A population-level prediction tool for the incidence of first-episode psychosis: translational epidemiology based on cross-sectional data
- James B Kirkbride1,
- Daniel Jackson2,
- Jesus Perez3,
- David Fowler4,
- Francis Winton5,
- Jeremy W Coid6,
- Robin M Murray7,
- Peter B Jones1,8
- 1Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain & Mind Sciences, Cambridge, UK
- 2MRC Biostatistics Unit, Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge, UK
- 3CAMEO, Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
- 4Norfolk and Suffolk Partnership Trust, Hellesdon Hospital, Norwich, UK
- 5Suffolk Early Intervention Psychosis Service, Norfolk and Suffolk Partnership Trust, Stowmarket, Suffolk, UK
- 6Forensic Psychiatry Research Unit, Queen Mary's University London, St. Bartholomew's Hospital, London, UK
- 7Department of Psychosis Studies, Institute of Psychiatry, London, UK
- 8NIHR Collaboration for Leadership in Applied Health Research & Care, Cambridge, UK
- Correspondence to Dr James Kirkbride;
- Received 23 August 2012
- Revised 21 December 2012
- Accepted 21 December 2012
- Published 11 February 2013
Objectives Specialist early intervention services (EIS) for people aged 14–35 years with first episodes of psychosis (FEP) have been commissioned throughout England since 2001. A single estimate of population need was used everywhere, but true incidence varies enormously according to sociodemographic factors. We sought to develop a realistically complex, population-based prediction tool for FEP, based on precise estimates of epidemiological risk.
Design and participants Data from 1037 participants in two cross-sectional population-based FEP studies were fitted to several negative binomial regression models to estimate risk coefficients across combinations of different sociodemographic and socioenvironmental factors. We applied these coefficients to the population at-risk of a third, socioeconomically different region to predict expected caseload over 2.5 years, where the observed rates of ICD-10 F10-39 FEP had been concurrently ascertained via EIS.
Setting Empirical population-based epidemiological data from London, Nottingham and Bristol predicted counts in the population at-risk in the East Anglia region of England.
Main outcome measures Observed counts were compared with predicted counts (with 95% prediction intervals (PI)) at EIS and local authority district (LAD) levels in East Anglia to establish the predictive validity of each model.
Results A model with age, sex, ethnicity and population density performed most strongly, predicting 508 FEP participants in EIS in East Anglia (95% PI 459, 559), compared with 522 observed participants. This model predicted correctly in 5/6 EIS and 19/21 LADs. All models performed better than the current gold standard for EIS commissioning in England (716 cases; 95% PI 664–769).
Conclusions We have developed a prediction tool for the incidence of psychotic disorders in England and Wales, made freely available online (http://www.psymaptic.org), to provide healthcare commissioners with accurate forecasts of FEP based on robust epidemiology and anticipated local population need. The initial assessment of some people who do not require subsequent EIS care means additional service resources, not addressed here, will be required.
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