| Equation | (# participants) x (% participants who receive treatment solely because of the program) x (% patients who respond to treatment) x (% increased earnings from avoided days of work lost as a result of the treatment) x ($ Average annual earnings of low-income population) |
| Explanation | This metric estimates the impact of mental health care access and treatment on increased earnings. This metric should be used in conjunction with HEA020. Number of participants: Reported by program. Percentage of participants who receive treatment solely because of the program: [54%]. This is estimated by Constellation Fund staff based on data from SAMHSA (2017) which shows that 46% of individuals under 200% of the federal poverty level with any mental illness receive mental health care. Thus, we estimate that about 54% of participants would have not received treatment in the absence of the program. Percentage of patients who respond to treatment: [60%]. We base this estimate on a wide reading of the research literature, including Berndt et al. (2000) and Kazak et al. (2010). Increased earnings from avoided days of work lost as a result of the treatment: [60%]. Banerjee, Chatterji & Lahiri (2017) report that individuals with mental illness lose, on average, 6.6 weeks of work during the previous year or 13% of worktime. Average annual earnings of low-income population: [$8,600 – $24,300], counterfactual earnings calculated from ACS 5-year estimates (U.S Census Bureau, 2016). $8,600 is the average annual earnings of the population in the Twin Cities under 180% of poverty for all earners and non-earners. $24,300 is the average annual earnings for individuals with high school diplomas. The Constellation Fund staff will determine which counterfactual is appropriate for the grantee. Example: Assuming average annual earnings of $24,300, the value of the lost work time is approximately $3,000 or about 13% of the average annual income. Other researchers have found similar results (Kaya & Chan, 2017; McIntyre, Liauw, & Valerie, 2011). These benefits are already discounted to present value. Note that this effect does not include potential job loss due to illness or as a consequence of work absences. |
| References | Banerjee, S., Chatterji, P., & Lahiri, K. (2017). Effects of psychiatric disorders on labor market outcomes: A latent variable approach using multiple clinical indicators. Health Economics. 26(2): 184–205. Berndt, E., Koran, L., Finkelstein, S., Gelenberg, A., Kornstein, S., Miller, I., Thase, M., Trapp, G. & Keller, M. (2000). Lost human capital from early-onset chronic depression. The American Journal of Psychiatry, 157(6), 940–947. Kaya, C., & Chan, F. (2017). Vocational rehabilitation services and outcomes for working age people with depression and other mood disorders. Journal of Rehabilitation, 83(3), 44–52. Kazak, A., Hoagwood, K., Weisz, K., Hood, J.R., Kratochwill, K., Vargas, L.A. & Banez, G.A. (2010). A meta- systems approach to evidence-based practice for children and adolescents. The American Psychologist, 65(2), 85–97. McIntyre, R., Liauw, S., & Taylor, V.H. (2011). Depression in the workforce: the intermediary effect of medical comorbidity. Journal of Affective Disorders, 128 Suppl 1, S29-36. Substance Abuse and Mental Health Services Administration (SAMHSA). (2017). Key substance use and mental health indicators in the United States: Results from the 2016 National Survey on Drug Use and Health (HHS Publication No. SMA 17-5044, NSDUH Series H-52). Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Retrieved from: https://www.samhsa.gov/data/ |
HEA021 Mental health care leading to increased earnings
