No description available.

No description available.

Purpose: Little is known about the heart rate (HR) responses at the onset of exercise, at peak exercise, and recovery in apparently healthy men and women. The purpose of this study was to determine the prevalence of abnormal HR acceleration at the onset of exercise, define the HR acceleration profile, determine if traditional cardiovascular disease (CVD) risk factors are associated with abnormal HR acceleration, and identify whether abnormal chronotropic responses cluster with abnormal HR acceleration in an asymptomatic, self-referred, adult population. Methods: A retrospective examination of a symptom-limited maximal treadmill test was performed for participants of a university-based fitness program between 1990 and 2006. Records were analyzed for various HR responses from individuals (N=947) of both sexes who represented a broad range of age and fitness levels (mean VO2: 32.9±9.4ml∙kg-1∙min-1). Abnormal HR acceleration at minute 1 and ⅓ total exercise time was defined as a HR increase from standing rest of ≤14 and ≤28 beats respectively. Results: The prevalence of abnormal HR acceleration at minute 1 and ⅓ total exercise time was 30.6% and 31.3% respectively. The mean HR increase during the first minute and ⅓ total exercise time was 20.5 ± 10.3 and 33.7 ± 10.4 beats respectively. Abnormal accelerators had a significantly better CVD risk factor profile (lower systolic and diastolic blood pressure, BMI, total cholesterol, triglycerides, higher VO2, and higher percent of regular exercise) than normal accelerators, despite consisting of a greater percentage of current smokers. Lastly, abnormal HR acceleration showed higher prevalence with abnormal HR recovery than with chronotropic incompetence. Conclusions: In the present study cohort, a lower HR increase at the onset of exercise was associated with a better CVD risk factor profile but the same differences were not seen at ⅓ total exercise time. / School of Physical Education, Sport, and Exercise Science

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (p. [32]-37).

No description available.

This dissertation studies prediction and classification models for disease risk through semiparametric modeling and statistical learning. It consists of three parts. In the first part, we propose several survival models to analyze the Cooperative Huntington's Observational Research Trial (COHORT) study data accounting for the missing mutation status in relative participants (Kieburtz and Huntington Study Group, 1996a). Huntington's disease (HD) is a progressive neurodegenerative disorder caused by an expansion of cytosine-adenine-guanine (CAG) repeats at the IT15 gene. A CAG repeat number greater than or equal to 36 is defined as carrying the mutation and carriers will eventually show symptoms if not censored by other events. There is an inverse relationship between the age-at-onset of HD and the CAG repeat length; the greater the CAG expansion, the earlier the age-at-onset. Accurate estimation of age-at-onset based on CAG repeat length is important for genetic counseling and the design of clinical trials for HD. Participants in COHORT (denoted as probands) undergo a genetic test and their CAG repeat number is determined. Family members of the probands do not undergo the genetic test and their HD onset information is provided by probands. Several methods are proposed in the literature to model the age specific cumulative distribution function (CDF) of HD onset as a function of the CAG repeat length. However, none of the existing methods can be directly used to analyze COHORT proband and family data because family members' mutation status is not always known. In this work, we treat the presence or absence of an expanded CAG repeat in first-degree family members as missing data and use the expectation-maximization (EM) algorithm to carry out the maximum likelihood estimation of the COHORT proband and family data jointly. We perform simulation studies to examine finite sample performance of the proposed methods and apply these methods to estimate the CDF of HD age-at-onset from the COHORT proband and family combined data. Our results show a slightly lower estimated cumulative risk of HD with the combined data compared to using proband data alone. We then extend the approach to predict the cumulative risk of disease accommodating predictors with time-varying effects and outcomes subject to censoring. We model the time-specific effect through a nonparametric varying-coefficient function and handle censoring through self-consistency equations that redistribute the probability mass of censored outcomes to the right. The computational procedure is extremely convenient and can be implemented by standard software. We prove large sample properties of the proposed estimator and evaluate its finite sample performance through simulation studies. We apply the method to estimate the cumulative risk of developing HD from the mutation carriers in COHORT data and illustrate an inverse relationship between the cumulative risk of HD and the length of CAG repeats at the IT15 gene. In the second part of the dissertation, we develop methods to accurately predict whether pre-symptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example age. To identify effective prediction rules using nonparametric decision functions, standard statistical learning approaches treat markers with clear biological importance (e.g., age) and other markers without prior knowledge on disease etiology interchangeably as input variables. Therefore, these approaches may be inadequate in singling out and preserving the effects from the biologically important variables, especially in the presence of potential noise markers. Using age as an example of a salient marker to receive special care in the analysis, we propose a local smoothing large margin classifier implemented with support vector machine to construct effective age-dependent classification rules. The method adaptively adjusts age effect and separately tunes age and other markers to achieve optimal performance. We derive the asymptotic risk bound of the local smoothing support vector machine, and perform extensive simulation studies to compare with standard approaches. We apply the proposed method to two studies of premanifest HD subjects and controls to construct age-sensitive predictive scores for the risk of HD and risk of receiving HD diagnosis during the study period. In the third part of the dissertation, we develop a novel statistical learning method for longitudinal data. Predicting disease risk and progression is one of the main goals in many clinical studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. We develop a statistical learning method for longitudinal data by introducing subject-specific long-term and short-term latent effects through designed kernels to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of distinctive feature of data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzhemeier's Disease (Alzhemeier's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to predict the conversion from mild cognitive impairment to dementia, and show a substantial gain in performance while accounting for the longitudinal feature of data.

Submitted by admin tede (tede@pucgoias.edu.br) on 2018-05-03T19:15:46Z No. of bitstreams: 1 Priscila Gonçalves Jacinto Figuerêdo (2).pdf: 1928842 bytes, checksum: e66c296592d0e4d12b86b6dd0b149758 (MD5) / Made available in DSpace on 2018-05-03T19:15:46Z (GMT). No. of bitstreams: 1 Priscila Gonçalves Jacinto Figuerêdo (2).pdf: 1928842 bytes, checksum: e66c296592d0e4d12b86b6dd0b149758 (MD5) Previous issue date: 2017-11-28 / Intestinal parasitosis are one of the determining indicators of the socioeconomic development of a country. Minority populations as quilombolas are included in the risk groups that present high rates of health problems, among them, the enteroparasitosis, related mainly to cultural and geographic isolation. A study was performed to investigate the occurrence of enteroparasitosis and associated factors in quilombolas from São Vicente Island community in the north part of the state of Tocantins. The experimente was a cross-sectional, descriptive study with a quantitative approach. It was carried out in April and May 2017, through the collection of stool samples for parasitological analysis and application of a questionnaire from the Brazilian Association of Research Institutes, additioned with socioeconomic, environmental and sanitary factors of the community. The data were analyzed with Statistical Package of Social Sciences (SPSS, 23.0) adopting a level of significance of 5% (p <0.05). From a total of 86 individuals surveyed, 39 (45.3%) presented intestinal parasites, nine (23.1%) of which were infested with two different intestinal worms. The geohelminths Ascaris lumbricoides and Trichuris trichiura were the most prevalent among pathogens, with 8 (16.6%) and 6 (12.5%) infested individuals, respectively. Regardless of the place of residence (urban or rural), risk factors such as: precarious or nonexistent basic sanitation, associated with the adoption of inadequate hygiene measures and low socioeconomic levels, exposed the quilombola community to intestinal parasitosis. Ensuring the improvement of environmental sanitation, housing habitability, facilitation of the availability of antiparasitics, accessibility to health services and information that mitigates health problems are important measures to reduce the prevalence of enteroparasites in the quilombola community. / As parasitoses intestinais se configuram um dos indicadores determinantes do desenvolvimento socioeconômico de um país. Populações minoritárias como quilombolas estão inclusas nos grupos de risco que apresentam elevados índices de agravos à saúde, entre eles, as enteroparasitoses, relacionadas principalmente ao isolamento cultural e geográfico. O estudo objetivou investigar a ocorrência de enteroparasitoses e fatores associados em quilombolas da comunidade Ilha de São Vicente no norte do Estado do Tocantins. Trata-se de um estudo observacional transversal, descritivo com abordagem quantitativa. Realizou-se nos meses de abril e maio de 2017, através da coleta de amostras de fezes para análise parasitológica e aplicação do formulário da Associação Brasileira de Empresas de Pesquisas acrescido de fatores socioeconômicos, ambientais e sanitários da comunidade. Os dados foram analisados com pacote estatístico Statistical Package of Social Sciences (SPSS, 23.0) adotando um nível de significância de 5% (p <0,05). Dos 86 indivíduos pesquisados, 39 (45,3%) apresentaram positividade para parasitos intestinais. Destes, 9 (23,1%) tiveram biparasitismo. Os geohelmintos Ascaris lumbricoides e Trichuris trichiura foram os mais prevalentes entre os patogênicos, com respectivamente 8 (16,6%) e 6 (12,5%). Independente do local de moradia (zona urbana ou rural) dos quilombolas, fatores de risco como: Saneamento básico precário ou inexistente, associado a adoção de medidas de higiene inadequadas e níveis socioeconômicos baixos, expuseram a comunidade quilombola às parasitoses intestinais. Garantir a melhoria do saneamento ambiental, habitabilidade das residências, facilitação na oferta de antiparasitários, acessibilidade aos serviços de saúde e informações mitigadoras dos agravos à saúde, são medidas importantes para diminuição da prevalência de enteroparasitoses na comunidade quilombola.

Bibliography: leaves 100-113.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2009. / Härtill 4 uppsatser.

Study methods: Mixed-method study design was used to investigate feasibility of implementing a mobile-phone based behavioural intervention to reduce CVD risk factors among the Chinese population. Patients, who were 45-79 years old, fair English literacy, had access to a JAVA enabled mobile phone and had no mental health problems, cognitive impairment or severe illness, were eligible to the study. Intervention: Patients recruited from three settings (1 GP, 1 specialist and 1 public clinics) had the study software installed to their phone. The software enabled patients to access their CVD risk profiles (including weight, BP, HbA1c, and lipoprotein profile), 10-year CVD risk prediction (based on Framingham Cardiac Risk Score), and pre-set behavioural recommendations. Patients’ CVD risk profiles were updated at 1-month and 3-month follow-up when their test results were available. Patients were alerted with healthy behaviours recommendations. Outcomes: Outcomes were measured at baseline and 3-month follow-up. Clinical outcomes included Cardiac Risk Factor Score and its components (BMI, Systolic & Diastolic BP, total cholesterol, HDL and HbA1c). Two sets of questionnaires were used to measure knowledge, risk reduction behaviour and attitude toward usefulness of medical records (pre-intervention) and perceived ease of use, usefulness, satisfaction and utilisation of the software (post-intervention). Results and Discussion: 19 patients were recruited at baseline. 75% (14) aged 45-55 years, 58% (11) were male, 79% (15) had secondary or lower education, 63% (12) were married, and 95% (18) never smoked. Patients’ understanding about CVD risk factors and risk reduction behaviour was moderate. Patients’ attitude toward electronic medical record was positive. Overall patients’ perception of usefulness, ease of use and satisfaction with the software was satisfactory. Post-intervention, a decreasing trend was observed in patients’ CVD risk profiles i.e. weight, BMI, SBP&DBP, HbA1c and Lipoprotein profile. Focus group discussions revealed that there was a mismatch between physicians and patients perspectives about the use of mobile phone in a behavioural intervention. Physicians tended to express their concern about the quality of records, security of technology, and patients’ actual benefit, while patients showed little concern about security and great excitement about further use of mobile phone technology in assisting their disease self-management. The public sector physicians admitted that their patients were passive in term of seeking information about their health. Patients were willing to use this software for future care if it could provide more real-time data, tailored recommendations for behavioural change, and an interactive communication tool with their physicians. Physicians would like to try the software if it could ease patient-management process, especially enhance patient-physician communication, and be a decision support system to help them keep track with changes that their patients made. Conclusion: This pilot study has provided preliminary evidence of the feasibility, acceptability, and utility of an e-platform in primary interventions for CVD in Hong Kong. / published_or_final_version / Community Medicine / Master / Master of Philosophy