{"paper_id":"46458364-4bec-4a1f-bfce-34cf2a4afd06","body_text":"Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: case study of a Chinese Tuberculosis hospital | 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 Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: case study of a Chinese Tuberculosis hospital Nili Ren, Xinliang Liu, Yi Luo, Guofei Li, Ying Huang, Desheng Ji, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4524748/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Sep, 2024 Read the published version in Global Health Research and Policy → Version 1 posted 5 You are reading this latest preprint version Abstract Healthcare-associated infections (HAIs) represent a major global health burden, necessitating effective frameworks to identify potential risk factors and estimate direct economic disease burden. We proposed a framework designed to address these needs through a case study conducted in a Chinese Tuberculosis hospital using data from 2018 to 2019. The framework incorporates a comprehensive multistep process, including ethical application, participant inclusion, risk factor identification, and direct economic disease burden estimation. In the case study, ethical approval was obtained, and patient data were anonymized to ensure privacy. All TB hospitalized patients over study period were included and classified into groups with and without HAIs after screening the inclusion and exclusion criteria. Key risk factors, including gender, age, and invasive procedures were identified through univariate and multivariate analyses. Then, propensity score matching was employed to select the balanced groups with similar characteristics. Comparisons of medical expenditures (total medical expenditure, medicine expenditure, and antibiotic expenditure) and hospitalization days between the balanced groups were calculated as the additional direct economic disease burden measures caused by HAIs. This framework can serve as a tool for hospital management and policy-making, enabling the implementation of targeted infection prevention and control measures. It has the potential to be applied in various healthcare settings at local, regional, national, and international levels to identify high-risk areas, optimize resource allocation, and improve internal and external hospital management, as well as inter-organizational learning. Challenges to implement the framework are also raised, such as data quality, regulatory compliance, considerations on unique nature of communicable diseases and other diseases, and training need for professionals. Healthcare-associated infections risk factors direct economic disease burden case study framework Figures Figure 1 Figure 2 Introduction Healthcare-associated infections (HAIs) pose a major global health burden. The World Health Organization (WHO) reported that in 2022, globally in acute-care hospitals, about 7 out of every 100 hospitalized patients in high-income countries (HICs) and 15 out of every 100 hospitalized patients in low- and middle-income countries (LMICs) obtained at least one HAI, and an average of 1 in every 10 hospitalized patients with HAIs died ( 1 ). HAIs also lead to the increased occurrence of antimicrobial resistance (AMR) - another major global health issue ( 2 ). However, from the perspective of the economic disease burden attributable to HAIs, the updated evidence is scant, especially in settings with limited resources. A framework is required to estimate the direct economic burden attributable to HAIs at minimum level-in individual hospitals, providing empirical evidence and support targeted interventions. In response to the global challenge of HAIs, the WHO announced its first-ever global strategy on infection and prevention control (IPC) at the 76th World Health Assembly in 2023, and an associated global action plan and monitoring framework will be completed by 2024 ( 3 ). Effective and tailored IPC measures are formulated based on identifying the risk factors associated with HAIs within hospitals. The set of risk factors associated with HAIs could be different in different hospitals. Therefore, a general framework should be developed to identify the risk factors associated with HAIs unique to each hospital setting. The framework on estimating the direct economic disease burden attributable to HAIs was developed before by our team and applied within general hospitals in China and Nepal ( 4 – 6 ). However, it is yet to be used in specialized hospitals, like Tuberculosis (TB) ones. The risk of HAIs is compounded by the nature of TB itself-a deadly respiratory infectious disease. According to the latest estimates reported by the WHO in 2023, around 10.6 million population were estimated to be infected with TB worldwide, and 1.3 million of them died in 2022 ( 7 ). Particularly, South-East Asia Region had the highest number of population infected with TB at 4.85 million, followed by African Region at 2.48 million ( 7 ). The high incidence and the complex nature of TB treatment environment can increase the vulnerability of patients to HAIs, thus escalating the economic and health burden on healthcare systems. To address above situation, a framework was developed by our team based on the case study conducted in a Chinese TB hospital to analyze the potential risk factors and estimate the direct economic burden attributable to HAIs. Furthermore, this framework aims to provide healthcare stakeholders with a tool for implementing effective IPC measures and evaluating the financial impact of HAIs. The Chinese TB hospital is located in Hubei Province, which is a tertiary pulmonary and tuberculosis control hospital that focuses on the prevention and control of TB, clinical diagnosis and treatment of pulmonary diseases, and medical/health education and research. Table 1 shows the information of the operational and treatment efficacy about the hospital from 2018 to 2019. The number of beds and average hospitalization days remained stable at 406 and 9.73 days, respectively. The mortality rate of hospitalized patients increased from 0.45–0.72%, and the number of outpatient and emergency patients rose significantly from 113,819 to 129,905. The annual HAIs prevalence decreased from 0.53–0.34%. Table 1 General information of the Chinese TB hospital Year 2018 2019 Number of beds 406 406 Average hospitalization days 9.73 9.73 Total mortality of hospitalized patients (%) 0.45 0.72 Number of discharged patients 16,445 16,649 Number of outpatient and emergency patients 113,819 129,905 Annual HAIs prevalence (%) 0.53 0.34 Framework development Figure 1 presents that a framework analyzes the impact of HAIs within a hospital setting, which includes four steps. The first step is an ethical application, ensuring that all data collection and analysis adhere to the highest standards of research ethics. The first step secures the approval of relevant ethics committees and establishes a foundation of trust and legality for research. Then, the second step is the inclusion of participants, which involves a detailed screening process to select all eligible hospitalized patients based on specific criteria. This selection process ensures that the data extracted are relevant and robust, providing a solid base for further analysis. The third step is that the framework identifies risk factors associated with HAIs, which involves a thorough analysis of patient data to ascertain factors that may increase the likelihood of HAIs, such as the length of hospitalization, the use of invasive procedures, or the presence of comorbid conditions. Understanding these risk factors is pivotal for developing targeted strategies to reduce the incidence of HAIs. Finally, the fourth step is the estimation of the direct economic disease burden attributable to HAIs, which involves calculating the additional medical expenditures and hospitalization days associated with HAIs. The economic burden analysis helps to quantify the financial impact of HAIs, highlighting the economic incentives for hospitals to invest in effective IPC measures. Overall, the framework employs a retrospective cross-sectional study design, allowing for the analysis of data from previously hospitalized patients. This approach is advantageous as it provides a snapshot of hospital performance over a specific period, enabling hospital managers and policy decision-makers to implement evidence-based improvements in patient care and IPC measures. This comprehensive and methodical approach ensures that every aspect of the impact of HAIs is captured and addressed, including from ethical considerations to risk factor analysis and economic burden analysis. Step 1: Ethical application Before extracting data from hospitals, it is essential to secure approval from the hospitals’ ethics committees, particularly when the data involve personal information about hospitalized patients. This case study obtained the approval from the TB hospital’s ethic committee. To ensure anonymity, original hospital numbers were replaced with unique identifiers created by a staff member from the Department of Medical Records. Personal information pertaining to the hospitalized TB patients was omitted during data extraction from the hospital information systems (HIS). As a result, there was no need for informed or verbal consent from the TB hospitalized patients. Step 2: Inclusion of participants This phrase primarily involves the inclusion of all hospitalized patients, followed by their classification into groups with and without HAIs for subsequent analysis of risk factors and economic burden. The specific steps taken in this TB hospital were as follows, with Fig. 2 illustrating the entire participant inclusion flowchart: 1) All TB hospitalized patients who were discharged from 0:00 1st January 2018 to 23:59 31st December 2022 were included. TB hospitalized patients information was retrieved from the HIS. As demonstrated in Fig. 2 , a total of 23,080 TB hospitalized patients were included during the study periods, with 11,332 patients in 2018 and 11,748 in 2019. 2) Those TB hospitalized patients staying in hospital less than 48 hours needed to be excluded, since the criteria for HAIs require a minimum hospital stay of more than two days ( 8 ). After applying this exclusion criterion, the total number of included TB hospitalized patients was reduced to 21,148. 3) The remaining TB hospitalized patients were then categorized based on whether they had acquired HAIs, according to the inclusion and exclusion criteria detailed in Table S1 (Suppl. 1). Following this categorization, 78 TB hospitalized patients were identified with HAIs, while 21,070 TB hospitalized patients did not have HAIs. Step 3: Identification on risk factors associated with HAIs The risk factors analysis on association with HAIs among hospitalized patients typically involves selecting potential risk factors, conducting univariate analysis, and performing logistic regression analysis. Here are the specific steps taken at this TB hospital: 1) Our research team has conducted a systematic review and meta-analysis to identify risk factors associated with HAIs among TB hospitalized patients in China. The study concluded a list of significant risk factors, including age older than 60 years, presence of complications, diabetes mellitus, invasive procedure, longer than 15 hospitalization days, secondary TB, smoking, presence of underlying disease, and use of antibiotics ( 9 ). Considering the data availability from this TB hospital, gender, age older than 60 years, diabetes mellitus, invasive procedure, more than 15 hospitalization days, presence of underlying disease, and used of antibiotics were selected as potential risk factors in this case study. Descriptions and assigned values of the selected potential risk factors are listed in Table S2 (Suppl. 1). 2) A univariate analysis was conducted to examine the association between the selected risk factors and HAIs among the TB hospitalized patients. The chi-squire test was used for those categorical variables with expected frequencies above five, while the Fisher exact probability test was used for those with frequencies below five. The codes for conducting the univariate analysis can be found in supplementary materials (Suppl. 2). Table 3 demonstrates that gender, longer than 15 hospitalization days, and the use of antibiotics were consistent risk factors, each showing a statistically significant association with HAIs among TB hospitalized patients in both 2018 and 2019 ( P < 0.05). Table 3 Univariate analyses on potential risk factors associated with HAIs in 2018 and 2019 Year Potential risk factors TB hospitalized patients with HAIs TB hospitalized patients without HAIs χ 2 P N (%) 2018 Gender Male 40 (90.91) 6,413 (62.82) 14.82 < 0.01* Female 4 (9.09) 3,796 (37.18) Age > 60 years 18 (40.91) 2,512 (24.61) 6.26 0.012* ≤ 60 years 26 (59.09) 7,697 (75.39) Invasive procedure Yes 12 (27.27) 492 (4.82) - < 0.01* No 32 (72.73) 9,717 (95.18) Length of hospitalization > 15 days 32 (72.73) 1,639 (16.05) 103.15 < 0.01* ≤ 15 days 12 (27.27) 8,570 (83.95) Diabetes mellitus Yes 5 (11.36) 1,293 (12.67) 0.07 0.796 No 39 (88.64) 8,916 (87.33) Underlying disease Yes 11 (25.00) 2,272 (22.25) 0.19 0.662 No 33 (75.00) 7,937 (77.75) Use of antibiotics Yes 40 (90.91) 5,095 (49.91) 29.46 < 0.01* No 4 (9.09) 5,114 (50.09) 2019 Gender Male 27 (79.41) 67,60 (62.24) 4.26 0.039* Female 7 (20.59) 4,101 (37.76) Age > 60 years 13 (38.24) 2,685 (24.72) 3.32 0.068 ≤ 60 years 21 (61.76) 8,176 (75.28) Invasive procedure Yes 7 (20.59) 659 (6.07) - 0.004* No 27 (79.41) 10,202 (93.93) Length of hospitalization > 15 days 30 (88.24) 1,660 (15.28) 137.64 < 0.01* ≤ 15 days 4 (11.76) 9,201 (84.72) Diabetes mellitus Yes 7 (20.59) 1,743 (16.05) 0.52 0.472 No 27 (79.41) 9,118 (83.95) Underlying disease Yes 7(20.59) 2,580 (23.75) 0.19 0.665 No 27 (79.41) 8,281 (76.25) Use of antibiotics Yes 33 (97.06) 5,180 (47.69) 33.10 < 0.01* No 1 (2.94) 5,681 (52.31) 3) A binary logistic regression model was applied to investigate the severity of potential risk factors associated with HAIs among TB hospitalized patients. To avoid ‘Table 2 Fallacy’ where multiple adjusted odd ratios (aOR) derived from a single logistic regression model misinterpret the impact of primary risk factors due to covariate heterogeneity ( 10 , 11 ), multiple logistic regression models were conducted by adjusting the common covariates including gender and age. The codes for this binary logistic regression analysis are available the in supplementary materials (Suppl. 2). Table 4 shows that consistently, the significant risk factors associated with HAIs were invasive procedure (aOR: 7.41 in 2018; 4.29 in 2019), longer than 15 hospitalization days (aOR: 13.15 in 2018; 39.76 in 2019), and use of antibiotics (aOR: 8.99 in 2018; 33.46 in 2019) ( P < 0.05). Table 4 Logistic regression analyses on potential risk factors associated with HAIs in 2018 and 2019 Year Potential risk factors Reference aOR 95%CI Z P 2018 Invasive procedure No 7.41 [3.78–14.54] 5.83 < 0.01* Length of hospitalization ≤ 15 days 13.15 [6.75–25.61] 7.57 < 0.01* Diabetes mellitus No 0.66 [0.26–1.70] -0.85 0.395 Underlying disease No 0.78 [0.38–1.63] -0.66 0.512 Use of antibiotics No 8.99 [3.21–25.18] 4.18 < 0.01* 2019 Invasive procedure No 4.29 [1.85–9.93] 3.40 0.001* Length of hospitalization ≤ 15 days 39.76 [13.98-113.08] 6.90 < 0.01* Diabetes mellitus No 1.08 [0.46–2.52] 0.17 0.864 Underlying disease No 0.56 [0.23–1.36] -1.28 0.201 Use of antibiotics No 33.46 [4.57-245.05] 3.46 0.001* Step 4: Estimation on the direct disease burden attributable to HAIs In order to accurately estimate the direct disease burden attributable to HAIs, the approach adopted involves a 1:1 matching method to compare medical expenditures and hospitalization durations between hospitalized patients with and without HAIs. This method focuses on various medical expenditures including total medical expenditure, medicine expenditure, and antibiotics expenditure, as well as hospitalization days. The following specific steps were implemented in this TB hospital, as illustrated in Fig. 1 : 1) Prior to the data analysis, we adjusted the medical expenditure data from 2018 to reflect 2019 values using the Consumer Price Indices (CPI) for medicines and healthcare services in Hubei Province ( 12 ). The adjustment formula is expressed as follows: \\({E}_{{Y}_{\\text{b}}}\\text{= }{E}_{{\\text{Y}}_{\\text{a}}}\\text{(}\\text{1}\\text{+ }{\\text{n}}_{{Y}_{\\text{a}+1}}\\text{%)( 1+}{\\text{n}}_{{Y}_{\\text{a}+2}}\\text{)… (1+}{\\text{n}}_{{Y}_{\\text{b}}}\\text{%)}\\) . Specifically, E represents the medical expenditure; Y denotes the year; and n indicates the annual increase in CPI. 2) Fig. 2 displays the respective numbers of TB hospitalized patients diagnosed with HAIs in the years 2018 and 2019, which were 44 and 34, respectively. Table S3 (Suppl. 1) provides an overview of the medical expenditures and hospitalization days for TB hospitalized patients with HAIs. Given that the data on medical expenditures and hospitalization days were skewed, the median, interquartile range (IQR), and the overall range (minimum to maximum values) were used to present the average levels of these variables. The average total medical expenditure increased from ¥30,730.70 in 2018 to ¥37,669.07 in 2019. Similarly, the average medicine expenditure rose significantly by 77.83%, while the average hospitalization days increased slightly. The range of antibiotics expenditure broadened considerably, despite the average costs remaining stable. These trends highlighted an upward shift in healthcare spending and resource utilization for TB hospitalized patients with HAIs over 2018 and 2019. 3) Propensity Score Matching (PSM) was utilized to select a balanced cohort of TB hospitalized patients with and without HAIs. PSM has been extensively applied in medical research to mitigate selection bias and estimate the effects of exposure in observational studies ( 13 , 14 ). It operates by matching two groups with similar propensity scores (PS), which represent the likelihood of a patient being exposed to HAIs based on predefined patient characteristics, with scores ranging from 0 to 1 ( 15 , 16 ). In this TB hospital, the Generalized Boosted Model (GBM) was used to generate the PS ( 17 ). A PSM method employing a caliper of 0.25 standard deviations (SD) of the PS facilitated the 1:1 matching without replacement, thus achieving a balanced comparison between TB hospitalized patients with and without HAIs. Based on the third step, which focuses on identification of the risk factors associated with HAIs, the covariates included in the models were gender, age, and use of invasive procedures such as central venous catheter, urine tube intubation, arteriovenous cannula, endotracheal intubation, mechanical ventilation, drainage, and tracheostomy. The variables including longer than 15 hospitalization days and use of antibiotics were excluded, since the antibiotics expenditure and hospitalization days were selected as measures for estimating the additional direct disease burden attributable to HAIs. The codes for the PSM analysis are available in the supplementary materials (Suppl. 2). The resulting matched pairs were 44 and 34 for the years 2018 and 2019, respectively. Table S4 (Suppl. 1) displays the comparisons of covariates between the two groups before and after performing PSM, confirming the effectiveness of the matching in balancing the covariates. 4) After selecting the balanced groups of TB hospitalized patients with and without HAIs, the Wilcoxon matched-pairs signed-rank test was conducted to compare differences in medical expenditure and hospitalization days, thus assessing the additional direct economic disease burden attributable to HAIs. The codes for this statistical test are also include in the supplementary materials (Suppl. 2). Table 4 shows that in both years of 2018 and 2019, TB hospitalized with HAIs consistently incurred much higher medical expenditure across all categories compared to those without HAIs. In 2018, the additional total medical expenditure was ¥15,417.31, with similar disparities in medicine and antibiotics expenditures, at ¥5,754.74 and ¥2,421.63 respectively ( P < 0.01). The trend continued in 2019, where the additional total medical expenditure increased to ¥26,978.70, indicating a rising cost burden associated with HAIs. The additional medicine and antibiotics expenditures also increased, rising to ¥10,595.32 and ¥2,218.66, respectively. Hospitalization days also reflected significant disparities, with HAIs patients hospitalized for much longer periods. In 2018, the additional hospitalization days were 11.5 days, and this gap widened in 2019 to 21.5 days. These indicate that HAIs were associated with substantially higher medical expenditure and longer hospital stays, with these disparities growing from 2018 to 2019. This underscores the critical financial and operational impacts of HAIs on healthcare systems. Table 4 Additional direct economic disease burden attributable to HAIs from 2018 to 2019 (¥) Year Measures/per patient TB hospitalized patients with HAIs TB hospitalized patients without HAIs Differences Z P Median (Q 25 , Q 75 ) 2018 Total medical expenditure 30,730.70 (18,438.59-59,474.81) 10,277.28 (7,028.36-21,898.79) 15,417.31 (6,633.08-41,634.12) 4.77 < 0.01* Medicine expenditure 9,181.29 (5,467.41-16,634.13) 2,450.13 (1,375.44-5,750.82) 5,754.74 (1,188.66-11,963.25) 4.93 < 0.01* Antibiotics expenditure 2,902.72 (2,125.28-5,584.55) 923.84 (0.00–2,111.95) 2,421.63 (607.97-4,768.49) 4.06 < 0.01* Hospitalization days 25 (14.5–34) 11.5 (6-18.5) 11.5 ( 4 – 22 ) 4.83 < 0.01* 2019 Total medical expenditure 37,669.07 (19,591.27-62,437.21) 8,906.16 (5,687.36-25,609.32) 26,978.70 (8,637.42-55,782.94) 5.05 < 0.01* Medicine expenditure 16,326.64 (9,363.05-28,571.62) 2,340.40 (1,015.68-5,564.57) 10,595.32 (5,030.52-26,701.55) 4.88 < 0.01* Antibiotics expenditure 2,878.10 (2,003.60-8,020.37) 365.88 (0.00–1,832.00) 2,218.66 (1,215.24-7,711.61) 4.49 < 0.01* Hospitalization days 28.5 (18–40) 9.5 ( 3 – 13 ) 21.5 (8–38) 4.92 < 0.01* 5) Rosenbaum bounds for robust test has been widely applied to assess the sensitivity to hidden bias in observational studies ( 18 ). Specifically, it is used to quantify the impact of unobserved confounding factors after performing the PSM analysis. In this case study, the sensitivity parameter Gamma (Γ) ranged from 1 to 2, which represents the degree of departure from random assignment due to an unobserved confounding factor. The codes for calculating Rosenbaum bounds are attached in supplementary materials (Suppl. 2). As indicated in Table S5, for all measures in both years 2018 and 2019, even when Gamma (Γ) equaled to a large value, such as 2, the P values (Sig+) were still lower than 0.05. These results indicated that the analyses on the additional direct economic disease burden were robust to hidden biases. 6) Different matching methods including 1 : 2, 1 : 3, and 1 : 4, when conducting the PSM analysis, were employed to conduct the sensitivity analysis in order to test the robustness of the results generated from 1 : 1 matching method in this case study. Table S6 shows that the additional total medical expenditure per TB hospitalized patient was ¥22784.37 using 1 : 3 matching method in 2018. It had a highest level of differences at 47.78%, compared to the remaining matching methods and measures. The lowest level of differences was 0.87% for the additional medicine expenditure per TB hospitalized patient using 1 : 2 matching method in the same year. Additionally, as indicated in Table S7 (Suppl. 1), the results of Rosenbaum bounds for robust test showed that for different matching methods, the analyses on the additional direct economic disease burden for all measures were still robust to hidden biases. Possible applications First, the application of this framework could be applied at the local level. Results generated from this framework could be applied within internal hospital management, providing critical insights for hospital managers. These generated results directly help hospital managers clearly understand the specific areas at high risk for HAIs, enabling targeted interventions. This data-driven approach allows for the refinement of IPC measures, improving patient safety and reducing the incidence of HAIs. Besides, by quantifying the economic burden attributable to HAIs, hospital managers can better allocate resources to areas that yield the highest return on investment in terms of infection prevention and patient care. Besides, integration of this framework into HIS could enable dynamic tracking and monitoring of HAIs, facilitating timely interventions. Particularly, if such framework could be implemented in an established alliance of hospitals, it is able to more effectively consolidate and share critical data, just as the function of real-time surveillance of existing HAIs systems ( 19 , 20 ). Additionally, optimizing resources allocation and tailoring IPC measures could be significantly enhanced based on the insights provided by this framework. Second, the application of this framework could be applied at the regional and national levels. Results generated from this framework can also be used in external hospital management, particularly for benchmarking across multiple hospitals ( 21 , 22 ). Benchmarking enables hospitals to determine the most cost-effective IPC measures and share best practices by comparing measures. For example, if one hospital exhibits a significantly lower direct economic disease burden attributable to HAIs compared to others, it can be identified as a model of best practice. Hospital managers and policy decision-makers can then figure out the measures or management mechanism formulated by the best practice, and promote them in a broader way to enhance healthcare outcomes. Such applications of benchmarking can improve patient safety and reduce the economic impact of HAIs, thereby benefiting the broader society. Additionally, results generated from this framework can be applied into inter-organizational learning ( 23 ). Hospitals are able to use these results as basis of training programs to improve the awareness of infection control among health professionals. Health professionals can learn the latest updates about theory, methodology, and technology on controlling HAIs via regular workshops, training sessions, and feedback meetings, so that they can enhance their own capacity to implement relevant measures. This needs the involvement of third-party evaluators, thus providing unbiased assessments of hospital performance ( 24 ). These evaluators can perform regular audits, ensuring compliance with established IPC standards and effectiveness of implemented strategies. Third, at the international level, the adaptability of this framework allows for its application in different countries, especially in settings with limited resources. More empirical evidence and experience could be generated to help local hospitals enhance the efficiency of infection control strategies, thereby reducing the attributable medical expenditures and improving quality of health. Moreover, this framework can be continuously refined and adjusted through its application across various international countries, leading to advancements in global health standards and policies. This can also be implemented by non-profit organizations, such as WHO. This collaborative international approach could address the global challenge of HAIs effectively and enhance the resilience and responsiveness of health systems around the world, as supported by the WHO recent reports on global IPC initiatives ( 3 ). Challenges of implementation on this framework First, the challenge to implement this framework is quality of data extracted from hospitals. Data extraction is highly related to the HIS capabilities within each hospital, since data for different variables are sourced from different subsystems of the HIS. Inconsistencies in data management and integration across these subsystems can lead to inaccuracies that compromise the reliability of the framework. The variance in technological infrastructure between hospitals, especially in lower-resource settings, poses significant challenges ( 25 ). Not all hospitals are equipped with advanced HIS systems that can provide the detailed and accurate data necessary for effective application of the framework. This technological disparity can result in significant differences in data quality and accessibility, complicating the implementation process and potentially skewing results. Second, the adoption of this framework in different countries must navigate varying levels of regulatory compliance, data privacy standards, and government support. For instance, data protection regulations in some countries may restrict the types of data that can be collected and how it can be used, limiting the framework’s applicability and effectiveness ( 26 ). Cultural differences in the management and operation of hospitals can also influence the consistency and completeness of data collection. Training and capacity building are also crucial for successful implementation of this framework. Hospital staff need to be trained in how to use the system and in understanding the importance of accurate data entry. Without proper training and a clear understanding of the framework’s objectives, the risk of data entry errors increases, which could compromise data quality and the subsequent analyses. Third, as some limitations of this case study, the unique nature of TB could pose a challenge to implement this framework. For example, since the medical expenditures for hospitalized patients with multi-drug-resistant TB (MDR-TB) are usually significantly higher than those for hospitalized patients with single-drug-resistant TB (SDR-TB), when conducting the PSM analysis, the covariate should include the status whether a hospitalized patient has MDR-TB or SDR-TB. This ensures that the PSM analysis accurately accounts for the medical expenditures associated with different types of TB resistance. Therefore, a comprehensive review of existing literature or consultation with relevant experts/doctors could provide specific risk factors related to TB, which should be included as covariates in the PSM analysis. Thus, the analysis can more precisely attribute the estimated direct economic disease burden solely to HAIs. This can be also applied to other communicable diseases or special diseases. Abbreviations AMR: Antimicrobial resistance CPI: Consumer Price Indices GBM: Generalized Boosted Model HAIs: Healthcare-associated infections HICs: High-income countries HIS: Hospital information system IPC: Infection and prevention control IQR: Interquartile range LMICs: Low- and middle-income countries MDR-TB: Multi-drug-resistant tuberculosis OR: Odd ratio PS: Propensity score PSM: Propensity Score Matching SD: Standard deviations SDR-TB: Single-drug-resistant tuberculosis TB: Tuberculosis WHO: World Health Organization Declarations Acknowledgements The authors thank Jiaxin He (PhD candidate from Wuhan University) for her support in drawing the flow charts of the framework and participant selection. Authors’ contributions HL and JS conceptualized the framework and supervised the whole work. NR applied the ethics application. NR, YL, GL, YH, DJ, and CP extracted the data from the TB hospital. NR, XL, and DJ processed the data. NR and XL analyzed the data. All authors reviewed the final manuscript and agreed on the submission. Funding This study was funded by the Wuhan City Medical Research Program (2021) (grant number: WG21D10) and China Academic Degrees and Graduate Education Development Center (2023) (grant number: ZT-231048623). Availability of data and materials Data and materials are accessible upon the reasonable request to the research team. Declarations Ethics approval and consent to participate The ethics committee of the TB hospital approved this study (Wuhan Pulmonary Ethic Committee (2021) 28). Anonymized data were adopted by replacing the original hospital number with the linkage between hospital number and sequence of admission. Information relevant to the personal information of TB hospitalized patients was removed from the hospital system. Therefore, informed and verbal consent was not required for patients hospitalized for TB in this study. Consent for publication All authors listed in this paper have read the manuscript and agreed to submit the manuscript for publication. Competing interests The authors declare that they have no competing interests. The co-first author, Xinliang Liu is Managing Editor , and the corresponding author, Hao Li is Editor in Chief from Global Health Research and Policy . Both were not involved in the review process. References World Health Organization. WHO launches first ever global report on infection prevention and control 2022 [cited 2024 April 1]. https://www.who.int/news/item/06-05-2022-who-launches-first-ever-global-report-on-infection-prevention-and-control . Weiner-Lastinger LM, Abner S, Edwards JR, Kallen AJ, Karlsson M, Magill SS, et al. Antimicrobial-resistant pathogens associated with adult healthcare-associated infections: Summary of data reported to the National Healthcare Safety Network, 2015–2017. Infect Control Hosp Epidemiol. 2020;41(1):1–18. World Health Organization. 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China Statistical Yearbook 2020 2020 [cited 2024 March 30]. https://www.stats.gov.cn/sj/ndsj/ . Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med. 2008;27(12):2037–49. Langworthy B, Wu YJ, Wang ML. An overview of propensity score matching methods for clustered data. Stat Methods Med Res. 2023;32(4):641–55. Abadie A, Imbens GW. Matching on the Estimated Propensity Score. Econometrica. 2016;84(2):781–807. Rosenbaum PR, Rubin DB. Propensity scores in the design of observational studies for causal effects. Biometrika. 2023;110(1):1–13. Hu LY, Gu CY, Lopez M, Ji JY, Wisnivesky J. Estimation of causal effects of multiple treatments in observational studies with a binary outcome. Stat Methods Med Res. 2020;29(11):3218–34. DiPrete TA, Gangl M. Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociol Methodol. 2004;34(1):271–310. Verberk JDM, Aghdassi SJS, Abbas M, Nauclér P, Gubbels S, Maldonado N, et al. Automated surveillance systems for healthcare-associated infections: results from a European survey and experiences from real-life utilization. J Hosp Infect. 2022;122:35–43. Takaya S, Hayakawa K, Matsunaga N, Moriyama Y, Katanami Y, Tajima T, et al. Surveillance systems for healthcare-associated infection in high and upper-middle income countries: A scoping review. J Infect Chemother. 2020;26(5):429–37. Li YK, Cao LY, Han YL, Wei JJ. Development of a Conceptual Benchmarking Framework for Healthcare Facilities Management: Case Study of Shanghai Municipal Hospitals. J Constr Eng Manag. 2020;146(1). Amarsy R, Granger B, Fournierc S, Monteil C, Trystram D, Siorat V, et al. Novel scores relevant to antimicrobial resistance and hospital-acquired infections developed with data from a multi-hospital consortium in the Parisian region of France. J Hosp Infect. 2024;143:97–104. 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Supplementary Files Supplementarymaterial1.docx Supplementary material 1 Table S1 Inclusion and exclusion criteria for TB hospitalized patients with and without HAIs Table S2 Description and assigned values of the potential risk factors associated with HAIs among TB hospitalized patients Table S3 Summaries of medical expenditures and hospitalization days among TB hospitalized patients with HAIs from 2018 to 2019 (¥) Table S4 Comparisons of covariates between TB hospitalized patients with and without HAIs before and after performing PSM Table S5 Rosenbaum bounds for robust test on the additional direct economic disease burden attributable to HAIs in 2018 and 2019 Table S6 Sensitivity analysis using different matching methods in 2018 and 2019 Table S7 Rosenbaum bounds for robust test across different PSM matching methods in 2018 and 2019 Supplementarymaterial2.docx Supplementary material 2 Codes for conducting univariate analysis in STATA software Codes for conducting multiple logistic regression analysis in STATA software Codes for performing PSM analysis in STATA software Codes for conducting Wilcoxon matched-pairs signed-rank tests in STATA software Codes for conducting Rosenbaum bounds for robust test in STATA software Cite Share Download PDF Status: Published Journal Publication published 09 Sep, 2024 Read the published version in Global Health Research and Policy → Version 1 posted Editorial decision: Major revision 11 Jul, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers invited by journal 12 Jun, 2024 Editor assigned by journal 12 Jun, 2024 First submitted to journal 03 Jun, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4524748\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":313476179,\"identity\":\"9dc76583-5134-4b43-8c9d-e187c75954ad\",\"order_by\":0,\"name\":\"Nili Ren\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Wuhan Pulmonary Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nili\",\"middleName\":\"\",\"lastName\":\"Ren\",\"suffix\":\"\"},{\"id\":313476181,\"identity\":\"15e5ba50-445a-4e9d-ac0c-d93a9a1602b7\",\"order_by\":1,\"name\":\"Xinliang Liu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACduaGAwwVNjxszMwHHwD5PHwEtTAzArWcSZPhZ29LNgBpYSNGCwNj22EbyZ4zahIgAYJaDA4zNh7mbWPmMbiRw1b5NcdOho2B+eGjG3i0SDYzNhzmOccG1JJ77LbstmSgw9iMjXPwaOFnBmkp4wFqyUu7LbmNGaiFh00anxY2sBY2CZDDzIolt9UT1gKxpc2AB+h9M8aP2w4T1gLyy8E5ZxJ4QIEszbjtODCCCPjF4Hjz4Q9vKv7bg6Ly489t1fb87M0PH+PTggKYecAkscpBgPEHKapHwSgYBaNgxAAAQC9EMeGqoFQAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0002-5679-8168\",\"institution\":\"Wuhan University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Xinliang\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":313476183,\"identity\":\"fc1b8db7-f8aa-4d78-bab3-403fba772917\",\"order_by\":2,\"name\":\"Yi Luo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Wuhan Pulmonary 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Li\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-5155-4033\",\"institution\":\"Wuhan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hao\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-06-04 02:56:12\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4524748/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4524748/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s41256-024-00375-w\",\"type\":\"published\",\"date\":\"2024-09-09T15:58:03+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":59964880,\"identity\":\"98e686b9-cab6-4004-a4ab-10513108e516\",\"added_by\":\"auto\",\"created_at\":\"2024-07-10 01:54:12\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":470327,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFlow chart of the framework on identification of risk factors and estimation of the direct economic disease burden attributable to HAIs\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4524748/v1/e1c53a721c9ff6144044bb4f.png\"},{\"id\":59964883,\"identity\":\"96c0ae61-5383-4ecb-a7e5-19424050f1bb\",\"added_by\":\"auto\",\"created_at\":\"2024-07-10 01:54:12\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":369932,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFlow chart of the included participants from the Chinese TB hospital in 2018 and 2019\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4524748/v1/b6e9103f1dabc2141c541d74.png\"},{\"id\":64619204,\"identity\":\"1cf16c49-a17c-4099-a390-fe982000f9d1\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 16:12:40\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1462481,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4524748/v1/5b71c886-ce4d-44c6-b35f-fa81e143481a.pdf\"},{\"id\":59964881,\"identity\":\"5fc7f4e9-0d30-4078-8cfe-00efe716dd3e\",\"added_by\":\"auto\",\"created_at\":\"2024-07-10 01:54:12\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":46544,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary material 1\\u003c/p\\u003e\\n\\u003cp\\u003eTable S1 Inclusion and exclusion criteria for TB hospitalized patients with and without HAIs\\u003c/p\\u003e\\n\\u003cp\\u003eTable S2 Description and assigned values of the potential risk factors associated with HAIs among TB hospitalized patients\\u003c/p\\u003e\\n\\u003cp\\u003eTable S3 Summaries of medical expenditures and hospitalization days among TB hospitalized patients with HAIs from 2018 to 2019 (¥)\\u003c/p\\u003e\\n\\u003cp\\u003eTable S4 Comparisons of covariates between TB hospitalized patients with and without HAIs before and after performing PSM\\u003c/p\\u003e\\n\\u003cp\\u003eTable S5 Rosenbaum bounds for robust test on the additional direct economic disease burden attributable to HAIs in 2018 and 2019\\u003c/p\\u003e\\n\\u003cp\\u003eTable S6 Sensitivity analysis using different matching methods in 2018 and 2019\\u003c/p\\u003e\\n\\u003cp\\u003eTable S7 Rosenbaum bounds for robust test across different PSM matching methods in 2018 and 2019\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarymaterial1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4524748/v1/e9e893043a085aff213efe9c.docx\"},{\"id\":59965872,\"identity\":\"f955f5a4-1bf6-4b71-ae8a-b16af43c8fa6\",\"added_by\":\"auto\",\"created_at\":\"2024-07-10 02:02:12\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":13524,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary material 2\\u003c/p\\u003e\\n\\u003cp\\u003eCodes for conducting univariate analysis in STATA software\\u003c/p\\u003e\\n\\u003cp\\u003eCodes for conducting multiple logistic regression analysis in STATA software\\u003c/p\\u003e\\n\\u003cp\\u003eCodes for performing PSM analysis in STATA software\\u003c/p\\u003e\\n\\u003cp\\u003eCodes for conducting Wilcoxon matched-pairs signed-rank tests in STATA software\\u003c/p\\u003e\\n\\u003cp\\u003eCodes for conducting Rosenbaum bounds for robust test in STATA software\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarymaterial2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4524748/v1/79949135aa8f667bf2080963.docx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: case study of a Chinese Tuberculosis hospital\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eHealthcare-associated infections (HAIs) pose a major global health burden. The World Health Organization (WHO) reported that in 2022, globally in acute-care hospitals, about 7 out of every 100 hospitalized patients in high-income countries (HICs) and 15 out of every 100 hospitalized patients in low- and middle-income countries (LMICs) obtained at least one HAI, and an average of 1 in every 10 hospitalized patients with HAIs died (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). HAIs also lead to the increased occurrence of antimicrobial resistance (AMR) - another major global health issue (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). However, from the perspective of the economic disease burden attributable to HAIs, the updated evidence is scant, especially in settings with limited resources. A framework is required to estimate the direct economic burden attributable to HAIs at minimum level-in individual hospitals, providing empirical evidence and support targeted interventions.\\u003c/p\\u003e \\u003cp\\u003eIn response to the global challenge of HAIs, the WHO announced its first-ever global strategy on infection and prevention control (IPC) at the 76th World Health Assembly in 2023, and an associated global action plan and monitoring framework will be completed by 2024 (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Effective and tailored IPC measures are formulated based on identifying the risk factors associated with HAIs within hospitals. The set of risk factors associated with HAIs could be different in different hospitals. Therefore, a general framework should be developed to identify the risk factors associated with HAIs unique to each hospital setting.\\u003c/p\\u003e \\u003cp\\u003eThe framework on estimating the direct economic disease burden attributable to HAIs was developed before by our team and applied within general hospitals in China and Nepal (\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). However, it is yet to be used in specialized hospitals, like Tuberculosis (TB) ones. The risk of HAIs is compounded by the nature of TB itself-a deadly respiratory infectious disease. According to the latest estimates reported by the WHO in 2023, around 10.6\\u0026nbsp;million population were estimated to be infected with TB worldwide, and 1.3\\u0026nbsp;million of them died in 2022 (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Particularly, South-East Asia Region had the highest number of population infected with TB at 4.85\\u0026nbsp;million, followed by African Region at 2.48\\u0026nbsp;million (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). The high incidence and the complex nature of TB treatment environment can increase the vulnerability of patients to HAIs, thus escalating the economic and health burden on healthcare systems.\\u003c/p\\u003e \\u003cp\\u003eTo address above situation, a framework was developed by our team based on the case study conducted in a Chinese TB hospital to analyze the potential risk factors and estimate the direct economic burden attributable to HAIs. Furthermore, this framework aims to provide healthcare stakeholders with a tool for implementing effective IPC measures and evaluating the financial impact of HAIs. The Chinese TB hospital is located in Hubei Province, which is a tertiary pulmonary and tuberculosis control hospital that focuses on the prevention and control of TB, clinical diagnosis and treatment of pulmonary diseases, and medical/health education and research. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e shows the information of the operational and treatment efficacy about the hospital from 2018 to 2019. The number of beds and average hospitalization days remained stable at 406 and 9.73 days, respectively. The mortality rate of hospitalized patients increased from 0.45\\u0026ndash;0.72%, and the number of outpatient and emergency patients rose significantly from 113,819 to 129,905. The annual HAIs prevalence decreased from 0.53\\u0026ndash;0.34%.\\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\\u003eGeneral information of the Chinese TB hospital\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYear\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2018\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2019\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber of beds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e406\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e406\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAverage hospitalization days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal mortality of hospitalized patients (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber of discharged patients\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16,445\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16,649\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber of outpatient and emergency patients\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e113,819\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e129,905\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnnual HAIs prevalence (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Framework development\",\"content\":\"\\u003cp\\u003eFigure \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents that a framework analyzes the impact of HAIs within a hospital setting, which includes four steps. The first step is an ethical application, ensuring that all data collection and analysis adhere to the highest standards of research ethics. The first step secures the approval of relevant ethics committees and establishes a foundation of trust and legality for research. Then, the second step is the inclusion of participants, which involves a detailed screening process to select all eligible hospitalized patients based on specific criteria. This selection process ensures that the data extracted are relevant and robust, providing a solid base for further analysis. The third step is that the framework identifies risk factors associated with HAIs, which involves a thorough analysis of patient data to ascertain factors that may increase the likelihood of HAIs, such as the length of hospitalization, the use of invasive procedures, or the presence of comorbid conditions. Understanding these risk factors is pivotal for developing targeted strategies to reduce the incidence of HAIs. Finally, the fourth step is the estimation of the direct economic disease burden attributable to HAIs, which involves calculating the additional medical expenditures and hospitalization days associated with HAIs. The economic burden analysis helps to quantify the financial impact of HAIs, highlighting the economic incentives for hospitals to invest in effective IPC measures. Overall, the framework employs a retrospective cross-sectional study design, allowing for the analysis of data from previously hospitalized patients. This approach is advantageous as it provides a snapshot of hospital performance over a specific period, enabling hospital managers and policy decision-makers to implement evidence-based improvements in patient care and IPC measures. This comprehensive and methodical approach ensures that every aspect of the impact of HAIs is captured and addressed, including from ethical considerations to risk factor analysis and economic burden analysis.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eStep 1: Ethical application\\u003c/h2\\u003e\\n \\u003cp\\u003eBefore extracting data from hospitals, it is essential to secure approval from the hospitals\\u0026rsquo; ethics committees, particularly when the data involve personal information about hospitalized patients. This case study obtained the approval from the TB hospital\\u0026rsquo;s ethic committee. To ensure anonymity, original hospital numbers were replaced with unique identifiers created by a staff member from the Department of Medical Records. Personal information pertaining to the hospitalized TB patients was omitted during data extraction from the hospital information systems (HIS). As a result, there was no need for informed or verbal consent from the TB hospitalized patients.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eStep 2: Inclusion of participants\\u003c/h2\\u003e\\n \\u003cp\\u003eThis phrase primarily involves the inclusion of all hospitalized patients, followed by their classification into groups with and without HAIs for subsequent analysis of risk factors and economic burden. The specific steps taken in this TB hospital were as follows, with Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e illustrating the entire participant inclusion flowchart:\\u003c/p\\u003e\\n \\u003cp\\u003e1) All TB hospitalized patients who were discharged from 0:00 1st January 2018 to 23:59 31st December 2022 were included. TB hospitalized patients information was retrieved from the HIS. As demonstrated in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, a total of 23,080 TB hospitalized patients were included during the study periods, with 11,332 patients in 2018 and 11,748 in 2019.\\u003c/p\\u003e\\n \\u003cp\\u003e2) Those TB hospitalized patients staying in hospital less than 48 hours needed to be excluded, since the criteria for HAIs require a minimum hospital stay of more than two days (\\u003cspan class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). After applying this exclusion criterion, the total number of included TB hospitalized patients was reduced to 21,148.\\u003c/p\\u003e\\n \\u003cp\\u003e3) The remaining TB hospitalized patients were then categorized based on whether they had acquired HAIs, according to the inclusion and exclusion criteria detailed in Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e (Suppl. 1). Following this categorization, 78 TB hospitalized patients were identified with HAIs, while 21,070 TB hospitalized patients did not have HAIs.\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003eStep 3: Identification on risk factors associated with HAIs\\u003c/h2\\u003e\\n \\u003cp\\u003eThe risk factors analysis on association with HAIs among hospitalized patients typically involves selecting potential risk factors, conducting univariate analysis, and performing logistic regression analysis. Here are the specific steps taken at this TB hospital:\\u003c/p\\u003e\\n \\u003cp\\u003e1) Our research team has conducted a systematic review and meta-analysis to identify risk factors associated with HAIs among TB hospitalized patients in China. The study concluded a list of significant risk factors, including age older than 60 years, presence of complications, diabetes mellitus, invasive procedure, longer than 15 hospitalization days, secondary TB, smoking, presence of underlying disease, and use of antibiotics (\\u003cspan class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). Considering the data availability from this TB hospital, gender, age older than 60 years, diabetes mellitus, invasive procedure, more than 15 hospitalization days, presence of underlying disease, and used of antibiotics were selected as potential risk factors in this case study. Descriptions and assigned values of the selected potential risk factors are listed in Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e (Suppl. 1).\\u003c/p\\u003e\\n \\u003cp\\u003e2) A univariate analysis was conducted to examine the association between the selected risk factors and HAIs among the TB hospitalized patients. The chi-squire test was used for those categorical variables with expected frequencies above five, while the Fisher exact probability test was used for those with frequencies below five. The codes for conducting the univariate analysis can be found in supplementary materials (Suppl. 2). Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e demonstrates that gender, longer than 15 hospitalization days, and the use of antibiotics were consistent risk factors, each showing a statistically significant association with HAIs among TB hospitalized patients in both 2018 and 2019 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u0026nbsp;\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eUnivariate analyses on potential risk factors associated with HAIs in 2018 and 2019\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eYear\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003ePotential risk factors\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTB hospitalized patients with HAIs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTB hospitalized patients without HAIs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026chi;\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eN (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"14\\\"\\u003e\\n \\u003cp\\u003e2018\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eGender\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40 (90.91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6,413 (62.82)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e14.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (9.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3,796 (37.18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;60 years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18 (40.91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,512 (24.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e6.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.012*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026le;\\u0026thinsp;60 years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e26 (59.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7,697 (75.39)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInvasive procedure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12 (27.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e492 (4.82)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e32 (72.73)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9,717 (95.18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLength of hospitalization\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;15 days\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e32 (72.73)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,639 (16.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e103.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026le;\\u0026thinsp;15 days\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12 (27.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8,570 (83.95)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eDiabetes mellitus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (11.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,293 (12.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.796\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e39 (88.64)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8,916 (87.33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eUnderlying disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11 (25.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,272 (22.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.662\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e33 (75.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7,937 (77.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eUse of antibiotics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40 (90.91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,095 (49.91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e29.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (9.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,114 (50.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"14\\\"\\u003e\\n \\u003cp\\u003e2019\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eGender\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27 (79.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e67,60 (62.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e4.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.039*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7 (20.59)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4,101 (37.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;60 years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13 (38.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,685 (24.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e3.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.068\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026le;\\u0026thinsp;60 years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e21 (61.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8,176 (75.28)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInvasive procedure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7 (20.59)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e659 (6.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.004*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27 (79.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10,202 (93.93)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLength of hospitalization\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;15 days\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 (88.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,660 (15.28)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e137.64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026le;\\u0026thinsp;15 days\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (11.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9,201 (84.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eDiabetes mellitus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7 (20.59)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,743 (16.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.472\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27 (79.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9,118 (83.95)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eUnderlying disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7(20.59)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,580 (23.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.665\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27 (79.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8,281 (76.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eUse of antibiotics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e33 (97.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,180 (47.69)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e33.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (2.94)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,681 (52.31)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e3) A binary logistic regression model was applied to investigate the severity of potential risk factors associated with HAIs among TB hospitalized patients. To avoid \\u0026lsquo;Table 2 Fallacy\\u0026rsquo; where multiple adjusted odd ratios (aOR) derived from a single logistic regression model misinterpret the impact of primary risk factors due to covariate heterogeneity (\\u003cspan class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e), multiple logistic regression models were conducted by adjusting the common covariates including gender and age. The codes for this binary logistic regression analysis are available the in supplementary materials (Suppl. 2). Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e shows that consistently, the significant risk factors associated with HAIs were invasive procedure (aOR: 7.41 in 2018; 4.29 in 2019), longer than 15 hospitalization days (aOR: 13.15 in 2018; 39.76 in 2019), and use of antibiotics (aOR: 8.99 in 2018; 33.46 in 2019) (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eLogistic regression analyses on potential risk factors associated with HAIs in 2018 and 2019\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYear\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePotential risk factors\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eReference\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eaOR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95%CI\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eZ\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003e2018\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInvasive procedure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[3.78\\u0026ndash;14.54]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLength of hospitalization\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026le;\\u0026thinsp;15 days\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[6.75\\u0026ndash;25.61]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDiabetes mellitus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[0.26\\u0026ndash;1.70]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.395\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnderlying disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[0.38\\u0026ndash;1.63]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.512\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUse of antibiotics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[3.21\\u0026ndash;25.18]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003e2019\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInvasive procedure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[1.85\\u0026ndash;9.93]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.001*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLength of hospitalization\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026le;\\u0026thinsp;15 days\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e39.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[13.98-113.08]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDiabetes mellitus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[0.46\\u0026ndash;2.52]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.864\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnderlying disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[0.23\\u0026ndash;1.36]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-1.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.201\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUse of antibiotics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e33.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e[4.57-245.05]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.001*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eStep 4: Estimation on the direct disease burden attributable to HAIs\\u003c/h2\\u003e\\n \\u003cp\\u003eIn order to accurately estimate the direct disease burden attributable to HAIs, the approach adopted involves a 1:1 matching method to compare medical expenditures and hospitalization durations between hospitalized patients with and without HAIs. This method focuses on various medical expenditures including total medical expenditure, medicine expenditure, and antibiotics expenditure, as well as hospitalization days. The following specific steps were implemented in this TB hospital, as illustrated in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e:\\u003c/p\\u003e\\n \\u003cp\\u003e1) Prior to the data analysis, we adjusted the medical expenditure data from 2018 to reflect 2019 values using the Consumer Price Indices (CPI) for medicines and healthcare services in Hubei Province (\\u003cspan class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). The adjustment formula is expressed as follows: \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({E}_{{Y}_{\\\\text{b}}}\\\\text{= }{E}_{{\\\\text{Y}}_{\\\\text{a}}}\\\\text{(}\\\\text{1}\\\\text{+ }{\\\\text{n}}_{{Y}_{\\\\text{a}+1}}\\\\text{%)( 1+}{\\\\text{n}}_{{Y}_{\\\\text{a}+2}}\\\\text{)\\u0026hellip; (1+}{\\\\text{n}}_{{Y}_{\\\\text{b}}}\\\\text{%)}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. Specifically, \\u003cem\\u003eE\\u003c/em\\u003e represents the medical expenditure; \\u003cem\\u003eY\\u003c/em\\u003e denotes the year; and n indicates the annual increase in CPI.\\u003c/p\\u003e\\u003cp\\u003e2) Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e displays the respective numbers of TB hospitalized patients diagnosed with HAIs in the years 2018 and 2019, which were 44 and 34, respectively. Table S3 (Suppl. 1) provides an overview of the medical expenditures and hospitalization days for TB hospitalized patients with HAIs. Given that the data on medical expenditures and hospitalization days were skewed, the median, interquartile range (IQR), and the overall range (minimum to maximum values) were used to present the average levels of these variables. The average total medical expenditure increased from \\u0026yen;30,730.70 in 2018 to \\u0026yen;37,669.07 in 2019. Similarly, the average medicine expenditure rose significantly by 77.83%, while the average hospitalization days increased slightly. The range of antibiotics expenditure broadened considerably, despite the average costs remaining stable. These trends highlighted an upward shift in healthcare spending and resource utilization for TB hospitalized patients with HAIs over 2018 and 2019.\\u003c/p\\u003e\\u003cp\\u003e3) Propensity Score Matching (PSM) was utilized to select a balanced cohort of TB hospitalized patients with and without HAIs. PSM has been extensively applied in medical research to mitigate selection bias and estimate the effects of exposure in observational studies (\\u003cspan class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). It operates by matching two groups with similar propensity scores (PS), which represent the likelihood of a patient being exposed to HAIs based on predefined patient characteristics, with scores ranging from 0 to 1 (\\u003cspan class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). In this TB hospital, the Generalized Boosted Model (GBM) was used to generate the PS (\\u003cspan class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). A PSM method employing a caliper of 0.25 standard deviations (SD) of the PS facilitated the 1:1 matching without replacement, thus achieving a balanced comparison between TB hospitalized patients with and without HAIs. Based on the third step, which focuses on identification of the risk factors associated with HAIs, the covariates included in the models were gender, age, and use of invasive procedures such as central venous catheter, urine tube intubation, arteriovenous cannula, endotracheal intubation, mechanical ventilation, drainage, and tracheostomy. The variables including longer than 15 hospitalization days and use of antibiotics were excluded, since the antibiotics expenditure and hospitalization days were selected as measures for estimating the additional direct disease burden attributable to HAIs. The codes for the PSM analysis are available in the supplementary materials (Suppl. 2). The resulting matched pairs were 44 and 34 for the years 2018 and 2019, respectively. Table S4 (Suppl. 1) displays the comparisons of covariates between the two groups before and after performing PSM, confirming the effectiveness of the matching in balancing the covariates.\\u003c/p\\u003e\\u003cp\\u003e4) After selecting the balanced groups of TB hospitalized patients with and without HAIs, the Wilcoxon matched-pairs signed-rank test was conducted to compare differences in medical expenditure and hospitalization days, thus assessing the additional direct economic disease burden attributable to HAIs. The codes for this statistical test are also include in the supplementary materials (Suppl. 2). Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e shows that in both years of 2018 and 2019, TB hospitalized with HAIs consistently incurred much higher medical expenditure across all categories compared to those without HAIs. In 2018, the additional total medical expenditure was \\u0026yen;15,417.31, with similar disparities in medicine and antibiotics expenditures, at \\u0026yen;5,754.74 and \\u0026yen;2,421.63 respectively (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). The trend continued in 2019, where the additional total medical expenditure increased to \\u0026yen;26,978.70, indicating a rising cost burden associated with HAIs. The additional medicine and antibiotics expenditures also increased, rising to \\u0026yen;10,595.32 and \\u0026yen;2,218.66, respectively. Hospitalization days also reflected significant disparities, with HAIs patients hospitalized for much longer periods. In 2018, the additional hospitalization days were 11.5 days, and this gap widened in 2019 to 21.5 days. These indicate that HAIs were associated with substantially higher medical expenditure and longer hospital stays, with these disparities growing from 2018 to 2019. This underscores the critical financial and operational impacts of HAIs on healthcare systems.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAdditional direct economic disease burden attributable to HAIs from 2018 to 2019 (\\u0026yen;)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eYear\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eMeasures/per patient\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eTB hospitalized patients with HAIs\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eTB hospitalized patients without HAIs\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eDifferences\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eZ\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\u003cp\\u003eMedian (Q\\u003csub\\u003e25\\u003c/sub\\u003e, Q\\u003csub\\u003e75\\u003c/sub\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e2018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eTotal medical expenditure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e30,730.70 (18,438.59-59,474.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e10,277.28 (7,028.36-21,898.79)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e15,417.31 (6,633.08-41,634.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e4.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eMedicine expenditure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e9,181.29 (5,467.41-16,634.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e2,450.13 (1,375.44-5,750.82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e5,754.74 (1,188.66-11,963.25)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e4.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eAntibiotics expenditure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e2,902.72 (2,125.28-5,584.55)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e923.84 (0.00\\u0026ndash;2,111.95)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e2,421.63 (607.97-4,768.49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e4.06\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eHospitalization days\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e25 (14.5\\u0026ndash;34)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e11.5 (6-18.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e11.5 (\\u003cspan class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e4.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e2019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eTotal medical expenditure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e37,669.07 (19,591.27-62,437.21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e8,906.16 (5,687.36-25,609.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e26,978.70 (8,637.42-55,782.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e5.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eMedicine expenditure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e16,326.64 (9,363.05-28,571.62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e2,340.40 (1,015.68-5,564.57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e10,595.32 (5,030.52-26,701.55)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e4.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eAntibiotics expenditure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e2,878.10 (2,003.60-8,020.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e365.88 (0.00\\u0026ndash;1,832.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e2,218.66 (1,215.24-7,711.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e4.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003eHospitalization days\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e28.5 (18\\u0026ndash;40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e9.5 (\\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e21.5 (8\\u0026ndash;38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e4.92\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/table\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e5) Rosenbaum bounds for robust test has been widely applied to assess the sensitivity to hidden bias in observational studies (\\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Specifically, it is used to quantify the impact of unobserved confounding factors after performing the PSM analysis. In this case study, the sensitivity parameter Gamma (\\u0026Gamma;) ranged from 1 to 2, which represents the degree of departure from random assignment due to an unobserved confounding factor. The codes for calculating Rosenbaum bounds are attached in supplementary materials (Suppl. 2). As indicated in Table S5, for all measures in both years 2018 and 2019, even when Gamma (\\u0026Gamma;) equaled to a large value, such as 2, the \\u003cem\\u003eP\\u003c/em\\u003e values (Sig+) were still lower than 0.05. These results indicated that the analyses on the additional direct economic disease burden were robust to hidden biases.\\u003c/p\\u003e\\u003cp\\u003e6) Different matching methods including 1 : 2, 1 : 3, and 1 : 4, when conducting the PSM analysis, were employed to conduct the sensitivity analysis in order to test the robustness of the results generated from 1 : 1 matching method in this case study. Table S6 shows that the additional total medical expenditure per TB hospitalized patient was \\u0026yen;22784.37 using 1 : 3 matching method in 2018. It had a highest level of differences at 47.78%, compared to the remaining matching methods and measures. The lowest level of differences was 0.87% for the additional medicine expenditure per TB hospitalized patient using 1 : 2 matching method in the same year. Additionally, as indicated in Table S7 (Suppl. 1), the results of Rosenbaum bounds for robust test showed that for different matching methods, the analyses on the additional direct economic disease burden for all measures were still robust to hidden biases.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePossible applications\\u003c/h2\\u003e\\u003cp\\u003eFirst, the application of this framework could be applied at the local level. Results generated from this framework could be applied within internal hospital management, providing critical insights for hospital managers. These generated results directly help hospital managers clearly understand the specific areas at high risk for HAIs, enabling targeted interventions. This data-driven approach allows for the refinement of IPC measures, improving patient safety and reducing the incidence of HAIs. Besides, by quantifying the economic burden attributable to HAIs, hospital managers can better allocate resources to areas that yield the highest return on investment in terms of infection prevention and patient care. Besides, integration of this framework into HIS could enable dynamic tracking and monitoring of HAIs, facilitating timely interventions. Particularly, if such framework could be implemented in an established alliance of hospitals, it is able to more effectively consolidate and share critical data, just as the function of real-time surveillance of existing HAIs systems (\\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). Additionally, optimizing resources allocation and tailoring IPC measures could be significantly enhanced based on the insights provided by this framework.\\u003c/p\\u003e\\u003cp\\u003eSecond, the application of this framework could be applied at the regional and national levels. Results generated from this framework can also be used in external hospital management, particularly for benchmarking across multiple hospitals (\\u003cspan class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). Benchmarking enables hospitals to determine the most cost-effective IPC measures and share best practices by comparing measures. For example, if one hospital exhibits a significantly lower direct economic disease burden attributable to HAIs compared to others, it can be identified as a model of best practice. Hospital managers and policy decision-makers can then figure out the measures or management mechanism formulated by the best practice, and promote them in a broader way to enhance healthcare outcomes. Such applications of benchmarking can improve patient safety and reduce the economic impact of HAIs, thereby benefiting the broader society. Additionally, results generated from this framework can be applied into inter-organizational learning (\\u003cspan class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Hospitals are able to use these results as basis of training programs to improve the awareness of infection control among health professionals. Health professionals can learn the latest updates about theory, methodology, and technology on controlling HAIs via regular workshops, training sessions, and feedback meetings, so that they can enhance their own capacity to implement relevant measures. This needs the involvement of third-party evaluators, thus providing unbiased assessments of hospital performance (\\u003cspan class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). These evaluators can perform regular audits, ensuring compliance with established IPC standards and effectiveness of implemented strategies.\\u003c/p\\u003e\\u003cp\\u003eThird, at the international level, the adaptability of this framework allows for its application in different countries, especially in settings with limited resources. More empirical evidence and experience could be generated to help local hospitals enhance the efficiency of infection control strategies, thereby reducing the attributable medical expenditures and improving quality of health. Moreover, this framework can be continuously refined and adjusted through its application across various international countries, leading to advancements in global health standards and policies. This can also be implemented by non-profit organizations, such as WHO. This collaborative international approach could address the global challenge of HAIs effectively and enhance the resilience and responsiveness of health systems around the world, as supported by the WHO recent reports on global IPC initiatives (\\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eChallenges of implementation on this framework\\u003c/h2\\u003e\\u003cp\\u003eFirst, the challenge to implement this framework is quality of data extracted from hospitals. Data extraction is highly related to the HIS capabilities within each hospital, since data for different variables are sourced from different subsystems of the HIS. Inconsistencies in data management and integration across these subsystems can lead to inaccuracies that compromise the reliability of the framework. The variance in technological infrastructure between hospitals, especially in lower-resource settings, poses significant challenges (\\u003cspan class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). Not all hospitals are equipped with advanced HIS systems that can provide the detailed and accurate data necessary for effective application of the framework. This technological disparity can result in significant differences in data quality and accessibility, complicating the implementation process and potentially skewing results.\\u003c/p\\u003e\\u003cp\\u003eSecond, the adoption of this framework in different countries must navigate varying levels of regulatory compliance, data privacy standards, and government support. For instance, data protection regulations in some countries may restrict the types of data that can be collected and how it can be used, limiting the framework\\u0026rsquo;s applicability and effectiveness (\\u003cspan class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). Cultural differences in the management and operation of hospitals can also influence the consistency and completeness of data collection. Training and capacity building are also crucial for successful implementation of this framework. Hospital staff need to be trained in how to use the system and in understanding the importance of accurate data entry. Without proper training and a clear understanding of the framework\\u0026rsquo;s objectives, the risk of data entry errors increases, which could compromise data quality and the subsequent analyses.\\u003c/p\\u003e\\u003cp\\u003eThird, as some limitations of this case study, the unique nature of TB could pose a challenge to implement this framework. For example, since the medical expenditures for hospitalized patients with multi-drug-resistant TB (MDR-TB) are usually significantly higher than those for hospitalized patients with single-drug-resistant TB (SDR-TB), when conducting the PSM analysis, the covariate should include the status whether a hospitalized patient has MDR-TB or SDR-TB. This ensures that the PSM analysis accurately accounts for the medical expenditures associated with different types of TB resistance. Therefore, a comprehensive review of existing literature or consultation with relevant experts/doctors could provide specific risk factors related to TB, which should be included as covariates in the PSM analysis. Thus, the analysis can more precisely attribute the estimated direct economic disease burden solely to HAIs. This can be also applied to other communicable diseases or special diseases.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eAMR: Antimicrobial resistance\\u003c/p\\u003e\\n\\u003cp\\u003eCPI: Consumer Price Indices\\u003c/p\\u003e\\n\\u003cp\\u003eGBM: Generalized Boosted Model\\u003c/p\\u003e\\n\\u003cp\\u003eHAIs: Healthcare-associated infections\\u003c/p\\u003e\\n\\u003cp\\u003eHICs: High-income countries\\u003c/p\\u003e\\n\\u003cp\\u003eHIS: Hospital information system\\u003c/p\\u003e\\n\\u003cp\\u003eIPC: Infection and prevention control\\u003c/p\\u003e\\n\\u003cp\\u003eIQR: Interquartile range\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eLMICs: Low- and middle-income countries\\u003c/p\\u003e\\n\\u003cp\\u003eMDR-TB: Multi-drug-resistant tuberculosis\\u003c/p\\u003e\\n\\u003cp\\u003eOR: Odd ratio\\u003c/p\\u003e\\n\\u003cp\\u003ePS: Propensity score\\u003c/p\\u003e\\n\\u003cp\\u003ePSM: Propensity Score Matching\\u003c/p\\u003e\\n\\u003cp\\u003eSD: Standard deviations\\u003c/p\\u003e\\n\\u003cp\\u003eSDR-TB: Single-drug-resistant tuberculosis\\u003c/p\\u003e\\n\\u003cp\\u003eTB: Tuberculosis\\u003c/p\\u003e\\n\\u003cp\\u003eWHO: World Health Organization\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors thank Jiaxin He (PhD candidate from Wuhan University) for her support in drawing the flow charts of the framework and participant selection.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHL and JS conceptualized the framework and supervised the whole work. NR applied the ethics application. NR, YL, GL, YH, DJ, and CP extracted the data from the TB hospital. NR, XL, and DJ processed the data. NR and XL analyzed the data. All authors reviewed the final manuscript and agreed on the submission.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was funded by the Wuhan City Medical Research Program (2021) (grant number: WG21D10) and China Academic Degrees and Graduate Education Development Center (2023) (grant number: ZT-231048623).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData and materials are accessible upon the reasonable request to the research team.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclarations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe ethics committee of the TB hospital approved this study (Wuhan Pulmonary Ethic Committee (2021) 28). Anonymized data were adopted by replacing the original hospital number with the linkage between hospital number and sequence of admission. Information relevant to\\u0026nbsp;the personal information of\\u0026nbsp;TB hospitalized patients was removed from the hospital system. Therefore, informed and verbal consent was not required for\\u0026nbsp;patients hospitalized\\u0026nbsp;for TB in this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors listed in this paper have read the manuscript and agreed to submit the manuscript for publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests. The co-first author, Xinliang Liu is Managing Editor\\u003cem\\u003e,\\u0026nbsp;\\u003c/em\\u003eand the corresponding author, Hao Li is Editor in Chief\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003efrom \\u003cem\\u003eGlobal Health Research and Policy\\u003c/em\\u003e. Both were not involved in the review process.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eWorld Health Organization. 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Environmental Health Check: How third-party environmental evaluation project affects corporate environmental responsibility. Sustain Dev. 2023.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBehnke M, Valik JK, Gubbels S, Teixeira D, Kristensen B, Abbas M, et al. Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections. Clin Microbiol Infect. 2021;27:S29\\u0026ndash;39.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003evan Rooden SM, Aspevall O, Carrara E, Gubbels S, Johansson A, Lucet JC, et al. Governance aspects of large-scale implementation of automated surveillance of healthcare-associated infections. Clin Microbiol Infect. 2021;27:S20\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"global-health-research-and-policy\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ghrp\",\"sideBox\":\"Learn more about [Global Health Research and Policy](http://ghrp.biomedcentral.com)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/GHRP/default.aspx\",\"title\":\"Global Health Research and Policy\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Healthcare-associated infections, risk factors, direct economic disease burden, case study, framework\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4524748/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4524748/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eHealthcare-associated infections (HAIs) represent a major global health burden, necessitating effective frameworks to identify potential risk factors and estimate direct economic disease burden. We proposed a framework designed to address these needs through a case study conducted in a Chinese Tuberculosis hospital using data from 2018 to 2019. The framework incorporates a comprehensive multistep process, including ethical application, participant inclusion, risk factor identification, and direct economic disease burden estimation. In the case study, ethical approval was obtained, and patient data were anonymized to ensure privacy. All TB hospitalized patients over study period were included and classified into groups with and without HAIs after screening the inclusion and exclusion criteria. Key risk factors, including gender, age, and invasive procedures were identified through univariate and multivariate analyses. Then, propensity score matching was employed to select the balanced groups with similar characteristics. Comparisons of medical expenditures (total medical expenditure, medicine expenditure, and antibiotic expenditure) and hospitalization days between the balanced groups were calculated as the additional direct economic disease burden measures caused by HAIs. This framework can serve as a tool for hospital management and policy-making, enabling the implementation of targeted infection prevention and control measures. It has the potential to be applied in various healthcare settings at local, regional, national, and international levels to identify high-risk areas, optimize resource allocation, and improve internal and external hospital management, as well as inter-organizational learning. Challenges to implement the framework are also raised, such as data quality, regulatory compliance, considerations on unique nature of communicable diseases and other diseases, and training need for professionals.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: case study of a Chinese Tuberculosis hospital\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-07-10 01:54:07\",\"doi\":\"10.21203/rs.3.rs-4524748/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Major revision\",\"date\":\"2024-07-11T05:14:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2024-06-12T08:44:24+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-06-12T08:08:16+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-06-12T05:20:12+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Global Health Research and Policy\",\"date\":\"2024-06-03T22:55:32+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"global-health-research-and-policy\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ghrp\",\"sideBox\":\"Learn more about [Global Health Research and Policy](http://ghrp.biomedcentral.com)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/GHRP/default.aspx\",\"title\":\"Global Health Research and Policy\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0e57e037-bcea-4aa4-be09-7b41b760766d\",\"owner\":[],\"postedDate\":\"July 10th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-09-16T16:04:32+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4524748\",\"link\":\"https://doi.org/10.1186/s41256-024-00375-w\",\"journal\":{\"identity\":\"global-health-research-and-policy\",\"isVorOnly\":false,\"title\":\"Global Health Research and Policy\"},\"publishedOn\":\"2024-09-09 15:58:03\",\"publishedOnDateReadable\":\"September 9th, 2024\"},\"versionCreatedAt\":\"2024-07-10 01:54:07\",\"video\":\"\",\"vorDoi\":\"10.1186/s41256-024-00375-w\",\"vorDoiUrl\":\"https://doi.org/10.1186/s41256-024-00375-w\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4524748\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4524748\",\"identity\":\"rs-4524748\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}