Using epidemiological evidence to forecast population need for early treatment programmes in mental health: a generalisable Bayesian prediction methodology applied to and validated for first-episode psychosis in England.

ABSTRACT:

Background

Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable.

Aims

To develop and validate a population-level prediction model for need for early intervention in psychosis (EIP) care for first-episode psychosis (FEP) in England up to 2025, based on epidemiological evidence and demographic projections.

Method

We used Bayesian Poisson regression to model small-area-level variation in FEP incidence for people aged 16-64 years. We compared six candidate models, validated against observed National Health Service FEP data in 2017. Our best-fitting model predicted annual incidence case-loads for EIP services in England up to 2025, for probable FEP, treatment in EIP services, init

SUBMITTER: McDonald K 

PROVIDER: S-EPMC7611597 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature