Resumo: Objetivo: Calcular e mapear as desigualdades em saúde na cidade de São Paulo por meio da metodologia do índice de saúde urbana (UHI). Métodos: Sete indicadores foram selecionados do censo brasileiro: (1) proporção de domicílios com acesso a esgoto, (2) proporção de domicílios com coleta regular de lixo, (3) proporção de domicílios com dois ou mais banheiros, (4) proporção de domicílios que recebem água encanada, (5) renda média por domicílio, (6) porcentagem de pessoas brancas e (7) taxa de alfabetização. Usando a metodologia UHI, todos os indicadores de saúde foram padronizados e agregados em uma única métrica para o setor censitário. Os valores de UHI foram classificados e plotados. A razão de disparidade e a inclinação do gráfico foram calculadas. A correlação entre os indicadores foi testada. Os resultados foram geocodificados, produzindo um mapa de risco à saúde. Resultados: A distribuição dos valores do índice apresentou uma seção intermediária linear e desvios nas extremidades. A taxa de disparidade encontrada foi de 2,95, enquanto o coeficiente angular foi 0,30. Todos os indicadores apresentaram correlação significativa. O mapa exibiu um arranjo característico de desigualdade em saúde entre o centro e a periferia. Os setores localizados na região central da cidade apresentaram valores de UHI mais elevados do que os da periferia. Conclusão: Os resultados deste estudo apresentaram uma distribuição visual das disparidades de saúde na cidade de São Paulo, demonstrando ser um método valioso para a identificação de áreas que requerem atenção da saúde pública.

ABSTRACT: Objective: To calculate and map the health inequalities in the city of São Paulo using the Urban Health Index (UHI) methodology. Methods: Seven indicators were selected from the Brazilian census: (1) proportion of households with access to sewage systems, (2) proportion of households served by regular waste collection, (3) proportion of households with two or more toilets, (4) proportion of households receiving tap water, (5) average income per household, (6) percentage of white people, and (7) literacy rate. Based on the UHI methodology, all health indicators were standardized and aggregated into a single metric at the census tract level. The UHI scores were ranked and plotted. The disparity ratio and the graph slope were calculated. The correlation between indicators was tested. Results were geocoded to produce a map of health risks. Results: The distribution of index values showed a linear middle section and deviations at each end. The disparity ratio found was 2.95, while the slope was 0.30. All indicators were significantly correlated. The map displayed a typical pattern of health inequality between the downtown and the periphery. The tracts located in the city’s downtown had higher UHI values than those on the outskirts. Conclusions: The results of this study presented a visual distribution of health disparities in the city of São Paulo, proving to be a valuable method for identifying areas that require public health attention.

Resumo: Objetivo: Calcular e mapear as desigualdades em saúde na cidade de São Paulo por meio da metodologia do índice de saúde urbana (UHI). Métodos: Sete indicadores foram selecionados do censo brasileiro: (1) proporção de domicílios com acesso a esgoto, (2) proporção de domicílios com coleta regular de lixo, (3) proporção de domicílios com dois ou mais banheiros, (4) proporção de domicílios que recebem água encanada, (5) renda média por domicílio, (6) porcentagem de pessoas brancas e (7) taxa de alfabetização. Usando a metodologia UHI, todos os indicadores de saúde foram padronizados e agregados em uma única métrica para o setor censitário. Os valores de UHI foram classificados e plotados. A razão de disparidade e a inclinação do gráfico foram calculadas. A correlação entre os indicadores foi testada. Os resultados foram geocodificados, produzindo um mapa de risco à saúde. Resultados: A distribuição dos valores do índice apresentou uma seção intermediária linear e desvios nas extremidades. A taxa de disparidade encontrada foi de 2,95, enquanto o coeficiente angular foi 0,30. Todos os indicadores apresentaram correlação significativa. O mapa exibiu um arranjo característico de desigualdade em saúde entre o centro e a periferia. Os setores localizados na região central da cidade apresentaram valores de UHI mais elevados do que os da periferia. Conclusão: Os resultados deste estudo apresentaram uma distribuição visual das disparidades de saúde na cidade de São Paulo, demonstrando ser um método valioso para a identificação de áreas que requerem atenção da saúde pública.

ABSTRACT: Objective: To calculate and map the health inequalities in the city of São Paulo using the Urban Health Index (UHI) methodology. Methods: Seven indicators were selected from the Brazilian census: (1) proportion of households with access to sewage systems, (2) proportion of households served by regular waste collection, (3) proportion of households with two or more toilets, (4) proportion of households receiving tap water, (5) average income per household, (6) percentage of white people, and (7) literacy rate. Based on the UHI methodology, all health indicators were standardized and aggregated into a single metric at the census tract level. The UHI scores were ranked and plotted. The disparity ratio and the graph slope were calculated. The correlation between indicators was tested. Results were geocoded to produce a map of health risks. Results: The distribution of index values showed a linear middle section and deviations at each end. The disparity ratio found was 2.95, while the slope was 0.30. All indicators were significantly correlated. The map displayed a typical pattern of health inequality between the downtown and the periphery. The tracts located in the city’s downtown had higher UHI values than those on the outskirts. Conclusions: The results of this study presented a visual distribution of health disparities in the city of São Paulo, proving to be a valuable method for identifying areas that require public health attention.

Recently, digital wax-up is proposed as a tool to aid prosthodontic planning. However, there are no data about the effect of prosthodontic planning on lateral occlusion scheme.Objective: This study aims to evaluate the impact of conventional and digital prosthodontic planning on lateral occlusion scheme.Material and Methods : Dental models of 10 patients were collected. All models had Angle Class I occlusion and were undergoing prosthodontic treatment that would influence the lateral occlusion scheme. Each set of models had received both conventional wax-up and digital wax-up. In relation to the lateral occlusion scheme, the following variables were evaluated: the prevalence of the different lateral occlusion scheme, number of contacting teeth and percentage of each contacting tooth. Four excursive positions on the working side were included: 0.5, 1.0, 2.0 and 3.0 mm from the maximal intercuspation position.Results : The lateral occlusion scheme of the two wax-up models was subjected to alterations following excursion. There was a tendency for the prevalence of canine-guided occlusion to increase and for the prevalence of group function occlusion to decrease with increasing excursion. The number of contacting teeth was decreasing with the increasing magnitude of excursion. For the 0.5 mm and 1.0 mm positions, the two wax-ups had significantly greater contacts than the pre-treatment models, while at the 2.0 mm and 3.0 mm positions, all the models were similar. For all models, canines were the most commonly contacting teeth, followed by the teeth adjacent to them. No difference was observed between the two wax-ups in relation to the number of contacting teeth.Conclusion : Although the prosthodontic planning had influenced the pattern of the lateral occlusion scheme and contacts, there was no difference between the conventional and digital prosthodontic planning.