. If the provider has a more severe
case-mix, then the adjustment is negative (population rate \< expected
rate) and the risk-adjusted rate is lower than the observed rate. The
risk-adjusted rate is the rate the provider would have if it had the
same case-mix as the reference population given the provider's actual
performance.

Finally, users can compare the risk-adjusted rate to the smoothed or
"reliability-adjusted" rate to determine whether this difference between
the risk-adjusted rate and reference population rate is likely to remain
in the next measurement period. Smoothed rates are weighted averages of
the population rate and the risk-adjusted rate, where the weight
reflects the reliability of the provider's risk-adjusted rate.

A ratio of (smoothed rate - population rate) / (risk-adjusted rate -
population rate) greater than 0.80 suggests that the difference is
likely to persist (whether the difference is positive or negative). A
ratio less than 0.80 suggests that the difference may be due in part to
random differences in patient characteristics (patient characteristics
that are not observed and controlled for in the risk-adjustment model).
In general, users may want to focus on areas where the differences are
more likely to persist.

# References

Access to Health Care in America. Washington, DC: National Academy
Press; 1993.

Bagg W, Sathu A, Streat S, et al. Diabetic ketoacidosis in adults at
Auckland Hospital, 1988-1996. Aust NZ J Med 1998;28(5):604-8.

Billings J, Zeital L, Lukomnik J, et al. Analysis of variation in
hospital admission rates associated with area income in New York City.
Unpublished report.

Billings J, Zeitel L, Lukomnik J, et al. Impact of socioeconomic status
on hospital use in New York City, Health Aff (Millwood)
1993;12(1):162-73.

Bindman AB, Grumbach K, Osmond D, et al. Preventable hospitalizations
and access to health care. JAMA 1995;274(4):305-11.

Blais L, Ernst P, Boivin JF, et al. Inhaled corticosteroids and the
prevention of readmission to hospital for asthma. Am J Respir Crit Care
Med 1998; 158(1):126-32.

Blumberg MS, Juhn PI. Insurance and the risk of ruptured appendix
\[letter; comment\]. N Engl J Med 1995;332(6):395-6; discussion 397-8.

Blustein J, Hanson K, Shea S. Preventable hospitalizations and
socioeconomic status. Health Aff (Millwood) 1998;17(2):177-89.

Bratton SL, Haberkern CM, Waldhausen JH. Acute appendicitis risks of
complications: age and Medicaid insurance. Pediatrics 2000;106(1 Pt
1):75-8.

Braveman P, Schaaf VM, Egerter S, et al. Insurance-related differences
in the risk of ruptured appendix \[see comments\]. N Engl J Med
1994;331(7):444-9.

Brunwald E, Antman EM, Beasley JW et al. ACC/AHA guidelines for the
management of patients with unstable angina and non-ST-segment elevation
myocardial infarction. A report of the American College of
Cardiology/American Heart Association Task Force on Practice Guidelines
(Committee on the Management of Patients with Unstable Angina). J Am
Coll Cardiol 2000;36(3):970-1062.

Burkhart DM. Management of acute gastroenteritis in children. American
Family Physician 1999;60(9):2555-63, 2565-6.

Centers for Disease Control and Prevention (CDC). National Diabetes Fact
Sheet: National Estimates and General Information on Diabetes in the
United States. Atlanta, GA: U.S. Department of Health and Human
Services, 1999.

Centers for Disease Control and Prevention (CDC). Asthma mortality and
hospitalization among children and young adults---United States,
1980-1993. MMWR Morb Mortal Wkly Rep 1996;45(17):350-3.

Chassin MR, Galvin RW, The urgent need to improve health care quality.
Institute of Medicine National Roundtable on Health Care Quality. JAMA
1998;280(11):1000-5.

Donahue JG, Weiss ST, Livingston JM, et al. Inhaled steroids and the
risk of hospitalization for asthma. JAMA 1997;277(11):887-91.

Edep ME, Shah NB, Tateo IM, et al. Differences between primary care
physicians and cardiologists in management of congestive heart failure:
relation to practice guidelines. J Am Coll Cardiol 1997;30(2):518-26.

Feinleib M, Rosenberg HM, Collins JG, et al. Trends in COPD morbidity
and mortality in the United States. Am Rev Respir Dis 1989;140(3 pt
2):S9-18.

Foster DA, Talsma A, Furumoto-Dawson A, et al. Influenza vaccine
effectiveness in preventing hospitalization for pneumonia in the
elderly. Am J Epidemiol 1992;136(3):296-307.

Gaster B, Hirsch IB. The effects of improved glycemic control on
complications in type 2 diabetes. Arch Intern Med 1998;158(2):134-40.

Gibbons RJ, Chatterjee K, Daley J, et al. ACC/AHA/ACP-ASIM guidelines
for the management of patients with chronic stable angina: a report of
the American College of Cardiology/American Heart Association Task force
on Practice Guidelines (Committee on Management of Patients with Chronic
Stable Angina) \[published erratum appears in J Am Coll Cardiol 1999
Jul;34(1):314\]. J Am Coll Cardiol 1999;33(7):2092-197.

Hackner D, Tu G, Weingarten S, et al. Guidelines in pulmonary medicine:
a 25-year profile. Chest 1999;116(4):1046-62.

Harris MI. Diabetes in America: epidemiology and scope of the problem.
Diabetes Care 1998;21 Suppl 3:C11-4.

Healthy People 2010. Office of Disease Prevention and Health Promotion,
U.S. Department of Health and Human Services.

Hessol NA, Fuentes-Afflick E, Bacchetti P. Risk of low birth weight
infants among black and white parents. Obstet Gynecol 1998;92(5):814-22.

Hiss RG. Barriers to care in non-insulin-dependent diabetes mellitus.
The Michigan Experience. Ann Intern Med 1996;124(1 Pt 2):146-8.

Humphrey LL, Palumbo PJ, Butters MA, et al. The contribution of
non-insulin-dependent diabetes to lower-extremity amputation in the
community. Arch Intern Med 1994;154(8):885-92.

Institute of Medicine Division of Health Care Services. Medicare: a
strategy for quality assurance. Washington, DC: National Academy Press;
1990.

Lin, S, Fitzgerald E, Hwang SA, et al. Asthma hospitalization rates and
socioeconomic status in New York State (1987-1993) J Asthma
1999;36(3):239-51.

Mayfield JA, Reiber GE, Sanders LJ, et al. Preventive foot care in
people with diabetes. Diabetes Care 1998;21(12):2161-77.

McConnochie KM, Russo MJ, McBride JT, et al. Socioeconomic variation in
asthma hospitalization: excess utilization or greater need? Pediatrics
1999;103(6):375.

Millman M, editor. Committee on Monitoring Access to Personal Health
Care Services. Washington, DC: National Academy Press; 1993.

Murray JL, Bernfield M. The differential effect of prenatal care on the
incidence of low birth weight among blacks and whites in a prepaid
health care plan. N Engl J Med 1988;319(21):1385-91.

Musey VC, Lee JK, Crawford R, et al. Diabetes in urban
African-Americans. I. Cessation of insulin therapy is the major
precipitating cause of diabetic ketoacidosis. Diabetes Care
1995;18(4):483-9.

National Heart, Lung, and Blood Institute/National Asthma Education and
Prevention Program. Expert Panel Report 2: Guidelines for the diagnosis
and management of asthma. In: National Institutes of Health pub. no.
97-4051. Bethesda, MD; 1997.

O'Campo P, Xue X, Wang MC, et al. Neighborhood risk factors for low
birthweight in Baltimore: a multilevel analysis. Am J Public Health
1997;87(7):1113-8.

Patout CA, Jr., Birke JA, Horswell R, et al. Effectiveness of a
comprehensive diabetes lower-extremity amputation prevention program in
a predominantly low-income African-American population. Diabetes Care
2000;23(9):1339-42.

Pecoraro RE, Reiber BE, Burgess EM. Pathways to diabetic limb
amputation. Basis of prevention. Diabetes Care 1990;13(5):513-21.

Philbin EF, Andreaou C, Rocco TA, et al. Patterns of
angiotensin-converting enzyme inhibitor use in congestive heart failure
in two community hospitals. Am J Cardio. 1996;77(1):832-8.

Ray NF, Thamer M, Fadillioglu B, et al. Race, income, urbanicity, and
asthma hospitalization in California: a small area analysis. Chest
1998;113(5):1277-84.

Reiber GE, Pecoraro RE, Koepsell TD. Risk factors for amputation in
patients with diabetes mellitus. A case-control study. Ann Intern Med
1992;117(2):97-105.

Reis, SE, Holubkov R, Edmundowicz D, et al. Treatment of patients
admitted to the hospital with congestive heart failure:
specialty-related disparities in practice patterns and outcomes. J Am
Coll Cardiol 1997;30(3):733-8.

Rosenthal GE, Harper DL, Shah A, et al. A regional evaluation of
variation in low-severity hospital admissions. J Gen Intern Med
1997;12(7):416-22.

Selby JV, Zhang D. Risk factors for lower extremity amputation in
persons with diabetes. Diabetes Care 1995;18(4):509-16.

Silver MP, Babitz ME, Magill MK. Ambulatory care sensitive
hospitalization rates in the aged Medicare population in Utah, 1990 to
1994: a rural-urban comparison. J Rural Health 1997;13(4):285-94.

Trudeau ME, Solano-McGuire SM. Evaluating the quality of COPD care. Am
J. Nurs 1999;99(3):47-50.

Weinberg AD, Minaker KL. Dehydration. Evaluation and management in older
adults. Council on Scientific Affairs, American Medical Association.
JAMA 1995;274(19):1552-6.

Weinberger M, Oddone EZ, Henderson WG. Does increased access to primary
care reduce hospital readmissions? VA Cooperative Study Group on Primary
Care and Hospital readmission. N Engl J Med 1996;334(22):1441-7.

Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable hospitalization
by insurance status in Massachusetts and Maryland. JAMA
1992;268(17)2388-94.

Zoorob RJ, Hagen MD. Guidelines on the care of diabetic nephropathy,
retinopathy and foot disease. Am Fam Physician 1997;56(8):2021-8,
2033-4.

######### 

# Appendix A: Links {#appendix-a-links .HeadOther}

The following links may be helpful to users of the AHRQ Prevention
Quality Indicators.

**Prevention Quality Indicators Version 3.1 Documents and Software**

> Available at <http://www.qualityindicators.ahrq.gov/pqi_download.htm>

+---------------------+------------------------------------------------+
| ***Title***         | ***Description***                              |
+=====================+================================================+
| *Guide to           | Describes how the PQIs were developed and      |
| Prevention Quality  | provides detailed evidence for each indicator. |
| Indicators*         |                                                |
+---------------------+------------------------------------------------+
| *Prevention Quality | Provides detailed definitions of each PQI,     |
| Indicators          | including all ICD-9-CM and DRG codes that are  |
| Technical           | included in or excluded from the numerator and |
| Specifications*     | denominator. Note that exclusions from the     |
|                     | denominator are automatically applied to the   |
|                     | numerator.                                     |
+---------------------+------------------------------------------------+
| *PQI Covariates     | Tables for each PQI provide the stratification |
| used in Risk        | and coefficients used to calculate the         |
| Adjustment*         | risk-adjusted rate for each strata.            |
+---------------------+------------------------------------------------+
| *Prevention Quality | This document provides the average Area Rate,  |
| Indicators          | Area Standard Deviation, Population Rate, and  |
| Comparative Data*   | Rating for each PQI.                           |
+---------------------+------------------------------------------------+
| *SAS® PQI Software  | This software documentation provides detailed  |
| Documentation*      | instructions on how to use the SAS ® version   |
|                     | of the PQI software including data             |
|                     | preparation, calculation of the PQI rates, and |
|                     | interpretation of output.                      |
+---------------------+------------------------------------------------+
| *Change Log to PQI  | The Change Log document provides a cumulative  |
| Documents and       | summary of all changes to the PQI software,    |
| Software*           | software documentation, and other documents    |
|                     | made since the release of version 2.1 of the   |
|                     | software in March 2003. Changes to indicator   |
|                     | specifications that were not a result of new   |
|                     | ICD-9-CM and DRG codes, are also described in  |
|                     | the Change Log.                                |
+---------------------+------------------------------------------------+
| *Fiscal year 2007   | This document summarizes the changes to the    |
| Coding Changes*     | indicator definitions resulting from FY 2007   |
|                     | changes to ICD-9-CM coding and DRG changes.    |
|                     | These changes will only affect data from FY    |
|                     | 2007 (October 1, 2006) or later.               |
+---------------------+------------------------------------------------+
| SAS® PQI Software   | Requires the SAS® statistical program          |
|                     | distributed by the SAS Institute, Inc. The     |
|                     | company may be contacted directly regarding    |
|                     | the licensing of its products:                 |
|                     |                                                |
|                     | > [http://www.sas.com](http://www.sas.com/)    |
+---------------------+------------------------------------------------+

**AHRQ QI Windows Application**

The AHRQ QI Windows Application calculates rates for all of the AHRQ
Quality Indicators modules and does not require SAS®. It is available
at:

> <http://www.qualityindicators.ahrq.gov/winqi_download.htm>

**Additional Documents**

The following documents are available within the \"Documentation\"
section of the **AHRQ QI Downloads** Web page:

> [[http://www.qualityindicators.ahrq.gov/downloads.htm]{.underline}](http://www.qualityindicators.ahrq.gov/downloads.htm)

*Refinement of the HCUP Quality Indicators (Technical Review), May 2001*

*Refinement of the HCUP Quality Indicators (Summary), May 2001*

*Measures of Patient Safety Based on Hospital Administrative Data - The
Patient Safety Indicators, August 2002*

*Measures of Patient Safety Based on Hospital Administrative Data - The
Patient Safety Indicators (Summary), August 2002*

In addition, these documents may be accessed at the AHRQ QI
Documentation Web page:

> <http://www.qualityindicators.ahrq.gov/documentation.htm>

-   *Guidance for Using the AHRQ Quality Indicators for Hospital-level
    Public Reporting or Payment, August 2004*

-   *AHRQ Summary Statement on Comparative Hospital Public Reporting,
    December 2005*

-   *Appendix A: Current Uses of AHRQ Quality Indicators and
    Considerations for Hospital-level*

-   *Comparison of Recommended Evaluation Criteria in Five E*xisting
    National Frameworks

The following documents can be viewed or downloaded from the page:

> <http://www.qualityindicators.ahrq.gov/newsletter.htm>

-   *2006 Area Level Indicator Changes*

-   *Considerations in Public Reporting for the AHRQ QIs*

-   *June 2005 Newsletter -* Contains the article, \"Using Different
    Types of QI Rates\"

**Other Tools and Information**

PQI rates can be calculated using the modified Federal Information
Processing Standards (FIPS) State/county code. A list of codes is
available at:

> <http://www.census.gov/popest/geographic/codes02.pdf>

AHRQ provides a free, on-line query system based on HCUP data that
provides access to health statistics and information on hospital stays
at the national, regional, and State level. It is available at:

> <http://hcup.ahrq.gov/HCUPnet.asp>

The CDC National Diabetes Surveillance System provides state level
estimates of diabetes prevalence by age.

> <http://www.cdc.gov/diabetes/statistics/index.htm>

[^1]: Individual hospitals that are sole providers for communities and
    that are involved in outpatient care may be able to use the PQI
    programs. .Managed care organizations and health care providers with
    responsibility for a specified enrolled population can use the PQI
    programs but must provide their own population denominator data.

[^2]: HCUPnet can be found at
    [[http://hcup.ahrq.gov/HCUPnet.asp]{.underline}](http://hcup.ahrq.gov/HCUPnet.asp)
    and provides instant access to national and regional data from the
    Healthcare Cost and Utilization Project, a Federal-State-industry
    partnership in health data maintained by the Agency for Healthcare
    Research and Quality.

[^3]: Ball JK, Elixhauser A, Johantgen M, et al. *HCUP Quality
    Indicators, Methods, Version 1.1: Outcome, Utilization, and Access
    Measures for Quality Improvement*. (AHCPR Publication No. 98-0035).
    Healthcare Cost and Utilization project (HCUP-3) Research notes:
    Rockville, MD: Agency for Health Care Policy and Research, 1998.

[^4]: *Impact: Case Studies Notebook -- Documented Impact and Use of
    AHRQ\'s Research*. Compiled by Division of Public Affairs, Office of
    Health Care Information, Agency for Healthcare Research and Quality.

[^5]: Institute of Medicine Division of Health Care Services. . .
    Medicare: a strategy for quality assurance. . . Washington, DC:
    National Academy Press; 1990.

[^6]: Information on the 3M™ APR-DRG system is available at
    <http://www.3m.com/us/healthcare/his/products/coding/refined_drg.jhtml>.

[^7]: Billings J, Zeitel L, Lukomnik J, et al. Impact of socioeconomic
    status on hospital use in New York City, Health Aff (Millwood)
    1993;12(1):162-73.

[^8]: Weissman, JS, Gatsonis C, Epstein AM. Rates of avoidable
    hospitalization by insurance status in Massachusetts and Maryland.
    JAMA 1992;268(17):2388-94.

[^9]: Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable
    hospitalization by insurance status in Massachusetts and Maryland.
    JAMA 1992;268(17)2388-94.

[^10]: Bindman AB, Grumbach K, Osmond D, et al. Preventable
    hospitalizations and access to health care. JAMA 1995;274(4):305-11.

[^11]: Billings J, Zeital L, Lukomnik J, et al. Analysis of variation in
    hospital admission rates associated with area income in New York
    City. Unpublished report.

[^12]: Silver MP, Babitz ME, Magill MK. Ambulatory care sensitive
    hospitalization rates in the aged Medicare population in Utah, 1990
    to 1994: a rural-urban comparison. J Rural Health 1997;13(4):285-94.

[^13]: Millman M, editor. Committee on Monitoring Access to Personal
    Health Care Services. Washington, DC: National Academy Press; 1993.

[^14]: *Dartmouth Atlas of Health Care, 1999*. Center for the Evaluative
    Clinical Sciences at Dartmouth Medical School, 2000.

[^15]: Bindman AB, Grumbach K, Osmond D, et al. Preventable
    hospitalizations and access to health care. JAMA 1995;274(4):305-11.

[^16]: Culler SD, Parchman ML, Przybylski M. Factors related to
    potentially preventable hospitalizations among the elderly. Med Care
    1998;36(6):804-17.

[^17]: Blustein J, Hanson K, Shea S. Preventable hospitalizations and
    socioeconomic status. Health Aff (Millwood) 1998;17(2):177-89.

[^18]: Billings J, Anderson GM, Newman LS. Recent findings on
    preventable hospitalizations . Health Aff (Millwood)
    1996;15(3):239-49.

[^19]: Billings J, Zeitel L, Lukomnik J, et al. Impact of socioeconomic
    status on hospital use in New York City. Health Aff (Millwood)
    1993;12(1):162-73.

[^20]: Pappas G, Hadden WC, Kozak LJ, et al. Potentially avoidable
    hospitalizations: inequalities in rates between US socioeconomic
    groups. Am J Public Health 1997;87(5):811-6.

[^21]: Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable
    hospitalization by insurance status in Massachusetts and Maryland.
    Jama 1992;268(17):2388-94.

[^22]: Komaromy M, Lurie N, Osmond D, et al. Physician practice style
    and rates of hospitalization for chronic medical conditions. Med
    Care 1996;34(6):594-609.

[^23]: Schreiber S, Zielinski T. The meaning of ambulatory care
    sensitive admissions: urban and rural perspectives. J Rural Health
    1997;13(4):276-84.

[^24]: Bindman A, Grumbach K, Osmond D, et al. Accuracy of preventable
    hospitalization rates for measuring access to care in rural
    communities. JGIM 1996;11\[Suppl 1\]:64.

[^25]: Parchman ML, Culler S. Primary care physicians and avoidable
    hospitalizations . J Fam Pract 1994;39(2):123-8.

[^26]: Epstein A. The role of the medical market in preventable
    hospitalizations. Abstract Book/Association of Health Services
    Research 1998;15(316-7).

[^27]: Gill JM, Mainous AG, 3rd. The role of provider continuity in
    preventing hospitalizations. Arch Fam Med 1998;7(4):352-7.

[^28]: Falik M, Needleman J, McCall N, et al. Ambulatory care sensitive
    conditions: hospitalization rates by usual source of care. Abstract
    Book/Association for Health Services Research 1998;15:236-7.

[^29]: Shi L, Samuels ME, Pease M, et al. Patient characteristics
    associated with hospitalizations for ambulatory care sensitive
    conditions in South Carolina. Southern Medical Journal
    1999;92(10):989-98.

[^30]: Gill JM. Can hospitalizations be avoided by having a regular
    source of care? Fam Med 1997;29(3):166-71.

[^31]: Parchman ML, Culler SD. Preventable hospitalizations in primary
    care shortage areas. An analysis of vulnerable Medicare
    beneficiaries. Arch Fam Med 1999;8(6):487-91.

[^32]: Krakauer H, Jacoby I, Millman M, et al. Physician impact on
    hospital admission and on mortality rates in the Medicare
    population. Health Serv Res 1996;31(2):191-211.

[^33]: Bagg W, Sathu A, Streat S, et al. Diabetic ketoacidosis in adults
    at Auckland Hospital, 1988-1996. Aust N Z J Med 1998;28(5):604-8.

[^34]: Musey VC, Lee JK, Crawford R, et al. Diabetes in urban
    African-Americans. I. Cessation of insulin therapy is the major
    precipitating cause of diabetic ketoacidosis. Diabetes Care
    1995;18(4):483-9.

[^35]: Bindman AB, Grumbach K, Osmond D, et al. Preventable
    hospitalizations and access to health care. JAMA 1995;274(4):305-11.

[^36]: Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable
    hospitalization by insurance status in Massachusetts and Maryland.
    JAMA 1992;268(17)2388-94.

[^37]: Billings J, Zeital L, Lukomnik J, et al. Analysis of variation in
    hospital admission rates associated with area income in New York
    City. Unpublished report.

[^38]: Weissman, et al., 1992.

[^39]: Braveman P, Schaaf VM, Egerter S, et al. Insurance-related
    differences in the risk of ruptured appendix \[see comments\]. N
    Engl J Med 1994;331(7):444-9.

[^40]: Braveman et al., 1994.

[^41]: Bratton SL, Haberkern CM, Waldhausen JH. Acute appendicitis risks
    of complications: age and Medicaid insurance. Pediatrics 2000;106(1
    Pt 1):75-8.

[^42]: Blumberg MS, Juhn PI. Insurance and the risk of ruptured appendix
    \[letter; comment\]. N Engl J Med 1995;332(6):395-6; discussion
    397-8.

[^43]: Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable
    hospitalization by insurance status in Massachusetts and Maryland.
    JAMA 1992;268(17)2388-94.

[^44]: Gaster B, Hirsch IB. The effects of improved glycemic control on
    complications in type 2 diabetes. Arch Intern Med
    1998;158(2):134-40.

[^45]: Zoorob RJ, Hagen MD. Guidelines on the care of diabetic
    nephropathy, retinopathy and foot disease. Am Fam Physician
    1997;56(8):2021-8, 2033-4.

[^46]: Hiss RG. Barriers to care in non-insulin-dependent diabetes
    mellitus. The Michigan Experience. Ann Intern Med 1996;124(1 Pt
    2):146-8.

[^47]: Harris MI. Diabetes in America: epidemiology and scope of the
    problem. Diabetes Care 1998;21 Suppl 3:C11-4.

[^48]: Hackner D, Tu G, Weingarten S, et al. Guidelines in pulmonary
    medicine: a 25-year profile. Chest 1999;116(4):1046-62.

[^49]: Feinleib M, Rosenberg HM, Collins JG, et al. Trends in COPD
    morbidity and mortality in the United States. Am Rev Respir Dis
    1989;140(3 pt 2):S9-18.

[^50]: Bindman AB, Grumbach K, Osmond D, et al. Preventable
    hospitalizations and access to health care. JAMA 1995;274(4)305-11.

[^51]: Millman M, editor. Committee on Monitoring Access to Personal
    Health Care Services. Washington, DC: National Academy Press; 1993.

[^52]: Weinberger M, Oddone EZ, Henderson WG. Does increased access to
    primary care reduce hospital readmissions? VA Cooperative Study
    Group on Primary Care and Hospital readmission. N Engl J Med
    1996;334(22):1441-7.

[^53]: Billings J, Zeital L, Lukomnik J, et al. Analysis of variation in
    hospital admission rates associated with area income in New York
    City. Unpublished report.

[^54]: Blustein J. Hanson K, Shea S. Preventable hospitalizations and
    socioeconomic status. Health Aff (Millwood) 1998;17(2):177-89.

[^55]: Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable
    hospitalization by insurance status in Massachusetts and Maryland.
    JAMA 1992;268(1):2388-94.

[^56]: Bindman AB, Grumback K, Osmond D, et al. Preventable
    hospitalizations and access to health care. JAMA 1995;274(4):305-11.

[^57]: Weissman, et al. 1992.

[^58]: Millman M, editor. Committee on Monitoring Access to Personal
    Health Care Services. Washington, DC: National Academy Press; 1993.

[^59]: Billings J, Zeitel L, Lukomnik J, et al. Impact of socioeconomic
    status on hospital use in New York City. Health Aff (Millwood)
    1993;12(1):162-73.

[^60]: Access to Health Care in America. Washington, DC: National
    Academy Press; 1993.

[^61]: Edep ME, Shah NB, Tateo IM, et al. Differences between primary
    care physicians and cardiologists in management of congestive heart
    failure: relation to practice guidelines. J Am Coll Cardiol
    1997;30(2):518-26.

[^62]: Reis, SE, Holubkov R, Edmundowicz D, et al. Treatment of patients
    admitted to the hospital with congestive heart failure:
    specialty-related disparities in practice patterns and outcomes. J
    Am Coll Cardiol 1997;30(3):733-8.

[^63]: Philbin EF, Andreaou C, Rocco TA, et al. Patterns of
    angiotensin-converting enzyme inhibitor use in congestive heart
    failure in two community hospitals. Am J Cardio. 1996;77(1):832-8.

[^64]: Billings J, Zeital L, Lukomnik J, et al. Analysis of variation in
    hospital admission rates associated with area income in New York
    City. Unpublished report.

[^65]: Millman M, editor. Committee on Monitoring Access to Personal
    Health Care Services. Washington DC: National Academy Press.

[^66]: Rosenthal GE, Harper DL, Shah A, et al. A regional evaluation of
    variation in low-severity hospital admissions. J Gen Intern Med
    1997;12(7):416-22.

[^67]: Healthy People 2010. Office of Disease Prevention and Health
    Promotion, U.S. Department of Health and Human Services.

[^68]: Hessol NA, Fuentes-Afflick E, Bacchetti P. Risk of low birth
    weight infants among black and white parents. Obstet Gynecol
    1998;92(5):814-22.

[^69]: O'Campo P, Xue X, Wang MC, et al. Neighborhood risk factors for
    low birthweight in Baltimore: a multilevel analysis. Am J Public
    Health 1997;87(7):1113-8.

[^70]: Hessol, et al. 1998.

[^71]: O'Campo, et al. 1997.

[^72]: Hessol, et al. 1998.

[^73]: Murray JL, Bernfield M. The differential effect of prenatal care
    on the incidence of low birth weight among blacks and whites in a
    prepaid health care plan. N Engl J Med 1988;319(21):1385-91.

[^74]: Weinberg AD, Minaker KL. Dehydration. Evaluation and management
    in older adults. Council on Scientific Affairs, American Medical
    Association. JAMA 1995;274(19):1552-6.

[^75]: Billings J, Zeital L, Lukomnik J, et al. Analysis of variation in
    hospital admission rates associated with area income in New York
    City. Unpublished report.

[^76]: Millman M, editor. Committee on Monitoring Access to Personal
    Health Care Services. Washington, DC: National Academy Press. 1993.

[^77]: Foster DA, Talsma A, Furumoto-Dawson A, et al. Influenza vaccine
    effectiveness in preventing hospitalization for pneumonia in the
    elderly. Am J Epidemiol 1992;136(3):296-307.

[^78]: Billings J, Zeital L, Lukomnik J, et al. Analysis of variation in
    hospital admission rates associated with area income in New York
    City. Unpublished report.

[^79]: Millman M, editor. Committee on Monitoring Access to Personal
    Health Care Services. Washington, DC: National Academy Press. 1993.

[^80]: Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable
    hospitalization by insurance status in Massachusetts and Maryland
    JAMA 1992;268(17)2388-94.

[^81]: Billings J, Zeital L, Lukomnik J, et al. Analysis of variation in
    hospital admission rates associated with area income in New York
    City. Unpublished report.

[^82]: Millman M, editor. Committee on Monitoring Access to Personal
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