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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.

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.

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

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.

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.

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.

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.

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.

Background UK Biobank is a well-characterised cohort of over 500 000 participants that offers unique opportunities to investigate multiple diseases and risk factors. Aims An online mental health questionnaire completed by UK Biobank participants was expected to expand the potential for research into mental disorders. Method An expert working group designed the questionnaire, using established measures where possible, and consulting with a patient group regarding acceptability. Case definitions were defined using operational criteria for lifetime depression, mania, anxiety disorder, psychotic-like experiences and self-harm, as well as current post-traumatic stress and alcohol use disorders. Results 157 366 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 than the general population across a range of indicators. Thirty-five per cent (55 750) of participants had at least one defined syndrome, of which lifetime depression was the most common at 24% (37 434). There was extensive comorbidity among the syndromes. Mental disorders were associated with high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation. Conclusions The questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed owing 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. Declaration of interest G.B. received grants from the National Institute for Health Research during the study; and support from Illumina Ltd. and the European Commission outside the submitted work. B.C. received grants from the Scottish Executive Chief Scientist Office and from The Dr Mortimer and Theresa Sackler Foundation during the study. C.S. received grants from the Medical Research Council and Wellcome Trust during the study, and is the Chief Scientist for UK Biobank. M.H. received grants from the Innovative Medicines Initiative via the RADAR-CNS programme and personal fees as an expert witness outside the submitted work.

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.

Purpose: ‘Big data’ are accumulating in a multitude of domains and offer novel opportunities for research. The role of these resources in mental health investigations remains relatively unexplored, although a number of datasets are in use and supporting a range of projects. We sought to review big data resources and their use in mental health research to characterise applications to date and consider directions for innovation in future. Methods: A narrative review. Results: Clear disparities were evident in geographic regions covered and in the disorders and interventions receiving most attention. Discussion: We discuss the strengths and weaknesses of the use of different types of data and the challenges of big data in general. Current research output from big data is still predominantly determined by the information and resources available and there is a need to reverse the situation so that big data platforms are more driven by the needs of clinical services and service users.