Validation of the Patient Activation Measure in a Multiple Sclerosis Clinic Sample and Implications for Care

Purpose. Patient engagement in multiple sclerosis (MS) care can be challenging at times given the unpredictable disease course, wide range of symptoms, variable therapeutic response to treatment and high rates of patient depression. Patient activation, a model for conceptualising patients’ involvement in their health care, has been found useful for discerning patient differences in chronic illness management. The purpose of this study was to validate the patient activation measure (PAM-13) in an MS clinic sample. Methods. This was a survey study of 199 MS clinic patients. Participants completed the PAM-13 along with measures of MS medication adherence, self-efficacy, depression and quality of life. Results. Results from Rasch and correlation analyses indicate that the PAM-13 is reliable and valid for the MS population. Activation was associated with MS self-efficacy, depression and quality of life but not with self-reported medication adherence. Also, participants with relapse-remitting MS, current employment, or high levels of education were more activated than other subgroups. Conclusions. The PAM-13 is a useful tool for understanding health behaviours in MS. The findings of this study support further clinical consideration and investigation into developing interventions to increase patient activation and improve health outcomes in MS

Investigating Determinants of Multiple Sclerosis in Longitunal Studies: A Bayesian Approach

Modelling data from Multiple Sclerosis longitudinal studies is a challenging topic since the phenotype of interest is typically ordinal; time intervals between two consecutive measurements are nonconstant and they can vary among individuals. Due to these unobservable sources of heterogeneity statistical models for analysis of Multiple Sclerosis severity evolve as a difficult feature. A few proposals have been provided in the biostatistical literature (Heijtan (1991); Albert, (1994)) to address the issue of investigating Multiple Sclerosis course. In this paper Bayesian P-Splines (Brezger and Lang, (2006); Fahrmeir and Lang (2001)) are indicated as an appropriate tool since they account for nonlinear smooth effects of covariates on the change in Multiple Sclerosis disability. By means of Bayesian P-Spline model we investigate both the randomness affecting Multiple Sclerosis data as well as the ordinal nature of the response variable

SUBSISTENCE URBAN MARKETS AND IN-COUNTRY REMITTANCES: A SOCIAL NETWORK ANALYSIS OF URBAN STREET VENDORS IN GHANA AND THE TRANSFER OF RESOURCES TO RURAL VILLAGES

This dissertation uses a mixed method approach to examine the determinants of internal remittances that are connected to the social networks of urban migrant street vendors. Urban street markets are a point of entry for many migrants moving from rural areas to cities in the Global South. The qualitative portion of the dissertation uses an ethnographic approach including participant observation, interviews and focus groups to examine the social networks of street vendors in a market in the municipality of Madina, Ghana. The quantitative analysis codes data from the ethnography in order to conduct a social network analysis using quadratic assignment procedure and logistic regression quadratic assignment procedure to analyze the relationship between attributes of street vendors and remittance behavior. Findings lead to several policy recommendations for the international community, as well as locally based non-governmental organizations, microfinance organizations, national and local governments providing funding or designing interventions affecting street markets or working with individual street vendors

Improving the clinico-radiological association in neurological diseases

Despite the key role of magnetic resonance imaging (MRI) in the diagnosis and monitoring of multiple sclerosis (MS) and cerebral small vessel disease (SVD), the association between clinical and radiological disease manifestations is often only moderate, limiting the use of MRI-derived markers in the clinical routine or as endpoints in clinical trials. In the projects conducted as part of this thesis, we addressed this clinico-radiological gap using two different approaches. Lesion-symptom association: In two voxel-based lesion-symptom mapping studies, we aimed at strengthening lesion-symptom associations by identifying strategic lesion locations. Lesion mapping was performed in two large cohorts: a dataset of 2348 relapsing-remitting MS patients, and a population-based cohort of 1017 elderly subjects. T2-weighted lesion masks were anatomically aligned and a voxel-based statistical approach to relate lesion location to different clinical rating scales was implemented. In the MS lesion mapping, significant associations between white matter (WM) lesion location and several clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers. In the SVD lesion mapping, the same WM fibers and the caudate nucleus were identified to significantly relate to the subjects’ cerebrovascular risk profiles, while no other locations were found to be associated with cognitive impairment. Atrophy-symptom association: With the construction of an anatomical physical phantom, we aimed at addressing reliability and robustness of atrophy-symptom associations through the provision of a “ground truth” for atrophy quantification. The built phantom prototype is composed of agar gels doped with MRI and computed tomography (CT) contrast agents, which realistically mimic T1 relaxation times of WM and grey matter (GM) and showing distinguishable attenuation coefficients using CT. Moreover, due to the design of anatomically simulated molds, both WM and GM are characterized by shapes comparable to the human counterpart. In a proof-of-principle study, the designed phantom was used to validate automatic brain tissue quantification by two popular software tools, where “ground truth” volumes were derived from high-resolution CT scans. In general, results from the same software yielded reliable and robust results across scans, while results across software were highly variable reaching volume differences of up to 8%

Fitness, physical activity, and exercise in multiple sclerosis

Background A moderate to high level of physical activity, including regular exercise, represents an established behavioral and rehabilitative approach for persons with multiple sclerosis (pwMS). Although being increasingly proposed to limit disease activity and progression, high-quality evidence is lacking. Objective The objective of the study is to provide valuable information for MS clinicians and researchers by systematically evaluating the current state of evidence (i) whether exercise interventions affect established clinical measures of disease activity and progression in pwMS (i.e., EDSS, relapse rate, lesion load, brain volume, MSFC) and (ii) how the physical activity and fitness level interact with these measures. Methods Literature search was conducted in MEDLINE, EMBASE, CINAHL, and SPORTDiscus. Evaluation of evidence quality was done based on standards published by The American Academy of Neurology. Results It is likely that exercise improves the MSFC score, whereas the EDSS score, lesion load, and brain volume are likely to remain unchanged over the intervention period. It is possible that exercise decreases the relapse rate. Results from cross-sectional studies indicate beneficial effects of a high physical activity or fitness level on clinical measures which, however, is not corroborated by high evidence quality. Conclusions A (supportive) disease-modifying effect of exercise in pwMS cannot be concluded. The rather low evidence quality of existing RCTs underlines the need to conduct more well-designed studies assessing different measures of disease activity or progression as primary end points. A major limitation is the short intervention duration of existing studies which limits meaningful exercise-induced effects on most disability measures. Findings from cross-sectional studies are difficult to contextualize regarding clinical importance due to their solely associative character and low evidence quality

Participation in exercise and sport for individuals with minimal disability from multiple sclerosis

Moira Smith developed and tested the feasibility of a novel flexible exercise participation program for individuals with multiple sclerosis. She found that the program was safe, feasible and highly acceptable. Importantly, the program enabled individuals with multiple sclerosis to find the right balance with participation in exercise and sport

Staging evaluation of posttraumatic stress disorder : a machine learning study

Os transtornos de estresse relacionados a um evento traumático, como o transtorno de estresse agudo (TEA) e o transtorno de estresse pós-traumático (TEPT), são caracterizados por alta morbidade e prejuízo social significativo. No Brasil, estima-se que 80% da população já foi exposta a pelo menos um evento traumático ao longo da vida em grandes centros urbanos, como São Paulo e Rio de Janeiro; o crescente problema da violência urbana mostra-se fator importante para a gênese dos transtornos relacionados ao trauma. Devido à etiologia do TEPT ser multicausal e complexa, técnicas de Machine Learning (Aprendizado de Máquina – ML) tem sido usadas para desenvolver escores de risco, para predição diagnóstica e para definição de tratamento. Contudo, considerando sua heterogeneidade clínica e etiológica, realizar o diagnóstico e definir um tratamento adequado pode ser muitas vezes desafiador. O uso do estadiamento clínico surge como um método mais refinado de diagnóstico, procurando definir a progressão do transtorno em momentos específicos durante o continuum da enfermidade. Esta abordagem pode auxiliar em um diagnóstico mais aprimorado, conhecer melhor o prognóstico e escolher o melhor tratamento de acordo com o estágio do transtorno. Assim, o TEPT aparece como um exemplo importante de como um método de estadiamento pode trazer benefícios. O objetivo desta tese é avaliar como os aspectos pessoais, clínicos e relacionados ao trauma dos pacientes atendidos em ambulatórios especializados em trauma psíquico podem estar relacionados à predição do estadiamento clínico de TEPT usando técnicas de ML.Stress disorders related to a traumatic event, such as acute stress disorder (ASD) and posttraumatic stress disorder (PTSD), are characterized by high morbidity and significant social impairment. In Brazil, it is estimated that 80% of the population has already been exposed to at least one traumatic event throughout life in large urban centers, such as São Paulo and Rio de Janeiro; the growing problem of urban violence proves to be an important factor in the genesis of trauma-related disorders. The etiology of PTSD is multicausal and complex; techniques of Machine Learning (ML) have been used to develop PTSD risk scores, to predict its diagnosis and to choose better treatments. However, considering its clinical and etiological heterogeneity, making the diagnosis and defining an appropriate treatment can often be challenging. The use of clinical staging appears as a refined method of diagnosis, aiming to define the progression of the disorder at specific times during the continuum of the illness. This approach may provide improved diagnosis, better understand the prognosis and choose the best treatment according to the stage of the disorder. Thus, PTSD appears as an important example of how a staging method can bring benefits. The objective of this thesis is to evaluate how the personal, clinical and trauma-related aspects of patients who sought care at outpatient clinics specialized in emotional trauma can be related to the prediction of the PTSD staging using ML techniques

Challenges in biomedical data science: data-driven solutions to clinical questions

Data are influencing every aspect of our lives, from our work activities, to our spare time and even to our health. In this regard, medical diagnosis and treatments are often supported by quantitative measures and observations, such as laboratory tests, medical imaging or genetic analysis. In medicine, as well as in several other scientific domains, the amount of data involved in each decision-making process has become overwhelming. The complexity of the phenomena under investigation and the scale of modern data collections has long superseded human analysis and insights potential