ABSTRACT
Background and Aim
This study aims to compare the mortality rate, length of stay (LOS), and hospitalization cost in patients with and without healthcare‐associated infections (HCAIs).
Methods
This prospective cohort study is conducted on 396 matched patients admitted to a tertiary teaching hospital with 800 beds in the southwest of Iran from July 2021 to January 2022. The cases were patients admitted during the study period who had, at minimum, one type of HCAI. For a comparison group, we considered all patients without HCAIs who hospitalized in the same ward and at the same time with the same age and gender in a ratio of one case to three controls. Descriptive analyses were done based on direct medical costs, LOS, and mortality rate in patients. The magnitude of the relationship between potential risk factors and HCAI was quantified using logistic regression.
Results
The most common HCAI and microorganisms were urinary tract infection (UTI) and Escherichia coli, respectively. The mean LOS for infected patients was 20.3 (±16) days, compared to 8.7 (±8.6) days for noninfected patients (p value ≤ 0.05). Lengths of stay (odds ratio [OR] = 1.09; 95% CI = 1.06–1.19; p value = 0.000), ICU lengths of stay (OR = 1.08; 95% CI = 1.02–1.15; p value = 0.003), presence of central Catheter (OR = 0.127; 95% CI = 0.51–0.319; p value = 0.000), and urinary catheter (OR = 0.225; 95% CI = 0.122–0.415; p value = 0.000), mechanical ventilation (OR = 0.136; 95% CI = 0.57–0.325; p value = 0.000), receipt of immunosuppressors (OR = 1.99; 95% CI = 11.12–3.56; p value = 0.01), were Significantly associated with HCAI. Patients with infections had a more costly hospital stay than noninfected patients (mean diff: $2037.46 ([SD]: 482.25$) (p value = 0.000). The highest cost component was the cost of medication expenditure (mean: $1612.66 ([SD]: $2542.27).
Conclusions
UTI was the most common HCAI in our study. An infection acquired during a hospital stay may be associated with higher hospitalization costs, prolonged hospitalization, and an increase in the rate of mortality. Longer lengths of stay, presence of central and urinary Catheters, receipt of immunosuppressors, use of mechanical ventilator were common risk factors for HCAI. This study reveals that the median reimbursement cost per hospitalization of patients with HCAIs was higher than patients without HCAIs. This highlights the necessity for implementation of HCAI prevention and control measures.
Keywords: case‐control study, costs, healthcare‐associated infections, length of stay, mortality
1. Introduction
Healthcare‐associated infection (HCAI) is an infection acquired during hospitalization in a patient that was not present or incubating before admission. According to the World Health Organization (WHO) report, “healthcare‐associated infections (HCAIs) represent the most common adverse event among hospitalized patients” [1]. The incidence of HCAIs ranged from 3.6% to 12% in high‐income countries and 10.1%–15.5% in low‐ and middle‐income countries [1]. The prevalence of HCAIs in Iran was 4.5% % in 2017 [2]. However, according to the WHO report, due to the limited and low‐quality data especially in low‐ and middle‐income countries, the burden of nosocomial infections is underestimated [1]. The Centers for Disease Control and Prevention (CDC) estimates that HCAIs occur in one out of every 25 acute care hospitalizations [3].
However, the rates and types of HCAIs differ across various hospitals and wards. Studies have confirmed that the risk of acquiring HCAIs is significantly higher in intensive care units (ICUs) [4, 5] with approximately 30% of patients affected by at least one episode of HCAIs with substantial associated morbidity and mortality [4].
HCAI can bring a severe burden to patients and healthcare systems. In addition to increasing morbidity and mortality, it can prolong hospitalization stays and raise hospitalization costs [6, 7]. Stewart et al. [8] found that the excess length of stay (LOS) attributable to HCAI was 7.8 days. They estimated a reduction of 10% in HCAI incidence could make 5800 bed‐days available to treat 1706 elective patients. Stewardson et al., estimated that the average extra LOS attributable to various types of HCAIs was 12.22 days for acute care admissions at 10 European hospitals [9]. For different infection sites, the extra LOS was reported to exceed 27 days [10]. Another study found that the mean LOS attributable to Central Line–Associated Blood Stream Infections (CLABSIs) was 19 days [11]. The extra LOS was 3.48 days for blood stream infection (BSI), 3.59 days for urinary tract infections (UTIs), 7.23 days for surgical site infections (SSIs), and 11.52 days for ventilator‐associated pneumonia (VAP) in the medical‐surgical ICUs [6].
HCAIs contribute to increase patient mortality rate. According to the US Center for Disease Control and Prevention (CDC), 1.7 million hospitalized patients acquire HCAIs per year while being treated for other health‐related problems; one out of these seventeen infected patients dies as a result of HCAIs [12]. In Spain, the mortality rate among patients with SSIs after surgery was about 2.4 times higher than among those who did not develop an SSI [13]. Similarly, in France, the mortality rate was significantly higher in infected patients relative to controls (8.3% vs. 2.0%, respectively) [14]. Although comprehensive data are limited, available evidence indicates that the burden of HCAIs may be even more substantial in low‐ and middle‐income countries. For example, Yepez et al. reported mortality rates in ICUs of 30.9% for CLABSIs, 14.5% for VAP, and 17.6% for catheter‐associated urinary tract infections (CAUTIs) [15]. Likewise, Kumar et al. found that over one‐third (39.8%) of patients who acquired nosocomial infections died, compared to a mortality rate of 5.0% in patients without such infections [16].
HCAIs also have a substantial impact on financial burden on healthcare systems. Baier et al. reported that the additional hospital costs directly associated with CLABSIs amount to €8810 per case in Germany [17]. In China, a study estimated that the excess costs attributable to HCAIs reaches $6127.65 annually [18]. Similarly, Arefian et al. found that HCAIs result in considerable additional costs, ranging from $7453 to $15,155 per patient [19]. In Iran, Soleymani et al. demonstrated that the hospitalization costs for patients with HCAIs (mean: $3264 ± 6078) were higher compared to controls ($2451 ± 3098) [20].
Few studies focused on a comprehensive assessment of the HCAI burden. Many studies concentrate solely on one type of infection like BSIs [21, 22, 23], often omitting other highly prevalent HCAIs, such as UTIs [24]. Our literature search revealed a knowledge gap on the burden, the mortality rate, LOS, and cost attributable to the HCAI in low‐ and middle‐income countries in a comprehensive assessment. Therefore, this study was conducted as a nested case‐control study to estimate the mortality rate, LOS, and hospitalization cost for patients with and without HCAIs in a large academic hospital.
2. Methods
2.1. Study Design
The study is a prospective nested case‐control investigation conducted on patients admitted to a tertiary teaching hospital with 800 beds in the southwest of Iran from July 2021 to January 2022.
2.2. Case‐ and Control Patients
A total of 396 patients were enrolled in the study. Of these, 100 cases (with HCAIs) and 296 controls (without HCAIs) were recruited into the study (ratio 1:3). Sampling was done a non‐probability method; from the beginning of the study, all patients who met the inclusion criteria were selected as the sample, and this process continued throughout the study period.
In our study, the cases were patients admitted during the study period who had at least one type of HCAI and a length of hospitalization greater than 2 days. One hundred infected cases were recorded across different wards during the study period. For the comparison group, we selected all patients hospitalized in the same ward and at the same time, matching them by age (±5 years) and gender, with a ratio of one case to three controls. Thus, a total of 296 patients were enrolled as the control group. Our inclusion criteria were hospitalization at the facility, any sex, and age 18 years or older.
2.3. Ethical Consideration
Written informed consent was obtained from each participant. In addition, ethics approval was obtained from the Ethical Review Committee of Ahvaz Jundishapur University of Medical Sciences. This study was conducted under the principles of the Helsinki declaration (Ethics code: IR.AJUMS.REC.1400.193).
2.4. Data Collection
Demographic and clinical information related to patients in both groups included age, gender, diagnosis, types of antibiotics used, types and number of invasive medical procedures performed, LOS, and discharge status.
The research team developed a data collection tool to record variables for both cases and controls. Relevant information was extracted from patient records and the hospital information system (HIS).
Researchers contacted patients or their families using the phone numbers provided in the patient records to capture actual data on 30‐day mortality. If there was no response, they made up to three additional calls over 3 days to the indicated relative of hospitalized patients. Non‐responses were recorded as missing data.
Microorganism identification was conducted based on laboratory tests.
Additionally, several risk factors were considered based on previous studies, including ICU admission, immunosuppressive therapies, surgical procedures, exposure to invasive medical procedures, and mechanical ventilation.
The hospitalization cost in the study was based on the total cost of treatment from admission to discharge, as detailed in the patient's bill. The cost for each patient was obtained from the HIS. Cost elements were examined in detail, and comparisons were made by calculating total treatment costs for each patient. Hospitalization costs were categorized into seven groups: drug costs, expendable supplies, paraclinical services (e.g., laboratory tests and imaging), surgery charges, nursing care charges, physiotherapy, and other costs. All costs were calculated in US dollars in 2021.
2.5. Statistical Analysis
Data were entered into Microsoft Excel, cleaned, and then transferred to SPSS version 24. Values were expressed as means, standard deviations, and percentages. Our variables were age, gender, discharge status, types of invasive medical procedures, types of HCAI and microorganisms, LOS, ICU LOS, mortality rate, and hospitalization costs. Normality was tested using the Kolmogorov‐Smirnov test. Subsequently, patient characteristics in both groups were compared using nonparametric tests (Mann–Whitney U test for continuous variables and chi‐square test for categorical variables). The relationship between risk factors and HCAIs was quantified using logistic regression. A p‐value of less than 0.05 was considered statistically significant.
3. Results
3.1. Characteristics of Cases and Controls
In this study, the case group consisted of 100 patients, while the control group comprised 296 patients, resulting in a total of 396 patients analyzed. The mean ages of patients in the case and control groups were 54.4 and 53.7 years, respectively. Additionally, 52% of the patients were female (see Table 1).
Table 1.
Characteristics of cases and controls.
| Variable | With HCAI (n = 100) | Without HCAI (n = 296) | p‐value* |
|---|---|---|---|
| Age (years), mean (SD) | 54.40 (14.99) | 53.79 (14.27) | 0.9 |
| LOS (days) mean (SD) | 20.3 (16) | 8.7 (8.6) | 0.000 |
| ICU LOS (days) mean (SD) | 21.25 (24.24) | 5.33 (4.43) | 0.007 |
| Discharge type | 0.000 | ||
| Discharge | 83 (83) | 282 (95.3) | |
| Dead | 17 (17) | 14 (4.7) | |
| Gender | 0.8 | ||
| Male | 48 (48) | 141 (48) | |
| Female | 52 (52) | 155 (52) |
The percentages of each type of HCAI, listed in descending order of incidence, were as follows: UTIs (51%), primary BSIs (20%), SSIs (19%), and lower respiratory tract infections (10%). The four most common types of infections were associated with the most prevalent pathogens. Escherichia coli, Klebsiella species, and Pneumonia were the most common pathogens, accounting for 33% and 30% of all HCAIs, respectively (see Table 2).
Table 2.
Costs, LOS, and mortality rates for each type of the HCAI.
| Type of infection | N (%) | Total cost ($), mean (SD) | LOS, mean (SD) | ICU LOS, mean (SD) | Mortality, N (%) |
|---|---|---|---|---|---|
| UTI | 51 (51) | 2088.77 (2061.31) | 15.6 (12.4) | 23.4 (27.50) | 4 (12.9) |
| VAP | 10 (10) | 3753.23 (1690) | 41.7 (15.1) | 38.16 (19.36) | 4 (45.5) |
| BSI | 20 (20) | 2323.96 (1959.75) | 21.9 (18.7) | 27.4 (26.14) | 4 (12.9) |
| SSI | 19 (19) | 3822.20 (2721.99) | 19.7 (13.5) | 23 (0.86) | 5 (16.1) |
3.2. Risk Factors for HCAI
Regarding risk factors, 17% of patients in the case group had immunodeficiency, 85% had a urinary catheter, 16% had a blood catheter, 17% had mechanical ventilation, and 24% had tracheal intubation. Logistic regression analysis revealed that lengths of stay (odds ratio [OR] = 1.09; 95% CI = 1.06–1.19; p = 0.000), ICU lengths of stay (OR = 1.08; 95% CI = 1.02–1.15; p = 0.003), presence of central catheters (OR = 0.127; 95% CI = 0.51–0.319; p = 0.000), urinary catheters (OR = 0.225; 95% CI = 0.122–0.415; p = 0.000), mechanical ventilation (OR = 0.136; 95% CI = 0.57–0.325; p = 0.000), and receipt of immunosuppressive therapy (OR = 1.99; 95% CI = 1.12–3.56; p = 0.010) were significantly associated with HCAI (see Table 3).
Table 3.
Risk factors of cases and controls.
| Variables | With HCAI (n = 100) | Without HCAI (n = 296) | OR (95% CI) | p‐value |
|---|---|---|---|---|
| LOS (days), mean (SD) | 20.3 (16) | 8.7 (8.6) | 1.09 (1.06–1.19) | 0.000 |
| ICU LOS (days), mean (SD) | 21.25 (24.24) | 5.33 (4.43) | 1.08 (1.02–1.15) | 0.003 |
| ICU admission | 20 (20) | 40 (13) | 0.625 (0.34–1.13) | 0.12 |
| Immunosuppression, N (%) | 17 (17) | 86 (29) | 1.99 (1.12–3.56) | 0.01 |
| Surgery, N (%) | 65 (65) | 186 (62) | .993 (0.621–1.58) | 0.97 |
| Urinary catheter, N (%) | 85 (85) | 171 (57) | 0.225 (0.122–0.415) | 0.000 |
| Ventilator, N (%) | 17 (17) | 8 (2.7) | 0.136 (0.57‐.325) | 0.000 |
| Tracheal intubation, N (%) | 24 (24) | 62 (20.9) | 0.828 (0.48–1.41) | 0.492 |
| Blood Catheter, N (%) | 16 (16) | 7 (2) | 0.127 (0.051–0.319) | 0.000 |
3.3. LOS
The mean LOS for infected patients was 20.3 (±16) days, compared to 8.7 (±8.6) days for noninfected patients. The mean ICU LOS for cases and controls was 21.25 (±24.24) days and 5.33 (±4.43) days, respectively. There was a significant difference in both total LOS and ICU LOS between the case and control groups (p ≤ 0.05). Thus, we conclude that both total LOS and ICU LOS were longer for cases than for controls (see Table 1). The longest LOS was observed in patients infected with VAP, with a mean of 38.16 (±19.36) days, and BSI, with a mean of 27.4 (±26.14) days (see Table 3). Additionally, patients infected with Pseudomonas and Klebsiella had the longest LOS, with a mean of 26.7 (±12.2) days (see Table S1).
3.4. Infection‐Attributable Mortality
We recorded 31 deaths (7.8%) in our study; 27 (87%) of these patients died in the hospital, and 4 (13%) died after discharge. Of the deaths, 17 (54%) were related to the case group. A significant difference in the mortality rate between the two groups was observed (OR = 0.242; 95% CI = 0.115–0.512; p = 0.000). Among the infected patients who died in the hospital, the most common infection type was SSI, accounting for five deaths (see Table 2).
3.5. Infection‐Attributable Cost
Patients with infections had a more costly hospital stay compared to noninfected patients, with a mean difference of $2037.30 (SD = $347.30; p = 0.000). The median reimbursement cost per hospitalization for patients with HCAIs was 2.12 times higher than for patients without HCAIs ($3850.80 for cases vs. $1813.33 for controls; p = 0.000). Medication expenditure was the highest cost for infected patients, with a mean of $1612.66 (SD = $2,542.27), which was on average twice as high as for noninfected patients (p = 0.000). The second highest cost component was medical equipment, with a mean of $564.08 (SD = $809) in infected patients, which was on average 1.5 times higher than in noninfected patients (p = 0.000). Antibiotics represented a substantial cost, with a mean of $319.28 (SD = $513.49), which was 4.08 times higher in infected patients compared to noninfected patients. Overall, all costs were significantly higher in the case group compared to the control group (p = 0.000), except for the cost of surgery, where no significant difference was observed (p = 0.547) (see Table 4).
Table 4.
Infection‐attributable medical cost ($).
| Variables | With HCAI (n = 100) | Without HCAI (n = 296) | Mean diff (SE) | p‐value |
|---|---|---|---|---|
| Cost of antibiotics, mean (SD) | 319.28 (513.50) | 78.49 (210.69) | 240.79 (36.48) | 0.000 |
| Cost of nursing care, mean (SD) | 112.35 (159.23) | 39.60 (45.74) | 72.7 (10.30) | 0.000 |
| Cost of testing and pathology, mean (SD) | 364.08 (450.22) | 141.35 (208.44) | 222.73 (33.41) | 0.000 |
| Cost of imaging, mean (SD) | 171.90 (309.34) | 84.69 (104.96) | 87.21 (20.78) | 0.000 |
| Cost of surgery, mean (SD) | 524.26 (711.18) | 464.33 (651.89) | 59.92 (77.18) | 0.547 |
| Cost of physiotherapy, mean (SD) | 25.84 (71.48) | 1.99 (10.66) | 23.85 (4.28) | 0.000 |
| Cost of drugs, mean (SD) | 1612 (2542.27) | 565.70 (1185.55) | 1046.97 (189.22) | 0.000 |
| Cost of medical equipment, mean (SD) | 564.09 (809) | 374.53 (701.79) | 189.56 (84.45) | 0.000 |
| Other costs, mean (SD) | 156.33 (331.17) | 62.67 (146.10) | 93.66 (141.11) | 0.000 |
| Total cost, mean (SD) | 3850.80 (4652.20) | 1813.33 (2185.97) | 2037.30 (347.30) | 0.000 |
4. Discussion
We designed this prospective study to assess the impacts of HCAIs on LOS, mortality, and hospitalization costs in both infected and uninfected patients at an educational hospital in Ahvaz, Iran. Our results demonstrated that HCAIs were associated with increased in‐hospital mortality, longer LOS, and higher hospitalization costs.
Based on our results, the most common HCAI was UTI, followed by BSI and SSI, which is consistent with findings from other studies [25, 26]. It has been shown that up to 80% of HCAIs are associated with indwelling medical devices [27]. Additionally, the main microorganisms identified in our study that led to HCAIs were E. coli and Klebsiella. Elliott's review also noted that common pathogens responsible for these infections include E. coli, Klebsiella, and Pseudomonas [25].
We observed an 11‐day difference in LOS between patients with HCAIs and those without. Additionally, ICU stay was nearly four times longer for patients with HCAIs compared to those without. Previous studies have confirmed that HCAIs are associated with increased hospital stay. For instance, Lü et al. [28] reported that HCAIs increased the LOS by an average of 5 days. Similarly, Stewart et al. [8] showed that patients with HCAI had a significantly longer hospital stay compared to those without HCAIs, with an excess LOS attributable to HCAIs of 7.8 days. Ak et al., [29] reported the median LOS in the ICU was more than four times longer for infected patients compared to uninfected patients. Additionally, Dasgupta et al. [30] showed that the median ICU stay for patients with HCAI was nearly 11 days longer than for those without HCAIs. In other words, some studies have shown that a longer hospital stay is a risk factor for HCAIs. Extended hospitalization increases the risk of infection due to prolonged exposure to pathogenic microorganisms, which can result from extended durations of invasive procedures and pharmacotherapy [31, 32, 33].
The mortality rate was 17% in patients with HCAIs, and these patients faced nearly a fivefold increased risk of death compared to those without HCAIs. Several studies have confirmed the impact of HCAIs on the rate of mortality [34, 35]. Mortality rate of patients with HCAIs have been reported to range from 17.1% to 48% [36]. The impact of HCAI on patient mortality rates is controversial. Several factors, including patient demographics, co‐morbidities, the severity of illness, and antibiotic resistance, significantly influence patient outcomes [37, 38]. Prevalence rates of infections acquired in ICUs vary from 9% to 37% [39]. Even in advanced hospitals in developed countries with better standards of care, HCAIs can affect up to 25% of ICU patients [40].
The cost per case in our study is comparable to previous studies conducted in Iran [20] but is significantly lower than in high‐income settings. The differences in cost may be attributed to lower overall costs in Iran's public hospitals. Notably, human resources and hospitalization costs are substantially lower in the public sector compared to the private sector.
The results indicate that 8% of the total cost is attributed to antimicrobial expenses. The average cost of medical procedures was higher for patients with HCAIs compared to noninfected patients. Other studies have similarly reported increased treatment costs for patients with HCAI [41, 42]. Specifically, the costs of antibiotics, medications, and laboratory tests were higher for patients with HCAIs compared to those without infections. The results are consistent with other studies [43]. The most significant cost difference between cases and controls was for pharmaceuticals. VAP was the most costly type of HCAI in the infected group. Manookian et al. [41] also found that pneumonia increased hospitalization costs more than other types of HCAIs. The total medical cost in the case group was higher than the total medical cost in the control group. All costs, except for those related to surgery, showed significant differences between the case and control groups. Our cost analysis revealed a significant association between LOS and HCAIs.
Other studies have found results similar to ours regarding risk factors for HCAIs. Previous research has indicated that the presence of invasive devices [35], use urinary catheters [44], central vascular catheters [45], and mechanical ventilation [46] are significant risk factors for HCAIs. Our study confirmed that the use of ventilators, presence of central stream and urinary catheters, longer LOS, ICU LOS, receipt of receipt of immunosuppressive therapy were associated with higher odds for HCAIs. Additionally, hospital‐acquired infections [47, 48], the use of mechanical ventilation [47], prolonged hospitalization [47], and increased ICU LOS [49] are associated with a higher mortality rate.
4.1. Limitations
This study has some limitations. First, we focused on a relatively small number of risk factors for hospital‐acquired infections. Second, the costs of hospitalization were assessed using the patient's bill that did not account for indirect costs. Third, it was not possible in this study to investigate illness severity as a potential predictor of death. Finally, the COVID‐19 epidemic led to the expansion of certain hospital departments or their conversion into inpatient units for COVID‐19 patients, which resulted in limitations in sampling and matching.
5. Conclusion
UTI was the most common HCAI in our study. An infection acquired during a hospital stay may be associated with higher hospitalization costs, prolonged hospitalization, and an increase in the rate of mortality. Longer lengths of stay, presence of central and urinary Catheters, receipt of immunosuppressors, use of mechanical ventilator were common risk factors for HCAI. This study reveals that the median reimbursement cost per hospitalization of patients with HCAIs was higher than patients without HCAIs. This highlights the necessity for implementation of HCAI prevention and control measures.
6. Policy Implications
The confirmation of higher healthcare costs, prolonged hospital stays, and increased mortality rates associated with nosocomial infections underscores the need for targeted policy interventions. These findings suggest that allocating resources toward infection prevention campaigns and enhanced hospital hygiene protocols could reduce the economic and human burden of hospital‐acquired infections. Investments in staff training, improved sanitation practices, and infection monitoring systems would not only reduce costs and patient stay durations but also significantly improve patient outcomes, thereby reducing overall healthcare system strain.
Author Contributions
Conceptualized and designed the study and prepared the first draft of the manuscript: Zhila Najafpour. Data collection, helped to prepare the initial draft: Samaneh Moradi. Helped to revise the initial draft: Iman Keliddar and Razieh Mombeyni. Analyzed and interpreted data and helped revise the initial draft: Bahman Cheraghian. All authors read and approved the final manuscript.
Ethics Statement
Written informed consent was obtained from each participant. In addition, ethics approval was obtained from the Ethical Review Committee of Ahvaz Jundishapur University of Medical Sciences. This study was conducted under the principles of the Helsinki Declaration (Ethics code: IR.AJUMS.REC.1400.193).
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Transparency Statement
The lead author Zhila Najafpour affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Supporting information
Supporting information.
Acknowledgments
The authors would like to thank the participants. In addition, the authors would also like to thank the staff of Golestan hospital affiliated of the Ahvaz Jundishapur University of Medical Sciences for their help. Ahvaz Jundishapur University of Medical Sciences financially supported this research. However, the funder had no role in the study's design, data collection, analysis, or interpretation of data, the decision to publish, or the preparation of the manuscript.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
References
- 1. World Health Organization , Report on the Burden of Endemic Health Care‐Associated Infection Worldwide (Geneva, Switzerland: WHO Document Production Services, 2011).
- 2. Ghashghaee A., Behzadifar M., Azari S., et al., “Prevalence of Nosocomial Infections in Iran: A Systematic Review and Meta‐Analysis,” Medical Journal of the Islamic Republic of Iran 32 (2018): 273–282. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Magill S. S., Edwards J. R., Bamberg W., et al., “Multistate Point‐Prevalence Survey of Health Care–Associated Infections,” New England Journal of Medicine 370, no. 13 (2014): 1198–1208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Babbar P., Biswal M., Digamber B., and Gupta A., “Healthcare Associated Infections in Intensive Care Units: A Pilot Study in a Tertiary Care Public Hospital in India,” Journal of Prevention and Infection Control 5, no. 1 (2019): 1. [Google Scholar]
- 5. Falahi J., Khaledi A., Alikhani M. Y., et al., “Prevalence of Nosocomial Infection in Different Wards of Ghaem Hospital, Mashhad,” Avicenna Journal of Clinical Microbiology and Infection 4, no. 2 (2017): 40297. [Google Scholar]
- 6. Karkhane M., Pourhosiengholi M. A., Akbariyan Torkabad M. R., et al., “Annual Antibiotic Related Economic Burden of Healthcare Associated Infections; A Cross‐Sectional Population Based Study,” Iranian Journal of Pharmaceutical Research: IJPR 15, no. 2 (2016): 605–610. [PMC free article] [PubMed] [Google Scholar]
- 7. Ohannessian R., Gustin M.‐P., Bénet T., et al., “Estimation of Extra Length of Stay Attributable to Hospital‐Acquired Infections in Adult ICUs Using a Time‐Dependent Multistate Model,” Critical Care Medicine 46, no. 7 (2018): 1093–1098. [DOI] [PubMed] [Google Scholar]
- 8. Stewart S., Robertson C., Pan J., et al., “Impact of Healthcare‐Associated Infection on Length of Stay,” Journal of Hospital Infection 114 (2021): 23–31. [DOI] [PubMed] [Google Scholar]
- 9. Stewardson A. J., Allignol A., Beyersmann J., et al., “The Health and Economic Burden of Bloodstream Infections Caused by Antimicrobial‐Susceptible and Non‐Susceptible Enterobacteriaceae and Staphylococcus aureus in European Hospitals, 2010 and 2011: A Multicentre Retrospective Cohort Study,” Eurosurveillance 21, no. 33 (2016): 30319. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Al‐Mousa H. H., Omar A. A., Rosenthal V. D., et al., “Device‐Associated Infection Rates, Bacterial Resistance, Length of Stay, and Mortality in Kuwait: International Nosocomial Infection Consortium Findings,” American Journal of Infection Control 44, no. 4 (2016): 444–449. [DOI] [PubMed] [Google Scholar]
- 11. Goudie A., Dynan L., Brady P. W., and Rettiganti M., “Attributable Cost and Length of Stay for Central Line–Associated Bloodstream Infections,” Pediatrics 133, no. 6 (2014): e1525–e1532. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Klevens R. M., Edwards J. R., C. L. Richards, Jr. , et al., “Estimating Health Care‐Associated Infections and Deaths in US Hospitals, 2002,” Public Health Reports® 122, no. 2 (2007): 160–166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Gili‐Ortiz E., González‐Guerrero R., Béjar‐Prado L., López‐Méndez J., and Ramírez‐Ramírez G., “Surgical Site Infections in Patients Who Undergo Radical Cystectomy: Excess Mortality, Stay Prolongation and Hospital Cost Overruns,” Actas Urológicas Españolas (English Edition) 39, no. 4 (2015): 210–216. [DOI] [PubMed] [Google Scholar]
- 14. Lamarsalle L., Hunt B., Schauf M., Szwarcensztein K., and Valentine W. J., “Evaluating the Clinical and Economic Burden of Healthcare‐Associated Infections During Hospitalization for Surgery in France,” Epidemiology and Infection 141, no. 12 (2013): 2473–2482. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Salgado Yepez E., Bovera M. M., Rosenthal V. D., et al., “Device‐Associated Infection Rates, Mortality, Length of Stay and Bacterial Resistance in Intensive Care Units in Ecuador: International Nosocomial Infection Control Consortium's Findings,” World Journal of Biological Chemistry 8, no. 1 (2017): 95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Sahu M., Siddharth B., Choudhury A., et al., “Incidence, Microbiological Profile of Nosocomial Infections, and Their Antibiotic Resistance Patterns in a High Volume Cardiac Surgical Intensive Care Unit,” Annals of Cardiac Anaesthesia 19, no. 2 (2016): 281–287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Baier C., Linke L., Eder M., et al., “Incidence, Risk Factors and Healthcare Costs of Central Line‐Associated Nosocomial Bloodstream Infections in Hematologic and Oncologic Patients,” PLoS One 15, no. 1 (2020): e0227772. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Jia H., Li W., Hou T., et al., “The Attributable Direct Medical Cost of Healthcare Associated Infection Caused by Multidrug Resistance Organisms in 68 Hospitals of China,” BioMed Research International 2019 (2019): 1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Arefian H., Hagel S., Heublein S., et al., “Extra Length of Stay and Costs Because of Health Care–Associated Infections at a German University Hospital,” American Journal of Infection Control 44, no. 2 (2016): 160–166. [DOI] [PubMed] [Google Scholar]
- 20. Soleymani F., Safari palangi H., Sarabi Asiabar A., et al., “Costs of Hospital‐Acquired Infection for Patients Hospitalized in Intensive Care Unit of an Iranian Referral Hospital,” Medical Journal of the Islamic Republic of Iran 32 (2018): 388–393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Stewardson A., Allignol A., Beyersmann J., Graves N., Schumacher M., and Meyer R., “The Health and Economic Burden of Bloodstream Infections Caused by Antimicrobial‐Susceptible and Non‐Susceptiblend 2011: A Multicentre Retrospective Cohort Study,” Eurosurveillance 21 (2011): 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Stewardson A., Fankhauser C., De Angelis G., et al., “Burden of Bloodstream Infection Caused by Extended‐Spectrum β‐Lactamase–Producing Enterobacteriaceae Determined Using Multistate Modeling at a Swiss University Hospital and a Nationwide Predictive Model,” Infection Control and Hospital Epidemiology 34, no. 2 (2013): 133–143. [DOI] [PubMed] [Google Scholar]
- 23. Cassini A., Högberg L. D., Plachouras D., et al., “Attributable Deaths and Disability‐Adjusted Life‐Years Caused by Infections With Antibiotic‐Resistant Bacteria in the EU and the European Economic Area in 2015: A Population‐Level Modelling Analysis,” Lancet Infectious Diseases 19, no. 1 (2019): 56–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Mitchell B. G., Ferguson J. K., Anderson M., Sear J., and Barnett A., “Length of Stay and Mortality Associated With Healthcare‐Associated Urinary Tract Infections: A Multi‐State Model,” Journal of Hospital Infection 93, no. 1 (2016): 92–99. [DOI] [PubMed] [Google Scholar]
- 25. Elliott C. and Justiz‐Vaillant A., “Nosocomial Infections: A 360‐Degree Review,” International Biological and Biomedical Journal 4, no. 2 (2018): 72–81. [Google Scholar]
- 26. Haque M., Sartelli M., McKimm J., and Bakar M. A., “Health Care‐Associated Infections—An Overview,” Infection and Drug Resistance 11 (2018): 2321–2333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Guiton P. S., Hung C. S., Hancock L. E., Caparon M. G., and Hultgren S. J., “Enterococcal Biofilm Formation and Virulence in an Optimized Murine Model of Foreign Body‐Associated Urinary Tract Infections,” Infection and Immunity 78, no. 10 (2010): 4166–4175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Lü Y., Cai M. H., Cheng J., et al., “A Multi‐Center Nested Case‐Control Study on Hospitalization Costs and Length of Stay Due to Healthcare‐Associated Infection,” Antimicrobial Resistance & Infection Control 7, no. 1 (2018): 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Ak O., Batırel A., Ozer S., and Colakoglu S., “Nosocomial Infections and Risk Factors in the Intensive Care Unit of a Teaching and Research Hospital: A Prospecive Cohort Study,” Medical Science Monitor 17, no. 5 (2011): PH29–PH34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Hazra A., Dasgupta S., Das S., and Chawan N. S., “Nosocomial Infections in the Intensive Care Unit: Incidence, Risk Factors, Outcome and Associated Pathogens in a Public Tertiary Teaching Hospital of Eastern India,” Indian Journal of Critical Care Medicine 19, no. 1 (2015): 14–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Jeon C. Y., Neidell M., Jia H., Sinisi M., and Larson E., “On the Role of Length of Stay in Healthcare‐Associated Bloodstream Infection,” Infection Control and Hospital Epidemiology 33, no. 12 (2012): 1213–1218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Al‐Rawajfah O. M., Cheema J., Hewitt J. B., Hweidi I. M., and Musallam E., “Laboratory‐Confirmed, Health Care‐Associated Bloodstream Infections in Jordan: A Matched Cost and Length of Stay Study,” American Journal of Infection Control 41, no. 7 (2013): 607–611. [DOI] [PubMed] [Google Scholar]
- 33. Gravel D., Taylor G., Ofner M., et al., “Point Prevalence Survey for Healthcare‐Associated Infections Within Canadian Adult Acute‐Care Hospitals,” Journal of Hospital Infection 66, no. 3 (2007): 243–248. [DOI] [PubMed] [Google Scholar]
- 34. Massart N., Mansour A., Ross J. T., et al, “Mortality Due to Hospital‐Acquired Infection After Cardiac Surgery,” Journal of Thoracic and Cardiovascular Surgery 163, no. 6 (2022): 2131–2140. [DOI] [PubMed] [Google Scholar]
- 35. Nuckchady D. C., “Incidence, Risk Factors, and Mortality From Hospital‐Acquired Infections at a Hospital in Mauritius,” Cureus 13, no. 11 (November 2021): e19962, 10.7759/cureus.19962. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Rosenthal V. D., Duszynska W., Ider B.‐E., et al., “International Nosocomial Infection Control Consortium (INICC) Report, Data Summary of 45 Countries for 2013‐2018, Adult and Pediatric Units, Device‐Associated Module,” American Journal of Infection Control 49, no. 10 (2021): 1267–1274. [DOI] [PubMed] [Google Scholar]
- 37. Peters L., Olson L., Khu D. T. K., et al., “Multiple Antibiotic Resistance as a Risk Factor for Mortality and Prolonged Hospital Stay: A Cohort Study Among Neonatal Intensive Care Patients With Hospital‐Acquired Infections Caused by Gram‐Negative Bacteria in Vietnam,” PLoS One 14, no. 5 (2019): e0215666. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Baviskar A., Khatib K., Rajpal D., and Dongare H., “Nosocomial Infections in Surgical Intensive Care Unit: A Retrospective Single‐Center Study,” International Journal of Critical Illness and Injury Science 9, no. 1 (2019): 16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Trubiano J. A. and Padiglione A. A., “Nosocomial Infections in the Intensive Care Unit,” Anaesthesia & Intensive Care Medicine 16, no. 12 (2015): 598–602. [Google Scholar]
- 40. Van der Kooi T. I. I., Manniën J., Wille J. C., and Van Benthem B. H. B., “Prevalence of Nosocomial Infections in The Netherlands, 2007–2008: Results of the First Four National Studies,” Journal of Hospital Infection 75, no. 3 (2010): 168–172. [DOI] [PubMed] [Google Scholar]
- 41. Manoukian S., Stewart S., Graves N., et al., “Bed‐Days and Costs Associated With the Inpatient Burden of Healthcare‐Associated Infection in the UK,” Journal of Hospital Infection 114 (2021): 43–50. [DOI] [PubMed] [Google Scholar]
- 42. Leal M. and Freitas‐Vilela A., “Costs of Healthcare‐Associated Infections in an Intensive Care Unit,” Revista Brasileira de Enfermagem 74, no. 1 (2021): e20200275. [DOI] [PubMed] [Google Scholar]
- 43. Puchter L., Chaberny I. F., Schwab F., Vonberg R.‐P., Bange F.‐C., and Ebadi E., “Economic Burden of Nosocomial Infections Caused by Vancomycin‐Resistant Enterococci,” Antimicrobial Resistance and Infection Control 7, no. 1 (2018): 1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Despotovic A., Milosevic B., Milosevic I., et al., “Hospital‐Acquired Infections in the Adult Intensive Care Unit—Epidemiology, Antimicrobial Resistance Patterns, and Risk Factors for Acquisition and Mortality,” American Journal of Infection Control 48, no. 10 (2020): 1211–1215. [DOI] [PubMed] [Google Scholar]
- 45. Lima C. S. S. C., Rocha H. A. L., Araújo D. A. B. S., and Silva C., “Determinantes de infecção nosocomial tardia neonatal: estudo de caso‐controle no Ceará,” Revista de Saúde Pública 56 (2022): 40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Wang L., Zhou K.‐H., Chen W., Yu Y., and Feng S.‐F., “Epidemiology and Risk Factors for Nosocomial Infection in the Respiratory Intensive Care Unit of a Teaching Hospital in China: A Prospective Surveillance During 2013 and 2015,” BMC Infectious Diseases 19, no. 1 (2019): 145. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Ozdemir K. and Dizbay M., “Nosocomial Infection and Risk Factors in Elderly Patients in Intensive Care Units,” Journal of Microbiology and Infectious Diseases 5, no. 01 (2015): 38–43. [Google Scholar]
- 48. Alrahmany D., Omar A. F., Alreesi A., Harb G., and Ghazi I. M., “ Acinetobacter baumannii Infection‐Related Mortality in Hospitalized Patients: Risk Factors and Potential Targets for Clinical and Antimicrobial Stewardship Interventions,” Antibiotics 11, no. 8 (2022): 1086. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Otero M. L., Menezes R. C., Ferreira I. B. B., et al., “Factors Associated With Mortality in Critically Ill Patients Diagnosed With Hospital Acquired Infections,” Infection and Drug Resistance 13 (2020): 2811–2817. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting information.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.