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.

Keltie Mcdonald,Thandiwe Rebecca Dliwayo,David P.J Osborn,Jeremy W Coid,James B Kirkbride,Hannah Ker,Pia Wohland,Gianluca Baio,Peter B Jones,Paul French,Tao Ding

doi:10.1192/bjp.2021.18

Abstract

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

Highlights

Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable
Our evidence-based methodology provides an accurate, validated tool to inform clinical provision of early intervention in psychosis (EIP) services about future population need for care, based on local variation of major social determinants of psychosis
Ward- and regionallevel covariates were appended to this data-set. To overcome these limitations and facilitate generalisability of our methodology to other settings, we developed a new, populationbased prediction model applied to psychosis care in England, validated against observed national routine data

Summary

Introduction

Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable. The past decade has witnessed an unprecedented transformation in public perception of mental health,[1] with political commitment[2,3] to address substantial disparities between physical and mental healthcare.[4] This has begun to have an impact on mental healthcare provision, which is undergoing substantial reconfiguration in many regions, including the USA,[5] Canada,[6] Australia[7] and northern Europe.[2,8,9] One such example is early intervention in psychosis (EIP) services, which seek to provide multidisciplinary care for people with an emerging psychosis, informed by an evidence base that suggests that a longer duration of untreated psychosis is associated with less favourable health and social outcomes.[10] These services have rapidly gained traction in several countries, including England, based on clinical evidence for their efficacy,[11] effectiveness[11,12] and potential cost-effectiveness[13] over the short to medium term. Effects on longer-term outcomes, in the absence of sustained intervention, are less apparent,[14] and are a challenge to providing sustainable care models for people experiencing psychosis.

Methods

Results

Conclusion