CMHSU: An R Statistical Software Package to Detect Mental Health Status, Substance Use Status, and their Concurrent Status in the North American Healthcare Administrative Databases

TL;DR

The paper introduces CMHSU, an R package that implements a diagnostic method for detecting mental health, substance use, and their concurrent status in large healthcare databases, aiding policymakers and researchers.

Contribution

It provides the first standalone software tool for DDDM-based detection of MHSU in healthcare data, facilitating large-scale analysis.

Findings

Demonstrated application on simulated real-world data

Analyzed temporal patterns of MHSU detection

Discussed limitations and future extensions of the package

Abstract

The concept of concurrent mental health and substance use (MHSU) and its detection in patients has garnered growing interest among psychiatrists and healthcare policymakers over the past four decades. Researchers have proposed various diagnostic methods, including the Data-Driven Diagnostic Method (DDDM), for the identification of MHSU. However, the absence of a standalone statistical software package to facilitate DDDM for large healthcare administrative databases has remained a significant gap. This paper introduces the R statistical software package CMHSU, available on the Comprehensive R Archive Network (CRAN), for the diagnosis of mental health (MH), substance use (SU), and their concurrent status (MHSU). The package implements DDDM using hospital and medical service physician visit counts along with maximum time span parameters for MH, SU, and MHSU diagnoses. A working example using a simulated real-world dataset is presented to examine various analytical aspects, including three key dimensions of MHSU detection based on the DDDM framework, as well as temporal analysis to demonstrate the package's application for healthcare policymakers. Additionally, the limitations of the CMHSU package and potential directions for its future extension are discussed.