Aplicação da função intensidade no delineamento de clusters de doenças no Estado de Minas Gerais.

• Ano de defesa: 2011
• Autor(a) principal: Cleide Peronico de Almeida
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: Função intensidade; Estatística Espacial Scan; Detecção de Clusters espaciais; Vigilância epidemiológica; Estatística; Estatistica médica; Analise por conglomerados; Análise espacial (Estatística)
• Link de acesso: http://hdl.handle.net/1843/ICED-8NLHMN

The identification of areas at risk for incidence of discrepant illness is of great interest in epidemiological surveillance. The various proposed methods of detection and inference of clusters identify the spatial regions belonging to the most likely cluster, but do not give any information about the regions adjacent to it. The intensity function is a visualization method that studies the plausibility of a map region to belong or not to the most likely cluster detected by the scan statistic. This study is of great importance, directing the work of public health professionals. In this work we study cases of four diseases, namely dengue, diabetes, hypertension and tuberculosis in the State of Minas Gerais by analyzing the results obtained by the Circular Spatial Scan Statistics and the intensity function. For the analysis it was considered population at-risk and the number of cases of each of the 853 municipalities of Minas Gerais State. This information was obtained through the Data Center of the Unified Health System, Brazilian Health Ministry (DATASUS) and the Brazilian Institute of Geography and Statistics (IBGE). The events under study are aggregated by area (the 853 municipalities of Minas Gerais State), and for each disease a visual comparison was made between the disease incidence map and the population at risk map and also between the map with the most likely cluster detected by the circular scan statistics and the map with the intensity function showing the plausibility of the each map region to belong or not to the detected cluster. As a result the intensity function map gives a high value for the regions belonging to the primary cluster detected by the spatial scan statistic and an intermediate but significant value for the regions surrounding the primary cluster, indicating a reasonable plausibility for these regions to belong to the real cluster. Through the results, we illustrate the performance of the intensity function in the evaluation of areas of a map after the detection of a possible cluster by methods like the Circular Spatial Scan Statistics and its importance in aiding decision-making process of health professionals in disease prevention and control.