1 / 26

Excess Hospitalization in Medi-Cal Beneficiaries with Serious Mental Illness - Preliminary Findings

Study on hospitalization & emergency department use, costs, & quality of care among Medi-Cal beneficiaries with serious mental illness.

lowella
Download Presentation

Excess Hospitalization in Medi-Cal Beneficiaries with Serious Mental Illness - Preliminary Findings

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Excess Non-Psychiatric Hospitalization and Emergency Department Use Among Medi-Cal Beneficiaries with Serious Mental IllnessPreliminary ResultsCheryl E. Cashin, Ph.D.UC Berkeley School of Public Health California Institute of Mental HealthFebruary 7, 2008 California Mental Health Care Management Program (CalMEND): A Quality Improvement Collaborative

  2. Acknowledgments • Funding from the National Institute of Mental Health (Mental Health Economics Research Training Grant) • The CalMEND Team • Special thanks to the research team: • Dr. Barry Handon, DHCS • Marco Gonzales, DHCS • Pauline Chan, DHCS and • Julie Cheung, CalMEND • Karin Kalk, CalMEND • Jim Klein, DHCS

  3. The Problem • Compared with the general population, individuals with serious mental illness: • have higher ratesofphysical illness and reduced life expectancy • have greater likelihood of multiple co-occurring chronic conditions • may have less access to timely, appropriate primary health care • Untreated medical conditions may lead to lower quality of life, barriers to recovery, and overuse of costly services

  4. Evidence from Other States • Growing awareness that Medicaid beneficiaries with multiple chronic conditions are the costliest: • 4% of Medicaid beneficiaries nationally account for 50% of expenditures • Adults with chronic conditions make up 40% of the Medicaid population but > 80% of expenditures • Little evidence specific to the SMI population • Results from NY suggest total claims for the SMI population can be up to 2x claims for other disabled population (Billings and Mijanovich 2007)

  5. Objectives of the Study • Examine differences in the patterns of health care utilization between individuals with and without SMI • Identify excess hospitalization and emergency department use and costs attributable to having a SMI • Identify characteristics of health service delivery and quality of care associated with excess costs that are amenable to intervention

  6. Data • Medi-Cal eligibility and claims files for individuals with and without SMI from 2002-2006 • Criteria for identification of SMI: • Short-Doyle claim and/or • Antipsychotic prescription • Other selection criteria: • Continuous Medi-Cal eligibility (2002-2006) • Age between 18 and 64 • Fee-for-service only (not enrolled in managed care)

  7. Measures to Protect Confidentiality • CPHS approval of the research project • Protected health information analyzed contains no identifying information (e.g. name, address, SSN) • Data are encrypted, password-protected, and stored in a locked room • Results will be presented as aggregate statistical analysis only

  8. Sample Size

  9. The Study Population: Total Claims in 2006 billion billion

  10. The Study Population: Per Capita Claims in 2006

  11. Methods • Comparison of mean hospitalization rates between the SMI and control population • Unadjusted means • Logistic regression to adjust means to control for age, gender and ethnicity • Estimate the probability of hospitalization given specific individual characteristics • Examine statistical significance of the effect of SMI on the probability of hospitalization

  12. Results:Unadjusted Means Chi-2 = 710.56 Pr = 0.000**

  13. Results:Logistic Regression • SMI is associated with a 31.2% increase in the odds of being hospitalized in a given year, controlling for individual characteristics • This effect is statistically significant at the 1% level • Age, gender and ethnicity also have statistically significant effects

  14. Highest Hospitalization Rates Low Impact of SMI Gender and Ethnic Differences in Impact of SMI on Non-Psychiatric Hospitalization Age 56-64 % Hospitalized

  15. Gender and Ethnic Differences in Impact of SMI on Non-Psychiatric Hospitalization Age 56-64 Lowest Hospitalization Rates Low Impact of SMI % Hospitalized

  16. Gender and Ethnic Differences in Impact of SMI on Non-Psychiatric Hospitalization Age 56-64 Highest Impact of SMI % Hospitalized

  17. Highest Impact of SMI Difference= 9% Gender and Ethnic Differences in Impact of SMI on Non-Psychiatric Hospitalization Age 56-64 % Hospitalized

  18. Ambulatory Care-Sensitive Hospitalization • Ambulatory care sensitive (ACS) hospitalization  a hospital admission that should be avoidable with effective intervention at the primary health care level • ACS hospitalization is widely used: • As an indicator of access, quality and effectiveness of primary health care • To measure/monitor health disparities

  19. Ambulatory Care-Sensitive Diabetes Hospitalization • Used Institute of Medicine ICD-9 criteria for ambulatory care-sensitive diabetes hospitalization • Primary diagnosis ICD-9 code = 2500-2503, 2508, or 2509

  20. Results:Unadjusted Means Chi-2 = 39.65 Pr = 0.000**

  21. Results:Logistic Regression • SMI is associated with a 53.0% increase in the odds of being hospitalized in a given year, controlling for individual characteristics • This effect is statistically significant at the 1% level • Age, gender and ethnicity also have statistically significant effects.

  22. Highest hospitalization rates Gender and Ethnic Differences in Impact of SMI on ACS-Diabetes Hospitalization % Hospitalized No significant difference No significant difference

  23. Gender and Ethnic Differences in Impact of SMI on ACS-Diabetes Hospitalization Highest impact of SMI % Hospitalized No significant difference No significant difference

  24. What Does This Tell Us So Far? • Medi-Cal beneficiaries with SMI have significantly non-psychiatric hospitalization, even relative to another high-need population • African Americans, with and without SMI, have the highest rates of hospitalization • Females and Latinos are particularly vulnerable to the impact of SMI • Specific chronic conditions, such as diabetes, may be important causes of excess hospitalization among the SMI population

  25. How can the results be used? Next steps required: • Quantifying the excess costs of excess hospitalization among the SMI population can justify investment in interventions. • Identifying factors associated with increased hospitalization among the SMI population can contribute to the design of interventions.

  26. California Mental Health Care Management Program (CalMEND): A Quality Improvement Collaborative Thank you.Cheryl E. Cashin, Ph.D.

More Related