[A proposal for using data from antimicrobial prescriptions: the EUREQA experience]. / Proposta sobre uso de dados de receitas de antimicrobianos retidas: a experiência EUREQA.

This study demonstrates that the use of information from medical prescriptions is essential for understanding the dynamics of community bacterial resistance. The resulting analysis can also influence and help establish more adequate public health policies on the control and optimization of antimicrobial use. The article demonstrates the use of a logical model developed by the EUREQA project for acquisition, classification, interpretation, and analysis of data from prescriptions for oral antimicrobial use.

Proposta sobre uso de dados de receitas de antimicrobianos retidas: a experiência EUREQA / A proposal for using data from antimicrobial prescriptions: the EUREQA experience

A presente nota pesquisa demonstra que o uso das informações de receituário ou prescrição médica tem fundamental valor para a compreensão das correlações da dinâmica da resistência bacteriana comunitária. Além disso, a análise dos dados gerada pode ajudar a estabelecer medidas e políticas de saúde pública mais adequadas para o controle e a otimização do consumo de antimicrobianos. Para isso, o artigo usa como base o modelo lógico desenvolvido pelo Projeto EUREQA voltado para aquisição, classificação, interpretação e análise das informações relacionadas à prescrição dos antimicrobianos de uso oral.

This study demonstrates that the use of information from medical prescriptions is essential for understanding the dynamics of community bacterial resistance. The resulting analysis can also influence and help establish more adequate public health policies on the control and optimization of antimicrobial use. The article demonstrates the use of a logical model developed by the EUREQA project for acquisition, classification, interpretation, and analysis of data from prescriptions for oral antimicrobial use.

Spatial exploration of Streptococcus pneumoniae clonal clustering in São Paulo, Brazil

OBJECTIVES: To examine the spatial distribution of Streptococcus pneumoniae and its clonal patterns collected between 2002 and 2006 in São Paulo, Brazil. METHODS: As part of an observational study in São Paulo city, Brazil, S. pneumoniae isolates routinely cultured from blood, respiratory specimens, or cerebrospinal and other profound fluids were selected. Additionally, only isolates with either penicillin (PEN) intermediate (I) or resistant (R) status on routine antibiogram were included, in order to obtain a higher probability of clonal isolates. A single I/R S. pneumoniae isolate per patient was included and submitted to genotypic determination by pulsed field gel electrophoresis (PFGE). Minimum inhibitory concentrations (MICs) were determined for the isolates by Etest® to PEN and other antimicrobials. Each isolate was geocoded in a digital map. The Kernel function and ratio methods between total isolates vs. clones were used in order to explore possible cluster formations. RESULTS: Seventy-eight (78) S. pneumoniae community isolates from two major outpatient centers in São Paulo, Brazil, were selected from the databank according to their penicillin susceptibility profile, i.e. R or I to penicillin assessed by oxacillin disc diffusion. Of these, 69 were submitted to PFGE, 65 to MIC determination, and 48 to spatial analytical procedures. Preliminary spatial analysis method showed two possible cluster formation located in southwest and southeast regions of the city. CONCLUSION: Further analyses are required for precisely determining the existence of S. pneumoniae clusters and their related risk factors. Apparently there is a specific transmission pattern of S. pneumoniae clones within certain regions and populations. GIS and spatial methods can be applied to better understand epidemiological patterns and to identify target areas for public health interventions.

Spatial exploration of Streptococcus pneumoniae clonal clustering in São Paulo, Brazil.

OBJECTIVES: To examine the spatial distribution of Streptococcus pneumoniae and its clonal patterns collected between 2002 and 2006 in São Paulo, Brazil. METHODS: As part of an observational study in São Paulo city, Brazil, S. pneumoniae isolates routinely cultured from blood, respiratory specimens, or cerebrospinal and other profound fluids were selected. Additionally, only isolates with either penicillin (PEN) intermediate (I) or resistant (R) status on routine antibiogram were included, in order to obtain a higher probability of clonal isolates. A single I/R S. pneumoniae isolate per patient was included and submitted to genotypic determination by pulsed field gel electrophoresis (PFGE). Minimum inhibitory concentrations (MICs) were determined for the isolates by Etest® to PEN and other antimicrobials. Each isolate was geocoded in a digital map. The Kernel function and ratio methods between total isolates vs. clones were used in order to explore possible cluster formations. RESULTS: Seventy-eight (78) S. pneumoniae community isolates from two major outpatient centers in São Paulo, Brazil, were selected from the databank according to their penicillin susceptibility profile, i.e. R or I to penicillin assessed by oxacillin disc diffusion. Of these, 69 were submitted to PFGE, 65 to MIC determination, and 48 to spatial analytical procedures. Preliminary spatial analysis method showed two possible cluster formation located in southwest and southeast regions of the city. CONCLUSION: Further analyses are required for precisely determining the existence of S. pneumoniae clusters and their related risk factors. Apparently there is a specific transmission pattern of S. pneumoniae clones within certain regions and populations. GIS and spatial methods can be applied to better understand epidemiological patterns and to identify target areas for public health interventions.

[Mapping of homicide risk with binomial cokriging and simulation: a case study in São Paulo, Brazil]. / Mapeamento do risco de homicídio com base na co-krigeagem binomial e simulação: um estudo de caso para São Paulo, Brasil.

The impact of violence on the population's health profile in large Brazilian cities is a real concern and has been incorporated into the country's public health agenda. Although violent crimes affect the entire population, they are distributed unequally in the city. In this context, analytical tools to evaluate the risk and spatial distribution of homicide are important for surveillance and prevention. The current article is a contribution in this direction. A geostatistical methodology is used to estimate and map homicide risk, and a new semivariogram estimator is presented and its application is evaluated in the city of São Paulo, Brazil, for the period 2002 to 2004. Homicide risk scenarios are generated using binomial cokriging and stochastic simulation procedures, allowing the demarcation of areas with greater or lesser risk that can be used to study spatial determinants of violence and improve the understanding of problems through analysis of differences across urban territories.

Mapeamento do risco de homicídio com base na co-krigeagem binomial e simulação: um estudo de caso para São Paulo, Brasil / Mapping of homicide risk with binomial cokriging and simulation: a case study in São Paulo, Brazil

O impacto da violência sobre o perfil de saúde da população nos grandes centros urbanos brasileiros é uma realidade e tem sido incorporado na agenda das ações de saúde. Embora o crescimento dos crimes contra a vida seja sentido por toda a população, a sua distribuição pela cidade ocorre de modo desigual. Nesse contexto, ferramentas de análise que permitam produzir uma avaliação do risco de homicídio e de sua distribuição espacial potencializam os meios de vigilância e prevenção. Este artigo oferece uma contribuição nessa direção. Uma metodologia geoestatística é empregada para a estimação e mapeamento do risco de homicídio, sob o enfoque de um novo estimador de semivariograma, e tem sua aplicação avaliada na cidade de São Paulo, Brasil, para o período de 2002 a 2004. Cenários para o risco de homicídio são gerados com base na co-krigeagem binomial e de procedimentos de simulação estocástica, possibilitando a delimitação de áreas com maior ou menor risco, que podem ser usados para estudar condicionantes espaciais da violência e auxiliar para um melhor entendimento dos problemas por meio da análise de suas diferenças ao longo dos territórios das cidades.

The impact of violence on the population's health profile in large Brazilian cities is a real concern and has been incorporated into the country's public health agenda. Although violent crimes affect the entire population, they are distributed unequally in the city. In this context, analytical tools to evaluate the risk and spatial distribution of homicide are important for surveillance and prevention. The current article is a contribution in this direction. A geostatistical methodology is used to estimate and map homicide risk, and a new semivariogram estimator is presented and its application is evaluated in the city of São Paulo, Brazil, for the period 2002 to 2004. Homicide risk scenarios are generated using binomial cokriging and stochastic simulation procedures, allowing the demarcation of areas with greater or lesser risk that can be used to study spatial determinants of violence and improve the understanding of problems through analysis of differences across urban territories.