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BackgroundGrowing evidence suggests that air pollution exposure may adversely affect the brain and increase risk for psychiatric disorders such as schizophrenia and depression. However, little is known about the potential role of air pollution in severity and relapse following illness onset.AimsTo examine the longitudinal association between residential air pollution exposure and mental health service use (an indicator of illness severity and relapse) among individuals with first presentations of psychotic and mood disorders.MethodWe identified individuals aged ≥15 years who had first contact with the South London and Maudsley NHS Foundation Trust for psychotic and mood disorders in 2008–2012 (n = 13 887). High-resolution (20 × 20 m) estimates of nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM2.5 and PM10) levels in ambient air were linked to residential addresses. In-patient days and community mental health service (CMHS) events were recorded over 1-year and 7-year follow-up periods.ResultsFollowing covariate adjustment, interquartile range increases in NO2, NOx and PM2.5 were associated with 18% (95% CI 5–34%), 18% (95% CI 5–34%) and 11% (95% CI 3–19%) increased risk for in-patient days after 1 year. Similarly, interquartile range increases in NO2, NOx, PM2.5 and PM10 were associated with 32% (95% CI 25–38%), 31% (95% CI 24–37%), 7% (95% CI 4–11%) and 9% (95% CI 5–14%) increased risk for CMHS events after 1 year. Associations persisted after 7 years.ConclusionsResidential air pollution exposure is associated with increased mental health service use among people recently diagnosed with psychotic and mood disorders. Assuming causality, interventions to reduce air pollution exposure could improve mental health prognoses and reduce healthcare costs.

BackgroundMental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable.AimsTo 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.MethodWe 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.ResultsA 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 10 383–11 740). For every 10 new treated cases, 21 and 23 people would be assessed by and referred to EIP services for suspected psychosis, respectively.ConclusionsOur 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.