‘Big data’ in mental health research: current status and emerging possibilities

Abstract

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.

Introduction

‘Big data’ resources for research have attracted increasing interest across healthcare, but applications in mental health have remained relatively limited to date. Big data challenges are traditionally characterised as those of volume (size of datasets), velocity (rapid, ‘real-time’ acquisition of data), and variety (multiple sources and types), with ‘variability’ and ‘veracity’ more recently added to reflect the unreliability of information arising from some sources [1]. There are numerous examples of different sources of big data which might be utilised for health research, including those derived from large biological sample collections, complex investigations (e.g. imaging), devices, and social media. With growing accessibility to large volumes of data accumulating in routine clinical practice following the shift of medical records from paper to electronic formats, clinical notes are also potential big data resources for researchers. There has been a long history of using routine data in mental health research, from the earliest studies of asylum records through the growth of the ‘case register’ in the mid- to late twentieth century. However, larger volumes of information are now accumulating in electronic format than would have been conceivable 20–30 years ago, which potentially transforms data-based investigations. We feel that it is timely to review the information resources being used for big data research, their current scope and coverage, and the nature of the research emerging.

Method

In a narrative literature review, we sought to ascertain and collate studies where big data approaches had been used in research on mental illness and treatment. Aiming at a representative rather than exhaustive list, the authors used recent reviews [210] to extract names of projects to use as key words for a Google Scholar search, adding also the requirement for the terms “mental health” or “psychiat*” for non-specialist sources, restricted to those since 2009. Where no relevant papers were returned, a simple web search for the project was also carried out to check for name changes and non-academic outputs. Forward bibliographic searching was also carried out to identify papers that had cited the reviews above, in an attempt to identify more recent projects. The authors included projects that demonstrated a reach in terms of massed healthcare data, and papers that had used big data approaches for mental health research. Our review was limited to English-language papers, and quantitative and qualitative studies about opinion regarding use of healthcare data were not included. Information was extracted on the name of the project (where given), the database used, the data sources for the database, and the geographical setting. The studies themselves were categorised into disease- or medication-specific, or other topics.

Results

Data resources identified and their international distribution are summarised in Table 1. In all, we identified 84 examples of databases that had been used to provide big data answers to mental health research questions, of which 24 are specific to mental health and related topics. Geographically, most data resources were found in the United States, with few specific national resources identified outside North America and northern/western Europe. However, there were a number of examples found of multinational and multi-continent collaborative resources, centred mostly on neurodegenerative or neurodevelopmental disorders. What should also be evident from Table 1 is the large number of databases being used for mental health research which are not themselves specific to mental health over and above any other specialty.

Table 1 Resources arranged geographically

Distributions of identified reports by disorder and nature of research are summarised in Table 2 with examples, although it is important to bear in mind that percentages refer to studies identified in this review which will not have been exhaustive; they are included for illustrative purposes and inferences regarding the total literature should be appropriately circumspect. The disorders covered in the papers we identified show that big data resources had been used most commonly to research unipolar depression and dementia, followed by schizophreniform and autism spectrum disorders, and relatively uncommon output on bipolar disorder, substance use disorders and neurodevelopmental disorders. For most disorders, the output was reasonably equally split between epidemiological/aetiological research and analyses of treatments and outcomes. The distributions of medication-reporting publications are summarised in Fig. 1 and indicate a predominant focus on antipsychotic and antidepressant agents, with relatively few publications on mood stabilisers or treatments for dementia. Specific examples of papers on medication and other topics are given in Table 3. Beyond medication profiles and safety, there were a number of papers on suicide, service use and user characteristics. Few of the research studies that we found were directly focused on mental health policy, but their findings often have important policy implications. A more detailed narrative of the types of questions addressed forms the focus for discussion of the topic.

Table 2 Example topics in papers discussing mental illness epidemiology, treatment and outcome
Fig. 1
figure1

The relative number of papers found reporting on different classes of medication (57 papers on medication in total)

Table 3 Examples of other topics appearing in multiple papers

Discussion

A wide variety of big data resources are emerging as platforms for mental health research, and it is inevitable that the characteristics of these resources will shape the questions addressed, particularly data availability. At one end, there are databases that take full clinical data directly from the electronic health record (EHR) at primary care or hospital level; some databases are populated from specific patient-level information provided by health service staff for the process of research or surveillance; some make secondary use of unmodified administrative data; some rely on patient report. Some studies transcend boundaries by making use of massed service-level data—such as the European Observatory of Health Systems and Policies—or combine findings from different databases—such as the Psychiatric Genomics Consortium. We have sought in this review to provide a snapshot of big data resources which are now becoming available for clinical/epidemiological mental health research and the way in which these have been used to date. It would be difficult to guarantee comprehensiveness in coverage due to limitations in our search methodology, the fast pace of current development in this field, the under-acknowledgement of the role of databases, and the nature of much of the research (i.e. not published in peer-review/indexed journals). In addition, the data resources themselves do not exist within tightly definable boundaries. For example: general healthcare databases may contain mental health relevant information but may not have been used for research within this field; many biological databases might be classifiable as ‘big data’ because of the density of information contained; and there is no clear point at which information from a large survey, or series of surveys, or cohort study, becomes large and detailed enough to be called ‘big data’. We have referenced resources that have access to large numbers of individuals, and have sought to provide examples that are broadly representative of emerging information available. For example, we have cited administrative data registries with linked death certification records to investigate mortality in mental disorders, and we have described these as big data; however, there is no qualitative difference between this and the linkage of the large Norwegian HUNT survey of over 60,000 community residents to national data on mortality and occupation-related outcomes [11, 12], which tends to be described instead as a large cohort study rather than ‘big data’. Similarly, this review did not attempt to cover large cohort studies with an emphasis on original data collection rather than reliant on administrative data (e.g. in a UK context, cohorts such as ALSPAC, Whitehall, or the 1946, 1957 or 1970 birth cohorts)—whose boundaries with big data are inevitably indistinct. Big data resources, thus, tend to be defined by the challenges faced by the data and their interpretation, as will now be described, rather than solely by the size or complexity of a database.

Big data and the five V’s

Big data resources are often characterised by ‘Vs’: originally three (volume, velocity and variety), now five (adding variability and veracity), but with the potential for further expansion (e.g. visualisation and value: http://dataconomy.com/seven-vs-big-data/). Taking the five V’s as the most common current characterisation, it is worthwhile considering each in turn as it applies to the mental health relevant databases described here:

  1. 1.

    The examples we identified exemplify ‘volume’ in the large number of cases represented and, in many instances, the quantity of information on each person represented. This particularly applies to healthcare data which are linked to high-compute biological datasets (e.g. from ‘omics’ and imaging) and to those which include the full electronic health record—i.e. which contain both large case numbers and large amounts of detail on each case. While small compared to many ‘big data’ resources, electronic health records represent a step-change in volume compared to the administrative databases previously relied on for analysis.

  2. 2.

    ‘Velocity’ may be a feature of electronic health records databases if these accumulate in real time, although is less relevant to static and/or periodically updated sets, and depends on the way in which a database is used. At the moment most research use has been observational, using historic data extractions and therefore not encountering the velocity challenge, even in ‘live’ (i.e. continually accumulating) databases. This will change once interventions start being developed which rely on real-time data feeds from health records, and will be challenge not only for hardware (e.g. the demands on central or local processing hubs) but also for designing appropriately agile software to enable such processing.

  3. 3.

    ‘Variety’ has also been less relevant to date because most analyses are still focusing on relatively stereotyped datasets drawn from original or derived structured fields; however, this is changing with increasing interest in natural language processing to derive information from text—whether relatively simple information extraction applications to render pre-defined constructs available as structured fields, or more complex whole-text analytics (e.g. investigating subtle changes in health records text as a potential predictor of adverse events such as suicidal behaviour https://slamtwigops.wordpress.com/tag/e-host-it/). ‘Variety’ will also become an increasingly relevant consideration as health records databases begin to integrate with the large-volume information generated by devices and remote monitoring, as well as potentially from patient-entered data—or example, when considering the differences in wording used to describe the experience of a disorder between a clinician writing in the health record and someone with the condition contributing to an online forum.

  4. 4.

    ‘Variability’ is used to describe the phenomenon of data whose meaning is constantly changing. Within health records, data fields clearly do change over time in the way information is entered, although this is generally at a pace which is manageable. Text fields in health records may present more of a challenge, as there are likely to be more rapid and less manageable changes in the ways clinicians record information, although this is likely to be negligible compared to the rapid evolution in social media and the language used there (and thus in any development of shared records with the facility for accommodating patient-entered information).

  5. 5.

    ‘Veracity’ is perhaps the most important challenge in the use of any administrative database for research, simply because source data have not generally been collected with research in mind and thus it is important to be aware of factors influencing the recording of information or not, and the accuracy with which this is carried out. The veracity challenge will be considered later in this discussion, having first reviewed the data resources available.

Electronic health records

EHRs present novel opportunities for research because of the very large volumes of information which naturally accrue and, unlike paper-based records, are accessible without prohibitively time-consuming data entry. Considering volume of information, there is a major distinction between databases using only structured fields, and those using the free text [13, 14]. Structured data such as age, sex, diagnosis, and dates of service-level events (admissions, discharges, etc.) are routinely entered by clinical or administrative staff, can be made readily available for research use, and are relatively easily de-identified for data governance requirements. However, the fact that structured information is more readily available for analysis does not make it any more valid or accurate than unstructured information. Clinical uncertainties can be poorly translated into codes [1517], and the sustainability of imposed structured data entry in routine clinical care (e.g. through embedded checklists and scales in the EHR) remains to be established. Free text is typically extensive in case note fields and uploaded correspondence for mental health EHRs, but less accessible for analysis, and less easily anonymised; however, text-contained information is potentially the most valuable for research despite the inconvenience of having to design mechanisms for extracting the information.

To make better use of the whole record, text mining tools have attracted increasing interest as a means of facilitating research with free text alongside the structured record [1821]. This can increase sensitivity for record identification; for example, Vanderbilt University Medical Centre found that extraction of diagnosis of dementia from structured fields identified 38 % of cases found by manual notes review, whereas 91 % of these were identified through a free text information extraction application [22]. However, it should be noted that even searching the free text for a diagnosis will only give an accurate indication of the numbers of people identified with a disorder, which may be a substantial underestimate of community cases. For example, Mayo Clinic analyses found that, of people identified in research studies as having definite dementia or autism spectrum disorder, around 70 and 50 %, respectively, had any note of such in their EHR [23, 24].

A key potential advantage of using information derived from EHR free text is the quantity of phenotypic data beyond a diagnosis, both in terms of patients’ mental health—such as symptom profile [25] or treatment responsiveness [26]—and the context in which a disorder is occurring [27]. This can be used for highlighting patients who have inclusion criteria for recruitment into observational or interventional studies, or can be used to investigate treatment response directly within the database: all relevant for the development of personalised medicine [28, 29]. Furthermore, phenotypic signatures of direct clinical relevance, such as “high suicide risk” or “vulnerable to depression”, might be fed back in real time via the EHR to alert the treating clinician [30, 31], coupled with decision support software or information resources. Free text can also be mined to define groups or outcomes that are too rare to be studied conventionally—such as the use of Khat in South-East London [32] or neuroleptic malignant syndrome [33].

Primary Care EHRs are potentially valuable for investigating the wider health of people with common or severe mental illnesses. Examples include Canadian longitudinal research into changes in the diagnosis and treatment of depression [34, 35], or the use of a UK general practice database to investigate the risk of cardiovascular disease in people with severe mental illness and to derive a risk prediction model for this outcome [36]. While some countries benefit from large healthcare providers with associated data resources (e.g. National Health Service data in the UK, and the Taiwan National Health Insurance Research Database), others, such as the USA, have brought together healthcare providers in ‘virtual networks’ [37, 38]. Anonymised data derived from each provider’s EHRs can be brought together with tools such as the Health Care Systems Research Network’s online integration tool “PopMedNet” for research, or to compare practices, such as the benchmarking of psychotropic prescribing [39]. There are also EHR-genomic consortia, such as eMerge—a collaboration of Marshfield, Mayo Clinic, Northwestern, Group Health and Vanderbilt—which hosts some dementia cohorts [40].

It is important that the governance of these EHR databases and projects is planned to balance the concerns of patients and the needs of researchers. Full anonymization may not be possible for projects requiring phenotypic details [41]; other protections such as limited access and firewalls must therefore be considered so as not to lose “social licence” for these types of projects [4244]. Both researchers and patients should have input to the next generation of data repositories and projects to shape them towards the kinds of questions that remain outstanding, such as capturing traits as well as diseases for research compatible with the USA’s National Institute of Mental Health Research Domain Criteria (RDoC) paradigm [45, 46].

Case registers involving de novo data collection

Specialist databases form registries of people in contact with the mental health system, or have evolved from this to offer surveillance of both service users and the services themselves [6]. While it is possible to create and maintain such a register solely with electronic health records, many involve the collection of specific data, usually requested from the service providers. These databases are a helpful resource for research into patterns of service use and their individual and societal determinants. Some databases, such as the Mental Health National Outcomes and Casemix Collection in Australia and the DGPPN-BADO in Germany have made efforts to include valid measures of outcome for service users, which helps them monitor improvement [47, 48] and also for research, such as into treatments for depression in Germany. There are also examples of more specialised registries: a database in the Netherlands recording seclusion and restraint episodes looking for insights to drive service improvement [49]; and the GRiST mental health data set in the UK, which deals with risk assessment and aims to use the data to become a decision support tool [50, 51].

Administrative databases

We identified a number of examples of projects making secondary use of large-volume administrative data to draw conclusions about healthcare use through diagnoses on hospital discharge notifications, billing for procedures, or prescriptions. Some of these databases are long established, such as the Swedish population-based registers; while the expansion of Medicaid, and the requirement for billing with ICD-codes, combined with incentives for “meaningful use” of information technology [52], has led to large accumulations of new data resources. This information tends to be used to describe treated prevalences of disorders, patterns of prescribing, and comparisons of ‘real-world’ treatment with recommendations. Where data include both prescriptions and incident diagnosis, this can also be useful in pharmacovigilance, using retrospective cohort or nested case–control studies to investigate adverse events [53], such as differences in the safety of different antipsychotics in older patients examined using Medicaid billing data for nursing home residents [54]. Such data can also be used to describe treatment costs—which may have a specific focus, such as a US investigation of the cost of non-compliance in bipolar disorder [55], or a broader scope, such as an EU project investigating whether the financing of health services in different EU countries affects the quality of mental healthcare [56].

Surveys and biobanks

In most circumstances, surveys and interviews are not practical for assembling big data resources; however, the WHO Global Burden of Disease programme uses standardised mental health surveys (based on the Composite Diagnostic Interview—CIDI) carried out at intervals by local research teams in member countries [57], and could be claimed to come closest to being a ‘big data’ survey in the mental health field. Large samples have also been achieved by some biobanks, such as UK Biobank, which already has self-report data for 500,000 [58] and is looking to improve its mental health phenotyping through an online questionnaire based on the CIDI-short form. Genome Wide Association Studies (GWAS) for complex disorders require large independent datasets of genomes, therefore it makes sense for researchers on projects such as UK Biobank to co-operate with others. The international Psychiatric Genomics Consortium (PGC) is a means to achieve this, sharing both datasets and expertise [59]. International research collaborations have also allowed the leveraging of neuroimaging taking place in different locations through the ENIGMA program [60].

Record linkage

All of the above databases can be given new dimensions when data from other sources are linked at the level of the individual [3, 9]. This is facilitated in countries with a unique identification number for its residents, such as many of the Nordic countries: for example, allowing researchers using Sweden’s population-based registers to link reports of death by suicide to records of psychiatric and medical diagnoses, periods of sickness absence from work, and unemployment [6165]. Danish records that link also to parents and siblings have been used to investigate potential risk factors for schizophrenia, such as family history, season of birth, urban living, and trauma to mother during pregnancy [6668]. A number of observational studies have investigated the safety of psychotropic medication in pregnancy, but in Western Australia this approach has been taken one step further by assembling and following an e-cohort of children born to mothers who have schizophrenia, using health and social service administrative registers alone; associations with obstetric complications and subsequent intellectual disability have already been reported [69, 70].

Data veracity

Although it might be assumed that cohorts assembled through researcher interviews are preferable to those derived from administrative data, it is important to recognise that each research method has strengths and limitations. Research interviews do provide potentially highly accurate information about a person’s status at a particular time; however, conventional research projects are limited, and not just in the numbers of cases who can be interviewed and examined. They may also poorly capture variability or trajectories in health status over time (especially as recollection of episodes of mental disorder has been found to be so poor [71]) which may be better characterised from administrative data than retrospective interview. Furthermore, even highly trained interviewers might have difficulty ascertaining phenomena like physical signs or relatively rare symptoms, which may be better identified from clinician-derived text in health records.

Conversely, as previously discussed, a veracity challenge for all healthcare databases is that information used has not, generally, been collected for research reasons; therefore, data are vulnerable to influence from forces other than the underlying patterns of disease, and hence the incentives for record-keeping need to be taken into account (sometimes considered under a ‘data provenance’ heading). One important issue concerns diagnosis, as many studies rely on recorded diagnostic information and frequently do not have any further information on the disorder under investigation beyond this. However, many mental disorders do not result in a documented diagnosis because the person does not report the disorder to a healthcare practitioner, because the practitioner does not identify the disorder, or because they do not assign or record a diagnosis. For example, in 2009 it was estimated that one-third of all people living in England with dementia had received a formal diagnosis [72]. Since then, political pressure, availability of medication and other factors have changed the culture surrounding the making and recording of a dementia diagnosis. Registers of people with dementia kept by all GPs in primary care have consequently been increasing in size by an average of 8 % per year [73]—a change that does not reflect changing epidemiology of the disease. Primary Care diagnosis rates of anxiety and depression in the UK have also been found not to be representative of disease trends [74, 75]; however, a team at the Secure Anonymised Information Linkage Databank in Wales found that combining diagnosis and symptom terms appeared to be more reflective, which suggests the wisdom of working with people who are familiar with the data source being used [76].

Conclusion

Big data are being used for mental health research in many parts of the world, and for many different purposes. Secondary use of administrative data, especially where routine diagnostic information is included, is likely to become increasingly adopted for research as these information resources are relatively inexpensive and scalable. Furthermore, secondary use of clinical information is coming up fast behind. ‘Volume’ challenges can generally be addressed with current information storage capacity and availability. The ‘velocity’ challenge is yet to be addressed because most of these big data resources are static and updated periodically, with few ‘real-time’ applications currently developed; however, this situation will change if decision support applications are implemented, and/or if learning and artificial intelligence begin to be incorporated in records systems. ‘Variety’ and ‘variability’, like velocity, are not current challenges but are likely to become increasingly salient in the near future. ‘Veracity’ remains a key consideration and one which is unlikely to change with technological advances, because secondary data use continues to depend on the data actually being recorded in clinical practice. The other big considerations are data governance and security, which clearly require robust planning and an effective, ongoing public dialogue.

An over-arching conclusion from this review is that research questions continue to be shaped by the information that happens to be available and accessible in these data resources. For example, the fact that healthcare databases are used so extensively for medication-oriented research questions is likely to reflect the relative ease with which medication data can be extracted. Equally their lack of use for investigations of symptom profiles or illicit substance use reflects the lack of structured data on these constructs in most records systems. A transition is likely to be needed whereby the data resources themselves are shaped, at least to some extent, by research priorities; however, this is only likely to be effective if the research priorities, in turn, are shaped by the needs of clinical services and those who use them.

References

  1. 1.

    Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2:3. doi:10.1186/2047-2501-2-3

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Coorevits P, Sundgren M, Klein GO, Bahr A, Claerhout B, Daniel C, Dugas M, Dupont D, Schmidt A, Singleton P, De Moor G, Kalra D (2013) Electronic health records: new opportunities for clinical research. J Intern Med 274(6):547–560. doi:10.1111/joim.12119

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Allebeck P (2009) The use of population based registers in psychiatric research. Acta Psychiatr Scand 120(5):386–391. doi:10.1111/j.1600-0447.2009.01474.x

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Spiranovic C, Matthews A, Scanlan J, Kirkby KC (2016) Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges. Adv Ment Health 14(1):14–25

    Article  Google Scholar 

  5. 5.

    Perera G, Soremekun M, Breen G, Stewart R (2009) The psychiatric case register: noble past, challenging present, but exciting future. Br J Psychiatry 195(3):191–193

    PubMed  Article  Google Scholar 

  6. 6.

    Munk-Jørgensen P, Okkels N, Golberg D, Ruggeri M, Thornicroft G (2014) Fifty years’ development and future perspectives of psychiatric register research. Acta Psychiatr Scand 130(2):87–98

    PubMed  Article  Google Scholar 

  7. 7.

    Alaghehbandan R, MacDonald D (2013) Use of administrative health databases and case definitions in surveillance of depressive disorders: a review. OA Epidemiol 1:3

    Article  Google Scholar 

  8. 8.

    Monteith S, Glenn T, Geddes J, Bauer M (2015) Big data are coming to psychiatry: a general introduction. Int J Bipolar Disord 3:21. doi:10.1186/s40345-015-0038-9

    PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Weber GM, Mandl KD, Kohane IS (2014) FInding the missing link for big biomedical data. JAMA 311(24):2479–2480. doi:10.1001/jama.2014.4228

    CAS  PubMed  Google Scholar 

  10. 10.

    Jensen PB, Jensen LJ, Brunak S (2012) Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 13(6):395–405

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Mykletun A, Bjerkesset O, Dewey M, Prince M, Overland S, Stewart R (2007) Anxiety, depression and cause-specific mortality. Psychosom Med 69:323–331

    PubMed  Article  Google Scholar 

  12. 12.

    Mykletun A, Overland S, Dahl AA, Krokstad S, Bjerkeset O, Glozier N, Aaro LE, Prince M (2006) A population-based cohort study of the effect of common mental disorders on disability pension awards. Am J Psychiatry 163(8):1412–1418

    PubMed  Article  Google Scholar 

  13. 13.

    Häyrinen K, Saranto K, Nykänen P (2008) Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 77(5):291–304

    PubMed  Article  Google Scholar 

  14. 14.

    Rosenbloom ST, Denny JC, Xu H, Lorenzi N, Stead WW, Johnson KB (2011) Data from clinical notes: a perspective on the tension between structure and flexible documentation. J Am Med Inform Assoc 18(2):181–186

    PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Eason K, Waterson P (2014) Fitness for purpose when there are many different purposes: who are electronic patient records for? Health Inform J 20(3):189–198. doi:10.1177/1460458213501096

    Article  Google Scholar 

  16. 16.

    Morrison Z, Fernando B, Kalra D, Cresswell K, Sheikh A (2014) National evaluation of the benefits and risks of greater structuring and coding of the electronic health record: exploratory qualitative investigation. J Am Med Inform Assoc 21(3):492–500. doi:10.1136/amiajnl-2013-001666

    PubMed  Article  Google Scholar 

  17. 17.

    Whooley O (2010) Diagnostic ambivalence: psychiatric workarounds and the diagnostic and statistical manual of mental disorders. Soc Health Illn 32(3):452–469. doi:10.1111/j.1467-9566.2010.01230.x

    Article  Google Scholar 

  18. 18.

    Anderson HD, Pace WD, Brandt E, Nielsen RD, Allen RR, Libby AM, West DR, Valuck RJ (2015) Monitoring suicidal patients in primary care using electronic health records. J Am Board Family Med 28(1):65–71. doi:10.3122/jabfm.2015.01.140181

    Article  Google Scholar 

  19. 19.

    Hripcsak G, Albers DJ (2013) Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 20(1):117–121. doi:10.1136/amiajnl-2012-001145

    PubMed  Article  Google Scholar 

  20. 20.

    Perera G, Broadbent M, Callard F, Chang C-K, Downs J, Dutta R, Fernandes A, Hayes RD, Henderson M, Jackson R, Jewell A, Kadra G, Little R, Pritchard M, Shetty H, Tulloch A, Stewart R (2016) Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) case register: current status and recent enhancement of an electronic mental health record-derived data resource. BMJ Open. doi:10.1136/bmjopen-2015-008721

    PubMed Central  Google Scholar 

  21. 21.

    St-Maurice J, Kuo MH, Gooch P (2013) A proof of concept for assessing emergency room use with primary care data and natural language processing. Methods Inf Med 52(1):33–42. doi:10.3414/ME12-01-0012

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Wei W-Q, Teixeira PL, Mo H, Cronin RM, Warner JL, Denny JC (2015) Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. J Am Med Inform Assoc. doi:10.1093/jamia/ocv130

    Google Scholar 

  23. 23.

    Barbaresi WJ, Colligan RC, Weaver AL, Katusic SK (2008) The incidence of clinically diagnosed versus research-identified autism in Olmsted County, Minnesota, 1976–1997: results from a retrospective, population-based study. J Autism Dev Disord 39(3):464–470. doi:10.1007/s10803-008-0645-8

    PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Knopman DS, Petersen RC, Rocca WA, Larson EB, Ganguli M (2011) Passive case-finding for Alzheimer’s disease and dementia in two U.S. communities. Alzheimer’s Dement 7(1):53–60. doi:10.1016/j.jalz.2010.11.001

    Article  Google Scholar 

  25. 25.

    Patel R, Jayatilleke N, Broadbent M, Chang C-K, Foskett N, Gorrell G, Hayes RD, Jackson R, Johnston C, Shetty H (2015) Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method. BMJ Open 5(9):e007619

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Perlis R, Iosifescu D, Castro V, Murphy S, Gainer V, Minnier J, Cai T, Goryachev S, Zeng Q, Gallagher P (2012) Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol Med 42(01):41–50

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Gundlapalli AV, Redd A, Carter M, Divita G, Shen S, Palmer M, Samore MH (2013) Validating a strategy for psychosocial phenotyping using a large corpus of clinical text. J Am Med Inform Assoc JAMIA 20(e2):e355–e364. doi:10.1136/amiajnl-2013-001946

    PubMed  Article  Google Scholar 

  28. 28.

    INBIOMED Consortium, Kouskoumvekaki I, Mayer MA, Brunak S, Shublaq N (2013) The interface between systems biology and medical informatics. Promoting and Monitoring Biomedical Informatics in Europe. INBIOMEDvision, European Commission

  29. 29.

    Richesson RL, Hammond WE, Nahm M, Wixted D, Simon GE, Robinson JG, Bauck AE, Cifelli D, Smerek MM, Dickerson J, Laws RL, Madigan RA, Rusincovitch SA, Kluchar C, Califf RM (2013) Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory. J Am Med Inform Assoc JAMIA 20(e2):e226–e231. doi:10.1136/amiajnl-2013-001926

    PubMed  Article  Google Scholar 

  30. 30.

    Poulin C, Shiner B, Thompson P, Vepstas L, Young-Xu Y, Goertzel B, Watts B, Flashman L, McAllister T (2014) Predicting the risk of suicide by analyzing the text of clinical notes. PLoS One 9(1):e85733. doi:10.1371/journal.pone.0085733

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. 31.

    Huang SH, LePendu P, Iyer SV, Tai-Seale M, Carrell D, Shah NH (2014) Toward personalizing treatment for depression: predicting diagnosis and severity. J Am Med Inform Assoc JAMIA 21(6):1069–1075. doi:10.1136/amiajnl-2014-002733

    PubMed  Article  Google Scholar 

  32. 32.

    Tulloch AD, Frayn E, Craig TK, Nicholson TR (2012) Khat use among Somali mental health service users in South London. Soc Psychiatry Psychiatr Epidemiol 47(10):1649–1656

    PubMed  Article  Google Scholar 

  33. 33.

    Su YP, Chang CK, Hayes RD, Harrison S, Lee W, Broadbent M, Taylor D, Stewart R (2014) Retrospective chart review on exposure to psychotropic medications associated with neuroleptic malignant syndrome. Acta Psychiatr Scand 130(1):52–60

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Morkem R, Barber D, Williamson T, Patten SB (2015) A Canadian primary care sentinel surveillance network study evaluating antidepressant prescribing in Canada from 2006 to 2012. Can J Psychiatry 60(12):564–570

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Wong ST, Manca D, Barber D, Morkem R, Khan S, Kotecha J, Williamson T, Birtwhistle R, Patten S (2014) The diagnosis of depression and its treatment in Canadian primary care practices: an epidemiological study. CMAJ Open 2(4):E337–E342. doi:10.9778/cmajo.20140052

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Osborn DPJ, Hardoon S, Omar RZ, Holt RIG, King M, Larsen J, Marston L, Morris RW, Nazareth I, Walters K, Petersen I (2015) Cardiovascular risk prediction models for people with severe mental illness: results from the prediction and management of cardiovascular risk in people with severe mental illnesses (PRIMROSE) research program. JAMA Psychiatry 72(2):143–151. doi:10.1001/jamapsychiatry.2014.2133

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Sittig DF, Hazlehurst BL, Brown J, Murphy S, Rosenman M, Tarczy-Hornoch P, Wilcox AB (2012) A survey of informatics platforms that enable distributed comparative effectiveness research using multi-institutional heterogenous clinical data. Med Care 50(59):S49

    PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Toh S, Platt R, Steiner JF, Brown JS (2011) Comparative-effectiveness research in distributed health data networks. Clin Pharmacol Ther 90(6):883–887. doi:10.1038/clpt.2011.236

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Stewart C, Simon G, Miroshnik I, Ahmedani B, Lakoma M, Larkin D, Niedenfuer K, Whitebird R, Nyirenda C, Beck A, Adams M, Davis R, Schmidt M, Ahmed A, Gul J, Crawford P, Lynch F, Liu J, Coleman K (2011) C-B1-01: psychotherapeutic prescription patterns across health plans in the mental health research network. Clin Med Res 9(3–4):183. doi:10.3121/cmr.2011.1020.c-b1-01

    Google Scholar 

  40. 40.

    Kho AN, Pacheco JA, Peissig PL, Rasmussen L, Newton KM, Weston N, Crane PK, Pathak J, Chute CG, Bielinski SJ, Kullo IJ, Li R, Manolio TA, Chisholm RL, Denny JC (2011) Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med 3(79):79re71. doi:10.1126/scitranslmed.3001807

    Article  Google Scholar 

  41. 41.

    Fernandes AC, Cloete D, Broadbent MT, Hayes RD, Chang CK, Jackson RG, Roberts A, Tsang J, Soncul M, Liebscher J, Stewart R, Callard F (2013) Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records. BMC Med Inform Decis Mak 13:71. doi:10.1186/1472-6947-13-71

    PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Clemens NA (2012) Privacy, consent, and the electronic mental health record: the person vs. the system. J Psychiatr Pract 18(1):46–50

    PubMed  Article  Google Scholar 

  43. 43.

    Hartzler A, McCarty CA, Rasmussen LV, Williams MS, Brilliant M, Bowton EA, Clayton EW, Faucett WA, Ferryman K, Field JR, Fullerton SM, Horowitz CR, Koenig BA, McCormick JB, Ralston JD, Sanderson SC, Smith ME, Trinidad SB (2013) Stakeholder engagement: a key component of integrating genomic information into electronic health records. Genet Med 15(10):792–801

    PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Nuffield Council on Bioethics (2015) The collection, linking and use of data in biomedical research and health care: ethical issues. http://nuffieldbioethics.org/project/biological-health-data. Accessed 20 July 2016

  45. 45.

    Farber GK (2016) Can data repositories help find effective treatments for complex diseases? Submitted to Progress in Neurobiology. Prog Neurobiol. doi:10.1016/j.pneurobio.2016.03.008

    PubMed  Google Scholar 

  46. 46.

    Cuthbert BN (2014) Translating intermediate phenotypes to psychopathology: the NIMH research domain criteria. Psychophysiology 51(12):1205–1206. doi:10.1111/psyp.12342

    PubMed  Article  Google Scholar 

  47. 47.

    Burgess P, Coombs T, Clarke A, Dickson R, Pirkis J (2012) Achievements in mental health outcome measurement in Australia: reflections on progress made by the Australian Mental Health Outcomes and Classification Network (AMHOCN). Int J Ment Health Syst 6(1):1–11. doi:10.1186/1752-4458-6-4

    Article  Google Scholar 

  48. 48.

    von Wolff A, Meister R, Härter M, Kriston L (2015) Treatment patterns in inpatient depression care. Int J Methods Psychiatr Res. doi:10.1002/mpr.1487

    Google Scholar 

  49. 49.

    Janssen WA, van de Sande R, Noorthoorn EO, Nijman HLI, Bowers L, Mulder CL, Smit A, Widdershoven GAM, Steinert T (2011) Methodological issues in monitoring the use of coercive measures. Int J Law Psychiatry 34(6):429–438. doi:10.1016/j.ijlp.2011.10.008

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Buckingham C (2015) Galatean risk and safety tool: web-based decision support for mental-health risk, safety, and wellbeing. GRiST mental health data set http://www.egrist.org. Accessed 20 July 2016

  51. 51.

    Buckingham C, Adams A (2014) Integrating patients’ and clinical mental health expertise within a single online decision support system: myGRiST. Paper presented at the International Conference on Communication in Healthcare, Amsterdam

  52. 52.

    Blumenthal D (2009) Stimulating the adoption of health information technology. N Engl J Med 360(15):1477–1479. doi:10.1056/NEJMp0901592

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Harpe SE (2009) Using secondary data sources for pharmacoepidemiology and outcomes research. Pharmacother J Hum Pharmacol Drug Ther 29(2):138–153

    Article  Google Scholar 

  54. 54.

    Huybrechts KF, Schneeweiss S, Gerhard T, Olfson M, Avorn J, Levin R, Lucas JA, Crystal S (2012) Comparative Safety of antipsychotic medications in nursing home residents. J Am Geriatr Soc 60(3):420–429. doi:10.1111/j.1532-5415.2011.03853.x

    PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Gianfrancesco FD, Sajatovic M, Rajagopalan K, Wang R-H (2008) Antipsychotic treatment adherence and associated mental health care use among individuals with bipolar disorder. Clin Ther 30(7):1358–1374. doi:10.1016/S0149-2918(08)80062-8

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Sfetcu R, Katschnig H, Amaddeo F, REFINEMENT Study Group (2012) REsearch on FINancing systems’ Effect on the Quality of MENTal health care, eDESDR-LTC. http://www.edesdeproject.eu/images/news/newpdf9.pdf. Accessed 20 July 2016

  57. 57.

    Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, Charlson FJ, Norman RE, Flaxman AD, Johns N, Burstein R, Murray CJ, Vos T (2013) Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 382(9904):1575–1586

    PubMed  Article  Google Scholar 

  58. 58.

    Smith DJ, Nicholl BI, Cullen B, Martin D, Ul-Haq Z, Evans J, Gill JM, Roberts B, Gallacher J, Mackay D (2013) Prevalence and characteristics of probable major depression and bipolar disorder within UK biobank: cross-sectional study of 172,751 participants. PLoS One 8(11):e75362

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Cross-Disorder Group of the Psychiatric Genomics C (2013) Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet 45(9):984–994. doi:10.1038/ng.2711. http://www.nature.com/ng/journal/v45/n9/abs/ng.2711.html. Accessed 20 July 2016 (supplementary-information)

  60. 60.

    Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, Toro R, Jahanshad N, Schumann G, Franke B, Wright MJ, Martin NG, Agartz I, Alda M, Alhusaini S, Almasy L, Almeida J, Alpert K, Andreasen NC, Andreassen OA, Apostolova LG, Appel K, Armstrong NJ, Aribisala B, Bastin ME, Bauer M, Bearden CE, Bergmann O, Binder EB, Blangero J, Bockholt HJ, Boen E, Bois C, Boomsma DI, Booth T, Bowman IJ, Bralten J, Brouwer RM, Brunner HG, Brohawn DG, Buckner RL, Buitelaar J, Bulayeva K, Bustillo JR, Calhoun VD, Cannon DM, Cantor RM, Carless MA, Caseras X, Cavalleri GL, Chakravarty MM, Chang KD, Ching CR, Christoforou A, Cichon S, Clark VP, Conrod P, Coppola G, Crespo-Facorro B, Curran JE, Czisch M, Deary IJ, de Geus EJ, den Braber A, Delvecchio G, Depondt C, de Haan L, de Zubicaray GI, Dima D, Dimitrova R, Djurovic S, Dong H, Donohoe G, Duggirala R, Dyer TD, Ehrlich S, Ekman CJ, Elvsashagen T, Emsell L, Erk S, Espeseth T, Fagerness J, Fears S, Fedko I, Fernandez G, Fisher SE, Foroud T, Fox PT, Francks C, Frangou S, Frey EM, Frodl T, Frouin V, Garavan H, Giddaluru S, Glahn DC, Godlewska B, Goldstein RZ, Gollub RL, Grabe HJ, Grimm O, Gruber O, Guadalupe T, Gur RE, Gur RC, Goring HH, Hagenaars S, Hajek T, Hall GB, Hall J, Hardy J, Hartman CA, Hass J, Hatton SN, Haukvik UK, Hegenscheid K, Heinz A, Hickie IB, Ho BC, Hoehn D, Hoekstra PJ, Hollinshead M, Holmes AJ, Homuth G, Hoogman M, Hong LE, Hosten N, Hottenga JJ, Hulshoff Pol HE, Hwang KS, Jack CR Jr, Jenkinson M, Johnston C, Jonsson EG, Kahn RS, Kasperaviciute D, Kelly S, Kim S, Kochunov P, Koenders L, Kramer B, Kwok JB, Lagopoulos J, Laje G, Landen M, Landman BA, Lauriello J, Lawrie SM, Lee PH, Le Hellard S, Lemaitre H, Leonardo CD, Li CS, Liberg B, Liewald DC, Liu X, Lopez LM, Loth E, Lourdusamy A, Luciano M, Macciardi F, Machielsen MW, Macqueen GM, Malt UF, Mandl R, Manoach DS, Martinot JL, Matarin M, Mather KA, Mattheisen M, Mattingsdal M, Meyer-Lindenberg A, McDonald C, McIntosh AM, McMahon FJ, McMahon KL, Meisenzahl E, Melle I, Milaneschi Y, Mohnke S, Montgomery GW, Morris DW, Moses EK, Mueller BA, Munoz Maniega S, Muhleisen TW, Muller-Myhsok B, Mwangi B, Nauck M, Nho K, Nichols TE, Nilsson LG, Nugent AC, Nyberg L, Olvera RL, Oosterlaan J, Ophoff RA, Pandolfo M, Papalampropoulou-Tsiridou M, Papmeyer M, Paus T, Pausova Z, Pearlson GD, Penninx BW, Peterson CP, Pfennig A, Phillips M, Pike GB, Poline JB, Potkin SG, Putz B, Ramasamy A, Rasmussen J, Rietschel M, Rijpkema M, Risacher SL, Roffman JL, Roiz-Santianez R, Romanczuk-Seiferth N, Rose EJ, Royle NA, Rujescu D, Ryten M, Sachdev PS, Salami A, Satterthwaite TD, Savitz J, Saykin AJ, Scanlon C, Schmaal L, Schnack HG, Schork AJ, Schulz SC, Schur R, Seidman L, Shen L, Shoemaker JM, Simmons A, Sisodiya SM, Smith C, Smoller JW, Soares JC, Sponheim SR, Sprooten E, Starr JM, Steen VM, Strakowski S, Strike L, Sussmann J, Samann PG, Teumer A, Toga AW, Tordesillas-Gutierrez D, Trabzuni D, Trost S, Turner J, Van den Heuvel M, van der Wee NJ, van Eijk K, van Erp TG, van Haren NE, van’t Ent D, van Tol MJ, Valdes Hernandez MC, Veltman DJ, Versace A, Volzke H, Walker R, Walter H, Wang L, Wardlaw JM, Weale ME, Weiner MW, Wen W, Westlye LT, Whalley HC, Whelan CD, White T, Winkler AM, Wittfeld K, Woldehawariat G, Wolf C, Zilles D, Zwiers MP, Thalamuthu A, Schofield PR, Freimer NB, Lawrence NS, Drevets W (2014) The ENIGMA consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav 8(2):153–182

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Crump C, Sundquist K, Sundquist J, Winkleby MA (2014) Sociodemographic, psychiatric and somatic risk factors for suicide: a Swedish national cohort study. Psychol Med 44(2):279–289. doi:10.1017/s0033291713000810

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Ishtiak-Ahmed K, Perski A, Mittendorfer-Rutz E (2013) Predictors of suicidal behaviour in 36,304 individuals sickness absent due to stress-related mental disorders—a Swedish register linkage cohort study. BMC Public Health 13(1):1–11. doi:10.1186/1471-2458-13-492

    Article  Google Scholar 

  63. 63.

    Lundin A, Lundberg I, Allebeck P, Hemmingsson T (2011) Psychiatric diagnosis in late adolescence and long-term risk of suicide and suicide attempt. Acta Psychiatr Scand 124(6):454–461. doi:10.1111/j.1600-0447.2011.01752.x

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Lundin A, Lundberg I, Hallsten L, Ottosson J, Hemmingsson T (2010) Unemployment and mortality—a longitudinal prospective study on selection and causation in 49321 Swedish middle-aged men. J Epidemiol Community Health 64(01):22–28. doi:10.1136/jech.2008.079269

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Reutfors J, Brandt L, Ekbom A, Isacsson G, Sparén P, Ösby U (2010) Suicide and hospitalization for mental disorders in Sweden: a population-based case-control study. J Psychiatr Res 44(12):741–747. doi:10.1016/j.jpsychires.2010.02.003

    PubMed  Article  Google Scholar 

  66. 66.

    Khashan AS, Abel KM, McNamee R et al (2008) Higher risk of offspring schizophrenia following antenatal maternal exposure to severe adverse life events. Arch Gen Psychiatry 65(2):146–152. doi:10.1001/archgenpsychiatry.2007.20

    PubMed  Article  Google Scholar 

  67. 67.

    Pedersen CB, Mortensen PB (2001) Family history, place and season of birth as risk factors for schizophrenia in Denmark: a replication and reanalysis. Br J Psychiatry 179:46–52

    CAS  PubMed  Article  Google Scholar 

  68. 68.

    Pedersen CB, Mortensen PB (2006) Are the cause(s) responsible for urban-rural differences in schizophrenia risk rooted in families or in individuals? Am J Epidemiol 163(11):971–978

    PubMed  Article  Google Scholar 

  69. 69.

    Morgan VA, Croft ML, Valuri GM, Zubrick SR, Bower C, McNeil TF, Jablensky AV (2012) Intellectual disability and other neuropsychiatric outcomes in high-risk children of mothers with schizophrenia, bipolar disorder and unipolar major depression. Br J Psychiatry 200(4):282–289

    PubMed  Article  Google Scholar 

  70. 70.

    Morgan VA, Valuri GM, Croft ML, Griffith JA, Shah S, Young DJ, Jablensky AV (2011) Cohort profile: pathways of risk from conception to disease: the Western Australian schizophrenia high-risk e-Cohort. Int J Epidemiol 40(6):1477–1485. doi:10.1093/ije/dyq167

    PubMed  Article  Google Scholar 

  71. 71.

    Andrews G, Anstey K, Brodaty H, Issakidis C, Luscombe G (1999) Recall of depressive episode 25 years previously. Psychol Med 29(04):787–791

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Prime Minister’s Office (2015) Prime Minister’s challenge on dementia 2020. GOV.UK. https://www.gov.uk/government/publications/prime-ministers-challenge-on-dementia-2020/prime-ministers-challenge-on-dementia-2020. Accessed 14 Apr 16

  73. 73.

    NHS Digital (2015) Quality and outcomes framework publications. NHS digital (formerly health & social care information centre, HSCIC). http://www.hscic.gov.uk/qof. Accessed 20 July 2016

  74. 74.

    Rait G, Walters K, Griffin M, Buszewicz M, Petersen I, Nazareth I (2009) Recent trends in the incidence of recorded depression in primary care. Br J Psychiatry 195(6):520–524. doi:10.1192/bjp.bp.108.058636

    PubMed  Article  Google Scholar 

  75. 75.

    Walters K, Rait G, Griffin M, Buszewicz M, Nazareth I (2012) Recent trends in the incidence of anxiety diagnoses and symptoms in primary care. PLoS One 7(8):e41670. doi:10.1371/journal.pone.0041670

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    John A, McGregor J, Fone D, Dunstan F, Cornish R, Lyons RA, Lloyd KR (2016) Case-finding for common mental disorders of anxiety and depression in primary care: an external validation of routinely collected data. BMC Med Inform Decis Mak 16(1):1–10. doi:10.1186/s12911-016-0274-7

    Article  Google Scholar 

  77. 77.

    Hammerman A, Dreiher J, Klang SH, Munitz H, Cohen AD, Goldfracht M (2008) Antipsychotics and diabetes: an age-related association. Ann Pharmacother 42(9):1316–1322. doi:10.1345/aph.1L015

    PubMed  Article  Google Scholar 

  78. 78.

    Lichtenberg P, Kaplan Z, Grinshpoon A, Feldman D, Nahon D (1999) The goals and limitations of Israel’s psychiatr case register. Psychiatr Serv 50(8):1043–1048

    CAS  PubMed  Article  Google Scholar 

  79. 79.

    Cheung NT, Fung KW, Wong KC, Cheung A, Cheung J, Ho W, Cheung C, Shung E, Fung V, Fung H (2001) Medical informatics—the state of the art in the Hospital Authority. Int J Med Inform 62(2–3):113–119. doi:10.1016/S1386-5056(01)00155-1

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Park S, Kim J-W, Kim B-N, Bae J-H, Shin M-S, Yoo H-J, Cho S-C (2015) Clinical characteristics and precipitating factors of adolescent suicide attempters admitted for psychiatric inpatient care in South Korea. Psychiatry Investig 12(1):29–36

    PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Lin K-H, Chu P-C, Kuo C-Y, Hwang Y-H, Wu S-C, Guo YL (2014) Psychiatric disorders after occupational injury among National Health Insurance enrollees in Taiwan. Psychiatry Res 219(3):645–650. doi:10.1016/j.psychres.2014.06.015

    PubMed  Article  Google Scholar 

  82. 82.

    Lawrence D, Hancock KJ, Kisely S (2013) The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers. BMJ 346:f2539. doi:10.1136/bmj.f2539

  83. 83.

    Ngamphiw C, Assawamakin A, Xu S, Shaw PJ, Yang JO, Ghang H, Bhak J, Liu E, Tongsima S, Consortium HP-AS (2011) PanSNPdb: the Pan-Asian SNP genotyping database. PLoS One 6(6):e21451

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. 84.

    Bobes J, Iglesias García C, García-Portilla González MP, Bascarán MT, Jiménez Treviño L, Pelayo-Terán JM, Rodríguez Revuelta J, Sánchez Lasheras F, Sáiz Martínez P (2013) Changes in administrative prevalence of mental disorders over a 13-year period in Asturias (northern Spain). Revista de Psiquiatría y Salud Mental (English Edition) 6(2):60–66. doi:10.1016/j.rpsmen.2012.10.002

    Article  Google Scholar 

  85. 85.

    Sauer K, Barkmann C, Klasen F, Bullinger M, Glaeske G, Ravens-Sieberer U (2014) How often do German children and adolescents show signs of common mental health problems? Results from different methodological approaches—a cross-sectional study. BMC Public Health 14(1):1–14. doi:10.1186/1471-2458-14-229

    Article  Google Scholar 

  86. 86.

    Sultana J, Italiano D, Spina E, Cricelli C, Lapi F, Pecchioli S, Gambassi G, Trifiro G (2014) Changes in the prescribing pattern of antidepressant drugs in elderly patients: an Italian, nationwide, population-based study. Eur J Clin Pharmacol 70(4):469–478

    PubMed  Article  Google Scholar 

  87. 87.

    Bocquier A, Cortaredona S, Verdoux H, Sciortino V, Nauleau S, Verger P (2013) Social inequalities in new antidepressant treatment: a study at the individual and neighborhood levels. Ann Epidemiol 23(3):99–105. doi:10.1016/j.annepidem.2012.12.008

    PubMed  Article  Google Scholar 

  88. 88.

    Frick U, Frick H, Langguth B, Landgrebe M, Hübner-Liebermann B, Hajak G (2013) The revolving door phenomenon revisited: time to readmission in 17’415 patients with 37’697 hospitalisations at a German psychiatric hospital. PLoS One 8(10):e75612

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Donisi V, Jones J, Pertile R, Salazzari D, Grigoletti L, Tansella M, Amaddeo F (2011) The difficult task of predicting the costs of community-based mental health care. A comprehensive case register study. Epidemiol Psychiatr Sci 20(03):245–256

    CAS  PubMed  Article  Google Scholar 

  90. 90.

    Lay B, Nordt C, Rossler W (2007) Trends in psychiatric hospitalisation of people with schizophrenia: a register-based investigation over the last three decades. Schizophr Res 97(1–3):68–78

    PubMed  Article  Google Scholar 

  91. 91.

    Margulis AV, Kang EM, Hammad TA (2014) Patterns of prescription of antidepressants and antipsychotics across and within pregnancies in a population-based UK cohort. Matern Child Health J 18(7):1742–1752

    PubMed  Article  Google Scholar 

  92. 92.

    Fernandez-Pujals AM, Adams MJ, Thomson P, McKechanie AG, Blackwood DH, Smith BH, Dominiczak AF, Morris AD, Matthews K, Campbell A (2015) Epidemiology and heritability of major depressive disorder, stratified by age of onset, sex, and illness course in generation scotland: scottish family health study (GS: SFHS). PLoS One 10(11):e0142197

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  93. 93.

    Wilkinson J, Bywaters J, Simms S, Chappel D, Glover G (2008) Developing mental health indicators in England. Public Health 122(9):897–905. doi:10.1016/j.puhe.2007.10.012

    PubMed  Article  Google Scholar 

  94. 94.

    Coupland CA, Dhiman P, Barton G, Morriss R, Arthur A, Sach T, Hippisley-Cox J (2011) A study of the safety and harms of antidepressant drugs for older people: a cohort study using a large primary care database. Health Technol Assess 15(28):1–202, iii–iv. doi:10.3310/hta15280

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    John A, Marchant AL, McGregor JI, Tan JO, Hutchings HA, Kovess V, Choppin S, Macleod J, Dennis MS, Lloyd K (2015) Recent trends in the incidence of anxiety and prescription of anxiolytics and hypnotics in children and young people: an e-cohort study. J Affect Disord 183:134–141

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    Lloyd K, McGregor J, John A, Craddock N, Walters JT, Linden D, Jones I, Bentall R, Lyons RA, Ford DV, Owen MJ (2015) A national population-based e-cohort of people with psychosis (PsyCymru) linking prospectively ascertained phenotypically rich and genetic data to routinely collected records: overview, recruitment and linkage. Schizophr Res 166(1–3):131–136

    PubMed  Article  Google Scholar 

  97. 97.

    Munk-Jorgensen P, Ostergaard SD (2011) Register-based studies of mental disorders. Scand J Public Health 39(7 Suppl):170–174

    PubMed  Article  Google Scholar 

  98. 98.

    Thorgeirsson TE, Oskarsson H, Desnica N, Kostic JP, Stefansson JG, Kolbeinsson H, Lindal E, Gagunashvili N, Frigge ML, Kong A, Stefansson K, Gulcher JR (2003) Anxiety with panic disorder linked to chromosome 9q in Iceland. Am J Hum Genet 72(5):1221–1230. doi:10.1086/375141

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

    Maas J, Verheij RA, de Vries S, Spreeuwenberg P, Schellevis FG, Groenewegen PP (2009) Morbidity is related to a green living environment. J Epidemiol Community Health 63(12):967–973. doi:10.1136/jech.2008.079038

    CAS  PubMed  Article  Google Scholar 

  100. 100.

    Haukka J, Suominen K, Partonen T, Lönnqvist J (2008) Determinants and outcomes of serious attempted suicide: a nationwide study in Finland, 1996–2003. Am J Epidemiol 167(10):1155–1163. doi:10.1093/aje/kwn017

    PubMed  Article  Google Scholar 

  101. 101.

    Braam AW, van Ommeren OWHR, van Buuren ML, Laan W, Smeets HM, Engelhard IM (2016) Local geographical distribution of acute involuntary psychiatric admissions in subdistricts in and around Utrecht, the Netherlands. J Emerg Med 50(3):449–457. doi:10.1016/j.jemermed.2015.06.057

    PubMed  Article  Google Scholar 

  102. 102.

    Evensen S, Wisløff T, Lystad JU, Bull H, Ueland T, Falkum E (2015) Prevalence, employment rate, and cost of schizophrenia in a high-income welfare society: a population-based study using comprehensive health and welfare registers. Schizophr Bull. doi:10.1093/schbul/sbv141

    Google Scholar 

  103. 103.

    Hansen DG, Rosholm J-U, Gichangi A, Vach W (2007) Increased use of antidepressants at the end of life: population-based study among people aged 65 years and above. Age Ageing 36(4):449–454

    PubMed  Article  Google Scholar 

  104. 104.

    Katona L, Czobor P, Bitter I (2014) Real-world effectiveness of antipsychotic monotherapy vs. polypharmacy in schizophrenia: to switch or to combine? A nationwide study in Hungary. Schizophr Res 152(1):246–254. doi:10.1016/j.schres.2013.10.034

    PubMed  Article  Google Scholar 

  105. 105.

    Dlouhý M, Barták M (2013) Mental health financing in six eastern European countries. E + M Ekonomie a management 16(4):4–13

    Google Scholar 

  106. 106.

    Ritchie CW, Molinuevo JL, Truyen L, Satlin A, Van der Geyten S, Lovestone S (2016) Development of interventions for the secondary prevention of Alzheimer’s dementia: the European Prevention of Alzheimer’s Dementia (EPAD) project. Lancet Psychiatry 3(2):179–186. doi:10.1016/s2215-0366(15)00454-x

    PubMed  Article  Google Scholar 

  107. 107.

    Murphy D, Spooren W (2012) EU-AIMS: a boost to autism research. Nat Rev Drug Discov 11(11):815–816

    CAS  PubMed  Article  Google Scholar 

  108. 108.

    Zoëga H, Furu K, Halldórsson M, Thomsen PH, Sourander A, Martikainen JE (2011) Use of ADHD drugs in the Nordic countries: a population-based comparison study. Acta Psychiatr Scand 123(5):360–367. doi:10.1111/j.1600-0447.2010.01607.x

    PubMed  Article  Google Scholar 

  109. 109.

    Requena G, Huerta C, Gardarsdottir H, Logie J, González-González R, Abbing-Karahagopian V, Miret M, Schneider C, Souverein PC, Webb D, Afonso A, Boudiaf N, Martin E, Oliva B, Alvarez A, De Groot MCH, Bate A, Johansson S, Schlienger R, Reynolds R, Klungel OH, de Abajo FJ (2015) Hip/femur fractures associated with the use of benzodiazepines (anxiolytics, hypnotics and related drugs): a methodological approach to assess consistencies across databases from the PROTECT-EU project. Pharmacoepidemiol Drug Saf. doi:10.1002/pds.3816

    Google Scholar 

  110. 110.

    Lesage A, Rochette L, Emond V, Pelletier E, St-Laurent D, Diallo FB, Kisely S (2015) A surveillance system to monitor excess mortality of people with mental illness in Canada. Can J Psychiatry 60(12):571–579

    PubMed  PubMed Central  Google Scholar 

  111. 111.

    Hwang YJ, Dixon SN, Reiss JP, Wald R, Parikh CR, Gandhi S, Shariff SZ, Pannu N, Nash DM, Rehman F, Garg AX (2014) Atypical antipsychotic drugs and the risk for acute kidney injury and other adverse outcomes in older adults: a population-based cohort study. Ann Intern Med 161(4):242–248

    PubMed  Article  Google Scholar 

  112. 112.

    Perlman CM, Hirdes JP, Barbaree H, Fries BE, McKillop I, Morris JN, Rabinowitz T (2013) Development of mental health quality indicators (MHQIs) for inpatient psychiatry based on the interRAI mental health assessment. BMC Health Serv Res 13(1):1–12. doi:10.1186/1472-6963-13-15

    Article  Google Scholar 

  113. 113.

    Meng X, D’Arcy C, Tempier R (2013) Trends in psychotropic use in Saskatchewan from 1983 to 2007. Can J Psychiatry Rev Can Psychiatr 58(7):426–431

    Google Scholar 

  114. 114.

    Tung JY, Do CB, Hinds DA, Kiefer AK, Macpherson JM, Chowdry AB, Francke U, Naughton BT, Mountain JL, Wojcicki A (2011) Efficient replication of over 180 genetic associations with self-reported medical data. PLoS One 6(8):e23473

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  115. 115.

    Smith MW, Stocks C, Santora PB (2015) Hospital readmission rates and emergency department visits for mental health and substance abuse conditions. Community Ment Health J 51(2):190–197. doi:10.1007/s10597-014-9784-x

    PubMed  Article  Google Scholar 

  116. 116.

    McDavid A, Crane PK, Newton KM, Crosslin DR, McCormick W, Weston N, Ehrlich K, Hart E, Harrison R, Kukull WA, Rottscheit C, Peissig P, Stefanski E, McCarty CA, Zuvich RL, Ritchie MD, Haines JL, Denny JC, Schellenberg GD, de Andrade M, Kullo I, Li R, Mirel D, Crenshaw A, Bowen JD, Li G, Tsuang D, McCurry S, Teri L, Larson EB, Jarvik GP, Carlson CS (2013) Enhancing the Power of genetic association studies through the use of silver standard cases derived from electronic medical records. PLoS One 8(6):e63481. doi:10.1371/journal.pone.0063481

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  117. 117.

    Olfson M, Kroenke K, Wang S, Blanco C (2014) Trends in office-based mental health care provided by psychiatrists and primary care physicians. J Clin Psychiatry 75(3):247–253

    PubMed  Article  Google Scholar 

  118. 118.

    Estiri H, Chan Y-F, Baldwin L-M, Jung H, Cole A, Stephens KA (2015) Visualizing anomalies in electronic health record data: the variability explorer tool. AMIA Summits Trans Sci Proc 2015:56–60

    Google Scholar 

  119. 119.

    Lin EHB, Heckbert SR, Rutter CM, Katon WJ, Ciechanowski P, Ludman EJ, Oliver M, Young BA, McCulloch DK, Von Korff M (2009) Depression and increased mortality in diabetes: unexpected causes of death. Ann Fam Med 7(5):414–421. doi:10.1370/afm.998

    PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Clark RE, Weir S, Ouellette RA, Zhang J, Baxter JD (2009) Beyond health plans: behavioral health disorders and quality of diabetes and asthma care for medicaid beneficiaries. Med Care 47(5):545–552. doi:10.1097/MLR.0b013e318190db45

    PubMed  Article  Google Scholar 

  121. 121.

    Raebel MA, Penfold R, McMahon AW, Reichman M, Shetterly S, Goodrich G, Andrade S, Correll CU, Gerhard T (2014) Adherence to guidelines for glucose assessment in starting second-generation antipsychotics. Pediatrics 134(5):e1308–e1314. doi:10.1542/peds.2014-0828

    PubMed  Article  Google Scholar 

  122. 122.

    Young JQ, Kline-Simon AH, Mordecai DJ, Weisner C (2015) Prevalence of behavioral health disorders and associated chronic disease burden in a commercially insured health system: findings of a case–control study. Gen Hosp Psychiatry 37(2):101–108. doi:10.1016/j.genhosppsych.2014.12.005

    PubMed  Article  Google Scholar 

  123. 123.

    Sohn S, Kocher J-PA, Chute CG, Savova GK (2011) Drug side effect extraction from clinical narratives of psychiatry and psychology patients. J Am Med Inform Assoc 18(Supplement 1):i144–i149. doi:10.1136/amiajnl-2011-000351

    PubMed  PubMed Central  Article  Google Scholar 

  124. 124.

    Watkins KE, Smith B, Akincigil A, Sorbero ME, Paddock S, Woodroffe A, Huang C, Crystal S, Pincus HA (2016) The quality of medication treatment for mental disorders in the department of veterans affairs and in private-sector plans. Psychiatr Serv 67(4):391–396

    PubMed  Article  Google Scholar 

  125. 125.

    Medicaid Medical Directors Learning Network, Rutgers Center for Education and Research on Mental Health Therapeutics (2011) Antipsychotic medication use in medicaid children and adolescents, Rutgers University. http://rci.rutgers.edu/~cseap/MMDLNAPKIDS.html. Accessed 20 July 2016

  126. 126.

    Ahmedani BK, Solberg LI, Copeland LA, Fang-Hollingsworth Y, Stewart C, Hu J, Nerenz DR, Williams LK, Cassidy-Bushrow AE, Waxmonsky J, Lu CY, Waitzfelder BE, Owen-Smith AA, Coleman KJ, Lynch FL, Ahmed AT, Beck A, Rossom RC, Simon GE (2015) Psychiatric comorbidity and 30-day readmissions after hospitalization for heart failure, AMI, and pneumonia. Psychiatr Serv 66(2):134–140. doi:10.1176/appi.ps.201300518

    PubMed  Article  Google Scholar 

  127. 127.

    Ghassemi M, Marshall J, Singh N, Stone DJ, Celi LA (2014) Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to icu admission is associated with increased hospital mortality. Chest 145(4):745–752. doi:10.1378/chest.13-1722

    PubMed  Article  Google Scholar 

  128. 128.

    Alexander GC, Gallagher SA, Mascola A, Moloney RM, Stafford RS (2011) Increasing off-label use of antipsychotic medications in the United States, 1995–2008. Pharmacoepidemiol Drug Saf 20(2):177–184. doi:10.1002/pds.2082

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  129. 129.

    Melamed RD, Khiabanian H, Rabadan R (2014) Data-driven discovery of seasonally linked diseases from an electronic health records system. BMC Bioinform 15(6):1–10. doi:10.1186/1471-2105-15-s6-s3

    Google Scholar 

  130. 130.

    Goyal D, Wang EJ, Shen J, Wong EC, Palaniappan LP (2012) Clinically identified postpartum depression in Asian American mothers. J Obstet Gynecol Neonatal Nurs 41(3):408–416. doi:10.1111/j.1552-6909.2012.01352.x

    PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Castro VM, Clements CC, Murphy SN, Gainer VS, Fava M, Weilburg JB, Erb JL, Churchill SE, Kohane IS, Iosifescu DV, Smoller JW, Perlis RH (2013) QT interval and antidepressant use: a cross sectional study of electronic health records. BMJ. doi:10.1136/bmj.f288

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Berger A, Edelsberg J, Treglia M, Alvir JMJ, Oster G (2012) Change in healthcare utilization and costs following initiation of benzodiazepine therapy for long-term treatment of generalized anxiety disorder: a retrospective cohort study. BMC Psychiatry 12(1):1–10. doi:10.1186/1471-244x-12-177

    Article  Google Scholar 

  133. 133.

    Connolly Gibbons MB, Rothbard A, Farris KD, Wiltsey Stirman S, Thompson SM, Scott K, Heintz LE, Gallop R, Crits-Christoph P (2011) Changes in psychotherapy utilization among consumers of services for major depressive disorder in the community mental health system. Admin Policy Ment Health Ment Health Serv Res 38(6):495–503. doi:10.1007/s10488-011-0336-1

    Article  Google Scholar 

  134. 134.

    Kohane IS (2015) An autism case history to review the systematic analysis of large-scale data to refine the diagnosis and treatment of neuropsychiatric disorders. Biol Psychiatry 77(1):59–65. doi:10.1016/j.biopsych.2014.05.024

    PubMed  Article  Google Scholar 

  135. 135.

    Raj KS, Keane-Miller C, Golden NH (2012) Hypomagnesemia in adolescents with eating disorders hospitalized for medical instability. Nutr Clin Pract 27(5):689–694. doi:10.1177/0884533612446799

    PubMed  Article  Google Scholar 

  136. 136.

    Baillargeon JG, Paar DP, Wu H, Giordano TP, Murray O, Raimer BG, Avery EN, Diamond PM, Pulvino JS (2008) Psychiatric disorders, HIV infection and HIV/hepatitis co-infection in the correctional setting. AIDS Care 20(1):124–129. doi:10.1080/09540120701426532

    CAS  PubMed  Article  Google Scholar 

  137. 137.

    Hanauer DA, Ramakrishnan N, Seyfried LS (2013) Describing the relationship between cat bites and human depression using data from an electronic health record. PLoS One 8(8):e70585. doi:10.1371/journal.pone.0070585

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  138. 138.

    Crawford DC, Goodloe R, Farber-Eger E, Boston J, Pendergrass SA, Haines JL, Ritchie MD, Bush WS (2015) Leveraging epidemiologic and clinical collections for genomic studies of complex traits. Hum Hered 79(3–4):137–146

    PubMed  PubMed Central  Article  Google Scholar 

  139. 139.

    Bauer MS, Lee A, Miller CJ, Bajor L, Li M, Penfold RB (2015) Effects of diagnostic inclusion criteria on prevalence and population characteristics in database research. Psychiatr Serv 66(2):141–148. doi:10.1176/appi.ps.201400115

    PubMed  Article  Google Scholar 

  140. 140.

    Hofmann-Apitius M, Alarcon-Riquelme ME, Chamberlain C, McHale D (2015) Towards the taxonomy of human disease. Nat Rev Drug Discov 14(2):75–76. doi:10.1038/nrd4537

    CAS  PubMed  Article  Google Scholar 

  141. 141.

    Pratt N, Andersen M, Bergman U, Choi N-K, Gerhard T, Huang C, Kimura M, Kimura T, Kubota K, Lai EC-C, Ooba N, Ösby U, Park B-J, Sato T, Shin J-Y, Sundström A, Yang Y-HK, Roughead EE (2013) Multi-country rapid adverse drug event assessment: the Asian Pharmacoepidemiology Network (AsPEN) antipsychotic and acute hyperglycaemia study. Pharmacoepidemiol Drug Saf 22(9):915–924. doi:10.1002/pds.3440

    CAS  PubMed  Google Scholar 

  142. 142.

    Reichborn-Kjennerud T, Knudsen GP, Andreassen OA, Espeseth T, Lundervold A, Reinvang I, Steen VM, Le Hellard S, Mattingsdal M, Boraske V (2014) A genome-wide association study of anorexia nervosa. Mol Psychiatry 19(10):1085–1094

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  143. 143.

    Moran V, Jacobs R (2013) An international comparison of efficiency of inpatient mental health care systems. Health Policy 112(1–2):88–99. doi:10.1016/j.healthpol.2013.06.011

    PubMed  Article  Google Scholar 

  144. 144.

    Wong AYS, Hsia Y, Chan EW, Murphy DGM, Simonoff E, Buitelaar JK, Wong ICK (2014) The variation of psychopharmacological prescription rates for people with Autism Spectrum Disorder (ASD) in 30 Countries. Autism Res 7(5):543–554. doi:10.1002/aur.1391

    PubMed  Article  Google Scholar 

  145. 145.

    Lambert J-C, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, Jun G, DeStefano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thornton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin C-F, Gerrish A, Schmidt H, Kunkle B, Dunstan ML, Ruiz A, Bihoreau M-T, Choi S-H, Reitz C, Pasquier F, Hollingworth P, Ramirez A, Hanon O, Fitzpatrick AL, Buxbaum JD, Campion D, Crane PK, Baldwin C, Becker T, Gudnason V, Cruchaga C, Craig D, Amin N, Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans D, Lovestone S, Letenneur L, Moron FJ, Rubinsztein DC, Eiriksdottir G, Sleegers K, Goate AM, Fievet N, Huentelman MJ, Gill M, Brown K, Kamboh MI, Keller L, Barberger-Gateau P, McGuinness B, Larson EB, Green R, Myers AJ, Dufouil C, Todd S, Wallon D, Love S, Rogaeva E, Gallacher J, St George-Hyslop P, Clarimon J, Lleo A, Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossu P, Spalletta G, Proitsi P, Collinge J, Sorbi S, Sanchez-Garcia F, Fox NC, Hardy J, Naranjo MCD, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso M, Matthews F, European Alzheimer’s Disease I, Genetic, Environmental Risk in Alzheimer’s D, Alzheimer’s Disease Genetic C, Cohorts for H, Aging Research in Genomic E, Moebus S, Mecocci P, Del Zompo M, Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P, Nacmias B, Gilbert JR, Mayhaus M, Lannfelt L, Hakonarson H, Pichler S, Carrasquillo MM, Ingelsson M, Beekly D, Alvarez V, Zou F, Valladares O, Younkin SG, Coto E, Hamilton-Nelson KL, Gu W, Razquin C, Pastor P, Mateo I, Owen MJ, Faber KM, Jonsson PV, Combarros O, O’Donovan MC, Cantwell LB, Soininen H, Blacker D, Mead S, Mosley Jr TH, Bennett DA, Harris TB, Fratiglioni L, Holmes C, de Bruijn RFAG, Passmore P, Montine TJ, Bettens K, Rotter JI, Brice A, Morgan K, Foroud TM, Kukull WA, Hannequin D, Powell JF, Nalls MA, Ritchie K, Lunetta KL, Kauwe JSK, Boerwinkle E, Riemenschneider M, Boada M, Hiltunen M, Martin ER, Schmidt R, Rujescu D, Wang L-S, Dartigues J-F, Mayeux R, Tzourio C, Hofman A, Nothen MM, Graff C, Psaty BM, Jones L, Haines JL, Holmans PA, Lathrop M, Pericak-Vance MA, Launer LJ, Farrer LA, van Duijn CM, Van Broeckhoven C, Moskvina V, Seshadri S, Williams J, Schellenberg GD, Amouyel P (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45(12):1452–1458. doi:10.1038/ng.2802. http://www.nature.com/ng/journal/v45/n9/abs/ng.2711.html. Accessed 20 July 2016 (supplementary-information)

  146. 146.

    Garriock HA, Kraft JB, Shyn SI, Peters EJ, Yokoyama JS, Jenkins GD, Reinalda MS, Slager SL, McGrath PJ, Hamilton SP (2010) A genomewide association study of citalopram response in major depressive disorder. Biol Psychiatry 67(2):133–138. doi:10.1016/j.biopsych.2009.08.029

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  147. 147.

    WHO (2015) World Health Statistics. Data repository, World Health Organisation. http://www.who.int/gho/database/en/. Accessed 20 July 2016

  148. 148.

    Manson H (2013) The burden of mental illness and addiction in Ontario. Can J Psychiatry 58(9):529

    PubMed  Google Scholar 

  149. 149.

    Heggestad T, Lilleeng SE, Ruud T (2011) Patterns of mental health care utilisation: distribution of services and its predictability from routine data. Soc Psychiatry Psychiatr Epidemiol 46(12):1275–1282

    PubMed  Article  Google Scholar 

  150. 150.

    Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, Soeby K, Bredkjaer S, Juul A, Werge T, Jensen LJ, Brunak S (2011) Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 7(8):e1002141. doi:10.1371/journal.pcbi.1002141

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  151. 151.

    Perini G, Grigoletti L, Hanife B, Biggeri A, Tansella M, Amaddeo F (2013) Cancer mortality among psychiatric patients treated in a community-based system of care: a 25-year case register study. Soc Psychiatry Psychiatr Epidemiol 49(5):693–701. doi:10.1007/s00127-013-0765-0

    PubMed  Article  Google Scholar 

  152. 152.

    Lyalina S, Percha B, LePendu P, Iyer SV, Altman RB, Shah NH (2013) Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. J Am Med Inform Assoc 20(e2):e297–e305. doi:10.1136/amiajnl-2013-001933

    PubMed  PubMed Central  Article  Google Scholar 

  153. 153.

    Kyaga S, Landén M, Boman M, Hultman CM, Långström N, Lichtenstein P (2013) Mental illness, suicide and creativity: 40-Year prospective total population study. J Psychiatr Res 47(1):83–90. doi:10.1016/j.jpsychires.2012.09.010

    PubMed  Article  Google Scholar 

  154. 154.

    Steinberg S, de Jong S, Mattheisen M, Costas J, Demontis D, Jamain S, Pietilainen OPH, Lin K, Papiol S, Huttenlocher J, Sigurdsson E, Vassos E, Giegling I, Breuer R, Fraser G, Walker N, Melle I, Djurovic S, Agartz I, Tuulio-Henriksson A, Suvisaari J, Lonnqvist J, Paunio T, Olsen L, Hansen T, Ingason A, Pirinen M, Strengman E, Hougaard DM, Orntoft T, Didriksen M, Hollegaard MV, Nordentoft M, Abramova L, Kaleda V, Arrojo M, Sanjuan J, Arango C, Etain B, Bellivier F, Meary A, Schurhoff F, Szoke A, Ribolsi M, Magni V, Siracusano A, Sperling S, Rossner M, Christiansen C, Kiemeney LA, Franke B, van den Berg LH, Veldink J, Curran S, Bolton P, Poot M, Staal W, Rehnstrom K, Kilpinen H, Freitag CM, Meyer J, Magnusson P, Saemundsen E, Martsenkovsky I, Bikshaieva I, Martsenkovska I, Vashchenko O, Raleva M, Paketchieva K, Stefanovski B, Durmishi N, Pejovic Milovancevic M, Lecic Tosevski D, Silagadze T, Naneishvili N, Mikeladze N, Surguladze S, Vincent JB, Farmer A, Mitchell PB, Wright A, Schofield PR, Fullerton JM, Montgomery GW, Martin NG, Rubino IA, van Winkel R, Kenis G, De Hert M, Rethelyi JM, Bitter I, Terenius L, Jonsson EG, Bakker S, van Os J, Jablensky A, Leboyer M, Bramon E, Powell J, Murray R, Corvin A, Gill M, Morris D, O’Neill FA, Kendler K, Riley B, Craddock N, Owen MJ, O’Donovan MC, Thorsteinsdottir U, Kong A, Ehrenreich H, Carracedo A, Golimbet V, Andreassen OA, Borglum AD, Mors O, Mortensen PB, Werge T, Ophoff RA, Nothen MM, Rietschel M, Cichon S, Ruggeri M, Tosato S, Palotie A, St Clair D, Rujescu D, Collier DA, Stefansson H, Stefansson K (2014) Common variant at 16p11.2 conferring risk of psychosis. Mol Psychiatry 19(1):108–114. doi:10.1038/mp.2012.157

    CAS  PubMed  Article  Google Scholar 

  155. 155.

    Matheson FI, Smith KLW, Moineddin R, Dunn JR, Glazier RH (2013) Mental health status and gender as risk factors for onset of physical illness over 10 years. J Epidemiol Community Health. doi:10.1136/jech-2013-202838

    PubMed  PubMed Central  Google Scholar 

  156. 156.

    Wangel A-M, Molin J, Moghaddassi M, Östman M (2011) Prior psychiatric inpatient care and risk of cesarean sections: a registry study. J Psychosom Obstet Gynecol 32(4):189–197

    Article  Google Scholar 

  157. 157.

    Kosteniuk J, Morgan D, Quail J, Teare G, Kulyk K, O’Connell M, Kirk A, Crossley M, Stewart N, Dal Bello-Haas V, McBain L, Mou H, Forbes D, Innes A, Bracken J, Parrott E (2015) A Multi-method investigation of dementia and related services in saskatchewan: final report and recommendations. Rural dementia action research team. University of Saskatchewan, Saskatoon

    Google Scholar 

  158. 158.

    Exalto LG, Biessels GJ, Karter AJ, Huang ES, Katon WJ, Minkoff JR, Whitmer RA (2013) Risk score for prediction of 10 year dementia risk in individuals with type 2 diabetes: a cohort study. Lancet Diabetes Endocrinol 1(3):183–190

    PubMed  PubMed Central  Article  Google Scholar 

  159. 159.

    van den Bussche H, Kaduszkiewicz H, Koller D, Eisele M, Steinmann S, Glaeske G, Wiese B (2011) Antidementia drug prescription sources and patterns after the diagnosis of dementia in Germany: results of a claims data-based 1-year follow-up. Int Clin Psychopharmacol 26(4):225–231. doi:10.1097/YIC.0b013e328344c600

    PubMed  Article  Google Scholar 

  160. 160.

    Rait G, Walters K, Bottomley C, Petersen I, Iliffe S, Nazareth I (2010) Survival of people with clinical diagnosis of dementia in primary care: cohort study. BMJ. doi:10.1136/bmj.c3584

    Google Scholar 

  161. 161.

    Bonn-Miller MO, Harris AH, Trafton JA (2012) Prevalence of cannabis use disorder diagnoses among veterans in 2002, 2008, and 2009. Psychol Serv 9(4):404–416

    PubMed  Article  Google Scholar 

  162. 162.

    Nesvåg R, Knudsen GP, Bakken IJ, Høye A, Ystrom E, Surén P, Reneflot A, Stoltenberg C, Reichborn-Kjennerud T (2015) Substance use disorders in schizophrenia, bipolar disorder, and depressive illness: a registry-based study. Soc Psychiatry Psychiatr Epidemiol 50(8):1267–1276. doi:10.1007/s00127-015-1025-2

    PubMed  Article  Google Scholar 

  163. 163.

    Mark TL, Kassed CA, Vandivort-Warren R, Levit KR, Kranzler HR (2009) Alcohol and opioid dependence medications: prescription trends, overall and by physician specialty. Drug Alcohol Depend 99(1):345–349

    PubMed  Article  Google Scholar 

  164. 164.

    Okkels N, Vernal DL, Jensen SOW, McGrath JJ, Nielsen RE (2013) Changes in the diagnosed incidence of early onset schizophrenia over four decades. Acta Psychiatr Scand 127(1):62–68. doi:10.1111/j.1600-0447.2012.01913.x

    CAS  PubMed  Article  Google Scholar 

  165. 165.

    Harper S, Towers-Evans H, MacCabe J (2015) The aetiology of schizophrenia: what have the Swedish Medical Registers taught us? Soc Psychiatry Psychiatr Epidemiol 50(10):1471–1479

    PubMed  Article  Google Scholar 

  166. 166.

    Stroup TS, Gerhard T, Crystal S, Huang C, Olfson M (2016) Comparative effectiveness of clozapine and standard antipsychotic treatment in adults with schizophrenia. Am J Psychiatry 173(2):166–173

    PubMed  Article  Google Scholar 

  167. 167.

    Gal G, Munitz H, Levav I (2015) Health care disparities among persons with comorbid schizophrenia and cardiovascular disease: a case—control epidemiological study. Epidemiol Psychiatr Sci, 1–7. doi:10.1017/S2045796015000852

  168. 168.

    Vigod SN, Seeman MV, Ray JG, Anderson GM, Dennis CL, Grigoriadis S, Gruneir A, Kurdyak PA, Rochon PA (2012) Temporal trends in general and age-specific fertility rates among women with schizophrenia (1996–2009): a population-based study in Ontario, Canada. Schizophr Res 139(1–3):169–175. doi:10.1016/j.schres.2012.05.010

    PubMed  Article  Google Scholar 

  169. 169.

    Castro VM, Minnier J, Murphy SN, Kohane I, Churchill SE, Gainer V, Cai T, Hoffnagle AG, Dai Y, Block S (2015) Validation of electronic health record phenotyping of bipolar disorder cases and controls. Am J Psychiatry 172(4):363–372. doi:10.1176/appi.ajp.2014.14030423

    PubMed  Article  Google Scholar 

  170. 170.

    Schaefer C, Shen L, Kearney K, McCormick M, Hamilton SP, McInnes LA, Reus V, Wall J, Kwok P-Y, Kvale M, Hoffmann TJ, Jorgenson E, Risch N (2014) GWAS of bipolar 1 disorder in a multi-ethnic cohort of 72,823 identifies four novel Loci. Paper presented at the American Society of Human Genetics (ASHG), San Diego, CA

  171. 171.

    Hayes JF, Marston L, Walters K, Geddes JR, King M, Osborn DPJ (2016) Lithium vs. valproate vs. olanzapine vs. quetiapine as maintenance monotherapy for bipolar disorder: a population-based UK cohort study using electronic health records. World Psychiatry 15(1):53–58. doi:10.1002/wps.20298

    PubMed  PubMed Central  Article  Google Scholar 

  172. 172.

    Lee H-C, Lin H-C (2010) Maternal bipolar disorder increased low birthweight and preterm births: a nationwide population-based study. J Affect Disord 121(1–2):100–105. doi:10.1016/j.jad.2009.05.019

    PubMed  Article  Google Scholar 

  173. 173.

    Hoffmann F, Petermann F, Glaeske G, Bachmann CJ (2012) Prevalence and comorbidities of adolescent depression in Germany. Z Kinder Jugendpsychiatr Psychother 40(6):399–404. doi:10.1024/1422-4917/a000199

    PubMed  Article  Google Scholar 

  174. 174.

    Ul-Haq Z, Mackay DF, Martin D, Smith DJ, Gill JM, Nicholl BI, Cullen B, Evans J, Roberts B, Deary IJ (2014) Heaviness, health and happiness: a cross-sectional study of 163066 UK Biobank participants. J Epidemiol Community Health 68(4):340–348

    PubMed  Article  Google Scholar 

  175. 175.

    Musliner KL, Munk-Olsen T, Laursen TM, Eaton WW, Zandi PP, Mortensen PB (2016) Heterogeneity in 10-year course trajectories of moderate to severe major depressive disorder: a danish national register-based study. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2015.3365

    PubMed  Google Scholar 

  176. 176.

    Lacourt TE, Houtveen JH, Smeets HM, Lipovsky MM, van Doornen LJP (2013) Infection load as a predisposing factor for somatoform disorders: evidence from a dutch general practice registry. Psychosom Med 75(8):759–764. doi:10.1097/PSY.0b013e3182a3d91f

    CAS  PubMed  Article  Google Scholar 

  177. 177.

    Sandelin R, Kowalski J, Ahnemark E, Allgulander C (2013) Treatment patterns and costs in patients with generalised anxiety disorder: 1-year retrospective analysis of data from national registers in Sweden. Eur Psychiatry 28(2):125–133. doi:10.1016/j.eurpsy.2012.02.003

    CAS  PubMed  Article  Google Scholar 

  178. 178.

    Frayne SM, Chiu VY, Iqbal S, Berg EA, Laungani KJ, Cronkite RC, Pavao J, Kimerling R (2011) Medical care needs of returning veterans with PTSD: their other burden. J Gen Intern Med 26(1):33–39. doi:10.1007/s11606-010-1497-4

    PubMed  Article  Google Scholar 

  179. 179.

    Micali N, Hagberg KW, Petersen I, Treasure JL (2013) The incidence of eating disorders in the UK in 2000–2009: findings from the general practice research database. BMJ Open 3(5):e002646

    PubMed  PubMed Central  Article  Google Scholar 

  180. 180.

    Polachek IS, Fung K, Vigod SN (2016) First lifetime psychiatric admission in the postpartum period: a population-based comparison to women with prior psychiatric admission. Gen Hosp Psychiatry. doi:10.1016/j.genhosppsych.2016.01.007

    PubMed  Google Scholar 

  181. 181.

    Sprung J, Flick RP, Wilder RT, Katusic SK, Pike TL, Dingli M, Gleich SJ, Schroeder DR, Barbaresi WJ, Hanson AC (2009) Anesthesia for cesarean delivery and learning disabilities in a population-based birth cohort. J Am Soc Anesthesiol 111(2):302–310

    Article  Google Scholar 

  182. 182.

    Alexander M, Petri H, Ding Y, Wandel C, Khwaja O, Foskett N (2016) Morbidity and medication in a large population of individuals with Down syndrome compared to the general population. Dev Med Child Neurol 58(3):246–254. doi:10.1111/dmcn.12868

    PubMed  Article  Google Scholar 

  183. 183.

    Hsu S-W, Chiang P-H, Lin L-P, Lin J-D (2012) Disparity in autism spectrum disorder prevalence among Taiwan national health insurance enrollees: age, gender and urbanization effects. Res Autism Spectr Disord 6(2):836–841. doi:10.1016/j.rasd.2011.09.006

    Article  Google Scholar 

  184. 184.

    Clarke TK, Lupton MK, Fernandez-Pujals AM, Starr J, Davies G, Cox S, Pattie A, Liewald DC, Hall LS, MacIntyre DJ, Smith BH, Hocking LJ, Padmanabhan S, Thomson PA, Hayward C, Hansell NK, Montgomery GW, Medland SE, Martin NG, Wright MJ, Porteous DJ, Deary IJ, McIntosh AM (2016) Common polygenic risk for autism spectrum disorder (ASD) is associated with cognitive ability in the general population. Mol Psychiatry 21(3):419–425. doi:10.1038/mp.2015.12

    PubMed  Article  Google Scholar 

  185. 185.

    Surén P, Bakken IJ, Aase H, Chin R, Gunnes N, Lie KK, Magnus P, Reichborn-Kjennerud T, Schjølberg S, Øyen A-S, Stoltenberg C (2012) Autism spectrum disorder, ADHD, epilepsy, and cerebral palsy in Norwegian children. Pediatrics 130(1):e152–e158. doi:10.1542/peds.2011-3217

    PubMed  PubMed Central  Article  Google Scholar 

  186. 186.

    Leivonen S, Voutilainen A, Chudal R, Suominen A, Gissler M, Sourander A (2016) Obstetric and neonatal adversities, parity, and tourette syndrome: a nationwide registry. J Pediatr 171:213–219. doi:10.1016/j.jpeds.2015.10.063

    PubMed  Article  Google Scholar 

  187. 187.

    Hoffmann F, Steuber C, Günther J, Glaeske G, Bachmann CJ (2012) Which treatments do children with newly diagnosed non-organic urinary incontinence receive? An analysis of 3,188 outpatient cases from Germany. Neurourol Urodyn 31(1):93–98. doi:10.1002/nau.21177

    PubMed  Article  Google Scholar 

  188. 188.

    Abdullah-Koolmees H, Gardarsdottir H, Yazir D, Stoker LJ, Vuyk J, Egberts TCG, Heerdink ER (2015) Medication discontinuation in patients after discharge from a psychiatric hospital. Ann Pharmacother 49(10):1085–1095. doi:10.1177/1060028015593763

    CAS  PubMed  Article  Google Scholar 

  189. 189.

    Hartz I, Handal M, Tverdal A, Skurtveit S (2015) Paediatric off-label use of melatonin—a register linkage study between the norwegian prescription database and patient register. Basic Clin Pharmacol Toxicol 117(4):267–273. doi:10.1111/bcpt.12411

    CAS  PubMed  Google Scholar 

  190. 190.

    Chung S-D, Lin C-C, Wang L-H, Lin H-C, Kang J-H (2013) Zolpidem use and the risk of injury: a population-based follow-up study. PLoS One 8(6):e67459. doi:10.1371/journal.pone.0067459

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  191. 191.

    Eriksson R, Werge T, Jensen LJ, Brunak S (2014) Dose-specific adverse drug reaction identification in electronic patient records: temporal data mining in an inpatient psychiatric population. Drug Saf 37(4):237–247

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  192. 192.

    Hviid A, Melbye M, Pasternak B (2013) Use of selective serotonin reuptake inhibitors during pregnancy and risk of autism. N Engl J Med 369(25):2406–2415

    CAS  PubMed  Article  Google Scholar 

  193. 193.

    Palmsten K, Hernandez-Diaz S, Huybrechts KF, Williams PL, Michels KB, Achtyes ED, Mogun H, Setoguchi S (2013) Use of antidepressants near delivery and risk of postpartum hemorrhage: cohort study of low income women in the United States. BMJ 347(7922):10. doi:10.1136/bmj.f4877

  194. 194.

    Stewart SL, Baiden P, Theall-Honey L (2014) Examining non-suicidal self-injury among adolescents with mental health needs, in Ontario, Canada. Arch Suicide Res 18(4):392–409

    PubMed  Article  Google Scholar 

  195. 195.

    Simon GE, Rutter CM, Peterson D, Oliver M, Whiteside U, Operskalski B, Ludman EJ (2013) Does response on the PHQ-9 depression questionnaire predict subsequent suicide attempt or suicide death? Psychiatr Serv 64(12):1195–1202. doi:10.1176/appi.ps.201200587

  196. 196.

    Bardach NS, Coker TR, Zima BT, Murphy JM, Knapp P, Richardson LP, Edwall G, Mangione-Smith R (2014) Common and costly hospitalizations for pediatric mental health disorders. Pediatrics 133(4):602–609. doi:10.1176/appi.ps.201200587

    PubMed  PubMed Central  Article  Google Scholar 

  197. 197.

    Koopmans G, Uiters E, Devillé W, Foets M (2013) The use of outpatient mental health care services of migrants vis-à-vis Dutch natives: equal access? Int J Soc Psychiatry 59(4):342–350. doi:10.1177/0020764012437129

    CAS  PubMed  Article  Google Scholar 

  198. 198.

    Oram S, Khondoker M, Abas M, Broadbent M, Howard LM (2015) Characteristics of trafficked adults and children with severe mental illness: a historical cohort study. Lancet Psychiatry 2(12):1084–1091. doi:10.1016/S2215-0366(15)00290-4

    PubMed  Article  Google Scholar 

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Acknowledgments

This paper represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

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Stewart, R., Davis, K. ‘Big data’ in mental health research: current status and emerging possibilities. Soc Psychiatry Psychiatr Epidemiol 51, 1055–1072 (2016). https://doi.org/10.1007/s00127-016-1266-8

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Keywords

  • Big data
  • Mental disorders
  • Epidemiology
  • Electronic health records