The Use of Medicaid Claims Data to
Describe Patterns of Antipsychotic
Prescribing in U.S. Nursing Homes
Academy Health
June 28, 2009
Sam Simon, Ph.D.
Mathematica Policy Research, Inc.
1
Collaborators
 Mathematica Policy Research
– James Verdier, JD
– Christal Stone, MPH
 SAMHSA
– Jeffrey Buck, Ph.D
– Judith Teich, MSW
– Ken Thompson, MD
 Advisors
– Constantine Lyketsos, MD
– Jules Rosen, MD
– Joel Streim, MD
2
Background: Antipsychotic Use in Nursing Homes
 Documented link between adverse events and
antipsychotic use among nursing home residents
– Rochon, Normand, Gomes et al., 2008; Ray, Chung,
Murray et al., 2009
 Antipsychotic use in nursing homes is rising
– About 18% when OBRA implemented to 26% in 2008
 Although most instances of antipsychotic use in
nursing homes may be clinically indicated, some
prescribing is likely to be questionable
– Rates of antipsychotic prescribing vary widely across
facilities, regardless of clinical conditions (Rochon et
al. 2007)
3
Study Goals

Identify and implement definitions of
potentially inappropriate prescribing of
antipsychotic medication in Medicaid claims
data

Document the frequency of potentially
inappropriate prescribing practices

Identify facility characteristics associated with
multiple questionable prescribing practices
4
Data

CY 2002 Medicaid Analytic eXtract (MAX) data
– Links information on Medicaid-covered services for
nursing home care, prescription drugs and acute
care hospitalization
– Rx data includes information about drug type,
dosage, frequency and route of administration

2002 Online Survey Certification and Reporting
(OSCAR) data
– Facility-level variables for Medicare/Medicaid
certified nursing homes
5
Sample

Excluded data from NC and AZ due to data
problems

Excluded facilities < 20 residents to remove
hospital-based nursing wings or units

986,490 nursing home residents (13,603
facilities) with 6 months or longer continuous
nursing home residence during 2002
6
Criteria for Indicator Inclusion

Publication in peer-reviewed literature or
government-issued guidance document

Feasibility for use with Medicaid claims data
(behavioral symptoms are not recorded in
claims data, for example)
7
Indicators of Potentially Inappropriate
Antipsychotic Use
1. Antipsychotic use without appropriate diagnosis
–
Adaptation of CMS’s Quality Indicator (designed for
use with the Minimum Data Set)
2. Excessive antipsychotic average daily dose
–
Incorporates population specific thresholds from CMS
guidance to nursing home surveyors for 14
antipsychotic medications
3. Antipsychotic use with dementia diagnosis
–
Based on growing body of literature and FDA warnings
linking antipsychotic use with serious adverse events
for patients with dementia
8
Indicators of Potentially Inappropriate
Antipsychotic Use (continued)
4.
Antipsychotic polypharmacy
–
5.
Intramuscular antipsychotic use
–
6.
Residents who require longer acting (IM, decanoate)
medication may be inappropriate for nursing home
placement.
Contraindicated antipsychotic (1): Thioridazine or
Mesoridazine
–
7.
Residents should not be maintained on more than one
antipsychotic medication – titration is an exception.
Updated Beers’ criteria
Contraindicated antipsychotic (2): Use of conventional
antipsychotics among residents with Parkinson’s
disease
–
Use of atypical antipsychotics represents best practice for
those with Parkinson’s disease
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Prevalence of Indicators of Potentially Inappropriate
Antipsychotic Use Among Nursing Facilities
Percentage of
Facilities
(N=13,603)
Indicator
Percentage of Residents
Triggering Indicators
Any Resident
Triggering
Indicator
Lower-Use
Facilities
(25th percentile)
Higher-Use
Facilities
(75th percentile)
1: Use w/o appropriate dx
99.2
21.5
35.0
2: Excessive daily dose
84.8
4.7
13.6
3: Use with dementia dx
94.1
6.4
16.0
4: Polypharmacy
48.8
1.5
4.5
5: IM use
18.0
1.1
2.7
6: Contraindicated Rx 1
25.9
1.2
3.0
7: Contraindicated Rx 2
21.6
1.1
2.4
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Facility Level Summary Measure
Facility summary
counts each of the
following conditions:
–
–
35
Percentage of Facilities

National Distribution of
Summary Score
Indicators 1 -4: If the
percentage of
residents triggering
>= 75th percentile
Indicators 5 - 7: If
any resident triggers
32.0
30
27.2
25
18.8
20
15
11.4
10
6.4
5
3.1
0.9
0.2
6
7
0
0
1
2
3
4
5
Summary Score
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Facility Characteristics Associated with
Summary Score >= 4

Multivariate modeling
indicates ownership status,
skilled nurse staffing and
facility size had the strongest
associations with the
presence of four or more
potentially inappropriate
antipsychotic prescribing
practices
Characteristic
Odds Ratio
(95 % CI)
For-profit
1.87
(1.52 – 2.30)
Lowest quartile of licensed
nurse staffing
1.73
(1.47 – 2.03)
Smallest facility size
quartile
1.54
(1.24 – 1.91)
Alzheimer’s disease
special care unit
1.46
(1.23 – 1.74)
Lowest quartile unlicensed
nurse staffing
1.24
(1.05 – 1.46)
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Conclusions

Medicaid claims data are a potential source of
information to identify facilities that may have
problematic or questionable levels of
antipsychotic prescribing

Several facility characteristics are associated
with highest levels of questionable prescribing
practices
– For-profit ownership, low licensed nurse staff levels,
smaller facilities
13
Limitations

Excludes residents not covered by Medicaid

Diagnoses from claims data likely underestimates prevalence of
appropriate conditions
–
Results in ‘false positive’ for inappropriate use

Some triggering of indicators may be due to coding errors

Behavioral indicators not available in claims data
–
Cannot construct indicators that require information on resident behaviors
• Linking MAX with MDS is feasible

Lack of consensus: practices that are never appropriate

Medicaid claims data less useful for this purpose for 2006 and subsequent
years
–
–
Rx drug coverage for Medicare-Medicaid dual eligibles shifted from Medicaid to
the Medicare Part D program in 2006
CMS collects and makes available detailed Rx drug data under Part D, Part D data
can be used in much the same way MAX data
14
Implications

The role of facility staffing should be examined
as a potentially modifiable factor related to
inappropriate antipsychotic prescribing

Medicaid and Medicare prescription drug data
may be a viable source to supplement MDS
data to identify facilities at risk for poor clinical
or survey outcomes
– MDS and MAX can be linked
15