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D
Overview of Data Sources for
Measures of Health Care Quality
for Children and Adolescents
This appendix reviews sources of data on the quality of health care
services for children and adolescents, including both administrative data
sources (claims or claims and encounters) and population health surveys.
There are two key administrative data sources:
• the Medicaid Statistical Information System (MSIS), which con-
tains state-level claims and encounter data; and
• the Healthcare Effectiveness Data and Information System
(HEDIS©) data collection for managed care beneficiaries.
Administrative data, primarily from claims, are an important source of
information on how the system is performing. A bill is generated to obtain
reimbursement whenever a service is provided that requires payment. In
contrast to population health surveys, which provide household reports
and snapshots of the health of the population and experiences with care,
claims-based data tend to provide a more detailed picture of the services
received and costs of care for given diagnoses over time (as long as the
individual is enrolled in that system). Administrative data therefore serve
as a fundamental tool for monitoring the adequacy of care, although they
have significant limitations, as noted in an earlier chapter.
In addition to the above two administrative data sources, quality mea-
sures can be found in population health surveys, especially in the data
sets compiled by the National Health Information Survey (NHIS) for the
Centers for Disease Control and Prevention and three surveys conducted
by the Maternal and Child Health Bureau (MCHB): the National Survey
251
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252 CHILD AND ADOLESCENT HEALTH
of Children’s Health (NSCH), the National Survey of Children with Special
Health Care Needs (NS-CSHCN), and the National Survey of Early Child
Health (NSECH). The NSCH is the most far-reaching of the three MCHB
surveys in terms of the population covered, the sample size, and the topics
covered.
THE MEDICAID STATISTICAL INFORMATION SYSTEM
The MSIS is a national database of Medicaid claims and eligibility
data that is maintained by the Centers for Medicare and Medicaid Services
(CMS) and consists of an aggregation of individual state-level claims da-
tabases. Reporting by states to the MSIS is mandatory for state Medicaid
agencies. Thus, the MSIS contains data on all Medicaid children and Chil-
dren’s Health Insurance Program (CHIP) children who are part of Medicaid
expansions (although not children in separate CHIP programs). The state-
level data reported to CMS for the MSIS provide a base that is useful for
some measures, although it has some major limitations.
On the positive side, state-level files contain data on claims and encoun-
ters for services, which include data on health insurance, diagnoses, and the
services or procedures provided as core data elements. The records contain
a state-assigned unique personal identifier; this identifier can be used con-
sistently to identify a given individual across different years and different
enrollment periods, making it possible to track Medicaid beneficiaries over
time within that state (MacTaggart, 2010).
The major weakness of the state-level data reported to the MSIS lies
in its nature as a claims-based system. In most states, claims for services
rendered under Medicaid primary care case management and fee-for-service
care show a complete record of the services provided and generate a re-
imbursement for those services. However, contractors with managed care
organizations may receive a capitated payment for beneficiaries. In such
cases, their claims data may not necessarily describe the actual services
provided. Managed care organizations may submit encounters or “shadow
claims,” which, because they do not actually generate reimbursement, may
be incomplete. CMS has indicated it is working with states to improve
encounter data (MacTaggart, 2010). Furthermore, the Children’s Health In-
surance Program Reauthorization Act (CHIPRA) requires the Department
of Health and Human Services (HHS) to collect and analyze the MSIS data
from states within 6 months. MSIS data have not been used as a source
of reporting in the past (MacTaggart, 2010; Simpson et al., 2009), but
the new federal reporting requirements, combined with federal efforts to
improve state claims/encounter databases, may lead to more usable data in
those databases. In the interim, states may combine the use of their claims/
encounter databases with chart audits for a sample of children to report
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APPENDIX D
on quality measures, using an approach analogous to the HEDIS hybrid
methodology (see below).
A second weakness in the current MSIS database is the omission of
children who are enrolled in separate (non-Medicaid) CHIP programs. The
MSIS also does not include privately insured or uninsured children. Since
CHIPRA now requires states to compare the status of children and adoles-
cents served by public plans with that of the general population of children
and adolescents on a statewide basis, MSIS data can provide only a partial
picture of the services or outcomes of those who are enrolled in Medicaid
or Medicaid-expansion CHIP plans.
HEDIS
Currently, administrative data from the HEDIS collection of data from
managed care plans are a primary source of information at the state level
for reporting on current and new quality-of-care measures. It should be
noted that in a managed care environment, the state usually provides a ne-
gotiated payment to the managed care organizations (MCOs) for services,
and the MCOs pay the providers. In cases where the providers are paid on
a fee-for-service basis, claims data will exist. In cases where health plans
pay providers through a negotiated payment per member, there is no need
for claims, and providers instead generate shadow claims for the encounter.
Developed by the National Committee for Quality Assurance (NCQA),
HEDIS is a tool used by more than 90 percent of health plans to report
on quality (NCQA, 2010). In its annual State of Health Care Quality re-
port, NCQA releases detailed, plan-specific performance information for
both commercial and Medicaid plans. NCQA’s 2008 report for Medicaid
provided information on 52 measures of clinical quality (NCQA, 2008).
States also release their own reports. For example, Michigan releases an
annual report on its HEDIS results by MCO (MDCH, 2008). New York
has long issued annual report cards (Quality Assurance Reporting Require-
ment) on health plan performance on HEDIS as well as state-level measures
(NYDOH, 2010).
Many of the initial core measures published and posted for public
comment by the Secretary of HHS are HEDIS measures that health plans
currently use to report on quality. The measures on immunization, prenatal
care, chlamydia screening, and well-care visits are examples of the HEDIS
measures in the core set. This is not surprising given that the AHRQ
committee recommending measures and the CHIPRA legislation placed
a premium on measures that were grounded and in use. Further, because
claims data form the basis for HEDIS measures, these measures generally
are limited to whether a service has been delivered, rather than broader
care processes across episodes of care or outcomes. For example, there is a
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254 CHILD AND ADOLESCENT HEALTH
measure of whether chlamydia screening took place, but not whether ap-
propriate follow-up occurred if the result was abnormal.
HEDIS protocols for assessing measures specify either methods that
use administrative data alone or hybrid methods that combine the use of
administrative data with chart reviews for a sample of beneficiaries. These
HEDIS protocols form a strong base for CMS to use in guiding the states
on reporting, but there are important cautions. First, these measures were
designed to be used by managed care plans, and the protocol includes
features designed to ensure that members are “continuously” enrolled in
health plans long enough to benefit from their quality improvement policies
(frequently 11 out of 12 months, but the “continuous enrollment” period
can be longer for some measures). As a result, Medicaid children who are
not enrolled in a managed care plan for the required amount of time are
omitted from the measurement results, even if they have been registered in
Medicaid for the designated period. As an example, HEDIS specifications
for reporting immunization coverage specify that only children enrolled for
11 or more of the prior 12 months be included in the reporting denomina-
tor (NCQA, 1996). In one study, fewer than half of all enrolled Medicaid
children (39 percent) were included in the health plan denominator in the
12 state studies, although most (78 percent) had been on Medicaid for the
required length of time (Fairbrother et al., 2004). This problem becomes
more acute as the continuous enrollment periods increase (asthma measure-
ment, for example, requires 2 years of continuous enrollment).
A second problem is that data are not reported in a standardized
manner (Partridge, 2007). Thus, although almost 90 percent of Medicaid
programs and 100 percent of CHIP programs reported using HEDIS access
and effectiveness measures related to child health in 2009, the data may
not be comparable across states (Smith et al., 2009). Standard definitions
frequently are not used, with states modifying HEDIS definitions to ac-
commodate a Medicaid population with shorter coverage spells, as well
as other local concerns (Partridge, 2007). For example, although the 1997
State Children’s Health Insurance Program (SCHIP) statute required each
state to file an annual report—including the state objectives for SCHIP, the
performance measures used, and progress that year toward meeting the
objectives—it did not specify exactly how measures were to be reported.
A review of state reports in 2005 on four HEDIS measures showed great
variation in the number of states that reported on the measures, from a
high of 34 to a low of 10 (Partridge, 2007). Furthermore, states modified
the HEDIS specifications to accommodate their priorities, so that even
though states reported on the same measures, the data were not strictly
comparable. The reviewers concluded that comparable data were sufficient
to build a national SCHIP database and generate national averages for two
of the four measures (Partridge, 2007). This issue of the level of compara-
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APPENDIX D
bility will need to be addressed in developing the reporting format required
by CHIPRA.
The HEDIS protocols are an important starting point for measure-
ment under CHIPRA. But the measures will need to be respecified to be
appropriate for the entire Medicaid and CHIP population through inclusion
of a denominator that addresses enrollment in these two programs. And
with the emphasis in the Affordable Care Act (ACA) on all populations of
children, measures may need to be respecified again to include all children,
regardless of payer.
NATIONAL SURVEY OF CHILDREN’S HEALTH
The NSCH is a nationally representative household survey of children
aged 0−17 that includes state-level estimates. It has been administered twice
(in 2003 and 2007); a third fielding is planned for 2011 that is expected
to include additional items on child well-being/thriving, health insurance
and access to care, and items relevant to life-course research. The third
wave of survey data may also include nearest cross-street information to
enhance the geocoded linking of these data to other neighborhood-level
data systems.
The NSCH represents responses of parents/guardians of a randomly
selected child in each household. Survey questions encompass child health
status and health conditions, health insurance and medical home, parental
health, school engagement, media exposure, youth activities, and neigh-
borhood conditions. The NSCH produces estimates for numerous demo-
graphic, socioeconomic, and health status subgroups of children, including
whether their health insurance coverage is public or private, whether they
have special health care needs, their race/ethnicity, their primary language,
whether they are foreign born or adopted, the immigration status of their
parents, their household income, and the household’s use of public assis-
tance. NSCH national and state-level findings for numerous subgroups are
posted at www.childhealthdata.org.
The NSCH includes multiple patient-centered categories of data rele-
vant to the measurement of health care quality for children and adolescents
(these data components are in addition to measures of physical and dental
health, mental and emotional health, health insurance coverage, and other
topics relevant to the child’s physical and social environments). The cat-
egories include preventive medical care visits, preventive dental care visits,
getting needed mental health care, one or more unmet needs for care, medi-
cal home, personal doctor or nurse, usual sources for sick and well care,
family-centered care, problems in obtaining needed referrals, effective care
coordination, access to specialty care or services, receipt of care from spe-
cialist doctor, doctor asks about concerns, and developmental screenings.
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256 CHILD AND ADOLESCENT HEALTH
NATIONAL HEALTH INTERVIEW SURVEY
As described in Chapter 4, the NHIS is an annual household survey
conducted by the National Center for Health Statistics that collects in-
formation on all household members, including children and adolescents.
NHIS data provide the basis for the AHRQ reports on health care dispari-
ties, indicating how many children and adolescents have access to health
care coverage, as well as a specific source of usual health care, and how
many children and adolescents rely on hospital-based services (such as out-
patient or emergency departments) for usual or ongoing care. NHIS data
also are used in identifying sources of health care disparities, especially in
areas that involve access to care or treatment for conditions such as asthma
and mental and emotional disorders.
RESOURCES FOR DATA ANALYSIS AND LINKAGE
This section describes four key resources for data analysis and linkage:
• the databases and tools that are part of the AHRQ Healthcare Cost
and Utilization Project (HCUP);
• the application forms for public insurance, which contain demo-
graphic information on Medicaid and CHIP beneficiaries;
• the Physician Quality Reporting Initiative (PQRI); and
• examples of state-based data warehouse capacities that foster link-
age across multiple database systems.
HCUP Databases and Tools
The HCUP databases, supported by AHRQ, represent the largest col-
lection of multiyear, all-payer hospital and emergency room discharge data
that can be applied to hospital claims to assess safety events, ambulatory
care−sensitive hospitalizations, and other measures of potential interest.
More than 40 states provide data as part of the project, collectively repre-
senting more than 95 percent of all discharges (AHRQ, 2010). The HCUP
databases are constructed using a core set of clinical and nonclinical details
found in a typical discharge claim for hospitals and emergency rooms,
including data on primary and secondary diagnoses and procedures, ad-
mission source and discharge disposition, patient demographics, expected
payment source, total charges, length of stay, and hospital characteristics.
From this core set of discharge information, several subsets of data can
be extracted to create inpatient, ambulatory care, emergency care, and
child-specific databases, as shown in Table D-1. Each database in turn can
be used to examine quality of care with the AHRQ quality indicators, to
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APPENDIX D
TABLE D-1 HCUP Databases
Year Years Number of Number of
Started Available States Hospitals
National
Nationwide Inpatient 1988 Yearly 42 in 2008 1,056 in 2008
Sample (NIS)
Kids’ Inpatient Database 1997 1997, 2000, 38 in 2006 3,739 in 2006
(KID) 2003, 2006
Nationwide Emergency 2006 Yearly 27 in 2007 966 in 2007
Department Sample
(NEDS)
State
State Inpatient Databases 1990 Yearly 40
(SID)
State Ambulatory Surgery 1997 Yearly 28
Databases (SASD)
State Emergency 1999 Yearly 27
Department Databases
(SEDD)
aggregate data using clinical classification codes (the International Clas-
sification of Diseases [ICD]-9-CM and ICD-10 codes), and to identify and
measure coexisting conditions using Comorbidity Software (see Table 2 in
Fairbrother et al., 2010).
HCUP also includes software tools and indicators with which to mea-
sure quality (see Table D-2). AHRQ first developed three indicator sets:
the Inpatient Quality Indicators (IQI), for the quality of care received in
hospitals; the Prevention Quality Indicators (PQI), for potentially prevent-
able hospital admissions; and the Patient Safety Indicators (PSI), for pre-
ventable complications of care. These measures were constructed based on
adult health issues, complications, chronic conditions, and patterns of care
and were not adequate to address the complexity of child and adolescent
health care needs. Responding to this gap, AHRQ developed a fourth set of
indicators focused on the safety and quality of pediatric hospital care—the
Pediatric Quality Indicators (PDIs) (see Table D-3). These indicators focus
on potentially preventable complications arising from inpatient care and on
preventable hospitalizations for pediatric patients. This software could be
used, for example, in calculating pediatric catheter-associated blood stream
infection rates, one of the initial AHRQ core measures, using a state’s in-
patient database.
While the HCUP tools and indicators provide important ways to
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258 CHILD AND ADOLESCENT HEALTH
TABLE D-2 HCUP Software Tools and Indicators
Clinical Classification Systems (CCSs)
CCS for ICD-9-CM Provides a means of classifying ICD-9-CM diagnoses or
procedures into clinically meaningful categories, which
can be used for aggregate statistical reporting.
CCS for ICD-10 Provides a means of classifying ICD-10 diagnoses into
clinically meaningful categories. It will be used in 2012
when the tenth revision of the ICD codes is implemented.
CCS-MHSA for Mental Defines mental health variables that identify general
Health and Substance categories for MHSA diagnoses. Beginning in 2008, the
Abuse CCS-MHSA was permanently integrated into the CCS
tool and is no longer stand-alone.
CCS Tools
Chronic Condition Allows for categorizing conditions as chronic or not
Indicators chronic.
Comorbidity Software Assigns variables that identify coexisting conditions on
hospital discharge records.
Procedure Classes Allow for categorizing procedure codes as minor
diagnostic, minor therapeutic, major diagnostic, and
major therapeutic.
Utilization Flags Provide a means of assessing use of procedures or
services, such as intensive care unit (ICU), critical care
unit (CCU), neonatal intensive care unit (NICU), and
specific diagnostic tests and therapies.
Supplemental Files
Cost-to-Charge Ratio Supplements the data elements in the HCUP Nationwide
Inpatient Sample (NIS) and State Inpatient Databases
(SID) and permits conversion of hospital total charge data
to cost estimates.
Hospital Market Structure Hospital-level files designed to supplement the data
elements in NIS, the Kids’ Inpatient Database (KID), and
SID.
AHRQ Quality Indicators (QIs)
Prevention Quality Identify hospital admissions that evidence suggests could
Indicators have been avoided.
Inpatient Quality Used for quality of care inside the hospital.
Indicators
Patient Safety Quality Used for quality of care inside the hospital as well as
Indicators potentially avoidable complications.
Pediatric Quality Used for quality of care inside the hospital as well as
Indicators potentially avoidable complications for children (under
age 18).
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APPENDIX D
TABLE D-3 Pediatric Quality Indicators (PDIs)
Provider-Level Indicators
Accidental Puncture or Cases of technical difficulty (e.g., accidental cut
Laceration or laceration during procedure) per 1,000 eligible
discharges
Decubitus Ulcer Number of patients with decubitus ulcer per 1,000
eligible admissions
Foreign Body Left in During Number of patients with a foreign body left in
Procedure during a procedure per 1,000 eligible admissions
Iatrogenic Pneumothorax (in Number of patients with iatrogenic pneumothorax
Neonates at Risk) per 1,000 eligible admissions
Iatrogenic Pneumothorax (in Number of patients with iatrogenic pneumothorax
Non-Neonates) per 1,000 eligible admissions
Postoperative Hemorrhage and Number of patients with postoperative hemorrhage
Hematoma or hematoma requiring a procedure per 1,000
eligible admissions
Postoperative Respiratory Number of patients with respiratory failure per
Failure 1,000 eligible admissions
Postoperative Sepsis Number of patients with sepsis per 1,000 eligible
admissions
Postoperative Wound Number of abdominopelvic surgery patients with
Dehiscence disruption of abdominal wall per 1,000 eligible
admissions
Selected Infection Due to Number of patients with specific infection codes per
Medical Care 1,000 eligible admissions
Transfusion Reaction Number of patients with transfusion reaction per
1,000 eligible admissions
Pediatric Heart Surgery Number of in-hospital deaths in patients undergoing
Mortality Rate surgery for congenital heart disease per 1,000
patients
Pediatric Heart Surgery Volume Number of patients undergoing surgery for
Rate congenital heart disease
Area-Level Indicators
Asthma Admission Rate Number of patients admitted for asthma per
100,000 population
Diabetes Short-Term Number of patients admitted for short-
Complications Admissions term complications of diabetes (ketoacidosis,
Rate hyperosmolarity, coma) per 100,000 population
Gastroenteritis Admission Rate Number of patients admitted for gastroenteritis per
100,000 population
Perforated Appendix Admission Number of patients admitted for perforated
Rate appendix per 100 admissions for appendicitis within
an area
Urinary Tract Infection Number of patients admitted for urinary tract
Admission Rate infection per 100,000 population
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260 CHILD AND ADOLESCENT HEALTH
measure the quality of care in hospital and emergency room settings, their
capacity to measure disparities is limited: more than a quarter of the claims
for children do not indicate race/ethnicity (HCUP, 2006). Moreover, the
nature of the disparities varies with each measure. Finally, even though the
measures reflect the most prominent safety issues, the prevalence of these
complications is relative low, limiting the types of analysis that can be per-
formed. Another issue with HCUP is that income data are at the community
and not the individual level.
Application Forms for Public Insurance
Application forms for public insurance (Medicaid and CHIP) are a
source of demographic information because they ask parents about their
child’s or adolescent’s race, ethnicity, age, gender, income, and in some
cases language. A validation study conducted in New York comparing
race and ethnicity information collected from applications with informa-
tion collected directly from parents as part of the Consumer Assessment of
Healthcare Providers and Systems (CAHPS) surveys showed high levels of
concordance between the two for all races and ethnicities (Fairbrother and
Simpson, 2010).
Some states, such as New York, Kentucky, and Georgia, have the ca-
pacity to link demographic information from the application forms with
claims-based data. This approach enables these states to monitor services
and outcomes by selected demographic characteristics that are included
on the application form and thus monitor disparities by race, ethnicity,
language (if collected), and income. The federal MSIS data set also links
claims/encounters data to demographic data from encounters, thus creating
the potential to monitor race, ethnicity, language, and income disparities at
the federal level. However, the ability to monitor disparities at the national
level is restricted because the states do not collect their demographic enroll-
ment data in a systematic manner. A review of application forms from all
states (Fairbrother and Simpson, 2010) shows that only 18 states ask for
“Hispanic/Latino” ethnicity as a separate category, while 19 states merge
ethnicity with racial categories. Of these, 7 allow the applicant to choose
more than one “race”; hence, an individual could select both “black” and
“Hispanic” in these states but not in the others. Eight states have no race/
ethnicity categories, but leave a blank for applicants to fill in. With respect
to primary language, 14 states ask for “English,” “Spanish,” and “other”
or list specific languages. However, 21 states have only a blank for ap-
plicants to fill in with their primary language. The design of application
forms has been left to the states in the past; with the new emphasis on
monitoring disparities at both the federal and state levels, standardization
will be necessary.
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261
APPENDIX D
Physician Quality Report Initiative
The Medicare PQRI is a quality reporting system that supports incen-
tive payments for eligible professionals who report data on quality mea-
sures based on parameters established by CMS. The American Recovery
and Reinvestment Act (ARRA) Health Information Technology for Eco-
nomic and Clinical Health (HITECH) legislation significantly expanded
the significance of the PQRI and the PQRI registry, which now incorporate
providers who serve patients enrolled in Medicaid and CHIP plans as well
as Medicare. Most of the 179 quality measures in the 2010 PQRI system
are specified for adults. However, a significant number of measures are
designed explicitly for children (especially those associated with the treat-
ment of asthma, ear infections, childhood cancers, pediatric end-stage renal
disease, and HIV/AIDS). Other measures include children and adolescents
in the denominator, but the measurement age breaks limit the feasibility of
determining how many children are included in certain data sets.
The specifications for the quality measures under PQRI provide the
details for the numerator and denominator and therefore support analyses
of the percentage of a defined patient population that receives a particular
process of care or achieves a particular outcome. For example, PQRI mea-
sure 65 focuses on the avoidance of inappropriate use of antibiotic treat-
ment for children with upper respiratory infections.
Examples of Data Warehouses and State-based Linkage Activities
Although states vary in their capabilities to collect, store, and analyze
data, some states, such as New York, Georgia, and Kentucky, have strong
warehousing capabilities, including in some cases the ability to link state
databases. New York, for example, collects member-level data reported by
Medicaid managed care plans (for all members) as part of annual HEDIS
reporting and has created linkages of quality measurement results with
eligibility files and CAHPS surveys. The resulting linked data set is orga-
nized at the person level, and includes demographic and service delivery
information for Medicaid members in each measure. The resulting data
warehouses can be used to monitor quality on a variety of measures and
to display results by race/ethnicity, age, gender, and geography, making it
possible to monitor performance for the population as a whole and for
vulnerable groups.
Furthermore, some states have linked health data sets, giving them the
ability to monitor over time and across settings. For example, New York
has a linked data set consisting of childbirth and fetal death certificates,
maternal and child hospital discharges, and Medicaid claims before and
after the birth. Using this linked data set, New York can relate, for example,
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262 CHILD AND ADOLESCENT HEALTH
aspects of prenatal care to subsequent outcomes and health behaviors.
Linking data across time can also make it possible to monitor important
aspects of chronic care, such as whether a child has filled all prescriptions
for medications needed to treat specific conditions, whether there are du-
plicative or overlapping medications in a regimen, or whether a rehospital-
ization occurred.
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