Tag: Pharmacoepidemiology

Predictive modeling: the role of opioid use in suicide risk

Grant Details

Funder: NIDA

Grant Number: R01DA047724

Grant Period: 8/15/2018 – 6/30/2022

Narrative: Suicide deaths and opioid-related overdose deaths have both been increasing in recent years. These two public health crises have substantial overlap: our preliminary work suggests that between 22% and 37% of opioid-related overdoses are suicides or suicide attempts. Healthcare settings are ideal places to intervene to prevent suicides, however clinicians need better tools to recognize the patients at greatest risk. We developed models that predict risk of suicide attempt or death with 83% to 86% accuracy. However, these models do not include important opioid-related variables. In a parallel body of work, we developed algorithms based on coded electronic health record (EHR) data to identify opioid-related overdoses and classify them as unintentional or intentional suicides. The proposed project integrates these two existing lines of research. Our suicide risk prediction dataset includes seven large healthcare systems and approximately 20 million visits by 3 million patients; it is currently being expanded to include additional outcomes and visits through 2016, and additional predictors, however inclusion of opioid-related variables was not part of the funded supplement. In the proposed study, we will determine whether including variables related to illicit and prescribed opioid use, opioid use disorder, discontinuation or significant dose reductions of prescription opioids, or prior non-fatal opioid-related overdoses improves predictions of suicide attempts or death within 90 days following an outpatient healthcare visit. We will also develop models that specifically predict opioid- related suicide attempts and deaths in the sample as a whole and among people prescribed opioid medications, and determine if the predictors of opioid-related suicide attempts or deaths are consistent for men and women. The goal of the proposed work is to maximize the performance of our models in order to create the best available tools for clinicians to help reduce future suicides. We have an established collaboration with the largest national EHR vendor and are working to develop an EHR-based, point-of-care clinical tool to predict suicide attempts and deaths based on our research findings. This work will therefore have a direct impact on clinical practice by providing clinicians with an efficient, evidence-based tool to evaluate suicide risk. The work will also provide critical data on understudied opioid-related predictors and moderators of suicide.

Lead Site: KPNW (PI Bobbi Jo Yarborough)

Participating Sites: HFHS, HPI, KPCO, KPHI, KPSC, KPWA

Current Status

Summary of Findings

Publications

Improved tailoring of depression care using customized clinical decision support

Grant Details

Funder: NIMH

Grant number: R01MH114873

Grant period: 07/01/2018 – 04/30/2023

Narrative: Treatments for mental health conditions such as unipolar depression provide modest average benefit but have wide variation between individuals and within individuals over time. Evidence-based customized treatment protocols would improve the mental health care of many people by providing treatment recommendations for individuals that take into account potential variation because of personal characteristics such as current health status, symptoms, and response to earlier treatment. Generating customized treatment protocols requires large amounts of data, such as from networks of health systems that can link electronic health records from millions of individuals. Current statistical approaches for discovering customized treatment protocols are limited in three important ways. First, current approaches rely on scientists to select the patient characteristics to use to customize treatments instead of using data to find the patient characteristics that will lead to improved, customized care. Second, customized treatment protocols discovered with current statistical methods assume no unobserved differences between individuals who receive various treatment options. Third, investigators do not have ways to know if the available data contain enough information to discover and compare customized treatment protocols precisely enough to make clinical decisions. We will address these three limitations by developing new statistical tools for discovering customized treatment protocols using electronic health records data. Our research team has expertise and experience in statistics, epidemiology, and mental health care. We will integrate methods that have been successfully used in other settings to improve statistical approaches for discovering customized treatment protocols and address these three important limitations. We will extend machine learning tools for selecting important pieces of information to the time-varying data structure required for discovering customized treatment protocols. We will build approaches that use available knowledge about the size of unobserved differences between groups of people who received different treatments to assess how those differences change study results. By building on the math used to estimate the sample sizes needed for precision in randomized trials with complex designs, we will develop new formulas for determining how many people with a particular condition and who took a particular drug are needed in a health system to provide enough accurate information to discover customized treatment protocols. Using data from the electronic health records of more than 15,000 patients, we will discover customized treatment protocols for depression. By improving statistical tools and addressing current limitations, our customized treatment protocols will have immediate impact for people living with unipolar depression. The statistical tools we develop will also be useful for discovering customized treatment protocols for people with a wide variety of mental health conditions.

Lead site: KPWA (PI Susan Shortreed)

Participating site: McGill University (co-I Erica Moodie)

  • Funder contacts:
    • Program Official: Michael Freed

Current Status

We have published papers proposing approaches to sample size estimation, unmeasured confounding sensitivity analyses, and selecting tailoring variables. We are continuing to work on alternative methods for tailoring variable selection.

Summary of Findings

  • Shrinkage regression based methods can identify important tailoring variables
  • Distributed regression methods can optimize individual treatment rules while protecting individual privacy
  • Dynamic weighted survival modeling can identify more effective individualized antidepressant treatment strategies using health records data

Publications

  1. Coulombe J, Moodie EEM, Shortreed SM, Renoux C. Can the Risk of Severe Depression-Related Outcomes Be Reduced by Tailoring the Antidepressant Therapy to Patient Characteristics? Am J Epidemiol. 2021 Jul 1;190(7):1210-1219. doi: 10.1093/aje/kwaa260. PMID: 33295950; PMCID: PMC8245894.
  2. Bian Z, Moodie EEM, Shortreed SM, Bhatnagar S. Variable selection in regression-based estimation of dynamic treatment regimes. Biometrics. 2021 Nov 27. doi: 10.1111/biom.13608. Epub ahead of print. PMID: 34837380.
  3. Moodie EEM, Coulombe J, Danieli C, Renoux C, Shortreed SM. Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes. Lifetime Data Anal. 2022 Jul;28(3):512-542. doi: 10.1007/s10985-022-09554-8. Epub 2022 May 2. PMID: 35499604.

MRHN III Supplemental Project: Effect of Initiating Buprenorphine on Suicidal Behavior

Grant Details

Title: Buprenorphine Effect on Suicidal Behavior

Funder: NIMH

Grant Number: U19MH121738-02S2  (supplement to main MHRN cooperative agreement)

Grant Period: 9/17/2020 – 8/31/2022

Narrative: This large observational study will evaluate the effects of initiating buprenorphine treatment on subsequent suicidal behavior among people with opioid use disorder, including those with and without co-occurring mental health conditions or other known risk factors for suicidal behavior. We will use comprehensive health records data from four large health systems serving a combined member/patient population of approximately 11 million. Analyses will examine the overall effect of buprenorphine treatment on subsequent suicide attempts or death, heterogeneity of effects in patient subgroups, and specificity of effects to buprenorphine vs other medications.

We will be using previously developed suicide risk prediction tools to compare the outcomes of individuals who do and do not use buprenorphine with similar baseline suicide risk.

  • Lead Site: KPWA
    • Overall PI: Greg Simon
  • Participating Sites/Subcontractors:
    • KPNC – Site PI: Cynthia Campbell
    • KPSC – Site PI: Rulin Hechter
    • Henry Ford – Site PI: Brian Ahmedani
  • Funder Contacts:
    • Science Officer: Susan Azrin
    • Program Official: Michael Freed
    • Grants Management Official: Julie Bergerud
  • Awarded Budget (Total Cost):
    • Year 1: $514,616
    • Year 2: $274,321

Documents

Funding Announcement: PA-18-591

Notice of Award

Personnel Contact List

Human Subjects: YES

  • IRB Review:
    • KPWA is single IRB reviewing for KPWA, KPNC, and KPSC – Approved waiver of consent for use of records data
    • Henry Ford determined to be exempt
    • KPWA IRBnet File: [1649129]

Clinical Trial: NO

Current status

Exploratory analyses (in preparation for extraction of data at each site) have examined availability and quality of data regarding opioid medication use, availability and quality of data regarding injury and poisoning events, and types of visits occurring prior to initial buprenorphine prescriptions.  These analyses are informing refinements to research design and data specifications.  Final data extraction will occur during in October 2022 with analyses complete in early 2023.

Summary of findings

Not yet available

Publications

None

Diversity Supplement – Understanding factors that lead to disparities in depression treatment

Project Name:
Diversity Supplement – Understanding factors that lead to disparities in depression treatment
Principal Investigator:
Karen J Coleman, PhD
Principal Investigator Contact Information:
Karen.J.Coleman@kp.org
Principal Investigator institution:
Kaiser Permanente Southern California
Funder
NIMH
Funding Period:  
09/2014 – 06/2016 (no-cost extension through 06/2017)
Abstract:
Depression and other mental illnesses lead to more disability than the most prevalent physical chronic illnesses such as heart disease, diabetes, and cancer, and may cost the U.S. healthcare system as much as 300 billion dollars annually. There are clear racial and ethnic differences in depression treatment, however, it is unknown if these are patient, provider, or healthcare system driven. The diversity supplement was designed to build on previous work funded within the Mental Health Research Network (MHRN) on practice variation in the treatment of depression. The original aims of the diversity supplement were as follows: Aim 1: To understand the healthcare system-, provider-, and patient-level factors that predict taking the initial antidepressant medication prescribed and/or attendance at the initial psychotherapy visit (primary adherence) within 30 days of an initial depression diagnosis.
Aim 2: To identify the healthcare system-, provider-, and patient-level factors that predict continuation of depression-related treatment once started (secondary adherence).
AIM 3: To characterize racial/ethnic disparities in the achievement of depression improvement or remission with treatment as assessed with the patient health questionnaire (PHQ9), and to understand the role of adherence in this response to treatment.
Grant Number:  
U19MH092201 (Supplement under MHRN II)
Participating Sites Contributing Data:
Kaiser Permanente Southern California, Pasadena, CA
Group Health Cooperative, Seattle, Washington
HealthPartners Institute, Minneapolis, Minnesota
Kaiser Permanente Colorado, Denver, Colorado
Kaiser Permanente Hawaii, Honolulu, Hawaii
Henry Ford Healthcare Systems, Detroit, Michigan
Additional Sites Participating in the Study:
Baylor Scott & White, Temple, Texas
University of Utah, Salt Lake City, Utah
Investigators:
Karen J. Coleman, PhD
Gregory Simon, MD MPH
Rebecca Rossom, MD
Arne Beck, PhD
Beth Waitzfelder, PhD
John Zieber, PhD
Brian Ahmedani, PhD
Zach Imel, PhD
Major Goals: To provide a high-level understanding of how race/ethnicity contributes independently to the variation for initiation and continuation of depression treatment. To provide a dataset and documentation associated with this dataset and its analyses that can be used by other researchers interested in the treatment of depression in large healthcare systems. To provide a basis for testing culturally tailored or appropriate interventions that improve the adherence to depression treatment in a variety of patient populations.
Major Limitations: Questions about depression treatment outcomes cannot be addressed with this dataset because PHQ9 data collection in the five healthcare systems during the study period was not widespread. Questions about healthcare system variation in policies and guidelines for depression treatment cannot be addressed with this dataset as these variables were not available for study. Questions about provider-level variation in the treatment of depression can only be addressed for two sites in the study due to the lack of data collected for providers in the other sites. Thus, conclusions about provider-level variation and its contribution to depression treatment modalities and adherence cannot generalize to other healthcare settings.
Description of study sample:
There are two study samples included in this study. One is for initiation of treatment for patients newly diagnosed with depression and one is for adherence to a new episode of antidepressant medication and/or formal psychotherapy treatment in patients diagnosed with depression. Treatment in the Newly Diagnosed Patients 18 and older who had a new depression diagnosis in primary care clinics between 1/1/2009 and 12/31/2013 were included. Patients were excluded if they had a diagnosis of bipolar disorder, schizophrenia spectrum disorder, or other psychosis in the prior two years to the diagnosis date. To ensure the availability of data needed to create the patient sample for all analyses, the sample was limited to those who were continuously enrolled in the healthcare systems for at least 360 days prior to the diagnosis date, allowing a 60-day gap. New episodes of depression were defined as an ICD-9 code for depression made in a primary care setting, with no diagnosis or treatment for depression (either psychotherapy or antidepressant medication) during the 360 days prior to the diagnosis. These patients were followed for 90 days after the diagnosis date to look for the initiation of treatment (see definitions below for treatment). Patients who disenrolled from the healthcare systems in less than 90 days after diagnosis were excluded. Adherence in the Newly Treated
Patients 18 and older who had a new episode of formal psychotherapy treatment (PT) between 1/1/2010 and 12/31/2013 or a new antidepressant treatment (AD) between 1/1/2010 and 12/31/2013 were included. Patients were excluded if they had a diagnosis of bipolar disorder, schizophrenia spectrum disorder, or other psychosis in the prior two years to index date. The sample was also limited to those who were continuously enrolled in the healthcare systems for at least 270 days prior to the index AD/PT episode, allowing a 60-day gap. A new episode of AD/PT treatment was defined as not having any evidence of the same type of treatment (AD or PT) during the previous 270 days before the date of the new episode.  AD episodes with a prescription for trazodone were excluded because this drug is primarily prescribed for sleep disturbance and not depression. We did not consider appointments that were less than 30 minutes and/or clearly designated as only medication management to be formal psychotherapy.
Current Status:
The analytic dataset and its documentation have been compiled.  Further analyses funded by the project are limited to the following manuscripts which are currently in process: The Mental Health Provider as a Source of Racial and Ethnic Disparities in Adherence to Antidepressant Medication and Psychotherapy (Imel et al.)
Study Registration:
N/A
Publications: Coleman KJ, Stewart C, Waitzfelder BE, Zeber JE, Morales LS, Ahmed AT, Ahmedani BK, Beck A, Copeland LA, Cummings JR, Hunkeler EM, Lindberg NM, Lynch F, Lu CY, Owen-Smith AA, Trinacty CM, Whitebird RR, Simon GE. Racial-Ethnic Differences in Psychiatric Diagnoses and Treatment Across 11 Health Care Systems in the Mental Health Research Network. Psychiatr Serv. 2016 Jul 1;67(7):749-57. doi: 10.1176/appi.ps.201500217. Epub 2016 Apr 15.Rossom RC, Shortreed S, Coleman KJ, Beck A, Waitzfelder BE, Stewart C, Ahmedani BK, Zeber JE, Simon GE. Antidepressant adherence across diverse populations and healthcare settings. Depress Anxiety. 2016 Aug;33(8):765-74. doi: 10.1002/da.22532. Epub 2016 Jun 20.Simon GE, Coleman KJ, Waitzfelder BE, Beck A, Rossom RC, Stewart C, Penfold RB. Adjusting Antidepressant Quality Measures for Race and Ethnicity. JAMA Psychiatry. 2015 Oct;72(10):1055-6. doi: 10.1001/jamapsychiatry.2015.1437.Simon GE, Rossom RC, Beck A, Waitzfelder BE, Coleman KJ, Stewart C, Operskalski B, Penfold RB, Shortreed SM. Antidepressants are not overprescribed for mild depression. J Clin Psychiatry. 2015 Dec;76(12):1627-32. doi: 10.4088/JCP.14m09162.Zeber JE, Coleman KJ, Fischer H, Yoon TK, Ahmedani BK, Beck A, Hubley S, Imel ZE, Rossom RC, Shortreed SM, Stewart C, Waitzfelder BE, Simon GE. The impact of race and ethnicity on rates of return to psychotherapy for depression. Depress Anxiety. 2017 Dec;34(12):1157-1163. doi: 10.1002/da.22696. Epub 2017 Nov 2. PubMed PMID: 29095538; PubMed Central PMCID: PMC5718939.Waitzfelder B, Stewart C, Coleman KJ, Rossom R, Ahmedani BK, Beck A, Zeber JE, Daida YG, Trinacty C, Hubley S, Simon GE. Treatment Initiation for New Episodes of Depression in Primary Care Settings. J Gen Intern Med. 2018 Aug;33(8):1283-1291. doi: 10.1007/s11606-017-4297-2. Epub 2018 Feb 8. PubMed PMID: 29423624.
Resources:
A data dictionary and descriptive tables for the data file associated with this project will be available soon. Some research questions cannot be addressed by this dataset and require an initial review and possible discussion to make this determination. For immediate questions, contact Greg Simon at simon.g@ghc.org.
Lessons Learned:
For all systems contributing data to this project, electronic medical records, insurance claims, and other data systems were organized in a Virtual Data Warehouse (VDW) to facilitate population-based research. The VDW is a collection of common data definitions and formats to ensure equivalent de-identified data for analysis. Because the VDW relies on data availability from a diverse set of healthcare settings in the Health Care Systems Research Network customizing data abstraction such as healthcare system policy variables or provider-level descriptive information is difficult and in some cases impossible. This needs to be considered when studies are proposed that examine the interplay of healthcare system-, provider-, and patient-level factors in mental health-related treatment choices and outcomes.
What’s next?
Possible harvest of new PHQ9 data as implementation of screening and treatment follow-up have increased exponentially since 2013. Pursue an R01 to characterize heterogeneity of achievement of depression improvement or remission and incorporate more healthcare sites (only 6 of 13 MHRN sites were included) and use additional provider variation analytic methods. Other possible grant ideas that have been discussed: Culturally-tailored intervention to assist with the decisions around depression treatment (shared decision-making and motivational interviewing models)

Effects of Medical Products on Suicidal Ideation and Behavior

Project Name:
Effects of Medical Products on Suicidal Ideation and Behavior
Principal Investigator:
Gregory Simon, MD, MPH
Principal Investigator Contact Information: 
gregory.e.simon@kp.org
Principal Investigator Institution:
KP Washington Health Research Institute
Funder:
Food and Drug Administration (FDA)
Funding Period:
9/30/2018 to 9/30/2021
Abstract:
We propose a comprehensive program of infrastructure development and methods development to support future generation of real-world evidence addressing these critical gaps.  The project team will include health systems and embedded research organizations with deep expertise in stakeholder engagement, medical informatics, data science, clinical epidemiology, biostatistics, pragmatic clinical trial methods, implementation science, and innovations in care delivery. Specific Tasks include: Augment the existing FDA Sentinel Initiative data infrastructure to support study of severe mental illness, suicidal ideation, and suicidal behavior. Evaluate and improve generalizability of models predicting suicidal behavior for use in future observational research and pragmatic trials. This program will be embedded in 4 integrated health systems serving a combined population of approximately 10 million members.  This work will be conducted in collaboration with health system and patient/family stakeholders, to assure that methods and evidence developed will actually address real-world questions. This infrastructure and methods development will enable a robust program of research regarding the effects of medical products on suicidal ideation and behavior, including: Scalable and re-usable methods to assess suicidal ideation and behavior as an adverse effect of existing products. Scalable and re-usable methods to assess therapeutic effects of existing products for reducing suicidal ideation and behaviorScalable and re-usable methods to rapidly evaluate possible therapeutic and adverse effects of new medical products on suicidal ideation and behavior. Large pragmatic trials to evaluate therapeutic effects of promising new product(s) on suicidal behavior
Grant Number:
N/A
Participating Sites:
Kaiser Permanente Washington
Harvard Pilgrim Healthcare
Kaiser Permanente Northern California
Kaiser Permanente Southern California
Henry Ford Health System                
Investigators:
Gregory Simon MD MPH
Susan Shortreed PhD
Yates Coley PhD
Richard Platt MD MS
Jeffrey Brown PhD
Darren Toh ScD
Jessica Young PhD
Stacy Sterling PhD
Karen Coleman PhD
Jean Lawrence ScD
Brian Ahmedani PhD
Major Goals Augment the existing FDA Sentinel Initiative data infrastructure to support study of severe mental illness, suicidal ideation, and suicidal behavior. Evaluate and improve generalizability of models predicting suicidal behavior for use in future observational research and pragmatic trials.
Description of study sample:
Various analyses are using data regarding approximately 4.5 million members of participating health systems.
Current Status:
Completed data infrastructure work includes:
– A toolkit to assess re-identification risk when sharing data derived from healthcare records:
– More timely updating of mortality data in health system research data warehouses.
– Regular reporting of availability and quality of patient-reported outcome data in health system research data warehouses.
Analyses are complete regarding:
– Value of more detailed data representation and more complex modeling methods for prediction of suicidal behavior.
– Accuracy of ICD-10-CM encounter diagnoses for identifying self-harm events.
– Value of data typically only available from electronic health records for prediction of suicidal behavior.
Study Registration:
N/A
Publications:
Simon GE, Shortreed SM, Boggs JM, Clarke GN, Rossom RC, Richards JE, Beck A, Ahmedani BK, Coleman KJ, Bhakta B, Stewart CC, Sterling S, Schoenbaum M, Coley RY, Stone M, Mosholder AD, Yaseen ZS. Accuracy of ICD-10-CM encounter diagnoses from health records for identifying self-harm events. J Am Med Inform Assoc. 2022 Aug 26:ocac144. doi: 10.1093/jamia/ocac144.
Resources:
N/A
Lessons Learned:
For prediction of suicidal behavior following outpatient mental health visits, more detailed temporal representation and more complex model development methods (random forest or neural networks vs. penalized logistic regression) do not meaningfully improve prediction accuracy.
When using prediction models to account for confounding by indication in observational studies of medication effects on suicidal behavior, random forest models may be slightly – but not meaningfully – superior to penalized logistic regression.
When using health records data to predict suicidal behavior, additional data available only from electronic health records (race, ethnicity, patient-reported outcome results) do not significantly improve prediction over data typically available from insurance claims.
What’s next?
Additional analyses will examine:
– Similarities and differences in prediction of opioid vs. other overdoses
– Similarities and differences in prediction of self-harm vs. accidental overdoses
– Changes in accuracy of suicide risk prediction models with health system implementation of Zero Suicide care improvement programs.

FDA Black Box Warning and Suicide

Project Name:
Longitudinal Analysis of SSRI Warnings and Suicide in Youth
Principal Investigator
Stephen Soumerai, ScD
Principal Investigator Contact Information:          
ssoumerai@hms.harvard.edu
Principal Investigator institution:
Harvard Pilgrim Health Care
Funder
NIMH
Funding Period:
09/10 – 07/2013
Abstract:
Approximately 14-25% of youth experience major depression before adulthood; about 9% of adolescents attempt suicide and 2.9% make a suicide attempt requiring medical attention. Treatment with antidepressant medications has been shown to improve mood and decrease suicidal ideation. However, there has been concern that antidepressants paradoxically increase the risk of suicidal behaviors following initiation of SSRI treatment. The FDA issued several public health advisories and a boxed warning since October of 2003 and, beginning in 2005, all SSRI labeling has required a “black box” warning (BBW) regarding the increased risk of suicidality in children and adolescents taking antidepressants. However, conflicting evidence concerning the true effects of SSRIs on the risk of suicidal behaviors in youth has generated much controversy. Studies following the BBW reported decreased rates of pharmacologic treatment for depression. Another study reported an 18% increase in completed suicides among youth in 2004 and 2005.

This research will contribute to research regarding unintended consequences of regulatory actions. The secondary aim is to assess the utility of sequential analysis for prospectively assessing signals of health policy impacts using the antidepressant warnings as a policy example.
Grant Number:
U19MH092201
Participating Sites:
Harvard Pilgrim Health Care Institute (Lead Site)
Harvard Medical School
Northeastern University
Baylor Scott & White Health jointly with Central Texas Veterans Health Care System
Kaiser Permanente Washington
HealthPartners Institute
Henry Ford Health System
Kaiser Permanente Colorado
Georgia State University
Kaiser Permanente Hawaii
Kaiser Permanente
Kaiser Permanente Northwest
Kaiser Permanente Southern California
University of Tennessee Health Science Center
Harvard Medical School
Brigham and Women’s Hospital
Investigators:
Stephen B. Soumerai, ScD
Christine Y. Lu, PhD
Sengee Toh, ScD
Jessica L. Sturtevant, ScM
Jeanne M. Madden, PhD
Laurel Anne Copeland, PhD
Gregory Simon, MD, MPH
Rebecca Rossom, MD, MS
Brian K. Ahmedani, PhD
Gregory Clarke, PhD
Marsha A. Raebel, PharmD
Ashli Owen-Smith, PhD
Beth Waitzfelder, PhD
Yihe Daida, PhD
Robert Davis, MD, MPH
Stacy Sterling (Enid M. Hunkeler retired), MA, FAHA
Frances Lynch, PhD
Karen J. Coleman, PhD
Robert Penfold
Martin Kulldorff, PhD
Major Goals: Examine the combined effects of FDA warnings and media coverage on changes in antidepressant use, suicide attempts, and suicides among children/adolescents, young adults and adults. Evaluate the utility of sequential analysis for prospectively assessing signals of health policy impacts using FDA antidepressant warnings and related media coverage as policy example.
Description of study sample:
Records data from 11 MHRN health systems were used to examine time trends in rates of antidepressant use, suicide attempt, and suicide death before, during, and after FDA advisories regarding suicidality during antidepressant treatment.  The combined sample included approximately 1.1 million adolescents aged 10-17, 1.4 million adults aged 18-29, and 5 million adults aged 30-64.
Current Status: (write 1-2 sentences describing the project status; include current date)
Our latest publication in May 2018 evaluated the utility of sequential analysis for prospectively assessing signals of health policy impacts. As a policy example, we studied the consequences of the widely publicized Food and Drug Administration’s warnings cautioning that antidepressant use could increase suicidal risk in youth. Prospective, periodic evaluation of administrative health care data using sequential analysis can provide timely population-based signals of effects of health policies (see below). This method may be useful to use as new policies are introduced. Along with this publication Drs. Lu, Soumerai, Simon, and Kulldorff published point and counterpoint articles in Medical Care regarding the importance of surveillance (see below). Analysis for this project is complete and there will be no more publications. Using 28 years of US death certificate data collected and validated by the US CDC from 1990 to 2017, we are conducting the first longitudinal study of discontinuities in the trends of suicide rates before and after the warnings among adolescents and young adults. We hypothesized that the warnings and reductions in depression diagnosis and treatment would be associated with an increase in completed suicides among adolescents and young adults in the US. There are no extant national longitudinal data on the effects of this policy on completed suicides.
Study Registration:
N/A
Publications:Lu CY, Stewart C, Ahmed AT, Ahmedani BK, Coleman K, Copeland LA, Hunkeler EM, Lakoma MD, Madden JM, Penfold RB, Rusinak D, Zhang F, Soumerai SB. How complete are E-codes in commercial plan claims databases? Pharmacoepidemiol Drug Saf. 2014 Feb;23(2):218-20. doi: 10.1002/pds.3551.Lu CY, Zhang F, Lakoma MD, Madden JM, Rusinak D, Penfold RB, Simon G, Ahmedani BK, Clarke G, Hunkeler EM, Waitzfelder B, Owen-Smith A, Raebel MA, Rossom R, Coleman KJ, Copeland LA, Soumerai SB. Changes in antidepressant use by young people and suicidal behavior after FDA warnings and media coverage: quasi-experimental study. BMJ. 2014 Jun 18;348:g3596. doi: 10.1136/bmj.g3596.Lu, CY, Penfold RB, Toh S, Sturtevant J, Madden JM, Simon G, Ahmedani BK, Clarke G, Coleman KJ, Copeland L, Daida Y, Davis RL, Hunkeler EM, Owen-Smith A, Raebel MA, Rossom MA, Soumerai SB, Kulldorff M. Near real-time surveillance for consequences of health policies using sequential analysis. Med Care. 2018 May;56(5):365-372.Lu, CY, Simon, G, Soumerai, SB, Kulldorff, M. Early warning systems are imperfect, but essential.  Med Care. 2018 May;56(5):382-383.Lu, CY, Simon, G, Soumerai, SB. Staying honest when policy changes backfire. Med Care. 2018 May;56(5):384-390.
Resources:N/A
Lessons Learned: Completeness of e-codes varies significantly over time, across treatment settings and across study sites. Improvements in e-coding in commercial health plan datasets are critical for injury research. In the meantime, poisoning by psychotropic drugs appears to be a useful proxy for identifying suicide attempts leading to emergency room visits and hospitalizations. There were substantial reductions in antidepressant use among all age groups and simultaneous, small increases in psychotropic drug poisonings, a validated measure of suicide attempts, among adolescents and young adults following the FDA warnings. These results were consistent across 11 geographically diverse U.S. study sites. Media exaggeration about FDA reports of drug risks may reduce appropriate drug use and increase adverse outcomes. We did not detect changes in completed suicides after the warnings, which is an extremely rare outcome.
What’s Next?
The Virtual Data Warehouse (VDW) provides a rich resource for multi-site research.  The longitudinal nature of the VDW enables longitudinal analyses that are necessarily part of the interrupted time series method, a strong quasi-experimental study design for studying impacts of health policies. MHRN hosts a health policy special interest group for discussing these research ideas.

Pathways from Chronic Prescription Opioid Use to New Onset Mood Disorder

Grant Details

Funder: NIH, NIDA

Grant Number: R01ActDA043811

Grant Period: 4/1/2019 – 3/31/2023

Narrative: Research on the association between psychopathology and prescription opioid analgesic use (OAU) has established that mental illness influences risk of chronic OAU (i.e. >90-days), high dose OAU and misuse. We explored the reverse direction of association and found longer OAU and higher opioid doses are associated with increased risk of new onset depression (NOD), independent of pain. Using Veterans Health Affairs (VA) patient data revealed >90-day OAU was associated with a 35% (in VA patients) to 105% (in private sector patients) increased risk of NOD compared to patients with 1-30 day OAU. Our additional studies revealed that OAU is associated with depression recurrence and treatment resistant depression. If these results are confirmed in the present proposal, results have potential to greatly inform interventions to reduce chronic OAU (e.g. treating depression), elucidate pathways to OAU misuse, and generate a body of evidence that informs safe opioid prescribing. To reveal pathways from OAU to NOD and related depression phenotypes (i.e. dysthymia, bipolar, anhedonia, vital exhaustion) we must measure the patients’ pre-existing risk factors and post-OAU events. We will obtain diagnoses and symptom level data and covariates that are not available in the medical record data used in our R21 and strengthen the temporal relationships between OAU and NOD. The central hypothesis driving this research is that pre-OAU risk factors such as a history of depression and post-OAU events such as onset of opioid misuse contribute to NOD.
If NOD is explained by OAU alone and not by pre-existing risk factors, then the opioid epidemic is generating new cases of depression in a large population of middle-aged adults, otherwise not at risk for NOD. Findings will disentangle consequences or correlates of chronic pain per se from those of chronic, high dose OAU. We test whether the OAU-NOD association is moderated by pre-existing depression, substance use disorder (SUD), including opioid use disorder and trauma exposure. We next propose that post-OAU opioid misuse, SUD, poor functioning, low social support and poor sleep quality promote NOD. Using 12 monthly brief assessments, we will determine if change in OAU, independent of change in pain influences, depression trajectories and determine if there is a reciprocal relationship among these variables over time. We will determine if OAU is associated with different depression phenotypes and last determine which subtypes of depression contribute to incident opioid use disorder.

Lead Site: St. Louis University (PI Jeffrey Scherrer)

Participating Sites: HFHS (Site PI Brian Ahmedani)

Current Status:

Summary of Findings:

Publications:

Practice Variation in High- and Low-value Care for Mood Disorders

Project Name:
Practice Variation in High- and Low-Value Care for Mood Disorders
Principal Investigator:
Gregory Simon MD MPH
Principal Investigator Contact Information:
simon.g@ghc.org
Principal Investigator institution:
Group Health Research Institute
Funder
NIMH
Funding Period:
09/2010 – 06/2015
Abstract:
This multi-site observational study examined patient, provider, and health system influences on process of depression care in primary care and mental health specialty settings.  Comprehensive records data from five MHRN sites (Group Health Cooperative, HealthPartners, Kaiser Permanente Colorado, Kaiser Permanente Hawaii, and Kaiser Permanente Southern California) were used to identify three patient cohorts: Primary care patients receiving a new diagnosis of depression with no recent history of depression treatment. Primary care and mental health specialty patients initiating a new episode of antidepressant treatment with a diagnosis of depression. Mental health specialty patients initiating a new episode of psychotherapy with a diagnosis of depression
Grant Number:
U19 MH092201 (Mental Health Research Network Cooperative Agreement)
Participating Sites:               
Group Health Cooperative                                                     
HealthPartners Institute                                
Kaiser Permanente Colorado                        
Kaiser Permanente Hawaii
Kaiser Permanente Southern California        
Investigators:
Gregory Simon MD MPH
Robert Penfold PhD
Susan Shortreed, PhD
Rebecca Rossom MD
Arne Beck PhD
Beth Waitzfelder PhD
Karen Coleman PhD
Major Goals:
To examine patient and provider contributions to variation in care (medication and psychotherapy) for depression.
Description of study sample:
The sample includes new diagnoses and new treatment episodes between 1/1/2010 and 12/31/2012.  These data are being used to address the following specific questions: Among primary care patients receiving a new diagnosis of depression, how do specific patient characteristics (age, sex, race/ethnicity, severity of depression) influence both the likelihood of initiating any treatment for depression and the choice between treatments (medication or psychotherapy)Among patients initiating medication treatment for depression, how are medication selection, early medication adherence, and acute-phase treatment response related to specific patient characteristics (age, sex, race/ethnicity, severity of depression)?  How do these treatment processes vary among providers? Among patients initiating psychotherapy for depression, how are early treatment adherence and acute-phase treatment response related to specific patient characteristics (age, sex, race/ethnicity, severity of depression)?  How do these treatment processes vary among providers?
Current Status:
All analyses are complete.
Study Registration:
N/A
Publications:
Simon GE, Coleman KJ, Waitzfelder BE, Beck A, Rossom RC, Stewart C, Penfold RB. Adjusting Antidepressant Quality Measures for Race and Ethnicity. JAMA Psychiatry. 2015 Oct;72(10):1055-6. doi: 10.1001/jamapsychiatry.2015.1437. No abstract available. PMID:26352783Simon GE, Rossom RC, Beck A, Waitzfelder BE, Coleman KJ, Stewart C, Operskalski B, Penfold RB, Shortreed SM.J. Antidepressants are not overprescribed for mild depression. Clin Psychiatry. 2015 Dec;76(12):1627-32. doi: 10.4088/JCP.14m09162.PMID:26580702Simon GE, Johnson E, Stewart C, Rossom RC, Beck A, Coleman KJ, Waitzfelder B, Penfold R, Operskalski BH, Shortreed SM.  Does patient adherence to antidepressant medication actually vary between physicians?  J Clin Psychiatry.  2017 Oct 24 (epub ahead of print)
Resources:
None
Lessons Learned: In MHRN health systems, we see little evidence for over-prescribing of antidepressants for mild depression. Likelihood of prematurely discontinuing antidepressant medication is much higher in minority racial and ethnic groups than in non-Hispanic Whites, and these racial and ethnic differences are far larger than differences related to other demographic or clinical characteristics. Likelihood of prematurely discontinuing psychotherapy for depression is modestly higher in minority racial and ethnic groups – but racial/ethnic disparities in psychotherapy adherence are smaller than disparities in antidepressant medication adherence. Among primary care patients receiving a new diagnosis of depression, likelihood of initiating any specific treatment (medication or psychotherapy) is lower among minority racial or ethnic groups.  Patients from minority racial and ethnic groups are more likely to start psychotherapy than medication. Failure to adjust antidepressant treatment quality measures for race and ethnicity will significantly disadvantage health systems serving members from traditionally under-served racial and ethnic groups. After accounting for random variation, likelihood of prematurely discontinuing antidepressant medication varies only minimally across physicians.
 What’s next?
A follow-up study (funded during the second cycle of MHRN funding) will further explore racial and ethnic disparities in care identified in this project.