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Background databases of electronic health records are powerful tools for dementia research, but data can be influenced by incomplete recording. We examined whether people with dementia recorded in a specialist database (from a mental health and dementia care service) differ from those recorded in primary care. Methods a retrospective cohort study of the population covered by Lambeth DataNet (primary care electronic records) between 2007 and 2019. Documentation of dementia diagnosis in primary care coded data and linked records in a specialist database (Clinical Records Interactive Search) were compared. Results 3,859 people had dementia documented in primary care codes and 4,266 in the specialist database, with 2,886/5,239 (55%) documented in both sources. Overall, 55% were labelled as having Alzheimer’s dementia and 29% were prescribed dementia medication, but these proportions were significantly higher in those documented in both sources. The cohort identified from the specialist database were less likely to live in a care home (prevalence ratio 0.73, 95% confidence interval 0.63–0.85), have multimorbidity (0.87, 0.77–0.98) or consult frequently (0.91, 0.88–0.95) than those identified through primary care codes, although mortality did not differ (0.98, 0.91–1.06). Discussion there is under-recording of dementia diagnoses in both primary care and specialist databases. This has implications for clinical care and for generalizability of research. Our results suggest that using a mental health database may under-represent those patients who have more frailty, reflecting differential referral to mental health services, and demonstrating how the patient pathways are an important consideration when undertaking database studies.

Purpose People with dementia may have indications for aspirin prescription and clinicians are asked to balance the potential risks against benefits. This review examines the evidence for the risk and benefit of long-term aspirin use in people with dementia aged over 65 years, including randomised controlled trials and observational studies. Methods We searched three databases for research published between 2007 and 2020. Each eligible article was assessed for risk of bias, and confidence in findings was rated using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Results Four papers met inclusion criteria: one randomised controlled trial, two cohort studies, and one with pooled data. All looked only at dementia of Alzheimer’s type, and none addressed myocardial or cerebral infarction as outcomes. Dementia progression was reported by two studies, with conflicting results. The trial found no significant effect of aspirin on mortality (odds ratio aspirin vs. no aspirin 1.07, 95% confidence interval 0.58–1.97) but found more events of severe bleeding with aspirin (OR aspirin vs. no aspirin 6.9, 1.5–31.2). An excess in intracranial haemorrhage in the aspirin group was judged plausible based on two non-randomised studies. Conclusions The review findings are limited because studies include only people with Alzheimer’s-type dementia and lack confirmatory studies, although an increased risk of bleeding events is recognised. Further research that addresses the benefits and risks of aspirin in more representative groups of people with dementia is needed to guide prescribing decisions.

BackgroundMood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart.AimsTo identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability.MethodWe used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores.ResultsFive latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including ‘minimally affected’, ‘inactive restless’, active restless’, ‘focused creative’ and ‘extensively affected’ individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment.ConclusionsFindings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

Anxiety disorders are common, complex psychiatric disorders with twin heritabilities of 30-60%. We conducted a genome-wide association study of Lifetime Anxiety Disorder (n = 83 565) and an additional Current Anxiety Symptoms (n= 77 125) analysis. The liability scale common variant heritability estimate for Lifetime Anxiety Disorder was 26%, and for Current Anxiety Symptoms was 31%. Five novel genome-wide significant loci were identified including an intergenic region on chromosome 9 that has previously been associated with neuroticism, and a locus overlapping the BDNF receptor gene, NTRK2. Anxiety showed significant genetic correlations with depression and insomnia as well as coronary artery disease, mirroring findings from epidemiological studies. We conclude that common genetic variation accounts for a substantive proportion of the genetic architecture underlying anxiety.

ObjectivesTo characterise the baseline King’s College London Coronavirus Health and Experiences of Colleagues at King’s cohort and describe patterns of probable depression and anxiety among staff and postgraduate research students at a large UK university in April/May 2020.MethodsAn online survey was sent to current staff and postgraduate research students via email in April 2020 (n=2590). Primary outcomes were probable depression and anxiety, measured with the Patient Health Questionnaire-9 and Generalised Anxiety Disorder-7, respectively. Secondary outcomes were alcohol use and perceived change in mental health. Outcomes were described using summary statistics and multivariable Poisson regression was used to explore associations with six groups of predictors: demographics and prior mental health, living arrangements, caring roles, healthcare, occupational factors and COVID-19 infection. All analyses were weighted to account for differences between the sample and target population in terms of age, gender, and ethnicity.ResultsAround 20% of staff members and 30% of postgraduate research students met thresholds for probable depression or anxiety on the questionnaires. This doubled to around 40% among younger respondents aged ConclusionsOur study shows worrying levels of symptoms of depression, anxiety and alcohol use disorder in an occupational sample from a large UK university in the months following the outbreak of the COVID-19 pandemic.

BackgroundCOVID-19 antibody testing allows population studies to classify participants by previous SARS-CoV-2 infection status. Home lateral flow immune-antibody testing devices offer a very convenient way of doing this, but relatively little is known about how measurement and antibody variability will affect consistency in results over time. We examined consistency by looking at the outcome of two tests three months apart while COVID-19 infection rates were low (summer 2020 in the UK).MethodsThe KCL-Coronavirus Health and Experiences in Colleagues at King’s is an occupational cohort of staff and postgraduate research students. Lateral flow immune-antibody testing kits were sent to participant’s homes in late June 2020 and late September 2020. Participants also completed regular surveys that included asking about COVID-19 symptoms and whether they thought they had been infected.ResultsWe studied 1489 participants returned valid results in both June and September (59% of those sent kits). Lateral flow immune-antibody test was positive for 7.2% in June and 5.9% in September, with 3.9% positive in both. Being more symptomatic or suspecting infection increased the probability of ever being positive. Of those positive in June, 46% (49/107) were negative in September (seroreversion), and this was similar regardless of symptom characteristics, suspicion, and timing of possible infection. A possible outlier was those aged over 55 years, where only 3 of 13 (23%) had seroreversion.DiscussionThese results do not follow the pattern reported from studies specifically designed to monitor seropositivity, which have found greater consistency over time and the influence of presence, timing and severity of symptoms on seroreversion. We suggest several factors that may have contributed to this difference: our low bar in defining initial seropositivity (single test); a non-quantitative test known to have relatively low sensitivity; participants carrying out testing. We would encourage other studies to use these real-world performance characteristics alongside those from laboratory studies to plan and analyse any antibody testing.

Purpose Experiences of reported trauma are common and are associated with a range of mental health problems. Sex differences in how reported traumas are experienced over the life course in relation to mental health require further exploration. Methods 157,358 participants contributed data for the UK Biobank Mental Health Questionnaire (MHQ). Stratified Latent Class Analysis (LCA) was used to analyse combinations of reported traumatic experiences in males and females separately, and associations with mental health. Results In females, five trauma classes were identified: a low-risk class (58.6%), a childhood trauma class (13.5%), an intimate partner violence class (12.9%), a sexual violence class (9.1%), and a high-risk class (5.9%). In males, a three-class solution was preferred: a low-risk class (72.6%), a physical and emotional trauma class (21.9%), and a sexual violence class (5.5%). In comparison to the low-risk class in each sex, all trauma classes were associated with increased odds of current depression, anxiety, and hazardous/harmful alcohol use after adjustment for covariates. The high-risk class in females and the sexual violence class in males produced significantly increased odds for recent psychotic experiences. Conclusion There are sex differences in how reported traumatic experiences co-occur across a lifespan, with females at the greatest risk. However, reporting either sexual violence or multiple types of trauma was associated with increased odds of mental health problems for both males and females. Findings emphasise the public mental health importance of identifying and responding to both men and women’s experiences of trauma, including sexual violence.

Background: There is inconsistent evidence about the association between inflammatory disorders with depression and anxiety onset in a primary care context. The study aimed evaluate the risk of depression and anxiety within multisystem and organ-specific inflammatory disorders. Methods: Prospective cohort study with primary care patients from the UK Clinical Practice Research Datalink diagnosed with an inflammatory disorder between 1st of January 2001 and 31st of December 2016. These patients were matched on age, gender, practice, and index date with patients without an inflammatory disorder. The study exposures were seven chronic inflammatory disorders. Clinical diagnosis of depression and anxiety represented the outcome measures of interest. Findings: Among 538,707 participants, the incidence of depression ranged from 14 per 1000 person-years (severe psoriasis) to 9 per 1000 person-years (systemic vasculitis), substantively higher compared to their comparison group (5 to 7 per 1000 person-years). Hazard ratios (HR) of multiple depression and anxiety events were 16% higher within inflammatory disorders (HR, 1·16, 95%CI 1·12-1·21, p

BackgroundEpisodic changes in mood characterise disorders such as bipolar disorder, which includes distinct periods of manic excitability or irritability, along with additional symptoms experienced during these periods. Common clinical understanding informs diagnostic criteria and epidemiological studies reflect clinical thresholds.AimsTo use a data-driven approach to defining groupings of symptoms experienced during periods of manic or irritable mood, which could inform understanding of mood disorders and guide case classification by identifying subgroups with homogeneous clinical/functional outcomes.MethodsWe used latent class analysis (LCA) to conduct an exploration of the latent structure in symptom responses in the UK Biobank and PROTECT studies, by investigating how symptoms, experienced during periods of manic or irritable mood, formed latent subgroups. We tested associations of latent subgroups with sociodemographic characteristics, diagnoses of psychiatric disorders and polygenic risk scores (PRS).ResultsFive latent classes were identified that captured patterns of symptoms experienced during periods of manic or irritable mood (N=42,183) in UK Biobank. We identified one class that experienced disruptive episodes of mostly irritable mood that was largely comprised of cases of depression/anxiety, and a class of individuals with increased confidence/creativity that reported lower disruptiveness and lower functional impairment. The five latent classes were replicated in an independent cohort, the PROTECT study (N=4,445), with similar distinctions between classes.ConclusionOur data-driven approach to grouping individuals identified distinct latent classes. A dimensional classification of mood disorders informed by our findings will be able to better assess or subtype these disorders in future studies.

Background Researchers conducting cohort studies may wish to investigate the effect of episodes of COVID-19 illness on participants. A definitive diagnosis of COVID-19 is not always available, so studies have to rely on proxy indicators. This paper seeks to contribute evidence that may assist the use and interpretation of these COVID-indicators. Methods We described five potential COVID-indicators: self-reported core symptoms, a symptom algorithm; self-reported suspicion of COVID-19; self-reported external results; and home antibody testing based on a 'lateral flow' antibody (IgG/IgM) test cassette. Included were staff and postgraduate research students at a large London university who volunteered for the study and were living in the UK in June 2020. Excluded were those who did not return a valid antibody test result. We provide descriptive statistics of prevalence and overlap of the five indicators. Results Core symptoms were the most common COVID-indicator (770/1882 participants positive, 41%), followed by suspicion of COVID-19 (n = 509/1882, 27%), a positive symptom algorithm (n = 298/1882, 16%), study antibody lateral flow positive (n = 124/1882, 7%) and a positive external test result (n = 39/1882, 2%), thus a 20-fold difference between least and most common. Meeting any one indicator increased the likelihood of all others, with concordance between 65 and 94%. Report of a low suspicion of having had COVID-19 predicted a negative antibody test in 98%, but positive suspicion predicted a positive antibody test in only 20%. Those who reported previous external antibody tests were more likely to have received a positive result from the external test (24%) than the study test (15%). Conclusions Our results support the use of proxy indicators of past COVID-19, with the caveat that none is perfect. Differences from previous antibody studies, most significantly in lower proportions of participants positive for antibodies, may be partly due to a decline in antibody detection over time. Subsequent to our study, vaccination may have further complicated the interpretation of COVID-indicators, only strengthening the need to critically evaluate what criteria should be used to define COVID-19 cases when designing studies and interpreting study results.

BackgroundUnderstanding and improving outcomes for people with anxiety or depression often requires large studies. To increase participation and reduce costs, such research is typically unable to utilise “gold-standard” methods to ascertain diagnoses, instead relying on remote, self-report measures.AimsTo assess the comparability of remote diagnostic methods for anxiety and depression disorders commonly used in research.MethodParticipants from the UK-based GLAD and COPING NBR cohorts (N= 58,400) completed an online questionnaire between 2018-2020. Responses to detailed symptom reports were compared to DSM-5 criteria to generate algorithm-based diagnoses of major depressive disorder (MDD), generalised anxiety disorder (GAD), specific phobia, social anxiety disorder, panic disorder, and agoraphobia. Participants also self-reported any prior diagnoses from health professionals, termed single-item diagnoses. “Any anxiety” included participants with at least one anxiety disorder. Agreement was assessed by calculating accuracy, Cohen’s kappa, McNemar’s chi-squared, sensitivity, and specificity.ResultsAgreement between diagnoses was moderate for MDD, any anxiety, and GAD, but varied by cohort. Agreement was slight to fair for the phobic disorders. Many participants with single-item GAD did not receive an algorithm-based diagnosis. In contrast, algorithm-based diagnoses of the phobic disorders were more common than single-item diagnoses.ConclusionsAgreement for MDD, any anxiety, and GAD was higher for cases in the case-enriched GLAD cohort and for controls in the general population COPING NBR cohort. For anxiety disorders, single-item diagnoses classified most participants as having GAD, whereas algorithm-based diagnoses distributed participants more evenly across the anxiety disorders. Further validation against gold standard measures is required.

BackgroundDefinitive diagnosis of COVID-19 requires resources frequently restricted to the severely ill. Cohort studies must rely on surrogate indicators to define cases of COVID-19 in the community. We describe the prevalence and overlap of potential indicators including self-reported symptoms, suspicion, and routine test results, plus home antibody testing.MethodsAn occupational cohort of 2807 staff and postgraduate students at a large London university. Repeated surveys covering March to June 2020. Antibody test results from ‘lateral flow’ IgG/IgM cassettes in June 2020.Results1882 participants had valid antibody test results, and 124 (7%) were positive. Core symptoms of COVID-19 were common (770 participants positive, 41%), although fewer met criteria on a symptom algorithm (n=297, 16%). Suspicion of COVID-19 (n=509, 27%) was much higher than positive external tests (n=39, 2%). Positive antibody tests were rare in people who had no suspicion (n=4, 1%) or no core symptoms (n=10, 2%). In those who reported external antibody tests, 15% were positive on the study antibody test, compared with 24% on earlier external antibody tests.DiscussionOur results demonstrate the agreement between different COVID indicators. Antibody testing using lateral flow devices at home can detect asymptomatic cases and provide greater certainty to self-report; but due to weak and waning antibody responses to mild infection, may under-ascertain. Multiple indicators used in combination can provide a more complete story than one used alone. Cohort studies need to consider how they deal with different, sometimes conflicting, indicators of COVID-19 illness to understand its long-term outcomes.THUMBNAILWhat is already known on this subject?Research into the effects of COVID-19 in the community is needed to respond to the pandemic, and guidance is needed as to how cohort studies measure COVID-19 infection status retrospectively, particularly given that objective testing for infection was not widely available in the first wave of COVID-19 in many countries. Retrospective testing might be possible using antibodies as a proxy for previous COVID-19 infection.What this study adds?Antibody testing is feasible in community cohorts but sensitivity may be poor. Self-report of suspected infection, recall of symptoms and results of tests received elsewhere add different aspects to the ascertainment of COVID-19 exposure. Combining self-report and objectively measured indicators may enable tailored algorithms for COVID-19 case definition that suits the aims of different research studies.

Family studies have identified a heritable component to self-harm that is partially independent from comorbid psychiatric disorders. However, the genetic aetiology of broad sense (non-suicidal and suicidal) self-harm has not been characterised on the molecular level. In addition, controversy exists about the degree to which suicidal and non-suicidal self-harm share a common genetic aetiology. In the present study, we conduct genome-wide association studies (GWAS) on lifetime self-harm ideation and self-harm behaviour (i.e. any lifetime self-harm act regardless of suicidal intent) using data from the UK Biobank (n > 156,000). We also perform genome wide gene-based tests and characterize the SNP heritability and genetic correlations between these traits. Finally, we test whether polygenic risk scores (PRS) for self-harm ideation and self-harm behaviour predict suicide attempt, suicide thoughts and non-suicidal self-harm (NSSH) in an independent target sample of 8,703 Australian adults. Our GWAS results identified one genome-wide significant locus associated with each of the two phenotypes. SNP heritability (hsnp2) estimates were ~10%, and both traits were highly genetically correlated (LDSC rg > 0.8). Gene-based tests identified seven genes associated with self-harm ideation and four with self-harm behaviour. Furthermore, in the target sample, PRS for self-harm ideation were significantly associated with suicide thoughts and NSSH, and PRS for self-harm behaviour predicted suicide thoughts and suicide attempt. Follow up regressions identified a shared genetic aetiology between NSSH and suicide thoughts, and between suicide thoughts and suicide attempt. Evidence for shared genetic aetiology between NSSH and suicide attempt was not statistically significant.

Background: There is evidence that mental disorders are more frequently reported among emergency services personnel due to the stressful nature of the job in combination with a high exposure to traumatic events. However, most of this research is based on occupational surveys that may lead to a contextual bias in the prevalence estimates or lack an adequate comparison group. Objectives: To investigate mental health outcomes and associations with individual, job and trauma related characteristics among emergency services personnel compared to other workers. Method: Participants were identified from the UK Biobank, a large study that collected a variety of genetic, physical and health data on individuals from across the UK. UK Biobank participants were aged between 40–69 years at recruitment. Those employed in the emergency services were identified based on job titles. A comparison sample of other workers was selected and matched to the gender composition of emergency services personnel. Results: 5052 participants were included, and 842 were currently working in the emergency services. The majority were male (77.4%) and the mean age at Biobank enrolment was 52.5 years. Alcohol misuse was reported in 32.8% of emergency services personnel compared to 29.2% in non-emergency services personnel, followed by PTSD (9.2% vs 6.0%), depression (6.8% vs 5.1%) and anxiety (3.9% vs 3.6%). An increased risk of PTSD was found among emergency services personnel compared to other workers (odds ratio 1.58, 95% confidence interval 1.21–2.06), but this association was no longer significant after adjustment for exposure to traumatic events and job characteristics. Conclusions: The substantial levels of alcohol misuse and increased risk of PTSD, possibly as a result of traumatic exposures in the line of duty in combination with job stressors such as shift work, call for continued monitoring of the health and wellbeing of emergency services personnel.

People with dementia may have indications for aspirin prescription and clinicians are asked to balance the potential risks against benefits. This review examines the evidence for the risk and benefit of long-term aspirin use in people with dementia aged over 65 years, including randomised controlled trials and observational studies.We searched three databases for research published between 2007 and 2020. Each eligible article was assessed for risk of bias, and confidence in findings was rated using Grading of Recommendations Assessment, Development and Evaluation (GRADE).Four papers met inclusion criteria: one randomised controlled trial, two cohort studies, and one with pooled data. All looked only at dementia of Alzheimer's type, and none addressed myocardial or cerebral infarction as outcomes. Dementia progression was reported by two studies, with conflicting results. The trial found no significant effect of aspirin on mortality (odds ratio aspirin vs. no aspirin 1.07, 95% confidence interval 0.58-1.97) but found more events of severe bleeding with aspirin (OR aspirin vs. no aspirin 6.9, 1.5-31.2). An excess in intracranial haemorrhage in the aspirin group was judged plausible based on two non-randomised studies.The review findings are limited because studies include only people with Alzheimer's-type dementia and lack confirmatory studies, although an increased risk of bleeding events is recognised. Further research that addresses the benefits and risks of aspirin in more representative groups of people with dementia is needed to guide prescribing decisions.

We report test results for SARS-CoV-2 antibodies in an occupational group of postgraduate research students and current members of staff at King’s College London. Between June and July 2020, antibody testing kits were sent to n = 2296 participants; n = 2004 (86.3%) responded, of whom n = 1882 (93.9%) returned valid test results. Of those that returned valid results, n = 124 (6.6%) tested positive for SARS-CoV-2 antibodies, with initial comparisons showing variation by age group and clinical exposure.

IntroductionWe will use an occupational sample to study the impact of COVID-19 on current staff and postgraduate research students at a large UK university. The cohort study will address some of the key questions about COVID-19 for the international community, while also providing feedback to the employer and educator.Methods and analysisParticipants were recruited via email to their University email address. Administrative records were available to compare the composition of volunteer participants to underlying staff and postgraduate student populations of the University. The study comprises a baseline survey, longitudinal follow-up surveys and a viral antibody study. Baseline information was collected in April 2020 including demographics, working situation, current stresses and worries, mental health and neurological symptoms. Personal experiences of COVID-19, indirect experiences and attitudes towards the pandemic were queried, as well as satisfaction with communication and support at work. Longitudinal surveys will assess changes in COVID-19 exposure and mental health. A viral antibody detection component is being planned and will also be longitudinal in nature.Ethics and disseminationEthical approval has been gained from KCL’s Psychiatry, Nursing and Midwifery Research Ethics Committee (HR-19/20-18247). Participants were provided with information and agreed to a series of consent statements before enrolment. Data are kept on secure servers with access to personally identifiable information limited. Researchers may apply to have access to pseudonymised data. Findings will be disseminated internally to the University and participants, and externally through scientific publications.

Background The higher mortality rates in people with severe mental illness (SMI) may be partly due to inadequate integration of physical and mental healthcare. Accurate recording of SMI during hospital admissions has the potential to facilitate integrated care including tailoring of treatment to account for comorbidities. We therefore aimed to investigate the sensitivity of SMI recording within general hospitals, changes in diagnostic accuracy over time, and factors associated with accurate recording. Methods and findings We undertook a cohort study of 13,786 adults with SMI diagnosed during 2006–2017, using data from a large secondary mental healthcare database as reference standard, linked to English national records for 45,706 emergency hospital admissions. We examined general hospital record sensitivity across patients’ subsequent hospital records, for each subsequent emergency admission, and at different levels of diagnostic precision. We analyzed time trends during the study period and used logistic regression to examine sociodemographic and clinical factors associated with psychiatric recording accuracy, with multiple imputation for missing data. Sensitivity for recording of SMI as any mental health diagnosis was 76.7% (95% CI 76.0–77.4). Category-level sensitivity (e.g., proportion of individuals with schizophrenia spectrum disorders (F20-29) who received any F20-29 diagnosis in hospital records) was 56.4% (95% CI 55.4–57.4) for schizophrenia spectrum disorder and 49.7% (95% CI 48.1–51.3) for bipolar affective disorder. Sensitivity for SMI recording in emergency admissions increased from 47.8% (95% CI 43.1–52.5) in 2006 to 75.4% (95% CI 68.3–81.4) in 2017 (ptrend < 0.001). Minority ethnicity, being married, and having better mental and physical health were associated with less accurate diagnostic recording. The main limitation of our study is the potential for misclassification of diagnosis in the reference-standard mental healthcare data. Conclusions Our findings suggest that there have been improvements in recording of SMI diagnoses, but concerning under-recording, especially in minority ethnic groups, persists. Training in culturally sensitive diagnosis, expansion of liaison psychiatry input in general hospitals, and improved data sharing between physical and mental health services may be required to reduce inequalities in diagnostic practice., In a cohort study using linked registry data, Hassan Mansour and colleagues examine the factors related to and temporal trends in the accurate recording of severe mental illness diagnoses among individuals admitted to hospitals in England between 2006 and 2017., Author summary Why was this study done? People with severe mental illness (SMI) have increased mortality and morbidity, largely due to preventable medical conditions, and these disparities have the potential to be ameliorated through better healthcare integration. Accurate recognition of SMI during hospital admissions can be critical as it allows continuity of previous pharmacological and supportive treatments and tailoring of inpatient and discharge care to individual needs. What did the researchers do and find? We examined the hospital discharge records of 13,786 individuals with SMI diagnosis from a mental health service, who had 45,706 admissions to English general hospitals between 2006 and 2017. We found that a psychiatric condition is recorded in around two-thirds of general hospital admissions of people with SMI. Recording of SMI diagnosis increased between 2006 and 2017. However, people from ethnic minority and married backgrounds were less likely to have psychiatric diagnosis recorded. Similarly, those with less severe mental or physical health symptoms were also less likely to have diagnosis recorded. What do these findings mean? Despite improvements over the past decade, inequities related to ethnicity remain. Policy-makers and clinicians should endeavor to improve recognition and recording of SMI in general hospital settings to promote integrated physical and mental healthcare. A limitation of our study is that our use of electronic health records for the reference-standard means that some people with SMI may have been misclassified.

Objectives More people with dementia also fall into the category of high vascular risk, for which a statin is usually prescribed. However, these recommendations are based on studies in people without dementia. We aimed to evaluate the evidence for the long-term effectiveness and harm of statin therapy in patients with dementia. Design Systematic review of randomized controlled trials and observational research. Setting Publications from developed countries indexed in the PubMed, Web of Science, and Cochrane trial database between 2007 and 2019. Participants Trials including people with all types of dementia with a mean age older than 65 years. Intervention Treatment with a statin for 6 months or longer. Measurements Major adverse cardiovascular events, dementia progression, and general health at 2 years, or medication adverse events (AEs) at any time. Each article was assessed for bias using the Newcastle-Ottawa or Cochrane Collaboration tools. A narrative synthesis and pooled analyses are reported. Results Five articles met the inclusion criteria. They reported only on dementia of the Alzheimer's type. There was no evidence regarding cardiovascular events or general health. We made a very low confidence finding that statins reduce dementia progression based on three cohort studies of heterogeneous design. We made a very low confidence finding of no significant difference in AEs based on two randomized controlled trials of 18 months: odds ratios of any AE = 1.21 (95% confidence interval [CI] = .83-1.77), serious AE = 1.03 (95% CI = .76-1.87), and death = 1.69 (95% CI = .79-3.62). Conclusion Evidence was insufficient to fully evaluate the efficacy of statins in people with dementia. We found that statins may have a small benefit delaying progression in Alzheimer's dementia, although this conflicted with previous findings from shorter randomized trials. For safety, the trial data lacked power to show clinically important differences between the groups. We recommend that clinical data be leveraged for further observational studies to inform prescribing decisions. J Am Geriatr Soc 68:650-658, 2020.

Importance Previous research has linked a history of depression with arterial stiffness (AS) during midlife. Objective To assess the association of depression with elevated midlife AS and to investigate the extent to which this association is mediated via metabolic syndrome (MetS). Design, Settings, and Participants This population-based retrospective cohort study analyzed data collected between March 2006 and December 2010 from 124 445 participants aged 40 to 69 years from the UK Biobank. Participants without data on AS at baseline (n = 332 780) or who reported a previous diagnosis of cardiovascular disease (n = 45 374) were not eligible. Data analysis was performed from May to August 2019. Exposures Lifetime history of depression was assessed via verbal interview and linked hospital-based clinical depression diagnosis. Metabolic syndrome was defined as the presence of 3 or more of hypertension, dyslipidemia, hyperglycemia, hypertriglyceridemia, and unhealthy waist circumference. Main Outcomes and Measures Peripherally assessed AS index (ASI) using digital photoplethysmography. Results Of 124 445 included participants with ASI assessed, 71 799 (57.7%) were women, and the mean (SD) age was 56 (8) years. A total of 10 304 participants (8.3%) reported a history of depression. Study findings indicated a significant direct association between depression and ASI levels (β = 0.25; 95% CI, 0.17-0.32). A significant indirect association was also observed between depression and ASI levels (β = 0.10; 95% CI, 0.07-0.13), indicating that 29% of the association of depression with ASI was mediated by MetS. The proportion of mediation increased to 37% when C-reactive protein was added to the MetS criteria (direct association: β = 0.21; 95% CI, 0.15-0.28; indirect association: β = 0.13; 95% CI, 0.10-0.17). Concerning components of MetS, the strongest indirect association was for waist circumference, accounting for 25% of the association between depression and ASI levels (direct association: β = 0.26; 95% CI, 0.18-0.34; indirect association: β = 0.09; 95% CI, 0.06-0.11). Among men, hypertriglyceridemia accounted for 19% of the association between depression and ASI (direct association: β = 0.22; 95% CI, 0.05-0.40; indirect association: β = 0.05; 95% CI, 0.02-0.08). Conclusions and Relevance One-third of the association of depression with elevated ASI levels during midlife may be accounted for by combined MetS and inflammatory processes. Unhealthy waist circumference and hypertriglyceridemia emerged as the most important potential targets for preventive interventions within women and men, respectively.

BackgroundUnderstanding and improving outcomes for people with anxiety or depression often requires large sample sizes. To increase participation and reduce costs, such research is typically unable to utilise “gold-standard” methods to ascertain diagnoses, instead relying on remote, self-report measures.AimsAssess the comparability of remote diagnostic methods for anxiety and depression disorders commonly used in research.MethodParticipants from the UK-based GLAD and COPING NBR cohorts (N = 58,400) completed an online questionnaire between 2018 and 2020. Responses to detailed symptom reports were compared to DSM-5 criteria to generate symptom-based diagnoses of major depressive disorder (MDD), generalised anxiety disorder (GAD), specific phobia, social anxiety disorder, panic disorder, and agoraphobia. Participants also self-reported any prior diagnoses from health professionals, termed self-reported diagnoses. “Any anxiety” included participants with at least one anxiety disorder. Agreement was assessed by calculating accuracy, Cohen’s kappa, McNemar’s chi-squared, sensitivity, and specificity.ResultsAgreement between diagnoses was moderate for MDD, any anxiety, and GAD, but varied by cohort. Agreement was slight to fair for the phobic disorders. Many participants with self-reported GAD did not receive a symptom-based diagnosis. In contrast, symptom-based diagnoses of the phobic disorders were more common than self-reported diagnoses.ConclusionsAgreement for MDD, any anxiety, and GAD was higher for cases in the case-enriched GLAD cohort and for controls in the general population COPING NBR cohort. For anxiety disorders, self-reported diagnoses classified most participants as having GAD, whereas symptom-based diagnoses distributed participants more evenly across the anxiety disorders. Further validation against gold standard measures is required.

Background Anxiety and depression are common, debilitating and costly. These disorders are influenced by multiple risk factors, from genes to psychological vulnerabilities and environmental stressors, but research is hampered by a lack of sufficiently large comprehensive studies. We are recruiting 40,000 individuals with lifetime depression or anxiety and broad assessment of risks to facilitate future research. Methods The Genetic Links to Anxiety and Depression (GLAD) Study (www.gladstudy.org.uk) recruits individuals with depression or anxiety into the NIHR Mental Health BioResource. Participants invited to join the study (via media campaigns) provide demographic, environmental and genetic data, and consent for medical record linkage and recontact. Results Online recruitment was effective; 42,531 participants consented and 27,776 completed the questionnaire by end of July 2019. Participants’ questionnaire data identified very high rates of recurrent depression, severe anxiety, and comorbidity. Participants reported high rates of treatment receipt. The age profile of the sample is biased toward young adults, with higher recruitment of females and the more educated, especially at younger ages. Discussion This paper describes the study methodology and descriptive data for GLAD, which represents a large, recontactable resource that will enable future research into risks, outcomes, and treatment for anxiety and depression., Highlights • Online recruitment of 40,000 individuals with lifetime depression or anxiety. • Detailed online phenotyping combined with genetic and clinical data. • The study sample is severe, highly comorbid, with chronic psychopathology. • The study protocol enables recall of participants for future research and trials.

Importance Psychotic experiences, such as hallucinations and delusions, are reported by approximately 5% to 10% of the general population, although only a small proportion develop psychotic disorders such as schizophrenia. Studying the genetic causes of psychotic experiences in the general population, and its association with the genetic causes of other disorders, may increase the understanding of their pathologic significance.Objectives To determine whether genetic liability to psychotic experiences is shared with schizophrenia and/or other neuropsychiatric disorders and traits and to identify genetic loci associated with psychotic experiences.Design, Setting and Participants Analyses of genetic correlation, polygenic risk scores, and copy number variation were performed using data from participants in the UK Biobank from April 1, 2018, to March 20, 2019, to assess whether genetic liability to psychotic experiences is shared with schizophrenia and/or other neuropsychiatric disorders and traits. Genome-wide association studies of psychotic experience phenotypes were conducted to identify novel genetic loci. Participants in the final analyses after exclusions included 6123 individuals reporting any psychotic experience, 2143 individuals reporting distressing psychotic experiences, and 3337 individuals reporting multiple occurrences of psychotic experiences. A total of 121 843 individuals who did not report a psychotic experience formed the comparator group. Individuals with a psychotic disorder were excluded from all analyses.Main Outcomes and Measures Genetic associations with psychotic experience phenotypes.Results The study included a total of 127 966 participants (56.0% women and 44.0% men; mean [SD] age, 64.0 [7.6] years). Psychotic experiences were genetically correlated with major depressive disorder, schizophrenia, autism spectrum disorder, and attention-deficit/hyperactivity disorder. Analyses of polygenic risk scores identified associations between psychotic experiences and genetic liability for major depressive disorder, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder. Individuals reporting psychotic experiences had an increased burden of copy number variations previously associated with schizophrenia (odds ratio [OR], 2.04; 95% CI, 1.39-2.98; P = 2.49 × 10−4) and neurodevelopmental disorders more widely (OR, 1.75; 95% CI, 1.24-2.48; P = 1.41 × 10−3). Genome-wide association studies identified 4 significantly associated loci, including a locus in Ankyrin-3 (ANK3 [GenBank NM_020987]) (OR, 1.16; 95% CI, 1.10-1.23; P = 3.06 × 10−8) with any psychotic experience, and a locus in cannabinoid receptor 2 gene (CNR2 [GenBank NM_001841]) (OR, 0.66; 95% CI, 0.56-0.78; P = 3.78 × 10−8) with distressing psychotic experiences. The genome-wide association study of any psychotic experience had a low single-nucleotide polymorphism–based heritability estimate (h2 = 1.71%; 95% CI, 1.02%-2.40%).Conclusions and Relevance A large genetic association study of psychotic experiences from the population-based UK Biobank sample found support for a shared genetic liability between psychotic experiences and schizophrenia, major depressive disorder, bipolar disorder, and neurodevelopmental disorders.

Research that provides an evidence base for the pharmacotherapy of people with mental disorders is needed. The abundance of digital data has facilitated pharmacoepidemiology and, in particular, observational research on the effectiveness of real-world medication. Advantages of pharmacoepidemiological research are the availability of large patient samples, and coverage of under-researched subpopulations in their naturalistic conditions. Such research is also cheaper and quicker to do than randomised controlled trials, meaning that issues regarding generic medication, stopping medication (deprescribing), and long-term outcomes are more likely to be addressed. Pharmacoepidemiological methods can also be extended to pharmacovigilance and to aid the development of new purposes for existing drugs. Drawbacks of observational pharmacoepidemiological studies come from the non-randomised nature of treatment selection, leading to confounding by indication. Potential methods for managing this drawback include active comparison groups, within-individual designs, and propensity scoring. Many of the more rigorous pharmacoepidemiology studies have been strengthened through multiple analytical approaches triangulated to improve confidence in inferred causal relationships. With developments in data resources and analytical techniques, it is encouraging that guidelines are beginning to include evidence from robust observational pharmacoepidemiological studies alongside randomised controlled trials. Collaboration between guideline writers and researchers involved in pharmacoepidemiology could help researchers to answer the questions that are important to policy makers and ensure that results are integrated into the evidence base. Further development of statistical and data science techniques, alongside public engagement and capacity building (data resources and researcher base), will be necessary to take full advantage of future opportunities.

ObjectivesFor many research cohorts, it is not practical to provide a “gold‐standard” mental health diagnosis. It is therefore important for mental health research that potential alternative measures for ascertaining mental disorder status are understood.MethodsData from UK Biobank in those participants who had completed the online Mental Health Questionnaire (n = 157,363) were used to compare the classification of mental disorder by four methods: symptom‐based outcome (self‐complete based on diagnostic interviews), self‐reported diagnosis, hospital data linkage, and self‐report medication.ResultsParticipants self‐reporting any psychiatric diagnosis had elevated risk of any symptom‐based outcome. Cohen's κ between self‐reported diagnosis and symptom‐based outcome was 0.46 for depression, 0.28 for bipolar affective disorder, and 0.24 for anxiety. There were small numbers of participants uniquely identified by hospital data linkage and medication.ConclusionOur results confirm that ascertainment of mental disorder diagnosis in large cohorts such as UK Biobank is complex. There may not be one method of classification that is right for all circumstances, but an informed and transparent use of outcome measure(s) to suit each research question will maximise the potential of UK Biobank and other resources for mental health research.

This paper corrects and updates a paper published in BJPsych Open 2018 “Mental Health in UK Biobank” (https://doi.org/10.1192/bjo.2018.12) that was voluntarily retracted following the finding of errors in the coding of the variable for alcohol use disorder. Notably, the percentage of participants reaching threshold for alcohol use disorder on the Alcohol Use Disorder Identification Tool increased from 7% to 21%.BackgroundUK Biobank is a well-characterised cohort of over 500,000 participants that offers unique opportunities to investigate multiple diseases and risk factors. An online mental health questionnaire completed by UK Biobank participants expands the potential for research into mental disorders.MethodsAn expert working group designed the questionnaire, using established measures where possible, and consulting with a service user group regarding acceptability. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and alcohol use disorders.Results157,366 completed online questionnaires were available by August 2017. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was the most common finding in 24% of participants (37,434), with current alcohol use disorder criteria met by 21% (32,602), while other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.ConclusionsThe questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed due to selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.

BackgroundAnxiety and depression are common, debilitating and costly. These disorders are influenced by multiple risk factors, from genes to psychological vulnerabilities and environmental stressors but research is hampered by a lack of sufficiently large comprehensive studies. We are recruiting 40,000 individuals with lifetime depression or anxiety, with broad assessment of risks to facilitate future research.MethodsThe Genetic Links to Anxiety and Depression (GLAD) Study (www.gladstudy.org.uk) recruits individuals with depression or anxiety into the NIHR Mental Health BioResource. Participants invited to join the study (via media campaigns) provide demographic, environmental and genetic data, and consent for medical record linkage and recontact.ResultsOnline recruitment was effective; 41,892 consented and 26,877 participants completed the questionnaire by July 2019. Participants’ questionnaire data identified very high rates recurrent depression, severe anxiety and comorbidity. Participants reported high rates of treatment receipt. The age profile of sample is biased toward young adults, with higher recruitment of females and the better educated, especially at younger ages.DiscussionThis paper describes the study methodology and descriptive data for GLAD, which represents a large, recontactable resource that will enable future research into risks, outcomes and treatment for anxiety and depression.HighlightsOnline recruitment of 40,000 individuals with lifetime depression or anxiety (77 characters)Detailed online phenotyping combined with genetic and clinical data (66 characters)The study sample is severe, highly comorbid, with chronic psychopathology (62 characters)The study protocol enables recall of participants for future research and trials (82 characters)The views expressed are those of the authors and not necessarily those of the NHS, NIHR, Department of Health or King’s College London

Background People who opt to participate in scientific studies tend to be healthier, wealthier and more educated than the broader population. Although selection bias does not always pose a problem for analysing the relationships between exposures and diseases or other outcomes, it can lead to biased effect size estimates. Biased estimates may weaken the utility of genetic findings because the goal is often to make inferences in a new sample (such as in polygenic risk score analysis). Methods We used data from UK Biobank, Generation Scotland and Partners Biobank and conducted phenotypic and genome-wide association analyses on two phenotypes that reflected mental health data availability: (i) whether participants were contactable by e-mail for follow-up; and (ii) whether participants responded to follow-up surveys of mental health. Results In UK Biobank, we identified nine genetic loci associated (P Conclusions Re-contact availability and follow-up participation can act as further genetic filters for data on mental health phenotypes.

BackgroundDepression is a heterogeneous disorder with multiple aetiological pathways and multiple therapeutic targets. This study aims to determine whether atypical depression (AD) characterized by reversed neurovegetative symptoms is associated with a more pernicious course and a different sociodemographic, lifestyle, and comorbidity profile than nonatypical depression (nonAD).MethodsAmong 157 366 adults who completed the UK Biobank Mental Health Questionnaire (MHQ), N = 37 434 (24%) met the DSM-5 criteria for probable lifetime major depressive disorder (MDD) based on the Composite International Diagnostic Interview Short Form. Participants reporting both hypersomnia and weight gain were classified as AD cases (N = 2305), and the others as nonAD cases (N = 35 129). Logistic regression analyses were conducted to examine differences between AD and nonAD in depression features, sociodemographic and lifestyle factors, lifetime adversities, psychiatric and physical comorbidities.ResultsPersons with AD experienced an earlier age of depression onset, longer, more severe and recurrent episodes, and higher help-seeking rates than nonAD persons. AD was associated with female gender, unhealthy behaviours (smoking, social isolation, low physical activity), more lifetime deprivation and adversity, higher rates of comorbid psychiatric disorders, obesity, cardiovascular disease (CVD), and metabolic syndrome. Sensitivity analyses comparing AD persons with those having typical neurovegetative symptoms (hyposomnia and weight loss) revealed similar results.ConclusionsThese findings highlight the clinical and public health significance of AD as a chronic form of depression, associated with high comorbidity and lifetime adversity. Our findings have implications for predicting depression course and comorbidities, guiding research on aetiological mechanisms, planning service use and informing therapeutic approaches.

People who opt to participate in scientific studies tend to be healthier, wealthier, and more educated than the broader population. While selection bias does not always pose a problem for analysing the relationships between exposures and diseases or other outcomes, it can lead to biased effect size estimates. Biased estimates may weaken the utility of genetic findings because the goal is often to make inferences in a new sample (such as in polygenic risk score analysis). We used data from UK Biobank and Generation Scotland and conducted phenotypic and genome-wide association analyses on two phenotypes that reflected mental health data availability: (1) whether participants were contactable by email for follow-up) and (2) whether participants responded to a follow-up surveys of mental health. We identified nine genetic loci associated with email contact and 25 loci associated with mental health survey completion. Both phenotypes were positively genetically correlated with higher educational attainment and better health and negatively genetically correlated with psychological distress and schizophrenia. Recontact availability and follow-up participation can act as further genetic filters for data on mental health phenotypes.

Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD between individuals reporting and not reporting trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD was greater in participants reporting trauma exposure (24%) than in individuals not reporting trauma exposure (12%), taking into account the strong, positive genetic correlation observed between MDD and reported trauma exposure. The genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg = 0.24, p = 1.8×10-7 versus rg = −0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3×10-4). Our results suggest that the genetic contribution to MDD is greater when additional risk factors are present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.

BackgroundAdministrative data can be used to support research, such as in the UK Biobank. Hospital Episode Statistics (HES) are national data for England that include contain ICD-10 diagnoses for inpatient mental healthcare episodes, but the validity of these diagnoses for research purposes has not been assessed.Methods250 peoples' HES records were selected based on a HES recorded inpatient stay at the South London and Maudsley NHS Foundation Trust with a diagnosis of schizophrenia, a wider schizophrenia spectrum disorder, bipolar affective disorder or unipolar depression. A gold-standard research diagnosis was made using Clinical Records Interactive Search pseudonymised electronic patient records using, and the OPCRIT+ algorithm.ResultsPositive predictive value at the level of lifetime psychiatric disorder was 100%, and at the level of lifetime diagnosis in the four categories of schizophrenia, wider schizophrenia spectrum, bipolar or unipolar depression was 73% (68-79). Agreement varied by diagnosis, with schizophrenia having the highest PPV at 90% (80-96). Each person had an average of five psychiatric HES records. An algorithm that looked at the last recorded psychiatric diagnosis led to greatest overall agreement with the research diagnosis.DiscussionFor people who have a HES record from a psychiatric admission with a diagnosis of schizophrenia spectrum disorder, bipolar affective disorder or unipolar depression, HES records appear to be a good indicator of a mental disorder, and can provide a diagnostic category with reasonable certainty. For these diagnoses, HES records can be an effective way of ascertaining psychiatric diagnosis.

Data science uses computer science and statistics to extract new knowledge from high-dimensional datasets (ie, those\ud with many diff erent variables and data types). Mental health research, diagnosis, and treatment could benefi t from\ud data science that uses cohort studies, genomics, and routine health-care and administrative data. The UK is well\ud placed to trial these approaches through robust NHS-linked data science projects, such as the UK Biobank, Generation\ud Scotland, and the Clinical Record Interactive Search (CRIS) programme. Data science has great potential as a low-cost,\ud high-return catalyst for improved mental health recognition, understanding, support, and outcomes. Lessons learnt\ud from such studies could have global implications.

Background There is increasing availability of data derived from diagnoses made routinely in mental health care, and interest in using these for research. Such data will be subject to both diagnostic (clinical) error and administrative error, and so it is necessary to evaluate its accuracy against a reference-standard. Our aim was to review studies where this had been done to guide the use of other available data. Methods We searched PubMed and EMBASE for studies comparing routinely collected mental health diagnosis data to a reference standard. We produced diagnostic category-specific positive predictive values (PPV) and Cohen’s kappa for each study. Results We found 39 eligible studies. Studies were heterogeneous in design, with a wide range of outcomes. Administrative error was small compared to diagnostic error. PPV was related to base rate of the respective condition, with overall median of 76 %. Kappa results on average showed a moderate agreement between source data and reference standard for most diagnostic categories (median kappa = 0.45–0.55); anxiety disorders and schizoaffective disorder showed poorer agreement. There was no significant benefit in accuracy for diagnoses made in inpatients. Conclusions The current evidence partly answered our questions. There was wide variation in the quality of source data, with a risk of publication bias. For some diagnoses, especially psychotic categories, administrative data were generally predictive of true diagnosis. For others, such as anxiety disorders, the data were less satisfactory. We discuss the implications of our findings, and the need for researchers to validate routine diagnostic data. Electronic supplementary material The online version of this article (doi:10.1186/s12888-016-0963-x) contains supplementary material, which is available to authorized users.

At the current level of membership examination fees at the Royal College of Psychiatrists, a resident in psychiatry who passes every part first time pays a total of £2136.[1][1] These costs are intended to cover only expenses rather than to generate profit, a message which many of us have accepted