Innovations and changes in the ICD‐11 classification of mental, behavioural and neurodevelopmental disorders

Abstract

The development of the ICD-11 CDDG over the past decade, based on the principles of clinical utility and global applicability, has been the most broadly international, multilingual, multidisciplinary and participative revision process ever implemented for a classification of mental disorders. Innovations in the ICD-11 include the provision of consistent and systematically characterized information, the adoption of a lifespan approach, and culture-related guidance for each disorder. Dimensional approaches have been incorporated into the classification, particularly for personality disorders and primary psychotic disorders, in ways that are consistent with current evidence, are more compatible with recovery-based approaches, eliminate artificial comorbidity, and more effectively capture changes over time. Here we describe major changes to the structure of the ICD-11 classification of mental disorders as compared to the ICD-10, and the development of two new ICD-11 chapters relevant to mental health practice. We illustrate a set of new categories that have been added to the ICD-11 and present the rationale for their inclusion. Finally, we provide a description of the important changes that have been made in each ICD-11 disorder grouping. This information is intended to be useful for both clinicians and researchers in orienting themselves to the ICD-11 and in preparing for implementation in their own professional contexts.

Psychiatric Comorbidity as a Function of Severity: DSM‐5 Alcohol Use Disorder and HiTOP Classification of Mental Disorders

“…Dimensional approaches have a strong empirical basis (Krueger et al, ) and are increasingly incorporated into diagnostic systems. For instance, DSM‐5 (American Psychiatric Association, ) has initiated a transition toward dimensional approaches (e.g., autism spectrum disorder, AUD, SUDs), and the International Classification of Disease, Eleventh Revision (World Health Organization, ) has incorporated dimensional aspects of classification, such as including severity ratings within disorders (e.g., PDs) and grouping disorders based on common factors (e.g., multiple anxiety disorders clustered together given their commonality of a “fear” component; Reed et al, ).…”

The genetics of the mood disorder spectrum: genome-wide association analyses of over 185,000 cases and 439,000 controls

“…We defined three major depressive disorder subtypes for analysis. 4 Individuals meeting criteria for major depressive disorder were classed as recurrent 5 cases if they reported multiple depressed periods across their lifetime (rMDD, N = 6 17,451), and single-episode cases otherwise (sMDD, N = 12,024, Supplementary 7 Table 1). Individuals reporting depressive symptoms, but not meeting case criteria, 8 were excluded from the main analysis but used as a "sub-threshold depression" 9 subtype to examine the continuity of genetic associations with major depressive 10 disorder below clinical thresholds (subMDD, N = 21,596).…”

“…METACARPA adjusted adequately for known overlap 5 between cohorts (Supplementary Methods). For later analyses (particularly linkage 6 disequilibrium score regression) we used as the sample size a "non-overlapping N" 7 estimated for each meta-analysis (Supplementary Methods). The definition, 8 annotation and visualisation of each meta-analysis is described in the 9 Supplementary Materials.…”

“…Inflation not due to polygenicity was 5 quantified as (intercept-1)/(mean observed chi-square-1) (26). Genetic correlations 6 were calculated in LDSC between each analysis and 414 traits curated from 7 published GWAS. Local estimates of SNP-based heritability and genetic covariance 8 were obtained using HESS v0.5.3b (Supplementary Methods and Results) (27,28).…”

“…25 Association of PGC BD polygenic risk scores with major depressive disorder 1 subtypes 2 Polygenic risk score analyses were performed using PRSice2 to assess 3 whether rMDD was genetically more similar to PGC BD than were sMDD or subMDD 4 (Supplementary Methods) (36). 5 Gene-wise, gene-set, and tissue and single-cell enrichment analyses 6 For all analyses, gene-wise p-values were calculated as the aggregate of the 7 mean and smallest p-value of SNPs annotated to Ensembl gene locations using 8 MAGMA v1.06 (Supplementary Methods and Results) (37). Gene set analysis was 9 performed in MAGMA (Supplementary Methods and Results).…”