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Yun Wang,1 Zhongfeng Huang,2 Huijuan Chen,3 Ye Yuan,2 Edward B McNeil,4 Xiaolong Lu,5 Aihua Zhang1 1Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, Guizhou, People’s Republic of China; 2Department of Tuberculosis, Guiyang Public Health Clinical Center, Guiyang, Guizhou, People’s Republic of China; 3Department of Tuberculosis Prevention and Control, Guizhou Center for Disease Prevention and Control, Guiyang, Guizhou, People’s Republic of China; 4Department of Infectious Diseases, School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, People’s Republic of China; 5School of Medicine and Health Management, Guizhou Medical University, Guiyang, Guizhou, People’s Republic of ChinaCorrespondence: Aihua Zhang, Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, Guizhou, People’s Republic of China, Tel +86 851 88416187, Fax +86 851 88416187, Email 97349238@qq.comPurpose: We aimed to assess the household financial burden due to multidrug-resistant tuberculosis (MDR-TB) treatment and its predictors, examine its association with patient mobility, and test their impact on patient loss to follow-up (LTFU).Methods: A cross-sectional study combining follow-up data collection was conducted at the largest designated MDR-TB hospital in Guizhou. Data were collected from medical records and questionnaires. Household financial burden was measured by the incidence of 2 indicators: catastrophic total costs (CTC) and catastrophic health expenditure (CHE). Mobility was classified as mover or non-mover after the patient’s address was verified twice. A multivariate logistic regression model was used to identify associations between variables. Model I and Model II were separated by CHE and CTC.Results: Out of 180 households, the incidence of CHE and CTC was 51.7% and 80.6%, respectively. Families with low income and patients who were primary income earners were significantly associated with catastrophic costs. 42.8% of patients were movers. Patients from households with CHE (ORadj=2.2, 95% CI: 1.1– 4.1) or with CTC (ORadj=2.6, 95% CI: 1.1– 6.3) were more likely to move. Finding a job against financial difficulty (58.4%) was the top reason for movers. 20.0% of patients experienced LTFU. Patients from households with catastrophic payments (CHE: ORadj=4.1, 95% CI 1.6– 10.5 in Model I; CTC: ORadj=4.8, 95% CI 1.0– 22.9 in Model II), patients who were movers (ORadj=6.1, 95% CI 2.5– 14.8 in Model I; ORadj=7.4, 95% CI 3.0– 18.7 in Model II) and primary income earners (ORadj=2.5, 95% CI: 1.0– 5.9 in Model I; ORadj=2.7, 95% CI 1.1– 6.6 in Model II) had an increased risk of LTFU.Conclusion: There is a significant association between household financial burden due to MDR-TB treatment and patient mobility in Guizhou. They impact patients’ treatment adherence and cause LTFU. Being a primary breadwinner increases the risk for catastrophic household payments and LTFU.Keywords: household catastrophic costs, migration, loss to follow-up, Guizhou, MDR-TB

Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that generalizes Gaussian ARMA and ARFIMA processes to allow both non-Gaussian marginal behaviour and a non-Gaussian description of the serial partial dependence structure. Extensions of classical causal and invertible representations of linear processes to general s-vine processes are proposed and investigated. A practical and parsimonious method for parameterizing s-vine processes using the Kendall partial autocorrelation function is developed. The potential of the resulting models to give improved statistical fits in many applications is indicated with an example using macroeconomic data.