Device-associated healthcare-acquired infections (DA-HAIs) are among the principal threats to patient safety in the intensive care unit (ICU) and one of the primary causes of patient morbidity and mortality.Reference Mehta, Rosenthal and Mehta 1 , Reference Mehta, Jaggi and Rosenthal 2 According to previous studies conducted in India,Reference Mehta, Rosenthal and Mehta 1 – Reference Singh, Pandya, Patel, Paliwal, Wilson and Trivedi 4 the pooled rates of DA-HAIs are higher than in high-income countries.Reference Dudeck, Edwards and Allen-Bridson 5
The implementation of infection control programs has proven effective for the reduction and control of DA-HAI surveillance, as shown in different studies conducted in the United States, whose results reported not only that the DA-HAI rates can be reduced by 30%, but that a related reduction in healthcare costs was feasible, as well.Reference Dudeck, Edwards and Allen-Bridson 5 Likewise, the burden of antimicrobial-resistant infections and susceptibility to antimicrobials of DA-HAI pathogens are an issue to be urgently addressed, so that informed decisions can be made to prevent transmission of resistant strains and their determinants, such as strains with phenotypes with very few available treatments with chances of success.Reference Sievert, Ricks and Edwards 6
For more than 40 years, the US Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) has provided benchmarking of US ICU data on DA-HAIs, which has been invaluable for researchersReference Dudeck, Edwards and Allen-Bridson 5 and has served as an inspiration to the International Nosocomial Infection Control Consortium (INICC).Reference Rosenthal, Maki and Mehta 7 The INICC is an international nonprofit, multicenter, open, collaborative healthcare-associated infection control program with a surveillance system based on that of the CDC/NHSN.Reference Dudeck, Edwards and Allen-Bridson 5 Established in Argentina in 1998, INICC is the first multinational surveillance and research network whose main goal is to measure, prevent, and reduce DA-HAIs and surgical site infections hospital-wide through the surveillance and analysis of data collected by a pool of hospitals worldwide on a voluntary basis.Reference Rosenthal, Maki and Mehta 7
India has a large private sector, and there are more healthcare providers in the private sector than in the public sector, but their services are mostly provided in urban areas. By contrast, the public sector provides health services to India’s rural population, which comprises over 800 million people who live in 640,867 villages, through a network of 23,887 primary health centers and 4,809 community health centers staffed by doctors, and 148,124 subcenters staffed by auxiliary nurse midwives.Reference Gautham, Shyamprasad, Singh, Zachariah and Bloom 8
The aim of the present study is to report a summary of surveillance data on DA-HAI collected in 84 ICUs in 40 hospitals from 20 cities of India participating in the INICC from March 2004 to February 2013.
METHODS
Background on INICC
INICC comprises more than 2,000 hospitals in 500 cities of 66 countries in Latin America, Asia, Africa, the Middle East, and Eastern Europe and is currently the only source of aggregate standardized data on the epidemiology of healthcare-associated infections (HAIs) worldwide.Reference Rosenthal, Maki and Mehta 7 The focus of the INICC surveillance and prevention is the DA-HAI program in adult and pediatric ICUs and neonatal ICUs (NICUs), step-down units, and inpatient wards, and surgical site infections in surgical procedures hospital-wide.
Setting and Study Design
This prospective cohort surveillance study was conducted in 84 adult or pediatric ICUs or NICUs from 40 hospitals in 20 cities of India, through the implementation of the INICC Multidimensional Approach, as described below.
Hospitals were stratified by bed capacity (<200; 201–500; 501–1,000; and >1,000). Corresponding denominator data, patient-days, and specific device-days were also collected.
All NICUs were level III and infection control professionals collected data on central line–associated bloodstream infections (CLABSIs) and umbilical catheter–associated primary bloodstream infections or cases of ventilator-associated pneumonia (VAPs) for each of 5 birthweight categories (<750 g; 750–1,000 g; 1,001–1,500 g; 1,501–2,500 g; >2,500 g).
Detailed and aggregated data were used to calculate DA-HAI rates per 1,000 device-days. Only prospective data using INICC patient detailed forms were used to calculate extra mortality and length of stay (LOS).
The infection control professionals had previous experience conducting surveillance of DA-HAIs.
In accordance with the INICC’s Charter, the identity of all INICC hospitals and cities is kept confidential.
INICC Multidimensional Approach
The INICC Multidimensional Approach includes the implementation of CDC/NHSN’s methodology but adds the collection of other data essential to helping infection control professionals to detect HAIs and avoid underreporting. 9 According to standard CDC/NHSN methods, numerators are the number of HAIs of each type, and denominators are device-days collected from all patients, as pooled data—that is, without determining the number of device-days related to a particular patient, and without collecting features or characteristics per specific patient. 9 This design differs from the INICC surveillance system because the design of the cohort study through the INICC methods also includes collecting specific data per patient from all patients, both those with and those without HAI, collecting risk factors of HAIs, such as invasive devices, and surrogates of HAIs, which include, but are not limited to, high temperature, low blood pressure, results of cultures, antibiotic therapy, LOS, and mortality. By collecting data on all patients in the ICU, it is possible to match patients with and without HAI by several characteristics to estimate extra LOS, mortality, and cost.
The INICC Multidimensional Approach comprises the simultaneous implementation of the following 6 components for HAI control and prevention: (1) a bundle of interventions, (2) education, (3) outcome surveillance, (4) process surveillance, (5) feedback on HAI rates and consequences, and (6) performance feedback.
Outcome and process surveillance are conducted by means of an online platform called INICC Surveillance Online System (ISOS). The ISOS comprises 15 modules: 10 for outcome surveillance and 5 for process surveillance. The modules of the outcome surveillance and process surveillance components may be used singly or simultaneously, but once selected, they must be used for a minimum of 1 calendar month.
In this study, we present the results of the cohort surveillance of HAIs in adult, pediatric, and neonatal ICUs. The results of the remaining outcome surveillance modules—(1) Clostridium difficile infections, (2) antimicrobial consumption, (3) surveillance of needle stick injuries, (4) cohort surveillance of HAIs in inpatient wards and step-down units, and (5) cohort surveillance of surgical procedures and surgical site infections—and of the modules for process surveillance, feedback on HAI rates and consequences, and performance feedback were not included in this report because they will be published in another future study.
Outcome Surveillance
Outcome surveillance included cohort surveillance of HAIs in adult, pediatric, and neonatal ICUs conducted through the ISOS, which allows the classification of prospective, active, cohort surveillance data into specific module protocols that apply US CDC/NHSN’s definitions published in 2013. 9 The site-specific criteria include reporting instructions and provide full explanations integral to their adequate application. 9
Data Collection and Analysis
The ISOS follows the INICC protocol, and infection control professionals collected daily data on CLABSIs, catheter-associated urinary tract infection (CAUTIs), and VAPs as well as denominator data, patient-days, and specific device-days in the ICUs.
These data were uploaded to the ISOS and were used to calculate DA-HAI rates per 1,000 device-days, mortality, and LOS, according to the following formulas: Device-days consisted of the total number of central line–days, urinary catheter–days, or mechanical ventilator–days. Crude excess mortality of DA-HAI equals crude mortality of ICU patients with DA-HAI minus crude mortality of patients without DA-HAI. Crude excess LOS of DA-HAI equals crude LOS of ICU patients with DA-HAI minus crude LOS of patients without DA-HAI. The device use ratio equals the total number of device-days divided by the total number of bed-days.
Training
The INICC chairman trained the principal and secondary investigators at hospitals. Investigators were also provided with tutorial movies, manuals, and training tools that described in detail how to perform surveillance and upload surveillance data through the ISOS. In addition, investigators attended webinars and had continuous access to a support team at the INICC headquarters in Buenos Aires, Argentina.
Statistical Analysis
INICC ISOS, version 2.0, was used to calculate HAI rates, device use, LOS, and mortality. EpiInfo, version 6.04b (CDC), and SPSS, version 16.0 (IBM), were also used. Relative risk ratios, 95% CIs, and P values were determined for primary and secondary outcomes.
RESULTS
The data presented in this report are from 84 ICUs in 40 hospitals in 20 cities in India currently participating in the INICC Program. The mean (SD) length of participation of hospitals is 24.9 (22.3) months, with a range of 2 to 85 months.
For the outcome surveillance component, DA-HAI rates, device use ratios, crude excess mortality by specific type of DA-HAI, microorganism profile, and bacterial resistance for March 2004 through February 2013 are summarized in Tables 1–6.
TABLE 1 Pooled Means of CLABSI, CAUTI Rates, and VAP Rates by Hospital Size, Adult and Pediatric Patients, Device-Associated Module, 2004-2013
NOTE. CAUTI, catheter-associated urinary tract infection; CL, central line; CLABSI, central line–associated bloodstream infection; ICU, intensive care units; MV, mechanical ventilator; UC, urinary catheter; VAP, ventilator-associated pneumonia.
TABLE 2 Pooled Means and Key Percentiles of the Distribution of CLABSI Rates, VAP Rates, CAUTI Rates; CL Use Ratio, MV Use Ratio, UC Use Ratio, by Type of Location, Adult and Pediatric Patients, Device-Associated Module, 2004–2013
NOTE. CAUTI, catheter-associated urinary tract infection; CL, central line; CLABSI, central line–associated bloodstream infection; DUR, device use ratio; HAI, healthcare-associated infection; ICU, intensive care unit; MV, mechanical ventilator; UC, urinary catheter; VAP, ventilator-associated pneumonia.
a HAI, n: CLABSI, VAP, CAUTI.
b HAI rates: central line–associated bloodstream infection per 1,000 CL-days; ventilator-associated pneumonia per 1,000 MV-days; catheter-associated urinary tract infection per 1,000 UC-days.
TABLE 3 Pooled Means of the Distribution of CLABSI Rates, VAP Rates, CL and MV Use Ratios, for Level III NICUs, Stratified by Birthweight Category, Device-Associated Module, 2004–2013
NOTE. CL, central line; CLABSI, central line–associated bloodstream infection; DUR, device use ratio; ICU, intensive care unit; MV, mechanical ventilator; NICU, neonatal ICU; VAP, ventilator-associated pneumonia.
TABLE 4 Pooled Means of the Distribution of Crude Mortality and Crude Excess Mortality of Adult and Pediatric ICU Patients With and Without HAI
NOTE. CAUTI, catheter-associated urinary tract infection; CLABSI, central line–associated bloodstream infection; HAI, healthcare-associated infection; ICU, intensive care unit; LOS, length of stay; RR, relative risk ratio; VAP, ventilator-associated pneumonia.
TABLE 5 Antimicrobial Resistance Rates in the Participating ICUs
NOTE. CLABSI, central line–associated bloodstream infection; CAUTI, catheter-associated urinary tract infection; ICU, intensive care unit; VAP, ventilator-associated pneumonia.
TABLE 6 Benchmarking of Device-Associated Healthcare-Acquired Infection Rates in This Report Against the Report of INICC (2007–2012) and Report of the US CDC/NHSN (2013)
NOTE. CAUTI, catheter-associated urinary tract infection; CDC/NHSN, Centers for Disease Control and Prevention National Healthcare Safety Network; CL, central line; CLABSI, central line–associated bloodstream infection; DUR, device use ratio; ICU, intensive care unit; INICC, International Nosocomial Infection Control Consortium; MV, mechanical ventilator; UC, urinary catheter; VAP, ventilator-associated pneumonia.
Table 1 shows DA-HAI rates and device-days by infection type (CLABSI, CAUTI, VAP) in adult and pediatric ICUs stratified by hospital size. Device-days consisted of the total number of central line–days, urinary catheter–days, or mechanical ventilator–days. The pooled mean of the CLABSI rates was 5.10 (95% CI, 4.9–5.3). The pooled mean of the VAP rates was 9.4 (95% CI, 9.0–9.8), and the pooled mean of the CAUTI rates was 2.1 (95% CI, 2.0–2.3).
Table 2 shows pooled means and key percentiles of the distribution of DA-HAI rates, and device use ratios stratified by type of ICU. The highest pooled CLABSI rate was found in the pediatric ICU (8.46). The highest pooled VAP rate was found in the neurologic ICU (13.7), and the highest pooled CAUTI rate was found in the neurosurgical ICU (4.20).
Table 3 shows pooled means of the distribution of CLABSI and VAP rates, and device use ratios in all the participating NICUs stratified by weight categories of neonatal patients. The highest pooled CLABSI rate was found in the less-than-750 g birthweight category (52.63) and the highest pooled VAP rate was found in the 751–1,000 g birthweight category (4.57).
Table 4 shows crude ICU mortality and LOS in adult, pediatric, and neonatal patients hospitalized in each type of unit during the surveillance period, with and without DA-HAI. VAP had the highest mortality both in adult and pediatric ICUs (29.6 % [95% CI, 26.2%–33.1%]) and in NICUs (10.0% [95% CI, 0.2%–44.5%]). In NICUs, VAP had the highest LOS, whereas in adult and pediatric ICUs the LOS was similar in all DA-HAIs.
Table 5 provides data on bacterial resistance of pathogens isolated from patients with DA-HAI in adult and pediatric ICUs and NICUs. Coagulase-negative staphylococci and Escherichia coli counts are high in CLABSI and VAP.
Table 6 compares the results of this report from India, with the INICC international report for the period 2007–2012Reference Rosenthal, Maki and Mehta 7 and with CDC/NHSN report of 2013.Reference Dudeck, Edwards and Allen-Bridson 5 In the medical/surgical ICUs, the rate of VAP was higher in this study than in CDC/NHSN report, although it was lower than INICC’s rates. The CLABSI and CAUTI rates in this study were higher than CDC/NHSN’s rates, but similar to INICC’s rates. Device use ratios for central line, mechanical ventilator, and urinary catheter were lower in this study than in the INICC report. Similarly, device use ratios for mechanical ventilator and urinary catheter were lower than the CDC/NHSN report, although central line device use ratio was slightly higher in our study.Reference Dudeck, Edwards and Allen-Bridson 5 , Reference Sievert, Ricks and Edwards 6
DISCUSSION
In our study, it was shown that the DA-HAI rates found in the participating Indian ICUs were higher than the rates reported by the CDC/NHSN.Reference Dudeck, Edwards and Allen-Bridson 5 Our CLABSI rate was similar to the pooled rate found in a previous study conducted in India showing 7.92 CLABSIs per 1,000 central line–days.Reference Mehta, Rosenthal and Mehta 1 Likewise, our CAUTI rate was similar to the findings of another study from ICUs in India showing 10.6 CAUTIs per 1,000 urinary catheter–days.Reference Jaggi and Sissodia 3 The VAP rate in our study was 10.4 per 1,000 mechanical ventilator–days in adult ICUs. In 2010, Singh et alReference Singh, Pandya, Patel, Paliwal, Wilson and Trivedi 4 found a rate of 21.92 VAPs per 1,000 mechanical ventilator–days, and Mehta et alReference Mehta, Jaggi and Rosenthal 2 found a global VAP rate of 17.43 VAPs per 1,000 mechanical ventilator–days in a multicenter study performed in 14 hospitals in 2012.
Comparing DA-HAI rates at medical cardiac and medical ICUs, we found that CLABSI and VAP rates were similar, but CAUTI rates were higher at the medical cardiac ICU, and this is most probably due to the usually increased use of antibiotics at medical ICUs.
Although pooled device use ratios in our adult ICUs were similar to, and even lower than in some cases, CDC/NHSN’s data, Reference Dudeck, Edwards and Allen-Bridson 5 DA-HAI rates were markedly higher in our ICUs. This shows that there are other risk factors that need to be addressed to explain these higher rates. Likewise, the antimicrobial resistance rates found in our ICUs were higher than CDC/NHSN rates for Staphylococcus aureus and coagulase-negative staphylococci isolates as resistant to oxacillin; Enterococcus faecalis as resistant to vancomycin; Klebsiella pneumoniae as resistant to ceftriaxone, ceftazidime, imipenem, and meropenem; Pseudomonas aeruginosa as resistant to piperacillin-tazobactam, amikacin, imipenem, or meropenem and ciprofloxacin; and E. coli as resistant to ceftriaxone, ceftazidime, imipenem, meropenem, and ertapenem.Reference Sievert, Ricks and Edwards 6 For most pathogens, percent resistance differed little among DA-HAI types.
These high DA-HAI rates may reflect the typical ICU situation in hospitals in India, and several reasons can explain this fact. In India, adherence to practice bundles is irregular, hospital accreditation is not mandatory, and some of the technology applied is different from that of high-income countries.Reference Rosenthal, Udwadia and Kumar 10 , Reference Maki, Rosenthal, Salomao, Franzetti and Rangel-Frausto 11 This situation is further emphasized by the fact that administrative and financial support in public hospitals is insufficient to fund full infection control programs, which invariably results in extremely low nurse-to-patient staffing ratios—which have proved to be highly connected to high DA-HAI rates in ICUs—and hospital overcrowding.Reference Rosenthal, Maki and Salomao 12
The particular and primary application of this study’s data is to serve as a guide for the implementation of prevention strategies and other quality improvement efforts locally for the reduction of DA-HAI rates to the minimum possible level. To reduce the hospitalized patients’ risk of infection, DA-HAI surveillance is primary and essential because it effectively describes and addresses the importance and characteristics of the threatening situation created by HAIs. This must be followed by the implementation of practices aimed at DA-HAI prevention and control and limitations on the administration of anti-infectives in order to effectively control antibiotic resistance.
The effectiveness of implementing an integrated infection control program focused on DA-HAI surveillance was demonstrated in the many studies conducted in the United States.Reference Dudeck, Edwards and Allen-Bridson 5 For almost 20 years, INICC has undertaken a global effort in America, Asia, Africa, Middle East, and Eastern Europe to respond to the burden of HAIs and has achieved very successful results by increasing hand hygiene adherence, improving compliance with other infection control bundles and interventions as described in several INICC publications, and consequently reducing the rates of HAI and mortality.Reference Rosenthal, Maki and Mehta 7 , Reference Rosenthal, Maki and Rodrigues 13 – Reference Rosenthal, Rodrigues and Alvarez-Moreno 17
The methods applied by the INICC are based on those of the CDC/NHSN, in terms of definitions and criteriaReference Rosenthal, Maki and Salomao 12 ; however, through the INICC Multidimensional Approach and ISOS, INICC collects additional extra data as well, including specific data per patient from all patients, both those with and those without HAI, collecting risk factors of HAIs, such as invasive devices, and surrogates of HAIs. This approach is useful to increase the ability of infection control professionals to detect HAIs, and allows the matching of patients by several characteristics to estimate extra LOS, mortality, and cost.
According to the World Bank, 68% of countries are classified into low-income and lower middle-income economies, representing more than 75% of the world population. India is defined by the World Bank as a lower middle-income economy. The relation between DA-HAI rates and the country socioeconomic level, and between DA-HAI rates and their association to the type of hospital (public, academic, or private), has been analyzed in only 2 studies in pediatric ICUs and NICUs and should be further studied in adult ICU patients. These studies found a negative correlation in most types of DA-HAI—that is, a higher country socioeconomic level was correlated with a lower infection risk.Reference Rosenthal, Jarvis and Jamulitrat 18 , Reference Rosenthal, Lynch and Jarvis 19 Participation in INICC has played a fundamental role, not only in increasing the awareness of DA-HAI risks in the ICU of limited-resource countries, but also in providing an exemplary basis for the institution of infection control practices.
To compare a hospital’s DA-HAI rates with the rates identified in this report, it is required that the hospital concerned start collecting its data by applying the methods and methodology described for CDC/NHSN and INICC, and then calculate infection rates and device use ratios for the DA-HAI module.
This study presents some limitations. First, we must rely upon the member hospitals’ laboratories to reliably identify infecting pathogens and delineate bacterial resistance patterns, and different laboratories have varying levels of expertise and resource availability; however, similar concerns can be raised about any multi-institutional surveillance study. Second, the frequency of culturing and the use of other diagnostic tests are beyond the control of infection control programs; in hospitals where culturing and other laboratory testing is infrequent and suspected infections are treated empirically, the capacity of the surveillance program to detect most DA-HAIs is likely to be low. Finally, for ward, trauma, neurological, and neurosurgical ICUs, we present data of fewer than 5 ICUs; therefore, for those 4 ICU types percentiles are not presented. However, because the number of patients in each of them is higher than 1,000, their data represent a sufficient sample size.
In conclusion, the high DA-HAI rates presented in this report confirm that HAIs in India pose a higher risk to patient safety compared with the developed world.Reference Dudeck, Edwards and Allen-Bridson 5 It is INICC’s main goal to enhance infection control practices, by facilitating basic and inexpensive tools and resources to tackle this problem effectively and systematically, leading to greater and stricter adherence to infection control programs and guidelines, and to the correlated reduction in DA-HAI and its adverse effects in the hospitals participating in INICC, as well as at any other healthcare facility worldwide.
Additional Coauthors
The remaining coauthors are Jehangir Sorabjee, Hazel Oliveira (Bombay Hospital, Mumbai); Sohini Arora, Asmita Kamble, Neelakshi Kumari, Angelina Mendonca (Ruby Hall Clinic, Pune); B. N. Gokul, R. Sukanya, Leema Pushparaj (Fortis Hospitals, Bangalore); Arpita Bhakta, Mahuya Bhattacharyya (Advanced Medicare Research Institute Hospitals, Kolkata); Amit Gupta (Pushpanjali Crosslay Hospital, Ghaziabad); Ashit Hegd, Farahad Kapadia, Anjali Shetty (PD Hinduja National Hospital & Medical Research Centre, Mumbai); Sathya Narayanan (Malabar Institute of Medical Sciences, Calicut); Jayant Shelgaonkar, Anita Thigale, Dipali S. Chavan (Aditya Birla Memorial Hospital, Pune); H. K. Sale, Dileep Mane, Amol Harshe, Pradnya Joshi (Noble Hospital, Pune); Aruna Poojary, Geeta Koppikar, Lata Bhandarkar, Shital Jadhav, Neeraj Chavan, Shweta Bahirune, Shilpa Durgad (Breach Candy Hospital Trust, Mumbai); J. V. Divatia, Rohini Kelkar, Sanjay Biswas, Sandhya Raut, Sulochana Sampat (Tata Memorial Hospital, Mumbai); Tanu Singhal, Reshma Naik, Vatsal Kothari (Kokilaben Dhirubhai Ambani Hospital, Mumbai); Suvin Shetty, Sheena Binu, Preethi Pinto (Dr. L. H. Hiranandani Hospital, Mumbai); Kavitha Radhakrishnan (Amrita Institute of Medical Sciences & Research Center, Kochi); Kandasamy Subramani (Christian Medical College, Vellore); Charulata Pamnan Harvinder Kaur Wasan, Sonali Khamkar, Leena Steephen (Jupiter Hospital, Thane); Subodh Kumar (JPNA Trauma Centre–All India Institute of Medical Sciences, New Delhi); Chirag Modi, Chirag Patel (Shree Krishna Hospital, Karamsad); Rathi Sankar (Kovai Medical Center and Hospital, Coimbatore); Madhavi Latha (Krishna Institute of Medical Sciences, Secundebarad); Pallavi Surase, Gita Nataraj, Palabi Bla (Seth GS Medical College, Mumbai); Lic. Romini Dawhale, Lic. Sheena Mary Jacobs (Jehangir Hospital, Pune); Soniya Thorat, Bannatti Gedigeppa (Rao Nursing Home, Pune); V. Venkateshwar, S. S. Dalal (Command Hospital Air Force, Bangalore); Karpagam Murali, Baby Padmini (G Kuppuswami Naidu Memorial Hospital, Coimbatore); M. B. Shah, Sis Felcy Chacko (Holy Spirit Hospital, Mumbai); Lic. Amy Samuel, Lic. K. Anusha Anand (Bombay Hospital Indore, Mumbai); Nallagonda Ravindra (Nizam’s Institute of Medical Sciences, Hyderabad); K. N. Prasad, Arvind Baronia, Mohan Gurjar (Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow); Neeru Verma (Military Hospital, Jodhpur).
ACKNOWLEDGMENTS
We thank the many healthcare professionals at each member hospital who assisted with the conduct of surveillance in their hospital; Mariano Vilar and Débora López Burgardt, who work at INICC headquarters in Buenos Aires; the INICC Country Directors and Secretaries (Haifaa Hassan Al-Mousa, Hail Alabdaley, Areej Alshehri, Altaf Ahmed, Carlos A. Álvarez-Moreno, Anucha Apisarnthanarak, Bijie Hu, Hakan Leblebicioglu, Yatin Mehta, Toshihiro Mitsuda, and Lul Raka); and the INICC Advisory Board (Carla J. Alvarado, Nicholas Graves, William R. Jarvis, Patricia Lynch, Dennis Maki, Toshihiro Mitsuda, Cat Murphy, Russell N. Olmsted, Didier Pittet, William Rutala, Syed Sattar, and Wing Hong Seto), who have so generously supported this unique international infection control network.
Financial support. Victor D. Rosenthal; Foundation to Fight against Nosocomial Infections.
Potential conflicts of interest. All authors report no conflicts of interest related to this article.
