Download Title of Presentation - Collaborative Family Healthcare Association

Session #E3b
Friday, October 11, 2013
The Effectiveness of Narrowband and Broadband Tools for
Integrated Care Assessment: A Comparison of the PHQ-9
and Health Dynamics Inventory (HDI) Across Care Settings
James V. Wojcik, Ph.D., L.P., Chief Psychologist at Canvas Health
Samuel Hintz, Ph.D., Postdoctoral Resident at Minneapolis VAMC
Nicole Shackelford, Psy.D., L.P., Psychologist at Canvas Health
Jonathan Hoistad, Ph.D., L.P., Senior Psychologist at Natalis
Counseling and Psychology Solutions
Collaborative Family Healthcare Association 15th Annual Conference
October 10-12, 2013
Broomfield, Colorado U.S.A.
Faculty Disclosure
We currently have the following relevant financial relationships
(in any amount) during the past 12 months:
• Drs. Wojcik and Hoistad are co-owners of Natalis Outcomes, a
company currently distributing the HDI.
• Dr. Wojcik is an original author of the HDI.
Objectives
Participants will be able to
• Recognize the comorbidity of behavioral health
disorders
• Evaluate the pros and cons of broadband vs.
narrowband assessment in a variety of care settings
• Determine the relevant factors to consider when
deciding between various screening or outcome
measures
• Assess how site specific factors may affect the
performance of a given instrument
Learning Assessment
Audience Question & Answer
Program outline
•
•
•
•
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Background and epidemiology
Influence of comorbidity
Broad and narrowband instruments
Findings from the current study
Research implications
5
Epidemiology (NIMH, 2010)
• 26.2% of the adult population in the US has a 12month prevalence of mental disorder.
• 9.5% of the adult population has a 12-month
prevalence of a mood disorder.
• 18.1% of the adult population has a 12-month
prevalence of an anxiety disorder.
• 4.1% of the adult population has a 12-month
prevalence of an ADHD disorder.
• 1.1% of the adult population has a 12-month
prevalence of Schizophrenia.
6
Child and adolescent (NIMH, 2010)
• 46.3% of 13- to 18-year-olds in the US
have a lifetime prevalence of any disorder
• 14% of 13- to 18-year-olds have a lifetime
prevalence of a mood disorder
• 25.1% of 13- to 18-year-olds have a
lifetime prevalence of an anxiety disorder
• 9% of 13- to 18-year-olds have a lifetime
prevalence of ADHD
7
Prevalence in non-mental
health settings (CDC, 2006)
• In 2007, there were 58.2 million
ambulatory care visits (physician office,
hospital outpatient, and emergency
department) by individuals with a mental
disorder as their primary diagnosis
• 2.4 million had been discharged from hospital
inpatient care.
• In 2004, in nursing homes there were
996,000 people with a mental illness
8
Prevalence in non-mental health
settings (Adelmann & Asche, 2002; NIMH, 2010)
• Mood disorders account for 9.5% of
mental disorders in the U.S; anxiety
disorders, 18.1% across the U.S.
population
• Those with any disorder were found to
have an average of two disorders, or both
a mental disorder and a substance abuse
disorder
9
2010 Canadian Health Study
(Vermani, Marcus, and. Katzman, 2011)
Measured 840 primary care patients with
the Mini International Neuropsychiatric
Interview (MINI)
• 27.2% met criteria for depression,
• 11.4% for bipolar disorder
• 12.6% for panic disorder
• 31.2% for generalized anxiety disorder
• 16.5% for social anxiety disorder
10
Influence of co-morbidity
Depression with a co-morbid diagnosis of an
Anxiety Disorder
–
–
–
–
–
More severe symptoms (Rivas-Vasques et al., 2004)
Poorer response to treatment (Rivas-Vasques et al., 2004)
Increased risk for suicide (Rivas-Vasques et al., 2004)
Greater medical costs (Marciniak et al., 2005)
Higher morbidity and mortality (Brown & Ramsey, 2000)
11
Influence of co-morbidity
• Depression with psychotic symptoms
– Less responsive to treatment by medication
and psychotherapy (Carpenter & Price, 2000;
Salokangas et al., 2007)
– Psychotic symptoms increase the risk of
depression (Salokangas et al., 2007)
– Have slower recovery and experience chronic
depressive symptoms (Salokangas et al.,
12
2007)
Influence of co-morbidity
Depression with a comorbid diagnosis of a
Substance Use Disorder
– Alcoholism interferes with the treatment of
depression (Brown & Ramsey, 2000)
– People with depression are more likely to
relapse (Brown & Ramsey, 2000)
13
Influence of co-morbidity
Influence of co-morbid diagnoses in
disadvantaged populations
– Anxiety, substance use, and eating disorders
can significantly impair treatment adherence
and effectiveness (Peterson et al., 2011)
– Anxiety, substance use, and eating disorders
can lead to greater medical health care costs
(Peterson et al., 2011)
14
2010 Canadian Health Study
(Vermani, Marcus, and. Katzman, 2011)
Missed comorbid diagnosis rates
• 65.9% for major depression
• 92.7% for bipolar disorder
• 85.8% for panic disorder
• 71% for generalized anxiety disorder
• 97.8% for social anxiety disorder
15
2003, 2005 New Zealand
Health Studies
Mental Health and General Practice Investigation (MaGPIe)
• Composite International Diagnostic Interview (CIDI),
>33% of patients had diagnosable mental disorder
during the past 12 months
• More likely to recognize when the patient is wellknown
• Mental health patients took longer
• Increased waiting times for other patients
• Subsidized by not charging or writing-off longer visits
16
Implications
• Primary Caregivers get 8-15 minutes to evaluate
and design treatment
• There is a broad spectrum of problems that
need attention
• There is more to figure out than time allows
• Tools can capture information and track impact
of treatment
• Disease-specific measurement can clarify level
of care needs, triage, and stepped care
decisions.
17
Implications
18
Increasing use of
narrowband assessments
Heart Disease Studies
• (Lichtman et al., 2008)
Screening of OB-GYN Patients
• (Kronke Spitzer, & Williams, 2001)
Screening of HIV+ Patients
• (Justice et al., 2004)
Diabetes Treatment Studies
• (Glascow et al., 2004)
CDC Nationwide Depression Survey
• (Gonzalez et al., 2010)
Extensive NIMH treatment studies, including STAR*D
• (Trivedi et al., 2006)
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Comparative Value : Narrowband
Narrowband instruments (PHQ-2, PHQ-9, GAD-7, Beck
Depression Inventory) focus on single disorders.
• Other disorders then measured by adding other scales
• Tend to be shorter and may offer time and cost savings
• Longer narrowband instruments (BDI, RC-MAS, YBOCS) may offer increased specificity and potential to
probe a larger range of symptom expression defining a
particular disorder.
20
Comparative Value: Broadband
Broadband instruments measure several disorders
simultaneously, increasing detection and treatment
of disorders.
Examples: SCL-90, BSI, SCID, PDSQ, HDI
• Likely to be longer
• May have greater initial costs and time requirements
• Allow screening and tracking of multiple problems
with less coordination
• More comprehensive diagnoses
• More comprehensive treatment planning
• Improved risk surveillance
• Inform population-level health policy
21
Current Study:
Cases of illness predicted by PHQ 9 and
HDI Compared to Outpatient Base Rates
• Records review produced dataset of
patients who had completed both
instruments
• Multiple treatment settings were included.
• Predicted rates of psychopathology were
compared with base rates (from EHRs)
across instrument and treatment site.
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Demographics
• 342 Male (36%), 614 Female (64 %), seen
between 2008 and 2013 at the three sites
• 956 total patients seen in:
• Site 1: Natalis Counseling: 71
• Site 2: Canvas Health: 835
• Site 3: St. Croix Family Medical: 50
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Inclusion criteria
• Patients only included if HDI-S and PHQ-9
administered within 2 weeks of one another
• Adequate Internal Consistency (alpha)
– HDI-S Depression Scale: .88
– PHQ-9: .90 (Canvas only)
24
Psychopathology base rates
by site
50%
45%
47%
46%
40%
35%
34%
34%
30%
25%
20%
15%
15%
13%
10%
7%
5%
5%
0%
Depression
Bipolar Disorder
ADHD
Site 1
Anxiety
Site 2
*base rates were not available for Site 3
25
Correlation of HDI-S Depression
scale with PHQ-9
Total:
.82**
Site 1:
Site 2:
Site 3:
.56*
.82**
.77**
26
Base Rate and Predicted
Psychopathology: Site 1
70
66
66
63
60
55
50
40
34
34
30
20
11
10
4
0
Depression HDI
Depression PHQ
prediction
ADHD
base rate
Anxiety
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Base Rates and Predicted
Psychopathology: Site 2
600
550
500
490
445
400
384
384
362
300
284
200
100
125
0
Depression HDI
Depression PHQ
pred by HDI
ADHD
base rate
Anxiety
28
Predicted vs. Base Rate Percent
Difference by Site
100
90
80
70
60
S1BR
50
S1Pred
S2BR
40
S2Pred
30
20
10
0
Dep HDI
Dep PHQ
ADHD
Anx
29
Predicted vs. Base Rate Percent
Difference by Site
30
Predicted Comorbidity By Site
31
Predicted Comorbidity By Site
32
Implications
• Both measures over-estimate the
presence of full-criteria depression based
on actual diagnostic rates (sensitivity)
• Not a bad thing! Sub-threshold problems
need attention
• However, site-specific factors produce
significant differences in the rate of
prediction
• Surprising, because base rates across
sites are comparable
34
Implications
• Base rates for major categories of
psychopathology are similar across sites,
but vary more for less common disorders
• May be an artifact of diagnostic practice
variation
• Comorbidity likely an overlap of health
condition presentation, not multiple
discrete disorders, but still adds
complexity
35
Implications
• Both instruments can serve as an effective
screen for depression; a broadband
instrument can screen for other disorders
as well
• Disorders are there to find and often in
need of intervention
• Sites should conduct local analysis of both
broad and narrowband assessments
36
Primary care compared to a mental health
clinic population: What are the meaningful
differences?
Both settings reflect:
• Comorbid disorders, including substance abuse,
anxiety, trauma, personality disorders
• Severe psychosocial stress
• Presence or concentration of serious and
persistent mentally ill
• Patients with previous poor medication response
37
Implications for practice across
settings
38
Implications for
health systems
Narrowband benefits
• Quicker
• Cheaper
• Less demand on patients or clinicians
• Simpler treatment plans
• And, with longer instruments, more detail
39
Implications for
health systems
Broadband benefits
• More reliable capture of co-morbidity
• More accurate diagnosis
• Recognition of sub-threshold disorders
• Fewer unknown forces to interfere with
success and increase health care costs
• Better risk and needs assessment
40
Benefits of routine
measurement
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•
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Identifies scope of problems
Guides search for co-morbidity
Increases diagnostic information and accuracy
Signals the clinician’s interest
Helps patients understand their problems
Allows time for empathy
Aids in joint treatment planning
Allows systems to plan, measure, and improve
41
Screening for scope
• Tools help clinicians grasp the necessary
scope of intervention, and target resources
and risk management appropriately
• Patients add to appointment value by
doing this work before meeting the
clinician
• Clinicians can quickly assess the
diagnostic time and tasks needed
42
Planning for deeper
assessment
• Clinicians can plan for more detailed tools,
referrals, testing
• May select specific scales or interviews to
identify additional problems such as
posttraumatic stress disorder, ADHD,
substance abuse
• Prior authorization better described, with
more details and population comparison
43
Diagnostic accuracy
• Patients respond frankly to questionnaires
• Increased details support better diagnosis
• Patients more likely to define range of their
problems
• They get reminders of things they want to
describe
• More appropriate treatments will be used,
with diminished iatrogenic risk
44
Empathic benefits
• Use of diagnostic measurement tools does
not offend patients
Tools allow more time to
• Listen to distress
• Develop trust and empathy
• Plan intervention
45
Increased patient
insight
• Gives patients definition and explanation
for their distress and symptom experience
• Increases understanding of treatment
• Gives experience of communicating
specifically with providers
• Enhances personal investment in planning
and interventions
• Increases compliance with recommended
treatments
46
Treatment planning
• Rapid assessment of level of care
• Helps clinicians and patients jointly plan
treatment
• Focus and prioritize attention on salient
problems, which can be graphically
described
• Follow-up measurement can adjust focus
as treatment benefits accrue or as new
problems emerge
47
Systems investment
• Health systems can more appropriately
invest in care
• Fewer under-treatment events
• Diagnostic base rates possible
• Track individual and community response
to intervention
• Target investment, staff needs
48
Session Evaluation
Please complete and return the
evaluation form to the classroom monitor
before leaving this session.
Thank you!
References
•
Adelmann, P. K., & Asche, S. E. (2002). Minnesota behavioral health treatment need assessment:
Comparison of health plans participating in the prepaid medical assistance program (White Paper).
Retrieved from Minnesota Department of Human Services website:
http://www.dhs.state.mn.us/main/groups/healthcare/documents/pub/dhs_id_008301.pdf
•
Brown, R. A. & Ramsey, S. E. (2000). Addressing comorbid depressive symptomatology in alcohol
treatment. Professional Psychology: Research and Practice, 31(4), 418-422.
•
Carpenter, L. L. & Price, L. H. (2000). Psychotic depression: What is it and how should we treat it?
Harvard Review of Psychiatry, 8, 40-42.
•
Center for Disease Control and Prevention. (2010). Mental health fast stats (Fact Sheet). Retrieved from
Center for Disease Control and Prevention website: http://www.cdc.gov/nchs/fastats/mental.htm
•
Center for Disease Control (2006). Anxiety and depression [Fact sheet]. Retrieved from
http://www.cdc.gov/Features/dsBRFSSDepressionAnxiety
50
References
•
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59,1229-1235.
•
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Systems Improvement, Bloomington, Minnesota. (2010) Psychiatric Services, 61, 10, 1042-1044
•
Justice, A. C., McGinnis, K. A., Atkinson, J. H., Heaton, R. K., Young, C., Sadek, J., Madenwald, T.,
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•
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•
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References
•
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•
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•
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•
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•
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•
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