|Equation||(# participants) x (% patients enrolled solely due to the program) x (Q1:Impact of program on substance abuse) x (Q2: Impact of substance abuse on employment) x ($ average annual earnings low-income)|
|Explanation||This metric estimates the impact of substance abuse treatment on increased earnings. This metric should be used in conjunction with HEA028. This metric is used to estimate benefits for any addiction treatment program based on the average impact of all treatment methods. We determine in a case-by-case basis whether a specific metric can be developed based on the program’s methodology.|
Number of participants: Reported by program.
Percentage of participants receiving treatment solely because of program: [0.9], estimate for those who enroll in the substance abuse treatment program solely due to our grant is based on findings that only about 10 percent of people who need substance abuse treatment actually receive it (Substance Abuse and Mental Health Services Administration, 2012).
Q1: Impact of program on substance abuse: [0.22]. The program impact is the reduction in symptoms based on an average drug treatment program [-22%] (Aos, Mayfield, Miller & Yen, 2006).
Q2: Impact of substance abuse on employment: [0.14]. Q2 is estimated as the (percentage impact of substance abuse on probability of employment) x (employment rate of population of interest), where the percentage impact of substance abuse on employment is [-0.29] (WSIPP, 2017). The employment rate of low-income individuals is [47%] (U.S. Census Bureau, 2018). The resulting impact is (0.29 x 0.47 = 0.14).
Average annual earnings of employed low-income individuals: [$13,500]. This is estimated using ACS data for the Twin Cities metropolitan area (U.S. Census Bureau, 2016). We assume one year of additional income. In this metric, we use earnings of employed low-income individuals instead of all low-income individuals since the effect size refers to the impact on employment.
|References||Aos, S., Mayfield, J., Miller, M. & Yen, W. (2006). Evidence-based treatment of alcohol, drug, and mental health disorders: Potential benefits, costs, and fiscal impacts for Washington State. Olympia, WA: Washington State Institute for Public Policy.|
Minnesota Compass. (2018). Twin Cities region neighborhood profile data: Phillips community. Retrieved from: http://www.mncompass.org/profiles/communities/minneapolis/phillips
Substance Abuse and Mental Health Services Administration (2012). Results from the 2011 National Survey on Drug Use and Health: Mental Health Findings, NSDUH Series H-45, HHS Publication No. (SMA) 12-4725. Retrieved from: https://www.samhsa.gov/data/sites/default/files/2011MHFDT/2k11MHFR/Web/NSDUHmhfr2011.htm
U.S. Census Bureau. (2016). American Community Survey 5-year estimates – public use microdata sample, 2012-2016. Generated using Public Use Microdata Area (PUMA) in the Seven-county Twin Cities Metropolitan Area. Retrieved from http://factfinder.census.gov
Washington State Institute for Public Policy. (2017). Benefit-cost technical documentation. Olympia, WA: Author. Retrieved from: http://www.wsipp.wa.gov/TechnicalDocumentation/WsippBenefitCostTechnicalDocumentation.pdf