infection control and hospital epidemiology november 2009, vol. 30, no. 11
Incidence of and Risk Factors for Nosocomial Bloodstream
Infections in Adults in the United States, 2003
Omar M. AL-Rawajfah, PhD, RN; Frank Stetzer, PhD; Jeanne Beauchamp Hewitt, PhD, RN
case-fatality rates have seldom been reported.
Although many studies have examined nosocomial bloodstream infection (BSI), US national estimates of incidence and
identify risk factors for nosocomial BSI among adults hospitalized in the United States on the basis of a national probability sample.
The purposes of this study were to generate US national estimates of the incidence and severity of nosocomial BSI and to
fatality rate associated with nosocomial BSI in the total US population. Cases of nosocomial BSI were defined by using 1 or moreInternational
Classification of Diseases, 9th Revision, Clinical Modification codes in the secondary field(s) that corresponded to BSIs that occurred at least
48 hours after admission. The comparison group consisted of all patients without BSI codes in their NIS records. Weighted data were used
to generate US national estimates of nosocomial BSIs. Logistic regression was used to identify independent risk factors for nosocomial BSI.
This cross-sectional study used the US Nationwide Inpatient Sample for the year 2003 to estimate the incidence and case-
rate was 20.6%. Seven of the 10 leading causes of hospital admissions associated with nosocomial BSI were infection related. We estimate
that 541,081 patients would have acquired a nosocomial BSI in 2003, and of these, 111,427 would have died. The final multivariate model
consisted of the following risk factors: central venous catheter use (odds ratio [OR], 4.76), other infections (OR, 4.61), receipt of mechanical
ventilation (OR, 4.97), trauma (OR, 1.98), hemodialysis (OR, 4.83), and malnutrition (OR, 2.50). The total maximum rescaled R2was
The US national estimated incidence of nosocomial BSI was 21.6 cases per 1,000 admissions, while the estimated case-fatality
independent predictors of nosocomial BSI.
The Nationwide Inpatient Sample was useful for estimating national incidence and case-fatality rates, as well as examining
Infect Control Hosp Epidemiol 2009; 30:1036-1044
From the Faculty of Nursing, Al al-Bayt University, Mafraq, Jordan (O.M.A.-R.); and the Center for Nursing Research and Evaluation (F.S.), the College
of Nursing (J.B.H.), and the Institute of Environmental Health (J.B.H.), University of Wisconsin–Milwaukee, Milwaukee, Wisconsin.
Received March 11, 2009; accepted June 15, 2009; electronically published September 24, 2009.
? 2009 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2009/3011-0002$15.00. DOI: 10.1086/606167
Nosocomial bloodstream infection (BSI), the most severe
form of healthcare-associated infection,1is associated with
substantial morbidity and mortality,2,3as well as increased
length of stay and healthcare costs.4-7The reported incidence
and case-fatality rates of nosocomial BSI vary, especially be-
tween intensive care unit (ICU) and non-ICU populations.
Incidence ranged from 0.6 cases per 100 admissions across
all units4to 9.7 cases per 100 ICU admissions.8Case-fatality
rates ranged from a low of 21.1%9in a non-ICU population
to a high of 69% in an ICU population.7
Male sex occasionally has been shown to increase the risk of
nosocomial BSI,10-13but this result has not occurred con-
sistently.8,9,14However, female sex has been associated with an
increased risk of mortality.15Increased age has been shown to
be a significant risk factor for nosocomial BSI in many8,12,16,17
the number of preexisting comorbidities,19,20severityofillness,9,21
and the presence of heart disease,11,22cancer,11,13,18,23diabetes
mellitus,11,24chronic pulmonary disease,11,17alcoholism,11central
venous catheter (CVC),18,25,26peripheral intravenous catheter,27
ventilator-associated pneumonia,17urinary tract infection, pre-
existing infection,28multiple trauma,12burns,12anemia,25or
malnutrition,29,30the use of immunosuppressive drugs13,17,31or
H2blockers,7transfusion of multiple units of blood or blood
products,18,32receipt of total parenteral nutrition,33,34receipt of
hemodialysis,11,31,35presence of nasogastric tubes,7tracheosto-
mies,17receipt of mechanical ventilation,7,24and surgicalorother
invasive procedures.7,18,28Most published studies have been lim-
ited to nonprobability samples from 1 or a few tertiary care
estimates of the incidence and severity of nosocomial BSI and
to identify risk factors for nosocomial BSI among adults hos-
pitalized in the United States on the basis of a national prob-
nosocomial bsi in us adults, 20031037
Our cross-sectional study is based on the US Nationwide
Inpatient Sample (NIS) for the year 2003. The NIS data sets
are publically available and, therefore, lack the individual
identifiers that would be used to determine what proportion
of individuals had more than 1 nosocomial BSI event during
the study year. We therefore make the assumption that having
more than 1 nosocomial BSI event during the study year is
rare and would have a negligible effect on the incidence and
case-fatality rate estimates.
The NIS is reported to be the largest all-payer inpatient
care database that is publicly available in the United States.
This national probability data set,36which used a sample con-
sisting of all inpatient stays that occurred in 20% of US com-
munity hospitals, provides information on approximately 8
million inpatient stays from 994 hospitals in 37 states.37When
weighted analyses are reported, the findings represent the
target universe of 4,836 hospitals in the United States that
match the definition of community hospitals used by the
American Hospital Association.36The analysis was limited to
cases that occurred in patients 18 years of age or older. Miss-
ing demographic data were as follows: sex, less than 0.2%
(8,247/5,424,343); race, 26.2% (1,419,326/5,424,343); and
type of admission, 10.4%(563,823/5,424,343). Patientrecords
that were missing these variables were omitted from the anal-
ysis. Data on the type of organism that caused the infection
were missing in 73,490 of the 113,436 nosocomial BSI cases
(64.8%) and 37,213 of the community-acquired BSI cases
Case Definitions and Final Sample
Cases of community-acquired BSI were defined as those that
received a primary diagnosis based on a select set of Inter-
national Classification of Diseases, 9th Revision, Clinical Mod-
ification (ICD-9-CM) codes (Appendix, Table A) at the time
of admission or within the first 48 hours in the hospital
(Figure). Cases of nosocomial BSI were defined as those that
received 1 or more of the same ICD-9-CM codes (Appendix,
Table A) as a secondary diagnosis 48 hours or longer after
admission. This definition was based on the Centers for Dis-
ease Control and Prevention (CDC)38definition and has been
widely used in other studies.5,39-41The ICD-9-CM codes were
identified on the basis of a literature review and through
searching the online and hard copy manuals.42,43The use of
ICD-9-CM for identifying BSI and other related conditions
is considered a valid method and has been widely employed
in other studies.44-47
After the inclusion criteria were applied, the final sample
consisted of 5,424,343 adults, of whom 113,436 had a diag-
nosis that met the nosocomial BSI case definition. The un-
infected comparison group of 5,238,519 patients excluded
those with nosocomial or community-acquired BSI.
Data Analysis Procedures
Descriptive and bivariate analyses.
with SAS-PC, version 9.1 (SAS Institute). Frequencies, per-
centages, means, and their standard deviations were used to
describe the sample. After the frequencies were defined with
SAS, standard formulas48were used to calculate the incidence
and case-fatality rates of nosocomial BSI. Individuals whose
records were missing data (!5%) for the variables of interest
were excluded from the final analysis. The sampling design
and weights included in the data set allow statistically valid
calculation of national-level estimates36,49and were used as
described by Houchens and Elixhauser.49The SAS weighting
procedure was used to compute estimated population means
and their standard errors.
The risk factors available for analysis consisted of age, sex,
admission and secondary diagnoses, number of comorbidities
and procedures, and presence of existing infections, trauma,
anemia, malnutrition, alcoholism, smoking, blood transfu-
sion, total parenteral nutrition, invasive procedures (eg, lum-
bar puncture, angioplasty, bronchoscopy, urinary catheter),
CVC use, peripheral arterial or venous catheter use, hemo-
dialysis, nasogastric tube, tracheostomy, and mechanical ven-
tilation. Risk factors were determined by searching both the
diagnosis and the procedural fields. These fields were used
together because some risk factor definitions include both
diagnostic and procedural codes. For example, mechanical
ventilation was determined to be present if a diagnosis code
that indicates dependence on a respirator was present and/
or if a procedural code that indicates mechanical ventilation,
such as insertion of an endotracheal tube, was present. A list
of these diagnoses and procedures was reviewed by 2 expert
clinicians, who judged its comprehensiveness and appropri-
ateness for the purposes of this study. Any disagreements were
resolved through consensus.
Risk factors were dichotomized for ease of interpretation,
and cross-tabulations were computed. The decision to di-
chotomize age with the cutoff of 65 years and older versus
18–64 years was based on reports thatshowedlessaccessibility
of healthcare services for adults younger than 65 years of age3
and on our desire to be consistent with cutoffs used in pre-
vious studies.7,14Mantel-Haenszel odds ratios (ORs) were
used to determine whether interaction occurred. As a result
of the very large sample size, the Breslow-Day test of the
homogeneity of the OR almost uniformly was highly statis-
tically significant but not meaningfully different. Conse-
quently, interaction was determined on the basis of whether
the OR in 1 stratum was protective (!1) and the other a risk
factor (11). The significance level for ORs was set at a less
than or equal to .05 (95% confidence interval). In the absence
of interaction, age- and sex-adjusted ORs were computed.
However, no confounding by these factors was evident, and
therefore, we report univariate ORs.
Logistic regression.Stepwise logistic regression was used
with risk factors selected on the basis of previous studies and
Analyses were conducted
1038infection control and hospital epidemiologynovember 2009, vol. 30, no. 11
data). BSI, bloodstream infection.
Flow chart showing the results of applying case-finding definitions to the Nationwide Inpatient Sample, 2003 (unweighted
in which the univariate ORs were at least 2.0. Because CVC
use and peripheral venous catheter use were collinear (r p
), we used only CVC use in the multivariate models. We0.9
examined the R2with and without age (dichotomized) and
sex in the models. Age and sex were excluded from the final
model, as they did not contribute to the R2.
Description of the Sample
There were 7,977,728 admissions in the NIS in 2003 (Figure).
The majority of eligible patients admitted were women
(3,375,190 of 5,416,096 patients whose sex was recorded
[62.3%]). Emergency admissions were the most frequent type
of admission (48.4%), followed by elective admissions
(29.3%) (characteristics of infected and uninfected patients
are presented in Table 1). Patients with a diagnosis of nos-
ocomial or community-acquired BSI were older than unin-
fected patients. A greater proportion of men werehospitalized
for nosocomial BSI than community-acquired BSI. Most of
the nosocomial BSI patients were admitted on an emergency
basis, but only 45 (0.04%) were admittedasaresultoftrauma.
Patients with nosocomial BSI underwent, on average, more
procedures than other patients and had a greater number of
comorbidities. Of the nosocomial BSI cases with culture re-
sults recorded, only 2,518 of 40,403 (6.2%) were polymicro-
As noted previously, microorganisms were substantially
underreported. The most prevalent specific causative agent
noted in the data set for nosocomial BSI was Staphylococcus
aureus (12,983/113,436 cases [11.4%]). In contrast, Esche-
richia coli was the most prevalent agent among patients with
a diagnosis of community-acquired BSI (9,831/72,388 cases
For nosocomial BSI, the population (weighted) estimated
incidence was 21.6 cases per 1,000 admissions. The popula-
tion estimated case-fatality rate was 20.6%. These estimates
nosocomial bsi in us adults, 20031039
table 1. Characteristics of Unweighted Sample, Nationwide Inpatient Sample, 2003
(N p 113,436)
(N p 5,238,519)
Age, years, mean ? SE
64.6 ? 0.268
57.3 ? 0.230
Race or ethnicity(n p 86,994)(n p 3,864,560)
Asian or Pacific Islander
Type of admission(n p 100,323)(n p 4,696,178)
No. of diagnoses, mean ? SE
No. of procedures, mean ? SE
LOS, days, mean ? SE
10.2 ? 0.111
3.6 ? 0.062
16.0 ? 0.225
6.2 ? 0.043
1.6 ? 0.023
5.4 ? 0.036
of stay; SE, standard error.
Data are no. (%) unless otherwise indicated. BSI, bloodstream infection; LOS, length
were nearly identical to the unweightedsampleestimates(Fig-
ure). The projected number of patients in the United States
who would have acquired a nosocomial BSI in 2003 is
541,081. The projected number of deaths in 2003 attributed
tonosocomial BSI was111,427.Forcommunity-acquiredBSI,
the unweighted incidence was 13.3 cases per 1,000 admis-
sions, while the case-fatality rate was 13.6%.
Risk Factors for Nosocomial BSI
The leading comorbidity group associated with an increased
risk of nosocomial BSI was the group withinjuryorpoisoning
(Table 2). Other comorbidity groups associated with at least
a doubling of risk were the group with metabolic diseases
and immunity disorders and the groups with diseases of the
blood, nervous and sensory systems, respiratory system, or
skin and subcutaneous tissue.
More than 70% of patients with either nosocomial or com-
munity-acquired BSI had at least 1 other infection in addition
to the BSI (data not shown). In the univariate analyses, mul-
tiple factors were associated with an increased risk of noso-
comial BSI (Table 2). In particular, the highest risks for nos-
ocomial BSI were associated with mechanical ventilation,
CVC use, total parenteral nutrition, peripheral intravenous
and arterial lines, other infections, and malnutrition; smaller
risks were associated with blood transfusions, trauma, and
anemia. Increased age and male sex had small effects (60%
and 70%, respectively) on the risk of nosocomial BSI. Neither
alcoholism nor cigarette smoking contributed substantially to
The risk factors included in the final model consisted of
other infections, trauma, mechanical ventilation, CVC use,
hemodialysis, and malnutrition, which met the a criterion of
.05, as well as an adjusted OR of 2.0 or greater (Table 3).
Forcing age and sex into the model produced a total maxi-
mum rescaled R2of 0.22. In the final model, 21.7% of the
variance was accounted for by the model that excluded age
and sex. Finally, hemodialysis and malnutrition were pre-
served in the final model even though these 2 variables added
only 1% to the total maximum rescaled R2. This decision was
made on the basis of the nearly 5-fold increased risk of nos-
ocomial BSI associated with hemodialysis and the 2.5-fold
increased risk associated with malnutrition. In summary, the
final model consists of the following risk factors: CVC use,
other infections, mechanical ventilation, trauma, hemodial-
ysis, and malnutrition. The probability of a nosocomial BSI
at the .02 level in this model had a sensitivity of 84% and a
specificity of 71%. At this level of sensitivity and specificity,
the model was able to predict 71% of nosocomial BSIs cases
Most of the published studies on the incidence and burden
of nosocomial BSI in the United States are based on smaller,
nongeneralizable data sources. This study, however, is based
on a large and representative sample of US hospitalizations
in 2003 so that we could better estimate population incidence
1040infection control and hospital epidemiology november 2009, vol. 30, no. 11
(BSI) and in Uninfected Patients, Nationwide Inpatient Sample, 2003
Unadjusted Odds Ratios for Risk Factors in Patients with Nosocomial Bloodstream Infection
No. (%) of
(N p 113,436)
No. (%) of
(N p 5,238,519)
Metabolic diseases and immunity disorders
Diseases of blood
Diseases of nervous system and senses
Diseases of respiratory system
Diseases of genitourinary system
Diseases of skin and subcutaneous tissue
Injury or poisoning
Diagnostic and treatment factors
Central venous catheter use
Peripheral arterial line use
Peripheral intravenous catheter use
Nasogastric tube use
Receipt of mechanical ventilation
Receipt of blood transfusion
Receipt of parenteral nutrition
aAll comparisons were significant at
bAge dichotomized on age of 65 years; aged 65 years and older is the risk group.
CI, confidence interval.
.P ! .001
and case-fatality rates. To our knowledge, this is the first study
that used NIS weighted data. Only a few studies have used
data on the US national level to examine the epidemiology
of nosocomial BSI. One of these studies used a nonprobability
sample of clinical data collected during 7 years (1995–2002)
from 49 US hospitals.50Wisplinghoff and colleagues reported
an incidence of 6 cases per 1,000 admissions. However, de-
terminations of nosocomial BSI cases were based on reports
by infection control practitioners, which has been shown by
Stevenson et al51to produce a more conservative estimate
than the use of ICD-9-CM codes, perhaps because of a com-
bination of differences between these codes and the CDC/
National Healthcare Safety Network criteria and/or a more
refined application of the criteria by infection control prac-
titioners. Conversely, the incidence in our study was congru-
ent with that revealed in other reports based on smaller non-
Our case-fatality rate of 21% is lower than those reported
in the literature for nosocomial BSI, which varied between a
low of 27% for ICU cases4in 1 large-scale study and a high
of 57% for general cases and 67% for ICU cases in another
study.53One explanation for our lower case-fatality rate is
that we were unable to distinguish between ICU and non-
ICU patients because this variable was not included in the
data set. Many nosocomial BSI studies were conducted in
critical care units in which severity of illness would increase
the case-fatality rate. Another factor is that many studies did
not specify the age group of the patients. Nosocomial BSI is
the most common type of infection in neonatal ICUs.54,55
Results from this study on the incidence and case-fatality
rates of nosocomial BSI should not be interpreted in isolation
from the many factors that can affect the rate of nosocomial
BSI in different institutions. Previous studies have reported
that infection rates can be affected by the hospital size,56,57
teaching affiliation,57and the admission unit.7,53,58Although
case-mix analysis performed by using different surrogate
markers is reported in the literature, a previous report sug-
gested that case-mix indicators are overlapping in their im-
portance and that the best set of markers is yet to be deter-
mined.56Therefore, the foremost aim of this national study
nosocomial bsi in us adults, 2003 1041
tion, Nationwide Inpatient Sample, 2003
Final Logistic Regression Model for Predicting Nosocomial Bloodstream Infec-
Central venous catheter use
Receipt of mechanical ventilation
CI, confidence interval; OR, odds ratio.
was to assess the incidence rate of nosocomial BSI across the
country as these infections occurnaturallyindifferentsettings
and to establish priorities for infection control. Nevertheless,
case-mix analysis by means of different surrogate markers is
recommended in future studies.
This study showed that 7 of the 10 leading causes of hospital
admissions associated with nosocomial BSI were infection
related. This is consistent with the findings in other studies
that secondary nosocomial BSI rates are high, varying be-
tween 33%21and 84%.58The NIS data set recorded the un-
derlying pathogen in only 37% of the nosocomial BSI cases,
but of those reported, S. aureus was the most prevalent. This
finding is similar to results from the study by Wisplinghoff
et al,4in which they prospectively collected clinical data from
49 hospitals. Their findings revealed that S. aureus (20%),
enterococci (9%), and Candida species (9%) were the most
prevalent causes of nosocomial BSI. Similar what we found
in the case in our study, Martin and colleagues59found that
missing data were common (51%) in an existing data set.
About two-thirds of nosocomial BSI patients were admitted
because of injury or trauma, which were associated with a 4-
fold increased risk for nosocomial BSI, similar to the findings
of Pittet et al.12The consistency of these findings is expected
because trauma injuries are associated with the loss of skin
barriers, injury-associated immunosuppression, extensive use
of invasive procedures, and massive blood transfusions. In
the univariate analysis, an increased risk of nosocomial BSI
was associated with mechanical ventilation, CVC use, he-
modialysis, and malnutrition, which is consistent with the
findings of other studies.7,11,17,18,25,29-31,35,60
In the multivariate analysis, 6 variables—CVC use, other
infections, mechanical ventilation, trauma, hemodialysis, and
malnutrition—showed moderate to large effects (ie, adjusted
ORs) and also provided the most parsimonious model to
explain the risk of nosocomial BSI. In this model and on the
basis of the total maximum rescaled R2, 21.7% of the variation
in the occurrence of nosocomial BSI was explained by these
6 variables. The explanatory power of the model is low be-
cause many variables that would have been desired were not
available in the NIS data set, including some individual-level
factors (eg, use of immunosuppressant drugs, severity-of-ill-
ness rating) and system-level factors such as handwashing
practices, nurse-to-patient ratios, length of stay prior to the
diagnosis of the nosocomial BSI, type of unit (eg, ICU, re-
spiratory, medical-surgical), and presence of antimicrobial
resistance. For example, many studies have shown that se-
verity of illness is an independent predictor of nosocomial
BSI.9,17,21Although the data set does include a variable for
length of stay, we did not include it in the final model because
it was defined in the data set as “the period of time from
admission to discharge.” With this definition it was not pos-
sible to know the exact length of stay before the infection
was diagnosed. The 6 variables in our final model are con-
gruent with those in many studies that showed that all of
these factors or most of them are independent predictors of
The key modifiable risk factors for nosocomial BSI iden-
tified in this study necessitate strict observance of aseptic
technique and correction of preexisting or hospital-induced
malnutrition as important strategies to prevent nosocomial
BSI. Reports from the past 2 decades have consistently dem-
onstrated that risk of infection declines following standard-
ization of aseptic care.39,63,64Previous reports also have shown
that low caloric intake is associated with increased risk of
nosocomial BSI.29Moreover, hypoalbuminemia (ie, an in-
dicator of protein deficiency linked to malnutrition) was sig-
nificantly associated with increased risk of nosocomial BSI.30,65
Although the 60% increased risk associated with increased
age was statistically significant, it was considerably lower than
that associated with the other 6 factors of our final model
and contributed very little to the prediction of nosocomial
BSI. Another study showed that advanced age is a risk factor
for nosocomial BSI,12as well as increased mortality due to
nosocomial BSI.14,66Our findings showed, however, that other
modifiable risk factors play the major role in the development
of nosocomial BSI.
This study showed that men had a 70% greater risk of
nosocomial BSI, which is consistent with the results of other
1042infection control and hospital epidemiologynovember 2009, vol. 30, no. 11
studies.10-13,67This study demonstrated that age and sex are
not among the main influential risk factors for developing
nosocomial BSI. In fact, the findings revealed that clinical
risk factors had a large role in the process of acquisition of
nosocomial BSI compared with that of nonmodifiable per-
sonal risk factors. In post hoc analyses, we observed a small
(about 3%) increased prevalence of trauma and mechanical
ventilation among men compared with women. This small
difference does not seem to be able to explain the observed
increased risk of nosocomial BSI in men in this study. Further
study is needed to explain this sex-based difference.
Although this study has a number of strengths, including
the fact it was based on a probability sample of US national
data, several limitations exist. First, misclassifications may be
associated with the use of ICD-9-CM codes for identifying
nosocomial BSI cases. One possible explanation is that mis-
classification would be likely to be random, which biases the
ORs toward unity. Given the moderate to large ORs revealed
in this study, the conclusions would not be altered. Another
possibility is that ICD-9-CM codes produce inflated estimates
of the odds, as noted by Stevenson et al,51yet this would have
no observable effect when the same amount of inflation con-
tributes to both the numerator and the denominator. None-
theless, further research is needed to determine the optimal
use of ICD-9-CM codes to most closely approximate the cri-
terion standard—CDC/National Healthcare Safety Network
definitions and methods.
A second limitation of this study is the use of cross-sec-
tional data. With the current data set structure, it was im-
possible to know whether the risk factors occurred before or
after the outcome of interest (ie, nosocomial BSI). Some of
this disadvantage could be offset if the data set were modified
to include specific dates for secondary diagnoses and pro-
cedures (eg, dates of insertion and removal of CVCs). A third
limitation, already noted, is the lack of data on other inde-
pendent risk factors that, if routinely recorded in health rec-
ords and included in the NIS, may help to explain a greater
proportion of the variance in risk of nosocomial BSI.
Despite these limitations, results from this study can be used
to measure the effectiveness of primary prevention measures
that have been taken in the United States on a national scale
to control nosocomial BSI. The use of ICD-9-CM codes in
state and national NIS data sets represents an opportunity to
perform broad surveillance (both benchmark, as in this study,
and for trend analysis) of nosocomial BSI. This type of sur-
veillance should be carried out routinely. At the same time,
smaller scale and more refined surveillance studies conducted
by infection control practitioners in healthcare settings provide
invaluable data. Both types of studies provide important, yet
different, data to inform infection control practice.
Strategies and guidelines for preventing nosocomial BSI
have been established and reported adequately in the litera-
ture.39,68-70However, the effectiveness of applying many of
these guidelines is not well evaluated. Finally, the numbers
generated by this study can be used to alert healthcare policy
makers in the United States to the negative consequences of
nosocomial BSI on healthcare systems. Moreover, the results
of our research may encourage healthcare policy makers to
invest greater resources in infection control education, train-
ing, and surveillance programs.
From the results of this study we estimated that more than
one-half million patients in the United States incurred a nos-
ocomial BSI in 2003; 1 in 5 cases of nosocomial BSI were
fatal. To date, this seems to be the first study that has used
the NIS data set to generate national estimates of nosocomial
BSIs. A number of modifiable risk factors were identified.
Findings of this study were congruent with those of many
smaller clinical prospective studies on nosocomial BSI, which
provides validation of our results. This study demonstrated
that it is possible to use ICD-9-CM codes to examine the
effect of clinical factors on the risk of nosocomial BSI, al-
though only a limited number of key variables for nosocomial
infection surveillance are available at present. The NIS is an
efficient and cost-effective data setforconductingsurveillance
of nosocomial BSI and, potentially, other nosocomial infec-
tions. The lack of exact dates and times associated with key
variables such as the secondary diagnoses and medical and
surgical procedures prevents determination of whether nos-
ocomial BSIs can be causally attributed to the identified risk
factor. Adding the date associated with the secondary diag-
noses would, in essence, yield longitudinal rather than cross-
sectional data and would add considerably to the robustness
of the data set for nosocomial infection surveillance purposes.
Nosocomial BSI is a major preventable cause of morbidity
and mortality in the United States, andcontinuedsurveillance
and intervention studies are warranted.
We thank Drs Sandra McLellan and Sandra Plach for consultations regarding
infectious diseases and classification of procedures associated with an in-
creased risk of healthcare-associated infections, respectively.O.M.A.-R.grate-
fully acknowledges support from the Harriet H. Werley Doctoral Student
Research Award, College of Nursing, University of Wisconsin–Milwaukee,
and the Great Lakes Scholars in Environmental Health Award, University of
Wisconsin–Milwaukee Institute of Environmental Health.
Financial support. Support to conduct this study came from the Harriet
H. Werley Doctoral Student Research Award, College of Nursing, University
of Wisconsin–Milwaukee, and the Great Lakes Scholars in Environmental
Health Award, University of Wisconsin–Milwaukee Institute of Environ-
mental Health (to O.M.A.-R.).
Potential conflicts of interest. All authors report no conflicts of interest
relevant to this article.
nosocomial bsi in us adults, 20031043
Clinical Modification Diagnosis Codes Used to Identify Nosocomial
Bloodstream Infections, United States Nationwide Inpatient Sam-
International Classification of Diseases, 9th Revision,
Septicemia during labor
Other staphylococcal septicemia
Staphylococcus aureus septicemia
Septicemia due to anaerobes
Septicemia due to Haemophilus
Septicemia due to Escherichia coli
Septicemia due to Pseudomonas
Septicemia due to Serratia
Septicemia due to unspecified GNB
Other specified septicemias
Septicemia due to Listeria monocytogenes
Disseminated systemic candidiasis
Viremia, unspecified, and herpetic
038.1, 038.10, 038.19
038.49, 785.52, 038.40
GNB, gram-negative bacteria.
Address reprint requests to Omar M. AL-Rawajfah, Faculty of Nursing,
Al al-Bayt University, PO Box 130040, Mafraq, Jordan 25113 (rawajfah@
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