Abstract:

Objective: To analyze the main influencing factors of pulmonary tuberculosis-affected households facing catastrophic costs in Baoji City, and to establish a predictive model and formulate strategies to prevent pulmonary tuberculosis-affected households facing catastrophic costs. Methods: Using cross sectional survey design and random cluster sampling, a face-to-face questionnaire investigation was conducted among 813 pulmonary tuberculosis patients who were managed by designated hospitals of 12 counties and had received treatment for more than 2 weeks in Baoji City from March 2021 to February 2022. Lasso-Logistic regression was used to analyze influencing factors of pulmonary tuberculosis-affected households facing catastrophic costs, and a Nomogram prediction model was drawn, using area under curve (AUC) of receiver operating characteristic curve (ROC) and calibration curve to evaluate the model, and using decision curve analysis to assess its practical application value. A validation dataset was used simultaneously for internal validation. Results: A total of 813 patients with pulmonary tuberculosis were included. The incidence of pulmonary tuberculosis-affected households facing catastrophic costs was 54.12% (440/813). Lasso-Logistic regression showed that marital status (OR=1.705,95%CI:1.111-2.617) and hospitalization (OR=5.495,95%CI:3.488-8.656) were independent risk factors, while having family annual income of 28000-60000 yuan or >60000 yuan (OR=0.175,95%CI:0.101-0.302;OR=0.048,95%CI:0.025-0.091) were protective factors. AUC of ROC for the Nomogram prediction model was 0.806 (95%CI: 0.770-0.841), and its mean absolute error was 0.019 for internal validation by Bootstrap method. The Hosmer-Lemeshow test suggested that the fitting degree of the prediction model was good (χ²=4.109, P=0.216). The prediction model had practical value within the threshold range of 0.16-0.96 on the decision curve. Conclusion: The incidence of pulmonary tuberculosis-affected households facing catastrophic costs was relatively high, and their Nomogram prediction model had good discrimination, consistency, and practicality, which could provide reference for preventing pulmonary tuberculosis-affected households facing catastrophic costs.

Key words: Tuberculosis,pulmonary, Forecasting, Nomograms, Models, economic