A machine learning approach to unravel client and program-specific effects in opioid treatment retention

Abstract

This study examines the impact of workforce diversity, particularly the presence of Black/African American staff, on client retention in opioid use disorder (OUD) treatment, recognizing the historically low retention rates among Black and Hispanic populations in such programs. Using a novel machine learning technique called “causal forest,” we explored the heterogeneous treatment effects of staff diversity on client retention, aiming to identify strategies that enhance client retention and improve treatment outcomes. Analyzing data from four waves of the National Drug Abuse Treatment System Survey spanning the years 2000, 2005, 2014, and 2017 (n = 627), we focus on the relationship between workforce diversity and retention. The findings revealed diversity-related variations in retention across 61 out of 627 OUD treatment programs (<10%), with potential beneficial effects attenuated by other program characteristics. These characteristics include programs that are more likely to be private-for-profit, have lower percentages of Black and Latino clients, lower staff-to-client ratios, higher proportions of staff with graduate degrees, and lower percentages of unemployed clients. Our results suggest that workforce diversity alone is insufficient for improving retention. Programs with characteristics linked to greater retention are better positioned to leverage a diverse workforce to enhance retention, offering important implications for policy and program design to better support Black clients with OUDs.

Keywords

Workforce diversity / Opioid use disorder / Treatment retention / Causal forest / Heterogeneous treatment effect

Funding

Support for this research and manuscript preparation was provided by a National Institute on Minority Health and Health Disparities research grant (R01MD014639, CoPIs: Daniel Howard and Erick Guerrero) and a National Institute on Drug Abuse research grant (DA048176, CoPIs: Jeanne Marsh and Erick Guerrero).

Conflict of interest

The authors declare they have no competing interests.

References

[1] CDC/National Center for Health Statistics. U.S. Overdose Deaths Decrease in 2023, First Time Since 2018; 2024. Available from: https://www.cdc.gov/nchs/pressroom/nchs_press_releases/2024/20240515.htm [Last accessed on 2024 May 24].

[2] Bart G. Maintenance medication for opiate addiction: The foundation of recovery. J Addict Dis. 2012; 31(3):207-225. doi: 10.1080/10550887.2012.694598

[3] Chan B, Gean E, Arkhipova-Jenkins I, et al. Retention strategies for medications for opioid use disorder in adults. J Addict Med. 2020; 15(1):74-84. doi: 10.1097/adm.0000000000000739

[4] Timko C, Schultz NR, Cucciare MA, Vittorio L, Garrison- Diehn C. Retention in medication-assisted treatment for opiate dependence: A systematic review. J Addict Dis. 2015; 35(1):22-35. doi: 10.1080/10550887.2016.1100960

[5] Williams AR, Samples H, Crystal S, Olfson M. Acute care, prescription opioid use, and overdose following discontinuation of long-term buprenorphine treatment for opioid use disorder. Am J Psychiatry. 2020; 177(2):117-124. doi: 10.1176/appi.ajp.2019.19060612

[6] Carroll KM, Weiss RD. The role of behavioral interventions in buprenorphine maintenance treatment: A review. Am J Psychiatry. 2017; 174(8):738-747. doi: 10.1176/appi.ajp.2016.16070792

[7] Manhapra A, Petrakis I, Rosenheck R. Three-year retention in buprenorphine treatment for opioid use disorder nationally in the Veterans health administration. Am J Addict. 2017; 26(6):572-580. doi: 10.1111/ajad.12553

[8] Proctor SL, Copeland AL, Kopak AM, Hoffmann NG, Herschman PL, Polukhina N. Predictors of patient retention in methadone maintenance treatment. Psychol Addict Behav. 2015; 29(4):906-917. doi: 10.1037/adb0000090

[9] Weinstein ZM, Kim HW, Cheng DM, et al. Long-term retention in office based opioid treatment with buprenorphine. J Subst Abuse Treat. 2017; 74:65-70. doi: 10.1016/j.jsat.2016.12.010

[10] Acevedo A, Garnick D, Ritter G, Horgan C, Lundgren L. Race/ ethnicity and quality indicators for outpatient treatment for substance use disorders. Am J Addict. 2015; 24(6):523-531. doi: 10.1111/ajad.12256

[11] Mennis J, Stahler GJ, El Magd SA, Baron DA. How long does it take to complete outpatient substance use disorder treatment? Disparities among blacks, hispanics, and whites in the US. Addict Behav. 2019; 93:158-165. doi: 10.1016/j.addbeh.2019.01.041

[12] Guerrero EG. Managerial capacity and adoption of culturally competent practices in outpatient substance abuse treatment organizations. J Subst Abuse Treat. 2010; 39(4):329-339. doi: 10.1016/j.jsat.2010.07.004

[13] Guerrero EG. Enhancing access and retention in substance abuse treatment: The role of Medicaid payment acceptance and cultural competence. Drug Alcohol Depend. 2013; 132(3):555-561. doi: 10.1016/j.drugalcdep.2013.04.005

[14] Guerrero EG, Campos M, Urada D, Yang JC. Do cultural and linguistic competence matter in Latinos’ completion of mandated substance abuse treatment? Subst Abuse Treat Prev Policy. 2012; 7:827-836. doi: 10.1186/1747-597x-7-34

[15] Guerrero EG, Khachikian T, Kim T, Kong Y, Vega WA. Spanish language proficiency among providers and Latino clients’ engagement in substance abuse treatment. Addict Behav. 2013; 38(12):2893-2897. doi: 10.1016/j.addbeh.2013.08.022

[16] Guerrero E, Andrews CM. Cultural competence in outpatient substance abuse treatment: Measurement and relationship to wait time and retention. Drug Alcohol Depend. 2011; 119(1-2):e13-e22. doi: 10.1016/j.drugalcdep.2011.05.020

[17] Howard DL. Are the treatment goals of culturally competent outpatient substance abuse treatment units congruent with their client profile? J Subst Abuse Treat. 2003; 24(2):103-113. doi: 10.1016/s0740-5472(02)00349-5

[18] Howard DL. Culturally competent treatment of African American clients among a national sample of outpatient substance abuse treatment units. J Subst Abuse Treat. 2003; 24(2):89-102. doi: 10.1016/s0740-5472(02)00348-3

[19] Jordan A, Jegede O. Building outreach and diversity in the field of addictions. Am J Addict. 2020; 29(5):413-417. doi: 10.1111/ajad.13097

[20] Weller BE, Harrison J, Adkison-Johnson C. Training a diverse workforce to address the opioid crisis. Soc Work Mental Health. 2021; 19(6):568-582. doi: 10.1080/15332985.2021.1975014

[21] Guerrero EG, Kong Y, Khachikian T, Wang S, D’Aunno T, Howard D. Workforce Diversity and Disparities in Opioid Treatment Wait Time and Retention. Research Square. Preprint; 2022. doi: 10.21203/rs.3.rs-1651284/v1

[22] Alaa A, Schaar M. Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design. Proc Mach Learn Res. 2018; 80:129-38.

[23] Angus DC, Chang CC. Heterogeneity of treatment effect: Estimating how the effects of interventions vary across individuals. JAMA. 2021; 326(22):2312-2313. doi: 10.1001/jfama.2021.20552

[24] Kong Y, Zhou J, Zheng Z, Amaro H, Guerrero EG. Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment. Health Serv Res. 2021; 57(2):411-421. doi: 10.1111/1475-6773.13896

[25] Künzel SR, Sekhon JS, Bickel PJ, Yu B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci U S A. 2019; 116(10):4156-4165. doi: 10.1073/pnas.1804597116

[26] Hill JL. Bayesian nonparametric modeling for causal inference. J Comput Graph Stat. 2011; 20(1):217-240.

[27] Grimmer J, Messing S, Westwood SJ. Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods. Polit Anal. 25(4):413-434. doi: 10.1017/pan.2017.15

[28] Athey S, Tibshirani J, Wager S. Generalized random forests. Ann Stat. 2019; 47(2):1148-1178. doi: 10.1214/18-AOS1709

[29] Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc. 2018; 113(523):1228-1242. doi: 10.1080/01621459.2017.1319839

[30] D’Aunno T, Park SE, Pollack HA. Evidence-based treatment for opioid use disorders: A national study of methadone dose levels, 2011-2017. J Subst Abuse Treat. 2019; 96:18-22. doi: 10.1016/j.jsat.2018.10.006

[31] D’Aunno T, Pollack HA, Frimpong JA, Wuchiett D. Evidence-based treatment for opioid disorders: A 23-year national study of methadone dose levels. J Subst Abuse Treat. 2014; 47(4):245-250. doi: 10.1016/j.jsat.2014.06.001

[32] Liu J, Storfer-Isser A, Mark TL, et al. Access to and engagement in substance use disorder treatment over time. Psychiatr Serv. 2020; 71(7):722-725. doi: 10.1176/appi.ps.201800461

[33] Campbell B. A Proposed Legislative Fund Could Help to Close Racial, Health Gap; 2021. Available from: https://phadvocates.org/wp-content/uploads/2021/06/bkgrnd-for-fund_060321.pdf [Last accessed on 2024 May 24].

[34] Haffajee RL. The public health value of opioid litigation. J Law Med Ethics. 2020; 48(2):279-292. doi: 10.1177/1073110520935340