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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 • • • • • 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) 19 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. 22 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 23 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 27 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 • • • • • • • • 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 • Gonzalez, O., Berry, J. T., McKnight-Eily, L. R., Strine, T., Edwards, V. J., Lu, H., & Croft, J. B. (2010). Current depression among adults—United States, 2006 and 2008. Morbidity and Mortality Weekly Report, 59,1229-1235. • A New Direction in Depression Treatment in Minnesota: DIAMOND Program, Institute for Clinical 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., Becker, J. T., Conigliaro, J., Brown, S. T., Rimland, D., Crystal, S., & Simberkoff, M. (2004). Psychiatric and neurocognitive disorders among HIV-positive and negative veterans in care: Veterans Aging Cohort Five-Site Study. AIDS , 18 (suppl 1), S49–S59. • Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. The Journal of General Internal Medicine, 16, 606-613. • Erbes, C., Polusny, M. A., Billig, J. A., Mylan, M., McGuire, K., Isenhart, C., & Olson, D. (2004). Developing and applying a systematic process for evaluation of clinical outcomes instruments. Psychological Services, 1(1), 31-39 51 References • Glasgow, R. E., Nutting, P. A., King, D. K., Nelson, C. C., Cutter, G., Gaglio, B., Rahm, A. K., Whitesides, H., & Amthauer, H. (2004). A practical randomized trial to improve diabetes care. Journal of General Internal Medicine, 19,1167–1174 • Lichtman, J. H., Bigger, Jr,, J. T., Blumenthal, J. A., Frasure-Smith, N., Kaufmann, P. G., Lespérance, F., Mark, D. B., Sheps, D. S., Taylor, C. B., & Froelicher, E. S. (2008). Depression and coronary heart disease: Recommendations for screening, referral and treatment. Journal of the American Heart Association, 118, 1768-1775. • MaGPIe Research Group (2005). General practitioners’ perceptions of barriers to their provision of mental healthcare: A report on mental and general practice investigation (MaGPIe) Journal of the New Zealand Medical Association. 118: 1222, Retrieved from the New Zealand Medical Journal website; http://journal.nzma.org.nz/journal/118-1222/1654/ • Marciniak, M. D., Lage, M. J., Dunayevich, E., Russell, J. M., Bowman, L., Landbloom, R. P. & Levine, L. R. (2005). The cost of treating anxiety: The medical and demographic correlates that impact total medical costs. Depression and Anxiety, 21, 178-184. • National Institute of Mental Health. (2010). The numbers count: Mental disorders in America (Fact Sheet). Retrieved from National Institute of Mental Health website: http://www.nimh.nih.gov/health/publications/the-numbers-count-mental-diso rders-in-america/index.html 52 References • Peterson, S., Hutchings, P., Shrader, G., & Brake, K. (2011). Integrating health care: The clear advantage for underserved diverse populations. Psychological Services, 8(2), 69-81. • Rivas-Vazquez, R. A., Saffa-Biller, D., Ruiz, I., Blais, M. A., Rivas-Vazquez, A. (2004). Current issues in anxiety and depression: Comorbid, mixed, and subthreshold Disorders. Professional Psychology: Research and Practice, 35(1), 74-83. • Salokangas, R. K. R., Luutonen, S., Nieminen, M., Huttunen, J. & Karlsson, H. (2007). Vulnerability to psychosis increases the risk of depression. Results of the RADEP study. Nordic Journal of Psychiatry, 61(5), 393-402. • Trivedi, M. H., Rush, A. J., Wisniewski, S. R., . Nierenberg, A.A., Warden, D., Ritz, L., Norquist, G., Howland, R. H., Lebowitz, B., McGrath, P. J., Shores-Wilson, K., Biggs, M.M., Balasubramani, G. K., Fava, M., and STAR*D Study Team (2006). Evaluation of Outcomes With Citalopram for Depression Using Measurement-Based Care in STAR*D: Implications for Clinical Practice. American Journal of Psychiatry, 163: 28-40. • Vermani, M., Marcus, M., and Katzman, M.A., (2011). Rates of detection of mood and anxiety disorders in primary care: A descriptive, cross-sectional study . Primary Care Companion: CNS Disorders: 13,2, e1-10 53