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Impacting Entry into Evidence-Based Supported Employment: A Population-Based Empirical Analysis of a Statewide Public Mental Health Program in Maryland

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Abstract

We use discrete-time survival regression to study two empirical issues relating to take-up of individual placement and support (IPS) supported employment (SE) services for persons with serious mental illness: (1) the influence of client characteristics on take-up probability, and (2) the possible impacts of a major recent initiative in one state (Maryland) to overcome barriers to IPS-SE expansion. Our longitudinal analysis of population-based Medicaid cohorts, during 2002–2010, provides tentative evidence of positive state initiative impacts on SE take-up rates, and evidence of effects on take-up for clients’ diagnoses, prior work-history, health and demographic characteristics, and geographic accessibility to SE providers.

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Notes

  1. For more information about these initiatives, see Marrone et al. (2013), Becker et al. (2007), and Reeder and Johnson (2008). Further details are described in documents from the State of Maryland (Maryland State Department of Education 1995, 2000, 2007).

  2. Note that the great bulk of all funding for SE services in Maryland were provided by the PMHS. DORS funding is much more limited and private insurance funding for SE services is virtually non-existent.

  3. Maryland has 24 “county” jurisdictions, including the independent city of Baltimore.

  4. Further details and data sources are presented in the “Appendix” (accessible at DOI: 10.13140/RG.2.2.13542.42564), Table 1. As shown in that table, the numbers of SE claims grew even more rapidly in this period, with claims doubling from FY2007 to FY2008 due to changes in administrative processes for provider billings to and payments by the PMHS.

  5. Included ICD diagnoses were: 295.10 thru 295.95, 296.33, 296.34, 297.1, 298.9x, 301.22, 301.83, 296.43, 296.44, 296.53, 296.54. 296.63, 296.64, 296.80, and 296.89 (labels for these diagnoses are provided in the “Appendix”).

  6. For PMHS claims in FY 2001 through FY 2004, the coding scheme for SE services included several older codes that were recoded in our analysis as follows: W9530, W9531 and W9532 recoded to h2024; W9533 recoded to h2024-21; W9534 recoded to h2023; and W9535 recoded to h2026. The definitions of these codes were: W9530 thru W9532—pre-employment phase services; W9533—job placement; W9534—intensive job coaching; and W9535—extended support services.

  7. The construction of the diagnostic groupings used in our regressions is described in the “Appendix”.

  8. We view Medicare coverage in the cohort year as a proxy for being an SSDI recipient since (1) none of the persons in our study were older than 60 in the cohort year and (2) the principal way these persons qualified for Medicare was by virtue of being on SSDI. Persons receiving SSDI benefits had to qualify for eligibility based on a record of previous work.

  9. It is interesting to note, however, that prior literature evaluating SE programs (e.g., Kinoshita et al. 2013) suggests provider experience has primarily focused on persons with schizophrenia-related diagnoses.

  10. Note that only persons with no SE services in the pre-baseline year were included in our analyses. This accords with the idea that our observed take-up is the start of a new SE service episode. Also note that since SE services use could not be tracked prior to fiscal 2001, and our first cohort is from fiscal 2002, we applied the same 12-month criterion to defining take-up for all 3 of our cohorts.

  11. Marginal effects for all binary and categorical variables were estimated as discrete changes (from 0 to 1 for all binary variables and as changes from the reference category for other categorical variables). Also note that these estimates are the overall means of the estimated effect averaged across all 170,202 data points in our regressions.

  12. Other SE recipients are persons with Medicaid coverage from other cohorts (not in our study), as well as other persons whose SE services are funded by the State under block grant or “grey zone” provisions (i.e., funds for low-income persons who have no other funding for SE services). A very small amount of additional funding for SE services is also provided by Maryland DORS.

  13. The most common reason for exclusion in the baseline year was receipt of SE services in the pre-baseline year; this was true for 381 persons in the 2002 cohort, 101 persons in the 2003 cohort, and 94 persons in the 2004 cohort.

  14. Of all services we identified in claims as the first SE service in the take-up year, 82.3% were coded as h2024 (pre-placement). This is consistent with our defining the first SE service following a full year with no SE services as the start of a new SE services episode.

  15. Declining hazard rates could result from selection factors (i.e., persons most likely to take up SE do so more rapidly), or from observable time-varying factors (e.g., increasing age).

  16. This is consistent with the fact that the SE engagement rates in Table 1 were much larger than take-up rates in Table 2.

  17. This is also consistent with the fact (shown in Table 1) that overall rates of SE services use in the pre-baseline year were higher for the fiscal 2002 cohort.

  18. In cohort-specific regressions, statewide effects could not be identified separately from individual-level time effects since all persons in the same cohort had the same base year.

  19. The mean distance across all persons in our cohorts (and all years) fell from 5.00 miles to 4.52 miles with all the decline occurring after FY 2005. This may be a better indication of the increase in SE provider accessibility since it was not affected by selection due to deletion of post-take-up years from each person’s data.

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Acknowledgements

Support for this research was provided by Grant R01MH093374 from the National Institute of Mental Health. The research project underlying this work was in large part the result of key efforts by our late friend and colleague, Judy Shinogle. She was the Principal Investigator when the project began, shortly before her death, and her knowledge, insights and vision are reflected in all aspects of the project. We are also grateful to a number of other people who provided valuable insights, data, suggestions, and other assistance on this work. They include: Agnes Rupp, Ph.D., NIMH Program Officer, colleague and supplier of valuable perspectives, suggestions, and (occasionally) criticisms; Chrstine Yee UMBC, for her careful review of earlier versions of this work; Judith Leiman and Steven Reeder, Ph.D. of the Maryland Behavioral Health Administration, who gave us access to information and documents relevant to the study and helped us better understand Maryland’s supported-employment initiative; and Tim Santoni and the staff of the Systems Evaluation Center, Mental Health Systems Improvement Collaborative, Division of Services Research, Department of Psychiatry, University of Maryland School of Medicine. We further acknowledge many useful suggestions from the editor and reviewers on an earlier version of this work. We reluctantly acknowledge our own responsibility for remaining errors, and shortcomings.

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Authors and Affiliations

  1. School of Public Policy and Maryland Institute for Policy Analysis and Research, University of Maryland – Baltimore County (UMBC), 1000 Hilltop Circle, Baltimore, MD, 21250, USA

    David Salkever

  2. Hilltop Institute, University of Maryland Baltimore County, Baltimore County, MD, USA

    Michael Abrams

  3. Westat Inc., Rockville, MD, USA

    Kevin Baier

  4. IBM Watson Health, Bethesda, MD, USA

    Brent Gibbons

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  1. David Salkever
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Correspondence to David Salkever.

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All analyses used only de-identified, routinely collected administrative data and were therefore deemed by the University and State of Maryland IRB’s to be exempt from informed consent requirements.

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Salkever, D., Abrams, M., Baier, K. et al. Impacting Entry into Evidence-Based Supported Employment: A Population-Based Empirical Analysis of a Statewide Public Mental Health Program in Maryland. Adm Policy Ment Health 45, 328–341 (2018). https://doi.org/10.1007/s10488-017-0827-9

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