To investigate potential risk factors associated with Helicobacter pylori (Hp) infection, we performed a case-control study in 167 consecutively selected hospitalized children in Salvador, Brazil. Hp infection was identified by the presence of IgG against Hp in serum samples. Data were gathered using a structured questionnaire, 38.3% children were found to be seropositive and classified as cases, and 61.7% were seronegative controls. After multivariate analysis, independent variables associated with Hp infection included: the educational attainment of the child's provider > or = 11 years (OR 0.1, 95% CI 0.01-0.9), poor garbage disposal service (OR 2.2, 95% CI 1.0-4.9), thumb sucking (OR 4.6, 95% CI 1.1-19.8), brushing teeth more than once a day (OR 5.6, 95% CI 1.8-17.7), having a pet dog (OR 2.5, 95% CI 1.0-6.1), and a history of chronic urticaria (OR 4.0, 95% CI 1.5-10.8). The risk factors identified are consistent with some, but not all, previous studies supporting either oral-oral or faecal-oral transmission of Hp. Our data suggested that a higher educational attainment might play an important role in preventing Hp infection.

IntroducationIn Brazil, the National Health System (SUS) provides healthcare to the public. The system has multiple administrative databases; the major databases record hospital (SIH) and outpatient (SIA) procedures. Epidemiological information is collected for all populations in subsystems, such as mortality (SIM), live births (SINASC) and diseases of compulsory declaration (SINAN). Each subsystem has its own information system, which is able to provide information about consultations, clinical information and medicines dispensed. However, these systems are not linked, thereby preventing individual-centred analysis. ObjectiveTo describe the methods and results of parameter setting that are needed to execute the probabilistic deduplication of large administrative and epidemiological databases in Brazil and to create a National Health Database Centred on the individual. MethodsThis paper shows the results of a record linkage model to integrate data from SIH, SIA, SIM, and SINAN, which have different formats and attributes between them and over time. These data consist of 1.3 billion records from 2000-2015. Probabilistic and deterministic record linkages were used to deduplicate these data. The Kappa statistic and clerical review were used to ensure the quality of the linkage. The graph algorithm and depth-first search were used to generate the identifiers. ResultsThe deterministic deduplication process resulted in a database with 403,113,527 possible unique individuals. After the probabilistic deduplication process of the former database was performed, 159,703,805 unique individuals were identified. This result had an estimated a false positive error rate of 3.3%, and the false negative error was estimated at 12.3%. ConclusionsThe National Health Database centred on the individual was generated and will allow researchers to use real-world evidence to conduct clinical, epidemiological, economic and other studies. This database represents a significant cohort, spanning 15 years of historical data and preserving patient privacy. The success of the process described will allow repeating and appending the data for future years and enable important studies to promote SUS efficiency and provide better treatments for patients.

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RESUMO Este artigo analisa a série histórica de um conjunto de indicadores, de 2002 a 2014, relacionados ao Sistema Único de Saúde do Brasil, embasado na metodologia da Proposta de Avaliação de Desempenho do Sistema de Saúde. Os resultados mostram que houve uma sensível melhoria nos indicadores de dimensão socioeconômica e nos da dimensão condições de saúde. A melhoria dos indicadores de condições de saúde pode estar relacionada ao incremento de suporte financeiro; ao incremento de recursos humanos; ao aumento do acesso às consultas médicas e aos serviços de alta complexidade; e a uma maior disponibilização de horas de profissionais de saúde para a população residente. PALAVRAS-CHAVE

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MethodsSemantic analysis of data was performed to describe and understand different meanings of different fields existing in the studied bases. In addition, there were four main procedures, executed with database operations tools and PLSQL programming language: cleaning and standardization of databases(document's numbers was checked in the brazilian national people's database, with a string approximator algorithm to decide if the document's number belonged or no the register); registration information extraction, deterministic and probabilistic deduplication thereof. The procedures were first performed on each database separately and after the unification of the records, was held again a deterministic deduplication. Except the probabilistic deduplication which was performed only on the final deterministic deduplicated's database.Performed procedures allowed a decision-making to chose fields used in data model for the unified database creation. Nine database's representative fields related to patients were selected: patient's name; patient mother's name; sex; birth date; state; city; zip code; cpf and cns(brazilian documents). ResultsInitially, the unified registration database resulted in 705.599.785 records, after deterministic deduplication there was a reduction culminating in 198.400.762 records. This reduction is explained because these databases are not fully integrated. Moreover, there is not always agreement between systems' semantics and in some cases changes occur in the data format over the period within the same system. After probabilistic deduplication, the number of unique records decreased to 124.545.186 which is explained by non-linked pairs by deterministic process. This result is guaranteed with a estimate error of at most 3.3% of false positive and at most 12.3% of false negative pairs. ConclusionThe results show that data deduplication is necessary and should be carried out thoroughly. Where the databases had limited patients' registration information, the technique enabled to capture, in more complete basis, additional information. Futhermore, it allowed to identify and assist in the understanding of positive and negative aspects within systems and trace clinical condition of patients, enabling pharmacoeconomic and epidemiological studies that define effectiveness and efficiency of public policies and embedded technologies. As future work, is important ensure the univocity of records and link this database with past period.

Ações e iniciativas de monitoramento e avaliação acompanham o desenvolvimento dos sistemas de saúde. O estágio atual de reconhecimento da complexidade da gestão de sistemas e serviços, em particular no caso brasileiro, devido à abrangência, ao volume de serviços e procedimentos e às características das disposições legais relativamente à organização, mostra a necessidade de uma política e de um sistema de monitoramento e avaliação. Em decorrência dessa necessidade, o Departamento de Monitoramento e Avaliação do SUS (DEMAS) da Secretaria Executiva do Ministério da Saúde, vem sistematizando e apresenta a proposta de um "Sistema de Avaliação para a Qualificação do SUS". O sistema de avaliação do SUS se constitui por um conjunto de programas de avaliação, relativamente independentes, mas relacionados, concatenados e complementares entre si, de modo a formarem um complexo que vise produzir, por meio de avaliações, um conjunto de informações necessárias e estratégicas ao desenvolvimento e qualificação do SUS, quanto ao cumprimento de seus princípios e diretrizes. A apresentação e análise descritiva apresentadas neste artigo incluem os seguintes componentes: o Índice de

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