Research Opportunities After CHIPRA:
Using State Eligibility Data
to Inform Policy
Academy Health Meeting
Child Health Services Research Group
Christopher Trenholm
June 27, 2009
Agenda

Opportunities and Challenges of Using
State Eligibility Data for Research

Examples of How Research on State
Eligibility Data Can Inform Policy
Opportunities
and Challenges
What Are State Eligibility Data?

State file(s) containing child-specific record(s) of
coverage spell(s) in Medicaid and/or CHIP

Can typically link files to form a complete
coverage history for a given child in a program
and often between programs

Other data elements can form key covariates
– Typical: age, basis of eligibility, location (county, zip)
– Less typical: denied apps, disenrollment reasons
How Can Research Benefit From
State Eligibility Data?
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Measure outcomes central to coverage expansion
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–
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Enrollment: increased program entry?
Retention: longer program duration?
Transition: increased likelihood of transfer?
Cycling: reduced rates of program exit and reentry?
Examine links to key policies and procedures
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–
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Simplification/coordination measures
Citizenship requirements
Systems improvements
Outreach activities
What Are the Challenges Working
With State Eligibility Data?

Access is often not easy
– Low priority; limited support; formal restrictions
– Working collaboratively will help

Significant front-end resources required
– Files are not designed or organized for research

Quality can be a concern
– Anomalies may go unaddressed
– Files may be retroactively changed

Data for only one state limits both causal
inferences and generalizability
A (Near) Solution to Challenges:
MAX Files

Annual files created by merging quarterly
“MSIS files” submitted to CMS by each state
– standardized and validated (“easy” processing)

Can link to form a lengthy history of individual
kid’s Medicaid coverage in any state

31 states report CHIP data in MSIS; can form a
history of kid’s public coverage

Data include child’s start/end dates; eligibility
status; age; and county and zip code
MAX Files Have
Two Important Limitations

Timing: MAX data are currently lagged at least
three years

Breadth: CHIP data available for only 6 of 16
states with separate programs

BUT both issues may soon be addressed:
CHIPRA appropriates $5M for FY09 to “improve
the timeliness of data reported in MSIS for
purposes of providing more timely data on
eligibility and enrollment in Medicaid and CHIP”
Illustrative Research
Examining “Spillover” from
All-Kids Expansion

Santa Clara Children’s Health Initiative (CHI)
– Combined major (“Healthy Kids”) coverage expansion
with outreach aimed at insuring all kids in the county

Q: What impact did CHI expansion have on
enrollment in Medi-Cal & Healthy Families?
– Used CA eligibility data to estimate DD model on
enrollment for two years pre- and post- expansion
– Formed treatment group from county zip codes and
comparison group from matched CA codes
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Observation = new MC & HF enrollees per zip/quarter
Kids who cycle/transition excluded from count
Findings:
CHI Sharply Increased Enrollment
Exploring Outreach
“Best Practices”

Used MSIS data in close to 20 states to isolate
counties with “outlier levels” of new enrollment
during period of state CKF grant

Identified outliers by comparing the difference in
actual and predicted enrollment over time
– Could be point-in-time ‘spikes’, persistent differences

Conducted phone and site visit interviews to
identify source(s) of each outlier and the possible
link(s) to CKF and specific outreach activities
Findings:
Best Practices Are Hard to Find

Follow-up revealed no persistent evidence
linking outreach and local enrollment outliers
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Most evident links found for targeted, schoolbased outreach models (flyers don’t cut it)

Major shifts in local enrollments usually
paralleled statewide shifts, most of which
coincided with state policy change(s)
Measuring Effects of
State Policies and Procedures

Focused on 10 states that: had high quality MSIS
data (no unexplained anomolies); and had been
site visited for CKF evaluation case studies

Constructed indicators for the adoption of key
simplification policies across the ten states over a
seven year time period (1999-2005)

Estimated pooled (DD) and within state (ITS)
models to tease out effect of policies on changes
in new enrollment and retention rates
Findings:
Effective Simplification Easier to Find
Change in New Enrollment
Self-Dec Income1
Individual
Policy
10.4%**
All Four
Policies
7.7%**
Presumptive Elig1
4.7%**
5.4%**
No Face-to-Face1
3.9%*
Centralized Review
1 Policy
11.7%**
-0.2%
9.9%**
measure that counts toward CHIPRA performance bonus
**p-value < 0.01; * p-value <0.05
CHIPRA Requires Research
Into These Areas and More

Policymakers critically need information on what
outcomes to expect from CHIPRA changes

Failure to anticipate these outcomes has derailed
momentum to expand coverage in many states
– NJ: apps increase from parental coverage expansion
– KY: spillover from CHIP expansion, rebrand, outreach

Important directions for state eligibility research
– Performance bonus simplification conditions
– Outreach funding
– Citizenship documentation for CHIP