Health Services and
Pharmacoepidemiological
Research with CMS Datasets
Stephen Crystal
y
Center for Education and Research on
Mental Health Therapeutics
Rutgers University
Presentation for CMS Databases Session
Annual Meeting of AcademyHealth
June 29, 2009
1
Organization of Talk
• Potentials, limitations, strategies for HSR
and
d CER with
ith CMS d
datasets.
t
t F
For d
details,
t il
see handout (Medical Care paper).
• Wh
Whatt is
i MAX ((super-briefly).
b i fl ) F
For d
details,
t il
see handout (CMS MAX slides).
• Ill
Illustrative
t ti applications
li ti
ffrom work
k att our
center focusing on mental health treatments
– supported
t d by
b AHRQ CERT
CERTs award
d ffor the
th
Center for Education and Research on
Mental Health Therapeutics; NIMH; FDA;
Retirement Research Foundation.
2
Unique Potentials of CMS Datasets for HSR,
Pharmacoepi and Comparative
Effectiveness Research
• Ability to examine care for very large
large, “real-world”
real world patient
populations. Includes expenditures (but second payer for
duals). Window into care for many of the most vulnerable.
• Inclusion of subgroups typically underrepresented in
primary-data-collection studies.
• Statistical power to study many subgroups of interest and
l
low-frequency
f
characteristics
h
t i ti and
d events.
t
• Utilization information that does not rely on self-report.
• Ability to update results over time cost
cost-effectively.
effectively
• Diagnostic information provided directly by providers.
• Ability to follow longitudinal patterns of care across multiple
settings, including duration and consistence of prescribed
medication use.
3
Methodological Challenges and
Strategies to Address Them (Ongoing
Good News/Bad News Joke)
• Limited clinical detail (can be addressed with
strategies such as data linkage).
• Identification of clinical subgroups of interest may not
be straightforward.
– Need for more validation studies of case identification
algorithms using diagnostic codes in claims data
data.
• Lurie et al: 1 inpt/2 outpt algorithm highly specific
for schizophrenia.
• Walkup, Crystal et al: validated claims-based
measure of HIV/AIDS vs. a state registry.
– More such validation studies needed to support CER
CER.
– Linkage to registries can be powerful tool (eg SEER).
4
Who Are We Looking At? How Do We
Examine Treatment Use Over Time?
• MAX includes full utilization/diagnostic histories for FFS only.
Important to understand who is in FFS population in each state
(healthy kids? mostly disabled kids?).
• Measures of medication consistency/persistence.
– Prescription drug claims provide the ability to examine
longitudinal patterns of medication use during usual care
among
gp
persons with chronic illnesses in diverse settings.
g
Multiple measures: eg time-to-gap, proportion of observation
time covered by scripts.
– Most appropriate measures will vary depending on
circumstances. For example, for antiretrovirals, effective
therapy entails indefinite and highly consistent use. In
p
HEDIS measures p
provide a
contrast, for antidepressants,
benchmark for adequate duration of a treatment episode.
5
Other Challenges
• Dispensed does not necessarily mean taken. Requiring at least one
refill increases likelihood that medication is used, but creates
potentials for bias related to initial response. Many challenges in
choice
h i off d
designs
i
((e.g. “i
“intention-to-treat”/first
t ti t t t”/fi t exposure carried
i d
forward versus as-treated) depending on specific study questions
and circumstances.
• Challenge
Ch ll
off confounding
f
di b
by iindication
di ti ((unobserved
b
d clinical
li i l ffactors
t
associated with prescribing decisions).
• Limited range of outcomes to assess. Outcomes often non-specific
(
(e.g.,
“cardiac”,
“ di ” ttotal
t l mortality)
t lit ) and
d causes off d
deaths
th unclear.
l
• Financial considerations may bias clinical information on claims
(e.g., diagnoses).
– for more gory detail, see Crystal et al, “Studying Prescription Drug Use and
Outcomes with Medicaid Claims Data: Strengths, Limitations, and Strategies.”
Medical Care 2007 (paper from previous AHRQ methods conference).
6
Utility for Comparative
Effectiveness Research
• CER often requires very large study populations with power
to examine adverse events not detectable with typical RCT
sample sizes; examine outcomes in particular subgroups of
concern; compare outcomes across individual drugs within
and between classes;; assess risks of adverse outcomes not
detectable in typical clinical trials.
• Very large numbers also needed to examine treatment effect
h t
heterogeneity
it and
d effect
ff t modifiers;
difi
construct
t t carefullyf ll
tailored new-treatment cohorts; and utilize advanced
statistical methods such as high dimensionality propensity
scoring and instrumental variables. These designs are
remarkably demanding of power and may require multi-year
q
y address some important
p
national data to adequately
questions, as in our current study on CE of antipsychotics.
7
C ’t W
Can’t
We Just
J tR
Rely
l on RCT
RCTs?
?
The “Gold Standard” But...
• Trials not always feasible/ethical. Much use is offlabel and for indications/conditions on which
evidence is scarce or absent.
• Efficacyy in selected p
populations
p
vs. effectiveness in
usual care. Treated populations often highly
clinically heterogeneous while RCTs typically aim to
reduce
d
h
heterogeneity
t
it with
ith iinclusion/exclusion
l i /
l i
criteria. Result: available evidence often lacks
external validity
validity.
8
Typical RCTRCT-Based Evidence
vs.
vs Usual Care
RCTs
Usual Care
Duration
Typically Short-Term
Often Long-Term
Comparators
Typically PlaceboControlled; Head-toHead Trials Scarce
Pressing Need for
Data on
Comparative
Outcomes to Inform
Clinical Decisions
Complex
Comorbidity is
Common in Treated
Population
Population
Patients with Complex
Comorbidities Often
Excluded
9
What is MAX?
• Person
Person--based Medicaid data used for
– Research/evaluation
– Epidemiology/quality
– Statistics/forecasting
• Calendar Year (begins 1999, SMRF - prior years)
• Event Based
– Occurrence of eligibility
– Dates of service
– Final action events (hospital stays, visits, etc.)
• Derived from MSIS (7 calendar quarters)
10
Why Do We Need MAX?
• Eligibility
– Retroactive
R t
ti eligibility
li ibilit iin proper chronology
h
l
– Eligibility codes – verified and improved
– Eligibility data added to each claim
• Services (Claims)
– Final action events (interim claims combined)
– Organized by dates of service
– Type of service – verified and regrouped
• Person Summary File
– Calendar year eligibility and summary of claims
– Not available from
f
MSIS
SS
11
MAX Data Sets
• Person Summary File
– Eligibility (annual and monthly)
– Managed care enrollment
– Utilization and Medicaid payment by type of service
• Service Files
– Inpatient
I
ti t hospital
h
it l
– Long term care
– Prescription drug
– Other
Oth Services
S i
• Service file records include
– Fee
Fee--for
for--service
– Prepaid plans - premium payments and encounters
(incomplete)
• See handout as well as RESDAC and CMS websites for
more details on MAX datasets.
12
Illustrative Applications: Selected
Studies From Our Group Using MAX
• Off-Label Use of Atypical Antipsychotics in Youth and Elderly
(Crystal et al
al, in press
press, Health Affairs)
Affairs).
• Antidepressant Use and Suicidality Among Medicaid Youth
((Olfson et al, Archives of General Psychiatry,
y
y 2006).
)
• Mood Disorder Hospitalization Among Medicaid Benes
(Prince, Crystal, AJPH 2009)
• Comparative Effectiveness of Antipsychotics in Early Onset
Schizophrenia (current).
• G
Guideline
id li C
Consistency,
i t
C
Comparative
ti S
Safety
f t and
d
Effectiveness of Antipsychotics in Nursing Home Elderly
(current).
– support from AHRQ CERTs award, FDA, NIMH, Retirement
Research Foundation.
13
Off--Label Use of AAPs in Youth and
Off
Eld l --Overview
Elderly-Elderly
O
Overview
i
• Increased use of atypical antipsychotic (AP)
medications for a broadened range of patients and
indications, often off-label, has raised a range of
policy challenges for payers, patients and clinicians.
US is outlier in off-label use patterns, esp. in youth.
• Medicaid and Medicare beneficiaries utilize a large
share of AAPs and these data are critical for
understanding use and outcomes.
• Particular concerns about increased use among
youth and among elderly with dementia.
14
Earlier Antipsychotics:
P
Powerful
f l Meds,
M d Limited
Li it d Use
U
• Beginning in the 1950s with introduction of
chlorpromazine, AP drugs have transformed care for
the seriouslyy mentallyy ill.
• Until the 1990s, however, use of APs was largely
ese ed for
o adu
adults
s with sc
schizophrenia
op e a a
and
do
other
e
reserved
severe psychotic disorders, treated by psychiatrists.
Treatment was understood to involve balancing
b
beneficial
fi i l iimpactt on hi
highly
hl di
disabling
bli symptoms
t
with
ith
significant risks.
15
The Advent of the AAPs
• Approval of risperidone (1993), olanzapine (1996),
quetiapine (1997), ziprasidone (2001), aripiprazole
(2002) was ffollowed
ll
db
by iincreased
d and
db
broadened
d
d
use. AAPs perceived as much safer and more
your father’s antipsychotic.”
p y
efficacious – “this is not y
• Medication costs previously a relatively small part of
mental health budgets, but AAPs were much higherpriced, and more-aggressively marketed (including
DTC in recent years). For Medicaid, AAPs became
the most costly class of drugs. Total US sales: $13.1
billion by 2007.
• Use increasingly
g y expanded from psychiatrists
y
to
primary care. Some broadening of indications but
much of expanded use was off-label.
16
National Medicaid Expenditures on
A ti
Antipsychotic
h ti Agents
A
t
Highest Cost Drug Groups (in Millions $) Among Nondual Eligible Beneficiaries
Rank
1
1999
Antipsychotics
($700))
($
2001
Antipsychotics
($1,174)
($
,
)
2003
Antipsychotics
($1,898)
($
,
)
2
Antidepressants
($513)
Antidepressants
($807)
Antidepressants
($1,085)
3
Antivirals
($452)
Anticonvulsants
($619)
Antivirals
($950)
4
Anticonvulsants
($388)
Antivirals
($612)
Anticonvulsants
($966)
5
Ulcer Drugs
($328)
Ulcer Drugs
($574)
Antiasthmatics
($986)
Esposito, D et al., Trends in Medicaid Prescription Drug Use and Costs: 1999 to 2002. Evidence from Medicaid
Analytic eXtract Data. Presented at Academy Health 2007; and CMS, Chartbook: Medicaid Pharmaceutical Benefit
Use and Reimbursement in 2003, http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/downloads/Pharmacy_RX_Chartbook_2003.pdf
17
Use Among Children and Youth
• 5
5-fold
f ld iincrease iin AP use 1993
1993-2002;
2002 challenging
h ll
i b
behavior
h i
such as aggression thought to motivate much current use.
• FDA-approved indications in youth limited to schizophrenia,
autism,
ti
T
Tourette’s
tt ’ and
d bipolar
bi l mania.
i
• Concerns about off-label use include increasing evidence on
adverse metabolic side effects as well as worries about longt
term
developmental
d
l
t l effects.
ff t
• Medicaid programs among concerned stakeholders.
• How much of use is off-label
off label and what diagnoses account for
use? How much due to increased diagnosing of bipolar?
• Utilization analyses presented to Medicaid Medical Directors,
NASMD, CMS and other concerned parties. MAX analyses
served as “template” for analyses of more-recent data by ~15
collaborating states in Medicaid Medical Directors Learning
Network. MAX also useful to compare to privately insured.
• Utilization and policy challenges with AAPs -- in press, Health
Affairs).
18
Increasing Use of Antipsychotics Among
Youth 6-17
Percent w
P
with AP Us
se
Medicaid ((7 States*))
Privatelyy Insured
4.5
4
35
3.5
3
2.5
2
1.5
1
05
0.5
0
* CA, FL, GA, IL, NY, OH, TX
19
AP Use by Hierarchical Diagnostic Group in
Medicaid Youth Ages
g 6-17*
2001
N=51,093
2004
N=88,096
4 0%
4.0%
3 3%
3.3%
Group 2 - Autism
5.3
4.9
Group
p 3 - Bipolar
p
Disorder
14.2
18.7
Group 4 - Conduct Disorder or DBD w/o ADHD
10.9
8.9
Group 5 - Conduct Disorder or DBD w/ ADHD
10.4
9.0
Group 6 - ADHD
27.5
29.1
Group 7 - Anxiety or Depression
9.5
9.1
Group 8 - Substance Abuse
04
0.4
06
0.6
Group 9 - Adjustment Related Disorders
2.0
1.5
Group 10 - Other MH Disorders
6.3
5.9
Group 11 - None of Above
9.6
9.1
G
Group
1 - Schizophrenia
S hi
h
i
* 7 State Medicaid Data (CA, FL, GA, IL, NY, OH, TX).
20
Diagnosis Rates in Medicaid Youth Ages 6-17*
2001
N=1,841,048
2004
N=2,101,866
0.15%
0.19%
G
Group
2 - Autism
A ti
0 40
0.40
0 50
0.50
Group 3 - Bipolar Disorder
0.63
1.11
2 13
2.13
1 98
1.98
0.98
1.00
Group 6 - ADHD
6.09
7.28
Group 7 - Anxiety or Depression
2.51
2.89
Group 8 - Substance Abuse
0.40
0.52
Group 9 - Adjustment Related Disorders
1.77
1.61
Group 10 - Other MH Disorders
4.70
4.42
Group 11 - None of Above
80.24
78.51
Group 1 - Schizophrenia
Group 4 - Conduct Disorder or DBD w/o
ADHD
Group 5 - Conduct Disorder or DBD w/
ADHD
* 7 State Medicaid Data (CA, FL, GA, IL, NY, OH, TX)
21
AP Treatment Rates within Diagnostic Groups
in Medicaid Youth Ages 6-17*
6-17
2001
N=51,093
2004
N=88,096
75.6%
75.1%
Group 2 - Autism
36.4
41.3
Group 3 - Bipolar
G
Bi l Disorder
Di
d
Group 4 - Conduct Disorder or DBD w/o
ADHD
Group 5 - Conduct Disorder or DBD w/
ADHD
Group 6 - ADHD
62 4
62.4
70 8
70.8
14.2
18.7
29.4
37.7
12.5
16.8
Group 7 - Anxiety or Depression
10 5
10.5
13 2
13.2
Group 8 - Substance Abuse
2.7
4.6
Group 9 - Adjustment Related Disorders
3.1
3.8
Group 10 - Other MH Disorders
3.7
5.6
Group 11 - None of Above
0.3
0.5
Group 1 - Schizophrenia
* 7 State Medicaid Data (CA, FL, GA, IL, NY, OH, TX)
22
AP Use by Hierarchical Diagnostic Group in
Privately Insured Youth Ages 6-17*
6-17
Group 1 - Schizophrenia
Group 2 - Autism
Group 3 - Bipolar Disorder
Group 4 - Conduct Disorder or DBD w/o ADHD
1996
N=349
2001
N=4,061
2004
N=16,192
2006
N=17,523
8.0%
3.6%
2.5%
2.2%
3.4
4.4
4.4
5.2
11 5
11.5
23 8
23.8
22 9
22.9
25 2
25.2
6.6
6.3
4.5
4.5
Group 5 - Conduct Disorder or DBD w/
ADHD
Group 6 - ADHD
3.7
4.1
2.8
2.9
17.8
18.5
18.9
21.4
Group 7 - Anxiety or Depression
19.5
18.0
16.7
16.0
Group 8 - Substance Abuse
0.3
0.4
0.6
0.5
Group 9 - Adjustment Related Disorders
2.0
1.4
1.2
1.5
Group 10 - Other MH Disorders
4.6
6.4
5.7
6.0
Group 11 - None of Above
22.6
13.1
19.8
14.6
* MarketScan Research Databases
23
U A
Use
Among Child
Children and
d Youth
Y th
• In 2004, 73% of AP-treated Medicaid youth and 67%
of those privately insured had only conditions that lack
an FDA indication for treatment, for any AAP at any
non adult age
non-adult
age.
• Externalizing behavioral disorders appear to account
for much AP use.
use 38% of Medicaid youth and 24% of
privately insured were diagnosed with ADHD but not
more severe disorders.
• The proportion of users diagnosed with bipolar
disorder has also grown in both populations.
24
Has Use Also Broadened Among
N -Elderly
NonNon
Eld l Adults?
Ad lt ?
• Among MA benes age 18
18--64 in our 7
7--state dataset,
schizophrenia accounted for 46% of AP users in
2001 and 40% in 2001. Bipolar increased slightly
from 9
9.5%
5% to 12
12.4%.
4% Almost oneone-quarter in 2004
were diagnosed with depression or anxiety but not
y
or bipolar; 22% had range
g of other mental
psychoses
disorders.
• Use also broadened among
g privately
y insured adults
ages 1818-64, increasing to .92% of this population in
2006. Bipolar accounted for 27% in 2006 (vs. 20% in
2001) and Sz for 8%.
2001),
8% 33% of users had anxiety or
depression without higherhigher-listed dx.
dx.
25
AP Use by Hierarchical Diagnostic Group in
Medicaid Adults Ages 18
18-64*
64*
2001
,
N=198,915
2004
N=249,167
,
46.2%
40.4%
Group 2 - Bipolar Disorder
9.5
12.4
Group 3 - Autism or Mental Retardation
0.7
0.7
Group 4 - Anxiety or Depression
p 5 - Personality
y Disorders ((1st or 2nd
Group
dx)
Group 6 - ADHD
23.4
24.2
04
0.4
03
0.3
0.4
0.4
Group 7 - Substance Abuse
20
2.0
23
2.3
Group 8 - Adjustment Related Disorders
0.6
0.5
Group 9 - Other MH Disorders
7.3
6.9
Group 10 - None of Above
9.7
11.8
Group 1 - Schizophrenia
* 7 State Medicaid Data (CA, FL, GA, IL, NY, OH, TX)
26
Use Among Nursing Home Elderly
• As in youth, AP use to manage behavioral problems
increased among elderly following advent of AAPs.
• AP drugs widely used to treat behavioral sx of
dementia such as agitation
agitation, aggression
aggression, wandering
wandering,
anxiety.
• AP use in NH was controversial in the prepre-AAP
period and was a focus of 1987 NH reforms (OBRA).
Use rates declined about 30% in early 1990s
following regulatory reforms, but increased again
following the approvals of olanzapine and
risperidone,, perceived as “safe”
risperidone
safe in this population
population.
27
Use Among NH Elderly
• In mid
mid--2000s, evidence accumulated of increased
death rates associated with AP treatment of elderly.
• In April 2005, FDA issued public health advisory
((“black
black box warning”)
warning ) finding antipsychotic use to be
associated with increased risk for death. MetaMetaanalysis of 17 shortshort-term trials (averaging 88-12
weeks) concluded that relative risk of death
increased about 60%, and absolute mortality
increased by about 2% for AP treated patients as
compared with placebo treated patients.
28
Background

Safety concerns for the atypical antipsychotics:




Health Canada stated safety concerns for risperidone for
increased risk of cerebrovascular events observed in several
dementia trials (October 2002)
2002).
FDA issued black box warnings of increased stroke risk for
specific
p
AAPs ((April
p 2003 - Februaryy 2005).
)
FDA issued black box warning for all atypical antipsychotics,
with finding of increased risk of all cause death in patients with
d
dementia
ti (A
(Aprilil 2005)
2005). IIn ttrials
i l averaging
i 8
8-12
12 weeks,
k relative
l ti
risk of death increased ~60% vs. placebo. Absolute mortality in
drug-treated patients ~4.5% vs. 2.6% in patients randomized to
placebo
l
b ((number
b needed
d d tto h
harm approx. 53)
53).
Based largely on observational data, FDA extended black box
warning to all APs (June 2008)
2008).
29
Use Among NH Elderly
• Other studies confirm this picture and conclude that
the risk
risk--benefit ratio is generally unfavorable (e.g.,
CATIE--AD, Schneider meta
CATIE
meta--analysis, AHRQ
evidence report on offoff-label AAP use). Trial results
indicate that effectiveness is at best modest. Some
reduction in behavioral symptoms (if only via
sedation) no evidence of improved cognitive
sedation),
function. Mechanisms for increased total mortality
p
unclear but mayy include stroke, falls, pneumonia,
AMI (these issues are being examined in a current
study). Comparative outcomes across drugs are
unclear
unclear.
30
AP Discontinuation Trial (DART(DARTAD)
• Early in 2009, further experimental evidence of
increased mortality associated with AP use among
nursing home residents emerged from a UK
randomized trial of AP treatment discontinuation. In
the DARTDART-AD trial, residents with dementia who had
received AP medications for at least 3 months were
randomized to continue AP treatment for 12 months
or to switch their medication to placebo. TwelveTwelvemonth survival was 70% in the continue treatment
group vs. 77% in the placebo group. (Ballard, 2009,
Lancet Neurology).
31
AP Discontinuation Trial (DART(DARTAD)
• Differences were even g
greater in longerlonger
g -term
followup,, with 24
followup
24--month survival of 46% vs. 71%.
• These findings
g are p
particularly
y concerning
g since they
y
suggest that the increased risk identified in the shortshortterm trials in the FDA’s metameta-analysis is exacerbated
by long
long--term use
use, which is believed to be widespread
among NH residents in the U.S.
• DARTDART-AD also found that for most patients with
dementia, AP withdrawal had no overall detrimental
effect on functional and cognitive status.
32
AP Use (Last 7 Days) by Hierarchical Diagnostic
Group in Nursing Home Residents Age 65+*
1999
2006
N=72,341 N=97,939
Group 1 - Schizophrenia
15.5%
15.3%
4.1
5.4
Group 2 - Bipolar Disorder
Group 3 - Dementia & Aggressive Behavioral
Symptoms
Group 4 - Dementia & Non-Aggressive
Behavioral Symptoms
Group
p 5 - Dementia without Behavioral
Symptoms
Group 6 - Depression or Anxiety Disorder
19.8
14.1
19.5
19.3
21 4
21.4
28 9
28.9
10.6
11.4
Group 7 - None of Above
9.2
5.7
* Minimum Data Set. Last full non-admission assessment ; long-term stay
33
Results
Monthly Proportions of AP use Among Long-term Residents of non
Hospital-based Nursing Homes Diagnosed with Dementia
Proportion of Full MDS Assessmen
nts
with AP Us
se
50
Observed
Predicted
Pre-warning trend
45
40
35
30
25
20
34
Results
Trends in AP Use Rates By Diagnosis
Pro
oportion of F
Full MDS Ass
sessments
witth AP Use
♦ Schizophrenia or bipolar disorder
60
♦ No Dementia
*
80
70
♦ Dementia
1.0
2.3
-0.5
50
40
30
93
9.3
20
10
12.3
*
*
2.1
*
-5.4
*
0.8
-5.9
0
35
Results
Proportion o
P
of Full MDS A
Assessmentts
With Drug Us
W
se
Trends in Psychotropic Drug Use Among Residents with
Dementia
♦ Antidepressants
♦ Antipsychotics
♦ Antianxiety or Hypnotics
60
50
40
*
9.4
*
30
9.3
20
10
-0.5
0.5
*
6.2
2.1
4.4
*
*
-5.4
5.4
*
2.6
6.4
0
36
Limitations

Analyses are based on MDS assessments rather
than claims data.

No information on specific AP agents or dosing:


Analysis could not explore potential shifts
towards:
 first generation AP agents
 lower doses.
External
E
t
l events
t other
th than
th regulatory
l t
warnings
i
may explain the observed trend changes.
37
Discussion

While the changes in AP use following the regulatory
warnings have been significant, the size of these
changes
h
h
has b
been small.
ll

More than 1.5 years after the black box warning for
increased all
all-cause
cause mortality
mortality, AP use rates remained
significantly higher than at the beginning of the study
p
period.

Substitution with anxiolytics or hypnotics?

Future directions: Analysis of claims data will allow to
address some of the limitations (dosing, specific APs).
38
Use of Merged MAX and MDS Data
to Examine Guideline Consistency
of AP Use in NH Elderly
• Work in progress
progress—
—early results presented at CERTs
CMS Day, June 2009.
• Analyses
a yses u
utilized
ed 20012001
00 -0
04 da
data
a from
o 7 sstates;
a es; will be
extended to larger set of states and to 2005 data in
next phase.
• MDS annual assessments provided information on a
range of clinical, functional and other resident
characteristics MAX data provide information on
characteristics.
medication use and diagnostic histories; OSCAR
data provide facility information. AP use examined
i 90 d
in
day window
i d
surrounding
di assessmentt using
i
days--supply “calendar”.
days
39
E l Results
Early
R
lt
• Among NH residents receiving APs
APs, guidelineguidelineinconsistent use was more likely among residents
who were male, Hispanic, over age 86, or with
extensive ADL limitations.
• Rates of inconsistent use varied significantly
g
y across
states and were higher in facilities with higher
numbers of total deficiency citations and those with
hi h rates
higher
t off physical
h i l restraint
t i t use, controlling
t lli ffor a
range of other resident and facility characteristics.
Analyses in progress
progress, stay tuned
tuned...
40
AP Safety and Comparative Effectiveness-Substudies and Data Sources
41
Institutionalized Elderly Analyses
•Mortality.
•Medical harms.
•Functional
F ti l outcomes.
t
42
Comparative Safety Among Elderly Nursing
Home Residents (Analyses In Progress)
• Data sources:
– Linked Medicaid,
Medicaid Medicare,
Medicare MDS – 45 states.
states
• Study cohort:
– Medicaid beneficiaries 65+ who initiate antipsychotic
py
medications during long-term care nursing home stay –
beginning with 8 state cohort.
• Exposure:
– New use of specific APMs; referent group: risperidone
– Dose in chlorpromazine equivalents
• Outcomes:
– All cause mortality; cause-specific mortality: Ca, cv, infect,
suicide
– Major events: Hosp. for AMI, stroke, pneumonia.
43
Comparative Safety Among Elderly Nursing
H
Home
Residents
R id t (A
(Analyses
l
IIn P
Progress))
•
Covariates:
– Age,
A sex, race, time,
i
– Medical conditions, psych conditions, health services intensity
– MDS: functional status, cognitive status, behavioral factors
•
Balance:
– Cox PH regression modeling; Propensity score stratification (deciles)
•
Follow-up:
p
– 180 days since initiation; censoring: events, d/c of index drug
•
Analysis:
– As treated analysis: rate ratios, stratified by time since initiation
– Cumulative risk analysis: risk ratios, risk differences (FECF)
•
Sensitivity analyses:
– Instrumental variable analysis
– Residual confounding analysis
44
Comparative Effectiveness of APs
i Early
in
E l Onset
O
t Schizophrenia
S hi
h
i
• Illustrates important potential for MAX for CER in
situations where:
• RCT evidence is lacking due to ethical, practical
and
d costt issues.
i
• Very large study populations needed to study
relatively uncommon subgroups (diagnosed with
Sz before age 18) and to carefully tailor newnewonset cohorts. Is effectiveness equivalent across
drugs? Difficult to examine nonnon-inferiority without
large study populations.
• Very
V
early
l results
l presented
d at CMS earlier
li this
hi
month; 2005 data needed for next steps.
45
Drug Abuse and Mood Disorder
Hospitalizations Among MA Beneficiaries
• Mood disorders are leading cause of psychiatric
hospitalization.
• 19991999-2000 55-state MAX data used to examine factors
that predict mood disorder hospitalization and
rehospitalization among MA benes
benes.
• Among benes with mood disorder, those with comorbid
substance abuse (SA) were more than 3 times more
likely to have hospitalizations, accounting for 36% of MD
hospitalizations. If hospitalized, they were more likely to
be readmitted and accounted for 50% of all
readmissions.
• Results highlight importance of targeting community
services to this group to treat SA and MD disorders and
prevent decompensation. (Prince, Crystal, AJPH 2009).
46
Suicidality and Antidepressants
in Youth
Work on this topic also exemplifies importance of large, naturalistic
datasets.
OBJECTIVE: To estimate the relative risk of suicide attempt and suicide
death in severely depressed children and adults treated with
antidepressant drugs vs those not treated with antidepressant drugs.
DESIGN: Matched casecase-control study.
SETTING: Outpatient treatment settings in the United States.
PARTICIPANTS: Medicaid beneficiaries from all 50 states who received
inpatient treatment for depression, excluding patients treated for
pregnancy, bipolar disorder, schizophrenia or other psychoses, mental
retardation, dementia, or delirium. Controls were matched to cases for
age, sex, race or ethnicity, state of residence, substance use disorder,
recent suicide attempt, number of days since hospital discharge, and
recent treatment with antipsychotic, anxiolytic/hypnotic,
anxiolytic/hypnotic, mood
stabilizer, and stimulant medications.
MAIN OUTCOME MEASURES: Suicide attempts and suicide deaths.
47
Suicidality and Antidepressants
in Youth
• RESULTS: In adults (aged 19
19--64 years), antidepressant drug
treatment was
as not significantly
significantl associated with
ith ssuicide
icide attempts
attempts.
However, in children and adolescents (aged 66-18 years),
antidepressant drug treatment was significantly associated with
suicide attempts (OR, 1.52; 95% CI, 1.121.12-2.07 [263 cases and
1241 controls]) and suicide deaths (OR, 15.62; 95% CI, 1.651.65infinityy [[8 cases and 39 controls]).
])
• CONCLUSIONS: In these highhigh-risk patients, antidepressant
drug treatment does not seem to be related to suicide attempts
and death in adults but might be related in children and
adolescents. These findings support careful clinical monitoring
during antidepressant drug treatment of severely depressed
young people.
l
Source: Olfson et al, Archives of General Psychiatry, 2006
48
In a Medication-Oriented Society With
Extensive Drug Promotion, Much Work Needs
To Be Done to Understand and Improve Use
of Psychotropics. MAX Provides Key
E id
Evidence
Source
S
for
f This
Thi Work.
W k
“Could we up the dosage? I still have feelings.”
49
Q
Questions?
ti
?
scrystal@rci rutgers edu
scrystal@rci.rutgers.edu
50
Contact Information
Stephen Crystal, Ph.D.
Director,, Center for Education and Research on
Mental Health Therapeutics
Rutgers
g
University
y
30 College Avenue
New Brunswick, NJ 08901
voice
i 732
732-932-8579
932 8579
fax 732-932-8592
scrystal@rci.rutgers.edu
t l@ i t
d
51