Efficient Management Strategy of Malaria Patients in China: A Decision-Tree Modelling Approach

Abstract Background: Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis, and preventing them from progressing to severe or critical state. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics, as well as the factors that affect the accuracy in the context of the agenda for a world free of malaria.Methods: A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed.Results: Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level, and provincial-level hospitals were 27.5% (n=136), 26.3% (n=130), and 8.3% (n=41), respectively; the proportions of patients seeking care in clinic, township health center, and Centers for Disease Control and Prevention were 25.9% (n=128), 4.1% (n=20), and 7.9% (n=39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% of patients had complications. The median time from onset to the first visit was 2 days (IQR: 0 - 3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0 - 4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age.Conclusion: Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. Researchers and clinicians should consider strengthening the primary care facilities, time interval between onset and initial diagnosis, administrated residence, and health insurance status to reduce diagnostic errors.