Objective: To understand the current situation and influencing factors of catastrophic health expenditure (CHE) of migrant workers in Western China. Method: Sample data were obtained by cluster random sampling. Data were entered and sorted using Epidata 3.1 and SPSS 21.0. The statistical analysis involved a descriptive analysis, chi-square tests, multivariate unconditional logistic regression, and multiple correspondence analysis (MCA). Results: A total of 1271 households were surveyed, and the incidence of CHE was 12.5% (159/1271). The multivariate logistic regression showed that households with elderly people over 65 years old (0R = 2.05, 95% CI = 1.42–2.97), children under five years old (0R = 2.61, 95% CI = 1.53–4.48), at least one person with no basic medical insurance (0R = 2.96, 95% CI = 2.08–4.23), chronically ill patients (0R = 1.85, 95% CI = 1.23–2.77), and hospitalized patients (0R = 3.61, 95% CI = 2.31–5.62) contributed to the risk of CHE. Compared to migrant workers in the >30,000 Yuan household per capita annual income group, the 10,001–20,000 Yuan income group (0R = 2.35, 95% CI = 1.44–3.82) and ≤10,000 Yuan income group (0R = 3.72, 95% CI = 2.09–6.62) had a higher risk of CHE occurrence. Compared to migrant workers in the university and above head-of-household education group, those in the primary level or below education group (0R = 5.90, 95% CI = 3.02–11.5) had a higher risk of CHE occurrence. MCA revealed a strong interrelationship between the following risk factors and CHE: household per capita annual income ≤10,000 Yuan, primary school education level or below for the head of the household, and having at least one person in the household with no basic medical insurance. Conclusions: CHE incidence amongst migrant workers in Western China is a serious issue, and policymakers should pay more attention to these migrant workers’ households that are more prone to CHE than others, so as to decrease the incidence of CHE in this group. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Logistic regression Influencing factors Migrant workers Multiple correspondence analysis Catastrophic health expenditure

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