Abstract
Healthcare workers (HCWs) have a high risk of acquiring tuberculosis infection (TBI). However, annual testing is resource-consuming. We aimed to develop a predictive model to identify HCWs best targeted for TBI screening. We conducted a secondary analysis of previously published results of 708 HCWs working in primary care services in five Brazilian State capitals who underwent two TBI tests: tuberculin skin test and Quantiferon®-TB Gold in-tube. We used a classification and regression tree (CART) model to predict HCWs with negative results for both tests. The performance of the model was evaluated using the receiver operating characteristics (ROC) curve and the area under the curve (AUC), cross-validated using the same dataset. Among the 708 HCWs, 247 (34.9%) had negative results for both tests. CART identified that physician or a community health agent were twice more likely to be uninfected (probability = 0.60) than registered or aid nurse (probability = 0.28) when working less than 5.5 years in the primary care setting. In cross validation, the predictive accuracy was 68% [95% confidence interval (95%CI): 65 - 71], AUC was 62% (95%CI 58 - 66), specificity was 78% (95%CI 74 - 81), and sensitivity was 44% (95%CI 38 - 50). Despite the low predictive power of this model, CART allowed to identify subgroups with higher probability of having both tests negative. The inclusion of new information related to TBI risk may contribute to the construction of a model with greater predictive power using the same CART technique.
Highlights
Tuberculosis (TB) is a global epidemics that caused an estimated 1.2 million deaths in 20191
We built a predictive model to identify Healthcare workers (HCWs) with negative results for these two tests using a machine learning technique, the classification and regression trees (CART). Since both tests may provide false positive and negative results and there is no evidence that one is superior to the other, we considered that HCWs with negative results of both TST and interferon-gamma release assays (IGRA) were free of TBI4
Since the 1990s, primary care in Brazil has been progressively shifted to the Family Health Strategy model, in which residents of the adjacent area are actively taken in charge by family health teams composed by one medical doctor (MD), one registered nurse (RN), one to two technical nurse assistants (TNA) and six to ten community health agents (CHA) who pay regular home visits, regardless of the demand care[10]
Summary
Tuberculosis (TB) is a global epidemics that caused an estimated 1.2 million deaths in 20191. HCWs without evidence of previous TBI should be annually tested for conversion of one of the available tests, such as the tuberculin skin test (TST) or the interferon-gamma release assays (IGRA), and eventually treated[4]. Those with positive results should be carefully followed up, but no re-testing or treatment is recommended. We built a predictive model to identify HCWs with negative results for these two tests using a machine learning technique, the classification and regression trees (CART) Since both tests may provide false positive and negative results and there is no evidence that one is superior to the other, we considered that HCWs with negative results of both TST and IGRA were free of TBI4
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Schedule a callMore From: Revista brasileira de epidemiologia = Brazilian journal of epidemiology
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