TY - JOUR
AU - Zhang,, Lulu
AB - Abstract Introduction A retrospective review conducted in three hospitals of Guangdong and Hainan, China. To analyze the variation tendency of mean hospitalization costs and length of stay (LOS) in naval hospitals over nine years, paying special attention to the factors affecting hospitalization costs and LOS to provide a reference base for health resource allocation in naval hospitals. Materials and Methods A total of 21,375 cases of military patients who were hospitalized and treated in three naval hospitals between January 2008 to December 2016 were extracted from the military health system. There were 16,278 complete and effective cases during those nine years. The situation, trends, and factors influencing hospitalization costs and LOS were analyzed using descriptive statistics, Mann-Whitney U test, Kruskal–Wallis H test, and multiple linear regressions. Results The following factors showed statistically significant differences in hospitalization costs: special care, primary care, year, military rank, critical illness, allergies, and condition (p < 0.0001); and number of hospitalizations, gender, and age (p < 0.01). The following factors showed statistically significant differences in hospital LOS: year, number of hospitalizations, outcomes, military rank, special care, severity of illness, and condition (p < 0.0001); allergy (p < 0.01); and service and gender (p < 0.05). LOS between 2008–2016 showed a decreasing tendency, while hospitalization costs showed an increasing trend. There were 6 factors that affected Abstract (or Structured Summary) both the cost of hospitalization and LOS: special care, year, military rank, condition, allergy, and gender. Conclusions The results suggest that improving efficiency of military hospital require strengthening hierarchical referrals and controlling hospital LOS. Shortening LOS, optimizing clinical pathways, and reasonably controlling the costs associated with medicines and surgery can help reduce hospitalization costs for military patients. Controlling the growth of hospitalization costs can help avoid the physical and psychological burden of medical over-treatment on patients and may also optimize the allocation of military health resources. length of stay, hospital costs, influencing factors, naval hospital INTRODUCTION The patient composition of naval hospitals differs from that of local public hospitals, and research shows that patients admitted to naval hospitals include military personnel, civilians, children, and adults with a wide range of diseases [1]. The utilization efficiency of naval hospital resources is evaluated primarily using two indicators: hospitalization costs and hospital length of stay (LOS) [2]. The current rapid increase in medical expenses has become an issue of concern at all levels of governmental departments, medical institutions, and the general public, and is particularly prominent in large-scale tertiary hospitals. The China Health Statistics Yearbook shows that the growth rate of medical expenses has greatly exceeded the level of residents’ income growth [3], and the burden on the medical security system has increased, which is detrimental to the long-term sustainable development of medical institutions. Shortening patients’ hospitalization periods can effectively reduce medical expenses, increase the utilization rate of hospital beds and collection capacity, reduce patients’ medical expenses, and improve hospitals’ social and economic benefits. Hospital LOS is the average duration, in days, for hospital admissions and is influenced by clinical and socioeconomic factors [4,5]. It is an important index that measures work efficiency, technical level, medical quality, and rational allocation of hospital health resources, and reflects a hospital’s operational and managerial capacities as well as its medical, nursing, and technological capabilities. Research has reported that reducing the LOS is essential to reduce the cost of care [6]. There are also known associations between increased LOS and increased adverse outcomes such as hospital-acquired infections [7] and venous thromboembolic events [8]. Prior studies have demonstrated that LOS is an important component and measure of the overall cost and value of elective surgical services [9–11]. There are many studies on hospitalization costs and LOS in public hospitals to date, but they tend to focus on a single disease type, mainly involving local patients [10,12]; there is a lack of research on naval hospitals. The Chinese People’s Liberation Army began its reformation in 2015 and there are no studies comparing military patients’ status before and after the reformation, and using large samples to identify factors influencing hospitalization costs and LOS in naval hospitals. While there have been hospitalization costs and LOS studies on Chinese naval hospitals that focus on a single disease [13,14], there is a lack of research that consider hospitals as a whole, especially naval hospitals [15]. In-depth research on hospitalization costs and LOS of military patients is needed to identify and control the factors that drive costs and LOS. This information can help reduce the medical burden and increase the rational use of military health resources. The purpose of this study is to: 1) analyze trends of patients’ hospitalization costs and LOS in three naval hospitals between 2008 and 2016, 2) analyze the factors affecting hospitalization costs and hospital LOS, and 3) explore ways to reduce hospitalization costs and shorten LOS by controlling relevant influencing factors and providing a reference for the allocation of health resources in naval hospitals. MATERIALS AND METHODS Study Design and Setting We cooperated with the three Central Hospitals of the South China Sea Fleet to collect data on 21,375 patients who were hospitalized and treated since January 2008 to December 2016. These included a total of 16,278 valid cases (excluding military family members, employees, missing cases, and patients aged less than 18 years). Demographic, operative, postoperative, and discharge data were analyzed. Factors included in the medical record were divided into three categories: (1) demographic characteristics, including basic patient information (hospital number, ID number, unit, address, gender, age, service, and military rank); (2) disease characteristics, including admission diagnosis, admission department, admission date, number of hospitalizations, condition, critical illness, severe illness, first-level care, special care, intensive care unit (ICU), allergy, surgery, discharge diagnosis, discharge department, and discharge date; and (3) treatment results, hospitalization costs, and hospital LOS. To protect the privacy of patients, the data collection process filtered personally identifiable information such as names. Patients were not directly involved in this study. Data Analysis All analyses were conducted using the IBM SPSS Statistics, version 21.0 (SPSS Inc., Chicago, IL). The effects of demographic, disease characteristic, and treatment result variables on the outcome measures, hospitalization costs and LOS were evaluated. The hospitalization costs and LOS measures were not normally distributed; therefore, the median was used to describe the average level. If the grouping factor was a binary variable (gender, critical illness, severe illness, special nursing, primary care, surgery, etc.), Mann-Whitney U tests were used for between-group comparisons. If the grouping factor was a multi-class variable (year, age, number of hospitalizations, outcome, military rank, condition, service, etc.), Kruskal–Wallis H tests were used for comparisons. Factors showing statistically significant differences in the univariate analysis were analyzed using multiple linear regression; all tests were two-tailed and a significance level of α = 0.05 was used. Ethics Statement This study was approved by the ethics committee of the Second Military Medical University, Shanghai. All procedures performed in this study were in accordance with the ethical standards of the institutional and national research committees and the 1964 Declaration of Helsinki, and its later amendments or comparable ethical standards. Access to medical records was approved by the three participating hospitals and all patients, since we cooperated with the three hospitals from 2008. Informed consent was obtained from the participants. We guaranteed the privacy and personal information security of all participants. All participating centers accepted responsibility for their data and consented to source data monitoring. RESULTS General Characteristics A total of 16,278 military patients and 24 departments were included in this study. The top five hospitalization departments were general surgery (19.9%), traumatic orthopedics (13.5%), urological surgery (7.4%), respiratory medicine (7.2%), and otolaryngology head and neck surgery (6.8%). The top five patient birthplaces were the Hunan Province (13.9%), Guangdong Province (10.2%), Henan Province (10.2%), Hubei Province (10.0%), and Shandong Province (9.0%). Hans (96.6%), Manchu (0.3%), Zhuang (0.2%), and Tujia (0.1%) accounted for the top four ethnic groups. Between 2008 to 2016, the number of patients hospitalized was variable, with the highest number hospitalized in 2016 and the lowest in 2013. Patients were mostly 20–30 years old, naval, men, and soldiers. According to the 2016 National Bureau of Statistics data [16], hospitalization costs were adjusted according to the price index of urban residents’ health care and personal items. Excluding inflation, there was a statistically significant difference in hospitalization costs over the years (p < 0.0001). The median hospitalization cost for military patients in 2008 was 2318¥ (357 USD), and 7702¥ (1185 USD) in 2016; A 232% increase over nine years. Except for a slight decrease in 2015, the quartile and median hospitalization costs generally showed an upward year-over-year trend and were higher in naval hospitals compared to that in public hospitals. There was also a statistically significant difference in LOS across years (p < 0.0001), with the median LOS being 15 days in 2008 and 12 days in 2016, which is a reduction of 20% in nine years. Except for a slight increase in 2010, the median LOS showed a year-over-year decline and was higher in naval hospitals than in public hospitals. The median hospitalization costs in the top five departments were statistically different from year to year (p < 0.0001). The median hospitalization cost showed a significant upward trend in all five departments, with the smallest increase (66.0%) in urological surgery and the highest (847.8%) in otolaryngology head and neck surgery (Fig. 1). FIGURE 1 Open in new tabDownload slide Variation of median hospitalization costs between 2008 and 2016 (¥). FIGURE 1 Open in new tabDownload slide Variation of median hospitalization costs between 2008 and 2016 (¥). The median LOS changed significantly across the years for otolaryngology head and neck surgery, urological surgery, general surgery (p < 0.0001), and traumatic orthopedics (p < 0.01), all showing a downward trend, while the median LOS change for respiratory medicine (p < 0.01), showed an upward trend (Fig. 2). FIGURE 2 Open in new tabDownload slide Variation of median hospitalization costs between 2008 and 2016 (¥). FIGURE 2 Open in new tabDownload slide Variation of median hospitalization costs between 2008 and 2016 (¥). Outcomes Hospitalization Costs Gender had no significant influence on hospitalization costs (p > 0.05). Service type was determined to be significantly associated with hospitalization costs where median costs were highest for Air Force personnel and lowest for Navy personnel (p < 0.0001). Number of hospitalizations was determined to be significantly associated with hospitalization costs where median costs were highest for patients with more than three hospitalizations and lowest for those hospitalized for the first time (p < 0.0001). Military rank was determined to be significantly associated with hospitalization costs where median costs were highest for generals and senior military officers and lowest for lieutenant colonels and other officers (p < 0.0001). Critical illness was determined to be significantly associated with hospitalization costs where median costs were highest for patients with critical conditions and lowest for those with general conditions, (p < 0.0001). Age was determined to be significantly associated with hospitalization costs where median costs were highest for those older than 50 years and lowest for those aged 20–30 years old (p < 0.0001). Outcome was determined to be significantly associated with hospitalization costs where median costs were highest for the treatment invalid group and lowest for the patients who were untreated regarding outcome (p < 0.0001). Hospitalization costs was higher in the group characterized by severe illness, critical illness, special care, first-level care, allergy, surgery, and ICU admission (p < 0.0001). Multiple linear stepwise regression analysis was performed on factors that were significant in the univariate analysis of hospitalization costs. The following factors significantly affected hospitalization costs: LOS, special care, first-level care, years, military rank, critical illness, allergy, and condition (p < 0.0001); and number of hospitalizations, gender, and age (p < 0.01). Hospital LOS Except for ICU admission and allergy had no significant effect on LOS (p > 0.05). Service type was determined to be significantly associated with hospital LOS where median LOS was shortest for the air force (p < 0.0001). Condition was determined to be significantly associated with hospital LOS where median LOS was longest in the critical group and shortest in the acute group (p < 0.0001). Number of admissions was determined to be significantly associated with hospital LOS where median LOS was longest for patients with more than three hospitalizations and shortest for those hospitalized for the first time (p < 0.0001). Military rank was determined to be significantly associated with hospital LOS where median LOS was longest for generals and senior military officers and shortest for lieutenant colonels and other officers (p < 0.0001). Age was determined to be significantly associated with hospital LOS where median LOS was longest for those older than 50 years and shortest for those 30–40 years old (p < 0.0001). Outcome was determined to be significantly associated with hospital LOS where median LOS was longest for the invalid group and shortest for the deceased group (p < 0.0001). Inpatient department was determined to be significantly associated with hospital LOS where median LOS was longest in the traumatic orthopedics department and shortest in the obstetrics department (p < 0.0001). And longer for the male group and those who had severe illness, critical illness, special care, first-level care, and surgery (Table I). The following factors significantly affected LOS: hospitalization costs, year, number of hospitalizations, outcome, military rank, special care, severe illness, and condition (p < 0.0001); allergy (p < 0.01); and service and gender (p < 0.05). Factors affecting both hospitalization costs and LOS were special care, year, military rank, and condition (p < 0.0001); allergy (p < 0.01); and gender (p < 0.05) (Table II). DISCUSSION In the analysis of factors influencing hospitalization costs, the median cost of hospitalization for patients with severe disease, critical illness, special nursing care, first-level care, allergy, and surgery was consistent with existing studies [16–20]. Reasonable control of drug and surgery-related costs will help to control hospitalization costs and reduce over-treatment. It was observed that age has an important impact on hospitalization costs [21]. This study confirmed that hospitalization costs for patients, who had longer LOS, were critically ill, and undergoing surgery was higher. Hospitalization costs were also higher for patients in the ICU and lowest for those in the department of respiratory medicine, which may be related to the severity of illness of ICU patients and the low cost of medical supplies for respiratory medicine. When adjusted for the impact of inflation over time, the factors influencing hospitalization costs for patients in all departments included lower drug costs in the change of diseases spectrum, application of new technologies and methods, excessive use of inspections, high prices of drug consumables, inspection equipment upgrades, surgery, and improvement in consumables quality. The decrease in 2015 was related to the reformations in Chinese naval hospitals, indicating that naval hospitals were greatly affected by the policies. China has reduced the number of military personnel and the navy has been making great efforts to implement this reform. All these efforts affect the number of patients. Regarding the factors influencing hospital LOS, the median LOS for patients with severe disease, critical illness, special nursing, first-level care, and surgery was longer than that for patients without such conditions; furthermore, male patients had longer LOS than female patients. Compared with female soldiers, young male fighters may be exposed to heavier training tasks, stricter management, and may be more likely to be hospitalized [22]. Severe illness and surgery were important influencing factors for a prolonged LOS. The more complicated the condition, the longer the patient’s hospital LOS, which is consistent with public hospital research [22–25]. Although studies have shown that race affects hospital LOS [21,22], the sample size of ethnic minorities in this study was relatively small; thus, only a descriptive analysis was conducted, with the most common ethnic minority being Manchu. Moreover, hospital LOS in all departments (excluding respiratory medicine) showed a downward trend which was related to technological improvements, treatment procedure optimization, and improvements in managerial efficiency. In contrast, the LOS for patients in the respiratory medicine department showed an upward trend. This may be a result of hospital restructuring where respiratory medicine departments in all hospitals were separated from internal medicine departments in the last nine years. Changes in disease types before and after the division of the departments show that disease type has a great influence on hospital LOS. A previous study reported that age was not a significant factor in prolonged LOS [26]; however, most studies have shown that age is an important factor affecting LOS [21,22,25,27]. This study found that LOS was longest for patients older than 50 years, and shortest for those aged 30–40 and 40–50 years, indicating that senior military personnel use more medical resources, which is related to their importance in rank and lower physical strength, while military personnel aged 30–50 are the main force and need to return to work as soon as possible. Many other factors, such as diagnosis and social support, can also predict LOS [28–31]. This study found a positive association between hospitalization costs and hospital LOS, which was confirmed by multiple studies [32–36]. Sun et al. found that longer LOS, and an increased number of episodes of care, was the two main contributors to increase in expenses, and that drug expenses accounted for over half of the medical expenses [37]. Taheri et al. demonstrated that reducing LOS yielded large cost savings, namely, reducing LOS by as much as one full day reduced the total cost of care on average by 3% [38]. Health systems pay more attention to resource utilization efficiency, effectiveness, and quality [39,40]. Decreased hospital LOS can reduce utilization costs while longer LOS impacts the efficient utilization of surgeon time and departmental surgical volume [41]. Service providers may be interested in diminishing costs and remaining profitable, and insurers may wish to cut their expenses. This may be one of the reasons for the significant decline in mean LOS and quality of psychiatric admissions across the United States [42]. Shortening LOS can effectively improve the bed turnover rate, increase the number of inpatients, and reduce medical expenses. TABLE I Comparison of Median Hospitalization Costs and LOS in Terms of Influencing Factors Parameter . Category . Frequency(%) . Hospital costs(¥) . P . LOS(d) . P . Demographic characteristics  gender   male 15623(96.0) 4,636 0.713 13 <0.0001   female 655(4.0) 4,706 10  age   18–20 2,597(16.0) 4,722 <0.0001 13 <0.0001   20–30 9,594(58.9) 4,320 14   30–40 2,542(15.6) 4,571 11   40–50 642(3.9) 5,439 11   ≥50 903(5.5) 9,916 18  Military rank   General and senior military officers 65(0.4) 13265 <0.0001 11.5 <0.0001   Senior colonel officers 818(5.0) 9,258 11   Lieutenant colonel and other officers 2,611(16.0) 4,334 6   Soldiers 12784(78.5) 4,499 8  service   Navy 13465(82.7) 4,580 <0.0001 13 <0.0001   Army 1,785(11.0) 4,851 13   Air Force 1,028(6.3) 5,196 12 Disease characteristics  Number of hospitalizations   1 10612(65.2) 4,355 <0.0001 12 <0.0001   2 3,088(19.0) 4,699 14   ≥3 2,578(15.8) 6,495 17  Condition   Critically 2(0.0) 627471 <0.0001 122 <0.0001   Acute 453(2.8) 6,442 10   General 15823(97.2) 4,600 13  Severe illness   No 16208(99.6) 4,621 <0.0001 13 <0.0001   Yes 70(0.4) 33423 29  Critical illness   No 16241(99.8) 4,628 <0.0001 13 <0.0001   Yes 37(0.2) 49970 30  ICU   No 16207(99.6) 4,627 <0.0001 13 0.051   Yes 71(0.4) 14116 19  Special nursing   No 16208(99.6) 4,605 <0.0001 13 <0.0001   Yes 70(0.4) 29439 20  First-level care   No 14067(86.4) 4,056 <0.0001 13 <0.0001   Yes 2,211(13.6) 12026 14  Allergy   No 15846(97.3) 4,586 <0.0001 13 0.770   Yes 432(2.7) 7,048 13  Surgery   No 11229(69.0) 3,689 <0.0001 12 <0.0001   Yes 5,049(31.0) 6,543 14  Hbsag   Not checked 9,088(55.8) 3,490 <0.0001 13 0.637   Negative 7,112(43.7) 6,677 13   Positive 78(0.5) 5,985 14  Hcvab   Not checked 10888(66.9) 3,556 <0.0001 13 0.375   Negative 5,370(33.0) 7,677 13   Positive 20(0.1) 6,854 16  Hivab   Not checked 12245(75.2) 3,759 <0.0001 14 <0.0001   Negative 4,021(24.7) 8,347 13   Positive 12(0.1) 7,020 18 Treatment results  Outcome   Cure 9,656(59.3) 4,153 <0.0001 13 <0.0001   Improve 4,655(28.6) 5,169 14   Untreated 116(0.7) 4,070 14   Invalid 147(0.9) 5,190 15   Death 47(0.3) 4,642 12   Other 1,657(10.2) 6,372 15 Parameter . Category . Frequency(%) . Hospital costs(¥) . P . LOS(d) . P . Demographic characteristics  gender   male 15623(96.0) 4,636 0.713 13 <0.0001   female 655(4.0) 4,706 10  age   18–20 2,597(16.0) 4,722 <0.0001 13 <0.0001   20–30 9,594(58.9) 4,320 14   30–40 2,542(15.6) 4,571 11   40–50 642(3.9) 5,439 11   ≥50 903(5.5) 9,916 18  Military rank   General and senior military officers 65(0.4) 13265 <0.0001 11.5 <0.0001   Senior colonel officers 818(5.0) 9,258 11   Lieutenant colonel and other officers 2,611(16.0) 4,334 6   Soldiers 12784(78.5) 4,499 8  service   Navy 13465(82.7) 4,580 <0.0001 13 <0.0001   Army 1,785(11.0) 4,851 13   Air Force 1,028(6.3) 5,196 12 Disease characteristics  Number of hospitalizations   1 10612(65.2) 4,355 <0.0001 12 <0.0001   2 3,088(19.0) 4,699 14   ≥3 2,578(15.8) 6,495 17  Condition   Critically 2(0.0) 627471 <0.0001 122 <0.0001   Acute 453(2.8) 6,442 10   General 15823(97.2) 4,600 13  Severe illness   No 16208(99.6) 4,621 <0.0001 13 <0.0001   Yes 70(0.4) 33423 29  Critical illness   No 16241(99.8) 4,628 <0.0001 13 <0.0001   Yes 37(0.2) 49970 30  ICU   No 16207(99.6) 4,627 <0.0001 13 0.051   Yes 71(0.4) 14116 19  Special nursing   No 16208(99.6) 4,605 <0.0001 13 <0.0001   Yes 70(0.4) 29439 20  First-level care   No 14067(86.4) 4,056 <0.0001 13 <0.0001   Yes 2,211(13.6) 12026 14  Allergy   No 15846(97.3) 4,586 <0.0001 13 0.770   Yes 432(2.7) 7,048 13  Surgery   No 11229(69.0) 3,689 <0.0001 12 <0.0001   Yes 5,049(31.0) 6,543 14  Hbsag   Not checked 9,088(55.8) 3,490 <0.0001 13 0.637   Negative 7,112(43.7) 6,677 13   Positive 78(0.5) 5,985 14  Hcvab   Not checked 10888(66.9) 3,556 <0.0001 13 0.375   Negative 5,370(33.0) 7,677 13   Positive 20(0.1) 6,854 16  Hivab   Not checked 12245(75.2) 3,759 <0.0001 14 <0.0001   Negative 4,021(24.7) 8,347 13   Positive 12(0.1) 7,020 18 Treatment results  Outcome   Cure 9,656(59.3) 4,153 <0.0001 13 <0.0001   Improve 4,655(28.6) 5,169 14   Untreated 116(0.7) 4,070 14   Invalid 147(0.9) 5,190 15   Death 47(0.3) 4,642 12   Other 1,657(10.2) 6,372 15 *Mann–Whitney U test for binary variables; Kruskal–Wallis H test for ordinal or numerical variables. Open in new tab TABLE I Comparison of Median Hospitalization Costs and LOS in Terms of Influencing Factors Parameter . Category . Frequency(%) . Hospital costs(¥) . P . LOS(d) . P . Demographic characteristics  gender   male 15623(96.0) 4,636 0.713 13 <0.0001   female 655(4.0) 4,706 10  age   18–20 2,597(16.0) 4,722 <0.0001 13 <0.0001   20–30 9,594(58.9) 4,320 14   30–40 2,542(15.6) 4,571 11   40–50 642(3.9) 5,439 11   ≥50 903(5.5) 9,916 18  Military rank   General and senior military officers 65(0.4) 13265 <0.0001 11.5 <0.0001   Senior colonel officers 818(5.0) 9,258 11   Lieutenant colonel and other officers 2,611(16.0) 4,334 6   Soldiers 12784(78.5) 4,499 8  service   Navy 13465(82.7) 4,580 <0.0001 13 <0.0001   Army 1,785(11.0) 4,851 13   Air Force 1,028(6.3) 5,196 12 Disease characteristics  Number of hospitalizations   1 10612(65.2) 4,355 <0.0001 12 <0.0001   2 3,088(19.0) 4,699 14   ≥3 2,578(15.8) 6,495 17  Condition   Critically 2(0.0) 627471 <0.0001 122 <0.0001   Acute 453(2.8) 6,442 10   General 15823(97.2) 4,600 13  Severe illness   No 16208(99.6) 4,621 <0.0001 13 <0.0001   Yes 70(0.4) 33423 29  Critical illness   No 16241(99.8) 4,628 <0.0001 13 <0.0001   Yes 37(0.2) 49970 30  ICU   No 16207(99.6) 4,627 <0.0001 13 0.051   Yes 71(0.4) 14116 19  Special nursing   No 16208(99.6) 4,605 <0.0001 13 <0.0001   Yes 70(0.4) 29439 20  First-level care   No 14067(86.4) 4,056 <0.0001 13 <0.0001   Yes 2,211(13.6) 12026 14  Allergy   No 15846(97.3) 4,586 <0.0001 13 0.770   Yes 432(2.7) 7,048 13  Surgery   No 11229(69.0) 3,689 <0.0001 12 <0.0001   Yes 5,049(31.0) 6,543 14  Hbsag   Not checked 9,088(55.8) 3,490 <0.0001 13 0.637   Negative 7,112(43.7) 6,677 13   Positive 78(0.5) 5,985 14  Hcvab   Not checked 10888(66.9) 3,556 <0.0001 13 0.375   Negative 5,370(33.0) 7,677 13   Positive 20(0.1) 6,854 16  Hivab   Not checked 12245(75.2) 3,759 <0.0001 14 <0.0001   Negative 4,021(24.7) 8,347 13   Positive 12(0.1) 7,020 18 Treatment results  Outcome   Cure 9,656(59.3) 4,153 <0.0001 13 <0.0001   Improve 4,655(28.6) 5,169 14   Untreated 116(0.7) 4,070 14   Invalid 147(0.9) 5,190 15   Death 47(0.3) 4,642 12   Other 1,657(10.2) 6,372 15 Parameter . Category . Frequency(%) . Hospital costs(¥) . P . LOS(d) . P . Demographic characteristics  gender   male 15623(96.0) 4,636 0.713 13 <0.0001   female 655(4.0) 4,706 10  age   18–20 2,597(16.0) 4,722 <0.0001 13 <0.0001   20–30 9,594(58.9) 4,320 14   30–40 2,542(15.6) 4,571 11   40–50 642(3.9) 5,439 11   ≥50 903(5.5) 9,916 18  Military rank   General and senior military officers 65(0.4) 13265 <0.0001 11.5 <0.0001   Senior colonel officers 818(5.0) 9,258 11   Lieutenant colonel and other officers 2,611(16.0) 4,334 6   Soldiers 12784(78.5) 4,499 8  service   Navy 13465(82.7) 4,580 <0.0001 13 <0.0001   Army 1,785(11.0) 4,851 13   Air Force 1,028(6.3) 5,196 12 Disease characteristics  Number of hospitalizations   1 10612(65.2) 4,355 <0.0001 12 <0.0001   2 3,088(19.0) 4,699 14   ≥3 2,578(15.8) 6,495 17  Condition   Critically 2(0.0) 627471 <0.0001 122 <0.0001   Acute 453(2.8) 6,442 10   General 15823(97.2) 4,600 13  Severe illness   No 16208(99.6) 4,621 <0.0001 13 <0.0001   Yes 70(0.4) 33423 29  Critical illness   No 16241(99.8) 4,628 <0.0001 13 <0.0001   Yes 37(0.2) 49970 30  ICU   No 16207(99.6) 4,627 <0.0001 13 0.051   Yes 71(0.4) 14116 19  Special nursing   No 16208(99.6) 4,605 <0.0001 13 <0.0001   Yes 70(0.4) 29439 20  First-level care   No 14067(86.4) 4,056 <0.0001 13 <0.0001   Yes 2,211(13.6) 12026 14  Allergy   No 15846(97.3) 4,586 <0.0001 13 0.770   Yes 432(2.7) 7,048 13  Surgery   No 11229(69.0) 3,689 <0.0001 12 <0.0001   Yes 5,049(31.0) 6,543 14  Hbsag   Not checked 9,088(55.8) 3,490 <0.0001 13 0.637   Negative 7,112(43.7) 6,677 13   Positive 78(0.5) 5,985 14  Hcvab   Not checked 10888(66.9) 3,556 <0.0001 13 0.375   Negative 5,370(33.0) 7,677 13   Positive 20(0.1) 6,854 16  Hivab   Not checked 12245(75.2) 3,759 <0.0001 14 <0.0001   Negative 4,021(24.7) 8,347 13   Positive 12(0.1) 7,020 18 Treatment results  Outcome   Cure 9,656(59.3) 4,153 <0.0001 13 <0.0001   Improve 4,655(28.6) 5,169 14   Untreated 116(0.7) 4,070 14   Invalid 147(0.9) 5,190 15   Death 47(0.3) 4,642 12   Other 1,657(10.2) 6,372 15 *Mann–Whitney U test for binary variables; Kruskal–Wallis H test for ordinal or numerical variables. Open in new tab TABLE II Multiple Linear Regression Analysis of Hospitalization Costs and LOS Parameter . Standard B . t . P . B(95.0% CI) . Hospital costs  (Constant) −11.076 <0.0001 (−1650752.725,−1154331.647)  LOS 0.491 76.996 <0.0001 (487.153,512.604)  Special care 0.200 31.602 <0.0001 (49724.003,56300.169)  First-level care 0.098 14.470 <0.0001 (5,356.166,7,034.609)  Year 0.080 10.492 <0.0001 (536.337,782.779)  Military rank −0.047 −4.916 <0.0001 (−2,533.895,−1,089.265)  Critical illness 0.062 9.838 <0.0001 (22673.535,33956.905)  Hivab 0.070 9.324 <0.0001 (2,792.096,4,278.441)  Allergy 0.040 6.471 <0.0001 (3,787.585,7,079.575)  Condition −0.034 −5.458 <0.0001 (−6,139.369,−2,894.766)  Number of hospitalizations 0.023 3.164 0.002 (72.879,310.186)  Gender −0.018 −2.851 0.004 (−3,337.956,−618.049)  Age 0.026 2.729 0.006 (169.082,1,031.596) Hospital LOS  (Constant) 17.849 <0.0001 (2,096.303,2,613.523)  Hospital costs 0.540 77.425 <0.0001 (0.001,0.001)  Year −0.145 −17.873 <0.0001 (−1.299,−1.042)  Number of hospitalizations 0.132 17.875 <0.0001 (0.955,1.190)  Military rank 0.067 9.094 <0.0001 (1.986,3.078)  Special care −0.050 −7.315 <0.0001 (−16.592,−9.579)  Outcome 0.036 5.471 <0.0001 (0.323,0.684)  Hbsag 0.056 7.043 <0.0001 (1.716,3.040)  Hivab −0.045 −5.298 <0.0001 (−3.030,−1.394)  Severe illness 0.031 4.649 <0.0001 (5.805,14.269)  Conditon 0.023 3.529 <0.0001 (1.338,4.681)  Allergy −0.020 −3.134 0.002 (−4.413,−1.017)  Service −0.015 −2.267 0.023 (−1.060,−0.077)  Gender −0.014 −2.157 0.031 (−2.941,−0.140) Parameter . Standard B . t . P . B(95.0% CI) . Hospital costs  (Constant) −11.076 <0.0001 (−1650752.725,−1154331.647)  LOS 0.491 76.996 <0.0001 (487.153,512.604)  Special care 0.200 31.602 <0.0001 (49724.003,56300.169)  First-level care 0.098 14.470 <0.0001 (5,356.166,7,034.609)  Year 0.080 10.492 <0.0001 (536.337,782.779)  Military rank −0.047 −4.916 <0.0001 (−2,533.895,−1,089.265)  Critical illness 0.062 9.838 <0.0001 (22673.535,33956.905)  Hivab 0.070 9.324 <0.0001 (2,792.096,4,278.441)  Allergy 0.040 6.471 <0.0001 (3,787.585,7,079.575)  Condition −0.034 −5.458 <0.0001 (−6,139.369,−2,894.766)  Number of hospitalizations 0.023 3.164 0.002 (72.879,310.186)  Gender −0.018 −2.851 0.004 (−3,337.956,−618.049)  Age 0.026 2.729 0.006 (169.082,1,031.596) Hospital LOS  (Constant) 17.849 <0.0001 (2,096.303,2,613.523)  Hospital costs 0.540 77.425 <0.0001 (0.001,0.001)  Year −0.145 −17.873 <0.0001 (−1.299,−1.042)  Number of hospitalizations 0.132 17.875 <0.0001 (0.955,1.190)  Military rank 0.067 9.094 <0.0001 (1.986,3.078)  Special care −0.050 −7.315 <0.0001 (−16.592,−9.579)  Outcome 0.036 5.471 <0.0001 (0.323,0.684)  Hbsag 0.056 7.043 <0.0001 (1.716,3.040)  Hivab −0.045 −5.298 <0.0001 (−3.030,−1.394)  Severe illness 0.031 4.649 <0.0001 (5.805,14.269)  Conditon 0.023 3.529 <0.0001 (1.338,4.681)  Allergy −0.020 −3.134 0.002 (−4.413,−1.017)  Service −0.015 −2.267 0.023 (−1.060,−0.077)  Gender −0.014 −2.157 0.031 (−2.941,−0.140) Open in new tab TABLE II Multiple Linear Regression Analysis of Hospitalization Costs and LOS Parameter . Standard B . t . P . B(95.0% CI) . Hospital costs  (Constant) −11.076 <0.0001 (−1650752.725,−1154331.647)  LOS 0.491 76.996 <0.0001 (487.153,512.604)  Special care 0.200 31.602 <0.0001 (49724.003,56300.169)  First-level care 0.098 14.470 <0.0001 (5,356.166,7,034.609)  Year 0.080 10.492 <0.0001 (536.337,782.779)  Military rank −0.047 −4.916 <0.0001 (−2,533.895,−1,089.265)  Critical illness 0.062 9.838 <0.0001 (22673.535,33956.905)  Hivab 0.070 9.324 <0.0001 (2,792.096,4,278.441)  Allergy 0.040 6.471 <0.0001 (3,787.585,7,079.575)  Condition −0.034 −5.458 <0.0001 (−6,139.369,−2,894.766)  Number of hospitalizations 0.023 3.164 0.002 (72.879,310.186)  Gender −0.018 −2.851 0.004 (−3,337.956,−618.049)  Age 0.026 2.729 0.006 (169.082,1,031.596) Hospital LOS  (Constant) 17.849 <0.0001 (2,096.303,2,613.523)  Hospital costs 0.540 77.425 <0.0001 (0.001,0.001)  Year −0.145 −17.873 <0.0001 (−1.299,−1.042)  Number of hospitalizations 0.132 17.875 <0.0001 (0.955,1.190)  Military rank 0.067 9.094 <0.0001 (1.986,3.078)  Special care −0.050 −7.315 <0.0001 (−16.592,−9.579)  Outcome 0.036 5.471 <0.0001 (0.323,0.684)  Hbsag 0.056 7.043 <0.0001 (1.716,3.040)  Hivab −0.045 −5.298 <0.0001 (−3.030,−1.394)  Severe illness 0.031 4.649 <0.0001 (5.805,14.269)  Conditon 0.023 3.529 <0.0001 (1.338,4.681)  Allergy −0.020 −3.134 0.002 (−4.413,−1.017)  Service −0.015 −2.267 0.023 (−1.060,−0.077)  Gender −0.014 −2.157 0.031 (−2.941,−0.140) Parameter . Standard B . t . P . B(95.0% CI) . Hospital costs  (Constant) −11.076 <0.0001 (−1650752.725,−1154331.647)  LOS 0.491 76.996 <0.0001 (487.153,512.604)  Special care 0.200 31.602 <0.0001 (49724.003,56300.169)  First-level care 0.098 14.470 <0.0001 (5,356.166,7,034.609)  Year 0.080 10.492 <0.0001 (536.337,782.779)  Military rank −0.047 −4.916 <0.0001 (−2,533.895,−1,089.265)  Critical illness 0.062 9.838 <0.0001 (22673.535,33956.905)  Hivab 0.070 9.324 <0.0001 (2,792.096,4,278.441)  Allergy 0.040 6.471 <0.0001 (3,787.585,7,079.575)  Condition −0.034 −5.458 <0.0001 (−6,139.369,−2,894.766)  Number of hospitalizations 0.023 3.164 0.002 (72.879,310.186)  Gender −0.018 −2.851 0.004 (−3,337.956,−618.049)  Age 0.026 2.729 0.006 (169.082,1,031.596) Hospital LOS  (Constant) 17.849 <0.0001 (2,096.303,2,613.523)  Hospital costs 0.540 77.425 <0.0001 (0.001,0.001)  Year −0.145 −17.873 <0.0001 (−1.299,−1.042)  Number of hospitalizations 0.132 17.875 <0.0001 (0.955,1.190)  Military rank 0.067 9.094 <0.0001 (1.986,3.078)  Special care −0.050 −7.315 <0.0001 (−16.592,−9.579)  Outcome 0.036 5.471 <0.0001 (0.323,0.684)  Hbsag 0.056 7.043 <0.0001 (1.716,3.040)  Hivab −0.045 −5.298 <0.0001 (−3.030,−1.394)  Severe illness 0.031 4.649 <0.0001 (5.805,14.269)  Conditon 0.023 3.529 <0.0001 (1.338,4.681)  Allergy −0.020 −3.134 0.002 (−4.413,−1.017)  Service −0.015 −2.267 0.023 (−1.060,−0.077)  Gender −0.014 −2.157 0.031 (−2.941,−0.140) Open in new tab This study differs from research on local public hospitals, as military patients tend to be men and soldiers, with low average age and obvious military group characteristics [43]. The establishment of a virtual bed for patients and the use of advanced treatment methods, advance pre-hospital examinations, and elective surgery are beneficial for shortening the LOS. At the same time, establishing a unified and standardized clinical path that shortens waiting time and sets reasonable assessment goals that adequately mobilize departmental resources and other measures can also help shorten the LOS. In addition, it is important to reduce adverse events caused by arbitrarily decreasing hospitalization days, such as repeated admission to the hospital, selective admission of simple diseases, and so on. Therefore, for ensuring the effectiveness of diagnosis and treatment, shorter hospital LOS will help reduce hospitalization costs, speed up bed turnover, and rationally utilize limited resources. At the same time, shortening the LOS will also help speed up the rehabilitation of soldiers and help troops to quickly restore combat effectiveness. Strengths and Limitations of This Study The primary strength of this study was the large sample size. There is a lack of research using large samples to identify factors influencing hospitalization costs and LOS in naval hospitals. The limitation of this study was that only departmental factors were considered; the impact of different diseases on LOS and hospitalization costs was not taken into consideration, which may have introduced bias. CONCLUSION The gradual decrease in average LOS and continuous increase in hospitalization costs for military patients reflect the continuous advancement of medical technology and increase in the efficiency of hospital management; however, the pressure of increasing medical costs needs to be faced. In summary, shortening LOS, optimizing clinical pathways, and reasonably controlling the costs associated with medicines and surgery can help reduce hospitalization costs for military patients. Controlling the growth of hospitalization costs can help avoid the physical and psychological burden of medical over-treatment on patients and may also optimize the allocation of military health resources. FUNDING This research was funded by the National Natural Science Foundation of China (Lulu Zhang, grants 71804186, 71233008), the People’s Liberation Army (Lulu Zhang, grants 17CXZ001, AWS12J002), and Shanghai Municipal Health Planning Commission(Lulu Zhang, grant 2013ZYJB0006). ACKNOWLEDGMENTS Special thanks to Dr. QI-Chen and Dr. Feifei-Yu from the Department of Health Statistics, Second Military Medical University for their statistical advice. NOTE 1 ZX, FZ, and CX contributed equally to this article and are co-first authors. References 1. Brondex MA , Viant CE , Trendel LD : Medical activity in Kabul NATO role 3 hospital: a 3-month-long experience . Mil Med 2014 ; 179 : 197 – 202 . Google Scholar Crossref Search ADS PubMed WorldCat 2. 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TI - Hospitalization Costs and Length of Stay in Chinese Naval Hospitals Between 2008 and 2016 Based on Influencing Factors: A Longitudinal Comparison
JF - Military Medicine
DO - 10.1093/milmed/usz170
DA - 2020-02-13
UR - https://www.deepdyve.com/lp/oxford-university-press/hospitalization-costs-and-length-of-stay-in-chinese-naval-hospitals-mN6cDJ0HPs
SP - e282
VL - 185
IS - 1-2
DP - DeepDyve
ER -