Application of mixture distributions for identifying thresholds of frequent and high inpatient mental health service use in longitudinal data

Abstract

Background

There is a need for greater understanding about frequent and high use of inpatient mental health services, and those with ongoing increased needs. Most studies employ a threshold of frequent use (e.g. numbers of admissions) and high use (e.g. lengthy stays) without justification.

Aims

To identify model-driven thresholds for frequent/high inpatient mental health service use and contrast characteristics of patients identified using various models and thresholds.

Method

Retrospective population-based study using 12 years of longitudinal data for 5631 patients admitted with a mental health diagnosis. Two-component negative binomial and poisson mixture (truncated/untruncated) models identified thresholds for frequent/high use in a 12-month period.

Results

The two-component negative binomial mixture model resulted in the best model fit. Using negative binomial-derived thresholds, 5.3% of patients had a period of frequent use (admitted six or more times), 15.8% of high use (hospitalised for 45 or more days) and 3.5% of heavy use (both frequent and high use). The prevalence of specific mental health disorders (e.g. mood disorder and schizophrenia) among frequent and high use cohorts varied across thresholds.

Conclusions

This model-driven approach can be applied to identify thresholds in other cohorts. Threshold choice may depend on the magnitude and focus of potential interventions.

Keywords:

• Adults
• hospitalisation
• inpatients
• mental health
• statistical model
• threshold

Acknowledgements

The authors thank Brendan McAlister and David Webster for assistance with accessing data and Brendan McAlister for assistance with interpretation. Patients and the public were not involved in the design or conduct of this study. The authors thank Shae-Leigh Vella for her contribution to the discussion.

Disclosure statement

The authors have no conflicts of interest to declare.

Additional information

Funding

The authors acknowledge the Illawarra Health Information Platform (IHIP) research partnership established between the Illawarra Shoalhaven Local Health District (ISLHD) and the University of Wollongong, with ISLHD providing funding support and the data used in this study.