Prevalence of antibiotic resistance in US hospitals.

Diagnostic Microbiology and Infectious Disease 78 (2014) 255–262

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Diagnostic Microbiology and Infectious Disease journal homepage: www.elsevier.com/locate/diagmicrobio

Antimicrobial Susceptibility Studies

Prevalence of antibiotic resistance in US hospitals☆ John Edelsberg a, Derek Weycker a, Rich Barron b, Xiaoyan Li b, Hongsheng Wu a, c, Gerry Oster a, Sejal Badre b, Wendy J. Langeberg b, David J. Weber d,⁎ a

Policy Analysis Inc. (PAI), Brookline, MA, USA Amgen Inc., Thousand Oaks, CA, USA Wentworth Institute of Technology, Department of Computer Science and Networking, Boston, MA, USA d University of North Carolina at Chapel Hill, Schools of Medicine and Public Health, Chapel Hill, NC, USA b c

a r t i c l e

i n f o

Article history: Received 25 February 2013 Received in revised form 20 September 2013 Accepted 1 November 2013 Available online 15 November 2013 Keywords: Drug resistance Antibiotics Drug resistance Bacterial

a b s t r a c t The percentage of isolates resistant to essential antibiotics among clinically significant bacterial pathogens was evaluated using data from 80 089 qualifying admissions in 19 US hospitals (2007–2010). Percentage resistant was highest for the following pathogen/antibiotic pairs: Enterococcus faecium/vancomycin (87.1% [95% CI 86.0–88.1] of 4024 isolates), Staphylococcus aureus/oxacillin–methicillin (56.8% [56.1–57.4] of 23 477 isolates), S. aureus/clindamycin (39.7% [39.1–40.4] of 21 133 isolates), Pseudomonas aeruginosa/ fluoroquinolones (32.6% [31.8–33.5] of 10 982 isolates), and Escherichia coli/fluoroquinolones (31.3% [30.8– 31.8] of 30 715 isolates). The percentage resistant was 3.9% (3.2–4.9) for E. faecium/daptomycin (n = 2029 isolates). While these results are consistent with those from earlier studies in many respects, the percentage of E. faecium isolates resistant to daptomycin, while still small, is higher than has been reported to date. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Antibiotic resistance has been recognized as a major problem for decades, and the associated adverse effects on morbidity, mortality, and economic costs (e.g., in terms of longer and costlier stays in hospital) have been documented in a large body of literature (Carmeli et al., 2002; Lautenbach et al., 2009; Mulvey & Simor, 2009; Shorr, 2009). More than 20 years ago, the Infectious Diseases Society of America (IDSA) issued guidelines designed to improve the use of antibiotics and other antimicrobial agents in hospitals (Marr et al., 1988). In 1997, the IDSA and the Society for Healthcare Epidemiology of America jointly published guidelines for the prevention of antimicrobial resistance in hospitals; in 2007, they published new guidelines on antibiotic stewardship (Dellit et al., 2007; Shlaes et al., 1997). Of particular concern is the slow pace of antibiotic development in recent years, which highlights the importance of understanding current levels of resistance and preserving the effectiveness of existing antibiotics (APUA, 2012; Nelson, 2003). Information on patterns of antibiotic resistance in US hospitals, however, is fragmented at best. Existing studies either have been limited to specific institutions or geographical regions, or if

☆ Funding for this research was provided by Amgen Inc. to Policy Analysis Inc. (PAI). ⁎ Corresponding author. Tel.: +1-617-232-4400; fax: +1-617-232-1155. E-mail address: [email protected] (D. Weycker). 0732-8893/$ – see front matter © 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.diagmicrobio.2013.11.011

nationwide in scope, have focused on a single species/genus and/or specific settings or isolates from patients with specific types of infection (Master et al., 2011; Sanchez et al., 2012). The most comprehensive recent studies were evaluations of healthcareassociated infections among patients of all ages during the periods 2006–2007 and 2009–2010 based on data from the National Healthcare Safety Network (NHSN) (Hidron et al., 2008; Sievert et al., 2013). In the more recent of these studies, 69 475 healthcareassociated infections reported from 2039 hospitals were examined for the years 2009–2010 (Sievert et al., 2013). The focus in both NHSN studies was on problematic pathogen-antibiotic combinations and only selected infections were considered, namely those that were device-associated (i.e., central-line-associated bloodstream infections, catheter-associated urinary tract infections, and ventilatorassociated pneumonia) and those classified as surgical site infections. Moreover, while device-associated isolates were obtained from patients across all hospital units, the majority came from critical care units (e.g., 65% in Sievert et al.). In this study, we used a multi-hospital database to examine the percentage resistant among clinically significant bacterial pathogens in US hospitals over the period 2007–2010. We focused on the pathogen/antibiotic pairs considered of interest in the most recent NHSN study, which had been included on the basis of their greater frequency, their higher degree of clinical importance, and consideration of emerging resistance patterns, but we also examined several other such pairs that we believed warranted attention (Sievert et al., 2013).

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2. Methods 2.1. Data source Data for this study were obtained from the Cerner Health Facts Database, which contains patient-level clinical records—including information from hospital decision-support and/or cost-accounting systems—from approximately seven million admissions to more than 100 hospitals throughout the US. For each admission, available information included: patient demographics; primary payer; admission source; diagnoses and procedures (in ICD-9-CM format); laboratory data, including microbiology orders, specimens, specimen sources, and results of susceptibility testing; drugs dispensed (including intravenous [IV] and oral antibiotics); length of stay; discharge disposition and destination; and total inpatient charges. Dates of services rendered during hospital admissions also were provided, allowing information to be arrayed chronologically over the course of each hospital stay. For this study, data were limited to the 19 hospitals that contributed microbiology and susceptibility findings in a continuous and consistent manner from January 2007 through December 2010. Selected attributes of these hospitals, as well as those excluded from analyses, are set forth in Table 1 of the online supplement. The study database was de-identified prior to its release to study investigators, as set forth in the corresponding Data Use Agreement. The study database has been evaluated and certified by an independent third party to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study database for health services research is therefore fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects (US DHHS, 2009). 2.2. Study sample We identified all hospitalized adults (age ≥18 years) with ≥1 positive cultures for common, clinically significant bacterial pathogens, and results of in vitro susceptibility testing for selected antibiotics against ≥1 identified pathogens. The list of pathogen/ antibiotic pairs considered in this study was based on the aforementioned NHSN study (Sievert et al., 2013). Other pairs deemed to be of particular interest–including Enterococcus spp. resistance to linezolid and daptomycin, and Staphylococcus aureus resistance to several commonly used antibiotics—also were included. Each unique pathogen/antibiotic pair for all such patients constituted an observation; while each patient could contribute multiple observations, only the first isolate for any given pathogen was considered. All pathogens of interest were species specific with the exception of Enterobacter spp. and the combination of two Klebsiella species, pneumoniae, and oxytoca. 2.3. Study outcomes Bacterial pathogens were defined as “resistant” to antibiotic therapy if they were designated “not susceptible” or “intermediate” based on the interpretation criteria of the Clinical and Laboratory Standards Institute (CLSI), as determined by the designated clinical microbiology laboratories of participating hospitals (CLSI, 2008). Pathogens for which a final determination could not be made were excluded from the analysis. 2.4. Statistical analyses Percentage resistant for each pathogen was examined on an overall basis as well as in relation to the four primary culture sources

Table 1 Characteristics of study sample. Characteristics

Admissions (n = 80 089)

Age (years), mean (SD) 18–34, % 35–49, % 50–64, % 65–74, % ≥75, % Male, % Comorbid conditions, % Alcohol/drug abuse Congestive heart failure Diabetes Immunocompromising condition Cancer Human immunodeficiency virus/acquired immune deficiency syndrome Primary or secondary immunodeficiency Evidence of solid organ or bone marrow transplant Receipt of oral or intravenous corticosteroids (≤24 hours of admission) Hypoalbuminemia Liver disease Lupus Lymphedema Malnutrition Peripheral vascular disease Renal disease Septicemia/bacteremia Systemic inflammatory response Primary reason for admission, % Medical Respiratory system Infectious and parasitic diseases Kidney and urinary tract Circulatory system Digestive system Nervous system Skin, subcutaneous tissue and breast Other Surgical Musculoskeletal system and connective tissue Digestive system Circulatory system Infectious and parasitic diseases Nervous system Respiratory system Hepatobiliary system and pancreas Other Missing Admission source, % Emergency room Physician referral Transfer from another health care facility Transfer from skilled nursing facility Other Unit of care (at time of culture), % Emergency room Intensive care unit Medical/surgical Other Missing Year of admission 2007 2008 2009 2010 Length of stay in hospital (days), mean (SD) Min, median, max Discharge disposition, % Home Skilled nursing facility (SNF) Non-SNF facility Expired Other

66.0 (18.2) 7.3 11.9 22.5 18.4 39.9 39.7 6.7 12.0 14.6 6.1 0.2 0.1 1.3 5.9 0.3 2.5 0.1 0.2 5.6 2.3 18.1 5.8 9.4 45.2 16.8 15.7 15.6 12.4 7.7 6.9 6.3 18.7 19.9 16.7 16.6 15.6 10.1 8.6 6.5 5.3 20.7 34.9 54.9 25.1 5.4 4.5 10.2 25.8 8.9 2.8 13.3 49.2 23.9 24.6 24.3 27.1 13.5 (16.8) 1, 9, 614 55.0 23.2 13.4 7.8 0.5


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Table 2 Frequency of isolates that are resistant to antibiotic therapy. Pathogen/ antibiotic combinations

Acinetobacter baumannii Carbapenems Enterobacter spp. ESC4 Carbapenems Enterococcus spp. E. faecium VAN LIN DAP E. faecalis VAN LIN DAP E. coli ESC4 FQ3 Carbapenems K. pneumoniae/ oxytoca ESC4 Carbapenems P. aeruginosa AMINOS ESC2 FQ2 Carbapenems PIP/PIPTAZ S. aureus OX/METH LIN DAP DOX CLIN TMX

Isolates Obtained b48 Hours from Admit

All Isolates No. of isolates reported

No. (%) of isolates tested

Resistant isolates, % (95% CI)

243

No. of isolates reported



103 243 (100.0)

15.0 (11.1–20.1)

4694 14.3 (13.4–15.4) 1.3 (0.9–1.8)

4024 (94.2) 3571 (83.6) 2029 (47.5)

87.1 (86.0–88.1) 1.1 (0.8–1.5) 3.9 (3.2–4.9)

6885 (98.3) 3536 (50.5) 1619 (23.1)

13.8 (13.0–14.6) 1.8 (1.4–2.3) 0.2 (0.1–0.5)

29 791 (95.5) 30 715 (98.4) 12 509 (40.1)

6.0 (5.7–6.2) 31.3 (30.8–31.8) 0.2 (0.1–0.3)

4274

103 (100.0)

13.8 (8.4–21.7)

2148 (99.8) 1038 (48.2)

13.1 (11.7–14.6) 1.1 (0.6–1.9)

1452 (95.1) 1199 (78.5) 677 (44.3)

83.5 (81.5–85.4) 1.3 (0.8–2.2) 3.0 (1.9–4.5)

3822 (98.8) 1876 (48.5) 816 (21.1)

10.7 (9.8–11.7) 2.2 (1.7–3.0) 0.2 (0.1–0.9)

11.3 (10.8–11.9) 3.3 (2.9–3.8)

11 229 10 748 10 982 4435 10 756

(97.0) (92.8) (94.8) (38.3) (92.9)

9.3 (8.8–9.9) 16.8 (16.1–17.5) 32.6 (31.8–33.5) 18.4 (17.3–19.6) 9.3 (8.8–9.9)

23 477 13 804 3057 33 21 133 23 605

(95.3) (56.0) (12.4) (0.1) (85.8) (95.8)

56.8 (56.1–57.4) 0.0 (0.0–0.1) 0.1 (0.1–0.3) 24.2 (12.8–41.0) 39.7 (39.1–40.4) 3.3 (3.1–3.6)

11 579

5.6 (5.3–5.9) 31.8 (31.1–32.4) 0.1 (0.1–0.2)

15.4 (14.0–16.9) 1.5 (1.0–2.3)

2572 (93.6) 2372 (86.3) 1352 (49.2)

89.1 (87.8–90.2) 1.0 (0.7–1.5) 4.4 (3.5–5.7)

3063 (97.5) 1660 (52.9) 803 (25.6)

17.6 (16.3–19.0) 1.3 (0.9–2.0) 0.1 (0.0–0.7)

8837 (95.4) 9076 (98.0) 4015 (43.4)

6.8 (6.3–7.3) 30.3 (29.4–31.3) 0.3 (0.2–0.6)

5187 (95.6) 2802 (51.6)

14.6 (13.6–15.6) 5.1 (4.3–6.0)

5259 (97.6) 5052 (93.8) 5135 (95.3) 2184 (40.5) 5066 (94.0)

9.1 17.4 31.0 20.3 9.9

(8.3–9.9) (16.4–18.5) (29.7–32.2) (18.7–22.1) (9.1–10.7)

8859 (95.2) 5358 (57.6) 1199 (12.9) 9 (0.1) 8038 (86.4) 8902 (95.7)

59.6 0.0 0.3 22.2 45.6 3.5

(58.6–60.6) (0.0–0.1) (0.1–0.9) (6.3–54.7) (44.5–46.7) (3.2–3.9)

5426 7348 (95.3) 3251 (42.1)

8.9 (8.3–9.6) 1.7 (1.3–2.2)

6191

24 644

2538 (99.8) 1341 (52.8)

9261 20 954 (95.5) 21 639 (98.6) 8494 (38.7)

7713 12 535 (95.4) 6053 (46.1)

15.9 (10.8–22.9)

3140

21 947

13 139

140 (100.0)

2747

3867

31 208


2542

1527

7007


140

2152 4686 (99.8) 2379 (50.7)

Isolates Obtained ≥48 Hours from Admit No. of isolates reported

5388 5970 5696 5847 2251 5690

(96.4) (92.0) (94.4) (36.4) (91.9)

9.5 (8.8–10.3) 16.2 (15.3–17.2) 34.2 (32.9–35.4) 16.6 (15.1–18.2) 8.8 (8.1–9.6)

14 618 8446 1858 24 13 095 14 703

(95.3) (55.0) (12.1) (0.2) (85.3) (95.8)

55.0 (54.2–55.8) 0.0 (0.0–0.1) 0.0 (0.0–0.2) 25.0 (12.0–44.9) 36.1 (35.3–36.9) 3.2 (3.0–3.5)

15 343

9301

CI = confidence intervals; AMINOS = amikacin, gentamicin, tobramycin; Carbapenems = imipenem, meropenem; CLIN = clindamycin; DAP = daptomycin; DOX = doxycycline; ESC2 = cefepime, ceftazidime; ESC4 = cefepime, cefotaxime, ceftazidime, ceftriaxone; FQ2 = ciprofloxacin, levofloxacin; FQ3 = ciprofloxacin, levofloxacin, moxifloxacin; LIN = linezolid; OX/METH = methicillin, oxacillin; PIP/PIPTAZ = piperacillin, piperacillin-tazobactam; TMX = trimethoprim-sulfamethoxazole; VAN = vancomycin.

(urinary tract, lower respiratory tract, skin and soft tissue, and bloodstream) and timing of culture (b48 vs. ≥48 hours after admission). (While cultures obtained ≥48 hours after admission may be assumed to yield nosocomial pathogens, cultures obtained b48 hours of admission probably yield a mixture of communityassociated and healthcare-associated pathogens, e.g., pathogens obtained from dialysis patients or patients recently discharged from hospital.) Ninety-five percent confidence intervals (95% CI) for percentages were computed using the Wilson score interval. 3. Results A total of 96 788 isolates yielding 253 834 pathogen/antibiotic pairs were identified from 80 089 qualifying admissions in the 19 hospitals that contributed patient-level information to the study database (Table 1). These hospitals were located primarily in the Northeast (53%), with the remainder in the Midwest (26%) and South (21%) (online supplement). Most (58%) hospitals were teaching institutions, and all (100%) were located in urban environments. More than two-thirds (68%) had b300 beds and 21% had ≥500 beds. Urinary tract isolates constituted 46% of all isolates; lower respiratory tract isolates, 17%; skin and soft tissue isolates, 12%; and bloodstream isolates, 9%; other sources each constituted b4% of the total. Isolates obtained b48 hours after admission constituted 61% of all isolates.

Percentages resistant for all isolates, on an overall basis and stratified by timing of culture (b48 hours vs. ≥48 hours from hospital admission), are presented in Table 2. Percentage resistant was highest for the following pairs: Enterococcus faecium/vancomycin (87.1% [95% CI 86.0– 88.1] of 4024 isolates), S. aureus/oxacillin-methicillin (56.8% [56.1–57.4] of 23477 isolates), S. aureus/clindamycin (39.7% [39.1–40.4] of 21133 isolates), Pseudomonas aeruginosa/fluoroquinolones (32.6% [31.8–33.5] of 10 982 isolates), and Escherichia coli/fluoroquinolones (31.3% [30.8– 31.8] of 30 715 isolates). The percentage of E. faecium isolates resistant to daptomycin (n = 2029 isolates) was 3.9% (3.2–4.9). The percentage resistant was almost always higher for cultures obtained ≥48 hours after admission, but differences for most pathogen/antibiotic pairs were small. Klebsiella isolates obtained ≤48 hours after admission, however, were substantially less likely to be resistant to cephalosporins and carbapenems than those obtained later. Percentages resistant for isolates from the four primary culture sources, on an overall basis and by timing of culture, are reported in Tables 3A−3D. Resistance levels were generally comparable across culture sources. 4. Discussion Our study, based on data for N250 000 pathogen/antibiotic pairs from 19 hospitals over a four-year period, is one of the largest examinations of antibiotic resistance in the US to date. Unlike the

258


Table 3A Frequency of isolates from urinary tract that are resistant to antibiotic therapy. Pathogen/ antibiotic combinations


Isolates obtained b48 hours from admit

All Isolates No. of isolates reported



25




17 25 (100.0)

10.7 (3.5–28.4)

1776 18.0 (16.3–19.8) 1.3 (0.7–2.3)

2232 (97.4) 1958 (85.4) 1099 (47.9)

88.3 (86.9–89.6) 1.2 (0.8–1.8) 4.3 (3.2–5.6)

4162 (99.5) 2129 (50.9) 972 (23.2)

14.0 (13.0–15.1) 2.2 (1.6–2.9) 0.3 (0.1–0.9)

22 408 (96.1) 23 051 (98.8) 8654 (37.1)

5.6 (5.3–6.0) 32.4 (31.8–33.0) 0.1 (0.0–0.1)

2292

17 (100.0)

10.0 (2.5–32.2)

981 (100.0) 400 (40.8)

17.9 (15.6–20.4) 1.5 (0.7–3.2)

902 (98.2) 741 (80.6) 404 (44.0)

84.6 (82.1–86.8) 1.1 (0.5–2.1) 2.7 (1.5–4.8)

2392 (99.5) 1185 (49.3) 492 (20.5)

11.2 (10.0–12.5) 2.9 (2.1–4.0) 0.4 (0.1–1.5)

16 153 (96.0) 16 642 (98.9) 6074 (36.1)

5.6 (5.2–5.9) 33.4 (32.7–34.2) 0.1 (0.0–0.2)

11.7 (11.0–12.5) 1.5 (1.1–2.0)

3836 3745 3860 1317 3745

(96.3) (94.0) (96.9) (33.1) (94.0)

8.6 (7.7–9.5) 14.3 (13.2–15.4) 36.8 (35.3–38.3) 14.4 (12.6–16.4) 6.7 (6.0–7.6)

1814 (95.5) 1088 (57.3) 214 (11.3) 1 (0.1) 903 (47.5) 1778 (93.6)

65.4 (63.2–67.6) 0.0 (0.0–0.4) 0.0 (0.0–1.8) 0.0 (0.0–79.3) 57.5 (54.2–60.7) 4.4 (3.5–5.4)

3982

18.1 (15.5–20.9) 1.0 (0.4–2.6)

1330 (96.9) 1217 (88.6) 695 (50.6)

90.8 (89.2–92.3) 1.3 (0.8–2.1) 5.2 (3.8–7.1)

1770 (99.6) 944 (53.1) 480 (27.0)

17.9 (16.1–19.7) 1.3 (0.7–2.2) 0.2 (0.0–1.2)

6255 (96.3) 6409 (98.7) 2580 (39.7)

5.8 (5.3–6.4) 29.7 (28.6–30.8) 0.0 (0.0–0.2)

2424 (96.5) 1119 (44.6)

15.0 (13.6–16.5) 2.1 (1.4–3.1)

1687 (97.7) 1647 (95.4) 1684 (97.6) 639 (37.0) 1656 (95.9)

8.9 13.9 32.6 14.4 6.9

2511 4756 (95.9) 1864 (37.6)

9.9 (9.1–10.8) 1.2 (0.8–1.8)

2256

1900

794 (99.9) 391 (49.2)

6493

4960 7180 (96.1) 2983 (39.9)

12.5 (2.2–47.1)

1777

16 830

7471

8 (100.0)

1373

2404

23 323


795

919

4181


8

981 1775 (99.9) 791 (44.5)

Isolates obtained ≥48 hours from admit No. of isolates reported

1726 2149 2098 2176 678 2089

(95.3) (93.0) (96.5) (30.1) (92.6)

8.3 (7.2–9.5) 14.5 (13.1–16.1) 40.0 (38.0–42.1) 14.5 (12.0–17.3) 6.6 (5.6–7.7)

1207 (95.3) 706 (55.8) 121 (9.6) 1 (0.1) 603 (47.6) 1180 (93.2)

64.8 (62.1–67.4) 0.0 (0.0–0.5) 0.0 (0.0–3.1) 0.0 (0.0–79.3) 55.1 (51.1–59.0) 5.2 (4.0–6.6)

1266

(7.7–10.4) (12.3–15.7) (30.4–34.8) (11.9–17.3) (5.8–8.2)

634 607 (95.7) 382 (60.3) 93 (14.7) — 300 (47.3) 598 (94.3)

66.7 (62.8–70.3) 0.0 (0.0–1.0) 0.0 (0.0–4.0) — 62.3 (56.7–67.6) 2.8 (1.8–4.5)


recent Sievert et al. and Hidron et al. studies based on NHSN data, we included isolates from a wide variety of hospitalized patients, and from a wide variety of sources, not just specified device-associated infections and surgical site infections, and thus results from our study supplement those from the NHSN (Hidron et al., 2008; Sievert et al., 2013). While our estimates of the percentage resistant confirm those of other studies in many respects, they also suggest that the percentage of E. faecium isolates resistant to daptomycin may be higher than previously reported. Although our study and the recent NHSN study differ in a number of important respects, in Table 4 we compare the resistant percentages reported in that study with those for isolates obtained ≥48 hours after admission (“late isolates”) in our study (Sievert et al., 2013). We believe this is the appropriate comparison since all of the isolates in the NHSN study were healthcare-associated. Although Sievert and colleagues did not pool their results, we did so to obtain overall percentages of resistance to compare with our results, which we were unable to stratify similarly due to the limitations of our study database. Readers should bear in mind several important differences between the Sievert study and ours when examining Table 4. First, the majority of isolates in the Sievert study were device-associated (77%), which was almost certainly not the case in our study since we considered all isolates from common sources (e.g., blood, urine, and respiratory), most of which are typically not device-associated. Second, our study focused on

isolates obtained from adults, while the Sievert study included isolates from children; although the exact percentage was not reported in Sievert et al., in the prior NHSN study, 15% of isolates were from persons b20 years of age (Hidron et al., 2008). Third, the NHSN investigators could avail themselves of clinical data to distinguish true infection from contamination or carriage. Finally, while the Sievert et al. study was based on data (2009–2010) from a much greater number of hospitals (n = 2039) that are distributed more widely throughout the US, the number of isolates (n = 81 139) obtained from each of these hospitals was—on average— much smaller (20 isolates per hospital per year) versus our study (n = 96 788 isolates from 19 hospitals during 2007–2010, yielding 1274 isolates per hospital per year). Despite these and other differences between the two studies, there was a remarkable similarity in the resistance percentages for P. aeruginosa and E. coli isolates. There were, however, several discrepancies that should be noted. The pooled percentage of Acinetobacter baumannii isolates resistant to carbapenems in the Sievert study was almost four times higher than that for “late isolates” in our study (61.2% vs. 15.9%, respectively). The percentages of Enterobacter isolates and Klebsiella isolates resistant to cephalosporins and carbapenems were also greater in the Sievert study. In contrast, the percentage of S. aureus resistant to oxacillin/methicillin and the percentage of E. faecium resistant to vancomycin were greater in our study.


259

Table 3B Frequency of isolates from lower respiratory tract that are resistant to antibiotic therapy. Pathogen/ antibiotic combinations


Isolates obtained b48 h from admit

All isolates No. of isolates reported



105



Isolates obtained ≥48 h from admit


28 105 (100.0)

15.5 (9.8–23.6)

1230 13.4 (11.6–15.4) 1.3 (0.6–2.5)

32 (94.1) 28 (82.4) 9 (26.5)

84.4 (68.2–93.1) 0.0 (0.0–12.1) 0.0 (0.0–29.9)

46 (100.0) 27 (58.7) 8 (17.4)

4.3 (1.2–14.5) 0.0 (0.0–12.5) 0.0 (0.0–32.4)

34

28 (100.0)

20.7 (9.7–38.7)

286 (99.7) 131 (45.6)

9.6 (8.2–11.4) 36.4 (33.9–39.0) 1.0 (0.4–2.1)

2237

42.9 (15.8–75.0) 0.0 (0.0–56.1) 0.0 (0.0–79.3)

8 (100.0) 4 (50.0) 1 (12.5)

25.0 (7.1–59.1) 0.0 (0.0–49.0) 0.0 (0.0–79.3)

11.7 (10.4–13.1) 6.5 (5.3–8.1)

4076 3858 3917 1757 3874

(98.2) (92.9) (94.3) (42.3) (93.3)

11.8 21.3 34.9 23.6 12.4

(10.9–12.8) (20.0–22.6) (33.4–36.4) (21.7–25.7) (11.4–13.5)

6809 (95.1) 3725 (52.0) 572 (8.0) 8 (0.1) 6481 (90.5) 6920 (96.6)

55.9 0.0 0.0 50.0 47.3 3.4

(54.8–57.1) (0.0–0.1) (0.0–0.7) (21.5–78.5) (46.1–48.5) (3.0–3.8)

4152

14.2 (12.1–16.5) 1.2 (0.5–2.6)

25 (92.6) 25 (92.6) 8 (29.6)

96.0 (80.5–99.3) 0.0 (0.0–13.3) 0.0 (0.0–32.4)

38 (100.0) 23 (60.5) 7 (18.4)

0.0 (0.0–9.2) 0.0 (0.0–14.3) 0.0 (0.0–35.4)

863 500 (93.8) 518 (97.2) 192 (36.0)

9.8 (7.5–12.8) 41.8 (37.7–46.1) 1.0 (0.3–3.7)

818 (94.8) 841 (97.5) 438 (50.8)

9.5 (7.7–11.7) 33.2 (30.1–36.4) 0.9 (0.4–2.3)

1435 (95.4) 862 (57.3)

12.9 (11.3–14.8) 7.3 (5.8–9.2)

2221 2112 2141 994 2120

(98.2) (93.4) (94.7) (44.0) (93.8)

10.5 20.0 31.9 24.3 11.5

3844 2214 377 2 3672 3915

(95.0) (54.7) (9.3) (0.0) (90.8) (96.8)

1504 688 (93.9) 329 (44.9)

9.0 (7.1–11.4) 4.6 (2.8–7.4)

1891

7160

942 (99.9) 506 (53.7)

38

733 2123 (94.9) 1191 (53.2)

13.8 (7.8–23.2)

27 7 (100.0) 3 (42.9) 1 (14.3)

533 1318 (94.4) 1359 (97.3) 630 (45.1)

77 (100.0)

10.8 (7.7–14.9) 1.5 (0.4–5.4)

8

1396


943

7

46


77

287 1228 (99.8) 637 (51.8)


2261 1855 (98.1) 1746 (92.3) 1776 (93.9) 763 (40.3) 1754 (92.8)

13.5 22.7 38.8 22.7 13.5

(12.0–15.1) (20.8–24.7) (36.6–41.1) (19.8–25.8) (12.0–15.2)

2965 (95.2) 1511 (48.5) 195 (6.3) 6 (0.2) 2809 (90.2) 3005 (96.5)

53.1 (51.3–54.9) 0.0 (0.0–0.3) 0.0 (0.0–1.9) 33.3 (9.7–70.0) 45.3 (43.4–47.1) 3.8 (3.2–4.5)

3115

(9.2–11.8) (18.4–21.8) (29.9–33.9) (21.8–27.1) (10.2–13.0)

4045 58.1 (56.6–59.7) 0.0 (0.0–0.2) 0.0 (0.0–1.0) 100.0 (34.2–100.0) 48.9 (47.2–50.5) 3.0 (2.5–3.6)


Sievert and colleagues did not assess linezolid and daptomycin. Our finding of 0% linezolid resistance among S. aureus isolates is consistent with extremely low rates of such resistance reported through 2008 in the US and worldwide (Farrell et al., 2009; Jones et al., 2009; Sader et al., 2011). However, an outbreak of linezolidresistant S. aureus in an ICU was recently reported (Garcia et al., 2010). Our rate of linezolid resistance for “late” E. faecalis isolates, while low (1.3%), is slightly higher than previously reported rates (b1%) (Farrell et al., 2009; Jones et al., 2009; Sader et al., 2011). Linezolid resistance among E. faecium isolates (1.0% in our study) was b1% in two prior studies, but was 1.8% in another (Farrell et al., 2009; Jones et al., 2009; Sader et al., 2011). The extremely low rates of daptomycin resistance among S. aureus and E. faecalis isolates that we observed are consistent with prior research (Sader et al., 2011). However, we found that 4.4% of late E. faecium isolates were resistant to daptomycin, in contrast with the negligible rate previously reported (Sader et al., 2011). We note a number of study limitations. First, use of percentages (rather than incidence rates, for example) to describe antibiotic resistance may be misleading, since a reduction in the absolute number of susceptible pathogens results in an increase in the percentage of isolates that are resistant, but not the absolute number of isolates that are resistant (which may be a better measure of the absolute burden of resistance). Second, antibiotic resistance was assessed based on the conclusions of laboratories servicing the

hospitals, and not by a single central laboratory or by study investigators based on requisite information, and data on breakpoints and methods of susceptibility testing were not included in the study database. To the extent that hospital-specific propensity to test organisms, methods of ascertainment of antibiotic resistance, and/or the reporting of antibiotic resistance varies between hospitals or changed over time, results of analyses may be biased. It is likely, however, that participating hospitals employed CLSI recommended procedures and breakpoints to determine susceptibility/resistance, as accredited hospital labs are required to use CLSI standards. Moreover, other large surveillance systems (e.g., Centers for Disease Control and Prevention NHSN) also report susceptibility of pathogens based on results obtained at local hospital laboratories. Third, our study was based on data from only 19 hospitals, none of which were located in the western region or in rural areas of the US, and focused on a population of adults. Moreover, compared with the other 100 hospitals in the study database that did not contribute microbiology/susceptibility data on a continuous basis during the study period (and thus were excluded from analyses), the 19 hospitals were more likely to be teaching institutions and to have, on average, more beds (online supplement). Caution thus should be exercised in generalizing study results to other institutions, settings, and populations (e.g., pediatric populations). We note, however, that none of the other large databases from which earlier reports of antibiotic resistance have come claim generalizability. Finally, while this study focused on

260


Table 3C Frequency of isolates from skin/soft tissue that are resistant to antibiotic therapy. Pathogen/ antibiotic combinations


Isolates obtained b48 hours from admit




54




26 54 (100.0)

13.1 (6.5–24.6)

548 10.3 (8.0–13.1) 0.9 (0.3–2.5)

407 (87.3) 429 (92.1) 262 (56.2)

87.5 (83.9–90.3) 1.4 (0.6–3.0) 2.7 (1.3–5.4)

608 (95.0) 441 (68.9) 186 (29.1)

14.6 (12.1–17.7) 1.1 (0.5–2.6) 0.0 (0.0–2.0)

1549 (92.5) 1603 (95.7) 864 (51.6)

6.1 (5.0–7.4) 25.1 (23.1–27.3) 0.5 (0.2–1.2)

466

26 (100.0)

7.1 (1.9–23.4)

307 (100.0) 200 (65.1)

7.9 (5.4–11.5) 0.5 (0.1–2.8)

115 (83.3) 128 (92.8) 87 (63.0)

87.0 (79.6–91.9) 2.3 (0.8–6.7) 3.4 (1.2–9.7)

351 (98.0) 241 (67.3) 102 (28.5)

11.4 (8.5–15.1) 0.8 (0.2–3.0) 0.0 (0.0–3.6)

989 (92.7) 1026 (96.2) 535 (50.1)

4.5 (3.4–6.0) 22.1 (19.7–24.8) 0.2 (0.0–1.1)

7.8 (6.1–9.9) 2.4 (1.4–4.3)

1178 (95.6) (88.3) (91.8) (49.2) (87.9)

6.3 (5.0–7.9) 13.6 (11.6–15.8) 23.4 (21.0–26.0) 13.1 (10.6–16.1) 7.1 (5.7–8.8)

6180 3470 977 16 5821 6247

(94.9) (53.3) (15.0) (0.2) (89.4) (96.0)

57.5 (56.3–58.7) 0.0 (0.0–0.1) 0.1 (0.0–0.6) 25.0 (10.2–49.5) 28.6 (27.4–29.7) 2.2 (1.8–2.6)

6510

13.4 (9.7–18.3) 1.4 (0.4–4.8)

292 (89.0) 301 (91.8) 175 (53.4)

87.7 (83.4–91.0) 1.0 (0.3–2.9) 2.3 (0.9–5.7)

257 (91.1) 200 (70.9) 84 (29.8)

19.1 (14.7–24.3) 1.5 (0.5–4.3) 0.0 (0.0–4.4)

560 (92.1) 577 (94.9) 329 (54.1)

8.9 (6.9–11.6) 30.3 (26.6–34.1) 0.9 (0.3–2.6)

314 (91.0) 191 (55.4)

12.0 (8.9–16.1) 3.1 (1.4–6.7)

422 402 411 226 397

(95.3) (90.7) (92.8) (51.0) (89.6)

6.9 19.0 28.2 19.0 9.9

(4.8–9.7) (15.4–23.1) (24.0–32.7) (14.4–24.6) (7.4–13.3)

1621 (95.2) 927 (54.4) 266 (15.6) 2 (0.1) 1541 (90.5) 1624 (95.4)

58.0 0.0 0.4 0.0 35.1 2.2

(55.6–60.4) (0.0–0.4) (0.1–2.1) (0.0–65.8) (32.8–37.5) (1.6–3.1)

345 454 (94.0) 259 (53.6)

5.0 (3.3–7.4) 1.9 (0.8–4.4)

735 1126 1040 1081 580 1035

239 (99.2) 148 (61.4)

608

483 768 (92.8) 450 (54.3)

18.2 (8.1–36.0)

282

1067

828

28 (100.0)

328

358

1675


241

138

640


28

307 546 (99.6) 348 (63.5)

Isolates obtained ≥48 hours from admit No. of isolates reported

443 704 638 670 354 638

(95.8) (86.8) (91.2) (48.2) (86.8)

6.0 (4.5–8.0) 10.2 (8.1–12.8) 20.5 (17.6–23.7) 9.3 (6.7–12.8) 5.3 (3.8–7.3)

4559 2543 711 14 4280 4623

(94.8) (52.9) (14.8) (0.3) (89.0) (96.2)

57.3 (55.9–58.7) 0.0 (0.0–0.2) 0.0 (0.0–0.5) 28.6 (11.7–54.6) 26.2 (24.9–27.5) 2.1 (1.8–2.6)

4807

1703

CI = confidence intervals; AMINOS = amikacin, gentamicin, tobramycin; carbapenems = imipenem, meropenem; CLIN = clindamycin; DAP = daptomycin; DOX = doxycycline; ESC2 = cefepime, ceftazidime; ESC4 = cefepime, cefotaxime, ceftazidime, ceftriaxone; FQ2 = ciprofloxacin, levofloxacin; FQ3 = ciprofloxacin, levofloxacin, moxifloxacin; LIN = linezolid; OX/METH = methicillin, oxacillin; PIP/PIPTAZ = piperacillin, piperacillin-tazobactam; TMX = trimethoprim-sulfamethoxazole; VAN = vancomycin.

isolates of organisms obtained in the hospital setting that are typically nosocomial in nature, we cannot rule out the possibility that some organisms were acquired in the community setting (especially those obtained during the first 48 hours of admission [i.e., early isolates]) or were colonized in the patient. The fact that resistance rates for early and late isolates were often quite similar suggests that a large proportion of early isolates were healthcare-associated. It also suggests that community-acquired resistant pathogens may be increasing in frequency, as was well demonstrated for methicillinresistant S. aureus (MRSA) in the early part of this century (Fridkin et al, 2005; Klevens et al., 2007; Moran et al., 2006). In conclusion, our study of the percentages of common pathogens isolated from hospitalized patients that were resistant to antibiotics commonly used to treat such patients, which was based on data for over 250 000 pathogen/antibiotic pairs from 19 hospitals over a four-year period, confirms results from other large North American studies in many respects. However, one of our findings is unique: a higher percentage of E. faecium isolates resistant to daptomycin than previously reported. Although small, and possibly reflecting a trend that thus far has been limited to one or more of our study hospitals, this finding bears watching. Also, the relatively high frequency of fluoroquinolone resistance among E. coli isolates in our study is of concern, given the high rate of use of this class of antibiotics for prophylaxis among cancer patients receiving chemotherapy and for the treatment of urinary and respiratory

infections. If resistance continues to increase at the rate suggested in our study (as well as previous studies), fluoroquinolones may no longer retain their role as an attractive option in these clinical scenarios (Sievert et al., 2013). Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.diagmicrobio.2013.11.011. References Alliance for the Prudent Use of Antibiotics (APUA). APUA Clinical Newsletter Vol. 30 No. 1, 2012 APUA. Carmeli Y, Eliopoulos G, Mozaffari E, Samore M. Health and economic outcomes of vancomycin-resistant enterococci. Arch Intern Med 2002;162:2223–8. Clinical and Laboratory Standards Institute (CLSI). Performance standards for antimicrobial susceptibility testing—sixteenth informational supplement (M100S18). Wayne, PA: CLSI; 2008. Dellit TH, Owens RC, McGowan Jr JE, et al. Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin Infect Dis 2007;44:159–77. Farrell DJ, Mendes RE, Ross JE, Jones RN. Linezolid surveillance program results for 2008 (LEADER Program for 2008). Diagn Microbiol Infect Dis 2009;65:392–403. Fridkin SK, Hageman JC, Morrison M, et al. Methicillin-resistant Staphylococcus aureus disease in three communities. N Engl J Med 2005;352(14):1436–44. Garcia MS, De la Torre MA, Morales G, et al. Clinical outbreak of linezolid-resistant Staphylococcus aureus in an intensive care unit. JAMA 2010;303:2260–4. Hidron AI, Edwards JR, Patel J, et al. Antimicrobial-resistant pathogens associated with healthcare-associated infections: Annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 20062007. Infect Control Hosp Epidemiol 2008;29:996–1011.


261

Table 3D Frequency of isolates from bloodstream that are resistant to antibiotic therapy. Pathogen/ antibiotic combinations


Isolates obtained b48 h from admit




18




10 18 (100.0)

25.0 (10.7–48.1)

401 11.4 (8.7–14.9) 1.3 (0.4–3.7)

508 (93.7) 432 (79.7) 297 (54.8)

78.5 (74.8–81.9) 0.9 (0.4–2.4) 2.0 (0.9–4.3)

1013 (97.7) 346 (33.4) 173 (16.7)

9.1 (7.5–11.0) 1.4 (0.6–3.3) 0.0 (0.0–2.2)

2140 (95.8) 2222 (99.5) 966 (43.2)

6.4 (5.4–7.5) 26.8 (25.0–28.6) 0.2 (0.1–0.8)

542

10 (100.0)

20.0 (5.7–51.0)

219 (100.0) 133 (60.7)

8.6 (5.5–13.0) 0.0 (0.0–2.8)

154 (95.1) 113 (69.8) 80 (49.4)

66.9 (59.1–73.8) 2.7 (0.9–7.5) 3.8 (1.3–10.5)

545 (97.7) 166 (29.7) 73 (13.1)

5.5 (3.9–7.7) 2.4 (0.9–6.0) 0.0 (0.0–5.0)

1843 (95.5) 1921 (99.6) 802 (41.6)

5.7 (4.7–6.8) 25.6 (23.7–27.6) 0.1 (0.0–0.7)

12.5 (10.7–14.5) 6.3 (4.7–8.5)

498 (99.2) (96.2) (97.2) (45.0) (95.8)

6.8 (4.9–9.4) 12.2 (9.5–15.4) 23.2 (19.6–27.1) 16.5 (12.2–21.9) 8.0 (5.9–10.8)

3084 (97.9) 1902 (60.4) 562 (17.8) 3 (0.1) 2845 (90.3) 3083 (97.8)

52.3 (50.6–54.1) 0.1 (0.0–0.3) 0.4 (0.1–1.3) 0.0 (0.0–56.1) 42.4 (40.6–44.3) 3.5 (2.9–4.2)

3151

14.8 (10.4–20.7) 2.9 (1.0–8.2)

354 (93.2) 319 (83.9) 217 (57.1)

83.6 (79.4–87.1) 0.3 (0.1–1.8) 1.4 (0.5–4.0)

468 (97.7) 180 (37.6) 100 (20.9)

13.2 (10.5–16.6) 0.6 (0.1–3.1) 0.0 (0.0–3.7)

297 (97.4) 301 (98.7) 164 (53.8)

10.6 (7.6–14.7) 33.9 (28.8–39.4) 0.6 (0.1–3.4)

396 (97.8) 267 (65.9)

20.3 (16.7–24.6) 12.4 (8.9–16.8)

202 191 196 100 193

(99.0) (93.6) (96.1) (49.0) (94.6)

8.5 19.5 28.1 22.0 14.0

(5.4–13.2) (14.5–25.7) (22.2–34.7) (15.0–31.1) (9.8–19.6)

867 (97.9) 581 (65.6) 191 (21.6) 3 (0.3) 798 (90.1) 861 (97.2)

57.3 0.0 1.0 0.0 48.6 3.8

(54.0–60.6) (0.0–0.7) (0.3–3.7) (0.0–56.1) (45.2–52.1) (2.7–5.3)

405 720 (95.2) 369 (48.8)

7.6 (5.9–9.8) 1.9 (0.9–3.9)

294 494 479 484 224 477

182 (100.0) 103 (56.6)

305

756 1116 (96.1) 636 (54.8)

30.0 (9.6–63.4)

479

1929

1161

8 (100.0)

380

558

2234


182

162

1037


8

219 401 (100.0) 236 (58.9)

Isolates obtained ≥48 h from admit No. of isolates reported

204 292 288 288 124 284

(99.3) (98.0) (98.0) (42.2) (96.6)

5.7 (3.5–8.9) 7.3 (4.9–11.0) 19.8 (15.6–24.8) 12.1 (7.5–19.0) 3.6 (2.0–6.5)

2217 (97.9) 1321 (58.3) 371 (16.4) — 2047 (90.4) 2222 (98.1)

50.4 (48.3–52.4) 0.1 (0.0–0.4) 0.0 (0.0–1.0) — 40.0 (37.9–42.1) 3.4 (2.7–4.3)

2265

886


Jones RN, Kohno S, Ono Y, et al. ZAAPS International Surveillance Program (2007) for linezolid resistance: results from 5591 Gram-positive clinical isolates in 23 countries. Diagn Microbiol Infect Dis 2009;64:191–201. Klevens RM, Morrison MA, Nadle J, et al. Invasive methicillin-resistant Staphylococcus aureus infections in the United States. JAMA 2007;298:1763–71. Lautenbach E, Synnestvedt M, Weiner MG, et al. Epidemiology and impact of imipenem resistance in Acinetobacter baumannii. Infect Control Hosp Epidemiol 2009;30: 1186–92. Marr JJ, Moffet HL, Kunin CM. Guidelines for improving the use of antimicrobial agents in hospitals: a statement by the Infectious Diseases Society of America. J Infect Dis 1988;157:869–76. Master RN, Clark RB, Karlowsky JA, et al. Analysis of resistance, cross-resistance and antimicrobial combinations for Pseudomonas aeruginosa isolates from 1997 to 2009. Int J Antimicrob Agents 2011;38:291–5. Moran GJ, Krishnadasan A, Gorwitz RJ, et al. Methicillin-resistant S. Aureus infections among patients in the emergency department. N Engl J Med 2006;355(7): 666–74. Mulvey MM, Simor AE. Antimicrobial resistance in hospitals: how concerned should we be? CMAJ 2009;180:408–15. Nelson R. Antibiotic development pipeline runs dry. Lancet 2003;362:1726–7.

Sader HS, Moet GJ, Farrell DJ, Jones RN. Antimicrobial susceptibility of daptomycin and comparator agents tested against methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci: trend analysis of a 6-year period in US medical centers. Diagn Microbiol Infect Dis 2011;70:412–6. Sanchez GV, Master RN, Karlowsky JA, Bordon JM. In vitro antimicrobial resistance of urinary Escherichia coli isolates among U.S. outpatients from 2000 to 2010. Antimicrob Agents Chemother 2012;56:2181–3. Shlaes DM, Gerding DN, John JF, et al. Society for Healthcare Epidemiology of America and Infectious Diseases Society of America Joint Committee on the Prevention of Antimicrobial Resistance: Guidelines for the prevention of antimicrobial resistance in hospitals. Infect Control Hosp Epidemiol 1997;18:275–91. Shorr AF. Review of studies of the impact on Gram-negative bacterial resistance on outcomes in the intensive care unit. Crit Care Med 2009;37:1463–9. Sievert DM, Ricks P, Edwards JR, et al. Antimicrobial-resistant pathogens associated with healthcare-associated infections: summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 20092010. Infect Control Hosp Epidemiol 2013;34:1–14. US Department of Health and Human Services. Code of Federal Regulations: Title 45, public welfare; Part 46, protection of human subjects [Web site]. Cited 2009 July 14. Available: http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.html.

262


Table 4 Comparison of resistance levels between current study and Sievert et al. study. Sievert et al.—pooled findings

Current study No. of isolates tested Acinetobacter baumannii Carbapenems Enterobacter spp. ESC4 Carbapenems Enterococcus spp. E. faecium VAN E. faecalis VAN E. coli ESC4 FQ3 Carbapenems K. pneumoniae/oxytoca ESC4 Carbapenems P. aeruginosa AMINOS ESC2 FQ2 Carbapenems PIP/PIPTAZ S. aureus OX/METH

Resistant isolates

No. of isolates tested

Resistant isolates

140

15.9

1201

61.2

2538 1341

15.4 1.5

3633 2779

34.1 3.7

2572

89.1

3240

79.4

3063

17.6

5252

8.5

8837 9076 4015

6.8 30.3 0.3

7779 8992 6184

13.2 31.5 2.2

5187 2802

14.6 5.2

5564 4577

25.5 11.9

5259 5052 5135 2184 5066

9.1 17.4 31.0 20.3 9.9

3898 5866 5940 4899 4478

10.0 23.3 29.6 22.5 15.5

8859

59.6

12 327

48.2

AMINOS = amikacin, gentamicin, tobramycin; Carbapenems = imipenem, meropenem; CLIN = clindamycin; DAP = daptomycin; DOX = doxycycline; ESC2 = cefepime, ceftazidime; ESC4 = cefepime, cefotaxime, ceftazidime, ceftriaxone; FQ2 = ciprofloxacin, levofloxacin; FQ3 = ciprofloxacin, levofloxacin, moxifloxacin; LIN = linezolid; OX/METH = methicillin, oxacillin; PIP/PIPTAZ = piperacillin, piperacillin-tazobactam; TMX = trimethoprim-sulfamethoxazole; VAN = vancomycin.

Antibiotic resistance in hospitals.

Antibiotic use in US hospitals: quantification, quality measures and stewardship.

Antibiotic use in Chinese hospitals: a multicenter point-prevalence study.

Report reveals scope of US antibiotic resistance threat.

Antibiotic Resistance Prevalence in Routine Bloodstream Isolates from Children's Hospitals Varies Substantially from Adult Surveillance Data in Europe.

Antimicrobial resistance in eight US hospitals along the US-Mexico border, 2000-2006.

The prevalence of antibiotic resistance in Haemophilus influenzae in Ireland.

Prevalence of multiple antibiotic resistance in Neisseria gonorrhoeae.

Factors Associated With Inter-Hospital Variability of Inpatient Antibiotic Use in a Cohort of US Hospitals.

Nosocomial Infections and Epidemiology of Antibiotic Resistance in Teaching Hospitals in South East of Iran.

Prevalence of antimicrobial use in US acute care hospitals, May-September 2011.

Serotypes and antibiotic resistance of non-invasive Streptococcus pneumoniae circulating in pediatric hospitals in Moscow, Russia.

Efflux-based antibiotic resistance mechanisms: the evidence for increasing prevalence.

A multicenter point-prevalence survey of antibiotic use in 13 Chinese hospitals.

Inappropriate use of antibiotics in hospitals: the complex relationship between antibiotic use and antimicrobial resistance.

Antibiotic consumption and antibiotic stewardship in Swedish hospitals.

The Reduction in Antibiotic Use in Hospitals.

Prevalence of antibiotic resistance genes in antibiotic-resistant Escherichia coli isolates in surface water of Taihu Lake Basin, China.

A new approach to the analysis of antibiotic resistance data from hospitals.

Determining the prevalence of SCCmec polymorphism, virulence and antibiotic resistance genes among methicillin-resistant Staphylococcus aureus (MRSA) isolates collected from selected hospitals in west of Iran.

Antibiotic resistance in Ghana.

Antibiotic resistance.

High administrative costs at US hospitals.

Prevalence and antibiotic resistance pattern of methicillin-resistant Staphylococcus aureus from an orthopaedic hospital in Nigeria.