Jornal Brasileiro de Pneumologia (Oct 2005)

Abandono do tratamento da tuberculose pulmonar em Cuiabá - MT - Brasil Noncompliance with treatment for pulmonary tuberculosis in Cuiabá, in the State of Mato Grosso - Brazil

  • Silvana Margarida Benevides Ferreira,
  • Ageo Mário Cândido da Silva,
  • Clóvis Botelho

DOI
https://doi.org/10.1590/S1806-37132005000500011
Journal volume & issue
Vol. 31, no. 5
pp. 427 – 435

Abstract

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OBJETIVO: Analisar os fatores preditivos de abandono do tratamento da tuberculose pulmonar. MÉTODOS: Estudo de coorte histórica a partir da análise de 481 pacientes bacilíferos, de um total de 529 casos inscritos no Programa de Controle de Tuberculose de Cuiabá (MT), de 1998 a 2000. Os dados foram obtidos do livro de registro do programa e dos prontuários médicos. Para o cálculo das taxas de incidência utilizou-se o método de densidade de incidência. Na análise bivariada utilizou-se o teste do qui-quadrado para razões de taxas de abandono (risco relativo) - Cornfield, ou o exato de Fisher. Foi construído um modelo de regressão logística multivariada visando a identificar as variáveis mais relevantes como preditoras da variável resposta (p OBJECTIVE: To analyze factors predictive of noncompliance with pulmonary tuberculosis treatment. METHODS: A historical cohort study involving 481 of the 529 active tuberculosis patients registered with the Tuberculosis Control Program in the city of Cuiabá, located in the state of Mato Grosso, during the 1998-2000 period. Data were obtained by analyzing medical charts and registration records. In the bivariate analysis, the chi-square test was used to calculate noncompliance rate ratios (relative risk), 95% confidence intervals were determined, and Fisher's exact test was used. The choice to estimate the rate of incidence was the method of density and a multivariate logistic regression model was constructed in order to identify the variables that were most predictive of noncompliance, using a level of statistical significance of p < 0.05. RESULTS: The global rate of incidence was 27.3%, equivalent to 5.1 noncompliant patients/100 patients/month, increasing in the second and third months of treatment. In the final logistic regression model, the following were considered predictors of noncompliance: unsupervised treatment (odds ratio: 2.58; 95% confidence interval: 1.64 - 4.06; p < 0.001); having been treated during the 1998-1999 period (odds ratio: 1.43; 95% confidence interval: 1.14 - 1.80; p = 0.002); being male (odds ratio: 1.39; 95% confidence interval: 1.10 - 1.76; p = 0.005) and having been out of compliance with previous treatment regimes (odds ratio: 1.37; 95% confidence interval: 1.06 - 1.78; p < 0.017). CONCLUSION: The results indicate an elevated incidence of noncompliance and show that unsupervised treatment, year in which treatment was received, male gender and prior noncompliance were predictors of future noncompliance.

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