There are approximately 2.5 million cases of multiple sclerosis [MS] in the world. Each year, around 10,000 new cases of MS are diagnosed. There are approximately 0.1%. in the Czech Republic and that means that there are approximately 15,000 people with this disease. Multiple sclerosis is a progressive neurological inflammatory disease of the central nervous system in pathogenesis. The auto-immune mechanisms are appliedinvolves; both the affect the myelin and damaged axons. This damage is responsible for the permanent disability of MS. The main objective of this thesis was to use quantitative research to describe the occurrence of MS in the Regions of South Bohemia and Usti, and within objective to estimate the true prevalence of multiple sclerosis in the South and the Usti Regions. MUDr. Príkaszký and I used the method of descriptive epidemiological studies on the technique of data collection, as well as the analysis and the comparison of two sources using the double-capture method. As a research area, we used a set of patients with multiple sclerosis. The data were gathered in two regions of the Czech Republic: Usti nad Labem Region and South Bohemia. We investigated the number of patients from neurologists and MS centers in both regions.There is also the data on health insurance included with the Ústí Region in this research. According to the data, we estimated the prevalence of neurologists in South Bohemia to be 79.20 per 100,000 people. The MS Center at the hospital in Ceske Budejovice has registered 512 patients: 388 women and 124 men. The values of the MS Centre in Ceske Budejovice received from the neurologists are a total of 882 patients, which is a prevalence of 138.6 patients per 100,000 of the population. Regarding the Usti Region, the prevalence reported by neurologists is 208.2 per 100 000 people. The MS Center at the hospital in Teplice, has registered 1139 sick 730 women and 409 men. According to health insurance data in their database, of 1187 people listed with a diagnosis of G.35, 333 are men and 854 are women. This represents an estimate of the prevalence 305/100000 of the people who are registered in the NGA. We calculated the prevalence of illnesses from those sources in both of these regions is higher than the general estimates. When comparing the data source of health insurance and the model file from the MS center of Ústí Region, we calculated the overall sensitivity of 91.7% within a health insurance group. The calculation of the estimated prevalence has been reached by using the method of double-capture of those sources that provided an estimate of 2,681 patients in the Usti Region. The models of this data are mainly demonstrated as a possible approach to the estimate of the prevalence of this chronic disease in the case of multiple sclerosis. The information has been obtained from the collection of data. The results of this study can be used by other workers who have the same objective as this study.

Made available in DSpace on 2016-06-02T20:06:05Z (GMT). No. of bitstreams: 1 4034.pdf: 873658 bytes, checksum: 8c8e2d629291b4edab052dd0ee734f94 (MD5) Previous issue date: 2005-02-25 / Financiadora de Estudos e Projetos / In this work, a bayesian methodology is shown to estimate the size of a diabethic-su¤ering population through lists containing information data of patients. The applied methodology is analogous of capture-recaptures in animal population. We assume correct the registers of relative information to the patients as well as we take in account correct and incorrect registers of the information. In case the supposed registers are correct, the methodology is developed for two or more lists and the Bayes estimate is determined for the size of a population. In a second model, the occurrency of correct and incorrect registers are considered, presenting a two-stage estimation method for the model parameters using two lists. For both models there are results with simulated and real examples. / Nesta dissertação apresentamos uma metodologia bayesiana para estimar o tamanho de uma população de diabéticos através de listas contendo informações sobre dados dos indivíduos. A metodologia aplicada é análoga a de captura-recaptura em população animal. Supomos corretos os registros de informações relativas aos pacientes assim como levamos em consideração registros corretos e incorretos das informações. No caso da suposição dos registros serem corretos, a metodologia é desenvolvida para duas ou mais listas e determinamos estimativas de Bayes para o tamanho populacional. Em um segundo modelo, consideramos a ocorrência de registros corretos e incorretos dos dados relativos aos pacientes, e apresentamos um método de estimação em dois estágios para os parâmetros do modelo utilizando duas listas. Para ambos os modelos, apresentamos resultados com exemplos simulados e reais.