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

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, initial assessment by EIP services and referral to EIP services for 'suspected psychosis'. Forecasts were stratified by gender, age and ethnicity, at national and Clinical Commissioning Group levels. Results: A model with age, gender, ethnicity, small-area-level deprivation, social fragmentation and regional cannabis use provided best fit to observed new FEP cases at national and Clinical Commissioning Group levels in 2017 (predicted 8112, 95% CI 7623-8597; observed 8038, difference of 74 0.92%). By 2025, the model forecasted 11 067 new treated cases per annum (95% CI 10383-11740). For every 10 new treated cases, 21 and 23 people would be assessed by and referred to EIP services for suspected psychosis, respectively. Conclusions: Our evidence-based methodology provides an accurate, validated tool to inform clinical provision of EIP services about future population need for care, based on local variation of major social determinants of psychosis.