Modelo preditivo ao abandono do tratamento da tuberculose

Abstract

The abandonment of the tuberculosis treatment and the irregular therapy constitute most serious problems in controlling this disease in the country, promoting the morbimortality, and favoring the resistance in medications and constituting the negative impact factor on controlling the disease. In the accomplished researches referring to the abandon of the tuberculosis, it is still rare the usage of logistic aggression with an adequate model to realize a discriminant analysis, meaning, be able to identify the individuals in which the upshot will be the abandon, and proposal of a supervised treatment. The objective of this actual study was to propose a logistic regression model as viable explanations of the socio-demographic characteristics of the individuals with tuberculosis in Joao Pessoa, and decide, with basis on the model, which treatment would be more adequate. The research used a quantitative and inductive approach method, with a statistic-comparable procedure and intensive direct observation of secondary data registry. The sample (n=1925) was constituted by the notified population of care in the System of Information of Care of Notification (SINAN) database, referring to the city of Joao Pessoa in the state of Paraiba, in the period between January of 2011 through December of 2008. For the descriptive analysis and the logistic regression estimation model the software R version 2.11.1, was used the backward selection method, with a 5% significance level in a way that in the end of the process the variables, (sex, age, race, schooling, and type of entry), that better explained the answer of the variable (final situation) was stated in the model. In the final model the significant variables was to have a schooling level of High School (OR= 1,56), in College level (OR= 42,99), other races (OR= 2,29), the reinstatement after abandonment (OR= 2,71), all the risk factors to abandonment of the tuberculosis treatment. Therefore it s concluded that the identification and quantification of the variables that influence the final state, the decision making will have to be directed to the adequate model for identification of the individuals that will abandon the treatment, so that way they can have a supervised treatment, in a way that this decision doesn t involve unnecessary costs.