Variable reduction for past year alcohol and drug use in unmet need for mental health services among US adults

Abstract

Background: No previous study has focused on the inter-relationship among alcohol and drug use variables in the past year. This study aimed to classify the past year alcohol and drug use variables and investigate the selected variables in past year alcohol and drug use with the unmet need for mental health services among US adults.

Methods: Data came from the 2015 National Survey on Drug Use and Health (NSDUH). Oblique principal component cluster analysis (OPCCA) was used to classify 37 variables on alcohol and drug use in the past year into disjoint clusters. Weighted multiple logistic regression analysis was used to examine the associations of selected variables with the unmet need.

Results: 37 alcohol and drug use variables were divided into 7 clusters. The variable with the lowest 1-R² ratio (R² is the squared correlation) from each cluster was selected as follows: tobacco use, pain reliever use, tranquilizer use, stimulant use, zolpidem products use, illicit drug and alcohol use, and benzodiazepine tranquilizers misuse. Multiple logistic regression analysis showed that pain reliever use (OR = 1.33, 95% CI = 1.17-1.50), tranquilizer use (OR = 2.49, 95% CI = 2.16-2.86), stimulant use (OR = 1.22, 95% CI = 1.01-1.47), and illicit drug and alcohol use (OR = 1.54, 95% CI = 1.34-1.77) revealed positive associations with the unmet need for mental health services.

Conclusion: This is the first study using OPCCA to reduce the dominations of alcohol and drug use; several alcohol and drug use variables in the past year were associated with unmet need of mental health services.

Keywords: Alcohol use; Cluster analysis; Data mining; Drug use; Mental health services; Unmet need.