TY - JOUR
T1 - The Maudsley Environmental Risk Score for Psychosis
AU - Vassos, Evangelos
AU - Sham, Pak
AU - Kempton, Matthew
AU - Trotta, Antonella
AU - Stilo, Simona Ausilia
AU - Gayer-Anderson, Charlotte Emily Juliette
AU - Di Forti, Marta
AU - Lewis, Cathryn Mair
AU - Murray, Robin MacGregor
AU - Morgan, Craig
PY - 2019/9/19
Y1 - 2019/9/19
N2 - BackgroundRisk 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.MethodsWe 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.ResultsSix 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.ConclusionsThis 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.
AB - BackgroundRisk 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.MethodsWe 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.ResultsSix 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.ConclusionsThis 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.
KW - Environment
KW - liability
KW - psychosis
KW - risk prediction
KW - schizophrenia
UR - http://www.scopus.com/inward/record.url?scp=85072534242&partnerID=8YFLogxK
U2 - 10.1017/S0033291719002319
DO - 10.1017/S0033291719002319
M3 - Article
SN - 0033-2917
JO - Psychological Medicine
JF - Psychological Medicine
ER -