Multistate Point-Prevalence Survey of Health Care–Associated Infections

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original article

Multistate Point-Prevalence Survey of Health
Care–Associated Infections
Shelley S. Magill, M.D., Ph.D., Jonathan R. Edwards, M.Stat.,
Wendy Bamberg, M.D., Zintars G. Beldavs, M.S., Ghinwa Dumyati, M.D.,
Marion A. Kainer, M.B., B.S., M.P.H., Ruth Lynfield, M.D., Meghan Maloney, M.P.H.,
Laura McAllister-Hollod, M.P.H., Joelle Nadle, M.P.H., Susan M. Ray, M.D.,
Deborah L. Thompson, M.D., M.S.P.H., Lucy E. Wilson, M.D.,
and Scott K. Fridkin, M.D., for the Emerging Infections Program
Healthcare-Associated Infections and Antimicrobial Use Prevalence Survey Team*

A bs t r ac t
Background
From the Centers for Disease Control
and Prevention (S.S.M., J.R.E., L.M.-H.,
S.K.F.) and Emory University School of
Medicine (S.M.R.) — both in Atlanta;
Colorado Department of Public Health
and Environment, Denver (W.B.); Ore­
gon Public Health Authority, Portland
(Z.G.B.); New York–Rochester Emerging
Infections Program and University of
Rochester, Rochester (G.D.); Tennessee
Department of Health, Nashville (M.A.K.);
Minnesota Department of Health, St. Paul
(R.L.); Connecticut Department of Public
Health, Hartford (M.M.); California Emerg­
ing Infections Program, Oakland ( J.N.);
Georgia Emerging Infections Program
and the Atlanta Veterans Affairs Medical
Center, Decatur (S.M.R.); New Mexico De­
partment of Health, Santa Fe (D.L.T.); and
Maryland Department of Health and
Mental Hygiene, Baltimore (L.E.W.). Ad­
dress reprint requests to Dr. Magill at the
Division of Healthcare Quality Promotion,
Centers for Disease Control and Preven­
tion, 1600 Clif ton Rd., MS A-24, Atlanta,
GA 30333, or at [email protected].
*A complete list of members of the
Emerging Infections Program HealthcareAssociated Infections and Antimicrobial
Use Prevalence Survey Team is in the
Supplementar y Appendix, available at
NEJM.org.
N Engl J Med 2014;370:1198-208.
DOI: 10.1056/NEJMoa1306801
Copyright © 2014 Massachusetts Medical Society.

Currently, no single U.S. surveillance system can provide estimates of the burden of
all types of health care–associated infections across acute care patient populations.
We conducted a prevalence survey in 10 geographically diverse states to determine
the prevalence of health care–associated infections in acute care hospitals and gen­
erate updated estimates of the national burden of such infections.
Methods

We def ined health care–associated infections with the use of National Healthcare
Safety Network criteria. One-day surveys of randomly selected inpatients were performed
in participating hospitals. Hospital personnel collected demographic and limited
clinical data. Trained data collectors reviewed medical records retrospectively to iden­
tify health care–associated infections active at the time of the survey. Survey data and
2010 Nationwide Inpatient Sample data, stratified according to patient age and length
of hospital stay, were used to estimate the total numbers of health care–associated
infections and of inpatients with such infections in U.S. acute care hospitals in 2011.
Results

Surveys were conducted in 183 hospitals. Of 11,282 patients, 452 had 1 or more
health care–associated infections (4.0%; 95% conf idence interval, 3.7 to 4.4). Of
504 such infections, the most common types were pneumonia (21.8%), surgical-site
infections (21.8%), and gastrointestinal infections (17.1%). Clostridium dif f icile was
the most commonly reported pathogen (causing 12.1% of health care–associated
infections). Device-associated infections (i.e., central-catheter–associated bloodstream
infection, catheter-associated urinary tract infection, and ventilator-associated
pneumonia), which have traditionally been the focus of programs to prevent health
care–associated infections, accounted for 25.6% of such infections. We estimated
that there were 648,000 patients with 721,800 health care–associated infections in
U.S. acute care hospitals in 2011.
Conclusions

Results of this multistate prevalence survey of health care–associated infections
indicate that public health surveillance and prevention activities should continue to
address C. dif f icile infections. As device- and procedure-associated infections de­
crease, consideration should be given to expanding surveillance and prevention
activities to include other health care–associated infections.
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Prevalence of Health Care–Associated Infections

E

limination of health care–associ­
ated infections is a priority of the Depart­
ment of Health and Human Services.1 Con­
siderable success in prevention has been reported
for some infections, particularly central-catheter–
associated bloodstream infections.2-5 Continued
improvements in patient safety depend on main­
taining a comprehensive understanding of the
epidemiology of health care–associated infec­
tions. Currently, no single U.S. surveillance sys­
tem can provide estimates of the burden of all
types of such infections across acute care patient
populations. The most recent estimate produced
by the Centers for Disease Control and Preven­
tion (CDC) and published in 2007 — 1.7 million
health care–associated infections per year — re­
lied on historical data combined with contempo­
rary hospitalization data.6 The CDC surveillance
system for health care–associated infections, the
National Healthcare Safety Network (NHSN),
provides information on incidence rates of com­
mon infections. Most hospitals limit reporting to
device-associated infections, selected surgical-site
infections, and infections due to Clostridium dif f i­
cile and methicillin-resistant Staphylococcus aureus
(MRSA). Therefore, the NHSN cannot provide
national-scale data on the overall burden and
distribution of health care–associated infections
across acute care patient populations.
To address this knowledge gap, the CDC be­
gan a three-phase effort in 2009 to develop and
conduct a multistate prevalence survey of health
care–associated infections and use of antimicro­
bial agents. Prevalence surveys have been used in
other countries to describe the scope and mag­
nitude of the problem of such infections.7-30 The
CDC effort culminated in 2011 in a large-scale
survey that estimated the prevalence of health
care–associated infections in acute care hospi­
tals, determined the distribution of these infec­
tions according to infection site and pathogen,
and generated updated estimates of the national
burden of these infections.

M e th o ds

Survey Design and Hospital Selection

Survey methods were developed in two phases: a
single-city pilot in 200931 and a limited-rollout sur­
vey in 2010 that was performed in collaboration
with the Emerging Infections Programs (EIP), a

network of 10 state health departments (in Cali­
fornia, Colorado, Connecticut, Georgia, Maryland,
Minnesota, New Mexico, New York, Oregon, and
Tennessee) and academic partners. The current
survey was also conducted with the EIP.
Each EIP site was asked to recruit a total of
up to 25 general and children’s acute care hospi­
tals, with the following distribution according to
size, if possible: 13 small hospitals (<150 beds),
9 medium-sized hospitals (150 to 399 beds), and
3 large hospitals (≥400 beds). Eligible hospitals
were randomly selected within each size stratum
to participate in a 1-day survey. When a selected
hospital declined to participate, an alternative
hospital was used.
The CDC determined the survey to be a public
health surveillance activit y. Institutional review
boards at the state health departments, academic
partners, and participating hospitals (where ap­
plicable) reviewed the protocol and either deter­
mined that the survey did not constitute humansubjects research or approved the survey with a
waiver of the requirement for informed consent.
Patient Selection

Inpatients of any age in acute care hospitals were
eligible for inclusion. Patients in outpatient areas,
emergency departments, and psychiatry, skilled
nursing, and rehabilitation units were excluded.
Each hospital surveyed a random sample of eligi­
ble patients obtained from the morning census on
the survey date: 100 patients in each large hospital
and 75 patients (or all eligible patients if <75) in
each small or medium-sized hospital (for details,
see the Supplementary Appendix, available with
the full text of this article at NEJM.org).
Training and Data Collection

Two teams collected data in each hospital: a pri­
mary team of infection preventionists and other
personnel at the participating hospital and an
EIP team of staff members from state health de­
partments, academic partner institutions, or both.
Both teams received training in survey operations
and data-collection procedures; the EIP team also
received training in NHSN terms and definitions
for health care–associated infections.
Primary teams reviewed medical records on
the survey date to collect demographic and lim­
ited clinical information, including whether pa­
tients were receiving or were scheduled to receive
antimicrobial drugs at the time of the survey.

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Primary teams did not collect detailed anti­
microbial data or identify health care–associated
infections. In some cases, EIP teams assisted with
or performed the primary-team data collection.
EIP teams reviewed medical records retrospec­
tively to collect data on antimicrobial therapy and
identify active health care–associated infections
with the use of NHSN surveillance def initions in
place at that time.32 EIP teams were instructed to
use only information present in the medical record
on or before the survey date, including results of
cultures collected or other testing performed on
or before the survey date. EIP teams used the
NHSN definitions of gastrointestinal infections
for reporting C. dif f icile infection when possible;
in circumstances in which a patient with a posi­
tive test result for C. dif f icile infection did not
meet the NHSN gastrointestinal def initions, EIP
teams used a prevalence survey–specif ic definition
of C. dif f icile infection (described in the Supple­
mentary Appendix).
On the basis of pilot data31 and unpublished
data showing that antimicrobial therapy is a sensi­
tive proxy indicator for health care–associated
infections (sensitivity, 95 to 100%), EIP teams re­
viewed records for active health care–associated
infections only for those patients who were re­
ceiving antimicrobial agents for the treatment of
active infections or for no documented reason.
Additional information on the use of data on
antimicrobial therapy to identify patients with
active infections is presented in the Supplemen­
tary Appendix (Methods section and Fig. S1).
Active health care–associated infections were
def ined as infections not present or incubating
on admission to the survey hospital (with cer­
tain exceptions, noted below) that met NHSN
surveillance def inition criteria, with signs or
symptoms of infection present on the survey
date or with antimicrobial therapy still being
given on the survey date. Infections present on
admission to the survey hospital were consid­
ered health care–associated infections if they were
surgical-site infections related to surgery per­
formed at the survey hospital within the preced­
ing 30 days (or within 1 year if an implant was
in place), C. dif f icile infections related to a previous
stay in the survey hospital within 28 days before
specimen collection, or infections related to a
prior hospitalization in the survey hospital within
the preceding 48 hours.

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Statistical Analysis

Data were analyzed with the use of SAS software,
version 9.3 (SAS Institute), and OpenEpi software,
versions 2.3.1 and 3.01 (www.openepi.com). The
mid-P exact method was used to generate conf i­
dence intervals for infection prevalence. Compar­
isons of patients with and those without health
care–associated infections were performed with
the use of chi-square tests for categorical vari­
ables and Wilcoxon rank-sum tests for continu­
ous variables.
To generate estimates of the national burden of
health care–associated infections, we converted
infection prevalence to incidence using the formula
of Rhame and Sudderth33: I =P × [LA ÷ (LN −INT)],
where I denotes incidence, P prevalence, LA the
mean length of hospitalization for all patients,
LN the mean length of hospitalization for patients
who acquired one or more health care–associated
infections, and INT the mean interval between
admission and the onset of the first such infection.
Numbers of patients with health care–associated
infections were obtained by multiplying infec­
tion incidence by numbers of U.S. hospital dis­
charges, obtained from the 2010 Nationwide
Inpatient Sample (NIS).34 This database of hos­
pitalizations from a sample of U.S. community
hospitals was developed as part of the Healthcare
Cost and Utilization Project of the Agency for
Healthcare Research and Quality; discharge
weighting allows national estimates to be gener­
ated from the sample.
We sought to improve the precision of the
burden-estimation process by performing logbinomial regression modeling to identify factors
signif icantly associated with the prevalence of
health care–associated infections. Through a pro­
cess described in the Supplementary Appendix,
the results of regression modeling were used to
create multiple strata based on patient age and a
proxy measure of the length of the hospital stay.
Within each stratum, the predicted prevalence of
health care–associated infections was converted
to incidence with the use of the median length
of the hospital stay for surveyed patients for
whom such information was available (LA in the
formula of Rhame and Sudderth), the median
length of hospital stay for patients with health
care–associated infections (LN), and the median
interval from admission to the onset of the f irst
health care–associated infection (INT). Median

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Prevalence of Health Care–Associated Infections

rather than mean values were used owing to a
skewed distribution. The incidence in each stra­
tum of age and length of stay was multiplied by
the total number of U.S. discharges in that stra­
tum (with the use of weighted discharge data
from the NIS), under the assumption that each
discharge represented a unique patient, to get
stratum-specif ic numbers of patients with health
care–associated infections. These stratum-specific
numbers were summed to obtain an estimate of
the total number of inpatients with health care–
associated infections in U.S. acute care hospitals
in 2011. Because our estimates of the median
length of the hospital stay for all patients were
based on data from patients receiving antimicro­
bial therapy, who may have had a longer median
length of stay than patients not receiving such
therapy, we also performed the burden-estimation
process using data from the NIS for the median
length of the hospital stay for all patients in the
formula of Rhame and Sudderth.
Burden estimates for major types of health
care–associated infection were generated by mul­
tiplying the proportion of surveyed patients with
each infection type by the estimated total number
of patients with health care–associated infec­
tions. The numbers of each major type of infec­
tion were summed to obtain an estimate of the
total number of inpatient health care–associated
infections in U.S. acute care hospitals in 2011.

R e s u l t s

Hospitals and Patients

A total of 183 hospitals (79% of the goal of
232 hospitals) participated (Table S1 in the Sup­
plementary Appendix). Of the 183 hospitals, 93
(51%) were small, 68 (37%) were medium-sized,
and 22 (12%) were large — proportions that
were similar to those for all 406 hospitals in the
10 EIP sites (55% small, 35% medium-sized, and
10% large).
Overall, 11,290 patients were included in sur­
veys performed between May and September 2011;
data collection was completed for 11,282 patients
(99.9%). The median patient age was 58 years
(interquartile range, 32 to 74). Most patients
(71.2%) were in non-nursery wards; 15.1% were
in critical care units. Approximately 51.9% of
patients were receiving or were scheduled to re­
ceive antimicrobial agents at the time of the

survey (Table 1, and Fig. S1 in the Supplemen­
tary Appendix).
Prevalence and Distribution of Health Care–
Associated Infections

The medical records of 4504 patients (39.9%) —
those receiving antimicrobial agents for treatment
of active infections or for no documented reason
— were reviewed for health care–associated in­
fections (Fig. S1 in the Supplementary Appendix).
A total of 504 such infections were detected in 452
of 11,282 patients; therefore, 4.0% of patients had
at least 1 health care–associated infection (95%
confidence interval [CI], 3.7 to 4.4). Pneumonia
and surgical-site infection were most common,
followed by gastrointestinal infection, urinary
tract infection, and primary bloodstream infec­
tion (Table 2). In addition to 50 primary blood­
stream infections, there were 37 secondary blood­
stream infections. Device-associated infections
(i.e., ventilator-associated pneumonia, catheterassociated urinary tract infection, and central­
catheter–associated bloodstream infection) ac­
counted for 25.6% of all health care–associated
infections; together, device-associated infections
and surgical-site infections (21.8%) accounted
for 47.4% of all health care–associated infections
(239 of 504 infections). The remaining 52.6% of
infections were not associated with devices or
operative procedures.
Overall, 169 of 394 non–surgical-site infections
(42.9%) developed during or within 48 hours after
a stay in a critical care unit; 167 (42.4%) devel­
oped during or within 48 hours after a stay in a
non-nursery ward. The NHSN operative-procedure
types associated with the most surgical-site in­
fections were colon surgeries (accounting for 16
of 110 surgical-site infections [14.5%]), hip arthro­
plasties (11 [10.0%]), and small-bowel surgeries
(7 [6.4%]). Ten surgical-site infections (9.1%) were
attributed to other, unspecif ied procedures.
The median interval from hospital admission to
the onset of symptoms of a health care–associated
infection was 6 days (interquartile range, 2 to 13)
among 494 patients for whom symptom-onset
dates were reported. Overall, 98 health care–
associated infections (19.4%) were present on
admission and were therefore related to a previ­
ous admission to the same hospital. Most infec­
tions present on admission were surgical-site
infections (66 [67.3%]) and gastrointestinal in­

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fections (9 [9.2%]). The outcome was known for
436 of the 452 patients with health care–associated
infections (96.5%). Fifty of these 436 patients
(11.5%) died during their survey hospitalization.

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Pathogens Causing Health Care–Associated
Infections

A total of 481 pathogens were reported for 372 of
504 health care–associated infections (73.8%).

Table 1. Demographic and Clinical Characteristics of Surveyed Patients.*
Patients with
Health Care–
Associated
Infections
(N = 452)

All Patients
(N = 11,282)

Patients without
Health Care–
Associated
Infections
(N = 10,830)

Male

5,034 (44.6)

4,813 (44.4)

221 (48.9)

Female

6,236 (55.3)

6,006 (55.5)

230 (50.9)

Characteristic

0.13

Sex — no. (%)

Missing data

12 (0.1)

11 (0.1)

1 (0.2)
0.09

Age — no. (%)
<1 yr
1–17 yr

1,151 (10.2)

1,115 (10.3)

36 (8.0)

479 (4.2)

460 (4.2)

19 (4.2)

18–24 yr

462 (4.1)

448 (4.1)

14 (3.1)

25–44 yr

1,686 (14.9)

1,634 (15.1)

52 (11.5)

45–64 yr

3,060 (27.1)

2,927 (27.0)

133 (29.4)

65–84 yr

3,429 (30.4)

3,269 (30.2)

160 (35.4)

≥85 yr

1,014 (9.0)

976 (9.0)

38 (8.4)

Missing data

1 (<0.1)

1 (<0.1)

0
0.09

Race or ethnic group — no. (%)‡
American Indian or Alaska Native

119 (1.1)

117 (1.1)

2 (0.4)

Asian

254 (2.3)

244 (2.3)

10 (2.2)

Black

1,905 (16.9)

1,809 (16.7)

96 (21.2)

254 (2.3)

244 (2.3)

10 (2.2)

Multiple races or other unspecified race
Native Hawaiian or other Pacific Islander

20 (0.2)

18 (0.2)

2 (0.4)

White

7,537 (66.8)

7,244 (66.9)

293 (64.8)

Missing data

1,193 (10.6)

1,154 (10.7)

39 (8.6)
0.04

Hispanic or Latino ethnic group — no. (%)‡
846 (7.5)

826 (7.6)

20 (4.4)

Not Hispanic or Latino

3,715 (32.9)

3,564 (32.9)

151 (33.4)

Missing data

6,721 (59.6)

6,440 (59.5)

281 (62.2)

Hispanic or Latino

<0.001

Hospital size — no. (%)§
Small

4,073 (36.1)

3,964 (36.6)

109 (24.1)

Medium

4,995 (44.3)

4,794 (44.3)

201 (44.5)

Large

2,214 (19.6)

2,072 (19.1)

142 (31.4)

1,707 (15.1)

1,551 (14.3)

156 (34.5)

<0.001

Location of patient in hospital on survey date — no. (%)¶
Critical care unit
Mixed acuity unit

119 (1.1)

114 (1.1)

5 (1.1)

Newborn or special care nursery

485 (4.3)

482 (4.5)

3 (0.7)

Specialty care area

469 (4.2)

439 (4.1)

30 (6.6)

Step-down unit

466 (4.1)

443 (4.1)

23 (5.1)

8,036 (71.2)

7,801 (72.0)

235 (52.0)

Ward, not nursery

1202

P
Value†

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Prevalence of Health Care–Associated Infections

Table 1. (Continued.)

Characteristic

All Patients
(N = 11,282)

Patients without
Health Care–
Associated
Infections
(N = 10,830)

2,121 (18.8)

1,862 (17.2)

Patients with
Health Care–
Associated
Infections
(N = 452)

P
Value†

Central catheter in place on survey date — no. (%)‖
Any

259 (57.3)

54 (0.5)

44 (0.4)

10 (2.2)

Peripherally inserted

1,037 (9.2)

878 (8.1)

159 (35.2)

Other known type

1,057 (9.4)

958 (8.8)

99 (21.9)

32 (0.3)

29 (0.3)

Femoral

Unknown type
None

9,140 (81.0)

Missing data

21 (0.2)

8,948 (82.6)
20 (0.2)

3 (0.7)
192 (42.5)
1 (0.2)
<0.001

Urinary catheter in place on survey date — no. (%)
Yes

2,659 (23.6)

2,482 (22.9)

177 (39.2)

No

8,594 (76.2)

8,321 (76.8)

273 (60.4)

Missing data

29 (0.3)

27 (0.2)

2 (0.4)
<0.001

Patient receiving mechanical ventilatory support on survey date —
no. (%)
Yes

527 (4.7)
10,748 (95.3)

No

7 (0.1)

Missing data
Patient receiving or scheduled to receive antimicrobial therapy
at time of survey — no. (%)**
Patient receiving dialysis at time of survey — no. (%)

<0.001

432 (4.0)
10,391 (95.9)
7 (0.1)

95 (21.0)
357 (79.0)
0

5,860 (51.9)

5,408 (49.9)

452 (100)

—

446 (4.0)

410 (3.8)

36 (8.0)

<0.001

Interval from admission to survey — days
Median
Interquartile range

3

2

12

1–6

1–5

7–23

<0.001††

*
†
‡
§
¶

Percentages may not add up to 100 because of rounding.
P values were calculated with the use of the chi-square test, except where indicated.
Race and ethnic group were determined on the basis of medical-record documentation.

Small hospitals had fewer than 150 beds, medium-sized hospitals had 150 to 399 beds, and large hospitals had 400 or more beds.

Hospital units were defined according to the National Healthcare Safety Network classification. Critical care units included level II–III and

level III neonatal intensive care units.
‖ Patients could have more than one type of central catheter.
** For four patients without health care–associated infections, information on antimicrobial therapy was not available on the survey date;
medical records of these patients were reviewed retrospectively to collect data on antimicrobial therapy and health care–associated infec­
tions. By definition, all patients with health care–associated infections were receiving antimicrobial agents at the time of the survey.
†† The P value was calculated with the use of the Wilcoxon rank-sum test.

longer at the time of the survey, were in a large
hospital, had a central catheter in place, were re­
ceiving mechanical ventilatory support, or were in
a critical care unit had an increased risk of health
care–associated infection (Table S2 in the Supple­
mentary Appendix). The total estimated number
of patients with at least 1 health care–associated
infection in 2011 was 648,000 (95% CI, 246,400 to
Risk Factors for Health Care–Associated
987,300). The use of data on the median length
Infections and Overall U.S. Burden
of the hospital stay from the NIS in the burdenMultivariable regression analysis showed that pa­ estimation process, in place of data on the length
tients who were older, had been in the hospital of stay from surveyed patients receiving anti­
C. dif f icile was the most common pathogen, causing
61 health care–associated infections (12.1%) (Ta­
ble 3). S. aureus was the second most common
pathogen (54 infections [10.7%]), followed by
Klebsiella pneumoniae and K. oxytoca (50 infections
[9.9%]) and Escherichia coli (47 infections [9.3%])
(Table 3).

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microbial therapy, did not substantially change
the overall burden estimate (582,000 infections;
95% CI, 216,600 to 875,400). Estimated numbers
of selected major types of health care–associated
infection are shown in Table 4; summing the
estimates for each of the 13 major types shown
in Table 2 yielded an overall total estimate of
721,800 infections (95% CI, 214,700 to 1,411,000).

D i s c u s s ion
In this survey, 4.0% of inpatients in U.S. acute care
hospitals had at least 1 health care–associated
infection, yielding an estimate of 648,000 inpa­
tients with a total of approximately 721,800 such
infections in 2011. These estimates of the national
burden of health care–associated infections in
acute care hospitals were generated through the
use of a modeling process that accounted for se­
lected predictors of infection prevalence, includ­
ing age and length of stay, and application of the
results of this modeling to the NIS, a nationally
representative sample of U.S. community-hospital
Table 2. Distribution of 504 Health Care–Associated Infections.*

Type of Infection

Rank

No. of
Infections

Percentage of All
Health Care–
Associated
Infections
(95% CI)

Pneumonia†

1 (tie)

110

21.8 (18.4–25.6)

Surgical-site infection

1 (tie)

110

21.8 (18.4–25.6)

Gastrointestinal infection

3

86

17.1 (14.0–20.5)

Urinary tract infection‡

4

65

12.9 (10.2–16.0)

Primary bloodstream infection§

5

50

9.9 (7.5–12.8)

Eye, ear, nose, throat, or mouth
infection

6

28

5.6 (3.8–7.8)

Lower respiratory tract infection

7

20

4.0 (2.5–6.0)

Skin and soft-tissue infection

8

16

3.2 (1.9–5.0)

Cardiovascular system infection

9

6

1.2 (0.5–2.5)

Bone and joint infection

10

5

1.0 (0.4–2.2)

Central nervous system infection

11

4

0.8 (0.3–1.9)

Reproductive tract infection

12

3

0.6 (0.2–1.6)

Systemic infection

13

1

0.2 (0.01–1.0)

* Infections were defined with the use of National Healthcare Safety Network
criteria. CI denotes confidence interval.
† A total of 43 pneumonia events (39.1%) were associated with a mechanical
ventilator.
‡ A total of 44 urinary tract infections (67.7%) were associated with a catheter.
§ A total of 42 primary bloodstream infections (84.0%) were associated with a
central catheter.

1204

of

m e dic i n e

stays. The current estimates of the overall burden
are lower than older estimates, such as those
from the Study on the Eff icacy of Nosocomial
Infection Control in the 1970s (2.1 million infec­
tions)36 and those from analyses of National
Nosocomial Infections Surveillance system data
collected from 1990 through 2002 (1.7 million in­
fections),6 although it is difficult to draw conclu­
sions from these comparisons because of the
differences in patient populations, surveillance
def initions of health care–associated infections,
and data-collection and analytical methods among
these CDC efforts.31
Device-associated infections, which have been
a major focus of infection prevention in recent
decades, accounted for only 25.6% of all health
care–associated infections detected in the current
survey. Infections not associated with devices or
operative procedures — including C. dif f icile infec­
tions and other gastrointestinal infections and
non–ventilator-associated pneumonia — accounted
for approximately half of all health care–associ­
ated infections in the survey. This f inding should
expand the public health focus to include these
other types of infections, identifying patients at
risk and developing effective prevention measures.
An example is the recent focus of the CDC on sur­
veillance and prevention of C. dif f icile infections.37
Gastrointestinal infections, 70.9% of which
were C. dif f icile infections, were the third most
common type of health care–associated infection
in this survey, in contrast to the results of previ­
ous analyses.6,36 Although there is ample evi­
dence to support our f inding that C. dif f icile in­
fections are a major contributor to the overall
U.S. burden of health care–associated infections
in acute care hospitals,38-41 the high prevalence
of C. dif f icile infections in this survey may be
partially explained by the use of a sensitive def i­
nition. This def inition, as opposed to the more
restrictive NHSN surveillance def initions of gas­
trointestinal infections, was used for reporting
31 of the 61 cases of C. dif f icile infection (51%) de­
tected in the survey. It is also likely that nucleic
acid amplification testing for diagnosis of C. dif f i­
cile infection was used in some participating fa­
cilities, resulting in increased case detection.42
This survey has important limitations that
must be considered. First, although we are conf ident that the survey hospitals are representa­
tive of hospitals within the EIP catchment areas,
they may not be representative of all U.S. acute
care hospitals. Only 183 hospitals and 11,282 pa-

n engl j med 370;13 nejm.org march 27, 2014

Prevalence of Health Care–Associated Infections

Table 3. Reported Causative Pathogens, According to Type of Infection.*
All Health
Care–
Associated
Infections
(N = 504)†

Pathogen

Pneumonia
(N = 110)

Surgical-Site
Infections
(N = 110)

GI
Infections
(N = 86)

UTIs
(N = 65)

Bloodstream
Infections
(N = 50)

number (percent)

no. (%)

rank

Clostridium difficile

61 (12.1)

1

Staphylococcus aureus

54 (10.7)

2

18 (16.4)

17 (15.5)

Klebsiella pneumoniae or K. oxytoca

50 (9.9)

3

13 (11.8)

15 (13.6)

Escherichia coli

47 (9.3)

4

3 (2.7)

14 (12.7)

Enterococcus species‡

44 (8.7)

5

2 (1.8)

Pseudomonas aeruginosa

36 (7.1)

6

Candida species§

32 (6.3)

Streptococcus species¶

25 (5.0)

Coagulase-negative staphylococcus
species
Enterobacter species

0

0

1 (1.2)

2 (3.1)

7 (14.0)

1 (1.2)

15 (23.1)

4 (8.0)

1 (1.2)

18 (27.7)

5 (10.0)

16 (14.5)

5 (5.8)

11 (16.9)

6 (12.0)

14 (12.7)

7 (6.4)

1 (1.2)

7 (10.8)

2 (4.0)

7

4 (3.6)

3 (2.7)

3 (3.5)

3 (4.6)

11 (22.0)

8

7 (6.4)

8 (7.3)

2 (2.3)

2 (3.1)

2 (4.0)

24 (4.8)

9

0

7 (6.4)

0

1 (1.5)

9 (18.0)

16 (3.2)

10

3 (2.7)

5 (4.5)

0

2 (3.1)

2 (4.0)

Acinetobacter baumannii

8 (1.6)

11, tie

4 (3.6)

2 (1.8)

0

0

0

Proteus mirabilis

8 (1.6)

11, tie

1 (0.9)

5 (4.5)

0

1 (1.5)

0

Yeast, unspecified

8 (1.6)

11, tie

3 (2.7)

0

1 (1.2)

4 (6.2)

0

Stenotrophomonas maltophilia

8 (1.6)

11, tie

6 (5.5)

0

0

2 (3.1)

0

Citrobacter species

6 (1.2)

15, tie

2 (1.8)

1 (0.9)

0

1 (1.5)

0

Serratia species

6 (1.2)

15, tie

2 (1.8)

0

0

2 (3.1)

0

Bacteroides species

6 (1.2)

15, tie

0

5 (4.5)

1 (1.2)

0

0

Haemophilus species

6 (1.2)

15, tie

2 (1.8)

2 (1.8)

0

0

0

Viruses‖

3 (0.6)

19, tie

1 (0.9)

0

0

0

0

Peptostreptococcus species

3 (0.6)

19, tie

0

2 (1.8)

0

0

1 (2.0)

Klebsiella species other than
K. pneumoniae and K. oxytoca

2 (0.4)

21, tie

1 (0.9)

0

0

0

1 (2.0)

Clostridium species other than
C. difficile

2 (0.4)

21, tie

0

2 (1.8)

0

0

0

Prevotella species

2 (0.4)

21, tie

0

1 (0.9)

0

0

0

Morganella morganii

2 (0.4)

21, tie

0

1 (0.9)

0

1 (1.5)

0

2 (0.4)

21, tie

0

0

1 (1.2)

0

1 (2.0)

13 (2.6)

—

1 (0.9)

6 (5.5)

0

1 (1.5)

3 (6.0)

Lactobacillus species
Other organisms**

0

0

61 (70.9)

* One or more pathogens were reported for 372 of 504 infections (73.8%). No pathogens were reported for the remaining 132 infections
(26.2%).
† Values for all health care–associated infections include those for the 13 major types of infection listed in Table 2.
‡ Enterococcus species include E. faecalis (23 infections), E. faecium (8), other or unspecified enterococci (11), E. faecalis and E. faecium (1),
and E. faecalis and E. avium (1).
§ Candida species include C. albicans (18 infections), C. parapsilosis (6), C. glabrata (4), other or unspecified candida species (2), and C. albi­
cans and C. dubliniensis (2).
¶ Streptococcus species include S. pneumoniae (7 infections), viridans streptococci (7), group B streptococci (3), other or unspecified strep­
tococci (7), and group G streptococci and S. parasanguis (1).
‖ Viruses include adenovirus (1 infection), herpes simplex virus (1), and parainfluenza virus (1).
** Other organisms include Achromobacter xylosoxidans (1 infection), Aeromonas hydrophila (1), aspergillus species (1), bacillus species (1),
Finegoldia magna (1), fusobacterium species (1), Moraxella catarrhalis (1), propionibacterium species (1), Pseudomonas alcaligenes (1),
Rothia mucilaginosa (1), unspecified gram-negative rods (2), and Acinetobacter lwoffii and micrococcus species (1).

tients were included, of a total of approximately States.43 Second, because our survey was limited
5000 U.S. community hospitals and 34 million to acute care hospitals, we cannot estimate the
annual admissions (2012 data) in the United numbers of health care–associated infections
n engl j med 370;13 nejm.org march 27, 2014

1205

The

n e w e ng l a n d j o u r na l

occurring in other settings, such as skilled nurs­
ing facilities. Third, we were not able to validate
data across the 10 EIP sites. Data evaluations
were performed in the previous two phases of
survey development; the results showed that the
primary data-collection team and the evaluation

of

m e dic i n e

team identif ied similar proportions of patients
with health care–associated infections overall,
although there were many discrepancies in patient-level determinations of such an infection.31
Additional limitations are discussed in the Sup­
plementary Appendix.

Table 4. Estimated Numbers of Major Types of Health Care–Associated Infection in the United States in 2011.
Infections
Identified
in Survey

Surveyed
Patients
with Type of
Infection

Estimated Infections
in the United States*

no.

% (95% CI)

no. (95% CI)

Pneumonia

110

24.3 (20.6–28.5)

157,500 (50,800–281,400)

Surgical-site infection

110†

24.3 (20.6–28.5)

157,500 (50,800–281,400)

Gastrointestinal infection

86

19.0 (15.6–22.8)

123,100 (38,400–225,100)

Urinary tract infection

65

14.4 (11.4–17.9)

93,300 (28,100–176,700)

Primary bloodstream infection

50

11.1 (8.4–14.2)

71,900 (20,700–140,200)

Eye, ear, nose, throat, or mouth infection

28‡

6.2 (4.2–8.7)

40,200 (10,400–85,900)

Lower respiratory tract infection

20

4.4 (2.8–6.6)

28,500 (6900–65,200)

Skin and soft-tissue infection

16

3.5 (2.1–5.6)

22,700 (5200–55,300)

Cardiovascular system infection

6

1.3 (0.5–2.7)

8,400 (1200–26,700)

Bone and joint infection

5

1.1 (0.4–2.4)

7,100 (1000–23,700)

Central nervous system infection

4

0.9 (0.3–2.1)

5,800 (700–20,700)

Reproductive tract infection

3

0.7 (0.2–1.8)

4,500 (500–17,800)

Systemic infection

1

0.2 (0.01–1.1)

Type of Infection

All health care–associated infections

Total

1,300 (0–10,900)
721,800 (214,700–1,411,000)

Infections in non-neonatal intensive care units
Catheter-associated urinary tract infection

25

5.5 (3.7–7.9)

Central-catheter–associated primary bloodstream infection

11

2.4 (1.3–4.2)

15,600 (3200–41,500)

Ventilator-associated pneumonia

35

7.7 (5.5–10.5)

49,900 (13,600–103,700)

46

10.2 (7.6–13.2)

66,100 (18,700–130,300)

56

12.4 (9.6–15.7)

80,400 (23,700–155,000)

7

1.5 (0.7–3.0)

Surgical-site infections attributed to Surgical Care Improvement
Project procedures§

35,600 (9100–78,000)

Hospital-onset infections caused by specific pathogens
Clostridium difficile infection¶
MRSA bacteremia‖

9,700 (1700–29,600)

* Estimates are based on an overall estimate of 648,000 patients (95% CI, 246,400 to 987,300) with at least one health care–associated infec­
tion in 2011. To calculate the numbers of estimated infections, the point estimate of the percentage of patients with a particular type of in­
fection (e.g., 24.3% for pneumonia) was multiplied by the point estimate of the overall number of patients with health care–associated in­
fections. To calculate the 95% CIs, the lower bound of the 95% CI for the percentage of patients with a particular type of infection (e.g.,
20.6% for pneumonia) was multiplied by the lower bound of the 95% CI for the overall number of patients with health care–associated in­
fections, and the upper bound of the 95% CI for the percentage of patients with a particular type of infection (e.g., 28.5% for pneumonia)
was multiplied by the upper bound of the 95% CI for the overall number of patients with health care–associated infections.
† There were 110 surgical-site infections in 109 patients. For the purposes of estimating the total number of such infections in the United
States in 2011, we assumed that each of the 110 infections occurred in a unique patient.
‡ There were 28 eye, ear, nose, throat, or mouth infections in 27 patients. For the purposes of estimating the total number of such infections
in the United States in 2011, we assumed that each of the 28 infections occurred in a unique patient.
§ Surgical Care Improvement Project procedures included those with the following National Healthcare Safety Network procedure codes:
CARD, CBGB, CBGC, AAA, CEA, PVBY, COLO, REC, HYST, VHYS, HPRO, and KPRO.35
¶ C. difficile infection was defined by an onset of symptoms on or after the third day of hospitalization (with the day of admission counted as the first day).
‖ Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia was defined as primary or secondary MRSA bloodstream infection with an
onset of symptoms on or after the third day of hospitalization (with the first day of admission counted as the first day).

1206

n engl j med 370;13 nejm.org march 27, 2014

Prevalence of Health Care–Associated Infections

Despite these limitations, the national esti­
mates that we generated for selected types of
health care–associated infection are remarkably
similar to estimates from other data sources.
For example, we estimated that 15,600 central­
catheter–associated bloodstream infections oc­
curred in 2011 (not including such infections in
neonatal intensive care units), and NHSN data
yielded an estimate of 12,400.35 Our estimate of
9700 hospital-onset cases of MRSA bacteremia
is similar to that obtained from EIP popula­
tion-based surveillance, in which 71.3% of the
estimated 14,156 invasive, hospital-onset MRSA
infections, or 10,093 infections, involved bacte­
remia.44 Finally, we estimated that there were
66,100 Surgical Care Improvement Project proce­
dure-associated surgical-site infections, as com­
pared with the NHSN estimate of 52,567.35 The
similarity of these estimates from different data
sources bolsters our conf idence in the overall
estimates of health care–associated infections
that we have generated, as well as our estimates
of infections for which other data sources do not
currently exist.
In summary, our survey results indicate that
on any given day approximately 1 of every 25 in­
patients in U.S. acute care hospitals has at least

one health care–associated infection. Pneumonia
and surgical-site infection were the most common
infection types, and C. dif f icile was the most com­
mon pathogen. Infections other than those asso­
ciated with central catheters, urinary catheters,
and ventilators account for the majority of the
U.S. burden of health care–associated infections
and may warrant increased attention. A better
understanding of trends in the epidemiology of
health care–associated infections and prevention
success may be achieved through repeated prev­
alence surveys in which similar methods are used
each time.
The f indings and conclusions in this report are those of the
authors and do not necessarily represent the off icial position of
the Centers for Disease Control and Prevention (CDC) or the
Agency for Toxic Substances and Disease Registry.
Dr. Kainer reports receiving consulting, lecture, and board mem­
bership fees from and owning stock in the Infectious Disease Con­
sulting Corporation. Dr. Lynfield reports receiving travel support
from Parexel. No other potential conf lict of interest relevant to this
article was reported.
Disclosure forms provided by the authors are available with
the full text of this article at NEJM.org.
We thank the staff of each hospital that participated in
phases 2 and 3 of the Emerging Infections Program (EIP)
Healthcare-Associated Infections and Antimicrobial Use Prev­
alence Survey, our colleagues in the EIP sites and at the CDC
who contributed to this effort (listed in the Supplementary Ap­
pendix), and the Healthcare Cost and Utilization Project Data
Partners (www.hcup-us.ahrq.gov/db/hcupdatapartners.jsp).

References
1. National action plan to prevent health­

care-associated infections: roadmap to
elimination. Washington, DC: Department
of Health and Human Services (http://www
.hhs.gov/ash/initiatives/hai/exec_summary
.html).
2. Vital signs: central line–associated
blood stream infections — United States,
2001, 2008, and 2009. MMWR Morb Mortal
Wkly Rep 2011;60:243-8.
3. Anderson DJ, Miller BA, Chen LF, et
al. The network approach for prevention
of healthcare-associated infections: longterm effect of participation in the Duke
Infection Control Outreach Network. Infect
Control Hosp Epidemiol 2011;32:315-22.
4. Marsteller JA, Sexton JB, Hsu YJ, et al.
A multicenter, phased, cluster-randomized
controlled trial to reduce central lineassociated bloodstream infections in in­
tensive care units. Crit Care Med 2012;40:
2933-9.
5. Pronovost P, Needham D, Berenholtz S,
et al. An intervention to decrease catheterrelated bloodstream infections in the ICU.
N Engl J Med 2006;355:2725-32. [Erratum,
N Engl J Med 2007;356:2660.]
6. Klevens RM, Edwards JR, Richards CL
Jr, et al. Estimating health care-associated
infections and deaths in U.S. hospitals,
2002. Public Health Rep 2007;122:160-6.
7. Zarb P, Coignard B, Griskeviciene J, et

al. The European Centre for Disease Pre­
vention and Control (ECDC) pilot point
prevalence survey of healthcare-associated
infections and antimicrobial use. Euro Sur­
veill 2012;17(46):20316.
8. Smyth ET, McIlvenny G, Enstone JE,
et al. Four country healthcare associated
infection prevalence survey 2006: over­
view of the results. J Hosp Infect 2008;
69:230-48.
9. Lyytikäinen O, Kanerva M, Agthe N,
Möttönen T, Ruutu P. Healthcare-associated
infections in Finnish acute care hospitals:
a national prevalence survey, 2005. J Hosp
Infect 2008;69:288-94.
10. Gastmeier P, Kampf G, Wischnewski N,
et al. Prevalence of nosocomial infections in
representative German hospitals. J Hosp
Infect 1998;38:37-49.
11. Struwe J, Dumpis U, Gulbinovic J, La­
gergren A, Bergman U. Healthcare asso­
ciated infections in university hospitals in
Latvia, Lithuania and Sweden: a simple
protocol for quality assessment. Euro Sur­
veill 2006;11:167-71.
12. Scheel O, Stormark M. National preva­
lence survey on hospital infections in Nor­
way. J Hosp Infect 1999;41:331-5.
13. Vaqué J, Rosselló J, Arribas JL. Preva­
lence of nosocomial infections in Spain:
EPINE study 1990-1997. J Hosp Infect
1999;43:Suppl:S105-S111.

14. The French Prevalence Survey Study

Group. Prevalence of nosocomial infections
in France: results of the nationwide survey
in 1996. J Hosp Infect 2000;46:186-93.
15. Azzam R, Dramaix M. A one-day
prevalence survey of hospital-acquired in­
fections in Lebanon. J Hosp Infect 2001;
49:74-8.
16. Zotti CM, Messori Ioli G, Charrier L,
et al. Hospital-acquired infections in Italy:
a region wide prevalence study. J Hosp In­
fect 2004;56:142-9.
17. Gikas A, Pediaditis J, Papadakis JA, et
al. Prevalence study of hospital-acquired
infections in 14 Greek hospitals: planning
from the local to the national surveillance
level. J Hosp Infect 2002;50:269-75.
18. Plowman R, Graves N, Griff in MA, et
al. The rate and cost of hospital-acquired
infections occurring in patients admitted
to selected specialties of a district general
hospital in England and the national bur­
den imposed. J Hosp Infect 2001;47:198­
209.
19. Emmerson AM, Enstone JE, Griff in
MA, Kelsey MC, Smyth ET. The Second
National Prevalence Survey of infection in
hospitals — overview of the results. J Hosp
Infect 1996;32:175-90.
20. Pittet D, Harbarth S, Ruef C, et al.
Prevalence and risk factors for nosocomial
infections in four university hospitals in

n engl j med 370;13 nejm.org march 27, 2014

1207

Prevalence of Health Care–Associated Infections
Switzerland. Infect Control Hosp Epide­
miol 1999;20:37-42.
21. Faria S, Sodano L, Gjata A, et al. The
f irst prevalence survey of nosocomial in­
fections in the University Hospital Centre
‘Mother Teresa’ of Tirana, Albania. J Hosp
Infect 2007;65:244-50.
22. Hajdu A, Samodova OV, Carlsson TR,
et al. A point prevalence survey of hospitalacquired infections and antimicrobial use
in a paediatric hospital in north-western
Russia. J Hosp Infect 2007;66:378-84.
23. Lee MK, Chiu CS, Chow VC, Lam RK,
Lai RW. Prevalence of hospital infection
and antibiotic use at a university medical
center in Hong Kong. J Hosp Infect 2007;
65:341-7.
24. Duerink DO, Roeshadi D, Wahjono H,
et al. Surveillance of healthcare-associated
infections in Indonesian hospitals. J Hosp
Infect 2006;62:219-29.
25. Klavs I, Bufon Luznik T, Skerl M, et al.
Prevalence of and risk factors for hospitalacquired infections in Slovenia — results
of the f irst national survey, 2001. J Hosp
Infect 2003;54:149-57.
26. Kallel H, Bahoul M, Ksibi H, et al.
Prevalence of hospital-acquired infection
in a Tunisian hospital. J Hosp Infect
2005;59:343-7.
27. Metintas S, Akgun Y, Durmaz G,
Kalyoncu C. Prevalence and characteris­
tics of nosocomial infections in a Turkish
university hospital. Am J Infect Control
2004;32:409-13.
28. Danchaivijitr S, Judaeng T, Sripalakij S,
Naksawas K, Plipat T. Prevalence of noso­
comial infection in Thailand 2006. J Med
Assoc Thai 2007;90:1524-9.
29. Gravel D, Matlow A, Ofner-Agostini M,
et al. A point prevalence survey of health

care-associated infections in pediatric pop­
ulations in major Canadian acute care hos­
pitals. Am J Infect Control 2007;35:157-62.
30. Gravel D, Taylor G, Ofner M, et al.
Point prevalence survey for healthcareassociated infections within Canadian
adult acute-care hospitals. J Hosp Infect
2007;66:243-8.
31. Magill SS, Hellinger W, Cohen J, et al.
Prevalence of healthcare-associated infec­
tions in acute care hospitals in Jackson­
ville, Florida. Infect Control Hosp Epide­
miol 2012;33:283-91.
32. Horan TC, Andrus M, Dudeck MA.
March 2010 update to: CDC/NHSN surveil­
lance def inition of healthcare-associated
infection and criteria for specif ic types of
infections in the acute care setting. Am J
Infect Control 2008;36:309-32 (http://www
.cdc.gov/nhsn/pdfs/archive/17pscNosInf Def
_NOTcurrent.pdf ).
33. Rhame FS, Sudderth WD. Incidence
and prevalence as used in the analysis of
the occurrence of nosocomial infections.
Am J Epidemiol 1981;113:1-11.
34. Healthcare Cost and Utilization Proj­
ect (HCUP). Nationwide Inpatient Sample
(NIS), 2010. Rockville, MD: Agency for
Healthcare Research and Quality (http://
www.hcup-us.ahrq.gov/nisoverview.jsp).
35. 2011 National and state healthcareassociated infections standardized infection
ratio report: using data reported to the Na­
tional Healthcare Safety Network as of Sep­
tember 4, 2012. Atlanta: Centers for Disease
Control and Prevention (http://www.cdc.gov/
hai/pdfs/SIR/SIR-Report_02_07_2013.pdf ).
36. Haley RW, Culver DH, White JW, Mor­
gan WM, Emori TG. The nationwide noso­
comial infection rate: a new need for vital
statistics. Am J Epidemiol 1985;121:159-67.

37. Vital signs: preventing Clos tridium dif­

f icile infections. MMWR Morb Mortal
Wkly Rep 2012;61:157-62.
38. Dubberke ER, Olsen MA. Burden of
Clostridium dif f icile on the healthcare sys­
tem. Clin Infect Dis 2012;55:Suppl 2:S88­
S92.
39. Zerey M, Paton BL, Lincourt AE, Ger­
sin KS, Kercher KW, Heniford BT. The
burden of Clost ridium dif f icile in surgical
patients in the United States. Surg Infect
(Larchmt) 2007;8:557-66.
40. Dubberke ER, Butler AM, Nyazee HA,
et al. The impact of ICD-9-CM code rank
order on the estimated prevalence of Clos­
t ridium dif f icile infections. Clin Infect Dis
2011;53:20-5.
41. Dubberke ER, Butler AM, Yokoe DS, et
al. Multicenter study of Clost ridium dif f icile
infection rates from 2000 to 2006. Infect
Control Hosp Epidemiol 2010;31:1030-7.
42. Cohen J, MacCannell D, Clark LA, et
al. Changes in Clost ridium dif f icile testing
practices and their impact on stool rejec­
tion policies across multiple U.S. laborato­
ries. Presented at IDWeek 2012, San Diego,
CA, October 16–21, 2012. abstract (https://
idsa.confex.com/idsa/2012/webprogram/
Paper34539.html).
43. Fast facts on US hospitals. Washington,
DC: American Hospital Association (http://
www.aha.org/research/rc/stat-studies/fast
-facts.shtml).
44. Active Bacterial Core Surveillance
(ABCs) report: Emerging Infections Pro­
gram Network methicillin-resistant Staphy­
lococcus aureus, 2011. Atlanta: Centers for
Disease Control and Prevention (http://
www.cdc.gov/abcs/reports-findings/
survreports/mrsa11.pdf ).
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