Deborah Garnick1, Constance Horgan1, Tami L Mark2, Margaret Lee1, Andrea Acevedo3, Sarah Neager4, Peggy O'Brien4, Ali Hashmi4, Bill Marder4, Kay Miller4. 1. Institute for Behavioral Health, Heller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts, USA. 2. RTI International, Rockville, Maryland, USA. 3. Department of Community Health, Tufts University, Medford, Massachusetts, USA. 4. Truven Health Analytics Inc., IBM Watson Health, Cambridge, Massachusetts, USA.
Abstract
Background: Identifying and effectively treating individuals with substance use disorders (SUDs) is an important priority for state Medicaid programs, given the enormous toll that SUDs take on individuals, their families, and their communities. In this paper, we describe how the Healthcare Effectiveness Data and Information Set (HEDIS) measure "Identification of Alcohol and Other Drug Services" can be used, along with eligible population prevalence rates, to expand states' ability to track how well their Medicaid programs identify enrollees with SUDs and link them with treatment (measured by initiation and engagement performance measures). Methods: We use the 2009 Medicaid MAX data on utilization and enrollment along with information from the National Survey of Drug Use and Health (NSDUH) to obtain state-level estimates of alcohol and drug abuse and dependence among Medicaid beneficiaries for 7 illustrative states. We calculate identification, initiation, and engagement measures using specifications from the National Committee on Quality Assurance (NCQA). Results: NSDUH data showed that the eligible population prevalence rate (the average rate of alcohol or drug abuse or dependence) among the 7 states was 10.0%, whereas the average identification rate was 2.9%. The gap between the prevalence and identification rates ranged from 5.1% to 11.0% among the 7 states. The initiation rates ranged from 36.9% to 57.1%. The states' engagement rates ranged from 11.8% to 31.1%, although rates differ by age, gender, and race/ethnicity in some states. Conclusion: Including identification along with initiation and engagement measures allows states to determine how well they are performing in a more complete spectrum from need, to recognition and documentation of enrollees with SUDs, to initiation of treatment, to continuation of early treatment.
Background: Identifying and effectively treating individuals with substance use disorders (SUDs) is an important priority for state Medicaid programs, given the enormous toll that SUDs take on individuals, their families, and their communities. In this paper, we describe how the Healthcare Effectiveness Data and Information Set (HEDIS) measure "Identification of Alcohol and Other Drug Services" can be used, along with eligible population prevalence rates, to expand states' ability to track how well their Medicaid programs identify enrollees with SUDs and link them with treatment (measured by initiation and engagement performance measures). Methods: We use the 2009 Medicaid MAX data on utilization and enrollment along with information from the National Survey of Drug Use and Health (NSDUH) to obtain state-level estimates of alcohol and drug abuse and dependence among Medicaid beneficiaries for 7 illustrative states. We calculate identification, initiation, and engagement measures using specifications from the National Committee on Quality Assurance (NCQA). Results: NSDUH data showed that the eligible population prevalence rate (the average rate of alcohol or drug abuse or dependence) among the 7 states was 10.0%, whereas the average identification rate was 2.9%. The gap between the prevalence and identification rates ranged from 5.1% to 11.0% among the 7 states. The initiation rates ranged from 36.9% to 57.1%. The states' engagement rates ranged from 11.8% to 31.1%, although rates differ by age, gender, and race/ethnicity in some states. Conclusion: Including identification along with initiation and engagement measures allows states to determine how well they are performing in a more complete spectrum from need, to recognition and documentation of enrollees with SUDs, to initiation of treatment, to continuation of early treatment.
Authors: Deborah W Garnick; Constance M Horgan; Margaret T Lee; Lee Panas; Grant A Ritter; Steve Davis; Tracy Leeper; Rebecca Moore; Mark Reynolds Journal: J Subst Abuse Treat Date: 2007-05-23
Authors: Deborah W Garnick; Margaret T Lee; Constance Horgan; Andrea Acevedo; Michael Botticelli; Spencer Clark; Steven Davis; Robert Gallati; Karin Haberlin; Andrew Hanchett; Dawn Lambert-Wacey; Tracy Leeper; James Siemianowski; Minakshi Tikoo Journal: J Subst Abuse Treat Date: 2011-01-22
Authors: Deborah W Garnick; Margaret T Lee; Peggy L O'Brien; Lee Panas; Grant A Ritter; Andrea Acevedo; Bryan R Garner; Rodney R Funk; Mark D Godley Journal: Drug Alcohol Depend Date: 2012-02-23 Impact factor: 4.492
Authors: Deborah W Garnick; Constance M Horgan; Andrea Acevedo; Margaret T Lee; Lee Panas; Grant A Ritter; Robert Dunigan; Alfred Bidorini; Kevin Campbell; Karin Haberlin; Alice Huber; Dawn Lambert-Wacey; Tracy Leeper; Mark Reynolds; David Wright Journal: J Subst Abuse Treat Date: 2013-10-14
Authors: Robert Dunigan; Andrea Acevedo; Kevin Campbell; Deborah W Garnick; Constance M Horgan; Alice Huber; Margaret T Lee; Lee Panas; Grant A Ritter Journal: J Behav Health Serv Res Date: 2014-01 Impact factor: 1.505
Authors: Katharine A Bradley; Kristen R Bush; Amee J Epler; Dorcas J Dobie; Tania M Davis; Jennifer L Sporleder; Charles Maynard; Marcia L Burman; Daniel R Kivlahan Journal: Arch Intern Med Date: 2003-04-14