Influencing factors and burden of hospital-acquired infections among cancer patients 

Influencing factors and burden of hospital-acquired infections among cancer patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influencing factors and burden of hospital-acquired infections among cancer patients Lihua Huang, Huacheng Ning, Xin-Chen Liu, Yongjie Wang, Qing Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3605326/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To explore the influencing factors and evaluate the economic loss of hospital acquired infections (HAIs) in cancer patients so as to provide evidence for reasonable prevention policies and measures. Methods Patients who were discharged for the first time from Sun Yat-sen University Cancer Center between Jan 1 and Dec 31, 2022 were recruited. Data were obtained from the medical record system and the nosocomial infection surveillance system. Logistic regression model was adopted to analyze the influencing factors of HAIs. By using 1:1 case-control matching and Wilcoxon signed rank test, economic loss and length of hospital stay (LOS) caused by HAIs were estimated. Results A total of 45030 cancer patients were enrolled in this study, of which 457 suffered HAIs. Logistic regression analysis showed that older age, longer retention days of deep vein catheter, longer indwelling days of urinary catheter, diabetes mellitus, male, surgical operation, and myelosuppression were all independent risk factors for HAIs (odds ratios ranges from 1.01 to 10.68). The analysis of 256 matched pairs presented that the total hospitalization expenditure and self-paid expenditure of the HAI group (114.79, 51.56 thousand-yuan, respectively) were significantly higher than those of the non-HAI group (77.15, 34.68 thousand-yuan, respectively). Compared with non-HAI group, the LOS in HAI group was significantly longer by 8 days. Conclusions HAIs lead to the increase of direct economic burden and LOS in cancer patients. Cancer patients who are male, older age, administrated with invasive operations, with diabetes mellitus and myelosuppression are more susceptible to HAIs. Hospital acquired infection Cancer Influencing factors Economic loss Length of hospital stay Figures Figure 1 Background Hospital-acquired infections (HAIs) are considered the most prevalent adverse outcomes of healthcare worldwide[ 1 ]. Unlike other medical interventions, controlling HAIs does not directly generate revenue for both hospital and patient, making it difficult to observe their potential benefits. In China, the development of HAIs prevention and control was slow until the SARS epidemic in 2003. Although numerous studies have assessed the burden of HAIs over the past 2 decades, most studies have primarily focused on general hospital inpatients[ 2 , 3 ]. Only a limited number have evaluated their impact on cancer patients and these studies were often constrained by specific types of cancer[ 4 – 6 ]. It is estimated that there were 19.3 million new cases of cancer and 10 million cancer-related deaths worldwide in 2020[ 7 ], with a steady increase over time. Individuals with cancer are at a heightened risk of developing infections, due to immunodeficiencies associated with cancer, antineoplastic chemotherapy or radiation therapy. A systematic review of studies conducted in tertiary and specialty hospitals of mainland China in 2018 suggested that the weighted prevalence of HAIs in oncology hospitals (3.96%) was higher than that in general hospitals (3.12%)[ 8 ]. Considering the high prevalence of HAIs in cancer patients, assessing the burden of HAIs on cancer patients is important for strengthening the management of HAIs in cancer hospitals. Epidemiological data supported that HAIs are associated with multiple risk factors and can be prevented[ 9 ]. Although patients with cancer are susceptible to infections[ 10 ], studies exploring the influencing factors of HAIs in this high-risk population are limited worldwide[ 11 , 12 ]. Therefore, it is important to identify specific risk factors in the more susceptible population, which helps to develop targeted preventive measures. Based on these considerations, we conducted this retrospective study involving inpatients discharged for the first time from the Sun Yat-sen University Cancer Center, a large regional cancer hospital in Southern China, to identify the risk factors of HAIs and estimate the burden including economic loss and length of hospital stay (LOS) attributed to HAIs overall and that across different specific infection sites. Methods Study design and data collection The protocol of this study was approved by the ethics committee of Sun Yat-sen University Cancer Center. This study is a retrospective analysis without any intervention and the data are anonymous, the requirement for informed consent was therefore waived by the ethics committee of Sun Yat-sen University Cancer Center. Inpatients discharged for the first time from the Sun Yat-sen University Cancer Center between January 1, 2022 to December 31, 2022 were selected as the study subjects. Information including medical ID, age, gender, department admitted to hospital, principal diagnosis based on codes from International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10), other diseases (i.e., hypertension, diabetes or myelosuppression), surgical procedure, radiotherapy, chemotherapy, total medical expenditure, and self-paid expenditure (i.e., the balance after deducting the basic health insurance reimbursement) were extracted from the electric medical records information system. The data of HAIs including medical ID of HAI cases, infection site and their days of urinary catheter use and deep vein catheter indwelling, were collected through the nosocomial infection monitoring information system. The cases of HAI were identified according to the Diagnostic Standard for HAIs developed by China's Ministry of Health in 2001[ 13 ]. Figure 1 showed the study flow. Of these, 457 patients had been diagnosed with at least one type of HAIs and the rest had not been diagnosed with HAIs during their hospitalization. The direct economic burden associated with HAIs includes the medical expenditure that patients with HAIs spend more than patients without HAIs, and the lost wages of patients and their accompanying family member due to prolonged length of hospital stay. According to the first page of the medical records, total medical expenditure can be divided into comprehensive medical service cost, diagnosis cost, treatment cost, blood and blood products cost and disposable consumable cost. The lost wages is calculated by the formula of human capital method, that is, twice the extended days of hospitalization multiplied by the days of average wages. Data released by the National Bureau of Statistics showed that the average wage of urban non-private employees in 2022 is 114,029 yuan[ 14 ], the average wage of private employees is 65,237 yuan[ 15 ], the estimated average daily wage is 178.73 ~ 312.41 yuan. the estimated average daily wage is 178.73 ~ 312.41 yuan. Statistical analysis Data were analyzed with the use of SPSS software, version 26.0 (IBM, Armonk, NY, USA). The continuous data with normal distribution were presented as mean (standard deviation[SD]), and the non-normally distributed continuous data were reported as median (interquartile range[IQR]); The count data was described by frequency (proportion). To identify influencing factors associated with HAIs, we first compared the demographic and medical characteristics of patients with and without HAIs using Mann-Whitney U test and Chi-square test. Potential factors with P -values of 0.1 or less in the univariate analysis were selected and further included in a multivariate conditional logistic regression model by using a forward stepwise strategy, and odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs) were estimated. To evaluate the burden of HAIs, as done before in previous studies regarding burden[ 16 – 18 ], HAI cases were matched to non-HAI controls in a ratio of 1:1 using the following criteria: same sex, age difference of less than 3 years, same principal diagnosis (i.e., the main diseases in the hospital, based on ICD-10 diagnosis code), same surgical procedure if applied and same type of payment. Table 1 showed the balanced distribution of factors matched between paired case and control groups. We compared the total medical expenditure and hospital LOS between paired case and control groups using the Wilcoxon signed rank test. To understand the burden of different infection sites, we further performed the stratification analysis by infection sites. All tests were two-tailed and P -value of 0.05 or less was considered as statistical significance. Table 1 Characteristics between paired case and control groups Characteristics Paired case group (N=256) Paired control group (N=256) Z/ c 2 P Age median, IQR 60.0 (51.0, 67.0) 59.50 (52.0, 68.0) -0.198 0.843 Gender n, % <0.001 1.000 Male 162 (63.3) 162 (63.3) Female 94 (36.7) 94 (36.7) Principal disease n, % <0.001 1.000 Upper gastrointestinal tumor 57 (22.2) 57 (22.2) Colorectal tumor 36 (14.1) 36 (14.1) Hepato-bilio-pancreatic tumor 44 (17.2) 44 (17.2) Lung tumor 17 (6.6) 17 (6.6) Peritoneal tumor 2 (0.8) 2 (0.8) Breast tumor 3 (1.2) 3 (1.2) Genitourinary tumor 45 (17.6) 45 (17.6) Neurologic tumor 34 (13.3) 34 (13.3) Thyroid tumor 3 (1.2) 3 (1.2) Hematologic tumor 5 (1.9) 5 (1.9) Others 10 (3.9) 10 (3.9) Surgical operation n, % - - No 0 (0.0) 0 (0.0) Yes 256 (100.0) 256 (100.0) Payment method n, % <0.001 1.000 Basic medical insurance 21 (8.2) 21 (8.2) Free medical care 228 (89.1) 228 (89.1) Self-paid 7 (2.7) 7 (2.7) Abbreviation: IQR: interquartile range Notes: Given that age distributed non-normally, Mann-Whitney U test was used to compare the difference between paired case and control groups. As for gender, main disease and ways of payment, Chi-square tests were performed to compared the differences between two groups. Results Influencing factors of HAIs in cancer patients A total of 45,030 cancer patients were enrolled in this study, of which 457 suffered HAI and 44,573 not. The patients with HAIs were assumed as the case group and patients without any HAIs as the control group. Univariate analysis presented in Table 2 indicated that age, male, surgical treatment, chemoradiotherapy, days of urinary catheterization, days of deep vein catheter retention, diabetes mellitus, hypertension, and myelosuppression were the factors associated with HAIs in cancer patients ( P < 0.001). The results of multivariate conditional logistic regression were presented in Table 3 . Older age (OR: 1.01, 95% CI: 1.01 ~ 1.02), longer retention days of deep vein catheter (OR: 1.13, 95% CI: 1.11 ~ 1.15), longer indwelling days of urinary catheter (OR: 1.18, 95% CI: 1.15 ~ 1.21), diabetes mellitus (OR: 1.57, 95% CI: 1.15 ~ 2.15), male (OR: 1.66, 95% CI: 1.35 ~ 2.04), surgical operation (OR: 8.63, 95% CI: 4.53 ~ 16.46), and myelosuppression (OR: 10.68, 95% CI: 4.43 ~ 25.79) were identified as the independent risk factors for HAIs among cancer patients ( P < 0.05). Table 2 Characteristics between the study participants with and without HAIs Patients without HAIs (N = 44573) Patients with HAIs (N = 457) Z/ χ 2 P Age (median, IQR, years) 53.0 (41.0, 62.0) 59.0 (48.0, 67.0) -8.189 < 0.001 Days of urinary catheter use (days) 0 (0,1) 2 (1,6) -28.540 < 0.001 Days of deep vein catheter indwelling (days) 0 (0,2) 5 (1,12) -22.597 < 0.001 Gender (n, %) 22.85 < 0.001 Male 22888 (51.3) 286 (62.6) Female 21685 (48.7) 171 (37.4) Chemoradiotherapy (n, %) 23.35 < 0.001 No 39117 (87.8) 435 (95.2) Yes 5456 (12.2) 22 (4.8) Diabetes (n, %) 29.25 < 0.001 No 42106 (94.5) 405 (88.6) Yes 2467 (5.5) 52 (11.4) Hypertension (n, %) 34.21 < 0.001 No 39723 (89.1) 368 (80.5) Yes 4850 (10.9) 89 (19.5) Surgical operation (n, %) 118.166 < 0.001 No 10805 (24.2) 11 (2.4) Yes 33768 (75.8) 446 (97.6) Myelosuppression (n, %) 92.542 < 0.001 No 44522 (99.9) 449 (98.2) Yes 51 (0.1) 8 (1.8) Abbreviations : HAIs: hospital-acquired infections; IQR: interquartile range Notes: Given that age, days of urinary catheter use and days of deep vein catheter indwelling distributed non-normally, Mann-Whitney U tests were used to compared the differences between patients with and without HAIs. As for other count data including gender and medical characteristics, Chi-square tests were performed to compared the differences between two groups. Table 3 Multivariate analysis for risk factors of HAIs in cancer patients OR(95% CI) P Age (years) 1.014 (1.006, 1.021) < 0.001 Days of urinary catheter use (days) 1.180 (1.154, 1.206) < 0.001 Days of deep vein catheter indewlling (days) 1.129 (1.111, 1.148) < 0.001 Gender < 0.001 Male 1.658 (1.351, 2.037) Female Ref. Diabetes 0.005 No Ref. Yes 1.571 (1.149, 2.149) Surgical operation < 0.001 No Ref. Yes 8.633 (4.528, 16.458) Myelosuppression < 0.001 No Ref. Yes 10.683 (4.426, 25.785) Abbreviations : HAIs: hospital-acquired infections; OR: odds ratios; CI: confidence intervals; Ref.: reference Notes: Potential factors with P- values of 0.1 or less in the univariate analysis (i.e., age, gender, days of urinary cathether use and deep vein catheter indewlling, surgical operation or not, have diabetes or myelosuppression) were selected and further included in a multivariate conditional logistic regression model by using a forward stepwise strategy, and odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs) were estimated. Influence of HAIs on LOS As seen in Table 4 , compared with the control group, the LOS of patients with HAIs was 8 days longer on average (14 vs. 22 days, P < 0.001). The LOS was significantly longer in the patients with all different sites of HAIs, except for upper respiratory tract as well as urinary system. Among the patients with single site of infection, the patients with abdominal and digestive infection had the most prolonged hospitalization days (11 days, P < 0.001). Moreover, the patients with two sites of HAIs have longer LOS (i.e., 34 days), staying 19.5 days more than the patients without HAIs ( P = 0.002). Table 4 Comparing length of hospital stay (days) between paired control and case groups Site of infection Number of HAI cases Paired control group (N = 256) Paired case group (N = 256) Difference value P Overall 256 14.00 (10.00, 17.00) 22.00 (16.00, 30.00) 8.00 < 0.001 Upper respiratory tract 10 12.00 (5.25, 19.25) 20.00 (14.75, 26.00) 8.0 0.112 Lower respiratory tract 50 11.50 (10.00, 16.00) 18.00 (14.00, 24.00) 6.5 < 0.001 Surgical site 109 14.00 (10.00, 19.00) 24.00 (17.00, 35.00) 10.0 < 0.001 Hematologic system 12 12.00 (8.00, 14.00) 20.00 (12.75, 26.50) 8.0 0.022 Abdomen and digestive system 34 14.00 (7.75, 18.75) 25.00 (15.25, 34.75) 11.0 0.001 Urinary system 12 14.00 (11.50, 17.00) 19.50 (11.75, 26.75) 5.5 0.165 Others 19 10.00 (4.50, 14.00) 21.00 (17.50, 25.00) 11.0 < 0.001 Two sites simultaneously 10 14.50 (13.25, 16.00) 34.00 (23.00, 42.25) 19.5 0.002 Abbreviations : HAI: hospital-acquired infection Notes: Wilcoxon signed rank test was adopted to compare the length of hospital stay (days) between paired non-HAI and HAI groups. To understand the burden of different infection sites, the stratification analyses by infection sites were further performed. Direct economic loss due to HAIs As presented in Table 5 , the median total medical cost in the case group was 114,790.88 yuan, and that in the non-HAI group was 77,149.02 yuan. The direct economic loss caused by HAIs was 37,641.86 yuan ( P < 0.001). The median medical expenditure at patients’ own expenditure in the HAI group was 51,558.3 yuan, and that in the non-HAI group was 34,681.5 yuan. The medical economic loss due to HAIs was 16,876.8 yuan ( P < 0.001). The comprehensive medical service fee, diagnosis fee, treatment fee, blood and its products fee, and consumable fee were all higher in HAI group than non-HAI group ( P < 0.05). Based on the estimation above, the average delayed hospitalization days for hospital infection were estimated as 8 days among cancer patients, and the direct non-medical economic loss caused by HAIs was 2,859.68 ~ 4,998.56 yuan. Table 5 Comparing medical expenditure (yuan) between paired control and case groups Expenditure item Paired control group (N = 256) Paired case group (N = 256) Difference value P Comprehensive medical service cost 3584.20 (2629.94, 5032.58) 7240.49 (4636.66, 11121.60) 3656.29 < 0.001 Diagnosis cost 10681.19 (7464.48, 15342.15) 19082.86 (12561.06, 26515.30) 8401.67 < 0.001 Treatment cost 29699.42 (22721.22, 39744.55) 34324.16 (25481.63, 48672.30) 4624.74 < 0.001 Blood and blood products cost 2589.30 (119.73, 7061.12) 7326.64 (2589.30, 17614.72) 4737.34 < 0.001 Disposable consumable cost 25508.51 (11853.82, 36014.94) 31933.68 (15125.81, 42912.15) 6425.17 0.001 Self-paid 34681.50 (20824.34, 47272.72) 51558.33 (32451.80, 80523.90) 16876.83 < 0.001 Total 77149.02 (54942.70,96951.14) 114790.88 (82401.90,156337.37) 37641.86 < 0.001 Notes: Wilcoxon signed rank tests were adopted to compare medical expenditure (yuan) between paired non-HAI and HAI groups. To understand the source of the increment due to HAIs, the stratification analyses by composition of medical expenditure were further performed. As shown in Table 6 , the total hospital expenditure of lower respiratory tract infection, abdominal and digestive system infection, surgical site infection, other site infection and 2 site infections had statistical significance ( P < 0.05). Among the patients with single site of infection, the patients with lower respiratory tract infection caused the most loss of medical cost (55,837.89 yuan, P 0.05). Table 6 Comparing medical expenditure (yuan) between paired control and case groups across different infectious sites Site of infection Number of HAI cases Paired control group (N = 256) Paired case group (N = 256) Difference value P Upper respiratory tract 10 77819.10 (39911.46, 87733.87) 98880.03 (63988.06, 136537.12) 21060.93 0.112 Lower respiratory tract 50 80051.99 (61639.60, 101602.32) 135889.88 (113612.51, 177711.98) 55837.89 < 0.001 Surgical site 114 76039.63 (57635.35, 96771.09) 109062.60 (74211.15, 147181.79) 33022.97 < 0.001 Hematologic system 12 64508.04 (42914.66, 91368.08) 92911.97 (73390.43, 103428.69) 28403.93 0.094 Abdomen and digestive system 34 75308.47 (62391.74, 98107.65) 127085.64 (92617.58, 158341.45) 51777.17 < 0.001 Urinary system 12 84553.58 (74420.53, 89971.05) 91027.97 (65932.73, 104381.79) 6474.39 0.386 Others 19 46777.89 (29499.61, 69814.57) 86701.68 (53678.25, 127625.90) 39923.79 0.001 Two sites simultaneously 10 100474.93 (83280.28, 127676.20) 157541.50 (125139.18, 242845.63) 57066.57 0.028 Abbreviations : HAI(s): hospital-acquired infection(s) Notes: Wilcoxon signed rank test was adopted to compare medical expenditure (yuan) between paired non-HAI and HAI groups. To understand the burden of different infection sites, the stratification analyses by infection sites were further performed. Discussion Study Summary In this retrospective study, we found that older age, male, longer retention days of deep vein catheter, longer indwelling days of urinary catheter diabetes mellitus, surgical operation, and myelosuppression were the independent risk factors for HAIs among cancer patients. The estimated medical economic loss attributable to HAIs was 37,641.86 yuan per case, and the estimated non-medical economic loss was 2,859.68 ~ 4,998.56 yuan per case. Compared with the non-HAI group, the LOS in the HAI group was significantly longer by 8 days. Among the patients with single site of infection, the patients with lower respiratory tract infection caused the most loss of medical cost (55,837.89 yuan) and the patients with abdominal and digestive infection had the most prolonged hospitalization days (11 days). Besides, patients with more sites of infection were likely to have increased medical costs and prolonged hospitalization days than patients with single site of infection. Burden of HAIs Like patients in general hospitals, HAIs did increase the economic burden and prolong LOS among patients in the cancer hospital. In this study of 256 paired cancer patients in southern China, the total direct economic burden of HAIs was estimated to 37,641.86 yuan. As for specific types of tumors, the economic loss due to HAIs varied. For example, a prospective monitoring case-control study of 448 patients undergoing colorectal cancer surgery in China presented that the total direct economic burden due to HAI was 1,589.30 dollars[ 5 ]. While, a retrospective study in Europe on 139 patients diagnosed with brain tumors reported a mean cost 17,097 euros[ 4 ] and another previous study in USA on gynecologic tumor estimated the cost 58,000 dollars[ 6 ]. Certainly, the economic loss due to HAIs varies in different studies by the region of study, the characteristics of study population, the structure and composition of disease and the types of infection. The high economic burden attributable to HAIs implied that HAI patients need extra diagnosis and treatments. Regarding hospitalization costs, the cost of diagnosis (8,401.67 yuan) constituted the highest proportion of economic burden followed by consumable cost (6,425.17 yuan) and blood and blood products cost (4,737.34 yuan) in our study. The reason for the increased cost of diagnosis might be that further examinations such as multiple microbial examination and blood routine examination are often needed in order to clarify the diagnosis of HAIs. As 256 patients with HAIs have accepted surgery and almost half of them had infections in their surgical site, some patients might require reoperation and/or continued wound care, and blood transfusion might be needed during or after the surgery, resulting in higher expenditure on consumable cost and blood products. Our findings also indicated that there is a significant increase in LOS associated with HAIs. Patients with HAIs were estimated to have an average LOS of 8 days longer than patients without HAIs. This result is consistent with previous studies in general hospitals; however, the days of prolongation in LOS varied among studies[ 1 , 19 ]. HAIs might deteriorate patients’ recovery from surgery, resulting in extended LOS and incurring extra costs of treatment and other hospital expenditures. Prolongation of LOS not only can increase the healthcare related economic burden but also the social and labor-related economic burden because of the patients’ absence from work and occupation of medical resources. Based on the data released by the National Bureau of Statistics, the estimated non-medical economic loss was 2,859.68 ~ 4,998.56 yuan per case. However, in addition, the occurrence of hospital infection will also lead to the aggravation of patients' basic diseases, increase the physical and mental pain of patients, bring more anxiety and worry to family members, and some even cause the death of patients, which could not be simply measured in currency. Among various infection sites, our study showed that two site infections had the highest direct economic burden of 57,066.57 yuan and the longest LOS of 19.5 days. It is easy to understand that patients with multiple site infection are likely to have poor basic conditions, complex disease, complex anti-infection program, long course of disease, and corresponding increase in cost. Among the patients with single site of infection, the patients with lower respiratory tract infection caused the most loss of medical cost (51,777.17 yuan) and the patients with abdominal and digestive infection had the most prolonged hospitalization days (11 days). Most of the patients with lower respiratory tract infection are elderly patients with underlying diseases and need to use ventilators. The pathogens are mostly drug-resistant bacteria, which makes treatment difficult and medical cost high. Moreover, our studies found that both URTI and urinary tract infection (UTI) had non-significant impact on medical expenditure and LOS. A study from UK has also reported that UTI did not increase LOS[ 20 ]. While URTI usually results in a self-limiting, acute onset and a short course of illness. Risk factors of HAIs Considering that most HAIs identified were preventable, more efforts should be made in HAI control and prevention, for instance, the identification of the risk factors and the development of effective strategies for the prevention of infections. In our study, we found that older age, male, longer retention days of deep vein catheter, longer indwelling days of urinary catheter, diabetes mellitus, surgical operation, and myelosuppression were the independent risk factors for HAI among cancer patients. Our findings were similar to a previous study including 192,317 hospitalized cancer patients, which revealed a clear age-dependent trend of HAI rates[ 11 ]. With age, the body becomes less resistant to external pathogens. On the other hands, the skin’s basement membrane and dermis thin with increasing age, and the skin loses its reserve of cutaneous blood vessels and nerves that diminish wound healing. Several studies in general hospitals have also shown that compared with women, males were more likely to acquire HAIs[ 21 , 22 ]. It may be because of gender differences in behaviors that were potential risk factors, such as smoking behavioral. While people who smoke have a high risk of infection, especially acute respiration infection[ 23 ], our study was unable to verify it, since we did not collect any data of behavior factors. Although age and sex are inherent characteristics that cannot be changed, they should be considered in the risk assessment of HAIs. Patients with certain underlying diseases are more susceptible to infections. Consistent with the previous studies, the results indicated that hyperglycemia is another risk factor for HAIs[ 12 , 24 ]. Hyperglycemia impairs the innate immune system and promotes glycosylation of proteins, which compromises wound healing[ 25 , 26 ]. Consequently, attention should be paid to the monitoring and management of blood sugar and control of glucose intake, especially the surgical patients. This study also found that myelosuppression is associated with a high risk of HAIs. Myelosuppression is a frequent complication of cytotoxic chemotherapy that results from damage to hematopoietic stem and progenitor cells in the bone marrow and commonly manifests as neutropenia, anemia, and/or thrombocytopenia, thus placing patients at significant risk of serious infections, bleeding, sepsis, and even death[ 27 ]. However, nosocomial infection surveillance systems currently do not include it as a monitored factor. Moreover, protection measures are not mandatory enforcement for patients with myelosuppression in the clinic. Invasive operations (e.g. surgery, central venous catheterization and urinary catheter indwelling) had been widely used in tumor therapy, which benefit patients but also bring some risks of infections. Although these invasive operations are relatively mature and routine techniques, they might damage the tissue mucosa, destroying the body’s normal defense barrier[ 11 , 28 ]. This allows certain conditional pathogens invade into the body or ectopic implant of the normal flora to take place. When it comes to the prevention and control of hospital infections, daily assessment of the need for an indwelling central venous catheter and/or urinary catheter and early removal of the catheter are significant tasks. As for surgical infection, a previous study has shown that preoperative hospitalization time ≥ 6 days, surgery time ≥ 230 minutes, American Society of Anesthesiologists (ASA) score ≥ III were related to HAIs after surgery[ 12 ]. Therefore, it is of great significance to reduce preoperative hospitalization time and shorten the surgery time for surgical infections. Strength & Limitations Our study is one of the few studies to investigate burden due to HAIs amongst cancer patients. However, this study is subjected to at least four limitations. First, sample used to explore the risk factors and estimate the economic burden of HAIs was from a single hospital located in southern China, so the results cannot be extrapolated directly to hospitals with other foci or to the situation in China in general. Second, considering that the economic burden of HAIs includes direct loss of prolonged stay antiinfection treatment and readmission, as well as the indirect loss which mainly consists of the reduced working hours of family members due to hospital care and the declined labor capacity of patients themselves due to infection and even disability, the total losses attributable to HAIs were underestimated in our research[ 29 , 30 ]. Third, since this was a single-center, retrospective study, causal relationship of the risk factors found above with HAIs could not be demonstrated. Fourth, the study sample did not include those diagnosed after discharge because of data inaccessibility, therefore the incidence and rate of HAIs might be lower than the actual situation, which might result in underestimation of the burden due to HAIs. In all, in order to validate the present results, large-scale multicenter prospective studies on the economic burden and risk factors related to HAIs should be further performed in cancer hospitals. Conclusions In summary, HAIs prolong LOS and increase medical costs among hospitalized cancer patients in South China. Cancer patients who are male, older aged, administrated with invasive operations, with diabetes mellitus and myelosuppression are more vulnerable to HAIs. This study provides important instructions for the surveillance and prevention of HAIs in cancer patients. However, prospective studies are needed to further verify these risk factors and construct a risk assessment model to screen and manage these high-risk patients, thus taking effective measures to reduce the occurrence of HAIs. Abbreviations ASA: American Society of Anesthesiologists; CIs: confidence intervals; HAIs: hospital-acquired infections; ICD-10: International Statistical Classification of Diseases and Related Health Problems 10 th Revision; IQR: interquartile range; LOS: length of hospital stay; ORs: odds ratios; SD: standard deviation; URTI: upper respiratory tract infection; UTI: urinary tract infection. Declarations Ethics approval and consent to participate The protocol of this study was approved by the ethics committee of Sun Yat-sen University Cancer Center. This study is a retrospective analysis without any intervention and the data are anonymous, the requirement for informed consent was therefore waived. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Authors' contributions Lihua Huang, Huacheng Ning and Huan Li lead the design and conception of the study. Lihua Huang, Yongjie Wang and Qing Liu performed the data analysis. Lihua Huang and Huacheng Ning contributed to writing the first draft. Yongjie Wong, Xin-Chen Liu, Qing Liu and Huan Li reviewed and revised the manuscript. Huan Li is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. All authors contributed to the article and approved the submitted version. Acknowledgements Not applicable. Funding None. References Zimlichman E, Henderson D, Tamir O, Franz C, Song P, Yamin CK, Keohane C, Denham CR, Bates DW: Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system . JAMA Intern Med 2013, 173 (22):2039-2046. Liu X, Spencer A, Long Y, Greenhalgh C, Steeg S, Verma A: A systematic review and meta-analysis of disease burden of healthcare-associated infections in China: an economic burden perspective from general hospitals . J Hosp Infect 2022, 123 :1-11. Li P, Li Y, Zhang Y, Bao J, Yuan R, Lan H, Sun M: Economic burden attributable to healthcare-associated infections in tertiary public hospitals of Central China: a multi-centre case-control study . Epidemiol Infect 2022, 150 :e155. Undabeitia J, Liu BG, Catalan G, Bilbao G, Pomposo I, Garibi JM: Clinical and economic analysis of hospital acquired infections in patients diagnosed with brain tumor in a tertiary hospital . Neurocirugia (Astur) 2011, 22 (6):535-541. Liu Y, Xiao W, Wang S, Chan CWH: Evaluating the direct economic burden of health care-associated infections among patients with colorectal cancer surgery in China . Am J Infect Control 2018, 46 (1):34-38. Biscione A, Corrado G, Quagliozzi L, Federico A, Franco R, Franza L, Tamburrini E, Spanu T, Scambia G, Fagotti A: Healthcare associated infections in gynecologic oncology: clinical and economic impact . Int J Gynecol Cancer 2023, 33 (2):278-284. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA Cancer J Clin 2021, 71 (3):209-249. Wang J, Liu F, Tartari E, Huang J, Harbarth S, Pittet D, Zingg W: The Prevalence of Healthcare-Associated Infections in Mainland China: A Systematic Review and Meta-analysis . Infect Control Hosp Epidemiol 2018, 39 (6):701-709. Sheng WH, Wang JT, Lin MS, Chang SC: Risk factors affecting in-hospital mortality in patients with nosocomial infections . J Formos Med Assoc 2007, 106 (2):110-118. Zhao X, Wang L, Wei N, Zhang J, Ma W, Zhao H, Han X: Risk factors of health care-associated infection in elderly patients: a retrospective cohort study performed at a tertiary hospital in China . BMC Geriatr 2019, 19 (1):193. Zhao X, Li S, Sun X, Liu S, Duan F: Risk factors for hospital-acquired infection in cancer patients in a central Chinese hospital . Am J Infect Control 2016, 44 (9):e163-165. Sun Z, Zhu Y, Xu G, Aminbuhe, Zhang N: Regression analysis of the risk factors for postoperative nosocomial infection in patients with abdominal tumors: experience from a large cancer centre in China . Drug Discov Ther 2015, 9 (6):411-416. Diagnostic criteria of nosocomial infection (in Chinese) [http://www.nhc.gov.cn/yzygj/s3593/200804/463eaed2307840129912e5278833d2b3.shtml] The average annual salary of urban non-private employees in 2022 was 114,029 yuan [http://www.stats.gov.cn/sj/zxfb/202305/t20230509_1939290.html] The average annual salary of employees in urban private units in 2022 was 65,237 yuan [http://www.stats.gov.cn/sj/zxfb/202305/t20230509_1939286.html] Cai Q, Teeple A, Wu B, Muser E: Prevalence and economic burden of comorbid anxiety and depression among patients with moderate-to-severe psoriasis . J Med Econ 2019, 22 (12):1290-1297. Pilon D, Neslusan C, Zhdanava M, Sheehan JJ, Joshi K, Morrison L, Rossi C, Lefebvre P, Greenberg PE: Economic Burden of Commercially Insured Patients With Major Depressive Disorder and Acute Suicidal Ideation or Behavior in the United States . J Clin Psychiatry 2022, 83 (3). Amand C, Tong S, Kieffer A, Kyaw MH: Healthcare resource use and economic burden attributable to respiratory syncytial virus in the United States: a claims database analysis . BMC Health Serv Res 2018, 18 (1):294. Seidelman JL, Mantyh CR, Anderson DJ: Surgical Site Infection Prevention: A Review . JAMA 2023, 329 (3):244-252. Manoukian S, Stewart S, Graves N, Mason H, Robertson C, Kennedy S, Pan J, Kavanagh K, Haahr L, Adil M et al : Bed-days and costs associated with the inpatient burden of healthcare-associated infection in the UK . J Hosp Infect 2021, 114 :43-50. Askarian M, Yadollahi M, Assadian O: Point prevalence and risk factors of hospital acquired infections in a cluster of university-affiliated hospitals in Shiraz, Iran . J Infect Public Health 2012, 5 (2):169-176. Ezzi O, Majoub M, Ammar A, Dhaouadi N, Ayedi Y, Helali R, Bannour W, Njah M: Burden of Healthcare-associated infections in a Tunisian University Hospital in 2019 . Tunis Med 2021, 99 (12):1148-1155. Feldman C, Anderson R: Cigarette smoking and mechanisms of susceptibility to infections of the respiratory tract and other organ systems . J Infect 2013, 67 (3):169-184. Zhu C, Liu H, Wang Y, Jiao J, Li Z, Cao J, Song B, Jin J, Liu Y, Wen X et al : Prevalence, incidence, and risk factors of urinary tract infection among immobile inpatients in China: a prospective, multi-centre study . J Hosp Infect 2020, 104 (4):538-544. Zhang Y, Zheng QJ, Wang S, Zeng SX, Zhang YP, Bai XJ, Hou TY: Diabetes mellitus is associated with increased risk of surgical site infections: A meta-analysis of prospective cohort studies . Am J Infect Control 2015, 43 (8):810-815. Ban KA, Minei JP, Laronga C, Harbrecht BG, Jensen EH, Fry DE, Itani KM, Dellinger EP, Ko CY, Duane TM: American College of Surgeons and Surgical Infection Society: Surgical Site Infection Guidelines, 2016 Update . J Am Coll Surg 2017, 224 (1):59-74. Barreto JN, McCullough KB, Ice LL, Smith JA: Antineoplastic agents and the associated myelosuppressive effects: a review . J Pharm Pract 2014, 27 (5):440-446. Mirabile A, Vismara C, Crippa F, Bossi P, Locati L, Bergamini C, Granata R, Resteghini C, Conte E, Morelli D et al : Health care-associated infections in patients with head and neck cancer treated with chemotherapy and/or radiotherapy . Head Neck 2016, 38 Suppl 1 :E1009-1013. Cassini A, Plachouras D, Eckmanns T, Abu Sin M, Blank HP, Ducomble T, Haller S, Harder T, Klingeberg A, Sixtensson M et al : Burden of Six Healthcare-Associated Infections on European Population Health: Estimating Incidence-Based Disability-Adjusted Life Years through a Population Prevalence-Based Modelling Study . PLoS Med 2016, 13 (10):e1002150. Lydeamore MJ, Mitchell BG, Bucknall T, Cheng AC, Russo PL, Stewardson AJ: Burden of five healthcare associated infections in Australia . Antimicrob Resist Infect Control 2022, 11 (1):69. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3605326","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":250995379,"identity":"1406f6a0-8b5c-487e-8331-a0ec190437bb","order_by":0,"name":"Lihua Huang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Huang","suffix":""},{"id":250995380,"identity":"2438d656-cdc3-43a6-b449-b7171c0bf492","order_by":1,"name":"Huacheng Ning","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huacheng","middleName":"","lastName":"Ning","suffix":""},{"id":250995381,"identity":"c32fc8a3-b77c-4227-b098-5babf09a4451","order_by":2,"name":"Xin-Chen Liu","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin-Chen","middleName":"","lastName":"Liu","suffix":""},{"id":250995382,"identity":"2b659503-a09a-4aa8-bfd2-d2361ae2865a","order_by":3,"name":"Yongjie Wang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongjie","middleName":"","lastName":"Wang","suffix":""},{"id":250995383,"identity":"d0affcbf-2674-4919-bfa8-cabd21a8132b","order_by":4,"name":"Qing Liu","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Liu","suffix":""},{"id":250995384,"identity":"3ab20e84-ba12-46f4-9b29-fab67740039a","order_by":5,"name":"Huan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACPmYeEGVDghY2iJY0UrQwgLUcJkULO+8xiZ87ztsbnD9j+OEHg02+vAPzswf4HcaXJtl75nbihhs5xpI9DGmWGw+wmRvg18JjJsHbdjvB4AaPGTPQhQaGDTxsEoS0SP5tOwdyGAlapHnbDjBuOJAD0SLPQFiLsbVsW3LizBtpxZI9BmkGBsxsZni18POfMbz5ts3Onu/84Y0fflTYGMi3Nz/DqwUOFA6ASGBQGRAdR/IN6IxRMApGwSgYBVAAAOmHOjjH3BWMAAAAAElFTkSuQmCC","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-11-13 13:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3605326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3605326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46878564,"identity":"b56b35bb-3231-463e-ae1e-1d350a69f606","added_by":"auto","created_at":"2023-11-21 21:25:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe study flow diagram.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviation: HAIs: hospital-acquired infections\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3605326/v1/5e5606a080de88f8396ed9f7.png"},{"id":47652367,"identity":"e02ee0c7-a92f-488c-961b-a8d030b10741","added_by":"auto","created_at":"2023-12-05 16:52:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1358355,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3605326/v1/2650ebd3-67c1-4e89-8ec4-4dd00afddbe7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influencing factors and burden of hospital-acquired infections among cancer patients ","fulltext":[{"header":"Background","content":"\u003cp\u003eHospital-acquired infections (HAIs) are considered the most prevalent adverse outcomes of healthcare worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Unlike other medical interventions, controlling HAIs does not directly generate revenue for both hospital and patient, making it difficult to observe their potential benefits. In China, the development of HAIs prevention and control was slow until the SARS epidemic in 2003. Although numerous studies have assessed the burden of HAIs over the past 2 decades, most studies have primarily focused on general hospital inpatients[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Only a limited number have evaluated their impact on cancer patients and these studies were often constrained by specific types of cancer[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is estimated that there were 19.3\u0026nbsp;million new cases of cancer and 10\u0026nbsp;million cancer-related deaths worldwide in 2020[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with a steady increase over time. Individuals with cancer are at a heightened risk of developing infections, due to immunodeficiencies associated with cancer, antineoplastic chemotherapy or radiation therapy. A systematic review of studies conducted in tertiary and specialty hospitals of mainland China in 2018 suggested that the weighted prevalence of HAIs in oncology hospitals (3.96%) was higher than that in general hospitals (3.12%)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Considering the high prevalence of HAIs in cancer patients, assessing the burden of HAIs on cancer patients is important for strengthening the management of HAIs in cancer hospitals.\u003c/p\u003e \u003cp\u003eEpidemiological data supported that HAIs are associated with multiple risk factors and can be prevented[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although patients with cancer are susceptible to infections[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], studies exploring the influencing factors of HAIs in this high-risk population are limited worldwide[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, it is important to identify specific risk factors in the more susceptible population, which helps to develop targeted preventive measures.\u003c/p\u003e \u003cp\u003e Based on these considerations, we conducted this retrospective study involving inpatients discharged for the first time from the Sun Yat-sen University Cancer Center, a large regional cancer hospital in Southern China, to identify the risk factors of HAIs and estimate the burden including economic loss and length of hospital stay (LOS) attributed to HAIs overall and that across different specific infection sites.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy design and data collection\u003c/h2\u003e\n \u003cp\u003eThe protocol of this study was approved by the ethics committee of Sun Yat-sen University Cancer Center. This study is a retrospective analysis without any intervention and the data are anonymous, the requirement for informed consent was therefore waived by the ethics committee of Sun Yat-sen University Cancer Center. Inpatients discharged for the first time from the Sun Yat-sen University Cancer Center between January 1, 2022 to December 31, 2022 were selected as the study subjects. Information including medical ID, age, gender, department admitted to hospital, principal diagnosis based on codes from International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10), other diseases (i.e., hypertension, diabetes or myelosuppression), surgical procedure, radiotherapy, chemotherapy, total medical expenditure, and self-paid expenditure (i.e., the balance after deducting the basic health insurance reimbursement) were extracted from the electric medical records information system. The data of HAIs including medical ID of HAI cases, infection site and their days of urinary catheter use and deep vein catheter indwelling, were collected through the nosocomial infection monitoring information system. The cases of HAI were identified according to the Diagnostic Standard for HAIs developed by China\u0026apos;s Ministry of Health in 2001[\u003cspan\u003e13\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e showed the study flow. Of these, 457 patients had been diagnosed with at least one type of HAIs and the rest had not been diagnosed with HAIs during their hospitalization.\u003c/p\u003e\n \u003cp\u003eThe direct economic burden associated with HAIs includes the medical expenditure that patients with HAIs spend more than patients without HAIs, and the lost wages of patients and their accompanying family member due to prolonged length of hospital stay. According to the first page of the medical records, total medical expenditure can be divided into comprehensive medical service cost, diagnosis cost, treatment cost, blood and blood products cost and disposable consumable cost. The lost wages is calculated by the formula of human capital method, that is, twice the extended days of hospitalization multiplied by the days of average wages. Data released by the National Bureau of Statistics showed that the average wage of urban non-private employees in 2022 is 114,029 yuan[\u003cspan\u003e14\u003c/span\u003e], the average wage of private employees is 65,237 yuan[\u003cspan\u003e15\u003c/span\u003e], the estimated average daily wage is 178.73\u0026thinsp;~\u0026thinsp;312.41 yuan. the estimated average daily wage is 178.73\u0026thinsp;~\u0026thinsp;312.41 yuan.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eData were analyzed with the use of SPSS software, version 26.0 (IBM, Armonk, NY, USA). The continuous data with normal distribution were presented as mean (standard deviation[SD]), and the non-normally distributed continuous data were reported as median (interquartile range[IQR]); The count data was described by frequency (proportion). To identify influencing factors associated with HAIs, we first compared the demographic and medical characteristics of patients with and without HAIs using Mann-Whitney U test and Chi-square test. Potential factors with \u003cem\u003eP\u003c/em\u003e-values of 0.1 or less in the univariate analysis were selected and further included in a multivariate conditional logistic regression model by using a forward stepwise strategy, and odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs) were estimated. To evaluate the burden of HAIs, as done before in previous studies regarding burden[\u003cspan\u003e16\u003c/span\u003e\u0026ndash;\u003cspan\u003e18\u003c/span\u003e], HAI cases were matched to non-HAI controls in a ratio of 1:1 using the following criteria: same sex, age difference of less than 3 years, same principal diagnosis (i.e., the main diseases in the hospital, based on ICD-10 diagnosis code), same surgical procedure if applied and same type of payment. Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e showed the balanced distribution of factors matched between paired case and control groups. We compared the total medical expenditure and hospital LOS between paired case and control groups using the Wilcoxon signed rank test. To understand the burden of different infection sites, we further performed the stratification analysis by infection sites. All tests were two-tailed and \u003cem\u003eP\u003c/em\u003e-value of 0.05 or less was considered as statistical significance.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Characteristics between paired case and control groups\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaired case group (N=256)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaired control group (N=256)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ/\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;c\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e median, IQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003cp\u003e(51.0, 67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e59.50\u003c/p\u003e\n \u003cp\u003e(52.0, 68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003en, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e162 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e162 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e94 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e94 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrincipal disease\u0026nbsp;\u003c/strong\u003en, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eUpper gastrointestinal tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e57 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e57 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eColorectal tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e36 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e36 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eHepato-bilio-pancreatic tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e44 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e44 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eLung tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e17 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e17 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003ePeritoneal tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e2 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e2 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eBreast tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e3 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e3 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eGenitourinary tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e45 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e45 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eNeurologic tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e34 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e34 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eThyroid tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e3 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e3 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eHematologic tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e10 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003cp\u003e10 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical operation\u0026nbsp;\u003c/strong\u003en, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e256 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e256 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePayment method\u0026nbsp;\u003c/strong\u003en, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eBasic medical insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e21 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e21 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eFree medical care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e228 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e228 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.27598566308244%\" valign=\"top\"\u003e\n \u003cp\u003eSelf-paid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\"\u003e\n \u003cp\u003e7 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.63799283154122%\"\u003e\n \u003cp\u003e7 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAbbreviation:\u0026nbsp;IQR:\u0026nbsp;interquartile range\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eGiven that age distributed non-normally, Mann-Whitney U test was used to compare the difference between paired case and control groups. As for gender, main disease and ways of payment, Chi-square tests were performed to compared the differences between two groups.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eInfluencing factors of HAIs in cancer patients\u003c/h2\u003e\n \u003cp\u003eA total of 45,030 cancer patients were enrolled in this study, of which 457 suffered HAI and 44,573 not. The patients with HAIs were assumed as the case group and patients without any HAIs as the control group. Univariate analysis presented in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e indicated that age, male, surgical treatment, chemoradiotherapy, days of urinary catheterization, days of deep vein catheter retention, diabetes mellitus, hypertension, and myelosuppression were the factors associated with HAIs in cancer patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results of multivariate conditional logistic regression were presented in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. Older age (OR: 1.01, 95% CI: 1.01\u0026thinsp;~\u0026thinsp;1.02), longer retention days of deep vein catheter (OR: 1.13, 95% CI: 1.11\u0026thinsp;~\u0026thinsp;1.15), longer indwelling days of urinary catheter (OR: 1.18, 95% CI: 1.15\u0026thinsp;~\u0026thinsp;1.21), diabetes mellitus (OR: 1.57, 95% CI: 1.15\u0026thinsp;~\u0026thinsp;2.15), male (OR: 1.66, 95% CI: 1.35\u0026thinsp;~\u0026thinsp;2.04), surgical operation (OR: 8.63, 95% CI: 4.53\u0026thinsp;~\u0026thinsp;16.46), and myelosuppression (OR: 10.68, 95% CI: 4.43\u0026thinsp;~\u0026thinsp;25.79) were identified as the independent risk factors for HAIs among cancer patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics between the study participants with and without HAIs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients without HAIs (N\u0026thinsp;=\u0026thinsp;44573)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients with HAIs (N\u0026thinsp;=\u0026thinsp;457)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ/ \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (median, IQR, years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003cp\u003e(41.0, 62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003cp\u003e(48.0, 67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDays of urinary catheter use\u003c/strong\u003e (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-28.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDays of deep vein catheter indwelling\u003c/strong\u003e (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1,12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-22.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22888 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286 (62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21685 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemoradiotherapy\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39117 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e435 (95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5456 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42106 (94.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e405 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2467 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39723 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4850 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical operation\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10805 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33768 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e446 (97.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMyelosuppression\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44522 (99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e449 (98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: HAIs: hospital-acquired infections; IQR: interquartile range\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eGiven that age, days of urinary catheter use and days of deep vein catheter indwelling distributed non-normally, Mann-Whitney U tests were used to compared the differences between patients with and without HAIs. As for other count data including gender and medical characteristics, Chi-square tests were performed to compared the differences between two groups.\u003c/p\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariate analysis for risk factors of HAIs in cancer patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.014 (1.006, 1.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDays of urinary catheter use\u003c/strong\u003e (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.180 (1.154, 1.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDays of deep vein catheter indewlling\u003c/strong\u003e (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.129 (1.111, 1.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.658 (1.351, 2.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.571 (1.149, 2.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical operation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.633 (4.528, 16.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMyelosuppression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.683 (4.426, 25.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: HAIs: hospital-acquired infections; OR: odds ratios; CI: confidence intervals; Ref.: reference\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Potential factors with\u003cem\u003e\u0026nbsp;P-\u003c/em\u003evalues of 0.1 or less in the univariate analysis (i.e., age, gender, days of urinary cathether use and deep vein catheter indewlling, surgical operation or not, have diabetes or myelosuppression) were selected and further included in a multivariate conditional logistic regression model by using a forward stepwise strategy, and odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs) were estimated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eInfluence of HAIs on LOS\u003c/h2\u003e\n \u003cp\u003eAs seen in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e, compared with the control group, the LOS of patients with HAIs was 8 days longer on average (14 vs. 22 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The LOS was significantly longer in the patients with all different sites of HAIs, except for upper respiratory tract as well as urinary system. Among the patients with single site of infection, the patients with abdominal and digestive infection had the most prolonged hospitalization days (11 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the patients with two sites of HAIs have longer LOS (i.e., 34 days), staying 19.5 days more than the patients without HAIs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparing length of hospital stay (days) between paired control and case groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite of infection\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of HAI cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaired control group (N\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaired case group (N\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003cp\u003e(10.00, 17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.00\u003c/p\u003e\n \u003cp\u003e(16.00, 30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper respiratory tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003cp\u003e(5.25, 19.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003cp\u003e(14.75, 26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower respiratory tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.50\u003c/p\u003e\n \u003cp\u003e(10.00, 16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003cp\u003e(14.00, 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgical site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003cp\u003e(10.00, 19.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.00\u003c/p\u003e\n \u003cp\u003e(17.00, 35.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematologic system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003cp\u003e(8.00, 14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003cp\u003e(12.75, 26.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbdomen and digestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003cp\u003e(7.75, 18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003cp\u003e(15.25, 34.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrinary system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003cp\u003e(11.50, 17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.50\u003c/p\u003e\n \u003cp\u003e(11.75, 26.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003cp\u003e(4.50, 14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003cp\u003e(17.50, 25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo sites simultaneously\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.50\u003c/p\u003e\n \u003cp\u003e(13.25, 16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.00\u003c/p\u003e\n \u003cp\u003e(23.00, 42.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: HAI: hospital-acquired infection\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eWilcoxon signed rank test was adopted to compare the length of hospital stay (days) between paired non-HAI and HAI groups. To understand the burden of different infection sites, the stratification analyses by infection sites were further performed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eDirect economic loss due to HAIs\u003c/h2\u003e\n \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e, the median total medical cost in the case group was 114,790.88 yuan, and that in the non-HAI group was 77,149.02 yuan. The direct economic loss caused by HAIs was 37,641.86 yuan (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The median medical expenditure at patients\u0026rsquo; own expenditure in the HAI group was 51,558.3 yuan, and that in the non-HAI group was 34,681.5 yuan. The medical economic loss due to HAIs was 16,876.8 yuan (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The comprehensive medical service fee, diagnosis fee, treatment fee, blood and its products fee, and consumable fee were all higher in HAI group than non-HAI group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Based on the estimation above, the average delayed hospitalization days for hospital infection were estimated as 8 days among cancer patients, and the direct non-medical economic loss caused by HAIs was 2,859.68\u0026thinsp;~\u0026thinsp;4,998.56 yuan.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparing medical expenditure (yuan) between paired control and case groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpenditure item\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaired control group (N\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaired case group (N\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComprehensive medical service cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3584.20\u003c/p\u003e\n \u003cp\u003e(2629.94, 5032.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7240.49\u003c/p\u003e\n \u003cp\u003e(4636.66, 11121.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3656.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10681.19\u003c/p\u003e\n \u003cp\u003e(7464.48, 15342.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19082.86\u003c/p\u003e\n \u003cp\u003e(12561.06, 26515.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8401.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29699.42\u003c/p\u003e\n \u003cp\u003e(22721.22, 39744.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34324.16\u003c/p\u003e\n \u003cp\u003e(25481.63, 48672.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4624.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood and blood products cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2589.30\u003c/p\u003e\n \u003cp\u003e(119.73, 7061.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7326.64\u003c/p\u003e\n \u003cp\u003e(2589.30, 17614.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4737.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisposable consumable cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25508.51\u003c/p\u003e\n \u003cp\u003e(11853.82, 36014.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31933.68\u003c/p\u003e\n \u003cp\u003e(15125.81, 42912.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6425.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-paid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34681.50\u003c/p\u003e\n \u003cp\u003e(20824.34, 47272.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51558.33\u003c/p\u003e\n \u003cp\u003e(32451.80, 80523.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16876.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77149.02\u003c/p\u003e\n \u003cp\u003e(54942.70,96951.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114790.88\u003c/p\u003e\n \u003cp\u003e(82401.90,156337.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37641.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Wilcoxon signed rank tests were adopted to compare medical expenditure (yuan) between paired non-HAI and HAI groups. To understand the source of the increment due to HAIs, the stratification analyses by composition of medical expenditure were further performed.\u003c/p\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e, the total hospital expenditure of lower respiratory tract infection, abdominal and digestive system infection, surgical site infection, other site infection and 2 site infections had statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among the patients with single site of infection, the patients with lower respiratory tract infection caused the most loss of medical cost (55,837.89 yuan, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no significant differences in the total hospitalization cost of upper respiratory tract infection (URTI), blood system infection and urinary system infection (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparing medical expenditure (yuan) between paired control and case groups across different infectious sites\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite of infection\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of HAI cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaired control group (N\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaired case group (N\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper respiratory tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77819.10\u003c/p\u003e\n \u003cp\u003e(39911.46, 87733.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98880.03\u003c/p\u003e\n \u003cp\u003e(63988.06, 136537.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21060.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower respiratory tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80051.99\u003c/p\u003e\n \u003cp\u003e(61639.60, 101602.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135889.88\u003c/p\u003e\n \u003cp\u003e(113612.51, 177711.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55837.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgical site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76039.63\u003c/p\u003e\n \u003cp\u003e(57635.35, 96771.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109062.60\u003c/p\u003e\n \u003cp\u003e(74211.15, 147181.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33022.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematologic system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64508.04\u003c/p\u003e\n \u003cp\u003e(42914.66, 91368.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92911.97\u003c/p\u003e\n \u003cp\u003e(73390.43, 103428.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28403.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbdomen and digestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75308.47\u003c/p\u003e\n \u003cp\u003e(62391.74, 98107.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127085.64\u003c/p\u003e\n \u003cp\u003e(92617.58, 158341.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51777.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrinary system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84553.58\u003c/p\u003e\n \u003cp\u003e(74420.53, 89971.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91027.97\u003c/p\u003e\n \u003cp\u003e(65932.73, 104381.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6474.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46777.89\u003c/p\u003e\n \u003cp\u003e(29499.61, 69814.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86701.68\u003c/p\u003e\n \u003cp\u003e(53678.25, 127625.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39923.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo sites simultaneously\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100474.93\u003c/p\u003e\n \u003cp\u003e(83280.28, 127676.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157541.50\u003c/p\u003e\n \u003cp\u003e(125139.18, 242845.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57066.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: HAI(s): hospital-acquired infection(s)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Wilcoxon signed rank test was adopted to compare medical expenditure (yuan) between paired non-HAI and HAI groups. To understand the burden of different infection sites, the stratification analyses by infection sites were further performed.\u003c/p\u003e\u0026nbsp;\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Summary\u003c/h2\u003e \u003cp\u003eIn this retrospective study, we found that older age, male, longer retention days of deep vein catheter, longer indwelling days of urinary catheter diabetes mellitus, surgical operation, and myelosuppression were the independent risk factors for HAIs among cancer patients. The estimated medical economic loss attributable to HAIs was 37,641.86 yuan per case, and the estimated non-medical economic loss was 2,859.68\u0026thinsp;~\u0026thinsp;4,998.56 yuan per case. Compared with the non-HAI group, the LOS in the HAI group was significantly longer by 8 days. Among the patients with single site of infection, the patients with lower respiratory tract infection caused the most loss of medical cost (55,837.89 yuan) and the patients with abdominal and digestive infection had the most prolonged hospitalization days (11 days). Besides, patients with more sites of infection were likely to have increased medical costs and prolonged hospitalization days than patients with single site of infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBurden of HAIs\u003c/h2\u003e \u003cp\u003eLike patients in general hospitals, HAIs did increase the economic burden and prolong LOS among patients in the cancer hospital. In this study of 256 paired cancer patients in southern China, the total direct economic burden of HAIs was estimated to 37,641.86 yuan. As for specific types of tumors, the economic loss due to HAIs varied. For example, a prospective monitoring case-control study of 448 patients undergoing colorectal cancer surgery in China presented that the total direct economic burden due to HAI was 1,589.30 dollars[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While, a retrospective study in Europe on 139 patients diagnosed with brain tumors reported a mean cost 17,097 euros[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and another previous study in USA on gynecologic tumor estimated the cost 58,000 dollars[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Certainly, the economic loss due to HAIs varies in different studies by the region of study, the characteristics of study population, the structure and composition of disease and the types of infection.\u003c/p\u003e \u003cp\u003eThe high economic burden attributable to HAIs implied that HAI patients need extra diagnosis and treatments. Regarding hospitalization costs, the cost of diagnosis (8,401.67 yuan) constituted the highest proportion of economic burden followed by consumable cost (6,425.17 yuan) and blood and blood products cost (4,737.34 yuan) in our study. The reason for the increased cost of diagnosis might be that further examinations such as multiple microbial examination and blood routine examination are often needed in order to clarify the diagnosis of HAIs. As 256 patients with HAIs have accepted surgery and almost half of them had infections in their surgical site, some patients might require reoperation and/or continued wound care, and blood transfusion might be needed during or after the surgery, resulting in higher expenditure on consumable cost and blood products.\u003c/p\u003e \u003cp\u003eOur findings also indicated that there is a significant increase in LOS associated with HAIs. Patients with HAIs were estimated to have an average LOS of 8 days longer than patients without HAIs. This result is consistent with previous studies in general hospitals; however, the days of prolongation in LOS varied among studies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. HAIs might deteriorate patients\u0026rsquo; recovery from surgery, resulting in extended LOS and incurring extra costs of treatment and other hospital expenditures. Prolongation of LOS not only can increase the healthcare related economic burden but also the social and labor-related economic burden because of the patients\u0026rsquo; absence from work and occupation of medical resources. Based on the data released by the National Bureau of Statistics, the estimated non-medical economic loss was 2,859.68\u0026thinsp;~\u0026thinsp;4,998.56 yuan per case. However, in addition, the occurrence of hospital infection will also lead to the aggravation of patients' basic diseases, increase the physical and mental pain of patients, bring more anxiety and worry to family members, and some even cause the death of patients, which could not be simply measured in currency.\u003c/p\u003e \u003cp\u003eAmong various infection sites, our study showed that two site infections had the highest direct economic burden of 57,066.57 yuan and the longest LOS of 19.5 days. It is easy to understand that patients with multiple site infection are likely to have poor basic conditions, complex disease, complex anti-infection program, long course of disease, and corresponding increase in cost. Among the patients with single site of infection, the patients with lower respiratory tract infection caused the most loss of medical cost (51,777.17 yuan) and the patients with abdominal and digestive infection had the most prolonged hospitalization days (11 days). Most of the patients with lower respiratory tract infection are elderly patients with underlying diseases and need to use ventilators. The pathogens are mostly drug-resistant bacteria, which makes treatment difficult and medical cost high. Moreover, our studies found that both URTI and urinary tract infection (UTI) had non-significant impact on medical expenditure and LOS. A study from UK has also reported that UTI did not increase LOS[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While URTI usually results in a self-limiting, acute onset and a short course of illness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors of HAIs\u003c/h2\u003e \u003cp\u003eConsidering that most HAIs identified were preventable, more efforts should be made in HAI control and prevention, for instance, the identification of the risk factors and the development of effective strategies for the prevention of infections. In our study, we found that older age, male, longer retention days of deep vein catheter, longer indwelling days of urinary catheter, diabetes mellitus, surgical operation, and myelosuppression were the independent risk factors for HAI among cancer patients.\u003c/p\u003e \u003cp\u003eOur findings were similar to a previous study including 192,317 hospitalized cancer patients, which revealed a clear age-dependent trend of HAI rates[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. With age, the body becomes less resistant to external pathogens. On the other hands, the skin\u0026rsquo;s basement membrane and dermis thin with increasing age, and the skin loses its reserve of cutaneous blood vessels and nerves that diminish wound healing. Several studies in general hospitals have also shown that compared with women, males were more likely to acquire HAIs[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It may be because of gender differences in behaviors that were potential risk factors, such as smoking behavioral. While people who smoke have a high risk of infection, especially acute respiration infection[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], our study was unable to verify it, since we did not collect any data of behavior factors. Although age and sex are inherent characteristics that cannot be changed, they should be considered in the risk assessment of HAIs.\u003c/p\u003e \u003cp\u003ePatients with certain underlying diseases are more susceptible to infections. Consistent with the previous studies, the results indicated that hyperglycemia is another risk factor for HAIs[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Hyperglycemia impairs the innate immune system and promotes glycosylation of proteins, which compromises wound healing[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consequently, attention should be paid to the monitoring and management of blood sugar and control of glucose intake, especially the surgical patients. This study also found that myelosuppression is associated with a high risk of HAIs. Myelosuppression is a frequent complication of cytotoxic chemotherapy that results from damage to hematopoietic stem and progenitor cells in the bone marrow and commonly manifests as neutropenia, anemia, and/or thrombocytopenia, thus placing patients at significant risk of serious infections, bleeding, sepsis, and even death[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, nosocomial infection surveillance systems currently do not include it as a monitored factor. Moreover, protection measures are not mandatory enforcement for patients with myelosuppression in the clinic.\u003c/p\u003e \u003cp\u003eInvasive operations (e.g. surgery, central venous catheterization and urinary catheter indwelling) had been widely used in tumor therapy, which benefit patients but also bring some risks of infections. Although these invasive operations are relatively mature and routine techniques, they might damage the tissue mucosa, destroying the body\u0026rsquo;s normal defense barrier[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This allows certain conditional pathogens invade into the body or ectopic implant of the normal flora to take place. When it comes to the prevention and control of hospital infections, daily assessment of the need for an indwelling central venous catheter and/or urinary catheter and early removal of the catheter are significant tasks. As for surgical infection, a previous study has shown that preoperative hospitalization time\u0026thinsp;\u0026ge;\u0026thinsp;6 days, surgery time\u0026thinsp;\u0026ge;\u0026thinsp;230 minutes, American Society of Anesthesiologists (ASA) score\u0026thinsp;\u0026ge;\u0026thinsp;III were related to HAIs after surgery[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, it is of great significance to reduce preoperative hospitalization time and shorten the surgery time for surgical infections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrength \u0026amp; Limitations\u003c/h2\u003e \u003cp\u003eOur study is one of the few studies to investigate burden due to HAIs amongst cancer patients. However, this study is subjected to at least four limitations. First, sample used to explore the risk factors and estimate the economic burden of HAIs was from a single hospital located in southern China, so the results cannot be extrapolated directly to hospitals with other foci or to the situation in China in general. Second, considering that the economic burden of HAIs includes direct loss of prolonged stay antiinfection treatment and readmission, as well as the indirect loss which mainly consists of the reduced working hours of family members due to hospital care and the declined labor capacity of patients themselves due to infection and even disability, the total losses attributable to HAIs were underestimated in our research[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Third, since this was a single-center, retrospective study, causal relationship of the risk factors found above with HAIs could not be demonstrated. Fourth, the study sample did not include those diagnosed after discharge because of data inaccessibility, therefore the incidence and rate of HAIs might be lower than the actual situation, which might result in underestimation of the burden due to HAIs. In all, in order to validate the present results, large-scale multicenter prospective studies on the economic burden and risk factors related to HAIs should be further performed in cancer hospitals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, HAIs prolong LOS and increase medical costs among hospitalized cancer patients in South China. Cancer patients who are male, older aged, administrated with invasive operations, with diabetes mellitus and myelosuppression are more vulnerable to HAIs. This study provides important instructions for the surveillance and prevention of HAIs in cancer patients. However, prospective studies are needed to further verify these risk factors and construct a risk assessment model to screen and manage these high-risk patients, thus taking effective measures to reduce the occurrence of HAIs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASA: American Society of Anesthesiologists; CIs: confidence intervals; HAIs: hospital-acquired infections; ICD-10: International Statistical Classification of Diseases and Related Health Problems 10\u003csup\u003eth\u003c/sup\u003e Revision; IQR: interquartile range; LOS: length of hospital stay; ORs: odds ratios; SD: standard deviation; URTI: upper respiratory tract infection; UTI: urinary tract infection.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol of this study was approved by the ethics committee of Sun Yat-sen University Cancer Center. This study is a retrospective analysis without any intervention and the data are anonymous, the requirement for informed consent was therefore waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLihua Huang, Huacheng Ning and Huan Li lead the design and conception of the study. Lihua Huang, Yongjie Wang and Qing Liu performed the data analysis. Lihua Huang and Huacheng Ning contributed to writing the first draft. Yongjie Wong, Xin-Chen Liu, Qing Liu and Huan Li reviewed and revised the manuscript. Huan Li is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003ch1\u003eAcknowledgements\u003c/h1\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZimlichman E, Henderson D, Tamir O, Franz C, Song P, Yamin CK, Keohane C, Denham CR, Bates DW: \u003cstrong\u003eHealth care-associated infections: a meta-analysis of costs and financial impact on the US health care system\u003c/strong\u003e. \u003cem\u003eJAMA Intern Med \u003c/em\u003e2013, \u003cstrong\u003e173\u003c/strong\u003e(22):2039-2046.\u003c/li\u003e\n\u003cli\u003eLiu X, Spencer A, Long Y, Greenhalgh C, Steeg S, Verma A: \u003cstrong\u003eA systematic review and meta-analysis of disease burden of healthcare-associated infections in China: an economic burden perspective from general hospitals\u003c/strong\u003e. \u003cem\u003eJ Hosp Infect \u003c/em\u003e2022, \u003cstrong\u003e123\u003c/strong\u003e:1-11.\u003c/li\u003e\n\u003cli\u003eLi P, Li Y, Zhang Y, Bao J, Yuan R, Lan H, Sun M: \u003cstrong\u003eEconomic burden attributable to healthcare-associated infections in tertiary public hospitals of Central China: a multi-centre case-control study\u003c/strong\u003e. \u003cem\u003eEpidemiol Infect \u003c/em\u003e2022, \u003cstrong\u003e150\u003c/strong\u003e:e155.\u003c/li\u003e\n\u003cli\u003eUndabeitia J, Liu BG, Catalan G, Bilbao G, Pomposo I, Garibi JM: \u003cstrong\u003eClinical and economic analysis of hospital acquired infections in patients diagnosed with brain tumor in a tertiary hospital\u003c/strong\u003e. \u003cem\u003eNeurocirugia (Astur) \u003c/em\u003e2011, \u003cstrong\u003e22\u003c/strong\u003e(6):535-541.\u003c/li\u003e\n\u003cli\u003eLiu Y, Xiao W, Wang S, Chan CWH: \u003cstrong\u003eEvaluating the direct economic burden of health care-associated infections among patients with colorectal cancer surgery in China\u003c/strong\u003e. \u003cem\u003eAm J Infect Control \u003c/em\u003e2018, \u003cstrong\u003e46\u003c/strong\u003e(1):34-38.\u003c/li\u003e\n\u003cli\u003eBiscione A, Corrado G, Quagliozzi L, Federico A, Franco R, Franza L, Tamburrini E, Spanu T, Scambia G, Fagotti A: \u003cstrong\u003eHealthcare associated infections in gynecologic oncology: clinical and economic impact\u003c/strong\u003e. \u003cem\u003eInt J Gynecol Cancer \u003c/em\u003e2023, \u003cstrong\u003e33\u003c/strong\u003e(2):278-284.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(3):209-249.\u003c/li\u003e\n\u003cli\u003eWang J, Liu F, Tartari E, Huang J, Harbarth S, Pittet D, Zingg W: \u003cstrong\u003eThe Prevalence of Healthcare-Associated Infections in Mainland China: A Systematic Review and Meta-analysis\u003c/strong\u003e. \u003cem\u003eInfect Control Hosp Epidemiol \u003c/em\u003e2018, \u003cstrong\u003e39\u003c/strong\u003e(6):701-709.\u003c/li\u003e\n\u003cli\u003eSheng WH, Wang JT, Lin MS, Chang SC: \u003cstrong\u003eRisk factors affecting in-hospital mortality in patients with nosocomial infections\u003c/strong\u003e. \u003cem\u003eJ Formos Med Assoc \u003c/em\u003e2007, \u003cstrong\u003e106\u003c/strong\u003e(2):110-118.\u003c/li\u003e\n\u003cli\u003eZhao X, Wang L, Wei N, Zhang J, Ma W, Zhao H, Han X: \u003cstrong\u003eRisk factors of health care-associated infection in elderly patients: a retrospective cohort study performed at a tertiary hospital in China\u003c/strong\u003e. \u003cem\u003eBMC Geriatr \u003c/em\u003e2019, \u003cstrong\u003e19\u003c/strong\u003e(1):193.\u003c/li\u003e\n\u003cli\u003eZhao X, Li S, Sun X, Liu S, Duan F: \u003cstrong\u003eRisk factors for hospital-acquired infection in cancer patients in a central Chinese hospital\u003c/strong\u003e. \u003cem\u003eAm J Infect Control \u003c/em\u003e2016, \u003cstrong\u003e44\u003c/strong\u003e(9):e163-165.\u003c/li\u003e\n\u003cli\u003eSun Z, Zhu Y, Xu G, Aminbuhe, Zhang N: \u003cstrong\u003eRegression analysis of the risk factors for postoperative nosocomial infection in patients with abdominal tumors: experience from a large cancer centre in China\u003c/strong\u003e. \u003cem\u003eDrug Discov Ther \u003c/em\u003e2015, \u003cstrong\u003e9\u003c/strong\u003e(6):411-416.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiagnostic criteria of nosocomial infection (in Chinese) \u003c/strong\u003e[http://www.nhc.gov.cn/yzygj/s3593/200804/463eaed2307840129912e5278833d2b3.shtml]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe average annual salary of urban non-private employees in 2022 was 114,029 yuan \u003c/strong\u003e[http://www.stats.gov.cn/sj/zxfb/202305/t20230509_1939290.html]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe average annual salary of employees in urban private units in 2022 was 65,237 yuan \u003c/strong\u003e[http://www.stats.gov.cn/sj/zxfb/202305/t20230509_1939286.html]\u003c/li\u003e\n\u003cli\u003eCai Q, Teeple A, Wu B, Muser E: \u003cstrong\u003ePrevalence and economic burden of comorbid anxiety and depression among patients with moderate-to-severe psoriasis\u003c/strong\u003e. \u003cem\u003eJ Med Econ \u003c/em\u003e2019, \u003cstrong\u003e22\u003c/strong\u003e(12):1290-1297.\u003c/li\u003e\n\u003cli\u003ePilon D, Neslusan C, Zhdanava M, Sheehan JJ, Joshi K, Morrison L, Rossi C, Lefebvre P, Greenberg PE: \u003cstrong\u003eEconomic Burden of Commercially Insured Patients With Major Depressive Disorder and Acute Suicidal Ideation or Behavior in the United States\u003c/strong\u003e. \u003cem\u003eJ Clin Psychiatry \u003c/em\u003e2022, \u003cstrong\u003e83\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eAmand C, Tong S, Kieffer A, Kyaw MH: \u003cstrong\u003eHealthcare resource use and economic burden attributable to respiratory syncytial virus in the United States: a claims database analysis\u003c/strong\u003e. \u003cem\u003eBMC Health Serv Res \u003c/em\u003e2018, \u003cstrong\u003e18\u003c/strong\u003e(1):294.\u003c/li\u003e\n\u003cli\u003eSeidelman JL, Mantyh CR, Anderson DJ: \u003cstrong\u003eSurgical Site Infection Prevention: A Review\u003c/strong\u003e. \u003cem\u003eJAMA \u003c/em\u003e2023, \u003cstrong\u003e329\u003c/strong\u003e(3):244-252.\u003c/li\u003e\n\u003cli\u003eManoukian S, Stewart S, Graves N, Mason H, Robertson C, Kennedy S, Pan J, Kavanagh K, Haahr L, Adil M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBed-days and costs associated with the inpatient burden of healthcare-associated infection in the UK\u003c/strong\u003e. \u003cem\u003eJ Hosp Infect \u003c/em\u003e2021, \u003cstrong\u003e114\u003c/strong\u003e:43-50.\u003c/li\u003e\n\u003cli\u003eAskarian M, Yadollahi M, Assadian O: \u003cstrong\u003ePoint prevalence and risk factors of hospital acquired infections in a cluster of university-affiliated hospitals in Shiraz, Iran\u003c/strong\u003e. \u003cem\u003eJ Infect Public Health \u003c/em\u003e2012, \u003cstrong\u003e5\u003c/strong\u003e(2):169-176.\u003c/li\u003e\n\u003cli\u003eEzzi O, Majoub M, Ammar A, Dhaouadi N, Ayedi Y, Helali R, Bannour W, Njah M: \u003cstrong\u003eBurden of Healthcare-associated infections in a Tunisian University Hospital in 2019\u003c/strong\u003e. \u003cem\u003eTunis Med \u003c/em\u003e2021, \u003cstrong\u003e99\u003c/strong\u003e(12):1148-1155.\u003c/li\u003e\n\u003cli\u003eFeldman C, Anderson R: \u003cstrong\u003eCigarette smoking and mechanisms of susceptibility to infections of the respiratory tract and other organ systems\u003c/strong\u003e. \u003cem\u003eJ Infect \u003c/em\u003e2013, \u003cstrong\u003e67\u003c/strong\u003e(3):169-184.\u003c/li\u003e\n\u003cli\u003eZhu C, Liu H, Wang Y, Jiao J, Li Z, Cao J, Song B, Jin J, Liu Y, Wen X\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrevalence, incidence, and risk factors of urinary tract infection among immobile inpatients in China: a prospective, multi-centre study\u003c/strong\u003e. \u003cem\u003eJ Hosp Infect \u003c/em\u003e2020, \u003cstrong\u003e104\u003c/strong\u003e(4):538-544.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zheng QJ, Wang S, Zeng SX, Zhang YP, Bai XJ, Hou TY: \u003cstrong\u003eDiabetes mellitus is associated with increased risk of surgical site infections: A meta-analysis of prospective cohort studies\u003c/strong\u003e. \u003cem\u003eAm J Infect Control \u003c/em\u003e2015, \u003cstrong\u003e43\u003c/strong\u003e(8):810-815.\u003c/li\u003e\n\u003cli\u003eBan KA, Minei JP, Laronga C, Harbrecht BG, Jensen EH, Fry DE, Itani KM, Dellinger EP, Ko CY, Duane TM: \u003cstrong\u003eAmerican College of Surgeons and Surgical Infection Society: Surgical Site Infection Guidelines, 2016 Update\u003c/strong\u003e. \u003cem\u003eJ Am Coll Surg \u003c/em\u003e2017, \u003cstrong\u003e224\u003c/strong\u003e(1):59-74.\u003c/li\u003e\n\u003cli\u003eBarreto JN, McCullough KB, Ice LL, Smith JA: \u003cstrong\u003eAntineoplastic agents and the associated myelosuppressive effects: a review\u003c/strong\u003e. \u003cem\u003eJ Pharm Pract \u003c/em\u003e2014, \u003cstrong\u003e27\u003c/strong\u003e(5):440-446.\u003c/li\u003e\n\u003cli\u003eMirabile A, Vismara C, Crippa F, Bossi P, Locati L, Bergamini C, Granata R, Resteghini C, Conte E, Morelli D\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eHealth care-associated infections in patients with head and neck cancer treated with chemotherapy and/or radiotherapy\u003c/strong\u003e. \u003cem\u003eHead Neck \u003c/em\u003e2016, \u003cstrong\u003e38 Suppl 1\u003c/strong\u003e:E1009-1013.\u003c/li\u003e\n\u003cli\u003eCassini A, Plachouras D, Eckmanns T, Abu Sin M, Blank HP, Ducomble T, Haller S, Harder T, Klingeberg A, Sixtensson M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBurden of Six Healthcare-Associated Infections on European Population Health: Estimating Incidence-Based Disability-Adjusted Life Years through a Population Prevalence-Based Modelling Study\u003c/strong\u003e. \u003cem\u003ePLoS Med \u003c/em\u003e2016, \u003cstrong\u003e13\u003c/strong\u003e(10):e1002150.\u003c/li\u003e\n\u003cli\u003eLydeamore MJ, Mitchell BG, Bucknall T, Cheng AC, Russo PL, Stewardson AJ: \u003cstrong\u003eBurden of five healthcare associated infections in Australia\u003c/strong\u003e. \u003cem\u003eAntimicrob Resist Infect Control \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e(1):69.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hospital acquired infection, Cancer, Influencing factors, Economic loss, Length of hospital stay","lastPublishedDoi":"10.21203/rs.3.rs-3605326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3605326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo explore the influencing factors and evaluate the economic loss of hospital acquired infections (HAIs) in cancer patients so as to provide evidence for reasonable prevention policies and measures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients who were discharged for the first time from Sun Yat-sen University Cancer Center between Jan 1 and Dec 31, 2022 were recruited. Data were obtained from the medical record system and the nosocomial infection surveillance system. Logistic regression model was adopted to analyze the influencing factors of HAIs. By using 1:1 case-control matching and Wilcoxon signed rank test, economic loss and length of hospital stay (LOS) caused by HAIs were estimated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 45030 cancer patients were enrolled in this study, of which 457 suffered HAIs. Logistic regression analysis showed that older age, longer retention days of deep vein catheter, longer indwelling days of urinary catheter, diabetes mellitus, male, surgical operation, and myelosuppression were all independent risk factors for HAIs (odds ratios ranges from 1.01 to 10.68). The analysis of 256 matched pairs presented that the total hospitalization expenditure and self-paid expenditure of the HAI group (114.79, 51.56 thousand-yuan, respectively) were significantly higher than those of the non-HAI group (77.15, 34.68 thousand-yuan, respectively). Compared with non-HAI group, the LOS in HAI group was significantly longer by 8 days.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHAIs lead to the increase of direct economic burden and LOS in cancer patients. Cancer patients who are male, older age, administrated with invasive operations, with diabetes mellitus and myelosuppression are more susceptible to HAIs.\u003c/p\u003e","manuscriptTitle":"Influencing factors and burden of hospital-acquired infections among cancer patients ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-21 21:25:25","doi":"10.21203/rs.3.rs-3605326/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60d2bbe1-12dc-4af0-a107-17d387784a0d","owner":[],"postedDate":"November 21st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-05T16:44:29+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-21 21:25:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3605326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3605326","identity":"rs-3605326","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}