To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Three years after the beginning of the COVID-19 pandemic, better knowledge on the transmission of respiratory viral infections (RVI) including the contribution of asymptomatic infections encouraged most healthcare centers to implement universal masking. The evolution of the SARS-CoV-2 epidemiology and improved immunization of the population call for the infection and prevention control community to revisit the masking strategy in healthcare. In this narrative review, we consider factors for de-escalating universal masking in healthcare centers, addressing compliance with the mask policy, local epidemiology, the level of protection provided by medical face masks, the consequences of absenteeism and presenteeism, as well as logistics, costs, and ecological impact. Most current national and international guidelines for mask use are based on the level of community transmission of SARS-CoV-2. Actions are now required to refine future recommendations, such as establishing a list of the most relevant RVI to consider, implement reliable local RVI surveillance, and define thresholds for activating masking strategies. Considering the epidemiological context (measured via sentinel networks or wastewater analysis), and, if not available, considering a time period (winter season) may guide to three gradual levels of masking: (i) standard and transmission-based precautions and respiratory etiquette, (ii) systematic face mask wearing when in direct contact with patients, and (iii) universal masking. Cost-effectiveness analysis of the different strategies is warranted in the coming years. Masking is just one element to be considered along with other preventive measures such as staff and patient immunization, and efficient ventilation.
Current psychiatric diagnoses, although heritable, have not been clearly mapped onto distinct underlying pathogenic processes. The same symptoms often occur in multiple disorders, and a substantial proportion of both genetic and environmental risk factors are shared across disorders. However, the relationship between shared symptoms and shared genetic liability is still poorly understood.
Well-characterised, cross-disorder samples are needed to investigate this matter, but few currently exist. Our aim is to develop procedures to purposely curate and aggregate genotypic and phenotypic data in psychiatric research.
As part of the Cardiff MRC Mental Health Data Pathfinder initiative, we have curated and harmonised phenotypic and genetic information from 15 studies to create a new data repository, DRAGON-Data. To date, DRAGON-Data includes over 45 000 individuals: adults and children with neurodevelopmental or psychiatric diagnoses, affected probands within collected families and individuals who carry a known neurodevelopmental risk copy number variant.
We have processed the available phenotype information to derive core variables that can be reliably analysed across groups. In addition, all data-sets with genotype information have undergone rigorous quality control, imputation, copy number variant calling and polygenic score generation.
DRAGON-Data combines genetic and non-genetic information, and is available as a resource for research across traditional psychiatric diagnostic categories. Algorithms and pipelines used for data harmonisation are currently publicly available for the scientific community, and an appropriate data-sharing protocol will be developed as part of ongoing projects (DATAMIND) in partnership with Health Data Research UK.
Substantial progress has been made in the standardization of nomenclature for paediatric and congenital cardiac care. In 1936, Maude Abbott published her Atlas of Congenital Cardiac Disease, which was the first formal attempt to classify congenital heart disease. The International Paediatric and Congenital Cardiac Code (IPCCC) is now utilized worldwide and has most recently become the paediatric and congenital cardiac component of the Eleventh Revision of the International Classification of Diseases (ICD-11). The most recent publication of the IPCCC was in 2017. This manuscript provides an updated 2021 version of the IPCCC.
The International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD), in collaboration with the World Health Organization (WHO), developed the paediatric and congenital cardiac nomenclature that is now within the eleventh version of the International Classification of Diseases (ICD-11). This unification of IPCCC and ICD-11 is the IPCCC ICD-11 Nomenclature and is the first time that the clinical nomenclature for paediatric and congenital cardiac care and the administrative nomenclature for paediatric and congenital cardiac care are harmonized. The resultant congenital cardiac component of ICD-11 was increased from 29 congenital cardiac codes in ICD-9 and 73 congenital cardiac codes in ICD-10 to 318 codes submitted by ISNPCHD through 2018 for incorporation into ICD-11. After these 318 terms were incorporated into ICD-11 in 2018, the WHO ICD-11 team added an additional 49 terms, some of which are acceptable legacy terms from ICD-10, while others provide greater granularity than the ISNPCHD thought was originally acceptable. Thus, the total number of paediatric and congenital cardiac terms in ICD-11 is 367. In this manuscript, we describe and review the terminology, hierarchy, and definitions of the IPCCC ICD-11 Nomenclature. This article, therefore, presents a global system of nomenclature for paediatric and congenital cardiac care that unifies clinical and administrative nomenclature.
The members of ISNPCHD realize that the nomenclature published in this manuscript will continue to evolve. The version of the IPCCC that was published in 2017 has evolved and changed, and it is now replaced by this 2021 version. In the future, ISNPCHD will again publish updated versions of IPCCC, as IPCCC continues to evolve.
Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders.
We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific.
We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001).
Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
Individuals with schizophrenia are at higher risk of physical illnesses, which are a major contributor to their 20-year reduced life expectancy. It is currently unknown what causes the increased risk of physical illness in schizophrenia.
To link genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised National Health Service (NHS) records. To assess (a) rates of physical illness in those with schizophrenia, and (b) whether physical illness in schizophrenia is associated with genetic liability.
We linked genetic data from a clinically ascertained sample of individuals with schizophrenia (Cardiff Cognition in Schizophrenia participants, n = 896) to anonymised NHS records held in the Secure Anonymised Information Linkage (SAIL) databank. Physical illnesses were defined from the General Practice Database and Patient Episode Database for Wales. Genetic liability for schizophrenia was indexed by (a) rare copy number variants (CNVs), and (b) polygenic risk scores.
Individuals with schizophrenia in SAIL had increased rates of epilepsy (standardised rate ratio (SRR) = 5.34), intellectual disability (SRR = 3.11), type 2 diabetes (SRR = 2.45), congenital disorders (SRR = 1.77), ischaemic heart disease (SRR = 1.57) and smoking (SRR = 1.44) in comparison with the general SAIL population. In those with schizophrenia, carrier status for schizophrenia-associated CNVs and neurodevelopmental disorder-associated CNVs was associated with height (P = 0.015–0.017), with carriers being 7.5–7.7 cm shorter than non-carriers. We did not find evidence that the increased rates of poor physical health outcomes in schizophrenia were associated with genetic liability for the disorder.
This study demonstrates the value of and potential for linking genetic data from clinically ascertained research studies to anonymised health records. The increased risk for physical illness in schizophrenia is not caused by genetic liability for the disorder.
The nature and degree of cognitive impairments in schizoaffective disorder is not well established. The aim of this meta-analysis was to characterise cognitive functioning in schizoaffective disorder and compare it with cognition in schizophrenia and bipolar disorder. Schizoaffective disorder was considered both as a single category and as its two diagnostic subtypes, bipolar and depressive disorder.
Following a thorough literature search (468 records identified), we included 31 studies with a total of 1685 participants with schizoaffective disorder, 3357 with schizophrenia and 1095 with bipolar disorder. Meta-analyses were conducted for seven cognitive variables comparing performance between participants with schizoaffective disorder and schizophrenia, and between schizoaffective disorder and bipolar disorder.
Participants with schizoaffective disorder performed worse than those with bipolar disorder (g = −0.30) and better than those with schizophrenia (g = 0.17). Meta-analyses of the subtypes of schizoaffective disorder showed cognitive impairments in participants with the depressive subtype are closer in severity to those seen in participants with schizophrenia (g = 0.08), whereas those with the bipolar subtype were more impaired than those with bipolar disorder (g = −0.23) and less impaired than those with schizophrenia (g = 0.29). Participants with the depressive subtype had worse performance than those with the bipolar subtype but this was not significant (g = 0.25, p = 0.05).
Cognitive impairments increase in severity from bipolar disorder to schizoaffective disorder to schizophrenia. Differences between the subtypes of schizoaffective disorder suggest combining the subtypes of schizoaffective disorder may obscure a study's results and hamper efforts to understand the relationship between this disorder and schizophrenia or bipolar disorder.
It is not clear to what extent associations between schizophrenia, cannabis use and cigarette use are due to a shared genetic etiology. We, therefore, examined whether schizophrenia genetic risk associates with longitudinal patterns of cigarette and cannabis use in adolescence and mediating pathways for any association to inform potential reduction strategies.
Associations between schizophrenia polygenic scores and longitudinal latent classes of cigarette and cannabis use from ages 14 to 19 years were investigated in up to 3925 individuals in the Avon Longitudinal Study of Parents and Children. Mediation models were estimated to assess the potential mediating effects of a range of cognitive, emotional, and behavioral phenotypes.
The schizophrenia polygenic score, based on single nucleotide polymorphisms meeting a training-set p threshold of 0.05, was associated with late-onset cannabis use (OR = 1.23; 95% CI = 1.08,1.41), but not with cigarette or early-onset cannabis use classes. This association was not mediated through lower IQ, victimization, emotional difficulties, antisocial behavior, impulsivity, or poorer social relationships during childhood. Sensitivity analyses adjusting for genetic liability to cannabis or cigarette use, using polygenic scores excluding the CHRNA5-A3-B4 gene cluster, or basing scores on a 0.5 training-set p threshold, provided results consistent with our main analyses.
Our study provides evidence that genetic risk for schizophrenia is associated with patterns of cannabis use during adolescence. Investigation of pathways other than the cognitive, emotional, and behavioral phenotypes examined here is required to identify modifiable targets to reduce the public health burden of cannabis use in the population.
22q11.2 deletion syndrome (22q11.2DS) is associated with a high risk of childhood as well as adult psychiatric disorders, in particular schizophrenia. Childhood cognitive deterioration in 22q11.2DS has previously been reported, but only in studies lacking a control sample.
To compare cognitive trajectories in children with 22q11.2DS and unaffected control siblings.
A longitudinal study of neurocognitive functioning (IQ, executive function, processing speed and attention) was conducted in children with 22q11.2DS (n = 75, mean age time 1 (T1) 9.9, time 2 (T2) 12.5) and control siblings (n = 33, mean age T1 10.6, T2 134).
Children with 22q11.2DS exhibited deficits in all cognitive domains. However, mean scores did not indicate deterioration. When individual trajectories were examined, some participants showed significant decline over time, but the prevalence was similar for 22q11.2DS and control siblings. Findings are more likely to reflect normal developmental fluctuation than a 22q11.2DS-specific abnormality.
Childhood cognitive deterioration is not associated with 22q11.2DS. Contrary to previous suggestions, we believe it is premature to recommend repeated monitoring of cognitive function to identifying individual children with 22q11.2DS at high risk of developing schizophrenia.
A number of copy number variants (CNVs) have been suggested as
susceptibility factors for schizophrenia. For some of these the data
remain equivocal, and the frequency in individuals with schizophrenia is
To determine the contribution of CNVs at 15 schizophrenia-associated loci
(a) using a large new data-set of patients with schizophrenia
(n = 6882) and controls (n = 6316),
and (b) combining our results with those from previous studies.
We used Illumina microarrays to analyse our data. Analyses were
restricted to 520 766 probes common to all arrays used in the different
We found higher rates in participants with schizophrenia than in controls
for 13 of the 15 previously implicated CNVs. Six were nominally
significantly associated (P<0.05) in this new
data-set: deletions at 1q21.1, NRXN1, 15q11.2 and
22q11.2 and duplications at 16p11.2 and the Angelman/Prader–Willi
Syndrome (AS/PWS) region. All eight AS/PWS duplications in patients were
of maternal origin. When combined with published data, 11 of the 15 loci
showed highly significant evidence for association with schizophrenia
We strengthen the support for the majority of the previously implicated
CNVs in schizophrenia. About 2.5% of patients with schizophrenia and 0.9%
of controls carry a large, detectable CNV at one of these loci. Routine
CNV screening may be clinically appropriate given the high rate of known
deleterious mutations in the disorder and the comorbidity associated with
these heritable mutations.