7 resultados para EPIDEMIOLOGICAL SURVEY

em Universidade Federal do Rio Grande do Norte(UFRN)


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NORO, Luiz Roberto Augusto et al. Incidência de cárie dentária em adolescentes em município do Nordeste brasileiro, 2006, Cadernos de Saúde Pública, Rio de Janeiro, v. 25, n. 4, p. 783-790, abr. 2009.

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Toxoplasmosis, provoked by the intracellular parasite Toxoplasma gondii, is one of the most prevalent parasitoses in the world. In humans, transmission occurs by three evolutionary forms of the parasite: oocysts, tissue cysts and tachyzoites. Wild and domestic felines are definitive hosts. The ocular form of toxoplasmosis can be of congenital origin with early or late clinical manifestations, or acquired after birth. T. gondii is considered the main culprit for most cases of infectious uveitis. This study aimed at assessing ocular toxoplasmosis, relating it to factors associated to the patient s lifestyle and describing the epidemic-serological and clinical profile of affected individuals. A cross-sectional study was conducted with a population of 159 patients. Univariate analysis (odds ratio) was used to evaluate the data, with a confidence interval of 95% and p-value < 0.05. A prevalence of 4% of ocular toxoplasmosis was observed in the population of patients treated at an ophthalmological clinic. Of patients directly examined by immunoenzymatic assay (MEIA-AxSYM®- Microparticle Enzyme Immune Assay), considering only uveitis, a frequency of anti-T. gondii of 73%, most of whom exhibited titulation between 40-99 UI IgG/mL. With respect to location of ocular lesions, bilaterality was observed in 57% of patients assessed by the ophthalmoscopy technique. When compared with the results of an active search of medical records, a similarity in ocular toxoplasmosis (74%) and bilateral lesion location (55%) was observed. Type I lesion was the most frequent type observed, with intraocular disposition in the macula. An epidemiological survey revealed that direct contact with cats; consuming raw or poorly cooked meat and direct contact with the soil were significantly associated with greater likelihood of acquiring ocular toxoplasmosis. Sample characterization in relation to age range was significant for patients between 31 and 40 years [χ², chi-square test (p = 0.04)], but population traits such as schooling, sanitary district, and monthly income were not significant. Results confirm that ocular toxoplasmosis is widely distributed in the metropolitan area of Natal, Brazil, with significant prevalence of ocular lesions provoked by T.gondii. It is suggested that sanitary authorities exert greater control in order to minimize the risk of toxoplasmic infection, mainly in pregnant women.

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The SBBrasil 2010 Project (SBB10) was designed as a nationwide oral health epidemiological survey within a health surveillance strategy. This article discusses methodological aspects of the SBB10 Project that can potentially help expand and develop knowledge in the health field. This was a nationwide survey with stratified multi-stage cluster sampling. The sample domains were 27 State capitals and 150 rural municipalities (counties) from the country's five major geographic regions. The sampling units were census tracts and households for the State capitals and municipalities, census tracts, and households for the rural areas. Thirty census tracts were selected in the State capitals and 30 municipalities in the countryside. The precision considered the demographic domains grouped by density of the overall population and the internal variability of oral health indices. The study evaluated dental caries, periodontal disease, malocclusion, fluorosis, tooth loss, and dental trauma in five age groups (5, 12, 15-19, 35-44, and 65-74 years).

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The SBBrasil 2010 Project (SBB10) was designed as a nationwide oral health epidemiological survey within a health surveillance strategy. This article discusses methodological aspects of the SBB10 Project that can potentially help expand and develop knowledge in the health field. This was a nationwide survey with stratified multi-stage cluster sampling. The sample domains were 27 State capitals and 150 rural municipalities (counties) from the country's five major geographic regions. The sampling units were census tracts and households for the State capitals and municipalities, census tracts, and households for the rural areas. Thirty census tracts were selected in the State capitals and 30 municipalities in the countryside. The precision considered the demographic domains grouped by density of the overall population and the internal variability of oral health indices. The study evaluated dental caries, periodontal disease, malocclusion, fluorosis, tooth loss, and dental trauma in five age groups (5, 12, 15-19, 35-44, and 65-74 years).

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Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

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Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

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NORO, Luiz Roberto Augusto et al. Incidência de cárie dentária em adolescentes em município do Nordeste brasileiro, 2006, Cadernos de Saúde Pública, Rio de Janeiro, v. 25, n. 4, p. 783-790, abr. 2009.