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P1-261 Prediction of dropout tuberculosis treatment on prioritary cities to control in EspÍrito Santo State, Brazil

Bibliographic Data

ID11181193
AuthorsThiago Nascimento Do Prado (0000-0001-8132-6288, Universidade Federal do Espírito Santo), Reynaldo Dietze (0000-0002-6995-8001, Universidade Federal do Espírito Santo), Antonio Ruffino-Netto (0000-0001-9770-4896, Universidade Federal do Espírito Santo), A R Netto, Eliana Zandonade (0000-0001-5160-3280, Universidade Federal do Espírito Santo), Ethel Leonor Noia Maciel (0000-0003-4826-3355, Universidade Federal do Espírito Santo)
Year2011
Volume65
IssueSuppl 1
PagesA138.3-A138
Publication date2011-08-01
Open AccessNo
TypeARTICLE
VenueJournal of Epidemiology and Community Health (JOURNAL)
Journal identifiersISSN: 0143-005X • E-ISSN: 1470-2738
PublisherBMJ (PUBLISHER • GB)
DOI10.1136/jech.2011.142976e.53
OpenAlexW2331623172
LanguageEN

Objectives To identify the epidemiological factors related to the abandonment of TB treatment in the priority municipalities for TB control and to establish a score for use in TB control programs to identify patients most likely to abandon treatment. Methods case-control study matched by sex and notification place to compare patients who dropped out of the treatment (cases) with those who were cured (control) in eight priority municipalities for TB control in the Espirito Santo state, from January 2006 to July 2008. Patients were interviewed directly by one of the researchers, at the clinic or at home. To data analysis we performed a bivariate analysis and the significant results obtained from these analysis were for the logistic regression analysis and network neural artificial (NNA). The questionnaire score was created and validated. Results The study involved 21 cases and 41 controls. In the bivariate analysis, the epidemiological factors involved in the TB treatment dropout were identified as follows: average income, smoking, age, occupation, religion, drugs, previous treatment for TB and willingness to abandon. The logistic regression analysis and neural network revealed that the use of illicit drugs and patient without religion was strongly related to the abandonment. It was found that the neural classification was not more effective than logistic regression for the score marks preparation. Conclusions The score created was able to estimate the treatment dropout cases identified in the study and can be used in programs of TB control to identify patients most likely to abandon TB treatment

Abandonment (legal) · Bivariate analysis · Descriptive statistics · Dropout (neural networks) · Environmental health · Epidemiological method · Logistic regression · Pathology · Statistics · Tuberculosis · Demography · Epidemiology · Health Education and Validation · Internal Medicine · Maternal and Neonatal Healthcare · Medicine · Tuberculosis Research and Epidemiology

Highly citedNo

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