Invited commentary on Stewart and Davis " 'Big data' in mental health research-current status and emerging possibilities"

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Quantifying psychotropic treatment and illness outcome in cohort studies using record-linkage to administrative health data

The advent of powerful information technology and the increasing availability of socalled ‘Big Data’, in a multitude of forms, has had revolutionary impact on many aspects of society, such as commerce and communication. Within healthcare broadly and mental health research specifically, however, the progress of these techniques is considered relatively nascent. This is paradoxical, as the complexity and multifactorial nature of mental health conditions, such as major depression and self-harm, makes them particularly tractable for more sophisticated data-driven approaches. In this thesis I will apply the transformative potential of data science applications related to record-linkage for mental health research. I will demonstrate that recordlinkage of cohort studies to administrative health data enables: (i) improved signal and power for discoveries and the reduction of false associations (ii) validation of research data and the identification of inaccuracies (iii) transformation of cross-sectional studies into longitudinal studies; and (iv) identification of new phenotypes for study. Chapters 1, 2 and 3 provide an introductory overview. In Chapter 1, I will survey the current state of psychiatric research in major depressive disorder (MDD), antidepressant pharmacoepidemiology, self-harm and suicidal ideation. These interrelated aspects of mental illness are common, highly complex and place a high burden on society. They are thus particularly appropriate for the research methods I shall employ herein. In Chapter 2 I will discuss the evolution of data sciences approaches within psychiatry, and specifically of record-linkage techniques and their application in medical epidemiology. In Chapter 3 I will also review the demographics and characteristics of the datasets used in this thesis, namely Generation Scotland (GS:SFHS), UK Biobank (UKB), the Scottish Morbidity Records (SMR) and the Prescribing Information System (PIS) of NHS Scotland. Chapter 4 demonstrates the application of record-linkage to administrative health data for validation in psychiatric research. Using national prescribing data in PIS as the ‘gold standard’, I compare the accuracy of GS:SFHS cohort self-reported psychiatric drug use, which is often thought to be relatively under-reported for reasons such as self-stigma, compared to other commonly prescribed medications. Our study finds that under-reporting is not found for all psychiatric medications, indeed antidepressants show very good agreement between self-report and prescribing data (k=0.85,(95% Confidence Interval(CI)0.84-0.87)), similar to antihypertensives (k=0.90, (CI 0.89-0.91)) which are another commonly prescribed medicine. However, for mood stabilizers the agreement is relatively poor (k=0.42, CI 0.33-0.50). A number of medication-related and patient-level factors are analysed, with relevant past medical history being the strongest predictor of self-report sensitivity. By contrast, general intelligence is not found to be predictive. The chapter concludes that there is no simple relationship between psychiatric medication use and medication underreporting. In addition, that no patient-level factor produces greater accuracy of selfreport across all medications studied, although history of indicated illness – where this could be defined - predicted more accurate self-report. In Chapter 5 the potential of record-linkage to transform cross-sectional research studies into longitudinal studies, is investigated using the problem of quantifying antidepressant prevalence. Antidepressants are the most commonly prescribed psychiatric medication, but concerns have been raised about significant increases in their usage. By linking PIS prescribing data with the phenotypic data in a subset of GS:SFHS, the study is able to determine new measures of antidepressant prevalence, incidence, adherence, prescribing patterns with other medications, and patient-level predictors of usage. An antidepressant prevalence of almost one third of the cohort (28%, 95% CI 26.9-29.1), defined as dispensing of at least one PIS antidepressant prescription in the five-year period 2012-16, is described. This is a 36.2% increase in annual prevalence between 2010 and 2016. Incidence is calculated as 2.4(2.1-2.7)% per year, which is not significantly changed from previous estimates. The majority of antidepressant episodes (57.6%) are found to be greater than 9 months duration and adherence, using the Proportion of Days Covered (PDC) measure, is found to be generally high(69%). In time-to-antidepressant-use Cox regression analysis of the 5 years following individual GS:SFHS enrolment, predictors of new antidepressant use included: history of affective disorder; being female; physical comorbidities; higher neuroticism scores; and lower cognitive function scores. The chapter finds that this research supports the hypothesis that increased long-term use among existing (and returning) users, along with wider range of indications of antidepressants, has significantly increased the prevalence of these medications. In Chapter 6 the potential of record-linkage to identify new phenotypes for study within psychiatric cohorts is examined using the example of self-harm. Self-harm is a common and debilitating behaviour but often difficult to research as there may be unwillingness in sufferers to disclose. Using record-linkage to hospital morbidity data(SMR), I identified individuals with hospital-treated self-harm in GS:SFHS and compared these to a replication cohort drawn from UK Biobank, with self-reported hospital-treated self-harm. I further demonstrated that neuroticism, a stable personality trait associated with depression, is independently positively associated with self-harm (per Eysenck Personality Questionnaire Short-Form(EPQ-SF) unit Odds Ratio 1.2 95% Credible Interval 1.1-1.2, PFDR <0.001), even when adjusted for a range of relevant covariates. I further replicated this finding in UK Biobank (per EPQ-SF unit Odds Ratio 1.1, 1.1-1.2, pFDR <0.001). In a follow-up recontact study of GS:SFHS, STRADL, where self-reported suicidal ideation was recorded, I find that neuroticism, and the neuroticism-correlated coping style, emotion-oriented coping (EoC), were also associated with suicidal ideation in multivariable models. Therefore the chapter concludes that neuroticism is an independent predictor of hospital-treated self-harm risk, and is therefore independent of major depressive disorder in this respect, and is also (along with emotion-oriented coping), an independent predictor of suicidal ideation. Chapter 7 summarises the empirical findings presented in Chapters 4 to 6. The Chapter will also recapitulate the strengths and limitations of the record-linkage approaches used in this thesis. Finally, suggestions for future research avenues for record-linkage studies using psychiatric cohorts, and psychiatric data science as an evolving field, are discussed

A service evaluation of passive remote monitoring technology for patients in a high-secure forensic psychiatric hospital:a qualitative study

Background: Technology has the potential to remotely monitor patient safety in real-time that helps staff and without disturbing the patient. However, staff and patients’ perspectives on using passive remote monitoring within an inpatient setting is lacking. The study aim was to explore stakeholders’ perspectives about using Oxehealth passive monitoring technology within a high-secure forensic psychiatric hospital in the UK as part of a wider mixed-methods service evaluation.Methods: Semi-structured interviews were conducted with staff and patients with experience of using Oxehealth technology face-to-face within a private room in Broadmoor Hospital. We applied thematic analysis to the data of each participant group separately. Themes and sub-themes were integrated, finalised, and presented in a thematic map. Design, management, and analysis was meaningfully informed by both staff and patients.Results: Twenty-four participants were interviewed (n = 12 staff, n = 12 patients). There were seven main themes: detecting deterioration and improving health and safety, “big brother syndrome”, privacy and dignity, knowledge and understanding, acceptance, barriers to use and practice issues and future changes needed. Oxehealth technology was considered acceptable to both staff and patients if the technology was used to detect deterioration and improve patient’s safety providing patient’s privacy was not invaded. However, overall acceptance was lower when knowledge and understanding of the technology and its camera was limited. Most patients could not understand why both physical checks through bedroom windows, and Oxehealth was needed to monitor patients, whilst staff felt Oxehealth should not replace physical checks of patients as reassures staff on patient safety.Conclusions: Oxehealth technology is considered viable and acceptable by most staff and patients but there is still some concern about its possible intrusive nature. However, more support and education for new patients and staff to better understand how Oxehealth works in the short- and long-term could be introduced to further improve acceptability. A feasibility study or pilot trial to compare the impact of Oxehealth with and without physical checks may be needed

Cognitive apprenticeship in clinical practice; Can it be extended to postgraduate psychiatry training programmes?

Introduction: Postgraduate psychiatry training occurs in the workplace or situated learning settings. The Cognitive Apprenticeship Model [CAM] was introduced as an instructional model for situated learning. While undergraduate medical students' experience of the model has been tested, to our knowledge there has been no such reports from postgraduate psychiatry training. Methods: We surveyed 134 Oxford Deanery psychiatry trainees recruited between 2005 and 2013 through an online questionnaire. Respondents identified which CAM components [scaffolding, modelling coaching, articulation, reflection exploration] were the best aspects, and most needing improvement, in their clinical training. Results: Of 57 respondents, 80% were satisfied with and enjoyed [90%] their training. They recognised all individual CAM components; modelling and coaching were identified as the best methods. Exploration was identified as the one most in need of improvement. The behavioural [modelling, coaching and scaffolding] rather than the cognitive methods were identified as the best aspects of their training [54 v 35%, p < 0.001]. Conclusions: The results extend findings from undergraduate students in suggesting that the CAM is a useful model for training strategies. Greater awareness of the cognitive components may be needed. The training methods could be included as indicators of training quality in national quality assurance surveys

"What Do They Want Me To Say?" The hidden curriculum at work in the medical school selection process: a qualitative study

<p>Abstract</p> <p>Background</p> <p>There has been little study of the role of the essay question in selection for medical school. The purpose of this study was to obtain a better understanding of how applicants approached the essay questions used in selection at our medical school in 2007.</p> <p>Methods</p> <p>The authors conducted a qualitative analysis of 210 essays written as part of the medical school admissions process, and developed a conceptual framework to describe the relationships, ideas and concepts observed in the data.</p> <p>Results</p> <p>Findings of this analysis were confirmed in interviews with applicants and assessors. Analysis revealed a tension between "genuine" and "expected" responses that we believe applicants experience when choosing how to answer questions in the admissions process. A theory named "What do they want me to say?" was developed to describe the ways in which applicants modulate their responses to conform to their expectations of the selection process; the elements of this theory were confirmed in interviews with applicants and assessors.</p> <p>Conclusions</p> <p>This work suggests the existence of a "hidden curriculum of admissions" and demonstrates that the process of selection has a strong influence on applicant response. This paper suggests ways that selection might be modified to address this effect. Studies such as this can help us to appreciate the unintended consequences of admissions processes and can identify ways to make the selection process more consistent, transparent and fair.</p

Birth weight associations with DNA methylation differences in an adult population

The Developmental Origins of Health and Disease (DOHaD) theory predicts that prenatal and early life events shape adult health outcomes. Birth weight is a useful indicator of the foetal experience and has been associated with multiple adult health outcomes. DNA methylation (DNAm) is one plausible mechanism behind the relationship of birth weight to adult health. Through data linkage between Generation Scotland and historic Scottish birth cohorts, and birth records held through the NHS Information and Statistics Division, a sample of 1,757 individuals with available birth weight and DNAm data was derived. Epigenome-wide association studies (EWAS) were performed in two independently generated DNAm subgroups (n(Set1) = 1,395, n(Set2) = 362), relating adult DNAm from whole blood to birth weight. Meta-analysis yielded one genome-wide significant CpG site (p = 5.97x10(−9)), cg00966482. There was minimal evidence for attenuation of the effect sizes for the lead loci upon adjustment for numerous potential confounder variables (body mass index, educational attainment, and socioeconomic status). Associations between birth weight and epigenetic measures of biological age were also assessed. Associations between lower birth weight and higher Grim Age acceleration (p((FDR)) = 3.6x10(−3)) and shorter DNAm-derived telomere length (p((FDR)) = 1.7x10(−3)) are described, although results for three other epigenetic clocks were null. Our results provide support for an association between birth weight and DNAm both locally at one CpG site, and globally via biological ageing estimates

Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n = 4213, cases = 358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n = 3452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population-based observational studies may provide a tractable approach to achieving the necessary statistical power