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 • • • • 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 • • 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 • • • 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 • • • 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 • • 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 • • • • • • Agency for Healthcare Research and Quality (2009). Screening for Depression in Adults and Older Adults in Primary Care: An Updated Systematic Review. 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