Develop Your Own?

[]{#contents .anchor}Contents

[[Before You Begin]{.underline}](#before)

-   [[Have You Identified the Significant Health Problems of Your Target
    Population(s)?]{.underline}](#targetpop)

-   [[Do Measures Already Exist that Accurately Assess Quality in Your
    Area of Interest?]{.underline}](#measuresexist)

-   [[Have You Clearly Defined the Anticipated Uses of the Resulting
    Quality Information?]{.underline}](#anticipated)

-   [[Are There Other Methods of Obtaining Comparable
    Information?]{.underline}](#comparable)

-   [[Have You Examined Your Assumptions About
    Causality?]{.underline}](#causality)

[[Developing Detailed Specifications]{.underline}](#specs)\
[[Data Issues to Keep in Mind]{.underline}](#data)

-   [[Push the Envelope---But Not Too
    Hard]{.underline}](#push-the-envelopebut-not-too-hard)

-   [[Learn and Use the \"Typical\" Data Sources]{.underline}](#typical)

-   [[Consider Using Encounter or Claims Data---Very
    Carefully]{.underline}](#encounter)

[[Sampling]{.underline}](#sampling)\
[[Reliability and Validity]{.underline}](#reliability)\
[[Field Testing]{.underline}](#field)\
[[An Alternative to Consider: Customizing Existing
Measures]{.underline}](#alternative)\
[[State-Designed and State-Negotiated Quality
Measures]{.underline}](#performance)\
[[Want More Information?]{.underline}](#moreinfo)

Existing measurement sets assess a limited number of factors. There may
be health conditions, health services, or specific population subgroups
of great interest for which established measures are lacking.

*Examples*: A State may want to:

-   Measure whether its child population is receiving lead screening.

-   Assess whether enrolled children receive service for an urgent
    medical condition within a set time frame.

-   Assess services received by children who are severely overweight.

Where established measures are lacking, States may wish to develop their
own quality measures. Additionally, Title V Maternal and Child Health
Programs are required to develop their own \"State-negotiated\" quality
measures as part of their Title V Maternal and Child Health Block Grant
responsibilities.

This section:

-   Discusses questions to consider Before You Begin as you contemplate
    developing your own measure.

-   Describes some key characteristics of quality measures and the
    measurement development process, including:

    -   Specifications.

    -   Data Issues.

    -   Sampling.

    -   Reliability and Validity.

    -   Field Testing.

-   Discusses Customization of Existing Measures as a possible strategy.

-   Provides some examples.

**Online Resources:**

For more information on existing measurement sets, go to:
[[http://www.ahrq.gov/chtoolbx/measures.htm#measures]{.underline}](http://www.ahrq.gov/chtoolbx/measures.htm#measures)

For more information on Title V Maternal and Child Health Programs, go
to:
[[http://www.ahrq.gov/chtoolbx/measure5.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measure5.htm)

For more information on \"State-negotiated\" quality measures, go to:
[[http://www.ahrq.gov/chtoolbx/measure8.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measure8.htm)

[[]{#before .anchor}Return to Contents](#contents)
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# Before You Begin

The process of developing quality measures is time consuming and
resource intensive. The goal is to create accurate measures that can
withstand critical scrutiny. Quality measurement strives for results
that cannot be called into question because of unclear definitions,
ambiguous procedural specifications, or flawed assumptions concerning
whether and how the involved health care-providing entity affects the
factor being measured.

Development of measures that produce such solid results is a multiyear
venture, requiring:

-   Technical expertise.

-   Senior-level oversight and championing.

-   Explanation to and involvement of multiple stakeholders.

-   Significant funding.

Before quality measures are ready for use, the following are required:

-   Careful planning.

-   Expert input.

-   Rigorous testing.

Undertaking to develop your own quality measure is a significant
venture. As you contemplate developing your own quality measure, you may
wish to consider the following questions:

-   [[Have You Identified the Significant Health Problems of Your Target
    Population(s)?]{.underline}](#targetpop)

-   [[Do Measures Already Exist that Accurately Assess Quality in Your
    Area of Interest?]{.underline}](#measuresexist)

-   [[Have You Clearly Defined the Anticipated Uses of the Resulting
    Quality Information?]{.underline}](#anticipated)

-   [[Are There Other Methods of Obtaining Comparable
    Information?]{.underline}](#comparable)

-   [[Have You Examined Your Assumptions About
    Causality?]{.underline}](#causality)

[[]{#targetpop .anchor}Return to Contents](#contents)
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Have You Identified the Significant Health Problems of Your Target
Population(s)?

Even if your overall objectives are already set, alternative quality
measures are possible. In general, the aspects of health care most
useful to measure:

-   Have a significant impact on the health of the relevant
    population(s).

-   Are important to key stakeholder groups.

-   Consume significant health care resources.

-   Offer clear opportunities for improvement.

[[]{#measuresexist .anchor}Return to Contents](#contents)
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Do Measures Already Exist that Accurately Assess Quality in Your Area of
Interest?

Have you:

-   Looked at existing quality measures in general use?

-   Searched AHRQ\'s National Quality Measures Clearinghouse™ (NQMC)
    compendium of quality measures?

-   Consulted with your peers?

-   Consulted with professional organizations?

-   Reviewed the literature?

If you are considering a survey of perceptions of care, have you:

-   Reviewed existing survey instruments?

-   Done focus groups with relevant populations?

-   Consulted with consumers and advocates?

**Online Resources:**

For more information on existing quality measures, go to:
[[http://www.ahrq.gov/chtoolbx/measures.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measures.htm)

For more information from the National Quality Measures Clearinghouse™
(NQMC) on 74 child health measures, go to:
[[http://www.qualitymeasures.ahrq.gov/]{.underline}](http://www.qualitymeasures.ahrq.gov/)

[[]{#anticipated .anchor}Return to Contents](#contents)
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Have You Clearly Defined the Anticipated Uses of the Resulting Quality
Information?

Will you use the results for:

-   Quality improvement?

-   Accountability?

-   Program management?

Clear and meaningful plans for the use of the resulting quality
information should be in place before any measure development begins.

**Online Resource:** For more information on Uses of Quality
Measurement, go to:
[[http://www.ahrq.gov/chtoolbx/uses.htm]{.underline}](http://www.ahrq.gov/chtoolbx/uses.htm)

[[]{#comparable .anchor}Return to Contents](#contents)
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## Are There Other Methods of Obtaining Comparable Information?

Alternative ways of getting comparable information using existing data
or information sources should be thoroughly explored prior to beginning
measure development.

[[]{#causality .anchor}Return to Contents](#contents)
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## Have You Examined Your Assumptions About Causality?

The concept of quality measurement is based on the assumption that there
is a meaningful connection between the operations of an identifiable
health care-providing entity and the health care results being measured.

*Example*: School absenteeism may be affected by child health programs,
but there are too many other contributing factors for absenteeism to
serve as a useful measure of child health program effectiveness.

Many factors affect child health, and many of them may be considerably
beyond the reach of any health care plan, program, or system.

[[]{#specs .anchor}Return to Contents](#contents)
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# Developing Detailed Specifications

Quality measurement tools are designed to produce accurate measures of
quality that can withstand critical scrutiny. Well-designed quality
measures generate information that cannot be called into question
because of:

-   Unclear definitions.

-   Ambiguous procedural specifications.

-   Flawed assumptions concerning connections between health care
    providers and the aspects of health care being measured.

In order to ensure such a result, quality measurement tools must include
clearly defined detailed terminology or specifications that are:

**Objective**. The definitions of terms are quantifiable and capable of
external verification.

**Standardized**. Definitions of data elements, data collection, and
data analyses are sufficiently precise and comprehensible that they can
be understood and applied in the same way regardless of who refers to or
applies it.

**Comprehensible**. The terminology can be understood without
specialized training or expert knowledge.

A quality measure is a relatively simple concept that describes a rate;
a rate has a numerator and a denominator. Successful quality measure
development requires that the specifications of these key data elements
(numerator and denominator) must also be:

-   Objective.

-   Standardized.

-   Comprehensible.

This can be complex. To illustrate, consider a quality measure that
assesses \"the percentage of children with an urgent medical condition
receiving care within a specific time frame.\" This quality measure
could be illustrated as follows:

The number of children with an urgent medical condition receiving care
within a specific time frame

The total number of children with an urgent medical condition

To calculate the quality measure, the numerator (above the line) is
divided by the denominator (under the line), thus generating the quality
measurement or rate.

The denominator of this measure is the \"total number of children with
an urgent medical condition.\" Some of the questions that must be
answered in order to define this denominator are:

**Ages Included**. What are the ages of the children who are included?

**Measurement Period**. What is the measurement period?

-   A calendar year?

-   A State fiscal year?

-   Some other period?

**Age Specification**. If, for example, children ages 1 through 5 are
defined as the denominator population:

-   What about children who turned 1 during the reporting year?

-   Children who turned 6?

-   Which are included?

-   Which are excluded?

**Systems or Units Assessed**. What service delivery systems or units of
analysis are you assessing?

-   One managed care organization (MCO)?

-   All MCOs?

-   A primary-care case management (PCCM) program?

-   Fee for service (FFS)?

-   Local, regional, or statewide?

-   Specific program(s)?

**Title V**. In assessing Title V programs:

-   How is the relevant child population defined?

-   What is the link between entities providing public health care and
    the resulting care?

**SCHIP or Medicaid**. In assessing the State Children\'s Health
Insurance Program (SCHIP) or Medicaid, can SCHIP or Medicaid children be
accurately identified?

**Definitions**. It is necessary to determine:

-   What definition of an \"urgent medical condition\" will be used.

-   What authority will be used in establishing this definition.

-   What experts or established clinical standards will be called upon.

**Methods**. In determining methods to be used:

-   What standardized method will be used to ensure that all of the
    children whose care is surveyed have an \"urgent\" level of medical
    need?

-   What codes will be used?

-   What data sources will be surveyed?

Once these questions have been resolved, the answers are translated into
a comprehensive description of the denominator. The goal is to describe
the measure specifications so precisely and in such detail that
reasonably knowledgeable persons across a wide variety of settings would
be able to understand and apply them in a consistent manner. Some
examples of specifications of existing measures may be of interest.

**Online Resource:** For more information on examples of specifications
of existing measures, go to:
[[http://www.ahrq.gov/chtoolbx/uses.htm]{.underline}](http://www.ahrq.gov/chtoolbx/uses.htm)

After the denominator is defined, the numerator must also be defined. In
the example given, the numerator is \"the number of children with an
urgent medical condition receiving care within a specific time frame.\"
The definitions of ages, of the populations to be used (plan or program
enrollment, type of service delivery system, etc.), and of \"urgent
medical condition\" will have already been agreed upon and defined, as
noted. However, further questions remain:

-   What is the appropriate time frame within which a child with an
    urgent medical condition should receive care?

-   What authority will be used in establishing this definition?

-   What experts or established clinical standards will be called upon?

-   What data sources will be used to get this information?

Once these questions have been answered, the core definitions for this
quality measurement will be in place.

In 2002, the Centers for Medicare & Medicaid Services issued a protocol
for calculating performance measures as part of overall guidance for the
mandatory external quality review of Medicaid MCOs. The protocol
describes a State Medicaid agency\'s activities related to the
specification, collection, and verification of performance measures.

**Online Resource:** Go to:
[[http://www.cms.hhs.gov/home/medicaid.asp]{.underline}](http://www.cms.hhs.gov/home/medicaid.asp).

[[]{#data .anchor}Return to Contents](#contents)
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# Data Issues to Keep in Mind

For many child health issues, it may be hard to identify data that are:

-   Readily available.

-   Comparable.

-   Complete.

-   Accurate.

For statistical reasons, the number of cases to be studied must also be
large enough that information on the quality of health care services
received by the study group can be reliably assumed to apply to others.

Quality measurement can make difficult demands on already limited data
sources. In the example of determining the percentage of children
receiving the urgent care they need within a given time frame,
calculation of the quality measure requires accurate data on the timing
of the receipt of care and on a variety of diagnoses.

Quality measurement is a data-driven activity. The feasibility of
calculating a quality measure depends heavily on the availability of
routinely collected data or on the resources to collect data that are
not readily available. Specification of the nature and sources of the
data to be used in any given quality measure and of how the data are to
be collected is important for achieving credible measurement results.

The following sections present some issues to consider, particularly if
you are going to develop your own data specifications and data
collection methods.

[Return to Contents](#contents)
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## Push the Envelope---But Not Too Hard

Data availability issues are central to any quality measure. On the one
hand, ambitious measures that rely on data sets that are fragmentary,
incomplete, inaccurate, or just plain nonexistent ensure that the
resulting quality measurement results will be flawed. On the other hand,
exclusive reliance on existing and readily available data may limit what
is possible and prevent progress. Therefore:

-   Include those in charge of data in your planning so they can see the
    potential of quality measurement.

-   Consult them about increasing data sources and improving data
    collection.

Because data completeness and accuracy are critical to all quality
measures, whether they are part of standardized measurement sets or
measures you develop yourself, data specialists are a key to success.

[[]{#typical .anchor}Return to Contents](#contents)
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## Learn and Use the \"Typical\" Data Sources

Most quality measures use one of the following:

-   Administrative data.

-   Medical records data.

-   Survey data.

**Administrative Data**. All health programs, plans, and systems
maintain administrative data. You should:

-   Understand what information these data contain.

-   Learn the limits as well as the strengths of the data.

-   Investigate whether the data are complete and accurate.

Because administrative data are regularly and routinely collected, their
use in calculating quality measures will lessen both the burden and the
cost. Encounter and claims data are examples of administrative data.

**Online Resource:** For more information on these types of data, go to
*Consider Using Encounter or Claims Data---Very Carefully* at
[[http://www.ahrq.gov/chtoolbx/develop#encounter]{.underline}](http://www.ahrq.gov/chtoolbx/develop.htm#encounter).

**Medical Records**. All providers maintain medical records with
detailed information on patient conditions and treatments, including
important information on preventive and well-care services. In many
settings much of that information is found only in medical records,
particularly where the information does not pertain to a procedure or
service that is individually billable.

In some cases, systems have been established to transfer some medical
records information into administrative databases, making it easier to
use for quality measurement. In most settings, medical records review is
a costly but necessary component of the calculation of selected quality
measures.

**Survey Data**. Survey data are as good as the procedures used to:

-   Develop the survey instrument.

-   Draw the sample.

-   Administer the survey.

Study the protocols developed for such public domain survey instruments
as Consumer Assessment of Health Plans CAHPS®, Experience of Care and
Health Outcomes Survey (ECHO™), Promoting Healthy Development Survey
(PHDS), and the Young Adult Health Care Survey (YAHCS) for models in
these areas.

**Online Resources:**

For more information on CAHPS®, go to:
[[http://www.ahrq.gov/chtoolbx/measure2.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measure2.htm)

For more information on ECHO™, go to:
<http://www.ahrq.gov/chtoolbx/measure9.htm>

For more information on  PHDS, go to:
[[http://www.ahrq.gov/chtoolbx/measure6.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measure6.htm)

For more information on  YAHCS, go to:
[[http://www.ahrq.gov/chtoolbx/measure7.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measure7.htm)

[[]{#encounter .anchor}Return to Contents](#contents)
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## Consider Using Encounter or Claims Data---Very Carefully

Encounter and claims data are varieties of administrative data. Some
States use encounter or claims data as the basis for the calculation of
Health Plan Employer Data and Information Set (HEDIS®) measures or for
other variations on established quality measures. The data are on hand
and are gathered continuously as a part of normal operations.

Before embracing these data sources for the purposes of quality
measurement, however, two caveats apply:

-   First, are the data complete? Claims data are likely to be complete
    for those services or procedures that are individually reimbursed or
    billable and less likely to be complete for services or procedures
    that are not individually reimbursed. Encounter data completeness
    also varies.

-   Second, are the data accurate and consistent? This can be especially
    problematic with encounter data, where such issues as inconsistent
    coding can pose significant challenges.

Some States have made significant investments in cleaning claims or
encounter data to ensure accuracy and completeness. Where such
precautions are taken and maintained, such data can be an important data
source for the calculation of quality measures.

In May 2002, CMS released a protocol for *Validating Performance
Measures* submitted by MCOs. Although intended primarily for Medicaid
use, these protocols offer useful examples to others concerning how to
ensure data completeness and accuracy.

**Online Resources:**

For more information on HEDIS®, go to:
[[http://www.ahrq.gov/chtoolbx/measure4.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measure4.htm)

Download the protocol for *Validating Performance Measures*. Go to:
[[http://www.cms.hhs.gov/home/medicaid.asp]{.underline}](http://www.cms.hhs.gov/home/medicaid.asp).

[[]{#sampling .anchor}Return to Contents](#contents)
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# Sampling  {#sampling-1}

In many cases, a sample of children is chosen for quality measurement
studies. Methods of choosing samples must be precisely described and
followed to avoid inadvertent bias that undermines the credibility of
the result.

*Example*: If data collection methods make it easier to submit
electronic data rather than data on paper, bias is introduced, and data
on paper may be underrepresented in the sample.

If sampling methods are not scrupulously designed so as to select a
sample that is representative of the entire population in question,
results could be questioned.

*Examples*:

-   If a sample of children with urgent medical conditions
    overrepresents children in urban areas, the results cannot be
    applied to children in rural areas.

-   If a sample is not large enough, the results cannot be generalized
    beyond the sample itself.

The small numbers of children with specific special health care
conditions make it particularly difficult to develop quality measures in
this area.

[[]{#reliability .anchor}Return to Contents](#contents)
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# Reliability and Validity

Quality measures must be tested for reliability and validity as a part
of the measurement development process.

**Reliability.** A \"reliable\" measure will give the same result every
time that it is applied to the same aspect of health care. This can be
compared to a thermometer, which will show the same temperature each
time it is used to measure the temperature in a particular location in a
living room if, in fact, the temperature has not changed.

**Validity.** A \"valid\" measure will be reliable and will also measure
the intended aspect. This can be compared to ensuring that the reading
on the thermometer measuring the average temperature in the living room
is not instead measuring the temperature of the corner next to the hot
fireplace.

Specific statistical tests exist to assess reliability and validity.
Such testing requires specific expertise. If in-house expertise is not
available, outside assistance is needed to ensure that these critical
steps in measure development are successfully completed.

[[]{#field .anchor}Return to Contents](#contents)
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# Field Testing

Field testing is another essential component of the measurement
development process. In field testing, a quality measure that appears to
be ready for general use is used in an actual setting on a trial basis.

*Example*: If the quality measure is intended for use in MCOs and PCCM
programs, volunteer MCOs and PCCM programs with experience in quality
measurement will be identified. They will put the measure to use and
report on how it works.

Field testing answers questions such as:

-   Can the denominator and numerator specifications be interpreted
    readily and consistently?

-   Are the anticipated data sources actually available?

-   Can the procedures for data collection be followed in a reasonable
    fashion?

-   Can quality measure data be analyzed and rates calculated using the
    recommended procedures?

[[]{#alternative .anchor}Return to Contents](#contents)
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An Alternative to Consider: Customizing Existing Measures

Because quality measurement development is demanding and resource
intensive, some States have found that customization of existing,
rigorous, field-tested quality measures is a better alternative. This is
particularly true for HEDIS® and CAHPS®, two measurement sets
characterized by methodological precision and rigor.

Some States have used existing HEDIS® measures as a starting point, then
customized specifications.

*Examples* of such customization are:

-   The age ranges to which a measure is applied.

-   The required length of \"continuous\" enrollment.

New York and Arizona have used customized HEDIS® measures as part of
their Quality Assurance Reporting Requirements, which include a measure
of lead screening. Other States, including Massachusetts, have expanded
the use of HEDIS® and/or CAHPS® measures beyond MCOs to collect and
report data for PCCM programs and fee-for-service Medicaid or to
calculate State averages. Still others have calculated HEDIS® results
using MCO-reported encounter data rather than the data sources specified
in the HEDIS® measure descriptions.

Such HEDIS® customization builds on the strengths of the HEDIS®
measurement development process. However, any changes in specifications
can compromise the comparability of the results. True comparisons of
results using such quasi-HEDIS® measures can only be made with other
results calculated using the same customized specifications.

*Example*: If all MCOs in a State use the same customized quasi-HEDIS®
measure, comparisons can be made, but only among MCOs within that State.

Further, if specifications are customized, it is essential that the same
precision and rigor be used in making the change that was used in
developing the original measure. If customized specifications introduce
vagueness or ambiguity, precision is lost and both comparability and the
reliability and validity of the results suffer.

The CAHPS® measurement set is designed to allow user flexibility:

-   CAHPS® includes pretested modules of supplemental questions on a
    wide variety of issues.

-   CAHPS® specifications indicate where such modules can be added.

-   CAHPS® also allows user-designed questions to be added in accordance
    with CAHPS® procedures.

Nonetheless, CAHPS® also includes specifications and protocols designed
to ensure reliability, validity, and comparability. Customization of
CAHPS® must be done with care in order to avoid compromising the
interpretation of results. Keep in mind:

-   The sequencing of questions has been carefully structured and tested
    to ensure that the order of questions does not in itself bias
    responses.

-   The Child Core set itself cannot be altered without compromising
    comparability.

-   CAHPS® supplemental modules must be used in accordance with CAHPS®
    specifications concerning where they should be inserted in order to
    avoid bias.

-   For the same reason, any user-designed questions must be added with
    care.

To avoid biasing results through faulty positioning or through the
addition of too many new items, user-designed items should be added only
in consultation with CAHPS® user support staff.

**Online Resources:**

For more information on HEDIS®, go to:
[[http://www.ahrq.gov/chtoolbx/measure4.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measure4.htm)

For more information on CAHPS®, go to:
[[http://www.ahrq.gov/chtoolbx/measure2.htm]{.underline}](http://www.ahrq.gov/chtoolbx/measure2.htm)

For more information on the Quality Assurance Reporting Requirements of
New York, go to:
[[http://www.ahrq.gov/chtoolbx/measure8.htm#ny]{.underline}](http://www.ahrq.gov/chtoolbx/measure8.htm#ny)

For more information on the Quality Assurance Reporting Requirements of
Arizona, go to:
[[http://www.ahrq.gov/chtoolbx/measure8.htm#az]{.underline}](http://www.ahrq.gov/chtoolbx/measure8.htm#az)

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State-Designed and State-Negotiated Quality Measures

Since 1996, Ohio\'s Department of Mental Health has been designing the
Mental Health Consumer Outcomes System, which includes a standard,
statewide approach to identifying and measuring consumer outcomes and
quality of Ohio\'s mental health systems. Created through a broad-based
effort including consumers, advocates, providers, and community mental
health boards, the system measures outcomes in the areas of: clinical
status, quality of life, functional status, and safety and health.

The measures are intended to have multiple uses, including: local and
State benchmarking; quality improvement by provider agencies, community
mental health boards, and Ohio\'s Department of Mental Health; and
accountability to stakeholders, including consumers and consumer
advocates.

**Online Resource:** For extensive information on the Ohio Mental Health
Consumer Outcomes System, go to:
<http://www.mh.state.oh.us/oper/outcomes/outcomes.index.html>.

Rhode Island\'s RIteCare program includes Medicaid and SCHIP. It is
administered by the Rhode Island Department of Human Services (DHS),
which has developed and applied its own quality measures. The Rhode
Island DHS has named these \"health indicators.\" In 2003, Rhode
Island\'s DHS issued a report monitoring trends in the diagnosis and
treatment of asthma.

**Online Resources:**

Download the report in PDF format (179 KB). Go to:
[[http://www.dhs.ri.gov/dhs/reports/ristats%20vol%202%20issue%203.pdf]{.underline}](http://www.dhs.ri.gov/dhs/reports/ristats vol 2 issue 3.pdf)

For a manual describing the process Rhode Island used, go to:
[[http://www.dhs.state.ri.us/dhs/reports/dhcresys.htm]{.underline}](http://www.dhs.state.ri.us/dhs/reports/dhcresys.htm).

The Title V Maternal and Child Health Program includes numerous examples
of State-negotiated quality measures.

**Online Resource:** For more information on such measures, look under
\"search\" at:
[[https://performance.hrsa.gov/mchb/mchreports/states_narratives/states_narrative.asp]{.underline}](https://performance.hrsa.gov/mchb/mchreports/states_narratives/states_narrative.asp)

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Want More Information?

For more information on related issues, you may want to review the
following article:

McGlynn EA, Asch SM. Developing a clinical performance measure. *Am J
Prev Med* 1998;14:14-21.\
Abstract available on PubMed®:\
[[http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=9566932&dopt=Abstract]{.underline}](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=9566932&dopt=Abstract)

[Return to Contents](#contents)
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