There are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances ...suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 760). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10-5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.
Response to the early stages of the COVID-19 pandemic resulted in the temporary disruption of cancer screening in the UK, and strong public messaging to stay safe and to protect NHS capacity. ...Following reintroduction in services, we explored the impact on inequalities in uptake of the Bowel Screening Wales (BSW) programme to identify groups who may benefit from tailored interventions.
Records within the BSW were linked to electronic health records (EHR) and administrative data within the Secured Anonymised Information Linkage (SAIL) Databank. Ethnic group was obtained from a linked data method available within SAIL. We examined uptake for the first 3 months of invitations (August to October) following the reintroduction of BSW programme in 2020, compared to the same period in the preceding 3 years. Uptake was measured across a 6 month follow-up period. Logistic models were conducted to analyse variations in uptake by sex, age group, income deprivation quintile, urban/rural location, ethnic group, and clinically extremely vulnerable (CEV) status in each period; and to compare uptake within sociodemographic groups between different periods.
Uptake during August to October 2020 (period 2020/21; 60.4%) declined compared to the same period in 2019/20 (62.7%) but remained above the 60% Welsh standard. Variation by sex, age, income deprivation, and ethnic groups was observed in all periods studied. Compared to the pre-pandemic period in 2019/20, uptake declined for most demographic groups, except for older individuals (70-74 years) and those in the most income deprived group. Uptake continues to be lower in males, younger individuals, people living in the most income deprived areas and those of Asian and unknown ethnic backgrounds.
Our findings are encouraging with overall uptake achieving the 60% Welsh standard during the first three months after the programme restarted in 2020 despite the disruption. Inequalities did not worsen after the programme resumed activities but variations in CRC screening in Wales associated with sex, age, deprivation and ethnic group remain. This needs to be considered in targeting strategies to improve uptake and informed choice in CRC screening to avoid exacerbating disparities in CRC outcomes as screening services recover from the pandemic.
ObjectivesTo determine whether clinically extremely vulnerable (CEV) children or children living with a CEV person in Wales were at greater risk of presenting with anxiety or depression in primary or ...secondary care during the COVID-19 pandemic compared with children in the general population and to compare patterns of anxiety and depression during the pandemic (23 March 2020–31 January 2021, referred to as 2020/2021) and before the pandemic (23 March 2019–31 January 2020, referred to as 2019/2020), between CEV children and the general population.DesignPopulation-based cross-sectional cohort study using anonymised, linked, routinely collected health and administrative data held in the Secure Anonymised Information Linkage Databank. CEV individuals were identified using the COVID-19 shielded patient list.SettingPrimary and secondary healthcare settings covering 80% of the population of Wales.ParticipantsChildren aged 2–17 in Wales: CEV (3769); living with a CEV person (20 033); or neither (415 009).Primary outcome measureFirst record of anxiety or depression in primary or secondary healthcare in 2019/2020 and 2020/2021, identified using Read and International Classification of Diseases V.10 codes.ResultsA Cox regression model adjusted for demographics and history of anxiety or depression revealed that only CEV children were at greater risk of presenting with anxiety or depression during the pandemic compared with the general population (HR=2.27, 95% CI=1.94 to 2.66, p<0.001). Compared with the general population, the risk among CEV children was higher in 2020/2021 (risk ratio 3.04) compared with 2019/2020 (risk ratio 1.90). In 2020/2021, the period prevalence of anxiety or depression increased slightly among CEV children, but declined among the general population.ConclusionsDifferences in the period prevalence of recorded anxiety or depression in healthcare between CEV children and the general population were largely driven by a reduction in presentations to healthcare services by children in the general population during the pandemic.
BackgroundCare home residents have complex healthcare needs but may have faced barriers to accessing hospital treatment during the first wave of the COVID-19 pandemic.
ObjectivesTo examine trends in ...the number of hospital admissions for care home residents during the first months of the COVID-19 outbreak.
MethodsRetrospective analysis of a national linked dataset on hospital admissions for residential and nursing home residents in England (257,843 residents, 45% in nursing homes) between 20 January 2020 and 28 June 2020, compared to admissions during the corresponding period in 2019 (252,432 residents, 45% in nursing homes). Elective and emergency admission rates, normalised to the time spent in care homes across all residents, were derived across the first three months of the pandemic between 1 March and 31 May 2020 and primary admission reasons for this period were compared across years.
ResultsHospital admission rates rapidly declined during early March 2020 and remained substantially lower than in 2019 until the end of June. Between March and May, 2,960 admissions from residential homes (16.2%) and 3,295 admissions from nursing homes (23.7%) were for suspected or confirmed COVID-19. Rates of other emergency admissions decreased by 36% for residential and by 38% for nursing home residents (13,191 fewer admissions in total). Emergency admissions for acute coronary syndromes fell by 43% and 29% (105 fewer admission) and emergency admissions for stroke fell by 17% and 25% (128 fewer admissions) for residential and nursing home residents, respectively. Elective admission rates declined by 64% for residential and by 61% for nursing home residents (3,762 fewer admissions).
ConclusionsThis is the first study showing that care home residents' hospital use declined during the first wave of COVID-19, potentially resulting in substantial unmet health need that will need to be addressed alongside ongoing pressures from COVID-19.
Objectives Using anonymised linked data across primary care general practice (GP) and local authority (LA) services to (1) identify unpaid carers in Swansea and Neath Port Talbot (NPT), (2) describe ...their health and health service use and, (3) compare these with a matched non-carer population.
MethodsUnpaid carers were identified using a) LA carers’ assessment data and b) GP Read codes within the Secured Anonymised Information Linkage (SAIL) Databank. An age, sex and area-matched non-carers cohort was created using demographic data and assigned pseudo-index dates. Linked GP and secondary care data was used to establish GP interactions, hospital admissions, emergency department and outpatient attendances in the year prior to identification as a carer. Long-term conditions (LTCs) were identified using published Cambridge multimorbidity Read code lists. Chi-square, Mann Whitney U-test, and rate ratios were used to test differences in aforementioned factors between carers and non-carers.
Results We have identified a total of 2,950 unpaid carers (N=2,024 in NPT; N=926 in Swansea), primarily via Read codes (80% in NPT; 70% in Swansea). Overlap between LA and GP identified individuals is less than five percent, and GP identified individuals are significantly younger than LA identified (NPT: χ2=176, p<0.001; Swansea: χ2=35.0, p<0.001). Further research is currently ongoing to utilise these anonymised linked data to ascertain key differences between carers and non-carers in the two local authorities. Results will include the significance of differences in rates of GP interactions, emergency department attendances, hospital admissions, outpatient attendances, rates of multimorbidity (0, 1, 2+ conditions), and top five specific LTCs between carers and matched non-carers in NPT and Swansea.
Conclusion We demonstrate the novel use of local authority-held data linked to national anonymised data sources to provide locally informative evidence for this priority population. Results will provide novel insight into the health and health service usage of unpaid carers at a LA level, assisting evidence-informed local support for unpaid carers.
Abstract The mechanisms by which antidepressants have their effects are not clear and the reasons for variability in treatment outcomes are also unknown. However, there is evidence from candidate ...gene research that indicates gene expression changes may be involved in antidepressant action. In this study, we examined antidepressant-induced alterations in gene expression on a transcriptome-wide scale, exploring associations with treatment response. Blood samples were taken from a subset of depressed patients from the GENDEP study ( n =136) before and after eight weeks of treatment with either escitalopram or nortriptyline. Transcriptomic data were obtained from these samples using Illumina HumanHT-12 v4 Expression BeadChip microarrays. When analysing individual genes, we observed that changes in the expression of two genes ( MMP28 and KXD1 ) were associated with better response to nortriptyline. Considering connectivity between genes, we identified modules of genes that were highly coexpressed. In the whole sample, changes in one of the ten identified coexpression modules showed significant correlation with treatment response (cor=0.27, p =0.0029). Using transcriptomic approaches, we have identified gene expression correlates of the therapeutic effects of antidepressants, highlighting possible molecular pathways involved in efficacious antidepressant treatment.
There has been substantial progress in psychiatric genetics in recent years, through collaborative efforts to build large samples sizes for case/control analyses for a number of psychiatric ...disorders. The identification of replicated trait-associated genomic loci represents a large stride forward in a field where little is known about the biological processes involved in disorder. As researchers build on this early foundation, they are beginning to advance the field towards more fine-grained approaches that interrogate the many sources of heterogeneity within psychiatric genetics that can obscure the identification of genotypic influences on disorder. In this review, we provide a brief overview, across a range of psychiatric diagnoses, of recent approaches that have been employed to dissect heterogeneity to give a flavour of the current direction of the field. We group these into three main categories; tackling the heterogeneity in phenotype that is found within the diagnostic categories used within psychiatry, the many different forms of genetic variation that might influence psychiatric traits and then finally, the heterogeneity that is seen across individuals of different ancestries.
Obtaining sufficient statistical power has been a key issue in the study of genetics in depression; only recently have samples become large enough to identify any genome-wide significant hits for ...this phenotype. Researchers have used a range of different approaches to tackle the issue of balancing sample size with phenotypic depth, and here we evaluate the statistical power achieved with these different methods.
We collated details of published genome-wide association studies exploring major depression or depressive symptoms. For each study we assessed sampling methods, phenotyping criteria and sample size, and use GPC (Genetics Power Calculator, Purcell et al. 2003) and extension methods (Yang et al. 2010, Traylor et al. 2015) to calculate statistical power, and compare approaches within the literature. We compared the statistical power (as captured by the non-centrality parameter) of each cohort across a range of allele thresholds and genetic effect sizes, contrasting different methodologies of cohort ascertainment within the depression literature.
We evaluate how statistical power is affected by the selection of potentially more homogenous but less prevalent depression phenotypes, the potential impact of misclassification in large cohorts which have employed brief phenotypic screening questions, and the value of dimensional measures of depression symptoms in population-based cohorts.
We confirm the findings of Traylor et al. apply in the case of depression; that in the analysis of specific subtypes of depression performed in datasets including PGC2 (2013, secondary analysis of recurrent, early-onset recurrent and typical-like depression, observed in approximately 75%, 50% and 40% of cases respectively) and CONVERGE (2015, secondary analysis of melancholia observed in approximately 85% of cases), very modest increases in genotypic relative risk (GRR) are required to achieve equivalent power with the whole sample analyses, particularly for less common risk allele frequencies and lower index GRR. We also compare the statistical power of the PGC2 case-control sample (discovery sample n=18,759) and the study from the CHARGE Consortium (discovery sample n= 34,549, Hek et al. 2013) looking at depression symptoms, observing that with a sample size which is 54% of that in CHARGE, the PGC2 dataset has less than half the statistical power to detect genetic effects.
Given range of approaches, here we give evaluation of expected statistical power across a range of approaches specific to the field of major depression genetics. We compare the statistical power achieved in the current literature and evaluate how different phenotypic approaches affect this.
If the ultimate aim of our research is to improve outcomes for patients with depression, our phenotypic definitions must be informed by clinical relevance. But nevertheless, an understanding of how statistical power is impacted by these definitions is key to optimal study design for the identification of the genetic variants associated with depressive disorders.
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