Thesis (Ph. D.)--University of Sydney, 2006. / Title from title screen (viewed Aug. 29, 2007). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Public Health, Faculty of Medicine. Includes tables and questionnaires. Includes bibliography. Also issued in print.

Title: Analysis of Pharmacotherapy by patients with diagnosis of COPD Student: Marilena Kartali-Kaouni Tutor: Prof. RNDr. Jiri Vlcek, CSc Department of Social and Clinical Pharmacy, Charles University in Prague, Faculty of Pharmacy in Hradec Kralove Background: " Chronic Obstructive Pulmonary Disease (COPD) is characterized by airflow limitation that is not fully reversible. The airflow limitation is usually both progressive and associated with an abnormal inflammatory response of the lungs to noxious particles or gases". Tobacco smoking is the major risk factor in the development of COPD. COPD is a leading cause of morbidity and mortality worldwide. Aim: 1st from the current literature to understand the nature of COPD and obtain information about the aetiopathogenesis of the disease, diagnosis options and summarize the current view of strategies for achieving the goals of treatment. 2nd in a pilot study to analyze drug therapy in COPD patients visiting a pharmacy in Greece. Methods: 56 prescriptions with the diagnosis of COPD were collected during a period of 8 months from a Greek pharmacy. Information from the prescriptions with regard to COPD medications prescribed (active substances, trade names, strength, dosage scheme, pack size), patients characteristics (age and gender) and prescribing...

There is broad agreement that medicine ought to be `evidence based' and `personalized' and that data should play a large role in achieving both these goals. But the path from data to improved medical decision making is not clear. This thesis presents three methods that hopefully help in small ways to clear the path. Personalized medicine depends almost entirely on understanding variation in treatment effect. Chapter 1 describes latent class mixture models for treatment effect heterogeneity that distinguish between continuous and discrete heterogeneity, use hierarchical shrinkage priors to mitigate overfitting and multiple comparisons concerns, and employ flexible error distributions to improve robustness. We apply different versions of these models to reanalyze a clinical trial comparing HIV treatments and a natural experiment on the effect of Medicaid on emergency department utilization. Medical decisions often depend on observational studies performed on large longitudinal health insurance claims databases. These studies usually claim to identify a causal effect, but empirical evaluations have demonstrated that standard methods for causal discovery perform poorly in this context, most likely in large part due to the presence of unobserved confounding. Chapter 2 proposes an algorithm called Ensembles of Granger Graphs (EGG) that does not rely on the assumption that unobserved confounding is absent. In a simulation and experiments on a real claims database, EGG is robust to confounding, has high positive predictive value, and has high power to detect strong causal effects. While decision making inherently involves causal inference, purely predictive models aid many medical decisions in practice. Predictions from health histories are challenging because the space of possible predictors is so vast. Not only are there thousands of health events to consider, but also their temporal interactions. In Chapter 3, we adapt a method originally developed for speech recognition that greedily constructs informative labeled graphs representing temporal relations between multiple health events at the nodes of randomized decision trees. We use this method to predict strokes in patients with atrial fibrillation using data from a Medicaid claims database. I hope the ideas illustrated in these three projects inspire work that someday genuinely improves healthcare. I also include a short `bonus' chapter on an improved estimate of effective sample size in importance sampling. This chapter is not directly related to medicine, but finds a home in this thesis nonetheless.

Background: This thesis presents an empirical investigation into the efficacy, types, and performance of community health workers (CHWs)-trained lay people to whom simple medical procedures can be "task shifted" from doctors. It has three objectives: (1) assess the effects of CHW delivered interventions for primary health outcomes in low-and middle-income countries (LMICs), (2) develop a comprehensive taxonomy of CHW characteristics and programme design features, and (3) assess the relative efficacy of different types of CHW programme designs and how they can be used to optimise CHW performance. Structure and Methods: Following the logic of early stage intervention development, this thesis has an iterative and developmental structure in which each section flows out of and builds on the previous section. Objective one is addressed in Chapters 2-4: scoping review, systematic review, and meta-analytic methods are applied to establish the efficacy and effectiveness of CHW-led interventions in LMICs. Objective two is addressed in Chapter 5: inductive, thematic analysis of systematically identified trials, influential papers, and existing information classification systems is used to develop a formal CHW taxonomy for intervention reporting and coding. Objective three is addressed in Chapter 6: systematic review methods are employed to identify interventions for improving the performance of community health workers in LMICs. Results: Objective one: a systematic review of 155 papers reporting 86 trials found high quality evidence that CHW delivered interventions reduce perinatal mortality, improve child nutritional status, and improve tuberculosis completion rates versus facility-based care. There is also moderate quality evidence that CHW delivered interventions improve certain mental, infectious disease, paediatric, and maternal health outcomes. In undertaking this process, an additional, methodological contribution was made in the form of a tool to reduce risk of bias in overviews of reviews. This tool may facilitate early stage intervention development in the future. Objective two: 253 records were used to establish, in a faceted taxonomy, the definitional clarity required for theory building and knowledge accumulation. Two categories (CHW Characteristics and CHW Programme Features) and six dimensions (Integration, Recruitment, Training, Supervision, Incentives, and Equipment) emerged. Objective three: a systematic review of 14 trials identified moderate quality evidence of the efficacy of CHW performance interventions in improving certain behavioural outcomes for patients, utilisation of services, and CHW quality of care. There was no effect on the biological outcomes of interest. Conclusion: In bringing the tools of evidence based practice to bear on community health worker interventions, this dissertation has contributed to the theoretical, methodological, and empirical evidence base from which the field can continue to advance.

Medical decisions should be based on good evidence. But this does not mean that health care professionals should practice evidence-based medicine. This dissertation explores how these two positions come apart, why they come apart, and what we should do about it. I begin by answering the descriptive question, what are current standards of evidence in medicine? I then provide a detailed critique of these standards. Finally, I address the more difficult normative question, how should we determine standards of evidence in medicine? In medicine, standards of evidence have been established by the pervasive evidence-based medicine (EBM) movement. Until now, these standards have not been subjected to comprehensive philosophical scrutiny. I outline and defend a theory of knowledge – a version of Helen Longino’s Critical Contextual Empiricism (CCE) – which enables me to critically evaluate EBM. My version of CCE emphasizes the critical evaluation of background assumptions. In accordance with this, I identify and critically evaluate the three substantive assumptions underlying EBM. First, I argue that medicine should not be held to the restrictive definition of science assumed by proponents of EBM. Second, I argue that epidemiological evidence should not be the only “base” of medical decisions. Third, I argue that not only is the particular hierarchy of evidence assumed by EBM unjustified, but that any attempt to hierarchically rank research methods is incoherent and unjustifiably restricts medical knowledge. Current standards of evidence divert attention from many legitimate sources of evidence. This distorts medical research and practice. In the remainder of the dissertation I propose means for improving not only current standards of medical evidence but also the process of producing and defending future standards. On the basis of four CCE norms, I argue that we have reason to protect and promote those features of the medical community that facilitate diversity, transparency, and critical interaction. Only then can we ensure that the medical community retains its ability to produce evidence that is both rigorous and relevant to practice.

Medical decisions should be based on good evidence. But this does not mean that health care professionals should practice evidence-based medicine. This dissertation explores how these two positions come apart, why they come apart, and what we should do about it. I begin by answering the descriptive question, what are current standards of evidence in medicine? I then provide a detailed critique of these standards. Finally, I address the more difficult normative question, how should we determine standards of evidence in medicine? In medicine, standards of evidence have been established by the pervasive evidence-based medicine (EBM) movement. Until now, these standards have not been subjected to comprehensive philosophical scrutiny. I outline and defend a theory of knowledge – a version of Helen Longino’s Critical Contextual Empiricism (CCE) – which enables me to critically evaluate EBM. My version of CCE emphasizes the critical evaluation of background assumptions. In accordance with this, I identify and critically evaluate the three substantive assumptions underlying EBM. First, I argue that medicine should not be held to the restrictive definition of science assumed by proponents of EBM. Second, I argue that epidemiological evidence should not be the only “base” of medical decisions. Third, I argue that not only is the particular hierarchy of evidence assumed by EBM unjustified, but that any attempt to hierarchically rank research methods is incoherent and unjustifiably restricts medical knowledge. Current standards of evidence divert attention from many legitimate sources of evidence. This distorts medical research and practice. In the remainder of the dissertation I propose means for improving not only current standards of medical evidence but also the process of producing and defending future standards. On the basis of four CCE norms, I argue that we have reason to protect and promote those features of the medical community that facilitate diversity, transparency, and critical interaction. Only then can we ensure that the medical community retains its ability to produce evidence that is both rigorous and relevant to practice.

Background: Evidence-based medicine (EBM) receives significant attention worldwide. Many studies have used questionnaires to discuss the factors obstructing the practice of EBM. There has however been no large-scale data analysis on who and when to practice EBM. This study aims to fill this gap in research by applying nationally representative data to analyze EBM practice after the provision of new evidence regarding rosiglitazone prescription. Methods: We used the National Health Insurance Database in Taiwan to analyze the changes in the prescription of rosiglitazone after meta-analysis found the drug to increase the risk of myocardial infarction. The study period was 18 months from the second quarter of 2007 to the fourth quarter in 2008. Two models of doctors¡¦ prescription behaviors were analyzed in the study. We conducted univariate analyses to distinguish significant differences in the variable and applied multivariate logistic analyses to predict the probability of physicians ceasing to prescribe rosiglitazone. Results: We found a significant improvement in EBM practice from specialists and experienced physicians. When compared to other specialists, endocrinologists were four times more likely to change rosiglitazone prescription (OR: 4.129, 95% CI: 2.484-6.863). Doctors with more than 10 years of specialist experience performed better in EBM practice than younger doctors. The hospital level that a physician worked at was not a significant factor. Local urbanization and economic status did not affect the practice of EBM by physicians in clinics either. Conclusions: Our study found that the EBM was still not well practiced among doctors in Taiwan. The practice of new evidence depended on the specialty or professional experience. Younger doctors and doctors working in medical centers seemed not to practice EBM well. In clinics, patients did not have enough influence to modify the doctor¡¦s prescription behavior. There was a significant time lag between the EBM emergence and EBM practice. This suggests that setting up an authoritative organization to provide timely EBM recommendations is very important. Further improvements to the practice of EBM are still urgently needed within the medical community.

published_or_final_version / Public Health / Master / Master of Public Health

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Due to the complicated nature of medical information needs, the time constraints of clinicians, and the linguistic complexities and sheer volume of medical information, most medical questions go unanswered. It has been shown that nearly all of these questions can be answered with the presently available medical sources and that when these questions get answered, patient health benefits. In this work, we design and describe a framework for Evidence-Based medical information research and delivery, MedicInfoSys. This system leverages the strengths of knowledge-based workers and of mature knowledge-based technologies within the medical domain. The most critical element of this framework, is a search interface, PifMed. PifMed uses gold-standard MeSH categorization (presently integrated into medline) as the basis of a navigational structure, which allows users to browse search results with an interactive tree of categories. Evaluation by user study shows it to be superior to PubMed, in terms of speed and usability.