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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://wwwqualityindicatorsahrqgov

October 2001
Version 31 March 12, 2007

Preface

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 AHRQs 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://wwwqualityindicatorsahrqgov/downloadshtm

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://qualitytoolsahrqgov/qualityreport and National Healthcare
Disparities Report http://qualitytoolsahrqgov/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, PhD, Director
Center for Organization and Delivery Studies

Acknowledgments

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

Other Contributors

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

Contributors from the Agency for Healthcare Research and Quality:

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

Acknowledgments iv

10 Introduction to the AHRQ Prevention Quality Indicators 1
11 What Are the Prevention Quality Indicators? 1
12 How Can the PQIs Be Used in Quality Assessment? 2
13 What does this Guide Contain? 3

20 Origins and Background of the Quality Indicators 4
21 Development of the AHRQ Quality Indicators 4
22 AHRQ Quality Indicator Modules 4

30 Methods of Identifying, Selecting, and Evaluating the Quality
Indicators 6
31 Step 1: Obtain Background Information on QI Use 6
32 Step 2: Search the Literature to Identify Potential QIs 6
33 Step 3: Review the Literature to Evaluate the QIs According to
Predetermined Criteria 7
34 Step 4: Perform a Comprehensive Evaluation of Risk Adjustment 8
35 Step 5: Evaluate the Indicators Using Empirical Analyses 9

40 Summary Evidence on the Prevention Quality Indicators 11
41 Version 31 PQIs 11
42 Strengths and Limitations in Using the PQIs 14
43
Questions for Future Work 15

50 Detailed Evidence for Prevention Quality Indicators 17
51 Diabetes Short-term Complications Admission Rate PQI 1 20
52 Perforated Appendix Admission Rate PQI 2 22
53 Diabetes Long-term Complications Admission Rate PQI 3 24
54 Chronic Obstructive Pulmonary Disease Admission Rate PQI 5 26
55 Hypertension Admission Rate PQI 7 28
56 Congestive Heart Failure Admission Rate PQI 8 30
57 Low Birth Weight Rate PQI 9 32
58 Dehydration Admission Rate PQI 10 34
59 Bacterial Pneumonia Admission Rate PQI 11 36
510 Urinary Tract Infection Admission Rate PQI 12 38
511 Angina without Procedure Admission Rate PQI 13 40
512 Uncontrolled Diabetes Admission Rate PQI 14 42
513 Adult Asthma Admission Rate PQI 15 44
514 Rate of Lower-extremity Amputation among Patients with Diabetes PQI
16 46

60 Using Different Types of QI Rates 48

70 References 50

Appendix A: Links A-1

List of Tables

Table 1 Prevention Quality Indicators 12
Table 2 Diabetes-related Prevention Quality Indicators 14
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 31 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://wwwqualityindicatorsahrqgov/listserv_archive_2006htmOct13

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

1 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

2 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 States 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

3 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 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 patients 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

1 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

2 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 Medicines 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

1 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

2 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

3 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

4 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

5 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 US
hospital discharges The NIS is designed to approximate a
20 of US
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

1 Version 31 PQIs

A modified version of the process described in Section 30 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 US
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 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
146 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 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 50, Detailed Evidence for Prevention Quality Indicators that
starts on page 17, and in the document Prevention Quality Indicators
Technical Specifications See Appendix A

Table 1 Prevention Quality Indicators

|a |Notes under Literature Review Findings: |
| |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 |
| |- Indicates that the concern has been demonstrated in the |
| |literature |
|b |Uncontrolled diabetes is designed to be combined with diabetes |
| |short-term complications |

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

Table 2 Diabetes-related Prevention
Quality Indicators

2 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 papers10 included
adjustment only for demographic factors eg, 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

3 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 146, 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 indicators true value after accounting for case-
mix and
reliability The score for each individual test is an ordinal
ranking eg, very high, high, moderate, and low The final summary score
was derived by assigning a weight to each ranking eg, 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 colleagues16 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 hospitalization17
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 for
links to these and other documents

1 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

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 246

The signal ratio ie, 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 517, 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

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 areas 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
Weissmans[38] sets of avoidable hospitalization measures This indicator,
defined as a provider-level indicator, was an original HCUP QI

2 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

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 333 and a substantial standard
deviation of 144

Relative to other indicators, a higher percentage of the variation occurs
at the area level rather than the discharge level However, the signal
ratio ie, 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 265, 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

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 Weissmans set of avoidable hospitalizations

3 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

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 that 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 808 per 100,000 population and a standard deviation of 581

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

4 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

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 3240 per
100,000 population and a
standard deviation of 2038

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 58 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

5 6 Hypertension Admission Rate
PQI 7

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

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 05 of total admissions for ACSCs[54]

Based on empirical evidence, this indicator is moderately precise, with a
raw area level rate of 371 per 100,000 population and a substantial
standard deviation of 322 The signal ratio ie, 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 699, 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 areas 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 238 in
Massachusetts after adjustment for age and sex, while Maryland had
a
corresponding relative risk of 193[57] Millman et al reported that low-
income ZIP codes had 76 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 Weissmans set of avoidable
hospitalizations

7 8 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

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 5210 per 100,000 population and a standard
deviation of 2865

The signal ratio ie, 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 930, indicating that the observed
differences in age-sex adjusted rates very likely represent true
differences across areas

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 46
times more CHF hospitalizations per capita than high-income ZIP codes[64]
Millman et al reported that low-income ZIP codes had 61 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

9 10 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

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 09[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 39 and a standard
deviation of 23 The signal ratio ie, 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 671,
indicating that some of the observed differences in age-sex adjusted rates
do not represent true differences in area performance

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

11 12 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

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 1399 per 100,000 population and a standard
deviation of 1032

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

13 14 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

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 3956 per 100,000 population and a
standard deviation of 2085

The signal ratio ie, 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 929, 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

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 54
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 54 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 Weissmans set of avoidable
hospitalizations[80]

15 16 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

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 1451 per 100,000 population and
a standard deviation of 895 The signal ratio ie, 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
849, 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

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 22
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 28 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 Weissmans set
of avoidable hospitalizations[83]

17 18 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

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 163 of total admissions
for ACSCs[85] Based on empirical evidence, this indicator is adequately
precise, with a raw area level rate of 1660 per 100,000 population and a
standard deviation of 1357

The signal ratio ie, 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 916, 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

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 23
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 27 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

19 20 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

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 72 per 10,000 population to
54 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 347 per 100,000 population and a standard deviation
of 281

The signal ratio ie, 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 726, 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

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

21 22 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

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 1079 per
100,000 population and a standard deviation of 817 The signal ratio
ie, 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 836, indicating that the observed differences in
age-sex adjusted rates likely represent true differences across areas

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 64
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 58 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 Weissmans
set of avoidable hospitalizations[97]

23 24 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

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 18 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 305 per 100,000 population and a substantial
standard deviation of 427
The signal ratio ie, 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 685, 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

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 responses 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

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 users 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 eg, 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
providers
actual case-mix eg, age, gender, DRG, and comorbidity categories

If the observed rate is higher than the expected rate ie, the ratio of
observed/expected is greater than 10, 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 ie, the ratio of
observed/expected is less than 10, 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
providers case-mix is less severe than the reference population If the
population rate
is lower than the expected rate, then the providers 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 providers case-mix This is the
providers 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 providers 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 providers risk-adjusted rate

A ratio of smoothed rate - population rate / risk-adjusted rate -
population rate greater than 080 suggests that the difference is likely
to persist whether the difference is positive or negative A ratio less
than 080 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;285:604-8

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;121:162-73

Bindman AB, Grumbach K, Osmond D, et al Preventable hospitalizations and
access to health care JAMA
1995;2744:305-11

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; 1581:126-32

Blumberg MS, Juhn PI Insurance and the risk of ruptured appendix [letter;
comment] N Engl J Med 1995;3326:395-6; discussion 397-8

Blustein J, Hanson K, Shea S Preventable hospitalizations and
socioeconomic status Health Aff Millwood 1998;172:177-89

Bratton SL, Haberkern CM, Waldhausen JH Acute appendicitis risks of
complications: age and Medicaid insurance Pediatrics 2000;1061 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;3317:444-
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;363:970-1062

Burkhart DM Management of acute gastroenteritis in children American
Family Physician 1999;609:2555-63,
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: US 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;4517:350-3

Chassin MR, Galvin RW, The urgent need to improve health care quality
Institute of Medicine National Roundtable on Health Care Quality JAMA
1998;28011:1000-5

Donahue JG, Weiss ST, Livingston JM, et al Inhaled steroids and the risk
of hospitalization for asthma JAMA 1997;27711:887-91

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;302:518-26

Feinleib M, Rosenberg HM, Collins JG, et al Trends in COPD morbidity and
mortality in the United States Am Rev Respir Dis 1989;1403 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;1363:296-307

Gaster B, Hirsch IB The effects of improved glycemic control on
complications in type 2 diabetes Arch Intern Med 1998;1582:134-40

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;341:314] J Am Coll Cardiol 1999;337:2092-197

Hackner D, Tu G, Weingarten S, et al Guidelines in pulmonary medicine: a
25-year profile Chest 1999;1164:1046-62

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,
US 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;925:814-22

Hiss RG Barriers to care in non-insulin-dependent diabetes mellitus The
Michigan Experience Ann Intern Med 1996;1241 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;1548:885-92

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;363:239-
51

Mayfield JA, Reiber GE, Sanders LJ, et al Preventive foot care in people
with diabetes Diabetes Care 1998;2112:2161-77

McConnochie KM, Russo MJ, McBride JT, et al Socioeconomic variation in
asthma hospitalization: excess utilization or greater need? Pediatrics
1999;1036:375

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;31921:1385-91

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;184:483-9

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

OCampo P, Xue X, Wang MC, et al Neighborhood risk factors for low
birthweight in Baltimore: a multilevel analysis Am J Public Health
1997;877:1113-8

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;239:1339-42

Pecoraro RE, Reiber BE, Burgess EM Pathways to diabetic limb amputation
Basis of prevention Diabetes Care 1990;135:513-21

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;771:832-8

Ray NF, Thamer M, Fadillioglu B, et al Race, income, urbanicity, and
asthma hospitalization in California: a small area analysis Chest
1998;1135:1277-84

Reiber GE, Pecoraro RE, Koepsell TD Risk factors for amputation in
patients with diabetes mellitus A case-control study
Ann Intern Med
1992;1172:97-105

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;303:733-8

Rosenthal GE, Harper DL, Shah A, et al A regional evaluation of variation
in low-severity hospital admissions J Gen Intern Med 1997;127:416-22

Selby JV, Zhang D Risk factors for lower extremity amputation in persons
with diabetes Diabetes Care 1995;184:509-16

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;134:285-94

Trudeau ME, Solano-McGuire SM Evaluating the quality of COPD care Am J
Nurs 1999;993:47-50

Weinberg AD, Minaker KL Dehydration Evaluation and management in older
adults Council on Scientific Affairs, American Medical Association JAMA
1995;27419:1552-6

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;33422:1441-7

Weissman JS, Gatsonis C, Epstein
AM Rates of avoidable hospitalization by
insurance status in Massachusetts and Maryland JAMA 1992;268172388-94

Zoorob RJ, Hagen MD Guidelines on the care of diabetic nephropathy,
retinopathy and foot disease Am Fam Physician 1997;568:2021-8, 2033-4

Appendix A: Links

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

Prevention Quality Indicators Version 31 Documents and Software

Available at http://wwwqualityindicatorsahrqgov/pqi_downloadhtm

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://wwwqualityindicatorsahrqgov/winqi_downloadhtm

Additional Documents

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

http://wwwqualityindicatorsahrqgov/downloadshtm

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://wwwqualityindicatorsahrqgov/documentationhtm

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 Existing
National Frameworks

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

http://wwwqualityindicatorsahrqgov/newsletterhtm

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://wwwcensusgov/popest/geographic/codes02pdf

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://hcupahrqgov/HCUPnetasp

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

http://wwwcdcgov/diabetes/statistics/indexhtm
———————–
[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://hcupahrqgov/HCUPnetasp 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 11: 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 AHRQs
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://www3mcom/us/healthcare/his/products/coding/refined_drgjhtml
[7]Billings J, Zeitel L, Lukomnik J, et al Impact of socioeconomic status
on hospital use in New York
City, Health Aff Millwood 1993;121:162-73
[8]Weissman, JS, Gatsonis C, Epstein AM Rates of avoidable hospitalization
by insurance status in Massachusetts and Maryland JAMA 1992;26817:2388-
94
[9]Weissman JS, Gatsonis C, Epstein AM Rates of avoidable hospitalization
by insurance status in Massachusetts and Maryland JAMA 1992;268172388-
94
[10]Bindman AB, Grumbach K, Osmond D, et al Preventable hospitalizations
and access to health care JAMA 1995;2744:305-11
[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;134:285-94
[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;2744:305-11
[16] Culler
SD, Parchman ML, Przybylski M Factors related to potentially
preventable hospitalizations among the elderly Med Care 1998;366:804-17
[17] Blustein J, Hanson K, Shea S Preventable hospitalizations and
socioeconomic status Health Aff Millwood 1998;172:177-89
[18] Billings J, Anderson GM, Newman LS Recent findings on preventable
hospitalizations Health Aff Millwood 1996;153:239-49
[19] Billings J, Zeitel L, Lukomnik J, et al Impact of socioeconomic
status on hospital use in New York City Health Aff Millwood
1993;121:162-73
[20] Pappas G, Hadden WC, Kozak LJ, et al Potentially avoidable
hospitalizations: inequalities in rates between US socioeconomic groups Am
J Public Health 1997;875:811-6
[21] Weissman JS, Gatsonis C, Epstein AM Rates of avoidable
hospitalization by insurance status in Massachusetts and Maryland Jama
1992;26817:2388-94
[22] Komaromy M, Lurie N, Osmond D, et al Physician practice style and
rates of hospitalization for chronic medical conditions Med Care
1996;346:594-609
[23] Schreiber S, Zielinski T The meaning of ambulatory care sensitive
admissions: urban and rural perspectives J Rural Health 1997;134:276-84
[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;392:123-8
[26] Epstein A The role of the medical market in preventable
hospitalizations Abstract Book/Association of Health Services Research
1998;15316-7
[27] Gill JM, Mainous AG, 3rd The role of provider continuity in
preventing hospitalizations Arch Fam Med 1998;74:352-7
[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;9210:989-98
[30] Gill JM Can hospitalizations be avoided by having a regular source of
care? Fam Med 1997;293:166-71
[31] Parchman ML, Culler SD Preventable hospitalizations in primary care
shortage areas An analysis of vulnerable Medicare beneficiaries Arch Fam
Med 1999;86:487-91
[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;312:191-211
[33]Bagg W, Sathu A, Streat S, et al Diabetic ketoacidosis in adults at
Auckland Hospital, 1988-1996 Aust N Z J Med 1998;285:604-8
[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;184:483-9
[35]Bindman AB, Grumbach K, Osmond D, et al Preventable hospitalizations
and access to health care JAMA 1995;2744:305-11
[36]Weissman JS, Gatsonis C, Epstein AM Rates of avoidable hospitalization
by insurance status in Massachusetts and Maryland JAMA 1992;268172388-
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;3317:444-9
[40]Braveman et al, 1994
[41]Bratton SL, Haberkern CM, Waldhausen JH Acute appendicitis risks of
complications: age and Medicaid insurance Pediatrics
2000;1061 Pt 1:75-
8
[42]Blumberg MS, Juhn PI Insurance and the risk of ruptured appendix
[letter; comment] N Engl J Med 1995;3326:395-6; discussion 397-8
[43]Weissman JS, Gatsonis C, Epstein AM Rates of avoidable hospitalization
by insurance status in Massachusetts and Maryland JAMA 1992;268172388-
94
[44]Gaster B, Hirsch IB The effects of improved glycemic control on
complications in type 2 diabetes Arch Intern Med 1998;1582:134-40
[45]Zoorob RJ, Hagen MD Guidelines on the care of diabetic nephropathy,
retinopathy and foot disease Am Fam Physician 1997;568:2021-8, 2033-4
[46]Hiss RG Barriers to care in non-insulin-dependent diabetes mellitus
The Michigan Experience Ann Intern Med 1996;1241 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;1164:1046-62
[49]Feinleib M, Rosenberg HM, Collins JG, et al Trends in COPD morbidity
and mortality in the United States Am Rev Respir Dis 1989;1403 pt 2:S9-
18
[50]Bindman AB, Grumbach K, Osmond D, et al Preventable hospitalizations
and access to health care JAMA
1995;2744305-11
[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;33422:1441-7
[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;172:177-89
[55]Weissman JS, Gatsonis C, Epstein AM Rates of avoidable hospitalization
by insurance status in Massachusetts and Maryland JAMA 1992;2681:2388-
94
[56]Bindman AB, Grumback K, Osmond D, et al Preventable hospitalizations
and access to health care JAMA 1995;2744:305-11
[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;121:162-73
[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;302:518-26
[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;303:733-8
[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;771:832-8
[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;127:416-22
[67]Healthy People 2010 Office of Disease Prevention and
Health Promotion,
US 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;925:814-22
[69]OCampo P, Xue X, Wang MC, et al Neighborhood risk factors for low
birthweight in Baltimore: a multilevel analysis Am J Public Health
1997;877:1113-8
[70]Hessol, et al 1998
[71]OCampo, 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;31921:1385-91
[74]Weinberg AD, Minaker KL Dehydration Evaluation and management in
older adults Council on Scientific Affairs, American Medical Association
JAMA 1995;27419:1552-6
[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;1363:296-307
[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;268172388-94
[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 Health
Care Services Washington, DC: National Academy Press 1993
[83]Weissman JS, Gatsonis C, Epstein AM Rates of avoidable hospitalization
by insurance status in Massachusetts and Maryland JAMA 1992;268172388-
94
[84]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;341:314] J Am Coll Cardiol 1999;337:2092-197
[85]Blustein J, Hanson K, Shea S Preventable hospitalizations and
socioeconomic status Health Aff Millwood 1998;172:177-89
[86]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;363:970-1062
[87]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
[88]Millman M, editor Committee on Monitoring Access to Personal Health
Care Services Washington, DC: National Academy Press 1993
[89]Healthy People 2010, Office of Disease Prevention and Health Promotion
US Department of Health and Human Services
[90]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
[91]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
[92]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; 1581:126-32
[93]Donahue JG, Weiss ST, Livingston JM, et al Inhaled steroids and the
risk of hospitalization for asthma JAMA 1997;27711:887-91
[94]Ray NF, Thamer M, Fadillioglu B, et al Race, income, urbanicity, and
asthma hospitalization in California: a small area analysis Chest
1998;1135:1277-84
[95]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
[96]Millman M, editor Committee on Monitoring Access to Personal Health
Care Services Washington, DC: National Academy Press 1993
[97]Weissman JS, Gatsonis C, Epstein AM Rates of avoidable hospitalization
by insurance status in Massachusetts and Maryland JAMA 1992;268172388-
94
[98]Mayfield JA, Reiber GE, Sanders LJ, et al Preventive foot care in
people with
diabetes Diabetes Care 1998;2112:2161-77
[99]Pecoraro RE, Reiber BE, Burgess EM Pathways to diabetic limb
amputation Basis of prevention Diabetes Care 1990;135:513-21
[100]Healthy People 2010, Office of Disease Prevention and Health
Promotion US Department of Health and Human Services
[101]Centers for Disease Control and Prevention CDC National Diabetes
Fact Sheet: National Estimates and General Information on Diabetes in the
United States Atlanta, GA: US Department of Health and Human Services,
1999
[102]Pecoraro et al 1990
[103]Mayfield et al 1998
[104]Selby JV, Zhang D Risk factors for lower extremity amputation in
persons with diabetes Diabetes Care 1995;184:509-16
[105]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;239:1339-42
[106]Reiber GE, Pecoraro RE, Koepsell TD Risk factors for amputation in
patients with diabetes mellitus A case-control study Ann Intern Med
1992;1172:97-105

———————–

The programs for the Prevention Quality Indicators PQIs can be downloaded
from
http://wwwqualityindicatorsahrqgov/pqi_downloadhtm Instructions
on how to use the programs to calculate the PQI rates are contained in the
companion text, Prevention Quality Indicators: SAS Software Documentation
or AHRQ QI Windows Application Documentation

Source:aiha.com

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