Epilepsy in Bipolar Disorder: Impact on Clinical Features, Course and Outcome

Background and Aims: It is well recognized that mood disorders and epilepsy commonly co-occur. However, the relationship between epilepsy and the clinical features and course of illness in bipolar disorder (BD) is currently unknown. Here we explore the rate of epilepsy within a large sample of individuals with BD and examine bipolar illness characteristics according to the presence or absence of epilepsy. Methods: 1596 participants recruited to the Bipolar Disorder Research Network; a well-defined sample of UK subjects with a diagnosis of BD, completed a self-report questionnaire to assess lifetime history of epilepsy (Ottman et al., 2010). A subset of participants (n = 29) completed a telephone interview assessment to determine expert-confirmed epilepsy status. Lifetime clinical characteristics of illness were compared between BD subjects with and without a history of epilepsy. Results: 127 individuals (8%) screened positively for lifetime history of epilepsy. Bipolar subjects with epilepsy experienced higher rates of: suicide attempt (64.2% vs. 47.4%, p = 0.000367); panic disorder (29.6% vs. 16.1%, p = 0.001); phobias (13.6% vs. 5.7%, 0.004); alcohol abuse (18.6% vs. 10.6%, p = 0.017); and other substance abuse (10.2% vs. 4%, p = 0.009). History of suicide attempt (OR = 1.79, p = 0.013) remained significant within a multivariate model. Similar trends were observed within bipolar subjects with well-defined, expert-confirmed epilepsy (n = 29). Conclusions: Results demonstrate an increased rate of self-reported epilepsy in the BD sample, compared to the general population, and suggest differences in the clinical course of BD according to the presence of epilepsy. Comorbid epilepsy within BD may provide an attractive opportunity for subcategorising for future genetic studies, potentially identifying common underlying mechanisms.

Large-scale Roll Out of Online Prospective Measurement of Mood Symptoms in Affective Disorders

Background and Aims: True Colours is an online prospective mood-monitoring system developed at the University of Oxford to assist local patients and clinicians with monitoring course of illness in bipolar disorder. We report our initial experiences of using True Colours for research purposes in the Bipolar Disorder Research Network (BDRN; www.bdrn.org), a large research network of individuals with mood disorders spread throughout the UK. Methods: After initial piloting to ensure the practicality/acceptability of using True Colours within BDRN, we invited all BDRN participants (n = 7000) to participate in weekly True Colours ratings via three postal invitations sent over an 8-month period. Results: Following the three postal invitations, 915 individuals have so far expressed an interest in joining True Colours, and, of these, 662 (72.3%) have registered. 32 of those who registered for True Colours (5%) have so far asked to leave the system. Positive feedback from participants has focused around the ease of use and convenience of True Colours and potential clinical utility of the graphical representation of weekly mood scores. Conclusions: We have demonstrated that large-scale prospective mood monitoring for research purposes using a contemporary online approach is feasible. Challenges have included: (i) variation in participants’ technological ability; (ii) management of requests for clinical advice based on mood scores within a research setting; and, (iii) resources required to provide access and on-going support for participants using True Colours. We continue to expand recruitment to True Colours within BDRN, and plan to trial email invitations in the next phase of recruitment.

Autistic and Schizotypal Traits and Global Functioning in Bipolar I Disorder

Objective: To determine the expression of autistic and positive schizotypal traits in a large sample of adults with bipolar I disorder (BD-I), and the effect of co-occurring autistic and positive schizotypal traits on global functioning in BD-I. Method: Autistic and positive schizotypal traits were self-assessed in 797 individuals with BD-I recruited by the Bipolar Disorder Research Network. Differences in global functioning (rated using the Global Assessment Scale) during lifetime worst depressive and manic episodes (GASD and GASM respectively) were calculated in groups with high/low autistic and positive schizotypal traits. Regression analyses assessed the interactive effect of autistic and positive schizotypal traits on global functioning. Results: 47.2% (CI=43.7-50.7%) showed clinically significant levels of autistic traits, and 23.22% (95% CI=20.29-26.14) showed clinically significant levels of positive schizotypal traits. In the worst episode of mania, the high autistic, high positive schizotypal group had better global functioning compared to the other groups. Individual differences analyses showed that high levels of co-occurring traits were associated with better global functioning in both mood states. Limitations: Autistic and schizotypal traits were assessed using self-rated questionnaires. Conclusions: Expression of autistic and schizotypal traits in adults with BD-I is prevalent, and may be important to predict illness aetiology, prognosis, and diagnostic practices in this population. Future work should focus on replicating these findings in independent samples, and on the biological and/or psychosocial mechanisms underlying better global functioning in those who have high levels of both autistic and positive schizotypal traits.

History of Premenstrual Mood Change and Postpartum Episodes are Associated with Perimenopausal Episodes in Women with Bipolar Disorder

Background and Aims: Reproductive life events are potential triggers of mood episodes in women with bipolar disorder. We aimed to establish whether a history of premenstrual mood change and postpartum episodes are associated with perimenopausal episodes in women who have bipolar disorder. Methods: Participants were 339 post-menopausal women with DSM-IV bipolar disorder recruited into the Bipolar Disorder Research Network (www.bdrn.org). Women self-reported presence (N = 200) or absence (N = 139) of an illness episode during the perimenopausal period. History of premenstrual mood change was measured using the self-report Premenstrual Symptoms Screening Tool (PSST), and history of postpartum episodes was measured via semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry, SCAN) and inspection of case-notes. Results: History of a postpartum episode within 6 months of delivery (OR = 2.13, p = 0.03) and history of moderate/severe premenstrual syndrome (OR = 6.33, p < 0.001) were significant predictors of the presence of a perimenopausal episode, even after controlling for demographic factors. When we narrowed the definition of premenstrual mood change to premenstrual dysphoric disorder, it remained significant (OR = 2.68, p = 0.007). Conclusions: Some women who have bipolar disorder may be particularly sensitive to reproductive life events. Previous mood episodes in relation to the female reproductive lifecycle may help clinicians predict individual risk for women with bipolar disorder approaching the menopause. There is a need for prospective longitudinal studies of women with bipolar disorder providing frequent contemporaneous ratings of their mood to overcome the limitations of retrospective self-report data.

Identifying Risk Factors for Postpartum Mood Episodes in Bipolar Disorder – A UK Prospective Study

Background and Aims: Women with bipolar disorder are vulnerable to episodes postpartum, but risk factors are poorly understood. We are exploring risk factors for postpartum mood episodes in women with bipolar disorder using a prospective longitudinal design. Methods: Pregnant women with lifetime DSM-IV bipolar disorder are being recruited into the Bipolar Disorder Research Network (www.BDRN.org). Baseline assessments during late pregnancy include lifetime psychopathology and potential risk factors for perinatal episodes such as medication use, sleep, obstetric factors, and psychosocial factors. Blood samples are taken for genetic analysis. Perinatal psychopathology is assessed via follow-up interview at 12-weeks postpartum. Interview data are supplemented by clinician questionnaires and case-note review. Potential risk factors will be compared between women who experience perinatal episodes and those who remain well. Results: 80 participants have been recruited to date. 32/61 (52%) women had a perinatal recurrence by follow-up. 16 (26%) had onset in pregnancy. 21 (34%) had postpartum onset, 19 (90%) within 6-weeks of delivery: 11 (18%) postpartum psychosis, 5 (8%) postpartum hypomania, 5 (8%) postpartum depression. Postpartum relapse was more frequent in women with bipolar-I than bipolar-II disorder (45% vs 17%). 62% women with postpartum relapse took prophylactic medication peripartum and almost all received care from secondary psychiatric services (95%). Conclusions: Rate of postpartum relapse is high, despite most women receiving specialist care and medication perinatally. A larger sample size will allow us to examine potential risk factors for postpartum episodes, which will assist in providing accurate and personalised advice to women with bipolar disorder who are considering pregnancy.

Postpartum Psychosis in Bipolar Disorder: No Association with Personality, Cognitive Style and Temperament

Background and Aims: Bipolar disorder has been associated with a number of personality traits, cognitive styles and affective temperaments. Women who have bipolar disorder are at increased risk of experiencing postpartum psychosis, however no previous research has investigated these traits in relationship to postpartum episodes. Our aim was to establish whether aspects of personality, cognitive style and affective temperament, that have been associated with bipolar disorder, confer vulnerability to postpartum psychosis over and above their known association with bipolar disorder. Methods: Participants were 552 parous women with DSM-IV bipolar I disorder recruited into the Bipolar Disorder Research Network (www.bdrn.org). Postpartum psychosis group: lifetime episode of postpartum psychosis within 6 weeks of delivery (N = 284). Non-postpartum psychosis group: no history of any perinatal mood episodes (N = 268). Bipolar disorder-associated personality traits (neuroticism, extraversion, schizotypy and impulsivity), cognitive styles (low self-esteem and dysfunctional attitudes) and affective temperaments were measured using well validated self-report questionnaire measures. Results: After controlling for key demographic, clinical and pregnancy-related variables, and measures of current mood state, there were no statistically significant differences between the postpartum psychosis group and non-postpartum psychosis group on any of the personality, cognitive style or affective temperament measures. Conclusions: Personality traits, cognitive styles and affective temperaments associated with the bipolar disorder diathesis in general were not associated with the onset of postpartum psychosis specifically. We have found no evidence that these traits should play a key role when evaluating risk of postpartum psychosis in women with bipolar I disorder considering pregnancy.

The Influence of Borderline Personality Traits on the Course of Bipolar Disorder

Background and Aims: Bipolar disorder and borderline personality disorder are commonly comorbid. Borderline personality disorder is diagnosed categorically, but personality pathology may be better characterised dimensionally. The impact of borderline personality traits (not diagnosis) on the course of bipolar disorder is unknown. We examined the presence and severity of borderline personality traits in a large UK sample of bipolar disorder, and the impact of these traits on illness course. Methods: Borderline Evaluation of Severity over Time (BEST) was used to measure presence and severity of borderline traits in 1447 individuals with DSM-IV bipolar I disorder (n = 1008) and bipolar II disorder (n = 439) recruited into the Bipolar Disorder Research Network (www.bdrn.org). Clinical course was measured via semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry) and case-notes. Results: BEST score was higher in bipolar II than bipolar I (36 v 27, p < 0.001) and 9/12 individual BEST traits were significantly more common in bipolar II than bipolar I. Within both bipolar I and bipolar II higher BEST score was associated with younger age of bipolar onset (p < 0.001), history of alcohol misuse (p < 0.010), and history of suicide attempt (p < 0.001). Conclusions: Borderline personality traits are common in bipolar disorder, and more severe in bipolar II than bipolar I disorder. Borderline trait severity was associated with more severe bipolar illness course; younger age of onset, alcohol misuse and suicidal behaviour. Clinicians should be vigilant for borderline personality traits irrespective of whether criteria for diagnosis are met, particularly in those with bipolar II disorder and younger age of bipolar onset.

Affective Instability, Childhood Trauma and Major Affective Disorders

Background and Aims Affective instability (AI), childhood trauma, and mental illness are linked, but evidence in affective disorders is limited, despite both AI and childhood trauma being associated with poorer outcomes. Aims were to compare AI levels in bipolar disorder I (BPI) and II (BPII), and major depressive disorder recurrent (MDDR), and to examine the association of AI and childhood trauma within each diagnostic group. Methods AI, measured using the Affective Lability Scale (ALS), was compared between people with DSM-IV BPI (n = 923), BPII (n = 363) and MDDR (n = 207) accounting for confounders and current mood. Regression modelling was used to examine the association between AI and childhood traumas in each diagnostic group. Results ALS scores in descending order were BPII, BPI, MDDR, and differences between groups were significant (p < 0.05). Within the BPI group any childhood abuse (p = 0.021), childhood physical abuse (p = 0.003) and the death of a close friend in childhood (p = 0.002) were significantly associated with higher ALS score but no association was found between childhood trauma and AI in BPII and MDDR. Conclusions AI is an important dimension in bipolar disorder independent of current mood state. There is a strong link between childhood traumatic events and AI levels in BPI and this may be one way in which exposure and disorder are linked. Clinical interventions targeting AI in people who have suffered significant childhood trauma could potentially change the clinical course of bipolar disorder.

Autistic and Schizotypal Traits and Global Functioning in Bipolar Disorder

Background and Aims To determine the expression of autistic and positive schizotypal traits in a large sample of adults with bipolar disorder (BD), and the effect of co-occurring autistic and positive schizotypal traits on global functioning in BD. Methods Autistic and positive schizotypal traits were assessed in 797 individuals with BD recruited by the Bipolar Disorder Research Network (BDRN), using the Autism-Spectrum Quotient and Kings Schizotypy Questionnaire (KSQ), respectively. Differences in global functioning (rated using the Global Assessment Scale) during lifetime worst depressive and manic episodes (GASD and GASM respectively) were calculated in groups with high/low autistic and positive schizotypal traits. Regression analyses assessed the interactive effect of autistic and positive schizotypal traits on global functioning. Results 47.2% (CI = 43.7–50.7%) showed clinically significant levels of autistic traits. Mean of sample on the KSQ-Positive scale was 11.98 (95% CI: 11.33–12.62). In the worst episode of mania, the high autistic, high positive schizotypal group had better global functioning than the low autistic, low positive schizotypal group (mean difference = 3.72, p = 0.004). High levels of co-occurring traits were associated with better global functioning in both mood states in individuals with a history of psychosis (GASM: p < 0.001; GASD: p = 0.055). Conclusions Expression of autistic and schizotypal traits in adults with BD is prevalent, and may be important to predict course of illness, prognosis, and in devising individualised therapies. Future work should focus on replicating these findings in independent samples, and on the biological and/or psychosocial mechanisms underlying better global functioning in those who have high levels of both autistic and positive schizotypal traits.

Mania Triggered by Sleep Loss: Implications for Postpartum Psychosis

Background and Aims To examine whether a history of mood episodes triggered by sleep loss is associated with (1) postpartum psychosis (PP) and (2) more broadly-defined postpartum mood episodes that included postnatal depression (PND), in women with bipolar disorder (BD). Methods Participants were 622 parous women with a diagnosis of bipolar-I disorder recruited in the UK to the Bipolar Disorder Research Network. Diagnosis and perinatal episodes were assessed via interview and case note data. Women were also asked during the interview whether episodes of mania and/or depression were triggered by sleep loss. We compared the rates of PP and PND within women who did and did not endorse sleep loss as a trigger of mood episodes. Results Women who reported that their episodes of mania were usually triggered by sleep loss were twice as likely to have experienced an episode of PP (OR = 2.00, 95% CI = 1.20–3.36) than women who did not report this. This effect remained significant when controlling for clinical and demographic factors. We found no significant associations between depression triggered by sleep loss and PP. Analyses in which we defined postpartum episodes at a broader level to include both PP and PND were not significant. Conclusions In pregnant women with BD, a history of mania following sleep loss could be a potential marker of vulnerability to severe postpartum episodes. Further study in prospective samples is required in order to confirm these findings, which may have important implications for understanding the aetiology of PP and of mood disorders more generally.

Polygenic Interactions with Environmental Adversity in the Aetiology of Major Depressive Disorder

Major depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD. The RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them. PRS significantly predicted depression, explaining 1.1% of variance in phenotype (p = 1.9 × 10−6). SLEs and CT were also associated with MDD status (p = 2.19 × 10−4 and p = 5.12 × 10−20, respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p = 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples. CT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.

Seasonality Shows Evidence for Polygenic Architecture and Genetic Correlation With Schizophrenia and Bipolar Disorder

OBJECTIVE: To test common genetic variants for association with seasonality (seasonal changes in mood and behavior) and to investigate whether there are shared genetic risk factors between psychiatric disorders and seasonality. METHOD: Genome-wide association studies (GWASs) were conducted in Australian (between 1988 and 1990 and between 2010 and 2013) and Amish (between May 2010 and December 2011) samples in whom the Seasonal Pattern Assessment Questionnaire (SPAQ) had been administered, and the results were meta-analyzed in a total sample of 4,156 individuals. Genetic risk scores based on results from prior large GWAS studies of bipolar disorder, major depressive disorder (MDD), and schizophrenia were calculated to test for overlap in risk between psychiatric disorders and seasonality. RESULTS: The most significant association was with rs11825064 (P = 1.7 × 10⁻⁶, β = 0.64, standard error = 0.13), an intergenic single nucleotide polymorphism (SNP) found on chromosome 11. The evidence for overlap in risk factors was strongest for schizophrenia and seasonality, with the schizophrenia genetic profile scores explaining 3% of the variance in log-transformed global seasonality scores. Bipolar disorder genetic profile scores were also associated with seasonality, although at much weaker levels (minimum P value = 3.4 × 10⁻³), and no evidence for overlap in risk was detected between MDD and seasonality. CONCLUSIONS: Common SNPs of large effect most likely do not exist for seasonality in the populations examined. As expected, there were overlapping genetic risk factors for bipolar disorder (but not MDD) with seasonality. Unexpectedly, the risk for schizophrenia and seasonality had the largest overlap, an unprecedented finding that requires replication in other populations and has potential clinical implications considering overlapping cognitive deficits in seasonal affective disorders and schizophrenia.

Improving the Psychometric Utility of the Hypomania Checklist (HCL-32): a Rasch Analysis Approach

Background The HCL-32 is a widely-used screening questionnaire for hypomania. We aimed to use a Rasch analysis approach to (i) evaluate the measurement properties, principally unidimensionality, of the HCL-32, and (ii) generate a score table to allow researchers to convert raw HCL-32 scores into an interval-level measurement which will be more appropriate for statistical analyses. Methods Subjects were part of the Bipolar Disorder Research Network (BDRN) study with DSM-IV bipolar disorder (n=389). Multidimensionality was assessed using the Rasch fit statistics and principle components analysis of the residuals (PCA). Item invariance (differential item functioning, DIF) was tested for gender, bipolar diagnosis and current mental state. Item estimates and reliabilities were calculated. Results Three items (29, 30, 32) had unacceptable fit to the Rasch unidimensional model. Item 14 displayed significant DIF for gender and items 8 and 17 for current mental state. Item estimates confirmed that not all items measure hypomania equally. Limitations This sample was recruited as part of a large ongoing genetic epidemiology study of bipolar disorder and may not be fully representative of the broader clinical population of individuals with bipolar disorder. Conclusion The HCL-32 is unidimensional in practice, but measurements may be further strengthened by the removal of four items. Re-scored linear measurements may be more appropriate for clinical research.

Identifying health inequalities in individuals with major mental illness (MMI) using routine data

Abstract and Summary of Thesis: Background: Individuals with Major Mental Illness (such as schizophrenia and bipolar disorder) experience increased rates of physical health comorbidity compared to the general population. They also experience inequalities in access to certain aspects of healthcare. This ultimately leads to premature mortality. Studies detailing patterns of physical health comorbidity are limited by their definitions of comorbidity, single disease approach to comorbidity and by the study of heterogeneous groups. To date the investigation of possible sources of healthcare inequalities experienced by individuals with Major Mental Illness (MMI) is relatively limited. Moreover studies detailing the extent of premature mortality experienced by individuals with MMI vary both in terms of the measure of premature mortality reported and age of the cohort investigated, limiting their generalisability to the wider population. Therefore local and national data can be used to describe patterns of physical health comorbidity, investigate possible reasons for health inequalities and describe mortality rates. These findings will extend existing work in this area. Aims and Objectives: To review the relevant literature regarding: patterns of physical health comorbidity, evidence for inequalities in physical healthcare and evidence for premature mortality for individuals with MMI. To examine the rates of physical health comorbidity in a large primary care database and to assess for evidence for inequalities in access to healthcare using both routine primary care prescribing data and incentivised national Quality and Outcome Framework (QOF) data. Finally to examine the rates of premature mortality in a local context with a particular focus on cause of death across the lifespan and effect of International Classification of Disease Version 10 (ICD 10) diagnosis and socioeconomic status on rates and cause of death. Methods: A narrative review of the literature surrounding patterns of physical health comorbidity, the evidence for inequalities in physical healthcare and premature mortality in MMI was undertaken. Rates of physical health comorbidity and multimorbidity in schizophrenia and bipolar disorder were examined using a large primary care dataset (Scottish Programme for Improving Clinical Effectiveness in Primary Care (SPICE)). Possible inequalities in access to healthcare were investigated by comparing patterns of prescribing in individuals with MMI and comorbid physical health conditions with prescribing rates in individuals with physical health conditions without MMI using SPICE data. Potential inequalities in access to health promotion advice (in the form of smoking cessation) and prescribing of Nicotine Replacement Therapy (NRT) were also investigated using SPICE data. Possible inequalities in access to incentivised primary healthcare were investigated using National Quality and Outcome Framework (QOF) data. Finally a pre-existing case register (Glasgow Psychosis Clinical Information System (PsyCIS)) was linked to Scottish Mortality data (available from the Scottish Government Website) to investigate rates and primary cause of death in individuals with MMI. Rate and primary cause of death were compared to the local population and impact of age, socioeconomic status and ICD 10 diagnosis (schizophrenia vs. bipolar disorder) were investigated. Results: Analysis of the SPICE data found that sixteen out of the thirty two common physical comorbidities assessed, occurred significantly more frequently in individuals with schizophrenia. In individuals with bipolar disorder fourteen occurred more frequently. The most prevalent chronic physical health conditions in individuals with schizophrenia and bipolar disorder were: viral hepatitis (Odds Ratios (OR) 3.99 95% Confidence Interval (CI) 2.82-5.64 and OR 5.90 95% CI 3.16-11.03 respectively), constipation (OR 3.24 95% CI 3.01-3.49 and OR 2.84 95% CI 2.47-3.26 respectively) and Parkinson’s disease (OR 3.07 95% CI 2.43-3.89 and OR 2.52 95% CI 1.60-3.97 respectively). Both groups had significantly increased rates of multimorbidity compared to controls: in the schizophrenia group OR for two comorbidities was 1.37 95% CI 1.29-1.45 and in the bipolar disorder group OR was 1.34 95% CI 1.20-1.49. In the studies investigating inequalities in access to healthcare there was evidence of: under-recording of cardiovascular-related conditions for example in individuals with schizophrenia: OR for Atrial Fibrillation (AF) was 0.62 95% CI 0.52 - 0.73, for hypertension 0.71 95% CI 0.67 - 0.76, for Coronary Heart Disease (CHD) 0.76 95% CI 0.69 - 0.83 and for peripheral vascular disease (PVD) 0.83 95% CI 0.72 - 0.97. Similarly in individuals with bipolar disorder OR for AF was 0.56 95% CI 0.41-0.78, for hypertension 0.69 95% CI 0.62 - 0.77 and for CHD 0.77 95% CI 0.66 - 0.91. There was also evidence of less intensive prescribing for individuals with schizophrenia and bipolar disorder who had comorbid hypertension and CHD compared to individuals with hypertension and CHD who did not have schizophrenia or bipolar disorder. Rate of prescribing of statins for individuals with schizophrenia and CHD occurred significantly less frequently than in individuals with CHD without MMI (OR 0.67 95% CI 0.56-0.80). Rates of prescribing of 2 or more anti-hypertensives were lower in individuals with CHD and schizophrenia and CHD and bipolar disorder compared to individuals with CHD without MMI (OR 0.66 95% CI 0.56-0.78 and OR 0.55 95% CI 0.46-0.67, respectively). Smoking was more common in individuals with MMI compared to individuals without MMI (OR 2.53 95% CI 2.44-2.63) and was particularly increased in men (OR 2.83 95% CI 2.68-2.98). Rates of ex-smoking and non-smoking were lower in individuals with MMI (OR 0.79 95% CI 0.75-0.83 and OR 0.50 95% CI 0.48-0.52 respectively). However recorded rates of smoking cessation advice in smokers with MMI were significantly lower than the recorded rates of smoking cessation advice in smokers with diabetes (88.7% vs. 98.0%, p<0.001), smokers with CHD (88.9% vs. 98.7%, p<0.001) and smokers with hypertension (88.3% vs. 98.5%, p<0.001) without MMI. The odds ratio of NRT prescription was also significantly lower in smokers with MMI without diabetes compared to smokers with diabetes without MMI (OR 0.75 95% CI 0.69-0.81). Similar findings were found for smokers with MMI without CHD compared to smokers with CHD without MMI (OR 0.34 95% CI 0.31-0.38) and smokers with MMI without hypertension compared to smokers with hypertension without MMI (OR 0.71 95% CI 0.66-0.76). At a national level, payment and population achievement rates for the recording of body mass index (BMI) in MMI was significantly lower than the payment and population achievement rates for BMI recording in diabetes throughout the whole of the UK combined: payment rate 92.7% (Inter Quartile Range (IQR) 89.3-95.8 vs. 95.5% IQR 93.3-97.2, p<0.001 and population achievement rate 84.0% IQR 76.3-90.0 vs. 92.5% IQR 89.7-94.9, p<0.001 and for each country individually: for example in Scotland payment rate was 94.0% IQR 91.4-97.2 vs. 96.3% IQR 94.3-97.8, p<0.001. Exception rate was significantly higher for the recording of BMI in MMI than the exception rate for BMI recording in diabetes for the UK combined: 7.4% IQR 3.3-15.9 vs. 2.3% IQR 0.9-4.7, p<0.001 and for each country individually. For example in Scotland exception rate in MMI was 11.8% IQR 5.4-19.3 compared to 3.5% IQR 1.9-6.1 in diabetes. Similar findings were found for Blood Pressure (BP) recording: across the whole of the UK payment and population achievement rates for BP recording in MMI were also significantly reduced compared to payment and population achievement rates for the recording of BP in chronic kidney disease (CKD): payment rate: 94.1% IQR 90.9-97.1 vs.97.8% IQR 96.3-98.9 and p<0.001 and population achievement rate 87.0% IQR 81.3-91.7 vs. 97.1% IQR 95.5-98.4, p<0.001. Exception rates again were significantly higher for the recording of BP in MMI compared to CKD (6.4% IQR 3.0-13.1 vs. 0.3% IQR 0.0-1.0, p<0.001). There was also evidence of differences in rates of recording of BMI and BP in MMI across the UK. BMI and BP recording in MMI were significantly lower in Scotland compared to England (BMI:-1.5% 99% CI -2.7 to -0.3%, p<0.001 and BP: -1.8% 99% CI -2.7 to -0.9%, p<0.001). While rates of BMI and BP recording in diabetes and CKD were similar in Scotland compared to England (BMI: -0.5 99% CI -1.0 to 0.05, p=0.004 and BP: 0.02 99% CI -0.2 to 0.3, p=0.797). Data from the PsyCIS cohort showed an increase in Standardised Mortality Ratios (SMR) across the lifespan for individuals with MMI compared to the local Glasgow and wider Scottish populations (Glasgow SMR 1.8 95% CI 1.6-2.0 and Scotland SMR 2.7 95% CI 2.4-3.1). Increasing socioeconomic deprivation was associated with an increased overall rate of death in MMI (350.3 deaths/10,000 population/5 years in the least deprived quintile compared to 794.6 deaths/10,000 population/5 years in the most deprived quintile). No significant difference in rate of death for individuals with schizophrenia compared with bipolar disorder was reported (6.3% vs. 4.9%, p=0.086), but primary cause of death varied: with higher rates of suicide in individuals with bipolar disorder (22.4% vs. 11.7%, p=0.04). Discussion: Local and national datasets can be used for epidemiological study to inform local practice and complement existing national and international studies. While the strengths of this thesis include the large data sets used and therefore their likely representativeness to the wider population, some limitations largely associated with using secondary data sources are acknowledged. While this thesis has confirmed evidence of increased physical health comorbidity and multimorbidity in individuals with MMI, it is likely that these findings represent a significant under reporting and likely under recognition of physical health comorbidity in this population. This is likely due to a combination of patient, health professional and healthcare system factors and requires further investigation. Moreover, evidence of inequality in access to healthcare in terms of: physical health promotion (namely smoking cessation advice), recording of physical health indices (BMI and BP), prescribing of medications for the treatment of physical illness and prescribing of NRT has been found at a national level. While significant premature mortality in individuals with MMI within a Scottish setting has been confirmed, more work is required to further detail and investigate the impact of socioeconomic deprivation on cause and rate of death in this population. It is clear that further education and training is required for all healthcare staff to improve the recognition, diagnosis and treatment of physical health problems in this population with the aim of addressing the significant premature mortality that is seen. Conclusions: Future work lies in the challenge of designing strategies to reduce health inequalities and narrow the gap in premature mortality reported in individuals with MMI. Models of care that allow a much more integrated approach to diagnosing, monitoring and treating both the physical and mental health of individuals with MMI, particularly in areas of social and economic deprivation may be helpful. Strategies to engage this “hard to reach” population also need to be developed. While greater integration of psychiatric services with primary care and with specialist medical services is clearly vital the evidence on how best to achieve this is limited. While the National Health Service (NHS) is currently undergoing major reform, attention needs to be paid to designing better ways to improve the current disconnect between primary and secondary care. This should then help to improve physical, psychological and social outcomes for individuals with MMI.