2021
Context v. algorithm: evidence that a transdiagnostic framework of contextual clinical characterization is of more clinical value than categorical diagnosis
van Os J, Pries L, Have M, de Graaf R, van Dorsselaer S, Bak M, Kenis G, Lin B, Gunther N, Luykx J, Rutten B, Guloksuz S. Context v. algorithm: evidence that a transdiagnostic framework of contextual clinical characterization is of more clinical value than categorical diagnosis. Psychological Medicine 2021, 53: 1825-1833. PMID: 37310330, PMCID: PMC10106290, DOI: 10.1017/s0033291721003445.Peer-Reviewed Original ResearchConceptsClinical characterizationPolygenic risk scoresClinical valueProspective general population cohortGeneral population cohortUse of medicationsPopulation attributable fractionPrediction of needDSM-IV diagnosisHealth care outcomesSymptom dimensionsTransdiagnostic frameworkClinical factorsAttributable fractionEtiological factorsRisk scoreCare outcomesCategorical algorithmService usePopulation cohortSociodemographic factorsTransdiagnostic symptom dimensionsPhysical healthDiagnosisDSM diagnosesSchizophrenia and the Environment: Within-Person Analyses May be Required to Yield Evidence of Unconfounded and Causal Association—The Example of Cannabis and Psychosis
van Os J, Pries L, Have M, de Graaf R, van Dorsselaer S, Bak M, Wittchen H, Rutten B, Guloksuz S. Schizophrenia and the Environment: Within-Person Analyses May be Required to Yield Evidence of Unconfounded and Causal Association—The Example of Cannabis and Psychosis. Schizophrenia Bulletin 2021, 47: 594-603. PMID: 33693921, PMCID: PMC8084443, DOI: 10.1093/schbul/sbab019.Peer-Reviewed Original ResearchConceptsFixed-effects modelCannabis usePsychotic experiencesGeneral population cohortRandom-effects modelMental health outcomesRisk factorsTime-varying confoundersProspective associationsPopulation cohortHealth outcomesOwn controlCausal associationPsychosisCannabisLongitudinal studyAssociationBetween-person levelConfoundersCohortIncidenceSchizophrenia
2020
Predictive Performance of Exposome Score for Schizophrenia in the General Population
Pries L, Erzin G, van Os J, Have M, de Graaf R, van Dorsselaer S, Bak M, Rutten B, Guloksuz S. Predictive Performance of Exposome Score for Schizophrenia in the General Population. Schizophrenia Bulletin 2020, 47: 277-283. PMID: 33215211, PMCID: PMC7965069, DOI: 10.1093/schbul/sbaa170.Peer-Reviewed Original ResearchConceptsES-SCZOptimal cut pointGeneral populationPopulation cohortGeneral population cohortCut pointsExposome scoreSchizophrenia spectrum disordersMeta-analytical estimatesClinical outcomesRisk strataRisk stratificationMulticausal etiologyMedical outcomesPsychiatric diagnosisBipolar disorderSchizophrenia diagnosisExposure scoreSuicide planSum scoreGene-environment interaction studiesSchizophreniaRisk predictionCharacteristic curveScoresEvidence for an interrelated cluster of Hallucinatory experiences in the general population: an incidence study
Moriyama T, Drukker M, Guloksuz S, Have M, de Graaf R, van Dorsselaer S, Gunther N, Bak M, van Os J. Evidence for an interrelated cluster of Hallucinatory experiences in the general population: an incidence study. Psychological Medicine 2020, 51: 2034-2043. PMID: 32317030, DOI: 10.1017/s0033291720000793.Peer-Reviewed Original ResearchConceptsRisk factorsProspective general population cohortGeneral population cohortIncidence of hallucinationsNon-psychotic disordersEnvironmental risk factorsTerms of prevalenceSelf-reported ratesYearly incidenceOlfactory hallucinationsVisual hallucinationsFunctional impairmentNEMESIS-2General populationPsychotic disordersHallucinatory experiencesPopulation cohortIncidence studyAuditory hallucinationsDelusional ideationCommon underlying mechanismMental disordersIncidenceNEMESIS-1Subsequent course