ABSTRACT
Objective To summary the hospitalization costs of lung cancer patients, and analyze the influence factors in these patients, and provide basis for controlling hospitalization costs of lung cancer patients. Methods The hospitalization costs data of hospitalized lung cancer cases in Wuhan from 2018 to 2020 were collected from medical records. Nonparametric test was used to analysis the data for single factor analysis. The patients were divided into two groups according on the upper quartile value of hospitalization cost, that is high-cost group (the cost ≥ the upper quartile value) and normal cost group (the cost “four major hospitals in Hubei” respectively. The hospital type is an independent influencing factors, compared with specialized hospital, the OR is 4.726 for general hospital. The hospitalization days is the independent influencing factors, more hospitalization days has high cost. The treatment mode is the independent influencing factors, compared with non-operative treatment, the OR is 556.129, 18.156 and 5.212 for surgical model, radio therapy model and interventional model respectively. Conclusion The age, hospital level, hospital type, hospitalization days and treatment mode are the independent influencing factors of hospitalization costs. To reduce the hospitalization cost of lung cancer patients, we should standardize the diagnosis of lung cancer patients, and focus on standardizing the treatment mode, also considering other influencing factors, such as hospital level, hospital type.
ABSTRACT
Objective To analyze the equity of the allocation of oral medical resources and the accessibility of health service capabilities in Wuhan. Methods The equity of oral medical resources was calculated with Gini coefficient and Theil index, accessibility was assessed by two-step floating catchment area model, and the spatial autocorrelation was used to study the high-low clustering of accessibility. Results The Gini coefficient of oral medical resources based on population level was around 0.3, and the Gini coefficient of oral medical resources based on geographic area was greater than 0.6. Theil index calculation results were similar. In terms of overall accessibility, the area with poor accessibility was 2,428 square kilometers, reaching 28.38% of the total area, while the area with better accessibility accounted for 14.18%. Conclusion The allocation of oral medical care resources based on population distribution was fairer and better than that based on geographic area. Moreover, the geographical accessibility varies greatly between regions, showing the characteristics of high-high cluster and low-low cluster.