A Comparative Study of Hospitalization Mortality Rates between General and Emergency Hospitalized Patients Using Survival Analysis

Abstract

Background/objectives: In Korea's emergency medical system, when an emergency patient arises, patients receive on-site treatment and care during transport at the pre-hospital stage, followed by inpatient treatment upon hospitalization. From the perspective of emergency patient management, it is critical to identify the high death rate of patients with certain conditions in the emergency room. Therefore, it is necessary to compare and analyze the determinants of the death rate of patients admitted via the emergency room and generally hospitalized patients. In fact, previous studies investigating determinants of survival periods or length of stay (LOS) primarily used multiple or logistic regression analyses as their main research methodology. Although medical data often exhibit censored characteristics, which are crucial for analyzing survival periods, the aforementioned methods of analysis fail to accommodate these characteristics, presenting a significant limitation.

Methods: Therefore, in this study, survival analyses were performed to investigate factors affecting the dying risk of general inpatients as well as patients admitted through the emergency room. For this purpose, this study collected and analyzed the sample cohort DB for a total of four years from 2016 to 2019 provided by the Korean National Health Insurance Services (NHIS). After data preprocessing, the survival probability was estimated according to sociodemographic, patient, health checkup records, and institutional features through the Kaplan-Meier survival estimation. Then, the Cox proportional hazards models were additionally utilized for further econometric validation.

Results: As a result of the analysis, in terms of the 'city' feature among the sociodemographic characteristics, the small and medium-sized cities exert the most influence on the death rate of general inpatients, whereas the metropolitan cities exert the most influence on the death rate of inpatients admitted through the emergency room. In terms of institution characteristics, it was found that there is a difference in determinants affecting the death rate of the two groups of study, such as the number of doctors per 100 hospital beds, the number of nurses per 100 hospital beds, the number of hospital beds, the number of surgical beds, and the number of emergency beds.

Conclusions: Based on the study results, it is expected that an efficient plan for distributing limited medical resources can be established based on inpatients' LOS.

Keywords: Kaplan–Meier survival analysis; cox proportional hazards model; death rate; medical data; national health insurance services cohort DB; survival analysis; survival period.

Figure 1

Current state of EMS in Korea (Source: KOSIS (Korean Statistical Informational Service, Statistics of EMS, 21 November 2022)). The two represent (1) use of emergency room, and (2) admission and death rate in emergency (from left to right).

Figure 2

Example of censored data.

Figure 3

Research framework for investigating determinants of survival time.

Figure 4

Kaplan–Meier survival curves by socio-demographic characteristics (general inpatients). The three charts represent (1) sex, (2) age, and (3) region (from left to right).

Figure 5

Kaplan–Meier survival curves by patient characteristic (general inpatients). The three charts represent (1) type of insurance, (2) income, and (3) disability (from left to right).

Figure 6

Kaplan–Meier survival curves by institution characteristic (general inpatients). The three charts represent (1) hospital type, (2) number of doctors per 100 beds, and (3) number of hospital beds (from left to right).

Figure 7

Cox proportional hazards model indicating the hazard ratio of the general inpatients. (* p < 0.05, *** p < 0.001).

Figure 8

Kaplan–Meier survival curves by socio-demographic characteristics (inpatients admitted through the emergency room). The three charts represent (1) sex, (2) age, and (3) region (from left to right).

Figure 9

Kaplan–Meier survival curves by patient characteristics (inpatients admitted through the emergency room). The three charts represent (1) type of insurance, (2) incom, and (3) disability (from left to right).

Figure 10

Kaplan–Meier survival curves by institution characteristic (inpatients admitted through the emergency room). The three charts represent (1) hospital type, (2) number of doctors per 100 beds, and (3) number of hospital beds (from left to right).

Figure 11

Cox proportional hazards model indicating the hazard ratio of the inpatients admitted through the emergency room. (* p < 0.05, *** p < 0.001).