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The Maudsley environmental risk score for psychosis

BACKGROUND: Risk prediction algorithms have long been used in health research and practice (e.g. prediction of cardiovascular disease and diabetes). However, similar tools have not been developed for mental health. For example, for psychotic disorders, attempts to sum environmental risk are rare, un...

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Autores principales: Vassos, Evangelos, Sham, Pak, Kempton, Matthew, Trotta, Antonella, Stilo, Simona A., Gayer-Anderson, Charlotte, Di Forti, Marta, Lewis, Cathryn M., Murray, Robin M., Morgan, Craig
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557157/
https://www.ncbi.nlm.nih.gov/pubmed/31535606
http://dx.doi.org/10.1017/S0033291719002319
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author Vassos, Evangelos
Sham, Pak
Kempton, Matthew
Trotta, Antonella
Stilo, Simona A.
Gayer-Anderson, Charlotte
Di Forti, Marta
Lewis, Cathryn M.
Murray, Robin M.
Morgan, Craig
author_facet Vassos, Evangelos
Sham, Pak
Kempton, Matthew
Trotta, Antonella
Stilo, Simona A.
Gayer-Anderson, Charlotte
Di Forti, Marta
Lewis, Cathryn M.
Murray, Robin M.
Morgan, Craig
author_sort Vassos, Evangelos
collection PubMed
description BACKGROUND: Risk prediction algorithms have long been used in health research and practice (e.g. prediction of cardiovascular disease and diabetes). However, similar tools have not been developed for mental health. For example, for psychotic disorders, attempts to sum environmental risk are rare, unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the aggregate environmental risk score (ERS) for psychotic disorders. METHODS: We reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis that combine a significant effect and large-enough prevalence. Pooled estimates of relative risks were taken from the largest available meta-analyses. We devised a method of scoring the level of exposure to each risk factor to estimate ERS. Relative risks were rounded as, due to the heterogeneity of the original studies, risk effects are imprecisely measured. RESULTS: Six risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on simulated data showed that most of the population would be at low/moderate risk with a small minority at increased environmental risk for psychosis. CONCLUSIONS: This is the first systematic approach to develop an aggregate measure of environmental risk for psychoses in asymptomatic individuals. This can be used as a continuous measure of liability to disease; mostly relevant to areas where the original studies took place. Its predictive ability will improve with the collection of additional, population-specific data.
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spelling pubmed-75571572020-10-23 The Maudsley environmental risk score for psychosis Vassos, Evangelos Sham, Pak Kempton, Matthew Trotta, Antonella Stilo, Simona A. Gayer-Anderson, Charlotte Di Forti, Marta Lewis, Cathryn M. Murray, Robin M. Morgan, Craig Psychol Med Original Articles BACKGROUND: Risk prediction algorithms have long been used in health research and practice (e.g. prediction of cardiovascular disease and diabetes). However, similar tools have not been developed for mental health. For example, for psychotic disorders, attempts to sum environmental risk are rare, unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the aggregate environmental risk score (ERS) for psychotic disorders. METHODS: We reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis that combine a significant effect and large-enough prevalence. Pooled estimates of relative risks were taken from the largest available meta-analyses. We devised a method of scoring the level of exposure to each risk factor to estimate ERS. Relative risks were rounded as, due to the heterogeneity of the original studies, risk effects are imprecisely measured. RESULTS: Six risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on simulated data showed that most of the population would be at low/moderate risk with a small minority at increased environmental risk for psychosis. CONCLUSIONS: This is the first systematic approach to develop an aggregate measure of environmental risk for psychoses in asymptomatic individuals. This can be used as a continuous measure of liability to disease; mostly relevant to areas where the original studies took place. Its predictive ability will improve with the collection of additional, population-specific data. Cambridge University Press 2020-10 2019-09-19 /pmc/articles/PMC7557157/ /pubmed/31535606 http://dx.doi.org/10.1017/S0033291719002319 Text en © The Author(s) 2019 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Vassos, Evangelos
Sham, Pak
Kempton, Matthew
Trotta, Antonella
Stilo, Simona A.
Gayer-Anderson, Charlotte
Di Forti, Marta
Lewis, Cathryn M.
Murray, Robin M.
Morgan, Craig
The Maudsley environmental risk score for psychosis
title The Maudsley environmental risk score for psychosis
title_full The Maudsley environmental risk score for psychosis
title_fullStr The Maudsley environmental risk score for psychosis
title_full_unstemmed The Maudsley environmental risk score for psychosis
title_short The Maudsley environmental risk score for psychosis
title_sort maudsley environmental risk score for psychosis
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557157/
https://www.ncbi.nlm.nih.gov/pubmed/31535606
http://dx.doi.org/10.1017/S0033291719002319
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