# Subtyping Service Receipt in Personality Disorder Services in South London: Observational Validation Study Using Latent Profile Analysis

## TL;DR

This study identifies two subgroups of personality disorder patients based on their mental health service use in South London, using data from over 3,900 patients.

## Contribution

The study introduces a method to classify patients with personality disorders into subtypes based on service engagement patterns using latent profile analysis.

## Key findings

- Two subgroups were identified: 73% with low service use and 27% with high service use.
- Cluster membership was significantly associated with nursing contacts in both unconditional and conditional models.
- The 2-profile model was statistically preferred over a 3-profile model.

## Abstract

Personality disorders (PDs) are typically associated with higher mental health service use; however, individual patterns of engagement among patients with complex needs are poorly understood.

The study aimed to identify subgroups of individuals based on patterns of service receipt in secondary mental health services and examine how routinely collected information is associated with these subgroups.

A sample of 3941 patients diagnosed with a personality disorder and receiving care from secondary services in South London was identified using health care records covering an 11-year period from 2007 to 2018. Basic demographic information, service use, and treatment data were included in the analysis. Service use measures included the number of contacts with clinical teams and instances of did-not-attend.

Using a large sample of 3941 patients with a diagnosis of PD, latent profile analysis identified 2 subgroups characterized by low and high service receipt, denoted as profile 1 (n=2879, 73.05%) and profile 2 (n=1062, 26.95%), respectively. A 2-profile solution (P<.01) was preferred over a 3-profile solution, which was nonsignificant. In unconditional (t3941,3939=19.53; P<.001; B=7.27; 95% CI 6.54-8) and conditional (t3941,3937=−3.31; P<.001; B=−74.94; 95% CI −119.34 to −30.56) models, cluster membership was significantly related to receipt of nursing contacts, over and above other team contacts.

These results suggest that routinely collected data may be used to classify likely engagement subtypes among patients with complex needs. The algorithm identified factors associated with service use and has the potential to inform clinical decision-making to improve treatment for individuals with complex needs.