Citation

Papini S, Chi FW, Schuler A, Satre DD, Liu VX, Sterling SA. Drug Alcohol Depend. 2022; 239: e109607.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.drugalcdep.2022.109607

PMID

36084444

Abstract

BACKGROUND: The combination of unhealthy alcohol use and depression is associated with adverse outcomes including higher rates of alcohol use disorder and poorer depression course. Therefore, addressing alcohol use among individuals with depression may have a substantial public health impact. We compared the effectiveness of a brief intervention (BI) for unhealthy alcohol use among patients with and without depression.

METHOD: This observational study included 312,056 adult primary care patients at Kaiser Permanente Northern California who screened positive for unhealthy drinking between 2014 and 2017. Approximately half (48%) received a BI for alcohol use and 9% had depression. We examined 12-month changes in heavy drinking days in the previous three months, drinking days per week, drinks per drinking day, and drinks per week. Machine learning was used to estimate BI propensity, follow-up participation, and alcohol outcomes for an augmented inverse probability weighting (AIPW) estimator of the average treatment (BI) effect. This approach does not depend on the strong parametric assumptions of traditional logistic regression, making it more robust to model misspecification.

RESULTS: BI had a significant effect on each alcohol use outcome in the non-depressed subgroup (-0.41 to -0.05, all ps < .003), but not in the depressed subgroup (-0.33 to -0.01, all ps > .28). However, differences between subgroups were nonsignificant (0.00 to 0.11, all ps > .44).

CONCLUSION: On average, BI is an effective approach to reducing unhealthy drinking, but more research is necessary to understand its impact on patients with depression. AIPW with machine learning provides a robust method for comparing intervention effectiveness across subgroups.

Language: en

Keywords

Depression; AIPW; Alcohol brief intervention; Causal machine learning; SBIRT; Treatment heterogeneity