Universal Depression Screening - Collaborative Family Healthcare

Report
Session #_D1a_
Friday, October 11, 2013 or Saturday, October 12, 2013
Screening and Management of Depression in Primary
Care:
Feasibility, Utility, and its role in Clinical Outcomes
William Sieber, PhD
Zephon Lister, PhD, LMFT
Rusty Kallenberg, MD
Alita Newsome, MA
David Strong, Ph.D.
Darren Himeles
Collaborative Family Healthcare Association 15th Annual Conference
October 10-12, 2013
Broomfield, Colorado U.S.A.
Faculty Disclosure
We have not had any relevant financial relationships
during the past 12 months.
Objectives
At the conclusion of this presentation, the participant will be able to:
• Understand the evidence in determining the effectiveness of universal
depression screening.
• Discuss what patient and disease condition characteristics are most
benefited by universal depression screening
• Describe the challenges and implications of universal depression screening
in primary care settings.
• Describe factors that contribute to the feasibility and utility of universal
depression screening and management in primary care
UCSD Primary Care
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•
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Three clinics, each 10 miles apart
Providers
 40+ Faculty physicians; 18+ residents
 2 Psychiatrists
 3 Licensed Mental Health Providers and 12 Mental health
providers in training
35,000+ patients; 120 – 160 patient encounters/day/clinic
Patient population:
 56% female
 53% Caucasian, 28% Hispanic, 12% Asian/Pacific Islander,
7% African American
 Range of payers - Medicaid to PPO
Patient Centered Medical Home
designation
2011 NCQA PCMH standards: Plan and Manage Care- One of
three clinically important conditions identified by the practice
must be a condition related to unhealthy behaviors (e.g.,
obesity) or a mental health or substance abuse condition.
 EPIC Systems electronic health record (EHR), now used
throughout the UCSD Healthcare System (not psychiatry).
EPIC provides a complete view of all visits — from ER to
primary care to specialty to inpatient — and all laboratory,
radiology, and special testing results.
Data analyst who creates software for providers to create
patent registries tracking variables of choice (e.g., smokers,
LDL, PHQ-9)
Literature: Depression in Primary Care
• Prevalence
o MDD Lifetime prevalence: 13.2 %; 12-month: 5-7%
o Dysthymia: 2-4% in primary care
o 3rd leading cause of loss of QALYs in older adults
• Screening efficacy
o Healthy People 2010 reported a baseline rate of 62 percent of adults being
screened for ‘mental health’ in 2000, with the goal of achieving a 68 percent
screening rate by 2010; VA reported 85% of eligible patients were screened
annually
o Up to 40 percent of cases of depression may be missed by PCP’s if provided no
assistance in screening
o USPSTF estimate 12-50 % of screen positives would meet MDD criteria:
majority screen positives not meet MDD yet could benefit from intervention
o AHRQ review did not find any studies that included adverse events of
screening.
Care Access and
Depression Screening Recommendations
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•
Access to behavioral health care
 Roughly one third to one half of non-elderly adults and almost two
thirds of older adults who are treated for depression are treated in
primary care.
 A naturalistic study of “typical of local primary health care
delivery” found that only 49 % of the patients who screened
positive for depression and had their depression confirmed by a
diagnostic interview and were not already being treated for
depression received any treatment at al
U.S. Preventive Services Task Force (USPSTF)
◦ screening adults for depression when staff-assisted depression
care supports are in place to assure accurate diagnosis, effective
treatment, and follow-up (i.e., collaborative care).
◦ against routinely screening adults for depression when staffassisted depression care supports are not in place7.
Arguments Against Screening
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•
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False negatives: denials, imprecision of PHQ-2
False positives: ‘yield’ of depression screening in primary care
range widely from 10 - 50% (undetected by PCP);
large/unknown percentage of screen positives already were
diagnosed with MDD. PHQ-9 needs qualified interviewer to
determine MDD
Drs. Coyne & Thombs assert that
o prior studies have not taken into account how many
patients were identified as depressed prior to screening,
o that SSRI medication is not very effective for any but the
most severe depression,
o therefore screening has limited value.
Gaps in Research
• Controversy remains about the utility of universal
•
•
•
depression screening
Studies have not adequately addressed “newly
diagnosed due to screening” very well
Few studies have enumerated costs of such a screening
program (staff resources) even without $ amounts
Little is known about factors that influence screening
utility, referrals/treatment options offered (e.g., % Rx,
psychotherapy), and treatment received – we know risk
factors but not how screening may differentially detect
cohorts with these risk factors
© 2013 WJ Sieber
Universal Depression Screening
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•
•
All adult UCSD Family Medicine patients presenting to any of
3 clinics were to be screened for depression (i.e., PHQ-2).
Ineligibility: if screen negative < 90 days, labs, flu shots,
procedure clinics
Any patient with a positive screen using the PHQ-2 was
administered a PHQ-9
o PHQ-9 scores of 10 or greater were considered indicative of moderate
depression in this analysis. (Spitzer)
o Clinical protocols for patients with PHQ-9 of > 10 include an evidence
based treatment plan
Patient Given PHQ-2
Depression Screen
Depression Screening
Clinical Protocol
PHQ-Score
2 or more
Full PHQ-9
given to
patient
PHQ-9
Score
(0-9)
Provide
patient
standard PCP
interpersonal
support and
education
PHQ-9
Score (1019)
1.Provide Patient
Information Sheet on
Stress Management
Groups and Collaborative
Care
2.Assess for T-Care
referral/follow-up
3.Assess for Collaborative
Care referral
4.Assess benefit of meds
and other PCP intervention
PHQ-2
Score less
than 2
No further
clinical
action
needed
PHQ-9 Score
(>20 with no #9
endorsement)
1.On-site T-Care trainee or
intern assesses pt. to inform
PCP intervention plan
2. Patient referral to
Collaborative Care
3.Assess benefit of meds and
other PCP intervention
PHQ-9 (#9
positive
endorsement)
1.(a) Immediate on-site
assessment and intervention by
T-Care trainee or intern to
inform PCP intervention plan
(e.g., ER), (b) access any CC staff
in clinic to assess patient at
earliest opportunity (c) if no CC
staff is available send stat EPIC
message to Lead Therapist or
Supervisor or page for
immediate support
2.See PHQ-9 >20 protocol
Methods
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•
Multiple clinical, health utilization, and outcome variables
were collected 11 months prior to and 11 months after
initiation of universal screening (Jan. 2011 – Oct. 2012)
The Electronic Health Record (EHR) was utilized to gather the
information regarding diagnoses as well as referral rates
o This included data from the problem list, from encounter notes, and
visit records
o These records were searched using an algorithm our team established
to create a database for analysis
Results
Working hypotheses, based on the literature, included:
1. Patients with multiple chronic health conditions will more
often screen positive than healthier patients, as will women
more often than men.
2. Majority of ’screen positives’ were already identified as
depressed.
3. Given equivalent severity of depression, elderly patients will
be less likely to be referred and receive counseling.
Thus, analyses focused on:
o who most often screened positive for depression
o Who was most often offered various treatment options
o who most often followed through with treatment
recommendations.
Screening for depression pre-post
universal program implementation
X2 = 7462.38, df = 1, p-value < 0.001
Odds of PHQ2 ≥2 (n=864)
Intercept
Age.Elderly=65+
Female=F
Race.Category=Hispanic/Latino
Race.Category=Black
Race.Category=Asian/PI/AI
Race.Category= Other
Observations with PHQ2+
Not CC Connected
CC Connected
Coef
S.E.
Wald Z
Pr(>|Z|)
-0.7434
0.1434
-5.19
<0.0001
-0.7396
0.2136
-3.46
0.0005
0.3676
0.158
2.33
0.0199
-0.3253
0.2442
-1.33
0.1828
-0.0909
0.243
-0.37
0.7084
-0.5627
0.2534
-2.22
0.0264
0.4821
0.275
1.75
0.0795
864
575
289
Patient Characteristics Associated
with a Positive PHQ-2 Screen
95% Confidence Intervals that do not cross the dotted line are statistically significant p<0.05
Impact of prior depression diagnosis
on screening outcome
Rate of positive PHQ-2
excluding patients with known depression
Prevalence of Identified Cases
With Affective Disorder (Pre- vs Post Screening)
Previously identified Affective Disorder
and ‘positive’ PHQ-9
Intervention trajectories based on PHQ-9 score
Referred to and seen by therapists,
by PHQ score and age
PHQ-9
Age
N
% Referred to
CC
% of referred
seen by CC
<5
< 65
4796
5.9
18.3
> 65
902
3.2
24.1
< 65
332
22.6
22.7
> 65
59
8.5
40.0
< 65
210
38.1
23.8
> 65
41
34.2
35.7
< 65
154
44.2
30.9
> 65
35
25.7
33.3
< 65
106
52.8
39.3
> 65
13
38.5
40.0
6 - 10
11 - 15
16 - 20
21 +
Patient Characteristics associated
with the Odds of CC Visit once Referred
Coef
S.E.
Wald Z
Pr(>|Z|
Intercept
-1.11
0.21
-5.20
<0.0001
Age.Elderly=65+
0.40
0.30
1.32
0.19
Female=F
-0.17
0.22
-0.78
0.44
Race.Category=Hispanic/Latino
-0.43
0.36
-1.22
0.22
Race.Category=Black
-0.88
0.43
-2.05
0.04
Race.Category=Asian/PI/AI
-0.58
0.39
-1.50
0.13
Race.Category= Other
-0.09
0.35
-0.25
0.80
PHQ2Pos=PHQ2Pos
0.57
0.21
2.73
0.01
The effects are all contrasts: exp(Coef)=Odds Ratio: i.e. PHQ Pos OR=exp(0.57)=1.77
• Elderly vs non-Elderly
• Women vs Men
• Each Ethnic/Racial Group vs Non-Hispanic White
• PHQ2 Positive vs not positive
Odds of Connection to Collaborative Care
Coef
S.E.
Wald Z
Pr(>|Z|)
Intercept
-2.73
0.09
-30.16
<0.0001
Age.Elderly=65+
-0.59
0.14
-4.17
<0.0001
Female=F
0.57
0.10
5.93
<0.0001
Race.Category=Hispanic/Latino
-0.09
0.15
-0.62
0.54
Race.Category=Black
0.01
0.16
0.06
0.95
Race.Category=Asian/PI/AI
-0.63
0.15
-4.14
<0.0001
Race.Category= Other
0.02
0.16
0.12
0.91
PHQ9
0.13
0.01
17.86
<0.0001
Logistic regression analysis:
Dependent variable is Refer/Visit to CC coded Yes or No
Patient characteristics adjusted for level of PHQ score.
So Elderly less likely to be connected, Women more likely, Asians less likely
Rates of Contact with CC, by Positive PHQ-2 Screen,
and by Prior Depression Diagnosis
Rates of Contact with CC, by Positive PHQ-9,
and by Prior Depression Diagnosis
Continuous Quality Improvement
o No screening if completed PHQ-2 < 3 months ago
o Included in pre-visit questionnaire
o Factors that impact feasibility
 Understandable algorithms for physicians response
 Resources for STAT requests for therapist or SI
response
 Resources for brief (increase) in CC referrals
o How assess utility of screening?
 Compare # patients referred (and adherent) to CC or
Rx’d pre versus post screening
 Assess improvement (PHQ-9) over time for pre versus
post screening for those seen in CC
Methodological/Analytic issues
o Measuring PHQs over time (with therapy)
 Regularity of assessment
 Differentiating between PHQ-9 n treatment from PHQ9 as newly identified (clinical versus population
analysis)
o How treat ‘ever positive’ and ‘first positive’ given negative
screens at first visit underestimate utility of screening with
ensuing screens showing positive
o How do ‘refusals’ affect results?
o Chronically ill screen positive more often but is ‘efficiency’
of screening better (e.g., # chronically ill w/ prior Dx of
depression?)
Additional Next Steps:
Clinically & analytically
• Implement GAD-7 and health behavior assessment
with programmatic response to each
• Assess outcome trajectories for patients PHQ-9s <10
and no treatment
Gaps in research?
• controversy remains about the utility of
universal depression screening
o Answer: Utility is high when all staff and providers need to
devote little effort
o Answer: Less than 10% of those screening positive for
depression had current diagnosis of depression
o Answer: Screen positives who scored low on PHQ-9 were
infrequently referred
• Few studies have enumerated costs of such a
screening program(staff resources) even
without $ amounts
o Answer: Part of larger PCMH efforts, once running little
effort
Gaps in research?
• Most studies have not adequately addressed
“newly diagnosed due to screening” very well
o Answer: we identified 208 new patients with depression in
11 months of screening
• little is known about factors that influence
screening utility, referrals and treatment
options offered (e.g., % Rx, psychotherapy),
and treatment received
o Answer: gender, age, ethnicity all play roles in rates of
screening positive and use of CC services once referred
Learning assessment
o A substantial proportion of patients screening positive are already
recognized as depressed?
 True or false?
o Negatives/costs of screening have been clearly documented in the
literature.
 True or false?
o Which patients are most likely to screen positive for depression?
 Elderly?
 Asian /Pacific Islanders?
 African Americans?
 Chronically ill?
o Which factors impact feasibility of screening program?
 EHR
 Universality of eligible patients & clarity of rules
 CQI (low performing teams, low referral rates)
Objectives met ?
• Feasibility
o Universal screening less complicated than decision rules
o Training and CQI on developed protocols is essential (staff and low
performing MD/MA teams)
o EHR/automated triggering using evidence-based algorithms (PHQ-2 >2
 PHQ-9)
o Availability of CC therapists (T-CARE and leaders on-call)
• Utility
o 208 previously undiagnosed depressed patients in 11 months
o Increased consults/referrals to CC
o Reinforcement of CC services (chronic illness, improve follow-through)
• Role in Clinical Outcomes
o Future analysis of improved adherence, health behavior change,
proactive screening (MyChart) for at-risk cohorts (e.g., chronic illness)
References
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Agency for Healthcare Research and Quality (2009). Screening for
Depression in Adults and Older Adults in Primary Care: An Updated
Systematic Review. AHRQ Publication No. 10-05143-EF-1
Depression Guideline Panel.(1993) Depression in primary care: Detection
and Diagnosis. Clinical Practice Guideline: Number 5. AHCPR Publication
No. 93-0550 ed. United States Department of Health and Human Services;
Public Health Service; Agency for Health Care Policy and Research.
Harman JS, Veazie PJ, Lyness JM. (2006) Primary care physician office
visits for depression by older Americans. J Gen Intern Med 21:926-930
Hasin D, Goodwin RD, Stinson F, Grant B. (2005) Epidemiology of Major
Depressive Disorder: Results From the National Epidemiologic Survey on
Alcoholism and Related Conditions. Arch Gen Psychiatry;62:1097-1106.
Kessler, R. C., Chiu, W. T., Demler, O., Walters, E. E. (2005). Prevalence,
severity, and comorbidity of twelve-month DSM-IV disorders in the National
Comorbidity Survey Replication (NCS-R). Archives of General Psychiatry,
62(6), 617-627.
Kessler RC, Berglund P, Demler O et al. (2003) The epidemiology of major
depressive disorder: results from the National Comorbidity Survey
Replication (NCS-R). JAMA; 289:3095-3105.
References
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Luber, M., Meyers, B. S., Williams-Russo, P. G., Hollenberg, J. P.,
DiDomenico, T. N., Charlson, M. E., & Alexopoulos, G. S. (2001).
Depression and service utilization in elderly primary care patients. The
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Pignone MP, Gaynes BN, Rushton JL et al. (2002) Screening for
depression in adults: a summary of the evidence for the U.S. Preventive
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Narrow WE, Rae DS, Robins LN, Regier DA.(2002) Revised prevalence
estimates of mental disorders in the United States: using a clinical
significance criterion to reconcile 2 surveys' estimates. Arch Gen
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Questions?

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