Hospital Burden Inflicted by Pediatric SARS-CoV-2 Hospitalizations during the first Omicron Wave in Shenyang, Northeastern China: A Retrospective Observational Cohort Study

Hospital Burden Inflicted by Pediatric SARS-CoV-2 Hospitalizations during the first Omicron Wave in Shenyang, Northeastern China: A Retrospective Observational Cohort Study | 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 Hospital Burden Inflicted by Pediatric SARS-CoV-2 Hospitalizations during the first Omicron Wave in Shenyang, Northeastern China: A Retrospective Observational Cohort Study Yaru Zhang, Fei Xia, Feng Shi, Kai You This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849458/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 In the last few years, the 2019 coronavirus disease (COVID-19) has posed a significant global public health threat. The omicron variant of SARS-CoV-2 first emerged as a pandemic in China in December 2022;however, there are no data on hospitalization costs related to diseases in children. In view of the high transmissibility of the Omicron virus, in the present study, we conducted a retrospective analysis of hospitalization costs for children to provide crucial information for understanding the burden inflicted on the Chinese medical system. Methods This study comprised a partial economic assessment from a retrospective observational cohort study designed to assess the costs of hospitalization of children aged 0–18 year with confirmed COVID-19 in Shengjing Hospital of China Medical University treated between December 1, 2022, and January 31, 2023, and followed until discharge, death, or external transfer. Differences between groups were tested using Student’s t-test and the Mann-Whitney test, as appropriate. A multiple logistic regression model was constructed to determine the risk factors associated with high costs. Results A total of 167 children with moderate, severe, and critical illness were included in the analysis. Twenty-six (15.57%) and 18 (10.78%) children required NICU and PICU care, respectively. Overall, 107 (65.27%) children were males, and approximately half (50.90%) of the children were less than 3 years old. The average hospitalization cost was 2671.61 USD, and the average length of stay was 10 days. Hospitalization costs were significantly higher for males than for females. Children with comorbidities and special therapeutic measures who lived in intensive care units or neonatal units had higher hospitalization costs. Ward type, number of treatment measures, and comorbidities were significantly related to hospitalization costs. Thirty-five (20.96%) children required mechanical intervention, and five (2.99%) children underwent plasma exchange. Conclusion Clinical management of COVID-19 pediatric patients poses an economic burden on the healthcare system. Ward type, number of comorbidities, and special therapeutic measures all affect hospitalization costs. COVID-19 children healthcare cost China Figures Figure 1 Background Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has presented a significant global public health threat in recent years [ 1 – 3 ]. The resulting economic impact of the COVID-19 pandemic on global health systems is a major concern, and additional resources and financial investments are urgently required. The existing capacity of hospitals, including facilities, equipment, supplies, and healthcareprofessionals, has increased significantly to match this burden. In a prior study, the United States analyzed the hospitalization costs of 247,590 adult COVID-19 patients, and the results showed that the median total hospitalization costs was 11267USD[ 7 ]. Similarly, a laboratory from Ethiopia analyzed the hospitalization costs of 19,235 patients, but the costs varied depending on the severity of the disease: the average costs for moderate, severe, and critically ill cases were 998.1 USD, 1413 USD, and 1788 USD respectively[ 8 ]; Research data from Saudi Arabia showed that the average cost of COVID-19 patients admitted to the General Internal Medicine Ward (GMW) and ICU was 42704.49 ± 29811.25 and 79418.30 ± 55647.69 Saudi Riyals, respectively[ 9 ]. Since the first the first report of COVID-19 in December 2019, the disease has spread rapidly around the world, with five waves caused by different variants: Alpha, Beta, Gamma, Delta, and Omicron[ 2 , 3 ]. In December 2022, an Omicron outbreak occurred on a large scale throughout China, including the Northeast region, and many of the children who survived developed immunity to the COVID strain; as much, the severity of Omicron infection in hospitalized children who had not previously been exposed to COVID-19 is poorly understood. The high transmissibility of Omicron has led to an increase in the number of hospitalized children, adding an additional economic burden to the healthcare system [ 4 , 5 ]. Understanding the hospitalization costs associated with COVID-19 admissions can support health policymakers in developing a comprehensive approach to hospital preparedness, decision making, and future risk management planning [ 6 ]. Therefore, an economic evaluation is essential to determine the resources and costs associated with the healthcare needed to treat patients with this new disease, including an analysis of the cost of medical care for children with COVID-19[ 10 , 11 , 12 ]. This retrospective study was conducted at Shengjing Hospital of China Medical University, one of the largest public hospital complexes with the largest pediatric department in Northeast China, with 560 beds located in Shenyang, Liaoning Province, with a population of 7.564 million. This study aimed to describe the hospitalization costs of hospitalized children with COVID-19 and the associated factors that affect hospitalization costs. Methods Study Design This retrospective study was conducted at Shengjing Hospital of China Medical University. We enolled children aged 0–18 years confirmed to have SARS-CoV-2 through pharyngeal swab polymerase chain reaction (PCR) examination, clinical symptoms, radiological abnormalities, or molecular testing, admitted to the general ward, neonatal unit, and intensive care unit of Shengjing Hospital between December 1, 2022, and January 31, 2023. Children were diagnosed with moderate, severe, or critical illness according to the “Interim Guidance for Diagnosis and Treatment of Coronavirus Disease 2019” (10th edition). The children were discharged after meeting the criteria for cure. Demographic and clinical data were collected from patients’ medical records, while economic data were obtained from the financial management system. The study protocol was approved by our Institutional Ethics Committee (2023PS730K). Clinical Data and Outcome Definitions The data were organized into standardized forms using trained extractors. Collected variables included sex, age, preexisting diseases reported by the children or their relatives, special therapeutic measures (such as mechanical ventilation, noninvasive ventilation, oxygen therapy, cardiopulmonary resuscitation, plasma exchange, and continuous renal replacement therapy), comorbidities, clinical outcomes (cured or died), and ward type (neonatal units, pediatric intensive care units, and pediatric general wards). Cost Analysis The total cost was calculated based on the following cost factors listed: (i) medical service costs included bed costs, medical materials and equipment, (ii) nursing costs included staff expense, (iii) radiologic diagnosis costs included X-ray, computed tomography (CT), B-mode ultrasonography, nuclear magnetic resonance imaging and video EEG testing, (iv) laboratory diagnosis costs included microbiology, hematology, biochemistry, and blood gas analysis, (v) therapeutic measure costs included the cost of specialist therapeutic measures (mechanical ventilation, noninvasive ventilation, oxygen therapy, cardiopulmonary resuscitation, plasma exchange, continuous renal replacement therapy, bone marrow aspiration, lumbar puncture) and normal therapeutic measure (gastric catheterization, nasal feeding, intravenous injection, etc), (vi) drug acquisition costs included antivirals, antibiotics and gamma globulin; (vii) blood product costs included universal red blood cells, platelet and plasma. Expenditures were calculated in US dollars (exchange rate: CNY 7.0 = USD 1.0). Statistical Analysis To assess the impact of different variables on hospitalization costs and to estimate the average cost (total cost/number of admissions) for each subgroup. The hypothesis test assumed an alpha error of 0.05. The Mann-Whitney U test was used to compare continuous between-group variables from the independent samples. Statistical analyses were performed using IBM SPSS Statistics (version 26.0; (SPSS Inc., Armonk, NY, USA). Results Demographic and clinical characteristics of the patients In total, 167 children with COVID-19 were included in the analysis, of whom 109 (65.27%) were male and 58 (34.73%) were female. The percentage of children aged < 3 years was 50.90%. The median patient age was 3 years (interquartile range, 0–9 years). Pre-existing diseases included neurological disease (22, 13.17%), cardiovascular disease (19, 11.38%), and cancer (8, 4.79%). According to the National Disease Severity Grading Guidelines, 138 (82.63%) cases were moderate or severe, and 29 (17.37%) were critically ill. Common comorbidities included neurological disease (57, 34.13%), cardiovascular disease (49, 26.95%), other pathogen infection (30, 17.96%), liver injury (14, 8.38%), kidney damage (5, 2.99%), shock (5, 2.99%), septicemia (5, 2.99%), and laryngeal obstruction above 3 degrees (4, 2.39%). Special therapeutic measures included mechanical ventilation (17, 10.18%), noninvasive ventilation (18, 10.78%), oxygen therapy (4, 2.39%), cardiopulmonary resuscitation (3, 1.80%), plasma exchange (5, 2.99%), continuous renal replacement therapy (CRRT; 3, 1.80%), bone marrow puncture (19, 11.38%), and lumbar puncture (25, 7.19%). A total of 165 patients were cured, and two died from COVID-19. Cost analysis Among the 167 admitted patients, the total cost was 446231.02 USD, and the average cost was 2672.04 USD. The average hospitalization costs for children treated on the pediatric general ward, neonatal unit, and pediatric intensive care unit were 2203.92 ± 2895.54 USD, 3216.34 ± 6481.00 USD and 5040.41 ± 4562.52 USD, respectively (P < 0.05). The average length of hospitalization was 10 days. The effects of age, sex, ward type, comorbidities, therapeutic measures, and clinical outcomes are shown in Table 1 . Of the patients admitted to the pediatric intensive care unit, 12 were male and six were female. Children aged 0–3 years had the highest average hospitalization costs, followed by children aged 13–18 years, children aged 6–12 years, and children aged 4–5 years, who had the lowest hospitalization costs (2949.27 ± 4676.49 USD VS 2526.83 ± 2703.27 USD VS 2418.48 ± 3860.79 USD VS 2193.20 ± 1779.35 USD, P < 0.05). Compared with children without pre-existing diseases (2188.25 ± 2363.32 USD), the hospitalization cost for children with pre-existing neurological diseases was 2881.06 ± 5273.14 USD (P = 0.17), with pre-existing cancer was 1791.19 ± 1134.33 USD (P = 0.389), with pre-existing cardiovascular disease was 3889.97 ± 8171.76 USD (P < 0.05). The hospitalization cost for children without comorbidities was 1876.90 ± 1620.73 USD. The hospitalization cost for children with cardiovascular disease was 3317.48 ± 3473.79 USD(P < 0.05), laryngeal obstruction above 3 degrees was 5182.63 ± 1804.92 USD(P < 0.05), liver damage was 4738.15 ± 7287.15 USD(P < 0.05), kidney damage was 7584.56 ± 10466.57 USD(P < 0.05), neurological disease was 2575.73 ± 3936.47 USD(P = 0.056), other pathogen infections was 3115.48 ± 4730.53 USD(P = 0.207), shock was 8464.76 ± 9944.45 USD(P < 0.05), septicemia was 2721.82 ± 988.66 USD(P = 0.071), respiratory failure was 4880.40 ± 3137.00 USD(P < 0.05). Compared with children with normal therapeutic measures (1584.62 ± 1883.73 USD), hospitalization costs were higher for children with special therapeutic measures. The following costs were associated with: Children requiring ventilator therapy (≥ 96 hours), 11628.82 ± 11161.55 USD(P < 0.05); with ventilator therapy (< 96 hours), 5423.51 ± 5495.42 USD (P < 0.05); with noninvasive ventilator-assisted ventilation, 8019.38 ± 9200.91 USD(P 0.05); with plasma exchange, 12438.68 ± 8682.96 USD (P < 0.05); with CRRT, 15414.04 ± 10841.19 USD (P < 0.05), with bone marrow aspiration, 4459.44 ± 5981.76 USD (P < 0.05); with lumbar puncture, 4905.66 ± 5373.39 USD(P < 0.05). The hospitalization cost for children who died due to COVID-19 was higher than that for cured children (3129.61 ± 10218.87 USD VS 2573.72 ± 3893.13 USD, P = 0.111)(Table 1 ). Table 1 Demography, clinical characteristics, and hospitalization expenses of patients in this study Number Total usage cost(USD) Average usage cost(USD, Mean ± SD) P-value Total 167 446231.02 2671.61 Sex Male 109(65.27%) 314234.16 2882.88 ± 1964.93 P<0.05 Female 58(34.73%) 18885.56 325.61 ± 252.47 Age(years) 0–3 85(50.90%) 250687.91 2949.27 ± 4676.49 P = 0.917 4–5 20(11.98%) 43863.94 2193.20 ± 1779.35 6–12 46(21.56%) 111249.97 2418.48 ± 3860.79 13–18 16(15.56%) 40429.20 2526.83 ± 2703.27 Ward type Pediatric general ward 123(73.65%) 271082.06 2203.92 ± 2895.54 Neonatal unit 26(15.57%) 83624.93 3216.34 ± 6481.00 P < 0.05 Pediatric intensive care unit 18(10.78%) 90727.41 5040.41 ± 4562.52 P < 0.05 Pre-existing disease Neurological disease 22(13.17%) 63383.25 2881.06 ± 5273.14 P = 0.147 Cancer 8(4.79%) 14329.51 1791.19 ± 1134.33 P = 0.389 Cardiovascular disease 19(11.38%) 73909.36 3889.97 ± 8171.76 P<0.05 Comorbidities Myocardial injury 49(26.95%) 162556.72 3317.48 ± 3473.79 P = 0.296 Laryngeal obstruction above 3 degrees 6(3.59%) 20730.50 5182.63 ± 1804.92 P<0.05 Liver damage 14(8.38%) 66334.13 4738.15 ± 7287.15 P < 0.05 Kidney injury 5(2.99%) 12849.17 7584.56 ± 10466.57 P<0.05 Neurological disease 57(34.13%) 146816.85 2575.73 ± 3936.47 P = 0.056 Other pathogen infections 30(17.96%) 93464.48 3115.48 ± 4730.53 P = 0.207 Shock 5(2.99%) 42323.82 8464.76 ± 9944.45 P < 0.05 Septicemia 5(2.99%) 13609.09 2721.82 ± 988.66 P = 0.071 Respiratory failure 19(11.38%) 107368.50 4880.40 ± 3137.00 P<0.05 Number of Comorbidities 0 32(19.16%) 45165.55 1411.42 ± 1214.23 1 46(27.54%) 86695.58 1884.67 ± 1211.36 P = 0.416 2 42(25.15%) 108735.95 2588.95 ± 3438.15 P = 0.230 3 28(16.77%) 86138.74 3076.38 ± 2168.13 P<0.05 ≥ 4 19(11.38%) 118800.69 6252.67 ± 5815.95 P<0.05 Special therapeutic measures Mechanical ventilation [≥ 96 hours] 10(5.99%) 116287.90 11628.82 ± 11161.55 P < 0.05 Mechanical ventilation [<96 hours] 7(4.19%) 37964.59 5423.51 ± 5495.42 P < 0.05 Noninvasive ventilation 18(10.78%) 144344.43 8019.38 ± 9200.91 P < 0.05 Oxygen therapy 4(2.39%) 12897.77 3224.44 ± 1478.57 P = 0.082 Cardiopulmonary resuscitation 3(1.80%) 15343.40 5114.47 ± 1439.57 P < 0.05 Plasma exchange 5(2.99%) 62193.39 12438.68 ± 8682.96 P < 0.05 Continuous renal replacement therapy 3(1.80%) 46245.13 15414.04 ± 10841.19 P<0.05 Special therapeutic measure number 0 104(62.28%) 165415.27 1292.31 ± 957.15 1 ~ 2 51(30.54%) 196330.77 3849.62 ± 2154.05 P<0.05 ≥ 3 12(7.19%) 57526.58 4793.88 ± 5429.38 P<0.05 Clinical outcome Cure 165(98.8%) 423917.32 2573.72 ± 3893.13 Died 2(1.2%) 6259.23 3129.61 ± 10218.87 P = 0.111 *The p-value is based on the Mann-Whitney U test for independent samples. Multiple regression analysis identified sex, number of comorbidities, special therapeutic management number, and ward type were associated with hospitalization costs. Sex had the greatest impact on hospitalization costs, while ward type was negatively associated with hospitalization costs. Hospitalization costs for patients in general wards were significantly lower than the average hospitalization costs (Table 2 ). Table 2 Multiple regression analysis of inpatient wards, comorbidities, treatment measures, and hospitalization costs Unstandardized coefficients Normalization factor β SE β t P-value Constant -2648.78 9509.46 -0.279 P = 0.781 Sex -4243.03 4576.47 -0.72 3.730 P < 0.05 Comorbiditie number 10473.83 3658.90 0.209 2.863 P < 0.05 Special therapeutic measure number 11806.90 2975.15 0.29 3.969 P < 0.05 Ward type -6371.66 2359.09 -0.19 -2.701 P < 0.05 *The p-value is based on the Mann-Whitney U test for independent samples. *SE:Standard error. The average medical service costs in the general ward, neonatal unit, and intensive care unit respectively was 473.20 USD, 732.81 USD and 885.74 USD. Average nursing costs was 67.94 USD, 139.04 USD and 260.99 USD, average radiologic diagnosis costs was 182.08 USD, 88.26 USD and 164.81 USD, average laboratory diagnostic costs was 307.22 USD, 368.65 USD and 649.68 USD, average therapeutic measure costs was 458.27 USD, 1240.53 USD, and 1196.55 USD average cost of drug was 399.61 USD, 337.44 USD, and 887.51 USD, the average cost of blood product was 254.73 USD, 297.56 USD, and 392.29 USD (Fig. 1 , Table 3 ). Table 3 The main components of hospitalization expenses of general ward, neonatal unit and intensive care unit. Classification General wards Neonatal unit Intensive care unit P-value Medical service 473.20USD(22.08%) 732.81USD(22.87%) 885.74USD(19.96%) P<0.05 Nursing 67.94USD(3.17%) 139.04USD(4.33%) 260.99USD(5.88%) P<0.05 Radiologic diagnosis 182.08USD(8.50%) 88.26USD(2.75%) 164.81USD(3.71%) P = 0.129 Laboratory diagnostic 307.22USD(14.34%) 368.65USD(11.50%) 649.68USD(14.64%) P<0.05 Therapeutic measure 458.27USD(21.38%) 1240.53USD(38.71%) 1196.55USD(26.96%) P<0.05 Drug acquisition 399.61USD(18.65%) 337.44USD(10.53%) 887.51USD(20.00%) P<0.05 Blood product 254.73USD(11.89%) 297.56USD(9.29%) 392.29USD(8.84%) P = 0.296 Discussion Understanding the factors that influence the cost of hospitalization of COVID-19 patients is essential to provide important information for future risk preparedness and response plans, and to improve current understanding of the economic assessment of global health emergencies. With a large number of COVID-19 patients requiring hospitalization, including pediatric patients, it is necessary to conduct an economic assessment from a hospital perspective and assess an important part of the economic impact of the COVID-19 outbreak on the healthcare system[ 13 , 14 ]. In a prior study in China, the total average cost per treatment for 70 COVID-19 patients was found to be 6,827 USD. The highest average cost was for access to medicines, which accounted for 45.1 per cent of the total. The total average cost for patients with preexisting conditions was significantly higher than that for patients without pre-existing conditions[ 1 ]. In a similar study, the clinical hospital at the University of São Paulo University School of Medicine reported an average hospitalization cost of 12,637.42 USD, almost double the average cost reported in China, but similar to the 12,547 USD reported in a Saudi Arabian study[ 15 , 16 ]. However, the above data were from adult patients, and there are currently no statistics on hospitalization costs for children with COVID-19. In our study, all admitted patients were children with severe or critical illnesses. As expected, patients admitted to the intensive incurred the highest daily costs. Our study included 167 children with COVID-19 from Northeast China. Of these, 29 (17.37%) were critically ill and admitted to the intensive care unit or partially admitted to the neonatal unit. Our findings showed no statistically significant differences between sex, age, and preexisting conditions and hospital costs, except for cardiovascular disease. Conversely, the ward, comorbidities, and therapeutic measure all affect the cost of hospitalization. In addition, a statistically significant correlation was found between the number of comorbidities and the number of therapeutic measure and cost of hospitalization. According to the results of the multiple regression analysis, the effect of surgical therapeutic measures on hospitalization costs was the greatest, and there was an inverse correlation between ward and hospitalization costs; that is, the hospitalization cost of patients admitted to the general ward was significantly lower than the average hospitalization cost. This study has several limitations. First, although our hospital included all COVID-19 cases in the area, the small sample size and single center design may weaken the significance of our conclusion. Second, this study excluded some direct and indirect costs to patients, such as lost workdays. Third, the patients admitted to the hospital were all moderately, severely, and critically ill children; therefor our study would have underestimated the total cost of treating all children with COVID-19. Nevertheless, our study provides important insights into the direct medical costs of COVID-19 pediatric patients in Northeast China. Conclusions Our study showed that hospitalization costs for children with COVID-19 were affected by the ward type, number of comorbidities, and special therapeutic management. An improved understanding of the hospital costs associated with admission of children with COVID-19 and the associated factors that affect them can aid health policymakers in developing a comprehensive approach to hospital preparedness, decision-making, and future risk management planning. Abbreviations COVID-19: Coronavirus disease-2019 CRRT : continuous renal replacement therapy SE : standard error Declarations Ethics approval and consent to participate The ethics committee of Shengjing Hospital approved all procedures. All methods were carried out comply with the ethical standards of Shengjing Hospital. Informed consent was obtained from every participant and parents, or their legal guardians of minors involved in this study. Consent for publication Not Applicable Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author Kai You ( [email protected] ) on reasonable request. Co mpeting I nterest s The authors declare that they have no conflict of interest. Funding S tatement This work was supported by the National Natural Science Foundation of China Youth Fund (81501292), China Postdoctoral Fund Surface Project (2017M611285) , and Basic Research Program of Liaoning Province(2022JH2/101500053). Acknowledgements The authors would like to thank the research group at the Department of Pediatrics of Shengjing Hospital , affiliated with China Medical University . Author Contributions YZconceived and designed the study. FX and FS participated in the data collection. YZ and KY performed data analysis. YZ wrote the preliminary draft of the manuscript. All authors revised and approved the final version of the manuscript. 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Public Health 2020, 17 , 7458. Int J Environ Res Public Health. 2020;17(24):9458. 10.3390/ijerph17249458 . Erratum for: Int J Environ Res Public Health. 2020;17(20). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3849458","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268506453,"identity":"afa57fb8-2bcf-4a2f-8fa3-1073eb84be4b","order_by":0,"name":"Yaru Zhang","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaru","middleName":"","lastName":"Zhang","suffix":""},{"id":268506454,"identity":"b9b1cf66-ec33-494e-a329-76e7a6ddf512","order_by":1,"name":"Fei Xia","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Xia","suffix":""},{"id":268506455,"identity":"61ea5765-848e-4a81-b689-9d1df5069b74","order_by":2,"name":"Feng Shi","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Shi","suffix":""},{"id":268506456,"identity":"28030248-f6d2-488c-b3cf-319599e9cf64","order_by":3,"name":"Kai You","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3QvwqCQBzA8V8IP5ejVoOoHsEQ+gPSsxwIttTUWJAQ2FJ7jxH0AoGgiw/gqATS6Ojg0O8Marscg+473N3gR/0dgEr1m2lQAIIpjhyg14i0zm/CgTUimnisJtCEmNHRsuyqDRP9kKdZOWegn+4PKYljy1n5CLNjPDE5dxiwaDyVkmRpBSsPd5dkiQbn9JOGi+ZXMq1o/GSRE9k1Iw6gIHxMJBBES2WkG4fr0YlmoaFoFjdiyEKUCWhH+6tRViFd3SHPSnvT7+i+VsjI8FZvoVheL6cVDRkZePW2/RBK/hWVSqX6u55QKUOao9geNwAAAABJRU5ErkJggg==","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"You","suffix":""}],"badges":[],"createdAt":"2024-01-10 04:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3849458/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3849458/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50053898,"identity":"24a8542d-2bd2-4488-b4b6-7b924c497f7e","added_by":"auto","created_at":"2024-01-23 17:15:55","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67812,"visible":true,"origin":"","legend":"\u003cp\u003eThe main components of patient hospitalization expenses of general ward, neonatal unit and intensive care unit. \u003cstrong\u003eA\u003c/strong\u003e main component of patient hospitalization expenses in the general ward, \u003cstrong\u003eB\u003c/strong\u003e main components of patient hospitalization expenses in the neonatal unit, \u003cstrong\u003eC\u003c/strong\u003e main components of patient hospitalization expenses in the intensive care unit.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3849458/v1/d1b098de3d22dc7bb28edf7e.jpeg"},{"id":59909790,"identity":"0d90084e-a996-45ba-a807-8933d87a3d8d","added_by":"auto","created_at":"2024-07-09 07:46:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":705337,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3849458/v1/36c6b0f0-229c-463e-ba5d-a3dce7b00007.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hospital Burden Inflicted by Pediatric SARS-CoV-2 Hospitalizations during the first Omicron Wave in Shenyang, Northeastern China: A Retrospective Observational Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has presented a significant global public health threat in recent years [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The resulting economic impact of the COVID-19 pandemic on global health systems is a major concern, and additional resources and financial investments are urgently required. The existing capacity of hospitals, including facilities, equipment, supplies, and healthcareprofessionals, has increased significantly to match this burden. In a prior study, the United States analyzed the hospitalization costs of 247,590 adult COVID-19 patients, and the results showed that the median total hospitalization costs was 11267USD[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, a laboratory from Ethiopia analyzed the hospitalization costs of 19,235 patients, but the costs varied depending on the severity of the disease: the average costs for moderate, severe, and critically ill cases were 998.1 USD, 1413 USD, and 1788 USD respectively[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; Research data from Saudi Arabia showed that the average cost of COVID-19 patients admitted to the General Internal Medicine Ward (GMW) and ICU was 42704.49\u0026thinsp;\u0026plusmn;\u0026thinsp;29811.25 and 79418.30\u0026thinsp;\u0026plusmn;\u0026thinsp;55647.69 Saudi Riyals, respectively[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the first the first report of COVID-19 in December 2019, the disease has spread rapidly around the world, with five waves caused by different variants: Alpha, Beta, Gamma, Delta, and Omicron[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In December 2022, an Omicron outbreak occurred on a large scale throughout China, including the Northeast region, and many of the children who survived developed immunity to the COVID strain; as much, the severity of Omicron infection in hospitalized children who had not previously been exposed to COVID-19 is poorly understood. The high transmissibility of Omicron has led to an increase in the number of hospitalized children, adding an additional economic burden to the healthcare system [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Understanding the hospitalization costs associated with COVID-19 admissions can support health policymakers in developing a comprehensive approach to hospital preparedness, decision making, and future risk management planning [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, an economic evaluation is essential to determine the resources and costs associated with the healthcare needed to treat patients with this new disease, including an analysis of the cost of medical care for children with COVID-19[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis retrospective study was conducted at Shengjing Hospital of China Medical University, one of the largest public hospital complexes with the largest pediatric department in Northeast China, with 560 beds located in Shenyang, Liaoning Province, with a population of 7.564\u0026nbsp;million. This study aimed to describe the hospitalization costs of hospitalized children with COVID-19 and the associated factors that affect hospitalization costs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis retrospective study was conducted at Shengjing Hospital of China Medical University. We enolled children aged 0\u0026ndash;18 years confirmed to have SARS-CoV-2 through pharyngeal swab polymerase chain reaction (PCR) examination, clinical symptoms, radiological abnormalities, or molecular testing, admitted to the general ward, neonatal unit, and intensive care unit of Shengjing Hospital between December 1, 2022, and January 31, 2023. Children were diagnosed with moderate, severe, or critical illness according to the \u0026ldquo;Interim Guidance for Diagnosis and Treatment of Coronavirus Disease 2019\u0026rdquo; (10th edition). The children were discharged after meeting the criteria for cure. Demographic and clinical data were collected from patients\u0026rsquo; medical records, while economic data were obtained from the financial management system. The study protocol was approved by our Institutional Ethics Committee (2023PS730K).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical Data and Outcome Definitions\u003c/h2\u003e \u003cp\u003eThe data were organized into standardized forms using trained extractors. Collected variables included sex, age, preexisting diseases reported by the children or their relatives, special therapeutic measures (such as mechanical ventilation, noninvasive ventilation, oxygen therapy, cardiopulmonary resuscitation, plasma exchange, and continuous renal replacement therapy), comorbidities, clinical outcomes (cured or died), and ward type (neonatal units, pediatric intensive care units, and pediatric general wards).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCost Analysis\u003c/h2\u003e \u003cp\u003eThe total cost was calculated based on the following cost factors listed: (i) medical service costs included bed costs, medical materials and equipment, (ii) nursing costs included staff expense, (iii) radiologic diagnosis costs included X-ray, computed tomography (CT), B-mode ultrasonography, nuclear magnetic resonance imaging and video EEG testing, (iv) laboratory diagnosis costs included microbiology, hematology, biochemistry, and blood gas analysis, (v) therapeutic measure costs included the cost of specialist therapeutic measures (mechanical ventilation, noninvasive ventilation, oxygen therapy, cardiopulmonary resuscitation, plasma exchange, continuous renal replacement therapy, bone marrow aspiration, lumbar puncture) and normal therapeutic measure (gastric catheterization, nasal feeding, intravenous injection, etc), (vi) drug acquisition costs included antivirals, antibiotics and gamma globulin; (vii) blood product costs included universal red blood cells, platelet and plasma. Expenditures were calculated in US dollars (exchange rate: CNY 7.0\u0026thinsp;=\u0026thinsp;USD 1.0).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTo assess the impact of different variables on hospitalization costs and to estimate the average cost (total cost/number of admissions) for each subgroup. The hypothesis test assumed an alpha error of 0.05. The Mann-Whitney U test was used to compare continuous between-group variables from the independent samples. Statistical analyses were performed using IBM SPSS Statistics (version 26.0; (SPSS Inc., Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical characteristics of the patients\u003c/h2\u003e \u003cp\u003eIn total, 167 children with COVID-19 were included in the analysis, of whom 109 (65.27%) were male and 58 (34.73%) were female. The percentage of children aged\u0026thinsp;\u0026lt;\u0026thinsp;3 years was 50.90%. The median patient age was 3 years (interquartile range, 0\u0026ndash;9 years). Pre-existing diseases included neurological disease (22, 13.17%), cardiovascular disease (19, 11.38%), and cancer (8, 4.79%). According to the National Disease Severity Grading Guidelines, 138 (82.63%) cases were moderate or severe, and 29 (17.37%) were critically ill. Common comorbidities included neurological disease (57, 34.13%), cardiovascular disease (49, 26.95%), other pathogen infection (30, 17.96%), liver injury (14, 8.38%), kidney damage (5, 2.99%), shock (5, 2.99%), septicemia (5, 2.99%), and laryngeal obstruction above 3 degrees (4, 2.39%). Special therapeutic measures included mechanical ventilation (17, 10.18%), noninvasive ventilation (18, 10.78%), oxygen therapy (4, 2.39%), cardiopulmonary resuscitation (3, 1.80%), plasma exchange (5, 2.99%), continuous renal replacement therapy (CRRT; 3, 1.80%), bone marrow puncture (19, 11.38%), and lumbar puncture (25, 7.19%). A total of 165 patients were cured, and two died from COVID-19.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCost analysis\u003c/h2\u003e \u003cp\u003eAmong the 167 admitted patients, the total cost was 446231.02 USD, and the average cost was 2672.04 USD. The average hospitalization costs for children treated on the pediatric general ward, neonatal unit, and pediatric intensive care unit were 2203.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2895.54 USD, 3216.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6481.00 USD and 5040.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4562.52 USD, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The average length of hospitalization was 10 days. The effects of age, sex, ward type, comorbidities, therapeutic measures, and clinical outcomes are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the patients admitted to the pediatric intensive care unit, 12 were male and six were female. Children aged 0\u0026ndash;3 years had the highest average hospitalization costs, followed by children aged 13\u0026ndash;18 years, children aged 6\u0026ndash;12 years, and children aged 4\u0026ndash;5 years, who had the lowest hospitalization costs (2949.27\u0026thinsp;\u0026plusmn;\u0026thinsp;4676.49 USD VS 2526.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2703.27 USD VS 2418.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3860.79 USD VS 2193.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1779.35 USD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eCompared with children without pre-existing diseases (2188.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2363.32 USD), the hospitalization cost for children with pre-existing neurological diseases was 2881.06\u0026thinsp;\u0026plusmn;\u0026thinsp;5273.14 USD (P\u0026thinsp;=\u0026thinsp;0.17), with pre-existing cancer was 1791.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1134.33 USD (P\u0026thinsp;=\u0026thinsp;0.389), with pre-existing cardiovascular disease was 3889.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8171.76 USD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe hospitalization cost for children without comorbidities was 1876.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1620.73 USD. The hospitalization cost for children with cardiovascular disease was 3317.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3473.79 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), laryngeal obstruction above 3 degrees was 5182.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1804.92 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), liver damage was 4738.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7287.15 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), kidney damage was 7584.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10466.57 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), neurological disease was 2575.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3936.47 USD(P\u0026thinsp;=\u0026thinsp;0.056), other pathogen infections was 3115.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4730.53 USD(P\u0026thinsp;=\u0026thinsp;0.207), shock was 8464.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9944.45 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), septicemia was 2721.82\u0026thinsp;\u0026plusmn;\u0026thinsp;988.66 USD(P\u0026thinsp;=\u0026thinsp;0.071), respiratory failure was 4880.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3137.00 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eCompared with children with normal therapeutic measures (1584.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1883.73 USD), hospitalization costs were higher for children with special therapeutic measures. The following costs were associated with: Children requiring ventilator therapy (\u0026ge;\u0026thinsp;96 hours), 11628.82\u0026thinsp;\u0026plusmn;\u0026thinsp;11161.55 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); with ventilator therapy (\u0026lt;\u0026thinsp;96 hours), 5423.51\u0026thinsp;\u0026plusmn;\u0026thinsp;5495.42 USD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); with noninvasive ventilator-assisted ventilation, 8019.38\u0026thinsp;\u0026plusmn;\u0026thinsp;9200.91 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); with oxygen therapy, 3224.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1478.5 USD(P\u0026thinsp;=\u0026thinsp;0.082); with cardiopulmonary resuscitation, 5114.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1439.57 USD(P\u0026thinsp;\u0026gt;\u0026thinsp;0.05); with plasma exchange, 12438.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8682.96 USD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); with CRRT, 15414.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10841.19 USD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with bone marrow aspiration, 4459.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5981.76 USD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); with lumbar puncture, 4905.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5373.39 USD(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The hospitalization cost for children who died due to COVID-19 was higher than that for cured children (3129.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10218.87 USD VS 2573.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3893.13 USD, P\u0026thinsp;=\u0026thinsp;0.111)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemography, clinical characteristics, and hospitalization expenses of patients in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal usage cost(USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage usage cost(USD, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e446231.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2671.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109(65.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e314234.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2882.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1964.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(34.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18885.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e325.61\u0026thinsp;\u0026plusmn;\u0026thinsp;252.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(50.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250687.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2949.27\u0026thinsp;\u0026plusmn;\u0026thinsp;4676.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(11.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43863.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2193.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1779.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(21.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111249.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2418.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3860.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(15.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40429.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2526.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2703.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWard type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePediatric general ward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123(73.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271082.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2203.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2895.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(15.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83624.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3216.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6481.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePediatric intensive care unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(10.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90727.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5040.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4562.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-existing disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(13.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63383.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2881.06\u0026thinsp;\u0026plusmn;\u0026thinsp;5273.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(4.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14329.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1791.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1134.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(11.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73909.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3889.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8171.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(26.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162556.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3317.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3473.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaryngeal obstruction above 3 degrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(3.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20730.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5182.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1804.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(8.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66334.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4738.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7287.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(2.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12849.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7584.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10466.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(34.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146816.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2575.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3936.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther pathogen infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(17.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93464.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3115.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4730.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(2.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42323.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8464.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9944.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepticemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(2.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13609.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2721.82\u0026thinsp;\u0026plusmn;\u0026thinsp;988.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(11.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107368.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4880.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3137.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(19.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45165.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1411.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1214.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(27.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86695.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1884.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1211.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(25.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108735.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2588.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3438.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(16.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86138.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3076.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2168.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(11.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118800.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6252.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5815.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial therapeutic measures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation [\u0026ge;\u0026thinsp;96 hours]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(5.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116287.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11628.82\u0026thinsp;\u0026plusmn;\u0026thinsp;11161.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation [\u0026lt;96 hours]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(4.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37964.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5423.51\u0026thinsp;\u0026plusmn;\u0026thinsp;5495.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoninvasive ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(10.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144344.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8019.38\u0026thinsp;\u0026plusmn;\u0026thinsp;9200.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(2.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12897.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3224.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1478.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopulmonary resuscitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(1.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15343.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5114.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1439.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma exchange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(2.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62193.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12438.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8682.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous renal replacement therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(1.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46245.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15414.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10841.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial therapeutic measure number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(62.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165415.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1292.31\u0026thinsp;\u0026plusmn;\u0026thinsp;957.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(30.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196330.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3849.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2154.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(7.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57526.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4793.88\u0026thinsp;\u0026plusmn;\u0026thinsp;5429.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(98.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e423917.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2573.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3893.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6259.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3129.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10218.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*The p-value is based on the Mann-Whitney U test for independent samples.\u003c/p\u003e \u003cp\u003eMultiple regression analysis identified sex, number of comorbidities, special therapeutic management number, and ward type were associated with hospitalization costs. Sex had the greatest impact on hospitalization costs, while ward type was negatively associated with hospitalization costs. Hospitalization costs for patients in general wards were significantly lower than the average hospitalization costs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple regression analysis of inpatient wards, comorbidities, treatment measures, and hospitalization costs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNormalization factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2648.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9509.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4243.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4576.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbiditie number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10473.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3658.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial therapeutic measure number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11806.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2975.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWard type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6371.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2359.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*The p-value is based on the Mann-Whitney U test for independent samples. *SE:Standard error.\u003c/p\u003e \u003cp\u003eThe average medical service costs in the general ward, neonatal unit, and intensive care unit respectively was 473.20 USD, 732.81 USD and 885.74 USD. Average nursing costs was 67.94 USD, 139.04 USD and 260.99 USD, average radiologic diagnosis costs was 182.08 USD, 88.26 USD and 164.81 USD, average laboratory diagnostic costs was 307.22 USD, 368.65 USD and 649.68 USD, average therapeutic measure costs was 458.27 USD, 1240.53 USD, and 1196.55 USD average cost of drug was 399.61 USD, 337.44 USD, and 887.51 USD, the average cost of blood product was 254.73 USD, 297.56 USD, and 392.29 USD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe main components of hospitalization expenses of general ward, neonatal unit and intensive care unit.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral wards\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeonatal unit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntensive care unit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e473.20USD(22.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e732.81USD(22.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e885.74USD(19.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.94USD(3.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.04USD(4.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e260.99USD(5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiologic diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182.08USD(8.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.26USD(2.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.81USD(3.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory diagnostic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307.22USD(14.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e368.65USD(11.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e649.68USD(14.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapeutic measure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e458.27USD(21.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1240.53USD(38.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1196.55USD(26.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug acquisition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e399.61USD(18.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337.44USD(10.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e887.51USD(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254.73USD(11.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297.56USD(9.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e392.29USD(8.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding the factors that influence the cost of hospitalization of COVID-19 patients is essential to provide important information for future risk preparedness and response plans, and to improve current understanding of the economic assessment of global health emergencies. With a large number of COVID-19 patients requiring hospitalization, including pediatric patients, it is necessary to conduct an economic assessment from a hospital perspective and assess an important part of the economic impact of the COVID-19 outbreak on the healthcare system[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a prior study in China, the total average cost per treatment for 70 COVID-19 patients was found to be 6,827 USD. The highest average cost was for access to medicines, which accounted for 45.1 per cent of the total. The total average cost for patients with preexisting conditions was significantly higher than that for patients without pre-existing conditions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In a similar study, the clinical hospital at the University of S\u0026atilde;o Paulo University School of Medicine reported an average hospitalization cost of 12,637.42 USD, almost double the average cost reported in China, but similar to the 12,547 USD reported in a Saudi Arabian study[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the above data were from adult patients, and there are currently no statistics on hospitalization costs for children with COVID-19. In our study, all admitted patients were children with severe or critical illnesses. As expected, patients admitted to the intensive incurred the highest daily costs. Our study included 167 children with COVID-19 from Northeast China. Of these, 29 (17.37%) were critically ill and admitted to the intensive care unit or partially admitted to the neonatal unit.\u003c/p\u003e \u003cp\u003eOur findings showed no statistically significant differences between sex, age, and preexisting conditions and hospital costs, except for cardiovascular disease. Conversely, the ward, comorbidities, and therapeutic measure all affect the cost of hospitalization. In addition, a statistically significant correlation was found between the number of comorbidities and the number of therapeutic measure and cost of hospitalization. According to the results of the multiple regression analysis, the effect of surgical therapeutic measures on hospitalization costs was the greatest, and there was an inverse correlation between ward and hospitalization costs; that is, the hospitalization cost of patients admitted to the general ward was significantly lower than the average hospitalization cost.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, although our hospital included all COVID-19 cases in the area, the small sample size and single center design may weaken the significance of our conclusion. Second, this study excluded some direct and indirect costs to patients, such as lost workdays. Third, the patients admitted to the hospital were all moderately, severely, and critically ill children; therefor our study would have underestimated the total cost of treating all children with COVID-19. Nevertheless, our study provides important insights into the direct medical costs of COVID-19 pediatric patients in Northeast China.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study showed that hospitalization costs for children with COVID-19 were affected by the ward type, number of comorbidities, and special therapeutic management. An improved understanding of the hospital costs associated with admission of children with COVID-19 and the associated factors that affect them can aid health policymakers in developing a comprehensive approach to hospital preparedness, decision-making, and future risk management planning.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCOVID-19:\u003c/strong\u003e Coronavirus disease-2019\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRRT\u003c/strong\u003e: continuous renal replacement therapy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e: standard error\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch4\u003eEthics approval and consent to participate\u003c/h4\u003e\n\u003cp\u003eThe ethics committee of\u0026nbsp;Shengjing\u0026nbsp;Hospital approved all procedures. All methods were carried out comply with the ethical standards of\u0026nbsp;Shengjing\u0026nbsp;Hospital.\u0026nbsp;Informed consent was obtained from every participant and parents, or their legal guardians of minors involved in this study.\u003c/p\u003e\n\u003ch4\u003eConsent for publication\u003c/h4\u003e\n\u003ch4\u003eNot Applicable\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author Kai You ([email protected]) on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003cstrong\u003empeting I\u003c/strong\u003e\u003cstrong\u003enterest\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003cstrong\u003etatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNational Natural Science Foundation of China Youth Fund (81501292), China Postdoctoral Fund Surface Project (2017M611285)\u003cins cite=\"mailto:Author\"\u003e,\u003c/ins\u003e and Basic Research Program of Liaoning Province(2022JH2/101500053).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;would like to\u0026nbsp;thank the research group at the Department of Pediatrics of Shengjing Hospital\u003cins cite=\"mailto:Author\"\u003e,\u003c/ins\u003e affiliated with China Medical University\u003cins cite=\"mailto:Author\"\u003e.\u003c/ins\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZconceived and designed the study. FX and FS participated in the data collection. YZ and KY performed data analysis. YZ wrote the preliminary draft of the manuscript. All authors revised and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi XZ, Jin F, Zhang JG, Deng YF, Shu W, Qin JM, Ma X, Pang Y. Treatment of coronavirus disease 2019 in Shandong, China: a cost and affordability analysis. Infect Dis Poverty. 2020;9(1):78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40249-020-00689-0\u003c/span\u003e\u003cspan address=\"10.1186/s40249-020-00689-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoenhardt M, Gano C, Sorg AL, Diffloth N, Tenenbaum T, von Kries R, Berner R, Armann JP. Burden of Pediatric SARS-CoV-2 Hospitalizations during the Omicron Wave in Germany. 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Int J Environ Res Public Health. 2020;17(24):9458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph17249458\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17249458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Erratum for: Int J Environ Res Public Health. 2020;17(20).\u003c/span\u003e\u003c/li\u003e \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":"COVID-19, children, healthcare cost, China","lastPublishedDoi":"10.21203/rs.3.rs-3849458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3849458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn the last few years, the 2019 coronavirus disease (COVID-19) has posed a significant global public health threat. The omicron variant of SARS-CoV-2 first emerged as a pandemic in China in December 2022;however, there are no data on hospitalization costs related to diseases in children. In view of the high transmissibility of the Omicron virus, in the present study, we conducted a retrospective analysis of hospitalization costs for children to provide crucial information for understanding the burden inflicted on the Chinese medical system.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study comprised a partial economic assessment from a retrospective observational cohort study designed to assess the costs of hospitalization of children aged 0\u0026ndash;18 year with confirmed COVID-19 in Shengjing Hospital of China Medical University treated between December 1, 2022, and January 31, 2023, and followed until discharge, death, or external transfer. Differences between groups were tested using Student\u0026rsquo;s t-test and the Mann-Whitney test, as appropriate. A multiple logistic regression model was constructed to determine the risk factors associated with high costs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 167 children with moderate, severe, and critical illness were included in the analysis. Twenty-six (15.57%) and 18 (10.78%) children required NICU and PICU care, respectively. Overall, 107 (65.27%) children were males, and approximately half (50.90%) of the children were less than 3 years old. The average hospitalization cost was 2671.61 USD, and the average length of stay was 10 days. Hospitalization costs were significantly higher for males than for females. Children with comorbidities and special therapeutic measures who lived in intensive care units or neonatal units had higher hospitalization costs. Ward type, number of treatment measures, and comorbidities were significantly related to hospitalization costs. Thirty-five (20.96%) children required mechanical intervention, and five (2.99%) children underwent plasma exchange.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eClinical management of COVID-19 pediatric patients poses an economic burden on the healthcare system. Ward type, number of comorbidities, and special therapeutic measures all affect hospitalization costs.\u003c/p\u003e","manuscriptTitle":"Hospital Burden Inflicted by Pediatric SARS-CoV-2 Hospitalizations during the first Omicron Wave in Shenyang, Northeastern China: A Retrospective Observational Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 17:15:50","doi":"10.21203/rs.3.rs-3849458/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":"ceb22b50-2dac-4347-bb72-910d11d0919d","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-09T07:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-23 17:15:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3849458","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3849458","identity":"rs-3849458","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}