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
