Using the service needs index to quantify complexity and identify treatment needs across youth mental health service populations: an observational study.

BACKGROUND

Digital technologies can facilitate comprehensive mental health assessment of an individual's treatment needs, while also enabling data aggregation and analysis at the population or service level. The Service Needs Index (made up of clinical, psychosocial, and comorbidity components) collectively expresses a concise metric for the type, range, and complexity of young people's treatment needs. This study aimed to examine variation in the Service Needs Index across service settings and assess its potential to inform population-level mental health planning.

METHODS

Using data from 1611 young people, we examined the Service Needs Index (made up of Clinical, Psychosocial, and Comorbidity subscores) across four mental health service populations (headspace Camperdown, urban headspaces, regional headspaces, and Mind Plasticity [a private practice in Sydney, Australia]). ANCOVA and pairwise comparisons were conducted controlling for age and sex. Bayesian logistic regression was used to examine the association between index scores and the odds of exceeding the Kessler-10 threshold for moderate psychological distress (K-10 ≥ 25).

RESULTS

There was significant variability in Service Needs Index scores (and subscores) between the four service populations. The private practice (Mind Plasticity) and regional headspaces had greater complexity than urban headspace services and headspace Camperdown. Complexity was driven by different patterns: Mind Plasticity had relatively higher clinical and comorbidity needs, while regional headspace services had higher clinical and psychosocial needs. Higher index scores were associated with increased odds of scoring in the moderate psychological distress range, with the Service Needs Index requiring the smallest score increase (6.1 units) to double the odds of scoring 25 or above on the K-10 (OR = 2.0).

CONCLUSIONS

The differences across service groups provide examples on how indices may shape policy and system-level decision-making in headspace services and other Primary Health Networks. The Service Needs Index measures complexity and could inform system-level decision-making by providing insights into trends, resource allocation, and the efficacy of interventions across broader groups.