Forecasting Depression: Past, Present, and Future Mental Health in Los Angeles County

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Beverley Kelly
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Familiarize participants with the aim and scope of the UCLA UniHealth Mental Health

1 Forecasting Depression: Past, Present, and Future Mental Health in Los Angeles County Supported with a grant from July 24, 2017 Forum Objectives 1. Familiarize participants with the aim and scope of the UCLA UniHealth Mental Health Forecasting Project. 2. Provide participants with an opportunity to share innovative interventions to address mental health in Los Angeles County. 1

policies and programs that improve the health of all communities.

2 Introduction to the UCLA Center for Health Advancement and Health Forecasting Project UCLA Center for Health Advancement The Center produces knowledge that helps decision-makers in all sectors formulate policies and programs that improve the health of all communities. We provide tools to: support informed decision-making; build consensus; and leverage collaborative opportunities with a range of partners. 2

Center for Health Advancement decision-support tools Forecasting trends in physical and mental chronic diseases and the potential impact of interventions for states, counties, communities and

3 Center for Health Advancement decision-support tools Forecasting trends in physical and mental chronic diseases and the potential impact of interventions for states, counties, communities and hospitals Estimating the social and fiscal co-benefits of investments in health, education and criminal justice programs and policies. Analysis and recommendations to minimize potential harm and maximize benefits of proposed public policies and projects from highways and city planning to education and tax policy. Health Forecasting Forecasting Module Purpose: To foster data-driven decision-making using forecasts of health to strategically anticipate, prepare, and plan for healthier communities Tool: an innovative computer simulation model that predicts changes in population health and simulates impacts of evidence-based interventions Features Customizable results by ZIP code Granular analysis for costly chronic condition trends by subgroups, such as race/ethnicity, age group, education and gender 3

tailor programs Value as a community health improvement tool Place-based focus Tailored to your population (zip code) Supported by existing data accounting for past health trends & demographic shifts

4 What do we forecast? Costly preventable chronic conditions Forecasting Module Modifiable health behaviors that can lead to improved health Demographic shifts that can provide insight to strategically plan resources or tailor programs Value as a community health improvement tool Place-based focus Tailored to your population (zip code) Supported by existing data accounting for past health trends & demographic shifts Supports Data-Driven Decision-Making: identify high risk groups Demonstrate need Engage Prioritize Evaluate Which groups are disproportionately affected? What will the future look like if we don t intervene to reduce chronic disease? How can I communicate the magnitude of the problem with my board of directors, community members, elected officials? Which chronic conditions impact my community? Which evidencebased programs could have the most effect? How can I benchmark my efforts? Am I moving the dial? 4

5 Mental Health Forecasting The goal of this project is to support non-profit hospitals and community stakeholders to better address the mental health burden in their communities by creating a computerbased simulation model to forecast the prevalence of major depression through the year 2050 in Los Angeles County. Forecasting Module After establishing baseline trends, the model will be used to estimate the potential impact of different community-based treatment and prevention programs Explore the State of Depression and Mental Health 5

6 Mental health - a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community. (WHO) Mental illness includes diagnosable mental disorders; or conditions characterized by alterations in thinking, mood, and/or behavior associated with distress and/or impaired functioning. (UHHS) Depressive disorders Major depressive disorder (MDD) - unipolar Bipolar - alternating lows and highs Dysthymia chronic, low grade MDD or Clinical depression 5+ symptoms nearly every day for 2+ weeks and cause significant distress or interfere with functioning: Feelings of sadness, hopelessness, low mood Loss of interest/pleasure in activities that used to be enjoyable Change in weight or appetite (+ or -) Change in activity (+ or -) Sleeping too much or too little Fatigue or low energy Feelings of guilt or worthlessness Difficulties concentrating and paying attention Thoughts of death or suicide 6

Major Depression 12-month prevalence in US 2015 using DSM* 6.7% adults 12.5% age 12-17 8.5% 4.7% 19.5% 5.8% 23.2% Est. lifetime prevalence in the US at age 75 13.

7 Major Depression 12-month prevalence in US 2015 using DSM* 6.7% adults 12.5% age % 4.7% 19.5% 5.8% 23.2% Est. lifetime prevalence in the US at age % Ever diagnosed by Dr, Los Angeles County * Without some exclusions and adapted for adolescents Sources: National Survey on Drug Use and Health; National Comorbidity Survey; Los Angeles County Heath Survey. In the News 19.5% vs. 5.8% in previous slide 2.1 suicides per 100k, CDC

8 Depression Burden of Disease #5 of 291 conditions/injuries - DALY burden rank in US bil in 2010$ workplace costs, direct costs and suicide- related costs Sources: National Institute of Mental Health; Greenberg et al. doi: /JCP.14m09298;. Depression - Lifecourse Age of Onset: Bottom 5%: 12 Median: 32 Top 5%: 73 Ave duration: 16 weeks; 12% have chronic courses Recurrence: 8 out of 10 people will have 1 more event in their lifetime Median lifetime events: 4 Sources: National Comorbidity Survey; Judd doi: /archpsyc

9 Depression Substance use Physical activity Diabetes e.g. Depression Behaviors Diabetes Diabetes Stress Depression Depression Diabetes self-management Inflammation Depression Diabetes 45% of depressed undiagnosed Sources: National Institute of Mental Health; Mayo Clinic; Laake et al. and Moulton et al. doi: /S (15) ; Li et al., doi: /j.diabres Discuss Interventions to Prevent, Treat, and Manage Depression 9

10 Intervention Review of Literature Difficulty in identifying the incidence of depression, its relapses, its risk, which leads to confusion in intervention and prevention efforts There is also some confusion in how intervention programs are to be implemented: universal or selective prevention Guiding theory/ conceptual framework Components Level of Intervention Examples Cognitive Behavioral Therapy (CBT) Change thinking/behaviors Primary, Secondary Penn Resilience Program Depression Screening Identify cases and treatment Primary PHQ-9, multiple instruments Collaborative Care model Integrative care Primary, Secondary, Tertiary TIDES Project (VA Project) Magnitude, Trends, and Needs 1. What are the top 5 health issues in the populations that you serve? If mental health is not among the top 5, where would you say it ranks? Forecasting Module 2. What factors regarding mental health lead you to rank it the way you do? 3. Over the past 5 to 10 years what trends have you seen in the prevalence and severity of mental health issues in the populations that you serve? What factors are contributing towards these trends? 4. If you were going to assess the extent of major depression and associated health care needs in the population that you serve, how would you go about doing so? 10

11 Addressing these issues Forecasting Module 5. What organizations and agencies are involved in addressing mental health conditions in the populations that you serve? 6. What s one thing that would make the most impact over the next 5 years towards decreasing the burden of major depression in the population that you serve? What role do you see your organization playing in these efforts? What could funders or the government do to support your efforts in this area? Intervention Discussion 7. Ideally, what would be done to better address mental health needs in the populations that you serve? 8. Clinical management of physical health conditions, such as diabetes and hypertension, are augmented by a wide array of community-based prevention and management programs. Have you seen or heard about any promising communitybased interventions to address mental health issues? If so, what are they? 11

12 Intervention types: Discussion What are some promising interventions? Screening? Primary? Secondary? Discuss Data Sources and Implementation of Findings 12

13 Measures and data discussion What goals are realistic in your community? At what time point? What will we count/compare to know that outcomes have been achieved? Models are representation of real-world conditions but are not represented perfectly We have identified suicides and attempted suicides as potential data to help us evaluate our assumptions and modeling decisions Measures and Data sources Can vary Alignment with clinical diagnoses Data Sources Name National Health and Nutrition Examination Survey (NHANES) Type of Instrument Used (if any) PHQ-9, Depression screener -activity limitation -use of MH care pros -social and emo support -SU and AU -serve on active duty Study population demographics US adults and children, 18+ are only included for mental health module Type of data Forecasting Module National survey data Behavioral Risk Factor Surveillance System (BRFSS) Module of PHQ 8/9 questions, K6 scale -HRQOL -life satisfaction, social support -HC access and use -sexual/intimate partner violence US civilian, noninstitutionalized adult population National survey data National Health Interview Survey (NHIS) K6 scale -activity limit -MH care use -armed forces status in HH U.S HHs, mental health module includes adult and child National survey data California Health Interview Survey (CHIS) K6 scale, Repeated K6 Sheehan Scale (Anxiety) -access and utilization California HHs California survey data LA County Health Survey Questions adapted from BRFSS - ever diagnosed LA County residents, adults, and children County survey data 13

14 How can forecasting mental health add value? Provide local area estimates of depression for Los Angeles County cities and hospital catchment areas; Communicate evidence-based information that incorporates the best available research and surveillance data to support resource allocation decisions for preventing and treating depression; Provide a measure of information equity bringing understanding of the population burden of mental health disorders on par with physical health conditions; Bring a population health lens to understanding and planning responses to mental health conditions that have largely been dealt with from a clinical, individual patient basis. Facilitate inter-sectoral policy efforts to deal with depression involving schools, criminal justice, public health, social services and health-care. Review Modeling Criteria 14

Framework informed by Health Forecasts Stage one: develop an initial, concise, operational statement of the issue Stage two: determine what is known through the scientific literature Stage three:

Occurs within the simulation program Occurs outside the simulation program Occurs within the simulation program Set Population Baseline Integrate risk factors & projections Validate Calibrate Use CA

15 Framework informed by Health Forecasts Stage one: develop an initial, concise, operational statement of the issue Stage two: determine what is known through the scientific literature Stage three: quantify the issue Stage four: develop program or policy options Stage five: develop an action plan for the program or policy Stage six: evaluate the program or policy Developing a county model Occurs within the simulation program Occurs outside the simulation program Occurs within the simulation program Set Population Baseline Integrate risk factors & projections Validate Calibrate Use CA Department of Finance projections for each county Set parameters to adjust behavior & disease modules Upload the County study Define unique catchment areas Baseline numbers are adjusted Model is run again & validated Compare forecast baseline to other sources 15

16 The Depression Framework Decisions made so far Attributes of each simulated person: Age Gender Race/ethnicity Education Poverty status Population: adults Propensity applied at age 18 16

17 The UCLA Mental Health Forecasting Community Action Group Modeling impactful interventions Forecasting Module Cities/Gov t Hospitals Community cross-sectoral collaboration to transform health Society Cities/Gov t 17

The UCLA Mental Health Forecasting Community Action Group Aim Produce forecasts that are tailored to particular communities and assess interventions that are of interest to those communities.

18 The UCLA Mental Health Forecasting Community Action Group Aim Produce forecasts that are tailored to particular communities and assess interventions that are of interest to those communities. Commitment A community group or health service provider that would like to participate in planning or on ongoing implementation of an intx. Expectations Brief occasional phone calls Members will receive Baseline depression forecast report for adults in their service area Feedback on interventions modeled Technical assistance and translating model results Translating data and analysis to optimize community health

Photo courtesy Conrad N. Hilton Foundation. EVALUATION OF THE Conrad N. Hilton Foundation Chronic Homelessness Initiative 2015 REPORT

Photo courtesy Conrad N. Hilton Foundation. EVALUATION OF THE Conrad N. Hilton Foundation Chronic Homelessness Initiative 2015 REPORT December 2015 Executive Summary Under a September 2011 contract with