Spatial analysis of hospitalizations from tuberculosis in Natal/RN through the kernel intensity estimator

• Grant number: 14/09124-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2014
• End date: August 31, 2015
• Field of knowledge: Health Sciences - Nursing - Public Health Nursing
• Principal Investigator: Ricardo Alexandre Arcêncio
• Grantee: Luana Seles Alves
• Host Institution: Escola de Enfermagem de Ribeirão Preto (EERP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

Hospitalizations for tuberculosis (TB) represent an important marker capable of expressing the fragility of access and use of health services, as well as the precarious social and economic conditions of groups and geographic areas . Incorporate spatial approaches to epidemiological studies is a challenge for public health studies. In this sense, the objective is to analyze the spatial distribution of hospitalizations for TB in Natal / RN through the Kernel intensity estimator for identification and representation of the potentially most vulnerable area to the occurrence of such event. Ecological study, whose data for the period 2008 to 2012 will be obtained at the Hospital Information System of the Unified Health System. Will be used as the unit of analysis the census tracts defined by the 2010 Census, the Brazilian Institute of Geography and Statistics. Exploratory data analysis, using measures of central tendency and frequency distribution, with subsequent application of the chi- square test of proportions will be performed with the Statistica ® software. Regarding the spatial analysis will be developed geocoding of cases in cartographic database using the Terraview 4.2.2 software. Then, we will use the analysis of points density - the Kernel Intensity Estimator - for identification and representation of areas with higher densities of hospitalization for TB . The management of information and preparation of thematic maps will be performed in ArcGIS version 10.1 . The Kernel Estimator is very helpful to provide an overview of the distribution of sample points and is indicative of the occurrence of clusters suggests that spatial dependence. Thus, this technique also allows to observe clusters in order to make assumptions about external events that may be causing the event.