BackgroundWith the increasing of ageing population, tuberculosis in the elderly brings a challenge for the tuberculosis (TB) control in China. Enough social support can promote the treatment adherence and outcome of the elderly patients with TB. Exploring effective interventions to improve the social support of patients is of great significance for TB management and control.MethodsA community-based, repeated measurement trial was conducted. Patients with TB >65 years of age were allocated into the intervention or control group. Patients in the intervention group received comprehensive social support interventions, while those in the control group received health education alone. The social support level of patients was measured at baseline and at the first, third and sixth months during the intervention to assess the effectiveness of comprehensive social support interventions.ResultsA total of 201 patients were recruited into the study. Compared with the control group, social support for patients in the intervention group increased significantly over time (βgroup*time=0.61, P<0.01) in the following three dimensions: objective support (βgroup*time=0.15, P<0.05), subjective support (βgroup*time=0.32, P<0.05) and support utilisation (βgroup*time=0.16, P<0.05). The change in the scores in the control group was not statistically significant.ConclusionsThe intervention programme in communities, including health education, psychotherapy and family and community support interventions, can improve the social support for elderly patients with TB compared with single health education.Trial registration numberChiCTR-IOR-16009232

No abstract

Multidrug-resistant tuberculosis (MDR-TB) has become a major public health problem. We tried to apply the classification tree model in building and evaluating a risk prediction model for MDR-TB. In this case-control study, 74 newly diagnosed MDR-TB patients served as the case group, and 95 patients without TB from the same medical institution served as the control group. The classification tree model was built using Chi-square Automatic Interaction Detectormethod and evaluated by income diagram, index map, risk statistic, and the area under receiver operating characteristic (ROC) curve. Four explanatory variables (history of exposure to TB patients, family with financial difficulties, history of other chronic respiratory diseases, and history of smoking) were included in the prediction model. The risk statistic of misclassification probability of the model was 0.160, and the area under ROC curve was 0.838 ( < 0.01). These suggest that the classification tree model works well for predicting MDR-TB. Classification tree model can not only predict the risk of MDR-TB effectively but also can reveal the interactions among variables.

No abstract