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.

Acute phase proteins and biomarkers for health in chickens

Acute phase proteins (APPs) are proteins synthesised predominantly in the liver, whose plasma concentrations increase (positive APP) or decrease (negative APP) as a result of infection, inflammation, trauma and tissue injury. They also change as a result of the introduction of immunogens such as bacterial lipopolysaccharide (LPS), turpentine and vaccination. While publications on APPs in chickens are numerous, the limited availability of anti-sera and commercial ELISAs has resulted in a lot of information on only a few APPs. Disease is a threat to the poultry industry, as pathogens have the potential to evolve, spread and cause rapid onset of disease that is detrimental to the welfare of birds. Low level, sub-acute disease with non-specific, often undiagnosed causes can greatly affect bird health and growth and impact greatly on productivity and profitability. Developing and validating methods to measure and characterise APPs in chickens will allow these proteins to be used diagnostically for monitoring flock health. Using immune parameters such as APPs that correlate with disease resistance or improvements in production and welfare will allow the use of APPs as selection parameters for breeding to be evaluated. For APPs to be useful parameters on which to evaluate chicken health, information on normal APP concentrations is required. Ceruloplasmin (Cp) and PIT54 concentrations were found to be much lower in healthy birds form commercial production farms than the reported normal values obtained from the literature. These APPs were found to be significantly higher in culled birds from a commercial farm and Cp, PIT54 and ovotransferrin (Ovt) were significantly higher in birds classified as having obvious gait defects. Using quantitative shotgun proteomics to identify the differentially abundant proteins between three pools: highly acute phase (HAP), acute phase (AP) and non-acute phase (NAP), generated data from which a selection of proteins, based on the fold difference between the three pools was made. These proteins were targeted on a individual samples alongside proteins known to be APPs in chickens or other species: serum amyloid A (SAA), C-reactive protein (CRP), Ovt, apolipoprotein A-I (apo-AI), transthyretin (Ttn), haemopexin (Hpx) and PIT54. Together with immunoassay data for SAA, Ovt, alpha-1-acid glycoprotein (AGP) and Cp the results of this research reveal that SAA is the only major APP in chickens. Ovotransferrin and AGP behave as moderate APPs while PIT54 and Cp are minor APPs. Haemopexin was not significantly different between the three acute phase groups. Apolipoprotein AI and Ttn were significantly lower in the HAP and AP groups and as such can be classed as negative APPs. In an effort to identify CRP, multiple anti-sera cross reacting with CRP from other species were used and a phosphorylcholine column known to affinity purify CRP were used. Enriched fractions containing low molecular weight proteins, elutions from the affinity column together with HAP, AP and NAP pooled samples were applied to a Q-Exactive Hybrid Quadrupole–Orbitrap mass spectrometer (Thermo Scientific) for Shotgun analysis and CRP was not identified. It would appear that CRP is not present as a plasma protein constitutively or during an APR in chickens and as such is not an APP in this species. Of the proteins targeted as possible novel biomarkers of the APR in chickens mannan binding lectin associated serine protease-2, α-2-HS-glycoprotein (fetuin) and major facilitator superfamily domain-containing protein 10 were reduced in abundance in the HAP group, behaving as negative biomarkers. Myeloid protein and putative ISG(12)2 were positively associated with the acute phase being significantly higher in the HAP and AP groups. The protein cathepsin D was significantly higher in both HAP and AP compared to the NAP indicating that of all the proteins targeted, this appears to have the most potential as a biomarker of the acute phase, as it was significantly increased in the AP as well as the HAP group. To evaluate APPs and investigate biomarkers of intestinal health, a study using re-used poultry litter was undertaken. The introduction of litter at 12 days of age did not significantly increase any APPs measured using immunoassays and quantitative proteomics at 3, 6 and 10 days post introduction. While no APP was found to be significantly different between the challenged and control groups at anytime point, the APPs AGP, SAA and Hpx did increase over time in all birds. The protein apolipoprotein AIV (apo-AIV) was targeted as a possible APP and because of its reported role in controlling satiety. An ELISA was developed, successfully validated and used to measure apo-AIV in this study. While no significant differences in apo-AIV plasma concentrations between challenged and control groups were identified apo-AIV plasma concentrations did change significantly between certain time points in challenged and control groups. Apoliporotein AIV does not appear to behave as an APP in chickens, as it was not significantly different between acute phase groups. The actin associated proteins villin and gelsolin were investigated as possible biomarkers of intestinal health. Villin was found not to be present in the plasma of chickens and as such not a biomarker target. Gelsolin was found not to be differentially expressed during the acute phase or as a result of intestinal challenge. Finally a proteomic approach was undertaken to investigate gastrocnemius tendon (GT) rupture in broiler chickens with a view of elucidating to and identify proteins associated with risk of rupture. A number of proteins were found to be differentially expressed between tendon pools and further work would enable further detailing of these findings. In conclusion this work has made a number of novel findings and addressed a number of data poor areas. The area of chicken APPs research has stagnated over the last 15 years with publications becoming repetitive and reliant on a small number of immunoassays. This work has sought to characterise the classic APPs in chickens, and use a quantitative proteomic approach to measure and categorise them. This method was also used to take a fresh approach to biomarker identification for both the APR and intestinal health. The development and validation of assays for Ovt and apo-AIV and the shotgun data mean that these proteins can be further characterised in chickens with a view of applying their measurement to diagnostics and selective breeding programs.

Reactive Attachment Disorder in infants in foster care and associated mental health and cognitive functioning

Background: Reactive attachment disorder (RAD) has been described as one of the least researched and most poorly understood psychiatric disorders (Chaffin et al., 2006). Despite this, given what is known about maltreatment and attachment, it is likely that RAD has profound consequences for child development. Very little is known about the prevalence and stability of RAD symptoms over time. Until recently it has been difficult to investigate the presence of RAD due to limited measures for informing a diagnosis. However this study utilised a new observational tool Method: A cross sectional study design with a one-year follow-up explored RAD symptoms in maltreated infants in Scotland (n=55, age range= 16-62 months) and associated mental health and cognitive functioning. The study utilised the Rating of Inhibited Attachment Behavior Scale (Corval, et al., unpublished 2014) that has recently been developed by experts in the field along side The Disturbances of Attachment Interview (Smyke & Zeanah, 1999). Children were recruited as part of the BeST trial, whereby all infants who came in to the care of the local authority in Glasgow due to child protection concerns were invited to participate. The study sample was representative of the larger pool of data in terms of age, gender, mental health and cognitive functioning. Results: The sample was found to be representative of the population of maltreated children from which it was derived. Prevalence of RAD was found to be 7.3% (n=3, 95% CI [0.43 – 14.17]) at T1, when children are first placed in to foster care. At T2, following one year in improved care conditions, 4.3% (n=2, 95% CI [below 0 – 10.16]) met a borderline RAD diagnosis. Levels of observed RAD symptoms decreased significantly at T2 in comparison to T1 but carer reported symptoms of RAD did not. Children whose RAD symptoms did not improve were found to be significantly older and showed less prosocial behaviour. RAD was associated with some mental health and cognitive difficulties. Lower Verbal IQ and unexpectedly, prosocial behaviour were found to predict RAD symptoms. Conclusions: The preliminary findings have added to the developing understanding of RAD symptoms and associated difficulties however further exploration of RAD in larger samples would be invaluable.

Men's experiences of receiving objective feedback on physical activity and other indicators of health risk, within the context of a gender-sensitised weight loss intervention

Receiving personalised feedback on body mass index and other health risk indicators may prompt behaviour change. Few studies have investigated men’s reactions to receiving objective feedback on such measures and detailed information on physical activity and sedentary time. The aim of my research was to understand the meanings different forms of objective feedback have for overweight/obese men, and to explore whether these varied between groups. Participants took part in Football Fans in Training, a gender-sensitised, weight loss programme delivered via Scottish Professional Football Clubs. Semi-structured interviews were conducted with 28 men, purposively sampled from four clubs to investigate the experiences of men who achieved and did not achieve their 5% weight loss target. Data were analysed using the principles of thematic analysis and interpreted through Self-Determination Theory and sociological understandings of masculinity. Several factors were vital in supporting a ‘motivational climate’ in which men could feel ‘at ease’ and adopt self-regulation strategies: the ‘place’ was described as motivating, whereas the ‘people’ (other men ‘like them’; fieldwork staff; community coaches) provided supportive and facilitative roles. Men who achieved greater weight loss were more likely to describe being motivated as a consequence of receiving information on their objective health risk indicators. They continued using self-monitoring technologies after the programme as it was enjoyable; or they had redefined themselves by integrating new-found activities into their lives and no longer relied on external technologies/feedback. They were more likely to see post-programme feedback as confirmation of success, so long as they could fully interpret the information. Men who did not achieve their 5% weight loss reported no longer being motivated to continue their activity levels or self-monitor them with a pedometer. Social support within the programme appeared more important. These men were also less positive about objective post-programme feedback which confirmed their lack of success and had less utility as a motivational tool. Providing different forms of objective feedback to men within an environment that has intrinsic value (e.g. football club setting) and congruent with common cultural constructions of masculinity, appears more conducive to health behaviour change.

Estimation of whole body muscle, adipose/fat mass, validation in health and during weight loss. Development of prediction equations using MRI as reference method

Background: Body composition is affected by diseases, and affects responses to medical treatments, dosage of medicines, etc., while an abnormal body composition contributes to the causation of many chronic diseases. While we have reliable biochemical tests for certain nutritional parameters of body composition, such as iron or iodine status, and we have harnessed nuclear physics to estimate the body’s content of trace elements, the very basic quantification of body fat content and muscle mass remains highly problematic. Both body fat and muscle mass are vitally important, as they have opposing influences on chronic disease, but they have seldom been estimated as part of population health surveillance. Instead, most national surveys have merely reported BMI and waist, or sometimes the waist/hip ratio; these indices are convenient but do not have any specific biological meaning. Anthropometry offers a practical and inexpensive method for muscle and fat estimation in clinical and epidemiological settings; however, its use is imperfect due to many limitations, such as a shortage of reference data, misuse of terminology, unclear assumptions, and the absence of properly validated anthropometric equations. To date, anthropometric methods are not sensitive enough to detect muscle and fat loss. Aims: The aim of this thesis is to estimate Adipose/fat and muscle mass in health disease and during weight loss through; 1. evaluating and critiquing the literature, to identify the best-published prediction equations for adipose/fat and muscle mass estimation; 2. to derive and validate adipose tissue and muscle mass prediction equations; and 3.to evaluate the prediction equations along with anthropometric indices and the best equations retrieved from the literature in health, metabolic illness and during weight loss. Methods: a Systematic review using Cochrane Review method was used for reviewing muscle mass estimation papers that used MRI as the reference method. Fat mass estimation papers were critically reviewed. Mixed ethnic, age and body mass data that underwent whole body magnetic resonance imaging to quantify adipose tissue and muscle mass (dependent variable) and anthropometry (independent variable) were used in the derivation/validation analysis. Multiple regression and Bland-Altman plot were applied to evaluate the prediction equations. To determine how well the equations identify metabolic illness, English and Scottish health surveys were studied. Statistical analysis using multiple regression and binary logistic regression were applied to assess model fit and associations. Also, populations were divided into quintiles and relative risk was analysed. Finally, the prediction equations were evaluated by applying them to a pilot study of 10 subjects who underwent whole-body MRI, anthropometric measurements and muscle strength before and after weight loss to determine how well the equations identify adipose/fat mass and muscle mass change. Results: The estimation of fat mass has serious problems. Despite advances in technology and science, prediction equations for the estimation of fat mass depend on limited historical reference data and remain dependent upon assumptions that have not yet been properly validated for different population groups. Muscle mass does not have the same conceptual problems; however, its measurement is still problematic and reference data are scarce. The derivation and validation analysis in this thesis was satisfactory, compared to prediction equations in the literature they were similar or even better. Applying the prediction equations in metabolic illness and during weight loss presented an understanding on how well the equations identify metabolic illness showing significant associations with diabetes, hypertension, HbA1c and blood pressure. And moderate to high correlations with MRI-measured adipose tissue and muscle mass before and after weight loss. Conclusion: Adipose tissue mass and to an extent muscle mass can now be estimated for many purposes as population or groups means. However, these equations must not be used for assessing fatness and categorising individuals. Further exploration in different populations and health surveys would be valuable.