Evidence map›Paper›PMID 31535606›Full record

SynthesisPsychological medicine2020

The Maudsley environmental risk score for psychosis.

Evangelos Vassos, Pak Sham, Matthew Kempton, Antonella Trotta, Simona A Stilo, Charlotte Gayer-Anderson, Marta Di Forti, Cathryn M Lewis, Robin M Murray, Craig Morgan

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Psychological medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Evangelos VassosSocial, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.ORCID 0000-0001-6363-0438
Pak ShamState Key Laboratory of Brain and Cognitive Sciences, Department of Psychiatry and Centre for Genomic Sciences, University of Hong Kong, Hong Kong, China.
Matthew KemptonDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Antonella TrottaSocial, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Simona A StiloDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Charlotte Gayer-AndersonHealth Service and Population Research, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Marta Di FortiSocial, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Cathryn M LewisSocial, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Robin M MurrayDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Craig MorganHealth Service and Population Research, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.

Funding

Department of HealthMedical Research Council MR/M008436/1Medical Research Council MR/P025927/1
6 · The paper itself

Abstract

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.

Indexed as

EnvironmentRisk AssessmentAdverse Childhood ExperiencesEthnicityFemaleHumansMaleMarijuana AbuseMinority GroupsObstetric Labor ComplicationsPaternal AgePregnancyPsychotic DisordersRisk FactorsUrban PopulationEnvironmentliabilitypsychosisrisk predictionschizophrenia

Identifiers

PMID31535606
PMCPMC7557157

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.