Objective:To analyze the impact of diagnosis-intervention packet payment (DIP) reform on hospitalization service capacity and patients′ economic burden, for references for promoting China′s medical insurance payment reform.Methods:Data were collected from the discharge summarizes of 116 545 hospitalized patients from a tertiary hospital in Guangdong Province. Among them, there were 42 534 cases before the DIP reform (January 2016 to December 2017) and 74 011 cases after the reform (January 2018 to December 2020). The all-cause in-hospital mortality rate, length of hospital stay, disease severity, readmission rate within 30 days, total hospitalization costs, and patient out of pocket expenses were used as evaluation indicators for hospitalization service capacity and patient economic burden. Intermittent time series analysis was conducted to examine the changes in indicators before and after DIP reform.Results:The slope of the change trend of all-cause in-hospital mortality rate and readmission rate within 30 days before and after DIP reform was not statistically significant ( P<0.05); The length of hospital stay showed a decreasing trend before the reform ( P=0.047), but the trend after the reform was not statistically significant ( P=0.776); The change trend of disease severity before the reform was not statistically significant ( P=0.682), but showed a significant upward trend after the reform ( P=0.012); The total hospitalization costs significantly increased during the reform ( P<0.001), but the trend of change after the reform was not statistically significant ( P=0.431); The patient′s out of pocket expenses showed an upward trend before the reform ( P=0.001), but the change trend after the reform was not statistically significant ( P=0.757). Conclusions:DIP reform could help hospitals improve their inpatient service capabilities and enhance their functional positioning; Strengthen medical cost management and control the increase in economic burden on hospitalized patients.

Objective:To analyze the impact of diagnosis-intervention packet payment (DIP) reform on hospitalization service capacity and patients′ economic burden, for references for promoting China′s medical insurance payment reform.Methods:Data were collected from the discharge summarizes of 116 545 hospitalized patients from a tertiary hospital in Guangdong Province. Among them, there were 42 534 cases before the DIP reform (January 2016 to December 2017) and 74 011 cases after the reform (January 2018 to December 2020). The all-cause in-hospital mortality rate, length of hospital stay, disease severity, readmission rate within 30 days, total hospitalization costs, and patient out of pocket expenses were used as evaluation indicators for hospitalization service capacity and patient economic burden. Intermittent time series analysis was conducted to examine the changes in indicators before and after DIP reform.Results:The slope of the change trend of all-cause in-hospital mortality rate and readmission rate within 30 days before and after DIP reform was not statistically significant ( P<0.05); The length of hospital stay showed a decreasing trend before the reform ( P=0.047), but the trend after the reform was not statistically significant ( P=0.776); The change trend of disease severity before the reform was not statistically significant ( P=0.682), but showed a significant upward trend after the reform ( P=0.012); The total hospitalization costs significantly increased during the reform ( P<0.001), but the trend of change after the reform was not statistically significant ( P=0.431); The patient′s out of pocket expenses showed an upward trend before the reform ( P=0.001), but the change trend after the reform was not statistically significant ( P=0.757). Conclusions:DIP reform could help hospitals improve their inpatient service capabilities and enhance their functional positioning; Strengthen medical cost management and control the increase in economic burden on hospitalized patients.

Matching is a standard method for selecting research objects regarding the observational research, which controls confounding factors and improves statistical efficiency. However, its role in controlling confounding is not consistent in different observational studies. Matching can eliminate the confounding bias of matching variables in cohort studies, but checking on itself cannot eliminate confounding bias in case-control studies. In matched case-control studies, researchers may not accurately judge whether the variable is a confounder. Sometimes the variables that are not confounders are mistakenly matched. In that case, it will result in overmatching, which will lead to the decline of statistical efficiency or the introduction of unavoidable bias or increase of workload. If the real confounding factors are omitted, it will cause confounding bias. Therefore, researchers should consider what kind of matching variable selection criteria should be formulated. A directed acyclic graph is a visual graphic language that can show the complicated causality among different epidemiological research designs. This article analyzes the role of Matching in different observational research designs from the perspective of the directed acyclic graph, formulates the selection criteria for matching variables in matched case-control studies, and provides some reference suggestions for future epidemiological research design.