The XMM Cluster Survey: a new cluster catalogue and applications

In this thesis, we present the XMM Cluster Survey Second Data Release (XCS-DR2) and use it to test possible spectroscopic biases, fit scaling relations, and find massive, relaxed galaxy clusters. XCS finds clusters in the XMM public archive. The new cluster candidate list includes 15,642 objects found in the 688 square degrees of sky suitable for cluster detection. XCS-DR2 is the largest X-ray selected cluster catalogue to date. It contains 7,129 unique preliminary cluster identifications and 1,177 unique firm cluster identifications. Where redshifts were available, a spectral fitting was made leading to 4,987 unique cluster temperature and luminosity measurements. XCS-DR2 is more than an order of magnitude larger than XCS-DR1. As XCS-DR2 is a catalogue of homogeneously processed galaxy clusters, it is an ideal dataset to test possible spectroscopic biases during X-ray spectral fitting. This thesis answers seven questions related to the combining and fitting of multi-observational data and the instrumental calibration of XMM. Notably we present strong evidence that spectral selection must take place before any final X-ray spectral fitting takes place. XCS-DR1 clusters have been used to fit a luminosity temperature scaling relation. This thesis presents new spectral fitting pipelines, so the previous scaling relations work was revisited to ascertain how the results have changed. Additionally, by using the latest SPT cluster catalogue, a scaling relation was fit between the X-ray and the SunyaevZel'dovich effect properties of XCS-DR2 clusters. Massive, relaxed galaxy clusters have been used to fit cosmological parameters through measurements of their baryon fractions. XCS-DR2 contains 342 clusters observed on-axis with temperature, TX ≥ 4:5 keV. A morphological analysis of these clusters shows that 20 of them appear to be relaxed. When added to the latest analysis, a subsample of six relaxed clusters, can improve ΩM and w estimates by 18% and 12% respectively

OzDES multifibre spectroscopy for the Dark Energy Survey: first-year operation and results

The Australian Dark Energy Survey (OzDES) is a five-year, 100-night, spectroscopic survey on the Anglo-Australian Telescope, whose primary aim is to measure redshifts of approximately 2500 Type Ia supernovae host galaxies over the redshift range 0.1 < z < 1.2, and derive reverberation-mapped black hole masses for approximately 500 active galactic nuclei and quasars over 0.3 < z < 4.5. This treasure trove of data forms a major part of the spectroscopic follow-up for the Dark Energy Survey for which we are also targeting cluster galaxies, radio galaxies, strong lenses, and unidentified transients, as well as measuring luminous red galaxies and emission line galaxies to help calibrate photometric redshifts. Here, we present an overview of the OzDES programme and our first-year results. Between 2012 December and 2013 December, we observed over 10 000 objects and measured more than 6 000 redshifts. Our strategy of retargeting faint objects across many observing runs has allowed us to measure redshifts for galaxies as faint as mr = 25 mag. We outline our target selection and observing strategy, quantify the redshift success rate for different types of targets, and discuss the implications for our main science goals. Finally, we highlight a few interesting objects as examples of the fortuitous yet not totally unexpected discoveries that can come from such a large spectroscopic survey

Can the use of Bayesian analysis methods correct for incompleteness in electronic health records diagnosis data? Development of a novel method using simulated and real-life clinical data

Background Patient health information is collected routinely in electronic health records (EHRs) and used for research purposes, however, many health conditions are known to be under-diagnosed or under-recorded in EHRs. In research, missing diagnoses result in under-ascertainment of true cases, which attenuates estimated associations between variables and results in a bias towards the null. Bayesian approaches allow the specification of prior information to the model, such as the likely rates of missingness in the data. This paper describes a Bayesian analysis approach which aimed to reduce attenuation of associations in EHR studies focussed on conditions characterised by under-diagnosis. Methods Study 1: We created synthetic data, produced to mimic structured EHR data where diagnoses were under-recorded. We fitted logistic regression (LR) models with and without Bayesian priors representing rates of misclassification in the data. We examined the LR parameters estimated by models with and without priors. Study 2: We used EHR data from UK primary care in a case-control design with dementia as the outcome. We fitted LR models examining risk factors for dementia, with and without generic prior information on misclassification rates. We examined LR parameters estimated by models with and without the priors, and estimated classification accuracy using Area Under the Receiver Operating Characteristic. Results Study 1: In synthetic data, estimates of LR parameters were much closer to the true parameter values when Bayesian priors were added to the model; with no priors, parameters were substantially attenuated by under-diagnosis. Study 2: The Bayesian approach ran well on real life clinic data from UK primary care, with the addition of prior information increasing LR parameter values in all cases. In multivariate regression models, Bayesian methods showed no improvement in classification accuracy over traditional LR. Conclusions The Bayesian approach showed promise but had implementation challenges in real clinical data: prior information on rates of misclassification was difficult to find. Our simple model made a number of assumptions, such as diagnoses being missing at random. Further development is needed to integrated the method into studies using real-life EHR data. Our findings nevertheless highlight the importance of developing methods to address missing diagnoses in EHR data

Service User Perspectives on Engagement in an Occupational Therapy-Led Pulmonary Rehabilitation Programme: A Qualitative Interview Study

Introduction: Pulmonary rehabilitation (PR) is an intervention for people with chronic respiratory conditions. There are questions about which components are important to its success, including the nature of occupational therapy involvement. The aim of this research was to explore the experiences of people who had attended an occupational therapy-led PR programme in the United Kingdom to determine the most important components.Method: Semi-structured telephone interviews were conducted with service users who had experience of a community based PR programme. Interviews were transcribed verbatim. Data were analysed using the framework analysis method with three researchers contributing to the analysis. Findings: Nine people took part in the interviews, with a mean age of 72 years. Four themes were identified which were organised around the concepts of Doing, Being, Becoming Belonging. These were ‘Doing exercise and physical activity’, ‘being breathless’, ‘belonging as an individual within the group’ and ‘becoming a person who lives with COPD’. Conclusion: Doing physical activity, whilst coping with being breathless and belonging as an individual within a group can positively influence experiences and perceived outcomes during and after PR. These dimensions have the potential to shape occupation-focussed PR programmes and the occupational therapy contribution in this area of practice

Protocol: Are primary care consultations for insomnia associated with dementia in later life?

Insomnia has been defined as a difficulty initiating or maintaining sleep, leading to sleep that is either insufficient or unrefreshing. It is well-established that insomnia is more common in people with dementia, but it is not clear if insomnia predates dementia in these individuals. This latter question is an important one: if it can be shown that people with insomnia are more likely to develop dementia in later life, this may improve our ability to predict an individual’s dementia risk, and possibly to help manage that risk. Several recent studies have found a link between insomnia and later dementia, but typically give little information about the time between the onset of insomnia and the onset of dementia, raising the possibility that the insomnia is an early symptom of dementia, rather than a risk factor or potential cause of the disease. Furthermore, in some studies the link between insomnia and dementia becomes weaker when factors such as depression and sleeping tablet use are taken into account. The proposed study uses primary care records to learn whether people with dementia are more likely to have consulted with their general practitioner (GP) about insomnia 5-10 years earlier, compared to those who do not have dementia

Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches

Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. Methods We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. Results The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Conclusions Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time

Automated detection of patients with dementia whose symptoms have been identified in primary care but have no formal diagnosis: a retrospective case-control study using electronic primary care records

Objectives UK statistics suggest only two-thirds of patients with dementia get a diagnosis recorded in primary care. General practitioners (GPS) report barriers to formally diagnosing dementia, so some patients may be known by GPS to have dementia but may be missing a diagnosis in their patient record. We aimed to produce a method to identify these â known but unlabelled' patients with dementia using data from primary care patient records. Design Retrospective case-control study using routinely collected primary care patient records from Clinical Practice Research Datalink. Setting UK general practice. Participants English patients aged >65 years, with a coded diagnosis of dementia recorded in 2000-2012 (cases), matched 1:1 with patients with no diagnosis code for dementia (controls). Interventions Eight coded and nine keyword concepts indicating symptoms, screening tests, referrals and care for dementia recorded in the 5 years before diagnosis. We trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, random forest). Primary and secondary outcomes The outcome variable was dementia diagnosis code; the accuracy of classifiers was assessed using area under the receiver operating characteristic curve (AUC); the order of features contributing to discrimination was examined. Results 93 426 patients were included; the median age was 83 years (64.8% women). Three classifiers achieved high discrimination and performed very similarly. AUCs were 0.87-0.90 with coded variables, rising to 0.90-0.94 with keywords added. Feature prioritisation was different for each classifier; commonly prioritised features were Alzheimer's prescription, dementia annual review, memory loss and dementia keywords. Conclusions It is possible to detect patients with dementia who are known to GPS but unlabelled with a diagnostic code, with a high degree of accuracy in electronic primary care record data. Using keywords from clinic notes and letters improves accuracy compared with coded data alone. This approach could improve identification of dementia cases for record-keeping, service planning and delivery of good quality care